r r

Monthly Labor Review

Reader

U.$. Department of Labor 

Bureau of Labor Statistics 

1975

Bulletin 1868

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L ib ra ry o f C o n g r e ss C a ta lo g in g in P u b lic a tio n D a ta

Main e n try under t i t l e :

Monthly la b o r review reader*

Com pilation o f a r t i c l e s on fin d in g s of th e Bureau 

o f Labor S t a t i s t i c s , p u b lish ed between Jan . 19&9, and 

Jan* 1975, in th e Monthly la b o r review .

S u p t. o f Docs, n o .: L 2.2:M 76/2

1. Labor supply—U nited S ta te s —A ddresses, e ssa y s, 

le c tu r e s . 2. Labor economics—A ddresses, e ssa y s, 

le c tu r e s . I . U nited S ta te s . Bureau o f Labor 

S t a t i s t i c s . I I . U nited S ta te s . Bureau o f Labor 

S t a t i s t i c s . Monthly la b o r review .

HD5724.M65 3 3 1-1, 1*0973 75-619070

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Monthly Labor Review

Reader

U.S. Department of Labor 

John T. Dunlop, Secretary

Bureau of Labor Statistics 

Julius Shiskin, Commissioner 

1975

Bulletin 1868

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402, GPO Bookstore, or

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Make checks payable to Superintendent of Documents

Stock Number 029-001-01403-1

Catalog Number L2.3:1868

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CONTENTS

Introduction..................................................................................................................................................................... 1

CHAPTER I. MEASURING EMPLOYMENT AND UNEMPLOYMENT

Unemployment statistics and what they mean John E. Bregger..................................................... 6

Comparing employment estimates from household Gloria P. G reen...................................................... 14

and payroll surveys

A 25-year look at employment as measured by Christopher G. Gellner ........................................ 26

two surveys

Analyzing the length of spells of unemployment Hyman B. Kaitz ................................................... 36

Black and white unemployment: the dynamics Curtis L. Gilroy..................................................... 46

of the differential

Quits in manufacturing Paul A. Armknecht and John E. Early................ 56

Job losers, leavers, and entrants: a cyclical analysis Curtis L. Gilroy and Robert J. Mclntire.............. 63

Comparing employment shifts in 10 Constance Sorrentino .......................................... 68

industrialized countries

CHAPTER II. CHANGES IN THE LABOR FORCE

The U.S. labor force: projections to 1990 Denis F. Johnston................................................. 78

Determining the labor force status of men Deborah P. Klein................................................... 88

missed in the census

Discouraged workers and changes in unemployment Paul 0 . Flaim ........................................................ 95

Education of workers: projections to 1990 Denis F. Johnston................................................. 104

CHAPTER III. SPECIAL GROUPS IN THE LABOR FORCE

The economic status of families headed by women Robert L. S tein ...................................................... 116

Where women work— an analysis by industry Elizabeth Waldman and Beverly J. McEaddy . . . 124

and occupation

The employment situation of Vietnam-era veterans Kopp Michelotti and Kathryn R. Gover.............. 135

Occupational characteristics of urban workers Christopher G. Gellner ........................................ 144

Employment and unemployment among Americans Roberta V. McKay ............................................... 156

of Spanish origin

Multiple jobholding in 1970 and 1971 Howard V. Hayghe and Kopp Michelotti .......... 161

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CONTENTS-Continued

CHAPTER IV. PRICE MEASUREMENT AND PRICE TRENDS

Toward comprehensive measurement of prices Allan D. Searle ....................................................... 170

Updating the Consumer Price Index— an overview Julius Shiskin ......................................................... Ig4

Measuring changes in industrial prices Joseph A. Clorety, Jr............................................... 202

Determining the effects of quality change on the CPI Jack E. Triplett....................................................... 209

The use of price indexes in escalator contracts Francis S. Cunningham.......................................... 215

Postwar price cycles: a new chronology Geoffrey H. Moore ................................................ 220

Prices in 1972: an analysis of changes during Phase 2 JoelPopkin ........................................................... 227

CHAPTER V. PRODUCTIVITY AND TECHNOLOGICAL CHANGE

Industry indexes of output per man-hour Jerome A. Mark .................................................... 236

Productivity and costs in the private economy, 1973 J. R. Norsworthy and L. J. Fulco ........................ 241

Measuring productivity in the Federal Government Charles Ardolini and J. Hohenstein...................... 248

Modernization and manpower in textile mills Rose Zeisel ............................................................. 256

Productivity in telephone communications Horst Brand ........................................................... 264

Labor requirements for construction of single- Robert Ball and Larry Ludwig ............................ 271

family homes

Productivity and unit labor costs in 12 industrial Patricia Capdevielle and Arthur Neef ................. 274

countries

CHAPTER VI. WAGES AND EARNINGS

The relationship between changes in wage rates and Victor J. Sheifer ....................................................... 284

in hourly earnings

Developing a general wage index Norman J. Samuels................................................ 292

Usual weekly earnings of American workers Paul O. Flaim and Nicholas I. Peters ................. 298

Youth unemployment and minimum wages Thomas W. Gavett ................................................ 309

Trends in overtime hours and pay, 1969-74 Diane N. Wescott .................................................. 319

Occupational rankings for men and women by Dixie Sommers .................................................... 327

earnings

Measuring employee compensation in Alvin Bauman ....................................................... 345

U.S. industry

Measuring annual earnings of household heads Robert L. Stein and Paul M. Ryscavage ............. 353

in production jobs

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CONTENTS—Continued

CHAPTER VII. INCOME DISTRIBUTION AND PURCHASING POWER

Two measures of purchasing power contrasted Paul M. Schwab ................................................... 364

Compensation per man-hour and take-home pay Jack Alterman ...................................................... 376

Exploring the distribution of earned income Peter Henle ............................................................. 386

Earnings and family income Robert L. Stein and Janice Neipert Hedges . . . 398

CHAPTER VIII. UNIONS, BARGAINING, AND THE WORKPLACE

Changing policies in public employee relations Joseph P. Goldberg ............................................. 412

Union membership among government employees Harry P. Cohany and Lucretia M. Dweey .......... 422

Women’s participation in labor organizations Virginia Berquist ................................................. 428

When workers are discharged— an overview Robert W. Fisher .................................................. 435

Productivity bargaining in Britain H. M. Douty ........................................................ 449

New approach to occupational safety and Lyle R. Schauer and Thomas S. Ryder .............. 455

health statistics

CHAPTER IX. WORK SCHEDULING AND WORK LIFE

The future of work: three possible alternatives Denis F. Johnston ............................................... 462

Trends in labor and leisure Geoffrey H. Moore and Janice Neipert Hedges . 471

What’s wrong with work in America?— a review Harold Wool .......................................................... 480

essay

A look at the 4-day workweek Janice Neipert Hedges ......................................... 487

A new type of working life table for men Howard N. Fullerton, Jr......................................... 492

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INTRODUCTION

The Monthly Labor Review— the principal outlet

for the creative thinking, analytical skills, and statistical series of the Bureau of Labor Statistics and

its professional staff— is a valuable reference source

for studies of methods of collecting and compiling

labor statistics and for interpretations of their meaning

and significance. The Monthly Labor Review Reader

presents some of the best of its recent articles. The

selections were made by the staff of the Monthly

Labor Review to reflect (1) current economic or social

policy issues facing the Nation at the present time;

(2) progress in the development of statistical concepts and methodology; and (3) the statistical practices

of other countries. Since the Bureau’s constant emphasis

is on the quality and integrity of its output, attention— in the selection process— was also paid to the

quality of the writing and analytical logic.

As the pioneer statistical agency in the Federal

Government, the Bureau— over its 90 years— has developed a comprehensive core of labor statistics covering

employment and unemployment, prices and living conditions, wages and industrial relations, productivity

and technological change, occupational safety and health,

and special economic studies. This Reader includes

selections from each of these major areas. Although

the Monthly Labor Review itself is a mature 60

years old, the articles selected for this new volume

span roughly only the last 15 years: This restriction was

necessary to keep the Reader to manageable size

and limited to articles that are relevant to today’s

problems.

Nor does this Reader reflect the full scope of the

Monthly Labor Review's contents. Each month, the

Review reports on current developments in industrial

relations and labor law, reviews books, and presents

almost 40 pages of the Bureau’s latest statistics. The

MLR— and this Reader as well— should be of interest

to labor, business, and government officials, as well

as to research scholars, students and teachers of economics, industrial relations, management and public

relations.

The Reader’s nine chapters include—

Chapter I. Measuring Employment and Unemployment: The Bureau’s two series on employment (from

the household and from the establishment surveys)

and the unemployment rate are major national economic

indicators crucial to assessments of the state of the

economy. The unemployment rate is used also to measure

progress towards meeting the goals of the Full Employment Act of 1946. More recently, the administration

of the Comprehensive Employment and Training Act

of 1973 requires that State and local area unemployment estimates that are consistent with the national

rate be used in the allocation of Federal revenue sharing

funds. The properties of these Bureau measures— along

with analyses of their trends, both in the U.S. and

other industrialized countries— are included in this

chapter, as are related series on the duration of

unemployment and the rate of voluntary separations

of workers in manufacturing industries— the so-called

quit rate. Another article discusses the relative unemployment experiences of blacks and whites in the various

phases of the business cycle. A final article includes a

cyclical analysis of workers who come into and leave the

labor force— job losers, leavers, and new entrants.

Chapter II. Changes in the Labor Force, focuses on

some problems in labor force accounting— men missed

in the Decennial Census of Population and discouraged

workers who are not in the labor force because

they think they could not find a job. It also includes the

Bureau’s most recent projections of the labor force

and the educational attainment of workers to 1990.

Chapter III. Special Groups in the Labor Force,

deals with some problems and employment characteristics

of special groups of workers: Women, Vietnam-era

veterans, urban workers, Americans of Spanish origin,

and multiple jobholders.

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Chapter V. Price Measurement and Trends: This

chapter describes improvements under way in the

Bureau’s consumer and industrial price measures. Improvements in concept and methodology and expansions

of scope are all covered. A comprehensive description of the massive program under way to overhaul

the Consumer Price Index is the subject of another

article. The development of a new comprehensive

price measure— its objective, its construction, its assets

and shortcomings, is the subject of still another article.

Other subjects covered include the use of price indexes

in long-term contracts to escalate wages and other

income payments, the cyclical behavior of price changes

during the postwar period, and price experience during

a recent period of price stabilization— 1972.

Chapter V. Productivity and Technological Change,

includes descriptions of several Bureau measures of

productivity; an analysis of productivity and cost

performance in the private sector in 1973, a year

in which long-run productivity growth slowed; productivity in construction, textiles, and the telephone

industries, and in a substantial portion of the Federal

Government. The Federal Government measure, still in

its early stages, included more than 850 output indicators, and covered 61 percent of the employment in

Federal civilian government. A comparison of productivity and unit labor costs in 12 industrial countries

is the subject of the final article of this chapter.

Chapter VI. Wages and Earnings, examines such topics

as the relationship between changes in wage rates and

hourly earnings. What are a worker’s “usual earnings,

annual earnings of household heads? What is the effect

of the Federal minimum wage on youth unemployment?

How do we measure employee compensation?” One

article discusses the limitations of existing wage measures and describes plans for a new index of change in

total employee compensation designed to measure the

full range of employment costs. Development of the new

index is now under way in the Bureau. It will be published in stages: First, the measure of change in wage

rates is scheduled for publication in 1976 and the

measure of change in full employment costs in 1977.

Chapter VII. Distribution o f Income and Purchasing

Power: Widespread interest in welfare policies and in

measures of the economic hardship of unemployment

both point to the need for better information on

income distribution and worker purchasing power. This

chapter describes the differences in concept and scope

of two widely used statistical measures of workers’ purchasing power: the BLS series on real net spendable

earnings and the Department of Commerce’s series on

per capita real disposable personal income. These series

showed different trends from the mid-1960’s to the

early 1970’s and raise questions about the actual

course of purchasing power during the recent period of

serious inflationary pressures. Another article reports on

the increasing importance of the income of wives as a

proportion of total family income— up 3 percentage

points from 1958 to 1969. Another author reports on a

slight but persistent trend toward greater income inequality in this country.

Chapter VIII. Unions, Bargaining, and the Workplace,

groups several articles on collective bargaining in the

public sector, for example, women in unions (although

their number is increasing, their participation in union

leadership positions is not) and productivity bargaining

in Great Britain. A final article in the chapter describes

the Bureau’s new survey— one of the largest in the

history of the country— to collect data on the incidence

of occupational injuries and illness in U.S. industry.

Chapter IX. Work Scheduling and Worklife: One

article in this chapter suggests that the role of work

in our society will be affected greatly by changes

in fertility rates: Several possibilities are presented,

ranging from the assertion that work will continue

to provide a central focus for personal satisfaction

and status achievement to the argument that our

traditional work ethic is undergoing rapid erosion,

to be displaced by new criteria of personal worth

and achievement unrelated to work performance.

Another article reports that “little objective evidence

exists to support an inference of a rising wave of

discontent among workers, associated directly with

the nature of their jobs.” But the author goes on

to point out that “most of the available statistical

indicators are clearly much too aggregative to serve

as reliable indexes of worker discontent. Statistical

series such as productivity and labor turnover were

designed for quite different purposes.” One article in

the chapter examines the relations between labor and

leisure and another the 4-day workweek. The concluding article describes two different methods to

measure the number of years, on the average, men

will work. It contrasts “generation” worklife tables

against “period” tables in an attempt to provide

a more realistic means of estimating the length of

worklife.

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Authors of all the articles included in this new

Reader were on the BLS staff at some time in

their careers, though not always at the times the

articles were written. Virtually all of these articles

were originally published between January 1969 and

January 1975, a period in which many innovations

were made in editorial policies and typography of

the journal. Editors who served on the Monthly Labor

Review staff for at least part of that time included:

Herbert C. Morton, Editor-in-Chief; Henry Lowenstern,

Executive Editor; Georgena R. Potts and Robert W.

Fisher, managing editors; Olivia G. Amiss; Elizabeth E.

Barnes, Eugene H. Becker, Catherine C. Defina, Barbara V.

Freund, John Gusman, Mary D. Hogya, Mervyn S.

Knobloch, Constance S. McEwen, Craig E. Polhemus,

Carol A. Rosen, Louise M. Schlader, and Eugene Skotzko.

Their efforts and the devoted work of many other

anonymous BLS employees— data collectors, field

agents, clerks; statisticians, economists, programmers—

made this volume possible.

I hope the users of this Monthly Labor Review Reader

will profit from the collective wisdom contained in

the selections chosen from the Review.

JULIUS SHISKIN

Commissioner

June 1975

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Chapter I. Measuring Employment and Unemployment

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Unemployment

statistics

and what

they mean

“ J o b l e s s r a t e drops to 6.0 percent,” read a typical

headline in newspapers throughout the country on

October 8, 1971. The change— from 6.1 to 6.0 percent of the labor force between August and September 1971— was reported accurately, but the implication of an improvement in the employment situation

was misleading. This is so because the unemployment rate is obtained by a sample survey. Any

change of one-tenth of one percent may be attributable to sampling error and, therefore, not statistically

significant.

Unemployment, of course, is more than a statistic

that measures our economic well-being and the degree to which immediately available manpower is

utilized. Unemployment statistics represent people— people trying to support their families or

augment family income, people seeking their first

jobs, people changing jobs, people losing jobs, but

first and foremost, people. Whether viewed as a

measure of economic well-being or as people with

employment difficulties, however, the data are often

misused, misunderstood, and even criticized.

Data sources and concepts

To set the stage, it is first of all desirable to review

briefly the procedures by which these important statistics are collected and how the measure of unemployment itself is defined. National statistics on unemployment are derived from the Current Population

Survey (CPS), a monthly sample by personal interview of approximately 50,000 households. The survey is conducted by the Bureau of the Census for

the Bureau of Labor Statistics. Persons are classified

through a series of questions which determine

whether they were employed and, if not, whether they

John E. Bregger is an economist in the Division of Employment and Unemployment Analysis, Bureau of Labor

Statistics.

The jobless rate in perspective: 

some common misconceptions 

about what the data represent, 

along with pointers on how 

to interpret them

JOHN E. BREGGER

were looking for work or were not in the labor force.

The data relate to the status of individuals in the

week including the 12th day of the month (the “reference” or “survey week”) and are collected in the

subsequent week (the interview week).1

Employed persons are those who perform a minimum of an hour’s work for pay or profit during the

reference period; also included are those who are

temporarily absent from a job or business for such

reasons as illness, vacations, or strikes, as well as

persons who work 15 hours or more a week without

pay in a family farm or business.

To be classified as unemployed, the individual

must not have worked at all during the reference

week. In addition, he must have taken some specific

steps to obtain a job in the previous 4 weeks, such

as applying directly to an employer, or to a public

employment service, or checking with friends or relatives, and being available for work at the time of the

survey. Persons on layoff or waiting to begin a new

job (within 30 days) need not meet these jobseeking

requirements to be classified as unemployed. Those

persons who are neither employed nor unemployed

are “not in the labor force.” Information is collected

regularly on this group as well, many of whom are

housewives. It is worth noting that at no time during

the course of the interview is the term “unemployed”

used, and, as a consequence, the respondents themselves frequently do not know how they will be classified. Furthermore, no response is elicited as to

whether an individual has applied for or is receiving

unemployment compensation payments.

At the present time, each household in the survey

represents approximately 1,300 households throughout the United States, and, similarly, one person in

the sample represents 1,300 in the population. Therefore, a total of 5 million unemployed would be represented by 3,800 individuals. On the surface, this

appears to be a small sample for such an important

figure. However, the survey is the largest monthly

From the Review of November 1971

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household survey in the world, some 50 times larger

than many of the national public-opinion polls, and

uses a scientifically selected sample that is studied

and reviewed continually. Moreover, the fact that

the sample yields reasonably consistent results

month after month lends credence to the procedure.

Nonetheless, the fact the data are taken from a sample does mean that a degree of sampling error exists.

The statistics on employed and unemployed persons are tabulated to show a wide variety of characteristics: sex, age, color, educational attainment,

marital status, household relationship, whether working full or part time or seeking full-time or part-time

work, duration of unemployment, reasons for being

unemployed, major activity (for young persons— in

school or other), and industry and occupation for

those employed and previous industry and occupation, if any, for the unemployed (industry and occupation of last full-time job lasting 2 weeks or

more). Cross-classifications of a number of these

characteristics are also available, such as by color,

sex, and age.

Seasonal adjustment

There are a number of seasonal fluctuations in employment and unemployment that occur during the

year. These include crop seasons, weather conditions, opening and closing of schools, holiday buying

periods (Christmas and Easter, for example), and industry production schedules. To cite perhaps the

most dramatic shift, there is a tremendous influx of

young people into the labor market in June after

school is out— between May and June of 1971, for

example, the labor force showed a net increase of

nearly 1.9 million persons, with 1.1 million initially

unsuccessful in their job search.

To determine the economic meaning of a month’s

data relative to the previous month or months, it is

essential to differentiate between the change that

usually occurs in the month and the change, if any,

that exceeded the normal, or expected, change.

Therefore, all of the major labor force estimates are

“seasonally adjusted” to permit an easy— and more

revealing— comparison of data for 1 month with

any other.

Without this separation of the seasonal component

of changes, the continuing trend in the labor market

situation would be more difficult to discern. Moreover, since most other economic data, such as estimates of Gross National Product and industrial production, are widely used in seasonally adjusted form,

the adjusted labor force data are more comparable

with them. There is a trade-off, however; seasonal

adjustment tends to “depersonalize” the unemployment data.

In seasonally adjusting the unemployment estimates, as it has for many years, the Bureau of Labor

Statistics uses a traditional ratio-to-moving average

method.2 The unemployment rate is derived by

dividing the sum of four seasonally adjusted components (unemployed persons 16-19 and 20 and

over, by sex) by the civilian labor force, which is

itself the sum of 12 seasonally adjusted components.3

Therefore, there are no direct seasonal adjustment

factors for the rate itself but only for its components. However, implicit seasonal factors for the rate

may be derived as the ratio of the rate, not seasonally

adjusted, to the rate, seasonally adjusted.

Statistical significance

After the seasonal adjustment process has sorted

out the usual, recurring, and largely noneconomic

events from the more significant underlying, developments, there remains an unemployment change from

1 month to the next which may or may not be

“statistically significant.” Because the unemployment

estimates are derived from a probability sample, they

may, of course, differ from the figures that would

have been obtained if it were possible to take a complete census using the same questionnaire and procedures. In other words, the data are subject to some

degree of sampling error, which must be taken into

account in evaluating changes in the data.

The “standard error” is the measure of sampling

variability, that is, the variations that might occur

by chance because only a sample of the population

is surveyed. The chances are about 2 out of 3 that a

sample estimate would differ from the results of a

census by less than the standard error; the chances

are 9 out of 10 that it would differ by less than 1.6

times the standard error; the chances are 19 out of

20 that the difference would be less than twice the

standard error. In its analysis of labor force data,

the Bureau uses 1.6 times the standard error as a

basis for determining the sampling error of an estimate or a change of an estimate from one point in

time to another. For total unemployment, the

standard error is approximately 3 percent, and, at

1.6 times the standard error, the error on an estimate

of 5 million is on the order of plus or minus 150,000.

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Thus, the term “significant,” when applied to the

unemployment numbers, is used whenever the change

in the number from one period to another exceeds

1.6 times the sampling error of the change. When an

apparent change is within this confidence interval,

the likelihood that a change actually occurred is

diminished. For example, a change in the present

level of total unemployment should exceed 150,000

from 1 month to the next to be deemed significant

statistically. Similarly, the national unemployment

rate would have to change by 0.2 percentage point

or more on a monthly basis to be significant.4

Changes that are smaller than these can reasonably

be attributed to sampling variations.

As a general rule, smaller numerical estimates

have higher relative errors. In the case of unemployment rates, the absolute error is greater when the

labor force base is comparatively smaller and also

Table 1. Estimated error at 1.6 standard error for change

in selected seasonally-adjusted unemployment rates

Unem- Percentage error on change

ployment at 1.6 standard error 1

Category rate in

April

1971 Monthly Quarterly Annual

Total (all civilian workers)________ 6.1 .21 .15 .09

Men, 20 years and over.............. 4.4 .24 .17 .11

Wonien, 20 years and over.............. 6 0 .35 .25 .16

Both sexes,16-19 years________ 17.2 1.15 .83 .53

Married men........I......................... 3.1 .21 .15 .09

Full-time workers........... ............... 5.5 .22 .16 .10

Part-time workers_____ ________ 9.4 .70 .50 .32

RACE

Negro and other races......................... 10.0 .80 .56 .34

Wen, 20 years and over.................. 6.8 .94 .66 .40

9.3 1.24 .87 .53

32.1 4.77 3.34 2.05

White.................................................... 5.6 .21 .15 .08

Men, 20 years and over.......... . 4.1 .24 .17 .11

Women, 20 years and over........ . 5.5 .37 .26 .17

Both sexes, 16-19 years.................. 15.2 1.17 .83 .53

OCCUPATION

White-collar workers_____________ 3.8 .24 .17 .11

3.3 .43 .31 .19

Managers, officials, and proprietors. 1.6 .33 .23 .15

5.2 .48 .34 .22

4.5 .73 .52 .33

Blue-collar workers............................. 7.4 .39 .28 .18

Craftsmen and foremen........... ....... 4.5 .52 .37 .23

Operatives...... .......................... ..... 8.6 .62 .44 .28

10.2 1.18 .84 .53

Service workers______________ _ 6.3 .60 .43 .27

Farm workers....................... ............... 1.8 .51 .39 .23

INDUSTRY

Nonfarm wage and salary workers... 6.3 .27 .19 .12

Construction................................. 9.6 1.21 .86 .54

Manufacturing_______ ________ _ 7.0 .46 .33 .21

Durable goods.................... ......... 7.5 .63 .45 .28

NonduraBle goods____________ 6.3 .68 .48 .31

Transportation~and public utilities. 4.0 .73 .52 .33

6.5 .52 .37 .23

Finance and service industries....... 5.3 .46 .33 .21

Government workers........................... 2.8 .38 .27 .17

Agricultural wage and salary workers. 6.1 1.71 1.21 .77

> For consecutive periods only; error for nonconsecutive periods slightly greater.

when the rates are higher. Therefore, the jobless rate

for female Negro teenagers, of whom there were

350,000 in the labor force in 1970, would have an

exceptionally high sampling error for a month-tomonth change, whereas the error for married men

(of whom there were 38.9 million) would be quite

small. Sampling error, of course, is successively less

when the data to be compared are averaged over

successively longer time spans, such as quarterly, annually, and so on. Table 1 illustrates the standard

error of change for a number of unemployment rates,

representing the major labor force groups.

Although “significant” is a statistical and therefore technical term in the interpretation of labor

force developments, it is often used in a more general sense, and there may be instances in which confusion arises over the use of the word. If the overall

jobless rate declines by 0.2 percentage point from

1 month to the next, for example, this is a “significant” change statistically, in the sense that there

is a very small probability that it would have resulted

solely by chance (such as who happened to be

selected in the sample). However, the decline would

not be significant in the sense of being a very large

movement. Similarly, a change could be interpreted

as being “significant” from a policy point of view if

it reflected continued improvement or represented

a change in direction. It is clear, therefore, that care

must be taken in recognizing the multiple meanings

of the word when considering the numbers on employment and unemployment.

What the overall rate doesn’t tell

Although the total unemployment rate gets the

headlines, it is not always the most meaningful measure of the situation and sometimes conceals as much

as it reveals. As a global estimate, it measures joblessness among all groups of workers— men and

women, white and black, young and old, urban and

rural, experienced and inexperienced. For example,

it incorporates those who experience very high rates

of unemployment, such as black teenagers, and those

with very low rates, notably married men.

The Bureau has continually expounded on the aggregate measure of unemployment in its various

studies of the economic situation and in its monthly

“Employment Situation” press release. Key groups

are identified— by age and sex, color, persons seeking full-time or part-time jobs, occupation and industry of last job, duration of unemployment (the

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To show the importance of a thorough analysis of

disaggregated monthly unemployment figures, two recent examples are presented.

1. As reported on October 2, 1970, the overall

jobless rate jumped from 5.1 percent in August to

5.5 percent in September 1970. Anyone who caught

only the newspaper headline unfortunately got a

misleading impression. However, the Bureau’s analysis emphasized that the increase was wholly among

16- to 24-year-olds and adult women, many of whom

were new entrants or re-entrants to the labor force;

the jobless rate for males 25 and over— often thought

of as primary workers and also as breadwinners—

actually remained unchanged.

2. A few months earlier, the unemployment rate

was reported to have risen from 4.8 percent in April

to 5.0 percent in May. Following technical standards

of statistical significance, the rise of 0.2 percentage

point was barely more than a borderline change. As

the continuation of a sharp upsurge since the first of

the year, however, the increase could be viewed with

greater meaning. Moreover, the components of the

change were striking: the jobless rate for adult men

(20 and over) rose by 0.3 percentage point and that

for adult women shot up by 0.7 percentage point;

these very significant increases were countered by a

drop in the teenage rate from 15.7 to 14.3 percent,

which, of course, held the overall rate to its small

increase.

Many different interpretations could be placed on

the data that emerge from these two examples. A

purely surface analysis might suggest that the Augustto-September increase was far worse than that from

April to May, because the overall rate increased

twice as much. However, a more penetrating evaluation leads to the conclusion that the May increase

could well be the more meaningful one.

How is it determined which unemployment

changes are of importance? Several factors must be

considered. First of all, it is necessary to identify the

labor force groups that have the greatest economic or

social significance, both in general and in a particular

period. If employment in a certain industry or occupation is changing, the jobless rate for this industry or occupation should be examined. Developments

for Negroes are often compared with those of whites.

Developments in other economic time series— strike

reports, payroll employment, retail sales, industrial

production, insured unemployment, to name a few—

number of weeks persons are seeking work), reasons

for unemployment, and so on.

suggest areas for investigation. Trends among a number of worker groups should be closely watched—

for example, married men, blue-collar workers, manufacturing workers, full-time workers, and those who

lost their last job.

Secondly, for those categories that are analyzed,

it is necessary to determine what changes are statistically significant. As was discussed earlier, developments for particular labor force groups are generally

important only if deemed significant statistically.

Changes can also be analyzed when their movements

become statistically significant over several months

— thus, representing a short-term trend— even when

they may not be significant in consecutive months.

Third, the analyst studies those groups with higher

than average unemployment rates. This is typically

true of black workers, teenagers, women, and construction workers, among others. For example, the

ratio of Negro unemployment to the total level is

nearly twice the proportion of Negro workers in the

labor force, and partly for this reason their job situation is closely watched.

Finally, it is important to view changes in unemployment not just for the single month being examined, but also from a longer term perspective. Charts

can greatly assist in this determination. During the

1970 economic downturn, for example, the increase

in joblessness among adult men greatly exceeded advances among other age-sex groups, even though

their rate was comparatively low and many of the

individual monthly changes were small. Moreover,

current developments are frequently compared with

highs or lows of previous periods.

Cross-currents in the labor force

It is important to note that monthly statistics of

the labor force and unemployment conceal a vast

number of movements between labor force categories. Typically, about half of the unemployed in

1 month will have found jobs or left the labor

force in the next, and about an equal number will be

newly unemployed. This is evidenced by the fact

that more than 14.5 million persons experienced

some unemployment in 1970, contrasted with an

average monthly level of 4.1 million. Similarly, there

are many people moving into and out of the labor

force each month. These “gross flows” are illustrated

in table 2, which presents March-April 1971 changes

in the employment status of the population 16 years

and over.

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These data are not identical with published figures

for April 1971, because they are based on the portion

of the April sample that was also in the March sample (three-fourths of the Current Population Survey

sample) and because the full estimation procedures

obviously could not be applied.5 These technical

constraints notwithstanding, the gross movements

into and out of unemployment are extensive; only

about half of the April unemployed had been jobseekers in the previous month, whereas nearly equal

proportions were employed or out of the labor force.

Clearly, there is constant turnover in the ranks of

the unemployed.

Changes in the labor force— both in terms of level

and age-sex composition— can affect the unemployment rate even if the level of unemployment is unchanged. For example, if the employment level in

April 1971 had been 300,000 higher, with no change

in the level of unemployment, the unemployment

rate would have rounded to 6.0 percent instead of

6.1 percent. The additional workers would have been

enough to move a rate that was already on the “low

side” of 6.1 percent down one-tenth of a percentage

point (rounded).

Over a longer period, of course, the impact can

be even more substantial if the composition of the

labor force should shift, as has occurred over the

last 15 years. In 1956, for example, adult men (20

years and older) accounted for 64 percent of the

labor force; by 1970, the ratio had slipped to 57

precent, as greater numbers of both adult women

and teenagers— groups with higher unemployment

rates than men— entered the labor market. If the

age-sex labor force distribution (10-year age groups)

had not changed between 1956 and 1970, but allowing for unemployment rates for each age-sex group

to change as they did, the 1970 overall jobless rate

would have been 4.4 percent rather than the published 4.9 percent figure.8

Table 2. Gross flows in the employment status of persons 16 years and over between March and April 1971

[In thousands]

Employment status

category

Status

in

April

Status in March

Employed

UnemployedNotin

labor

force

Employed.............................................

Unemployed.........................................

Not in labor force................................

78,409

4,494

56,302

74,240

1,018

2,566

1,508

2,409

1,182

2,660

1,067

52,553

Labor force behavior during downturns

When business conditions begin to worsen, as during 1970, the level of unemployment increases, of

course, while employment either grows more slowly

or declines. The labor force, the combination of

these elements, usually continues to rise, but its rate

of increase may tend to decline, as people abandon

or defer the search for work. This diminution tendency is recognized in labor force theory as the “discouraged worker effect.”

Another concept of labor force behavior during

cyclical downturns is the “additional worker effect.”

It holds that secondary workers are induced to enter

the labor market as breadwinners lose their jobs or

take a pay cut (or perhaps fear these circumstances

may occur). Therefore, should a worker be laid off,

his wife and perhaps a teenage son might enter the

labor force. It is extremely difficult to substantiate

the “additional worker effect” from available labor

force data. There is more concrete evidence of the

“discouraged worker effect,” and it appears to be

the larger factor.7

The above discussion suggests a very important

aspect of the working population, the fact that decisions to participate or not participate in the labor

force— and thus to seek work or not seek work— are

often made for many personal reasons or in response

to factors and events about which we have no information. In other words, people cannot be assumed

to always act in a prescribed manner when it comes

to their participation in the labor force. This should

be borne in mind when one examines short-run labor

force growth. Under short-run conditions, in fact,

fits and starts in labor-force growth are more typical

than a smooth trend from month to month at an

annual rate of 1.5 to 2.0 million. This is also relevant

to an understanding of unemployment developments,

because people frequently cannot find a job as soon

as they enter the labor force. And if short-run labor

force behavior is not easily predictable, it is clear

that short-run unemployment movements are also

variable.

Problems with seasonal adjustment

Because the seasonal-adjustment process is based

upon experience of past years, to the extent that

seasonal patterns or short-run labor force behavior

change, the current data may be difficult to evaluate.

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Table 3. Seasonally adjusted unemployment rates as originally published and as revised in subsequent years, 1967-70

Month

1967 1968 1969 1970

Originally

published

1968

revision

1969

revision

1970

revision

1971

revision

Originally

published

1969

revision

1970

revision

1971

revision

Originally

published

1970

revision

1971

revision

Originally

published

1971

revision

January............................ 3.7 3.7 3.7 3.8 3.8 3.5 3.6 3.6 3.7 3.3 3.4 3.4 3.9 3.9

February...................... 3.7 3.7 3.7 3.8 3.8 3.7 3.7 3.8 3.8 3.3 3.3 3.3 4.2 4.2

March.............................. 3.6 3.7 3.7 3.8 3.7 3.6 3.7 3.7 3.7 3.4 3.4 3.4 4.4 4.4

April................................. 3.7 3.7 3.8 3.8 3.8 3.5 3.5 3.5 3.5 3.5 3.5 3.5 4.8 4.7

May.................................. 3.8 3.9 3.9 3.9 3.8 3.5 3.6 3.6 3.5 3.5 3.5 3.4 5.0 4.9

June_____________ _ 4.0 3.9 3.9 3.9 3.9 3.8 3.7 3.7 3.7 3.4 3.4 3.4 4.7 4.8

July.................................. 3.9 3.9 3.9 3.8 3.8 3.7 3.7 3.7 3.6 3.6 3.5 3.5 5.0 5.0

August........................... 3.8 3.8 3.8 3.8 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 5.1 5.1

September................... . 4.1 4.1 4.0 3.9 3.9 3.6 3.6 3.5 3.5 4.0 3.8 3.8 5.5 5.4

October______________ 4.3 4.3 4.2 4.1 4.1 3.6 3.6 3.5 3.4 3.9 3.8 3.7 5.6 5.5

November......... ............. 3.9 3.8 3.8 3.9 3.9 3.3 3.4 3.4 3.5 3.4 3.5 3.5 5.8 5.9

December......................... 3.7 3.7 3.7 3.7 3.8 3.3 3.3 3.3 3.4 3.4 3.5 3.6 6.0 6.2

This may come about due to transitional periods of

economic activity. Other complications can arise

from a shift in the timing of the survey week (whether

the 12th day is early or late in the week); in months

in which large labor force changes are taking place,

such as in June and September; possibly from unusual events such as strikes; the timing of holidays;

severe weather conditions; or changes in the survey

questionnaire (these, however, are rarely made). As

a consequence, the seasonally adjusted values may

exhibit erratic behavior or lack of smoothness over

certain months. As more experience is gained, after

a year or two, the new seasonal pattern will usually

emerge more clearly, and a more acceptable set of

seasonally adjusted values will become available.

This “wait-until-next-year” approach to determine

the more accurate seasonally adjusted monthly

changes is not a satisfactory answer to the policymaker or the newsman, whose concern is necessarily

with the present. However, it should be recognized

that revisions of the overall jobless rate rarely exceed

0.1 percentage point in the subsequent year. Moreover, since seasonally adjusted values are subject

to change, seemingly erratic movements in the current year should be viewed as approximations of

what occurred rather than as exact measures.

In the past several years, there has been some

evidence that the overall seasonally adjusted rate

has been behaving more erratically than in prior

years. This may have come about as a result of conceptual revisions and the changes in the questionnaire beginning in January 1967. Prior to 1967, an

individual not working was asked, during the course

of the survey interview, “Was . . . looking for work?”

In 1967, the question was changed to: “Has . . . been

looking for work during the past 4 weeks?” If the

answer is yes, the interviewer asks, “What has . . .

been doing in the last 4 weeks to find work?” A

specific activity must be cited or the person will not

be counted as unemployed. Finally, the question,

“Is there any reason why . . . could not take a job

last week?”, is asked to ascertain if the jobseeker

was available for work rather than seeking a job for

some future period.8

Although the precise effect of these changes is

difficult to quantify, there is evidence of their impact,

particularly the addition of the availability question.

The shift from an unspecified jobseeking period to

a 4-week period might well have produced a lower or

higher total, as previous respondents could have interpreted “looking for work” to imply either “last

week” or some vague earlier period. Similarly, the

introduction of the specific jobseeking method requirement (not asked, of course, of persons on layoff or those waiting to begin a new job within 30

days) may have lowered the jobless count as well,

screening out those for whom jobseeking is more a

state of mind rather than overt action.

And the effect of the “availability test” is clear,

particularly in March, April, and May, when youngsters still in school are seeking summer jobs. All

three of the questionnaire revisions appear to have

seasonality implications, with teenagers in the spring

being the most obvious example. One result has been

a number of unaccountable movements in the overall

rate, particularly in the August-through-November

periods. Another result has been the fact that May

is now the seasonal low of the year in terms of unemployment, instead of October. The original jobless rate and its revisions based upon seasonal adjustment for the years 1967-70 are shown in table 3.

Since the 1967 alterations, the seasonal adjustment

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process has had a chance to “settle down” somewhat,

and it appears fewer inexplicable month-to-month

jumps will be observed in 1971 and subsequent years

due to this factor. However, because changes in the

business cycle can affect seasonal patterns, it is also

likely the seasonal adjustment process has been affected by the rise in the level of unemployment since

1969.

This would be most evident in months in which

very large seasonal changes occur, such as between

May and June, August and September, and December and January. The seasonal-adjustment process,

which at present is essentially multiplicative in nature, may tend to overcompensate for these wide

variations. This accounts for a part of the unusual

drop in the jobless rate, from 6.2 percent in May to

5.6 percent in June, which was reported at the time

as somewhat exaggerating the “real” change that

took place between the 2 months.

It should be emphasized that seasonally-adjusted

values are, at best, approximations of the underlying

trend. One should therefore not expect them to be

uniformly smooth on a monthly basis or that they be

a precise reflection of cyclical movements.

The unemployment concepts reexamined

The question has been raised many times over the

years as to whether the unemployment concepts and

definitions should be revised. “We are counting too

many as unemployed.” “We are not counting

enough.” “Many of the unemployed don’t really want

to work and shouldn’t be counted.” “Many unemployed don’t need a job.” “Many no longer looking

have just given up but are still really unemployed.”

So go some of the complaints about the current concepts.

This subject has been examined almost continuously since the inception of the unemployment

measurement survey in 1940. The concept that has

been accepted and used has changed very little over

this tri-decade,9 or else comparability would be a

very real problem.

Those who feel that the number counted as unemployed is too high point to such groups as young

people, particularly those who are in school, and

married women as outstanding candidates for elimination from the conceptual base. The usual justifications given are that these types of workers do not

really need a job and/or are only temporarily in the

labor force. It would be interesting and significant

for public policy development if one could make an

estimate of the number of unemployed who need

jobs. But the subjectivity of such a measure would

be exceedingly great, since in some cases it cannot

be easily determined whether an unemployed individual really needs a job. For example, in many

families, the husband and wife are both active labor

force participants because they believe they need

the income; it cannot be arbitrarily concluded that

women with employed husbands do not need jobs.

Similarly, it seems unrealistic to count certain “marginal” workers as employed when they have jobs,

but to exclude them from the unemployed count

when they are looking for work.

On the other hand, it is also often said that the

unemployment concept excludes some persons who

should be counted as unemployed. Those who support this view point out that a number of persons

become discouraged over job prospects and cease to

look for work, even though they still desire a job.

These discouraged workers are sometimes referred

to as “hidden unemployed.” 10 Ever since the regular collection of unemployment statistics by the

Federal Government began, however, the criterion

has been that to be counted as unemployed a person

should be an active jobseeker, which lessens substantially the possibility of unemployment being a

state of mind. The President’s Committee to Appraise Employment and Unemployment Statistics

recommended in its 1962 report that these workers

be identified, but it was equally firm in its belief that

they not be included in the jobless count.11 At its

behest, this category, as well as all other “not in the

labor force” groups, has been identified in the Current Population Survey since 1967, and data are

published on a quarterly basis in Employment and

Earnings by a wide variety of characteristics. In

1970, discouraged workers as measured in the survey averaged nearly 650,000 persons,12 mostly teenagers and adult women.

Because of the vast array of statistics regularly

published on the employed, unemployed, and those

not in the labor force, it is possible for one to calculate an unemployment rate based on various definitions of labor force and unemployment. For example,

those who believe that labor force eligibility should

begin with age 18 and end, say, with age 69 can

exclude 16- and 17-year-olds and the over-70 group,

and then calculate a separate unemployment rate.

Similarly, estimates of discouraged workers could

be added to the unemployment and labor force totals

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to arrive at a rate that includes this group, or persons

whose major activity is going to school might be

excluded.

The major criterion that has been used over time

in estimating unemployment is objectivity of measurement. Need for work, intensity of desire, and family income are all potentially subjective factors and

as such are excluded from unemployment concepts,

which are under constant review to make them as

objective as possible.

In this context, it is appropriate to conclude with

the idea that perhaps the most important aspect in

the measurement of unemployment in the United

States is the maintenance of consistency over time.

This is another way of saying that the greater concern is necessarily focused upon the relative, rather

than the absolute, position. In other words, it is imperative to know how well off the economy is each

month compared with the preceding month or some

earlier period. It is relevant to continue to examine

who should be counted as unemployed, but such

examination should not interfere with public trust

in the figures, historical continuity of the data, or

objectivity of measurement. If these three standards

are followed, unemployment statistics will continue

to provide one of the best measures of the economic

status of the Nation. 

-FOOTNOTESxFor a more detailed discussion of the Current Population Survey and the concepts utilized, see Concepts and

Methods Used in Manpower Statistics From the Current

Population Survey (BLS Report 313, 1967).

*The original data of a series “are regarded as the

product of a trend-cycle component times a seasonal component times an irregular component. The trend-cycle represents the ‘real’ movement of the series, including cyclical

movements. The seasonal component is the annual repetitive pattern which makes certain months consistently higher

or lower than others. The irregular component is a residual,

including sampling errors and short-term fluctuations which

do not follow any consistent pattern. After a satisfactory

decomposition is achieved, the seasonally adjusted series

is computed by dividing each original value by the corresponding seasonal factor.” The foregoing is from ‘The

Method of Seasonal Adjustment for Labor Force Series,”

Employment and Earnings, February 1971, pp. 22-23.

A more technical description of the seasonal-adjustment

method may be found in “Appendix A. The BLS Seasonal

Factor Method,” BLS Handbook of Methods for Surveys

and Studies (BLS Bulletin 1458, 1966), pp. 222-228.

* The civilian labor force, seasonally adjusted, is the

aggregation of the four major age-sex components (male

and female, 16-19 years, and 20 years and over) for each

of three categories: agricultural employment, nonagricultural employment, and unemployment.

* In evaluating monthly estimates, the determination o f

whether a change is statistically significant is based upon

the sampling error of the estimate. It is recognized that

the seasonal-adjustment process itself is imperfect, especially on a current basis. However, the magnitude of any

error attributable to seasonal adjustment is not quantifiable

at the time the estimates first become available.

BSee Harvey J. Hilaski, ‘T he Status of Research on

Gross Changes in the Labor Force,” Employment and

Earnings and Monthly Report on the Labor Force, October

1968.

•Additional amplification of structural shifts in the labor

force and their effect upon the unemployment rate are

contained in an article by George L. Perry, “Changing

Labor Markets and Inflation,” Brookings Papers on Economic Activity 3 (Washington, Brookings Institution, 1970),

pp. 411-441.

7 For a discussion of the discouraged worker and additional worker hypotheses and some indication of their relative impacts, see William G. Bowen and T. Aldrich Finegan,

The Economics of Labor Force Participation (Princeton,

N.J., Princeton University Press, 1969).

•For further amplification of the 1967 changes, see

Robert L. Stein, “New Definitions for Employment and

Unemployment,” Employment and Earnings and Monthly

Report on the Labor Force, February 1967.

•For a discussion of the historical background of the

conceptual framework, see President’s Committee to

Appraise Employment and Unemployment Statistics, Measuring Employment and Unemployment (Washington, 1962),

Ch. I.

“ Prominent among those who support the view that

discouraged workers should be included in the jobless

counts is Professor Alfred J. Telia of Georgetown University. Professor Telia has performed a considerable amount

of research with labor force models which enables him

to estimate the number of persons not actually counted as

unemployed but who would be in the labor force under

what he defines as “full employment” conditions. See “The

Relation of Labor Force to Employment,” Industrial and

Labor Relations Review, April 1964, pp. 454—469, and

“Labor Force Sensitivity to Employment by Age, Sex,”

Industrial Relations, February 1965, pp. 69-83.

“ President’s Committee to Appraise Employment and

Unemployment Statistics, op. cit., pp. 52-56.

“ In 1967, the number of discouraged workers averaged

732,000; this dropped consecutively in 1968 to 667,000 and

to 574,000 in 1969 before rising to 638,000 in 1970 and

740,000 in the second quarter of 1971 (seasonally adjusted).

For further discussion of these data, see “Discouraged

Workers and Recent Changes in Labor Force Growth”

(BLS Report 396, 1971).

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The two series generally move 

in similar directions, 

but show different levels 

due to absences, second jobs, 

the census undercount, and other factors

GLORIA P. GREEN

S t a t i s t i c s o n n o n a g r i c u l t u r a l employment are

key indicators of the economic health of the

Nation. Because these data serve a variety of

purposes, no one source of data can adequately

provide all of the information that is needed for

a complete and balanced picture of the employment situation.

Each month, the Bureau of Labor Statistics

analyzes and publishes two independently derived

estimates of total employment in nonagricultural

industries— the household series and the establishment series.1 Each of these bodies of data makes

its own unique contiibution to the nonagricultural

employment picture— the household series as a

measure of the work status of individuals and the

payroll series as a count of jobs.

These series attempt to measure different aspects

of the nonagricultural employment situation—

people versus jobs—but for the most part they

tend to show the same underlying economic influences. At times, however, significant differences,

which may be confusing to the user, are observed

in the level of the estimates, in month-to-month

changes, in the timing and extent of business

cycles, and in certain longer run trends.

In part, these differences are inherent in the

concepts and scope of the two series. Nonagricultural employment measured through a household

survey cannot and should not be expected to

yield magnitudes identical with those of employment measured through an employer-payroll

reporting system. Conceptual differences between

the series can usually be reconciled or explained

in large part, and a number of the differences in

coverage can be adjusted. However, there are

also discrepancies caused by differences in samGloria P. Green is an economist in the Division of Em ­

ployment and U nem ploym ent Analysis, Bureau of Labor

Statistics.

Comparing

employment estimates

from household and

payroll surveys

pling techniques and collection and, estimation

methods, most of which cannot be readily measured in terms of impact on differences in the

levels of the two series.

It should be noted at the outset that the total

nonagricultural employment series from the household survey is much more comprehensive than

the establishment series. The household series

includes— in addition to wage and salary workers—

the self-employed, unpaid workers who worked

15 hours or more during the survey week in

family-operated enterprises, and private household workers, none of whom by definition would

appear on establishment payrolls. These three

groups are readily identified in the household

survey on the basis of major industry and classof-worker designations; in 1968, they totaled

7.5 million workers. When these groups are

subtracted from the household estimate of total

nonagricultural employment, a third series, reflecting employment of wage and salary workers of

generally comparable coverage to the establishment series, is obtained. In order to develop a

reconciliation, it is necessary to examine the

comparability of this derived household series

with the payroll employment series. The three

series— total nonagricultural employment (household survey), nonagricultural wage and salary

employment excluding private household workers

(household survey),2 and nonagricultural wage

and salary employment (establishment survey)—

are compared in table 1 on an annual average

basis from 1948 -68.

This study first examines the conceptual and

other differences between the two series. Second,

it attempts to reconcile the annual levels of the

two series over the 1962-68 period 3 insofar as

known discrepancies can be quantified. (It should

be kept in mind, however, that no attempt at

reconciliation provides a complete answer accounting for all of the factors that influence the levels

From the R eview of December 1969 14

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of the two series.) Finally, employment levels

are examined on both an annual and a monthly

basis to gain greater insight into the divergences

in levels.

Measurable factors affecting com parability

Of the many factors that influence the levels of

the series, only a few can be quantified to any

degree. The numbers involved and their relative

effect upon the levels of the series are discussed

in the sections that follow.

U n p a i d a b s e n c e s . One major source of discrepancy between the two series stems from

different treatment of workers absent for a full

week from their jobs. The household survey includes

among the employed all persons who had jobs

during the survey week but were temporarily

absent because of illness, bad weather, vacation,

labor-management disputes or various personal

reasons, whether they were paid by their employers

for the time off or whether they were seeking other

jobs. B y contrast, the establishment series includes

only those persons on paid leave for any part of

the pay period specified in the survey. Therefore,

persons who are absent without pay for the entire

period are not included in the payroll figures. On

the average, about 3.7 million employed nonfarm

wage and salary workers were picked up in the

Table 1. Nonagricultural employment, 1948-68

[In thousands]

Year

Household series'

Payroll 

series: 

wage and 

salary

employment

Difference3

Total

Total wage 

and salary 

employment2

1948___________________ 51,405 43,135 44,891 1,756

1949___________________ 50,684 42,308 43, 778 1,470

1950___________________ 52,450 43,982 45,222 1,240

1951___________________ 53,951 45,627 47,849 2,222

1952___________________ 54,488 46, 465 48,825 2,360

1953___________________ 55,651 47, 449 50,232 2,782

1954_________________ 54,733 46,490 49,022 2,532

1955___________________ 56,464 47,838 50,675 2,837

1956___________________ 58, 394 49,518 52, 408 2,890

1957___________________ 58, 789 49,745 52,894 3,149

1958___________________ 58,122 48,876 51,363 2,487

1959_________________ . 59,745 50,330 53,313 2,983

1960___________________ 60,958 51,487 54,234 2,747

1961_________________ 61,333 51,690 54, 042 2,352

1962___________________ 62, 657 53,136 55, 596 2,460

1963___________________ 63,863 54, 498 56,702 2,204

1964_________________ 65, 596 56,115 58,331 2,216

1965___________________ 67,594 58,217 60,815 2,598

1966______ ____ _ . . 69,859 60, 686 63,955 3,269

1967__________________ 70, 527 62,882 65,857 2,975

1968___________________ 72,103 64,601 67,860 3,259

1 Persons 14 years and over for 1948-66; 16 years and over for 1967-68.

2 Excludes private household workers.

3 Payroll series employment less household wage and salary employment.

household survey as being “with a job but not at

work” in 1968. Of this total, 1.6 million or 43

percent were not paid for the time off, as shown in

table 2.

M u l t i p l e j o b h o l d i n g . Another major source of

discrepancy relates to the treatment of persons

employed in more than one job. The household

survey counts each person by work status only

once since each person is classified as either

employed, unemployed, or not in the labor force.

Employed persons holding more than one. job

during the survey week are counted and classified

in the job at which they worked the greatest

number of hours. In the establishment survey, the

total number of employed persons is overstated to

the extent that those who worked in more than one

establishment during the reference period are

counted each time their names appear on a payroll.

Workers may be counted more than once, for

example, because they hold down two jobs or more

concurrently or because they leave one job and

obtain another within a single reference period and

thus appear on the payroll records of both em ­

ployers. Such a situation can also arise when a

worker is continued on a payroll after leaving his

job because he is being compensated for earned

vacation time.

While it is virtually impossible to identify

persons who work at two jobs or more from payroll

records, it is usually possible to obtain this

information from the worker or a member of his

household. To gain insight into multiple jobholding, special surveys of this phenomenon have

been conducted as a part of the household survey

periodically since 1943. In M ay 1966, the most

recent month for which survey data are available,

approximately 2 million workers held a secondary

nonagricultural wage and salary job that would not

have been reported in the usual monthly household

survey.4 As indicated in table 3, these additional

jobs were concentrated in trade (28 percent),

services (26 percent), and government (18 percent).

Accordingly, the household estimate of 60 million

persons who were employed in a nonagricultural

wage or salary primary job (excluding private

household workers) during the survey week in

M ay 1966 would have to be raised by 2 million to

approximate a payroll count.5 The count for that

period would be even higher, of course, if account

were taken of the number of persons holding 3

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Table 2. Employed nonagricultural wage and salary

workers on unpaid absences, by industry, 1962-68

(In thousands]

Industry 1962 1963 1964 1965 1966 1967 1968

Total1______________ 1,122 1,241 1,249 1,249 1,317 1,454 1,629

Private1 __________________ 913 993 1,006 1,024 1,065 1,168 1,314

Construction_____________ 95 95 83 92 97 114 140

Manufacturing___________ 339 365 377 381 394 460 488

Transportation and public 

utilities_______________ 68 80 78 76 81 70 95

Wholesale and retail trade.._ 201 239 227 237 242 256 285

Finance, insurance, and real 

estate___________ ____ _ 35 36 46 37 41 41 46

Services2_______________ 163 172 188 190 194 211 240

Government________ ________ 210 247 243 225 252 284 315

1 Also includes mining, not shown separately, and excludes private household workers.

2 Excludes private household workers.

additional paid jobs or more. These surveys have

been too infrequent to determine any definite

seasonal, cyclical, or secular trends, although in

recent surveys the rate of multiple jobholding has

remained substantially the same.

Even though several improvements in the

enumeration ol multiple jobholders have been

made in recent years, it is probable that no

survey has given what might approximate a

complete count. However, the extent of this

hypothetical “undercount” is uncertain. Many

persons may be reluctant to report secondary

jobs for various reasons, such as a distrust of the

confidentiality assurances of the survey, and

knowledge of such jobs might be deliberately

withheld from the survey interviewer. Another

possible reason for undercount is that the respondent (often the housewife) may not be aware

of the second job or may not realize that the

person in question is technically on two payrolls

or more. Examples of this latter case are teachers

paid on a 12-month basis and employed in other

jobs during the summer; lawyers acting as directors

of corporations, sometimes several corporations;

school board and other government officials who

are paid for limited services rendered; persons

performing as consultants on an irregular basis,

etc. It might be possible to develop techniques to

improve the count in the latter cases, but it is

unlikely that deliberate failures to report multiple

jobholding could be uncovered.

A g e l i m i t a t i o n s . While the household series

provides data on the full- or part-time employment status of the entire U.S. civilian noninstitutional population 16 years of age and over,

the establishment series has no age limitations,

although child labor laws, compulsory school

attendance, and general social custom prevent

most children under 16 from working. In 1967

and 1968 household survey estimates of nonagricultural wage and salary workers 14-15 years

ol age averaged nearly 440,000 and 480,000,

respectively. (Prior to January 1967, official c p s

statistics on nonagricultural wage and salary

employment had included 14- and 15-year-olds.)

The number of young persons under 14 years of

age who are employed in nonagricultural wage and

salary jobs is not known.

E f f e c t o f t h e c e n s u s u n d e r c o u n t . Investigation of the accuracy and completeness of the

1960 Census of Population has indicated that an

estimated total of 5.7 million persons of all ages

were missed in the enumeration. Since the decennial population censuses provide the basis for

projection of current estimates of the population,

which, in turn, serve as monthly controls for the

household survey sample, any undercount of the

population in the census can have a profound

effect upon the level of labor force and employment estimates derived from that survey. Advancing the ages of persons undercounted in 1960

by 7 years reveals a probable total employment

undercount of about 2.8 million persons 16

years of age and over in 1967.® Distributing the

2.8 million on a ratio basis, about 2.4 million are

estimated here to be nonagricultural wage and

salary workers. The nonfarm employment estimates based on payroll surveys are not similarly

affected, since these surveys cover all persons

on payrolls and do not depend upon probability

population controls.

A g r i c u l t u r a l s e r v i c e s . One minor discrepancy

between the two series which has been quantified

Table 3. Distribution of secondary nonagricultural wage

and salary jobs, by industry, May 1966

[In thousands)

Industry division Total1 Percent

Total____ _ ___________ ______________ 1,996 100.0

Private wage and salary....................................... ............. 1,640 82.2

Mining........ ........................................................ 9 0.5

Construction...................... ........................................ 129 6.5

Manufacturing.............................................................. 183 9.2

Transportation and public utilities............................. 123 6.2

Wholesale and retail trade__________ ___________ 551 27.6

Finance, insurance, and real estate............................. 128 6.4

Services2 .. . . . . ...................................... 517 25.9

Government................................... ....................................... 356 17.8

1 Data include only first additional Job.

2 Includes forestries and fisheries; excludes private household workers.

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on the basis of data obtained from the payroll

series stems from the different classification of

workers employed in agricultural services. The

payroll series includes them under services, while

the household series classifies them in agriculture.

They constitute nearly 90 percent of the payroll

subclassification, agricultural services, forestries,

and fisheries, and ranged from 134,000 in 1962 to

160,000 in 1968. This classification difference is

of significance primarily in comparing the services

industry component of the two series; its effect

on overall estimates of nonagricultural wage and

salary employment is of lesser importance.

Net effect of measurable differences

Taking into account all of the measurable differences between the two series, it is possible to develop reasonably comparable household and payroll estimates of nonagricultural wage and salary

employment. (See table 4.) However, most of the

adjustments must be made in the household data,

since they contain a wide variety of data on

other characteristics of workers, and it would

not be appropriate statistically to adjust one

series with data from another.

The adjustment technique requires the subtraction of the number employed in agricultural

services from the payroll series, since this group

is included in agriculture in the household series.

Except for this adjustment, all others are made to

the household data to achieve comparability with

Table 4. Measurable differences between payroll and

household estimates of nonagricultural wage and salary

employment, 1968

Item

Unadjusted

totals Adjustments

Additions

minus

reductions

Adjusted 

totals and 

difference

Payroll series........................... 67,860,000

Less: employment in 

agricultural services. . . 160,000

Adjusted payroll employment................................ 67,700,000

Household series1....................... 64,601,000

Less: unpaid absences___ 1,629,000

Total reductions....... 1,629,000

Plus: multiple jobholders 

(est.)2......... 2.184.000 

484,000

1968 undercount (est.). 2.400.000

Total additions____ 5.068.000

Net adjustments.. _ 3.439.000

Adjusted household 

employment................... 68,040,000

-340,000

Difference after adjustments 

(payroll less household 

series). . . .

1 Excludes private household workers <1,854,000).

2 Estimate includes only first additional nonagricultural wage and salary job.

the adjusted payroll series. This involves a subtraction of the number of persons on unpaid

absence from their jobs during the survey week,

and the addition of the estimated number of

secondary jobs, employment of 14- and 15-yearolds, and the estimated 1968 population undercount. The net result after allowance for these

measurable differences is to reduce the original

difference between the two series from 3.3 million

to an estimated 340,000 in 1968.

In evaluating this difference, it should be kept

in mind that estimates of the known sources of

discrepancy are subject to considerable uncertainty. It should not be assumed from this isolated

example that the net effect of allowing for all

measurable variables would always bring the two

series this close together. The net adjustment

might be greater or smaller than this figure in

another period. However, this attempt at reconciliation does illustrate the extent of the

problem, and the nature and approximate magnitudes of many of the discrepancies.

Other factors affecting com parability

While the foregoing factors can be quantified

to some degree, there are other factors which

influence the levels of the two series for which no

quantitative estimates can be made.

D i f f e r e n c e s i n s u r v e y c o v e r a g e . In several

respects, the establishment series is more inclusive

in terms of population coverage than is the household series. The establishment series includes

military personnel who hold civilian jobs in nongovernment establishments during their off-duty

hours. It may also include some inmates of institutions who are working in or outside the institution

if they are on payrolls. All military personnel and

institutional inmates are explicitly excluded from

the household data.

Residents of Canada or Mexico who commute to

nonagricultural jobs in the United States and thus

are included in the payroll count would also be

outside the scope of the household survey, which

covers people residing in the 50 States and the

District of Columbia. Commuting to the Uuited

States may be partly offset to the extent that some

U.S. residents are employed by establishments in

Canada or Mexico. The combined effect of these

differences, however, probably does not account

for much of the gap between the two series.

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T h e s u r v e y p e r i o d . The time period covered by

the household survey is always one calendar

week; since July 1955, it has been designated as

the week containing the 12th of the month. In

the payroll series, the time reference is to the

payroll period including the 12th of the month,

which is intended to be a single week. Despite

comparability in design and intent, there are a

number of differences in reality. Some establishments have 2-week or monthly payrolls (at least

one-fifth of the total) and thus are likely to reflect

more duplication due to multiple jobholding or

turnover than would be reported for a single week.

Moreover, during the longer time period a person

could be counted as employed in the payroll

series who, during the household survey week, was

either unemployed or not in the labor force.

For the Federal Government, employment

figures represent the number of persons occupying

civilian positions on the last day of the calendar

month plus any intermittent workers who worked

at anytime during the month. This not only

tends to magnify the general problem resulting

from turnover but also contributes to a special

problem of comparability with the household

series, which is peculiar to December alone. That

month the payroll series invariably rises substantially, reflecting the hiring of temporary

postal workers for the Christmas rush period. The

household survey, on the other hand, usually does

not show a rise of similar magnitude, mainly

because the survey week occurs relatively early in

the month and many of the workers counted as

employed in the payroll series are not working at

the time the household statistics are collected.7

D ata permitting a correction for the deviation

from the single-week reference period in the

establishment data are not available.

P r o b l e m o f s c h o o l t e a c h e r s . The manner in

which payrolls are handled in the education system

causes a special problem of comparability between

the two series, which is peculiar to the summer

months alone. Some teachers and other educational

staff are paid on a 12-month basis; other regular

faculty members are paid only during the academic

school year (9 or 10 months) and would not ordinarily appear on payrolls during the summer

months. As a consequence, special treatment is

accorded school teachers in the establishment

series. Instead of using payroll reports for the summer months, which would include only that part

of the total actually employed in summer schools

or on paid vacation, estimates of the number of

regular school teachers in M ay— the last full month

of the school year prior to the vacation period—

are substituted in tabulations covering the vacation months.

In the household series, school teachers who

have contracts (either written or verbal) to return

to teaching in the fall would be reported during

the summer months as “with a job but not at

work” (on vacation) unless they hold other jobs

and were thus classified according to the occupation and industry of that job during the survey

week. The possibilities for multiple jobholding

among teachers during the summer months, which

would have the effect of inflating the payroll count

compared with the household count, are obvious.

However, the magnitude of this conceptual difference is uncertain.

S a m p l i n g a n d e s t i m a t i n g p r o c e d u r e s . The

household survey covers a scientifically selected

probability sample of 50,000, designed to represent

the entire civilian noninstitutional population. As

an early step in estimating procedures, the sample

data (for persons 16 years of age and over) are

weighted by independently developed estimates of

the population by age, sex, and color. These estimates. are prepared by carrying forward the most

recent census data (1960) to take account of subsequent aging of the population, deaths, immigration, and emigration.

Because the household survey is based upon a

sample, the results may differ from the figures that

would be obtained if a complete census using the

same schedules and procedures were possible. In

this series, the relative sampling error for the estimate of nonagricultural wage and salary workers

is about 200,000 at present employment levels.

This means that the chances are about 2 out of 3

that an estimate from the sample would differ

from a complete census by less than this amount.

This estimate of sampling error would be 400,000

if a confidence level of 19 out of 20 times is

wanted.

As in any survey, the results are also subject to

errors of response and reporting. Furthermore, as

noted earlier, that part of the population missed in

the census (the undercount) is also presumably

missed in the sample survey and subsequent

“blow-up” of the sample as well.

On the other hand, the monthly payroll em18

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ployment estimates are derived from reports of a

relatively large survey sample (160,000 establishments having over 30 million employees), which

assures a high degree of accuracy.8 However, since

the estimating procedures employ the previous

month’s estimate as the base in computing the

current month’s level (link relative technique),

sampling and response errors may accumulate over

several months. To remove any accumulated error,

the employment estimates are adjusted annually

to new benchmarks (comprehensive counts of employment). The revision published each July also

adjusts the estimates for changes in the industrial

classification of individual establishments (resulting from changes in their product, which are not

reflected in the levels of estimates until the data

are adjusted to new benchmarks).9 Another cause

of differences, generally minor, arises from improvements in the quality of the benchmark data.

For the eight most recent benchmark revisions, the

estimates of total nonagricultural employment

have varied from benchmarks by less than 1 percent, averaging 0.3 percent.

Within a few industries, mostly in the service

sector, current monthly estimates are not obtained by direct reports from a sample of establishments (e.g., churches and other nonprofit

organizations), and monthly changes in these industries are based on movements shown in the

benchmark data for earlier years. Necessary adjustments are made at the time of adjustment to

new benchmarks. This procedure can result in

substantial error for a few individual industries,

especially those in which small establishments predominate, but its effect on the much larger nonfarm total is negligible.

The accuracy of the level of the establishment

series depends a great deal on the accuracy of the

benchmarks. These are primarily derived from

tax returns for unemployment insurance, supplemented by social security tax returns for small

employers and by a variety of other sources for

certain sectors. It is possible, for example, that

errors of omission or duplication can occur in the

reporting of social insurance tax returns for

the correct period or in fitting together the benchmark data gathered from a number of sources.

In addition, the benchmark occurring in March

of each year can mean that the other months,

particularly the summer and fall periods, are

less precisely estimated. M any State unemployment insurance laws do not cover firms unless

they operate 20 weeks or more during the calendar

year. As a result, a number of seasonal undertakings, such as summer resorts, hotels, and

amusement enterprises, may be missed by the

benchmark source and thus by the payroll statistics. The net effects of these problems are

unknown, and these uncertainties make comparisons of the levels of the two series even more

inconclusive.

The possibility of error in the population

censuses or the unemployment insurance benchmarks cannot be disregarded. There is no “true”

total against which the accuracy of either can

be measured. Although the benchmark data and

the population totals are among the best statistical

measures available, as we have seen neither is

perfect.

Comparison of annual em ploym ent levels

Although measured very differently, estimates

of nonagricultural wage and salary employment

in the household and establishment series, have

exhibited similar growth patterns over the 1962-

68 period. Employment in the household series

increased by 11.5 million (from 53.1 to 64.6

m illion);10 in the establishment series, it gained

12.3 million (from 55.6 to 67.9 million). B oth

series reflected a net expansion of about 22 percent

during the 7-year period. However, when related

to an earlier period employment levels reveal that

the two series have diverged somewhat, the

difference averaging 2.7 million between 1962

and 1968 and 2.4 million over the 1948-61 period.

(Household data for 1967 and 1968 are not strictly

comparable with that for earlier years. Footnote

10 describes the differences.)

In an effort to examine some of the divergences

in levels between 1962-68, the household and

payroll estimates by major industry groups11

have been adjusted to take into account all

measurable differences between the two series

that can be quantified on a year-to-year basis.

(See table 5.) This involves subtraction of the

number of persons on unpaid absences from thenjobs during the survey week, the addition of the

estimated number of nonagricultural wage and

salary secondary jobs to the household series, and

elimination of the number employed in agricultural

services from the payroll series. In table 5, all

quantifiable adjustments covered in table 4 have

been made on the industry data from both series,

with the exception of the undercount for every

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Table 5. Comparison off payroll and household estimates of nonagricultural wage and salary employment, 1962-68

[In thousands]

Industry and year

Total wage and salary employment:

1962 ..........................................

1963 ..........................................

1964 ..........................................

1965 ..........................................

1966 ..........................................

1967 ..........................................

1968 ..........................................

Private wage and salary employment:

1962 -------------------------------

1963 ..........................................

1964 ..........................................

1965 ..........................................

1966 .......... .............................

1967 ..........................................

1968 ..........................................

Mining:

1962 ..................................

1963 ..................................

1964 ..................................

1965 ...................................

1966 ..................................

1967 ..................................

1968 ..................................

Construction:

1962 ..................................

1963 ..................................

1964 ..................................

1965 ..................................

1966 ..................................

1967 ..................................

1968 ..................................

Manufacturing:

1962 ..................................

1963 ..................................

1964 ..................................

1965 ..................................

1966 ..................................

1967 ..................................

1968 .................................

Transportation:

1962 ..................................

1963 ..................................

1964 ..................................

1965 ..................................

1966 ..................................

1967 ..................................

1968 ...................................

Trade:

1962 .................................. .

1963 ..................................

1964. ..................................

1965 .................................. .

1966 .................................. .

1967 ..................................

1968 ..................................

Finance:

1962........................................

1963.......................................

1964 ...................................

1965. ..................................

1966...................................

1967.......................................

1968........................................

Services:»

1962 ..................................

1963 ..................................

1964 ...................................

1965 ..................................

1966 ..................................

1967 ..................................

1968 ...................................

Government:

1962........................................

1963 .............................. .......

1964 ..................................

1965 ..................................

1966 ...................................

1967 ..................................

1968 ..................................

Payroll series Household series Differences

Total i

Agricultural Adjusted Unpaid Additional Adjusted Payroll minus Adjusted payservices* total

(excluding

agricultural)

Total3 absences

paid jobs* to payroll 

basis9

household

(unadjusted)

roll minus 

adjusted household series

55,596 134 55,462 53,136 1,122 1,867 53,881 2, 460 1,581

56,702 139 56,563 54,498 1,241 2,132 55,389 2,204 1,174

58,332 143 58,189 56,115 1,249 2,005 56,871 2,217 1,318

60,815 148 60,667 58,217 1,249 2,018 58,986 2,598 1,681

63,955 152 63,803 60,686 1,317 1,996 61,365 3,269 2,438

65,857 155 65, 702 62,882 1,454 2,108 63, 536 2,975 2,166

67,860 160 67,700 64,601 1,629 2,184 65,156 3,259 2,544

46,706 134 46,572 44.433 913 1,549 45,069 2,273 1,503

47,477 139 47,338 45,405 993 1,808 46,220 2,072 1,118

48,735 143 48,592 46,752 1,006 1,582 47, 328 1,983 1,264

50,741 148 50, 593 48,594 1,024 1,626 49,196 2,147 1,397

53,163 152 53,011 50, 340 1,065 1,640 50,915 2,823 2, 096

54, 4b9 155 54,304 51,737 1,168 1,718 52,287 2,722 2,017

56,015 160 55,855 53,012 1,314 1,778 53, 476 3,003 2,379

650 650 541 12 12 541 109 109

635 635 525 8 7 524 110 111

634 634 512 8 8 512 122 122

632 632 505 11 7 501 127 131

627 627 524 15 9 518 103 109

613 613 536 18 9 527 77 86

610 610 508 20 9 497 102 113

2,902 2,902 2,990 95 143 3,038 -8 8 -136

2,963 2,963 2,980 95 181 3,066 -1 7 -103

3,050 3,050 3,103 83 133 3,153 -5 3 -103

3,186 3,186 3,253 92 118 3,279 -6 7 -9 3

3,275 3,275 3,283 97 129 3,315 - 8 -4 0

3,208 3,208 3,238 114 126 3,250 -3 0 -4 2

3,267 3,267 3,337 140 130 3,327 -7 0 -6 0

16,853 16,853 16,983 339 188 16,832 -130 21

16,995 16,995 17,582 365 258 17,475 -587 -480

17,274 17,274 17,986 377 192 17,801 -712 -527

18,062 18,062 18,726 381 225 18, 570 -664 -508

19,214 19,214 19,793 394 183 19, 582 -579 -368

19,447 19, 447 20,182 460 182 19,904 -735 -457

19,768 19,768 20,362 488 184 20, 058 -594 -290

3,906 3,906 3,866 68 101 3,899 40 7

3,903 3,903 3,888 80 92 3,900 15 3

3,951 3,951 3,942 78 103 3,967 9 -1 6

4,036 4,036 3,965 76 120 4,009 71 27

4,151 4,151 4,048 81 123 4,090 103 61

4,261 4,261 4,150 70 129 4,209 111 52

4,313 4,313 4,284 95 133 4,322 29 - 9

11,566 11,566 10, 239 201 495 10, 533 1,327 1,033

11,778 11,778 10, 484 239 598 10, 843 1,294 935

12,160 12,160 10,800 227 566 11,139 1,360 1,021

12,716 12,716 11,280 237 530 11,573 1,436 1,143

13,245 13,245 11,504 242 551 11,813 1,741 1,432

13,606 13,606 11,872 256 588 12, 204 1,734 1,402

14,081 14,081 12,210 285 605 12, 530 1,871 1.551

2,800 2,800 2,649 35 125 2,739 151 61

2,877 2,877 2,728 36 115 2, 807 149 70

2,957 2, 957 2. 849 46 107 2,910 108 47

3,023 3,023 2,953 37 119 3,035 70 -1 2

3,100 3,100 2,954 41 128 3,041 146 59

3,225 3,225 3,156 41 142 3,257 69 -3 2

3,383 3,383 3,271 46 147 3.372 112 11

8,028 134 7,894 7,164 163 485 7,486 864 408

8,325 139 8,186 7,219 172 557 7,604 1,106 582

8,709 143 8,566 7.561 188 473 7,846 1,148 720

9,087 148 8, 939 7,912 190 507 8,229 1,175 710

9,551 152 9, 399 8,233 194 517 8, 556 1.318 843

10.099 155 9,944 8,603 211 542 8,934 1,496 1,010

10, 592 160 10, 432 9,040 240 570 9,370 1,552 1,062

8,890 8,890 8,703 210 318 8,811 187 79

9,225 9,225 9,093 247 324 9.170 132 55

9,596 

10, 074

9,596 

10, 074

9,363

9,623

243 423 9, 543 

9,790

233 53

225 392 451 284

10, 792 10, 792 10,346 252 356 10,450 446 342

11,398 11,398 11,146 284 390 11,252 252 146

11,846 11,846 11,590 315 406 11,681 256 165

• Based on March 1968 benchmark data.

* Derived as 90 percent of agricultural services, forestries and fisheries, SIC 07-09.

J Excludes private household workers.

< Includes only secondary nonagricultural wage and salary paid iobs. Data for 1962 - 

6 reflect actual results of multiple jobholding surveys conducted during the month of

May. These surveys were notconducted in 1967 and 1968; data represent rough approximations calculated on the basis of May 1966 survey results.

* Equals households series (excluding private household workers), less unpaid 

absences plus additional paid jobs.

9 Excludes private household workers; includes forestries and fisheries.

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year and 14- and 15-year-olds for 1967 and 1968;

data by industry, which would permit these two

adjustments, are not available. For this reason,

the adjusted household totals in tables 4 and 5

will not coincide.

Adjusting the household series to a payroll basis

does not fully reconcile the overall levels of the

series but does tend to reduce existing differences.

For example, in 1968, total nonagricultural wage

and salary employment, as measured by the

household survey, was 64.6 million, compared

with 67.9 million in the payroll survey, a net

difference of 3.3 million. The number of unpaid

absences to be subtracted from the household

figures totaled 1.6 million, while the estimated

secondary-job count to be added was 2.2 million.

Subtracting the 160,000 wage and salary workers

in agricultural services from the payroll series

results in an adjusted payroll count of 67.7 million.

Thus a residual difference between the two series

of 2.5 million workers remains.

In light of the totally different sampling,

collection, and estimating methodology used in

the two series, it is perhaps more noteworthy that

there is a high degree of consistency between some

of the industry estimates. This is true not only for

large sectors like manufacturing and government

but also for some of the much smaller groups,

particularly transportation and public utilities,

and finance, insurance, and real estate. On the

other hand, certain industries show distinct

problems. Those industries with relatively extensive dual jobholding are, by and large, the

industries in which the largest differences persist.

M ost significant in contributing to the overall

disparities between the two series are the m o v ements in trade and services.

I n d i v i d u a l i n d u s t r i e s . Differences in estimates

of employment in trade during the 1962-68 period

accounted for over 50 percent of the net difference

in total nonagricultural wage and salary employment. In 1968, the discrepancy amounted to 1.9

million workers, as there were an estimated 14.1

million in the payroll series compared with 12.2

million persons in the household series. After

adjustment for unpaid absences (280,000) and

dual jobholding (600,000), the difference totaled

nearly 1.6 million.

It is quite probable that the number of dual

jobholders enumerated in the household survey is

somewhat understated, due to the continued

expansion in trade and service activities in recent

years, which has provided increased opportunities

for part-time work to persons already employed.

Another development in the job market that would

logically increase the supply of part-time workers

is the continuing downtrend in the full-time

workweek in various sectors.

Payroll employment in the service industries

was 1.5 million higher than the household count

in both 1967 and 1968. In 1968, there were 10.6

million workers in the service industry in the payroll series compared with 9.0 million workers

reported in the household series. Adjusting the

household series for unpaid absences (240,000)

and estimated secondary jobs (570,000), and the

payroll series for workers in agricultural services

(160.000) , results in a residual difference of about

I . 1 million. Aside from the probability of understatement of dual jobholding in the household

survey, some of the residual difference can possibly

be traced to benchmark problems. Since the

industry tends to have smaller units and a fast rate

of turnover among firms, deviations from benchmarks may be sizable. In addition, the timing of

benchmarks (March) could mean that seasonal

firms, which are typical in services, are inadequately accounted for both in the benchmark

and throughout the year as a moving constant.

The direction of this suspected deviation is not

known, however.

Employees on government payrolls totaled

over 11.8 million in 1968, compared with about

II. 6 million in the household series, a difference

of 260,000.12 The usual adjustment technique to

the household data does not significantly narrow

the difference, however, as the number of

secondary jobs added (about 410,000) was

nearly offset by the number of unpaid absences

(320.000) , resulting in a remaining difference of

160,000. One source of discrepancy can be traced

to the payroll series for the Federal Government,

which as explained earlier, counts all civilian

employees on the rolls on the last day of the

calendar month and any intermittent workers

who worked at any time during the month; this

contrasts with the household count covering a

single reference week. It is not possible to individually discuss Federal, State, and local government employment differences, however, because

the two subsectors are not separately identified

in the household series tabulations.

Household survey estimates of wage and salary

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employees in manufacturing industries exceeded

the number of workers in manufacturing in the

payroll series during the entire 7-year period.

The largest difference, nearly 740,000, occurred in

1967, when household estimates of nonfarm wage

and salary employment in manufacturing totaled

20.2 million workers compared with 19.4 million

workers in the payroll series. Of all the industry

divisions, manufacturing has the largest count of

persons on unpaid absences, numbering 460,000

in 1967, and more than offsetting the small number of persons holding secondary jobs (180,000).

As a result, the gap between the two series was

reduced to nearly 460,000. Considering the size

of the industry and the magnitude of the adjustment for pay status, residual differences in 1967,

as well as in the other years under discussion, are

probably not significant.

Household estimates of wage and salary employees in the construction industry also exceeded

the number of workers on construction payrolls

each year during the 7-year span. In 1968, however, there were 3.3 million in the household

series, slightly more than the number in the

payroll series. While the differences are not

significant either before or after adjustment, they

can partly be attributed to the fact that the payroll series covers workers in contract construction,

while the number reported in the household series

may not accurately differentiate between contract

construction, force-account, and speculative

construction.13 Aside from the problem of defining

construction wage and salary workers, other

problems probably stem from the number of

small, short-lived firms in the industry.

Monthly comparisons

In addition to a reconciliation of the annual

levels of the two series, further insights into the

problem of comparability may be gained by looking at month-to-month movements in employment levels. An examination of published monthly

household and payroll estimates of nonagricultural wage and salary employment levels for

1968 reveals divergences between the two series

ranging from 2.3 million in both July and August

to 4.0 million in September. (See table 6.) This

compares to the 3.3 million differential for 1968

on an annual average basis.

As in the annual comparisons, the largest

disparities in overall monthly levels took place

Table 6. Nonagricultural wage and salary employment,

monthly, 1968

[In thousands]

Month Payroll 

series1

Household 

series2

Payroll

minus

household

series

Month-t

cha

Payroll

series

o-month 

nge

Household

series

January______________ 65, 765 62, 740 3,025 3-l,9 2 0 3-l,4 7 7

February_____________ 66,115 63,313 2, 802 350 573

March_______________ 66,475 63, 446 3,029 360 133

April________________ 67,170 63,752 3,418 695 306

May _______________ 67, 465 64, 263 3,202 295 511

June_________________ 68, 470 65,104 3, 366 1,005 841

July_________________ 68, 036 65, 734 2,302 -434 630

August_________ _____ 68,205 65, 876 2,329 169 142

September.. __________ 68,610 64, 599 4,011 405 -1,277

October______________ 68,959 65,125 3,834 349 526

November____________ 69, 247 65, 358 3, 889 288 233

December____________ 69, 805 65, 902 3,903 558 544

1 Based on March 1968 benchmark data.

2 Excludes wage and salary workers in private households.

3 Change from December 1967 to January 1968.

in the trade and service sectors. Of the 4.0 million

net differential recorded in September, for example, 2.0 and 1.7 million were noted in trade

and services, respectively. On the other hand,

estimates for most of the other industries were

surprisingly consistent.

While month-to-month changes in each series

can be explained in large part, divergences between

the two in any given month are more difficult

to reconcile than divergences in the annual

averages. The only difference between the two

series that can be quantified and therefore

adjusted on a monthly basis (on both a total and

industry-by-industry basis) is the count of unpaid

absences. Such an adjustment should theoretically eliminate one major source of disparity but,

unfortunately, it also widens the gap in employment levels. Moreover, while year-to-year changes

in the two series have at least been in the same

direction, though not of the same magnitude,

significant differences in month-to-month changes

occur both in direction of movement and in size,

which cancel out to a certain extent in comparisons

of annual averages.

As an illustration of differences in the direction

of change, payroll employment decreased by

about 430,000 workers (from 68.5 to 68.0 million)

in July 1968, as declines in trade, manufacturing,

and government countered gains in the other

industry sectors. In contrast, employment in the

household series increased by 630,000 workers

(from 65.1 to 65.7 million), as pickups were registered in all industry sectors except mining and

manufacturing, both of which remained unchanged

from June. Similarly, from August to September,

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employment in the household series declined by

1.3 million workers (from 65.9 to 64.6 million),

with unemployment cutbacks in all industry sectors except government. On the other hand, the

payroll series showed a gain of 400,000 (from 68.2

to 68.6 m illion); employment pickups in manufacturing, transportation and public utilities, trade,

and government offset declines in mining, construction, finance, insurance and real estate, and

services.

Much of the disparity in monthly employment

levels is inherent in the different seasonal patterns

of the two series, although both series are subject

to the same general seasonal fluctuations. The

amplitude of their seasonal factor—original level

divided by the seasonally adjusted level—ranges

from a little over 98 percent to over 101 percent.

At current employment levels, this 3-percentage

point differential implies a seasonal expansion and

contraction of approximately 2 million employees

during the year for each series.

The two series move fairly consistently during

the first half of the year, as each shows steady

employment increases through June. (During the

January-June 1968 period, monthly payroll employment increases averaged 540,000 compared to

470,000 in the household series.) The only real

difference in the first half of the year is that the

payroll series has its seasonal low in February

while the household series’ low occurs in January.

After June, however, certain significant differences emerge. The establishment series dips

sharply between June and July, then resumes its

upward movement, reaching a seasonal peak in

December. The household series continues upward

from June to a seasonal peak in August, drops

significantly in September, then rises slightly during the remainder of the year, although December

edges off marginally. In part, these differences

can be attributed to different treatment in each

series of unpaid absences and dual jobholding.

However, other factors discussed earlier undoubtedly exert some influence on employment levels.

Seasonality caused by vacation-taking appears

an important reason for monthly variations in

unpaid absences, particularly in the summer

months. For example, in July and August 1968,

unpaid absences totaled 2.7 and 2.9 million,

respectively, compared with 1.2 million in both

May and September. The exclusion of workers on

unpaid absences from the establishment series

leads to a seasonal dip in the summertime, particularly in July, when vacations are common and

many vacationing employees are not eligible for

pay. Though diminishing in recent years, the number of persons on unpaid vacation is still considerable. The household count of employed nonagricultural wage and salary workers on vacation in

July 1968 totaled 7 million, and more than 1.6

million of these persons did not receive pay for the

time off. These absences were concentrated

primarily in manufacturing, trade, services, and

government.

Similarly, variations in the two series are also

reflected by the relatively high incidence of absences due to bad weather and illness, particularly

in the winter months and during periods of major

industrial disputes.14 The 1967 auto strike, involving over 150,000 workers, accounted for a

significant swing in the two series between

September and November 1967. When unpaid

absences are subtracted from the household series,

the month-to-month movements and consequently

the seasonal pattern more nearly resemble those

of the payroll series, particularly with respect to

July and September. (See table 7.)

Because of the design and timing of the multiple

jobholding survey— conducted in M ay of most

years since 1962 as part of the regular household

survey— the effects of multiple jobholding on

employment levels from month to month cannot

be quantified. It is likely, however, that changes

in employment levels during certain seasons of the

year can, in part, be attributed to wide seasonal

swings in the extent of dual jobholding. For

example, taking additional jobs at Christmas time

by persons who are already employed would be

reflected in the establishment series but not in the

household series, since workers holding more than

one job are classified according to the major

activity only.15 Multiple jobholding could largely

account for the sharp rise in employment in the

establishment series in December and the substantial decline in January, while changes in the

household series are much smaller, although

generally in the same direction. During the past

7 years, the November-to-December increase in

nonagricultural wage and salary employment has

averaged 490,000 in the establishment series

compared with about 325,000 in the household

series. By the same token, the decline in January

has averaged about 1.7 million in the establishment series compared with about 1.2 million in

the household series.

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Similarly, there may be seasonal fluctuations

in the number of workers who normally hold both

agricultural and nonagricultural jobs and spend

more time at one type of work or the other in any

one month, depending on the demands of the

harvesting season. Such workers are counted in

their nonagricultural jobs each month in the

establishment series, but by their major activity

during the survey week in the household series.

As noted earlier, different treatment of school

teachers and other educational staff creates divergences in employment levels, particularly in

the summer months. In similar fashion, the entrance and exit of young persons into and out of

the labor market during the summer have a

significant effect on employment levels. The

decline in employment in the household series in

September reflects primarily the large number of

youngsters leaving the labor force to return to

school. Part of the explanation as to why the payroll series actually increases in September is

that it may not be as responsive to changes in

teenage employment as the household survey;

as noted earlier, a number of summer resort,

hotel, and amusement undertakings where employment of youth is prevalent may be missed

by the benchmark source and thus in the current

employment estimates.

Selecting the best series

In addition to explanations of the reasons for

differences between the two series, many users

may wish to know which is the “best series”

under given circumstances. A meaningful answer to

this question can be given in terms of the purpose

for which the data are required. For cyclical

analysis, neither series should be overlooked. Each

has a long history, with data available for all of

Table 7. Current seasonal adjustment factors for nonagricultural wage and salary employment

Month

Payroll

series

(implicit)

Household

series

(implicit)

Household

(excluding

unpaid

absences)

January.. _______________ 98.6 98.3 98.4

February.................................. 98.4 98.9 99.0

March..’................................. 98.8 98.9 99 4

April____________________ 99.6 99.3 99.7

May............. ........................... 99.9 99.4 100.1

June. ............................. 101.0 100.7 100.4

July.......................................... 100.1 101.7 100.0

August___ ______ ________ 100.2 101.7 99.8

September................. .......... 100.6 99.5 100.2

October__ ______________ 100.8 100.1 100.7

100.8 100.7 101.1

December_______________ 101.4 100.6 101.3

the postwar business cycles for comparative purposes. The data, however, should be used with an

awareness of their coverage, concepts, methodology, and limitations.

The household survey places its primary emphasis on the work status of individuals and

relates this status to other characteristics, such

as age, sex, color, educational attainment, and

marital status. However, it is not well suited to

providing detailed information on the industrial

distribution of employment and because of this,

employment levels by industry are not published.

The payroll survey provides practically no information on personal characteristics of workers

(except sex), but is an excellent source for detailed industrial and geographic employment data.

It also provides hours aVid earnings data directly

related to the employment figures. Moreover, the

payroll series usually measures month-to-month

change more precisely than the household series

and therefore is more reliable for current analysis

of employment changes. Therefore, the payroll

and household surveys may be regarded as supplementary and complementary. Both serve a

useful purpose, and neither should be discarded

in favor of the other. 

FOO TNO TES

1 Household data are collected in a national sample survey of approximately 50,000 households (called the Current

Population Survey) conducted monthly by the Bureau of

the Census for the Bureau of Labor Statistics. A detailed

description of this survey appears in Concepts and Methods

Used in Manpower Statistics From the Current Population

Survey (bls Report 313, 1967).

Establishment data are based on payroll reports from a

sample of 160,000 establishments employing over 30 million

nonagricultural wage and salary workers, collected by

State agencies in a cooperative program with b l s. For a

more detailed discussion of this survey, see the technical

note in Employment and Earnings, published monthly by

bls, and bls Handbook of Methods for Surveys and Studies

(bls Bulletin 1458, 1966), chapter 2.

2 All subsequent references to nonagricultural wage

and salary employment in the household series in text,

and tables, exclude private household workers.

3 For a discussion and reconciliation of differences

between the two types of employment series in 1961 and

earlier years, see Chapter V' and Appendix I of Measuring

Employment and Unemployment, President’s Committee to

Appraise Employm ent and Unemploym ent Statistics,

1962.

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4 See Harvey R. Hamel, “Moonlighting— An Economic

Phenomenon,” Alonlhly Labor Review, October 1967, pp.

17-22, reprinted as Special Labor Force Report No. 90.

5 Multiple jobholding surveys were not conducted in

1967 and 1968. For purposes of reconciliation, rough approximations of the number of workers employed in secondary nonagricultural wage and salary jobs in these years

have been calculated on the basis of the May 1966 survey

results. In 1967 and 1968, it was estimated that these

workers totaled 2.1 and 2.2 million, respectively.

6 This estimate was made using a “comparability assumption” by Denis F. Johnston and James R. Wetzel in their

article, “ Effect of the Census Undercount on Labor Force

Estim ates,” Monthly Labor Review, March 1969, pp. 3-13.

It is assumed here that the estimated undercount for 1968

was the same as that for 1967. This undercount is an

extremely rough approximation and should not be accorded

a high degree of accuracy.

7 Because of special processing and enumeration problems of the Current Population Survey in December,

primarily due to the Christmas season, it is usually conducted 1 week earlier, i.e., the week containing the 5th

day instead of the week containing the 12th. This earlier

survey week may also result in the missing of a considerable number of temporary sales workers in retail trade,

accounting for still another source of deviation in the two

series.

8 A discussion of the sampling and estimating procedures

together with estimates of sampling variability for both

series are published monthly in Employment and Earnings.

9 These adjustments generally mean that the employment series have been revised back to the previous complete count and forward to the current month’s estimate.

For a detailed discussion of the adjustment of payroll employment levels to new benchmark levels, see “ BLS Establishment Employment Estimates Revised to March 1968

Benchmark Levels,” July 1969 Employment and Earnings.

19 For 1967 and 1968, annual average household totals

are not precisely comparable to those for earlier years.

Improvements in the methods of measuring labor force

data initiated in January 1967 have clarified and sharpened

the household statistics. (A detailed discussion of these

conceptual changes can be found in “ New Definitions for

Employment and Unem ploym ent,” reprinted from the

February 1967 Employment and Earnings and Monthly Report on the Labor Force.) Three particular changes affected

the household count of nonagricultural wage and salary

employment and consequently the differences between the

two series. First, the exclusion of 14- and 15-year-olds, as

discussed earlier, reduced total nonagricultural employment and at the same time widened the disparity between

the two series in subsequent periods. Secondly, a shift to

wage and salary employment of persons erroneously classified as self-employed had the effect of reducing the gap

between the series. In essence, estimates of the selfemployed, particularly in the trade, miscellaneous service,

and construction industries, had been too high prior to

1967, because some persons were enumerated as selfemployed, who actually operated their own incorporated

enterprises and were therefore listed on the payrolls as

salaried officers of the corporation. This classification problem was eliminated by asking an additional question in

the household survey of all persons reported as selfemployed in a nonfarm business as to whether the business

was incorporated. The effect of this additional question

was to reduce the average level of nonfarm self-employment

by about 750,000 (in 1966) and to raise nonagricultural

wage and salary employment by a corresponding amount.

In a third change, which wTas relatively insignificant, persons absent from their jobs during the survey week and

seeking other jobs were shifted from the unemployed to

the employed status (with a job but not at work). The

effect of this change increased total employment (in 1966)

by about 80,000, most of whom were nonagricultural wage

and salary workers. The net results of these changes were

to reduce the differential between the two series by approximately 400,000 in 1967 and subsequent years. The

fact that the 1968 differential equaled that of 1966

(table 1) suggests that a further (inexplicable) widening

occurred in the latter year.

11 The household survey has never been used to measure

industry employment, and industry absolutes are not

published. Such data are subject to sampling errors to a

much greater extent than are the totals. In addition,

industry data are also largely affected by response and

classification errors to the extent that respondents may

not accurately report the industry of employment. This

could be expected to occur most frequently between

construction and manufacturing (force-account construction, belonging in the latter industry) and wholesale trade

and manufacturing (where respondents report the latter

instead of the former). These data are provided here,

however, because they contribute to an understanding of

comparative movem ents in the totals.

12 Until July 1969, when the March 1968 benchmarks

were introduced, the deviation between the two series

had been nearly 3 times this amount. Because the

payroll employment estimate for the Federal Government

is a complete count, the series is not subject to benchmark

revision; the benchmark adjustment for State and local

government employment (based on the Quinquennial

Census of Government) is performed at 5-year intervals.

As a consequence, any deviations in current employment

levels from actual benchmark levels tend to accumulate.

13 If construction activities are classified in other industries in the payroll series, this could also account for

minor reconciliatory differences. However, the differences

are probably concentrated in manufacturing and trade

(force-account construction) and finance, insurance, and

real estate (speculative construction). Force-account

construction refers to construction work performed by an

establishment primarily engaged in some business other

than construction, for its own account and use, and by

its own employees.

14 Workers on strike for the entire reference period arc

counted as employed (with a job but not at work) in the

household series but are not so counted in the payroll

series.

15 The c p s survey week in December is usually 1 week

earlier than the payroll survey reference period, which

would also have an impact upon the probable extent of

multiple jobholding as well as upon divergences between

the two surveys for the month.

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A 25-year look

at employment

as measured

by two surveys

How h a v e t r e n d s in the data developed in the

payroll employment series and the household employment series of the Bureau of Labor Statistics

compared over a considerable period of time? The

computer has made it possible to analyze data on

nonagricultural wage and salary employment in

eight major industry groups and for the nonfarm

economy as a whole over a 25-year period, 1948 to

1972. This study of a large number of observations

shows that movements in the two series are strikingly similar despite their separate sources.

The series compared

Information on the number of jobs and rate of

growth of employment by industry is developed by

the Bureau of Labor Statistics from data collected

by the Bureau of the Census in monthly interviews

at 45,000 households (the Current Population Survey— CPS) and from payroll data collected by BLS

monthly from 155,000 business establishments (the

Current Employment Statistics program— CES).

Data from the payroll series have generally been

considered more reliable than those from the household series. There are two reasons for this. First,

BLS believes that the industry of a worker can be

determined more precisely from authentic payroll

records of establishments whose industry classifications are periodically reviewed than from the answers of household respondents. Second, the payroll

figures are derived from a much larger sample than

the household data— about 40 percent of payroll

employment while the household data are derived

from interviews conducted at less than a tenth of a

percent of the households in the universe. Movements in the monthly payroll estimates of employChristopher G. Gellner is an economist in the Division of

Employment and Unemployment Analysis, Bureau of

Labor Statistics.

Industry employment estimates based 

on household interviews 

and on payroll records 

move similarly but differ 

in levels and other specifics

CHRISTOPHER G. GELLNER

ment have tended to show much more smoothness

and stability historically than the household estimates largely because of these factors but also because of different procedures used in compiling the

two series.

This does not mean, however, that the household

estimates are not a useful measure of employment in

major industries. The household series, in fact, has

some unique virtues. It is the only source providing

detailed insights into employment by industry in

terms of age, color, occupation, and similar characteristics. It also extends coverage to some industries,

such as agriculture and private household services,

not covered by the payroll estimates. Furthermore,

it provides unemployment estimates by industry and,

thus, is the only source from which comparisons of

both employment and unemployment can be made

on an industry basis. Because both series of estimates, are important, this article contrasts their behavior during the 1948-72 period for each of the

major industry groups, describing and explaining the

differences exhibited.

General trends and differences

The underlying movements of the industry data

from each series have been comparable over time,

but the series often differ in the levels of the estimates, in month-to-month and annual changes, in

the timing and extent of business cycles, and in

other shortrun trends. These differences arise because the two series actually measure different phenomena— the Current Population Survey counting

the number of persons employed and the Current

Employment Statistics program counting the number

of jobs occupied.1 This results in different treatment

in each survey of persons holding more than one job

and of those on unpaid absence. For example, in

the CPS, persons working during the survey week in

more than one job are counted once and are classiFrom the Review of July 1973

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fied according to the job in which they worked the

greatest number of hours. By contrast, in the establishment survey, workers on the payroll of more

than one business establishment would be counted

every time their names appeared on payrolls. Also,

people who have jobs but are temporarily absent

from them during the survey week because of illness, bad weather, vacation, a labor management

dispute, or for various other reasons are counted

nonetheless as employed— with a job but not at

work— in the household survey even if they did not

receive pay for this period of absence. In the payroll

survey, workers on unpaid absence are not counted.

The two surveys also differ in coverage, sources

of information, methods of collection, and estimation procedures. These and other differences have

been comprehensively discussed and, wherever possible, quantified in two earlier analyses of the two

series. For a thorough discussion of the overall differences which are only cursorily touched on in this

article, these two sources should be consulted.2 This

article also differs from the earlier analyses in that it

focuses on the historical trends of the two series,

but does not attempt a further reconciliation of their

differences. (For such reconciliations, see the two

earlier studies.)

The establishment survey covers only wage and

salary workers on the payrolls of nonagricultural establishments. The household survey includes all persons who worked at least 1 hour for pay or profit

during the survey week— whether as wage and salary workers in establishments or private households

or as self-employed workers— as well as those who

worked 15 hours or more without pay in familyoperated enterprises. In this article, the household

survey estimates of employment by industry have

been adjusted to exclude private household workers,

the self-employed, and unpaid family workers. This

eliminates major coverage problems and facilitates a

more direct comparison with the payroll estimates.

However, differences caused by the two surveys’

contrasting treatment of multiple jobholding, workers on unpaid absences, and other factors such as

the population undercount in the household survey

and the inclusion of persons under 16 years of age

in the establishment survey still affect the levels.3

Computer-drawn charts are presented in this article, tracing the monthly movements of the household and payroll series over the 1948-72 period for

total nonagricultural wage and salary employment

and in eight major industry divisions. The charts

clearly show (1) the two series have remarkably

similar trends over time, and (2) the payroll series is

significantly smoother, in terms of month-to-month

fluctuations. This fact is attributable primarily to the

much larger size of the establishment sample. It is

also due in large part to use of a link-relative technique to estimate monthly payroll employment. This

means that the previous month’s estimate is used as

a base in computing the current month’s estimate.4

In addition, the payroll series are adjusted once a

year to a benchmark— a complete and independent

count of employment for each industry.5 As a result

of this process the series is considered relatively free

of error over the long span. It is still possible, however, that the benchmark counts for certain industries, such as construction and services which

abound with short-lived and seasonal firms, may be

imperfect. On the other hand, the error on consecutive months change for total nonagricultural employment estimated from the household survey is on the

order of plus or minus 225,000 workers based on

the current size of the sample (45,000 households).

Since the sample was considerably smaller during

the 1950’s and early 1960’s, the errors on monthto-month changes were larger.

Chart 1 shows that during the entire 1948-72 period total nonagricultural wage and salary employment was consistently higher in the payroll than in

the household series. One possible cause for this gap

may be the so-called population undercount. The

household survey findings are applied each month to

a population count projected forward from the decennial Census. There is evidence that a sizable

number of persons have been missed in each of the

recent censuses. Consequently, household survey estimates also tend to understate the true level of employment.

Both series have shown virtually the same growth

pattern over the period. Despite the short-term declines in employment during some recessions both

series showed nearly the same average annual rate

of growth (compounded) during the period— 2.1

percent for the household estimates and 2.0 percent

for the payroll. Thus, there was only a slight narrowing of the absolute gap between the series over

the years (2.3 million in 1948 compared to 2.0 million in 1972). It is noteworthy, however, that this

narrowing was not of a long-term nature, occurring

largely during the recovery stages of the 1970-71

recession. In addition to this cyclical development,

which also occurred to some extent during previous

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Chart 1. Comparison of household and establishment survey employment— total nonagricultural industries, wholesale

and retail trade, and manufacturing— seasonally adjusted, 1948-72

Employment in thousands Employment in thousands

16,000

15.000

14.000

13.000

12.000 

11,000 

10,000 

9,000

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recessions, the adjustment of the household series to

new population controls introduced in January 1972,

based on the 1970 census, had the effect of raising

the household figures for nonfarm wage and salary

employment by 250,000.® During 1961-69, years of

uninterrupted economic expansion, there had been a

widening of the actual gap to about 2.7 million. In

percentage terms, however, the gap between the two

series has narrowed somewhat over the past 25

years. In 1948 the payroll estimate exceeded the

household estimate by 5.4 percent; in 1972, it exceeded it by only 2.9 percent.

Differences in levels and movements in the two

series are clarified through an examination of individual industry data. For example, the net difference

between the aggregate levels from the two series has

stemmed from differences in the estimation of trade

and services employment.

Patterns by industry

Mining. The only industry group to show a decline

in employment during the 1948-72 period was mining. Chart 2 shows that the decline in the payroll series was gradual and long term, occurring mostly

between 1948 and 1962. Laborsaving technology in

metal and coal mining accounted for the industry’s

employment shrinkage.

The household estimates of mining employment

have behaved more irregularly, but most of the decline also occurred between 1948 and 1962. A

phenomenally large part of the decline occurred in

early 1954 and was probably related to a change in

the Current Population Survey sample introduced at

that time.7

Since 1962, the payroll series has remained rather

stable. On the other hand, the household figures

held steady until late 1970 and then rose enough to

close the gap which had existed during most of the

25-year period. Due to the recent relative narrowing

of the gap between the series, the average rate of

decline of mining employment over the period was

somewhat greater with respect to the payroll series

than the household series (table 1).

Month-to-month and short-term disparities between the two series with respect to mining employment are partly explained by (1) the relatively high

sampling variability of the household data, and (2)

the two surveys’ different treatment of workers on

strike. Since the mining industry is comparatively

small, only a small number— about 500— of mining

workers are picked up in the Current Population

Survey sample each month and estimates derived

from the sample are subject to considerable sampling variability. Because of the small mining sample, a month-to-month change must exceed 50,000

before one can be 90-percent confident that a

change in employment levels occurred. By contrast,

the payroll series is subject to very little sampling

error. The sharp drops in the payroll figures in October-November 1949 and January-March 1950, as

well as the declines in 1966, 1968, and 1971, are

attributable to strikes. Employees on strike are not

included in the establishment series if they are off

the payroll during the entire reference week. In the

household survey, on the other hand, workers on

strike are counted as employed— with a job but not

at work. Consequently, the household figures did

not drop during heavy strike periods.

Construction. Wage and salary employment in construction showed nearly identical trends and rates of

growth in the payroll and household series between

1948 and 1972. Generally, the household estimates

were slightly above the payroll estimates, with the

exception of relatively short spans in 1951-52 and

1971-72. Why the two series diverged in the early

1950’s and 1970’s, yet were relatively close otherwise, is difficult to ascertain. Residential construction

reached record levels in the early 1950’s and 1970’s

and may have caused the divergence. Because of differences in coverage, the household survey may have

been more likely to reflect employment increases during periods of rapidly expanding construction activity, particularly in the residential area. (See chart 2.)

In the household survey all persons whose major

job is reported to be construction work are counted

in the construction industry. By contrast, the payroll

survey includes only workers employed by bona fide

contractors as defined in the Standard Industrial

Classification (SIC) system. It omits so-called

“force-account” construction workers— those employed by an establishment, the main product or

service of which is something other than contract

construction. Therefore, workers performing construction in industries such as real estate development, steel, automobiles, and trade are not included

in the contract construction classification but rather

in the industry called for by their establishment’s

main product. The extent of this classification difference is probably minimal during “normal” times but

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Chart 2. Comparison of household and establishment survey employment— construction, finance, insurance, and real

estate, and mining— seasonally adjusted, 1948-72

Employment in thousands Employment in thousands

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Table 1. Household and payroll estimates of nonagricultural wage and salary employment by industry groups, selected

years 1948-72

Item

Total

nonagricultural wage

and salary

Mining Construction Manufacturing

Transportation and

public

utilities

Wholesale

and retail

trade

Finance,

insurance

and real

estate

Services Government

HOUSEHOLD SURVEY

1948___________________ 42,603 871 2,421 15,477 4,141 8,474 1,658 4,300 5,261

1950___________________ 43,493 829 2,501 14,903 3,943 9,029 1,754 4,745 5,789

1960_______________ . 51,235 552 2,962 16,552 4,033 10,092 2,567 6,542 7,935

1970___________________ 67,691 499 3,525 20,224 4,476 12,945 3,578 10,000 12,424

1971....................... .............. 68,209 551 3,668 19,119 4,431 13,789 3,696 10,188 12,764

1972___________________ 70,729 583 3,890 19,437 4,553 14,392 3,934 10,611 13,329

Change in level, 1948 to

1972_________ ________ +28,126 -288 + 1,469 +3,960 + 412 + 5,918 +2,276 +6,311 + 8,068

Average annual rate of

growth (compounded)___ +2.1 - 1 .7 + 2.0 + 1.0 + 0.4 + 2 .2 + 3.7 + 3.8 + 4.0

PAYROLL SURVEY

1948___________________ 44,891 994 2,169 15,582 4,189 9,272 1,829 5,206 5,650

1950___________________ 45,222 901 2,333 15,241 4,034 9,386 1,919 5,382 6,026

1960___________________ 54,234 712 2,885 16,796 4,004 11,391 2,669 7,423 8,353

1970___________________ 70,593 623 3,381 19,349 4,493 14,914 3,688 11,612 12,535

1971___________________ 70,645 602 3,411 18,529 4,442 15,142 3,796 11,669 12,856

1972___________________ 72,764 607 3,521 18,933 4,495 15,683 3,927 12,309 13,290

Change in level, 1948 to

1972______ ___________ +27,873 -387 + 1,352 +3,351 + 306 + 6,411 +2,098 7,103 +7,640

Average annual rate of

growth (compounded)___ + 2.0 - 2 .0 + 2 .0 + .8 + .3 + 2.2 + 3.2 + 3.7 + 3.6

may increase during booms in residential construction when developers may do more of their own

construction work.8

Also during boom periods in homebuilding,

many small firms or associations of individuals enter

the industry “to get a piece of the action.” Many

may not register under the unemployment compensation laws (even if required to do so by State law)

because of the added expense or because in the past

they were too small to qualify. The payroll survey

only covers firms or establishments registered under

State unemployment insurance programs. Even if

registered under the unemployment insurance laws,

however, firms entering the industry would not be

picked up in the payroll sample until after the annual complete census of establishments— the benchmark. Thus, employment in firms which did not remain in operation until the next benchmark would

never be included in the payroll count.

Thus it is possible that the divergence between

the two series in 1971-72 may have been related to

the surge in residential homebuilding, which pushed

housing starts to record levels. The divergence in

1951-52 cannot be connected as easily to housing

starts— since residential housing starts peaked in

1950, then fell somewhat in the ensuing 2 years

when the gap between the series developed.

But the recent divergence between the two series

may not be tied solely to increased construction activity in the residential sector. When the series

began to drift apart in 1969, residential construction

was not particularly strong. Furthermore, when

housing starts showed strong spurts at other times

between 1948 and 1972, such as in 1954-55, 1959,

and 1963-64, the household series did not show an

inordinate degree of increase compared with the payroll data. Compared with the booms in the early

1950’s and 1970’s, however, the increased residential construction activity during these other periods

was relatively mild.

Manufacturing. Chart 1 shows that during most of

the 1948-60 period the payroll estimates of wage

and salary employees in manufacturing slightly exceeded the number of workers in manufacturing estimated in the household series. In 1961-62, the series moved together, and since 1962, there has been

a reversal of the previous pattern, with the

household employment levels exceeding the payroll.

Nevertheless, both series showed fairly similar average annual rates of growth for the 1948-72 period

— 0.8 percent for the payroll estimates and 1.0 pcr31

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Chart 3. Comparison of household and establishment survey employment— services, government, and transportation

and public utilities— seasonally adjusted, 1948-72

Employment in thousands

13,000

12,000

Employment in thousands

11,000

4,250

4,000

3,750

Household survey 

Payroll survey

1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972

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cent for the household (table 1). The two surveys

also showed about the same rates of employment

growth for both the durable and nondurable goods

sectors of manufacturing. For both sectors, payroll

estimates of employment were generally a little

above the household estimates during the 1950’s but

moved below them in the early 1960’s. This reversal

in position occurred first in nondurable goods and

was followed shortly thereafter by durable goods.

In terms of cyclical developments, chart 1 also

shows that both series of data on manufacturing employment have behaved similarly. Between 1962 and

1969, a period of sustained economic growth, both

expanded at the average annual rate of about 3 percent, and both series plummeted at a particularly

rapid pace during the 1970-71 recession.

Current differences between the two series in levels of manufacturing employment can be ascribed in

large part to the contrasting ways of treating workers on unpaid absence and with more than one job.

Manufacturing has a relatively large number of unpaid absences— more than any other industry group

— yet a comparatively small number of secondary

jobholders. Since persons on unpaid absence are

counted as employed only in the household survey,

theoretically the household estimates should exceed

the payroll figures. However, as chart 1 shows, this

has been the case only since the two series switched

relative positions in the early 1960's.

In 1960, the industrial (and occupational) classification was revised in the household series. The

purpose of this revision was to improve the quality

of the data in terms of industry detail by making

household industry groups conform to the 1957 revision of the Standard Industrial Classification

(SIC) system. Following this revision, an additional

question was added to the CPS questionnaire in

1960: What is the name of the employer of each

worker in the household? The purpose of this question was to improve the accuracy of industry reporting. In troublesome classification cases, this question

permitted matching the individual’s firm with an appropriate list of industry classification codes. It is

reasonable to assume that the increase in the household measurement of manufacturing employment resulting from the addition of this question and the

use of the new classification system improved the reliability of the household estimates substantially,

since, theoretically, the household series should have

been yielding estimates above those of the establishment series because the number of unpaid absences

in the industry exceeds the number of secondary

jobholders.

Transportation and public utilities. Employment

growth in transportation, communications, and public utilities as measured by both surveys fluctuated

widely during the 1948-72 period. Apart from cyclical effects, the payroll series exhibited considerably

more stability than the household series, which

shows a significant degree of month-to-month variation. Trends in both series during the period were

similar, although the payroll series generally was

above the household series, particularly during the

1950’s. As in manufacturing, industry classification

revisions introduced in 1960 may have contributed

significantly to the levels of the household data for

transportation more closely approximating those of

the payroll series (chart 3).

Chart 3 also indicates that both employment series moved downward during the 1950’s. Long-term

declines in employment in railroad transportation

and local and interurban passenger service were the

major causes of this. After the 1960-61 recession,

employment expanded relatively rapidly until the

1970-71 economic slowdown. Gains in trucking and

air transportation, communications, and public utilities figured importantly in the growth of the 1960’s.

However, because of declines in the 1950’s, the 25-

year growth in the industry was slight— 410,000 in

the household survey and 300,000 in the payroll.

Trade. Historically, the payroll estimates of employment in wholesale and retail trade have been substantially above the household estimates for the industry. In fact, the industry has generally accounted

for about half the net difference between the two

surveys in total nonagricultural wage and salary employment. The difference in trade employment did,

however, vary during the 1948-72 period, ranging

from 400,000 in 1950 to 2.2 million in 1969. By

1971, the gap had narrowed to 1.3 million. Despite

occasional disparities in growth in the short term,

the annual rates of expansion of both sets of estimates over the 1948-72 period were identical— 2.2

percent (table 1).

A significant amount of moonlighting occurs in

trade and is the major reason the payroll estimates

have consistently exceeded those of the household

survey. Probably, the increase in moonlighting

causes the two series to diverge during business expansions and, conversely, the contraction of dual

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jobholding causes them to converge during cyclical

downturns. Another, relatively minor, reason for the

gap between the two series is that the payroll survey, in contrast to the household survey, includes

some military personnel and inmates of institutions

employed in trade. Moreover, many wage and salary

workers in the industry were misclassified as selfemployed in the household survey before 1967.9

This happened because some owners of small retail

businesses, or salesmen with a loose relationship to

their organization, may regard themselves or may be

considered by the respondents in their households as

self-employed, although they are listed as salaried

officers or salesmen on the payroll of the business.

Services. As with trade, payroll estimates of wage

and salary employment in services historically have

been substantially higher than those of the household estimates. As chart 3 shows, the gap between

the two series gradually widened after the late

1950’s. Over the entire 25 years, it ranged between

0.5 and 1.5 million workers. When combined with

that in trade, this gap has accounted for the entire

net difference in household and payroll levels of

nonagricultural employment since World War II.

The average annual rate of increase of the two

series over 1948-72 was nearly identical— 3.7 percent for the payroll series and 3.8 percent for the

household, a faster rate than any other industry

group except government during the 25-year period.

Differences in household and payroll estimates of

employment in the service industries stem in large

part from many of the factors that account for the

gap in trade: The number of dual jobholders in the

industry has consistently exceeded the number of

workers on unpaid absence. The gradual widening

of the difference between the two series is probably

due to increased dual jobholding. Another minor

factor is that services, like trade, is also an industry

in which members of the Armed Forces are likely to

hold jobs during off-duty hours.

Another source of discrepancy is that service employment in the payroll series includes workers engaged in agricultural services— about 250,000 in

1972— who are classified in agriculture in the

household survey. Working in the other direction

the payroll benchmark, because of its nature and

timing (March), may not pick up employment in

some of the short-lived or seasonal firms (mostly

businesses in resort areas open only during the summer months).

Finance, insurance, and real estate. Employment in

this group of industries, as measured by both the

payroll and household series, expanded continuously

during 1948-72. Employment shown in the payroll

series rose consistently while the household series

fluctuated markedly, undoubtedly due to sampling

variability. (See chart 2.) There was, in addition,

some disparity in the rates of growth of the series.

The average annual rate of increase of the household survey was 3.7 percent, exceeding the payroll

survey’s 3.2 percent, the largest single industry difference during the period between the two series in

terms of rates of growth. As a result, the gap between the two series, which had been substantial

during the 1950’s, was completely closed by 1972.

Government. Among the major industry groups,

government (Federal, State, and local) posted the

largest increase in employment between 1948 and

1972, both in terms of the absolute change and in

average annual percent changes (table 1). The

household series, however, increased at a somewhat

faster rate than the payroll series, such that the

small gap between the two series that prevailed

throughout much of the period was completely

closed by 1972.

Persistent discrepancies between the levels of the

two series can be traced in part to the different

times when monthly employment in the Federal

Government is sampled in each survey, in addition

to a different treatment of multiple jobholders and

unpaid absences. The monthly household estimates

reflect government employment in a single week in

the month (the reference week). By contrast, the

payroll series for the Federal sector counts all civilian employees on the rolls on the last day of the

month plus all intermittent employees who worked

during the month. Thus, some persons counted as

employed in government in the payroll survey were

not so classified in the household survey. There is at

present no method devised which can quantify the

effect of this difference in reference periods. However, since turnover among Federal employees is relatively low, it should be fairly small. A major

exception would be in December when many

temporary workers are hired by the U.S. Postal

Service.

Another difference between the two series stems

from the treatment of teachers during the summer.

In the payroll survey, teachers are counted as employed regardless of whether they are paid only during the school year or on a 12-month basis. There34

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fore, teachers taking jobs during their summer

vacation would be counted twice. In the household

series, teachers would be counted only once, either

as an employed teacher (with a job but not at

work) or as employed in the job obtained over the

summer. The overall effect of these procedures

would be to lower the household level of government employment relative to that of the payroll

level.

B e c a u s e o f d i f f e r e n c e s in methodology and concepts, the payroll and household employment series

by industry cannot be expected to yield the same

magnitudes, even when the differences in coverage

have been eliminated. In terms of total nonagricultural wage and salary employment, the household

survey levels have historically been lower than the

payroll levels, primarily because the payroll series

counts workers more than once if they are on two

or more payrolls during the reporting period. On an

industry-by-industry basis, there may be other factors intrinsic to each industry which have been the

major causes of the discrepancies between levels.

Generally, however, the two series showed the same

long-term trends and rates of increase between

1948-72, both for total nonagricultural employment

and for employment in major individual industries.

Moreover, the cyclical movements of the two series

during the 25-year period also have been very much

alike, especially with respect to turning points.

Although some inconsistencies between the series

continue to prevail, each possesses unique qualities.

Since the payroll series is derived from reports of

industry establishments, it furnishes extremely reliable employment estimates by industry. The household survey, on the other hand, provides demo--

graphic and labor force detail not available from the

employer reports. 

FOOTNOTES

1 To be more precise, the payroll series counts the total

number of persons appearing on the payrolls of business

establishments at any time during the survey week. Thus,

any job held by more than one person during the week

would be counted more than once.

- President’s Committee to Appraise Employment and

Unemployment Statistics, Measuring Employment and Unemployment (Washington, 1962), chapter IV and appendix

I; and Gloria P. Green, “Comparing employment estimates

from household and payroll surveys,” Monthly Labor Review, December 1969, pp. 9-20.

1 For a comprehensive discussion of the differences between employment data from the household and

establishment surveys, including the effect of the population

undercount in the household survey and the inclusion of

14- and 15-year-olds in the payroll series, see the sources

cited in footnote 2.

1 For a description of the “link relative” technique used

in estimating monthly payroll levels of employment, see the

Technical Note in any recent issue of Employment and

Earnings.

•r’ A BLS benchmark is a comprehensive count of the number of workers on the payrolls of business establishments. It

is derived from a complete census of all establishments

covered by State unemployment compensation laws with

supplementary information from a number of Federal and

private agencies. Annually the payroll figures are updated to

reflect information from the March benchmarks of the

previous year. For a description of the payroll benchmarks,

see “BLS Establishment Estimates Revised to March 1971

Benchmarks Level,” Employment and Earnings, October

1972.

(i For an explanation of the changes and an indication of

the differences in the household employment estimates resulting from adjusting the CPS to population controls

based on information from the 1970 Census, see “Revisions

in the Current Population Survey,” Employment and Earnings, February 1972.

7 In February 1954, the CPS sample was expanded from

68 to 230 sample areas, although the overall sample size of

25,000 households was retained. Contemporaneously, a substantially improved estimation procedure (composite estimate) was introduced which took advantage of the large

overlap in the sample from month to month. These two

changes improved the reliability of most of the major statistics by an amount equivalent to that of doubling the

sample size.

s According to a survey by the National Association of

Home Builders of 450 major U. S. homebuilders, who in

1971 planned to build one-fourth of all housing units, 80

percent were involved in activities other than homebuilding.

The largest group (61 percent) were in land development

See “Major Homebuilders in 1971,” Economic News Notes

for the Building Industry (National Association of Homebuilders, 1971).

!* In 1967, a clarifying question was added to the CPS

questionnaire which asked all persons reported as self-employed whether or not the business was incorporated. Operators of small incorporated enterprises were claissified as

wage and salary workers instead of self-employed. See

Robert L. Stein, “New Definitions for Employment and

Unemployment,” Employment and Earnings and Monthly

Report on the Labor Force, February 1967.

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New findings show 

average jobless spells 

are shorter than previously 

believed, but there are 

more of them

HYMAN B. KAITZ

How long does a person remain unemployed on

average? A simple question, yet one that cannot

be easily answered despite the wealth of data

available on the unemployed.

For many years the Bureau of Labor Statistics

has been reporting regularly an estimate of the

average duration of unemployment for those who

are unemployed in a particular month. During

1969 this figure was about 8 weeks.

Now it is possible to supplement this measure of

the duration of unemployment. This article describes a method for estimating the number and

the average length of all of the spells of unemployment completed during the year. During 1969,

for example, this estimating procedure indicates

that, on the average, a person who became unemployed remained unemployed for about 5 weeks.

The differences between these two averages will

be explained below, but it is important to note

here that the two averages differ primarily because

they measure two essentially distinct groups.

It should also be pointed out that each measurement is an estimate since at the present time we do

not have so-called longitudinal surveys that follow

unemployed individuals week-by-week during

their spells of unemployment. Instead, we must

infer the length of the spells from a series of snapshots (surveys) at monthly intervals.

Because these estimates are derived by using

analytical techniques relatively unfamiliar to labor

force analysts, a detailed development of the

method used is presented in the appendix.

Earlier analysis

Each month during the week including the 19th

(the survey week), Census Bureau interviewers

Hym an B. K aitz is chief of the Division of Statistical

Standards, Bureau of Labor Statistics.

Analyzing

the length of

spells of

unemployment

visit over 52,000 households throughout the

United States and ask about the labor force

status during the preceding week of all household

members 16 years of age and older. (The preceding week, which includes the 12 th of the

month, is called the reference week.) Individuals

looking for work during the reference week arc

asked how long they have been unemployed. On

the basis of these responses, a distribution of

unemployed persons by duration of unemployment to the end of the reference week is published

regularly, together with the average duration of

unemployment. The published material represents

a cross section of the unemployed, for the most

part before their unemployment spell ends.

Let us begin by examining regularly published

distributions of unemployment by duration for

the average reference week in each of the years

from 1948 through 1969.

In table 1, we find, for example, that during an

average reference week in 1969 about 133,000

people were unemployed more than 27 weeks.

This number represents the long-term unemployed

in 1 week, but it cannot easily be converted into

an estimate of the number of long-term unemployed persons in a month or year for several

reasons. Many of these people remain among the

long-term unemployed from week to week and are

counted repeatedly, while others find work or

leave the labor force, and still others enter the

long-term unemployed from among those who

previously had lesser amounts of unemployment.

At the other end of the scale, we know that among

the 1,629,000 with less than 5 weeks of unemployment in the average reference week in 1969, many

will ultimately become long-term unemployed.

The long-term and short-term unemployed can

be separated if we consider only the average numFrom the Review of November 1970

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ber who left unemployment in the reference week.

The average number who end unemployment spells

of varying lengths in an average week multiplied

by the number of weeks in a year yields the

estimated annual number of spells by duration.

Table 2 contains these data.

In table 2 we find, for example, 570,000 spells of

27 weeks or longer in 1969. Because of their length,

these spells probably correspond closely to the

number of people who were jobless this long in 1969,

since it is unlikely that they had more than

one spell in that year. Among the 24 million spells

of less than 5 weeks, many were completed by

individuals who had more than one spell. Consequently, the shorter spells cannot readily be converted into a corresponding number of people.

Similarly, the estimated total of 32 million spells

of unemployment ending in 1969 correspond to a

smaller number of people with some unemployment in that year since some people experienced

more than one spell.

Two views of the unemployed

It should be emphasized that the data in tables

1 and 2 constitute two distinct ways of looking

at the unemployed by duration, and both are

essential to an understanding of patterns of unemployment. However, while we have been accustomed to looking at the type of data presented

in table 1, the estimates in table 2 are new.

Table 1. Distribution of the unemployed by duration of

unemployment (up to the reference week)

[Annual averages in thousands]

Year Total

Number of weeks

Average 

duration 

Less (in weeks

than 5

5-10 11-14 15 26 27 and 

over

1948______ 2, 278 1,300 505 164 193 116 8.

1949_ . . . . . . 3,634 1,756 863 331 428 256 10.

1950______ 3,287 1,450 754 301 425 357 12.

1951.. ... 2,054 1,177 421 153 166 137 9.

1952......... . .. 1,883 1,135 390 126 148 84 8.

1953................ 1,834 1,142 368 114 132 78 8. 1954 3, 533 1,605 811 305 495 317 11.

1955____ 2, 852 1,335 598 217 366 336 13.

1956.. ___ 2,750 1,412 594 211 301 232 11.

1957_______ 2,859 1,408 651 240 321 239 10.

1958____ 4,601 1,753 958 438 785 667 13.

1959________ 3,738 1,585 778 335 469 571 14.

1960________ 3,852 1,719 823 353 503 454 12.

1961.. _____ 4,713 1,806 964 411 728 804 15.

1962______ . 3,913 1,659 812 323 534 585 14.

1963________ 4,070 1,751 877 354 535 553 14.

1964____ 3,787 1,697 798 319 491 482 13.

1965_____ 3,366 1,628 707 276 404 351 11.

1966 2,878 1,573 573 206 287 239 10.

1967______ 2,975 1,634 675 218 271 177 8.

1968____ 2,816 1,594 613 197 256 156 8.

1969............ 2, 831 1,629 627 200 242 133 8.

Table 2. Distribution of completed unemployment spells

by duration

|ln thousands]

Year Total

Number of weeks

Average 

duration 

Less (in weeks)

than 5

5-10 11-14 15-26 27 and 

over

1948________ 25, 580 18,880 4,190 900 1,140 470 4.6

1949________ 30,450 20,310 5,150 1,630 2,320 1,040 6.2

1950________ 25, 510 16,870 4,150 1,270 2, 050 1,170 6.7

1951________ 26,010 20,700 3, 000 780 1,070 460 4.1

1952________ 24, 540 19,150 3, 510 680 850 350 4.0

1953________ 25, 580 20,470 3, 370 630 790 320 3.7

1954.... .. 28, 60C 18, 720 5, 300 1,320 2, 020 1,240 6.4

1955________ 27, 300 19,720 4,360 860 h 360 i; 030 5.4

1956________ 28, 680 21,010 4,480 960 1,420 810 5.0

1957________ 26,190 18,150 4,420 1,130 1,650 840 5.7

1958________ 32, 350 21,890 4.160 1,470 2,650 2,180 7.4

1959________ 31,220 22,190 4,200 1,190 2,180 1,460 6.2

1960________ 33, 590 24,190 4,280 1,380 2, 320 1,420 6.0

1961________ 34,230 23, 060 5,220 1,330 2,450 2,170 7.2

1962________ 32,900 22,980 5,230 1,150 1,930 1,610 6.2

1963________ 33,210 22, 590 5, 500 1,320 2,220 1,580 6.4

1964________ 33, 860 24,130 5,090 1,200 2,010 1,430 5.8

1965________ 33,670 25, CC0 4,61C 1,120 1,810 1,130 5.2

1966............ 35,900 28,410 4,420 900 1,370 800 4.2

1967________ 31,110 22, 090 5,680 1,160 1,500 680 5. C

1968________ 32, 340 23,980 5, 390 1,020 1,320 630 4.5

1969________ 32,10C 23,67C 5, 360 1,110 1,390 570 4.6

In table 3, the distribution of unemployment

by duration found in the average cross section is

compared with the distribution of average spells

of unemployment in 1969.

The contrast between the two duration distributions shown in table 3 is a marked one.

While only 2.8 million people were unemployed in

an average week of 1969, 32 million spells of unemployment occurred during the year. The distribution of completed spells is more heavily

skewed toward the shorter durations than is the

case in the cross-section distribution. In the latter,

a little more than half of the unemployed in an

average Aveek are shown with fewer than 5 weeks

of unemployment, while almost three-fourths of

completed spells fall in this interval. As a result,

the average duration of unemployment spells of

4.6 weeks is only six-tenths of the average duration

in the cross section. The greater skewness and

loAA er average duration of completed spells is due

to the probability of leaving unemployment being

inversely related to the length of unemployment.

(See the appendix for detailed deA^elopment of

this point.)

The structure of unemployment Avhich emerges

from the data on spells is quite different from

previous ideas based on the characteristics of

cross-section data. Unemployment duration is

much shorter on the a\'erage than had previously

been thought the case. By far, the bulk of those

who become unemployed experience spells of only

a few weeks. These spells undoubtedly reflect,

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among other things, the influx of young people in

the summer, intermittent looking for work by

marginal workers, and seasonal activities. These

spells are not necessarily terminated by finding a

job. During 1969, for example, about half of those

leaving unemployment found work and the remainder left the labor force. (These proportions

are inferred, under equilibrium conditions, from

the known fact that people who became unemployed were drawn, in approximately equal proportions, from the employed and from those out

of the labor force.) Many people look for short

periods because they must go back to other

duties such as attending school or keeping house.

Longer spells are more likely to be terminated

when jobseekers become discouraged and no

longer look for work. In addition, multiple short

unemployment spells are experienced by some

people, which add up to a substantial amount of

unemployment for them during the year, with

concomitant low annual earnings.

Data on completed spells offers a variety of

avenues for analysis. The rest of this article

considers two: (1) implications in the distribution

of unemployment for one’s chances of leaving the

unemployed; and (2) the behavior of spells

of unemployment under changing economic

conditions.

To begin, we examine the distribution of unemployment by duration under relatively stable

conditions with respect to one’s chances of leaving

the unemployed. We call this probability the “continuation rate” and it is fully explained in the

appendix. It is the probability that a person unemployed n weeks remains unemployed for an

additional week. Rates for 1969 are in table 4.

In this table we find, for example, that for

people who have had 5 weeks of unemployment

to the reference week, almost four-fifths (78 percent) will go on to experience a sixth week, and

Table 3. Comparison of distributions of unemployment

by duration, 1969

Weeks of unemployment Average cross 

section

Completed spells

Total: number__________________ 2,831,000 32,100, 000

percent___________________ 100.0 100.0

Under 5 weeks___________ ____ 57.6 73.7

5-10 weeks__________________ 22.1 16.7

11-14 weeks_________________ 7.1 3.5

15-26 weeks_________________ 8.5 4.3

27 weeks and over_____________ 4.7 1.8

Average duration__________ _______ 8. 0 weeks 4.6 weeks

Table 4. Weekly continuation rates for 1969

Weeks of unemployment experienced up to reference week Continuation rates

n = l _______________________________________________ .76

2______________ ________________ ____ ____ . .67

3....... ............................. ............................................ .72

4_______ ____ ____________________________ .75

5_______________ _______________ _____________ .78

6________________ ___________ _____ .80

7................................................................................ ............. .82

8____________________________________ __________ .84

9_______________ _____________ .86

Average of 10 to 14_____ ____ _________ .89

Averege of 15 and over._________ ____ _______ ____ ____ .92

so on. We note that continuation rates generally

increase with the length of unemployment already

experienced. Two separate reasons for this phenomenon may be offered, although this may not

exhaust the possibilities. Since these continuation

rates are calculated for all of the unemployed

combined, they are undoubtedly influenced by

the heterogeneity present among the unemployed

in their separate chances of leaving the unemployed. For example, if we had two groups among

the unemployed, each with a constant but different

continuation rate, the cross-section distribution

for both combined would show the continuation

rate increasing with the length of unemployment.

A second possible hypothesis is that of feedback

effect in which the longer a person is unemployed,

the less chance he has of reemployment or of

otherwise leaving the ranks of the unemployed

the relative importance of these two factors' may

be investigated by considering the duration distributions for unemployed with very specific

characteristics, such as age, sex, color, occupation

family role, and so on. If each of these groups is

sufficiently homogeneous, increasing continuation

rates will reflect only the feedback effect. More

investigation of homogeneity is needed.

Spells and the business cycle

The basic data for the next analysis is given in

table 5. In this table we find, for example, that 3.51

percent of the civilian labor force was unemployed

in an average week of 1969, and 21.8 percent were

in their first week of unemployment.

The data are given in percentages to remove the

effects of population growth and cyclical labor

force changes from the corresponding numbers

which might otherwise have been used. The percent of unemployed with 1 week of unemployment in the average cross section for each year is

the same as the percent of unemployed in the aver38

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age week who are beginning new periods of unemployment. This percent (s) was regressed on the

unemployment rate (u) for the 22 years with the

following results:

s= 33.617 —3.140u ? = .8 8 2 , D — W = 2.33

(12. 6)

The inverse relationship is very significant, as

indicated by the high correlation coefficient and

the t value of 12.6 for the coefficient of u. There is

no particular serial correlation as indicated by the

value of the Durbin-Watson (D-W) coefficient.

One alternate form of this equation was computed with two additional independent variables:

the unemployment rate lagged 1 year, and a

dummy variable equal to unity for 1967, 1968,

and 1969 to pick up possible effects of a change in

the labor force questionnaire in 1967. These added

variables proved not to be significant. Other

regression forms were not considered in the

present analysis. It should be noted that changes

in the age-sex-color composition (heterogeneity) of

the unemployed over the postwar period may

well affect this aggregate relationship. Also, since

the observations are annual averages, phases of

the business cycle are not well articulated in it.

Nevertheless, the aggregate relationship does

give some approximate results of interest to us.

First of all, increases in the unemployment rate

are accompanied by declines in the percent of

unemployed starting new spells, as the historical

data indicate. Specifically, a one-percentage-point

increase in the unemployment rate is accompanied

by a three-pcrcentage-point decline in the percent

of the unemployed starting new spells. As detailed

development in the appendix shows, the average

duration of spells is equal to the reciprocal of the

proportion with new spells (100/s). The number of

spells initiated in a year is given by the product

of the unemployment rate (u) and the percent of

unemployed with 1 week of joblessness in the

reference week. As noted, this number excludes

the effects of population growth and cyclical labor

force changes.

Total unemployment (u) is equal to the product

of the number of new spells (us) and the average

duration per spell (100/s). Consequently, small

percentage changes in the unemployment rate

from one period to another are approximately

equal to the sum of the percent changes in the

number of new spells and in the length of the

Table 5. Data for business cycle analysis, 1948-69

Year

1948

1949

1950

1951

1952

1953

1954

1955

1956

1957

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

Percent of unemployed 

in first week of 

unemployment in 

average reference week

21.6

16.1

14.9

24.3

25.1 

26.8

15.6

18.4 20.0

17.6

13.5

16.0 

16.8

14.0

16.2

15.7

17.2

19.2

24.0

20.1

22.1

21.8

average spell. Table 6 presents such percent

changes for increments of 0.5 in the unemployment

rate. All estimates come from the equation

discussed earlier.

It must be emphasized that these results derive

from the form of the relationship specified. Until

a better form of the relationship is determined,

these results should be considered tentative and

should be evaluated against common sense considerations. We note that at low unemployment

rates (between 3.0 and 3.5), the percent of newly

unemployed changes more rapidly than the

average spell length. This is not inconsistent

with the assumption that in a tight labor market,

the ranks of the unemployed contain a higher

than average proportion of people only marginally

connected with the labor force who swell the

unemployed for relatively short periods.

As unemployment rises (up to about 5.5 percent), the rate of increase in the length of average

spells rises, but the rate of increase in new unemployment spells declines. Above an unemployment rate of 5.5 percent, the length of the average

spells increases faster than the unemployment

rate, with accompanying declines in the number

with new spells. This may reflect the conversion

of a potential series of short spells for some workers

into one long spell, with some workers being discouraged enough to leave the labor force, keeping

them from entering the ranks of the unemployed

with new spells. It undoubtedly reflects more

substantially the behavior of workers with strong

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attachment to the labor force. The particular

sequence in which unemployment rates change

through the business cycle may also have effects

on the patterns of new spells and spell duration

which should be explicitly considered. In general,

the changing mix of workers with various degrees

of marginality at different levels of the unemployment rate, and its contribution to these results,

can only be investigated through a disaggregated

analysis.

Both of the brief analyses presented in the latter

part of this article are intended to serve mainly

as examples of what can be done with data based

on or related to spells of unemployment. They are

not intended to present substantial analyses in

their own right.

There are many avenues of research. We plan to

explore current estimates of completed spells

Table 6. Percent of unemployed with new spells and average

spell length for selected values of unemployment rates

Unemployment rate Average duration Unemployed with new spells

Level Percent Weeks Percent Proportion Percent

change change change

3.0_________ 4.13 .726

16.7 6.9 9.1

3.5. 4.42 .792

14.3 7.5 6.4

4.0. 4.75 .842

12.5 8.0 4.1

4.5. 5.13 .877

11.1 8.8 2.2

5.0. 5.58 .896

10.0 9.6 0.4

5.5. 6.12 .899

9.1 10.6 —1.4

6 .0 . 6. 77 .887

8.3 11.9 - 3 .2

6.5. 7. 57 .859

month by month. Satisfactory estimates should

be possible from the available data but their

development awaits further empirical work.

APPENDIX

We first consider unemployment in a “steady

state” or equilibrium condition, in which the level

of unemployment remains the same from week to

week. The number of people leaving the ranks of

the unemployed each week is balanced by the

addition of an equal number of newly unemployed,

and the distribution of the unemployed by duration (in weeks) remains constant. This assumption

of a steady state in unemployment would approximately represent the years 1968 and 1969, for

example, apart from seasonal change.

Three groups of unemployed

Three categories of the unemployed are separately and explicitly considered in this analysis:

(1) all those with some unemployment in the

reference week; (2) those who have just begun

spells of unemployment in the reference week; and

(3) those who have just completed spells in the

reference week. These categories are not mutually

exclusive, since the first category includes those

in the other two categories and those with 1-week

spells will be in both the second and third categories.

The first category is the only one we observe

directly and for which we have the distribution

by weeks of unemployment. The question is

sometimes asked: How long on the average will

these people remain unemployed? The answer is

that in a steady state condition, their average

unemployment subsequent to the reference week

will be the same as their average period of unemployment thus far. That is, their average completed spell of unemployment will be twice their

average period of unemployment thus far. The

reasoning for this may be given in nontechnical

terms. All those in the first category may be classified in subgroups by the length of their completed

spells. Consider, for example, the subgroup which

will ultimately complete spells exactly 14 weeks in

length. Under the steady-state hypothesis, within

the reference week some of these people will have

just begun their spells, while others are just concluding theirs, with the remaining people in between. For this group we should therefore expect

to find that on the average they have already had

7 weeks of unemployment up to the reference

week.

In the same way we would expect to find that

on the average, each of the other subgroups is

halfway through its spells of unemployment. For

all of these subgroups combined we would therefore expect to find that, on the average, they are

halfway through their spells. To put it another

way, the average length of completed spell for

all people in the reference week will be twice the

average duration up through the reference week.

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If the average duration up through the reference

week is 8 weeks, then we can say that these

people will ultimately complete their spells with

an average of 16 weeks. (These computations

are subject to a small adjustment as will be noted

in the examples below.) This result is summarized

in the following statement:

Under equilibrium conditions, within a given

week, the unemployed are halfway through their spells

of unemployment on the average; thus, their average

duration of completed spells will be twice the average

duration of the unemployment already experienced to

the given week.

This method of reasoning does not, however,

tell us, for example, how many people will have

14-week spells. The number in the reference week

with 7 weeks include those with 7 weeks or more

in their spells, but some people who will have 14-

week spells will also come from those listed with

fewer than 7 weeks of unemployment in the reference week. As will become evident, there is little

need to further pursue the question of the duration

of unemployment spells of people in the first category. We note that people in that category have

begun their spells at different times and will conclude them at different times, but they all are

unemployed in the reference week. The findings

for this category cannot be generalized to cover a

longer reference period, such as a month.

In order to extend our analysis to cover unemployment in the other two categories, it will be

helpful to work with some simple duration

patterns. There is only one unemployment pattern for which the average completed spell for all

people in the first category is the same as that

for the people in the other two categories: when all

the unemployed have exactly the same length of

spell, for example 8 weeks. Consider such an equilibrium pattern with two people becoming unemTable A -l. First simple duration pattern

Duration of unemployment (in weeks) to—

Number of unemployed in 

reference week

Middle of reference 

week

End of reference 

week

.5 1 2

1.5 2 2

2.5 3 2

3.5 4 2

4.5 5 2

5.5 6 2

6.5 7 2

7.5 8 2

Total........................................................................ 16

ployed, and two people leaving unemployment

each week. The unemployment distribution in the

reference week is shown in table A—1.

If we make our calculations to a single point of

time—for example, to the middle of the reference

week (the left hand column of table A - l) — we

find that the average elapsed duration of unemployment up to that time for all the unemployed

is 4 weeks. But we have specified that every unemployed person will go on to experience 8 weeks

of unemployment. The relationship between these

two averages is therefore in accord with the general

rule previously stated.

More realistic pattern

We next look at a more typical pattern of unemployment by duration in a given reference week.

In a steady state, in any given reference week,

more people will be shown as having 1 week of

elapsed unemployment than as having 2 weeks;

more will have 2 weeks than 3, and so on. In

general terms, we need say only that the number

with n weeks of unemployment will not exceed the

number with in-1) weeks, whatever the value

of n. This statement allows us to include the

pattern presented in table A -l.

This generalization of the longitudinal pattern

of unemployment is consistent with reality as the

following argument show's. Suppose there are 100

people in the reference wreek with 4 weeks of unemployment. In the following w'eek, at most, 100

people will now' be showrn with 5 w eeks of unemployment. It is more likely that some will have

withdrawn from the ranks of the unemployed for

some reason, so that the number with 5 w'eeks of

unemployment will be less than 100. That the

number with 5 wreeks of unemployment in the

week following the reference week equals the

number with 5 w'eeks of unemployment in the

reference week is an essential characteristic of the

steady-state distribution, which has the same

duration pattern from week to week. Table A-2

shows howr this pattern keeps regenerating itself

in the case of this second simple duration pattern.

Each w eek five new people become unemployed.

Four of these will continue to remain unemployed

for a second week. Three of the four will remain

unemployed for a third week, and so on. Finally,

one person will remain unemployed through a

fifth week and then leave. No one is unemployed

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for more than 5 weeks in a spell. In this pattern

(which is longitudinal in nature contrasted with

the cross section or latitudinal pattern in the

reference week), we follow a particular group or

cohort from 1 week to the next as indicated by the

diagonal arrows. Any cohort followed in this way

for 5 weeks will have the same duration distribution as in the cross section in a single week. As

already noted, in a steady state, the cross-section

and longitudinal distributions are identical.

To compute the desired average durations we

can go through a simple arithmetic exercise. If we

take as our reference point the middle of the week

(see left hand column of table A-2), then the

average elapsed duration in the cross-section distribution is 1% weeks for the 15 unemployed

people. If we take the first cohort of five people

with 1 elapsed week to the end of the reference

week, then the average subsequent unemployment for this group from the middle of the reference week to the end of their respective spells is

2% weeks. Of these five people, one drops out after

1 week, so he has an additional duration of x/2

week. Another drops out at the end of the following week with a total additional duration of 1%

weeks, and so on. In the same way we follow the

second cohort of four people and find that their

average additional duration of unemployment is 2

weeks. Similar calculations for the remaining three

original cohorts yield average additional durations

of iy2, 1, and y2 weeks, respectively. The average

additional duration for all five cohorts combined is

1% weeks— the same as the cross-section duration,

confirming the rule stated earlier. The final average length of completed spell for all of those with

unemployment in a selected reference week (the

first category) is twice this number or 3 % weeks.

Table A-3 shows how we obtain the duration distribution of completed spells for those who are

leaving unemployment in the reference week or for

those who enter unemployment.

The preceding discussion shows that we can

use the cross-section pattern in a single week to

deduce the experience of a group of unemployed

persons from the time they become unemployed

to the end of their spells. B y taking first differences

in the cross-section distribution (second column

from left) we get the number who leave unemployment by the length of their completed spells

whether begun or ended in the reference week.

In a steady state condition, the pattern of completed spells fo r those who enter unemployment at the

Table A-2. Second simple duration pattern

Duration of unemployment (in weeks) to—

Number of unemployed in reference week

Middle of 

reference 

week

End of 

reference 

week

Week 1 Week 2 Week 3 Week 4 Week 5

0.5 1 5

1.5 2 4 \ ^ 4

2.5 3 3 \ ^ 3 \ ^ 3

3.5 4 2 \ ^ 2

4.5 5 1 ^ 1 ^ 1 ^ 1 ^ 1

Total________ . 15 15 15 15 15

same time is the same as fo r those who leave unemployment at the same time.

Because our specified cross-section distribution

includes individuals with different lengths of completed spells, we find in table A-3- that the cross

section of unemployment by duration differs from

the pattern of spells that will occur for all persons

with some unemployment, and from the pattern

for people entering or leaving unemployment.

Continuation rates

Before we examine actual cross-section duration

distributions of the unemployed, it will be helpful

to develop some additional properties of steadystate distributions. We start with a concept

already familiar in actuarial and demographic

analysis (the “survival rate” ) which is designated

here as the “continuation rate.” For our purposes,

the continuation rate (rn) is the proportion of

people unemployed n weeks who continue to

remain unemployed for the n-{-lst week. Clearly

rn must fall somewhere between zero and unity

and may change as n changes, or remain constant.

In a steady-state distribution, the value of rn

for a specified value of n does not change from

1 reference week to another. Consider the continuation rates associated with the two simple

distributions we have alread}'- discussed. (See table

A -4).

Apart from the fact that rn falls between zero

and unity, there is no other requirement it must

satisfy, other than that a value of zero ends the

pattern. We reject the case in which rn is constant

and equal to unity, since this gives us a distribution with infinite average duration, and no

movement out of the unemployed. However, we

admit consideration of duration patterns with

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Table A-3. Second simple duration pattern by cross

section and completed spells of unemployment

Cross section

Distribution by completed spells

Duration of 

unemployment 

(weeks)

(number of unemployed in 

reference week)

All persons 

with some 

unemployment

Persons

beginning

spells

Persons

ending

spells

l . _ _____ _________ 5 1 1 1

2________________ 4 2 1 1

3________________ 3 3 1 1

4____ 2 4 1 1

5________________ 1 5 1 1

Total________ 15 (people) 15 (people) 5 (spells) 5 (spells)

Average durations__ 156 (to middle of 

reference week) (

3

o end of spell

3

infinite and reasonable averages which may be

represented by mathematical functions which

theoretically allow durations of infinite length.

This situation is the same as in ordinary statistical

practice when we assume that variables may be

represented by a normal distribution, which has

infinite tails to the left and to the right.

In order to understand the characteristics of

actual data, we shall need some additional insights into completed spell distributions.

In a steady-state condition, with a constant continuation rate (less than unity), the cross-section

distribution pattern to end oj the reference week is

the same as the pattern oj completed spells for those

either beginning or ending their spells at the same

time.

The reasoning behind this statement is as follows. Let ip represent the number of people in

the cross-section distribution with 1 week of

completed unemployment; u2, those with 2 weeks;

and un, those with n weeks. Thus, u2 also represents

that part of the Ui who go on to have a second week

of unemployment, and so on. If r= constant continuation rate, then by definition, u2H-Ui =

r = . . . = u n+1-Hiin whatever the value of n.

Consequently, u2= r u 1, . . ., un+1= r u n whatever

the value of n. The number of unemployed in the

reference week who complete a spell of 1 week is

Ui — u2==Ui (1— r). Similarly, the number who

complete spells of n weeks is un—un+1 = un (1— r).

The two distributions may be set down together

as follows:

Wteks of Cross-section Completed spell

unemployment distribution distribution 1

1 Ui u i (1 —r)

2 uz uz (1—r)

n u„ un (1-r)

It is apparent that, apart from a constant, (1—r),

the two distributions are the same, and they will

consequently have the same distributions and the

same averages. But only in the case of a constant

continuation rate will the two patterns coincide.

A critical difference between distributions by

duration of unemployment for all unemployed

persons and for those beginning or ending their

unemployment spells must be noted. As already

indicated, the results for persons in the first category cannot be aggregated or averaged over various time periods easily, but those for the other two

categories can. For example, spells begun (or concluded) in 1 week do not overlap information for

spells begun (or ended) in any other week. An

answer to the rather imprecise question of what

is the character of unemployment spells in a year

can be provided by taking all those with some

unemployment in the first week of the year and all

those beginning unemployment in the other 51

weeks of the year, in order to develop unduplicated

averages or aggregates for the year. The characteristics developed in this way would approximate the

spells for those becoming unemployed in the average reference week of the year. If the question were

stated in terms of all the spells begun or ended

during the year, it could be answered directly in

terms of the data for those beginning or ending

unemployment in the reference week.

Other general characteristics

Two additional general statements which are

needed for our use can be made for any duration

distribution:

The number oj new spells oj unemployment in any

period is the sum oj the number oj people in each week

in the period identified as having completed 1 week

oj unemployment.

In a steady-state distribution, the average duration

oj completed spells is equal to the ratio oj the total

number oj unemployed in the reference week to the

number with 1 week oj unemployment.

This last statement is confirmed by a simple

algebraic formulation. Let un be the number of

people in the cross-section distribution with n

completed weeks of unemployment through the

end of the reference week. Let 11! = the number

with 1 week and u = to ta l number of unemployed’

in the reference week. Then u = T ^ u n. The number

71=1

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un—un+i for all values of n.

Consequently, the average spell duration is the

sum of the number of spells weighted by the length

of spell divided by the total number of spells.

Average duration = X j

71 = 1 U l

Expand the numerator in term by term detail:

S n(un—un+1) = u , —u2+ 2 (u 2—u3)

+ 3 (u3—u4)-f- . . .

= u ! + u 2-t-u3+ . . . = u,

Thus the average spell duration=u-f-U!.

Test this method on the two sample duration

distributions we have examined in tables A -l and

A -2. For the first one, u = 1 6 , and ^ = 2 , so the

average duration of completed spells is 16/2 = 8

weeks. For the second one, u = 1 5 , and u, = 5, so

the average duration of completed spells is

15/5 = 3 weeks. In the special case of a distribution

with a constant continuation rate (r), the average

duration of completed spell is 1 /(1—r) which is also

the average duration through the end of the reference week of the cross-section distribution.

Applications to data

Before we can apply the findings developed so

far to actual household survey data, we must

emphasize one particular point: some of these

results apply most accurately to steady state

distributions. In the real world, as unemployment grows or declines or changes in distribution

by duration, these properties may hold less precisely. For example, 4 weeks after there are heavy

layoffs, we may find that the number of people

with 4 weeks of unemployment is greater than

the number with 3 weeks in a given cross-section

distribution. In a steady state distribution this

result is impossible. On the other hand, in the

corresponding longitudinal distribution by duration after the layoffs, the number with 4 weeks

of unemployment will be no greater than the

number in the preceding week with 3 weeks of

unemployment. The cross-section and longitudinal distributions may thus be temporarily different from each other. Similarly, the completed

spell distribution for those starting new spells

of people who complete spells n weeks in length is may well look different for a time from that for

people concluding spells. Finally, the average

duration of completed spell for those in the first

category may be temporarily less than or more

than twice the average in a given cross-section

distribution. However, in the process of aggregating or averaging to annual levels, these shortterm anomalies are smoothed away, so that in

this article it was legitimate to consider the average cross-section distribution for the year as

equivalent to a longitudinal distribution, and to

derive the corresponding distributions of completed spells by duration.

The methodology for obtaining completed spell

distributions may also be used for estimating these

distributions month by month from the household

survey data to show how they change in response

to economic and other influences.

The balance of this appendix is confined to

description of the procedure for estimating the

distribution by duration of completed spell for

1969. Cross-section distributions by duration are

ordinarily published within the class intervals

shown in table 1. Unpublished data are available

for a few categories of unemployed for single

weeks of duration. However, this detailed frequency pattern reveals that: (1) some biases are

present in the recall of the first several weeks of

unemployment, and possibly elsewhere; and (2)

frequencies tend to bunch up at durations which

are multiples of a month; that is, quarters and

half years. It was therefore deemed advisable to

attempt to fit smooth mathematical functions to

the data within the published class intervals to

derive the estimates of the completed spell distributions. A major indication of the erratic nature

of some of the frequencies lay in the variation of

continuation rates above unity, and in wide fluctuations in adjacent values of the continuation

rates.

No single mathematical curve appeared to fit all

of the intervals simultaneously. On the other hand,

reasonable results were obtained by fitting curves

to two or three adjacent intervals at a time. A

logarithmic normal curve was fitted to the data for

the two bottom intervals and used to estimate the

number with unemployment of 1, 2, 3, or 4 weeks.

The top two intervals were fitted with an exponential function that was used to estimate the

number of people with 27 weeks of unemployment.

Treating the bottom three intervals together, we

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fitted a mixed exponential (a weighted sum of

two exponentials) to estimate the number of persons unemployed 5 weeks, 11 weeks, and 15 weeks,

respectively.

At this point two additional general statements

must be made to help us in our computations and

in our understanding of the results.

In a steady state distribution, the number oj spells

involving from m to n weeks of unemployment is

equal to the number oj unemployed persons in the

cross-section distribution with m weeks oj unemployment m inus the number with n - f 1 weeks.

The reasoning here is similar to that employed

before in deriving the simple estimate for the

average duration of completed spells. As already

indicated, the number of people in the crosssection who complete spells of exactly n weeks

in length is u n — un+i, where u„ is the number

of people with n elapsed weeks of unemployment

through the reference week. The number of

persons with completed spells of from m weeks

through n weeks is therefore (u„ — um+i) +

(U m+ i — U m+2) 4 * • • • 4 " (* l n U n + l ) — U m u n+lIn a steady state distribution, ij the duration

pattern has an increasing (decreasing) set oj continuation rates associated with increasing weeks oj

duration, then the average duration oj completed

spell will be less than (more than) the average duration in the cross-section duration distribution.

In other words, if the likelihood of a person

continuing to be unemployed increases the longer

he is unemployed, then the average duration of

completed spells will be less than the average

duration found during the reference week. The

opposite is true when he is less likely to continue

to be unemployed the longer he has been unemployed.

Increasing continuation rates will yield relatively higher frequencies of spells at the lower

end of the distribution than for a distribution

with constant continuation rates, while a distribution with constant continuation rates will have

relatively higher frequencies at the lower end than

a distribution with declining continuation rates.

In each of these two comparisons, the distribution

with the greater weight at the lower end will

clearly have a lower average duration than the

other. Since the constant continuation rate distribution has the same average for completed spells

as for the cross-section, the results follow.

Table 4 of the article showrs that continuation

rates generally increase with the length of unTable A-4. Continuation rates applicable to patterns in

tables A -l and A-2

Duration of unemployment to end of

Continuation rates

reference week (in weeks)

Table A-l Table A-2

1__________________ ___________ 1.00 0.80

2_____ ____ _____________ ______ 1.00 0. 75

3________ _____ ________________ 1.00 0. 67

4______________________________ 1.00 0. 50

5____________________ __________ 1.00 0

6______________________________ 1.00

7............................................................. 1.00

8______________________________ 0

employment. Possible explanations for this pattern

have already been given. In accord with the

general rule just stated, we expect in this situation that the completed spell distribution would

have a smaller average duration than the crosssection distribution.

Since we want to derive the distribution of

spells within the same intervals as for the crosssection distribution, we shall need estimates of

the numbers of people in the cross section with

the following specified weeks of unemployment,

in accord with the estimating procedure described earlier: iq, u5, un, Ui5, and u27.

The derived estimates are generally satisfactory, although they are undoubtedly susceptible

to improvement. Other mathematical functions

might also be considered in the curve-fitting

process. The estimation of the number of people

with 1 week of unemployment was particularly

critical and difficult. The logarithmic normal fit

gave results which were closest to the actual data

in the bottom interval for individual weeks and

gave acceptable estimates for the continuation

rates. Nevertheless, the estimated total number

of spells in each of the years may be subject to

an estimating error of 5 percent or more. This

margin of error also will be present in the duration

of average spell because of their direct connection.

Estimates for the rest of the duration distributions should be reasonably good otherwise.

An excellent single reference is D. J. Bartholomew, Stochastic Processes in the Social Sciences

(New York, John Wiley and Sons, 1967), which

also has an extensive bibliography. Two other

references are R. F. Fowder, Duration oj Unemployment on the Register oj Wholly Unemployed

(London, H. M. Stationery Office, 1968), and

B. Craig, Development oj Manpower Statistics

(Paris, Organization of Economic Cooperation

and Development, 1969). Q

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Black and white

unemployment: the

dynamics of

the differential

T h e w i d e l y a c c e p t e d v i e w in the economic literature is that the unemployment situation of black

workers improves relative to that of whites when

the demand for labor is strong and deteriorates visa-vis whites when the demand for labor slackens.

Yet, observed changes in the ratio of black-to-white

unemployment rates— roughly 2-to-l throughout

most of the post-World War II period— suggest that

the unemployment situation of blacks improves compared with that of whites when the demand for labor

slackens and deteriorates when the demand for labor

is strong. Why is it that changes in the ratio of blackto-white unemployment rates appear to run counter

to the generally accepted view? Can the apparent

contradiction be reconciled?

In attempting to analyze the disproportionate share

of unemployment experienced by black workers 1 and

to compare changes in unemployment among blacks

and whites, most analysts use the ratio of black-towhite unemployment rates which will be called the

relative unemployment differential,2 following the

terminology used in the literature. Others have focused on the difference between the rates which is

called the absolute unemployment differential. This

article introduces another measure— ratio of the percentage-point changes in the unemployment rates of

blacks and whites— which is termed the incremental

ratio. Because no single statistical measure can be

expected to tell the whole story, the incremental ratio

can be used in concert with existing measures in describing the relative incidence of black and white

unemployment. Its main contribution is to enable the

analyst to describe more accurately the changing

Curtis L. Gilroy is a labor economist in the Division of

Employment and Unemployment Analysis, Bureau of Labor

Statistics. Roberta V. McKay, an economist in the same

division, assisted in the preparation of this article.

New analysis adds support to beliefs 

that blacks fare better 

in prosperity than in other 

phases of business cycle

CURTIS L. GILROY

unemployment burden of the two groups during the

business cycle.3

No attempt will be made in this paper to describe

the employment situation of black workers, which is

well documented;4 nor will an attempt be made to

investigate the causes of excessive black unemployment.'' Rather, this paper, while emphasizing the

usefulness of the two popular measures of black and

white unemployment differences and exploring some

inconsistencies between and limitations in them, seeks

to demonstrate that the incremental ratio is preferable in comparisons of changes in the incidence of

black and white joblessness over the business cycle.

In particular, this study will use the incremental ratio

to support the hypothesis that blacks are affected

more (proportionate to their numbers) than whites

by changes in the demand for labor.

The measures

In comparing unemployment of black and white

workers, it is concluded that blacks experience a

disproportionate share of unemployment if the relative unemployment differential (B /W ) is greater

than 1.00 and the absolute differential between the

rates (B -W ) is positive.

Neither of these popular measures is completely

satisfactory by itself, however, in measuring changes

in the incidence of unemployment. A comparison of

the relative differential at two points in time can

easily create a false impression if it measures changes

in unemployment rates with widely divergent bases.

On the other hand, a comparison of the absolute differentials in rates does not describe the relative

change in unemployment attributable to each group.

The problems in both measures may be demonstrated

as follows: Consider, for example, a situation in

which blacks and whites have initial unemployment

rates of 8.0 and 4.0 percent, respectively. If both

From the Review of February 1974

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rates decrease by the same absolute amount, say 2

percentage points, the relative unemployment differential would rise from 2.0 to 3.0, while the absolute

differential in the rates would remain at 4.0 percentage points. If both the white and black rates were

halved (whites from 4.0 to 2.0 and blacks from 8.0

to 4.0) the relative differential would remain unchanged at 2.0 and the absolute differential would

decline from 4.0 to 2.0 percentage points. Finally, if

the white rate were reduced from 4 to 1 percent and

the black rate from 8 to 4 percent, the relative differential would increase from 2.0 to 4.0 while the

absolute differential would fall from 4.0 to 3.0 percentage points. The policy implications emanating

from this last example are somewhat unclear. Short

of out-and-out equality of black and white unemployment rates, there is a question as to whether blacks

are better off, in that they experienced a greater

decline in their rate vis-a-vis whites, or worse off, in

that their relative position deteriorated.

The question therefore remains: Which measure is

the more appropriate? Many analysts, in relying on

the relative differential, have pointed to a relative

improvement in the black employment situation

whenever the differential decreased. Other writers

argue that the situation may not have improved at

all and insist that what is important is not so much

that blacks have higher unemployment rates but

whether an increase affects them more adversely than

whites, say from the peak of a business cycle to its

trough.6

A comparison of the relative differential in rates

in two distinct periods— denoted by (B /W )ti and

(B /W )t2— is useful but its usefulness is limited to a

comparison of those periods when economic conditions are similar. It is informative, for example, as a

measure of long-term changes between two periods

with similar overall unemployment rates. The relative differential in rates is most appropriate in measuring relative unemployment burdens of blacks and

whites at a point in time. The absolute differential

between rates— (Btj — Wtl) or (Bt2 — Wt2)— implies the correct unemployment relationship between

time periods, but it does not go far enough. To gauge

more precise relative changes in unemployment another measure is desirable— the incremental ratio.

The incremental ratio

The incremental ratio incorporates the favorable

aspects of the relative and the absolute differentials.

It does not show the change in relative unemployment

rates but rather depicts the absolute change in unemployment rates and expresses it in relative terms; that

is, (Bt2 — B tl) / (W .2 — Wtj) m A B /A W . The

prime advantage of the incremental ratio is that it

takes into account the widely different bases from

which were measured the changes in the rates in

percent terms. For example, it shows that during the

1970-71 recession, proportionate to their share of

the labor force, 14 persons were added to the already

high unemployment rolls of blacks for every 10 that

were added to the unemployment of whites—

A B / AW = (6.3 - 9.2) / (3.3 - 5.4) = 1.4.

(See table 1.)

Let us translate this incremental ratio into numbers

of unemployed people. Assuming that the sizes of the

black and white labor forces were 1,000 and 10,000

respectively (blacks make up, in fact, about 10 percent of the labor force), the number of unemployed

blacks would increase from 63 to 92, whereas the

number of jobless whites would rise from 330 to 540.

Thus, on a per-thousand basis, the unemployment

rolls of blacks increased by 29 workers over the last

recession; that for whites by only 21 workers.

By contrast, a comparison of the relative unemployment differential over that period shows an improvement in the black unemployment situation as

the black-white ratio declined from 1.91 at the peak

of the business cycle to 1.70 at its trough. This

occurred because the percentage increase in the unemployment rate for whites exceeded that for blacks.

The decline in the ratio is misleading, however, because it does not account for the greater increase,

proportionately, in unemployment among blacks than

whites. Blacks are then worse off relative to whites

even though the relative differential decreased.

If, during the prosperity phase of the cycle, black

unemployment fell from 9.2 to 6.3 percent and that

for whites dropped from 5.4 to 3.3 percent, the relative differential would rise. Looking solely at changes

in the relative differential, one could argue that the

economy should be in a continuing state of recession

to allow the relative differential to fall to the ideal of

1.00. On the other hand, the incremental ratio of 1.4

would indicate that blacks experienced a greater

decrease in unemployment (proportionate to the size

of their labor force) than their white counterparts

during the cyclical upswing.

The apparent inconsistency in the measures disappears, however, when the incremental ratio takes

on a value greater than the initial value of the rela47

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five differential. For example, during the expansionary phase of the 1960’s, black unemployment fell

from 12.4 to 6.3 percent while white joblessness declined from 6.1 to 3.3 percent (table 1). The incremental ratio was 2.2, and the relative differential

declined from 2.03 to 1.91. In this case the value

of the incremental ratio (2.2) exceeded that of the

initial relative differential (2.03). To clarify its

meaning, then, if the incremental ratio exceeds 1.0,

the black unemployment rate is changing more than

the white rate; to produce a decline in the relative

differential over time, its value must exceed that of

the initial value of the black unemployment rate

divided by the white rate (B-f-W typically must be

greater than 2.0).

Three limitations of the incremental ratio, however, are important to consider. First, the ratio is not

relevant to measuring unemployment differences at

one point in time. The relative and absolute differentials in rates are most useful here. Second, the

incremental ratio is inapplicable over very short periods of time when any change in a group’s unemployment rate (for example, A B /A W = (8-7) /

(4-4) = 1/0 = undefined) is unlikely. Third, using

the incremental ratio to measure changes in unemployment over long periods of time also may be

misleading since it would tend to hide the short-run

ups and downs in the economy for which it is a most

useful measure.

Despite the black-white unemployment rate ratio

Table 1. Peak-to-trough and trough-to-peak changes in unemployment rates by color, by age and sex, 1954-70

Age and sex

Peak Trough

Over-theperiod

change

Peak Trough

Over-theperiod

change

Peak Trough

Over-theperlod

July change

1957

April

1958

May

1960

February

1961

November

1969

November

1970

Total, 16 years and over:

White................................................................. 3.7 6.4 2.7 4.7 6.1 1.4 3.3 5.4 2.1

Black................................................................. 7.9 13.4 5.5 10.0 12.4 2.4 6.3 9.2 2.9

Incremental ratio...................................... 2.0 1.7 1.4

Both sexes, 16-19 years:

White................... ..................................... 10.5 14.8 4.3 12.9 15.6 2.7 10.6 15.4 4.8

Black......................................................... 19.8 25.4 5.6 24.8 30.7 5.9 23.6 32.3 8.7

Incremental ratio.............................. 1.3 2.2 1.8

Men, 20 years and over:

White________ -..................................... 3.1 5.9 2.8 3.9 5.3 1.4 2.1 4.0 1.9

Black....................... ................................ 7.0 13.8 6.8 9.1 11.6 2.5 3.7 6.5 2.8

Incremental ratio.............................. 2.4 1.8 1.5

Women, 20 years and over:

White......................................................... 3.7 5.9 2.2 4.3 5.7 1.4 3.4 5.1 1.7

Black......................................................... 6.8 10.5 3.7 8.3 10.2 1.9 5.5 7.8 2.3

Incremental ratio.............................. 1.7 1.4 1.4

Trough Peak Trough Peak Trough Peak

Over-the- Over-the- Over-theperiod period period

August July change April May change February February change

1954 1957 1958 1960 1961 1969

Total, 16 years and over:

White................................................................ 5.6 3.7 -1 .9 6.4 4.7 -1 .7 6.1 3.3 -2 .8

Black................................................................ 10.3 7.9 -2 .4 13.4 10.0 -3 .4 12.4 6.3 -6 .1

Incremental ratio...................................... 1.3 2.0 2.2

Both sexes, 16-19 years:

White......................................................... 13.2 10.5 -2 .7 14.8 12.9 -1 .9 15.6 10.6 -5 .0

Black......................................................... 16.9 19.8 -2 .9 25.4 24.8 - . 6 30.7 23.6 -7 .1

Incremental ratio.............................. 1.1 .3 1.4

Men, 20 years and over:

White......................................................... 5.0 3.1 -1 ,9 5.9 3.9 - 2 .0 5.3 2.1 -3 .2

Black......................................................... 10.2 7.0 -3 .2 13.8 9.1 -4 .7 11.6 3.7 -7 .9

Incremental ratio.............................. 1.7 2.4 2.5

Women, 20 years and over:

White......................................................... 5.2 3.7 -1 .5 5.9 4.3 -1 .6 5.7 3.4 -2 .3

Black........................................................ 9.2 6.8 -2 .4 10.5 8.3 -2 .2 10.2 5.5 -4 .7

Incremental ratio.............................. 1.6 1.4 2.0

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falling during the trough-to-peak period in the 1960’s,

the ratio characteristically rises in times of prosperity

and falls in recessionary periods. This is evident from

chart 1, which traces the ratio of the major age-sex

groups over several recent business cycles. By considering only the ratio, we would be led to believe

that blacks are generally better off relative to whites

in cyclical downturns than they are when economic

activity is strong. However, this is not the case.

Blacks become worse off than their white counterparts in recessions, as the incremental ratio shows.

This is supported by James Tobin, who has presented

the conventional view, in his statement:

People who stand at the end of the hiring line and

the top of the lay-off list have the most to gain

from a tight labor market. It is not surprising that

the position of Negroes relative to that of whites

improves in a tight labor market and declines in a

slack market.7

Peak to trough; trough to peak

Characteristic of recessions is a slackening in the

demand for labor; a feature of every recovery is an

increase in the demand for labor services. Black

workers have experienced a greater absolute increase

in their unemployment rate than white workers in

almost all cyclical downturns (peak-to-trough) and

a greater absolute decrease in their unemployment

rate than whites in the recovery periods (trough-topeak). (See chart 2.) This differing cyclical unemployment experience of blacks relative to whites is

revealed by the incremental unemployment rate

ratio, which is consistently greater than 1.0.

The peak-to-trough and trough-to-peak changes in

the seasonally adjusted unemployment rates for white

and black workers are shown in tables 1 and 2 for

selected age, occupational, and industry groups. The

Chart 1. Black-white unemployment rate ratio at peaks and troughs of business cycles, 1954-73

NOTE: Unemployment rates for the peaks and troughs were obtained at each turning point of the cycle,

by averaging the three seasonally adjusted monthly observations centered

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unemployment rates for the peaks and troughs were

obtained by averaging the three monthly observations

made at each turning point of the cycle.8 Constrained

by the availability of unemployment data by color for

the various age groups, our observations of three

peak-to-trough and trough-to-peak movements occur

during 1954-73; for occupations and industries, observations covering two peak-to-trough and one

trough-to-peak movements during 1959-73 are presented.9

The phenomenon of blacks being more adversely

affected than whites by business downturns has been

mitigated over the last several business cycles. For

every successive peak-to-trough period, black workers have shared less of the increase in unemployment.

In the 1957-58 downturn, for example, proportionate

to the size of their labor force, 20 black workers were

added to the unemployment totals for every 10

white workers; however, during the 1969-70 recession, only 14 blacks became jobless for every 10

white workers. (See table 1.) Moreover, proportionately more blacks than whites have left the ranks of

the unemployed with each successive recovery period. During the 1954—57 recovery, 13 black workers

for every 10 white workers left the unemployed

ranks, while in the 1961-69 period of prosperity,

there was a decrease of 22 unemployed black workers for every 10 white workers.

Among the age-sex groups, adult black men have

borne the brunt of the increase in unemployment in

the cyclical downturn but have also experienced proportionately more of the decline in unemployment

when economic activity picked up. While adult black

men and women shared about equally in the increase

in unemployment in the most recent downturns— the

incremental ratios are similar in size— black women

Chart 2. White and black unemployment rates, all workers, 16 years and over, 1954-73

[Seasonally adjusted quarterly averages]

1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1973

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do not benefit as much as black men in the upswing.

In most of the major occupational groups, the

ratio was smaller in the most recent recession than

in the 1960-61 downturn. (See table 2.) This indicates that black workers suffered a smaller increase

in joblessness than white workers during the 1969-70

slowdown. This occurred particularly among clerical

and sales workers, operatives, and service workers—

those occupations in which two-thirds of the black

labor force are employed.

An example of the particular usefulness of the incremental ratio arises from examining the experience

of the professional and managerial occupational

group. The negative sign of the incremental ratio for

this group indicates that the rates for blacks and

whites moved in opposite directions. In this case, not

Table 2. Peak-to-trough and trough-to-peak changes in unemployment rates by color, by selected occupation and industry groups, 1959-70

Occupation and industry

Peak Trough

Over-theperiod

change

Peak Trough

Over-theperiod

change

Trough Peak

Over-theperiod

May change

1960

February

1961

Novembw.»

1969

November

1970

February

1961

November

1969

Occupation

Professional, technical, and managerial:

White_______________________________ 1.5 2.0 0.5 1.1 1.8 0.7 2.0 1.1 -0 .9

Black________________________________ 2.8 3.6 .8 2.1 1.4 - . 7 3.6 2.1 -1 .5

Incremental ratio...................................... 1.6 -1 .0 1.7

Clercial and sales workers:

White................................................... ............ 3.6 4.6 1.0 2.7 4.3 1.6 4.6 2.7 -1 .9

Black........ ........................................................ 7.5 9.8 2.3 5.6 8.4 2.8 9.8 5.6 -4 .2

Incremental ratio..................... ................ 2.3 1.8 2.2

Craft and kindred workers:

White..........-.................................................... 4.3 9.1 4.8 2.0 4.1 2.1 9.1 2.0 -7 .1

Black................................................................. 10.4 15.3 4.9 3.2 5.5 2.3 15.3 3.2 -1 2 .2

Incremental ratio...................................... 1.0 1.1 1.7

Operatives:

White................................................................. 7.7 11.5 3.8 4.4 7.7 3.3 11.5 4.4 -7 .1

Black................................................................. 10.5 17.4 6.9 5.2 9.3 4.1 17.4 5.2 12.2

Incremental ratio...................................... 1.8 1.2 1.7

Nonfarm workers:

White................................................................. 11.2 19.8 8.6 7.0 10.2 3.2 19.8 7.0 -1 2 .8

Black...................................... ......................... 13.1 20.6 7.5 6.9 11.7 4.8 20.6 6.9 -13.7

Incremental ratio...................................... .9 1.5 1.1

Service workers:

W hite.............................................................. 5.5 6.8 1.3 3.4 5.5 2.1 6.8 3.4 -3 .4

Black................................................................. 9.4 11.8 2.4 6.3 7.9 1.6 11.8 6.3 -5 .5

Incremental ratio...................................... 1.8 .8 1.6

Industry

Construction:

White................................................................. 10.9 24.0 13.1 5.4 9.2 3.8 24.0 5.4 -1 8 .6

Black................................................................. 17.2 30.1 12.9 7.1 14.1 7.0 30.1 7.1 -2 3 .0

Incremental ratio...................................... 1.0 1.8 .7

Manufacturing:

White................................................................. 5.4 8.7 3.3 3.3 6.3 3.0 8.7 3.3 —5.4

Black................................................................ 11.3 19.2 7.9 5.1 10.2 5.1 19.2 5.1 -14.1

Incremental ratio...................................... 2.4 1.7 2.6

Trade:

White................................................................. 5.3 7.3 2.0 3.3 5.3 2.0 7.3 3.3 -4 .0

Black................................................................. 12.1 14.3 2.2 7.0 9.1 2.1 14.3 7.0 -7 .3

Incremental ratio...................................... 1.1 1.1 1.8

Services:

White................................................................. 3.8 5.7 1.9 2.9 5.0 2.1 5.7 2.9 -2 .8

Black................................................................. 9.1 12.8 3.7 5.9 7.6 1.7 12.8 5.9 -6 .9

Incremental ratio...................................... 1.9 .8 2.5

Government:

White................................................................ 1.9 2.2 .3 1.5 1.9 .4 2.2 1.5 - . 7

Black................................................................. 3.6 5.6 2.0 3.9 5.2 1.3 5.6 3.9 -1 .7

Incremental ratio...................................... 6.7 3.3 2.4

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only did blacks experience a lower incidence of unemployment relative to whites around the bottom of

the cycle, but their incidence of joblessness actually

decreased in the recession while that for whites rose.

This was due in large part to the increase in overall

unemployment during the last recession being relatively more concentrated in the professional and technical occupations; for example, in aerospace, electronics, and other defense-related industries in which

only a small proportion of black workers were employed. Throughout the period of recovery, the

incremental ratios show that blacks, proportionate to

the size of their labor force, experienced a greater

decrease in unemployment than white workers. In

each of the occupational groups, the incremental

ratio was in excess of 1.0.

Patterns similar to the occupational ones exist for

the major industry groups from peak to trough and

the trough to peak. Although the incremental ratio

was greater than 1.0, substantial decreases in the

ratios occurred within manufacturing, service, and

government over the last two recessions. In the recovery period, black unemployment decreased at a

faster rate than white with the exception of the construction industry, where black workers became unemployed in greater relative numbers than white

workers and have been less likely than whites to be

rehired.

Black-white differences over time

The degree to which changes in overall economic

conditions affect the unemployment rates of various

labor market subgroups has been the concern of

several analysts.10 Few, however, have attempted to

systematically measure the impact on the various

demographic subgroups by color.11 This section will

show the impact of the business cycle on the unemployment rate of black and white workers and will

measure the extent to which the jobless rates of

blacks have been affected proportionately more than

those for whites over time. Thirty regressions were

run by the selected age-sex, occupational, and industry subgroups by color. The dependent variable (Y )

was the subgroup unemployment rate by color, and

the independent variables— (Xi and X 2)— the unemployment rate of males 35-44 years old, and time,

respectively.12 By using the jobless rate of men 35-44

years old as a proxy for the changing level of economic activity, the regression equations will permit

the estimation of what the unemployment rates of

various subgroups would be as economic conditions

change. The labor market becomes “loose” or “tight”

roughly coincident with changes in aggregate demand

defining the various phases of the business cycle.

Quarterly data from the Current Population Survey

were used covering the years 1954—73 for the age-sex

groups and 1959-73 for occupations and industries.

The results of the regressions appear in table 3.

All the equations show a high degree of statistical significance and demonstrate a close and positive relationship between the surrogate measure for

economic change (henceforth referred to as the

“prime age unemployment rate”) and the incidence

of unemployment among the various labor market

subgroups. The extent to which a given change in

the prime rate would affect the subgroup rate is

substantially greater for blacks than for whites in all

equations. For example, a change of one percentage

point in the prime rate (X x) results in a change in

the same direction of 1.05 percentage points for

adult white males but an increase of 2.26 percentage points in that for adult black males. Among

both blacks and whites, the coefficients are larger—

indicating greater movement in rates— for bluecollar than for white-collar workers, since the former group, which has proportionately more semiskilled and unskilled labor, is considerably more

affected by changing business conditions.13 Moreover, the coefficients of the goods-producing relative

to the service-producing industries are larger, due

to the faster growth of the latter and the fact that

they are cyclically less sensitive.

A time variable was included in the regression

equations to show the extent to which a trend could

be discerned in the various unemployment rate

series. Although care must be taken in interpreting

the effect of the time variable because it tends to

include all factors varying with time, the results of

the regressions do show a worsening in the overall

employment situation for both blacks and whites.

The sign of the coefficient (b2) of the time variable

(X 2) is positive, which indicates that over the period

1954-73, the subgroup unemployment rates have

trended upward. A significant exception was that for

adult black men, whose rate has trended downward

secularly. The upward trend was greatest in teenage

unemployment, both among blacks and whites; the

upward trend among teenage blacks was significantly

greater than for all other groups and confirms the

fact that much of the black unemployment problem

is a youth problem as well; that is, a greater num52

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Table 3. Regression results showing relationships between unemployment rates of various labor market subgroups,

prime male unemployment rate, and time

Dependent

variable

Color « ) (bi) (bj (r,) (s) D-W Dependent

variable

Color (a) (bi) (bj) (r») (s) D-W

Age-sex-color Operatives........... White -.0 0 7 1.881 .033 .77 .85 1.91

1954(1)—1973(1) (.01) (12.15) (3.67)

Black .423 2.937 .025 .77 1.41 2.24

Both sexes, 16 (.40) (11.46) (1.66)

years and over.. White .680 1.024 .019 .93 .24 1.11 Nonfarm laborers White 2.255 2.466 .017 .67 2.23 2.22

(4.96) (32.09) (12.84) (1.23) (5.60) (.69)

Black 2.957 1.852 .019 .89 .60 .85 Black 4.287 3.014 -0.25 .46 2.42 2.05

(8.68) (23.38) (5.31) (2.54) (7.42) (1.06)

Men, 20 years and Service workers,

over................... White .043 1.051 .007 .95 .22 1.25 excluding private

(.34) (35.84) (5.48) household_____ White 1.803 .941 .019 .72 .47 1.90

Black 1.657 2.256 -.0 1 8 .94 .65 1.15 (5.04) (10.94) (3.91)

(4.48) (26.25) (4.71) Black 5.215 1.317 -.010 .71 .88 1.47

Women, 20 years (7.80) (8.19) (1.12)

and over........ . White 1.339 .814 .016 .87 .27 1.00

(8.58) (22.42) (9.58) Industry-color

Black 3.643 1.240 .018 .68 .77 .74 1959(0-1973(1)

(8.37) (12.26) (3.83)

Both sexes, 16-19 Manufacturing... White 6.445 2.482 -.1 1 8 .82 1.89 .99

years.................. White 4.941 1.817 .062 .72 1.02 .75 (4.52) (7.23) (5.95)

(8.52) (13.48) (10.20) Black' .619 3.081 .006 .82 1.37 2.13

Black 7.603 2.875 .233 .77 2.48 1.20 (.60) (12.40) (.43)

(5.40) (8.79) (15.71) Construction____ White 3.038 2.637 -.0 1 0 .31 3.98 2.10

(1.01) (3.65) (.24)

Occupation-color Black 10.191 4.955 -.219 .52 7.44 1.44

1959(1)—1973(1) (1.81) (3.66) (2.81)

Wholesale and

Professional, retail trade___ White 1.086 1.116 .026 .68 .60 2.19

technical, and (2.40) (10.24) (4.22)

managerial____ White -.371 .456 .021 .76 .20 2.15 Black 3.977 2.210 .029 .60 1.53 1.57

(2.52) (12.85) (10.43) (3.45) (7.96) (1.84)

Black .864 .603 .008 .35 .70 1.60 Services, exclud-

(1.63) (4.72) (1.11) ing private

Clerical and sales household____ White 1.003 .894 .025 .73 .41 1.47

workers............ White .560 .774 .026 .78 .31 2.18 (3.21) (11.89) (5.88)

(2.55) (13.77) (8.04) Black 3.105 1.726 .021 .62 1.16 1.90

Black 1.614 1.470 .048 .42 1.34 1.97 (3.56) (8.22) (1.74)

(1.60) (6.16) (3.43) Government......... White .379 .388 .015 .50 .29 2.19

Craftsmen and (1.74) (7.40) (5.02)

kindred workers. White -.235 1.383 .010 .56 1.11 2.07 Black 1.010 .726 .014 .37 .77 i.60

(.28) (6.85) (.86) (1.73) (5.15) (5.11)

Black 1.249 2.285 -.0 2 7 .70 1.65 2.04

(1.00) (7.63) (1.59)

NOTE: t values are in parentheses.

ber of teenagers entering the labor market relative

to job opportunities available to them.

During 1959-73, the trend in unemployment

among the various occupational groups has been

generally upward. Those downward trends that occurred for blacks in the blue-collar and service

occupations, were statistically insignificant. The significant upward 'rends were predominant in the

white-collar professions, which may partly be explained by the difficulty in securing employment for

a more highly educated and trained labor force at a

time when there were severe cutbacks— as in 1970-

71— in the aerospace, electronics, and other defenserelated industries.

Unemployment appears to have become more

concentrated among the service-producing industries for both blacks and whites and less prevalent

in the goods-producing sector. This may be due in

part to the service-producing sector’s having attracted

many women and young workers who characteristically have higher unemployment rates.

The coefficient of determination (r2) shows the

extent to which the regression equations explain the

variations in the unemployment rate of the subgroups. In general, a greater amount of the variation

in the white than of the black subgroup unemployment rates could be explained by the regression

equation.14 The greater unexplained portion of the

variation in black unemployment (in addition to

greater sampling variability) is undoubtedly due to

such structural factors as educational deficiencies,

growth rates of various labor force subgroups, regional distribution, employment discrimination, and

the like. The standard error of the estimate(s),

which is a measure of how much, on the average,

the actual unemployment rate deviates from that

calculated from the regression equation is larger

for the black regressions than those for the white.

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The proposition that blacks are affected relatively

more than whites by changes in the demand for

labor is further substantiated by a comparison of

the coefficients (bx) of the prime male unemployment rate (X j). Expressing these coefficients as

the ratio Bbi/W bi (where B and W denote the

black and white coefficients), the relative change

in black and white unemployment rates can be

seen. This is clearly the incremental ratio. Since

Bbi and Wbj are consistently positive, and Bbi >

Wbj, the ratio is positive and greater than 1.00.

(See table 4.) This means that over the long period,

whenever business conditions have deteriorated,

black unemployment has risen proportionately more

than white; however, when conditions improved,

Table 4. A comparison of black and white coefficients

of the independent variable Xi (male prime unemployment

rate)

Subgroup

Black

coefficient

(Bb,)

White

coefficient

(Wb,)

Black

coefficient

-!-White

coefficient

(Bbl/Wbl)

Age-sex

Both sexes, 16 years and over_____________ 1.852 1.024 1.809

Men, 20 years and over.................................... 2.256 1.051 2.147

Women, 20 years and over________________ 1.240 .814 1.523

Both sexes, 16-19 years______________ _ _ 2.875 1.817 1.582

Occupation

Professional, technical, and managerial.......... .603 .456 1.322

Clerical and sales workers________________ 1.470 .744 1.899

Craftsmen and kindred workers___________ 2.285 1.383 1.652

O peratives...____ ___ ______ _____ 2.937 1.881 1.561

Nonfarm laborers...______ ________ _____ 3.014 2.466 1.222

Service workers, excluding private household. 1.317 .941 1.400

Industry

Construction ______________ __________ 4.955 2.637 2.093

Manufacturing_____________ __________ 3.081 2.482 1.241

Wholesale and retail trade________________ 2.210 1.116 1.980

Services, excluding private household______ 1.726 .894 1.931

Government_____ _______ ______________ .726 .388 1.871

SOURCE: Table 3.

blacks have left the ranks of the unemployed in

greater relative proportions.

W e m a y c o n c l u d e from our analysis that in recovery and downturn, blacks are affected relatively more than whites by changes in the demand

for labor. The differing cyclical unemployment experience of blacks relative to whites is more accurately portrayed by a measure introduced in this

article— the incremental ratio. Utilization of this

ratio of the difference between the unemployment

rates of blacks and whites (A B /A W ) reconciles

the inconsistency in patterns which emerges when

comparing black and white unemployment rates at

two points in time using the traditional measures—

the absolute unemployment differential (B-W) and

the more popular relative unemployment differential

(B /W ).

A primary advantage of the incremental ratio is

that it takes into account the widely different bases

from which the changes in unemployment are measured. Consequently, use of the incremental ratio is

desirable because it permits a fuller understanding

of the dynamics of black and white unemployment

over the business cycle. For example, a narrowing

of the traditional ratio, which generally occurs during recessions, has created a misleading impression

that blacks were less affected than whites by increases in unemployment. The incremental ratio

shows, in fact, that black unemployment has risen

proportionately more than that for whites during

such periods; this finding was buttressed in this

article by regression and variance analysis. Likewise,

a widening of the differential is characteristic of

recovery periods. This is also misleading because it

implies that blacks become worse off than whites

when the economy expands. The incremental ratio,

however, shows that black unemployment has decreased proportionately more than white unemployment during such periods. 

-FOOTNOTES1 Statistics for members of the black and other U.S. minority races— called “Negro and other races”— are used to

indicate the situation for black workers. Blacks constitute

89 percent of the larger group.

2 In a pioneering study, Harry Gilman examines the cyclical variability of the relative incidence of black and white

unemployment in “The White/Non-White Unemployment

Differential,” in Mark Perlman, ed., Human Resources in

the Urban Economy (Washington, D.C., Resources for the

Future, Inc., 1963), pp. 75-113. See also Curtis L. Gilroy,

Investment in Human Capital and the Nonwhite-White Unemployment Differential, unpublished Ph. D. dissertation,

State University of New York (Binghamton), 1973.

3 This procedure was suggested by Paul O. Flaim who

utilized it in an unpublished Bureau of Labor Statistics

analysis, “The Negro-White Unemployment Relationship,”

March 1970.

4 See Black Americans, a chartbook, Bulletin 1699 (Bureau of Labor Statistics, 1971); Gloria P. Green, Employment in Perspective: The Negro Employment Situation, Re54

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port 391 (Bureau of Labor Statistics, 1971); and The Social

and Economic Status of the Black Population in the United

States, 1972, Current Population Reports, Series P-23, No.

46 (Bureau of the Census, 1973) and similar Census reports

in previous years.

cSee, for example, Gary S. Becker, The Economics of

Discrimination (Chicago, 1967); Harry Gilman, “Economic

Discrimination and Unemployment,” American Economic

Review, December 1965, pp. 1077-1096; Ralph E. Smith

and Charles C. Holt, “A Job Search-Turnover Analysis of

the Black-White Unemployment Ratio,” in Industrial Relations Research Association, Proceedings of the Twenty-Third

Annual Winter Meeting, December 1970, pp. 76-86; Lester

Thurow, Poverty and Discrimination (Washington, The

Brookings Institution, 1969).

6 Harry Gilman, for example, has dismissed the relative

unemployment differential (B /W ) as a desirable index, instead using the difference between white and black unemployment rates (B -W ) at two points in time as the appropriate measure. See Harry J. Gilman, “The White/Nonwhite Unemployment Differential,” p. 92.

7 James Tobin, “Improving the Economic Status of the

Negro,” Daedulus, Fall 1965, p. 406.

8 The cycle turning point dates used are those defined by

the National Bureau of Economic Research. They are, peakto-trough: July 1957-April 1958; May 1960-February 1961;

and November 1969-November 1970.

Three-month averages were computed to smooth out inherent sampling variability (particularly among blacks because of the relatively small sample size from the Current

Population Survey) and to mitigate somewhat the discrepancy which may occur between the NBER cycle turning

points and the turning points in unemployment.

9 The year 1959 was chosen because that is the year the

data first became available for occupations and industries by

color. The choice of 1959 as a starting date for this analysis

may be questioned, however, because it is viewed by some

as not a representative year in that it was sandwiched in

between two recessions. In addition, a major steel strike in

that year may have adversely affected the employment situation for blue-collar workers.

10 See, for example, Paul M. Ryscavage, “Impact of Higher

Unemployment of Major Labor Force Groups,” Monthly

Labor Review, March 1970, pp. 21-25; Robert A. McMillan, “What Happens When the Unemployment Rate

Changes?,” Economic Review, Federal Reserve Bank of

Cleveland^ June-July 1972, pp. 3-16; Vladimir Stoickov,

“Increasing Structural Unemployment Re-examined,” Industrial and Labor Relations Review, April 1966, pp. 368-376;

Comment by Arthur Butler, “Identifying Structural Unemployment,” and Reply by Stoickov in Industrial and Labor

Relations Review, April 1967, pp. 441-446; and Lester

Thurow, “The Changing Structure of Unemployment: An

Econometric Study,” Review of Economics and Statistics,

May 1965, pp. 137-149.

11 Barbara R. Bergman and David E. Kaun looked at

detailed age groups by color in their Structural Unemployment in the United States (U.S. Department of Commerce,

Economic Development Administration, 1967) pp. 77-81.

12 The equation took the form U = a -f- biXi + b2X2

where U represents the subgroup unemployment rate; a, a

constant term; b; and b2, coefficients of the independent

variables Af, and X t\ and X t and X 2, the unemployment rate

of “prime” age males 35-44 years old and time respectively.

The use of the “prime” age male unemployment rate as a

cyclical indicator rather than the more popular aggregate

unemployment rate was to avoid having much the same

variable on both sides of the equation. See Bergman and

Kaun, p. 78.

13 See Malcolm S. Cohen and William H. Gruber, “Variability by Skill in Cyclical Unemployment,” Monthly Labor

Review, August 1970, pp. 8-11.

14 The cyclical variability of the various black and white

unemployment rate series can be seen by comparing the

standard deviations of the respective series. If blacks are

more affected than whites by changes in the demand for

labor, it will be reflected in a greater variability in their

unemployment rate series through a larger standard deviation. The standard deviation, while reflecting the particular

cycle amplitudes, will also include additional variability.

Some of this variability is due to the fact that the black and

white unemployment rates have different size bases. When

the variances of two such series are to be compared, a measure of relative variance may be more useful because it expresses the magnitude of the variation relative to the size of

the quantity that is being measured. If the absolute variability of the unemployment rate is assumed to depend in part

upon the average level of unemployment, then the standard

deviation as a percentage of the mean— coefficient of variation— is an appropriate measure. In all cases, the standard

deviations as well as the coefficients of variation were greater

for the black than for the white series.

Some of this variability will be due to sampling error.

For the white series, the Current Population Survey is large

enough to make the random sampling error variance small.

On the other hand, the unemployment rate series for black

workers rest on smaller samples for which the sampling

error variance is substantially larger. Harry Gilman found

that, even after adjusting for differing sample size among

blacks and whites, black workers still had a greater absolute

variability in their unemployment rates than whites in 64

percent of the intermediate occupational groups studied.

Those occupations within which the variability was similar

for both blacks and whites were the higher skilled professional jobs where blacks are relatively few in number. See

Gilman, “The White/Non-White Unemployment Differential,” pp. 90-92.

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The rate of voluntary separations 

is a good economic indicator; 

the reasons for quitting are 

changeable and derive from workers’ 

attitudes toward the economy

PAUL A. ARMKNECHT AND

JOHN F. EARLY

L abor m o b il it y is the sine qua non for the efficient

allocation of labor factors in the production process.

The only reliable labor mobility data available on a

continuing and current basis are those reported by

the Bureau of Labor Statistics in its monthly series

on labor turnover in manufacturing, particularly the

rate of voluntary separations. This article undertakes

to lay a foundation for the use of the series in current economic analysis and to discover the reasons

for variation in the quit rate over time and among

industries.

The quit rate as a cyclical indicator

In the post-World War II era, the quit rate in

manufacturing has been a smooth, well-behaved series that has rather consistently led the business cycle

at its peak and coincided with it at the trough. (See

chart 1.) A test of its adequacy as an economic

indicator by means of the methods adopted by Geoffrey H. Moore and Julius Shiskin1 placed it on a

par with the most commonly accepted indicators. Of a

possible summary score of 100, the quit rate scored

71, compared with 69 and 65, respectively, for the

layoffs and total accession rates. Tables 1 and 2

show the smoothness and small revisions in the quit

rate which are two of the important factors contributing to its quality as an indicator. These desirable

traits may arise, in part, from the fact that while the

BLS labor turnover survey is based on a sample of

approximately 38,000 establishments, the true size of

the sample underlying the quit rate estimate is the

10.4 million workers employed in these establishPaul A. Armknecht and John F. Early are economists in the

Division of Industry Employment Statistics, Bureau of

Labor Statistics. An earlier version of this article was presented at the meeting of the American Statistical Association

in Montreal, Canada, on August 16, 1972. A more detailed

study will appear in a forthcoming BLS staff paper.

Quits

in manufacturing:

a study of

their causes

ments, since quit decisions are made by individual

workers.

Since the beginning of the series, the quit rate has

exhibited a median lead of 15 months at the business

cycle peak and a median lead of 1 month at the

trough. This long lead and the desirable statistical

properties of the series make it a good forecaster of

possible downturns in the economy.

Time series regression

The highly cyclical nature of the quit rate has

already been noted, but the literature on the subject

has developed a controversy over the question of

whether the rate also has a trend.2 It has been

argued (1 ) that there is no trend in the rate, (2)

that there is a decline in the rate because of nontransferability of pensions and other fringe benefits

— the so-called industrial feudalism hypothesis, and

(3) that there has been a decline in the quit rate

because of endemic factors, such as the aging of the

work force. Our study supports the view that there

has been no trend.

To determine whether there has been any measurable trend in the quit rate in the past two decades, a

number of time-series regression models were tested,

using both quarterly and annual data. Only the final

equations for the quarterly model will be presented

and discussed here. A more detailed description of

other hypotheses tested and of statistical difficulties

that had to be overcome will be found in a forthcoming BLS staff paper. The following is the two-stage

least-squares estimate of the model which explains

the data best over time. All insignificant terms, including the constant, have been removed.

(1) qt = .238 A(ht) + .405 D(ht) + .310 hUl

(.074) (.064) (.043)

R2 = .763

c Durbin-Watson = 2.18

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Chart 1. Manufacturing quit rate, seasonally adjusted, 1947-71

Rate

NOTE: Peaks (P) and troughs (T) refer to business cycle turning points determined by the National Bureau of Economic Research.

The standard errors are contained in parentheses,

and the following definitions apply:

q t = the change in the quit rate in quarter t.

A(ht) = the positive change in the new hire rate

in quarter t, zero if the change was

negative.

D(ht) = the negative change in the new hire

rate in quarter t, zero if the change was

positive.

ht_, = the change in new hires in the quarter

previous to t.

The new hire rate explains the quit rate so well

probably because it is a measure of the jobs available

and of job security, and it seems quite likely that the

more jobs there are and the more secure a worker

feels the more inclined he will be to seek a better

paying job. As already indicated, the constant term

in this equation was not significant, which means that

there was no constant change in the quit rate for the

past two decades— that is, there was no trend. Our

model differs from the models used by those who

have found negative trends in the quit rate in at least

two important ways. First, our model was statistically much more rigorous than that employed by

some who used a rather impressionistic mode of

analysis. Second, the new hire rate seems to be a

more appropriate measure of the cyclical swings in

job availability and security than were the variables

used by Pencavel to remove cyclical effects. The absence of a trend in the quit rate does not mean that

there have been no long term shifts in the patterns of

mobility. We will, in fact, show later that there have

been some rather dramatic shifts. But the absence of

a trend does suggest that, on the average, the manufacturing worker is no more or less mobile in seeking

new employment than he was in the years immediately following World War II.

It will be noted that for the current quarter the

effects of the new hire rate have been divided into

two parts— the increases, or “absorption,” and the

decreases, or “disabsorption.”3 There appears to be

a distinct asymmetry of behavior here. A decline in

hiring during the current quarter will depress the

propensity to quit by 70 percent more than a similar

expansion in hiring would have increased it. In short,

the manufacturing worker is very cautious and can

have his confidence shaken much more readily than

restored. Such behavior helps explain the difference

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in the leading behavior of the quit rate at business

cycle peaks and troughs.

One other hypothesis that we wanted to test was

whether there was an additional, forward-looking attitudinal factor in the determination of the quit rate.

It was our hypothesis that the workers’ decisions to

quit were based not only on recent hiring practices,

but also on their views of the future, which might

depart from past experience. We further hypothesized that this future expectation about the condition

of the labor market would also be closely tied with

the workers’ consumption plans. As a result we expected two things: that the quit rate and the savings

rate should be positively correlated; and that, even

after the removal of current and past hiring effects,

there should remain an unexplained portion of variation in the quit rate that would correlate positively

with the growth of aggregate economic activity in the

following quarter.

Our first test found a significant positive correlation between the savings ratio and the quit rate. The

second resulted in the following equation, where

G (Y t+i) is the rate of growth of the real Gross

National Product in the next quarter:

(2) q t = .1 6 0 A (h t) + .424 D (h t) + .292 h t_t

(.077) (.062) (.042)

+ -019 G (Y t+I)

(.007)

R* — .781 Durbin-Watson = 2.11

C

Equation 2 preserves the essential characteristics of

equation 1. The G (Y t+I) term has a significant positive coefficient, indicating the presence of a forwardlooking attitude on the part of workers in their quit

decisions. The only difference between equations 1

and 2 is the spread between the absorption and

Table 1. Labor turnover economic indicators, 195S-71

Measure Quits Layoffs Accessions

New

hires

Average percent change:

Original series____ ______ _______ 18.61 15.20 16.59 19.23

Seasonal factors............... ................. 18.02 12.87 16.61 19.24

Seasonally adjusted series...... ........... 3.87 8.09 4.18 4.27

Irregulars______________________ 3.41 6.94 3.72 3.48

Trend-cycle____________________ 1.86 2.62 1.16 2.14

Irregular/trend-cycle ratio____________ 1.83 2.65 3.21 1.63

Number of months of cyclical dominance

(MCD)....... .......................................... . 2 3 4 2

NOTE:.These statistics for the layoff and accession rates differ slightly from those 

published by the Bureau of the Census in Business Conditions Digest since seasonal 

adjustment methods used by the bureaus differ.

Table 2. Labor turnover rate revisions, 1966-69

Change Total

accessions

New hires Quits Layoffs

Average monthly change............. 0.8 0.7 0.5 0.3

Average monthly revision............ .1 .1 0 .1

Percent revision to change_____ 11.9 9.0 9.0 27.2

1 Less than .05.

disabsorption coefficients for new hires. This increase

further emphasizes the cautious nature of the American manufacturing worker. When future expectations

are indirectly entered into the equation, it becomes

even more difficult to restore lost confidence unless

expectations for future growth reinforce current

improvements.

Cross-section regression

As noted above, changes in the quit rate over time

seem to be largely caused by changes in economic

factors as well as expectations about future changes.

But there still remain questions about the causes of

the variations in quit rate behavior among industries.

One should certainly expect low-paying industries to experience higher quit rates since their employees are most likely to find higher paying jobs and

have less to lose by quitting. Industries that are hiring large numbers of new employees may experience

higher quit rates since workers will be less concerned

about job security. Highly seasonal industries may

offer lower job security, attract the casual worker,

and, as a result, show a higher proportion of quits.

In addition to the characteristics of the industry,

characteristics of the workers may also contribute to

quit behavior. Women, for instance, may either exhibit a casual attachment to the labor force and thus

have low opportunity costs associated with high quit

propensities, or they may believe that they will face

discrimination in hiring and thus be reluctant to quit.

Production workers, who are generally affected more

than other workers by seasonal and cyclical changes

in the economy, may exhibit greater propensities to

quit since the nature of their work is marked by such

problems as work hazards, lack of opportunity for

promotion, poor supervision, and low wages, all of

which weigh more heavily in their evaluation of their

jobs. On the other hand, it may be true that the

lower education of the production worker may

impede his mobility by reducing his knowledge of the

market.

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Procedure. To test these hypotheses we ran ordinary

least-squares regressions for each year from 1959

through 1971, using annual averages for the 94 industry groups in manufacturing for which the Bureau

of Labor Statistics publishes labor turnover data.

The model we used regressed the quit rate for each

industry (Q ) on the average hourly earnings of

production workers (E ), the ratio of production

workers to all employees (P ), the amplitude of

the seasonal factors for employment (S ),4 the net

new hire rate— calculated as the difference between

the quit and new hire rates— (Hn), and the ratio of

women to all employees (W ) for that industry.

An equation was estimated for each year, using

index forms of the data with the manufacturing average for that year as the base to remove secular trends

from some of the data. The final regression coefficients were transformed to beta coefficients. This

transformation was made for the purpose of allowing

for differences in variation among the variables. (See

table 3.) Those coefficients which are not significantly different from zero are in parentheses. With

these transformations of the data it was possible to

establish the importance and direction of each variable in determining the interindustry variation in quit

behavior. It is interesting to note that for 1960 the

results we obtained were very similar to those obtained by Pencavel using a somewhat different

model.5

Findings. The results substantiate our qualitative assessments of the relationship between quits and the

explanatory causes, even down to the indeterminacy

of the role of women and production workers in

overall quit behavior. By far the most important

Table 3. Beta coefficients for variables in cross sectional

analysis, 1959-71

Year E P S Hn W

1959.................................. -0.579 (0.048) 0.143 0.371 (0.114)

1960.................................. - .559 (.030) (.026) .394 .249

i961................................ - 452 (.026) (.040) .422 .272

1962.................................. - .602 (- .011) (.000) .408 .172

1963.................................. - .695 (- .016) (.054) .364 (.056)

1964......... .................... . - .788 (- .005) (.041) .386 (- .075)

1965.................................. - .856 (.100) (.074) .301 - .219

1966............................... . - .874 .209 .116 .298 - .287

1967.................................. - .844 .192 (.046) .300 - .197

1968............................... - .911 .180 .127 .184 - .234

1969.................................. - .943 .149 .121 .253 - .229

1970.................................. - .914 .164 (.165) .224 - .188

1971.................................. - .904 .164 (.110) (.126) - .289

NOTE: The variables in this table are: E = average hourly earnings of production 

workers; P= ratio of production workers to all employees; $ = seasonal amplitude; 

H=net new hire rate; W= ratio of women to all employees.

Numbers in parentheses indicate insignificant coefficients.

factor determining interindustry variations in voluntary separations is the relative level of earnings. Next

in order of importance are relative net hires, followed

closely by the relative proportion of female employment. Finally, in the latter years of the decade, the

relative proportion of production workers proved

to be significant, while variations in seasonality were

of minimal significance in all but a few key years.

Earnings versus security

Pecuniary motivations cause relatively high levels of voluntary separations in low paying industries.

Skill requirements in such jobs are generally low.

Such positions are readily available to new or inexperienced workers, only to be vacated as soon as the

workers develop some skill and become aware of

other job opportunities. In high paying industries,

voluntary turnover is lower because of the low probability of obtaining a better paying job.

The earnings variable may also reflect other related market phenomena. For example, industries

with relatively low wage levels may be highly competitive, labor-intensive industries where cost conscious entrepreneurs have minimal regard for human

capital. In such situations poor working conditions

reflected in the low levels of earnings may also explain quit behavior. On the other hand, industries

with higher wage levels may be highly unionized, in

which case unionization may be a contributor to the

higher earnings level as well as better working conditions. In addition, the greater importance of human

capital in these latter industries may give management a stake in reducing turnover.

Earnings differentials may reflect, in part, skill and

age differentials among industries. However, when

variables for occupational and age differences among

industries were introduced by Pencavel, the results

were highly insignificant. Such industry occupational

and age composition items are only available from

the decennial census and could, of course, have

changed substantially during the 1960’s.

An examination of the coefficients for earnings in

table 3 reveals that this pecuniary factor has become

an increasingly important one in determining interindustry variations in quits. With the exception of periods of economic recession in the manufacturing sector (1960-61, 1967, and 1970-71), there has been

a steady progression in the importance of this variable over the decade of the 1960’s. The slight decline

in relative importance for this factor in times of

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business cycle downswings reflects the shift in importance from wage betterment to job security motivations.

The job security factor itself tends to show a gradual decline over the decade as net new hires become

a less important variable, although this trend is also

interrupted during periods of cyclical downturns. The

shifts in degree of importance between the pecuniary

and job security factors over the years tested, point

out the counterbalancing relationship of these two

factors in workers’ motivations to leave their jobs

voluntarily.

In analyzing these two trends, one must remember

that the years studied are the only postwar period

characterized by prolonged economic growth. Therefore, the increasing importance of pecuniary factors

and the decreasing importance of job security may

have been influenced to a degree by this extended

period of growth. To some extent, the expansion of

industrial centers from urban to suburban areas resulting in extended labor market areas has probably

increased the worker’s knowledge of opportunities

within the market. Increasing educational attainment

and mass communication also may have increased

the information reaching the worker. Such information makes the jobholder’s behavior more consistent

with the neoclassical concept of “economic man”

trying to increase his earnings and consumption

power under the constraint of his pains for laboring.

Women workers

In the manufacturing sector women tend to have

higher quit rates than men, partially owing to the

fact that industries with a high proportion of women

employees are also among the lower paying ones.

Hence, part of the reason for differences in quit

propensities between the sexes is the concentration of

women in lower paying jobs. Our model, however,

takes account of earnings differentials, so that it can

measure more accurately the true effect of women’s

employment as a factor in determining variations in

quits among industries. Considering the beta coefficients shown in table 3, one can see that the role

women play in determining quit propensities underwent a drastic reversal during the last decade. From

1960 to 1962 the proportion of women employed in

an industry was a significant factor directly affecting

the frequency of quits. As the relative proportion of

women increased so did the quit rate. In the next 2

years their effect was not significant, but the direction of the relationship changed. In 1965, the proportion of female workers again became a significant

factor, but the relationship with quits was inverse. As

the relative proportion of women workers among

industries increased, the quit rate decreased.

There were several important undercurrents in the

labor market during the last half of the 1960’s which

could account for this reversal. The manufacturing

labor market became very tight. This in part was due

to the Vietnam war buildup, which increased the

demand for war related goods, generated more income which increased demand for consumer goods,

and produced a manpower shortage arising from the

increased manpower needs in the military services.

As a result, there was a large influx of women into

the labor force. In addition, demographic factors

may have had their effect as there was evidence of a

slight “marriage squeeze” in 1963 and a more drastic

one beginning in 1966.6 This would also account for

the rapid increase in labor force participation among

women as well as declines in labor force withdrawal

for reasons related to marriage.

Social, cultural, and technological changes are also

quite relevant to this shift in quit behavior among

women. Such factors as the social approval and safer

methods of contraception, increasing educational attainment, antidiscriminatory legislation, introduction

of labor saving equipment for household and office

use, and many others have led to the acceptance of

the modem woman as a productive worker and economic competitor.

Despite the decline in the attitude that a “woman’s

place is in the home,” sex discrimination in hiring still

may serve as a deterrent to voluntary job mobility

for women. Since social and technological changes

have lessened the necessity for the casual attachment

of women in the labor force, the previously mentioned discrimination factor would seem to be a

more plausible explanation of the women’s influence

on voluntary separations in recent years.

Production workers and seasonality

The relative concentration of production workers

does not emerge as a significant factor until 1966. It

was in this period that demographic factors became

important in labor supply. Many young workers bom

during the postwar period entered the labor market.

With a tight market and low skill requirements,

members of this group were available for many semiskilled production line positions which may not have

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been entirely to their liking. The sudden change in

age composition and labor supply may account for

part of this shift. Still another factor, somewhat related, is that in the tight market job information was

diffused more widely to a workforce of increasing

education and sophistication. This situation resulted

in better knowledge of alternative opportunities and

made it possible for the worker to behave more like

the classical economic man. Combined with disillusionment of the young, job satisfaction among production employees may also have declined. As the

labor market slackened in 1969 and 1970 the production worker effect became less important, as is

borne out by the coefficients in table 3. Even in a

very slack labor market the greater propensity of the

production workers to quit remained, indicating the

presence of a shift in the basic pattern of manufacturing quit behavior.

Finally, we come to the question of seasonality.

As our beta coefficients indicate, it is the least important of our variables and proves to be significant only

in the years when the manufacturing business cycle is

at a peak (1959, 1966, 1969). This fact suggests

that seasonality becomes an important factor only

when jobs and alternative opportunities are plentiful.

The combined effects of these trends and shifts in

the individual variables are manifested in differences

among the various equations. We tested all pairs of

regression equations based on Chow’s test for differences between pairs of equations.7 We noted that

there are no significant differences among equations

which are separated by 1 or 2 years. There are no

significant differences in the quit experience among

the 15 pairs of equations preceding 1965, and there

are only two significant differences among the pairs

which lie entirely in the latter half of the period. Of

the remaining 36 pairs of equations that span both

subperiods, however, there are only three which do

not exhibit a significant difference in quit experience,

and these are separated in time by 1 or 2 years. We

can safely conclude, therefore, that the changes in

the effects of the individual variables resulted in a

sudden, dramatic shift in the overall basis for the

interindustry quit rate variation in the middle of the

last decade.

Summary and conclusions

We have viewed voluntary separations in American manufacturing industries from three different

perspectives: the properties of the average quit rate

for all manufacturing which make it a good economic

indicator, the variations in the quit rate through time

and the sources of these variations, and the differences in quit rates among industries and the changing

bases for these differences. Through these analyses

we have obtained several results which should be

helpful in the examination of the quit rate itself, the

functioning of the labor market, and the economic

situation as a whole.

• The total manufacturing quit rate is a statistically reliable and well behaved series. Preliminary

estimates are revised only rarely and only in the most

unusual cases does this revision exceed 0.1 of a percentage point. The seasonally adjusted series is quite

smooth and serves as a reasonably reliable economic

indicator.

• Workers are very conscious of job security and

can have their confidence easily shaken, while restoration of that confidence is quite difficult. As a result,

the changes in the quit rate may precede aggregate

economic activity by as much as five quarters during

periods of prosperity, but remain quite close to

movements in the total economy during periods of

slowed economic activity. Worker assessment of job

security seems to be built largely on the behavior of

the labor market during the past two quarters or so,

with extra weight being given to recent adverse developments. The variations in hiring among industries explains some of the variation in quits, although

this effect has been declining in recent years, with the

exception of recession years. This result suggests that

a worker draws his clues to the labor market situation not only from the closest period in time but also

from the situation that exists in the plant and industry in which he is employed. The decline in the

importance of job security in the interindustry variations suggests that, with time, the worker’s horizons

are broadening and he keys his behavior to wider

economic occurrences, although there is some reversion to the most immediate clues during times of

uncertainty and insecurity.

• The quit rate may be the best summary measure

of manufacturing workers’ attitudes, which in turn

make an important contribution to aggregate demand

and the course of the total economy. It is possible

that the observed correlation of quits and future aggregate economic activity arises from the fact that an

uncertain worker is a cautious consumer. Such a

dynamic of aggregate demand suggests that the public policy of creating jobs in time of slack economic

activity will do more than just increase aggregate

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demand through the usual accelerator-multiplier

principles: it will also serve to restore the confidence of the worker as consumer and thereby increase aggregate demand in a shorter period of time.

• Through time, the average worker has based his

decision to quit on different factors, and the importance he has attributed to each of them has been

changing. But he seems to have retained essentially

the same risk-taking posture which is modified only

1 Geoffrey H. Moore and Julius Shiskin, Indicators of

Business Expansions and Contractions (New York, Columbia University Press, 1967).

2 For example: Ewan Clague, “Long-Term Trends in Quit

Rates,” Employment and Earnings, December 1956, pp.

iii-ix; Arthur Ross, “Do We Have a New Industrial Feudalism?,” The American Economic Review, December 1958,

pp. 903-920; John E. Parker and John F. Burton, Jr.,

“Voluntary Labor Mobility in the U. S. Manufacturing

Sector,” Proceedings of the Twentieth Annual Winter M eeting of the Industrial Research Association, pp. 61-70; John

H. Pencavel, An Analysis of the Quit Rate in American

Manufacturing Industry (Princeton, Industrial Relations

Section, Princeton University, 1970).

3 This type of formulation has been suggested, in a somewhat different context, by Lester C. Thurow, “The Changing

Nature of Unemployment,” Review of Economics and Statistics, May 1965, pp. 137-149.

4 A detailed discussion of this method is presented in The

BLS Seasonal Factor Method, which is available upon request at the Bureau of Labor Statistics.

by changes in the availability of jobs. The absence of

a secular decline in the quit rate, the increasing importance of earnings levels in quit decisions, and the

sudden emergence of the production worker’s greater

propensity to quit, all suggest that there are no structural shifts taking place in the economy which would

impede the mobility of labor. The data on women,

however, suggest that there still remain some structural deficiencies in the labor supply process. □

5 In the Pencavel model (equation I A ) the beta coefficient for the earnings variable was —0.428, for the female

ratio 0.227, and for the hiring variable (accessions lagged)

0.321. The R* value for this equation is 0.778. His equation also contained a significant unionization variable and an

insignificant one for earnings variability. (See Pencavel, op.

cit., p. 21.)

6 The marriage squeeze occurs when there is an abundance of women of marriageable age over men of marriageable age. See Current Population Reports, Series P-25, No.

388, U.S. Bureau of the Census, for a more detailed explanation.

7 G. C. Chow, “Tests for Equality Between Sets of Coefficients in Two Linear Regressions,” Econometrica, July

1960, pp. 591-605. The test outlined by Chow uses the

F-ratio. The numerator is the difference between the sum of

squared residuals from the regression of the pooled data less

the sum of the squared residuals for the individual

regressions. The denominator is the latter sum. Both numerator and denominator are adjusted for degrees of freedom.

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New models trace shifts among 

job losers, leavers, and entrants 

during economic 

downturns and recoveries

CURTIS L. GILROY AND ROBERT J. MclNTIRE

W h a t h a p p e n s to unemployment when there is a

significant drop in economic activity? To what extent does it increase, how fast, and what happens

to four groups that make up the unemployed— job

losers, job leavers, reentrants, and new entrants into

the labor force— the groups denoted by the Bureau

of Labor Statistics’ “reasons for unemployment.”

This study shows that job losers account for the

greatest increase in unemployment and that their

response is more immediate than for other groups.

A recent Monthly Labor Review article 1 provided

a description of the unemployed by indicating which

ones have lost their last job (job losers), voluntarily

quit their last job (job leavers), reentered the labor

force after a period of absence (reentrants), or

entered the job market for the first time, never before

having held a full-time job (new entrants).

As that article emphasized, the job-loser group is

of particular interest to analysts. First, this category

comprises the largest single grouping of the unemployed by reason (about 40 percent) and is the

most cyclically sensitive. Second, the plight of job

losers is viewed by some as being more acute than

that of the other groups, since over one-half of the

job losers are heads of households who generally

bear substantial family responsibility. Third, job

losers are the only group of unemployed whose

joblessness stems, not from voluntary action, but

from forces outside their control (that is, decisions

by employers).

Because the main purpose of the previous article

was an analysis of the changing characteristics of

the unemployed by reason over the 1967-72 period,

only cursory examination was given to the cyclical

behavior of the groups. The average extent to which

Curtis L. Gilroy is a labor economist in the Office of Current Employment Analysis, and Robert J. Mclntire is a

mathematical statistician in the Office of Systems and

Standards, Bureau of Labor Statistics.

Job losers, leavers,

and entrants:

a cyclical

analysis

the “unemployment rate” for each of the groups,

as well as their proportion of total unemployment,

exhibits different cyclical patterns was shown in the

original article by a simple regression equation

(Y t = oc - f /?Qt + tt). In that equation, Y represented either the rate or proportion of the unemployed for each group, oc was the constant term,

/? was the coefficient of Q, a cyclical indicator (the

overall jobless rate), and e was the error term.

This basic model, while useful, did not adequately

specify the relationship between movements of the

various groups of the unemployed and changes in

the level of economic activity. First, the use of the

aggregate unemployment rate as a proxy for the

business cycle was questionable, since the dependent

variables (particularly job losers and reentrants)

comprise much of the independent variable (the

overall jobless rate). Second, as the low DurbinWatson statistics suggested, serious correlation problems existed. Third, no time variable was incorporated into the model. Finally, an additional

problem arose because the rates of unemployment

by group were used instead of the actual number of

unemployed by group as a dependent variable.2

A new model

To remedy some of these deficiencies, the first

approach in this followup study was to design a

new model which takes the form

Y t = oc + j9Qt -f \T t + c, (1)

where Y represents the number of unemployed by

reason; oc is the constant; /3 and A are the coefficients

of Q and T (a cyclical indicator and a time variable,

respectively); and e is the error term.

The index of industrial production (1967 = 1 0 0 )

compiled by Federal Reserve Board is used here

as a coincident cyclical indicator. This eliminates

the problem which may arise when much the same

variable appears on both sides of the equation.

From the Review of November 1974

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A time variable was included in the regression

equation because a linear trend is assumed to be

prominent in the various series, independent of

cyclical movements— an upward trend related to

growth in the size of both the labor force and output over time. Thus, a correction for trend is necessary to obtain a more accurate measure of cyclical

effects.

The results from this initial step appear in table 1,

part (a ), and show that as industrial production

rises (falls), signaling a pickup (downturn) in economic activity, the number of unemployed in all of

the categories falls (rises). A comparison of the

standardized (beta) coefficients indicates that job

losers are the most cyclically sensitive because they

experience the greatest relative change.

Even in this form, however, the equations generated low Durbin-Watson statistics, the patterns of

residuals indicating that serial correlation was still

present. This typically occurs when observations are

made over time and the effect of a disturbance

occurring at time period t carries over into the next

period, t -f- 1. It is crucial, because it violates a

basic assumption of ordinary least-squares estimation, that the disturbances be independent of one

another; that is, that the error terms be generated

randomly. The regression equations above assumed

there was no serial correlation, that rho (£), the

coefficient of serial correlation, equals zero (where

serial correlation is assumed to be first order of the

form et = pet-i + vt). If, in fact, the disturbances

are not independent, the least-squares estimators

would not be the best linear unbiased estimators.

They would lose efficiency, because the dependence

among the disturbances reduces the effective number of independent pieces of information in the

sample. Thus, conventional formulas for carrying

out tests of significance or constructing confidence

limits with respect to regression coefficients would

lead to incorrect inferences. If such positive serial

correlation exists, standard errors would be underestimated (t-statistics overestimated) and confidence

intervals narrowed. In short, the test would be likely

to show more statistical significance than it really

should.

In an attempt to overcome this problem, the regressions were rerun using the method of first differences, with the equation taking the form

(Yt — Y,.j) = ex' + /3 (Q, — Q,_i) + f' (2)

where a ' = 0 and e' = et — et-i- This method recognizes that there is serial correlation but further assumes that the true value of p is unity. The results

indicated negative serial correlation (high DurbinWatson) and continued to cast doubt on the significance of the regression coefficients for all equations.

The serial correlation indicates that the functional form of the model may have been misspecified

or that relevant independent variables may have

been missing. The authors investigated various functional forms for the independent variables without

Table 1. Regression results showing relationships between unemployed persons by reason and the index of industrial

production and time, 1967-73

[Numbers in thousands]

(a) Dependent variable (Yt) Constant Tt R* S DurbinWatson

• t*

Job losers________ ______ ______ 9,275 39 

(32 18)

—83 98

(28 74)

36 68

(38 56)

.949 112.77 55 — 1 34

Job leavers. ................................... 672 96 

(6.11)

- 3 04

(2 73)

4 64

(12.77)

.825 43 08 84 -.2 4

Reentrants....................... ....................... 3,509.75

(17.55)

—27 16 

(13 40)

15 52

(23 52)

.894 78.24 1 16 - 90

New entrants................................................................ 1,165.76

(9.86)

—8 48

(7 07)

6.82

(17.49)

861 46 25 .79 - 54

(b) Dependent variable (Yt — Constant <«t ~ P«t-i) (Tt - p T t.J R* s DurbinWatson

(«t - P « t-J

Job losers.................................. p - 715 2,301 74 

(14 18)

—72 20 

(12 46)

34 10

(16 52)

.769 74 78 2 21 — 1.13

Job leavers............. p - 566 269 84

(3 17)

—2 59 

(1.30)

4 67 

(6 89)

.572 35 59 2 15 -.1 7

Reentrants........ ......... p - 361 2,196 57 

(13 07)

—26 74 

(9 99)

15.73

(17.67)

826 67.23 2 14 -.8 5

New entrants.................... p - 592 447.94

(5.16)

-7.91

(3.64)

6.74

(9.07)

.611 36.70 2.07 - 45

NOTE: t — statistics are in parentheses. Qt* and (Qt — pQt_j)* = standardired (beta) coefficients.

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achieving any significant reduction in serial correlation. This paper reports on the results of adding

lagged monthly differences as independent variables.

But, for a given model, with a given set of variables,

a method exists for obtaining improved estimates of

the coefficients if the value of p, the coefficient of

serial correlation, can be estimated.

An estimate of p, which we label p, was obtained

from the regression procedure on the original equation Yt = oc -f- ftQt -f--ATt -f- €t. Constructing new

variables, then, the following equation was run for

each of the groups:

(Y t — pYt) = cc* + $ (Q t — pQt-j)

+ \ (T t — pIYi) + (3)

where oc* = oc (1 - p) and f* = e, - p«t.x.

These results are shown in table 1, part (b), and

are not materially different from the original estimates. The coefficients ft and % estimated with the

rho transformed variables, are improved estimates

of ft and A in the original equation. The coefficients

are more efficient and more reliable for tests of

significance.

Run in this form, the regression equations indicated that, on the average, 72,000 workers (or twothirds of the overall increase in unemployment)

would lose their jobs with a 1.0 absolute decrease

in the industrial production index. The smallest

increase in unemployment would occur among job

leavers, who would account for only 2 percent of

increased joblessness. This finding is consistent with

workers’ reluctance to voluntarily leave their jobs

when the economy is weakening, and is in line with

the behavior of the quit rate in manufacturing which

falls in times of job scarcity. In fact, with respecification of the model, the coefficient of Q was no

longer statistically significant for job leavers.

The model with lagged monthly differences

Although the credibility of the equations has been

enhanced, one might rightly inquire as to whether

the response of the various groups to changes in

economic activity is immediate, or whether it is

drawn out over a number of time periods.

To test for lagged responses, the following model

was run for each of the groups:

Y t = oc -f- <p Qt-t + fix (Qt — Qt-i) -}- £2 (Qt-i — Qt-2)

+ £ 3 (Qt-2 — Qt-3) /34 (Qt-n — Qt-i) + A Tt + f t (4)

where Y represents, as before the number of unemployed by reason; (Qt — Qt-i) . . • (Qt-3 —

Qt t) are month-to-month differences lagged from

the most recent monthly change (Qt — Qt-i) back

through the fourth previous month (Qt-3 — Qt-4).

The coefficients of these lagged differences represent

the relatively “short-run” cumulative effects of a

“once-and-for-all” change in economic activity; that

is, ft, measures the effect after 1 month, ft2 measures

the accumulated effect after 2 months, and so forth.

Four successive periods were included in the model

after investigation with up to six periods. These

investigations indicated that the short-run effects of

a change in Q on each of the reasons groups run

out by the fifth month; that is, monthly differences

beyond the fourth previous month did not have

significant coefficients and did not change the results

for the first four periods. The variable Qt-4 serves

to represent the cyclical influence not accounted for

by the short-run differences, and therefore is tantamount to the relatively “long-run” effect.

Because there appeared to be serious auto-correlation problems in the regression equations run in this

form, the model was reestimated with first differences

adjusted using an estimate of the coefficient of first

Table 2. Regression results showing relationships between unemployed persons by reason and ‘short-run’ and ‘long-run’

changes in the index of industrial production and time, 1967-73 1

[Numbers in thousands)

Dependent variable 

< * t - f r t - i >

Constant («t-4 - f a t J X, x 5. X, X, (Tt - f r t- i) R' s DurbinWatson

Job losers................ p = . 760 2,236 46 —85 25 —25 98 —61 17 — 57 47 —75 51 37.69 .794 62 58 2.14.

(15.22) (13 66) (2.84) (5 93) (5.44) (7.42) (17.30)

Job leavers_______ p = .5 7 2 268 85 —2 76 2 37 —4 32 4 33 - 9 82 4 88 567 35 62 2 09

(2 92) (1 26) (0.46) (0 79) (0 79) (1.84) (6.78)

Reentrants.............. p =.131 3,180 03 —29 13 1 28 -14 92 -27 69 -21 52 16 44 915 62 02 1 98

(18 71) (14.66) (0 14) (1.63) (3.04) (2 38) (26 21)

New entrants_____ p = 292 996 80 —11 02 7 21 6 46 - 2 68 0 15 7 26 848 34.01 1 92

(10 85) (8.36) (1.47) (1 30) (0.54) (0 03) (17 26)

NOTE: t — statistics are in parentheses.

1 The "short-run” lagged independent variables, are:

X, = [(Qt - Q«.J - - Qt J1

65

x .= I ( Q t- i - Q t - J - p ( Q t - . - Q t J ]

Xj = [(Q.-2 - Q , J - p(Q,-a - Qt-*)]

X4 = P f . - Q t J - p(Qt -4 - Q t j ]

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order serial correlation as shown by

(Yt — pYt-i) = oc* 4- 0 (Qt-4 — pQ ts) + fa. [(Qt — Qt-0

— £(Q»-i — Q*-»)l + • • • -+- ^ (Tt — pTt-i) + e* (5)

where cc* = « (1 — p), «* = «t — pct-i, and all

coefficients are estimates of the coefficients in the

original lagged equation; that is, the coefficient calculated for <f> in equation (5 ) is an estimate of 4> in

equation (4 ), and so forth for the other coefficients.

The results appear in table 2 and conform in general

to what would be intuitively expected.

The number of unemployed job losers shows a

substantial degree of cyclical sensitivity and the

Chart 1. Actual and projected numbers of unemployed by

reason, October 1973-July 1974

response is distributed over the four periods. The

instantaneous response by employers to a downturn in business activity (here defined as an absolute decrease of 1.0 in the index of industrial production) is to lay off 25,000 workers. In the next

month, an additional 36,000 workers lose their jobs

for a total of 61,000 after 2 months. In the “longrun”— after 4 months— 85,000 workers are laid off.

The number of unemployed job leavers, on the

other hand, is not cyclically sensitive, and the group’s

short-term response to changing economic conditions is erratic. In the long run, the increase in the

number of job leavers due to a drop in industrial

production is the smallest of all the groups. This is

consistent with the findings in the previous section.

Reentrant unemployment, however, clearly responds to cyclical swings in the economy. As in the

case of job losers, the effect is distributed over

several months; unlike job losers, however, there is

little instantaneous response. There is virtually no

effect in the current period to a 1.0 decrease in the

production index. In the second month, however,

15,000 of the additional unemployed workers are

reentrants; in the “long run,” there are nearly 30,000

unemployed reentrants. This development probably

reflects the fact that a greater proportion of the

labor force reentrants must pass through the unemployment stream when the demand for labor

slackens.

The number of new entrants appear to be somewhat sensitive to cyclical swings, although their

response is less pronounced and rather different.

The positive, although insignificant, instantaneous

response reflects perhaps the fact that new entrants

are motivated in the short run by phenomena other

than changing levels of economic activity. The

impact of the business cycle appears to take hold

after several months, however, as indicated by the

significant and negative long-run response. Presumably, uncertain economic conditions do not encourage additional new entrants to the labor force. But

it does seem reasonable that, if economic conditions

are deteriorating, new entrants unemployed in previous months are more likely to remain unemployed

and any additional new entrants are more likely to

be unemployed.

The results of this study are not strictly comparable to those of the study of this type previously

reported in the Monthly Labor Review because of

conceptual and methodological differences. However, both studies show job losers, the largest group,

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to be most affected by changes in economic activity.

Application to recent data

Although the purpose of this article is not so

much to develop a predictive model as to obtain

better estimates of selected coefficients, the results

of the equation, when applied to dates for the most

recent economic slowdown, seem to track fairly

well with the actual changes in the number o f unemployed by reason. This can be seen from chart 1,

which traces the predicted changes and the actual

changes over the October 1973 to July 1974 period.

October 1973, in this case, coincides roughly

with the beginning of the energy crisis which caused

a very abrupt slowdown in economic activity in

some sectors. Because of this abrupt slowdown,

which caused a sudden decline in the industrial production index, the actual number of unemployed job

losers increased rapidly from October to February,

but then remained relatively stationary through July.

As shown in the chart, the projected number of

job losers— derived by using the coefficients of the

rho adjusted model with lagged monthly differences

— rose much more gradually. Although its steepest

rise occurred in the first months of 1974, when the

effects of the energy crisis were becoming more

widespread, it did not catch up with the level of

the actual series until April. There was then a relatively large gap between the actual and the projected series from January to March.

However, given the unusual nature of the recent

slowdown, and the fact that the regression line

measures average movements, this gap is not too

surprising. Compared with previous slowdowns, the

one which occurred last winter had a serious impact

on only a limited number of industries— principally

automobile factories and gasoline stations. It was

the sharp cutbacks in these industries which caused

the surge in the number of unemployed job losers.

Other industries were hardly affected during this

period, and this may account for the fact that the

number of unemployed reentrants, although projected to rise gradually, did not show an actual

increase until spring. In sum, given the abrupt

nature and industry concentration of the recent

slowdown, the divergences between the projected

and actual series appear to be within reasonable

bounds. 

-FOOTNOTES1 Curtis L. Gilroy, “Job losers, leavers, and entrants:

traits and trends,” Monthly Labor Review, August 1973, pp.

3-15.

2 The main drawback of the use of component rates is

that each rate is really not a rate in and of itself. For a

true unemployment rate, the numerator and denominator

must consist of groups with like characteristics. For example, the “true” unemployment rate for job losers would be

the number of job losers divided by the job-loser labor force,

not the entire civilian labor force. But the job-loser labor

force does not exist; it has no meaning. Thus, use of the

component rates, though an interesting and sometimes useful

breakdown of the aggregate unemployment rate, is little

more than a tautology.

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Canada, the United Kingdom, 

Belgium, the Netherlands, 

and Sweden, like the 

United States earlier, have become 

primarily service economies

CONSTANCE SORRENTINO

Generally, with a nation’s economic development

and its progress in industrialization, the distribution

of the employed population shifts from agricultural

to industrial activities, particularly manufacturing,

and further from these sectors to service activities.

The United States emerged as the world’s first

service economy— over 50 percent of employment

in service industries— shortly after World War II.

With some lag, the other industrial nations of the

world appear to be following that pattern. By 1970,

6 of the 10 industrial countries had over half their

civilian employment in the service sector.

Sectoral shifts in employment largely reflect differing rates of change in demand and productivity.

In turn, these changes affect overall rates of change

in productivity and economic growth. Manpower

shifts from the low productivity farm sector to higher

productivity sectors result in increases in productivity

growth. On the other hand, shifts from the industrial

sector to the services sector generally have a moderating effect on overall productivity growth.

This article presents data on comparative civilian

employment by sector in 10 developed countries at

5-year intervals from 1950 to 1970. The data for

1950, and perhaps for 1955, were affected by the

recovery from wartime conditions in many of the

European countries and Japan. These recovery elements may have distorted the usual relationship in

some countries. Certainly, the substantial gains in

industrial employment experienced by Italy, Japan,

and Germany from 1950 to 1970 should be viewed

against this background.

Data are provided for three broad sectors: (1)

agriculture, forestry, hunting and fishing (called

“agriculture” in the text); (2) industry (comprising

mining, manufacturing, and construction); and (3)

Constance Sorrentino is an economist in the Division of

Foreign Labor Statistics and Trade, Bureau of Labor Statistics.

Comparing

employment shifts

in 10 industrialized

countries

services (transportation, communication, public

utilities, trade, finance, public administration, private

household services and miscellaneous services1).

Employment in government enterprises is classified

according to the sector appropriate to the output

of the enterprise. In addition to information for the

three broad breakdowns, separate figures are provided for manufacturing.

Foreign country data were adjusted to United

States concepts wherever significant conceptual differences existed. The adjustments made as well as

the sources of the data are discussed in an appendix.

Shifts in employment

A vast reallocation of sectoral manpower took

place in Canada, France, Italy, Germany, Japan,

and Sweden during 1950-70. More moderate shifts

occurred in Belgium, the Netherlands, the United

Kingdom, and the United States. In general, employment disparities among the 10 countries narrowed

significantly.

Table 1 provides employment data by economic

sector, and table 2 shows percent distributions of

employment by sector. Data for 1950 were not

available for France’s industry and services; therefore, the French data cover only the period from

1955, except for agriculture.

Agriculture. Employment in agriculture declined in

all countries, usually quite rapidly. In conjunction

with the growth in total employment in most countries, this resulted in a significant fall in agriculture’s

share of employment.

Large differences among countries in the proportion of employment in agriculture narrowed between

1950 and 1970. In 1950, agriculture dominated in

Italy and Japan, accounting for 40 percent of all

workers. One-fifth to one-fourth of total employment in Canada, Germanv. and Sweden was in agriFrom the Review of October 1971 68

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culture. The United Kingdom had, by far, the lowest

proportion of workers in agricultural activities, at 5

percent. The United States and Belgium were next,

at 12-13 percent.

By 1970, agriculture accounted for more than

10 percent of employment in only France, Italy,

and Japan. The United Kingdom continued to have

the smallest proportion, 3 percent, and the United

States and Belgium followed closely with about

5 percent.

In most countries, the rate of decline in agricultural employment accelerated in the sixties (table 3).

Table 1. Civilian employment by economic sector, 1950-70

[In thousands]

Agricultural employment in Italy fell by 3 percent

a year between 1951 and 1960 and at almost

6 percent a year from 1960 to 1970. The only

exception was Canada, where the decline slowed

from 3.7 percent a year in the 1950’s to 2.6 percent

in the 1960’s.

Movement out of agriculture generally makes

additional manpower available for industry and

services. However, rural to urban migration in Italy

and Japan actually tended to curb the total labor

supply during 1950-70. Many women and children

who formerly worked as unpaid farm laborers withYear United

States

Belgium Canada France Germany Italy Japan Netherlands

Sweden United

Kingdom

Civilian employment

1950 *................................................. .................................. 58,920 3,402 4,976 18,752 22,869 19,098 34,940 3,575 3,424 22,608

1955........................................................ ............................. 62,171 3,414 5,364 18,727 (*) 19,701 39,250 3,815 (*) 23,527

1960........................... .......................................................... 65,778 3,438 5,965 18,712 25,954 19,877 43,370 4,019 3,558 24,256

1965...................................... ................................................ 71,088 3,608 6,862 19,560 26,699 18,915 46,200 4,349 3,704 25,327

1970 *....................................... ........................................... 78,627 3,670 7,879 19,967 26,327 18,698 50,150 4,477 3,852 24,710

Agriculture1 *4 *

1950 ' ..................................................................................... 7,268 430 1,139 5,631 5,183 8,510 15,070 572 795 1,228

1955.......................................................... ................. ......... 6,551 361 954 5,041 (*) 7,624 14,070 525 (») 1,122

I960...................................................................................... 5,572 300 795 4,189 3,623 6,470 12,800 465 570 1,028

1965...................................................................................... 4,477 230 694 3,480 2,966 4,884 10,500 388 432 847

1970'........ ........................................................................... 3,566 191 613 3,009 2,533 3,639 8,500 340 325 711

Industry9

1950*................................................................................... 19,850 1,584 1,722 (*) 9,854 5,702 8,200 1,465 1,323 10,507

1955............................................................................ ......... 21,825 1,612 1,850 6,849 (J) 6,540 9,910 1,592 (*) 11,235

1960......................... ................................ ........................... 21,995 1,584 1,906 7,136 12,449 7,267 12,380 1,678 1,431 11,462

1965....................................................... ............................... 24,311 1,670 2,233 7,819 13,183 7,594 15,010 1,845 1,565 11,739

1970 ' ................................................................................... 26,066 1,621 2,377 7,918 12,899 8,048 17,850 1,818 1,480 11,081

Manufacturing

1950 *................. .................................... ............... ........... 15,448 1,165 1,316 (*) 7,415 4,448 6,180 1,103 1,069 8,194

1955....................................................................................... 17,097 1,191 1,373 5,043 (’) 4,773 7,530 1,177 (*) 8,852

1960.................................................................................... 17,149 1,201 1,471 5,240 9,718 5,344 9,430 1,241 1,128 9,122

1965............... ..................................................................... 19,190 1,278 1,636 5,570 10,288 5,518 11,450 1,331 1,215 9,242

1970*............................................................................ 20,737 1,249 1,790 5,662 10,306 5,954 13,730 1,309 1,089 9,026

Services'

1950 *..................................................................................... 31,800 1,388 2,116 (*) 7,832 4,885 11,670 1,538 1,306 10,873

1955............................ ....................................................... 33,796 1,441 2,560 6,837 (*) 5,538 15,270 1,698 (*) 11,170

I960....................................................................................... 38,212 1,554 3,264 7,387 9,882 6,141 18,190 1,876 1,557 11,766

1965..................................................................................... 42,301 1,708 3,934 8,261 10,550 6,437 20,690 2,116 1,707 12,741

1970'.................................................................................. 48,994 1,858 4,888 9,040 10,895 7,012 23,800 2,319 2,047 12,918

1 1951 data lor Italy.

1 Not available.

1 1969 data for Belgium, France, Germany, and the Netherlands. Data for other 

countries, except the United States, are preliminary estimates for 1970. For the United 

States, 1970 data are final.

4 Includes forestry, hunting, and fishing.

9 Manufacturing, mining, and construction.

'Transportation, communication, public utilities, trade, finance, public administration, private household services, and miscellaneous services.

NOTE: Wherever significant conceptual differences occur, data have been adjusted 

to U.S. concepts. Modifications have also been made so that the data for each country 

reflect a compatible time series.

SOURCE: Organization for Economic Cooperation and Development, Labor Force 

Statistics (various issues); International Labor Office, Yearbook of Labor Statistics 

(various issues); and national statistical publications. Some data based partly on 

estimates.

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Table 2. Percent distribution of civilian employment by economic sector, 1950-70

United Belgium Canada France Germany Italy Japan Nether* Sweden United

States lands Kingdom

Agriculture2

19501..................................................................................... 12.3 12.6 22.9 30.0 22.7 44.6 43.1 16 0 23.2

A

1955....................................................................................... 10.5 10.6 17.8 26.9 (’)

14.0

38.7 35.8 13.8

1960....................................................................................... 8.5 8.7 13.3 22.4 32.6 29 5 11 6

1965....................................................................................... 6.3 6.4 10.1 17.8 11.1 25.8 22.7 8 9 11.7

1970 4..................................................................................... 4.5 5.2 7.8 15.1 9.6 19.5 16.9 7.6 8.4

Industry*

1950 1..................................................................................... 33.7 46.6 34.6 & 43.1 29.9 23.5 41.0 38.6

1955....................................................................................... 35.1 47.2 34.5 (*)

48.0

33.2 25.2 41.7 (•)

1960....................................................................................... 33.4 46.1 32.0 38.1 36.6 28.5 41.8 40.2

1965....................................................................................... 34.2 46.3 32.5 40.0 49.4 40.1 32.5 42.4 42.3

1970 4..................................................................................... 33.2 44.2 30.2 39.7 49.0 43.0 35.6 40.6 38.4

Manufacturing

19501..................................................................................... 26.2 34.2 26.4 (')

26.9

32.4 23.3 17.7 30.9 31.2

1955....................................................................................... 27.5 34.9 25.6

3 %

24.2

26.9

19.2 30.9

I960....................................................................................... 26.1 34.9 24.7 28.0 21.7 30.9

1965....................................................................................... 27.0 35.4 23.8 28.5 38.5 29.2 24.8 30.6 32.8

1970 4..................................................................................... 26.4 34.0 22.7 28.4 39.1 31.8 27.4 29.2 28.3

Services

1950»..................................................................................... 54.0 40.8 42.5 (’)

36.5

34.2 25.6 33.4 43.0 38.1

1955....................................................................................... 54.4 42.2 47.7 (*)

38.1

28.1 38.9 44.5 (J)

1960...................................................................................... 58.1 45.2 54.7 39.5 30.9 41.9 46.7 43.8

1965.............................................. -....................................... 59.5 47.3 57.3 42.2 39.5 34.0 44.8 48.7 46.1

1970 4..................................................................................... 62.3 50.6 62.0 45.3 41.4 37.5 47.5 51.8 53.1

1 1951 data for Italy.

* Includes forestry, hunting, and fishing. 

' Not available.

4 1969 data for Belgium, France, Germany, and the Netherlands. 

• Manufacturing, mining, and construction.

SOURCE: Calculated from data in table 1.

drew from the labor force entirely when their

families left agriculture. Thus, the female participation rate declined in both countries.2 In most other

countries, this effect was outweighed by the increasing number of married women entering the labor

force when their children reached school age.

Industry and manufacturing. Industrial employment

rose in all countries during 1950-70. However,

in six countries, the increase did not keep pace

with overall employment expansion; consequently,

the proportion in industry actually declined. Canada’s

industrial sector experienced the greatest loss in

share, falling over 4 percentage points. The industrial

sectors in Belgium and the United Kingdom lost

about 2 percentage points, while losses of less than

1 percentage point occurred in the United States,

the Netherlands, and Sweden. (See table 4.)

The industrial sectors in Italy and Japan experienced substantial gains of 12-13 percentage points

in share of total employment. France and Germany

were the only other countries having industry gain

in employment share from 1950 to 1970. In the

period from 1960 to 1970, France, Germany, Italy,

and Japan were also the only countries with industry

employment rising as a proportion of total employment. However, the 1960-70 gain in Germany was

only 1 percentage point, after a rise of almost

5 percentage points in the earlier decade.

Japan’s industrial sector grew most, with employment more than doubling between 1950 and 1970,

greatly outpacing the rise in total civilian employment. Italy’s industrial employment rose 41 percent

but increased in relative size even more than

Japan’s because total civilian employment declined.

In the United States, industrial activities now

account for one-third of total employment, about

the same as in 1950. By 1970, all other countries

except Canada had more than a third of their manpower in industry. With almost half of total employment in industry, Germany had the highest relative

proportion. The United Kingdom, the Netherlands,

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Italy, and Belgium had over 40 percent of their

employed manpower working in industry.

Employment in manufacturing grew slightly faster

than employment in overall industry in the United

States, Belgium, Germany, Japan, and the United

Kingdom. Manufacturing growth matched industrial

growth in Canada. In the remaining countries, rapid

increases in construction employment pushed industrial growth ahead faster than manufacturing growth.

Foreign trade is an important factor in analysis

of comparative trends in manufacturing employment.

In many other countries, exports of manufactured

goods are larger relative to total GNP than in the

United States and have grown faster than U.S.

exports in the postwar years.

Postwar expansion of Japanese, Italian, and

German exports of goods has been a particularly

important factor in their rapid rise in manufacturing

employment. Japanese manufacturing employment

expanded from 18 to 27 percent of total employment between 1950 and 1970; Italy’s from about

23 to 32 percent of the total; and Germany’s from

32 to 39 percent. In contrast, U.S. manufacturing

employment remained at about 26 to 27 percent

of the total. U.S. exports of goods had a slower rate

of increase than those of any other country studied

except the United Kingdom. According to data of

the Organization for Economic Cooperation and

Development (OECD) and the United Nations,

trade in manufactured goods during 1955-68 increased faster than manufacturing output in all major

industrial countries except the United Kingdom and

the United States.3

Services. Prior to the shift from a predominantly

industrial to a predominantly service economy

shortly after World War II, over half of the employed population in the United States was producing goods— agricultural commodities and industrial

products. Until 1958, the United States was the

only industrialized country with over half its employment in services. Then Canada crossed the 50

percent level. The United Kingdom joined them

around 1965. Since 1965, Belgium, the Netherlands,

and Sweden have also become predominantly service

economies. Only France, Italy, Japan, and Germany

continue to have more workers employed in the

production of goods than of services. Japan and

France appear likely to become service economies

during the 1970’s.4 But Italy and Germany will

probably not shift until later, because service

employment constitutes only 38 percent of total

employment in Italy and 41 percent in Germany.

In 1970, the United States and Canada had about

Table 3. Average annual rates of change in employment by sector, 1950-70, 1950-60, and 1960-70

Sector United

States

Belgium Canada France Germany Italy Japan Netherlands

Sweden United

Kingdom

1950-70 1

Civilian employment............................................................. 1.5 0.4 2.3 0.3 0.7 -0 .1 1.8 1.2 0.6 0.4

Agriculture.................. ................................................. -3 .6 -4 .4 -3 .2 -3 .4 -3 .8 -4 .6 -2 .9 -2 .8 -4 .6 -2 .8

Industry.................... ........................................ ......... 1.4 .1 1.6 (') 1.4 1.8 4.0 1.1 .6 .3

Manufacturing........................................................ 1.5 .4 1.6 (*) 1.7 1.6 4.1 .9 .1 .5

Services.......................................................................... 2.2 1.6 4.3 (l) 1.8 1.9 3.6 2.2 2.3 .9

1950-601

Civilian employment............................................................. 1.1 0.1 1.8 (4) 1.3 0.5 2.2 1.2 0.4 0.7

Agriculture............................................................— -2 .7 -3 .7 -3 .7 -3 .3 -3 .7 -3 .1 -1 .6 -2 .1 -3 .4 -1 .8

Industry......................................................................... 1.0 0 1.0 (*) 2.4 2.7 4.2 1.4 .8 .9

Manufacturing........................................................ 1.1 .3 1.1 (*) 2.8 2.1 4.3 1.2 .5 1.1

Services.......................................................................... 1.9 1.1 4.4 (*) 2.4 2.6 4.5 2.0 1.8 .8

1960-70*

Civilian employment............................................................. 1.8 0.7 2.8 0.7 0.2 -0 .6 1.5 1.2 0.8 0.2

Agriculture..................................................................... -4 .6 -5 .1 -2 .6 -3 .8 -4 .1 -5 .9 -4 .2 -3 .5 -5 .3 -3 .8

Industry........................................................................ 1.7 .3 2.2 1.2 .4 1.0 3.7 .9 .3 - . 3

Manufacturing........................................................ 1.9 .4 2.0 .9 .7 1.1 3.8 .6 - . 4 - . 1

Services................................ .-...................................... 2.5 2.0 4.1 2.3 1.1 1.3 2.7 2.3 2.8 .9

1 1950-69 for Belgium, France, Germany, and the Netherlands; 1951-70 for Italy. 4 Less than .05 percent per year.

* Not available. ' 1960-69 for Belgium, France, Germany, and the Netherlands.

* 1951-60 for Italy. SOURCE: Calculated from data in table 1.

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[Percentage points]

Table 4. Change in share of total employment by sector,

19 50 -70 1

Agrlculture

Industry

Country

Total Menu*

facturlng

Services

United States....................................... -7 .8 -0 .5 0.2 8.3

Belgium................................................ - 7 .4 -2 .4 - . 2 9.8

Canada................................................. -15.1 -4 .4 -3 .7 19.5

France.................................................. -1 1 .8 3.1 1.5 8.8

Germany............................................... — 13.1 5.9 6.7 7.2

Italy...................................................... —25.1 13.1 8.5 11.9

Japan.................................................... —26.2 12.1 9.7 14.1

Netherlands......................................... -8 .4 - . 4 -1 .7 8.8

Sweden................................................ -1 4 .8 - . 2 -2 .9 15.0

United Kingdom................................... -2 .5 -1 .7 .3 4.2

1 1950-69 for Belgium, Germany, Netherlands; 1955-69 for France; 1951-70 for 

Italy.

SOURCE: Calculated from data intable 1.

62 percent of their employment in services. The

next highest proportion was in Sweden with 53

percent. Canada experienced the most dramatic

increase in services, with employment growing at

4.3 percent a year and going from about 43 to 62

percent of total employment between 1950 and 1970.

Thus Canada accomplished in 12 years what it took

the United States 25 years to do— move from half

to over three-fifths of total employment in services.

During the 1950-70 period, the services sector

expanded more rapidly than the industrial sector

in all countries except Japan. In Italy, the annual

rate of growth in services was only slightly higher

than that in industry. In the other countries, however,

the rate of growth in services was much faster than

in industry.

Output and productivity trends

Changes in employment structure are the net

result of varying rates of change in demand and in

productivity. Other things being equal, increased

output requires more employees. However, productivity increases reduce the number of workers

required for a given output.

Nine countries’ rates of growth in real output

(gross domestic product) by sector during 1950-69

are presented in table 5. Data by sector on the

growth of output per employed person (includes

wage and salary employees, unpaid family workers,

and the self-employed) are also provided. Constant

price data by sector are not available for Japan.

In general, the agricultural sector experienced the

slowest rate of growth in output and the fastest

rate of increase in productivity. These two factors

combined to cause the sharp reduction in agricultural employment discussed earlier.

Industry grew fastest in output in all countries

except the United States, where services led in

growth. Of the countries covered in table 5, Germany

and Italy had the sharpest increases in industrial

output. Output in Japan’s industrial sector probably

grew faster than in either of these countries, however,

as overall gross domestic product in Japan increased

at a rate of almost 10 percent a year during 1952-69.

Productivity growth in industry was rapid in most

countries, although not as rapid as in agriculture.

Growth in services output was generally rapid,

and outpaced growth in total output in the United

States, Belgium, and Sweden. However, productivity

increases in services were relatively low, resulting

in the sharp rise in services employment in most

countries.5

In the United States, the level of productivity

in agriculture is well below that in industry and

services. The industry sector has the highest level

of productivity of the three sectors, and services

ranks second. It should be recognized, however,

that within each sector productivity can vary considerably. In services, for example, productivity

ranges from a level lower than agriculture in

miscellaneous services to a level 4 times that of

agriculture in finance, insurance, and real estate.

Furthermore, in the industry sector, mining productivity is over double that of manufacturing, while

output per man-hour in construction is well below

manufacturing. In general, the relationships discussed above also hold true for most of the foreign

countries studied.6

Movement of employment out of the low productivity agricultural sector, which occurred in all

countries studied, to sectors with higher output per

worker tends to raise the level of productivity in

the whole economy, and hence, contributes to

economic growth. The following tabulation is based

on OECD calculations of the contribution to economic growth of shifts in employment between

sectors during 1955-68.7 The proportion of the

total increase in output attributed to such shifts

in eight countries was:

United States ........................................................................ 11.3

B elgiu m ................................................................................... 12.9

Canada ................................................................................... 15.3

F r a n c e .................................................................................. 18.3

Germany ............................................................................... 14.9

Italy ....................................................................................... 36.8

Netherlands ...................... 10.3

United K ingdom ................................................................... 5.2

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Table 5. Average annual rate of change in output1 and in output per employed person by sector, 1950-69

Country 2

Output Output per employed person

Total Agriculture Industry Services Total Agriculture Industry Services

United States.................................................. .......... . . 3.8 1.0 3.8 4.2 2.3 4.7 2.2 2.0

Belgium 3__________ _______________ ______ ___ 3.6 1.7 3.8 3.7 3.3 6.4 4.1 1.9

Canada « ...................... .............. ................ .................. 4.8 2.0 5.6 4.8 2.4 5.2 3.8 .4

France 5........................................................................... 5.6 1.6 6.6 5.6 5.1 5.4 5.5 3.6

Germany................. ......................................................... 6.7 2.6 8.0 5.8 5.9 6.6 6.5 4.0

Italy ............................................................................ 5.4 2.6 7.6 5.0 5.6 7.0 5.7 3.1

Netherlands4................................................................... 5.1 2.7 6.0 4.8 3.9 5.5 4.9 2.6

Sweden........................................................................... 4.0 .4 5.0 4.4 3.5 5.1 4.3 2.3

United Kingdom............................................................... 2.6 2.2 2.9 2.2 2.1 5.1 2.5 1.3

1 Gross domestic product at constant (1963) market prices for the United States, 

France, Germany, and Sweden and at factor cost for all other countries.

2 Not available for Japan.

2 1956-68.

4 1950-68.

* 1955-69.

• 1951-69.

SOURCE: Calculations based on output data from the Organization for Economic 

Cooperation and Development, National Accounts of OECD Countries, 1950-1968 

(Paris, OECD, 1970) and estimates for 1969; and employment data comparable to 

the statistics in table 1.

Although the OECD did not make calculations for

Japan, it is probable that the shift from agriculture

contributed substantially to Japanese economic

growth, as was the case in Italy. The effect of the

shift was, of course, strongest in countries where

agriculture acounted for a large proportion of

employment in the base year, 1955.

Not all sectoral shifts necessarily result in increased growth of productivity for the economy.

The movement into services employment, which

has occurred in all countries studied, generally

tends to reduce the rate of growth in productivity.

As table 5 indicates, the service sector has a slower

rate of growth in productivity than agriculture and

industry in all countries. Movement out of agriculture into many of the service industries represents

a shift to a sector with a higher level of productivity,

but lower rate of growth in productivity. Movement

from industry to services generally represents a

shift to a sector with a lower level of productivity

and a lower rate of growth in productivity.

Projected shifts in the structure of the U.S.

economy during the 1970’s are not likely, therefore,

to promote a faster rate of productivity growth.

Sectors with typically low rates of growth are expected to expand employment faster than those with

high rates.8 For example, rapid expansion of employment in miscellaneous services is expected. This

sector has both a low rate of growth in productivity

and a relatively low level. OECD projections for

foreign countries also indicate a slowdown in the

contribution to growth in productivity of sector

shifts in the 1970’s. In Italy, the contribution of

sectoral shifts to economic growth in the present

decade is expected to drop to less than half the

level estimated for 1955-68.9 

-FOOTNOTES1 Miscellaneous services include hotel, repair, recreational,

personal, medical, legal and educational services.

2 In Italy, the female labor force participation rate dropped

from 32 percent to 25 percent between 1960 and 1970; in

Japan, it fell from 57 percent in 1955 to 47 percent by 1970.

In contrast, female participation in the U.S. labor force rose

from 36 percent in 1955 to 38 percent in 1960 and to 43

percent by 1970.

* Organization for Economic Cooperation and Development, The Growth of Output, 1960-1980 (Paris, OECD,

1970), p. 61.

* According to the Statistics Bureau of the Prime Minister’s

Office, Japan will probably have half of its employed popuation in services by 1975.

BPart of the slow productivity rise in services reflects a

measurement problem: in the absence of better methods,

output in constant prices in services is often measured by

statistics on employment and wages. The largest sector

affected is government services, where only Belgium and

Germany make some allowance (necessarily arbitrary) for

productivity increases. Clearly, it would have been desirable

to show government services separately from other services,

but comparable data are not available. Also, the use of

employment data rather than man-hours may somewhat overestimate labor input and therefore underestimate productivity

in services, as compared with other sectors, because there is

probably more part-time work in services. Canadian authorities suggest this may be one reason for the apparent small

increase in output per employed person in Canadian service

industries.

8 For rough calculations of relative output per employed

person by sector in foreign countries, see OECD, op. cit., p.

36.

7 OECD, op. cit., p. 39. In the OECD calculations, it was

assumed that the overall increase in output is the sum of

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increases in four independent components: (1) agricultural

output; (2) output attributable to the growth in productivity

in industry and services; (3) output due to the increase in

total employment; and (4) output due to shifts in employment between sectors.

The increase in output due to shifts in employment between sectors was based on the following assumptions: (1)

output in agriculture at the end of the period would have

been the same even if labor had not left this sector; and

(2) productivity in industry and services would have been

the same whether or not labor had moved into these

sectors. Given these assumptions, the sectoral shift effect is

measured by the difference between actual output at the

end of the period and output as it would have been with

end-period productivity in industry and services, but the

same percent distribution of employment as the beginning of

the period.

* Patterns of U.S. Economic Growth (BLS Bulletin 1672,

1970).

* OECD, op. tit., p. 92.

Appendix: concepts used and limitations of data

Sources. With the exception of the United Kingdom,

the employment data used in this study refer to

total civilian employment; that is, wage and salary

workers, unpaid family workers, and the selfemployed. Data for the United Kingdom exclude

unpaid family workers; however, such workers account for a very small fraction of total employment.

Employment statistics for the United States and

Canada were derived solely from sample surveys of

households. Statistics for most recent years (generally 1960 onward) for Germany, Italy, Japan, and

Sweden are from household surveys while data for

the 1950’s were from other sources, such as population censuses and official estimates based on censuses

and various other sources. Data for France, Belgium,

and the Netherlands are derived from official estimates by their statistical offices. Employment figures

for the United Kingdom are based on compulsory

national insurance statistics and official estimates of

the self-employed. Distribution of the self-employed

by sector for the United Kingdom was estimated

by the Organization for Economic Cooperation and

Development.

For all countries, employment in government

enterprises is classified according to the sector appropriate to the output of the enterprise. This is the

procedure followed by the International Standard

Industrial Classification (ISIC).1 In the BLS establishment survey, government-operated establishments

are classified in a separate economic division, as

provided for in the U.S. Standard Industrial Classification (SIC). However, the U.S. employment data

in this report are on the ISIC basis as regards the

treatment of government enterprises. These data

are obtained from the U.S. labor force survey in

which the economic division claimed by the respondent is recorded. Thus, a person working in a

government-operated manufacturing establishment is

classified in the manufacturing sector.

Output (gross domestic product) figures used in

this article are based on OECD definitions; therefore, data for the United States differ somewhat

from the statistics published by the U.S. Office of

Business Economics (OBE). The major difference

is that OECD figures include an estimate of capital

consumption by government, whereas no such estimate is made by OBE in the national accounts.

Figures for output per employed person in the

United States presented in this article differ from

the indexes regularly published by the Bureau of

Labor Statistics. BLS data relate solely to the private economy whereas the data in this article include

output and employment in the government sector.2

BLS figures on output per employed person are

based on output figures from OBE. In contrast,

output figures used in the calculations in this article

are based on the OECD definitions. There are also

some minor differences in the employment data

used. BLS data for the farm sector are somewhat

different in coverage from the agricultural statistics

used in this report. (The agricultural sector includes

forestry, hunting, and fishing as well as farming.)

Adjustments. Certain modifications in the basic

employment data were necessary for greater comparability among countries. Military personnel are

included in the basic employment data in some

foreign countries and allocated to the services sector.

Adjustment to omit the military has been made

in all cases. Unpaid family workers who work fewer

than 15 hours are excluded from employment data

in the United States, but usually are included in

other countries. Adjustments were made to exclude

such workers from the Japanese and Italian data.

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Numbers of unpaid family workers in the other

countries were not large enough to make a significant difference in employment proportions; consequently, no other adjustments for unpaid family

workers were made.

No adjustment was made for the varying lower

age limits used for employment statistics in different

countries. The lower age limit for U.S. data was 16,

while the limit in the other countries was 14 or 15.

Other adjustments to achieve consistency of the

employment data within certain countries were made.

For the United States, adjustments were made in

1950 and 1955 data to reflect changes in survey

definitions occurring in 1957, when persons on

temporary layoff and persons waiting to begin a

new job were shifted from the employed to the

unemployed count. Since 14- and 15-year-olds were

excluded from the U.S. labor force by 1967 changes

in definition, data for all years prior to 1967 were

adjusted to exclude them. However, no adjustments

were made in the U.S. data for inconsistencies in

the series resulting from the introduction of data

from the decennial censuses into the estimation

procedure in 1953 and 1962, the inclusion of Alaska

and Hawaii in 1960, and the shifting from unemployed to employed status in 1967 of persons

absent from their jobs during the survey week and

seeking other jobs.3

Adjustments were also made for such breaks in

the comparability of time series as the 1967 introduction of a redesigned labor force survey in Japan and

the introduction of three different Standard Industrial Classification systems (in 1948, 1958, and

1968) in the United Kingdom. Also, where population census data were used in lieu of labor force

survey statistics for 1950, these data were adjusted

to a compatible basis with the survey data based

on a comparison of survey and census data in a

year when both were available.

Limitations. The adjustments discussed above have

accounted for all major definitional differences in

employment statistics between countries and all

significant time series differences within countries.

However, it should be emphasized that only approximate comparability was achieved among countries.

In some instances, it was necessary to make adjustments based on incomplete data or on overlapping

data in one year which may not be fully applicable

to other years. Nevertheless, the adjusted figures

provide a more accurate basis for international

comparisons and for trends over time than the

figures regularly published for each country.

The average annual rates of change in tables 3

and 5 were calculated by the method of selected

points. In this method, the growth rate is obtained

by connecting the logs of the beginning and terminal

values of the period of years considered with a

straight line (this trend line is given by the compound

interest rate formula). Therefore, the growth rates

are affected by the selection of the terminal years.

This study omits the effects of intrasectoral shifts

in civilian employment. There have been large

differences in the employment experience of industries within the major sectors studied. There is

considerable heterogeneity in the service sector. For

example, the U.S. has seen a dramatic growth in

services supplied to businesses, and in educational

and health services; at the other end of the scale,

the number of persons employed in domestic household service and in transporation has been sharply

reduced.

Comparisons in this study relate to the number

of persons employed. Distribution of hours by

sector would have shown somewhat different results

since trends in and levels of hours may differ.

A significant portion of the increase in U.S. services

employment, for example, was in part-time work.

The U.S. services sector has shown a consistent

postwar decline in average hours paid for. The

industry sector, on the other hand, showed very

little change in hours paid for from 1950 until a

rise in overtime hours occurred in 1964-65.4 Office

of Business Economics’ figures on the number of

full-time equivalent employees by sector eliminate

the effects of part-time work on the sectoral distribution of employment in the United States. The

1950 and 1970 percent distributions by sector of

full-time equivalent wage and salary workers and

the self-employed according to OBE statistics are

as follows:

1950 1970

Agriculture ................................................... 11.8 4.5

Industry .......................................................... 34.9 32.2

Services .......................................................... 52.0 61.7

Government enterprises ............................. 1.3 1.7

The United States employment data used in this

report show a similar percent distribution by sector.

(See table 2.) It.should be remembered that employment in government enterprises is distributed

according to the sector most appropriate to the

output of the enterprise in tables 1 and 2.

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By limiting the analysis to civilian employment,

two important segments of the labor force are

omitted— military personnel and the unemployed.

The size of a nation’s military forces can have major

economic implications, but the forces themselves

are not considered to be engaged in economic

activity. The unemployed are omitted because by

------------------APPENDIX

1 United Nations, International Standard Industrial Classification of All Economic Activities, Statistical Papers, Series

M, No. 4, Revision 2 (New York, United Nations, 1968).

*BLS figures On the private economy do include output

and employment of government enterprises, but exclude

public administration.

’ Beginning in 1953, population levels were raised by

about 600,000 and labor force and employment raised by

about 350,000; beginning in 1962, the introduction of figures

definition they are not productively engaged and

their association with a particular economic sector

is tenuous at best. In the United States, these excluded groups represented 7 to 10 percent of the

labor force between 1950 and 1970. Elsewhere, the

military and unemployed generally accounted for

much smaller proportions of the labor force. □

from the 1960 census reduced the population by 50,000 and

labor force and employment by 200,000. The inclusion of

Alaska and Hawaii beginning in 1960 resulted in an increase

of about 500,000 in the population and about 300,000 in the

labor force. The 1967 shift of persons absent from their jobs

and seeking other jobs to the employed category increased

total employment by about 80,000.

* Patterns of UJS. Economic Growth (BLS Bulletin 1672,

1970), p. 13.

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Chapter II. Changes in the Labor Force

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Special Labor Force Report shows 

work force expanding to 101.8 million by 1980; 

rate of growth is then expected to decline, 

with labor force reaching 107.7 million 

by 1985 and 112.6 million by 1990

DENIS F. JOHNSTON

D uring the 1970’s, the total labor force of the

United States is estimated to expand by 15.9 million, reaching 101.8 million by 1980, according to

the latest projections of the Bureau of Labor Statistics. This increase implies an average annual growth

rate of 1.7 percent, about the same as the average

annual rate for the 1960’s. After 1980, the rate of

growth is expected to decline, averaging only 1.0

percent a year during the eighties. At this decelerated

rate, the labor force is estimated to reach 107.7

million by 1985 and 112.6 million by 1990.

Projected changes in the labor force are of necessity closely related to changes projected in the size

and age composition of the working-age population

— those 16 and over. Projected changes in labor

force participation rates (the percent of the population in the labor force) for specific age-sex groups

are also significant, but their impact is overshadowed by the effect of the projected population

changes. Between 1970 and 1990, for example, 89

percent of the projected change in the male labor

force and 68 percent of that of the female labor

force can be attributed to projected population

changes. Only among men 65 and over, and women

20 to 24 and 45 to 54, do projected changes in

labor force participation rates have a greater effect

on the labor force than changes in population.

This article presents projections of the total labor

force of the United States, by age and sex, for 1980,

1985, and 1990.1 It includes a discussion of past

trends, as background for the analysis of changes

implied by the projections, together with a brief

summary of the assumptions which underlie the

Denis F. Johnston is Senior Demographic Statistician,

Office of Manpower Structure and Trends, Bureau of

Labor Statistics. William V. Deutermann, Jr., of the Division of Labor Force Studies, assisted in developing the statistical materials.

The U.S.

labor force:

projections

to 1990

projections and the methods employed in their development.

Changes in the 1970’s

The projected 1980 labor force differs markedly

from the actual labor force of 1960 and 1970 in its

composition by age and sex. The median age of the

labor force, which declined from 40 to 38 years

during the 1960’s, is expected to fall still more rapidly during the present decade, reaching 35 years by

1980. The major factor in this decline is the sharp

rise in the number of young adult workers aged 25

to 34 years— from 17.7 to 26.8 million— as the

“baby boom” generation moves inexorably through

the life cycle. This age group— one-fifth of the labor

force in 1970— is estimated to make up over onefourth of the labor force 10 years later (table 1).

These projections also indicate a continuing increase in the proportion of the labor force that are

women— from 36.7 percent in 1970 to 38.5 percent

in 1980. This projected increase is much less pronounced, however, than the rise since 1960, when

32.1 percent of those in the labor force were women

(table 2 ). Two major reasons may be cited in support of the more modest growth projected for

women workers during the present decade. First, the

largest changes in the female population in the

1970’s are in the age group (25 to 34 years) whose

labor force participation rate is lower than for those

age groups where the largest population increases

occurred in the 1960’s. Second, the unusually rapid

increase in women’s labor force participation rates

during the past decade is associated with the precipitous decline in the birth rate. These projections assume that no further drastic declines in birth rates

are in prospect. Thus, the labor force participation

rate of women 25 to 34 years old, which rose from

35.8 percent in 1960 to 44.8 percent in 1970, is_

projected to rise only 5.4 percentage points during

From the Review of July 1973

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[Numbers in thousands]

Table 1. Total population, total labor force, and labor force participation rates, by age and sex, actual 1960 and 1970

and projected 1980, 1985, and 1990

Total population, July 1 Total labor force, annual averages

Labor force participation rates, 

annual averages (percent of 

population in labor force)

Sex and age group

Actual Projected Actual Projected Actual Projected

1960 1970 1980 1985 1990 1960 1970 1980 1985 1990 1960 1970 1980 1985 1990

BOTH SEXES

Total, 16 years and over. 121,817 142,366 167,339 175,722 183,078 72,104 85,903 101,809 107,716 112,576 59.2 60.3 60.8 61.3 61.5

16 to 24 years........................ 21,773 32,257 37,463 34,405 31,643 12,720 19,916 23,781 22,184 20,319 58.4 61.7 63.5 64.5 64.2

25 to 54 years.................. 67,764 71,777 84,740 94,028 103,309 46,596 51,487 61,944 69,202 76,421 68.8 71.7 73.1 73.6 74.0

55 years and over............ ......... 32,279 38,333 45,136 47,289 48,126 12,788 14,500 16,084 16,330 15,836 39.6 37.8 35.6 34.5 32.9

MEN

Total, 16 years and over. 59,420 68,641 80,261 84,285 87,911 48,933 54,343 62,590 66,017 68,907 82.4 79.2 78.0 78.3 78.4

16 to 19 years................... 5,398 7,649 8,339 7,141 7,045 3,162 4,395 4,668 3,962 3,901 58.6 57.5 56.0 55.5 55.4

16 and 17 years............. 2,880 3,937 4,111 3,515 3,373 1,322 1,840 1,887 1,603 1,530 45.9 46.7 45.9 45.6 45.4

18 and 19 years.................. 2,518 3,712 4,228 3,626 3,672 1,840 2,555 2,781 2,359 2,371 73.1 68.8 65.8 65.1 64.6

20 to 24 years........... .......... 5,553 8,668 10,666 10,305 9,021 4,939 7,378 8,852 8,496 7,404 88.9 85.1 83.0 82.5 82.1

25 to 34 years______ ______ 11,347 12,601 18,521 20,540 21,040 10,940 11,974 17,523 19,400 19,853 96.4 95.0 94.6 94.4 94.4

35 to 44 years___________ 11,878 11,303 12,468 15,409 18,378 11,454 10,818 11,851 14,617 17,398 96.4 95.7 95.1 94.9 94.7

45 to 54 years................. ......... 10,148 11,283 10,781 10,630 11,922 9,568 10,487 9,908 9,744 10,909 94.3 92.9 91.9 91.7 91.5

55 to 64 years......... .......... 7,564 8,742 9,776 9,874 9,424 6,445 7,127 7,730 7,716 7,307 85.2 81.5 79.1 78.1 77.5

55 to 59 years.................... 4,144 4,794 5,263 5,129 4,787 3,727 4,221 4,558 4,421 4,112 89.9 88.0 86.6 86.2 85.9

60 to 64 years__________ 3,420 3,948 4,513 4,745 4,637 2,718 2,906 3,172 3,295 3,195 79.5 73.6 70.3 69.4 68.9

65 years and over............ ......... 7,530 8,395 9,710 10,386 11,081 2,425 2,164 2,058 2,082 2,135 32.2 25.8 21.2 20.0 19.3

65 to 69 years..... .............. 2,941 3,139 3,633 3,852 4,065 1,348 1,278 1,289 1,322 1,365 45.8 40.7 35.5 34.3 33.6

70 years and over_______ 4,590 5,256 6,077 6,534 7,016 1,077 886 769 760 770 23.5 16.9 12.7 11.6 11.0

WOMEN

Total, 16 years and over. 62,397 73,725 87,078 91,437 95,167 23,171 31,560 39,219 41,699 43,669 37.1 42.8 45.0 45.6 45.9

16 to 19 years....... .................... 5,275 7,432 8,057 6,910 6,776 2,061 3,250 3,669 3,203 3,188 39.1 43.7 45.5 46.4 47.0

16 and 17 years................. 2,803 3,828 3,969 3,397 3,243 801 1,324 1,427 1,247 1,205 28.6 34.6 36.0 36.7 37.2

18 and 19 years................. 2,472 3,604 4,088 3,513 3,533 1,260 1,926 2,242 1,956 1,983 51.0 53.4 54.8 55.7 56.1

20 to 24 years............................ 5,547 8,508 10,401 10,049 8,801 2,558 4,893 6,592 6,523 5,826 46.1 57.5 63.4 64.9 66.2

25 to 34 years............................ 11,605 12,743 18,442 20,301 20,750 4,159 5,704 9,256 10,339 10,678 35.8 44.8 50.2 50.9 51.5

35 to 44 years........................ . 12,348 11,741 12,903 15,741 18,524 5,325 5,971 6,869 8,560 10,219 43.1 50.9 53.2 54.4 55.2

45 to 54 years______________ 10,438 12,106 11,625 11,407 12,695 5,150 6,533 6,537 6,542 7,364 49.3 54.0 56.2 '57.4 58.0

55 to 64 years............. .............. 8,070 9,763 11,307 11,492 10,934 2,964 4,153 5,057 5,213 5,003 36.7 42.5 44.7 45.4 45.8

55 to 59 years..................... 4,321 5,257 5,966 5,804 5,396 1,803 2,547 3,055 3,033 2,853 41.7 48.4 51.2 52.3 52.9

60 to 64 years........... ......... 3,749 4,506 5,341 5,688 5,538 1,161 1,606 2,002 2,180 2,150 31.0 35.6 37.5 38.3 38.8

65 years and over__________ 9,115 11,433 14,343 15,537 16,687 954 1,056 1,239 1,319 1,391 10.5 9.2 8.6 8.5 8.3

65 to 69 years......... ........... 3,347 3,780 4,595 4,942 5,267 579 644 758 814 864 17.3 17.0 16.5 16.5 16.4

70 years and over.............. 5,768 7,653 9,748 10,595 11,420 375 412 481 505 527 6.5 5.4 4.9 4.8 4.6

SOURCE: Population and labor force data for 1960 are from Special Labor Force 

Report 119 and differ slightly from later estimates. Corresponding 1970 data are from

the current decade, reaching 50.2 percent in 1980.

The large increase in the number of young adult

workers, and the continued rise in the number of

women in the labor force, are the salient features of

the changes in prospect during the remainder of the

present decade. However, the changes which are

foreseen in the other age-sex groups of the working-age population are also significant (table 3).

First, the teenage labor force, which increased

from 5.2 million in 1960 to over 7.6 million in

1970, is projected to increase still further, but at a

slower pace, reaching a peak in the late 1970’s.

Thereafter, this group may be expected to decline

slowly in number, reaching 8.3 million in 1980.

Current Population Survey estimates. Projected population data are from Current 

Population Reports, Series P-25, No. 493, Series l .

This development has important implications for the

absorption of these new young labor force entrants

into the Nation’s economy. During the 1960’s when

the number of teenage workers was rising by about

240,000 a year, on average, teenage unemployment

fluctuated between 12.2 and 17.2 percent (on an

annual average basis). In contrast, the size of the

teenage labor force is estimated to increase by only

about 70,000 a year, on average, during the current

decade. This slower pace of increase should enhance

the effectiveness of measures designed to reduce the

unemployment rate among teenagers. (See chart 1.)

Second, the group aged 20 to 24 is projected to

continue to grow rapidly in size during the current

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decade, but again at a slower pace than during the

1960’s. This group increased by an average of

480,000 a year during the 1960’s, but is expected to

increase at the more moderate pace of 320,000 a

year during the current decade, reaching 15.4 million workers by 1980.

Third, the group aged 35 to 44, which was the

same size in 1970 as in 1960, is projected to increase by 1.9 million during the current decade,

reaching 18.7 million in 1980, as the larger number

of persons born between 1935 and 1944 moves into

this age group of workers.

Fourth, the group aged 45 to 54, which increased

by 2.3 million between 1960 and 1970, is projected

to decline by nearly 600,000 during the present decade, reaching 16.4 million workers in 1980. At that

time, this group will consist mostly of the relatively

small number of persons born between 1925 and

1934— the “depression” cohort.

Fifth, the Nation’s older workers (aged 55 and

over) are projected to continue to increase in number at a somewhat more moderate pace during the

current decade. This group increased by about 1.7

million during the 1960’s, and is expected to increase by an additional 1.6 million during the

1970’s, reaching 16.1 million workers in 1980.

Within this group, the number of workers aged 65

and over is projected to remain nearly constant, rising from 3.2 million in 1970 to 3.3 million in 1980.

This trend is in contrast to the steady increase in

the size of the population 65 and over, which is expected to grow from 19.8 million in 1970 to over

24 million by 1980. Projected reductions in the

labor force participation rates of persons in this age

group yield a nearly constant number of workers despite the substantial increase in the population.

Comparison with earlier projections

In general, the present set of labor force projections differs from the previous BLS study in two

major respects. First, the participation rates for men

in all age groups are now estimated to decline over

time, reflecting the observed downward movement

over the 1955-72 period. 2 Second, the participation

rates for women are considerably higher than those

Table 2. Distribution of total labor force, by age and sex, actual 1960 and 1970 and projected 1980, 1985, and 1990

Number (in thousands) Percent distribution

Sex and age group Actual Projected Actual Projected

1960 1970 1980 1985 1990 1960 1970 1980 1985 1990

BOTH SEXES

Total, 16 years and over___ 72,104 85,903 101,809 107,716 112,576 100.0 100.0 100.0 100.0 100.0

16 to 24 years.................... ............. 12,720 19,916 23,781 22,184 20,319 17.6 23.2 23.4 20.6 18.0

16 to 19 years.......................... 5,223 7,645 8,337 7,165 7,089 7.2 8.9 8.2 6.7 6.3

20 to 24 years_____ ____ _ 7,497 12,271 15,444 15,019 13,230 10.4 14.3 15.2 13.9 11.8

25 to 54 years_____ . . ............ 46,596 51,487 61,944 69,202 76,421 64.6 59.9 60.8 64.2 67.9

25 to 34 years_____________ 15,099 17,678 26,779 29,739 30,531 20.9 20.6 26.3 27.6 27.1

35 to 44 years_____ _____ 16,779 16,789 18,720 23,177 27,617 23.3 19.5 18.4 21.5 24.5

45 to 54 years_____ ___. . . 14,718 17,020 16,445 16,286 18,273 20.4 19.8 16.2 15.1 16.2

55 years and over________ _____ 12,788 14,500 16,084 16,330 15,836 17.7 16.9 15.8 15.2 14.1

55 to 64 years........................... 9,409 11,280 12,787 12,929 12,310 13.0 13.1 12.6 12.0 10.9

65 years and over__________ 3,379 3,220 3,297 3,401 3,526 4.7 3.7 3.2 3.2 3.1

Median age..................................... 39.9 38.2 35.2 35.8 37.0

MEN

Total, 16 years and over___ 48,933 54,343 62,590 66,017 68,907 67.9 63.3 61.5 61.3 61.2

16 to 24 years.............................. 8,101 11,773 13,520 12,458 11,305 11.2 13.7 13.3 11.6 10.0

25 to 54 years................................. 31,962 33,279 39,282 43,761 48,160 44.3 38.7 38.6 40.6 42.8

55 years and over.................... ....... 8,870 9,291 9,788 9,798 9,442 12.3 10.8 9.6 9.1 8.4

Median age________ ____ 39.7 38.2 35.2 35.8 36.9

WOMEN

Total, 16 years and over___ 23,171 31,560 39,219 41,699 43,669 32.1 36.7 38.5 38.7 38.8

16 to 24 years............... ............... . 4,619 8,143 10,261 9,726 9,014 6.4 9.5 10.1 9.0 8.0

25 to 54 years............... ................. 14,634 18,208 22,662 25,441 28,261 20.3 21.2 22.3 23.6 25.1

55 years and over_____________ 3,918 5,209 6,296 6,532 6,394 5.4 6.1 6.2 6.1 5.7

Median age_____________ 40.3 38.2 35.1 35.9 37.1

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The uses of projections

. . . The basic distinction between a projection and

a forecast reflects the purpose it is intended to serve

rather than the method of its preparation or the

degree o f understanding which it reflects. A forecast

may be defined as a projection which has been

selected as representing the “most likely” outcom e

in situations whose determinants are insufficiently

known or controlled to permit outright prediction.

Its distinguishing characteristic is the element of

judgment or decision which is necessary in making

such a selection. If projections are race horses, the

forecast is the horse you decide to bet on.

W hereas projections may serve a number of functions, the basic function on a forecast is to delineate

the most probable outcom e in a specified area of

concern over a specified period in the future. The

need for a forecast does not arise until and unless

the user must com m it him self to a definite plan of

action extending into the future. Given such a com ­

mitment, the preparation or adoption of some kind

of forecast is inescapable.

Projections in general, and economic-demographic

projections in particular, may be used to meet a

number of purposes. First, they are most comm only

designed to fulfill an anticipatory function— allowing

the user to anticipate the probable magnitude or

impact o f some probable or postulated set of conditions or changes at some future time. . . .

Second, projections— or the forecast which is selected from among them— are an essential input for

planning and program developm ent. If our plans and

programs are rational, they must be future-oriented,

and they must therefore incorporate some systematic

appraisal of the environment in which these plans

are likely to operate in the future. . . .

Third, projections are an essential— though som etimes implicit— ingredient in program evaluation.

Attempts at program evaluation, especially in areas

involving social behavior, com m only encounter the

problem that program benefits cannot be estimated

with nearly the confidence or accuracy that surrounds estimates o f program costs. The social researcher recognizes in this difficulty the truism that

the impact of any social program is entangled in a

web o f cross-impacts reflecting the totality of interactions occurring in the society. One way to avoid

this difficulty is to project the course of developments which might be anticipated in the absence of

the particular program, so that comparison of this

projection with actual post-program outcom es may

yield an estimate, however crude, of program impact

or “benefit.”

Fourth, projections may be viewed as essential

links in a chain of conjecture; each projection includes among its underlying assumptions certain

conditions which are derived from a prior projection,

and most projections are likely, in turn, to provide

inputs to other projections. . . .

Fifth, projections serve a public information function. Our justifiable concern with the manipulative

and propagandistic elem ents which may be found in

projections prepared for public effect should not

obscure the fact that projections, when freed o f such

influences, have a unique educational value. . . .

Finally, projections serve an exploratory or heuristic function, insofar as they may be developed in

order to delineate the probable (or possible) consequences of alternative sets o f initial conditions and

determining factors. W hile the chief value of such

exercises may be educational, they may be o f considerable practical value to the decisionmaker as

well. To the extent that they expand his awareness

of the “degrees of freedom ” which he enjoys in a

given situation, they may prompt his consideration

o f alternative solutions which he might not otherwise have recognized.

Each of these six functions provides a perspective

from which to suggest a course of action in “building upon” the available econom ic and demographic

projections. However, it is the last o f these functions

which most clearly reflects the nature and potential

value of projections in their purest sense, and it is

the fulfillment of this function which most nearly

implies a capacity to carry out the other functions

as well.

. . . T o build upon econom ic and demographic

projections, it is necessary to recognize the different

purposes for which projections are developed and

the different strategies which are called for in pursuing these purposes. From the standpoint o f the

technician, the necessary strategy is straightforward:

we need to integrate our econom ic and demographic

models, incorporating additional indicators of relevant social processes, so as to develop more inclusive

social systems models. But for the decisionmaker and

social critic alike, a different strategy must be

employed— one which recognizes in the failures of

past predictions not the need for improved analytical

systems, but rather the existence of opportunities for

the expression of human values which alone give

meaning to our decisions.

— D e n is F . Jo h n s t o n ,

“Building on Economic and Demographic Projections,”

a paper presented at a meeting of the

Society of Actuaries, Toronto.

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Table 3. Net changes in total labor force 16 years old and over, by age and sex, 1960-70, 1970-80, and 1980-90

Sex and age group

Net change 

(in thousands) Percent change Average annual rate of change1 

(in percent)

1960-70 1970-80 1980-90 1960-70 1970-80 1980-90 1960-70 1970-80 1980-90

BOTH SEXES

Total, 16 years and over.............................. 13,799 15,906 10,767 100.0 100.0 100.0 1.75 1.70 1.01

16 to 19 years.................... ........... 2,422 692 -1,248 17.6 4.4 -1 1 .6 3.81 .87 -1 .6 2

20 to 24 years.............................. 4,774 3,173 -2,214 34.6 19.9 -2 0 .6 4.93 2.30 -1 .5 5

25 to 34 years_____ _____ ____ 2,579 9,101 3,752 18.7 57.2 34.8 1.58 4.15 1.31

35 to 44 years__________ . _ 10 1,931 8,897 .1 12.1 82.6 (2) 1.09 3.89

45 to 54 years..... ........... 2,302 -575 1,828 16.7 - 3 .6 17.0 1.45 - .3 4 1.05

55 to 64 years....................... 1,871 1,507 -477 13.6 9.5 - 4 .4 1.81 1.25 - .3 8

65 years and over.............. -159 77 229 - 1 .2 .5 2.1 - .4 8 .24 .67

MEN

Total, 16 years and over__ . . . . 5,410 8,247 6,317 39.2 51.8 58.7 1.05 1.41 .96

16 to 24 years______ 3,672 1,747 -2,215 26.6 11.0 -2 0 .6 3.74 1.38 -1 .7 9

25 to 54 years_______ 1,317 6,003 8,878 9.5 37.7 82.5 .40 1.66 2.04

55 years and over.............. 421 497 -346 3.0 3.1 - 3 .2 .46 .52 - .3 6

WOMEN

Total, 16 years and over....... ...................... 8,389 7,659 4,450 60.8 48.2 41.3 3.09 2.17 1.07

16 to 24 years...................... ............................... 3,524 2,118 -1,247 25.5 13.3 -1 1 .6 5.67 2.31 -1 .3 0

25 to 54 years_____ _______ ________ _______ 3,574 4,454 5,599 25.9 28.0 52.0 2.18 2.19 2.21

55 years and over........................................ ........... 1,291 1,087 98 9.4 6.8 .9 2.85 1.90 .15

1 Compounded continously. 2 Less than .05 percent.

formerly estimated, although the rate of increase is

generally slower than that observed during the 1960

decade (table 4 ). The net effect of these changes is

to reduce the 1980 male labor force by 1.0 million

(in comparison with the previous BLS projection)

and to raise the female labor force by 2.1 million,

for a net increase of 1.1 million (from 100.7 to

101.8 million workers).

The direction of both of these major changes is

the same as that of earlier revisions in the BLS

projections (as shown in table 4 ). However, unlike

the earlier revisions, the present projection does not

hold the participation rates for men in the central

working ages (25 to 54) at a constant level. Instead

it allows these rates to edge downward slowly, on

the assumption that the observed reductions between

1955 and 1972 are not attributable to cyclical factors, but rather reflect a long-term secular trend. As

has been noted in previous BLS projections, the

projected declines in the participation rates of

younger men (16 to 24) are assumed to reflect the

net effect of continued growth in school enrollment,

while the declines projected among men 55 and

over reflect a long-term trend toward earlier retirement— an option which is increasingly supportable

by virtue of the improved terms and increased coverage afforded by a host of private and public pension

plans and personal savings. 3

In regard to the upward revision in the participation rates for women, three major points should be

made. First, the current projection implies a substantial reduction in the rate of increase of participation rates of women under 35. This is particularly

noticeable in comparison with the very rapid gains

observed among women in this age group during the

1964-72 period, when their participation rates increased by 10 percentage points, reaching 50.4 percent in 1972. The projected gain over the following

8-year period (1972-80) is only 2.5 percentage

points. As noted previously, the more modest

growth projected in the labor force participation

rates of these younger women reflects the judgment

that the extraordinary growth observed during the

past decade was accelerated by certain factors which

are not expected to have a significant impact in the

future. The most important of these is the rapid decline in fertility that occurred during the 1960’s. Between 1960 and 1972, the general fertility rate declined from 118.0 to 73.4— a drop of 38 percent.4

Since the presence of young children in the home

limits the availability of mothers for work outside

the home (ceteris paribus), this reduction in fertility

allowed a growing proportion of young women to

enter the labor force. In addition, the Vietnam

buildup of the late 1960’s afforded unusually favorable employment opportunities for these women.

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Chart 1. Change in labor force (annual average) over

successive decades, 1960 to 1990, by age group

Annual average change (in millions)

Age -.2 0 .2 .4 .6 .8 1.0

1 An increase of one thousand per year, on average.

Also, in the late 1960’s the number at or near the

median age at which women married for the first

time was considerably larger than the number of

men 2 to 3 years older than themselves whom they

would normally have married. This temporary imbalance was exacerbated by the Vietnam buildup,

thus inducing considerable delay in marriage. Each

of these factors is assumed to have had a strong

positive influence on the participation rates of young

women in the recent past, and none of these factors

is expected to be operative in the future.

Second, the current projection implies a more

moderate reduction in growth of the participation

rate among women 35 to 54 years old. Between

1964 and 1972, their participation rate increased by

5 percentage points, reaching 52.7 percent in 1972.

The corresponding increase for 1972-80 is only 2.0

percentage points. This slower projected growth reflects in part the slow increase in labor force participation among women 45 to 54 years old observed

during the past decade. In addition, it is felt that the

very large increases projected in the number of

young women workers 25 to 34 years old may have

a limiting effect on the employment opportunities of

older women.

Finally, the projection for older women (55 and

over) shows a small increase in their rate of labor

force participation during the remainder of the current decade. This projected increase occurs only

among women 55 to 64 years old; the long-term decline in participation among women 65 and over is

expected to continue. Although the projected labor

force of women 55 and over in 1980 is practically

identical with the previous BLS projection, the projected participation rates are somewhat lower, reflecting the stabilized rates observed in the recent

past. This apparent discrepancy is accounted for by

the larger size of the population of women 55 and

over currently projected for 1980.

Changes in the 1980’s

The outstanding feature of the projected 1980-90

increase in the total labor force is the slower pace of

growth— from an average annual rate of 1.7 percent

in the 1970’s to 1.0 percent in the 1980’s. At this

reduced rate, the labor force is projected to increase

by 10.8 million during the 1980 decade, reaching

107.7 million by 1985 and 112.6 million by 1990.

Also significant is the expected shift in the locus of

major expansion, from the 25- to 34-year-old group

in the 1970’s to the 35- to 44-year-old group, during

the 1980’s. The latter group, whose number is projected to increase by about 190,000 a year, on average, during the current decade, is projected to grow

by nearly 900,000 a year, on average, during the

1980’s. One manifestation of this shift is the estimated rise in the median age of the labor force—

from 35.2 years in 1980 to 37.0 years in 1990.

The number of young workers (16 to 24 years

old) is projected to decline by nearly 350,000 a

year, on average, during the 1980’s from 23.8 million in 1980 to 22.2 million by 1985 and 20.3 million by 1990— only 400,000 more than their number in 1970. (See chart 2.) However, this younger

group in 1990 is expected to differ sharply from

that of 1970, with nearly 500,000 fewer men and

900,000 more women workers— reflecting the assumed continuation in both the downward trend in

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Table 4. Comparison of current labor force projection with earlier BLS projections, 1980 and 1985

[In thousands]

Total labor force 16 years old and over, by age and sex

1980 1985

Sex and age group Current

projection

SLFR 

1191

1980

SLFR

Differences Current

projection

1985

SLFR

Difference 

( 6 ) - (7)

(1) (2)

492

(3)

( D - (2) 

(4)

( 1 ) - (3) 

(5) (6)

119'

(7) (8)

BOTH SEXES

Total, 16 years and over........ 101,809 100,727 99,942 1,082 1,867 107,716 107,156 560

16 to 24 years________ 23,781 23,130 22,554 651 1,227 22,184 22,242 -5 8

25 to 54 years_______ 61,944 61,377 60,431 567 1,513 69,202 68,525 677

55 years and over.............................. 16,084 16,220 16.957 -136 -873 16,330 16,389 -5 9

MEN

Total, 16 years and over____ 62,590 63,612 64,061 -1,022 -1,471 66,017 67,718 -1,701

16 to 24 years.................. . __ 13,520 13,690 13,888 -170 -368 12,458 13,179 -721

25 to 54 years..................... 39,282 39,983 39,893 -701 -611 43,761 44,542 — 781

55 years and over........ ...................... 9,788 9,939 10,280 -151 -492 9,798 9,997 -199

WOMEN

Total, 16 years and over____ 39,219 37,115 35,881 2,104 3,338 41,699 39,438 2,261

16 to 24 years................................... 10,261 9,440 8,666 821 1,595 9,726 9,063 663

25 to 54 years____ _____________ 22,662 21,394 20,538 1,268 2,124 25,441 23,983 1,458

55 years and over_______________ 6,296 6,281 6,677 15 -381 6,532 6,392 140

1 Sophia C. Travis, "The U.S. labor force: projections to 1985,” Monthly Labor 

Review, May 1970, pp. 3-12, reprinted as Special Labor Force Report 119.

2 Sophia C. Travis and Denis F. Johnston, “Labor Force Projections for 1970-80,”

Monthly Labor Review, February 1965, pp. 129-40, reprinted as Special Labor Force 

Report 49.

Table 5. Effect of alternative fertility assumptions on projected total labor force of women 16 to 49 years old, by age,

1980, 1985, and 1990 ’

[In thousands]

Sex and age group

1980 1985 19902

Series

D

Series

E

Series

F

Series

D

Series

E

Series

F

Series

D

Series

E

Series

F

BOTH SEXES

Total, 16 years and over_________ _____________ 101,138 101,809 102,166 106,932 107,716 108,247 112,119 112,576 113,031

MEN

Total, 16 years and over.............................................. 62,590 62,590 62,590 66,017 66,017 66,017 69,102 68,907 68,834

WOMEN

Total, 16 years and over......... __............._.................. 38,548 39,219 39,576 40,915 41,699 42,230 43,017 43,669 44,197

16 and 17 years..................... ....................................... 1,425 1,427 1,429 1,245 1,247 1,247 1,356 1,205 1,149

18 and 19 years_______ __________________ _____ ___ 2,228 2,242 2,253 1,943 1,956 1,964 1,971 1,983 1,991

20 to 24 years......... ............................................ _.............. . 6,372 6,592 6,730 6,307 6,523 6,686 5,643 5,826 5,965

25 to 29 years________________ ____________________ 4,770 5,038 5,176 5,167 5,505 5,743 5,042 5,387 5,646

30 to 34 years............. ........................................................... 4,104 4,218 4,268 4,689 4,834 4,920 5,116 5,291 5,416

35 to 39 years_______ ________________________ ____ 3,593 3,632 3,646 4,588 4,641 4,668 5,202 5,268 5,307

40 to 44 years........................................................................... 3,225 3,237 3,241 3,904 3,919 3,927 4,931 4,951 4,963

45 to 49 years______ _____________ ________ _____ _ 3,203 3,205 3,205 3,384 3,386 3,387 4,052 4,054 4,056

50 years and over_______________ ______ ____ _____ _ 9,628 9,628 9,628 9,688 9,688 9,688 9,704 9,704 9,704

1 As currently defined by the Bureau of the Census in Current Population Reports,

Series P-25, No. 493, Series D implies an ultimate completed cohort fertility rate of 

2/500, that is, 1,000 women would have, on average, 2,500 births throughout their 

childbearing period. Series T. implies a corresponding rate of 2,100, and Series F 

implies a rate of 1,800. The basic projections in this report are based on the Series E

population projections.

2 The differences in the projected male labor force in 1990 are due to differences 

among the three series in the number of births projected for 1973 and 1974—cohorts 

which would be 16 and 17 years old in 1990. The projected female labor force 16 and 

17 in 1990 is similarly affected.

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the participation rates of young men and the upward trend for young women.

Workers in the 25- to 34-year-old group are estimated to continue to increase in number during the

1980’s but at a much slower pace than in the

1970’s, reaching 29.7 million by 1985 and 30.5 million by 1990. Moreover, this gain is expected to

occur primarily during the first half of the 1980

decade, with an annual average increase of 600,000

a year, in contrast to an increase of only 160,000 a

year, on average, between 1985 and 1990.

The prospects among workers 45 to 54 years old

imply a reversal of the trend foreseen for the current decade— from an annual average decline of

nearly 60,000 in the present decade to an average

gain of 180,000 a year in the 1980’s. Meanwhile,

the smaller number of persons born in the 1925-34

period will be moving into the 55- to 64-year-old

age group, whose labor force numbers are therefore

expected to decline by nearly 50,000 a year, on

average.

Finally, the outlook for workers 65 and over during the 1980’s is for a slow but steady increase in

Chart 2. Age-sex profile of total labor force, 1970 actual

and 1990 projected

| j 1990 excess over 1970

1970 excess over 1990

AGE GROUPS

Men Women

7.5

number (20,000 a year), as the assumed continuing

decline in their participation rates is more than

offset by the continued rise in the underlying population of older persons— from 24 million in 1980 to

25.9 million in 1985 and 27.8 million by 1990.

The sex distribution of the projected labor force

is not expected to change greatly in the 1980’s. The

proportion of workers who are women is expected

to rise from 38.5 percent in 1980 to 38.7 percent in

1985 and 38.8 percent in 1990. This stabilization

reflects primarily the changing age composition of

the working-age population during the decade, with

declines in the number of young women and very

small increases in the number of women 45 to 64.

years old— the two age groups whose participation

rates have been relatively high (chart 3).

Alternative projections

The alternative projections shown in table 5 describe the estimated effect of specified changes in a

single variable (fertility) upon the size and age-sex

distribution of the projected labor force.5 Table 5

shows the projected total labor force of women 16

to 49 years old, by age, for 1980, 1985, and 1990,

under three alternative assumptions concerning fertility: Series “D,” “E,” and “F.” As is explained in

the following section on methodology, Series E

(defined as 2,100 births per 1,000 women) is the

series adopted for the basic set of projections in this

report; it represents a level of fertility whereby each

generation is barely replaced by the next one, so

that the population eventually stops growing (except

for immigration). Series D implies a higher fertility rate of 2,500 births per 1,000 women, while Series F implies a lower rate of 1,800 births per

1,000 women. Thus Series E is somewhat closer

to F than to D. In developing these alternative

projections, the assumed participation rates for

women with and without children under 5 years old

are the same for each series; the only difference

among the three series is the difference in the proportions of the population of women with and without children under 5. Series D implies a larger

proportion of women in each childbearing age group

with children under 5, while Series F implies a

lower proportion, with Series E falling in between.

The effect of these alternative fertility assumptions (ceteris paribus) can be illustrated by examining the 1980 projection. As noted previously, the

basic Series E projection yields a total labor force

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of 101.8 million. A shift to Series D has the effect

of reducing the female labor force (and thus the

total labor force) by about 670,000, while a shift to

Series F increases the labor force by about

360,000. Thus, the range of the projected variation

in the size of the labor force, as we move from Series D to Series F, amounts to about 1.0 million, or 1 percent of the basic projection for 1980.

Among all women workers, however, that range

amounts to 2.6 percent of the basic projection, and

among women in the principal childbearing ages

(16 to 49), it amounts to 3.5 percent of the basic

projection.0

It should be noted, parenthetically, that the previous projections assumed continuation of the Series

C fertility levels (the level which approximates

the actual fertility rate of the mid-1960’s). Since

that time, fertility has declined to its present level,

which is close to Series E. On the basis of the

above calculations, the shift from Series C to Series E would account for an increase in the size of

the female labor force of about 700,000 in 1970.

Thus, the “error” in the fertility assumption alone

accounts for over one-third of the 1.9 million underestimate of the 1970 female labor force in the BLS

projections prepared in 1964.7

Methods and assumptions

The projections in this report reflect anticipated

changes in the demographic composition of the population of working age, combined with our judgments as to the changes which might be expected in

the labor force participation rates of the several

age-sex groups in the population. The predominant

factor in these projections is the anticipated change

in the size and age-sex composition of the population. The projections assume no drastic changes in

the propensity of the several population groups to

seek work. They also assume a generally favorable

demand situation, together with the absence of

major wars or other major social or economic disturbances. Finally, the projections assume no major

legislative or social changes which would alter the

conditions under which individuals choose to enter

or remain out of the labor force, or which would

alter the prevailing definitions of “labor force,” “employment,” or “unemployment.” 8

The general approach is to extrapolate observed

trends in the participation rates of each age-sex

group to the terminal date of the projection (1990),

Chart 3. Labor force participation rates of women, by

age, 1960, 1980, and 1990

[Percent of total population in total labor force]

Percent of

population in

labor force

to to to to to to to to to to to and

19 24 29 34 39 44 49 54 59 64 69 over

Age. groups

and to apply the projected rates to the projected

population to obtain the labor force. The major

steps in this procedure are as follows:

Step 1. Annual average rates of labor force participation (the percent of the total population in the total

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labor force) were obtained for each year, 1955

through 1972, for men and women separately in the

following age groups: 16-17, 18-19, and 5-year

groups thereafter to 70 and over. By means of linear regression, the average annual change in the

participation rates of each age-sex group over the

1955-72 period was obtained. That average annual

change, times 5, was taken as representative of the

average observed 5-year change in the participation

rate of each age-sex group.

Step 2. Each of the observed 5-year changes in

participation rates was then gradually reduced by a

constant proportion for successive 5-year periods,

so as to reduce all changes to approximately zero in

50 years (that is, in 10 5-year periods). Such a

“tapering” of trends is designed to prevent the occurrence of future rates that might otherwise fall

outside plausible (or possible) limits. It also reflects

the assumption that each rate is moving toward some

asymptotic level which can only be defined arbitrarily. To accomplish this reduction, a constant

multiplier (M ) was applied to each observed average

5-year change to obtain the projected change over

the first projected period (1970-75). That change

was again multiplied by M to obtain the projected

change for the next 5-year period (1975-80), and

so on to 1985-90. For example, the largest observed

5-year change was —4.64 percentage points (among

males 65 to 69); the multiplier (M ) was assigned

a value such that 4.64 X M10 < 0.05. In this case,

M = .63. Similarly, the smallest observed 5-year

change (among women 65 to 69) was —.22; here,

the appropriate value for M is .84.

Step 3. The projected 5-year change for each age-sex

group, 1970-75, added algebraically to the 1969-71

average labor force participation rate for the specified group (used as a base) yields the projected participation rate for 1975. Repeating this procedure

yields projected participation rates for 1980, 1985,

and 1990.

Step 4. For women in the childbearing ages (16 to

49), the trends in the ratio of the observed participation rates among women with children under 5

and those without children under 5 to the participation rates for all women in the specified age groups

were estimated and projected. The projected ratios

were then used to obtain projected participation

rates for women with and without children under 5,

by age, to 1990.

Step 5. The percentages of women in each age

group (16 to 49) who would have children under 5,

consistent with the fertility levels of the Bureau of

the Census’ Series C, D, E, and F projections of

population (as given in Current Population Reports,

Series P-25, No. 493) were estimated for the years

1975, 1980, 1985, and 1990. These percentages

were then applied to the projected total population of

women in these ages to obtain the number of women

with and without children under 5 for the target

years.

Step 6. The projected participation rates for women

with and without children under 5 (as obtained in

step 4) were then applied to the projected numbers

of women with and without children under 5 (by

age), yielding a projected labor force consistent with

the Series C, D, E, and F population projections.

Step 7. An analysis of recent trends in the fertility

of American women and of information relating to

the fertility expectations of young married women

led to the decision to adopt the Series E projections for the basic set of labor force projections. Aggregating the projected labor force of women 16 to

49, by presence of children under 5, and dividing by

the corresponding population produced a final projected set of participation rates for all women 16 to

49 for the target years, consistent with Series E

population projections.

Step 8. On the assumption that changes in fertility

would not affect the participation rates for men or

for women 50 and over, the projected labor force

for these latter groups was obtained by multiplying

the projected population by the projected participation rates obtained in step 3. 

-FOOTNOTES1 These projections supersede those which were presented

by Sophia C. Travis in “The U.S. labor force: projections

to 1985,” Monthly Labor Review, May 1970, pp. 3-12, reprinted as Special Labor Force Report 119. Information by

color or race, a category included in the earlier report, is

not yet available, and will be published in a forthcoming

report in 1974. The new projections are based on the Series

E projections of population, as given in the Current Pop87

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Determining the

labor force status

of men missed

in the census

Recent attempts have been made to obtain

heretofore unavailable social and economic data

about men missed in the census—especially men

from minority groups between 20 and 50 years

of age who are estimated to have high rates of

undercount. The studies, which were conducted

in New Haven, Conn., Central Harlem in New

York, N .Y ., and Trenton, N.J., used a “casual

interview” technique. This approach consisted of

interviews in bars, poolrooms, restaurants, on

street comers, park benches, and similar

locations. This article discusses the results of the

new approach, which is one way to obtain more

extensive social and economic data for those

parts of the population that have been difficult

to folly enumerate in censuses.

Background: the undercount

It is estimated that about 3 percent of the

population was missed in the 1960 census. All the

studies undertaken to estimate the number of

persons missed indicate that the undercount rate

(percent of persons missed) varies significantly by

race, age, and sex. The 1960 census enumerated

98 percent of white persons but only 90 percent of

persons of other races,1 according to Census

Bureau estimates.2 The total number of unenumerated persons has been estimated to be 5.7

million, of whom 38 percent were members of races

other than white. Thus, while the number of

uncounted white persons is greater than the number of uncounted persons of other races, the

proportion of white persons missed is considerably

smaller than the proportion of persons of other

races.

Deborah P. Klein is an economist in the Urban Employment Studies Group of the Office of Manpower and

Employment Statistics, Bureau of Labor Statistics.

Special Labor Force Report

describes pilot use of a

new technique for securing

labor force data

in urban poverty areas

DEBORAH P. KLEIN

Completeness of coverage varies by age and sex,

as well as by race and ethnic group. Coverage is

proportionately better for children than adults,

and better for females than for males. A relatively

large number of persons over 65 years of age were

missed. The highest rates of undercount were found

among men 20-34 years old and 50-54 years.

Women’s undercount rates are lower than men’s

at every age below 50, and it is possible that age

misstatement accounts for part of the undercount

for women at the older ages.

The census data provide benchmarks for preparing monthly population estimates between censuses. These estimates are used to weight the data

from the Current Population Survey (cps) which

provide monthly statistics on economic characteristics of the population. Thus, any undercount

in the decennial census is transmitted to the intercensal statistics and may affect the reliability of

the published labor force data.

The Bureau of Labor Statistics tried to quantify

the possible effect of the undercount on national

unemployment rates. Two different assumptions

about the labor force status of uncounted persons

were used to analyze population data that had

been adjusted for the estimated undercount.8

Under the “comparability” assumption, missed

persons were assigned the same labor force status

as counted persons in the same age-sex-color group.

Under the “poverty-neighborhood” assumption,

missed individuals were assigned the characteristics of persons living in^ urban poverty areas and

in the same age-sex-color cell.

Regardless of which assumption was used, the

resulting estimates of labor force size and employment were substantially larger when account was

taken of the missed persons. Distributions by age,

sex, and color changed only slightly, but the levels

were higher than those indicated by the published

From the Review of March 1970 88

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figures. The national unemployment rate was not

appreciably different from the published one under

either assumption. It would have required a very

high undercount rate, coupled with a grossly

higher unemployment rate among the uncounted

persons, for the national published unemployment

rate to have been significantly in error (table 1).

In some local areas, the undercount may constitute a greater proportion of the population than

it does in the Nation as a whole. In these areas,

including the estimated undercount might make a

significant difference in labor force data as well as

population data. It has been suggested that

undercount rates are highest in crowded urban

poverty areas and sparsely populated rural areas.

Particular concern has been expressed about the

quality of the population and labor force estimates

for the N ation’s largest cities. At the city level we

do not know what percentage of the population is

missed and what the characteristics of these

missed people are. The demographic analysis which

is used to obtain national estimates has not been

done on a local level, primarily because adequate

birth, death, and migration rates are available only

on a national basis.

Bureau research on missed persons

The issue of severe unemployment in urban

poverty areas highlighted the fact that failure to

obtain information about all residents could significantly affect the labor force data for these areas.

Consequently, the Bureau of Labor Statistics

designed a pilot research program to improve statistics for urban poverty areas. This program,

which included among its aims the gathering of

more information about persons not counted in

household surveys in these areas, was conducted in

the spring of 1967. The undercount portion of the

program, which complemented the previous work

of the Census Bureau in this area, was concerned

with identifying the labor force characteristics of

men not enumerated in household surveys, such

as the decennial census or the c p s .

The basic procedures of the b l s undercount

study were to obtain a set of names and addresses

through some source other than a household survey; to determine whether the individual would be

reported in a household survey; and then to compare the characteristics of those individuals reported by the household to the characteristics of

those who were not reported. One source used to

obtain the names and addresses was *'‘casual interviews,” that is, interviews conducted in casual

settings such as bars, poolrooms, and on street

corners. A second source was lists obtained from

establishments, such as restaurants, laundries, and

hospitals, which often hire large numbers of lowpaid workers. Another aspect of the b l s research

project was to compare the effects of conducting

an undercount study in conjunction with a complete census count, and conducting such a study

without a complete population count.

The pilot program was conducted in two areas—

the Negro poverty areas of New Haven, Conn.,

and the Central Harlem area of New York, N.Y.

New Haven was selected because it was the site of

a pretest of the 1970 decennial census. The b l s

research project was timed to follow shortly after

this pretest. In New York (which had had no

recent census), two sources—employers’ lists and

casual interviews—were used to obtain names and

addresses. The target populations in both areas

were Negro men between the ages of 20 and 50

years, because the estimated rates of undercount

were highest for this group.4

In New Haven, where casual interviews were

the only source of names and addresses, the Census

Bureau was able to check the names and addresses

obtained from the casual interviews against the

listing obtained in the census pretest. Followup

interviews were conducted at households where the

names and addresses obtained in the casual interviews could not be matched with records of that

census pretest. These interviews inquired about

the whereabouts of the individual in question. In

New York, the procedure called for a household

interview at every address obtained from either

Table 1. Effect on the unemployment rate of including

omitted persons under selected assumptions, by color

and sex, X967

Unemployment rate

Color and sex Official

estimate

Comparability

assumption

Poverty neighborhood

assumption

Omitted

persons

Adjusted

rates

Omitted

persons

Adjusted

rates

White:

Both sexes.............. 3.4 3.4 3.4 5.1 3.4

Male....................... 2.7 3.1 2.8 4.9 2.8

Female.................... 4.6 3.7 4.6 5.7 4.6

Negro and other races:

Both sexes.............. 7.4 6.0 7.2 6.9 7.3

Male....................... 6.0 4.9 5.9 6.5 6.2

Female.................... 9.1 7.4 9.0 8.1 9.0

Source: Monthly Later teview, March 1969, tables on pages 10,11, and 12.

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the casual interview or the establishment lists. The

household interview was used to determine whether

the person would be listed in a household interview

such as the c p s , and to ascertain whether those

persons not listed as household members were part

of the undercount. This method revealed itself to

be considerably less effective than the method of

conducting such a study in conjunction with a

census count.

The New Haven study identified 39 cases of

persons missed in the census pretest. Obviously,

the number of cases was too small to permit

inferences about the characteristics of all uncounted Negro men in urban poverty areas. The

results, however, were significant in providing some

insight, albeit inconclusive, into the social and

economic characteristics of the undercount, and

into a method which would increase identification

of the uncounted persons. (Even fewer cases were

found in New York.5)

The primary finding of the study was that the

labor force status of the undercount group was

very much like the labor force status of their

neighbors who were counted. (See table 2.) Two

significant differences between the enumerated

group and the undercount group support the

hypothesis that men are missed in census counts

because they do not have family responsibilities

and, as a result, frequently shift their places of

residence. The differences were: (1) the undercounted group tended to have more casual

attachments to their places of residence; that is,

the proportion of those who had lived at their last

place of residence 1 year or less was nearly 4 times

higher for the undercount group than for the

enumerated group, and (2) a large proportion of

the undercount group had never been married. In

addition, the New Haven study suggested that

economic and social characteristics of undercounted Negro men in urban poverty areas could

be identified through the technique of obtaining

names and addresses through casual interviews

following a complete census of the area.

Trenton undercount study

A recent study in Trenton, N.J., employed the

casual interview technique used in the b l s New

Haven Undercount Project already described.

The study was undertaken by the Trenton Model

Cities Agency to gain additional information about

the situation of persons in poverty areas.

The primary finding of the Trenton study

substantiated the tentative conclusions of the New

Haven study—the labor force status of men who

are not counted in a census is similar to that of

men who are counted. (See table 3.)

The Trenton Model Cities Agency used a

slightly revised version of the questionnaire

designed by b l s for use in the New Haven study.

The schedule covered the areas of educational and

marital status, age, place of birth, residential

history, labor force status, occupation, earnings,

and hours worked.

Unlike the New Haven study, in the Trenton

study there was no followup probe at addresses

which were unmatched in the census record. In

New Haven, there had been a complete census

count, a series of casual interviews, a matching of

names, and then a followup household interview.

The address of each person who had not been

enumerated in the census pretest was visited and

the respondent was asked about the individual in

question. If the respondent acknowledged that the

individual did live at the address, then that person

was considered to be part of the undercount. The

Trenton study omitted this followup household

interview. The Census Bureau classified all persons

whose names could not be matched with enumeration lists and whose addresses were within the

enumeration district as persons missed in the

census pretest.

The Trenton survey was about twice as large

as the one in New Haven. Over 900 names and

Table 2. Comparison of selected characteristics of casual

interview respondents in New Haven

(Percent distribution]

Total Persons Persons not matched

persons matched in in census records

Characteristics in census records

casual

study

(men counted

in the census) Men found in Men not found

field followup

(undercount)

in field

followup

Marital status:

Married.................. 48 57 33 40

Separated................. 16 9 18 23

Widowed or divorced. 5 6 0 4

Never married.......... 30 25 46 32

Information not

available............... 2 2 3 1

Years at residence:

1 year or less............ 21 12 46 27

More than 1 year.......

Information not

70 75 51 68

available............... 9 12 3 5

Labor force status:

Employed................. 78 78 77 77

Unemployed............. 11 11 13 12

Unemployment rate..

Not in labor force___

13

8

12

8

14

8

13

7

Information not

available............... 3 3 3 3

Number of responses....... 507 249 39 219

Source: BLS Report 354, pp. 25-26.

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addresses were obtained from casual interviews in

Trenton. These names were divided into three

groups. The first group consisted of 283 names that

were matched with the census lists; that is, men

who were enumerated in the census. The second

group consisted of 290 names that could not be

matched with census lists but whose addresses were

within the city limits. This group was considered to

be part of the undercount. The third group (350

names) contained persons whose enumeration

status was unclear; they may or may not have

been enumerated. Included in this group were

schedules that could not be matched because of

problems in address classifications and schedules

which arrived past the deadline for Census Bureau

checking. There were about 150 additional schedules that could not be classified because their

addresses were outside Trenton city limits.

Duplicate schedules (which typically occurred

when more than one enumerator interviewed the

same individual) were also excluded from the

tabulations.

When the characteristics of the persons interviewed in a casual setting and counted in the

census were compared with the characteristics

of the persons interviewed in the same area and

not counted in the census, the general finding was

that the two groups were very similar in regard to

labor force status. The unemployment rate for

the missed individuals was almost identical to

that of their counted neighbors. Furthermore, the

unemployment rate for the men whose enumeration

status was not known (the men for whom no

census match could be made) was about the same

as the others. The rate of nonparticipation in the

labor force was somewhat larger for the underTabl« 3. Comparisons of labor forco status of casual

intorviaw respondents in Trenton

[Percent distribution]

Labor forct status

Total men

in study

Men classified as

enumerated

in the

census

pretest

Men classified as

part of the

undercount

Men who

could not

be

classified

Employod........................... 83 88 79 85

Unemployed....................... 9 9 9 10

Unemployment rate___ 10 10 10 10

Not in labor force............... 6 4 9 5

Information not available.... 1 1 2 0)

Number of responses.......... 923 283 290 350

i Less than 1 percent

Table 4. Comparisons of selected characteristics of

casual interview respondents in Trenton

(Percent distribution]

Characteristics

Total

men in

study

Men

classified

as

enumerated

in the

census

pretest

Men

classified

as

part of

the

undercount

Men who

could not

be

classified

Afe:

Less than 20 years.............. 6 6 8 5

20-29................................. 38 28 46 40

30-39................................. 24 24 20 28

40-49................................. 26 36 20 23

50 or more......................... 5 6 5 3

Information not available.... 1 <•> 1 <•)

Marital status:

Married............................. 44 58 35 39

Separated, widowed or

divorced......................... 18 13 21 21

Never married.................... 34 26 41 34

Information not available.... 4 3 3 6

Years at residence:

Leu than 1........................ 13 9 14 15

lor 2................................ 22 20 25 21

3 or more........................... 62 69 58 59

Information not available.... 3 2 4 4

Number of responses................. 923 283 290 350

> Less than 1 percent

count group than for the enumerated. However,

the rate was less than 10 percent for both groups.

Another characteristic in which the unenumerated and the uncounted were similar was educational

attainment. Among the men interviewed in the

Trenton study, about 30 percent of each group

had not attended high school and about 65 percent

had not graduated from high school.

Despite the similarity of the two groups in terms

of labor force status and educational attainment,

there were some characteristics in which they

differed. The uncounted group was somewhat

younger, less likely to be married, and more mobile.

(See table 4.) The differences in age distribution,

of course, affected the other characteristics.

Furthermore, the mobility aspects could reasonably be expected to affect enumeration. Young,

unmarried men are more likely to shift their living

arrangements, thus making it difficult to enumerate them.

The Trenton study added another dimension to

the undercount question; it was possible to tabulate the results by race and ethnic group. The

sample was approximately three-quarters Negro;

another fifth were persons with Spanish surnames

(primarily of Puerto Rican birth); the remainder

were other Caucasians, some Orientals, and a few

men of undetermined race. The Spanish surname

group was younger, and less likely to have ever

been married, than the Negro group. M ost of the

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men with Spanish surnames were born in Puerto

Rico; the Negroes were evenly divided between

those born in New Jersey and those born in a

southern State. The persons with Spanish surnames were newer to the area than the Negroes.

In the Trenton study, the unemployment rate

for Puerto Ricans was significantly higher than

the rate for Negroes. The differential was maintained for each of the classification groups,

although the extent of the differential varied. (See

table 5.)

Characteristics of the undercount

Both the Trenton and New Haven studies were

primarily methodological; that is, they were

designed to test the feasibility of using the casual

interview technique to collect data about persons

ordinarily missed in a census of an urban poverty

area. The data obtained from these surveys are not

sufficient to describe the characteristics of all men

living in urban poverty areas—counted or uncounted in a census—because the data were

limited to two areas and we do not know whether

the casual interview technique reaches a representative sample of the local population. However,

some conclusions may be drawn about the relationships between the characteristics of counted and

uncounted persons in urban poverty areas.

The significant social relationships deal with the

ties of counted and uncounted men to a particular

family and residence. In both New Haven and

Trenton, the major difference between the group

of men who would have been enumerated in a

census and those who would not was in the

strength of these ties. (See tables 2 and 4.) For

example, in New Haven, only 33 percent of the

undercount were married, compared with 57

percent of the enumerated. In Trenton, the

percentages were 35 and 58, respectively. Length

of time at current residence is another variable

which may distinguish between the enumerated

and the unenumerated. In New Haven, 46 percent

of the undercount had lived at their current

residence 1 year or less, compared with 12

percent of the enumerated; in Trenton the rates

were 14 and 9 percent, respectively. While the

differences were greater in New Haven, they were

in the same direction as in the larger Trenton

study. The general finding seems to be that a

married man living with his wife at a stable

address is more likely to be reached in a household

survey than a single man who moves frequently.

The significant conclusion that can be drawn

from these studies is that the labor force status of

the uncounted is very similar to that of the counted

in the same urban poverty area. (See tables 2

and 3.) In New Haven, the unemployment rate

for the enumerated was quite close to that of the

uncounted; in the larger Trenton study, the rates

were virtually identical.

Equally important, from the standpoint of

evaluating published unemployment statistics, is

the implication that enumeration of all persons in

an urban poverty area would not significantly

change the unemployment rate for that area—and

perhaps this is true for other areas as well. If this

is true, it would provide greater credence to the

estimates of labor force size and employment prepared by Johnston and Wetzel and discussed

earlier. (See table 1 and discussion on page 26.) The

findings of the studies in Trenton and New Haven

provide evidence that could support either the

comparability assumption or the poverty neighborhood assumption. If similar studies were conducted

in nonpoverty areas, it might become apparent

which assumption is more valid.

Table 5. Labor force status of casual interview respondents, by race or ethnic group, Trenton

(Percent distribution]

Labor force status

Negro men Men with Spanish surnames

Total

Classified as

enumerated

in the census

pretest

Classified as

part of

the undercount

Unclassified Total

Classified as

enumerated

in the census

pretest

Classified as

part of

the undercount

Unclassified

Employed................................................... 85 88 81 88 75 80 73 70

Unemployed............................................... 8 6 9 9 17 19 12 18

Unemployment rate................................ 9 7 10 9 18 19 14 20

Not in labor force......................................... 5 5 8 3 9 1 16 10

Information not available.............................. 1 1 3 0 1 0 0 1

Number of responses................ ................. 695 203 231 261 188 70 51 67

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An evaluation of these studies indicates that

the technique of conducting casual interviews in

conjunction with a complete census count merits

serious consideration in any attempt to collect

data on missed persons. This data collection technique produced, for the first time, information

about the economic characteristics of men missed

in a census.

There are several advantages to the casual interview technique. First, it can reach persons not

usually contacted in household surveys. Whether

the individual is missed because his entire household is not located, because he does not maintain a

a stable relation with any one household, or

because his household chooses not to acknowledge

his presence, the casual technique offers a prospect

of reaching him. Thus, this technique is suitable

for identifying the characteristics of persons subject to various types of undercount. Second, it can

be employed selectively; that is, it can be directed

to a specific group by the designation of the interview locations and instructions to the interviewers.

Furthermore, the questionnaire can be designed

specifically for the selected group. For example,

the choice of language and the approach of the

enumerators can be tailored to fit the target population. Third, the use of the casual interview

technique permits the enumerator to speak directly

to the desired respondent during the initial contact. In household and other random surveys, on

the other hand, the initial contact is often made

with the wife, roominghouse owner, or other person. When a follow up with the desired respondent

is not possible, the data that was obtained from

the secondary respondent is less reliable than data

from the desired respondent would have been.

Fourth, the casual nature of the questioning and

the relaxed atmosphere of the interview locations

may induce candid responses. There is some evidence that this technique can obtain information

of a kind not readily available from household

surveys. For example, the New Haven study

obtained information about illegal activities that

had not been available from regular household

surveys. Fifth, the technique is a relatively inexpensive method of obtaining a large number of

responses in a short period of time. The elimination

of callbacks to locate specific individuals resulted

in a lower cost per schedule than in household

interviews.

Characteristics of the method A major disadvantage of the casual interview

technique is that it does not provide a sample with

a scientifically delineated universe. This makes it

difficult to establish the representativeness of the

survey findings. This objection is partially blunted

when the survey is done in conjunction with a

complete census count. Under these circumstances,

the individuals reached were members of the

census universe, although not necessarily a random

sample of this group. Despite this objection, the

advantages of selectivity, direct access, candid

responses, and low expense appear to make this

technique a useful tool for determining the

characteristics of the undercount.

There is no set requirement for the type of

enumerator to use for casual interviews. In New

Haven, all of the interviewers were men experienced

in field work and familiar with the area of enumeration. In Trenton, the interviewers were young men

and women of various ages with some survey

experience. Both male and female enumerators

were successful. Although experience with using

nonindigenous interviewers in these situations is

limited, a strong case could be made for the use of

interviewers who are indigenous to the area.

Variation in the hours of enumeration served to

prevent labor force bias. It appeared best to

interview during day and evening hours, and over

the weekend where that is possible.

The samples in both New Haven and Trenton

were not designed to be representative of the city

as a whole but rather of specific areas—minority

group poverty areas. The enumerators were

instructed to interview men from minority racial

or ethnic groups between the ages of 20 and 50.

This group was selected because of undercount

rates estimated to be very high. In New Haven,

the 500 men were primarily Negro; in Trenton the

900 men were primarily Negro and Puerto Rican.

The data indicate that in each city about 10

percent of the men had ages outside of the boundaries set. However, this percentage was substantially lower than it would have been had there

been no attempt to restrict the sample.

In each city, the casual interviews were conducted in poor areas, and the sites were such places

as bars, restaurants, poolrooms, street corners, and

park benches. In New Haven, this was done to

increase the percentage of unemployed and

marginally employed men (working in low-skilled,

low-paying jobs) because it had been suggested

that these men constituted a disproportionate

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share of the undercount, whose characteristics

were the focus of these studies. In Trenton, the

sections of the city where interviews were conducted yielded a similar sample of men.

Thus, any differences between the men in each

sample and the total population of their city would

reflect the method of sample selection and would

have no necessary correlation with the social and

economic distribution of the undercount or the

population of that city. However, the characteristics of the sample group are not atypical of other

samples that have been drawn from urban poverty

areas.8

The small sample size and the restricted nature

of the selection process have precluded the drawing

of any definitive conclusions about the characteristics of all persons not counted. We have no way of

knowing whether the characteristics of unenumerated men reached through the casual interview

technique are typical of the entire undercount.

There are two reasons for this uncertainty. First,

the characteristics of the undercount in other

geographic areas, economic strata, or age groups

may be very different from the characteristics of

the undercount in an urban poverty area. Second,

even within an urban poverty area, the technique

of casual interviews may not reach all of the

undercount. For example, there may be some men

who never go to bars or stand on street comers.

However, the quality of the findings that have

been made thus far suggests that additional

studies should be undertaken. The question now

is whether the insights thus far obtained from

studying the undercount among minority groups in

urban poverty areas would be supported in similar

or dissimilar studies of other groups in other areas.

Wider application of the method described above

may bring us closer to obtaining a better definition

of the characteristics of the undercount, better

understanding of the reasons for the undercount,

insight into techniques that might reduce the

extent of undercount, and a better appreciation of

published data that is affected by the

undercount. 

■FOOTNOTES

1 R efers to N egroes, Orientals, and A m erican Indians.

N ationw ide, N egroes m ake up about 92 percent of races

other than w hite, and a higher proportion in urban povertyareas.

* For sources of estim ates and m ore detail, see Jacob S.

Siegel, “ C om pleteness of C overage of the N on w hite Population in th e 1960 C ensus and Current E stim ates, and

Som e Im p lication s,” in D avid M . Heer, ed., Social Statistics

and the City (C am bridge, M ass., Join t C enter for U rban

Studies of M assachusetts In stitu te of T echnology and

H arvard U n iversity, 1968). A sum m ary of th e m ethods

used to estim ate th e exten t of th e undercount w ill be found

in b l s R eport 354, Pilot and Experimental Program of the

Urban Employment Survey. For a more detailed description,

see Jacob S. Siegel and M elvin Zelnik, “ An E valuation of

C overage in th e 1960 C ensus of P opulation by T echniques

of D em ographic A nalysis and by C om posite M ethods,”

1966 Proceedings of the Social Statistics Section, American

Statistical Association; Leon Pritzker and N . D . R oth well,

“ Procedural D ifficulties in T aking P ast Censuses in Predom inantly N egro, Puerto R ican, and M exican A reas,”

and E li S. M arks and Joseph W aksberg, “E valu ation of

C overage in th e 1960 C ensus of Population through Caseby-C ase C hecking,” in D a v id M . Heer, ed., op. cit.

* See D enis F. Johnston and Jam es R . W etzel, “ E ffect

of th e Census U ndercount on Labor Force E stim a tes,”

Monthly Labor Review, M arch 1969, pp. 3 -1 3 .

4 For a detailed description of th is research, see b l s

R eport 354, cited in footn ote 2.

* In N ew York only three cases of undercount w ere identified. B ecause of th is sm all num ber and because of th e large

num ber of unlocated addresses, m eaningful com parisons

betw een found and m issed persons could n ot be m ade.

There w as considerable difficulty in locating apartm ent

dw ellers in th e m ultiunit ten am en ts w ith poor or nonexisten t ten an t identification typ ical of th e p overty areas

in N ew York and other large cities.

6 T ables providing detailed data on th e characteristics

of the respondents in th e T renton stu d y are availab le from

th e Bureau of Labor S tatistics.

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Discouraged

workers and

changes in

unemployment

U n t i l a couple of decades ago, the many millions

of working-age persons outside the labor force were

of limited concern to labor economists and policymakers, either as a potential source of manpower or

as a possible threat to the stability of the job market.

It was then the general assumption that the Nation’s

labor supply consisted only of persons actually working or actively seeking work. The notion that many

persons outside the labor force might have wanted

work but were not seeking it because they believed

that their search would be fruitless was not widely

entertained.

This popular concept of the labor supply was

probably relevant in the 1930’s, when the ranks of

the unemployed contained an apparently inexhaustible reservoir of. manpower. It had to be gradually

abandoned, however, as evidence accumulated during the post-World War II period showed that millions of persons entered and left the labor force

each year, not only because of personal reasons but

also in apparent response to changing labor market

conditions.

Recognizing these facts, the President’s Committee

to Appraise Employment and Unemployment Statistics (more familiarly known as the Gordon Committee) stated in 1962 that “the relatively simple

dichotomy between those in and out of the labor

force . . . [no longer provides] . . . a satisfactory

measure of the labor supply.” The Committee went

on to recommend that special efforts be made,

through the Current Population Survey (CPS), to

collect detailed data on persons not in the labor

force, particularly on the so-called “discouraged

workers” or “hidden unemployed”— those persons

who want work but are not looking for a job beFirst time series analysis of data from the 

Current Population Survey indicates 

the number of discouraged workers 

rises as unemployment increases

PAUL 0. FLAIM

cause of a belief that their search would be in vain.

In so doing, it should be noted, the Committee also

recommended that these persons not be included in

the unemployment count.

In 1964-66, following the recommendation of

the Gordon Committee, the Bureau of Labor Statistics began to experiment with a special set of

survey questions designed to elicit detailed information on the reasons persons outside the labor

force did not participate in the job market. In January 1967, these questions were incorporated into the

regular CPS questionnaire. The data which they have

yielded have been published quarterly since late

1969 in a special set of tables in the monthly BLS

periodical, E m ploym en t and Earnings.

The earlier analyses of these findings were, by

necessity, limited to cross-sectional examinations

done in snapshot fashion. Obviously, no time-series

analysis could have been undertaken until a number

of years had elapsed. Moreover, the first 3 years of

data were collected in a period of very low unemployment, so that one could hardly draw any conclusion about their cyclical sensitivity.

Since the data have now been accumulated for 6

years— the last 3 years being a period in which vast

cyclical changes took place in the Nation’s economy

—it is possible to determine, at least tentatively, to

what extent workers will refrain from entering the

job market or may be induced to leave it because of

rising unemployment. Two variables are of particular interest for this purpose: (1) the number of “discouraged workers,” and (2) the number of workers

leaving the labor force because of “slack work,”

who may or may not wind up as “discouraged

workers” under current definitions.

Paul O. Flaim is an econom ist in the D ivision o f Em ploy- Identifying those discouraged

ment and U nem ploym ent Analysis, Bureau o f Labor Statistics. This article is based on a paper presented at the

August 1972 meeting o f the American Statistical Associa- Determining the extent of discouragement over

tion in Montreal, Canada. job prospects is a very difficult task. It involves the

From the Review of March 1973

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measurement of what are essentially subjective phenomena, specifically one’s desire for work and one’s

perception of his or her chances of obtaining a job.

The pinning down of these “states of mind” is rendered particularly uncertain by the fact that the

housewife is typically the only person interviewed in

each CPS household, and she must answer for all

members of the household.

Even if interviewed individually, however, some

persons may still not always admit their “real”

reason for leaving the labor force. It is possible, for

example, that some, although having been unable to

find a job, may attribute their nonparticipation status

to ill health or other “socially acceptable reasons”

rather than admit that they have failed in the job

market. Conversely, there may be some who indicate

that they want a job and who then explain their failure to look for one in terms of unavailability, even

though their desire for work is actually of very

limited intensity. Given the subjective and elusive

nature of “discouragement,” the extent of its possible overstatement or understatement cannot be

measured.

In order to identify the discouraged workers, the

CPS interviewer asks first if the persons not in the

labor force “want a regular job now, either full time

or part time.” If the answer is yes, or even a tentative yes, there is a follow-up question as to the

reasons they are not looking for work. In order to

be classified as discouraged, a person’s principal

reasons for not looking for work must fall into one

of the following five categories:

1. B elieves no w ork available in line o f w ork or area;

2. H ad tried but co u ld not find work;

3. L acks necessary sch oolin g, training, skills, or ex ­

perience;

4. E m ployers think to o y ou n g or to o old;

5. O ther personal handicap in finding a job.

It may be argued that this screening process, particularly the requirement that a person must first be

reported as wanting a job in order to be questioned

about possible discouragement, yields a rather restrictive definition of hidden unemployment. What

about those persons, one might ask, who, upon losing

their job, may decide to return to school and who

would then not want a job “now”? Should they not

also be regarded as discouraged workers? To answer

this, it should be noted that if the discouraged workers’ data are to be useful as a measure of underutilization of manpower for policy purposes, they should

hardly include persons who do not want a job,

especially when their current activity may actually

prevent them from taking a job.

It is also important to note that separate data are

collected and published, from the same survey, on

the reasons for leaving the last job for those persons

who have recently left the labor force. As will be

discussed later, these “flow” data are an important

adjunct to the figures on discouraged workers in

terms of understanding the dynamics of the labor

force under changing economic conditions.

How many discouraged workers?

The first examinations of the data on persons not

in the labor force, based on 1967-68 findings,

showed that less than one-tenth of these persons professed any desire to be holding a job.1 Among these,

only about 700,000 were classified as discouraged—

that is, as not looking for work because of a belief

that they could not find a job. As shown in table 1,

the other nonparticipants reported as wanting a job

turned out either to be in school, in poor physical

condition, or prevented from seeking work by household responsibilities. The ranks of the 700,000 discouraged workers, furthermore, were found to contain relatively few men of prime working age— less

than 200,000. The great majority of discouraged

consisted, instead, of teenagers, housewives, and

elderly persons. These findings seemed to run counter

to the contentions that there were virtually millions

of discouraged workers and that they included large

numbers of men.2

However, the data being analyzed in the late

1960’s had been collected in a period of unusually

low unemployment, when the jobless rate held below

4 percent. Not until 1970, when unemployment rose,

could the relationship between changes in the unemployment rate and in the number of discouraged

workers be discerned.

That the rise in unemployment in 1970 produced

‘Hidden unemployment’

In this issue, three articles and a bibliography

deal with “hidden unemployment,” a problem

that is also referred to as a “manpower gap,”

or “discouraged workers.” The authors recognize that hidden unemployment may mean different things to different researchers. Consequently, each sets forth what the concept means

within the context of his analysis.

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Table 1. Distribution of persons not in the labor force, by reason, 1967-72

[Numbers in thousands]

Labor force status 1967 1968 1969 1970 1971 1972

Civilian noninstitutional population.......... ........................................................................ 129,873 132,026 134,335 136,995 139,775 143,325

In civilian labor force...... ............................................................................................ 77,347 78,737 80,734 82,715 84,113 86,542

Not in the labor force........ .......................................................................................... 52,484 53,289 53,596 54,275 55,662 56,783

Do not want job now, total................................................................................. 47,787 48,810 49,137 50,396 51,259 52,321

Current activity: 1 n school...... .................................................................. 5,641 5,892 5,958 6,051 6,373 6,301

Ill, disabled..................................................................... 3,741 3,684 3,826 3,869 4,077 4,313

Homemaker..................................................................... 31.239 31,667 31,384 32,162 32,203 32,384

Retired, old..................................................................... 5,313 5,540 5,795 5,918 6,160 6,691

Other................................................................................ 1,853 2,027 2,174 2,396 2,446 2,632

Want a job now, total.............. ....................................................................... 4,698 4,477 4,459 3,877 4,404 4,461

Reason not looking: School attendance......... ......................................... 1,104 1,115 1,126 1,075 1,242 1,200

Ill health, disability................................................. 768 656 627 489 555 632

Home responsibilities............................................... 1,325 1,263 1,257 926 1,020 1,098

Think cannot get job............................. .................. 732 667 574 638 774 765

All other reasons...................................................... 769 777 875 749 813 766

NOTE: Because of separate computation, the figures on the civilian labor force 

and on persons not in the labor force may not in all cases add up precisely to the

at least a temporary slackening in labor force participation is now a historical fact. The slackening

was most evident in the first half of 1971, when the

labor force hardly grew at all. The question is the

extent to which this slackening in participation can

be attributed to discouragement over job prospects

caused by the rise in unemployment.

As chart 1 shows, there is, indeed, a positive relationship between the unemployment rate and the

number of discouraged workers. Both series trended

downward, though in differing degrees, during the

1967-69 period; both rose substantially during 1970;

both showed little distinct movement during 1971;

and both moved downward during 1972. In terms

of the actual number of persons involved, however,

it should be noted that the 1969-71 increase in the

number of discouraged workers was relatively small

when compared with the rise in the number of jobless persons. While the number of unemployed rose

by 2.2 million between 1969 and 1971 (on an annual

average basis), the number of discouraged workers

increased by only 200,000.

Despite the positive relationship between unemployment and discouragement, the two series did not

correlate very highly with each other. The coefficient

of correlation between these two variables, derived

on the basis of seasonally adjusted monthly data for

the 1967-71 period,3 was only 0.53. Nor was the

coefficient raised when the relationship between the

two series was tested on the basis of data disaggregated by age, sex, and race. (Correlation and regression results are shown in appendix table 1.)

Since it may be reasonably assumed that changes

civilian noninstitutional population.

in the number of discouraged workers lag behind

the changes in the unemployment rate, some experimentation with lags was also conducted. By lagging

the discouraged workers’ series by 3 and also by 6

months behind the unemployment rate, the coefficients of correlation were raised somewhat—to 0.61

in both cases—but were still far from indicating a

very close relationship between the two variables.

‘Cyclical' vs. 'structural' discouragement

A closer examination of the disaggregated data on

discouraged workers for the 1967-71 period revealed a significant change in composition in terms

of the specific reason cited by these persons for their

belief that they could not obtain a job. Specifically,

there was an increase in the proportion of workers

whose discouragement appears to have been directly

related to the changing conditions of the job market.

Conversely, there was a decline in both the number

and proportion of persons attributing their discouragement to personal situations or deficiencies.

Table 2 groups discouraged workers into these two

broad categories. The first included the workers reported as believing that there were no jobs in their

line of work or area and those who had tried unsuccessfully to find a job before giving up the search.

The second category includes those workers reported

as thinking they could not get a job due to their very

young or advanced age, those who saw their lack of

education or training as the major obstacle, and those

who cited other personal handicaps, Such as language difficulties.

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It would appear, given the different nature of the

reasons for discouragement, that the first category

of discouraged workers should be much more cyclically sensitive than the second. Discouragement

among the second category appears to be more of a

“structural” nature and thus not necessarily related

to the tightness, or looseness, of the job market. The

data in table 2 confirm this hypothesis. As shown, all

of the 200,000 increase in the number of discouraged

workers between 1969 and 1971 took place among

those blaming their situation on job-market weaknesses.

Chart 1. Unemployment rate and number of discouraged

workers, 1967-72

In thousands

900 ---------

Number discouraged

because Qf personal

reasons 1

0 L I I I 1 I I I 1 I I I 1 I I I I I I 1 1.1 1,1

Percent

Table 2. Composition of discouraged workers by reason

for believing they cannot find a job, 1967-72

[Numbers in thousands]

Reason 1967 1968 1969 1970 1971 1972

Total.............................. 732 667 574 638 774 765

Job-market factors..................... 383 371 311 437 537 540

Had looked but could not

find job........................... 168 161 161 244 300 300

Thinks no job available....... 215 210 150 193 237 240

Personal factors......................... 349 297 263 201 236 226

Employers think too young

or too old........................ 216 171 139 105 112 111

Lacks education, skills,

training........................... 84 74 78 60 85 78

Other personal handicap___ 49 52 46 36 39 37

Percent distribution.......... 100.0 100.0 100.0 100.0 100.0 100.0

Job-market factors..................... 52.3 55.5 54.2 68.5 69.5 70.6

Had looked but could not

find job............................ 23.0 24.1 28.0 38.2 38.8 39.2

Thinks no job available....... 29.4 31.4 26.1 30.3 30.7 31.4

Personal factors......................... 47.7 44.5 45.8 . 31.5 30.5 29.5

Employers think too young

or too old........................ 29.5 25.6 24.2 16.5 14.5 14.5

Lacks education, skills,

training.................... ...... 11.5 11.1 13.6 9.4 11.0 10.2

Other personal handicap___ 6.7 7.8 8.0 5.6 5.0 4.8

NOTE: Because of rounding, sums of individual items may not equal totals.

Correlation analysis also lent support to this hypothesis. Whereas, as noted above, the total number of

discouraged workers did not correlate highly with the

overall unemployment rate, yielding a coefficient of

only 0.53, the number of workers discouraged because of job market reasons yielded a much higher

correlation coefficient — 0.79 — when regressed

against the unemployment rate.

On the other hand, when the number of persons

whose discouragement seemed to hinge on personal

factors was regressed against the unemployment rate,

the result was a negative coefficient of correlation—

—0.47. There is no ready explanation for this negative relationship, but some possibilities may be raised.

For example, the passage of legislation designed to

reduce job discrimination because of age may have

accounted for a downward trend in the number of

elderly workers who thought that they could not get a

job due to their advanced age. It may also be hypothesized that when the unemployment rate rises, some

workers who had previously been attributing their

discouragement to personal reasons may then attribute their situation to the deteriorating job market.

It is clear, nevertheless, that if we limit our analysis to the group of discouraged workers who link

their situation to the conditions of the job market,

we find that their number did increase and decrease

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in line with the underlying movement of the unemployment rate during the 1967-71 period.

Unexpected discontinuity

One of the findings from the 6 years of experience

in obtaining statistics on labor force nonparticipants

is that it apparently makes quite a bit of difference

whether the questions about current desire for work

and future jobseeking plans are asked in the first

month in which they are visited by the CPS interviewer or in subsequent months.

Since a person’s reasons for nonparticipation in

the labor force are not likely to change from one

month to another, this information is asked in only

one of the four consecutive monthly interviews conducted in households falling in the CPS sample.

From 1967 through 1969, the questions were asked

in the month in which a given household first entered

the CPS sample and then again 1 year later, when

the same household reentered the sample for the

second and final 4-month stint after an 8-month

hiatus. In January 1970, the questions were switched

from the first and fifth month-in-sample to the fourth

and eighth. In effect, instead of being asked when a

household enters or reenters the sample, they are

now being asked only when the household leaves the

sample.4

This switch turned out to have a noticeable effect

on the data on persons not in the labor force. Following the switch, proportionately fewer persons,

particularly among the housewives, were reported as

either wanting a job at present or as planning to look

for work in the near future. Evidently, having become increasingly more at ease with the interviewer

with each passing month, a respondent is less likely

to exaggerate his (or her) attachment to the labor

force in the fourth monthly interview than in the

first one.

As far as the data on discouraged workers are

concerned, the switch appears to have caused a small

drop in the number and proportion of persons attributing their discouragement to personal reasons (a

factor which no doubt contributed to the negative

relationship between this variable and the unemployment rate). Although this discontinuity did not have

a great effect on the overall numbers, it is a good

illustration of the difficulties which arise in the measurement of what are essentially attitudes on the part

of workers or potential workers.

Profile, 1972

As was the case during the first years for which

data on discouraged workers are available, the proportion of men of prime working age among this

group is still relatively small. Of the 765,000 persons

classified as discouraged workers in 1972, only about

70,000, or less than one-tenth, were men 25 to 59

years of age. (See table 3.)

Blacks are even more overrepresented among the

discouraged workers than they are among the unemployed. They made up only one-ninth of all the

persons of working age outside the labor force but

one-fourth of the discouraged workers in 1972.

(Blacks also make up one-ninth of the civilian labor

Table 3. Discouraged workers by time elapsed since last job and jobseeking intentions, 1972

Percent distribution by time elapsed since last lob Percent who

Total Intend to

Sex, age, and color discouraged seek work

(In thousands) Total Less than 1 to 5 More than Never within

1 year years 5 years worked 12 months

Total 16 years and over......................................................... 765 100.0 36.3 30.6 19.0 14.1 77.1

Male, 16 years and over........................................................ .......... 240 100.0 45.0 30.0 9.6 15.4 77.1

16-19 years............................................................................... 64 100.0 40.0 12.3 1.5 46.2 84.4

20-24 years............................................................................... 33 100.0 0 0 0 0 0

25-59 years............... .............................................................. 67 100.0 55.2 32.8 9.0 3.0 83.6

60 years and over..................................... .............. 75 100.0 37.3 42.7 18.7 61.3

Female, 16 years and over................................................................ 525 100.0 32.4 30.9 23.2 13.5 77.0

16-19 years........................................ 68 100.0 35.3 10.3 54.4 82.4

20-24 years............................................................................... 79 100.0 44.3 32.9 3.8 20.3 89.9

25-59 years............................................................................... 299 100.0 29.8 33.4 32.1 5.4 77.9

60 years and over...................................................................... 79 100.0 28.8 37.5 30.0 3.8 55.7

White*................. 578

Negro and other races * 188

1 Percent distribution not shown where base is less than 50,000. * Breakdown of discouraged workers in terms of time elapsed since last job and

future job-seeking intentions is not available separately for whites and Negroes.

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force and account for about one-fifth of the unemployed.)

In terms of previous work history, nearly (two-fifths

of the discouraged workers had been out df the job

market less than 1 year when interviewed. Only 14

percent had never worked before. These findings,

however, differ by age and sex.

Evidently, most discouraged workers regard their

status as only temporary. Although they do not deem

it worthwhile to look for a job at the time of the

interview, they are apparently more hopeful in terms

of their future prospects. Nearly 80 percent of the

total were reported as planning to actively seek work

within the next 12 months. It would thus be erroneous to assume that most discouraged workers have

permanently given up on the job market.

Examining out-flows

While the data on discouraged workers may not

fully explain how cyclical changes in the employment

situation affect the dynamics of the labor force, other

data gathered through the same survey shed additional light on this phenomenon. For example,

through the special set of questions asked of the nonparticipants since 1967, it has been possible to group

those with recent work experience according to their

reasons for leaving their last job—regardless of

whether or not they are currently counted as discouraged workers.

Unlike the questions designed to identify the discouraged workers, those designed to determine when

and why a person left his last job deal with facts

which are of a more overt, observable nature. As

such, these questions should present fewer problems,

particularly when the respondents, as is often the

Table 4. Persons exiting from the labor force, by reason

for leaving last job, 1967-72

Reason for leaving 

job during previous 

12 months

1967 1968 1969 1970 1971 1972

Number (in thousands).................. 9,327 9,752 10,175 10,130 10,098 9,624

Percent........................ 100.0 100.0 100.0 100.0 100.0 100.0

School, home responsi-

. bilities______________ 49.2 50.3 50.5 49.3 47.7 46.8

III health, disability........... 9.5 9.2 9.6 8.9 8.7 9.1

Retirement, old age_____ 5.3 6.0 6.1 6.7 7.4 8.1

Economic reasons............ . 17.1 17.8 16.6 18.0 19.5 19.3

End of seasonal job... 9.2 9.1 8.5 8.1 8.5 8.6

Slack work_______ 3.3 3.1 3.1 4.3 5.2 4.9

End of temporary job. 4.6 5.6 5.1 5.7 5.8 5.8

All other reasons............ 18.9 16.7 17.2 17.2 16.7 16.7

case, are persons other than those to whom the questions relate. The “flow” data which these questions

produce should thus be subject to less response error

than those on the actual number of discouraged

workers.

As shown in table 4, the volume of the gross flows

out of the labor force has not changed much in recent

years, averaging close to 10 million. This would

indicate that the cyclical changes in labor force

growth during this period have stemmed primarily

from fluctuations in the in-flow of new entrants and

reentrants into the job market. There have been,

however, some cyclical changes in the composition

of the out-flows by reasons for leaving last job.

Chart 2. Unemployment rate and percent of persons

leaving labor force because of “slack work," 1967-72

Percent

Percent

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Specifically, there was an increase between 1969

and 1971 in the proportion of persons attributing

their exit from the labor force to the fact that their

jobs had been terminated, either temporarily or permanently, due to economic reasons. Of the three

categories under the “economic” heading, “slack

work” appears to have been most cyclically sensitive. As illustrated on chart 2, the changes in this

variable have been closely related to the changes in

the unemployment rate.

This relationship was also tested through regression and correlation analysis. The coefficient of correlation between the overall unemployment rate and

the number of persons reporting they had left the

labor force after having lost their jobs due to slack

work was 0.83 on the basis of monthly data for the

1967-71 period. The substitution of data on unemployment due to job loss for the overall measurements of unemployment yielded coefficients of

roughly similar magnitude. (See appendix table 2.)

Summary and conclusion

After 6 years of experience in the collection of

data on discouraged workers through the Current

Population Survey, it appears that this survey is,

indeed, a viable vehicle for such a purpose. Although the definition of “discouragement” used for

the purposes of the survey might not be universally

agreed upon, the data gathered so far have shed

important light both on the discouraged-worker phenomenon and other aspects of labor force dynamics.

While 6 years of data may not be sufficient to

enable us to establish with any certainty the relationship between two variables, the hypothesis that

changes in the number of discouraged workers are

closely related to changes in the unemployment rate

can now be verified at least tentatively. The same

can also be said for changes in the number of workers leaving the labor force because of slack work.

To the extent that this is true, it would appear that

we should take into account these variables, as well

as the data on unemployment and underemployment,

when assessing the waste of manpower which accompanies an economic recession. In the most recent

slowdown, however, the increase in the number of

discouraged workers, as currently defined, was relatively small when compared with the magnitude of

the changes in unemployment. That being the case,

it would be unreasonable to assume that the return

of these workers to the job market as economic conditions improve could be of such magnitude as to act

as a major brake against the lowering of the unemployment rate. 

----------FOOTNOTES----------

1 See Robert L. Stein, “Reasons for Nonparticipation in

Labor Fcvce," Monthly Labor Review, July 1967, pp. 22-7,

and Paul O. Flaim, “Persons not in the labor force: Who

they are and why they don’t work,” Monthly Labor Review,

July 1969, pp. 3-14.

2 These contentions were based largely on econometrically

derived estimates of hidden unemployment published in

various journals during the mid-1960’s. Among the first to

construct such estimates were Alfred Telia and Thomas

Dernbu.g and Kenneth Strand. See A. Telia, “The Relation

of Labor Force to Employment,” Industrial and Labor Relations Review, April 1964, pp. 454-69, and T. Dernburg and

K. Strand, “Cyclical Variation in Labor Force Participation,” Review of Economics and Statistics, November 1964.

Many other economists, using a variety of econometric techniques, have since undertaken similar research. Essentially,

they have attempted to measure the elasticity of labor force

participation rates, especially for women and youth, in response to the intensity of the demand for labor as reflected

by the unemployment rate, the wage rate, and other variables. Optimal participation rates, those consistent with conditions of “full employment,” were then applied to the

population to obtain a “full employment labor force.” To

the extent that the actual labor force, as measured through

the Current Population Survey, fails to match this theoretical

labor force, they ascribe the gap to the discouraged workers

phenomenon or hidden unemployment.

For an analytical discussion of the early econometric estimates of “hidden unemployment” or “discouraged workers,”

see Jacob Mincer, “Labor Force Participation and Unemployment: A Review of Recent Evidence” in R. A. Gordon

and M. S. Gordon, eds., Prosperity and Unemployment

(New York, Wiley, 1966), pp. 73-112. For a comparison

of the more recent econometric estimates with the survey

data presented in this article, see the discussions by Joseph

Gastwirth and Jacob Mincer elsewhere in this issue.

3 Although the not-in-the-labor-force data are published

only on a quarterly basis, they are being tabulated monthly.

They have also been seasonally adjusted experimentally,

although not yet regularly published in this form.

4 The switch was instituted in an attempt to determine

whether these added questions were having an effect on the

so-called “first-month bias” in the unemployment figures.

It had long been evident that the reported incidence of joblessness in households entering or reentering the CPS sample

was higher than in households which had been in the sample

for 2 consecutive months or more. This “first-month bias”

became even larger around 1967, and it was hypothesized

that the increase might have been related to the introduction

of the not-in-the-labor-force questions. The reduction in the

reported incidence of unemployment for the first and fifth

month-in-sample groups and concomitant rise for the fourth

and eighth following the January 1970 switch of the not-inthe-labor-force questions seems to have amply confirmed

this hypothesis.

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APPENDIX

In order to determine quantitatively to what extent workers may refrain from entering the labor

force or may be induced to leave it because of rising

unemployment, recourse was made to regression

analyses. A large number of simple, or two-variable,

regressions were run, with the variables consisting

in each case of 60 monthly observations covering

the 1967-71 period.

The independent variable (X ) consisted in all

cases of seasonally adjusted observations concerning

the unemployment situation, either at the aggregate

or disaggregated level. The dependent variable (Y )

consisted of observations concerning either the number of “discouraged workers” or the number of

workers having left the labor force after being laid

off because of economic factors affecting their jobs.

As was the case with the unemployment data used

for the independent variable (X ), all observations

on the number of discouraged workers had also been

seasonally adjusted. The data on the number and/or

percent of persons who had left the labor force because of various economic reasons, on the other

hand, were not seasonally adjusted. Although these

latter data are obtained monthly, they refer to exits

from the labor force occurring over a 12-month span.

As such, these data are, in effect, 12-month moving

averages, which should be relatively devoid of seasonal fluctuations.

The result of a selected number of regression

analyses focusing on the relationship between unemployment and the number of discouraged workers

are shown in appendix table 1. As shown, it is only

when the number of discouraged workers is reduced

to include only those whose discouragement is directly attributable to job market reasons, and which

is thus of cyclical nature, that the regression yields a

reasonably good fit— as denoted by relatively high

values of the coefficient of correlation (r), the coefficient of determination (r2), and the T-Ratio.

A smaller number of regressions were run speAppendix table 1. Regression of selected categories of discouraged workers against various measurements of unemployment

Variables Regression results

Independent(X) Dependent(Y)

Unemployment rate, overall.........................

Unemployment rate, men age 20 or over... 

Unemployment rate, women age 20 or over

Unemployment rate, teens 16-19.................

Unemployment rate, overall.......................

Unemployment rate, overall.........................

Persons unemployed 15 weeks and over__

Average duration of unemployment.......... .

Unemployment rate, overall........................

Unemployment rate, overall.........................

Unemployment rate, overall.........................

Unemployment rate, overall.........................

Discouraged, total.

Discouraged, men age 20 or over.

Discouraged, women age 20 or over

Discouraged, teens 16-19.................

Discouraged, total lagged 3 months.

Discouraged, total lagged 6 months .

Discouraged, total.............................

Discouraged, total.............................

Discouraged, job market reasons...

Discouraged, personal reasons.........

Discouraged, job market reasons

lagged 3 months..........

Discouraged, job market reasons

lagged 6 months..........

Regression

equation

r r* s T ratio DurblnWatson

Y=472 + 47. OX 

(43.7) (10.0)

0.53 0.28 77.8 4.70 0.88

Y=145.6 + 6.8X 

(11.8) (4.1)

0.21 0.05 29.8 1.66 1.51

Y=257.4 + 31.7X 

(44.1) (9.9)

0.39 0.15 _ 62.2 3.18 1.31

Y=-14.4 + 9.4X 

(31.9) (2.3)

0.47 0.22 35.8 4.10 1.90

Y=432.7 + 57.5X 

(42.2) (9.9)

0.61 0.36 72.7 5.80 1.02

Y=413.2 + 63.8X 

(44.9) (10.8)

0.61 0.37 72.3 5.90 1.08

Y= 568.1 +142.3X 

(20.2) (26.4)

0.58 0.34 74.5 5.41 0.98

Y=285.6 + 43.IX 

(65.6) (7.2)

0.62 0.38 71.9 5.97 1.18

Y= 77.2 + 76.OX 

(33.8) (7.7)

0.79 0.62 60.3 9.81 1.54

Y=387.8 - 27.IX 

(31.2) (7.1)

-0 .4 5 0.20 55.5 -3 .8 0 0.67

Y= 58.6 + 82.4X 

(37.7) (9.1)

0.81 0.65 58.0 10.41 1.73

Y= 44.1 + 88. IX

(37.7) (9.1)

0.79 0.62 60.7 9.70 1.61

102

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Appendix table 2. Regression of selected categories of workers leaving labor force against various measurements ol

unemployment

Variables

Independent(X) Dependent (Y) Regression

equation

r r* s T ratio DurblnWatson

Unemployment rate................................................ Total leaving due to economic reasons___ Y=1271.4+113.5X 

(68.7) (15.7)

0.69 0.47 112.3 7.2 1.17

Unemployment rate.............................................. Left due to slack work................................... Y= 2 .1 + 86.3X 

(33.0) (7.6)

0.83 0.69 58.8 11.4 1.92

Number of unemployed who lost last job............ Left due to slack work................................... Y= 140.7+ 0.15X 

(21.8) (1.46)

0.82 0.67 60.6 10.9 1.88

Job-losers rate............................................ ........... Left due to slack work as percent of total 

leaving labor force.

Y= 1.4+ 1.3X 

(0.2) (0.1)

0.82 0.67 0.6 11.0 2.04

Regression results

cifically to determine the relationship between

changes in unemployment and in the flow of workers

out of the labor force following a job loss stemming

from economic factors in general and slack work in

particular. Results are shown in appendix table 2.

These equations indicate that there is, indeed, a

rather close and positive relationship between

changes in unemployment and in the number and

proportion of workers who drop out of the labor

force after losing their jobs. □

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Education

of workers:

projections

to 1990

T h e l a t e st p r o j e c t io n of the educational attainment of adult workers points to an accelerated rise

in the number of college graduates and a rapid decline in the number of workers at the lower end of

the educational ladder.1 Starting with the March

1970-72 average, the number of workers 25 years

and over with different amounts of formal education, as shown in table 1, is projected to change as

follows:

— W ith 4 years o f college or more— from 9.6 million

to 14.3 m illion by 1980 and 21.8 million by 1990,

increasing from 14.6 percent to 23.8 percent o f the

civilian labor force.

— W ith 1 to 3 years o f college— from 7.9 m illion to

10.8 million by 1980 and 15.0 million by 1990,

increasing from 12 percent to 16.4 percent o f the

labor force.

— W ith 4 years o f high school— from 24.6 million to

31.4 million by 1980 and 37.7 million by 1990,

from 37.5 percent to 41.2 percent of the labor

force.

— W ith 1 to 3 years o f high school— from 11.1 million to 11.7 m illion by 1980 and 11.4 million by

1990, from 16.9 percent to 12.5 percent o f the

labor force.

— With 8 years o f elementary school or less— from

12.5 million to 9.1 m illion by 1980 and 5.6 m illion by 1990, from 19 percent to 6.1 percent o f

the labor force.

The imbalance between the rates of growth of the

adult male civilian labor force and of the number of

college graduates among them has been especially

pronounced. Over the 13 years between 1958 and

1971, the labor force has increased by only 0.6 percent a year, on average, while its component of men

with at least 4 years of college has grown at the average annual rate of 3.8 percent, or about six times

as rapidly. The corresponding disparity among adult

Denis F. Johnston is senior demographic statistician in the

Office of Manpower Structure and Trends, Bureau of

Labor Statistics.

Special Labor Force Report 

shows rapid advances 

in educational attainment 

of workers during 

the next two decades

DENIS F. JOHNSTON

working women has been much smaller— 2.5 and

4.8 percent.

According to the projections, this disparity in

growth rates may be expected to continue in the future. Between the early 1970’s and 1990, the adult

male civilian labor force is estimated to increase at

an annual average rate of 1.6 percent, with the

holders of college degrees increasing at the average

rate of 4.0 percent. The corresponding increases

during that period for the adult female civilian labor

force are estimated at average rates of 1.9 and 5

percent a year. It is apparent that one of the major

challenges to be met by the economy, both during

the current decade and the 1980’s, is the continued

absorption of this rapidly growing supply of well educated workers.

The prospective change in the number of less educated workers is equally dramatic. In the late

1950’s, over one-third of the adult civilian labor

force— 19.3 million workers— had completed 8

years or less of formal education. By the early

1970’s, this group had been reduced to about 12.5

million, or less than one-fifth of the entire labor

force. It is projected to decrease to about one-eighth

of the labor force by 1980 and to about one-sixteenth by 1990. This continuing drop in the number

of less educated adult workers implies an average

annual rate of decline of 3.9 percent throughout the

1958-90 period (chart 1).

Changes in the 1970’s

Between 1972 and 1980, the civilian labor force

16 years old and over is projected to increase by

nearly 13.3 million, reaching 99.8 million in 1980.2

Nearly 60 percent, or 7.6 million, of this increase is

expected to consist of workers aged 25 to 34 years.

The concentration of the projected increase in the

number of workers with at least 4 years of college

education is similar, with 55 percent of the 4.7-milFrom the Review of November 1973

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[Percent distribution]

Table 1. Years of school completed by persons 25 years old and over in the civilian labor force, actual 1957-72,

projected to 1980, 1985, and 1990

Sex and year

Total Elementary school High school College

Number in 

thousanda

Percent Less than 

5 years1

5 to 7 

years

8 years 1 to 3 

years

4 years 1 to 3 

years

4 years 

or more

4 years 5 years 

or more

BOTH SEXES

1957-59 average1______ 55,909 100.0 6.3 11.4 16.8 19.2 27.8 8.4 10.2 (>) (*)

1964-65-66 average____ 60,067 100.0 4.1 8.7 13.4 18.9 32.8 9.6 12.5 7.4 5.1

1967-68-69 average........ 63,618 100.0 3.1 7.2 11.0 17.6 36.4 11.0 13.7 8.1 5.6

1970-71-72 average........ 65,655 100.0 2.6 6.4 10.0 16.9 37.5 12.0 14.6 8.3 6.3

Projected: 1980............. 77,227 100.0 1.5 3.9 6.4 15.1 40.7 14.0 18.5 10.4 8.1

1985______ 84,731 100.0 1.0 2.7 4.8 13.8 41.3 15.2 21.2 11.6 9.6

1990............ 91,456 100.0 .6 1.9 3.6 12.5 41.2 16.4 23.8 12.7 11.1

MEN

1957-59 average1______ 38,527 100.0 7.1 12.1 17.6 19.2 25.1 8.2 10.8 (3) (*)

1964-65-66 average____ 39,821 100.0 4.8 9.3 14.1 18.7 30.0 9.7 13.6 7.7 5.9

1967—68—69 average........ 40,941 100.0 3.6 7.7 11.7 17.3 33.0 11.5 15.2 8.5 6.7

1970-71-72 average____ 41,668 100.0 3.1 7.1 10.7 16.6 34.0 12.2 16.3 8.9 7.4

Projected: 1980______ 48,283 100.0 1.8 4.3 6.9 14.4 37.5 14.7 20.4 10.6 9.8

1985............. 52,772 100.0 1.2 3.0 5.3 12.8 38.4 16.2 23.1 11.5 11.6

1990............. 56,815 100.0 .8 2.2 4.0 11.4 38.6 17.6 25.5 12.2 13.3

WOMEN

1957-59 average*______ 17,382 100.0 4.5 9.9 15.2 19.1 33.7 8.9 8.7 (•) (*)

1964-65-66 average____ 20,246 100.0 2.8 7.8 12.0 19.3 38.5 9.5 10.3 6.7 3.6

1967-68-69 average......... 22,677 100.0 2.2 6.2 9.6 18.2 42.5 10.3 11.1 7.3 3.8

1970-71-72 average........ 23,987 100.0 1.8 5.2 8.6 17.6 43.6 11.4 11.8 7.5 4.3

Projected: 1980............. 28,944 100.0 1.0 3.2 5.5 16.4 46.1 12.7 15.3 10.0 5.3

1985........... 31,959 100.0 .6 2.2 4.0 15.4 46.1 13.6 18.1 11.7 6.4

1990............. 34,641 100.0 .4 1.5 2.9 14.4 45.3 14.5 21.0 13.4 7.7

1 Includes persons reporting no formal education.

* Totals exclude persons whose educational attainment was not reported.

lion increase in their number to be found among

workers 25 to 34 years old. In 1972, about 32 percent of the 11.8 million college graduates in the

labor force were in the 25-to-34 age group; by

1980, it is estimated that about 38 percent, or 6.3

million, of the projected 16.4 million college graduates in the labor force will be in this age group.

Among women workers, the sharpest increase in the

number of college graduates in the labor force is

projected to occur in the 25-to-34 age group. In

March 1972, this group of women college graduates

numbered about 1.2 million; by 1980, its number is

estimated to reach nearly 2.1 million— an average

annual gain of 6.8 percent. The corresponding rate

of increase among men college graduates in this age

group is 6.2 percent a year.

The current decade is also expected to witness

significant changes in the median age of workers

with different amounts of formal schooling. During a

period when the median age of the labor force is

rapidly declining (from about 39.5 years in 1970 to

3 Data not available.

NOTE: Data for combined years are averages of March Current Population Survey 

figures.

about 35.6 years anticipated in 1980), the median

age of workers with 8 years education or less rises

slowly to about 50.4 years, while that of workers

with at least 4 years of college falls below 35 years.

The steady decline in the number of less educated

workers is largely attributable to their growing concentration in the older age groups, whose labor force

participation rates have been dropping steadily.

Changes in the 1980’s

The anticipated slowdown in the growth rate of

the labor force during the 1980’s (1.0 percent a year,

as compared with 2.2 percent during the 1970’s),

will be accompanied by an accelerated rate of decline in the number of less educated workers (8

years of school or less), and a more moderate rate

of growth among the more highly educated workers

(table 2). The differences between men and women

workers with respect to these projected rates of

change are primarily attributable to differences in

105

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Federal Reserve Bank of St. Louis
Chart 1. Educational attainment of the civilian labor

force 25 years old and over, 1957-59 average, 1970-72

average, and projected 1980 and 1990

their projected rates of labor force participation, and

the effect of the changing age distribution of workers with different amounts of schooling.

By 1990, 4 out of 5 workers are projected to

have completed at least 4 years of high school, with

a range from just over 60 percent among workers

65 years and over to nearly 90 percent among

workers 25 to 34 years old. A somewhat wider

range is evident in the 1990 projection for those

with at least 4 years of college: they are expected to

make up over 20 percent of the labor force, ranging

from about 16 percent among those 65 years old

and over to nearly 30 percent among workers 25 to

34 years old (chart 2 ). It is also evident that the

differences between working men and women with

respect to high school graduation are expected to

narrow somewhat over the projection period. The

percentage of high school graduates is projected to

increase somewhat faster among working men than

among working women but the reverse is true as regards college graduates: except for workers 55 years

old and over, the percent of women with college degrees is expected to rise somewhat faster than that

of men. By 1990, the gap between the educational

levels of both sexes will be considerably smaller

than it is today.

A major feature of the projected educational attainment of workers over the next 17 years is the

growing educational attainment of the several age

groups of the labor force (table 3). For example,

the percentage of workers with at least 4 years of

Table 2. Civilian labor force 16 years old and over, by

sex and years of school completed, 1950, 1960, and 1970

censuses, projected to 1980 and 1990

Sex and year

Years of school completed

Total

8

or

le ss1

9 to 11 12 13 to 15

16

or

more

BOTH SEXES

1950 census........................ 57,141 23,671 11,222 13,593 4,545 4,110

1960 census........................ 67,545 20,832 15,016 18,623 6,855 6,219

1970 census........................ 80,393 14,431 17,157 28,168 10,556 10,081

Projected 1980................... 99,809 10,002 17,262 40,302 15,844 16,399

Projected 1990................... 110,576 6,139 15,683 44,771 19,960 24,023

MEN

1950 census........................ 41,051 18,607 8,049 8,584 2,950 2,861

1960 census........................ 45,339 15,315 10,044 11,161 4,373 4,446

1970 census........................ 49,634 10,034 10,688 15,647 6,424 ■ 6,841

Projected 1980.................. 60,630 6,933 10,301 22,568 9,895 10,933

Projected 1990................... 66,947 4,316 8,955 25,468 12,615 15,593

WOMEN

1950 census........................ 16,090 5,064 3,173 5,009 1,595 1,249

1960 census___________ 22,206 5,517 4,972 7,462 2,482 1,773

1970 census...................... 30,759 4,397 6,469 12,521 4,132 3,240

Projected 1980_________ 39,179 3,069 6,961 17,734 5,949 5,466

Projected 1990_________ 43,629 1,823 6,728 19,303 7,345 8,430

Average annual percent change

BOTH SEXES

1950-60........................... 1.7 - 1 .3 2.9 3.1 4.1 4.1

1960-70............................... 1.7 - 3 .7 1.3 4.1 4.3 4.8

1970-80........................... 2.2 - 3 .7 .1 3.6 4.1 4.9

1980-90..................... ......... 1.0 - 4 .9 - 1 .0 1.0 2.3 3.8

MEN

1950-60..................... ......... 1.0 - 1 .9 2.2 2.6 3.9 4.4

1960-70....___________ .9 - 4 .2 .6 3.4 3.8 4.3

1970-80.............................. 2.0 -3 .7 - . 4 3.7 4.3 4.7

1980-90............................ 1.0 -4 .7 - 1 .4 1.2 2.4 3.6

WOMEN

1950-60_______________ 3.2 .8 4.5 4.0 4.4 3.5

1960—70.............................. 3.2 - 2 .3 2.6 5.2 5.1 6.0

1970-80____________ _ 2.4 - 3 .6 .7 3.5 3.6 5.2

1980-90______ ______ _ 1.1 - 5 .2 - . 3 .8 2.1 4.3

1 Includes persons reporting no formal education.

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Table 3. Projected educational attainment of persons 16 years old and over in the civilian labor force, by age and sex,

1980, 1985, and 1990

[ Percent distribution)

Age and years of school completed

1980 1985 1990

Both

sexes Men Women Both

sexes Men Women Beth

sexes Men Women

16 YEARS AND OVER

Total: Number (in thousands)....................... ......... ............ 99,809 60,630 39,179 105,716 64,057 41,659 110,576 66,947 43,629

Percent................... ................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1.......................... 27.3 28.4 25.6 23.0 23.5 22.2 19.8 19.8 19.6

4 years of high school or more....................... 72.7 71.6 74.4 77.0 76.5 77.8 80.2 80.1 80.3

Elementary: Less than 5 years1.................. 1.3 1.6 .9 .9 1.1 .6 .6 .7 .4

5 to 7 yeari............................ 3.3 3.8 2.6 2.4 2.8 1.9 1.8 2.0 1.3

8 years................................... 5.4 6.1 4.4 4.2 4.7 3.3 3.2 3.7 2.5

High school: 1 to 3 years........... .......... 17.3 17.0 17.8 15.5 14.9 16.4 14.2 13.4 15.4

4 years....... ................................... 40.4 37.2 45.3 40.7 37.9 45.0 40.5 38.0 44.2

College: 1 to 3 years........................ 15.9 16.3 15.2 17.1 17.8 16.2 18.0 18.8 16.8

4 years or more....................................................... 16.4 18.1 14.0 19.2 20.8 16.7 21.7 23.3 19.3

4 years............................................................... 9.7 9.8 9.6 11.0 10.8 11.3 12.0 11.6 12.7

5 years or more................................................... 6.7 8.3 4.4 8.2 10.0 5.4 9.7 11.7 6.6

16 AND 17 YEARS

Total: Number (in thousands)........................ 3,295 1,868 1,427 2,831 1,584 1,247 2,716 1,511 1,205

Percent.............................................. 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1............ 97.9 98.7 96.9 98.0 98.9 97.0 98.0 98.8 97.0

4 years of high school or more................................................................ 2.1 1.3 3.1 2.0 1.2 3.1 2.0 1.2 3.0

Elementary: Less than 5 years1........... .5 .5 .5 .4 .4 .4 .3 .3 .3

5 to 7 years........................ 1.4 1.6 1.2 1.1 1.3 .9 .8 .9 .7

8 years...................... ....... 4.0 5.0 2.6 3.5 4.6 2.2 3.1 4.0 1.8

High school: 1 to 3 years................................... 92.0 91.6 92.6 93.0 92.6 93.5 93.8 93.6 94.2

4 years................................... 2.0 1.2 3.0 1.9 1.1 3.0 1.9 1.1 2.9

College: 1 to 3 years........................ .1 .1 .1 .1 .1 .1 .1 .1 .1

4 years or more............

18 AND 19 YEARS

Total: Number (in thousands)............................................................... 4,803 2,569 2,234 4,095 2,147 1,948 4,134 2,159 1,975

Percent.................................................. 100.0 100.0 100.0 ioo;o 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1____________ 31.0 37.3 23.7 29.5 35.6 22.6 27.9 33.7 21.6

4 years or high school or more..................................................... 69.0 62.6 76.3 70.5 64.3 77.5 72.0 66.3 78.4

Elementary: Less than 5 years1........................... .8 .8 .7 .6 .6 .5 .4 .5 .4

5 to 7 years................................ 1.3 1.7 .9 1.1 1.4 .7 .9 1.2 .5

8 years.................................. 1.7 2.1 1.1 1.4 1.8 .9 1.1 1.4 .7

High school: 1 to 3 years.............................. 27.2 32.7 21.0 26.4 31.8 20.5 25.5 30.6 20.0

4 years........................... 55.5 49.3 62.6 56.8 50.7 63.6 57.9 52.2 64.2

College: 1 to 3 years............................ 13.4 13.2 13.6 13.6 13.5 13.8 14.0 14.0 14.1

4 years or m o re .......................................................... .1 .1 .1 .1 .1 .1 .1 .1 .1

20 TO 24 YEARS

Total: Number (in thousands)...... ........................ ..................... 14,484 7,910 6,574 14,059 7,554 6.505 12,270 6,462 5,808

Percent________________________ _________ 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1........................................................... 12.6 15.3 9.4 10.4 12.8 7.5 8.0 10.1 5.6

4 years of high school or more........ ....................................................... 87.4 84.7 90.6 89.7 87.3 92.5 92.0 89.8 94.5

Elementary: Less than 5 years1.................... ......................... .6 .7 .6 .5 .6 .4 .4 .5 .3

5 to 7 years______________ 1.5 1.9 1.0 1.3 1.7 .8 1.0 1.4 .6

8 years.................................................... ........................ 1.9 2.3 1.3 1.6 2.0 1.1 1.3 1.6 .9

High school: 1 to 3 years___________ _______________ _______ 8.6 10.4 6.5 7.0 8.5 5.2 5.3 6.6 3.8

4 years_____________________ ____________ 42.3 40.2 44.7 40.1 38.4 42.1 38.0 36.6 39.6

College: 1 to 3 years..................................................................... •30.5 31.0 30.0 33.1 33.6 32.5 35.7 36.2 35.1

4 years or m ore................................. ............. ........... 14.6 13.5 15.9 16.5 15.3 17.9 18.3 17.0 19.8

4 years........... ............................ ...................... 11.5 10.0 13.3 12.8 11.2 14.7 14.1 12.4 16.0

5 years or more________ __________________ 3.1 3.5 2.6 3.7 4.1 3.2 4.2 4.6 3.8

25 TO 34 YEARS

Total: Number (in thousands).................. ................................ ........... 26,299 17,052 9,247 29,259 18,929 10,330 30,051 19,382 10,669

Percent____ ___________________ _______ ____________ 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1........................................................... 16.0 15.9 16.7 13.3 12.7 14.2 10.8 10.1 12.1

4 years of high school or more____________ ______ ____ ________ 83.9 84.2 83.4 86.9 87.5 85.8 89.2 89.9 87.9

Elementary: Less than 5 years1.............. ................ ......................... .3 .4 .2 .2 .2 .1 .2 .2 .1

5 to 7 years.......................................... ............. ........... 1.2 1.4 1.0 .8 .9 .6 .4 .5 .3

8 years........................................................................ 2.6 3.1 1.8 2.0 2.4 1.3 1.4 1.8 .8

High school: 1 to 3 years.................................. ....................... ....... 11.9 11.0 13.7 10.3 9.2 12.2 8.8 7.6 10.9

4 years_____ _____ ______________ ___________ 42.2 40.7 44.9 41.0 40.1 42.8 39.8 39.2 40.8

College: 1 to 3 years................................................................... 17.6 18.5 18.8 20.0 16.5 19.7 21.2 17.0

4 years or more............................................................ 24.1 25.0 22.6 27.1 27.4 26.5 29.7 29.5 30.1

4 years................................... .............................. 13.4 12.5 15.1 14.5 13.0 17.2 15.3 13.3 18.8

5 years or more....................................................... 10.7 12.5 7.5 12.6 14.4 9.3 14.4 16.2 11.3

See footnotes at end of table.

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Table 3. Continued—Projected educational attainment of persons 16 years old and over in the civilian labor force

[Percent distribution]

Age and years of school completed

1980 1985 1990

Both

sexes Men Women Both

sexes Men Women Both

sexes Men Women

35 TO 44 YEARS

Total: Number (in thousands)_________________ _____ ___ 18,450 11,584 6,866 22,907 14,350 8,557 27,347 17,131 10,216

Percent____ ________________ _______ ______ 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1___________________________ 24.4 24.4 24.5 19.6 19.0 20.8 16.1 15.1 18.0

4 years of high school or more________________ _______ 75.6 75.7 75.5 80.3 81.1 79.1 83.7 84.9 82.0

Elementary: Less than 5 years1_____ _____ .9 1.1 .5 .4 .5 .2 .2 .2 .1

5 to 7 years............................................... 3.0 3.5 2.3 1.7 2.0 1.3 1.0 1.2 .7

8 years......... ........................ 4.5 4.8 3.9 3.4 3.8 2.8 2.5 2.9 2.0

High school: 1 to 3 years______ ____ 16.0 15.0 17.8 14.1 12.7 16.5 12.4 10.8 15.2

4 years_______________________ 42.9 39.3 48.9 42.5 39.6 47.5 41.8 39.4 45.9

College: 1 to 3 years_____________________ 13.9 14.8 12.4 15.7 16.8 13.9 17.0 18.3 14.9

4 years or more_______________ _______________ 18.8 21.6 14.2 22.2 24.7 17.7 24.9 27.2 21.2

4 years__________________ . . 10.7 11.4 9.6 12.2 12.4 11.7 13.2 13.0 13.6

5 years or m ore.._______ _________________ 8.1 10.2 4.6 10.0 12.3 6.0 11.7 14.2 7.6

45 TO 54 YEARS

Total: Number (in thousands)_______________________________ 16,397 9,862 6,535 16,238 9,698 6,540 18,225 10,863 7,362

Percent____ _______ ______ _____ . . ___. . . 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1_______________ _____________ 33.4 35.5 30.4 28.5 30.2 26.3 23.3 23.9 22.2

4 years of high school or more_______________________________ 66.5 64.6 69.6 71.5 69.9 73.8 76.7 76.0 77.8

Elementary: Less than 5 years1. . ______ __________________ 2.4 3.2 1.1 1.6 2.2 .7 .9 1.3 .4

5 to 7 years__________________________________ 5.3 6.2 4.0 4.0 4.9 2.9 2.6 3.1 1.7

8 years___ _________________________________ 8.2 9.3 6.6 5.9 6.6 4.9 4.2 4.6 3.5

High school: 1 to 3 years___ ______ _________________ _____ 17.5 16.8 18.7 17.0 16.5 17.8 15.6 14.9 16.6

4 years_____________________________________ 40.1 34.9 48.1 42.7 37.7 50.1 43.5 39.4 49.6

College: 1 to 3 years.._________ ______________________ 11.3 11.8 10.6 12.4 13.0 11.6 14.2 14.9 13.2

4 years or more______________________________ 15.1 17.9 10.9 16.4 19*2 12.1 19.0 21.7 15.0

4 years___________________________________ 8.5 9.7 6.8 9.4 10.4 7.9 10.8 11.4 10.0

5 years or more________ ____________ ______ 6.6 8.2 4.1 7.0 8.8 4.2 8.2 10.3 5.0

55 TO 64 YEARS

Total: Number (in thousands)_______________________________ 12,784 7,727 5,057 12,926 7,713 5,213 12,307 7,304 5,003

Percent__________ ____ _____________________ 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1____ __________________ _____ 37.4 39.9 33.8 34.0 36.2 30.8 30.5 32.5 27.7

4 years of high school or m ore.._____________________________ 62.6 60.2 66.2 66.1 63.9 69.2 69.6 67.5 72.3

Elementary: Less than 5 years1_____ ______________________ 2.5 3.0 1.8 2.0 2.5 1.4 1.6 2.0 1.0

5 to 7 years__________________________________ 6.4 7.1 5.5 5.6 6.2 4.6 4.7 5.4 3.8

8 years_____________________________________ 11.1 12.1 9.6 9.4 10.6 7.7 '7 .7 8.9 6.0

High school 1 to 3 years_____ ____________________________ 17.4 17.7 16.9 17.0 16.9 17.1 16.5 16.2 16.9

4 years_____________________________________ 39.4 34.8 46.3 40.5 35.4 48.0 41.6 36.4 49.2

College: 1 to 3 years...___________________________ . . . 11.1 11.7 10.3 11.5 12.1 10.6 11.9 12.4 11.1

4 years or more___________________ ____ ______ 12.1 13.7 9.6 14.1 16.4 10.6 16.1 18.7 12.0

4 years________________ ___________________ 7.0 7.5 6.0 8.0 8.9 6.6 9.1 10.0 7.6

5 years or more_______ _____________ _______ 5.1 6.2 3.6 6.1 7.5 4.0 7.0 8.7 4.4

65 YEARS AND OVER

Total: Number (in thousands)_______________________________ 3,297 2,058 1,239 3,401 2,082 1,319 3,526 2,135 1,391

Percent_____________ ______ _______________________ 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Less than 4 years of high school1___ _________________________ 51.9 54.9 47.0 43.9 45.9 40.8 38.3 40.1 35.2

4 years of high school or more________________ _______________ 48.1 45.1 53.0 56.0 54.1 59.0 61.8 59.9 64.8

Elementary: Less than 5 years * ...__________ ______________ 5.4 5.8 4.8 4.3 4.7 3.6 3.0 3.1 2.7

5 to 7 years___________________ ______________ 12.8 14.1 10.6 9.4 9.8 8.8 7.9 8.5 6.8

8 years___ _____ _______________ __________ _ 19.2 20.4 17.2 15.3 15.9 14.4 12.4 13.2 11.3

High school: 1 to 3 y ears....--------------- ----------------------------- 14.5 14.6 14.4 14.9 15.5 14.0 15.0 15.3 14.4

4 years............................................................................ 25.6 23.3 29.4 31.4 29.4 34.6 34.8 31.8 39.4

College: 1 to 3 years_______ _____________ _____ _____ _ 9.0 8.0 10.7 9.7 8.8 11.1 10.6 10.1 11.4

4 years or more__________ ___________________ 13.5 13.8 12.9 14.9 15.9 13.3 16.4 18.0 14.0

4 years................................. ....................... ............ 6.7 6.5 7.0 7.3 7.4 7.2 8.0 8.4 7.5

5 years or more.......................................................... 6.8 7.3 5.9 7.6 8.5 6.1 8.4 9.6 6.5

1 Includes persons reporting no formal education. NOTE: Because of rounding, percentages may not add to exactly 100.

high school in March 1972 ranged from 78.6 percent among workers 25 to 34 years old to 41.5 percent among those 65 years old and over. By 1980, the

corresponding percentages are projected at 83.9 and

48.1 percent, respectively, and by 1990, at 89.2 and

61.8 percent, respectively. Thus the range narrows

from 37.1 percentage points in 1972 to 27.4 percentage points in 1990. However, the gap widens

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between the projected percentages of college graduates in the two age groups. In March 1972, the

range was from 20.1 percent among workers 25 to

34 years old to 12.4 percent among those aged 65

years and over. By 1980, the corresponding percentages are projected at 24.1 and 13.5 percent, respectively, and by 1990, at 29.7 and 16.4 percent, respectively— a range increase from 7.7 to 13.3

percentage points.

These trends suggest that while the projected

labor force at all ages will tend increasingly to have

achieved at least a high school education, the

younger workers will enjoy a growing advantage

over their elders with respect to the completion of

college studies.

The new projection also reveals the highly significant proportion of the college graduates who will

have pursued some form of graduate education in

the future. Among the nearly 16.4 million college

graduates projected to be in the labor force in 1980,

about 6.7 million (41 percent) are expected to have

completed at least 1 year of graduate work. By

1990, the number of workers with at least 5 years

of college education rises to 10.7 million, or 45 percent of the college graduates in the labor force. The

corresponding proportions among working men are

46 percent in 1980 and 50 percent in 1990; among

working women, 31 percent in 1980 and 34 percent

in 1990. Here also, the disparity between the

younger and older workers is pronounced. By 1980,

10.7 percent of the workers 25 to 34 years old are

estimated to have completed at least 5 years of college, compared with 6.8 percent of the workers 65

years and over; by 1990, the corresponding proportions are 14.4 and 8.4 percent, respectively.

The educationally disadvantaged

In recognizing the growing preponderance in

the civilian labor force of persons with higher education, one may overlook the plight of the less educated ones, whose competitive disadvantage grows

apace with the rising educational attainment of the

majority. Their problems are exacerbated by their

growing concentration in the older age groups of

the work force. As previously noted, the median

age of workers- with 8 years of formal education or

less increases over the projection period, while that

of the more highly educated declines. In March

1972, about 38 percent of the workers with 8 years

or less of formal schooling were aged 55 years and

Chart 2. Percent of civilian labor force with at least 4

years of high school and at least 4 years of college, by

age, 1970-72 actual and 1990 projected

over. By 1980, this proportion is expected to remain

unchanged, despite the decline in the median age of

all workers; and by 1990, it rises to 41 percent.

Thus, the employment and retraining problems to be

overcome in fitting the educationally disadvantaged

into our increasingly sophisticated economy will be

complicated by the relatively advanced age of these

less educated workers.

Workers who have attended, but failed to complete, high school have experienced particularly severe employment problems during the past 20 years.

If workers under age 18 (most of whom are still enrolled in high school) are excluded, the number of

workers with 1 to 3 years of high school is projected to rise slightly, from 13.7 million in March

1972 to 14.2 million in 1980. It declines steadily

thereafter, reaching 13.2 million by 1990. The proportion of younger workers (18 to 24 years old)

among these high school dropouts has apparently

reached a peak and is expected to decline in the fu109

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ture. In March 1962, 15.6 percent of workers 18

and over with 1 to 3 years of high school completed

were in the group 18 to 24 years old. By March

1972, the corresponding percentage had risen to

18.5 percent, reflecting the large inflow of new

young workers which has occurred since the mid1960’s. By 1980, the corresponding proportion is

projected at 17.9 percent, and by 1990, it is projected to have declined sharply to 13.0 percent. In

numerical terms, these young high school dropouts

increased from about 2 million in 1962 to 2.5 million in 1972. They are projected to remain about

constant in number to 1980, and to decline to about

1.7 million by 1990. Thus, the demand for manpower programs designed to assist these young but

educationally disadvantaged workers to improve

their employment qualifications will remain steady

throughout the 1970’s, but will decline thereafter.

The college graduates: supply and demand

The performance of the U.S. economy in absorbing the growing number of college graduates during

the past two decades warrants some optimism with

respect to the employment prospects of highly educated workers in the future. Between 1950 and

1970, the number of employed men 25 years and

over increased by 19 percent— 0.9 percent a year,

on average— while that of the college graduates

among them rose by 134 percent, or an average of

4.3 percent a year. Over the same 20-year period,

the number of employed women workers in the

same age group increased by 89 percent— an average gain of 3.2 percent a year— while the number of

college graduates among them increased by 147 percent, or 4.5 percent a year, on average.

However, the recent employment experience of

new college graduates suggests that their short-term

employment prospects may be quite sensitive to

cyclical changes in the economy and to the changing

mix of demand for highly trained professional and

technical workers in particular fields.3 In particular,

a note of caution should be expressed in regard to

the employment needs of women college graduates

whose numbers are projected to grow so rapidly.

This growth must be considered in relation to the

limited job opportunities expected in primary and

secondary education, where a large proportion of

these women have been employed in the past. The

absorption of these women into the labor force may,

therefore, require a movement by these women in

unprecedented numbers into traditionally male-dominated professional and technical occupations.4

These highly qualified workers may also displace

increasing numbers of less educated workers in occupations which have formerly been the preserve of

those without college education, particularly if the

kinds of jobs which typically have been held by college graduates do not increase fast enough to absorb

the prospective growth of college graduate jobseekers. The upgrading of job requirements already observed suggests that the employers’ expectations

with respect to the educational qualifications of their

prospective employees tend to rise with increases in

such qualifications of the jobseekers themselves.

Thus, if college graduates are forced to seek jobs

which have not traditionally attracted them, they are

likely to be hired in preference to the less educated,

quite apart from the actual education needed to perform such jobs adequately. Should such displacement take place on a large scale, the potential consequences could be damaging both to the

college-educated workers and to the less educated

workers they displace. For the former, limited opportunity to utilize and develop the skills and

perspectives acquired in college could give rise to alienation, frustration, and other problems associated

with this type of underemployment. For the latter,

the prospect of competition with the educationally

advantaged for jobs and promotions could also give

rise to serious strains.5 But it is also possible to envision more favorable consequences, such as the improved job performance which may be expected to

accompany the educational upgrading of workers in

different occupations. In addition, increased competition between workers with different amounts of

formal education might eventually lead to less exclusive reliance upon academic credentials in hiring

and promotion, and the supplementing of such criteria with more valid indicators of work-related ability

and potential.6

Comparison with earlier projection

In comparison with the 1970 projection it supersedes, the current projection of the male civilian

labor force 25 years old and over is smaller by

about 400,000 for 1980 and about 500,000 for

1985.7 As for the female civilian labor force of the

same age span, the current projection is larger by

about 1.3 million for 1980 and 1.6 million for

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1985. In the aggregate, the current projection yields

an adult civilian labor force that is larger than the

earlier projection by about 900,000 for 1980 and

1.1 million for 1985 (table 4 ). In general, the current projection shows a more rapid advance in the

educational attainment of the adult civilian labor

force than was indicated earlier. This advance is

more pronounced among men than among women

for both 1980 and 1985. It is also more pronounced

for 1985, for each sex, than for 1980.

The differences between the current and the previous projection in the size of the adult civilian

labor force are largely those resulting from changes

in the projected rates of labor force participation.

The recent changes in the size of the projected population 25 years old and over are generally minor.

The more rapid rate of increase in the projected educational attainment of the labor force, on the other

hand, reflects primarily the higher levels of educational attainment projected for the adult population

as a whole by the Bureau of the Census, since the

projected educational attainment of the labor force

has been linked to that of the population. (See explanation of the linkage in the following section.)

Assumptions and methodology

These projections were developed by a method

which provides a systematic linkage with the latest

available projections of educational attainment of

the population, by age and sex, prepared by the Bureau of the Census.8 The projections were developed in the following steps:

A. Civilian labor force 25 years old and over

Step J. Percentage distributions by educational

attainment categories were obtained for m en and

wom en in the population and the civilian labor force

for age groups 25 to 34, 35 to 44, 45 to 54, 55 to

64, and 65 and over. T he follow ing educational

attainment categories were used: less than 5 years

o f schooling (including no school years com pleted),

5 to 7 years, 8 years, 9 to 11 years, 12 years, 13 to

15 years (that is, 1 to 3 years o f college), 16 years,

and 17 years or more. These data were obtained

from the M arch Current Population Surveys for

four periods— an average of 1957 and 1959; an

average o f 1964, 1965, and 1966; an average of

1967, 1968, and 1969; and an average o f 1970,

1971, and 1972.9

Step 2. The observed differences in the educational distribution o f the population and civilian

labor force (by age and sex) were projected to 1990.

Table 4. Comparison of current projection of educational attainment of adult workers 25 years and over

with previous BLS projection, 1980 and 1985

1980 1985

Sex and year* of 

school completed

C urrent

projection

(1)

Report 

122 ‘

(2)

Differen ce 

(1H 2)

(3)

C urrent

projection

(4)

Report

1221

(5)

Difference

(4W5)

(6)

MEN

Civilian labor force:

Number (in thousands). 

Percent........................

48,283

100.0

48,665

100.0

-382 52,772

100.0

53,282

100.0

-510

Less than 8 years1............. 6.1 6.4 - . 3 4.2 4.7 - . 5

8 to 11 years___________ 21.3 23.2 - 1 .9 18.1 19.9 - 1 .8

12 years.............................. 37.5 39.7 - 2 .2 38.4 42.3 - 3 .2

13 to 15 years__________ 14.7 12.1 2.6 16.2 12.6 3.6

16 years or more_______

WOMEN

20.4 18.6 1.8 23.1 20.5 2.6

Civilian labor force:

Number (in thousands). 

Percent..........................

28,944

100.0

27,662

100.0

1,282 31,959

100.0

30,362

100.0

1,597

Less than 8 years*.......... 4.2 4.5 - . 3 2.8 3.1 - . 3

8 to 11 years...................... 21.8 22.5 - . 7 19.4 19.8 - . 4

12 years......... ................... 46.1 47.2 -1 .1 46.1 48.2 -2 .1

13 to 15 years.................... 12.7 12.0 .7 13.6 12.9 .7

16 years or more........ ....... 15.3 14.0 1.3 18.1 16.0 2.1

>Denis F. Johnston, "Education of adult workers: projections to 1985,” Monthly 

Labor Review, August 1970, pp. 43-56, reprinted as Special Labor Force Report 122.

* Includes persons reporting no formal education.

These differences reflect observed trends over the

period 1 9 5 7 -5 9 to 1970-72, either toward increasing

or decreasing differences. Otherwise, the differences

were assumed to remain constant.

Step 3. The differences— positive or negative—

were applied to the projected educational distributions of the population to obtain a first approximation o f the educational attainment of the labor force

in 1980, 1985, and 1990.

Step 4. These educational distributions (in percentage terms) were then applied to the previously

projected civilian labor force totals for each age-sex

group. The resultant numbers were then divided by

the corresponding population to obtain a labor force

participation rate for the population in each age,

sex, and educational attainment category for the

target dates.

Step 5. The labor force participation rates obtained in step 4 were then compared with observed

trends in these rates over the period 1 9 5 7 -5 9 to

1970-72. Relatively minor adjustments in these rates

were then introduced wherever necessary to maintain

consistency with the observed trends, w hile also preserving consistency with the previous projection o f

the civilian labor force in each age-sex group.

Step 6. The adjusted rates o f labor force participation were then applied to the projected population

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to obtain the projected labor force by age, sex, and

years of school com pleted for 1980, 1985, and 1990.

B. Civilian labor force 16 to 24 years old

For som e planning purposes, it is useful to have

a projection of the educational attainment o f workers

under the age o f 25 even though many o f these

workers have not yet completed their formal schooling. The Bureau of the Census does not develop projections o f the educational attainment o f the population under 25; therefore, it was decided to develop

a projection o f the educational attainment o f both

the population and the civilian labor force 16 to 24

years old by m eans of a direct extrapolation o f trends

in their reported educational attainment. The reported educational attainment distribution o f the

population and labor force 16 and 17 years old (by

sex) was obtained from the 1950, 1960, and 1970

censuses and from the M arch 1972 Current Population Survey. Corresponding data for the population

and labor force age groups o f 18 and 19 and 20

to 24 (by sex) w ere obtained from the published

Current Population Survey reports for the years

1957, 1959, and 1964-72.

For each of these groups, observed trends in the

percentage distribution of their educational attainment were extrapolated to 1990, and the resultant

projections were applied directly to the projected

population and civilian labor force of each group to

obtain the numbers by years of school completed.

The labor force participation rates implicit in these

projections, by age, sex, and years of school completed were then computed for 1980, 1985, and

1990 by dividing the projected civilian labor force

by the projected population. These rates were thei*

compared and adjusted, as necessary, to ensure consistency with trends in the reported participation

rates over the period for which actual data were

available. The adjusted rates were then applied to

the projected population to obtain the final projected labor force in each educational attainment

category. 

-FOOTNOTES1 These projections supersede those presented in Denis F.

Johnston, “Education of adult workers: projections to 1985,”

Monthly Labor Review, August 1970, pp. 43-56, reprinted

as Special Labor Force Report 122. Information by color

or race, which was provided in the earlier report, is not yet

available. The size and age-sex composition of the civilian

labor force in this report are consistent with those of the

total labor force as projected in Denis F. Johnston, “The

U.S. labor force: projections to 1990,” Monthly Labor Review, July 1973, pp. 3-13. In addition, the projected educational attainment of the adult civilian labor force (25

years and over) is consistent with the latest projection of

the educational attainment of the adult population published

by the Bureau of the Census. For a description of the

assumptions and methodology of these projections, see

Demographic Projections for the United States, Current

Population Reports, Series P-25, No. 476 (Bureau of the

Census, February 1972).

2 Unless otherwise specified, the projections in this article

relate to the entire civilian labor force, 16 years old and

over. The projected years of school completed refer to the

workers’ estimated attainment at the time they are in the

labor force, and not to their ultimate attainment upon

completion of their formal education. Where data for 1972

are used for discussion of emerging changes during the current decade, the 1972 data are taken from William V. Deutermann, “Educational attainment of workers, March

1972,” Monthly Labor Review, November 1972, pp. 38-42,

reprinted as Special Labor Force Report 148.

3 Information on the employment experience of recent

college graduates is provided in Vera C. Perrella, “Employment of recent college graduates,” Monthly Labor Review,

February 1973, pp. 41-50, reprinted as Special Labor

Force Report 151. Also see Michael F. Crowley, “Professional manpower: the job market turnaround,” Monthly

Labor Review, October 1972, pp. 9-15; and Persons in

Engineering, Scientific, and Technical Occupations: 1970

and 1972, Current Population Reports, Special Studies,

Series P-23, No. 45 (Bureau of the Census, July 1973).

4 For additional perspective on the employment o f college-educated women, see Pamela Roby, “Women and

American Higher Education,” The Annals o f the American

Academy of Political and Social Science, November 1972,

pp. 118—39, and Alan L. Sorkin, “Occupational Status of

Women, 1870-1970,” The American Journal of Economics

and Sociology, July 1973, pp. 235-43.

6 For additional information on the prospective occupational distribution of college-educated workers, see the

forthcoming article by Neal H. Rosenthal in the December 1973 issue of the Monthly Labor Review.

6 “Years of school completed” is obviously a crude criterion of either ability to perform well in a given job or o f

potential for further training and development; exclusive or

even primary reliance upon such a criteron for purposes of

job placement, promotion, or training is unlikely to provide

an adequate screening of the more capable individuals in

most work situations. Among the factors which are not

necessarily reflected by educational attainment are the quality of schooling received, the possible loss of knowledge

and skills learned in the past, learning and experience acquired outside of school, and such personal attributes as

discipline, interest, and motivation. On this general issue,

see Thomas F. Green, Work, Leisure, and the American

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Schools (New York, Random House, 1968), and Credentials and Common Sense: Jobs for People Without Diplomas (U.S. Department of Labor, Manpower Administration, December 1968), Manpower Report 13.

7 These comparisons are restricted to the civilian labor

force 25 years old and over in 1980 and 1985 because the

1970 projection was limited to that age group and those

dates.

8 Demographic Projections for the United States, Current

Population Reports, Series P-25, No. 476 (Bureau of the

Census, February 1972), table 5. These projections relate to persons 25 years old and over, by age and sex. For

the age groups where two series of educational distributions

were developed (persons 25 to 34 years old in 1980 and

persons 25 to 44 years old in 1985 and 1990), an arithmetic average of the two series was adopted.

9 Current Population Survey data on the educational attainment of the population for the mentioned years are

published by the Bureau of the Census in Current Population Reports, Series P-20, Numbers 77, 99, 138, 158, 169,

182, 194, 207, 229, and 243. Corresponding data for die

civilian labor force for these years are published by the

Bureau of Census in Current Population Reports, Series

P-50, No. 78 (for 1957) and by the Bureau of Labor Statistics in its Special Labor Force Reports, 1, 53, 65, 83,

92, 103, 125, 140, and 148 (covering March 1959 through

March 1972). The latter reports were reprinted, with additional tables, from the Monthly Labor Review.

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Chapter III. Special Groups in the Labor Force

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Nearly 5.6 million families

in the United States are headed by women;

despite employment growth of the 1960’s

about 2 million of these families

remain in poverty

ROBERT L. STEIN

O n e of the important domestic problems facing

the Nation in the 1970’s is how to improve the

economic status of families headed by women.

According to the latest estimates— for March

1970— 5.6 million families in the United States

are headed by women, or more than 1 family in 10.

The number has been increasing more rapidly

than the total of all families. Between 1960 and

1970, for example, it rose by 24 percent, whereas

total families increased by 14 percent.

Historically the employment and income situation of such families has generally been bleak.

M ost of the women are ill-equipped to earn an

adequate living. M any suffer from one handicap or

more to successful competition in the labor

market—lack of sufficient education or training,

irregular and unstable work histories, sex or racial

discrimination in hiring, ill health, and the difficulty of arranging for satisfactory child care.

As a result, these women have not been able to

share fully in the Nation’s economic growth,

with its associated expansion in jobs and advances

in earnings. During the 1960’s, the income of

families headed by men remained more than

double the income of families headed by women.

While the number of families headed by men with

incomes below the poverty line ($3,700 for a family

of four in 1969) was reduced by one-half between

1959 and 1969, the number of poor families

headed by women remained virtually unchanged

at about 1.8 million. Employment growth, the

most powerful weapon in the antipoverty arsenal,

has not significantly reduced the number of poor

families headed by women.

Robert L. Stein is an economist in the Office of Economic and Social Research, Bureau of Labor Statistics.

Carol Milner of the same office assisted in the preparation

of the article.

The economic

status of

families headed

by women

Public assistance, a primary source of income

for many of the families headed by women, has

been expanding in coverage and in benefit levels,

but payments are still generally very low—in

most States below the poverty line.

The welfare system has been caught in a crossfire of public criticism. The target for most of the

hostility is the a f d c program—Aid to Families

with Dependent Children— designed to provide

income assistance to the families of children whose

fathers have died or deserted or are absent for a

variety of other reasons. On the one hand, welfare

programs are criticized because their payment

levels are considered too low to provide economic

security to families in need. On the other hand,

the programs are criticized on the grounds that

work, as well as need, should be a requirement for

eligibility. The welfare system has also been

faulted because of the widely disparate State

benefit levels, because it may discourage some

women from seeking employment, and because

it may induce some families to break up.

The attacks have become sharper in recent

years because of steady growth in the welfare

population during a period of rapid economic

growth and very low unemployment. B y March

1970, about three-fifths of the 3.4 million families

with children headed by women were already on

welfare and the rolls were still rising. These developments were placing a growing burden on the

already hardpressed taxpayer. One result of the

resistance to the rising welfare bill has been a

heightened interest in the possibility of employment for welfare mothers. One important aspect

of welfare reform involves the development of

training and job placement programs for ablebodied adult welfare recipients. The manpower

provisions of the Administration’s proposed Family

Assistance Act of 1970 include a training and work

requirement for mothers of school-age children.

From the Review of December 1970 116

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Scope of the problem

In March 1970, 5.6 million women were heads

of families (table 1); 2.4 million of these women

(43 percent; were widows and 2.6 million (46

percent) were divorced or separated from their

husbands. The remaining 600,000 had never

been married. About a third of these single women

had children under 18.

From the standpoint of society, foremost concern is centered on the status of those families

with dependent children. The environment in

which these children are growing up is inevitably

affected by the stresses and strains on the mother

who must take over the responsibility for the

discipline, training, and guidance of the young as

well as their financial support. In March 1970,

there were 3.4 million such families, comprising 8

million children under 18 years of age (an average

of 2.4 per family) and 13 million persons altogether.

The remaining 2.2 million—women without

children under 18— were nearly all past the age of

45. Two-thirds were widows, and all but a few

were heads of small families consisting of only

two or three persons. These older family heads

were not without employment and income problems. B y and large, however, their situation was

Table 1. Selected characteristics of families headed by

women

Characteristic

Thousands of 

families

Percent of 

families in each 

category

March

1970

March

1960

March

1970

March

1960

ALL RACES

Total, all families............................. . . . ............. 5,580 4,494 11 10

With children........................ . ............ 3,363 2,542 11 9

Below the poverty line................................ 1,803 1,916 36 23

With children........................................ 1,488 1,525 47 28

In central cities of metropolitan areas............... 2,269 1,764 15 12

Below the poverty level................. ............. 738 585 50 29

WHITE

Total, all families................................................ 4,185 3,545 9 9

With children........................................ 2,255 1,834 9 8

Below the poverty line................................. 1,063 1,233 30 20

With children........................................ 831 948 40 25

In central cities of metropolitan areas............... 1,418 1,240 12 10

Below the poverty line................................ 337 303 39 24

NEGRO AND OTHER RACES

Total, all families................................................ 1,395 949 27 22

With children........................................ 1,108 708 31 25

Below the poverty line................. .............. 739 683 53 32

With children........................................ 657 577 59 35

In central cities of metropolitan areas.............. 851 524 29 23

Below the poverty line................................ 402 282 66 28

less serious than that of younger families with

children since they had more freedom to accept

employment, they had more income from other

sources, and they had fewer dependents. Half

had fully grown children in the household who

could contribute to the family’s income. In 1969,

the median income of families headed by women

45 to 64 years of age who had no children under

18 in the household was $7,000, whereas the income

of families headed by women 24 to 44 years of

age who did have children was only $4,000.

Between 1960 and 1970, the number of women

heading families with children rose by 800,000.

Roughly one-third of this increase could be

attributed to general population growth. There has

been considerable speculation that rising welfare

benefits in the large industrial States of the North

have contributed to the breaking up of poor

families. However, it would be extremely difficult

to isolate this factor from the entire complex of

forces that leads to family disorganization. (Onethird lived in the South where welfare payments

are still comparatively low.)

The proportion of families headed by women is

highest among poorly educated and low income

groups, among minority groups, and among city

residents. On the other hand, the group is also more

heterogeneous than might be supposed. Among

women 25 and over, most of whom have completed

their formal schooling, one-third of the family

heads have no more than an elementary school

education (compared with one-fourth of other

women), but 13 percent have some college education. Although one-third have incomes below the

poverty line, a small minority (nearly 300,000)

have incomes of $15,000 or more. These are mainly

older white families without children.

Among the black urban poor, the proportion of

families headed by women was 66 percent in March

1970. Here, as in the Nation as a whole, the proportion has been increasing; the trend is much more

pronounced among the urban poor.

Even among the 3.4 million families with children, the situation is uneven. About 65 percent

have only one or two children and their incomes

are somewhat higher than the incomes of larger

families. However, those with few or no children

tend to be at the extremes of the age scale. Among

women family heads age 25 to 44, presumably

the prime candidates for training and employment,

nearly half had three children or more. The prob117

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lems confronting women with many children are

compounded by the fact that they are also the

least educated and therefore the least equipped to

find employment.

Fam ily income

The relationship between income and family

stability is complex. When a breadwinner dies or

leaves his family, the loss or reduction of financial

support may be only partly offset by the wife’s

earnings and Social Security, private pensions or

insurance, welfare payments or other benefits.

Poverty or low income may itself create tensions

leading to family breakup. Or the fact that a man

does not have a steady job at good pay may induce

him to leave so that his family can obtain public

assistance. These situations are not easily quantified. In any case, the data show a very strong correlation between income and the presence or absence of fathers.

As table 2 shows, the percentage of families

headed by women moves down steadily as family

income rises. The proportion starts out at 63 per

100 families with incomes under $2,000, and then

moves down progressively to reach 2 per 100 families with incomes of $10,000 and over.

Negro families with children are much more

likely than white families to be headed by a

woman— 1 in every 3 Negro families is in this

category, compared with 1 in every 10 white

families. The difference in family structure is one

reason for the lower average income of Negro

families. Although the proportion of black families

without husbands and fathers is higher than for

whites at every income level, it moves down

sharply and continuously from about 3 in 4 among

the lowest income families to about 1 in 20 among

the higher income families.

The median income of the families of 8 million

children who were being brought up by their

mothers— or other female relatives— was $4,000

in 1969. This contrasts with a median family income of $11,600 for the 61 million children living

with both parents.

Only 38 percent of the families headed by women

had incomes over $5,000 and only 9 percent had

incomes over $10,000. B y contrast, 55 percent of

the husband-wife-children families had incomes

over $10,000. Although husband-wife families tend

to be larger than families headed by women, the

Table 2. Income in 1969 of families with children, headed

by women

Family income All races White

Negro 

and other 

races

Total: Number (in thousands)......... .................... 3,363 2,255 1,108

Percent................................................ ....... 100 100 100

Under $2,000.................................................... 21 18 26

$2,000 to $2,999____________ ___________ 15 13 18

$3,000 to $3,999............................................... 14 12 18

$4,000 to $4,999____ ______ ______ ______ 12 12 11

$5,000 and over............................................... 38 45 27

$5,000 to $5,999................................................ 10 10 10

$6,000 to $6,999_________________ ______ 8 9 6

$7,000 to $7,999....................................... 5 6 3

$8,000 to $8,999........................................ 3 4 3

$9,000 to $9,999... ....................................... 3 5 1

$10,000 and over............................................ 9 11 4

Median income.................................. ..................... $4,008 $4,523 $3,327

Families headed by women as percent of all families

with children............... ........................................ 11 9 31

Under $2,000................................................... 63 57 74

$2,000 to $2,999..________________ ____ 54 48 67

$3,000 to $3,999............................................. 40 33 56

$4,000 to $4,999............................................... 28 26 36

$5,000 to $5,999............................. .................. 20 17 31

$6,000 to $6,999...................... ....................... 14 12 22

$7,000 to $7,999......................... ____.............. 8 7 12

$8,000 to $8,999_______________________ 5 4 14

$9,000 to $9,999...............................................

r

5 6

$10,000 and over______ _____ _____ _____ 2 2 5

differences in income between the two types of

families far exceed any differences in need.

Families headed by women account for a large

and growing proportion of the remaining poverty

in the United States. In 1969, 47 of every 100 poor

families with children were headed by women. In

1959, the proportion was 28 out of 100.

The poverty line takes account of both family

income and family size. In 1969, the line was set

at $3,700 for a nonfarm family of four headed by

a woman. It goes up (or down) by roughly $700

for each additional person (or each person less) in

the family.

The poverty thresholds as used in this discussion 1 are not intended to provide a measure of

income adequacy; that is, it should not be inferred

that those with incomes above the poverty line

have necessarily achieved a minimally adequate

level of living. The cutoffs do provide a useful

device for measuring the prevalence of, and trends

in, very low income levels among various familytype and family-size groups, and are more realistic

than are fixed dollar amounts of income (for example, families with incomes under $3,000) because

they are graduated by family size. They are

varied over time to reflect annual changes in the

average price level as measured by the Consumer

Price Index.

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The poverty statistics point up the importance

of fam ily size. If a family headed by a woman has

only one or two children, it has about a 2 out of 3

chance of staying above the poverty line. However, as the number of children increases, the

probability that the family’s income is under the

poverty line rises sharply. Among those families

with four children or more, over two-thirds are

poor.

Additional children might have been economically helpful to poor families in an earlier era.

But in modern urban society with its complex

technology and its unrelenting emphasis on education and skill, each additional child diminishes the

woman’s prospects for economic independence and

security through employment. The bearing and

rearing of children m ay interfere with the completion of her education, and most certainly will

interfere with the continuity of her employment.

Unless a woman can acquire at least a high school

education or can acquire meaningful job training

and job experience, and unless she can work full

time most of the year, it is unlikely that her annuaj

Table 3. Extent of poverty in 1969 among families headed

by women, by number of children

(Numbers in thousands]

Total

Poor families

Race and number of 

children under 18 >

number

of

families Number

Percent

of

total

Median deficit 

between total 

income and 

poverty line 2

ALL RACES

Total................................... 5,580 1,803 32 $1,200

No children under 18.................. 2,218 315 14 700

One child___________________ 1,211 360 30 1,100

Two children________________ 960 386 40 1,200

Three children_______________ 545 279 51 1,500

Four children_____ __________ 303 202 67 1,700

Five children or more_________ 344 262 76 2,400

WHITE

Total................................... 4,185 1,063 25 1,200

No children under 18.................... 1,931 232 12 700

One child................................. . .. 906 227 25 1,100

Two children................................. 702 258 37 1,300

Three children.._____________ 353 163 46 1,700

Four children............................... 163 97 60 1,700

Five children or more................... 130 86 66 2,400

NEGRO AND OTHER RACES

Total............................. . 1,395 739 53 1,400

No children under 18............ ....... 286 83 29 700

One child............................. ......... 306 133 43 1,100

Two children......... ..................... 258 128 50 1,100

Three children.................. ......... 191 116 61 1,500

Four children................................ 140 105 75 1,600

Five children or more.................. 214 174 81 2,400

> Own or related.

> Based on data for 1968.

earnings alone would be sufficient to lift the income of a family of four above the poverty line.

Additional children tend to reduce her earning

power, while raising family expenses. The extra

welfare allowance for each additional family member is too small to prevent the gap from widening.

The situation is illustrated statistically in table 3.

On the average, poor families headed by women

had total incomes in 1969 which were $1,200

below the poverty threshold, but this income

deficit increased with each child added to the

family. The median difference between income

level and the poverty line (the “poverty gap”)

was $1,100 for those with one child, $1,500 for

those with three children, and $2,400 for those

with five children or more.

One-quarter of all families headed by a woman

arc black. For these families, the rate of poverty is

greater than for white families irrespective of the

number of children. Moreover, large families are

more common among blacks; one-third of the

Negro families headed by women has four children

or more compared with only one-eighth of the

white families.

Among families with children, nearly twothirds had only one child or two children. But when

the children themselves are considered by family

size, a different picture emerges— three-fifths lived

in families with three children or more. These are

the families where the poverty rate ranged from 51

to 76 percent and the poverty gap averaged from

$1,500 to $2,400.

Extent of employment

The proportion of women holding paid jobs

outside the home has been climbing steadily for

25 years and by March 1970, 43 of every 100 women

16 years of age and over were in the labor force

(that is, either employed or seeking work).

The typical pattern has been for a woman to

enter the labor force after completion of her

education and prior to marriage, to leave after

starting a family, and to reenter the labor force

as family responsibilities diminish. During the

last 10 years, however, there has been some

modification of this pattern with the increasing

entry into the labor force of mothers with young

children. Their participation rate, although still

comparatively low, has increased much faster

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than the rate for other mothers. From 1960 to

1969, the rate for mothers with children under 6

years of age increased from 20 percent to 30

percent, while for mothers with children 6 to 17

years of age it increased from 43 to 51 percent.

The data indicate that the labor force participation of mothers responds to economic need. In

March 1969, divorced, separated, or widowed

women with young children under 6 had a participation rate of 47 percent, compared with

29 percent for married women with children

under 6. The higher rate for women without

husbands reflects in part an insufficiency of income from sources other than employment (alimony, child support, welfare, and Social Security).

From the standpoint of developing programs

geared to assist women to earn their way off

welfare, these labor force trends appear somewhat

encouraging. However, the statistics on labor

force participation of women can be misleading

because they reveal nothing about the duration

of employment. It is readily apparent that there

is a high rate of turnover in the female work

force. During 1968, an average of 28 million were

employed, but 37 million different women were

employed at some time during the year. For

insight into the duration of employment, it is

necessary to turn to data on work experience

during the entire calendar year rather than in an

average survey week. Because of concern with

the capacity of women not merely to hold jobs

but to support their families on the basis of their

earnings, it is particularly important to examine

the extent of full-time and part-time labor force

activity, and the extent of year-round work

compared with seasonal or temporary work.

Special tabulations of data on work experience

in 1967, compiled for the Manpower Administration of the U.S. Department of Labor, were

summarized for female heads of families age 16

to 44 years. These are women who still have many

years of potential working life remaining and for

whom job training is a realistic possibility. They

are also the ones, however, who are most likely

to be prevented from working steadily by the

presence of children. Altogether, 70 percent worked

at some time during the year, but only 38 percent

worked throughout the year at full-time jobs.

As table 4 shows, working only part of the year

is not enough to enable many female family heads

Table 4. Work experience of women1 heading families

and extent of poverty among these families in 1967

(Numbers in thousands]

Work experience 

and race

Total

number

of

families

Poor families Percent distribution

Number

Percent

of

total

Total Poor

ALL RACES

Total................................ 2,263 1,029 45 100 100

Year round full time.................. 862 135 16 38 13

All other workers............... ....... 728 373 51 32 36

Part year full tim e............ 439 217 49 19 21

Part time............................. 289 156 54 13 15

No work at all..... ..................... 673 521 77 30 51

WHITE

Total................................ 1,509 557 37 100 100

Year round full time.................. 599 53 9 40 10

All other workers....................... 498 215 43 33 39

Part year full time.............. 305 131 43 20 24

Part time............................. 193 84 44 13 15

No work at all............................ 413 289 70 27 52

NEGRO AND OTHER RACES

Total................................ 752 470 63 100 100

Year round full time.................. 262 82 31 35 17

All other workers...................... 231 157 68 31 33

Part year full time.............. 134 85 63 18 18

Part time............................. 97 72 74 13 15

No work at all........................... 260 232 89 35 49

1 16 to 44 years of age.

to support their families at a level of living above

the poverty line. Of the families headed by women

who were employed only part time or part year,

about half were poor. On the one hand, where

the mother was employed year round full time,

only 16 percent were poor. Of course, supplementary income was a factor in some cases, but

the mother’s earnings were clearly the most decisive factor. On the other hand, three-fourths of

the families headed by nonworkers were poor.

If a woman can hold a professional, managerial,

or clerical job, her chances of keeping her family

above the poverty line are very good (table 5);

only 16 percent of these families were poor. Over

two-fifths of the mothers who worked at all had a

job in one of these white-collar occupations.

Half of all female heads of poor families did not

work at all during the year so that any skills or

experience they might have were not being used.

Of those who did work, nearly half had low-paid

service jobs such as kitchen helpers, maids,

hospital attendants and aides, and laundry

workers. A fifth held semiskilled factory jobs.

Only one-fifth of those with any employment experience (one-tenth of the overall total) worked

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at some time during the year in the better-paid

white-collar occupations.

Weekly earnings of women

Data on the usual weekly earnings of wage and

salary workers in full-time jobs reveal that in

general the median earnings of women full-time

workers are not very high. (See table 6.) The

overall median weekly earnings for all women fulltime workers in M ay 1969 were $87. Even among

white women with high school diplomas, who were

employed mainly in clerical jobs, usual weekly

earnings were only $88.

The data by educational attainment (years of

formal schooling completed) and occupation from

the M ay 1969 earnings survey are instructive.

They reveal that only among the college-educated

professional and managerial groups did a majority

of women working full time earn over $100 a

week. Among those with no college attendance

(three-fourths of the total), only 3 out of every 10

white women and 2 out of every 10 black women

Table 5. Occupation of women heads1 of families, by

poverty status in 1967

[Numbers in thousands]

Total

number

Poor family 

heads

Percent

distribution

Occupation, according to 

longest job held

of

family

heads Number

Percent

of

total

Total Poor

ALL RACES

Total with work experience........ . 1,584 504 32 100 100

Professional and managerial...................... 185 21 11 12 5

Clerical.____ _______ ______________ 483 84 17 30 17

Sales.......................................................... 77 32 42 5 6

Operatives and other blue collar_______ 354 106 30 22 21

Private household___________________ 109 73 67 7 14

Other service workers________________ 331 159 48 21 32

Farm workers______________________ 45 29 C) 3 6

WHITE

Total with work experience______ 1,091 268 25 100 100

Professional and managerial................... 145 17 12 13 6

Clerical and sales___________________ 470 83 18 43 31

Operatives and other blue collar_______ 231 53 23 21 20

Private household and other services___ 217, 101 47 20 38

Farm workers______________________ 28 14 0) 3 5

NEGRO AND OTHER RACES

Total with work experience______ 490 234 48 100 100

Professional and managerial__________ 39 5 (2) 8 2

Clerical and sales___________________ 89 33 37 18 14

Operatives and other blue collar_______ 123 52 42 25 22

Private household and other services___ 222 130 59 45 56

Farm workers______________________ 17 14 <2) 3 6

116 to 44 years of age.

2 Percent not shown where base is less than 75,000.

Table 6. Educational attainment of women Heads 1 of

poor families and usual weekly earnings of full-time

women workers in May 1969

[Numbers in thousands]

Educational attainment by race

Heads of pc 

Number

or families2 

Percent

Usual weekly earnings 

of full-time 

workers (median)

Total................................... 1,025 100 $87

White............................... .............. 556 54 88

8 years or less......... .............. 140 14 70

9-11 years________ ______ 188 18 76

12 years.................................. 163 16 88

13-15 years________ _____ 53 5 100

16 years or more__________ 12 1 138

Negro and other races_________ 469 46 74

8 years or less____________ 147 14 54

9-11 years_______________ 212 21 66

12 years...... ........................... 97 9 80

13 years or more.................... 13 1 115

1 16 to 44 years of age.

2 Poverty status as of 1967.

earned $100 a week or more.

The earnings potential of women heading poor

families is even more restricted because of limited

formal education. Nearly 70 percent of the 1

million in the 16- to 44-year age bracket never

completed high school; 300,000 never went beyond

elementary school. More than half of the least

educated are black. Negro women with less than

a high school education were earning only $60 a

week in the spring of 1969, even working at fulltime jobs. Many were working in domestic and

other service activities not covered by minimum

wage legislation and where hourly pa}' scales are

still comparatively low.

If all women heading poor families were to

become employed at jobs with weekly earnings

commensurate with their education levels, and

assuming that they would be subject to prevailing

practices of racial and sex discrimination in hiring

and pay scales, the}* would earn an average of

about $74 per week (as of the spring of 1969).

Data from the Work Incentive Program show that

the average w i n graduate in a followup sample

was earning about $2 an hour or roughly $80 a

week. A woman who earned that much, and who

worked every week of the year, would make enough

to support herself and her family above the poverty

standard if she had no more than three children.

Women who can be trained to fill clerical,

technical, and lower grade professional jobs, and

who stay on those jobs on a regular year-round

basis, could expect to earn between $5,000 and

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$7,500 a year, on the average. On the other hand,

average earnings are much lower in semiskilled

manual occupation and in service (excluding

domestic) occupations, where about two-fifths of

the female heads age 16 to 44 who work at all are

clustered. Year-round work in these occupations

would yield annual earnings of about $4,500 and

$3,500, respectively.

Programs to upgrade em ployability

Paid work would appear to be a logical solution

to the income problems of many welfare mothers.

However, the data point up several constraints

operating against any employment strategy. If

employment is to be effective in raising family

standards, it must be full time and year round.

Even for the mother of a small or average-sized

family, the cost and difficulty of finding adequate

child care, and the lack of sufficient education and

job training, are formidable barriers to steady

work at good wages.2 For mothers of large families,

these problems are compounded because their

family responsibilities are greater, and their

income needs are larger.

In an effort to overcome these barriers to

employment, Federal programs such as the Work

Incentive Program (w i n ) and the proposed Family

Assistance Act ( f a p ) have been developed in

recent years.3 Both of these programs have training, job placement, and child care provisions which

are designed to enable employable adult members of poor families to find jobs and gain economic

independence.

The benefit and tax rate schedules under f a p

provide some idea of how much a mother would

have to earn to get off welfare completely. If a

four-person family received $3,920 or more in

earned income, its Federal income supplement

would be eliminated entirely. The earnings equivalent of that annual income would be roughly $2

an hour for 2,000 hours of work, or $80 a week for

at least 50 weeks. The head of a six-person family

would have to earn more than $2.50 an hour or

over $100 a week all year long before the income

supplement would phase out completely. In many

northern States (Connecticut, Massachusetts,

New Jersey, New York, Pennsylvania, Minnesota,

in particular), where a f d c payments are relatively

high, the woman’s earnings would have to be

considerably higher to equal welfare payments,

since State welfare benefits would not be reduced

under the proposal.

Of course, any increase in a woman’s earning

power would at least reduce her welfare subsidy.

It would be important, therefore, to take account

of trends in the average payment per family, in

addition to the total number of beneficiaries, if

an integrated income support and employability

program were to go into effect.

The main issue in any employment strategy is

whether the incentives can be made strong enough

to induce welfare recipients to accept training and

jobs. In the recent controversy over the Family

Assistance Program, proponents of the bill pointed

to the provisions for child care, training, job

counseling, and job placement, and to the flexibility in program design to meet the individual

needs of each beneficiary. They stressed that the

poor in this country are imbued with a strong work

ethic, needing only the opportunity to exercise it.

They emphasized that the act was so designed that

the tax and benefit provisions would always make

it more profitable for a recipient to work than not

to work. For the small minority who might otherwise reject the opportunity, the act includes a

provision requiring adults to register with the U.S.

Employment Service unless exempted because of

illness, age, or in the case of female family heads,

the presence of children under 6. Opponents of the

act raised a number of questions about the appropriateness and effectiveness of the work requirement in the case of mothers. Skepticism was voiced

about the availability of jobs; about the costeffectiveness of child care and training; and, above

all, as to whether the monetary incentives would

be strong enough to offset the loss of welfare payments and in-kind benefits (food stamps, medicaid,

etc.) associated with increased earnings.

Perhaps some answers will be forthcoming from

experimentation with income maintenance programs which is now under way in several communities. In the meantime, the data available on

the work experience, occupational and educational

backgrounds, and, particularly, the earnings of

women family heads do give some useful perspective on the feasibility of providing employment

as a substitute for welfare. 

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FOOTNOTEST h e d a ta in th e ta b le s a n d m u ch of th e d a ta u n d erly in g 

th e te x t fo r th is article w ere o b ta in e d from th e C u rre n t 

P o p u la tio n S u rv ey ( c p s ) w hich is co n d u cted b y th e B u reau 

of th e C ensus, in p a r t for th e B u reau of L a b o r S tatistics. 

T he th re e p rin c ip a l sources of in fo rm atio n w ere th e su p p lem e n ta ry in q u iries on fam ily incom e, on w ork experience, 

a n d on w eekly earnings. D etailed ta b u la tio n s on th ese 

su b jects w ere m ad e av ailab le b y th e P o p u latio n D ivision, 

B u reau of th e C ensus; th e Office of M an p o w er a n d E m ­

p lo y m en t S tatistics, B u reau of L a b o r S ta tistic s; a n d th e 

Office of R esearch , M an p o w er A d m in istratio n , D e p a rtm e n t 

of L abor. F o r a d escrip tio n of th e C u rre n t P o p u la tio n 

S urvey, see b l s R e p o rt 313, “ C o n cep ts a n d M eth o d s used 

in M an p o w er S ta tistic s from th e C u rre n t P o p u la tio n S u rv e y .” A n ex p lan a tio n of th e incom e a n d p o v e rty concepts

a n d a discussion of th e reliab ility of th e d a ta are co n tain ed 

in C u rre n t P o p u la tio n R e p o rts Series P -6 0 , p u b lish ed by 

th e B u reau of th e C ensus.

1 F o r a discussion of th e uses a n d lim ita tio n s of p o v e rty 

sta tistic s, sec M ollic O rsh an sk y , “ H ow P o v e rty is M easu re d ,” Monthly Labor Review, F e b ru a ry 1969, p p . 37-41.

2 Sec G enevieve W . C a rte r, “ T h e E m p lo y m e n t P o te n tia l 

of a f d c M o th e rs,” Welfare in Review, J u ly -A u g u st 1968,

pp . 1-11.

3 F o r a d escrip tio n of th ese p ro g ram s, sec th e Work

Incentive Program, F irst A n nual R e p o rt of th e U .S. D ep a rtm e n t of L ab o r on T rain in g a n d E m p lo y m en t, 1970. 

Also see T h e F a m ily A ssistance A ct of 1970, now p e n d in g 

in C ongress.

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Women find jobs in the 

fastest growing industries, 

but remain clustered 

in fewer occupation groups 

than men

ELIZABETH WALDMAN AND

BEVERLY J. McEADDY

Where women

work— an analysis

by industry

and occupation

The last three decades have been years of

extraordinary economic and social change in the

status of women—the tremendous response of

married women to labor market demand; an

increasingly service-oriented economy, accompanied by an increased need for white-collar workers;

changing attitudes toward careers for women

outside the home; the trend toward smaller families; the increase in the number of households

headed by women; and landmark legislation prohibiting employment discrimination based on sex.

Despite these changes, today’s figures on the

employment of women in American industry bear

a striking resemblance to those o f yesterday. In

1970, just as in the three previous census years—

1940, 1950, and 1960—the service industry ranked

first in the employment o f women. Over this 30-

year span, about 60 percent of all employees in

the service industry were women— some 60 percent o f the workers in educational services;

around 75 percent in the medical-health industry;

and about 75 percent in personal services, including those in hotels and private homes. Within

other major industrial categories, such as manufacturing and trade, certain subgroups remain as

female-intensive today as they were yesterday.

Examples are the manufacture of clothing and general merchandising, where at least 50 percent of all

employees are women. (See table 1.)

The recordbreaking growth achieved by American industry since 1940 was made possible, in

part, by the phenomenal increase in the number

and proportion o f w om en, especially married

women, who were able and willing to join the

work force. From 1940 to 1970, nonagricultural

employment of all persons expanded from 32.1

million to 69.1 million, with women nearly half of

Elizabeth Waldman is an economist and Beverly J. McEaddy

a social science research analyst in the Division of Labor

Force Studies, Bureau of Labor Statistics.

the increase (18 million). In 1940, women were 31

percent of all workers in nonagricultural industries; by 1970, 40 percent, as their numbers

almost tripled to 27Vi million. In 1940, almost

half the women in the labor force were single and

only 30 percent were married; in 1970, about 20

percent were single and 60 percent were married.

Over these three decades, the labor force participation rate o f married women rose from 15 to 41

percent and the rate o f mothers with children

under age 6 from 9 to 30 percent.

The enorm ous expansion in the labor force

participation of women has sometimes been referred to as the response of married women to the

tidal w ave o f paperwork that occurred in the

industrial world o f the 1950’s and 1960’s. The

population explosion of post-World War II contributed to the need for expanding all types of

services— among them, medical, educational, personal, and recreational—thus generating more jobs

of the types considered to be traditionally female.

Many jobs in the service industry can be described as exten sion s o f what w o m en do as

homemakers— teach children and young adults,

nurse the sick, prepare food.

Another factor contributing to the concentration

o f women in the service industries is that parttime employment is more readily available there

than in other major industry categories (with the

exception of retail trade). In recent years, about

one-fourth o f all employed women held part-time

jobs. Also, many service industries employ fulltim e w orkers but operate at other than the

standard 9-to-5 schedule—for example, hospitals,

schools, libraries, and hotels. Shift work or other

atypical hours o f em ploym ent may be more

attractive to women who have children.

The following sections discuss in greater detail

the trends in wom en’s employment by industry

and occupation, and are based on (a) establishment data from the monthly nationwide sample

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survey o f nonagricultural payrolls; (b) the Current

Population Survey (CPS), a monthly nationwide

sample survey of households; and (c) the U .S.

Decennial Census of Population. The establishment series provides a count o f job s; the CPS and

Census provide a count of individuals. Despite

these differences, data on the industrial employment of women from one series complements and

confirms the trends indicated by the others.1

Establishment data

Payroll statistics from establishments in nonagricultural industries provide one of the most

detailed, up-to-date appraisals of the employment

o f women in American industry. These data also

permit more precise industry identification than

that obtained through the household interviews of

either the decennial censuses or the monthly

Table 1. Women employed as wage and salary workers in nonagricultural industries, 1940-70

[Numbers in thousands]

Industry

1940 1950 1960 1970

Total

employed

Women

Total

employed

Women

Total

employed

Women

Total

employed

Women

Number

Percent of 

total

Number

Percent of 

total

Number

Percent of 

total

Number

Percent of 

total

Total nonagricultural industries...................... 32,058 9,794 31 43,478 14,113 32 54,579 19,449 36 69,115 27,496 40

Mining____________________________________ 869 12 1 880 22 2 626 30 5 616 50 8

Construction__________ ______ ______________ 1,603 33 2 2,752 86 3 3,062 130 4 3,976 244 6

Manufacturing............................. .............................. 10,317 2,323 23 14,053 3,594 26 17,142 4,354 25 19,566 5,623 29

Durable goods___________________ _______ 5,162 604 12 7,460 1,219 16 9,621 1,707 18 11,596 2,483 21

Nondurable_____________________________ 5,155 1,719 33 6,478 2,337 36 7,464 2,627 35 7,970 3,140 39

Knitting m ills_______ ______________ 217 130 60 189 119 63 195 132 68 962 445 46

Apparel, etc________________________ 768 522 68 1,036 754 73 1,131 858 76 1,201 939 78

Leather and leather products..................... 363 139 38 371 167 45 345 173 50 282 160 57

Food and Kindred products____ ________ 1,054 197 19 1,328 311 23 1,757 417 24 1,364 356 26

Meat products...................................... 207 32 15 267 55 21 311 76 24 0 0 0

Canned and preserved food and seafood.. _____________________ 89 32 36 140 57 41 202 85 42 0 0 0

Confectionary and related products_____ ________ ________ 70 36 51 73 37 51 75 38 51 0 0 0

Chemicals and allied products__________ 433 76 18 642 129 20 850 163 19 980 222 23

Transportation and public utilities_____________ _ 2,911 345 12 4,138 666 16 4,268 757 18 5,039 1,106 22

Telecommunications__________ . . . . . .. 392 210 54 637 389 61 818 426 52 1,071 522 49

Radio and TV______ 23 5 22 0 87 22 25 131 33 25

Trucking and warehouse.______ ___________ 330 17 5 545 42 8 776 61 8 992 106 ii

Wholesale and retail trade____________________ 5,522 1,669 30 8,122 3,013 37 9,653 3,835 40 13,810 5,871 43

Wholesale______*_______________________ 1,009 174 17 1,687 362 21 1,943 422 22 2,907 699 24

Retail_________________________________ 4,514 1,495 33 6,434 2,651 41 7,710 3,413 44 10,903 5,172 47

General merchandise.______________ _ 718 468 65 873 595 68 1,211 821 68 2,005 1,392 69

Eating and drinking__________________ 817 385 47 1,266 719 57 1,429 912 64 2,061 1,271 62

Apparel and accessory stores... ______ l 356 . 181 51 408 266 65 442 317 72 667 437 66

Finance, insurance and real estate........................... 1,294 435 34 1,670 739 44 2,417 1,181 49 3,610 1,870 52

Services_________ . . . ____ ________ ___ 6,984 4,321 62 8,584 5,007 58 11,668 7,241 62 18,282 11,436 63

Personal, including private household and hotels 3,268 2,449 75 2,930 2,097 72 3,247 2,464 76 3,010 2,256 75

Private households.. ___ .. ___._ 2,196 1,931 88 1,598 1,405 88 1,880 1,701 90 0 (1,124) 0

Business and repair_____________ ________ 583 64 11 999 155 16 1,181 287 24 2,006 624 31

Professional service______________________ 2,796 1,736 62 4,166 2,623 63 6,803 4,352 64 12,707 8,352 66

Medical and health________ _________ 745 543 73 1,365 1,006 74 2,223 1,710 77 3,907 3,096 79

Medical and health, except hospitals.. 0 0 393 302 77 544 448 82 1,249 1,039 83

0 0 972 704 72 1,679 1,262 75 2,658 2,057 77

Education__________________ ____ _ 1,514 976 64 2,019 1,283 64 3,292 2,062 63 6,080 3,788 62

0 0 470 281 60 762 464 61 1,550 957 62

0 0 1,549 1,002 65 2,530 1,598 63 4,530 2,831 62

Legal2_____________________________ 156 85 54 120 81 68 144 109 76 386 178 46

Other services, including recreation and amusement 338 72 21 489 131 27 437 138 32 559 204 36

Public administration 1_______________________ 1,758 350 20 2,471 648 26 3,194 909 28 4,216 1,297 31

Postal service___________________________ 309 36 12 451 52 12 551 65 12 719 144 20

Federal public administration____ __________ 299 104 35 1,003 339 34 1,266 444 35 1,528 546 36

State and local____________________ _____ 848 203 24 1,017 258 25 1,377 400 29 1,824 557 31

('1 264 100 38 396 152 38 538 202 38

Local . . . . . __________ 0 753 158 21 981 248 25 1,286 355 28

800 305 38 809 338 42 2,548 1,013 40

1 Data not available.

2 1940 figures include engineers and miscellaneous professionals.

1 1940 figures are for government instead of public administration.

NOTE: Because some industries are not included in this table, subgroups do not 

always add to total for major industrial division.

SOURCE: Census of Population, Industrial Characteristics, 1940 (Vol. Ill), 

1950 (P-E No. ID), 1960 (PC(2) 7F), 1970 (PC(2) 7C); (Bureau of the Census).

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Current Population Surveys. Payroll data were

first collected in 1919, but until 1964 information

on women was available on a regular basis for

only a few selected industries. Today, detailed

tables on the employment of women in more than

400 industrial categories are published quarterly by

the Bureau of Labor Statistics.2

The following discussion uses establishment

data to review recent changes for women in major

industry divisions, with an eye on prospective

trends, and describes changes in the occupational

mix within industries.

From January 1964 to January 1973, the number o f wom en on payrolls in nonagricultural

industries expanded from 19.1 to 27.9 million.

(S ee table 2.) Married or form erly married

w om en, many responsible for sch ool-age or

younger children, accounted for the largest share

of the increase in payroll employment o f women.

Most of the 8.8 million labor force entrants or

reentrants found jobs in the four major industry

divisions that were the fastest growing:

Millions

of workers

Services ................................................................ 2.5

Government ........................................................ 2.4

Wholesale and retail trade ................................ 1.9

Manufacturing .................................................... 1.1

In the late 1960’s, the service industry maintained its position as a principal em ployer o f

women, and by 1973 had more female workers

(6.8 million) than any other industry. O f the

several industries within the service sector that

recorded a robust expansion, the most spectacular

was health care services. The forces that contributed to this industry’s growth—gains in the size of

the population, a rising affluence that enabled

more persons to afford health care and to demand

improved services, and increases in the roles of

special programs covering medical and health

services, such as medicare and medicaid— are

expected to continue and to bring similar rapid

employment increases in the near future. It seems

likely that this industry, in which 8 out of 10

em ployees are w om en, can continue to be a

source of jobs for women.

In January 1973, the trade industry was the

second largest employer o f women (6.3 million),

most o f whom held jobs in retail stores. Women

were only one-fourth (900,000) of the employees

in wholesale trade, but nearly half (5.4 million) in

retail trade. Within retail trade, women made up

two-thirds o f the employees in department stores,

clothing and accessory shops, and drugstores, and

over half in restaurants and other eating and

drinking establishm ents. N ew job openings in

trade during the rest of the 1970-80 decade are

expected to be little more than half what they

were in the 1960’s, because of the greater use of

such laborsavers as computers, automated equipment, self-service stores, and vending machines.3

Manufacturing still employs the largest share of

the male work force, but since the mid-1960’s has

dropped to fourth place for women. In part, this

reflects the fact that in some nondurable goods

industries employing relatively larger proportions

o f wom en— textile mill products, apparel and

related item s, and food and kindred item s—

increased automation and other improved plant

processes have boosted output without any great

increase in employment. During the 1970’s, the

need for additional workers in manufacturing is expected to be largest in such durable goods industries

as machinery, rubber and plastic products, and instruments, all currently male-dominated.

In the same way that the phenomenal growth of

the service industry in the private sector made

jobs available for women, services provided by

governm ent agencies were responsible for the

soaring employment of women on government

payrolls, especially at the local and State levels.

Nearly half (1.1 million) of the entire 1964-73

increase in women’s jobs in government occurred

at the local level in one industry— education.

Two-thirds o f all employees in schools and related

educational activities supported by city, county,

and other local tax jurisdictions were women. In

January 1973, the local and State education industry accounted for nearly 60 percent of the 6.1

million women on government payrolls. Demand

for workers in the education field is expected to

taper off considerably as a result of the decline in

birth rates that began in the late 1950’s. In

contrast, government health and welfare services,

industries in which women are also prominent,

are expected to increase at a rapid pace through

the remainder of the 1970’s.

The industry division encompassing finance,

insurance, and real estate became predominantly

female during the 1960’s, and by January 1973

women were 52 percent of the em ployees (the

1940 and 1950 censuses reported much smaller

proportions, 34 and 44 percent, respectively). The

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Table 2. Women employees on nonagricultural payrolls, by selected industries, January 1964 and January 1973

[Numbers in thousands]

Industry group

1964 1973

Number of 

women

Percent of total 

employed

Number of 

women

Percent of total 

employed

Total nonagricultural industries____ ___________________ __________ _. 19,096 34 27,920 38

Private...___________________ ____________________ _______________ 15,421 33 21,854 37

Mining_________ ____ __________________ ____ ______________________ 34 6 37 6

Construction_______________________________________________________ 143 6 193 6

Manufacturing_____________________________________________________ 4,385 26 5,464 28

Durable goods____ . ___________________ _________ _______ 1,717 18 2,357 21

Fabricated metal products________________ _____ 192 17 264 19

Machinery, except electrical____ __________________ _______ 201 13 297 15

Electrical equipment and supplies____ _______________________ 571 37 781 41

Transportation equipment____________________________________ 168 10 199 11

Instruments and related products...______ ____________________ 123 34 183 38

Miscellaneous manufacturing_______ ______ _______________ 145 40 179 43

Nondurable goods___ _ _____________ . . . .. .. .. 2,668 37 3,107 39

Food and kindred products___________ _____ . . . ___________ 387 23 420 25

Meat products_________________________________ _________ 79 25 94 28

Poultry dressing plants_______________________________ 35 53 52 55

Canned, cured, and frozen foods_____________ ____________ 85 42 89 39

Canned, cured, and frozen seafoods_________ _ _____ 20 58 21 56

Confectionary and related products____________ ____ ________ 39 51 41 51

Tobacco manufacturers____________________ _______ _________ 40 46 30 42

Textile mill products_____ ______________________ ____________ 373 43 467 46

Knitting mills__________ _____________________ _________ 134 67 174 65

Apparel and other textile products_________________ ___________ 994 79 1,062 81

Printing and publishing_________________ _______ . . . 270 29 366 34

Periodicals... ________ _ . . . _____________ ______ . . . 33 48 34 50

Blankbooks and bookbinding______ _______________ _____ .. 21 45 29 51

Chemicals and allied products_________________________________ 160 19 208 21

Leather and leather products.__ _ __________________ _______ 179 53 175 60

Transportation and public utilities_____ . . . . . . _. 706 18 949 21

Communications.. . . . . . . . . . . . ___ 410 50 542 47

Telephone communication... . . . __________________ _ _____ 380 56 493 51

Radio and television broadcasting________ ______ ____ ___________ 22 22 34 25

Wholesale and retail tra d e ____________________ . _____ _______ . . . 4,404 37 6,338 40

Wholesale trade.. _ . . . ___________ _____ _________ _______ 686 22 912 23

Retail trade.. . . . . __________________ _______ _______ _ 3,718 43 5,426 46

Retail general merchandise__________ __________ _____ _______ 1,163 70 1,708 68

Food stores__________________________________ _____ ________ 451 32 694 37

Apparel and accessory stores____ ___________________ ________ 387 65 505 66

Eating and drinking places__________________ ______ _ _______ 969 56 1,431 55

Miscellaneous retail stores____________________ ______ . . . . . 427 42 620 46

Drug stores and proprietary stores__________________________ 222 58 295 62

Finance, insurance, and real estate.__________________ ____ ____ ____ ___ 1,445 50 2,070 52

Banking.. ________ _ _____ . ______ . . . _________ 454 60 721 64

Credit agencies other than banks___ ______ ____________ _______ 167 54 234 57

Security, commodity brokers, and services._ . . ____ . . . . . . . . 38 31 68 35

Insurance carriers______________ ______________________ .. _____ 435 49 578 52

Insurance agents, brokers, and service_______ . ___________________ 124 56 172 59

Real estate____________________________________________________ 190 36 250 34

Services__________ . . . . ___________________________ 4,304 51 6,803 55

Hotels, tourist courts, and motels_______________ __________________ 245 48 346 52

Personal services__________ _____________ __ ________ _______ 553 60 555 62

Miscellaneous business services________________ __________________ 333 34 600 35

Advertising_____ ___________ ______________________________ 40 37 50 43

Credit reporting and collection__ _______ _______________________ 43 70 57 71

Services to buildings.__________________________ ____________ 42 27 119 35

Medical and other health services_____________ _____________________ 1,474 78 2,850 80

Hospitals._____ ________ _______ _____ ______________________ 1,029 82 1,641 80

Legal services_____________________ _____ ___________ ______ ____ 105 62 171 63

Educational services_____________________________________________ 398 44 593 49

Elementary and secondary schools..----------- ------- ------------- ------ - 175 58 255 61

Colleges and universities............................................................................ 197 37 272 42

Government............. .............................................................................. ....................... 3,675 39 6,066 45

Federal___________ ______ _______ ______________ _____ ___ _____ ____ 520 22 767 29

State......................................... ............................................................................. . 692 38 1,248 43

State education................................................................................................. 245 40 535 43

Other State government.......... ......................................................................... 448 37 713 43

Local........................................................................................ .............. .................. 2,463 46 4,050 50

Local education............................................................... ................................ 1,831 63 2,956 63

Other local government..---------------------------- --------------------------------- 633 26 1,095 32

NOTE; Because some industries are not included in this table, subgroups do not SOURCE; Bureau of Labor Statistics, 

always add to total for major industrial division.

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1964-73 expansion in this industry’s jobs for

women occurred primarily in banking and insurance. These industries and credit agencies are

expected to continue to expand through the

remainder of this decade, providing new opportunities for women.

Occupation by industry

W om en, like men, find jobs in the fastest

growing industries. However, no matter what

industry women are in, they remain clustered in

fewer occupation groups than men. (See table 3.)

Service. As pointed out earlier, the service industry employs the largest number of working women

and ranks third in the employment o f men. In

1970 as in 1940, most women in this industry were

employed in the same three occupation groups:

professional-technical, services, and clerical-sales

(chart 1). Yet there was a striking redistribution

which stemmed from the extraordinary demands

for clerical support made by the education and

health service industries.

In the 30-year span, the proportion of women in

the service industry who perform service jobs

increased from 20 to 28 percent. Examples of

occupations still dominated by women are food

services, practical nurses, and dental assistants.

In p rofession al-tech nical occup ation s, 2

million women are teachers in elementary and

high school. Women also predominate in registered nursing, social work, libraries, dietetics,

physical therapy, and dental hygiene.

The 1940-70 redistribution shifted men into the

more prestigious, better paying professionaltechnical group. In the education industry, about 70

percent of the teachers in colleges and universities

are men; about 70 percent of the teachers in elementary and high sch ools are w om en. D octors,

lawyers, engineers, and many other professionalTable 3. Occupation group of employed wage and salary workers in nonagricultural industries, by sex, 1970

Total

Percentage in each occupation group

Industry and sex

Number 

(in thousands)Percent

Professional, 

technical and 

kindred 

workers

Managers

and

administrators

Sales

workers

Clerical

and

kindred

workers

Craft

and

kindred

workers

Operatives,

except

transport

Transport

equipment

operatives

Laborers,

except

farm

Service

workers,

except

private

household!

Total: Women.......................................... 26,373 100.0 16.6 3.2 7.4 37.5 1.8 15.0 0.5 0.9 17.1

Men................................................ 41,619 100.0 14.6 10.5 6.9 8.6 22.2 15.2 6.4 7.0 8.8

Mining and construction: Women__________ 294 100 0 5.1 3.4 1.0 72.1 8.5 3.7 .7 2.7 2.4

Men_____________ 4,297 100 0 5.9 7.2 .7 2.8 53.2 9.8 5.0 14.4 1.1

Manufacturing: Durable goods: Women_____ 2,483 100 0 3.8 1.1 .5 33.7 4.8 52.6 2 1.9 1.3

Men_______ 9,113 100.0 14.0 5.8 1.9 6.4 26.7 34.0 3.3 5.5 2.4

Nondurable goods: Women... 3,140 100.0 3.9 1.0 1.3 22.5 4.4 63.9 .2 1.7 1.0

Men_____ 4,831 100.0 10.0 7.8 5.9 ,6.8 23.8 30.8 5.9 5.7 3.3

Transportation and public utilities: Women___ 1,106 100 0 4.2 3.2 1.3 74.6 2.0 1.1 7.7 .8 5.2

Men........... 3,934 100.0 8.8 8.1 1.3 11.0 28.6 3.9 25.3 10.2 2.8

Wholesale trade: Women........................... ......... 699 100 0 2.7 4.1 5.3 67.0 2.3 13.7 .7 2.6 1.6

Men______ _______ ______ 2,208 100.0 5.0 16.9 24.6 10.0 12.5 7.3 14.2 7.9 1.5

Retail trade: Women__________ ____ _____ 5,171 100 0 1.3 4.8 31.8 29.2 1.6 3.6 .2 1.3 26.2

Men............................................. 5,732 100.0 2.8 17.0 19.3 6.8 14.4 11.3 5.5 9.3 13.5

Finance, insurance, and real estate: Women... 1,870 100.0 2.9 6 2 7.3 79.7 .3 .3 .2 3.2

Men.......... 1,740 100.0 7.9 26.1 32.4 19.6 3.4 .5 .5 2.3 7.5

Services: W omen............................................ 10,312 100.0 36.9 2.5 .7 28.0 .6 3.1 .1 .4 27.7

Men.................................................... 6,846 100 0 40.4 9.6 1.4 6.0 11.8 3.9 2.3 3.8 20.8

Medical and other health services: Women. 3,096 100 0 33.1 1.5 .2 20.4 .4 1.6 (l) .3 42.6

Men___ 811 100.0 34.5 7.2 .2 5.4 10.0 3.0 1.4 1.8 36.4

Educational services: Women...................... 3,788 100.0 59.0 2.6 .2 22.7 .4 .4 .2 .1 14.5

Men.......................... 2,292 100.0 60.0 9.2 .3 5.2 4.4 1.0 .7 1.8 17.5

Public administration: Women____________ _ 1,297 100.0 11.3 6.3 .3 72.6 .8 .8 .1 .6 7.2

Men............................... 2,919 100.0 18.9 12.5 .2 25.3 9.7 2.0 2.0 4.0 25.5

1 Excludes all women, but includes a few men, who were private household workers. SOURCE: Census of Population, 1970: Occupation by Industry, Report (PC(2) 

* Less than 0.05 percent. 7C), (Bureau of the Census).

NOTE: Because of rounding, sums of individual items may not equal totals.

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technical occupations have remained substantially

male-intensive.

Chart 1. Occupational distribution of wage and salary workers, selected industries, 1940 and 1970

WOMEN MEN

Percent 

100 ‘

75-

50'

25'

SERVICE

Professional and 

technical workers

, Managers - —.

Clerical and / 

/ sales workers

/Craft workers x

Operatives —

Service workers 

(excl. domestic)

^ Laborers

* 100

■75

50

■25

(2.4 mil.) (10.3 mil.: (2.4 mil.) (6.8 mil.)

TRADE

„

... -^technical workers

^ M a na g e rs/^

Clerical and 

/ sales workers

/

Operatives-----

^ Service workers \

^ Laborers -—

(1.7 mil.) (5.9 mil.) (3,9 mil.) (7.9 mil.)

MANUFACTURING

Trade. Since 1940, very little occupational change

has taken place for women in trade. Nearly 9 out

of 10 women in this industry were in retail trade,

where sales, clerical, and service jobs predominated, in that order.

In general m erchandising, wom en held the

greatest proportion of sales jobs, as well as such

clerical positions as bookkeepers, cashiers, office

machine operators, secretaries, and typists. A few

more were in managerial and administrative jobs

as buyers and sales managers, and a relatively

small number were in the operative group as

dressmakers and seamstresses. Women working

in eating and drinking establishments were mostly

waitresses, cooks, and clerical workers.

A smaller proportion of men (7 out of 10) were

in retail trade. Changes from 1940 to 1970 in their

occupational pattern reflect mainly the relative

increase in the need for managers and skilled craft

workers (carpenters, electricians, mechanics, and

repairers) and a decrease in the need for clerical

and sales workers.

Manufacturing. In both 1940 and 1970, approxim ately 9 out o f 10 wom en working in the

manufacturing industry held semiskilled operative

or white-collar clerical jobs. Nearly three-fifths

were engaged in the production of nondurable

goods. In this sector, most women work in the

production end as operatives (for example, assemblers), as checkers, examiners, and inspectors,

and as sewers and stitchers. About 11 percent of

all professionals in nondurable goods were women.

G overn m en t. About 22 percent o f women on

nonfarm payrolls are governm ent em ployees,

mostly (86 percent) in State and local governments working as teachers, administrators, clerical workers, maintenance workers, librarians,

nurses, and counselors.

A ccording to a survey by the U .S . Civil

Service Commission4 of women employed by the

Federal Government in 1970, most worked in

nonprofessional administrative, clerical, and office

service jobs. Roughly 77 out of 100 (compared

with 44 of 100 men) were in grades GS-1 through

-6 in 1970. A nother 20 out o f 100 wom en

employees (compared with 32 of 100 men) were in

grades GS-7 through -11, and only 3 out of 100

women (but 23 out o f 100 men) were in the

highest paying grades, G S-12 and above.

Women’s share o f full-time white-collar Federal

em ploym ent by individual grade reveals even

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more dramatically their concentration in the nonprofessional grade series. (See table 4.) Grades in

which women predominated, G S-1 to -4 , are

entry-level clerical and support positions in the

nonprofessional job series. G S-5 is the entry

grade for professional employment. Positions in

G S-5, -7 , -9 , and -11 are primarily professional,

technical, or administrative jobs, requiring a baccalaureate or higher degree or equivalent professional, technical, or administrative experience.

Other industries. Most of the occupations women

hold in the finance, insurance, and real estate

industry are clerical—about 80 percent. Approximately 9 out o f 10 bank clerks and tellers are

women, but very few bank officers are. In the

transportation and public utilities industry, about

20 percent of the employees are women; over half

o f these women work in communications, where 9

out o f 10 are telephone operators. Of women

employed in the construction and mining industries, most are in clerical occupations.

Occupational shifts

Overall, the 1940-70 changes in the occupational pattern o f women, as well as of men, mirror

the shift from the predominantly goods-producing

economy prior to World War II to the serviceproducing econom y o f the 1970’s. For women,

the occupational pattern changed from half blue,

half white collar to one that is decidedly white collar. Today, over 60 percent of women and 40 percent of men employed in nonagricultural industries

are in white-collar work. (See chart 2.)

In 1940, women working in nonagricultural

industries were concentrated in three broad occupational groups. Roughly half held service and

blue-collar operative jobs (30 and 21 percent), and

a third were in the white-collar clerical-sales

group. For those in services, prominent occupations were waitress, hairdresser, cook, and practical nurse. Operatives were mostly engaged in the

manufacture of clothing (sewing machine operators). The clerical-sales group were mainly stenographers, typists, secretaries, bookkeepers,

cashiers, and retail saleswomen. Of the 14 percent

in white-collar professional-technical jobs, 2 out

of 4 were teachers and 1 out of 4 were nurses.

Thirty years later, working women were still

highly concentrated in the same three broad

occupational groups, but a much larger share

were in the clerical-sales field, the professionaltechnical proportion had edged up, and the service

and operative proportions had declined.

Do the changes represent an improvement in

the lot of em ployed women? The shift out o f

service jobs as domestics and into white-collar

jobs is a profound improvement, especially among

Negro wom en. And women now are a larger

share of employees in a few o f the more prestigious, better paying occupations. In 1940 women

were only 1 out o f 20 physicians, compared with 1

of 8 today (1973 average). From 1940 to 1973, the

proportion of real estate agents and brokers who

were women grew from 9 to 36 percent.

In the area of job discrimination by sex, there

are a few “ breakthroughs” into typically masculine jobs. For example, today 30 percent of all

bartenders are women, compared with percent in 1940, and about 37 percent of the busdrivers are women, a rarity in 1940 when they were

less than 1 percent. However, many of today’s

female busdrivers may operate school buses part

time, part o f the year, and for low pay. Thus,

what appears to some persons to be an occupational improvement—the movement o f women out

of their homes with its unpaid housework and into

the paid labor force— to others represents no gain.

Self-employed women

Women have made considerable inroads into

the traditionally male-intensive province of selfTable 4. Women as full-time white-collar employees in

Federal Government agencies,1 October 31, 1970

General schedule

(GS)

grade

Salary1

Number of

employed

women

Women as

percent of

total employed

1.......................................... $4,125 2,913 68

2.......................................... 4,621 18,576 76

3.......................................... 5,212 86,274 78

4.......................................... 5,853 139,664 63

5...................................... - 6,548 191,678 32

6.......................................... 7,294 65,089 48

7.......................................... 8,098 54,037 38

8.......................................... 8,956 12,431 26

9.......................................... 9,881 43,441 24

10........................................ 10,869 3,890 12

11........................................ 11,905 19,325 12

12........................................ 14,192 9,870 7

13........................................ 16,760 4,622 5

14........................................ 19,643 1,817 4

15..............- ...................... 22,885 942 3

16 and higher..................... 26,547+ 158 2

1 Excludes employees of Central Intelligence Agency, National Security Agency, 

Board of Governors of Federal Reserve System, and foreign nationals overseas.

1 The rate for basic pay for employees is step 1 of the grade.

SOURCE: "Study of Employment of Women in the Federal Government, 1970,"

(Washington, U.S. Civil Service Commission, Bureau of Manpower Information Systems, 1971), pp. 17, 235.

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em ploym ent, where their share rose from 17

percent in 1940 to 26 percent in 1973. A total of

1.4 million women were self-employed in nonagricultural industries in 1973, nearly 600,000 more

than in 1940. Over this period, the number of selfemployed men rose only slightly to 4 million— a

minor increase, especially when compared with

the doubled employment of men in nonfarm wage

and salary work. The shift from small owneroperated businesses to corporate ownership contributed to the lack of increase for men. Increased

demands in the more female-intensive service and

trade industries drew more women into entrepreneurship.

N early all o f the self-em ployed wom en in

nonagricultural industries in 1973 were in service

and retail trade. In the service industry, over 6

out of 10 were in personal services (operating

beauty shops, laundries, dressmaking shops, and

child care facilities) and 3 out of 10 in professional

services (medical enterprises, such as nursing

homes, and educational services).

The occupational distribution of self-employed

women differs from that of women in wage and

salary jobs. Proportionately more o f the selfemployed were managers or proprietors (24 versus 4 percent) and fewer were in clerical-sales (18

versus 43 percent), where self-employed women

are found in such fields as court stenography or

real estate sales. Self-employed women are generChart 2. Occupational distribution of wage and salary workers, nonagricultural industries, 1940 and 1970

WOMEN MEN

Professional and

ally older than wage and salary women, in part a

reflection o f the greater experience and maturity

necessary to run their own businesses or careers.

Median ages in 1973 were about 46 and 36 years.

Earnings

Further evidence that wom en have not yet

penetrated the high-skill, high-paying jobs is found

in the payroll data on weekly and hourly earnings

in nonagricultural industries. In January 1973,

most industries paying average weekly earnings of

less than $100 were fem ale-intensive. Several

were paying under $90 a week, while the weekly

paycheck for all industries averaged $138.5 Figures on hourly earnings, which exclude the effect

of part-time and overtime work, support conclusions based on weekly earnings. (See table 5.)

In 1972, w om en were 28 percent (or 572

million) of the total workers in the manufacturing

industry, yet most of these women were concentrated in the lower paid and less-skilled jobs. The

average salary for all manufacturing workers was

$159 a w eek in January 1973. For those in

manufacturing industries that were female-intensive, the average was much lower—for example,

the apparel industry, in which 81 percent of

em ployees were wom en, paid average weekly

salaries of only $93.

The service industry—the most female-intensive of the major industry groups, with 55 percent

o f its workers wom en— em ployed 6.8 million

women in January 1973; earnings averaged $111 a

week. About 1.6 million women worked in hospitals, where weekly earnings averaged $108. Another 600,000 women worked in hotels and laundries-drycleaners, where average weekly wages

were $76 and $87, respectively.

Another low-paying female-intensive industry is

retail general m erchandise, with an average

weekly wage of $82. Although part-time work

undoubtedly accounts for some of the low earnings, most jobs in department stores and restaurants are known to be low paying.

M ale-intensive industries are on the higher

rungs of the wage ladder: Construction—6 percent

female, paying $223 average a week; transportation and public utilities—21 percent female, paying an average o f $196 a w eek (switchboard

operators averaged $126, line construction employees $228); m anufacturing—here the list is

extremely long. In transportation equipment, 10

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percent fem ale, average earnings were $210 a

week; in food products, the malt liquor industry

em ployed 7 percent women and the average

worker earned $229 a week. Among retail trade

industries, the most female intensive were the

lowest paying. Yet employees on the payrolls of

motor vehicle dealers, only 11 percent of whom

were women, were among the highest paid workers in retail trade—$152 a week.

Male-female earnings differentials have always

existed, but in recent years with the increase in

women’s labor force activity these differentials

have become the focus of great concern. One

form of discrimination was the barring of women

from the type of jobs men held, such as skilled

workers and executives. A typical example was

cited in a standard college-level economics text;

. . . in a large electrical-goods plant, job evaluation experts divide all factory work into tw o parts:

w om en’s jobs and m en’s jobs. The low est pay o f the

men begins about where the w om en’s highest pay

leaves off; yet both management and the union will

admit, off the record, that in many borderline jobs

the productivity o f the wom en is greater than that of

the m en.6

Many researchers believe the earnings differential results partially from the role that society has

traditionally arrogated to women. In a study on

wage differentials by sex, using 1959 and 1960

Census of Population and Housing data, Victor

Fuchs suggested that the percentage point difference in male-female wages “ can be explained by

the different roles assigned to men and women.

Role differentiation, which begins in the cradle,

affects the choice o f occupation, labor force

attachment, location of work, postschool investment, hours of work, and other variables that

influence earnings.” 7 He believes a reduction in

this role discrimination would eventually result in

a narrowing o f the male-female differential in

earnings. Professor Fuchs also stated that consumer discrimination (such as the preference of

customers in expensive restaurants for waiters

rather than waitresses) may be more significant

than employer discrimination.

Area wage surveys covering six industry divisions recently published by the Bureau of Labor

Statistics indicate that on a nationwide basis, pay

levels were consistently higher for men than for

women working in the same occupation.8 In a

study of pay differences between men and women

in the same job, John Buckley— while neither

denying nor confirming that wage discrimination by sex existed—acknowledged that “ experience in implementing the Equal Pay Act indicates

that some discriminatory practices do exist.” 9

Differences in female-male earnings in the Federal Government occur in part because women

remain clustered in the lowest paid grades.

Table 5. Gross hours and earnings of production or non*

supervisory workers 1 on private nonagricultural payrolls,

selected industries, January 1973

Industry

Average

earnings

Average

hours

Weekly Hourly Weekly Overtime

Total private................................................ $138 $3.77 3 6.6 -

Mining..................................................................... 190 4 .60 41.3 -

Contract construction................. ........................... 2 2 3 6 .42 3 4 .8 _

Manufacturing___________________________ 159 3 . 9 8 4 0.0 3.6

Durable goods________________________ 173 4 . 2 3 4 1.0 3.9

Fabricated metal products...................... 169 4 . 1 3 4 1 .0 3.9

Machinery, except electrical................... 188 4 . 4 4 42.4 4.5

Electrical equipment and supplies____ 153 3 .80 40.3 3.0

Transportation equipment___________ 210 5.00 4 1.9 4.8

Miscellaneous manufacturing________ 124 3 . 2 4 38.4 2.4

Nondurable goods_____________________ 140 3 .61 3 8.7 3.2

Food and kindred products__________ 149 3 . 7 5 3 9.8 3.8

Canned, cured, and frozen foods... 119 3.15 3 7 .8 3.0

Confectionery and related products. 125 3 .29 3 7.9 2.2

Textile mill products_______________ 112 2 .87 39.1 3.9

Knitting mills_________________ 99 2 . 7 6 35.7 2.4

Apparel and other textile products____ 93 2 . 7 2 34.1 1.2

Chemical and allied products............... 181 4 .36 41.5 3.5

Leather and leather products________ 103 2 .77 37.2 1.9

Transportation and public utilities___________ 196 4 . 8 7 40.2 —

Telephone communication______________ 175 4 . 4 7 39.1 —

Switchboard operating employees2___ 126 3 .65 3 4.4 —

Line construction employees3........ ....... 2 2 8 5.23 4 3.6 —

Wholesale and retail trade__________ ______ 107 3.11 3 4 .5 —

Wholesale trade___ __________________ 1 5 8 3 .99 39.5 —

Retail trade__________________________ 91 2 .78 32.9 —

Retail general merchandise__________ 82 2 . 6 1 31.3 —

Food stores_______________________ 102 3.20 3 2 .0 —

Apparel and accessory stores............... . 78 2 . 5 4 30.6 —

Eating and drinking places4 _________ 62 2.08 29.7 —

Motor vehicle dealers______________ 152 3 . 7 7 4 0 .2 —

Drug stores and proprietary stores........ 82 2 . 6 7 30.7 —

Finance, insurance, and real estate...................... 131 3 .54 3 7 .0 —

Banking.......................................................... 114 3 . 0 7 3 7 .0 —

Services................................................................... 111 3.27 3 3 .9 —

Hotels, tourist courts, and motels4............... 76 2 . 3 5 3 2.3 —

Laundries and drycleaning plants.................. 87 2 .50 34.7 —

Hospitals.......................................................... 108 3 .15 3 4.3

1 Data relate to production workers in mining and manufacturing; to construction 

workers in contract construction; and to nonsupervisory workers in wholesale and retail trade; finance, insurance, and real estate; transportation and public utilities; and 

services.

1 Data relate to employees in such occupations in the telephone industry as switchboard operators; service assistants; operating room instructors; and pay station 

attendants. In 1971, such employees made up 29 percent of the total number of 

nonsupervisory employees in establishments reporting hours and earnings data.

3 Data relate to employees in such occupations in the telephone industry as central 

office craft workers; installation and exchange repair craft workers; line, cable, and 

conduit craft workers; and laborers.

4 Money payments only; tips not included.

SOURCE: Bureau of Labor Statistics.

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It is not within the scope of this article to

explore in depth the various reasons for malefem ale differentials in pay, but research has

continued in this area.10 Data presented here takes

the differential into account as an important factor

in women’s industrial characteristics.

Education

For today’s working woman to achieve professional status in the higher paying, traditionally

male-intensive occupations in many industries,

she must acquire more years of formal higher

education. To illustrate, doctors, dentists, airline

pilots, metallurgists, architects, and certified public accountants must have more years of schooling

than are needed for most occupations. For men,

the returns on the investment in education are

usually high in terms o f money and prestige.

Women, even with the required years of schooling,

often do not obtain returns equal to men’s.

The 1972 amendments to the Equal Pay Act

and the Civil Rights Act have outlawed many

Table 6. Women and men employed in nonagricultural

industries, by educational attainment, March 1973

[Percent distribution]

Years of school completed

Sex and Industry Total

Less

than

8

8

9

to

n

12

13

to

15

16

or

more

WOMEN

Total: Number (thousands). 29,968 1,153 1,547 4,987 13,995 4,376 3,910

Percent______ ____ 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Mining and construction.......... . 1.2 0.4 0.8 0.5 1.4 2 0 0.6

Manufacturing________ _____ _ 19.4 41.1 35.0 28.3 19.9 9.7 4.1

Transportation and public

utilities__________________ 3.7 1.0 1.0 2.3 4.9 4.2 2.1

Trade......................... .................. 22.9 21.9 27.1 33.7 24,6 19.1 6.0

Finance, insurance, and real

estate_______ ________ _ 7.5 2.2 2.3 3.3 10.3 10.3 3.5

Service, except private household........................................ . 40.9 31.5 32.2 29.5 33.2 48.8 80.2

Public administration................... 4.4 2.0 1.6 2.3 5.6 5.8 3.5

MEN

Total: Number (thousands). 48,152 3,344 3,399 8,575 17,585 7,223 8,026

Percent....................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Mining and construction.............. 11.2 17.4 18.3 13.9 11.9 7.9 4.0

Manufacturing....... ................. 30.3 37.2 36.1 32.5 33.6 25.5 19.4

Transportation and public

utilities..................................... 9.0 10.3 9.5 8.8 10.7 9.7 3.9

Trade........................................... 20,7 15.5 18.4 25.6 21.7 24.7 12.8

Finance, insurance, and real

estate....................................... 4.4 2.5 2.0 1.8 3.5 7.1 8.7

Service.................................... . 18.2 14.0 13.0 13.3 11.2 16.8 43.6

Public administration................... 6.3 3.1 2.8 4.1 7.4 8.3 7.6

SOURCE: Bureau of Labor Statistics.

barriers to employment, among them job quotas

by sex and unequal male-female wage scales for

the same job. While educational attainment alone

is not a cure-all for working women (legislation

helps), the statistics on women’s education and

labor force participation indicate that more years

of formal schooling would assist in equalizing

women’s position with that of men.

Annual surveys on educational attainment show

that, for both men and women, participation in the

labor force is lowest and unemployment rates highest for those who complete the fewest years of

school. The March 1973 survey shows:

U nem ploym ent Labor

Y ears o f sch ool co m p leted rate fo rce rate

11 years or less ......................... 32 8.6

12 years ...................................... 51 5.3

4 years o f college or more .. 61 2.7

Women who have graduated from college earn

over twice as much annually as women at the

lowest end of the education scale. For women

employed year round in full-time jobs, median

earnings in 1972 were $8,925 for the college

graduates, $5,770 for high school graduates, and

$4,305 for those who did not complete elementary

school. A similar education-earnings relationship

was evident for employed men, but their yearround, full-time earnings were substantially above

women’s—$14,660, $10,075, and $7,575, respectively, for the corresponding groups.

Earnings in the different educational categories

are also a reflection of the differences in the

industrial distributions o f employment. Of the

women in nonagricultural jobs who were college

graduates, 8 out of 10 were in service industries,

mostly professional services, in March 1973.11

(See table 6.) Women who were high school

graduates, but had no college education, were more

widely distributed: (1) one-third in the service industry, largely professional services; (2) one-fourth

in trade, largely retail stores; and (3) one-fifth in

manufacturing. Among working women at the bottom of the educational scale, mostly older women

who had either not completed or never attended

elementary school, two-fifths were in manufacturing, largely semiskilled employees.

Toward tomorrow

Today’s working women are in the throes of

obtaining equal consideration with men in the job

market through the legislation that prohibits dis133

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crimination in employment. Tomorrow’s working

women will be affected not only by the measure

of success achieved today, but also by economic

conditions, changes in lifestyle (for exam ple,

smaller fam ilies), the mode of enforcement of

legislation prohibiting discrimination, provision of

child care services for mothers on industry payrolls, and the extent o f formal education or

technical training of women for traditionally male

occupations. CH

-FOOTNOTES

1 Establishment data are based on payroll records compiled

monthly from mail questionnaires by the Bureau of Labor

Statistics in cooperation with State agencies. The payroll

surveys provide detailed industry information on wage and

salary employees in nonagricultural establishments. The Current Population Survey (CPS) is conducted each month for the

Bureau of Labor Statistics by the Bureau of the Census. It is

based on household interviews obtained from a sample of the

population 16 years old and over

The CPS definition of nonagricultural employment comprises persons in nonagricultural industries who were wage

and salary workers (including domestics and other private

household workers), self-employed, or unpaid and working 15

hours or more during the survey week in family-operated

enterprises. The payroll survey covers only wage and salary

employees on the payrolls of nonagricultural establishments.

In the household approach, employed persons holding more

than one job are counted only once and are classified

according to the job at which they worked the greatest number

of hours during the survey week. In the payroll series, persons

who worked in more than one establishment during the

reporting period are counted each time their names appear on

payrolls. For example, workers may be counted more than

once if they hold two jobs concurrently or leave one job for

another during the same reference period and thus appear on

the payrolls of both employers.

The household survey includes among the employed all

persons who had jobs but were not at work during the survey

week—that is, were not working but had jobs from which they

were temporarily absent because of illness, bad weather,

vacation, labor-management dispute, or because they were

taking time off for various other reasons, even if they were not

paid by their employers for the time off. In the payroll series,

persons on leave paid for by the company are included, but

not those on leave without pay for the entire payroll period.

For a detailed description of these series and differences

between them, the following publications are available from

the Bureau of Labor Statistics: Concepts and Methods Used

in Manpower Statistics from the Current Population Survey,

Report 313 (Bureau of Labor Statistics, 1967); Handbook of

Methods for Surveys and Studies, Bulletin 1711 (Bureau of

Labor Statistics, 1971), ch. 2; and Gloria P. Green, “ Comparing employment estimates from household and payroll surveys,” Monthly Labor Review, December 1969, pp. 9-20,

reprinted as BLS Reprint 2651.

The Decennial Census o f Population is conducted by the

Bureau of the Census to obtain a house-to-house enumeration

of each person by questionnaire. For an employed person,

1970 census information pertains to one specific job held

during the reference week. If employed at two jobs or more,

the job at which the person worked the greatest number of

hours during the reference week was to be represented.

Census reports on specific subjects, such as occupation by

industry, are frequently based on representative samples of the

total population. The intercensal statistics provided by the

Current Population Survey are generally designed to be

comparable to decennial census statistics. For a detailed

description of the 1970 decennial census and comparability

with earlier censuses and other data, see Census o f Population: 1970, General, Social and Economic Characteristics,

Final R eport P C (1)-C 1, U.S. Summary (Bureau of the

Census).

2 Detailed establishment data on women employees were

first published in the May 1963 Employment and Earnings.

Since 1967, the data have been published in Employment and

Earnings once each quarter (February, May, August, and

November) as table B-3, “Women Employees on Nonagricultural Payrolls, by Industry.” In addition, annual and monthly

data appear in Employment and Earnings, United States,

1909-72, Bulletin 1312-9 (Bureau of Labor Statistics, 1973),

and the Handbook of Labor Statistics, 1973, Bulletin 1790

(Bureau of Labor Statistics, 1974).

3 See Occupational Outlook Handbook, 1972-73 Edition,

Bulletin 1700 (Bureau of Labor Statistics, 1973).

4 See Study o f Employment o f Women in the Federal

Government, 1970, Pamphlet SM 62-06 (Washington, U.S.

Civil Service Commission, Bureau of Manpower Information

Systems, 1971), table B.

5 See Employment and Earnings, May 1973, table B-3,

“ Women Employees on Nonagricultural Payrolls, by Industry,” and Employment and Earnings, Aprif T973v~4able^ Cr2,

“ Gross Hours and Earnings of Production or Nonsupervisory'

Workers on Private Nonagricultural Payrolls, by Industry.”

6 Paul R. Samuelson, Economics (New York, McGraw-Hill

Book Co., 1967), p. 120.

7 Victor R. Fuchs, “Differences in hourly earnings between

men and women,” Monthly Labor Review, May 1971, pp. 9-

15. See also pp. 23-26, this issue.

8 Area Wage Surveys: Metropolitan Areas, United States

and Regional Summaries, 1969-70, Bulletin 1660-92 (Bureau

of Labor Statistics, 1971).

9 John E. Buckley, “ Pay differences between men and

women in the same job,” Monthly Labor Review, November

1971, pp. 36-39.

10 See, for example, Paul O. Flaim and Nicholas I. Peters,

“ Usual weekly earnings of American workers,” Monthly

Labor Review, March 1972, pp. 28-38. Reprinted as Special

Labor Force Report 143.

11 See William V. Deutermann, “ Educational attainment of workers, March 1973,” Monthly Labor Review,

January 1974, pp. 58-62. Reprinted as Special Labor Force

Report 161.

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Special Labor Force Report reviews 

employment gains of veterans 

during the year ending in June 1972, 

and new'data on occupations, 

industry, and residence

KOPP MICHELOTTI AND KATHRYN R. GOVER

Job prospects brightened for veterans during the

year ending in June 1972, as young, newly separated

servicemen returned to an economy in which employment was generally on the rise while unemployment remained stable. The number of veterans with

jobs increased steadily during this period, and the

unemployment rate for Vietnam Era veterans1 in

ages 20 to 29 dropped a full percentage point to 8.0

percent (seasonally adjusted) in the second quarter

of 1972. Subsequently, the rate fell even further to

7.2 percent in the third quarter.

The civilian economy had to absorb fewer new

veterans, as military discharges declined. In fiscal

1972, discharges numbered 880,000, down from an

average of one million in each of the 3 preceding

years, reflecting in part the drop in Armed Forces

inductions that began about 3 years earlier.

At the close of fiscal 1972, the United States had

been engaged in the war in Southeast Asia for 8

years, and 5.7 million men were Vietnam Era veterans. About 80 percent of the veterans were in their

twenties and another 12 percent were 30 to 34 years

old. The older group has been increasing in size as

the men separated several years ago move out of

their twenties. In the second quarter of 1972, there

were about 660,000 in this age group compared with

420,000 a year earlier. About 97 percent were in the

labor force and their unemployment rate (not seasonally adjusted) was 2.7 percent, not materially different from the 3.0 percent rate for nonveterans 30

to 34 years old.

The number of veterans in ages 30 to 34 is still

too small to permit either reliable adjustment for

recurring seasonal patterns in their employment or

detailed tabulations for such basic characteristics as

race and duration of unemployment. Since the jobfinding problems of veterans 30 to 34 years old are

Kopp M ichelotti and Kathryn R. Gover are Social Science

Research Analysts in the Division o f Labor Force Studies,

Bureau o f Labor Statistics.

The employment

situation of

Vietnam Era

veterans

much less serious than for the group under age 30,

this analysis will continue to focus on those 20 to 29

years old.

This annual review of the employment situation of

male Vietnam era veterans includes, for the first

time, information on occupation and industry of employment, residence, household relationship, and reasons for being unemployed or out of the labor force.

Employment

During fiscal 1972 all of the net growth in the

veterans’ labor force was in employment, as the

number of 20- to 29-year-old veterans with jobs rose

by 550,000 to average 3.9 million. Similar patterns

of increase occurred with respect to the nonveteran

labor force and employment levels. (See table 1.) A

year earlier employment had accounted for only

three-fourths of the labor force increase for veterans

and two-thirds for nonveterans.

Occupation. The occupational distribution of employed veterans and nonveterans 20 to 29 years old

is generally the same, with the exception of professional and technical workers and craftsmen. (See

table 2.) In the second quarter of 1972, about onefourth of the veterans were craftsmen (such as skilled

construction workers and mechanics), compared

with one-fifth of the nonveterans. A smaller proportion of veterans than nonveterans were in professional and technical jobs (11 and 17 percent, respectively). For the 20- to 24-year-olds, the proportion of veterans in these occupations was less than

half that of nonveterans. This gap reflects the lower

percentage of college graduates among the veterans.

Younger veterans (age 20-24) were more concentrated in jobs which generally require less education, training, and experience. In the second quarter

of 1972, about two-thirds of the employed younger

veterans but only half of the veterans 25 to 29 years

old were blue-collar workers— craftsmen, operatives,

From the Review of December 1972

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and nonfarm laborers. On the other hand, less

than a third of the younger veterans were in whitecollar jobs, compared with 40 percent of the older

veterans. Only 6 percent of the younger group but

14 percent of the older were in professional and

technical occupations.

Negro1 2 veterans were more heavily concentrated

than white veterans in the less skilled laborer and

service occupations. (See chart 1.) These differences

result from a combination of several factors, such as

job discrimination and the somewhat larger proportion of employed Negro veterans who were in the

less experienced age group 20 to 24 years— 50 percent, compared with 41 percent of the young whites.

Table 1. Employment status of male Vietnam Era veterans and nonveterans 20 to 29 years old, quarterly averages,

1971 and 1972

[Numbers in thousands]

Seasonally adjusted

Veteran status and employment status

1971 1972

1971 1972

1 II III IV II 1 II III IV 1 II

VETERANS1

Total, 20 to 29 years:

Civilian noninstitutional population 2_____ 3,809 3,981 4,145 4,293 4,429 4,515 3,809 3,981 4,145 4,293 4,429 4,515

Civilian labor force_________ _____ 3,459 3,623 3,844 3,931 4,058 4,174 3,470 3,632 3,814 3,951 4,076 4,180

Percent of population_____________ 90.8 91.0 92.7 91.6 91.6 92.4 91.1 91.2 92.0 92.0 92.0 92.6

Employed___________________ 3,087 3,314 3,525 3,626 3,658 3,862 3,160 3,302 3,463 3,623 3,743 3,848

Unemployed________________ 372 309 319 304 400 312 310 330 351 328 332 332

Unemployment rate_______ 10.8 8.5 8.3 7.8 9.8 7.5 8.9 9.1 9.2 8.3 8.2 8.0

20 to 24 years:

Civilian noninstitutional population 2.......... 1,902 1,947 1,974 1,990 2,000 1,967 1,902 1,947 1,974 1,990 2,000 1,967

Civilian labor force_______________ 1,668 1,711 1,782 1,782 1,788 1,788 1,676 1,719 1,768 1,783 1,801 1,792

Percent of population_____________ 87.7 87.9 90.3 89.5 89.4 90.9 88.1 88.3 89.6 89.6 90.0 91.1

Employed_____ _____________ 1,424 1,499 1,583 1,587 1,544 1,606 1,471 1,490 1,551 1,579 1,596 1,596

Unemployed_________________ 244 212 199 195 244 182 205 229 217 204 206 196

Unemployment rate_______ 14.6 12.4 11.2 11.0 13.6 10.2 12.2 13.3 12.3 11.4 11.4 10.9

25 to 29 years:

Civilian noninstitutional population2_____ 1,907 2,035 2,171 2,303 2,429 2,549 1,907 2,035 2,171 2,303 2,429 2,549

Civilian labor force_______________ 1,791 1,912 2,062 2,149 2,270 2,387 1,794 1,912 2,046 2,168 2,274 2,388

Percent of population_____________ 93.9 94.0 95.0 93.3 93.5 93.6 94.1 94.0 94.2 94.1 93.6 93.7

Employed____ ______________ 1,663 1,815 1,942 2,039 2,114 2,256 1,689 1,811 1,912 2,044 2,148 2,251

Unemployed_________________ 128 97 120 109 156 130 105 101 134 124 127 136

Unemployment rate........... .

NONVETERANS

7.2 5.1 5.8 5.1 6.9 5.5 5.8 5.3 6.5 5.7 5.6 5.7

Total, 20 to 29 years:

Civilian noninstitutional population 2____ 9,209 9,334 9,454 9,567 9,716 9,930 9,209 9,334 9,454 9,567 9,716 9,930

Civilian labor force_______________ 7,844 8,093 8,436 8,200 8,264 8,604 7,997 8,076 8,136 8,371 8,435 8,586

Percent of population_____________ 85.2 86.7 89.2 85.7 85.1 86.6 86.8 86.5 86.1 87.5 86.8 86.5

Employed____________ ______ 7,188 7,524 7,852 7,633 7,566 8,006 7,419 7,502 7,544 7,727 7,816 7,978

Unemployed_________________ 656 569 584 567 698 598 578 574 592 644 619 608

Unemployment rate...... ......... 8.4 7.0 6.9 6.9 8.4 7.0 7.2 7.1 7.3 7.7 7.3 7.1

20 to 24 years:

Civilian noninstitutional population 2_____ 5,327 5,468 5,582 5,620 5,825 5,980 5,327 5,468 5,582 5,620 5,825 5,980

Civilian labor force_______________ 4,158 4,439 4,741 4,456 4,573 4,860 4,321 4,421 4,448 4,610 4,753 4,842

Percent of population_____________ 78.1 81.2 84.9 79.3 78.5 81.3 81.1 80.9 79.7 82.0 81.6 81.0

Employed___________________ 3,709 4,016 4,321 4,061 4,072 4,421 3,911 4,004 4,028 4,162 4,293 4,404

Unemployed____________ ___ 449 423 420 394 501 439 410 417 420 448 460 437

Unemployment rate_______ 10.8 9.5 8.9 8.8 10.9 9.0 9.5 9.4 9.4 9.7 9.7 9.0

25 to 29 years:

Civilian noninstitutional population 2_____ 3,882 3,866 3,872 3,947 3,891 3,950 3,882 3,866 3,872 3,947 3,891 3,950

Civilian labor force_______________ 3,686 3,654 3,695 3,744 3,691 3,744 3,676 3,654 3,687 3,762 3,682 3,745

Percent of population_____________ 95.0 94.5 95.4 94.9 94.9 94.8 94.7 94.5 95.2 95.3 94.6 94.8

Employed___________________ 3,479 3,508 3,531 3,572 3,494 3,585 3,508 3,497 3,516 3,566 3,523 3,574

Unemployed_________________ 207 146 164 172 197 159 168 157 171 196 159 171

Unemployment rate_______ 5.6 4.0 4.4 4.6 5.3 4.2 4.6 4.3 4.6 5.2 4.3 4.6

1 Vietnam Era veterans are those who served after August 4, 1964; they are all classified as war veterans. About 80 percent of the Vietnam Era veterans of all ages are 

20 fo 29 years old. Post-Korean peacetime veterans are not included in this table.

2 Since seasonal variations are not present in the population figures, identical numbers appear in the unadjusted and seasonally adjusted columns.

NOTE: Because of rounding, sums of individual items may not equal totals. Rates

are based on unrounded numbers. Data are subject to sampling variability which 

may be relatively large in cases where numbers are small. Therefore, differences 

between numbers or percents based on them may not be significant. Eor a detailed 

explanation of the reliability of estimates, including standard error tables, see the 

Technical Note in the October 1972 issue of Employment and Earnings.

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Industry. The distribution by industry of employed

veterans 20 to 29 years old was virtually the same as

that of employed nonveterans the same ages— nearly

a third held jobs in manufacturing, primarily in the

durable goods industries, and a fifth were in trade,

mostly in retail trade.

Among the veterans, Negro men, to a greater extent than white men, seem to take advantage of preferential hiring programs in the public sector. In the

second quarter of 1972, 20 percent of the employed

Negro veterans 20 to 29 years old worked for Federal, State, or local governments, compared with 12

percent of the white veterans. (See chart 2.)

Table 2. Major occupation and industry group of

employed male Vietnam Era veterans and nonveterans

20 to 29 years old, second quarter averages, 1972

[Percent distribution)

Major occupation and 

industry group

Veterans Non veterans

20 to 29 

years

20to24 

years

25 to 29 

years

20 to 29 

years

20 to 24 

years

25 to 29 

years

Total employed (in thousan d s)...___________ 3,862 1,606 2,256 8,006 4,421 3,585

OCCUPATION

Total_____ ___________ 100.0 100.0 100.0 100.0 100.0 100.0

Professional and technical

workers__________________ 10.6 5.7 14.1 17.4 13.0 22.8

Managers and administrators,

except farm_______________ 7.8 6.2 8.9 8.3 5.8 11.0

Clerical workers______________ 9.7 10.0 9.6 7.4 8.6 5.8

Sales workers_______________ 6.7 5.7 7.4 5.6 5.5 5.9

Craftsmen and kindred workers.. 23.5 22.7 24.1 18.5 17.8 19.4

Operatives and kindred workers.. 23.6 27.9 20.5 21.8 23.4 19.8

Service workers______________ 7.7 7.5 7.9 7.6 9.4 5.4

Farmers and farm laborers,

foremen_______ ____ ______ 1.9 2.6 1.4 3.8 4.0 3.5

Laborers, excluding farm and

mine________ : ___________ 8.4 11.5 6.1 9.8 12.5 6.5

INDUSTRY

Total____ ____________ 100.0 100.0 100.0 100.0 100.0 100.0

Agriculture__________________ 2.4 3.3 1.8 4.6 5.1 4.1

Nonagricultural industries______ 97.6 96.7 98.2 95.4 94.9 95.9

Wage and salary workers___ 94.8 94.4 95.0 91.9 92.6 91.0

Construction_________ 9.2 10.6 8.1 8.5 9.1 7.8

Manufacturing________ 31.6 31.6 31.5 28.1 27.4 28.9

Durable goods......... 20.1 19.9 20.3 18.0 17.2 18.9

Nondurable goods.. 11.5 11.8 11.2 10.1 10.2 10.9

Transportation, communication, and

public utilities______ 9.0 8.0 9.7 6.0 5.7 6.3

Trade_______________ 19.1 21.1 17.7 19.0 21.9 15.6

Finance, insurance, and

real estate......... ......... 3.8 3.2 4.3 3.8 3.4 4.4

Service............................ 8.1 8.3 8.0 11.4 11.7 11.1

Government__________ 12.8 10.0 14.8 14.0 12.4 15.9

Self-employed and unpaid

family workers_____ _______ 2.8 2.3 3.1 3.5 2.4 4.8

NOTE: For definitions and notes on data limitations, see table 1.

Unemployment

The unemployment rate of veterans 20 to 29 fell

from 9.1 percent to 8.0 percent (seasonally adjusted) in the year ended in the second quarter

1972, while the rate of nonveterans remained the

same at 7.1 percent (seasonally adjusted). All of the

improvement in the veterans’ unemployment rate occurred among the veterans in ages 20 to 24, whose

average rate dropped to 10.9 percent in second

quarter 1972 from 13.3 percent a year earlier. At

5.7 percent, the unemployment rate of veterans 25 to

29 was roughly the same as in second quarter 1971.

The gap between the unemployment rate of veterans and nonveterans narrowed substantially between

mid-1971 and 1972. For the second quarter of 1972,

the difference was 0.9 percentage points compared

with 2.0 percentage points a year before. Although

most of the narrowing reflects an improved job situation for veterans, some reflects a shift in the age

composition of the veterans compared to the nonveterans. Very little of the increase in the 20- to 29-

year-old veteran population and labor force was in

ages 20 to 24, where unemployment problems are

more severe than for older veterans. With fewer men

going into military service (draft calls fell from

152,000 in fiscal 1971 to 25,000 in fiscal 1972), the

nonveteran population and labor force increased primarily in ages 20 to 24. Regardless of veteran status,

the jobless rate for men 25 to 29 is lower than that

for men 20 to 24, for such reasons as greater work

experience, more familiarity with the job market, and

higher seniority.

By the third quarter of 1972, the unemployment

rate for veterans 20 to 29 years old had dropped

to 7.2 percent, and in October the veterans’ rate of

6.4 percent was little different from the 6.6 percent

rate for nonveterans the same ages.

Duration. Following the economic downturn of

1970, the duration of unemployment for both veterans and nonveterans lengthened. The percentage of

unemployed veterans looking for work for 15 weeks

or more increased from an annual average of 9 percent in 1969 to 15 percent in 1970 to 25 percent in

1971. The comparable statistic for nonveterans has

increased in a similar fashion. In the second quarter

of 1972, about 30 percent of the jobless veterans and

nonveterans had been unemployed for at least 15

weeks, the same proportions as in the second quarter

a year earlier. (See table 3.)

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Reasons for unemployment. Some persons become

unemployed by losing or quitting a job, while others

are unemployed as a consequence of coming into the

labor force and starting to look for work. As the

following percentages for the second quarter of 1972

indicate, veterans and nonveterans differed slightly in

their reasons for unemployment:

Veterans N onveterans

Total unem ployed (in thousands) 312 598

Percent ............................................... 100.0 100.0

Job losers ................................ 45.2 50.2

On layoff ....................... 11.5 9.4

Other job lo s e r s.......... 33.7 40.8

Job le a v e r s.............................. 12.5 14.2

Labor force e n tr a n ts.......... 42.3 35.6

Reentrants .................... 34.6 30.1

N ew workers ............... 7.7 5.5

In the second quarter of 1972, veterans and nonveterans were about equally likely to be on layoff,

but the veterans were less likely to have lost their

jobs for such reasons as dismissal, expiradon of a

temporary job, or plant closing. A somewhat greater

percentage of the veterans than of the nonveterans

were either reentrants to the labor force or had never

worked before.

Younger veterans were more likely than older vetChart 1. Occupational distribution of male Vietnam

Era veterans 20 to 29 years old, by race, 2d quarter

averages, 1972

Percent 

100

75 -

50

25 -

Professional,

technical

Managerial

Clerical

Sales

Craftsmen

Operatives

_ Laborers,

excluding farm

Farm workers 

Service

White Negro and 

other races

Table 3. Duration of unemployment of male Vietnam

Era veterans and nonveterans 20 to 29 years old,

quarterly averages, 1971 and 1972

[Percent distribution]

Veteran status and duration 

of unemployment

1971 1972

1 II III IV -

II

VETERANS

Total unemployed:

Number (in thousands)......... 372 309 319 304 400 312

Percent__________ ______ 100.0 100.0 100.0 100.0 100.0 100.0

Less than 5 weeks............. . 38.4 40.8 42.9 42.6 41.1 40.6

5 to 14 weeks................. ....... 37.9 29.8 33.5 33.6 33.9 28.6

15 weeks or more_________ 23.7 29.4 23.5 23.9 25.0 30.8

NONVETERANS

Total unemployed:

Number (in thousands)......... 656 569 584 567 698 598

Percent........................... ....... 100.0 100.0 100.0 100.0 100.0 100.0

Less than 5 weeks................. 39.3 45.1 44.0 39.8 35.7 46.0

5 to 14 weeks_____ ____ 38.9 25.2 35.3 36.4 34.7 24.7

15 weeks or more.............. . 21.8 29.8 20.7 23.9 29.6 29.3

NOTE: For definitions and notes on data limitations, see table 1.

erans to be labor force entrants, because more of

them had only recently left the Armed Forces. Jobfinding problems for newcomers to the labor force,

whether veterans or nonveterans, tend to be exacerbated by the fact that they may not be as familiar

with the intricacies of the job market as those who

left or lost a job.

Men not in the labor force

In the second quarter of 1972, about 8 percent

(340,000) of the veterans were neither working nor

looking for work, compared with 13 percent (1.3

million) of the non veterans. The proportion not in

the labor force was smaller for veterans because of a

combination of demographic and social factors.

Among these is the larger proportion of veterans in

their late twenties, an age group in which the labor

force participation rate is higher than for those 20 to

24 years old. Another factor is the larger proportion

of veterans than nonveterans who head households.

In the April-June quarter of 1972, about two-thirds

of the veterans but only half of the nonveterans were

household heads, with a wife and, perhaps, young

children to support. All of this difference was accounted for by the 20- to 24-year-old men, among

whom about half of the veterans compared with

about a third of the nonveterans had these family

responsibilities.

Attendance at school was by far the most impor138

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Chart 2. Employment of male Vietnam Era veterans 20 to 29 years old, by race and major industry group, 2d quarter

White Negro and other races

1 Excluding government.

tant reason given for not being in the labor force. In

the second quarter of 1972, about two-thirds of the

veterans and three-fourths of the nonveterans not in

the labor force gave school as their reason for nonparticipation. The next most frequently given reason

was not wanting a job. (See chart 3.)

On an annual average basis for 1971, veterans 20

to 24 years old and those 25 to 29 years old differed

little in their reasons for nonparticipation in the

labor force. In contrast, nonveterans exhibited large

differences by age. Younger nonveterans were almost

twice as likely as older nonveterans to be in school,

while they were less likely than older nonveterans to

mention ill health or disability as a reason for nonparticipation. Few of the veterans in either their

early or late twenties gave this reason. Relatively few

(2 to 3 percent) of the veterans and nonveterans not

in the labor force in the first half of 1972 gave as

their reason the belief that they could not find a job.

Education

Vietnam Era veterans are better educated when

they leave the service than were World War II or

Korean Conflict veterans at the time of their separation from military service, reflecting in part a general

increase in the educational attainment of the population. For all the servicemen discharged from

August 1964 through the end of 1971, the median

years of schooling completed at time of separation

was 12.5 years. This compares with 12.3 years for

Korean Conflict veterans and 11.5 years for World

War II veterans. Educational attainment at separation was highest for Vietnam Era veterans 25 to 29

years old (12.9 years). Nearly half (46 percent) of

the men in this age group had completed at least

1 year of college.3

Since the midsixties, the median educational attainment of veterans at time of separation has increased gradually from 12.4 to 12.6 years. In fiscal

1965 through 1967, about 17 percent of the separatees had completed a year of college or more. This

proportion reached 27 percent in the first half of

fiscal 1972, including 13 percent who had graduated

from college.4

Roughly 10 percent of the veteran population 20

to 24 years old in the year ending in June 1972,

reported school as their major activity.5 The proportion for nonveterans of the same age was twice as

high. Among 25- to 29-year-olds, about 6 percent of

the veterans and 3 percent of the nonveterans were

in school.

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Students generally have a much lower labor force

participation rate than those whose major activity is

something else. In the second quarter of 1972, 30

percent of the veterans 20 to 29 years old in school

were in the labor force in contrast to 97 percent of

the veterans out of school. The labor force participation rate of students was the same for veterans and

nonveterans, but among nonstudents veterans had a

slightly higher rate.

About a tenth of the unemployed veterans and

nonveterans were students, and most of these were

seeking part-time work. In the second quarter of the

year, however, the proportion seeking part-time jobs

usually decreases, probably because students begin

working or looking for full-time summer jobs before

the end of the school year, as shown by the following

tabulation for veterans in the first and second quarters

of 1972:

I II

Total unemployed (in th ou san ds). 400 312

P e r c e n t....................................................... 100.0 100.0

Major activity: s c h o o l................................... 13.0 9.0

Looking for full-time w o r k ............. 4.3 4.8

Looking for part-time w o r k ............. 8.7 4.2

Majority activity: other .............................. 87.0 91.0

Looking for full-time w o r k ............. 84.3 88.8

Looking for part-time w o r k ............. 2.7 2.2

In contrast, the overwhelming majority of unemployed nonstudents look for full-time jobs the year

round.

The unemployment rate of men 20 to 29 years old

in school is far higher than that of those not in

school. For veterans, in the second quarter of 1972,

the unemployment rate was 29 percent for students

in contrast to 7 percent for nonstudents. The corresponding unemployment rates for nonveterans were

13 percent for students and 7 percent for nonstudents.

Race and residence

Race. Negroes constitute a smaller proportion of

Vietnam Era veterans than of nonveterans. In the

first half of 1972, they made up about 9 percent of

the 20- to 29-year-old veteran population and labor

force but almost 13 percent of the nonveteran population and labor force. The smaller proportion of

Negroes among veterans is primarily due to two reasons. Relatively more Negroes than whites are disqualified from entering the Armed Forces,6 and relaChart 3. Reasons for nonparticipation in the labor force

of male Vietnam Era veterans and nonveterans 20 to 29

years old, 2d quarter averages, 1972

tively more eligible Negroes than whites reenlist

when their enlistments expire.7

The employment situation of veterans of Negro

and other minority races can be discussed only in

general terms because the data are based on small

numbers of sample cases and sampling variability is

high. The unemployment rates of Negro veterans

have not been significantly different statistically from

those of Negro nonveterans, but have been substantially higher than the unemployment rates of white

veterans. (See table 4.) During 1971 and the first

half of 1972, the quarterly average unemployment

rate of Negro veterans was in the range of 12 to 15

percent, compared with 7 to 10 percent for white

veterans.

Residence. Following the national pattern,8 more

Vietnam Era veterans and nonveterans 20 to 29

years old reside in the Southern and North Central regions of the United States than in the Northeast and West. The unemployment rates for veterans

and nonveterans in the Southern and North Central

regions are considerably lower than comparable rates

the Northeast and West. (See table 5.) In the second

quarter of 1972, the jobless rates for veterans 20 to

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29 years old were 5.6 and 6.7 percent, respectively,

in the Southern and North Central regions, compared

with 8.9 and 9.9 percent in the West and Northeast.

About half the Negro veterans 20 to 29 years old

live in the South, in contrast to about one-quarter of

the white veterans; this is comparable to the distribution of the total population by race. The unemployment rate for the Negro veterans in the South,

at 13.8 percent, was about three times as high as for

white veterans, though not significantly higher than

Table 4. Employment status of male Vietnam Era veterans

averages, 1971 and 1972

[Numbers in thousands]

for Negro veterans living elsewhere (11.7 percent).

Outside the South, the unemployment rate of Negro

veterans was only one and a half times as high as for

white veterans.

Special programs

Among the continuing programs and benefits for

veterans are the longstanding GI Bill administered by

the Veterans Administration, Project Transition,

and nonveterans 20 to 29 years old, by race, quarterly

White Negro and other races

Employment status 1971 1972 1971 1972

•

II III IV 1 II 1 II III IV II

VETERANS

Total, 20 to 29 years:

Civilian noninstitutional population, _........ 3,446 3,596 3,721 3,878 4,028 4,102 363 386 425 415 401 413

Civilian labor force_______________ 3,135 3,274 3,456 3,558 3,708 3,799 324 350 388 373 350 375

Percent of population_____________ 91.0 91.0 92.9 91.7 92.1 92.6 89.3 90.7 91.3 89.9 87.3 90.8

Employed______ ______ ______ 2,812 3,008 3,191 3,306 3,361 3,535 275 308 334 322 297 327

Unemployed_________________ 323 266 265 252 347 264 49 42 54 52 53 48

Unemployment rate.............. 10.3 8.1 7.7 7.1 9.3 7.0 15.1 12.1 14.0 13.8 15.3 12.7

20 to 24 years:

Civilian noninstitutional population______ 1,699 1,737 1,761 1,798 1,800 1,748 203 210 214 192 200 219

Civilian labor force_______________ 1,489 1,527 1,593 1,615 1,617 1,595 179 184 189 167 171 193

Percent of population_____________ 87.6 87.9 90.5 89.8 89.8 91.2 88.2 87.6 88.3 87.0 85.5 88.1

Employed.................................... 1,282 1,347 1,424 1,447 1,411 1,442 142 153 159 140 133 164

Unemployed____________ ____ 207 180 169 168 206 153 37 31 30 27 38 2.9

Unemployment rate....... ....... 13.9 11.8 10.6 10.4 12.7 9.6 20.9 17.0 16.0 15.9 22.4 15.1

25 to 29 years:

Civilian noninstitutional population_____ 1,747 1,859 1,961 2,080 2,228 2,354 ’ 160 176 211 223 201 195

Civilian labor force........ ................... 1,646 1,747 1,863 1,943 2,091 2,205 145 165 199 206 179 182

Percent of population.......................... 94.2 94.0 95.0 93.4 93.8 93.7 90.6 93.8 94.3 92.4 89.1 93.3

Employed____ ______________ 1,529 1,661 1,767 1,859 1,950 2,093 133 154 175 181 164 164

Unemployed__________ ____ 117 86 96 84 141 112 12 11 24 25 15 2.9

Unemployment rate. _ __ 7.1 4.9 5.2 4.3 6.7 5.1 8.0 6.7 12.0 12.0 8.6 10.2

NONVETERANS

Total, 20 to 29 years:

Civilian noninstitutional population........ . 7,964 8,072 8,183 8,260 8,463 8,652 1,245 1,262 1,271 1,307 1,253 1,278

Civilian labor force_______________ 6,798 7,020 7,338 7,116 7,232 7,539 1,045 1,073 1,098 1,084 1,032 1,065

Percent of population_____________ 85.4 87.0 89.7 86.2 85.5 87.1 83.9 85.0 86.4 82.9 82.4 83.3

Employed_____ _____________ 6,277 6,567 6,888 6,679 6,678 7,053 910 958 963 955 888 953

Unemployed________________ 521 453 450 437 553 486 135 115 135 129 145 112

Unemployment rate......... . 7.7 6.5 6.1 6.1 7.6 6.4 12.9 10.7 12.3 11.9 14.0 10.5

20 to 24 years:

Civilian noninstitutional population______ 4,616 4,739 4,834 4,838 5,066 5,220 711 729 748 782 759 761

Civilian labor force_______________ 3,604 3,850 4,119 3,853 3,994 4,263 554 589 621 603 579 597

Percent of population_____________ 78.1 81.2 85.2 79.6 78.8 81.7 77.9 80.8 83.0 77.1 76.3 78.4

Employed____ ______________ 3,252 3,519 3,795 3,549 3,596 3,913 457 497 525 513 476 508

Unemployed_________________ 352 331 324 304 397 350 97 92 96 90 104 89

Unemployment rate_______ 9.8 8.6 7.9 7.9 9.9 8.2 17.4 15.6 15.5 15.0 17.9 14.9

20 to 29 years:

Civilian noninstitutional population........ . 3,348 3,333 3,349 3,422 3,397 3,433 534 533 523 525 494 517

Civilian labor force____________ 3,195 3,170 3,219 3,263 3,238 3,277 491 484 477 481 453 467

Percent of population_____________ 95.4 95.1 96.1 95.4 95.3 95.5 91.9 90.8 91.2 91.6 91.7 90.3

Employed___________________ 3,026 3,048 3,093 3,130 3,082 3,140 453 460 438 442 412 444

Unemployed. ...................__ 169 122 126 133 156 136 38 24 39 39 41 23

Unemployment rate,............ 5.3 3.8 3.9 4.1 4.8 4.2 7.8 4.9 8.1 8.0 9.0 4.9

NOTE: For de'mitions and notes on data (imitations, see table 1.

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Table 5. Employment status of male Vietnam Era veterans and nonveterans 20 to 29 years old, by region and race,

second quarter averages, 1972

[Numbers in thousands]

Labor force status and race

Veterans Nonveterans

Total Northeast

North

Central

South West Total NortheastNorth

Central

South West

ALL MEN

Civilian noninstitutional population___________________________ 4,515 997 1,293 1,344 881 9,931 2,427 2,675 3,091 1,738

Civilian labor force......................................................................... 4,174 921 1,209 1,242 802 8,603 2,034 2,370 2,699 1,500

Percent of population__________________________________ 92.4 92.4 93.5 92.4 91.0 86.6 83.8 88.6 87.3 86.3

Employed_______________ _____ _____ _____________ 3,862 830 1,128 1,173 731 8,005 1,856 2,210 2,570 1,369

Unemployed__________ ___________________________ 312 91 81 69 71 599 178 161 129 131

Unemployment rate._______________ ____ _______ 7.5 9.9 6.7 5.6 8.9 7.0 8.8 6.8 4.8 8.7

Not in labor force........................................ .................................. 341 76 84 102 79 1,328 393 305 392 238

WHITE

Civilian noninstitutional population...................................................... 4,102 927 1,214 1,139 822 8,653 2,190 2,429 2,466 1,568

Civilian labor force________________ _____ ______________ 3,799 859 1,134 1,055 751 7,539 1,851 2,164 2,162 1,362

Percent of population_________ ____ ____________________ 92.6 92.7 93.4 92.6 91.4 87.1 84.5 89.1 87.8 86.9

Employed________________________________________ 3,535 775 1,061 1,012 687 7,054 1,697 2,034 2,072 1,251

Unemployed______________ _______ ___________ ____ 264 84 73 43 64 486 154 131 90 111

Unemployment rate____________________________ 6.9 9.8 6.4 4.1 8.5 6.4 8.3 6.1 4.2 8.1

Not in labor force........................................................................ 303 68 80 84 71 1,114 339 265 304 206

NEGRO AND OTHER RACES

Civilian noninstitutional population...................................................... 413 70 79 205 59 1,278 237 246 625 170

Civilian labor force________ ____ ___________ ______ _____ 375 62 75 187 51 1,064 183 206 537 138

Percent of population___________ ______________________ 91.8 (l) 95.0 91.2 0 83.3 77.2 83.7 85.9 81.2

Employed_______ ______ __________________________ 327 55 67 161 44 951 159 176 498 118

Unemployed.____ _____ _____________ _____ ________ 48 7 8 26 7 113 24 30 39 20

Unemployment rate.......................................................... 12.8 0 10.7 13.8 0 10.6 13.1 14.6 7.3 14.5

Not in labor force____ ____ _______ ____ ________________ 38 8 4 18 8 214 54 40 88 32

1 Percent not shewn where base is less than 75.CC0.

under the Department of Defense, and Employment

Services, Unemployment Compensation for Ex-Servicemen, and Reemployment Rights, all under the Department of Labor. In the past year, many of these

programs have been expanded and new ones added.

The President’s 6-point veterans program spurred

substantial increases in veterans’ job counseling,

placement, and training benefits, and also prompted

increased job opportunities in private industry

through such organizations as the National Alliance

of Businessmen.

Through June 1972, 41 percent of all Vietnam

Era veterans have participated in educational programs under the current GI Bill (effective June

1966). The comparable proportions of veterans participating under previous GI Bills after a similar

length of time were 40 percent of Korean Conflict

servicemen and 46 percent of World War II veterans. On October 24, 1972, amendments were signed

into law raising the amount of the current GI Bill

educational benefits for full-time students from the

$175 per month for a single veteran to $220, with

corresponding increases for veterans with dependents.

NOTE: For definitions and notes on data limitations, see table 1.

By the end of June 1972, about 1.5 million servicemen had received some type of job counseling for

civilian jobs under Project Transition which began in

January 1968. In addition, some 258,000 had participated in a job-training program, frequently run on

or near military bases by private industry. Although

Project Transition is primarily for those servicemen

most in need of vocational training and education for

civilian life, it recently incorporated many other special programs. One such program is Military Experience Directed Into Health Careers (MEDIHC), a

joint program of the Departments of Defense and

Health, Education, and Welfare, in which servicemen

who received military training in the health or medical fields are assisted in obtaining jobs in the civilian

health fields. The placement rate in mid-1972 ranged

from 40 to 70 percent depending upon the State. A

companion program, the Veterans Construction Jobs

Clearinghouse, assists servicemen who have been

trained as construction mechanics. The program is

supported by Department of Labor funds and

manned by representatives of the construction industry.

Other examples of new or amplified benefits for

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Vietnam Era veterans were additional payments to

eligible veterans (as well as others) under the Temporary Unemployment Compensation Program and

the employment of veterans (and others) under the

Public Employment Program.

These are only a few examples of the nationwide

efforts which helped Vietnam Era veterans get educational and vocational training and contributed to

the improvement in their employment situation during fiscal year 1972. 

-FOOTNOTES1 About 83,000 women veterans of the Vietnam Era are

not included in this report because employment data are not

available for them. In this report, Vietnam Era veterans are

those who served in the Armed Forces after Aug. 4, 1964,

have been separated from active duty, and are now in the

civilian noninstitutional population. Korean Conflict veterans

served during the period June 27, 1950, to Jan. 31, 1955.

World War II veterans served at any time from Sept.

16, 1940, to July 25, 1947. Nonveterans include those who

have never served in the Armed Forces or who served only

in peacetime prior to June 27, 1950. Post-Korean Conflict

veterans— men who served between Feb. 1, 1955, and Aug.

4, 1964— are not included in this report.

Unless otherwise indicated, data on the civilian noninstitutional population, labor force, employment status, and educational attainment are derived from the nationwide Current

Population Survey (CPS) sample of about 50,000 households. The CPS, conducted each month by the Bureau of the

Census for the Bureau of Labor Statistics (BLS), is the

source of special tabulation by veteran status prepared for

the Veterans Administration and BLS. The data are subject

to sampling variability, which may be relatively large for the

smaller figures and for small differences between figures.

Standard errors of monthly sample estimates are published by

BLS in Employment and Earnings. These standard errors

must be reduced by a factor of .7070 for quarterly averages,

and .4472 for annual averages. Details about basic labor

force concepts, sample design, and estimating methods are

decribed in Concepts and Methods Used in Manpower Statistics From the Current Population Survey (BLS Report

313, 1967).

The latest in this series of annual reports on the employment situation of Vietnam Era veterans was published in the

Monthly Labor Review, September 1971, pp. 3-11, and

reprinted as Special Labor Force Report 137.

2 Data for all persons other than white are used in this report to represent data for Negroes, since the latter constitute

about 92 percent of all persons other than white persons in

the United States.

3 See Data on Vietnam Era Veterans, December 1971

(Veterans Administration, 1972), p. 8.

4 Ibid., p. 7.

5 Respondents in the Current Population Survey were

asked, “What were you doing most of last week?” On the

basis of their replies, persons were classified into two groups

— Major activity: going to school and Major activity: other.

In this report, those whose major activity was going to school

are referred to as “students” and those whose major activity

was something else are classified as “not in school.”

8 Data on disqualifications on the basis of medical, mental,

and trainability tests were provided by the Medical Statistics

Agency, Office of the Surgeon General, Department of the

Army. In these data, statistics for Negroes refer to Negroes

only and statistics for whites refer to all others (non-Negro).

7 Data on reenlistment rates and ineligibility to reenlist

were provided by the Director of Procurement Policy, Office

of the Assistant Secretary of Defense for Manpower and

Reserve Affairs. In these data, statistics for Negroes refer to

Negroes only and statistics for whites refer to whites only.

8 See Geographic Profile of Employment and Unemployment, 1971 (BLS Report 402, 1972).

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Special Labor Force Report shows workers 

in metropolitan areas to be highly skilled, 

but with substantial differences 

between residents of the central cities 

and those living in the suburbs

CHRISTOPHER G. GELLNER

T w o - t h i r d s o f a l l w o r k e r s in the United States

now reside in metropolitan areas— the centers of

economic activity and growth for the Nation.1 The

economic importance of these areas is reflected in the

high proportion of highly skilled workers within their

populations. Professional, technical, and managerial

occupations are more common in such areas (particularly the 20 largest) than in the Nation as a whole.

This article is based on occupational data for

Standard Metropolitan Statistical Areas (SMSA’s)

that have recently become available on an annual

average basis from the Current Population Survey

(CPS). It explores the major differences in the

occupational distribution of employment among our

large metropolitan areas and between their central

cities and suburban rings. It also attempts to determine whether such skill differences have any direct

bearing on the disparity between central city and

suburban unemployment rates.

Data on the occupational distribution of the labor

force are essential in order to study the purported

skill gap between central city workers and suburban

workers.2 In the absence of such information, it has

been widely assumed that the great majority of

central city workers are concentrated in semiskilled

and low skilled jobs and that when unemployed they

seek work in similar fields.

Skill pattern by nature of area

As table 1 shows, over one-half of all workers

residing in metropolitan areas are employed in whitecollar work and 16 percent are in professional and

managerial occupations. In nonmetropolitan areas,

slightly less than two-fifths of the workers are in

white-collar work and 11 percent in professional

Christopher G. Gellner is a labor economist in the Division

of Employment and Unemployment Analysis, Bureau of

Labor Statistics.

Occupational

characteristics

of urban

workers

and managerial occupations. Nonmetropolitan area

workers are more likely to be employed in bluecollar work. The proportion of workers in the service

occupations is roughly the same in metropolitan and

nonmetropolitan areas.

The higher skill level of the metropolitan area

labor force is apparent among both Negro and white

workers. One-third of the Negro workers residing

in metropolitan areas were engaged in white-collar

work in 1970, with 14 percent working as skilled

professionals and managers. Outside these areas,

only 14 percent were in white-collar work and 8

percent in the professional and managerial occupations. Among whites, the percentage employed in

white-collar occupations is also significantly higher

in metropolitan areas (56 percent) than in nonmetropolitan areas (41 percent). Moreover, the

proportion of whites employed in the professional

and managerial occupations in metropolitan areas,

at 28 percent, is also significantly higher than the

22 percent in nonmetropolitan areas. Outside metropolitan areas, about one-tenth of the whites and

one-eighth of the Negroes were in farming occupations in 1970.

Generally speaking, the larger a metropolitan area,

the larger its proportion of higher skilled workers.

This is confirmed by data on the 20 largest SMSA’s,

which contain about half of the workers of all metropolitan areas.3 Approximately 56 percent of the

workers residing in these areas are in white-collar

occupations, a slightly greater proportion than in

all metropolitan areas. Moreover, these large urban

areas have a slightly larger proportion of professional

and technical workers than do all metropolitan areas.

Conversely, the proportion of workers in both bluecollar and service occupations is somewhat lower

in these large SMSA’s than in smaller urban areas.

Differences in occupational distribution between

the labor force in the 20 SMSA’s and that for all

metropolitan areas are evident both for Negroes and

for whites. In the 20 largest areas, approximately

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37 percent of Negro employment and 59 percent of

white was in white-collar fields— both larger percentages than for all SMA’s combined. The relatively

large proportion of white-collar workers in the Negro

labor force of the 20 largest areas compared with

that of all SMSA’s, however, results almost entirely

from a greater representation in clerical jobs, generally occupied by women. White employment, on

the other hand, stems from a greater representation

in professional and technical as well as clerical

occupations. In blue-collar and service occupations,

the percentages of both Negro and white workers

are slightly lower in the 20 largest areas than in all

metropolitan areas combined. The smaller proportion of Negro workers in these two occupational

groups in the 20 areas is due chiefly to the fact that

a smaller proportion hold nonfarm laborer and private household jobs in the large urban areas than

in smaller areas.

Central city versus suburb

As metropolitan areas have grown in size and

importance, the socioeconomic dichotomy between

the central city and its surrounding surburban ring

has increased. Each component of the SMSA is

dependent upon the other for economic survival.

Central cities, however, have experienced a disproportionate amount of the economic hardship in

metropolitan areas, as reflected by their higher unemployment rates and less skilled work forces.

Today, the central cities of the Nation’s metropolitan areas contain approximately three-tenths of

all U.S. employment. This is less than half, however,

of total metropolitan employment. Over the past two

decades, the net number of employed persons living

in central cities has remained almost the same, and

occupational upgrading has proceeded slowly. In

contrast, in the surrounding suburban rings the resident labor force has grown rapidly in size and has

experienced substantial occupational upgrading. The

relative change in the skill levels of the suburban and

central city residents has occurred principally because higher skilled workers have moved to the

suburbs, while the central cities have received large

numbers of less skilled workers from smaller towns

and rural areas.

Half the workers living in the central cities were

employed in white-collar jobs in 1970. This is slightly

lower than the suburban proportion. However, the

highly skilled professional and technical fields accounted for 14 percent of all central city workers—

the same as the U.S. average, but over 2 percentage

points below the suburban average. (See table 2.)

In comparison to their suburban counterparts, central city residents were generally less represented in

all of the skilled white-collar occupations and in the

craftsmen trades, but were more represented in

the lower skilled occupations (operative, nonfarm

laborer, and service).

Among central city residents, about one-third of

the employed Negroes were working in white-collar

jobs. The same proportion was found among Negroes

living in the suburbs. While about one-half the

Table 1. Employed persons in the United States, by major occupation group and color, 1970 annual averages

[Percent distribution]

Total White Negro and other races

Occupation group

United

States

All nonmetropolitan 

areas

All

metropolitan

areas

20 largest 

metropolitan 

areas

United

States

All nonmetropolitan 

areas

All

metropolitan

areas

20 largest 

metropolitan 

areas

United

States

All nonmetropolitan 

areas

All

metropolitan

areas

20 largest 

metropolitan 

areas

Total employed (thousands)____ 78,627 27,011 51,616 26,180 70,182 24,798 45,384 22,643 8,445 2,213 6,232 3,537

Percent............ .............. ....... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

White-collar workers._________ 48.3 38.8 53.3 55.7 50.8 41.1 56.1 58.6 27.9 13.6 33.0 37.2

Professional and technical... 14.2 11.4 15.6 16.5 14.8 11.9 16.4 17.4 9.1 5.6 10.3 10.6

Managers, officials, and 

proprietors..................... . 10.5 9.7 11.0 11.1 11.4 10.3 12.0 12.1 3.5 2.3 3.9 4.3

Clerical workers......... .......... 17.4 12.8 19.9 21.4 18.0 13.5 20.4 21.7 13.2 4.4 16.3 19.7

Sales workers....... ................ 6.2 5.0 6.8 6.7 6.7 5.4 7.4 7.4 2.1 1.3 2.4 2.6

Blue-collar workers_____ _____ 35.3 38.4 33.7 32.3 34.5 37.7 32.8 31.2 42.2 45.9 40.8 39.4

Craftsmen and foremen........ 12.9 13.0 12.9 12.5 13.5 13.5 13.5 13.1 8.2 7.0 8.6 8.3

Operatives...................... ....... 17.7 20.0 16.5 15.8 17.0 19.5 15.6 14.8 23.7 26.2 22.9 22.5

Nonfarm laborers......... ......... 4.7 5.4 4.4 4.0 4.1 4.8 3.7 3.4 10.3 12.9 9.3 8.1

Service workers............... ............ 12.4 12.8 12.1 11.7 10.7 11.5 10.3 9.9 26.0 27.7 25.4 23.1

Private household workers... 2.0 2.4 1.8 1.5 1.3 1.6 1.1 1.0 7.7 11.5 6.4 4.8

Other service workers........... 10.4 10.4 10.3 10.2 9.4 9.9 9.2 8.9 18.3 16.2 19.0 18.3

Farm workers............. ................. 4.0 9.9 .9 .4 4.0 9.7 .9 .4 3.9 12.7 .7 .3

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Table 2. Employed persons in the central cities and suburban rings of all SMSA's and the 20 largest SMSA’s, by major

occupation group and color, 1970 annual averages

[Percent distribution]

Occupation group

Total White Negro and other races

Central city Suburban ring Central city Suburb: n ring Central city Suburban ring

ALL METROPOLITAN AREAS

Total employed (thousands)...................................... 23,234 28,382 18,471 26,913 4,764 1,469

Percent................................................................ 100.0 100.0 100.0 100.0 100.0 100.0

White-collar workers........................................... 516 54.7 56.4 55.9 32.8 33.6

Prof essional and technical.......................... U .4 16.6 15.6 16.9 19.7 12.3

Managers, officials, and proprietors............ 9.6 12.1 11.2 12.5 3.7 4.8

Clerical workers........................................... 21 2 18.8 22 3 9.1 17.1 13.8

Sales workers.............................................. 6.3 7.2 7.3 7.4 2.3 2.7

Blue-collar workers............................................. 34.0 33.5 32.1 33.2 41.3 39.2

Craftsmen and foremen.............................. 17.6 14.0 12.2 14.4 8.9 7.8

Operatives ............................................... 11.5 15.6 16.1 15.2 23.2 21.9

Nonfarm laborers........................................ 4.9 3.9 3.8 3.6 9.3 9.5

Service workers................................................... 14.2 10.4 11.2 9.6 25.7 24.6

Private household workers......................... 2.1 1.5 1.0 1.2 6.3 6.5

Other service workers................................. 12.1 8.9 10.2 8.4 19.4 18.1

Farm workers...................................................... .2 1.4 .2 1.3 .2 2.6

20 LARGEST METROPOLITAN AREAS

Total employed (thousands)...................................... 11,223 14,957 8,399 14,244 2,823 714

Percent................................................................ 100.0 100.0 100.0 100.0 100.0 100.0

White-collar workers........................................... 52.8 57.9 58.3 58.7 36.3 40.7

Professional and technical....................... 14.4 18.0 16.0 18.2 9.6 14.9

Managers, officials, and proprietors........... 9.2 12.5 11.0 12.8 4.0 5.8

Clerical workers........................................... 23.4 19.9 24.4 20.0 20.3 17.0

Salesworkers...................................... ......... 5.7 7.5 6.8 7.7 2.4 3.0

Blue-collar workers.......................... ................. 33.3 31.5 31.1 31.2 39.9 37.1

Craftsmen and foremen........ ...................... 11.0 13.7 11.6 13.9 9.0 8.2

Operatives.................................................... 17.8 14.3 16.1 14.0 22.8 21.4

Nonfarm laborers........................................ 4.6 3.5 3.4 3.3 8.2 7.5

Service workers................................................. 13.8 10.1 10.5 9.5 23.6 21.1

Private household workers.......................... 1.8 1.3 .8 1.1 4.8 5.1

Other service workers................................. 12.0 8.8 9.7 8.4 18.8 16.0

Farm workers...................................................... .1 .6 .1 .6 .2 1.0

white-collar Negro suburbanites were in professional,

technical, managerial, or official jobs, less than half

the Negro white-collar workers living in central

cities were in this highly skilled group. This indicates that a considerable proportion of Negroes with

high skilled— and thus high paying— jobs have

moved to the suburbs. Such a selective process does

not appear to have been at work among Negroes

outside the white-collar sector, however, as the skill

distribution of Negro blue-collar and service workers

living in the suburbs is not measurably different from

that of central city Negroes. In contrast, white

workers living in the suburbs are in higher skilled

jobs than their central city counterparts, within both

the white-collar and blue-collar categories. The proportion of suburban Negroes working in farm jobs

(2.6 percent) is twice as large as the proportion of

suburban whites.

Workers living in the central cities of the 20 largest

metropolitan areas have essentially the same array

of jobs as those in the central city of all SMSA’s

combined. The suburbanites in the largest areas, on

the other hand, hold higher skilled jobs than suburbanites in general. Especially, they are more concentrated in professional and technical occupations.

As a result, the skill gap between central city and

suburban residents is wider in the 20 largest areas

than in all SMSA’s combined.

The relatively wide skill gap is evident among

both whites and Negroes. In the 20 largest metropolitan areas, suburban workers— both white and

Negro— hold a relatively larger proportion of professional, managerial, and sales jobs than do their

city counterparts (table 2 ). In addition, a larger

proportion of the white suburban labor force than

of the white central city labor force are skilled

craftsmen. The proportion of professional and technical workers is IV2 times as large among Negroes

living in the suburban rings of the 20 largest

SMSA’s as among Negroes in the central cities of the

same areas, and equals the proportion of these

highly skilled workers in the total U.S. labor force.

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The majority of Negro white-collar workers living

in the central cities of these areas are in clerical

occupations.

Reason for the skill gap

General differences in occupational levels between residents of central cities and those of suburbs

are explained in part by the differences in the racial

composition of the labor force in the central cities

and the suburbs. Negroes hold a disproportionate

share of the lower skilled, less desirable jobs, and

their dense concentration in central cities tends to

skew the occupational distribution of city workers

in the low skilled direction. This is especially the

case in the 20 largest SMSA’s, where four-fifths of

the Negro labor force resides in the central cities.

The nature and geographic location of the industrial growth within or in the vicinity of a metropolitan

area may have some effect on the occupational distribution of its central city and suburban labor forces.

For years most new metropolitan industry and business has been placed in the suburbs. The majority

of building permits for office buildings and stores

issued in the early 1960’s were for suburban sites.4

If the new higher skilled better paying jobs are

available only in one section of the metropolitan

area— that is, the suburban ring— workers with the

appropriate qualifications for these jobs may prefer

to live in this section in order to be close to the

expanding employment opportunities.

The nature and location of industry growth, however, has probably had a greater effect on the differences in occupationrl distribution among the labor

forces of individual SMSA’s than on the differences

in occupational distribution between the labor forces

of a particular city and its surrounding suburb.

Another factor that has affected the occupational

gap between the central cities and suburbs is patterns

of population growth and migration. From 1950 to

1970, there has been virtually no growth in the

number of workers residing in the central cities,

if annexations are excluded. Extensive growth in

the number of workers residing in the suburban

rings has, in the meantime, pushed the number of

suburban workers past the number of city workers.

Many workers, when they attain a sufficient level of

education and skill to obtain a more remunerative

job, move to the suburbs. Continuance of this trend

is a serious obstacle to closing the gap between the

suburban labor force and that in the cities.

There has, however, been a great deal of worker

movement into and from the city over the past two

decades. Occupational upgrading of the city labor

force has been hindered not only by outmigration

of highly skilled workers (largely whites) to the

more affluent suburbs, but also by a substantial

inmigration of unskilled, untrained Negroes (many

of them coming from rural areas). Because it is

mainly the younger whites who have been moving to

the suburbs, white workers in the city tend to be

older than white workers in the suburbs. In contrast, Negro workers in the city are relatively young.

However, they often lack appropriate skills or eduA note concerning data

The labor force data discussed in this article

were collected and tabulated for the Bureau of

Labor Statistics by the Bureau of the Census as

part of the Current Population Survey (CPS) program. The CPS is a national survey conducted

monthly in about 50,000 households. The data for

Standard Metropolitan Statistical Areas and their

central cities and suburban rings have larger sampling errors than national data collected through

the CPS, even when averaged over 12 months. For

this and other reasons, the metropolitan area estimates in this article may differ somewhat from

1970 Census estimates that are scheduled to be

released in 1972. This should be taken into account

when making further use of the data.

Standard Metropolitan Statistical Areas as defined by the Office of Management and Budget

consist of large cities and their adjacent suburban

counties. Central cities include the corporate limits

of the city or cities named in the SMSA title, while

the suburban rings include all SMSA territory outside the central city or cities.

The metropolitan areas used in the report refer

to the 212 SMSA’s as defined and ranked in 1960.

This means that for the purposes of this report the

geographic boundaries of the 212 SMSA’s are

those which were in effect in 1960 even though,

subsequently, the boundaries of some of these

areas have been redefined to include additional

counties or exclude counties. Since 1960, the number of areas defined to be metropolitan in character

has been expanded to over 240. SMSA’s added

since 1960 are not included in the data in this

report.

It should also be noted that the data in this

report have been tabulated according to the place

of residence of workers rather than their place of

work.

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Table 3. Employed persons in the 20 largest SMSA’s,

their central cities, and their suburban rings, by occupation, 1960 1 and 19702

[Percent distribution]

Occupation group

Total employed 

(thousands).................. .

Percent5............................

White-collar workers.........

Professional and

technical.................

Managers, officials,

ana proprietors___

Clerical workers.........

Sales workers........... .

Blue-collar workers..........

Craftsmen and

foremen................. .

Operatives..................

Nonfarm laborers___

Service workers............... .

Private household___

Other service workers.

Farm workers................... .

20 largest 

SMSA's

Central cities 

of 20 largest 

SMSA’s

Suburban rings 

of 20 largest 

SMSA’s

1960 1970 1960 1970 1960 1970

22.287 26.180 11.628 11.223 10.659 14.957

100.0 100.0 100.0 100.0 100.0 100.0

50.4 55.7 48.6 52.8 52.4 57.9

13.6 16.5 11.9 14.4 15.4 18.0

9.2 11.1 8.0 9.2 10.5 12.5

19.3 21.4 21.0 23.4 17.5 19.9

8.3 6.7 7.6 5.7 9.0 7.5

37.6 32.3 37.9 33.3 37.2 31.5

14.5 12.5 12.7 11.0 16.5 13.7

18.7 15.8 20.4 17.8 16.9 14.3

4.3 4.0 4.8 4.6 3.8 3.5

11.3 11.7 13.4 13.8 9.1 10.1

2.2 1.5 2.5 1.8 1.9 1.3

9.1 10.2 10.9 12.0 7.2 8.8

.7 .4 .2 .1 1.2 .6

1 1960 Decennial Census data, collected in April 1960. Persons 14 and 15 years old 

are included (unlike the 1970 data collected by the CPS). However, the number of 

employed 14- and 15-year-olds is small and should have only minor effect on the distribution of employment.

* 1970 annual averages collected by the Current Population Survey.

5 Percentage distribution of 1960 Census data is the distribution of those persons 

who reported an occupation.

cation for the many available jobs that call for

managerial, professional, or technical personnel.

the blue-collar sector. The proportion of service

workers remained the same. (See table 3.)

Workers residing in the suburbs accounted for all

the employment growth shown by the 20 largest

SMSA’s during the 1960’s. Their number increased

by about two-fifths and was accompanied by a

general occupational upgrading of the labor force.

Today, workers residing in the suburbs have a much

higher representation in the major white-collar

occupations (except as sales personnel) than they

did in 1960. Today’s suburban workers also have a

smaller representation in all blue-collar jobs (particularly as craftsmen and operatives) than they

had a decade ago. The proportion of private household workers and the proportion of farm workers

have also declined in the suburbs since 1960.

Over the same period, the central city labor force

declined slightly in size and exhibited a somewhat

slower rate of occupational upgrading. The number

of employed persons residing in the central cities

of the 20 largest SMSA’s declined by 400,000 (or

nearly 4 percent) between 1960 and 1970. Although

these workers have achieved a higher representation

in professional, technical, and managerial occupations, the disparity in skill level between city and

suburban residents is slightly greater today than a

decade ago.

Occupation and joblessness

1960-70 changes in 20 SMSA’s

Since 1960, the work force in our 20 largest

metropolitan areas has grown numerically. Its quality, measured by its occupational distribution, has

also increased. A decade ago, just half the employed

in these areas were working in white-collar occupations; today, 56 percent of employment in these

areas is white collar.

Within the white-collar sector, there has been a

marked increase in the proportion of professional

and managerial workers. The proportion of clerical

workers has also increased, while the proportion

of sales workers has decreased. This decline does

not stem from a lack of proportionate growth in

the number of sales jobs relative to other jobs.

Instead, the decline can probably be attributed to

the greater use by retail establishments of part-time

sales personnel whose primary job is in another field.

While the percentage of white-collar workers

increased during this period, there was a commensurate decline in the percentage of workers in

For several years, the unemployment rates in the

central cities of metropolitan areas have been significantly higher than the jobless rates in suburban

rings.5 In 1970, for example, the jobless rate in all

central cities combined was 5.6 percent, in all

suburban rings combined 4.7 percent. Several

hypotheses have been offered to explain the central

city v. suburban differences in joblessness— a mismatch between skills and jobs, life style, and differential occupational status.

The mismatch hypothesis argues that the main

cause of the urban unemployment problem is not

a shortage of jobs, but a mismatch between the

skill requirements of the jobs available in the

central city and the actual skills of the resident labor

force.6 It argues that jobs in the central cities have

been growing very slowly and those jobs that have

been created are of a highly skilled, white-collar,

“professional” character, for which central city

residents do not have the training to compete successfully. It further maintains that jobs in the suburbs

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have been growing extensively because of the relocation of manufacturing, retail trade, and services outside the city, and that many of these suburban jobs

require the low skilled or semiskilled labor which

city residents could provide.

A test of the validity of this hypothesis requires

reliable data both on the skills of city workers and

on the location and quality of job growth. A recent

study undertaken with limited data concluded that

a so-called job-worker mismatch in the city is largely

imaginary.7 It found that low skilled jobs had continued to grow in the central cities, though not as

fast as in the suburbs. According to this study,

based on data for 1965-67, almost enough jobs

were being created in the cities studied to eliminate

all unemployment even if all the jobless were semiskilled or low skilled workers. In light of this fact,

the persistence of an unemployment gap between

the central city and the suburbs was attributed

largely to discriminatory employment practices.

The life style hypothesis is in direct opposition to

the mismatch hypothesis. It argues that there is an

abundance of unfilled low skilled job vacancies in

or near city areas. It also holds, however, that most

of these job vacancies are for jobs with low wages

or bad working conditions. The availability of a

large number of unfilled low skilled jobs has allegedly

created excess labor demand and tends to make

workers very independent of their employers, thus

creating a high rate of voluntary termination.8 This

hypothesis also argues that many of the jobs that

are concentrated in the cities (warehouses, maintenance services, and so on) are compatible with

a high rate of worker turnover, which, in turn, is

considered a norm in city slum areas.

According to this rationale, high unemployment

in the inner city has been caused not only by the

low skill level of the workers who live there, but

mainly by their cultural norms and life style. As with

the mismatch hypothesis, adequate testing of this

argument cannot begin until data on the quality of

the central city job growth become available.

Under the occupational hypothesis, the higher

jobless rates in the city compared with the suburbs

stem from the fact that the city has greater proportions of workers in those occupations with traditionally high unemployment rates (operative, nonfarm laborer, service, and so forth). Even when

comparisons are made by broad occupational categories, central city unemployment rates are higher

than suburban unemployment rates. (See table 4.)

The disparity between city and suburban unemployment rates by occupation must be attributed

at least partly to the higher proportion of Negroes

in the city labor force. This can be seen if we look

at the central city and suburban occupational jobless

rates by race. The absolute differences between

city and suburban unemployment rates by race for

most occupations are smaller than the differences

for all races combined. Since Negroes generally

have substantially higher unemployment rates than

whites for the same occupation, their concentration

in the city tends to increase the overall gap between

the city and suburban occupational jobless rates.

Table 4. Unemployment rates by occupation for all SMSA’s, their central cities, and their suburban rings, by occupation and color, 1970 annual averages

Total White Negro and other races

Occupational group

All

SMSA’s

All

central

cities

All

suburban

rings

All

SMSA’s

All

central

cities

All

suburban

rings

All

SMSA’s

All

central

cities

All

suburban

rings

1970

All workers........................................................................ . 5.1 5.6 4.7 4.7 4.9 4.5 8.1 8.3 7.4

White-collar workers_______________ ___________ 3.0 3.4 2.7 2.8 3.1 2.7 5.0 5.3 4.2

Professional and technical...................................... 2.2 2.5 1.9 2.2 2.5 1.9 2.1 2.4 0

Managers and officials.............................................. 1.5 1.9 1.3 1.5 1.9 1.3 2.0 (l) 0

Clerical workers......................................... .............. 4.1 4.3 3.9 3.8 3.8 3.8 6.9 7.0 6.3

Sales workers......... ................................................. 4.0 4.5 3.6 3.7 4.0 3.5 8.9 9.5 0

Blue-collar workers......... ............................... ............... 6.4 6.9 5.9 6.0 6.3 5.7 8.7 8.8 8.5

Craftsmen and foremen-------------------------------- 3.8 4.4 3.5 3.7 4.3 3.4 5.2 4.8 6.8

Operatives....... ....................................................... 7.3 7.4 7.1 6.9 6.8 6.9 9.0 9.0 8.9

Nonfarm laborers........................ ........................... 10.2. 10.8 9.5 9.9 10.3 9.6 11.0 11.6 9.0

Service workers............... ................................................ 5.4 5.6 5.2 5.0 4.3 5.1 6.7 6.8 6.6

Farm workers................................................ -............... 0 0 0 (2) (2) (2) (2) (2) 0

1 Not shown where unemployment estimate is less than 5,000 or where labor force 1 Rates for farm workers are not shown since these workers constitute a very minute

is less than 50,000. percent of metropolitan employment.

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This effect can most clearly be seen in the clerical,

operative, and service occupations where Negroes

are most concentrated.

A factor which may be influencing the disparity

between central city and suburban jobless rates for

the same occupation is that central city and suburban workers of the same broad occupational

grouping may not have the same level of skills.

Within the same broad occupational grouping the

city workers may be in lower skilled suboccupations,

with relatively higher unemployment rates, than suburban workers. This effect cannot be quantified with

the limited occupational data available.

Notwithstanding that, for the same occupation,

urban residents have higher unemployment rates

than suburban residents, the occupational hypothesis

seems to be supported by the data in tables 2 and 4.

It would be spurious reasoning, nevertheless, to

conclude that the central city-suburban jobless differential is attributable entirely to differences in the

occupational levels of the respective labor forces.

Individual area highlights

Different political, social, and economic circumstances have contributed to the nature of the occupational distribution within each metropolitan area.

Among these are:

1. The racial composition of the area’s labor

force. If the area (specifically the central city) houses

a large proportion of minority workers, the occupational distribution of the labor force will gravitate

toward the low skilled occupations.

2. The nature of the industries most important

to the area’s economy. Workers living in or near an

area will generally be in occupations associated with

the industries that dominate its economy.

3. The rate of labor force growth. In a period

when the number of workers living in a particular

area is expanding rapidly, the labor force tends to

be relatively skilled, because usually only workers

with high paying jobs can afford the housing and

other economic amenities common to areas of this

nature.

4. Delineation of the areas in question. Boundaries between central city and suburbs are drawn

according to political criteria and not according

to economic differentiation. Some central cities may

be so defined as to contain large neighborhoods of

“suburban” character, while the suburban rings of

some SMSA’s (especially the older ones on the

Eastern seaboard and in the Midwest) may contain

relatively large cities that share most of the problems

of the urban cores.

The boundaries of the metropolitan areas listed

in table 5 correspond to 1960 definitions. Since

then, approximately half of these SMSA’s have been

redefined either to include additional suburban counties or to exclude counties. However, the effect on

most areas is probably very slight. Table 6 shows the

additions and deletions of territory since 1960 to

the areas affected and the proportion of the 1970

SMSA population attributed to the change in

definition.

Areas where white collars predominate

As table 5 shows, in eight metropolitan areas

(New York, Los Angeles-Long Beach, San Francisco-Oakland, Washington, D.C., Minneapolis-St.

Paul, Boston, Cincinnati, and Dallas), relatively

large proportions of the work force— over 56 percent— are employed in white-collar jobs. In virtually

all, no more than 30 percent of the workers are

employed in blue-collar occupations.

New York. Because of the local concentration of

corporate headquarters and other public and private

offices in New York, both central city and suburban

workers are primarily white collar. However, nearly

half the central city white-collar workers are in

clerical jobs, while two-thirds of the white-collar

suburbanites are in professional, managerial, and

sales occupations.

Since 1960, the number of workers living in New

York’s suburbs has grown extensively, by about 30

percent, while the number living in the city has not

increased. However, work forces in both the central

city and the suburbs have been occupationally upgraded during this period, at a fairly even rate, thus

maintaining the relative skill relationships between

residents of the two areas.

Los Angeles-Long Beach and San FranciscoOakland. The relatively high proportion of professional, technical, and managerial workers in the

Los Angeles and San Francisco SMSA’s is a reflection of the industrial mix in the two areas. Los

Angeles has many aerospace and research-related

industries, while San Francisco has a heavy concentration of service-producing industries (transportation and public utilities, trade, finance, insurance,

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and real estate, and government) that require whitecollar workers. Both areas also have relatively large

educational institutions, which employ large numbers

of professional workers.

Los Angeles-Long Beach is an anomaly in that

workers living in the city hold proportionately more

highly skilled jobs than workers living in the

suburbs.9 This can be attributed in part to the fact

that the suburban ring contains areas and cities 10

of an urban nature.

In both California metropolitan areas, suburban

and central city workers have been occupationally

upgraded fairly evenly since 1960. In San FranciscoOakland, about the same proportion of city workers

are in professional and technical occupations (19

percent) as in the suburbs. However, proportions of

Table 5. Total employment by occupation for the 20 largest SMSA’s, their central cities, and their suburban rings,

1970 annual averages

(Percent distribution]

Occupation group

Total employed (thousands)......................... .

Percent............................................................

White-collar workers...............................

Professional and technical...............

Managers, officials, and proprietors.

Clerical workers...............................

Sales workers...................................

Blue-collar workers.................................

Craftsmen and foremen...................

Operatives........................................

Nonfarm laborers.............................

Service workers.......................................

Private household workers..............

Other service workers......................

Farm workers................. .........................

Total employed (thousands)......................... .

Percent............................................................

White-collar workers...............................

Professional and technical...............

Managers, officials, and proprietors.

Clerical workers...............................

Sales workers...................................

Blue-collar workers.................................

Craftsmen and foremen...................

Operatives........................................

Nonfarm laborers.............................

Service workers.......................................

Private household workers..............

Other service workers......................

Farm workers...........................................

Total employed (thousands)......................... .

Percent............................................................

White-collar workers...............................

Professional and technical...............

Managers, officials, and proprietors.

Clerical workers...............................

Sales workers..................................

Blue-collar workers.................................

Craftsmen and foremen...................

Operatives........................................

Nonfarm laborers.............................

Service workers.......................................

Private household workers..............

Other service workers......................

Farm workers..........................................

SMS A

Central

city

Suburban

ring

SMSA

Central

city

Suburban

ring

SMSA

Central

city

Suburban

ring

SMSA

Central

city

Suburban

ring

NEW YORK LOS ANGELES- CHICAGO PHILADELPHIA

LONG BEACH

4,517 3,132 1,385 3,364 1,281 2,083 2,865 1,366 1,499 1,876 777 1,099

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

60.1 59.1 62.3 56.1 59.0 54.3 53.9 48.9 58.6 52.6 46.8 56.6

16.7 15.2 20.0 17.0 18.4 16.1 15.9 13.5 18.2 15.7 10.8 19.1

11.9 10.8 14.6 12.4 12.9 12.1 10.5 7.2 13.5 9.8 7.1 11.8

25.2 27.7 19.5 19.5 20.6 18.7 21.2 23.2 19.2 19.9 23.1 17.5

6.3 5.5 8.2 7.3 7.1 7.4 6.3 4.9 7.7 7.2 5.7 8.3

27.5 28.0 26.5 32.3 29.3 34.2 35.5 39.8 31.4 36.0 39.8 33.4

10.7 9.5 13.4 12.5 10.7 13.6 13.2 12.8 13.6 13.1 12.3 13.6

13.9 15.6 10.1 16.1 15.1 16.7 18.0 21.5 14.8 18.9 22.2 16.6

2.9 2.9 3.0 3.8 3.6 3.8 4.2 5.5 3.0 4.1 5.3 3.2

12.3 12.9 10.8 11.0 11.3 10.8 10.3 11.2 9.4 10.8 13.4 9.0

1.3 1.3 1.2 2.0 2.3 1.8 .8 .7 .9 1.5 1.7 1.5

11.0 11.6 9.6 9.0 9.0 9.1 9.5 10.5 8.4 9.3 11.7 7.5

.1 0 .3 .6 .4 .7 .3 0 .6 .6 0 1.0

DETROIT SAN FRANCISCO— BOSTON PITTSBURGH

OAKLAND

1,571 582 989 1,371 451 920 1,165 239 926 876 174 702

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

47.4 39.8 52.0 60.9 60,6 61.0 59.9 58.1 60.4 48.0 47.8 48.2

13.5 10.2 15.5 19.1 19.7 ' 18.9 18.3 15.5 19.1 15.5 13.5 16.1

8.2 4.6 10.3 12.2 10.9 12.8 10.8 7.7 11.6 8.7 8.0 8.9

20.1 20.4 19.9 22.9 24.3 22.3 23.6 30.0 21.9 16.8 21.1 15.6

5.6 4.5 6.3 6.7 5.8 7.1 7.1 4.9 7.6 7.0 5.2 7.5

40.5 45.4 37.5 26.4 25.6 26.7 28.0 28.7 27.8 38.7 33.1 40.1

14.7 12.7 15.9 11.2 8.7 12.4 11.5 10.8 11.7 16.8 13.0 17.8

21.8 28.0 18.1 10.9 12.0 10.3 12.7 12.2 12.9 15.3 12.7 15.9

4.0 4.7 3.5 4.2 4.8 4.0 3.7 5.7 3.2 6.6 7.4 6.3

12.0 14.9 10.2 12.0 13.7 11.2 11.9 13.1 11.6 12.7 19.0 11.1

1.6 1.8 1.5 1.9 2.5 1.6 .9 0 .8 1.6 0 1.6

10.4 13.1 8.7 10.1 11.2 9.6 11.0 0 10.8 11.1 0 9.6

(l) 0 0 .7 0 1.1 0 0 0 .6 0 .9

ST. LOUIS WASHINGTON, D.C. CLEVELAND BALTIMORE

909 228 681 1,140 343 797 770 203 567 766 348 418

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

52.2 41.0 55.9 69.5 54.0 76.3 53.2 35.2 59.6 49.8 37.9 59.6

14.1 8.0 16.2 25.3 15.0 29.7 12.8 7.9 14.4 15.0 10.1 19.1

10.5 6.6 11.9 11.4 5.9 13.8 12.3 5.1 14.9 8.8 5.8 11.5

19.6 21.1 19.1 26.9 28.7 26.2 21.4 17.3 22.8 20.2 18.0 21.8

8.0 5.4 9.0 5.9 4.3 6.5 6.8 4.9 7.6 5.8 4.1 7.4

35.2 39.9 33.6 17.8 23.8 15.2 35.5 47.7 31.1 36.6 43.8 30.5

13.1 10.9 13.8 8.6 7.0 9.3 14.0 13.7 14.1 13.9 13.2 14.4

17.7 23.3 15.9 5.6 9.5 4.0 17.5 26.7 14.2 16.1 21.1 12.0

4.4 5.7 4.0 3.6 7.3 2.0 4.0 7.3 2.8 6.5 9.5 4.1

12.2 19.1 9.8 12.5 22,1 8.4 11.2 17.1 9.2 13.4 18.3 9.2

1.5 3.3 1.0 2.4 4.5 1.5 .7 0 0 2.2 3.3 1.2

10.7 15.8 8.8 10.1 17.6 6.9 10.5 0 0 11.2 15.0 7.9

0 0 0 0 0 0 0 0 0 0 0 0

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Table 5. Continued—Total employment by occupation for the 20 largest SMSA’s, their central cities, and their

suburban rings, 1970 annual averages

Percent distribution]

Occupation group

SMS A

Central

city

Suburban

ring

SMS A

Central

city

Suburban

ring

SMSA

Central

city

Suburban

ring

SMSA

Central

city

Suburban

ring

NEWARK MINNEAPOLISST. PAULBUFFALO HOUSTON

Total employed (thousands)........................... ............. .......... 752 92 660 759 291 468 509 167 342 758 550 208

Percent..................................................................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

White-collar workers........................................................ 52.0 27.4 55.6 56.3 52.5 58.7 53.7 48.0 56.0 50.2 54.8 38.8

Professional and technical......................................... 16.0 4.7 17.7 18.1 16.6 19.2 18.0 15.4 19.2 12.5 14.3 7.9

Managers', officials, and proprietors________ ____ 11.6 (l) 13.0 11.0 6.9 13.6 9.8 5.6 11.7 11.0 12.3 7.5

Clerical workers......................................................... 18.7 15.0 19.2 20.8 24.3 18.6 18.0 20.4 17.0 19.2 20.2 16.8

Sales workers............................................................. 5.6 0 5.8 6.4 4.7 7.3 7.8 6.7 8.4 7.5 8.0 6.5

Blue-collar workers........................................................... 38.1 55.9 35.6 28.9 30.0 28.1 34.5 36.8 33.6 36.0 30.7 49.5

Craftsmen and foremen............................................. 13.0 13.1 13.0 10.9 10.8 11.0 13.7 11.8 14.5 14.7 11.8 22.0

Operatives................................................................... 21.1 34.3 19.2 14.0 14.6 13.6 16.1 19.1 14.8 16.3 13.5 23.4

Nonfarm laborers....................................................... 4.0 8.5 3.4 4.0 4.5 3.7 4.8 5.9 4.2 5.0 5.4 4.2

Service workers................................................................. 9.8 16.6 8.9 13.7 17.5 11.2 11.4 15.2 9.7 13.6 14.3 11.7

Private household....................................................... 1.4 0 1.4 1.5 0 1.7 .9 0 0 2.7 3.3 0

Other service workers................................................ 8.4 0 7.6 12.2 0 9.5 10.5 0 0 10.9 11.0 0

Farm workers..................................................................... 0 (l) 0 1.0 0 1.7 0 0 0 0 0 0

MILWAUKEE PATERSON- CINCINNATI DALLAS

CLIFTON-PASSAIC

Total employed (thousands)..................................................... 526 285 241 556 132 424 435 197 238 696 385 311

Percent...................................................................................... 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

White-collar workers.......................................................... 49.4 43.9 56.2 53.5 39.2 58.2 56.7 58.8 55.3 59.3 59.1 59.6

Professional and technical......................................... 13.4 12.1 15.0 13.9 12.8 14.4 18.7 22.6 15.4 16.0 14.7 18.2

Managers, officials, and proprietors.......................... 11.8 8.8 15.5 11.3 7.2 12.5 11.5 9.7 13.0 13.1 13.1 13.0

Clerical workers.......................................................... 16.8 16.7 16.7 20.9 14.4 22.9 19.3 20.4 18.3 21.7 22.0 21.2

Sales workers............................................................. 7.5 6.3 8.6 7.3 4.7 8.3 7.2 6.2 7.7 8.4 9.2 7.4

Blue-collar workers........................................................... 36.6 40.4 32.2 37.1 46.6 34.2 32.7 29.4 35.0 28.6 27.2 30.9

Craftsmen and foremen............................................. 14.0 14.8 12.9 14.2 11.8 14.9 12.3 10.2 13.8 11.4 8.8 15.2

Operatives................................................................... 18.7 21.7 14.6 19.0 29.4 15.8 16.6 15.7 17.1 13.3 13.6 13.0

Nonfarm laborers....................................................... 3.9 3.9 4.3 3.9 5.4 3.5 3.8 3.5 4.1 3.9 4.8 ■2.6

Service workers................................................................. 13.9 15.7 11.6 9.4 14.2 7.8 9.5 11.8 8.1 11.5 13.5 8.2

Private household................................................... 1.4 0 0 1.3 0 1.2 2.0 2.7 0 1.9 2.0 0

Other service workers................................................ 12.5 0 0 8.1 0 6.6 7.5 9.1 0 9.6 11.5 0

Farm workers..................................................................... 0 0 0 0 0 0 0 0 0 0 0 0

1 Percent not shown where employment estimate is less than 5,000. Percent not shown where private household employment estimate is less than 5,000.

managers, salesmen, and craftsmen are larger in

the suburbs, as was the case in 1960.

Boston, Minneapolis-St. Paul, and Dallas. The high

proportion of white-collar workers in Boston,

Minneapolis-St. Paul, and Dallas is also attributable

largely to the relative importance of the serviceproducing industries— especially trade and finance,

insurance, and real estate. The high proportion of

white-collar workers in these three areas (particularly

in Minneapolis-St. Paul) may be explained in part

by the great majority of workers residing in the

central city being white. However, central citysuburban skill gaps of average magnitude are still

evident in Boston and Minneapolis-St. Paul.

In Dallas, the number of workers has increased

substantially in both the city and suburbs over the

decade. The central city and suburban ring of the

metropolitan area have also experienced nearly

equal occupational upgrading during this period.

Cincinnati. The occupational distribution in the central city and the suburbs of Cincinnati is atypical,

in that the proportion of workers in the professional

and technical field is one-third larger in the city

than in the suburban ring 11. However, larger proportions of managers and craftsmen live in the

suburbs. In both the city and suburbs, workers have

been occupationally upgraded extensively since

1960, and the number of workers has also increased.

Washington, D.C. In the metropolitan area that includes the Nation’s capital, seven-tenths of all

workers are white-collar, with one-fourth in professional and technical occupations. This is, of

course, a reflection of the dominant position of the

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Federal Government in the area. The skill level of

workers living in the Washington, D.C., suburban

ring is the highest of all 20 suburbs: three-fifths of

the workers are either professionals, technicians,

managers, officials, or craftsmen.

Central city residents, though holding more skilled

jobs in comparison with workers in many central

cities, have a relatively small share of the higher

skilled jobs available in the metropolitan area. In

the District of Columbia itself (the central city),

seven-tenths of the resident workers are Negro.

Because of extensive migration during the past

decade, both in and out, the present city residents

show virtually no occupational upgrading over the

1960 residents.

Moderate proportions of both blue and white

Chicago, Philadelphia, St. Louis, Cleveland,

Buffalo, and Houston have relatively moderate proportions of both blue-collar workers (around 35

percent) and white-collar workers (between 50 and

54 percent). No particular industry predominates

in these areas, although, with the exception of

Houston, manufacturing is relatively strong.

Chicago, Philadelphia, and Buffalo. In these three

areas, about 55 percent of the workers living in the

suburbs are employed in professional and technical,

managerial, sales, and craftsmen occupations. In

comparison, only about 37 percent of the workers

who live in the central cities are in these occupations.

In all three areas, the number of workers living

in the central city has not grown since 1960. In

Philadelphia and Chicago, occupational upgrading

of the work force has been slight; in Buffalo, more

substantial. In comparison, the suburban work force

in these areas has grown, both numerically and in

terms of the proportion of higher skilled jobs.

Cleveland. Here the difference in occupational array

between those living in the central city and in the

suburbs is wide. Three-tenths of suburban workers

are in professional and managerial occupations, but

only one-eighth of all city workers. The majority of

city workers (about two-thirds) are employed in

blue-collar and service jobs.

Sf. Louis. The central city-suburban occupational

gap is also wide in St. Louis. Suburban residents

hold a disproportionate number of the skilled jobs

available in the metropolitan area. The central city

labor force has decreased both numerically and in

terms of the proportion of skilled jobs since 1960.

Migration of workers helps to explain the slight

occupational downgrading of the inner city. A large

Table 6. Definitional changes in the 20 largest Standard Metropolitan Statistical Areas, 1960-70

SMSA No change Additions Deletions

Percent to which use of 1960 boundary

definitions overestimate (underestimate) 1970 population 1

SMSA Suburban ring

No change

Orange Co. +20 +37

No change 

No change 

No change

Solano Co. + 5

—less than 1

+ 8

Sherborn Town, Middlesex Co. —less than 1

Millis Town, Norfolk Co.

Loudoun Co., Va., Prince 

William Co., Va.

- 5 - 7

No change

Franklin Co., Mo. - 2 - 3

No change

Geauga Co., Medina Co............... - 7 -11

Hartford Co.................................. - 6 -1 0

No change

Brazoria Co., Fort Bend Co., 

Liberty Co., Montgomery Co.

-1 2 -3 2

Kaufman Co., Rockwall Co......... - 3 - 6

No change 

No change

Ozaukee Co., Washington, Co__ - 8 -1 7

Clermont Co., Ohio, Warren Co. -1 8 -2 6

Ohio, Dearborn Co., Ind. 

Boone Co., Ky.

1 Positive percent denotes an overestimation and a negative percent denotes an SOURCE: 1970 Census of Population, Preliminary Report PC (P3)-3 (U.S. Depart*

underestimation. ment of Commerce, Bureau of the Census, 1971).

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outmigration of white workers decreased the city

labor force by one-fifth. Consequently, the city

labor force became increasingly Negro, due both

to the shift of white workers and inmigration of

Negroes. Today, two-fifths of the city’s workers

are black.

Houston. Today, one-half of Houston’s metropolitan

work force is white collar. Workers living in the city

are in white-collar and service jobs. About onehalf of the workers in the suburban ring (as defined

in 1960 12) are blue collar— the converse of the

situation in most metropolitan areas. One reason

for the high proportion of craftsmen and operatives

in the suburban ring may be that over the last

decade employment in contract construction and

the oil industry has grown extensively in the

Houston metropolitan area.

Newark and Paterson-Clifton-Passaic. In New

Jersey’s two largest metropolitan areas— Newark

and Paterson-Clifton-Passaic— the occupational gap

between the city and suburban work forces is wide.

In both areas, employment is moderately heavy in

both durable and nondurable goods manufacturing.

The suburbs of both areas have fairly high proportions of professional and managerial workers, while

the central cities are peopled mainly by blue-collar

workers, especially operatives.

In the Paterson area, workers residing in either

the city and suburbs have been occupationally upgraded only slightly since a decade ago. Most of

the occupational change has been in movement of

workers among the lower skilled occupations.

Workers in the Newark and Paterson metropolitan

areas (especially in the city of Newark) are strongly

represented in operative occupations. Moreover,

about seven-tenths of the workers living in Newark

are employed in blue-collar and service occupations,

a slightly larger proportion than a decade ago.

The relatively low skill level of workers in Newark’s

central city results in part from the inmigration of

untrained Negroes and Puerto Ricans.

Areas with strong blue-collar orientation

In Detroit, Pittsburgh, Baltimore, and Milwaukee,

one-half or less of the work force is employed in

white-collar occupations. Stated conversely, at least

half of the workers in these areas are in blue-collar

and service occupations. In all of these SMSA’s

except Baltimore, employment is very heavy in the

durable goods industries.

Detroit and Pittsburgh. About two-fifths of all metropolitan workers in Detroit and Pittsburgh are in

blue-collar jobs. The influence of the automobile

industry is strong in Detroit, with about 22 percent

of the labor force being operatives. Close to one-half

of the workers who live in Detroit’s central city are

in blue-collar jobs (mostly as operatives), and

15 percent are in service occupations.

Over half the workers living in Pittsburgh’s central

city and the same proportion in its surrounding

suburban ring are in blue-collar and service occupations. Service workers living in the city account for

one-fifth of total city employment. Pittsburgh has

a higher proportion of blue-collar workers in the

suburbs (40 percent) than in the city (33 percent).

The suburban blue-collar workers, however, are

more likely to be craftsmen and foremen and less

likely to be nonfarm laborers.

Baltimore. In the Baltimore metropolitan area, onehalf the labor force is in white-collar occupations.

In the central city, this proportion is 38 percent

and in the suburban ring 60 percent. About 30

percent of the suburban work force is in professional

and managerial occupations— twice as large a proportion as that for the central city work force.

Since 1960, the labor force living in the Baltimore

suburbs has grown extensively (passing the number

of city workers) and has become increasingly white

collar. On the other hand, the labor force living in

the city has not grown nor shown any significant

upgrading. Today, half the workers who live in

Baltimore city are Negroes.

Milwaukee. In the Milwaukee SMSA, one-half of

the labor force is in white-collar occupations. Its

central city-suburban skill gap is not of the magnitude of Baltimore’s, however. Because of the

importance of durable goods manufacturing in this

area, approximately one-third of the workers are

employed as craftsmen and operatives.

Conclusions

Reflecting their important and growing role in

the Nation’s economy, metropolitan areas have

larger proportions of professional and technical,

managerial and official, and clerical workers than

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other areas of the country. These areas (especially

the 20 largest) are the Nation’s centers of industrial

and business activity, and their labor forces can

be expected to lead the path of occupational evolution in the future as they have in the past.

So long as workers tend to move from the central

city as their occupational level rises, the central

city labor force will remain below that of the

suburbs in terms of skill levels. This situation is

unlikely to change until there are substantial changes

in housing patterns and in socioeconomic conditions.

Earlier, it was concluded that in the 20 largest

SMSA’s the gap in skills between central city

residents and those in the suburbs is only slightly

larger today than a decade ago, notwithstanding

significant upgrading in both SMS A components.

However, in many individual SMSA’s the gap has

substantially increased over the decade.

The same factors that have affected the occupational gap in the past will probably continue to

do so. Two interrelated factors— the racial composition of the city labor force and the rate of

city-to-suburb migration— may have the most effect

on the future occupational distribution of city

workers and the chance of closing the gap in skills

between them and their suburban counterparts.

At present, the proportion of Negro workers in the

central cities of the 20 largest metropolitan areas

is about one-fourth. If the central city-suburban

skill gap is to be narrowed, greater effort must be

made to make city living attractive to skilled

workers, to equip relatively unskilled city residents

with sufficient skills to enable them to compete for

good jobs, and to remove discriminatory hiring and

housing practices. 

FOOTNOTES1 D ata in this article on m etropolitan areas refer to the 

212 Standard M etropolitan Statistical A reas as defined in 

1960. It should be noted th at the d ata in this report represent the place of residence of w orkers rather than their 

place of work.

2 U ntil recently, d ata on the occupational distribution of 

m etropolitan w orkers have been available only from the 

decennial census and to a lim ited extent from the U rban 

E m ploym ent Survey (U E S ), w hich obtained som e industry 

and occupational data on residents living in C oncentrated 

E m ploym ent Frogram (C E P ) areas of six m ajor U.S. 

cities betw een July 1968-June 1969. See BLS R eport 370, 

O ctober 1969.

3 These are the 20 largest SM SA ’s as defined and ranked 

in 1960. D ata from the 1970 decennial census show that 

during the 1960’s three SM SA ’s m oved out of the top 20 

(C incinnati, P aterso n -C lifto n -P assaic, and Buffalo) and 

were replaced by A naheim -S anta A n a-G ard en G rove, 

Seattle-E verett, and A tlanta.

4 See D orothy K. N ew m an, “D ecentralization of Jobs,” 

Monthly Labor Review, M ay 1968, table 1, p. 8.

5 See Paul O. Flaim , “Jobless T rends in 20 Large M etropolitan A reas,” Monthly Labor Review, M ay 1968,

pp. 16-28.

8 N ew m an, op. cit.

7 Study by C harlotte Frem on, “T he O ccupational Patterns 

in U rban E m ploym ent C hange, 1965-67” (w orking p ap er)

The U rban Institute, W ashington, D .C., Jan u ary 1970.

8 See Peter B. D oeringer, “L abor M arket R eport from the 

Boston G hetto,” Monthly Labor Review, M arch 1969, 

pp. 55-56.

9 D ata from the 1960 Census show th a t this relationship 

was also true a decade ago.

10 T he suburban ring of the Los A ngeles-L ong Beach 

SMSA as defined in 1960 contained cities in O range C ounty, 

such as A naheim , Fullerton, G arden G rove, Santa A na, 

and so forth, w ith som e u rban characteristics. In 1963, 

O range C ounty was deleted from the Los Angeles SMSA 

and m ade a separate SM SA (A n ah eim -S an ta A n a-G ard en 

G ro v e). Since the data in this article are based on 1960 

definitions, these urban areas are included in the Los 

A ngeles-Long Beach suburban boundaries.

11 O ne reason for this m ay be the fact th at the four 

counties added to the C incinnati SM SA since 1960 are not 

included in the d ata in this article. These counties in 1970 

housed one-fourth of the C incinnati suburban population 

(table 6 ). Because of the absence of these counties from 

the suburban figures, the suburban proportion of professional and technical w orkers is no doubt understated.

12 The H ouston suburban ring as defined in 1960 did not 

contain four counties subsequently added to the SMSA. 

These counties in 1970 contained three-tenths of the H ouston suburban population (table 6 ). T he absence of these 

counties from the suburban figures show n here has no 

doubt affected the occupational distribution of the H ouston 

suburban w ork force.

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Employment and

unemployment

among Americans

of Spanish origin

Of t h e 6 m i l l i o n Americans age 16 and over

who identified them selves as being of Spanish

origin or d escent in 1973, an average o f 3.6

million were in the labor force, and they had an

unem ploym ent rate of 7.5 percent. These are

summary findings o f a new Bureau o f Labor

Statistics data series on the employment status of

Americans o f Spanish origin, now available for

the first time on a regular basis.

In the recent past, data on A m ericans o f

Spanish origin have been collected only once a

year. Moreover, very little detail had been available on a consistent and continuous basis. Since

March 1973, monthly information on labor force

characteristics of the civilian noninstitutional population of Spanish origin 16 years of age and over

have been tabulated separately by the Bureau of

the Census as a part of the ongoing monthly

survey of the N ation’s labor fo rce.1 Under a

program sponsored by the U .S. Department of

Labor’s Manpower Administration, these data

will be published quarterly by the Bureau o f Labor

Statistics, beginning in April 1974.

This article introduces the continuous labor

force data series for Americans of Spanish origin.

It first traces the evolution of the self-identification method for classifying persons of Spanish

origin and discusses a few of the major technical

caveats. Its main focus, however, is on analyzing

initial findings from the survey based on 1973 annual averages.2

Data comparability

Before 1973, the two major sources o f published data on the labor force characteristics of

persons o f Spanish origin have been the decennial

R o b e rta V . M cK ay is an eco n o m ist in the D ivision of 

Em ploym ent and U nem ploym ent A nalysis, Bureau of L abor 

Statistics.

Quarterly publication of new series 

begins this month; data for 1973 

show persons of Spanish origin 

had an unemployment rate of 7.5 percent, and 

were more likely to be jobless than white workers

ROBERTA V. McKAY

censuses and once-a-year supplem ents to the

Current Population Survey (CPS) in 1969, 1971,

and 1972.3 In addition to labor force data, both

the census and Current Population Survey supplemental series included data on a wide range of

characteristics o f the population.

The socioeconom ic data collected during the

decennial census were derived from a population

universe based on a changing characterization of

Spanish background and ethnicity. The earliest

published social and economic data on Spanish

ethnicity from decennial censuses were derived

from questions on the country of birth of the

individual (1850) and subsequently, birthplace of

parents (1890). Still later, when the first direct

question on Spanish ethnicity was introduced

(1930), Mexican Americans were identified from a

“ race” question (a one-time question in which

M exican Am ericans were considered to be a

racial group). In subsequent decennial years, the

characterization of Spanish Americans was progressively expanded by including questions on

Spanish mother tongue (1940) and (1950) classification by Spanish surname in the five Southwestern States where many Americans of Spanish

origin reside. In 1970, Spanish Americans were

identified by use o f four identifiers: origin or

descent, mother tongue, surname, and place of

birth or parent’s birth. Three of these definitions

are utilized in defining Spanish heritage, a term

used in many 1970 census reports.4 The Spanish

origin or descent definitions, used in many of the

Census of Population Subject Reports, relies on

self-identification of Spanish ethnicity.

In the annual ethnic origin supplement to the

Current Population Survey, initiated in Novembei

1969 and then conducted in March of 1971 and

1972, estimates have been based on the respondent’s identifying himself as of Spanish origin or

descent. Self-identification essentially consists of

From the Review of April 1974 156

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asking all survey respondents, “ What is . . . ’s

origin or descent?’’, with the enumerator coding

according to seven Spanish categories.5

With changing terms and differing collection

methods, the population counts resulting from the

1970 census and the estimates from the annual

ethnic supplements to the Current Population

Survey have varied widely. A lack of comparability

between the census and the Current Population

Survey and, over the past 5 years, within the Current Population Survey series has therefore

emerged. The Current Population Survey estimates

were found to yield differing population growth

counts because of technical factors such as sample

redesign (in 1972-73), reclassification of the origin

of children under age 14, revision of Mexican-origin

categories, and sampling variability.6

The new series

The new Spanish origin labor force estimates

are derived from the ongoing monthly Current

Population Survey, which collects information on

labor force activities o f all persons age 16 and

over in the United States. The data on persons of

Spanish origin in this article are somewhat limited

in detail. The relatively small size o f this group-

—less than 5 percent o f the total population

—particularly subjects these estimates to a high

degree o f sampling variability, that is, the variation

that might occur by chance because only a sample

o f the population has been surveyed. C onsequently, only the larger estimates can be reliably

reported. However, the availability and use of annual averages serve to enhance data reliability, thus

permitting a number o f comparisons between the

labor force experience o f Spanish origin workers

and other workers.7

Persons classified as o f Spanish origin also are

counted as white or black. For purposes of labor

force analysis, persons o f Spanish origin are

tabulated separately, without regard to race. According to the 1970 census, approximately 98

percent of the population group is white. The size of

the Spanish ethnicity component within each color

group is not large enough to either affect the movement of the entire group or substantially bias any

one estimate.8

(The comparisons in this article also introduce

specially tabulated data for black workers from

the Current Population Survey. Previously, only

the “ Negro and other races’’ classification, of

which blacks comprise 89 percent, has been used

in racial comparisons.)

Population and labor force

The 6 million persons 16 years and over of

Spanish origin in 1973 accounted for 4 percent of

the Nation’s civilian noninstitutional population of

this age group. The sex com position o f the

population was about the same as that o f both

racial groups, but persons of Spanish origin, like

blacks, had a lower median age than the white

population. Teenagers of Spanish origin com ­

prised a proportion of their working age population nearly 1 Yi times that of their white counterparts.

Table 1. Employment status of persons of Spanish origin,

whites, and blacks, by sex and age, annual averages, 1973

[In thousands]

Employment status Total Spanish

origin

White Black

TOTAL, 16 YEARS OLD AND OVER

Civilian noninstitutional population............. 145,936 5,997 129,302 14,788

Civilian labor force................................ 88,714 3,603 78,689 8,890

Percent of population............. 60.8 60.1 60.9 60.1

Employment.................................... 84,409 3,333 75,278 8,061

Agriculture___ _____ _____ 3,452 222 3,144 258

Nonagricultural industries__ 80,957 3,111 72,134 7,803

Unemployment............................... 4,304 280 3,411 829

Unemployment rate........ 4.9 7.5 4.3 9.3

Not in the labor force................ .......... 57,222 2,394 50,613 5,898

MALES, 20 YEARS OLD AND OVER

Civilian noninstitutional population............. 60,943 2,425 54,503 5,662

Civilian labor force............................... 49,539 2,084 44,490 4,430

Percent of population__ 81.3 85.9 81.6 78.2

Employment.................................... 47,946 1,973 43,183 4,170

Agriculture.............................. 2,500 167 2,269 193

Nonagricultural industries... 45,445 1,806 40,915 3,977

Unemployment_____________ _ 1,594 111 1,307 260

Unemployment rate........ 3.2 5.3 2.9 5.9

Not in the labor force............................ 11,404 341 10,013 1,232

FEMALES, 20 YEARS OLD AND OVER

Civilian noninstitutional population--------- 69,249 2,718 61,319 7,050

Civilian labor force------------------------ 30,713 1,118 26,647 3,635

Percent of population__ 44.4 41.1 43.5 51.6

Employment.................................. 29,228 1,038 25,494 3,325

Agriculture............................ 550 28 506 37

Nonagricultural industries... 28,678 1,010 24,988 3,288

Unemployment............................. 1,485 81 1,153 310

Unemployment rate........ 4.8 7.2 4.3 8.5

Not in the labor force............................ 38,536 1,599 34,672 3,415

BOTH SEXES, 16 TO 19 YEARS OLD

Civilian noninstitutional population............. 15,744 855 13,481 2,076

Civilian labor force.........................— 8,461 401 7,552 824

Percent of population... 53.7 46.9 56.0 39.7

Employment............................... 7,236 321 6,602 566

Agriculture.............................. 402 27 370 28

Nonagricultural industries... 6,834 294 6,232 537

Unemployment............................... 1,225 79 950 259

Unemployment rate____ 14.5 19.8 12.6 31.4

Not in the labor force........................... 7,283 454 5,929 1,251

NOTE: Since persons of Spanish origin are also counted as white or black, 3 groups 

shown will not sum to total.

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The Spanish origin civilian labor force averaged

3.6 million persons in 1973, com posed o f 2.1

million adult men, 1.1 million adult women, and

400,000 teenagers (table 1). In percentage terms,

this age-sex distribution of the labor force was

similar to that for whites. Compared with blacks,

however, the work force of Spanish origin had a

larger proportion o f adult men and a smaller

proportion of adult women.

On an overall basis, the labor force participation rates— the civilian labor force as a percent of

population—o f Spanish origin persons did not

differ much from those of their white and black

counterparts. In 1973, 60.1 percent of the Spanish

origin were in the labor force, identical with the

black proportion but slightly lower than that of

white workers.

The major activity o f persons in the three

groups who were not participating in the labor

force were likewise similar. (See table 2.) At least

7 out o f 10 workers in each of the population

groups cited home responsibilities or school as

reasons for not working or looking for work.

The labor force participation rate of adult men

of Spanish origin, at 85.9 percent in 1973, was

Table 2. Major activity of persons not in the labor force,

Spanish origin, whites, and blacks, by age and sex, annual

average, 1973

Major activity

Spanish origin White Black

Age 

16 to 

19

Age 

20 and 

over

Age 

16 to 

19

Age 

20 and 

over

Age 

16 to 

19

Age 

20 and 

over

TOTAL

Not in the labor force (in

thousands)__________ ____ 454 1,940 5,929 44,685 1,251 4.647

Percent distribution--------- 100 0 100 0 100.0 100 0 100 0 100.0

Home responsibilities, _ 19 6 76.1 12 7 69 9 13 5 61 9

School_____________ 62.9 3.9 71.6 3.8 70.4 5.3

Unable to work,.......... 2 6.0 5 5 0 5 11.5

Other........................ 17.3 14.1 15 2 21 3 15.7 21 2

MALES

Not in the labor force (in

thousands)............................... 185 341 2,551 10,013 542 1,232

Percent distribution. ____ 100.0 100 0 100.0 100.0 100.0 100.0

Home responsibilities.. 1.1 2.3 6 1.8 9 3.2

School_____________ 76 8 11.7 81 3 9.8 78 8 9.6

23 8 .7 14.1 .6 25.2

Other_______ ______ 22.1 62.2 17.4 74.3 19.7 62.0

FEMALES

Not in the labor force (in

thousands)_______________ 269 1,599 3,377 34,672 709 3,415

Percent distribution_____ 100 0 100.0 100.0 100 0 100.0 100.0

Home responsibilities.. 32.1 91.9 21 8 89.6 23.0 83 1

School_____________ 53.7 2.2 64 3 2.0 64.0 3.8

Unable to work______ .4 2.2 4 2 4 4 6.6

Other........................... 13.8 3.8 13.5 6.0 12.6 6.5

above the 81.6-percent rate for white men and

considerably higher than the 78.2 percent for

black men. (See table 3.) At every age level,

except for those age 65 and over, adult Spanish

men were considerably more likely to participate

in the labor force than black adult men. However,

adult Spanish men participated at a higher rate

than white men only in the age group 20 to 24

years— 88.5 compared to 85.8 percent—reflecting

the fact that a smaller proportion of Spanish men

are still in school at that age.9 The dominant

influence on the differing overall labor force

participation rates of adult men, Spanish versus

white, has been the dissimilar age distributions of

their populations and labor forces. Young adult

men, who have higher participation rates than

those age 55 years and over, comprise a relatively

greater proportion o f the Spanish origin than

white population and labor force groups. The

proportionately older age of white men serves to

lower their overall participation rate.

The small proportion o f adult men of Spanish

origin who did not work or look for work was

similar to that o f whites and blacks. The main

reasons were inability to work and voluntary idleness, retirement, waiting to enter school or Armed

Forces, and discouragement over job prospects.

The labor force participation rate of Spanish

teenage boys was 55.2 percent, less than that for

whites but greater than that for blacks. When not

in the labor force, Spanish teenage boys were

as likely to be in school as other teenage boys.

Table 3. Civilian labor force participation rates of persons

of Spanish origin, whites, and blacks, by age and sex, annual

average, 1973

Spanish origin White Black

Age

Males FemalesMales Females

Males Females

Total, IS years old and 

over________ _______ 81.5 40.9 79.5 44 1 73.3 49.3

Both sexes, 16 to 19 

years old................. 55.2 39.1 62.0 50.1 45.6 34 2

20 years old and over. 85.9 41.1 81.6 43.5 78.2 51.6

20 to 24 years 

old................... 88.5 48.6 85.8 61.6 83.6 58.0

25 to 34 years 

old__________ 94,3 44.0 96.3 48.5 91.8 62.7

35 to 44 years 

old__________ 94.6 45.5 96.8 52.2 90.9 61.6

45 to 54 years 

old............ ....... 91.5 45.2 93.5 53.4 87.5 56.1

55 to 64 years 

old__________ 74.8 29.4 79.0 40 8 69.4 44.7

65 years old and 

over.................. 21.5 7.1 22.8 8.7 22.4 11.4

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At 41 percent, the labor force participation rate

of adult women of Spanish origin was slightly

lower than the participation rate of white women

and considerably lower than that of black women.

It was even below that o f white teenage girls, a

group whose participation has been among the

lowest of all race-sex groups. The high degree of

nonparticipation of Spanish women undoubtedly

reflects the traditional role of women in the Spanish

hom e.10 Household responsibilities were the reason

92 percent of these women not in the labor force

were neither working nor looking for work.

Although the major reason that teenage girls of

Spanish origin were not in the labor force was

“ school,” the proportion citing school was lower

than that for white or black teenage females.

Moreover, they were more likely to cite household

responsibilities as keeping them from working or

looking for work than did other teenage girls.

Employment

An average of 3.3 million persons of Spanish

origin were em ployed in 1973. Adult men accounted for nearly 2.0 million of this number,

adult women, 1.0 million, and teenagers, 320,000.

The occupational distribution of employed persons of Spanish origin was essentially similar to

that of blacks, except that a smaller proportion of

Americans of Spanish origin were employed in

service occupations and slightly larger proportions

were blue-collar and farm workers. The differences between the occupational distribution of

white and Spanish workers are striking. Whereas

two-thirds o f Spanish workers were employed in

blue-collar and service occupations and fewer

than one-third held jobs in white-collar occupations, half of the whites held jobs in white-collar

occupations. The proportion of Spanish workers in

the professional and managerial occupations was

less than half the proportion for white workers in

such occupations. (See table 4.)

Underlying the occupational distribution of employed persons o f Spanish origin were som e

important sex differences. Six out of 10 men of

Spanish origin held jobs in blue-collar occupations, a proportion comparable to that of blacks

but higher than the 46-percent figure for white

men. White-collar employment among Spanish

males was again roughly the same as for black men,

but less than half the proportion of white men.

Women of Spanish origin were employed in

blue-collar occupations to a greater extent than

either white or black women. Thirty-five percent

of women of Spanish origin compared with 16

percent of white and 19 percent o f black women

were so employed. A lesser proportion held jobs

in white-collar occupations than white women.

Forty percent o f women of Spanish origin had

white-collar jobs; 63 percent of white women did.

But Spanish women were only half as likely as

black women to work in service occupations,

including domestic jobs. These sex differences

help to explain the more than proportional concentration of Spanish workers in blue-collar occupations, white workers in white-collar occupations, and black workers in service occupations.

Unemployment

Unemployment is a severe problem for persons

of Spanish origin. In 1973, an average of 270,000

were jobless. At 7.5 percent, their unemployment

rate was more than halfway between the 4.3-

percent rate for white workers and the 9.3 percentage for blacks. Workers of Spanish origin

accounted for over 6 percent of total unemployment but only 4 percent of the labor force.

Table 4. Employment and unemployment rates, experienced workers only of Spanish origin, whites, and blacks, by

occupation, annual average, 1973

Occupation

.Employment 

(Numbers in thousands)

Unemployment rate 

(Percent of labor 

force)

Spanish

origin

White Black Spanish

origin

White Black

Total experienced workers__

Percent distribution__

3,333 

100 0

75,278 

100 0

8,061 

100 0

6.6 3.7 7.8

White-collar workers.........

Professional and techni28.9 49 8 28.6 4.3 2.7 6.7

cal________________

Managers and adminis6.5 14.4 8.5 3.3 2.0 4.5

trators, except farm__ 5 5 10 0 3.5 1.4 1.4 2.2

Sales workers________ 3.7 6.9 2.1 5.9 3.4 11.5

Clerical workers............... 13.2 17.5 14.5 5.5 3.8 8.2

Blue-collar workers______

Crafts and kindred

49.8 34.7 42.3 7.7 5.0 8.0

workers____________

Operative, except trans13.0 13.9 8.8 6.4 3.6 5.3

port_______________

Transport equipment

24.3 12.5 17.5 8.3 5.6 9.4

operatives__________ 4.5 3.7 5.8 4.4 3.9 5.1

Nonfarm laborers............. 8 0 4.6 10.2 9.5 8.1 9.5

Service workers_____ _____ 15.8 11.7 26.4 6.2 5.0 8.7

Private household______ 1.8 1.1 6.3 3.2' 2.9 6.8

Other service workers___ 14.0 10.6 20.1 6.6 5.2 9.2

Farm workers____ ______ 5.6 3.7 2.7 8.7 2.2 6.0

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As with the total population, men, women, and

teenagers o f Spanish origin are not equally affected by unemployment. The jobless rate for

adult men averaged 5.3 percent, while the rates

w ere 7.2 percent for adult wom en and 19.8

percent for teenagers. These unemployment rates

were higher than those for whites for each age-sex

group, but lower than those for blacks (table 5).

The burden of joblessness among persons of

Table 5. Unemployment rates and ratios, persons of

Spanish origin, whites and blacks, by age and sex, annual

average, 1973

Age and sex

Unemployment rate Ratio

Spanish

origin

White Black Spanishwhite

Blackwhite

Total, 16 years and over.............. 7.5 4.3 9.3 1.7:1 2.2:1

Both sexes, 16 to 19 years____ 19.8 12.6 31.4 1.6:1 2.5:1

Males, 20 years and over....... 5.3 2.9 5.9 1.8:1 2.0:1

20 to 24 years____ ______ 8.2 6.5 12.8 1.3:1 2.0:1

25 to 54 years____ ______ 4.6 2.3 4.6 2.0:1 2.0:1

55 years and over________ 5.2 2.5 3.2 2.1:1 1.3:1

Females, 20 years and over... 7.2 4.3 8.5 1.7:1 2.0:1

20 to 24 years__________ 9.0 7.0 18.3 1.3:1 2.6:1

25 to 54 years.............. ....... 7.0 4.0 7.0 1.8:1 1.8:1

55 years and over_______ 4.5 2.7 3.4 1.7:1 1.3:1

Spanish origin can also be demonstrated by the

use o f a Spanish-white unemployment rate ratio,

similar to that commonly used to examine the

relationship between black and white unemployment. The ratio of Spanish to white unemployment rates of 1.7:1 indicates that relative to the

sizes o f their respective labor forces, for every 10

white workers unemployed there were 17 unemployed w orkers o f Spanish origin. This was

considerably less than the 2.2:1 black-white ratio

in 1973. However, for adult men in prime working

years— ages 25 to 54— Spanish-white ratio was

2.0:1, about the same as the black-white ratio.

The new current labor force data available for

1973 tend to confirm the results of earlier surveys

on the labor force characteristics of workers of

Spanish origin. Joblessness affects a significantly

higher proportion of Spanish than white workers,

but their unem ploym ent rates are lower than

those for black workers. Adult men of Spanish

origin are more likely than black men to participate in the labor force, but with the exception of

those 20 to 24 years old, their labor force

participation rates are lower than those of their

white counterparts in every age group. Moreover,

adult Spanish wom en participate to a lesser

degree than both black and white workers. □

-FOOTNOTES

1 F o r a d etailed d escrip tio n of the C u rren t P opulation 

S u rv e y , see C oncepts and Methods Used in Manpower

Statistics from the Current Population Survey, R eport 313 

(B ureau of L abor Statistics, 1967). This report is available 

from the Bureau on request.

2 Although collection of the data began in March of 1973, a 

12-month series is provided which includes estim ated levels for 

January and February.

3 See Persons o f Spanish Origin in the United States:

November 1969, Current Population Reports, Population

Characteristics. Series P-20, No. 213 and No. 249 (Bureau of 

the C ensus, 1971). See also C ensus o f Population: 1970, Subject

Reports, Persons of Spanish Origin, Series PC(2)-1C (Bureau of 

the C ensus, 1973).

4 Spanish heritage includes perso n s with the follow ing 

characteristics: persons of Spanish language (Spanish m other 

tongue and all other persons in families in which the head or 

the wife reported Spanish spoken in the home as a child) and 

persons of Spanish surnam e in the five Southw estern States of 

A rizona, C alifornia, C olorado, New M exico, and T exas; 

persons of Puerto Rican birth or parentage in the three Middle 

Atlantic States of New Jersey, New York, and Pennsylvania: 

persons of Spanish language in the remaining 42 States and the 

D istrict of Columbia.

5 The seven Spanish origin categories are Mexican American, C hicano, M exican (M exicano), Puerto Rican, Cuban,

Central or South A merican, and “ O ther Spanish.”

6 The most significant of these technical adjustm ents took 

place betw een M arch 1972 and M arch 1973, resulting in a 

gross population increase of 1.4 million. The basis for these 

changes and their im pact on the population estim ates have 

been detailed in a recent C ensus Bureau report. See Persons

o f Spanish Origin in the United States: March 1973 (A dvance 

report), Current Population Reports, Population Characteristics, Series P-20, No. 259 (Bureau of the C ensus, 1974).

7 Sampling errors for Spanish estim ates may be obtained 

from the author upon request.

8 In the 1970 Decennial Census of Population, it was found 

that the proportion of N egroes among persons of Spanish 

origin at the national level is probably in the range of IV2 to 2 

percent. See Census o f Population: 1970, Subject Reports,

Persons o f Spanish Origin, Series PC(2)-1C (Bureau of the 

C ensus, 1973) and its addendum issued August 1973.

9 See Anne M. Young, “ The high school class of 1972: 

more at work, few er in college” Monthly Labor Review, June 

1973, pp. 26-32.

10 F or a discussion of labor force participation rates of 

A m erican w om en of Spanish origin in term s of num ber of 

children, see Paul M. Ryscavage and Earl F. Mellor, “ The 

econom ic situation of Spanish A m ericans,” Monthly Labor

Review, April 1973, pp.3-9.

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Multiple

jobholding

in 1970

and 1971

T h e n u m b e r a n d p r o p o r t io n of American workers

who held two jobs or more in May 1971 were virtually unchanged from May of 1969 and 1970, even

though the unemployment rate increased sharply

over that period. In May 1971, 4 million or 5.1

percent of all employed workers had two jobs or

more.

The multiple jobholding rate for men has remained more than double that for women and the

rate for whites continues to be higher than for

workers of Negro and other races. Between May of

1970 and 1971, the number with two wage or salary

jobs in the nonagricultural sector was unchanged.

A small decline in the number with one job in agriculture was offset by an increase in the number

combining wage or salary jobs with self-employment.

(See box.)

This report on multiple jobholders, frequently

called moonlighters, includes a discussion of the

relationship between the unemployment rate and

multiple jobholding, earnings of multiple jobholders

on their second jobs, and seasonality and trends in

multiple jobholding.1

Unemployment and moonlighting

The increase in unemployment during the past 2

years again has focused interest on the relationship

between unemployment and multiple jobholding

rates. The unemployment rate increased from 2.9

percent in May 1969 to 4.1 percent a year later and

reached 5.3 percent in May 1971. In each of these

months, however, the multiple jobholding rate was

either 5.1 or 5.2 percent.

Based on the limited number of over-the-year

comparisons available for the same month and one

Howard V. Hayghe and Kopp Michelotti are economists in

the Division of Labor Force Studies, Bureau of Labor

Statistics.

Special Labor Force Report, 

based on May 1971 survey, 

shows moonlighters earned 

an average of $30 a week 

on second jobs

HOWARD V. HAYGHE AND KOPP MICHELOTTI

comparison over a 3-year period, changes in multiple jobholding rates appear to be unrelated to

changes in unemployment rates. (See chart 1.) Of

the 10 pairs of observations covering a span of 15

years, in only three periods did significant changes

in the same direction occur in both multiple jobholding and unemployment rates (in years ending

in 1963, 1964, and 1966). In two periods (ending

Survey definitions

For purposes of this survey, multiple jobholders are defined as those employed persons

who, during the survey week, (1) had jobs as

wage or salary workers with two employers or

more, (2) were self-employed and also held

wage or salary jobs, or (3 ) worked as unpaid

family workers but also Led secondary wage

or salary jobs. The primary job is the one at

which the greatest number of hours were

worked. Also included as multiple jobholders

are persons who had two jobs during the survey week only because they were changing

from one job to another. This group is very

small— only 1 percent of all multiple jobholders

in May 1969.

Persons employed only in private households

(as a maid, laundress, gardener, babysitter, and

so on) who worked for two employers or more

during the survey week were not counted as

multiple jobholders. Working for several employers was considered an inherent characteristic of private household work rather than an

indication of multiple jobholding. Also excluded were self-employed persons with additional farms or businesses, and persons with

second jobs as unpaid family workers.

From the Review of October 1971

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Chart 1. Comparison of changes in multiple jobholding and unemployment rates

Percentage points

in 1958 and 1969), the unemployment rates and

multiple jobholding rates moved in opposite directions. In the remaining five periods, in which the

unemployment rate increased in three and decreased

in two, the multiple jobholding rates remained essentially unchanged.

Personal characteristics

Multiple jobholding is almost entirely a male

phenomenon. In May 1971, only 765,000 out of a

total of 4 million multiple jobholders were women.

Although there was no overall change in the number

of multiple jobholders between May 1970 and May

1971, the number of women with two jobs or more

rose by about 130,000 while the number of men

with two jobs declined correspondingly. The multiple jobholding rate for women, at 2.6 percent, was

higher than in May 1970, as shown in table 1. The

rate for men, on the other hand, decreased slightly

over the year to 6.7 percent from 7.0 percent.

Multiple jobholding was most prevalent among

employed men 25 to 44 years old; 7.9 percent of

these men, representing over half of all male multiple

jobholders, held more than one job in May 1971.

It is not surprising that the rate for men in these

ages is higher than for older or younger men. Almost

9 out of 10 of all employed men in the central

age group were married and most had children under

age 18. Thus, for many, the need for additional

income was strong enough to induce them to seek

second jobs. A recent survey showed that half the

male moonlighters gave meeting household needs or

paying off debts as reasons for holding more than

one job.2

Married women who work generally have family

responsibilities which preclude holding more than

one job. Thus, the multiple jobholding rate for married women was lower than that for other women.

Also, a smaller proportion of female than male

moonlighters, about 50 and 90 percent respectively,

were married, reflecting in part the household responsibilities of married female workers.

The likelihood of the husband working at two

jobs does not appear to be affected by the presence

of the wife in the labor force. The multiple jobholding rate for married men whose wives were in the

work force was about the same as for those whose

wives were not.

There is a sharp difference in the proportions of

male and female multiple jobholders who combine

a full-time job with a part-time job. The following

tabulation shows that in May 1971 the largest proportion of men who moonlighted worked full time

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on their first job and part time on their second:

Men Women

Total ..................................................... 100 100

Worked at two part-time j o b s ...................... 19 52

Worked full time on first job

and part time on s e c o n d .......................... 75 47

Worked at two full-time j o b s..................... 6 1

It should be noted that, among the women, about

the same proportions worked at two part-time jobs

or combined a full-time with a part-time job.

Industry and occupation

One-half of all multiple jobholders worked in

manufacturing or in service and finance on their first

jobs. This reflected the fact that more persons work

in these two industries than in any others, rather

than high proportions of the workers in these industries holding second jobs. (Multiple jobholding rates

in these two industries were about average.) Rates

significantly above average are found among workers

whose first jobs are in public administration, and in

transportation and public utilities.

The multiple jobholding rate of men was highest

in May 1971 for those whose primary jobs were in

State and local government, 15 percent. The multiple

jobholding rates of postal service workers and those

in educational services were about as high. In comparison, those who worked in mining had a rate of

only 4 percent.

By occupation, men who were employed on their

primary jobs as teachers or as protective service

workers (guards, policemen, and firemen) had the

highest multiple jobholding rates— IS and 16 percent,

respectively. Laborers and waiters, cooks, or bartenders had the lowest multiple jobholding rates, at

about 4 percent.

The opportunity to work at a second job may be

just as important a factor in multiple jobholding

as the need for extra income. Many of the workers

in the industries and occupations where multiple jobholding rates are high have flexible hours on their

principal jobs as among farmers, or working hours

are different from the usual ones, as for many postal

and protective service workers.

About one-third of the multiple jobholders worked

at wage or salary jobs in service and finance on their

second jobs. An additional 15 percent took second

jobs in retail trade. It is not surprising that large

proportions found jobs in these industries, because

they typically provide the part-time or off-hours jobs

that multiple jobholders seek. About one-third of

the multiple jobholders were self-employed in farm

and nonfarm industries on their second jobs. Between May 1970 and 1971, the number of selfemployed in nonagricultural industries rose by 166,-

000 to 728,000 (table 2). Almost all of the increase

was among those who had their own businesses in

trade and service industries. It may be that some

persons who wanted but could not find a second

Table 1. Employed persons with two jobs or more, by sex, race, and unemployment rates, 1955-70

[In percent]

D a te

N u m b e r

(th o u s a n d s )

M u lt ip le jo b h o ld in g r a t e 1 U n e m p lo y e d 

a s p e rc e n t 

o f c iv ilia n 

B o th s e x e s M e n W om en W h ite N e g ro a n d la b o r f o r c e 1

o t h e r r a c e s

J u ly 1 9 5 6 . . . . ............................................................................................................ 3 ,65 3 5 .5 6 .9 2 .5 ( s) (») 4 .4

J u ly 1957.............................................................................. ...................................... 3 ,57 0 5 .3 6 .6 2 .5 (s) (*) 4.1

J u ly 1958......................... ............................................................................................ 3 .09 9 4 .8 6 .0 2 .2 (*) (J) 7 .4

D ecem b e r 1959.......................................................................................................... 2 ,96 6 4 .5 5 .8 2 .0 4 .6 4 .2 5.1

D ecem b e r I9 6 0 4....................................................................................................... 3,01 2 4 .6 5 .9 2 .0 4 .6 4 .1 6 .4

M ay 1 9 6 2 ...................................... .......................................................................... - 3 ,34 2 4 .9 6 .4 2 .0 4 .9 4 .6 5.1

M ay 1963..................................................................................................................... 3,921 5.7 7 .4 2 .4 5 .7 5 .2 5 .5

M a y 1964................ ................... ................................................................................. 3 ,72 6 5 .2 6 .9 2 .1 5 .3 4 .7 4 .8

M a y 1 9 6 5 . . . . ............................................................ .........................................— 3 ,75 6 5 .2 6 .7 2 .3 5 .3 4 .0 4 .3

M a y 1966 ...................................... ............... ............................................................... 3,63 6 4 .9 6 .4 2 .2 5 .0 4 .3 3 .7

M ay 1969..................................................................................................................... 4 ,00 8 5 .2 6 .9 2 .3 5 .3 4 .5 2 .9

M ay 1970..................................................................... ............................................... 4,04 8 5 .2 7 .0 2 .2 5 .3 4 .4 4 .1

N o vem b er 1970.......................................................................................................... 3 ,83 2 4 .9 6 .3 2 .5 5 .0 3 .4 5 .5

M a y 1971..................................................................................................................... 4,03 5 5.1 6 .7 2 .6 5 .3 3 .8 5 .3

1 M u ltip le jo b h o ld e rs as p e rce n t of a ll em p loyed persons. 

1 N ot se a so n a lly ad ju sted .

* Data not av a ila b le .

4 Data fo r A la s k a and H aw a ii in clu d e d beg in n in g 1960.

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[N u m b e rs in tho usands]

Table 2. Type of industry and class of worker of primary and secondary jobs for persons holding two jobs or more, May

and November 1970 and May 1971

Date, type of Industry, and

class of worker of primary job

Total

employed

Persons holding two

jobs or more

Type of industry and class of worker of secondary job

Agriculture Nonagricultural industries

Number

Percent

of total

employed

Total

Wage and

salary

workers

Selfemployed

workers

Total

Wage and

salary

workers

Selfemployed

workers

M A Y 1970

Total..................................... 7 8,358 4 ,0 4 8 5 .2 738 122 616 3 ,3 1 0 2 ,7 4 8 562

A g ric u ltu re ............................................. .. 3 ,7 2 5 276 7 .4 71 47 24 205 1% 9

W age and s a la ry w o r k e r s ... .......... 1,20 0 89 7 .4 44 20 24 45 36 9

S e lf-e m p lo y e d w o rk e rs ............. .. 1,927 154 8 .0 27 27 0 ) 127 127 0

598 33 5 .5 0 33 33 0

N o n a g ric u ltu ra l in d u strie s ....................... 7 4 ,633 3 ,77 2 5 .1 667 75 592 3 ,10 5 2 ,5 5 2 553

W age an d s a la ry w o r k e r s ... .......... 6 8 ,9 0 5 3 ,57 0 5 .2 661 69 592 2 ,90 9 2 ,35 6 553

S e lf-e m p lo y e d w o rk e rs .................... 5 ,22 6 194 3 .7 6 6 0 188 188 0

502 8 1 6 (2) 8 8 0

N O V E M B E R 1970

Total..................................... 78,7 4 0 3,83 2 4 .9 614 88 526 3 ,2 1 8 2 ,64 8 570

A g ric u ltu re .................................................... 3 ,2 2 8 198 6.1 44 26 18 154 149 5

W age and sa la ry w o rk e rs ................ 1,042 51 4 .9 26 8 18 25 20 5

S e lf-e m p lo y e d w o rk e rs .................... 1,762 129 7 .3 18 18 ( l ) 111 111 0

424 18 4 .2 (») 18 18 0

N o n a g ric u ltu ra l in d u s trie s ....................... 75 ,5 1 2 3 ,63 4 4 .8 570 62 508 3 ,06 4 2 ,49 9 565

W age and s a la ry w o rk e rs ................ 6 9 ,613 3 ,4 4 3 4 .9 563 55 508 2 ,88 0 2 ,3 1 5 565

S e lf-e m p lo y e d w o rk e rs ........... ........ 5 ,35 5 180 3 .4 7 7 0 173 173 0

544 11 2 .0 0

11 11 0

M A Y 1971

Total..................................... 7 8 ,708 4 ,03 5 5 .1 700 96 604 3 ,3 3 5 2 ,60 7 728

A g ric u ltu re .................................................... 3 ,59 8 217 6 .0 66 41 25 151 147 4

W age and s a la ry w o rk e rs ................ 1,24 5 65 5 .2 38 13 25 27 23 4

S e lf-e m p lo y e d w o rk e rs .................... 1,812 129 7.1 20 20 0 109 109 0

U np aid fa m ily w o rk e rs ..................... 541 23 4 .2 8 8 0 15 15 0

N o n a g ric u ltu ra l in d u strie s ....................... 75 ,1 1 0 3 ,81 8 5 .1 634 55 579 3 ,18 4 2 ,4 6 0 724

W age and sa la ry w o rk e rs ................ 6 9 ,1 5 0 3,641 5 .3 629 50 579 3 ,01 2 2 ,28 8 724

S e lf-e m p lo y e d w o rk e rs .................... 5 ,42 9 167 3 .1 4 4 0 163 163 0

U np aid fa m ily w o rk e rs ..................... 531 10 1 .9 1 1 0 9 9 0

1 S e lf-e m p lo y e d p e rso n s w ith a seco nd ary b u sine ss o r farm , bu t no w age or s a la ry 

job , w e re not counted a s m u ltip le jo b h o ld e rs.

wage or salary job turned to self-employment in

order to earn additional income.

Half the women who were multiple jobholders

were employed in service and finance on their secondary jobs, compared with a quarter of the men,

and a much larger proportion of women than men

worked in trade. In contrast to the men, about a

fifth of whom were employed in agriculture on their

secondary jobs, nearly all of the women had second

jobs in nonagricultural industries.

Greater proportions of Negro 3 than white moonlighters have second jobs in service industries, and

smaller proportions are self-employed. In May 1971,

39 percent of the Negroes, but about 29 percent of

the white multiple jobholders worked in the service

industries. Only 24 percent of the Negroes were

* P e rso n s w h o se p rim a ry jo b w as as an u n p aid fa m ily w o rke r w ere counted a s m u ltip le jo b h o ld e rs o n ly if th e y also h eld a w age o r sa la ry job .

self-employed on the second job, compared with 34

percent of the whites.

Multiple jobholders do not tend to work in the

same major occupational groups on their secondary

jobs as on their primary jobs, with one exception.

A majority of the professional and technical workers

who held at least two jobs were employed in the

same occupational group on both jobs. About a third

of the service workers (except private household)

worked in the same occupational groups on their

second jobs. In none of the other occupation groups

did more than one-fourth of the workers do the

same kind of work on both jobs.

Blue-collar workers on their first jobs were more

often farmers or farm managers on their second jobs

than persons in other occupations. Nearly a quarter

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of the blue-collar workers— compared with fewer

than 10 percent of the professional, clerical, and

service workers— operated farms on their secondary

jobs. Similarly, 35 percent of those who were farmers

and farm managers on their first jobs worked as

craftsmen or operatives on their second jobs. Many

of the construction and maintenance skills that are

needed on the farm can be used off the farm as well.

The difference by race in the occupational distribution of multiple jobholders on their second jobs

corresponds roughly to the differences in occupations

by race of all workers. Thus, white dual jobholders

are considerably more likely to work in white-collar

occupations, especially as managers, than their Negro

counterparts. About 46 percent of the whites worked

at white-collar jobs on their second jobs, compared

to 31 percent of the Negroes.

At the same time, proportionately over twice as

many Negroes as whites worked in service occupations on their second jobs. The same proportions of

whites and Negroes were blue-collar workers on their

second jobs.

Earnings on secondary jobs

Before the May 1970 survey, there was no information on a nationwide basis on how much moonlighters earned on their second jobs. Data obtained

in May of 1970 and 1971 indicate that multiple

jobholders who were wage or salary workers on their

second jobs had median earnings of $30 on those

jobs during the survey week. (See table 3.)

On average, men earn more than women in their

primary jobs; this tendency also prevailed among

multiple jobholders. Thirty-four percent of the men,

but only 10 percent of the women, added at last $50

to their weekly income through moonlighting in May

1971. Earnings of under $20 were reported by onefourth of the men and one-half of the women. Median

earnings on the second job were $35 for men and

$19 for women. These sharp differences reflect the

variation in the distributions of occupations and industries in which men and women find secondary

jobs, and the difference in the number of hours they

work at these jobs.

Earnings depend not only on the rate of pay but

also on the number of hours worked. During the

May 1971 survey week, men worked a median of

13 hours on their second wage or salary jobs, in

contrast to 9 hours for women. Only 7 percent of

the women worked as many as 22 hours, while onefifth of the men worked at least this long at secondary

wage or salary jobs.

For both men and women, secondary job earnings

increased as hours worked increased. The following

tabulation shows that median weekly earnings for

persons who worked 22 to 34 hours on their second

jobs were about four times as high as earnings of

those who worked 1 to 7 hours.

Both sexes Men Women

1 to 7 h o u r s................. ............... $15 $17 $12

8 to 14 h o u r s............... ............... 26 28 20

15 to 21 h o u r s............ ............... 43 46 34

22 to 34 h o u r s............ ............... 61 62 0 )

1 Median not shown where base is less than 75,000.

By age and marital status. Wage or salary earnings

on the second jobs of men in the central age groups

tended to be higher than those of younger men.

About 24 percent of the men 25 to 44 years old

earned $70 or more on their second jobs during the

survey week, and a similar proportion of men. 45 to

64 years old earned as much (table 3). On the other

hand, much smaller proportions of the men 20 to 24

years old and of male teenagers earned as much

as $70. About three-fourths of the teenagers earned

under $20, reflecting the relatively small number of

hours that they worked on their second jobs.

Married men earned twice as much as single

men— $37 and $18, respectively. This is not surprising since men in the central age groups had the

highest earnings and most of these men were married.

By industry and occupation. Men with secondary

jobs in educational services and in manufacturing

earned substantially more than the average moonlighter. One-third of the men working in these industries earned $70 or more on their second jobs

during the survey week, compared with only one-fifth

of those in service (other than educational) and

one-tenth of those in trade. The longer-than-average

hours worked by men in manufacturing help to account for their higher than average weekly earnings.

On the other hand, the high earnings of men in

educational services, many of them teachers, reflect

high hourly rates of pay since they worked fewer

hours than average. Men with secondary jobs in

finance and in agriculture had the lowest earnings.

Almost half of those in finance and two-thirds of

those in agriculture earned less than $20 during the

survey week.

Relatively high earnings were most frequent among

men who were professional and managerial workers

on the second job, A third of each earned at least

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[Percent distribution]

Table 3. Wage or salary earnings on second job for persons holding two jobs or more, by age, sex, and marital status,

May 1971

W e e k ly e a rn in g s on s e c o n d jo b

A g e , s e x , a n d m a r it a l s ta tu s T o ta l M e d ia n

U n d e r $20 $20 to $29 $30 to $39 $40 to $49 $50 to $69 $70 o r m o re

B O T H S E X E S

T o ta l..................................................................................................... 100 .0 3 2 .0 17.7 1 3.0 8 .7 1 2.7 1 5.9 $30

M E N

T o ta l, 16 y ea rs o ld an d o v e r........................................................ 100.0 2 5.6 1 7.2 13.6 9.1 1 5.0 1 9.5 $35

16 to 19 y e a rs _____ ___________________ _______ ____________________ 10 0.0 7 4 .0 15.9 3 .1 1.3 4 .0 1 .7 $14

20 to 24 y e a rs ....................................... ........................................... ............ 10 0.0 3 2.1 2 0 .6 13.4 10.1 1 1.9 1 1.9 $28

25 to 44 y e a rs ________________________________________________ ____ 10 0.0 2 1 .8 1 7.0 13.T 9 .3 14.7 2 4.1 $38

45 to 54 y e a rs ............ ............................................................................... 10 0.0 1 4.8 1 3.5 18.2 1 3.7 1 8.9 2 0 .9 $42

55 to 64 y e a rs .............................. ................................................................. 10 0.0 1 9.4 1 9.0 14.9 4 .4 2 3 .8 18.5 $37

65 y ea rs and o v e r................... . ........................................... (»)

(*)

S in g le ............................................................................................................... 10 0 .0 5 3 .9 1 6.5 6 .8 5 .0 6 .8 1 1.0 $18

M a rrie d , sp o u se p re s e n t....................................... .................................... 10 0.0 2 1 .5 17.1 1 4.9 9 .7 1 6.0 2 0 .8 $37

O ther m a rita l sta tu s..................................................................................... (l ) C1)

W O M E N

T o ta l, 16 y ea rs o ld and o v e r........................................................ 10 0 .0 5 2.2 1 9.5 11.1 7 .4 5 .3 4 .5 $19

16 to 24 y e a r s . . . ....................................................................... ................. 10 0 .0 6 4 .5 2 2 .7 5 .7 2 .8 3 .5 .7 $15

25 to 44 y e a rs ................................................................................ ............... 100.0 42.1 1 5.7 1 0.3 1 4.4 9 .2 8 .3 $25

45 to 64 y e a rs ................................................................................................ 10 0.0 5 1.7 22.1 1 6.8 3 .4 2 .7 3 .4 $19

65 y e a rs and o v e r......................................................................................... (l) (i)

S in g le ................................................................................................................ 10 0.0 6 2 .2 1 8.9 6 .7 2 .9 5 .6 3 .7 $16

M a rrie d , sp o u se p r e s e n t . . . ...................................... .............................. 10 0 .0 4 8 .0 2 0 .2 1 1.9 9 .3 5 .6 4 .9 $20

O ther m a rita l s ta tu s .................................................................................... 10 0.0 4 6 .9 1 9.0 1 5.4 9 .8 4 .0 4 .9 $21

1 Percent and median not shown where base is less than 75.CC0. NOTE: Because of rounding, sums of individual items may not equal totals.

$70 on their extra jobs during the survey week. The

high earnings of the professionals reflect their high

wage rates since they worked fewer hours than

average. In contrast, only one-fifth of the sales workers earned as much as $50; the median number of

hours worked by sales workers was about equal to

the average on the second job for all workers.

By earnings on primary job. The multiple jobholders

with the highest weekly wage or salary earnings on

the primary job tended to have the highest earnings

on their second jobs. Median weekly earnings on

the second job in May 1971 increased from $14 for

men who earned under $60 a week on their first

job to $52 for those who earned $200 or more, as

shown in the following tabulation:

Weekly earnings on

primary job

Less than $60 . . .

$60-$99 ...........

$100-$124 .............

$125-$149 ...........

$150—$199 ...........

$200 or m o r e ____

Median weekly

earnings on

secondary job

$14

33

27

36

38

52

Percent of

earnings on

primary job

50

40

24

26

22

23

Among the men earning $200 a week or more on

their primary jobs, 22 percent made at least $100

on their second job, in contrast to only 6 percent of

those who primary jobs were less remunerative.

The tendency for the high earners on first job

also to earn the most on the second job is to be

expected since they may be assumed to have highly

valued skills. They are also less likely than others

to be moonlighting to meet regular household expenses and therefore may be more selective in their

ch oices o f secondary jobs.

Although low earners on the first job also earned

comparatively little on the second job, the supplemental earnings were a much larger proportion of

their basic earnings than they were for the much

better paid men. Men who earned between $60 and

$99 on their primary jobs averaged an additional 40

percent on their second jobs; for those who earned

$150 or more, the average earnings on the second

job were less than one fourth of their primary job

earnings.

Trends

In most of the years since 1962, data on multiple

jobholding have been obtained for the month of May.

Over this period, the number of moonlighters gen166

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erally fluctuated within a narrow range. It reached

the decade’s high of 4 million in May 1969 and

remained at that level in May 1970 and May 1971,

about 700,000 more than in 1962. During this

period, the multiple jobholding rates remained relatively constant, at about 5 percent.

Most of the increase in the total number of multiple jobholders between May 1962 and May 1971 was

among persons who were wage and salary workers

in nonfarm industries on both their first and second

jobs (table 4 ). Nearly 2.3 million multiple jobholders

held two such jobs in May 1971; they represented

57 percent of all multiple jobholders, up from 52

percent in May 1962.

About 900,000 workers combine a wage or salary

job with self-employment in nonfarm industries.

Examples of persons in this group are policemen,

firemen, postal workers, and teachers who on their

second jobs drive their own taxis, do home maintenance or repair, or are free lance writers or artists.

That number was relatively stable between 1962 and

1970 but in 1971, as previously indicated, it increased because of the rise in the number selfemployed on one job. About four-fifths of all who

combine a nonfarm wage or salary job with selfemployment are self-employed on the second job.

A third group of moonlighters, about 850,000 or

roughly one-fifth of the total (somewhat lower than

Table 4. Agricultural and nonagricultural employment of

persons holding two jobs or more, 1953-70

[Numbers in thousands]

D a te

A t le a s t o n e jo b 

in a g r ic u lt u r e

T w o jo b s in n o n ­

a g r ic u lt u r a l in d u s t r ie s

T o ta l N u m b e r

P e r ­

c e n t 

o f d u a l 

jo b ­

h o ld e rs

T o ta l

T w o

w a g e

an d

s a la r y

jo b s

W age

a n d

s a la r y

jo b

an d

s e lf -

e m -

p lo y -

m e n t

J u ly 1956 _____________ 3 ,65 3 1,503 4 1.1 2,15 0 1,611 539

J u ly 1957 .................... 3 ,5 7 0 1,414 3 9 .6 2,156 1,558 598

J u ly 1958......................... 3 ,09 9 1,122 3 6 .2 1,977 1,427 550

D ecem b er 1959............. 2 ,99 6 829 2 8 .0 2,137 1,533 604

D ecem ber 1960 1.......... 3 ,01 2 781 2 5 .9 2,231 1,647 584

M a y 1962 _____________ 3 ,3 4 2 868 2 6 .0 2,47 4 1,749 725

M a y 1963...................... .. 3,921 1,071 2 7 .3 2,85 0 2,07 3 777

M ay 1964........................ 3 ,7 2 6 1,06 9 2 8 .7 2,657 1,928 729

M ay 1965........................ 3 ,7 5 6 1,06 5 2 8 .4 2,691 1,914 777

M ay 1966........................ 3 ,63 6 936 2 5 .7 2,70 0 1,934 766

M ay 1969______ _______ 4 ,0 0 8 939 2 3 .4 3,06 9 2 ,32 6 743

M ay 1 97 0..................... .. 4 ,0 4 8 943 2 3 .3 3,10 5 2 ,3 5 6 749

N o vem b er 1970 ............ 3 ,8 3 2 768 2 0 .0 3,06 4 2 ,31 5 749

M ay 1971......................... 4 ,0 3 5 851 2 1.1 3,18 4 2 ,28 8 8%

1 Data for Alaska and Hawaii included beginning 1960.

in May 1970), have one or both jobs in agriculture.

It is remarkable that this number has not changed

materially over the decade, since the total number of

persons employed in agriculture declined by a third

between 1962 and 1971.

Most of the persons who hold at least one job in

agriculture are self-employed farmers on the second

job. Typically, they are wage and salary workers in

nonagricultural industries on their first jobs who work

the family farm in their free time. Many of these

persons may have taken nonfarm jobs in nearby

towns because they could not earn an adequate

living on their marginal or submarginal farms.

Seasonality and multiple jobholding

In an effort to ascertain whether multiple jobholding varies from one season to another, information on multiple jobholding was obtained in November 1970 as well as in May of 1970 and 1971. Both

the number and rate of multiple jobholding in

November were somewhat lower than in either spring

month. (See table 2.) About 3.8 million persons

had two jobs or more in November 1970— 200,000

fewer than in May 1970— and the multiple jobholding rate, at 4.9 percent, was also somewhat

below the May 1970 level of 5.2 percent. By May

1971, both the number of moonlighters and the rate

had returned to their prior levels. The drop from May

to November 1970 appeared to be in line with the

seasonal decline in agriculture: the number of multiple job holders with at least one job in agriculture

fell by 175,000 to 770,000. However, the upswing

in this group between November 1970 and the

following May was not as large as the decline had

been, so that it is difficult to assess how much of

either the drop or increase was seasonal, although it

is reasonable to assume that a substantial part was

seasonal.

As indicated earlier, the multiple jobholding rate

for Negroes generally has been slightly lower than

for whites in the past decade. Between May and

November of 1970 the difference widened because

the rate for Negroes declined more sharply than for

whites. The reduction for Negro multiple jobholders

occurred in both agricultural and nonagricultural

industries, but for whites the decline was only among

those in agriculture.

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Although the overall multiple jobholding rates

declined between May and November, the changes

in rates by industry and by occupation were generally

not significant. Also, the median number of hours

worked in May of each year and in November 1970

was the same (13), with little variation by industry.

The last time a multiple jobholding survey was

made during a winter month was in December 1960.

Between that time and November 1970, the number

of multiple jobholders with two nonfarm jobs has

increased by 800,000 or nearly 40 percent, but the

number who had at least one job in agriculture has

remained about the same.

The stability over the decade in the number of

persons with at least one job in agriculture in a

winter month is the balancing off of two divergent

trends. Over this period, the number of farm workers

holding a second job in a winter month decreased,

primarily because of a decline in the total number

employed in agriculture, rather than a material

change in the multiple jobholding rate. On the other

hand, the number of nonfarm workers holding a

second job in agriculture increased. 

----------FOOTNOTES----------

1 D ata in this report are based primarily on information

from supplementary questions to monthly survey o f the

labor force, conducted for the Bureau of Labor Statistics

by the Bureau of the Census through its Current Population Survey. The data for the three surveys relate to

the weeks o f M ay 11-17 and N ovem ber 15-21, 1970, and

May 16-22, 1971. The most recent report in this series was

published in the Monthly Labor Review, August 1970, pp.

57-64, and reprinted with additional tabular data and

explanatory notes as Special Labor Force Report N o. 123.

! See Vera C. Perrella, “Multiple jobholders in May 1969,”

Monthly Labor Review, August 1970, pp. 58-59.

*D ata for all persons other than white persons are used

in this report to represent data for Negroes, since the latter

constitute about 92 percent o f all persons other than white

persons in the United States.

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Chapter IV. Price Measurement and Price Trends

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How a general price index

could be constructed,

what it should accomplish,

and virtues and limitations

of various approaches

ALLAN D. SEARLE

T here is no adequate comprehensive measure of

price change in the U.S. economy. Such a system

is needed. It would measure price change at intermediate steps of production and at the level of

final distribution would lend itself to a variety of

analytical and statistical uses. It would provide a

check on what is happening to prices in any

important segment of the economy and, at the

same time, gage price movement for the economy

as a whole.

This article’s principal purpose is to discuss a

number of possible general price indexes and an

underlying system of price indexes for primary and

intermediate production and distribution which

would be consistent with the general indexes. An

attem pt will be made to show how interindustry

(input-output) measures and industrial sector

measures could be fitted into a total structure,

which itself would be consistent with National

Accounting (Gross National Product) concepts.1

Available price measures

In the absence of a comprehensive measure,

analysts turn to a number of important but

limited sources of information about price trends.

These are the Consumer Price Index ( c p i) of the

Bureau of Labor Statistics, which measures price

change in goods and services purchased by urban

wage earner and clerical workers’ families; the

Wholesale Price Index ( w p i ) of the b l s , which

measures price changes for commodities sold on

primary markets; the Indexes of Prices Paid and

Received by Farmers of the U.S. Department of

Agriculture; and the Implicit Price Deflator

( i p d ) of the Office of Business Economics of the

U.S. Department of Commerce, which measures

price change in the Gross National Product.

A llan D . Searle is an econom ist in th e Office of Prices

and L iving C onditions, Bureau of Labor Statistics.

T oward

comprehensive

measurement

of prices

All of these sources have drawbacks as general

measures of price change. The c p i is not intended

as a measure of price change for the goods and

services bought by consumers other than wage

earners and clerical workers and omits purchases

by Government and business. While the w p i

covers many industrial commodities, it does

not cover construction, transportation, communications, Government purchases and financing, or

industrial services. (However, individual commodity series in both indexes would be used in

constructing comprehensive indexes.) The agricultural indexes are confined to price changes that

affect farmers. The i p d depends for its coverage

of prices very largely upon the detailed price

information collected in the w p i and c p i programs

and is similarly limited.2 Moreover, the i p d ’ s

weighting scheme causes it to reflect changes in

composition of goods and services as well as

changes in prices. In 1969, the Office of Business

Economics began publishing a fixed-weight index

quarterly starting with 1965: This index has the

same limitations as to commodity coverage as

the i p d .

An adequate, timely measure of price change,

broad enough to assess accurately inflationary

and deflationary forces and detailed enough to

provide insight into the interplay of economic

forces in the economy, should do at least two

things: It should provide measures of price change

for all goods and services purchased by consumers,

business, Government, and separately for foreign

countries, and it should provide sufficient detail

by industry to promote understanding of price

behavior at intermediate levels of demand (both

for transactions that enter final demand and those

that do not).

The measures of price change described in this

article, as well as the underlying industry sector

structure, are consistent with an industrial classification scheme rather than a commodity groupFrom the Review of March 1971

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ing, such as that used in the Bureau’s Wholesale

Price Index. The general indexes are designed to

deal with current price developments in terms of

analysis of increasing and decreasing price trends.

Thus, the pricing of existing capital assets is not

discussed, although this would be particularly

important if a census of wealth were undertaken.

For the present, however, it is considered more

urgent to move pricing concepts and development

toward a comprehensive system generally consistent with the framework of the current accounts

portion of the National Accounts (consumer

purchases, business investment, Government purchases, and net foreign sales), modified only to

the extent necessary for current price analysis.

The first part of the article will deal with

approaches to a general price index and the

remainder, with approaches to price indexes for

industrial sectors, concluding with a detailed

consideration of problems involved and the

relationship among the indexes.

A general price index

One way to visualize a general price index is as

the end product of all interindustry purchases

and sales, unduplicated and totaled at the level of

final demand. Such an index would measure the

average price change of goods and services entering final markets in proportion to their values.

A general index can be constructed in two

principal ways: (1) indirectly through the concept

of “Gross Product Originating in Industry” ;

that is, the aggregation of indexes developed

industry by industry for the value added per unit

at each stage of production, or (2) directly by

pricing the goods and services in final demand.

The latter scheme can, in turn, rely on one of two

routes: producers’ prices or purchasers’ prices.

Each approach— value added or final demand—

has its own virtues and limitations.

Gross output method

Construction of a general price index by the

gross-product-originating route emphasizes industrial classification of the Nation’s sales or production. Price indexes would be developed for the

output of each industry (similar to those b l s publishes for about 100 for manufacturing and mining

industries). In addition, price indexes of purchases

made by producers (inputs) would be developed

for each industry. These two sets of indexes would

be applied to data on the dollar value of output

and input to derive constant-dollar output and

input, and constant-dollar value added (the

difference between output and input), as described

in the section on double deflation (p.15)

These three sets of indexes are especially valuable in industrial economic analysis, permitting

consistency in comparisons with series of wage

rates, productivity, unit labor cost, employment,

average weekly hours, and hourly earnings in a

particular industry. However, these indexes do

not provide for a complete assessment of the

impact of changes in prices of materials purchased

and in the prices of the output of the industry.

Final demand

The other approach to constructing a general

price index— directly pricing goods and services

in final markets— can be accomplished by following the interindustry framework (with its advantages of an industry classification scheme) in

such a way that the spreads between prices at

producers’ and purchasers’ levels are evident

industry by industry at each point in final demand.

The general outline of the approach can be

developed within the limits of the concepts currently used in constructing the Gross National

Product, or the national income concepts can

(for this purpose) be modified in order to present

a price index more responsive to measuring price

changes as they occur in ac.tual markets.

S t a n d a r d a n d m o d i f i e d g n p . Adherence to g n p

concepts is desirable because the g n p is a production-oriented measure and a system of price

measurement consistent with production values,

input values, employment, man-hours, and so

on, is essential to the understanding of output,

inputs (in real and monetary terms), and productivity. However, it may be advantageous to

make certain modifications in those concepts in

order to restrict measurement to those price

changes which are continually occurring in the

market place:

1. Because the general price index would focus

on the prices at which goods are sold in domestic

markets, the measure would differ from the g n p

deflator by eliminating prices related to inventory change which o b e must take into account

so that g n p measures the total quantity and price

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of production. Also, export sales would be

beyond the modified domestic-market scope of

the general price index conceived here, and the

foreign trade part of final demand would require

different handling.

2 . The g n p deflator includes prices imputed to

nonmarket transactions, such as use by homeowners of their own dwellings and the consumption of homegrown food. A General Price Index

would measure only prices of goods and services

actually sold.

3 . The production concept underlying the g n p

requires that used cars and resale of homes be

measured to include only that production which

is involved in their resale. Assigning proper weights

and pricing such items in a General Price Index

would reflect the total transaction viewed from

the purchaser’s point of view.

If a general price measure is viewed as the end

product of the multitude of interindustry transactions (many of which are not passed on except

as components or constituent parts of final sales),

then the interindustry structure can be made to

serve as a reference framework for the development of a generalized price measure.3

Table 1 shows how the structure of the Bureau’s

currently published Stage of Processing Indexes

relate to the interindustry structure. The significance of these indexes in price analysis is set

forth in a later section.

Input-output data show the dollar value of

transactions among the various industries (including sales among establishments within the

same industry) for the reference year. Each row

in the table would show how the output of goods

and services of each industry would be distributed

among other industries, and to final users. For

example, part of the output of agriculture is

sold to agriculture (for example, livestock feeds)

with some output going to manufacturers for

further processing and the renainder going to

consumers as unprocessed foods. Were numerical

values shown, the columns would show the value

of each industry’s intake (input) of raw materials,

Table 1. Input-output flows, classified by the codes used in the Stage of Processing Indexes, showing coverage gaps

Industry

Industry Final demand

Agriculture MiningConstructionManufacturing

Transportation,

other

public

utilities

Trade Finance Services Government Other

Personal

consumption

expenditures

Gross

private

capital

formation

Net

exports

Government 

purchases 

(Federal, 

State, and 

local)

Agriculture...................................

Mining..........................................

Construction.................................

Manufacturing..............................

Transportation, other public 

utilities.

Trade......................... .................

Finances.......................................

Services........................................

Government..................................

Other.............................................

2622

2621

2622

2500

1220

2200

2420

1100

1210

1310

1210

2110

2120

2130

2140

2410

2500

2610

1210

1320

2420

2622

2500

2622

3111

3112 

3120 

3130

3210

3220

NOTE: The codes in the BLS Stage of Processing Price Indexes are as follows:

1000 Crude materials for further processing 

1100 Crude foodstuffs and feedstuffs 

1200 Crude nonfood materials except fuel

1210 Crude nonfood materials except fuel, for manufacturing 

1220 Crude nonfood materials except fuel, for construction 

1300 Crude fuel

1310 Crude fuel for manufacturing industries 

1320 Crude fuel for nonmanufacturing industries 

2000 Intermediate materials, supplies and components

2100 Intermediate materials and components for manufacturing 

2110 Intermediate materials for food manufacturing 

2120 intermediate materials for nondurable manufacturing 

2130 intermediate materials for durable manufacturing 

2140 Components for manufacturing 

2200 Materials and components for construction 

2400 Processed fuels and lubricants

2410 Processed fuels and lubricants for manufacturing 

2420 Processed fuels and lubricants for nonmanufacturing

2500 Containers 

2600 Supplies

2610 Supplies for manufacturing industries 

2620 Supplies for nonmanufacturing industries

2621 Manufactured animal feeds

2622 Other supplies 

3000 Finished goods

3100 Consumer finished goods 

3110 Consumer foods

3111 Consumer crude foods

3112 Consumer processed foods 

3120 Consumer other nondurable goods 

3130 Consumer durable goods

3200 Producer finished goods

3210 Producer finished goods for manufacturing industries 

3220 Producer finished goods for nonmanufacturing industries

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semifinished products and services used in producing output for final sale.

Pricing for this part of the input-output table

calls for development of price indexes for each

cell of the table (or at least to represent each cell)

in order to promote understanding of how price

changes of supplying industries are related to

price indexes of the recipients. The prices which

enter this part of the table would be producers’

(sellers’) prices.

The final demand part of table 1 is aggregated

by consumers’ purchases (personal consumption

expenditures), business capital purchases (gross

private capital formation and net inventory

change), foreign purchases (net exports), government purchases (Federal, State, and local), and

total. In this approach to a general index, the

indexes would be presented at two levels of pricing:

producers’ and purchasers’.

The spread between the two price indexes

would represent the “margin” added to values of

crude and intermediate goods exchanged and

channeled from the producers’ segment of industry

by wholesale and retail trade. The General Price

Index would be the index representing the allindustry average in both the producers’ and

purchasers’ columns of total final demand.

While indexes of producers’ and purchasers’

prices for goods and services entering final demand

would generally differ at the industry level, the

indexes for producers’ and purchasers’ prices should

be equal at the total level and are in essence indexes of price change for the Gross National

Product regardless of how g n p is structured. (However, indexes based upon the traditional g n p

structure would differ from those based on the

modified g n p structure described in this article.)

The reason for this equality in the totals (but

inequality elsewhere) lies in the fact that the same

totals for the transportation and trade margins

are treated as separate purchases in the producers’

price column and as part of the value of each good

in the purchasers’ price column. That is, the purchasers’ price column would include only that part

of transportation (either passenger or freight) in

personal consumption expenditures which is purchased by the consumer. (A third portion of transportation values are intermediate sales and do not

appear in either the producers’ or purchasers’ price

columns.)

In reality, the bulk of transactions of industries

in the producing sector are made with wholesale

trade and to a lesser extent with retailers. The

developers of the interindustry tables, however,

felt that much more economic insight could be

gained if the system of input-output accounts

could be set up to show the flow of product from

producer directly to final purchasers by industry

of origin. Consequently, each sector of final demand is viewed as purchasing most goods and

services directly from producers, purchasing from

trade only “the margin”— operating expense and

profit. In the concept outlined here, then, the

total final demand at producers’ prices would

include separately a substantial value for trade.

The total at purchasers’ prices, however, would

include no value for trade because the remainder

would have been distributed among the various

other segments.

Relation to broad price indicators

A General Price Index represents only one

approach to measuring general price trends.

Another avenue is the Implicit Price Deflator of

the Office of Business Economics. This series is a

measure of price change for final demand, but

differs from the measure described in at least two

(and possibly three) principal respects: (1) The

o b e measure is built upon a system of classification

which varies from category to category of final

demand and does not conform to an industry or

commodity group structure, so that it is not

possible to trace effects of price change by industry. (2) The o b e measure is of the Paasche

type; that is, the weighting of prices changes as the

composition of output changes or, more precisely,

the weights for each year or quarter are used to

average the relative price change between that

year or quarter and the base year. The index

proposed in this article would be of the fixedweight, Laspeyres type,4 organized along industrial lines. A General Price Index would deal only

with market sales, excluding the imputations in

the Implicit Price Deflator. (3) It would also be

useful to construct an index corresponding identically with g n p concepts.

The price deflator implied by another o b e

program ( g n p by Major Industries) has an

industrial structure but again the index is of the

Paasche type. The industrial breakdown at present

goes to no further detail than the 2-digit sic level

and is presented as a value-added price measure

as described in the section on “Standard and

modified g n p . ”

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In comparison, a General Price Index would

have the following attributes:

1. The industrial structure of the index permits

interindustry analysis of price change and provides

insight into the effect on prices of the flow of goods

and services among industries at stages of production prior to final sale.

2. The index’s industrial structure will alloAv

analyses of price trends at the individual industry

level for both outputs and (ultimately) inputs of

materials, adding to the store of understanding of

the functioning of the economy.

3. Basing the index on an industrial structure

of price measures will enhance the construction

and understanding of the Inter-Industry Program

by providing the means for interim extension of

the input-output data and contributing to improvement of the constant-value data themselves.

4. Because the index would be prepared with

fixed weights, its form will be comparable to that

of other leading price indicators, such as the

Consumer Price Index, the Wholesale Price Index,

and the U.S. Department of Agriculture’s indexes

of Prices Paid and Received by Farmers.

5. In one version, a modification of the g n p

concept would permit a better tie-in with other

price indexes and with market sales.

6. In another version, a more conventional

form, the tie-in with g n p (in common with other

implicit deflators) could unify the concept of the

entire price structure into a cohesive whole.

Another measure sometimes used in place of a

general price measure is the Wholesale Price Index.

The w p i , however, is limited in scope compared

with the proposed general index and is classified

along different lines. The w p i is aimed at measuring

price change of commodities at the first level of

transactions (primary m arkets); the general index

would include prices of both commodities and

services at the level of final sale (final demand)

with detail by industry representing all interindustry transactions. The classification structure

of the w p i is principally based on groupings of

competitive commodities; the industrial portion

of the structure underlying the general index

would be based on an industry structure. The

w p i excludes from coverage retail and wholesale

trade while the general index includes these sectors.

Other areas included in the industrial substructure

of the general index, which are excluded from the

w p i , are interplant transfers and sales of military

items to the Government.

The structure and coverage of the Consumer

Price Index is even further removed from an

industrial concept. While much of the data used

in its construction are obtained from retail outlets,

the index is not a retail trade index. It is a

purchase price index for a broadly defined but

specific group of consumers—urban wage earners

and clerical workers. Hence, it excludes some

sales at retail (for example, qualities and types of

goods and services purchased by higher income or

lower income consumers) and is classified in line

with consumption categories (for example, housing,

medical care) rather than by industry. Because,

pricing for the c p i has been oriented toward the

urban wage earner and clerical purchaser, pricing

for retailing as a whole needs to be augmented to

include higher- and lower-priced stores, farm and

nonfarm, and types of business not covered or

only lightly covered.5 Also, the c p i provides only

spotty coverage for certain types of stores

(examples: lumber yards, retail bakeries, hardware stores, and farm equipment dealers). Furthermore, even where a given product is represented,

it does not represent price differences for the

same product sold in particular types of retail

industry—shoes for example, are purchased in

shoe stores and in department stores.

Industrial sector price indexes

Where a prime purpose of a General Price

Index is to measure price change of goods entering

final demand, the purpose of a set of industrial

sector price indexes is to measure, separately,

price change of products and services moving

between industries at all stages of production,

processing, and distribution including final demand. Thus, industrial sector price indexes can

provide increased insight into the interrelationships of costs and prices at all stages of the

productive process and add to the understanding

of the resultant general price index.

In this context, the General Price Index may

be viewed as a summary indicator of price movements within the entire industry structure

(including imports). Properly formulated, price

changes in intermediate industries could be

traced through the structure to final demand and

price change effected by demand changes could be

traced back through the structure in instances

when “demand pull” is the dominant force upon

prices.

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An Industrial Sector Price Index (ispi) is, essentially, a composite index made up from several

series of prices that closely match the economic

activity of a defined industry or industry sector.

Industrial sector price indexes may be subdivided

into indexes of output or input prices based upon

either the products and services sold or the products and services purchased by an industry sector.

For example, an output price index for a given

industry represents a set of individual price indexes

for all the important products of the industry,

averaged together according to the relative importance of each product to the industry. An input

price index for an industry consists of an aggregation of price indexes for the commodities and

services purchased by the industry, weighted together according to the relative magnitude of the

purchases.

History. Early work in the field of industrial

price measurement was devoted largely to supplying data for specific statistical purposes (deflation) but a consistent, continuous program was

not developed. In the early 1950’s, a set of annual

industry sector price indexes covering the years

1947-53 was prepared as part of a Bureau of Labor

Statistics project on interindustry economics.

These indexes were designed to revalue industry

outputs. Later, in 1959, a similar set of indexes

was compiled in connection with the need of the

Bureau of the Census to construct the 1958 production index benchmark. Then, in 1961, the Price

Statistics Review Committee of the National

Bureau of Economic Research recommended to

the Bureau of the Budget that bls begin a

permanent program to develop price indexes organized along an industrial structure, and in 1962

the Bureau initiated its present modest program.

At present, the program consists of monthly

output price indexes for about 100 manufacturing, mining, and agricultural industries (at the

4-digit sic level of detail). Price indexes for commodities primary to an industry are weighted

with values covering only the production within

the industry (that is, excluding the portion which

constitutes secondary production in other industries). Secondary production values of the industry

are used to weight the price movements of items

of types primarily made elsewhere but made as

secondary output of the industry. Thus, a given

price index for a commodity may be used in

several places in the ispi system: where output is

primary and in the industry or industries where it

is secondary. The inclusion of price series for

secondary output represents a departure from

the methodology of the earlier indexes, which

represented primary output only. Ultimately,

input indexes (pricing industry purchases) will

also be constructed.6

The ispi in deflation. Price measures may be

used as deflators to estimate change in physical

quantities. Among the most noteworthy government statistical programs using price indexes for

deflation are those connected with the National

Accounts: Gross National Product in constant

dollars, the final demand accounts and gross

product originating in industry. Other programs

include the bls productivity measures where

value data are deflated to obtain constant dollar

output per man-hour, and the Federal Reserve

Board’s production index which uses price data

to supplement physical output data, as does the

Census Bureau every 5 years.

Meaning of quantity measure. One of the primary purposes of the ispi is for use in deflation of

value figures in order to estimate changes in physical volume— value or value-added in “constant

dollars.” The deflator which is developed for this

purpose must be constructed (at least in concept)

in such a way that the price index is comparable

in all respects with the value data being deflated—

or at least with the value concept which the compilers of the value data had in mind. For example,

pricing must be timed to coincide with the value

data. Timing may be crucial where there are large

inventory holdings or sudden large changes in inventory so that shipments and production may

have to be valued at different price levels and in

situations where the production cycle is very long

(for example, shipbuilding or construction).

The price index also must be constructed according to a concept which will lead to a quantity

( “constant dollar”) figure which carries the meaning which the user requires. For example, a price

index in which a quality adjustment for a new

machine is based on the measurable improved

performance of the machine results in a deflated

index in which quantity is measured in units of

performance. This is a different measure from one

in which the quality adjustment is made on the

basis of additional resources required to provide

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the additional performance, except for the one

(unlikely) situation in which the resources used

are proportional to the added performance.

Another measure would result if the quality adjustment is based on the additional amount buyers

are willing to pay for the added performance. This

matter is treated further, later in the paper. The

important point here is that it is essential not only

that the price measure represent the same scope

or coverage as the dollar values to be deflated, but

also that the meaning of the quantity measure be

constantly borne in mind, for any change in the

definition of price automatically changes the definition of quantity.

Real o u t p u t . Real output can be viewed as

either the value of production at constant prices

(gross output) or as “value-added at constant

values-added-per unit.” The first approach is

akin to physical output measures in which output

is expressed in tons, car-miles, dozens, and so on.

It can also be represented in index form as value

at constant prices,

SP0Qt/2 P 0Q0, or SP.Q./SP.Qo

The first expression results from deflating a value

series by the Paasche-style formula for an index

of prices; the second, from use of a Laspeyresstyle (fixed market basket) price index.7

The second approach has no counterpart in

physical terms (for example, a quantum of valueadded is not readily visualized) but nonetheless

has an economic meaning as the utility added to

the materials and other purchased inputs in the

course of production. Deflated value-added can

be represented in index form as value added at

constant output and input prices:

SPqQ. Sp0 qt SP,Q,— 2pt qj

2 P 0Qo— 2p0qo ° P SP,Q 0— 2p ,q 0

where “P ” and “Q” are output price and quantity,

“p” and “q” input price and quantity, respectively, and “o” and “i” signify data in the base

and current periods. In practice, however, the

values of output and input are deflated separately

by indexes of output prices and of input prices,

respectively, of either the current-weight or

fixed-weight form.

Gross output values (current or deflated)— and

the weights for the corresponding price indexes—

are not additive from industry to industry. For

example, the value of steel in automobiles would

be counted in the automobile data and again in

the steel data. In contrast, the value-added data

derived in the value-added approach are additive,

industry-to-industry without duplication.

D o u b l e d e f l a t i o n . Because direct measurement

of value added per unit (value-added pricing) is

not feasible, the deflation of value-added data to

obtain the real net value of output must be done

in stages. The output price indexes and the input

price indexes are used to deflate total value of

production and of purchases, respectively. The

difference between the undeflated output and

input values (value added), when divided by the

difference between deflated output and input

(real value added), yields an implicit index of

value-added price or of unit value-added.

N e t s e c t o r m e a s u r e m e n t . Another approach to

price measurement is that suggested by the Price

Statistics Review Committee.8 In this proposal,

at each stage of aggregation, the weights assigned

represent only sales or output, which moves to

buyers outside that sector or stage of aggregation.

For example, as indexes are prepared for progressively larger industry groupings (3-digit, 2-digit,

total industry division) in the Standard Industrial

Classification system, intrasector values and prices

are discarded as the sector definition becomes

broader. Weights are not additive to the total,

as they are in the double deflation approach.

However, this alternate method provides a concept

similar to that resulting from the double deflation

process. Furthermore, at the level of final demand

the results are consistent with other general price

indexes, but this system does not provide for

tracing and analyzing price movements as readily

as the i s p i system.

The ispi and price analysis

Price indexes constructed by industry have an

important use in economic analysis. They would

complement indexes which are classified by market

structure or similarity of use, such as the w p i .

Industry Sector Price Indexes allow comparison

of price trends for an industry with other comparable economic series for the industry.

For example, a consistent set of measures for

the basic steel industry under the industrial price

structure would cover price change of steel pro176

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duced and sold, price change for goods and services

purchased and, through the technique of double

deflation, a value-added pi ice index summarizing or netting out the input- and output-steel

price changes, so that an assessment of changing

industrial price spreads can be made. These trends

cOuld then be assessed in relation to changes in

production, average hourly earnings, productivity,

and unit labor cost, all of which are also compiled

by industry.

Price indexes developed according to an industrial structure can also serve as tools in the analysis of relationships between prices and wages,

materials costs, other costs, and profits. Experimental work in this field is now underway to test

the feasibility of relating various aspects of changing costs and profits to price change in selected

industries.

While development and analysis of indexes

along industrial lines are proceeding, the improvement of data and analysis of price trends according

to stages of the production process must not be

neglected. The Bureau’s currently published Stage

of Processing ( s o p ) indexes show price movements

for commodities at various stages of production:

crude, intermediate, and finished. In this set of

indexes each product is classified according to the

amount of processing, manufacturing, or assembling it undergoes before entering the market.

Commodities may fall into more than one category.

For example, some fresh fruit is sold as a crude

material for further processing by canneries and

some directly to final demand. Because the s o p

indexes are currently tied into the w p i structure,

with its incomplete universe, the attempt to reconcile the s o p and i s p i has results similar to that of

Procrustes and his bed. (See table 1.)

One of the principal limitations of the s o p

indexes is failure actually to price the various

different markets for the same product. This

should be largely corrected as a concomitant of

increased pricing for the i s p i .

In the long run, improvement of these indexes

will provide an additional facet to price analysis.

The s o p indexes will provide the connections

between prices at farm and mine through manufacturing and trade to the final consumer, to

supplement the industrial analyses provided by

the i s p i and the analysis of final demand price

movements provided by the general price indexes.

Table 1 not only shows the relationship of the

sop structure to the ispi, but also presents some

of the gaps which must be filled.

A fam ily of indexes

The i s p i should be viewed, not as a single index,

as the w p i or c p i is, but as a system, or family,

of indexes which are flexible enough to serve as

deflators for a number of the more important sets

of economic statistics that relate to the National

Accounts as well as for assessment of the inflationary and deflationary forces at work in the

economy. The dual organization of the General

Price Index by producers’ and purchasers’ prices

for final demand is but one example of the utility

of alternate sets of price indexes. To meet a variety

of needs, the i s p i component series must be collected in sufficient detail to permit regrouping

(for example, imports and domestic prices should

be in separate series so that price changes for the

domestic industry can be separately analyzed).

Also, the i s p i components should be shown both

with and without taxes and possibly with and

without transport charges as discussed under the

sections on taxes and transportation.

All sets of alternative indexes will have certain

attributes in common. Comprehensive industry

coverage (or at least representation) would be

required. The i s p i should represent all industries

in the economy including importers and exporters.

The indexes should represent price movement for

goods not only as produced by the primary industry (where the product takes its final form)

but also as exchanged, transported, marketed,

and further processed by the intermediate industries (wholesalers and jobbers) through which

the product passes and as sold at retail.9 In

addition, pricing for an industry would have to

cover not only the industry’s primary output but

price movements of its secondary products—goods

of a type normally made in other industries.

Thus, a product might be represented in the i s p i

system at any one stage of production in the industry where it is produced as a primary product

and as many times as it appears as, secondary

output in other industries, or as it moves through

channels of distribution. Pricing is thus multidimensional—horizontal throughout industries and

vertical along the lines of progress from raw

materials to retail or other final distribution.

N ot only must industrial coverage be compre177

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hensive but all activities of the industries must be

represented—not simply the price movements

for commodities sold in the marketplace, as in the

wpi. Specifically, pricing should encompass production and distribution of commodities, provision

of industrial services, interplant transfers between

establishments owned by the same company,

production of items for direct sale to “ultimate

consumers,” military items (which are excluded

from wpi coverage) sold to the Government, and

purchase of commodities and services by industry,

including imports.

If the pricing system is comprehensive, the

variety of price data collected at the most detailed

level would be suitable to combine in a variety of

classification structures to meet most major needs.

Price data could then be classified and group indexes computed for the Standard Industrial Classification System covering agriculture, forestry, and

fisheries; mining; construction; manufacturing;

transportation, communication, electric, gas, and

sanitary services; wholesale and retail trade;

finance, insurance, and real estate; services; and

Government; the interindustry (input-output)

structure; and the National Income and Product

Accounts.

Product accounts. These consist of purchases of

(or sales to) individual consumers, Government,

business investors, and foreign countries and the

net “sales” to business inventory. Price indexes

developed for this set of data should be set at the

purchaser’s level rather than at the producers’

level, because that is the level at which the accounts presently are aggregated. Separate indexes

would be compiled to show price change for personal consumption expenditures, gross private

domestic investment, net exports, and Government purchases of goods and services.

Pricing of inputs in the government sector is

important for a number of reasons. Federal, State,

and local purchases amounted to about one-fifth

of gnp in 1969. The largest single consumer

was the Federal Government. Because Federal

spending is used as a fiscal counterbalance to

inflationary and deflationary forces in the economy,

information on price trends of Government purchases complement the price picture in the private

sector.

Indexes of Government purchase prices would

serve a number of budgetary purposes. Such

indexes could provide (through their use as deflators) estimates of actual quantity purchased

(deflated value), and by this means answer the

question whether additional expenditure results

from increased quantity or higher prices. As an

example of detailed application, they could also

permit more accurate escalator clauses to be

written into government purchase agreements.

Income accounts. The income account side of the

national accounts consists of the returns to the

factors of production (wages, profits, rent, and so

on) plus nonfactor charges, such as direct business

taxes and depreciation. The gross product of an

industry measured from the income side is not

convertible to constant dollars according to the

same concept as measured from the product side.

This difference arises because of the definition of

quantity. The quantities implied in constantdollar product are the usual “final” outputs or

inputs measured in terms of tons, cubic feet, or

service rendered. The quantities implied in

constant-dollar income (when current-dollar income is deflated by indexes of wage rates, rents,

and so on) are measured in such “physical” units

as man-hours, use of a building for a year, and so

forth. The two approaches should be reconcilable

in terms of the total if suitable weights or inputoutput ratios (productivity ratios) could be

developed. In the foreseeable future, hQwever,

deflation will be confined to the product accounts

and to Gross Product Originating in industry

to which we now turn.

Gross product originating. This approach

focuses on the industrial origin of gross product.

While in the product account approach gnp is the

total of all final purchases, and in the income

approach the total of all factor incomes, plus

nonfactor charges such as indirect business taxes

and depreciation, in the gross product originating

approach gnp is the sum of the gross product of

all industries, representing each industry’s contribution to the total output of goods and services^

Price indexes required for the constant-dollar

estimate of Gross Product Originating in each

industry consist of industrial output prices for

each industry and purchase price indexes. Output

price indexes should measure not only the primary

products but also the secondary products of industry. The “double-deflation” method described

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earlier is used to obtain implicit “value-addedprice” indexes for the industry. Output prices for

this approach are at the ‘producers' level rather

than at the purchasers' level, in contrast to pricing

in the Product Accounts of the g n p . Since excise

taxes are included in the output data, the price

indexes should include them. Also, purchase of

services should be included in the input index.

Pricing and concepts

Pricing for the various systems of Industry

Sector Price Indexes must be consistent with the

precepts of pricing— that the object priced be

standardized with respect to some highly specified

set of attributes—but within the general concept

of quantity (output or input) relating to the structure of the index of which the price series is a part.

If the price series is to be used as a deflator to convert current-dollar data to constant-dollar data,

every conscious decision which is made concerning

the price series automatically results in an implicit

decision concerning deflated value, and, as a result,

production, quantity, and the unit of quantity.

Through its effect on quantity, the pricing

decision may, in turn, influence the measure

of productivity. For this reason, it is essential

that all decisions concerning the commodity or

service to be priced, the specification, level of

pricing, and the unit be determined with the

specific purpose in mind. After pricing is established, it is essential that decisions concerning

adjustments for quality, timing of discounts,

changes in industrial vertical integration, treatment of transportation charges, and taxes should

be made with the larger goal in mind.

Sometimes several different or alternate decisions might be required concerning particular

problems of pricing, because of the various uses

to which the i s p i would be put. For example, the

question whether the transportation charge for a

commodity should be included in the purchase

price would be answered in the affirmative in the

case of a deflator for materials inputs in a manufacturing industry whose input values, as reported

in the Census of Manufactures, include transportation implicitly. It would be answered in the negative for the interindustry chart, where transportation is treated as a separately purchased input and

materials are priced f . o . b .

Some problems that might be encountered in

constructing an Industrial Sector Price Index and

possible decisions regarding them are presented in

the following sections:

I n t e r p l a n t t r a n s f e r s . Transfers to other plants

of the same company can be either included or

excluded from industry data on value of production. (Census data carry the totals both ways.) In

the currently published industry price indexes of

the b l s , the decision was made to include the interplant transfers in the concept because they are

part of total output. Their inclusion makes the

value and price data consistent with data on

man-hours, employment, and payrolls, all of which

implicitly include them. (In practice, however,

only the weights are included because price movements of interplant transfers are assumed to

parallel those of marketed products.) Moreover,

value added, excluding transfers, would be difficult

to estimate because data on cost of materials are

not collected according to whether the finished

product will be an interplant transfer. (In contrast,

such indexes as the w p i , which is market-oriented,

exclude interplant transfers.)

Q u a l i t y c h a n g e . It is not our purpose to deal

exhaustively with the quality change problem,

but only to indicate how decisions on this question

relate to the production measurement problem.

Unfortunately, there has never been agreement

among the various agencies of Government as to

the purpose to be served by any particular type of

quality adjustments— a statement which implies

that output itself has not been clearly defined.

Two aspects of the subject warrant attention in

particular: the nature of the measured quality

change and its incidence.

It is apparent th a t not all changes in products

or services will be greeted by purchasers as

quality improvements. Some will find new styling

appealing; others will object or at least be unmoved. These are the subjective features of

quality change with which the psychologist, not

the economist, might deal. However, there are a

great number of changes in product which receive

a consensus—improved safety, contribution to

better health, performance, or economy of operation represent improvements while moves in the

opposite direction represent deterioration.

The nature of a change may be generally

accepted, but the nature of the measurement may

not be, largely because the question is approached

from opposite ends of the production-consump179

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tion cycle. The production-oriented view would

recognize as quality changes only those specific

additions or deletions which require the use (or

removal) of productive resources in their creation.

The consumer-oriented view accepts changes

which contribute (positively or negatively) to the

utility, enjoyment, and so on of products or

services without regard to resource use. These

divergent attitudes lead to different types of

measures or adjustments for quality change, even

when there is agreement that quality has in fact

changed in a specific manner.

Some examples may serve to illustrate the

choices. Should adjustment for an improvement

in electric light bulbs, which extends the length of

life, be made on the basis of the additional lumens

provided or on the basis of the costed-price of the

added feature which made the improved performance possible? Should an improvement in the

ability of earthmoving equipment be based on the

additional tonnage-per-hour of earth moved or on

the cost-price of the improvement? Should changes

in motors be measured by changes in horsepower

ratings or by cost? Should quality change of a

new type, thinner, tin plate which allows more

beer cans to be produced per ton be measured by

the additional cans which may be produced or by

the incremental cost of the improvement? 10

The criterion used will determine the method

employed in the quality adjustment, which, in

turn, affects the magnitude of the measure because

the increment in resource inputs is rarely proportional to the increment of usefulness or of

performance.

A third method— “let the consumer decide”—

shifts the decision to the market place. When the

product before improvement (or debasement) and

the original product are selling on the same market

at the same time, the price differential is taken

as the measure of quality change. This has considerable appeal, especially where subjective matters, such as style, are involved, in that consumer

taste dictates and willingness to pay provides the

key to the adjustment. In some technical areas,

however, the universal application of this principle

m ay be less satisfactory because it assumes a high

degree of consumer sophistication. Rather than

an adjustment based on the increased tire miles

provided by an improved tire (consumer-oriented)

or the cost of providing more miles (produceroriented), the decision is based on the consumers’

belief or faith that the product performs better.

It is perhaps valid to observe that even some

sophisticated consumers may not be willing to pay

for increased safety either on the basis of the

amount of safety or the cost of it but m ay value

risk, dash, and adventure more. In this case, the

third method would be better. In any case, this

approach does not seem to match either the

consumer-oriented or market-oriented approach,

but may lie closer to the former.

Economists of the Office of Business Economics

have expressed the view that the appropriate

quality-change measure should be based on the

resource-use approach for their purpose.11 The

reason is that this method of measurement provides a production measure which can be used to

gage capital productivity change. In the earthmoving equipment example previously cited, the

percentage increase in performance would exceed

the cost increase and the difference would represent capital productivity gain. If the price index

were adjusted by the full amount of the performance increase (using the consumer-oriented

method) the quantity-of-machinery purchased or

used (input) would show a larger increase and

offset the increase in work done (output), in the

numerator of the productivity measure. For the

General Price Indexes and the i s p i , it seems that

the appropriate type of quality adjustment would

be cost-oriented.

For the c p i (an index outside the i s p i system)

it appears that the consumer-utility or performance adjustment has some merit. This method

would, however, recognize the c p i as primarily a

consumer welfare-type index. On the other hand,

adoption of this approach for the c p i would limit

that measure’s usefulness as a contributor of

building blocks for the retail-trade-industry segment of the i s p i system, and would limit the

usefulness of comparisons between industrial

prices and their c p i counterparts.

A cost of living index would go even further

than the c p i in the direction of a consumeroriented approach to the quality problem. This

index would “take into account the fact that, for

most commodities, there is a rate at which the

consumer could substitute one for the other in

response to changes in relative prices and still

remain on the same plane or level of satisfaction,”

and a forced substitution—replacement by producers of a low-priced item with one of higher

quality and price—would be treated as a price

increase.12

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In connection with the Federal requirement

that smog-control devices be installed on new

automobiles, an interesting question has arisen:

Should the price indexes be adjusted at all for

these devices? The argument against adjustment

(and thus for treating the list price increase upon

introduction as a genuine price increase) holds

that the buyer does not benefit from the device—

others do. Also, the argument runs, acceptance

of such devices as improvement in quality (and

not price increases) overcompensates because of

failure to penalize for the environmental deterioration which necessitated the device in the

first place. The opposing view is that the buyer

does benefit in a social sense. His payment “in

consideration of the payment by others’’ is of

value to him, hence a quality improvement. In

addition, this view states that the antismog

device does represent additional production and

a car with a device represents more than one

without (as does one with a radio or heater),

and failure to adjust ignores the additional

output— a consideration important for price series

used in deflation. This view of the index as a

deflator results in attaching a production-oriented

definition to the cpi, and the resulting index

measures social cost by the market cost of the

device.

If environmental improvement (or cessation of

deterioration) is paid for by price increases rather

than by taxes, this question will arise more frequently in the future. The problems of quality

change associated with environmental improvem ent illustrate what has been said earlier concerning pricing to meet concept requirements:

that all decisions on pricing, unit of measure,

quality adjustments, and so on, should be made

with the particular goal of measurement in mind

and, in case of deflators, with the particular effect

on the production measure in mind. Owing to the

variety of needs for price data, it seems reasonable

to assume that many questions of this kind will

be solved by the construction of alternate measures, each for a specific purpose.

Turning to the problem of the incidence of

quality change, it is obvious that a change in

quality of the product of a given industry m ay

affect the performance and quality of goods and

services produced by other industries. In an

example already cited, the steel industry developed a better electrolytic tin plate that boosted

the number of cans manufacturers could produce

per ton. A decision to adjust the price index for

steel quality improvements by the full amount

of the increased performance “credits” the producing industry with the full amount of the

improvement. Use of the cost-price adjustment,

on the other hand, can be shown to result in a

sharing of the effect between the producing and

using industries. This comes about because the

cost-price adjustment is usually of smaller magnitude than the full-performance adjustment,

and the price of steel falls less and steel production rises less than in the performance approach.

This in turn credits the using industry with less

steel consumption per can, and both industries

show a gain in productiveness.

Taxes. If taxes are viewed as payment to government for either specific or general service, then it

follows that they should be converted into some

sort of price paid for the general or specific service and used in deflating government output. As

a consequence of considering taxes pa}unent for

government service, value figures and price indexes for the private sector would exclude taxes

to avoid double counting. In addition, price series

needed for analysis of the interrelationship of

price, production, and productivity trends must

be comparable—hence, with taxes excluded.

However, there are practical necessities to consider. Value-of-output-data used by obe in

deflation, for example, often contain the excise tax

both on the items in the output total and the hidden

taxes in the materials and components. In this

instance, the deflator should include excise and

sales taxes. The preference for inclusion of taxes

because of their inclusion in the value data is based

on the view that a tax increase should be reflected

as a price increase so that the deflated value series

will remain constant. This throws any concomitant

production increase which may flow from the tax

increase (new schools, roads, and so on) into the

government sector.

Because of variations in the value data with

respect to inclusion or exclusion of excise and sales

taxes, it seems likely that series should be available

with and without taxes in both the retail and nonretail price programs.

Income taxes and other taxes which are not

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sidered as payment to government and converted

to price indexes for the government output price

index, at least in concept.

Transport charges. A s in the case of taxes, the

decision whether to include these charges is

based partly on the concept used, partly on the

nature of the data to be deflated. Values for

purchased materials reported to the Census of

Manufactures include the transportation charges,

so purchase price indexes would include transport.

As indicated, the interindustry concept views

purchasers as purchasing transportation separately, so purchase prices of goods would exclude

transportation charges for this purpose. Transport

would be separately deflated by indexes of

freight or passenger rates, as appropriate.

At this point, it is important to note that the

different treatments do not lead to the same

results. When transportation is included in the

price, the series implicitly prices the transport

charge per unit of product; when separately

priced, transport charge is standardized and

expressed as the charge for a given product

hauled a given distance, or a fixed number of

ton-miles. Thus, a change in the distance hauled

(change in the amount of transportation purchased) becomes an integral part of price when

transportation is included— a situation which

does not apply when transport is treated as a

separate item. The handling of transportation

data is a special case of the general problem of

reconciling input pricing with output pricing.

Both types of pricing are affected by productivity

changes or, in other terms, changes in input-output

ratios.

Several other categories of pricing may have to

be covered to meet various needs. Inventory

pricing and goods in process pricing, while perhaps

not generally necessary, would be important in

industries such as shipbuilding where production

cycles are long or varying, and where it is essential

to adjust real value of shipments to represent

real output. Changes in vertical integration would

have to be watched carefully in dynamic situations

to assure continuous sample adjustment, as

today’s onsite production (for example, housing)

becomes tomorrow’s purchased component. Pricing

of large scale output (the purchase of the entire

crop by a cannery, for instance), and of long-term

purchase contracts are all part of the total picture

and must be taken into account.

Present program coverage

At present, the Bureau’s Industry Sector Price

Indexes are published for only 100 4-digit manufacturing and mining industries out of a total of

Table 2. Coverage of Gross National Product (by sector) and of industries by available price indexes

Percent of sector GNP (value of 

shipments) covered2

Number of 4-digit industries covered2

Sector

GNP

accounted

for* To- InformaInformation not 

published

To- InformaInformation not 

published

(percent) tal tion

publishedGood

coverage

No to 

fair

coverage

tal tion

published Good

coverage

No to 

fair

coverage

Total............................................................................................................... 100.0 100 13 16 *59 382 101 79 702

Agriculture, forestry, fisheries................................................................................. 3.8 100 0 88 12 37 0 10 27

Mining___I . ............................................................................................................. 2.4 100 74 0 26 50 10 1 39

Contract construction................................................................................................ 3.5 100 0 37 63 22 0 0 22

Manufacturing........................................................................................................... 30.7 100 28 13 59 421 91 33 297

Transportation, communication, electric, gas, and sanitary.................................... 10.1 100 0 100 0 72 0 4 68

Wholesale trade............................................................... ...................................... 7.2 100 0 0 100 48 0 0 48

Retail trade..................................................................... ......................................... 9.7 100 0 55 45 67 0 16 51

Finance, insurance, and real estate......................................................................... 13.4 100 0 79 0 0 79

Services..................................................................................................................... 9.3 100 0 86 0 15 71

Government............................................................................................................... 9.9 100 0 0

* The Office of Business Economics of the U.S. Department of Commerce is the source 

of these data.

2 Percent of shipments values covered is derived from the following: For agricultural 

sectors, from unpublished material of the U.S. Department of Agriculture; for mining and 

manufacturing, the Censuses of Minerals Industries, and Manufactures, 1963; contract 

construction estimated from residential construction as proportion of all construction; 

for transportation and warehousing, communications, and so forth, and parts of services, 

from unpublished data of the Office of Business Economics; and for retail trade from the 

1963 Census of Business.

2 These industries are defined in the Standard Industrial Classification Manual, 1967 

(Bureau of the Budget, Office of Statistical Standards).

* The remaining 12 percent is accounted for by industries for which detail on value 

coverage does not permit adequate evaluation.

NOTE: Dashes indicate information on availability of data is not known.

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a b o u t 500 in th e se tw o d iv isio n s a n d of a to ta l of 

a b o u t 900 in all d iv isio n s. T h e se 100 in d u s trie s 

co v e r a b o u t 13 p e rc e n t of th e to ta l d o m e stic v alu e 

of U .S . o u tp u t. C o v e ra g e ac c o u n te d fo r b y 

p u b lish e d in d e x es is h ig h e st fo r m in in g — a b o u t 

75 p e rc e n t— follow ed b y m a n u fa c tu rin g w ith 28 

p e rc e n t. F o r th e la tte r, a d d itio n a l p ro d u c t-c la ss 

(5 -d ig it) in d e x es a re a v a ila b le w hich w o u ld b rin g 

co v e rag e to a b o u t 45 p e rc e n t. (See ta b le 2.)

Coverage in agriculture is reasonably good,

although there is some question as to the level of

pricing (whether close enough to the farm). There

is fairly good coverage in some utilities and in

retail trade (largely from the c p i ) , but insufficient

for publication of industry indexes.

1 See Ja c k A lterm an a n d M a rtin L. M arim o n t, Prices

find Price Analysis in the Framework of the National Accounts, a p a p e r p resen ted a t th e 11th G eneral C onference 

of th e In te rn a tio n a l A ssociation for R esearch in In co m e 

a n d W ealth, A u g u st 24-31, 1969, N a th a n y a , Israel.

2 See Inflation and the Price Indexes (U.S. Congress, Joint 

Economic Com m ittee, 89th Cong., 2d sess., 1966). In terms of 

base year weights the Im plicit Price Deflator relies on the CPI 

for 46 percent of its coverage, the WPI for 12 percent, agriculture for 7 percent, and other prices and nonprice estimates 

for the remainder.

3 See th e sta te m e n t b y th e C om m issioner of L ab o r 

S tatistics, G eoffrey H . M oore, before th e S u b co m m ittee 

on E conom ic S ta tistic s of th e J o in t E conom ic C o m m ittee, 

C ongress of th e U n ite d S tates, M ay 15, 1969.

4 T h e Im p lic it P rice D eflato r is d eriv ed b y d iv id in g to ta l 

e x p en d itu res v a lu e d in c u rre n t prices b y to ta l ex p en d itu res 

v alu ed in base p erio d prices. In co n stru ctin g th e D eflato r, 

v alu e d a ta are d eflated b y a p p ro p ria te price indexes (w hich 

m a y be fixed-w eight g roup indexes) a t th e finest degree of 

d e ta il feasible a n d su m m ed to o b ta in th e to ta l c o n sta n td o llar figure. I t can be show n th a t th e to ta l p rice index 

d eriv ed is of th e P aasch e form in so fa r as w eights betw een 

th e m o st d etailed level of ag g reg atio n is concerned.

5 F o r a m ore d etailed an aly sis of th e need for re ta il 

pricing, see A llan D . Searle a n d M ary E. L aw rence, “ R e ­

ta il P rices a n d th e C o n su m er P rice In d e x ,” F e b ru a ry 1969, 

m im eographed.

9 F o r a m ore co m p lete d escrip tio n of th e B u reau of 

L ab o r S ta tistic s c u rre n t p ro g ram , see “ In d u stry -S e c to r 

In d ex es,” Handbook of Methods for Surveys and Studies

(B L S B u lletin 1458, 1966).

Some data are available for transportation from

the Interstate Commerce Commission and other

regulatory and ratemaking agencies, and the

Bureau of the Census has published a price index

for new single-family dwellings. Exploratory work

in other aspects of construction is continuing in

both b l s and the Census Bureau. Research on

import and export pricing is also underway.

Table 2 shows in detail the coverage available

in terms of the interindustry classification structure. It is evident that much remains to be done.

Coverage is low as a whole. Even in manufacturing,

the gaps are considerable and are characterized by

nonhomogeneous or rapidly changing products,

such as aircraft, electronics, and shipbuilding. □

7 2Po0i _ Z PjQ, . Z P iQ j ZPiQi 2P iQ i_Z P iQ o

2PoQo ZPoQo ‘ 2 P 0Qi ~LPiQ0 2 P 0Q0 ■ 2 P qQ0

8 The Price Statistics of the Federal Government, a re p o rt 

pf th e P rice S ta tistic s R eview C o m m ittee of th e N a tio n a l 

B u reau of E conom ic R esearch to th e B u reau of th e B u d g et, 

h earin g s before th e S u b co m m ittee on E conom ic S ta tistic s 

of th e J o in t E conom ic C o m m ittee, Ja n u a ry 24, 1961.

9 See fo o tn o te 5.

10 D iscussion of th ese issues can be fo u n d in E d w a rd F. 

D enison, P ro b lem s of C a p ita l F o rm a tio n , S tu d ies in In ­

com e an d W ealth, V olum e 19 (N ew Y ork, N atio n al 

B u re a u of E conom ic R esearch , 1957), pp . 217-234; M ilto n 

G ilb ert, “ Q u ality C h an g es a n d In d ex N u m b e rs,” Economic

Development and Cultural Change, A pril 1961, pp . 287 -2 9 4 ; 

a n d Z vi G riliches, “ Q u ality C h an g e a n d In d ex N u m b ers: A 

C ritiq u e ,” an d M ilto n G ilb ert, “ A R e p ly ,” b o th in 

Monthly Labor Review, M ay 1962, p p . 542-545, a n d in th e 

m in u te s of th e C o m m ittee on C onsum er a n d W holesale 

P rices, B usiness R esearch A d visory C ouncil to th e B u reau 

of L ab o r S ta tistic s, F e b ru a ry 18, 1964.

11 See G eorge Jaszi, R o b e rt C. W asson, a n d L aw ren ce 

G rose, “ E x p an sio n of F ixed B usiness C a p ita l in th e U n ite d 

S ta te s ,” Survey of Current Business, N o v em b er 1962, p. 10; 

a n d L aw rence G rose, Irv in g R o tte n b e rg , a n d R o b e rt C. 

W asson, “ N ew E stim a te s of Fixed B usiness C a p ita l in th e 

U n ite d S ta te s, 1925-65,” Survey of Current Business,

D ecem b er 1966, p p . 37-38.

12 F o r a discussion of differences b etw een a cost-of-living 

index a n d o th e r co n su m er p rice indexes, see Jo el P o p k in 

“ T h e P ro g ram for th e 1975 R ev isio n of th e c p i ,” a p a p e r 

p resen ted before th e N a tio n a l P la n n in g A sso c ia tio n 

O ctober 1970.

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The place of the 

Consumer Price Index 

in today’s economy 

and some of the problems 

in keeping it up to date

JULIUS SHISKIN

T h e m o n t h l y Consumer Price Index is the only

index compiled by the U.S. Government that is designed to measure changes in the purchasing power

of the urban consumer’s dollar. It serves two major

functions:

• It is a yardstick for revising wages, salaries, and

other income payments to keep in step with rising

prices; and

• It is an indicator of the rate of inflation in the

American economy.

Because of changes in consumer buying patterns,

it is necessary to update and revise the Consumer

Price Index periodically. The Bureau of Labor Statistics is now in the midst of a major revision, scheduled for completion in 1977. The index will be

tested beginning in 1976. Starting in April 1977,

BLS will publish two Consumer Price Indexes:

an improved index for urban wage earners and clerical workers to meet the requirements of collective

bargaining, and an index for all urban households,

which will provide a new comprehensive measure of

price change for the economy.

This article briefly describes uses of the Consumer

Price Index, defines what it measures and describes

its limitations as a cost-of-living index, reviews

earlier revision programs,1 reports on some of the

problems encountered in the current revision, and

describes the additional data that will be available after the revision has been completed and the

new indexes are published in 1977.

Uses of the Consumer Price Index

Today, as in earlier years, the Consumer Price

Index plays an important role in consumers’ attempts

to assess the degree to which their purchasing power

Julius Shiskin is Commissioner of Labor Statistics.

Updating the

Consumer Price

Index—

an overview

is eroded by price increases, and serves as a major

economic indicator.

As an escalator. It is estimated that there are more

than 5.1 million workers covered by collective bargaining contracts which provide for increases in wage

rates when the CPI rises. The number and application of these escalator clauses is increasing, and

escalator clauses based on the index show signs of

changing. In the spring 1974 settlements in the

aluminum industry, for instance, a new step was

taken when aluminum producers agreed to provide

for annual automatic cost-of-living escalator adjustment in pension benefit levels, so that pension payments to retired workers will rise partially (65 percent) along with a rise in the Consumer Price Index.

In addition to workers whose wages or pensions

are adjusted according to changes in the CPI, some

44 million persons now find their incomes affected

by the index, largely as a result of statutory action:

almost 29 million social security beneficiaries, about

2 million retired military and Federal Civil Service

employees and survivors, 600,000 postal workers,

and about 13 million food stamp recipients. When

dependents are taken into account, the incomes of

somewhere in the neighborhood of one-half the population already are or soon will be pegged to the Consumer Price Index.

Another group whose living standard is affected by

changes in the Consumer Price Index are the 24 million children who eat lunch or breakfast at school,

under the National School Lunch Act and the Child

Nutrition Act of 1966. Under Public Law 93-150,

national average rates for these lunches and breakfasts are adjusted semiannually by the Secretary of

Agriculture on the basis of the change in the CPI

series, “Food away from home.”

Also, the poverty threshold estimate, which is the

basis of eligibility in many health and welfare programs of both Federal and State and local goveraFrom the Review of July 1974 184

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ments, is updated periodically to keep in step with

the Consumer Price Index. Under the Comprehensive Employment and Training Act of 1973, the “low

income” standard specified as one of the criteria for

distribution of manpower revenue-sharing funds is

kept current by reference to the index.

In addition, escalator clauses in an increasing number of rental, royalty, and child support agreements

automatically increase payments to an undetermined

number of people. The CPI is also used as a guide in

drawing up contracts and in wage negotiations.

As an economic indicator. Beyond its application to

wages and other income payments to individual

Americans, the index has direct impact on the formulation and evaluation of government economic

policy that affects virtually everyone. The Consumer

Price Index is, in fact, a major yardstick by which

the success or failure of government economic policies is judged.

As an indicator of cyclical change in the economy,

the index itself has typically lagged behind other

measures of economic performance, such as real

GNP (output) and unemployment. The Wholesale

Price Index also tends to lead the CPI, although the

leads are quite variable. On the other hand, the

Consumer Price Index seems to lead the GNP implicit price deflator, although the lead is less clear

when comparisons are made with the GNP price

deflator computed with fixed weights.

In the light of these important issues involving the

CPI, it is clear that an accurate Consumer Price

Index is of the utmost importance. At present, a 1-

percent change in the index triggers at least a $1

Changes in nomenclature

Never a static instrument, the Consumer Price

Index has been responsive to changes in expenditures

and earnings patterns, as well as in its uses and in the

economy. Changes in use and in concept brought

changes in nomenclature as well.

Between 1913 and 1945, the Bureau of Labor Statistics referred to The Cost-of-Living Index for the

United States. In 1945, the name was changed to

Consumers’ Price Index for Moderate Income Families in Large Cities. In 1964, the current title, Consumer Price Index for Urban Wage Earners and

Clerical Workers, was adopted.

Beginning in 1977, a new Consumer Price Index

for All Urban Households will be published, in addition to an updated Consumer Price Index for Urban

Wage Earners and Clerical Workers.

billion increase in income under escalation provisions. An error of only 0.1 percent can thus lead to

the misallocation of over $100 million.

Furthermore, while it is difficult to estimate the

effects of an error in the Consumer Price Index on

economic policy decisions, it is also clear that— with

inflation the major economic problem of the day—

the stakes involved in an accurate Consumer Price

Index are very great relative to the costs.

What the CPI measures— and doesn’t

The Consumer Price Index compares what the

“market basket” of goods and services cost this

month against what it cost a month ago, or a year

ago, or 10 years ago, or in 1967 (the base year for

the current index). Say that in 1967 the prescribed

market basket could have been purchased for $100.

In February 1974 the CPI was 141.5 and in March

1974 it reached 143.1. That means that the same

combination of goods and services that could have

been obtained for $100 in 1967 cost $141.50 in

February 1974 and $143.10 in March.

This does not necessarily mean the average consumer actually spent $143.10 in March 1974 as

against $100 in 1967. Consumers tend to adjust their

shopping practices to the prices they encounter in the

marketplace and to substitute less costly items, or do

without, in order to hold their spending within their

means. For example, if the price of certain cuts of

beef rises rapidly, the purchasers may shift to poultry

or less expensive meat. If the charge for repair services increases more than the customer believes is acceptable, householders tend to postpone having the

repairs made or to “do it themselves.”

The index does not take this sort of substitution

into account, but rather is predicated on the purchase

of the same market basket, in the same proportions

(or weight), month after month. This is one reason

why it is called a price index and not a cost-of-living

index— although the public often refers to it as a

cost-of-living index, and it is often used in that way.

There are other major differences between the two

types of indexes. For instance, the CPI does not include income and social security taxes since (unlike

sales taxes) these costs are not directly associated

with retail prices of specific goods and services,

whereas a true cost-of-living index would explicitly

include them.

The CPI does not immediately reflect changes in

expenditure patterns, nor can it immediately adjust

to the introduction into the economy of new products

or services. For example, the increased use of con185

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venience foods as more and more women entered the

labor market and the rise in “fast-food” eating places

— these social and economic phenomena were in

place for some time before they could be adequately

reflected in the index. Similarly, a product which has

fallen from public favor— either because its place is

usurped by a better product, or simply because of a

change in fashion or consumer preference— may continue for a time to carry a disproportionate weight in

the index until it can appropriately be phased out.

But even within the fixed market basket concept of

the CPI, provision is made for some changes in products between the main decennial revisions.

The Consumer Price Index does not attempt to

report these changes in the style of living. It simply

measures the changes in prices for a scientifically selected market basket based on the average experience

of certain population groups. Items in the market

basket for which the CPI measures price changes

run the gamut from bread and butter to television

and bowling fees, from prenatal and obstetrics services to funeral expenditures, from popular paperbacks to college textbooks. The CPI never has been

limited to price changes of so-called necessities.

Expenditures by a cross section of consumers living in a representative selection of urban places, as

disclosed by Consumer Expenditure Surveys, provide

the basis both for the selection of items to be priced

and the importance of each of these items in the

index structure. The relative importance (or weight)

given to each item also is derived from the Consumer

Expenditure Survey. The weights reflect the experience of renters and of homeowners; of car owners

and of earless families and individuals; of families

with many children, childless families, and single

consumers.

Since the CPI is based on expenditures, it does not

reflect noncash consumption, such as food grown at

home, fringe benefits received as part of a job, services supplied by government agencies without payment of a special tax fee, and so on. When the relative importance of such an item changes over time—

as with medical care, for which employers and the

government have in recent years assumed an increased proportion of the expense— these changes

must be taken into account in interpretation of the

index.

Origin of the CPI

Although studies of prices and living conditions

in the United States had been conducted by government agencies since the late 19th century,2 the Bureau of Labor Statistics first consumer price index,

called a cost-of-living index,3 grew out of a decision

by the Shipbuilding Labor Adjustment Board during

World War I. In arriving at a “fair wage scale,” the

Board determined in November 1917 that readjustment of wages in the shipbuilding yards was warranted when there had been a general and material

increase in the cost of living.4 During 1918-19, in

cooperation with the Board, the Bureau investigated

the cost of living in a number of shipbuilding and

other industrial centers. Details of expenditures on

goods in the family market basket were obtained

from each of 12,000 wage-earner families in 92

cities, and records of retail establishments in 32

cities provided prices for a large number of articles.

Regular price collection was initiated after 1917 in

these 32 cities, with price information collected 1 to

4 times a year for about 145 commodities and services. In 1919, the Bureau began the publication of

complete “cost of living” indexes at semiannual intervals for 32 large shipbuilding and industrial centers, using a weighting structure based on expenditures of wage-earner and clerical-worker families in

1917-19.5 In February 1921, regular, periodic publication of the U.S. index in roughly its present form

was established. Although over the years there have

been many changes in scope, coverage, frequency,

and publication format, the index has remained a

measure of change in the cost of a fixed market

basket of goods and services. Quarterly indexes were

initiated in 1935, and monthly indexes began in

Forthcoming articles on the CPI revision

Revision of the Consumer Price Index, as this article indicates, is a long and complex endeavor, involving the work of many staff members. The present

article is an overview of the revision process, and

interim progress report on some of the actions taken

and plans underway. Additional articles are planned

describing both methodology and survey results.

The principal BLS staff contributors to this overview article were Kenneth Dalton, Chief of the Division of Consumer Prices and Price Indexes, Office of

Prices and Living Conditions and Robert Gillingham,

Economist, CPI Revision Group. Georgena Potts of

the Office of Publications assisted in bringing the materials for this article together and in writing the text.

Much of the work described above on the current revision was carried out under the direction of Janet L.

Norwood, now Deputy Commissioner of Labor Statistics.

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October 1940 at the request of the National Defense

Advisory Commission.6

First major revision— 1940

In 1933 the Secretary of Labor requested that

the American Statistical Association appoint a committee to advise the Department on its statistical programs. The Advisory Committee paid particular

attention to cost-of-living indexes, and on its recommendation the Bureau of Labor Statistics initiated

steps leading to a comprehensive revision of its

indexes.

In 1934-36, the Bureau undertook a comprehensive survey of “Money Disbursements of Wage Earners and Clerical Workers,” which covered 14,500

families of two persons or more in 42 cities with

50,000 inhabitants or more. Price collection procedures were altered and methodological changes in

index calculation were made, modifying both the

weights used in combining group indexes to obtain

the “all-items” index and the population weights for

combining cities.7 The system of weights was revised,8 with specific weights based on city food

expenditure patterns replacing the regional weights

formerly used. New commodities were added, and

food indexes were constructed on the new basis back

to March 1919. Also, the Bureau adopted the principle of imputation— that is, ascribing to a sample

item that could not be priced the price change for

groups of items presumed to have price movements

similar to the sample item.

The comprehensive revision of the index was completed in 1940.9 At the same time, the reference

base period was shifted to 1935-39 = 100, on

advice of the Central Statistical Board (predecessor

of the present Statistical Policy Division of the

Office of Management and Budget).

Post-World War II revision

During World War II, temporary adjustments in

data collection procedures and in weights for foods,

fuels, transportation, and other selected items had

been made to take account of rationing and wartime

shortages.10 These adjustments were necessarily imperfect. In 1946, when wartime restrictions were removed, prewar weight patterns were restored, with

adjustments to validate the actual change.

In 1946, also, a number of important changes

were made in the calculation of food prices. Separate average prices were computed for chain and

independent stores, and these averages were combined using fixed weights. Food outlet samples were

Publication of the Consumer Price Index

The national Consumer Price Index is compiled by the

Bureau of Labor Statistics and published about 3 weeks

following the month to which the data refer. The index

refers to the entire month, not any specific day of the

month. Prices are collected early in the month for foods,

around mid-month for rents and utilities, and over the

entire month for other goods and services. Approximately

15,000 retail stores and other retail outlets (bowling alleys,

doctors’ offices, and so on) are visited each month and

approximately 400 items are priced. A press release

contains a brief analysis of prices movements during the

month, as well as the latest available indexes and percent

changes over selected periods. A more detailed report

is published subsequently in the Monthly Labor Review

(table 25, pp. 103-08) and in a special periodical, The

Consumer Price Index. U.S. average indexes are published

monthly for “all items” and for groups, subgroups, and

selected items.

Individual “city” indexes for Standard Metropolitan

Statistical Areas, identified by the names of their central

cities, are published in the monthly press release, in

the Monthly Labor Review (table 27, p. 110) and in a

detailed report for individual Standard Metropolitan Statistical Areas (SMSA’s) having 1 million inhabitants or

more in 1960, based on the pricing of full samples of

items. These indexes are computed monthly for five areas:

Chicago, Ill.-Northwestern Indiana; Detroit, Mich.; Los

Angeles-Long Beach, Calif.; New York, N.Y.-Northeastern N.J., and the Philadelphia metropolitan area, and

once every 3 months on a rotating cycle, for the other

published “city” areas. Indexes are published monthly

for the food component for published “city” areas.

Because many users misinterpret the city indexes as

measures of intercity differences in prices, each report

cautions the user of these indexes that comparisons of

indexes for individual SMSA’s show only that prices in

one location changed more or less than in another. The

metropolitan area indexes cannot be used to measure differences in price levels or in living costs between areas.

Besides publication of city indexes in a national press

release, statements are issued from the Bureau’s regional

offices on the same day as the national release. These

contain indexes and analyses of price movements in the

individual areas.

Starting in 1973, indexes have been published for

cities in five population-size groups, and in 1974 regional

indexes were added.

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revised, taking into account type of store, sales

volume and location.

The 1953 revision

Expenditure surveys conducted in a few cities in

1947-49 showed significant post-war changes in consumption patterns of wage-earner and clerical-worker

families, indicating a serious need for revision of the

index weights used and the market basket items.11

In 1949, the Congress authorized a large-scale 3-year

program for modernization of the index. By this time,

the postwar rise in prices, which followed elimination of price controls in mid-1946, appeared to have

run its course; prices had begun to decline from their

postwar peaks, and the period 1951-52 was expected

to be characterized by relatively stable economic

conditions.

The outbreak of hostilities in Korea, however, was

accompanied by sharp and diverse price increases

in the United States. These price changes, coupled

with widespread use of the index in wage escalation

clauses, made adjustment of the index weights to

post-World War II spending patterns extremely

urgent. In an interim revision,12 using data from

1947-49 expenditure surveys in seven cities, group

weights were adjusted, and 25 additional items were

selected for pricing. Both the “old series” index and

the adjusted index were published simultaneously

from 1950 through 1952, when the old series was

discontinued.

The comprehensive revision which was begun in

1949, was completed in 1953. Surveys of consumer

expenditures were conducted in 91 cities, the index

concepts were reexamined completely, and the

index reference base was changed from 1935-39

to 1947-49. The general concept of the index as

a measure of price change for a fixed market basket

of goods and services was retained, but a major

change was made by including the purchase of a

home in the weighting pattern. The classification

of goods and services into groups and subgroups

was revised, and indexes were computed retroactively

on the new base period (1947-49) for the new

major groups. The revision introduced a new sample

of 46 index cities out of the 91 cities in the Consumer

Expenditure Survey, including for the first time small

urban places (including areas with as few as 2,500

inhabitants) as well as large cities; revised weights

reflected the 1950 spending pattern of wage-earner

and clerical-worker families adjusted to 1952; and

the list of items priced was expanded to include

new products (such as television sets and frozen

foods) and items that had not been previously covered, such as restaurant meals and owned homes.13

The new index was linked to the adjusted index in

December 1952 to form a continuous series.14

The 1964 revision

By the late 1950’s, it became apparent that the

index weights should not go unrevised for more than

a decade. The Bureau of Labor Statistics asked for

and received authorization for a revision program, to

take 5 years, which was begun in 1959. It included a

Consumer Expenditure Survey conducted in 1960-

61 that provided information on the entire population. These data were basic in selecting a new market

basket, new weights to reflect the distribution of consumers’ expenditures, and a new and larger sample

of cities and retail stores. Chart 1 indicates how the

weighting pattern has changed over the years.

Since the 1950’s the population had mushroomed,

but, more important, it presented a composition

markedly different from that in 1950. The proportion

of persons at each end of the life cycle had increased.

Major changes had occurred in its geographic distribution. About 1 in 5 family units was moving

each year, many to the South and West, from farm

to city, from the central city to the suburbs, and to

peripheral areas in the process of urbanization.

Personal disposable income had moved upward

since 1950— about 37 percent between 1950 and

1956— and more than two-thirds of the rise was reflected in increased real income. Shifts in consumer

spending patterns were apparent. Further extension

of credit on easy terms made the consumer less and

less willing to defer purchasing a house, major appliances, an automobile, and other large-ticket items.

Also, the decline of price maintenance laws and the

rise of the discount house had altered retail distribution patterns. Many new products or qualities had

come into being, ranging from deep freezers to new

household items made of plastics. Greater use was

being made of frozen foods, and there were important changes in housing, including a large number of

new units and a continuing shift from rental to owner

occupancy. Particularly significant was the increasing share of consumer services in the economy as

a whole.

The basic objective of the index continued to be

the measurement of change in the price of a fixed

market basket of goods and services for urban

wage earners and clerical workers. A number of

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Chart 1. The consumer market basket, selected periods

1935-39 1952 1963

1 Includes personal finance charges other than automobile financing and mortgage interest. Imputed, not directly priced.

important changes were introduced, however: (1)

the population coverage of the index was expanded

to include persons living alone; (2) the definition

of an urban wage-earner or clerical-worker family

was modified, so that a family was considered within

the scope of the survey if 50 percent or more of its

income came from wage and clerical occupations

and if at least one member of the family worked

for a minimum of 37 weeks of the year (in the old

series, this working member had to be the head of

the household; the change was made because of the

increasing importance of families with two workers

or more and of family units whose household head

was retired, but which had other working members);

and (3) the income limitation was dropped.15

These changes raised the population coverage to

about 55 percent of the urban population and under

45 percent of total population. At the time of the

1964 revision it was estimated that single workers

living alone represented about 10 percent of all urban

wage-earner and clerical-worker consumer units to

which the index applied, and family units 90 percent. (On an expenditure weights basis, however,

the importance of single consumer units is only 6

percent of the composite wage-earner and clericalworker index.)

The average income of the population covered

was $5,963 in 1960-61 on the basis of the revised

definition, compared with $6,230 prior to revision.

This decline resulted from inclusion of single workers, whose average income of $3,560 was considerably below that of the family groups.

A new and expanded sample of metropolitan

areas and small urban places was introduced, based

on the 1960 Census of Population, and pricing was

extended to suburban areas. The sample of retail

stores was also revised and expanded. Probability

sampling techniques were used for the first time in

the selection of items for pricing. A system for

measuring sampling error was developed, and improvements were made in price collection methods.

The revision was completed in 1964. As before,

the new series was linked to the old in order to

maintain continuity. To provide an opportunity for

examining differences in price movements and to

allow persons using the index in contractual agreements (such as labor contracts) to shift to the

revised index, both the old and new series indexes

were published for the period January to June 1964.

The two indexes did not diverge substantially.

Current revision program

The current revision of the Consumer Price Index

has been a major project of the Bureau of Labor

Statistics over the past 4 years. As in the past, the

revision program involves the development of a

greater amount of data and a review of the economic

and statistical concepts and operational procedures

used in constructing the index. Exhibit 1 shows the

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progression of various steps in the process. Major

elements of the current revision, simply stated, are:

• On the basis of a survey o f consum er expenditures, to determine

a. the proportion o f spending for food, shelter,

medical care, and so on (to be used in the index

w eights), and

b. the specific goods and services to be included

in the market basket.

• To obtain a new sample o f stores where people

buy, reflecting shifts in retail purchases, such as from

central cities to suburbs and from retail stores to mail

order houses;

• To modernize the conceptual framework to make

the index more relevant to current and prospective

econom ic conditions, and to improve statistical methodology, particularly sampling techniques.

Present goals are that the revised CPI will have

sampling errors that are substantially lower than

those of the current CPI.

Surveys

Consumer Expenditure Survey— what people buy.

Information for the decennial revision of the Consumer Price Index is collected from a series of

sample surveys. The most important of these is the

Consumer Expenditure Survey, which collects information on what people buy. The latest such survey, conducted for the current revision, covers the

years 1972 and 1973. It differs from previous surveys in several aspects of design and collection

methods, notably in combining the resources of the

Nation’s two major economic statistical agencies, the

Bureau of the Census and the Bureau of Labor

Statistics. BLS developed the questionnaire content

and specified the output. Census selected the household sample, spread throughout 216 areas of the

country, and conducted the interviews. Most of the

information was obtained in a series of quarterly

interviews involving about 20,000 families.

The remaining information was obtained from

another sample of about 20,000 families, who were

asked to complete a 2-week diary, in order to obtain data on small, frequent purchases, such as food

and personal care items, which are typically difficult

to recall. The diary collection program started and

ended 6 months later than the quarterly survey.

Exhibit 1. CPI revision calendar

1972 1973 1974. 1975 1976 1977 197S

Consumer Expenditure Survey—1st year quarterly data collection.

Consumer Expenditure Survey—1st year diary data collection.

Bureau of Census—processing and delivery of 1st year diary data.

Consumer Expenditure Survey—2d year quarterly data collection.

Bureau of Census—processing and delivery of 1st year quarterly data.

Consumer Expenditure Survey—2d year diary data collection.

Preparation for Point-of-Purchase Survey.

Bureau of Census—processing and delivery of 2d year diary data.

Preparation for rent survey.

Bureau of Census—processing and delivery of 2d year quarterly data.

Point-of-Purchase Survey data collection.

Screening, listing, and initiation of Rent Survey.

Item sample selection.

Rent test index.

Bureau of Census—delivery of Point-of-Purchase results.

Outlet sample selection.

Initiation of revised item and outlet samples.

Test indexes collected and compiled.

Publication of two indexes.

Evaluation and comparison of two indexes.

1972 1973 1974 1975 1976 1977 1979

NOTE: Dates above refer to start of projects but not necessarily to their completion.

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Substantial gains in the accuracy of the index

should result from significant improvements in both

the sample and survey design of the Consumer Expenditure Survey, such as improved stratification,

lower nonresponse rate, reduced length of recall

period, and improved estimating procedures. Also, a

substantially larger sample of items will be selected

for pricing. (Approximately 400 items are priced

in compiling the CPI each month.)

The Consumer Expenditure Survey is expected to

provide more accurate and more complete data than

have previously been available, and thus a sounder

basis for the selection and weighting of items in the

market basket. As with the last revision, data from

the Consumer Expenditure Survey will be used to

select a stratified probability sample of items (the

market basket) within the universe of items classified

into expenditure classes.

Information on such items as clothing, utilities,

and small household appliances is expected to be

more accurate because of the shorter recall period

resulting from the change to a quarterly survey.

The diary survey will provide greater detail on items

ordinarily purchased on a daily or weekly basis,

such as food and beverages, and on personal care

products, not otherwise covered in the survey.

Point-of-Purchase Survey— where people buy. Pricing of items included in the Consumer Price Index

takes place in outlets selected to be as representative

as feasible of types and sizes of places where urban

wage earners and clerical workers shop. The Pointof-Purchase Survey, now underway, will provide

additional data on the retail stores, mail order

houses, bowling alleys, doctors’ offices, and other

places where goods and services are bought. Approximately 20,000 families are being asked where

they purchased various types of goods and services.

From the survey results, for the first time, a full

probability sample of retail stores and other outlets

to be used in collecting data for the monthly index

will be developed. Optimization principles are being

used to assure proper balance between the number

of outlets and the number of price quotations collected in each. Here again the Bureau of the Census

serves as collection agent, under contract with BLS.

Rent survey. Still another survey is underway, to

provide more accurate and current data for the rent

index. Under the present system, change in rents is

measured from large samples of rental units which

include the same units at successive periods. No

substitution of nearby units is permitted. Six-month

rent changes are obtained in each city by part-time

agents every 2 or 3 months, by personal visit or telephone inquiry to tenants of specified units in different samples. The agent uses a detailed checklist covering fuels, gas, and electricity, telephone, garage,

furniture, water, maid service, switchboard service,

and so forth. In most cities, two subsamples of up to

500 rental units each are drawn, with each sample

priced semiannually in different months. In the five

largest cities, three subsamples of about 500 each are

priced semiannually in different calendar months,

providing data for one of the subsamples every 2

months.

In the 1974 survey, as a first step, BLS data collectors in specified areas first list housing unit

addresses in the sample neighborhoods, including

both single and multifamily dwelling units. In the

second stage, the data collectors visit randomly

selected dwelling units and interview the occupants

to obtain information on whether the units are

owner- or renter-occupied, the type and amount of

rent, type of occupancy (year-round, transient, or

seasonal), age and type of structure and whether the

unit has complete kitchen facilities. In the final stage

of the survey, respondents will be asked to provide

information on the amount of rent paid and the

kinds of equipment and services included in the

rent. Thereafter, contact will be made semiannually

with samples of those dwelling units which meet the

specifications for inclusion in the Consumer Price

Index. The samples will be rotated and information

will be obtained each month on changes in the

amount of rent paid and the services and facilities

provided in the current and the previous month.

The new sample design will improve the timeliness

of the rent index, as well as its accuracy. Rent for

the current month will be compared with rent for

the immediately preceding month, rather than at 6-

month intervals as at present. The measurement of

short-term changes is a critical requirement for the

Consumer Price Index. The current rent system does

not provide an adequate measure of monthly change,

nor does it provide for time intervals of a few months

between changes. The revised system will yield

accurate short-term changes while allowing for close

to a 50-percent reduction in sample size.

In addition, attention is being given to the development of better methods for adjusting for

changes in the quality of the rental units priced. Current plans call for incorporating the new rent sample

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sumer Price Index some time before completion of

the entire revision program.

Output of the surveys. In addition to its application

to the expenditure weights and the market basket,

information from the Consumer Expenditure Survey

will be analyzed and published in a number of other

formats. Comparisons of the changing expenditure,

savings, and income patterns which have occurred

since the last such survey (in 1960-61) will provide

a wealth of material for sociologists, urban planners,

and other economic and manpower policymakers.

These will include analyses of the differences in levels

of living among various demographic groups using

characteristics such as family income, family size, age

of family head, occupation of head, and so forth.

The quarterly Consumer Expenditure Survey will

provide data similar to that obtained in previous

such surveys, though in some cases in greater detail

and of greater applicability. Information on clothing,

for example, will be collected with great specificity—

Coats: heavy-weight coats, light-weight coats, snowski suits, all-weather coats, plastic raincoats, and

other coats— and will carry an age-sex code for purchases for members of the household as well as for

gifts purchased for persons outside the household.

Major household equipment items will be identified as to whether they were purchased new or old,

whether they were purchased for own use, received

as a gift, included with dwelling, rented, or purchased as a gift to others. Purchases of this type will

also carry a code to indicate whether the item was

bought for cash, on 30-day credit, installment credit,

or other credit.

The diary survey uses a complex system of more

than 1,700 commodity codes. Examples of the level

of detail provided by this coding structure are:

M ilk—buttermilk, chocolate, condensed, evaporated,

imitation, malted, powdered, or skim-whole;

B eefsteak—chuck, rib round, sirloin, T-bone, or other

steak.

These food products will also include a net weight

or volume per unit identification. (The quantity

code may appear only on the tapes for public use

and not in published data.)

In addition to BLS publication of these data,

computerized data of extraordinary detail and specificity will be available on public use tapes to

econometricians and other researchers from outside

the Bureau of Labor Statistics for their individual

study projects. These data are expected to be available beginning in 1976.

Sample of cities

Improvements are being planned for the sample

of Standard Metropolitan Statistical Areas (designated by the names of their central cities). The

present sample of “cities,” which numbers 56, was

selected on the basis of the 1960 Census of Population using probability methods. It was designed to

represent the entire urban portion of the country.16

For the revised index, prices will be collected in

85 areas, with the area selection based on the 1970

Census of Population. The 85-area design lends

itself to further expansion to at least 156 areas,

if needed. Of the 85 areas, 28 are self-representing

and 57 are representative of the balance of the

SMSA’s and the remainder of the urban population.

The increase in the number of areas to be sampled will make it possible not only to improve the

reliability of the national Consumer Price Index and

the indexes recently introduced for different regions

of the country and for urban areas classified by size

of population, but also to provide, for the first time,

regional indexes for cities of different populationsize classes. Monthly or quarterly indexes will be

published for 28 cities in 1977, compared with 23

at present.

Population coverage

One of the major problems related to the current

revision program has been to determine just who

should make up the index population. Historically,

the index has been oriented toward the urban

worker. As the characteristics of the urban wageearner family have changed over the years, this fact

has been reflected both in the titles of the index and

in the index structure.

In earlier periods, wage earners and clerical workers could be characterized realistically as being of

“low income.” Clerical and salesworkers were

identified as “lower salaried” employees, and the

index was referred to as one for “low and moderate

income” families. These were renters primarily,

living in the more densely populated city centers, and

including relatively more of the older established

households and larger families.

The large increase since World War II in the

size of the middle-income group and population

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movement to the suburbs reflected to a large degree

the improving economic status of the “worker” included in the index population. Expenditures are

by and large based on income, and the large increase

in the number of two-earner families has raised many

wage-earner and clerical-worker families into the

middle-income group. Also, the shift toward a service economy and the increasing unionization of

salaried white-collar employees has caused the occupational classification of “wage earner and clerical

worker” to lose much of its significance, because of

the similarity in the manner of living of this group

and that of the total urban population in the middleincome range. As a result of these demographic and

economic changes, questions were raised about the

coverage of the index.

More than a decade ago, the Stigler Committee

recommended that an index with broad population

coverage be developed.17

A more comprehensive index for the entire population, not only the wage and salary earners, should

be made. . . .

From the viewpoint of general public policy and

scientific study, our basic need is for a comprehensive

Consumer Price Index . . . that is appropriate to the

measurement of the changes in welfare of the Nation

and to the measurement of inflation (and hence the

guidance of monetary and fiscal policy).18

Along with this recommendation, the committee

stated that “a price index comparable to the present

CPI, suitable to the current wage escalation clauses,

should be maintained for several years even if an

extensive revision of the scope of the index is undertaken by the Bureau of Labor Statistics.”

During 1961 appropriations hearings, Ewan

Clague, then Commissioner of Labor Statistics, made

a point of the Bureau’s plans for extending the scope

of the price indexes to cover single-person families

and “further extensions which may eventually expand

the index to represent all nonfarm families.” In May

1966, then Commissioner Arthur M. Ross, testifying

before the Senate Subcommittee on Economic Statistics, indicated an urgent need to expand the Consumer Price Index to represent purchases by all consumers and all retail sales,19 as well as the need for

separate indexes to be compiled within this framework.

The idea of broadening the population coverage

of the index was introduced early in the current

revision program. In May 1970 the question was

discussed with the Research Advisory Councils

(from business and from labor) that regularly meet

with the Bureau of Labor Statistics. In a Review

article in March 1972, discussing Bureau programs,

then Commissioner Geoffrey H. Moore wrote:

In the past the index has reflected expenditures only

for urban wage earners and clerical workers, but consideration is being given to broadening this base by

expanding the coverage to include other types of

workers or retired persons.20

Views on the coverage issue

In April 1974 the Bureau of Labor Statistics announced its intention to broaden the coverage of the

Consumer Price Index to include all urban households beginning in April 1977. The index limited to

urban wage earners and clerical workers would have

to be discontinued because of time and cost constraints.21 This announcement stirred up a lively debate and led to the surfacing of the many different

points of view on this issue. To a large extent, the

interest in the issue was prompted by the recent high

rate of inflation and the great increase in the use of

the index as an escalator for many different types of

income payments.

In discussion of background papers prepared by

the Bureau of Labor Statistics for its Research Advisory Councils, it became clear that there was general agreement among council members that broadening the population coverage of the Consumer Price

Index would be acceptable. However, union spokesmen on the Labor Research Advisory Council urged

that the Bureau reconsider its plans to discontinue

the urban wage-earner index.

The controversy was brought to public attention

by AFL-CIO President George Meany and United

Auto Workers President Leonard Woodcock. Mr.

Meany stated his opposition to the dropping of the

wage-earner and clerical-worker index in a letter to

Secretary of Labor Peter Brennan, which was widely

reported in the press. Mr. Woodcock, in testimony

before a subcommittee of the Joint Economic Committee, argued forcefully for continuation of the

wage-earner index:

Trade unions have a vital interest in the CPI as it

currently stands. It is absolutely essential for effective,

responsible, and rational collective bargaining that

there be available a consistent and reliable index reflecting changes in the cost of living actually experienced by working people. . . .

. . . We have had nearly 30 years of experience

dealing with [the Consumer Price Index]; we understand its strengths and weaknesses; we are familiar

with its behavior and we know how to incorporate it

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responsibly into our contracts. . . . In principle we

are totally opposed to the abolition of a CPI geared

to workers in favor of one geared to nobody. . . .

In practice neither we nor anyone else have any concrete experience as to how this new index would

behave. However, there is a presumption that it would

record lower rates of inflation than the current OPI,

at least if prices continue to behave as they have done

in the last decade. This is because items whose prices

have generally been rising fas*: l are precisely those

which figure most prominently in the budgets of lower

income families.

The UAW, and the labor movement in general,

clearly recognize that there are purposes for which

the existing CPI is not suited. Certain macroeconomic

analyses require more general indicators along the

lines of the proposed All-Consumer Index. Other

purposes (for example, setting the appropriate level

for social security payments) require more specific

measures covering groups currently excluded from

the CPI. Such functions are legitimate and we would

support the creation of indexes appropriate to them.22

In hearings before another Senate subcommittee,

later in the month, union spokesmen stressed the

need for continuity of statistical data, especially in

an area such as labor-management negotiations

where faith in the reliability of that data is basic to

its acceptance as a tool in bargaining. Lazare Teper,

Director of Research for the International Ladies’

Garment Workers’ Union, pointed out that ‘‘Neither

workers nor management are likely to accept the

new set of figures just because it covers other groups

whose marketplace experience is different from

that of wage and salaried workers.” 28

Cost-of-living adjustments are not generally considered by labor leaders to represent a real gain for

the worker in terms of the labor share of expenditures. Escalator clauses— which in effect freeze

real wages— are merely a defensive factor to prevent

there being any loss in real income. Improvement in

the workers’ share must be negotiated in other forms

(in some contracts the escalator clause is tied in with

an annual improvement factor).24 Thus, labor leaders felt it essential that this defensive tool not be

weakened by changing its effective coverage.

Many users, however, spoke out in favor of the

expansion of population coverage. For example, the

Interagency Subcommittee on Economic Statistics

(headed by Gary L. Seevers) of the Council on

Economic Policy (then headed by George P. Shultz)

expressed “general agreement that the family definition in the CPI should be as broad as possible. The

Subcommittee encouraged the Bureau of Labor Statistics to take steps to enlarge the definition in the

revised index.” 25

Senator William Proxmire, at appropriations hearings, noted the value of both the current wage-earner

index and the comprehensive index with broader

coverage:

You know how very concerned some people are because their escalation clauses are tied to it [the present

index for urban wage earners and clerical workers].

Fifty million people in this country, including 5 million in organized labor, and a very large number of

government workers, and many others, have their

Population coverage in other countries

In other countries that publish consumer price indexes,

the most common variables used to determine whether

or not a family should be included in the weights of an

index are (1 ) location— urban or rural; (2 ) source of

income; (3) income level; and (4) family structure or

size. The definitions used range from total population to

quite narrow definitions, but in most industrial countries

coverage is broader than it is in the United States at

present.

Among the countries with complete coverage of all

consumers are the Federal Republic of^Germany, Belgium, Denmark, Norway, Sweden, Italy, and the Netherlands. The Federal Republic of Germany regularly publishes indexes for three subclasses in addition to its

overall index. Italy and the Netherlands publish two

indexes, one for the total population and one for nonfarm wage earners and salaried workers. Both countries

utilize a maximum income cutoff.

Although the coverage is not complete, it is very broad

scale in Austria, the United Kingdom, and Japan. Austria

covers all urban households. The United Kingdom conducts a continuous expenditure survey of all households

and publishes two indexes. One index excludes pensioners

and upper level income earners; this refers to about ninetenths of the population. The United Kingdom also publishes quarterly indexes for pensioner households. Japan

excludes only agricultural and single-person households.

Canada’s index includes urban, middle-income families

ranging in size from two to six persons, living in metropolitan areas with over 30,000 inhabitants. Middle income

is considered to be $4,000 to $12,000 as of the base year

1967.

France publishes a monthly index representing households headed by urban wage earners or non-supervisory

salaried employees. Their index excludes upper-income

salaried workers.

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compensation directly tied to this index. They know

it, they have faith in it, and they feel it relates to

their actual income. And I think you are very wise,

a n d the government is wise, in deciding that they

sh o u ld have this new index to make it more comprehensive, and cover 80 percent o f the people— and

have a much more representative index of the cost

o f living.26

At its April 24, 1974, meeting the BLS Business

Research Advisory Council passed a resolution in

“support [of] the efforts of the Commissioner to expand the population coverage of the revised Consumer Price Index,” and laid the ground for further

exploration of the desirability of maintaining more

than one index.27

A family of indexes

The determination of the target population for

the Consumer Price Index must recognize the major

uses of the index— the traditional as well as the

recent ones. In theory, one way of satisfying this

need is through a family of indexes. In this approach,

indexes would be developed that represent not only

the price experience of all consumer units, but also

the separate experience of particular subgroups of

the population, such as wage earners and clerical

workers, the aged, the poor, and the rural population. In practice, production of such a family of

indexes does not appear feasible as part of the 1977

revision program, given current constraints of time

and funds. BLS will be studying the prospects for

such indexes as time and resources become available.

A family of CPI indexes would be roughly analogous to the complex of unemployment data published

by the Bureau of Labor Statistics. There, in addition

to the total unemployment rate, numerous components are shown— for example, the unemployment

rates for household heads, for adult men, for women,

for Negroes and other races, for veterans. In order to

show this large variety of data, the total size of the

sample must be large enough that the figures for each

component are reliable. (The Current Population

Survey sample used to obtain the unemployment

data now includes 47,000 households monthly, compared with 15,000 retail outlets in the current CPI

sample.) Technically and operationally, a similar

program could be developed for the Consumer Price

Index; it is a matter of time and money, on the one

hand, and the usefulness of the additional output, on

the other.

Even the urban wage-earner and clerical-worker

segment of the population is not a completely homogeneous group; it is made up of many individuals,

each with an individual way of life. If a price index

were calculated for each of the individuals in this

group, some of these indexes would rise more rapidly

than others. So even under the current wage-earner

and clerical-worker index, there are some covered

individuals who gain when wages are escalated by

the Consumer Price Index, and some who lose, in

relation to their actual expenditures.28

New BLS plan— two indexes in 1977

On May 24, 1974, the Bureau of Labor Statistics

announced a decision to issue two indexes starting

in April 1977— an updated version of the current

Consumer Price Index for Urban Wage Earners and

Clerical Workers, and a broader Consumer Price

Index for All Urban Households.

Both indexes will incorporate improvements being developed as part of the revision program— for

instance, it is anticipated that both will be produced

with smaller measurement errors than the present

index, and that the full array of city and other detail

will be included in both indexes.29 In addition, an

evaluation plan will be built into the program.

Both indexes will be calculated and published for

at least 3 years, 1977-80. During that period, the

comparative movements of these two indexes and

their components will be studied. Results of these

studies will be discussed periodically with the Research Advisory Councils, as well as with Administration officials, the Congress, and professional economic and statistical groups. Finally, a determination

will be made as to whether one index is adequate, or

whether both and perhaps an index representing the

difference between them is needed, or whether a

whole family of indexes best meets the demands

placed upon the CPI index program.

The comprehensive index will cover all urban

households in Standard Metropolitan Statistical

Areas. Some of these include rural areas as well as

cities and suburbs. Nonfarm families living in these

rural areas within SMSA’s will be included, but the

index will exclude other rural families and the military and institutional population. The result will be

to increase the population coverage to about 80

percent of the total noninstitutional population (from

the past coverage of under 45 percent). Other

rural families make up about 18 percent of the total

and military personnel about 2 percent.

As in 1964, the change in population coverage

will change the average annual income of the index

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population. Rough estimates based on 1971 data

from the Current Population Survey indicate that

broadening the population coverage to all urban

households would lower the mean income of the

index population from about $10,500 to $10,100.

Although the other workers (professional and selfemployed, for instance) added to the covered population had 1971 average annual incomes higher than

the wage-earner and clerical-worker group, incomes

of the unemployed and those not in the labor force,

who will also be included in the index, were markedly

lower. The following tabulation shows total money

income in 1971 for consumer units (families and

unrelated individuals):

In In

1960-61 1971

C onsum er units headed by dollars dollars

Wage earners and clerical workers. $ 7,745 $10,539

Other salaried and self-employed

workers ...................................... 11,803 16,062

Unemployed persons..................... 5,544 7,544

Persons not in the labor force .... 3,760 5,116

All of above groups........... 7,396 10,064

The difference in 1971 average annual income between the urban wage-earner and clerical-worker

group and the all-urban-households group was $475

in 1971 dollars and $349 in 1960-61 dollars. As

noted above, a similar effect (of lowering the average family income of the population group covered

by the index) occurred in the previous revision, when

inclusion of single urban wage earners and clerical

workers lowered the income of the index population

group during 1960 and 1961 by $267 (in 1960-61

dollars).

To produce separate indexes for wage earners

and clerical workers and for all urban households

will increase the costs in terms of both the revision

program and the ongoing program after 1977. However, the increase should be relatively modest. The

two indexes will be of high quality and both are

planned to be as good as or better than the present

index.

No one today can tell which components of the

index— foods, fuels, services— are likely to be rising

most rapidly in the future. Thus in the 1960’s, food

price increases averaged 2.7 percent a year, fuels

and utilities 1.0 percent, and services 3.5 percent a

year. From 1972 to 1973, foods rose 20.1 percent,

fuels and utilities 11.5 percent, and services 6.2

percent. Nor can anyone say whether an all-urbanhouseholds index would rise more or less rapidly

than an index for wage earners and clerical workers

alone. Some students of the index speculate that

movement of the comprehensive index would closely

parallel that of the urban wage-earner index. But no

one can speak authoritatively on this until there is

empirical evidence.

The issue depends on more than just the weights

assigned to various items— it depends also on the

items priced and the kinds of outlets sampled. Some

people have argued, for example, that the prices of

lobster and imported caviar have risen much more

rapidly than have the prices of other, more prosaic

food items; others have noted that prices of some

very low-cost items, not now priced, have also risen

more than the average. This implies that prices of

goods purchased by groups not covered by the

present index— professional workers, the unemployed, retired persons— have risen more than average. But in fact we know very little about differences

in the movements of price indexes which might be

constructed for different population groups.

Other conceptual problems

A number of other basic conceptual issues remain,

on which there is also considerable controversy. Two

of the most vexatious are briefly described below.

Housing. The treatment of owner-occupied housing

presents a two-tiered problem. At the first level, a

decision must be made as to the concept under

which housing is to be priced. After that decision

is made, a second is required on the most accurate

and most efficient way of measuring prices and price

changes under that concept.

Up to the present time, the price of the house

itself has been used. For other index items, a loaf of

bread, for instance, purchase of the bread implies

consumption of the bread within that month.

The problem with housing— and with all durable

goods— is that the purchase of a house is not the

same as consumption. In effect, the index treatment

of housing has said that those individuals who purchase a house this year consume the total purchase price, as well as total financing costs, this year.

And those individuals living in previously purchased houses spend nothing on housing in this

year. In other words, the entire “consumption” of

the purchase price plus financing costs is attributed

to the year of purchase.

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Another way of looking at it is that what is really

being “consumed” by the owner living in a house

are housing services— that is. shelter, and accommodation for food preparation and consumption,

recreation, entertaining, laundry, and so forth. Obviously, the owner does not consume all these services in the year of purchase, but continues to consume them over the years of living in that house.

Some questions for BLS raised by the “indexing” proposal

The possibility of “indexing” the U.S. economy was

recently brought to public attention by Milton Friedman

of the University of Chicago. Under such a system— the

most notable current example is the one in use in Brazil

— when the CPI rises, so do not only salaries and wages,

but also the tax structure, rents, interest rates, and other

items. Thus the objective is to keep all or at least most

of the economy in step, by reference to the Consumer

Price Index.

The basic question is, of course, what effect would

indexing have on inflation and, in turn, on unemployment,

real economic growth, distribution of real income, and

other economic conditions. The debate on this basic

question is just getting under way.

Answers are lacking to some troublesome questions

that arise from the increasing use of the Consumer Price

Index as a basis for escalating income for an increasingly

large proportion of the population. What is the effect if

an inaccurate index is used, or if an index is used which

represents a portion of the population whose costs are

rising either slower or faster than the average for the

country as a whole? What is the impact of escalation by

a single index upon groups which experience changes in

living costs different from the average? Upon those groups

in the population whose incomes are not escalated? What

additional requirements would indexing put upon the

accuracy of price indexes?

Beyond the effect on income payments, what is the

impact of an incorrect or inappropriate index on statistics on real economic growth— especially real personal

consumption expenditures and real retail sales, which are

deflated in part or in whole by the CPI? If an inappropriate or inaccurate index is used as a deflator, measures

of real economic growth will be correspondingly off the

mark. What distortions appear in income distribution

data when the same CPI is used to deflate the incomes of

all classes?

Let us consider some of the possible economic implications of a situation in which the economy is indexed

and there is only one CPI, with coverage limited to a

subgroup of the population. Let us assume that this price

index rises more rapidly than an index which covered the

other segments of the population. Under this assumption,

these additional groups would be getting a CPI adjustment exceeding that which they would receive if their

incomes were escalated by their own index. This would

give them a greater than warranted increase in money

income and, in this way, the measure of price change

could become a source of inflation in itself.

On the other hand, suppose that the single index

covered all consumer units, and suppose that prices for

an important subgroup of the population were rising

more rapidly than the broadly based index. Then tying

wage escalation agreements for this subgroup to the allconsumers CPI would result in income payments for this

group that are smaller than would be the case if their

incomes were moved by their own index. This loss would

be offset, in the aggregate, by the fact that the other

groups would receive larger income increases than those

which would be triggered by their own CPI, though this

would provide little solace for the groups that lose. The

loss by one group would be offset by the gains of the

other, so use of a broadly based measure would result in

the appropriate aggregate income adjustment— though it

would also involve a shift in income shares. These hypothetical examples do not, of course, take into account the

dynamic effects of the indexing which add to the complexities.

Thus far we have assumed that all income payments

are escalated by the CPI. But what if some are and

some are not, as is, in fact, the situation in the U.S.

today. And, especially, how would those whose income

payments are not pegged to the CPI fare in an economy

where most income payments are pegged. The unescalated groups may very well be starting off with a handicap in obtaining their income share— unlike the others,

they would not have any automatic increases nor a floor

to their income payment increases.

Events may lead us to a statistical program in which

indexes are developed that represent not only the price

experience of all consumer units, but also the separate

experience of many subgroups of the population. The

existence of multiple indexes would create uncertainties

in the minds of many groups regarding the particular

index to which it would be most appropriate to tie their

own income payments. Expansion in the number of CPI

indexes would, however, only complicate problems that

already exist because of the availability of city and commodity indexes. In a recent contract, the wages of New

York transit workers were tied to the CPI for New

York-Northeastern New Jersey area, rather than the

national index. The Food Stamp Allotment program is

escalated on the basis of average price data from the

food-at-home component series of the CPI and the children’s lunch program by the food-away-from-home component. A degree of familiarity with the statistical

methods used in compiling the CPI far above what exists

today will be required for effective use of a multiple index

approach.

A single all-consumer-units index would probably be

easiest to administer, but it will be hard to convince

groups who think their cost of living will rise more

rapidly than the average that this is the best route to take.

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(In the same way, a renter consumes housing services during the time of residence in a rented house

or apartment.)

If a decision is made to price the flow of housing

services, the problem will be to develop a technique

for estimating the price of owner-occupied housing.

There are two methods which can be used. The first

is to use a rental equivalence technique— in effect,

measure what you would charge if you rented the

house to yourself in an assumed arms-length transaction. The second is to establish a user-cost function

for the provision of housing services— that is, to

measure the major cost components that an owner

incurs in providing himself housing. These would include mortgage and equity financing costs, maintenance costs, taxes, and the variety of other expenses

that go into providing housing services.

Both approaches present considerable data problems. The rental equivalence approach requires the

development of a sample of rental units which can

provide an adequate measure of the changes in

owner-occupied housing costs. Another aspect that

must be considered is the increasing share of owneroccupied apartments and townhouses in condominium developments. It is difficult to construct a good

sample for this purpose since housing units which are

typically rented differ in various ways from those

which are normally owner-occupied. For example,

owner-occupied houses are often located in areas

where there is very little rental housing such as,

for instance, suburban developments.

Implementation of this pricing technique does not

require that the average owner-occupied house be

equal to the average rental house, but only that there

is enough overlap to pick from the sample of rental

housing the houses which are similar in their most

important aspects to those that are owner-occupied.

We must determine whether there is sufficient overlap between the distribution of rental single-family

housing and the distribution of owner-occupied

housing.

There are measurement problems associated with

the user cost approach also. First of all, this approach requires a source of house prices. The current CPI obtains price data from the Federal Housing Administration on FHA-insured houses. But,

these houses represent a small and unrepresentative

segment of the market. Similarly, the user cost approach must take into account in some way the

capital gains which arise from appreciating home

values. In addition, since the same houses are not

sold in successive periods, it would be necessary to

develop methods for factoring quality change out

of the house price data collected.

Some of these same problems of data collection

also exist with the current method of pricing housing

costs. The problem of quality change is a particularly

difficult one. Also, as pointed out above, data from

the Federal Housing Authority on prices for new and

existing housing purchased under FHA commitment

have serious limitations for use in the Consumer Price

Index. These FHA-guaranteed purchases represented

only about 6 percent of the home purchase market

in 1973. In addition, there are considerable differences between the typical house financed under the

FHA program and those financed under conventional mortgages and thus the FHA sample may not

be representative of all houses sold.

In the current method, prices of houses, classified

by age and size, are converted to price per square

foot. This is reflected in the index by a 3-month moving average, to eliminate erratic fluctuations in each

month’s data.

Investigations have been made into the availability

of data from other sources, such as lending institutions and real estate associations, as well as the census series on new housing prices. They have not,

as yet, uncovered data that would be useful for the

CPI.

Quality change. The Bureau of Labor Statistics is

also investigating new methods to improve the hanComparison of costs and income effect

To put the costs o f the revision program into perspective as they relate to the amounts affected by

changes in the Consumer Price Index, let us assume

that all escalators we know of today had been in effect at the beginning o f 1974, when the index had

risen by some 10 percent over the early part of 1973.

Under that assumption, income payments would have

been increased by at least $10 billion.

If that figure is used, the cost of preparing the

monthly Consumer Price Index, including the authorized cost of updating and revising the index decennially, works out to something like 70 cents for each

$1,000 increase in payments as a result of escalation

alone. If two indexes (the present one plus the more

comprehensive one covering all urban households)

had been calculated during this period, the cost per

$1,000 increase in escalated income payments would

have been between 85 and 90 cents. Of course, if inflation rates are lower, as has usually been the case,

these cost figures would be higher. However, they do

not take into acount the important uses of the CPI

as a measure of inflation.

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dling of quality change. Quality change is one of the

most difficult problems faced in compiling a price

index, since both products and consumption patterns

are constantly changing. An example familiar to

many consumers is that of passenger automobiles,

where— with each model change— the Bureau faces

the problem of separating out the actual price rise

from the changes in quality, some of the latter

necessitated by statute (such as emission control

and safety belt legislation)

Frequently a model currently being priced must

be replaced, either because it is discontinued or

because consumption patterns have changed to such

an extent that the model no longer accurately reflects

consumer spending patterns. The value of the quality

change in the new model should not be reflected as

a price change, since the goal of the index is to

measure the cost to consumers of purchasing a constant market basket of goods and services of constant quality through time. Ideally, estimates would

be obtained for the value of each change in quality

that occurred as a result of a change in the model or

item priced; and this estimate would be based on

the consumers’ valuation of a change in quality,

rather than that of the producer.

At present, most changes in quality are handled

in one of two ways: 1

1. The quality change is deemed to be minor, and

any price change that occurred simultaneously is

reflected in the index just as if there were no quality

change— the prices are compared directly; or

2. The quality change is judged to be significant,

and the simultaneous price change is assumed to be

an accurate measure of the value of the quality

change— no price change is reflected in the index.

Since in most cases a large number of different

models are priced for each item (such as a console

color television set), and since sales of these models

are not discontinued at the same time in various

stores, the problem created by the above procedure

can easily be overemphasized. However, it is true

that many times price changes do occur simultaneously with model changes and that in order to

have an accurate measure of price change (holding

quality constant), an estimate of the consumers’

valuation of the quality change is needed to separate

out the price and quality components of the price

change. The problem is even more difficult when

quality changes and there is no price change. For

some important items in the index, producers’ cost

estimates of quality changes are being used for this

purpose. Although this eliminates the “all or nothing” nature of the usual procedure, there is still no

reason to assume that the producers’ estimates will

reflect accurately the consumers’ valuation of the

changes.

The Stigler Committee report recommended that

the Bureau investigate another approach to quality

measurement, one which does measure the consumers’ valuation. For certain items such as houses,

cars, and major household appliances, there are at

any time a wide variety of models available, models

which possess a large number of different characteristics. From cross-sections of data on retail prices

and characteristics of models, taken over a period

of several years, it is possible to estimate the consumers’ marginal valuation of quality change— that

is, what the consumer is willing to pay for the addition of a particular characteristic, such as a meatkeeper in a refrigerator. This is done using standard

statistical techniques. These marginal prices for the

characteristics can then be used as estimates in the

current index of the value of changes in quality.

Research on using this approach to measuring

quality change is currently underway in the Bureau.

The technique has been applied to data for rental

housing, automobiles, and refrigerator-freezers. However, results are preliminary and, as yet, new price

indexes have not been computed using the implicit

prices yielded from the research.

There is great interest in whether quality change

results in any bias in the Consumer Price Index.

A recent article in the Monthly Labor Review

pointed out:

Many econom ists believe that quality changes in

goods and services are not adequately taken into

account in the preparation o f the Consumer Price

Index ( c p i ) . As a result, they believe, the cpi has a

systematic and persistent upward drift w hich makes

the index a questionable indicator of the course o f

inflationary price movements.

To what extent is the belief that price indexes are

biased upward borne out by existing evidence? N o

assessment of the quality error in the c p i as a whole

has yet been made, but a number o f investigations

have produced estimates o f quality error in individual

index components. The present article is a survey of

existing studies, which present contradictory evidence.

Some investigators found upward bias, but others

reported that quality error might be negative— that is,

when the b l s failed to correct adequately for quality

changes, it resulted in a price index that rose too

slowly, rather than too rapidly.

After reviewing key studies in the field, the conclusion reached was that there is no conclusive

evidence which indicates a particular bias in the

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Consumer Price Index due to quality change: . .

we have not proved that price indexes are biased

either upward or downward; rather, they establish

only that the proposition that indexes are systematically upward-biased is not conclusively confirmed by

the available evidence.” 30

Future updating and revision programs

Perhaps even more significant than the immediate

problems of this current index revision is the question of long-run improvement in the revision process.

BLS records show that the 1950-52 revision took 3

years and cost $4 million; the 1960-64 revision took

5 years and cost $6.5 million; and present estimates

are that the current revision will take 8 years and

cost more than $40 million (after adding the cost

of the second index). The endless delays in issuing

the results and the ever-rising costs suggest that a

better method of updating the CPI and making revisions must be found.

Over the past decade, statistical agencies over the

world have been shifting away from large-scale de1 F or a m ore com prehensive history o f the C onsum er

P rice Index, along w ith a description o f the 1964 revision

and detailed description o f techniques, see The Consum er

Price Index: H istory and Techniques, B ulletin 1517 (B ureau

o f L abor Statistics, 1 9 6 6 ). See also Prices, Escalation, and

Econom ic Stability (B ureau o f Labor Statistics, 19 7 1 ).

2 See, for exam ple, studies o f fam ily expenditures covering the years 1 8 8 8 -9 0 in the Annual R eport of the C om ­

m issioner of L abor in 1890 and 1891, and the im posing c o llection o f w h olesale price data included in the “A ldrich

R eports” by the Senate C om m ittee on F inance in 1892 and

1893.

* T he term “cost o f livin g” w as used to describe the B ureau’s index until its nam e w as changed fo llo w in g controversy in the W orld W ar II period over the ind ex’s validity

as a m easure o f cost o f living. It has alw ays been m erely a

m easure o f changes in prices for goods and services purchased for fam ily living.

‘ “Labor and the W ar: A djustm ent o f Shipbuilding D isputes on the Pacific C oast,” M onthly R eview of the U.S. Bureau of Labor Statistics, M arch 1918, pp. 6 7 -7 6 .

0 C ost of living in the United States, B ulletin 357 (B ureau

o f L abor Statistics, 1 9 2 4 ).

•C h a n g es in C ost o f L ivin g F rom Septem ber 15 to N o ­

vem ber 15, 1940,” M onthly Labor R eview , January 1941,

p. 146.

7 “R evision o f Index o f C ost o f G oods Purchased by W age

E arners and L ow er Salaried W orkers,” M onthly Labor R eview, Septem ber 1935, pp. 8 1 9 -3 7 .

8 R etail Prices of Food, 1923-36, B ulletin 635 (B ureau o f

Labor Statistics, 1 9 3 8 ).

cennial programs to smaller decennial or quinquennial programs supplemented by annual sample surveys. The President’s budget for fiscal year 1975

includes funds to plan such a shift in the decennial

revision of the Consumer Price Index. An ongoing

quarterly Consumer Expenditure Survey would provide more timeliness and greater flexibility, at

roughly the same cost over a 10-year period. The

ongoing Consumer Expenditure Survey could also

have the advantage that numerous analytical studies

could be made on a current basis, including prompt

information of the effect of the rise in food and fuel

prices upon spending patterns. Further, a continuing

consumer expenditure survey could, after a break-in

period, be tabulated rapidly, so that shifts in spending patterns, market baskets, and retail stores samples could be analyzed and information could be

provided on the need for more frequent updating

of the Consumer Price Index. New market baskets

and new retail store samples could be phased in

more often, say once in 5 years rather than once in

10, but this would not be a necessary part of the new

approach. □

* Changes in C ost of Living in Large C ities in the United

States, 1913-41, B ulletin 699 (B ureau o f Labor Statistics,

1 9 4 1 ).

10 “B L S C ost o f L iving Index in W artim e,” M onthly Labor

R eview , July 1943, pp. 8 2 -9 5 , and Consum ers’ Prices in

the U nited States, 1942—48, B ulletin 966 (B ureau o f Labor

Statistics, 1 9 4 9 ).

11 “R evision o f the C onsum ers’ Price In dex,” M onthly

Labor R eview , July 1950, pp. 1 2 9 -3 2 .

12 Interim A djustm ent of Consum ers’ Price Index, B ulletin

1039 (B ureau o f Labor Statistics, 1 9 5 2 ).

“ Consum er Prices in the United States, Price Trends and

Indexes, 1953-58, B ulletin 1256 (B ureau o f L abor Statistics,

1 9 5 9 ).

14 A t the behest o f certain labor groups, the old index was

continued for an additional 6 m onths. T his action prom pted

som e parts o f the professional com m unity to charge that

political judgm ent w as being substituted for scientific decisionm aking in the statistical field. See, for exam ple, the

report o f the T ech nical C om m ittee o f the A m erican Statistical A ssociation , appointed by A S A P resident Sim on

K uznets in the sum m er o f 1949 to advise the Bureau o f

L abor Statistics on Price Index N um ber R evisions. T he

com m ittee, chaired by Bruce D . M udgett, held its last m eeting June 30, 1953. O ther m em bers w ere D u d ley C ow den,

R eavis C ox, and S olom on Fabricant.

T he report stated:

A governm ental bureau can attract scientific personnel

o f the highest com petence on ly if it creates w orking co n ­

ditions that assure the unfettered pursuit o f their w ork.

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B y the sam e token any restriction upon their freedom

arising from the pressures o f special interests w ill destroy

the very conditions that attract m en o f com petence, and

any bureau yielding to such pressures m ay lose not only

its qualified w orkers but also its reputation for objectivity

and for the m aintenance o f high standards o f scientific

w orkm anship. Should not the association alw ays stand

ready to support any bureau resisting these pressures?

T hese thoughts were aroused by an incident w hich took

place w hen it w as announced in January that the revised

C onsum ers’ Price Index w ould displace the old index, and

that the old index w ould be discontinued. A t the urging

o f a num ber o f groups w hich have collective bargaining

agreem ents w ith w age escalator clauses based on the old

index, the P resident asked the D epartm ent o f L abor to

resum e com pilation and publication o f the old index

through June 30 o f 1953, and the D epartm ent acceded

to this request. . . . T hat this m atter could constitute a

case o f dangerous pressure w as recognized by Secretary

[o f Labor] D urkin, w ho cautioned that no new contracts

should be based upon the old index, and by C om m issioner

[o f L abor Statistics] C lague, w h o urged that all users

consider the revival o f the old index as purely tem porary.

It w ill be recognized also by technical w orkers in this

field, and it becom es their duty to support these w arnings

and to call attention to the possibility that this kind o f

step m a y b e th e first a lo n g th e d a n g er o u s road to w a rd

partisan control o f econ om ic m easurem ent.

15 A side from generally higher incom e levels for occupations w ithin the scope o f the index, an incom e lim itation on

fam ilies included (at a level o f $ 1 0 ,000 after taxes in 1950)

w as discarded because o f the higher incom e per fam ily unit,

resulting from the increased num ber o f fam ilies w ith m ore

than one worker, and greater precision in the occupational

classification o f the survey.

,a Both the 1 9 6 1 -6 2 C onsum er E xpenditure Survey and

subsequent data collection are based on a 56-area probability sam ple, o f w h ich 18 are self-representing areas. T he

balance are areas selected by a stratified controlled-selection

probability procedure to represent the balance o f urban

areas, classified by region and size.

17 A com m ittee appointed in 1959 by the N ation al Bureau

o f E conom ic R esearch, under contract w ith the Office o f

Statistical Standards o f the Bureau o f the Budget, and

chaired by P rofessor G eorge Stigler o f the U niversity o f

C hicago. See The Price Statistics of the Federal Government: Review, Appraisal, and Recommendations, H earing

B efore the Subcom m ittee on E conom ic Statistics o f the

Joint E conom ic C om m ittee, 87th C on g., 1st sess., 1961.

18 Hearings on Government Price Statistics, Pt. I. See also

Zvi G riliches, “H ed onic P rice Indexes for A u tom obiles: A n

E conom etric A n alysis o f Q uality C hange,” in that volum e.

18 H arold S. T aylor, “W hat Price D ata? D istortions

N o ted ,” The New York Times, A ug. 21, 1966.

80 G eoffrey H . M oore and M axine Stewart, “N ew developm ents in labor statistics,” Monthly Labor Review, M arch

1972, pp. 3 -1 3 .

21 Statem ent by Julius Shiskin, C om m issioner o f Labor

Statistics, before the Senate Subcom m ittee on P roduction

and Stabilization, C om m ittee on Banking, H ousin g and

U rban Affairs, Apr. 23, 1974.

22 Statem ent o f Leonard W oodcock, President, U nited

A u tom obile, A erospace and A gricultural Im plem ent W orkers o f A m erica (U A W ), before the Subcom m ittee on Priorities and E conom y in G overnm ent o f the Joint E conom ic

C om m ittee, A pr. 5, 1974.

23 “T he N eed to Preserve the Current C overage o f the

C onsum er Price Index for U rban W age Earners and C lerical W orkers,” statem ent by L azare Teper, D irector o f R esearch, International L adies’ G arm ent W orkers’ U n ion ,

A F L -C IO , to the Senate Subcom m ittee on Production and

Stabilization o f the C om m ittee on B anking, H ousing and

U rban Affairs, Apr. 23, 1974.

28 For other discussions o f the use o f the escalator clause in

collective bargaining, see H enry L ow enstern, “Adjusting

w ages to living costs: a historical note,” and Jerom e M .

Staller and Loren M. Solnick, “Effect o f escalators on

w ages in contracts expiring in 1974,” pp. 21 and 27, in this

issue.

25 Report o f m eeting o f Sept. 10, 1973. T he subcom m ittee

is chaired by G ary L. Seevers, m em ber o f the C ouncil o f

E conom ic A dvisers, and includes representatives o f the

Treasury D epartm ent, the Office o f M anagem ent and

Budget, the Federal R eserve Board, the C ost o f L iving C ouncil, and the D epartm ents o f A griculture, C om m erce, and

Labor.

“ Statem ent by Sen. W illiam Proxm ire (D ., W is.) at

H earings o f Senate A ppropriations C om m ittee, A pr. 25,

1974.

27 M inutes o f the Business R esearch A dvisory C ouncil,

A pr. 24, 1974.

28 M ost escalator clauses are tied to the national C P I, but

som e attem pt to m ake the relationship m ore directly applicable to the w orkers’ ow n experience by using the index

for the area in w h ich they are located or another relevant

“city.” F or exam ple, in the spring o f 1974, a new 2-year

contract betw een the city o f Salem , O reg., and the Salem

P olice A ssociation provided, in the contract’s second year,

a 5- to 9-percent increase depending on the increase in the

January 1974 C P I for the G reater Portland area. (T h e

Portland, O reg.-W ash. SM SA is one o f the areas priced

each 3 m onth. See table 27, p. 110.)

28 Since there w ill be tw o index populations (on e o f urban

w age earners and clerical workers, and one o f all urban

h o u seh o ld s), item s w ill be selected to be representative o f

each o f these populations. Selected item s m ay vary from

region to region and betw een index populations, but probability sam pling procedures w ill be used to m axim ize the

overlap for efficiency in collection . W ithin selected item s,

in general, the goal is to use an objective probability process

for the selection o f goods “specified in detail,” including

proper representation o f both big-volum e and other goods.

“ Jack E. T riplett, “D eterm ining the effects o f quality

change on the C P I,” Monthly Labor Review. M ay 1971,

pp. 2 7 -3 8 .

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Measuring

changes

in industrial

prices

“ The reliability of an index number obviously

depends upon the judgment and the accuracy

with which the original price quotations were

collected. This . . . work is not only fundamental,

it is also laborious, expensive, and perplexing

beyond any other part of the whole investigation. . . . To judge from the literature about index

numbers, one would think that the difficult and

important problems concern methods of weighting

and averaging. But those who are practically

concerned with the whole process of making an

index number from start to finish rate this . . .

work lightly in comparison with the . . . work of

getting the original data.”

Thus Wesley C . Mitchell in his classic The Making

and Using of Index Numbers,1 originally published

in 1915 and with minor modifications republished

in 1921, prefaced his discussion of price collection

problems.

C o n cisely a n d in cisiv ely M itc h e ll lim n e d all of 

th e m a jo r a n d m a n y of th e m in o r p ro b le m s: th e 

m u ltip lic ity of p rices fo r a n y im p o r ta n t co m m o d ­

ity ; th e d ifficulties in se lec tin g a re p re s e n ta tiv e 

sa m p le ; th e n ee d fo r q u a lity a d ju s tm e n ts ; th e 

n eed to “ g u a rd a g a in s t th e p itfa lls of c a sh d isc o u n ts, p re m iu m s, re b a te s, d efe rred p a y m e n ts , a n d 

allo w an ces of all s o rts ” ;2 an d m a in te n a n c e of 

c o m p a ra b ility . M itc h e ll w ro te as a p ra c tic a l in d e x 

m a k e r; h e w as th e p rin c ip a l a rc h ite c t of th e 

W h o lesa le P ric e In d e x (w p i) in th e g en e ral fo rm 

i t h a s since d ev elo p ed .

Publication of The Behavior of Industrial Prices 3

(reviewed in the October 1970 Monthly Labor

Review) in substantial measure qualifies its

J o se p h A . C lo r ety , Jr., is C h ief of th e D iv isio n - of

In d u str ia l P rices an d P rice In d e x es, B u reau of L abor

S ta tistic s.

New study adds wealth of data 

on transaction prices 

for use in continuing BLS effort 

to improve wholesale indexes

JOSEPH A. CLORETY, JR.

a u th o rs , G eorge J . S tig le r a n d J a m e s K . K in d a h l, 

as p ra c tic a l in d e x m a k e rs. T h e m in o r c a v e a t rises 

fro m th e im p o r ta n t d is tin c tio n b e tw e e n p ro b le m s 

in v o lv e d in a o n e-tim e s tu d y a n d th o se w h ich 

p la g u e th e m a k e rs of a m o n th ly , c o n tin u in g in d ex . 

M o re im p o r ta n t, S tig le r a n d K in d a h l no d o u b t 

w ill s tim u la te g o v e rn m e n ta l, p ro fessio n al, a n d 

p u b lic in te r e s t in th e m a jo r p ro b le m of tra n s a c tio n 

p rices. M o s t im p o rta n t, th e w e a lth of d a ta 

p re se n te d p ro v id e s a m in e of no m e a n v a lu e fo r 

re se a rc h e rs in ac ad e m ic , p riv a te , a n d p u b lic 

circles. In d e e d , th e B u r e a u ’s c o m m o d ity a n a ly s ts 

resp o n sib le fo r c o m m o d ities co v e red b y S tig le r 

a n d K in d a h l are s tu d y in g th e d a ta in d e ta il 

p rim a rily to in itia te c o rre c tiv e a c tio n w h ere 

in d ic a te d , feasib le, and n ec essary .

I n so m e in sta n c e s, s u b s ta n tia l p ro g re ss in obta in in g a c tu a l tra n s a c tio n p rices h a s b ee n m a d e 

since 1966— th e la s t y e a r co v e red b y S tig le r a n d 

K in d a h l. T h is reflects th e v ir tu a lly d a ily a tte n tio n 

to th e p ro b lem s of p rice co llec tio n o u tlin e d b y 

M itc h e ll, w h ich la rg e ly c h a ra c te riz e s n o t o n ly th e 

c u rre n t bls sta ff b u t th e ir p red ecesso rs since 

M itc h e ll’s d a y . A lth o u g h p e rso n a l q u a litie s co n ­

trib u te , th e B u r e a u ’s successes a n d failu re s are 

la rg e ly a fu n c tio n of th e reso u rce s c o m m itte d to 

a g iv e n in d ex . F o r ex am p le, p rio r to W o rld W a r I I 

th e e n tire sta ff p ro d u c in g th e w p i w ere h o u se d 

re a so n a b ly c o m fo rta b ly in one m e d iu m la rg e office, 

w ith a v e ry sm a ll cubicle fo r th e ir chief.

The first major challenge

C o n sid erin g th e reso u rce s a v a ila b le , th e q u a lity 

of th e ir w o rk w as re m a rk a b le . T h is w as f o r tu n a te 

since th e firs t m a jo r ch a lle n g e to th e r e lia b ility of 

th e w p i as a m e a su re of p rice ch a n g e s in p rim a ry 

m a rk e ts cam e in th e la te 1930’s. T h a t c o n tro v e rs y 

ro se fro m a series of stu d ie s b y G a rd in e r C . M e a n s

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d ev e lo p in g h is th e o ry of a d m in iste re d p rices, w h ich 

relied h e a v ily on use of in d iv id u a l c o m m o d ity d a ta 

fro m th e w p i. N o t o n ly th e th e o ry b u t th e v a lid ­

ity a n d re lia b ility of th e d a ta w ere c h a lle n g ed 

sh a rp ly . T h e gist of th e c h a rg e w as t h a t th e in d e x 

d a ta failed d ism ally to re flec t e ith e r th e fre q u e n c y 

or th e m a g n itu d e of a c tu a l ch an g es in tr a n s a c ­

tio n prices. B o th th e m a jo r c o n tro v e rs y a n d d e ta ils 

of th e a c c o m p an y in g ch a rg es a re b e y o n d th e sco p e 

of th is article, b u t re p re s e n ta tiv e article s are c ite d 

in th e ac c o m p a n y in g b o x fo r th o se in te re ste d .

B e ca u se th e M e a n s a p p ro a c h to use of w p i

d a ta w as c e n tra l to th e c h a p te r on price s tr u c tu re 

in th e N a tio n a l R e so u rce s C o m m itte e ’s The

Structure of the American Economy,4 S a u l N elso n 

w as co m m issio n ed to in v e stig a te th e v a lid ity of 

th e w p i for th e p a rtic u la r use m a d e of it. S tig ler 

a n d K in d a h l n o te h is conclusion, w hich s u b s ta n tia te d th e o v erall v a lid ity of th e w pi d a ta for “ th e 

s ta te m e n t a n d in te r p re ta tio n of su ch d iffe re n t 

[rigid a n d flexible] ty p e s of price b e h a v io r.” 5 I n 

his p re s e n ta tio n of th e su p p o rtin g ev id en ce (w hich 

p ro b a b ly w as a b s tra c te d fro m a c o n sid e ra b ly 

g re a te r b o d y of d a ta ), N elso n c o m m e n te d on tw o 

p o in ts of som e rele v a n c e to c u rre n t p ro b lem s. H e 

n o te d t h a t th e R o b in s o n -P a tm a n A ct h a d b een in 

effect o n ly p a r t of th e p e rio d co v ered a n d t h a t 

p re su m a b ly i t w o u ld in h ib it c e rta in fo rm s of se c re t 

concessions. (In te re stin g ly , m u c h c u r r e n t d isc u ssion assu m es t h a t t h a t s ta tu te in h ib its re p o rtin g 

to th e B u re a u a c tu a l concessions m a d e. T h is 

im p lies a g en e ral w illingness to v io la te th e law , 

w h ich seem s u n d u ly cy n ical. A ctu a lly , d a ta re ­

p o rte d to th e B u re a u b y in d iv id u a l b u sin esses— or 

in d iv id u a l h o u se h o ld s for th e m a tte r — a re tre a te d 

as a b so lu te ly c o n fid e n tia l b y th e B u re a u .)

N elso n w as also co n c ern ed b y th e s u b s ta n tia l 

n u m b e r of c o m m o d ities for w h ich th e re p o rtin g 

so u rce w as a tra d e p u b lic a tio n . T h e n as now , of 

course, a d istin c tio n is n ec essary b e tw e e n th o se 

w hich r e p o r t p rices on an o rg an ized ex ch an g e 

(w hich o b v io u sly are c le a rly a c tu a l tra n s a c tio n 

prices) a n d c e rta in o th e rs w ith v a rio u s im p e rfec tio n s. T a b le 1 su m m a riz e s differences b e tw e e n 

re p o rtin g so u rces fo r th e w pi in F e b r u a r y 1937,

w hich N elso n u sed , a n d J a n u a r y 1970, fro m a 

c o m p ila tio n m a d e fo r p u rp o ses of th is co m p ariso n . 

D a ta fo r J a n u a r y 1970 are also a v a ila b le b y 

n u m b e r of price series, w hich th ro w fu rth e r lig h t 

on co v erag e b y re p o rtin g source.

Selected readings

Gardiner C. Means, “ Notes on Inflexible Prices,”

American Economic Review, March 1936, pp. 28-35.

Price Behavior and Business Policy (Washington,

Temporary National Economic Committee, 1940),

Monograph 1, appendix 1, pp. 165-168.

The Structure of Industry (Washington, Temporary

National Economic Committee, 1941), Monograph

27.

Jules Backman, “ Price Inflexibility and Changes

in Production,” American Economic Review, September 1939, pp. 480-486.

John K. Galbraith, “ Monopoly Power and Price

Rigidities,” Quarterly Journal of Economics, May

1936, pp. 456-475.

Frederick C. Mills, “ Price D ata and Problems of

Price Research,” Econometrica, October 1936, pp.

289-309.

Don D. Humphrey, “ The Nature and Meaning of

Rigid Prices, 1890—1933,” Journal of Political Economy, February-December 1937, pp. 651-661; of

particular interest for use of an earlier study by

Frederick C. Mills covering the period 1890-1925.

Willard L. Thorp, “ Price Theories and Market

Realities,” Papers and Proceedings of the American

Economic Association, March 1936, pp. 15-22.

Reopening the controversy

The old controversy flared again in the late

1950’s, when Means presented relatively current

w pi data to the Senate Antitrust Subcommittee of 

the Judiciary Committee to sustain his contention

that the industries characterized by administered

prices caused the bulk of the increase in the w pi

during the last half of the 1950’s. To the considerable extent to which the validity and reliability of the w pi for this purpose were the center

of controversy, John M. Blair— then chief economist for the subcommittee— and Stigler led the

debate.6

Prior to the exchange of views cited above,

the Price Statistics Review Committee (popularly

referred to as the Stigler Committee, reflecting

his chairmanship) functioned during 1959 and

1960. Both their summary recommendation and

a more fully stated version relative to use of

buyers’ prices deserve full quotation in that order:

The individual product prices should, where feasible,

be collected from buyers (not from sellers, as at

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present) to get more accurate information on actual

transaction prices.7

We recommend that a major shift be made to the

collection of buyers’ prices. Large and continuous

buyers of manufactures should be able to supply prices

which truly represent the effective terms on which

transactions are made. We do not believe that this

shift to buyers’ prices will be simple or free of new

difficulties, but it is the most promising source of

comprehensive, continuous, and reliable price

quotations.

Where buyers’ prices are not available, we recommend extensive use of unit values, at least as benchmarks to which the monthly prices are adjusted.

U nit values are inferior to specification transaction

prices, but when unit values are calculated for fairly

homogeneous commodities, they are more realistic

than quoted prices in a large number of industrial

markets.8

T h e b rief te x t of th e r e p o r t p rec ed in g th e m o re 

d e ta ile d re c o m m e n d a tio n m a k es clea r th a t th e se 

re c o m m e n d a tio n s r e s t b a sica lly on tw o sta ff 

p a p e rs a tta c h e d to th e r e p o r t b u t specifically 

p re se n te d as th e re sp o n sib ility of th e ir in d iv id u a l 

a u th o rs . H a r r y E . M c A lliste r (w ho also se rv e d as 

s e c re ta ry of th e S tig le r C o m m itte e ) e v a lu a te d th e 

w p i in te rm s of in te rn a l d a ta , fre q u e n c y of p rice 

c h a n g e in r e la tio n to n u m b e r of re p o rte rs , co m ­

p a riso n of w p i d a ta b a se d o n se lle rs’ p rices w ith 

th o se w h ich h e co llected fro m a sa m p le of la rg e 

b u y e rs, a n d a c o m p a riso n w ith C en su s u n it v a lu e s .9 

J o h n A. F lu e c k ’s e v a lu a tio n re ste d on e n u m e ra tin g 

som e of th e m a in re a so n s th a t a c tu a l tra n s a c tio n 

p rices m a y differ fro m lis t prices (b u ttre s s e d b y 

q u o ta tio n s fro m v a rio u s p u b lic a tio n s), a n d a 

c o m p a riso n of w p i p rices w ith g o v e rn m e n t b id 

p rice s.10

C o n sid e rin g th e d u b io u s q u a lity (for e v a lu a tin g 

a p rice index) of u n it v a lu e d a ta d u e to th e p ro d u c t 

m ix p ro b le m a n d of g o v e rn m e n t b id p rices b e ­

ca u se of m a rk e d differences in th e m a rk e t le v e ra g e

of a g o v e rn m e n t a n d of th e b u y e r s ’ p rice d a ta 

b ec au se of a n o b v io u s b ia s as w ell as o th e r m e a su re ­

m e n t p ro b le m s s h a re d b y se lle rs’ a n d b u y e r s ’ 

p ric e s, it is r e g re tta b le t h a t a la w y e r w as n o t a 

m e m b e r of th e c o m m itte e . E c o n o m ists a n d s ta tis ­

tic ia n s p e rh a p s sh o u ld b e fo rg iv e n fo r fo rg e ttin g 

u p o n w h o m th e b u rd e n of p ro o f re s ts .

The BLS response

B e t h a t as it m a y , th e B u r e a u ’s official re sp o n se 

to th e re c o m m e n d a tio n s , as g iv e n b y C o m m issio n e r E w a n C lag u e, w as to “ p lace e m p h a sis firs t 

o n m o re in te n siv e effo rts to o b ta in a c tu a l tr a n s a c ­

tio n s p rice s fro m sellers, o b ta in in g p rice s fro m 

b u y e rs o n ly w h ere a b s o lu te ly n e c e ssa ry , b e c a u se 

of th e g r e a t d ifficu lty a n d ex p en se in v o lv e d in th e 

la tte r m e th o d ” ; 11 a n d to q u e s tio n th e so u n d n e ss of 

th e re c o m m e n d e d u se of u n it v a lu e d a t a in th e 

w p i “ b ec au se te s ts w h ich w e h a v e m a d e sh o w th a t 

re a l p rice ch a n g es c a n n o t b e s e p a ra te d fro m 

ch a n g es in p ro d u c t m ix . . .” 12 H is p re p a re d s ta te ­

m e n t d o c u m e n ts b o th p o sitio n s in c o n sid e ra b le 

d e ta il.13

L a z a re T e p e r, a n o u ts ta n d in g e x p e rt o n p rice 

in d ex es, te stifie d a t th e sa m e h e a rin g s th a t “ th e 

su g g e stio n t h a t p rice q u o ta tio n s b e co llected fro m 

b u y e rs does n o t seem re a listic . I t c e rta in ly w o u ld 

re p re s e n t a v e ry c o stly p ro c e d u re w h ich m a y n o t 

n ec essarily y ie ld w h a t is ex p e cted of it. . . [as to 

use of u n it values] T h e C o m m itte e is c lea rly o n a 

w ro n g p a t h .” 14 H is full s ta te m e n t p re se n ts h is 

rea so n s. A fo o tn o te , in c id e n ta lly , illu m in a te s p itfalls in v o lv e d in u se of g o v e rn m e n t b id p rices to 

e v a lu a te th e w p i .15

T h e S u b c o m m itte e ’s r e p o r t p ith ily su m m a riz e s 

th e g en e ra l re a c tio n to th e re c o m m e n d a tio n t h a t 

th e B u re a u m o v e as ra p id ly as p o ssib le to collecTable 1. Reporting sources for the Wholesale Price Index and number of price series used

Reporting source

Sources used Price series used

February 1937 January 1970 January 1970

Number Percent Number Percent Number Percent

Number used 

per item

Total____________________________ 784 100.0 2,445 100.0 7,726 100.0 3.2

Company reports________________________ 383 48.8 1,906 78.0 7,107 92.0 3.7

Trade publications__________________ ____ 367 46.8 394 16.1 415 5.4 1.1

Trade associations_______________________ 31 4.0 11 .4 11 0.1 1.0

Government agencies.. ..................................... 3 0.4 134 5.5 193 2.5 1.4

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tio n of p rices fro m b u y e rs : “ T h e e n th u sia sm of th e 

Review C o m m itte e for th is re c o m m e n d a tio n w as 

n o t sh a re d g e n e ra lly b y th e w itn e sse s.” 16

A p p a re n tly th e A p p ro p ria tio n s C o m m itte e s 

of th e C o n g ress sh a re d th is la ck of e n th u sia sm , 

fo r no a d d itio n a l reso u rce s w ere m a d e av a ila b le . 

A m o d e st in c re m e n t w as a p p ro v e d to p e rm it th e 

B u re a u to in itia te d e v e lo p m e n t of th e I n d u s tr y 

S e c to r In d e x e s (is p i). D e v e lo p m e n t of th ese 

indexes, in w h ich p rices are classified b y th e S ta n d ­

a rd I n d u s tr ia l C lassific atio n sy ste m , w as re c o m ­

m e n d e d b y th e S tig le r C o m m itte e .

User views

I n 1965-66, th e J o in t E c o n o m ic C o m m itte e ’s 

S u b c o m m itte e o n E c o n o m ic S ta tis tic s c o n d u c te d 

a m a jo r e x p lo ra tio n of th e n ee d s for im p ro v e d 

sta tis tic s . A s a first ste p , th e su b c o m m itte e solic ite d th e view s of a la rg e g ro u p of u sers of 

g o v e rn m e n t s ta tis tic s . A m o n g o v er 70 re s p o n d ­

e n ts, tw o called a tte n tio n to th e p ro b le m of 

tra n s a c tio n s prices. A rth u r F . B u rn s co m m e n te d , 

“ T o o fre q u e n tly , s ta tis tic s o n w holesale p rices 

re p re s e n t lis t p rices r a th e r th a n a c tu a l prices 

c h a rg e d .” 17 H e r b e r t S te in s u b m itte d a re c e n t 

sp e ec h b y A lfred C . N e a l in w h ich N e a l a sse rte d 

t h a t “ I n th e a re a of w holesale p rices, I am in ­

clin e d to p la ce a t th e to p of th e p rio ritie s a n a t ­

te m p t to o b ta in d a ta o n a c tu a l prices p a id b y th e 

b u y e r, n o t th e p rice su p p lie d b y sellers . . . ” 18

L ik e o th e r g o v e rn m e n t agencies, th e B u re a u 

w as in v ite d to c o m m e n t on th e co m p e n d iu m of 

re c o m m e n d a tio n s . A s to N e a l’s su g g e stio n , th e 

B u re a u re sp o n se w as th a t “ T h e p ro je c t h a s m e rit, 

a t le a s t o n a se lec tiv e b asis, b ec au se it w ill e n a b le 

a b e tte r e v a lu a tio n to b e m a d e of p rice tre n d s fo r 

in d u s trie s c h a ra c te riz e d b y c o m p lic a te d re b a te 

a n d d is c o u n t s tru c tu re s . . . . T h is w o u ld b e a 

c o s tly p ro je c t b u t o n e w h ic h bls h a s re c o m ­

m e n d e d fo r se lec ted p ro je c ts for a n u m b e r of 

y e a rs .” 19

A s one p h a se of its p ro je c t, th e s u b c o m m itte e 

h eld h e a rin g s in M a y 1966 o n p rice s ta tis tic s , 

fo cu sin g on th e e x te n t to w hich th e S tig le r 

C o m m itte e re c o m m e n d a tio n s h a d b e e n im p le ­

m e n te d d u rin g th e in te rv e n in g 5 y e a rs. W ith 

re sp e c t to th e re c o m m e n d a tio n to m o v e to w a rd 

u se of b u y e rs ’ p rices, R a y m o n d B o w m a n (th e n 

A s s is ta n t D ire c to r of th e B u d g e t B u re a u for 

S ta tis tic a l S ta n d a rd s ) s ta te d :

There is general agreement with the objective of

this recommendation, i.c., that the w p i should reflect realistic, actual transaction prices, not quoted

prices. Price respondents (sellers) arc requested to

report all discounts applicable to quoted prices . . .

It is recognized that such discounts are not universally

reported.

We agree with h l s that the first step toward implementation of this recommendation should be

through a limited and experimental program to identify

commodity areas in which the differences are important. . . . Because of the heavy costs and respondent burden involved, collection of price data

from buyers should be undertaken only if other

methods are not successful.20

B o w m a n ’s s ta te m e n t concisely su m m a riz e d th e 

m o re d e ta ile d s ta te m e n t b y C o m m issio n er A rth u r 

M . R o ss.21 L a z a re T e p e r, ag reein g t h a t re p o rte rs 

do n o t alw a y s r e p o r t all d isc o u n ts, w e n t o n :

These short-term distortions on the index may

make it at times a bit less sensitive to current price

changes. Collection of actual transaction prices, of

course, is a massive and costly operation. On the other

hand, it is conceivable that ways may be found to secure better cooperation from respondents and to get

more accurate responses from them. Experimental

research is called for in this area.22

T h e s u b c o m m itte e ’s re p o rt, c itin g th e differences 

b etw e en lis t a n d a c tu a l tra n s a c tio n p rices as a 

d eficiency in th e w p i ,23 re c o m m e n d e d t h a t “ C o lle ctio n of d a t a on p rices p a id b y b u y e rs fo r sele c te d p ro d u c ts, su c h as m e ta ls a n d m a c h in e ry , 

sh o u ld be in itia te d in o rd e r to in su re o b ta in in g th e 

te rm s of a c tu a l tra n s a c tio n s w h ich o ften differ 

sig n ific a n tly fro m lis t p rice s . . . ” 24

Expansion of industry sector indexes

W ith reso u rce s u n a u g m e n te d , th e B u re a u d e ­

v o te d su c h of its in d u s tria l p rice p ro g ra m reso u rce s 

as co u ld be m u s te re d to ex p a n d in g th e I n d u s tr y 

S e c to r In d e x e s— a p p ro x im a te ly d o u b lin g th e n u m ­

b e r of s ic 4 -d ig it in d u s try in d e x es p u b lish ed , 

w ith a ro u g h ly p ro p o rtio n a te in c re ase in 5 -d ig it 

p ro d u c t class indexes. Im p le m e n ta tio n of th is 

S tig le r C o m m itte e re c o m m e n d a tio n w as d ee m e d 

th e w iser use of scarce reso u rce s. T h is re c o m m e n ­

d a tio n h a s b e e n s u p p o rte d n o t o n ly u n a n im o u sly 

b u t e n th u s ia s tic a lly — ex c e p t fo r p ro v id in g th e 

a d d itio n a l reso u rce s w h ich are a p re re q u is ite for 

p ro v id in g th e c o m p le te b a tte r y of o u tp u t p rice 

in d ex es a n d in p u t p rice in d e x es e sse n tia l to ach iev e 

th e ir m a jo r p u rp o ses. T h u s fa r, th e p ro g ra m h a s 

b e e n lim ite d to o u tp u t p rice in d ex es, fo r w h ich

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th ere are few er problem s in using d a ta collected 

p rim arily for th e w p i.

W ith o u t fa n fa re b u t p e rsis te n tly th e B u re a u 

th ro u g h its c o m m o d ity a n a ly s ts h a s p resse d to w a rd 

o b ta in in g a c tu a l tra n s a c tio n prices fro m sellers. 

A s T e p e r su g g e sted , th e re are w ay s to im p ro v e 

c o o p e ra tio n a n d th u s o b ta in m o re a c c u ra te r e ­

sponses. S u b s ta n tia l p ro g re ss h a s b ee n m a d e in a 

n u m b e r of in d u strie s, th a n k s la rg e ly to c o m m o d ity 

a n a ly s ts ab le to co n v in ce re p o rte rs t h a t a c c u ra te 

r e p o r ts of a c tu a l p rice s se rv e n o t o n ly th e p u b lic 

in te r e s t in a c c u ra te official p rice in d ex es, b u t also 

th e ir o w n lo n g -te rm b e s t in te re s ts . S u b s ta n tia l 

c re d it is d u e to th o se in th e b u sin e ss c o m m u n ity 

Who b y a n d la rg e h a v e d ee p en e d a n d e x te n d e d 

th e ir v o lu n ta r y c o o p e ra tio n . A ll of th e se re p o rte rs 

are v o lu n ta r y , as h a s b e e n th e case th r o u g h o u t 

th e h is to ry of th e w p i, b u t th e re h a s b e e n a 

m a rk e d im p ro v e m e n t in th e q u a lity of c o o p e ra tio n .

T o illu s tra te : T h e w p i fo r m o to r v eh icles reflec ts 

n o t o n ly a c tu a l tra n s a c tio n p rices b u t also a 

so p h is tic a te d a d ju s tm e n t fo r q u a lity ch a n g es, o n ly 

b ec au se of th e w e a lth of d e ta ile d d a ta su p p lie d 

b y th e m a n u fa c tu re rs , a t no sm all expense to th e m . 

O n a less s tr u c tu r e d a n d m o re in fo rm a l b asis, 

th e B u re a u rec eiv e s th e n e c e ssa ry d a ta fro m m a n y 

if n o t m o s t of th e co m p an ie s w h ich r e p o r t m a ­

c h in e ry p rices. R e c e n tly , th e re p re s e n ta tiv e of a 

c o m p a n y w h ic h a t one tim e re p o rte d p r e tty m u c h 

o n a “ ta k e i t o r le av e i t ” b asis s p e n t m o s t of a 

d a y w ith bls sta ff a ssistin g in th e c o rre c t c a lc u la ­

tio n of a p a r tic u la rly co m p lex p rice ch a n g e. T h e 

o v e ra ll p ic tu re is n o t as ro s y as th e se ex a m p le s 

m a y im p ly ; th e p o in t is t h a t ap p re c ia b le p ro g re ss 

is b ein g m a d e .

A lth o u g h p e rso n a l v isits to re p o rtin g c o m p a n ie s 

a re lim ite d (u su a lly in c o n n e c tio n w ith e s ta b lish in g 

a n ew re p o rte r), ex te n siv e u se of th e te le p h o n e is 

efficient, re la tiv e ly in e x p en siv e (a ro u g h ru le of 

th u m b fo r e s tim a tin g co sts of field trip s is $100 

p e r m a n -d a y ), a n d a c h a n n e l for b u ild in g r e la tio n ­

sh ip s c o n d u c iv e to r e p o rtin g a c c u ra te ly . I t is 

m o re ty p ic a l th a n n o t, w h en th e tra d e p ress or 

o th e r p u b lic a tio n s r e p o r t d isc o u n tin g , fo r th e 

a p p ro p ria te c o m m o d ity a n a ly s t to ch e ck th e 

r e p o rts im m e d ia te ly w ith re p o rtin g co m p an ie s 

a n d o th e r so u rces of in fo rm a tio n . L ik e q u a lity 

a d ju s tm e n t, th e p u r s u it of a c tu a l tra n s a c tio n 

p rice s is v ir tu a lly a d a ily p ro b le m in c a lc u la tin g 

the wpi.

B y the m id-1960’s it was apparent that bls

was unable to pursue the recommendation for

use of buyers’ prices. The National Bureau of

Economic Research evidently considered such a

study a worthwhile project. Stigler’s own strong

convictions on the relative merits of sellers’ versus

buyers’ prices evidently made him willing to invest

much of his time in the tedious if challenging

task of data collection and index calculation.

Although field work covered the period from the

fall of 1965 to mid-1967, the data and their indexes

are for 1957 through 1966. A few sellers were

included in their sample, but it consisted predominately of large manufacturers, government

agencies, and a few hospitals. The study was

limited to relatively few industries, constituting

less than one-fifth of the weight of the w p i. For

the commodity groups to which the indexes for

individual series were aggregated and which are

given the titles corresponding to the appropriate

w p i major groups, the coverage varies sharply.

The bls indexes shown in Stigler and Kindahl

were constructed by them from bls series corresponding to theirs, and at the group level, of

course, are not those published by b l s. Its appearance and the attendant publicity raise a variety

of questions to which relatively brief answers may

be given on my personal responsibility. Bureau

review and assessment are not complete.

Assessing the data

Does the study invalidate the w p i? Absolutely

not. As Stigler and Kindahl very properly point

out, the study was not a test of the w p i.25 Stigler

personally may think so, or that Stigler and

Kindahl tends to indicate so.26 For the other pole

of opinion, see testimony before the Joint Committee on July 14, 1970, by Means and Blair.27

D o es th e s tu d y d e m o n s tra te t h a t b u y e rs ’ p rices 

m o re n e a rly a p p ro x im a te a c tu a l tr a n s a c tio n p rices 

th a n do se lle rs’ p rices? N o t u n le ss one a c c e p ts 

e ith e r ty p e of p rice as a n a c tu a l tr a n s a c tio n p rice 

as u sed in c a lc u la tio n of a n in d e x of p rice c h a n g e . 

O b v io u sly , S tig le r a n d K in d a h l’s in d e x es d iffer 

fro m bls in d e x es in v a rio u s w a y s a n d b y v e ry 

rfa rro w to v e ry w id e a m p litu d e s. G iv e n th e 

differences in sa m p le s, sp ecificatio n s, c o llec tio n 

a n d c a lc u la tio n p ro c e d u re s, d ifferen ces a re to b e 

e x p e cted .

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T h is d isc u ssio n assu m es t h a t S tig le r a n d K in - 

d a h l ag ree s u b s ta n tia lly w ith th e b l s d efin itio n of 

an a c tu a l tra n s a c tio n p rice, w h ich is lis t o r b o o k 

“ p rices less all d isc o u n ts, allow ances, re b a te s , free 

d eals, e tc ., so t h a t th e re s u ltin g n e t p rice is th e 

a c tu a l sellin g p rice of th e c o m m o d ity for th e 

specified b a sis of q u o ta tio n .28 T o th is m ig h t b e 

a d d e d “ p lu s a n y p re m iu m , e tc .” T h is d ig ressio n 

m a y seem u n n e c e ssa ry , b u t th e lite ra tu re a b o u n d s 

w ith u se of th e te rm in v e ry d iffe ren t a n d u su a lly 

m u c h b ro a d e r c o n n o ta tio n s. A c tu a lly m u c h c riticism of th e w p i (an d c p i ) ste m s fro m f ru s tra tio n 

in v o lv e d in a tte m p tin g to u se it for la c k of an 

a p p ro p ria te index.

The Bureau usually obtains prices f.o.b. production or central marketing point, to avoid reflecting changes in transportation costs. Stigler

and Kindahl’s data from buyers normally would

include these charges which introduces another

source of differences.

Can b l s use Stigler and Kindahl as a pilot

study? No, but both data in the book and data

which its authors may have in their possession

could be very helpful. A b l s pilot study of buyers’

prices necessarily would be constructed to test the

feasibility of collecting such prices on a continuing

basis (rather than an essentially one time), using

the mail or average b l s agents (rather than two

distinguished professors supported by the prestigious n b e r ) , and including small buyers as well as

large.

S tig le r a n d K in d a h l p u b lish e d th e n u m b e r of 

p rice series u se d e a ch y e a r fo r each co m m o d ity . 

T h e se d a t a sh o w a* c o n s is te n t p a tte r n . T h e y rise 

fro m a sm a ll n u m b e r of re p o rte rs in th e e a rly y e a rs 

to a p e a k in 1964 a n d 1965, follow ed b y a p ro ­

n o u n c e d d eclin e in 1966. T h e fo rm e r p h en o m en o n , 

w h ich in d ic a te s th e in a b ility o r u n w illin g n ess of 

th e r e p o r te r to p ro d u c e re c o rd s in e a rlie r y e a rs o r 

S tig le r a n d K in d a h l’s in a b ility to d e te rm in e co m ­

p a ra b le p rice s fro m su c h re c o rd s, w o u ld n o t be 

re le v a n t fo r b l s if d a ta w ere collected c u rre n tly . 

T h e d ro p in 1966, h o w ev e r, is d ec id e d ly p e r tin e n t 

in a p p ra isin g th e p o te n tia l success in o b ta in in g 

re p o rte rs o n a c o n tin u in g b asis.

If Stigler and Kindahl recorded man-days spent

in data collection, such information would be most

useful, b l s must consider any major change in

terms of its costs. For the industrial price program,

these must be regarded as almost fixed. Much more

lig h t on th e p ro b le m s of m a in ta in in g c o m p a ra b ility , ev e n w ith in th e b ro a d e r sp e cific atio n s S tig le r 

a n d K in d a h l e m p lo y e d , an d of p ro b le m s in id e n ­

tify in g th e specified ite m a n d its p rice w o u ld b e 

m o s t h e lp fu l. In v o ic e s w ere n o t d esig n ed w ith a 

view to a id in g th e p rice co llecto r. D u rin g W o rld 

W a r I I , I w as in v o lv e d in co llec tin g b u y e r s ’ p rices 

an d ca n te s tify t h a t th e p ro b le m s p ro g re sse d alm o st g e o m e tric a lly as I m o v e d fro m food to te x tile s 

a n d a p p a re l to m a c h in e ry .

W ill th e B u re a u m o v e to a w p i b a se d on b u y e r s ’ 

p rices? I t is as u n lik e ly as it is u n d e sira b le . T o 

p ro d u c e en o u g h p rice q u o ta tio n s fo r re lia b le 

m e a su re s of p rice ch a n g e fro m m o n th to m o n th 

w ould re q u ire a m u c h la rg e r sa m p le of re p o rte rs. 

T o ta k e S tig le r a n d K in d a h l’s a v e ra g e of 17 p e r 

c o m m o d ity (a fig u re w ith v e ry w ide v aria n ce s) 

v e rsu s th e b l s a v e ra g e of slig h tly m o re th a n th re e 

as a v e ry c o n s e rv a tiv e e s tim a te of th e re q u ire d 

sa m p le size, b l s w o u ld b e fo rced to c u rta il sh a rp ly 

th e n u m b e r of c o m m o d itie s in c lu d e d in th e w p i —

u n less its reso u rce s w ere e x p a n d e d tre m e n d o u sly . 

D isc u ssin g th e q u e s tio n of w h e th e r a n in d e x sh o u ld 

in c lu d e a sm a ll o r la rg e n u m b e r of co m m o d ities, 

M itc h e ll o b se rv e d th a t “ E v e ry re s tric tio n in th e 

scope of th e d a ta im p lies a lim ita tio n in th e 

significance of th e re s u lts .” 29

T h e B u re a u h a s lo n g b e e n in te re s te d in tw o uses 

of b u y e rs ’ prices. F o r th e w p i , w h ere it is e s ta b ­

lish ed c o n c lu siv ely th a t a c tu a l tra n s a c tio n prices 

c a n n o t b e o b ta in e d in a n y o th e r w ay , b u y e rs ’ 

p rices sh o u ld b e u sed g iv e n th e n ec essary reso u rces. 

L o o k in g fo rw a rd to c o n s tru c tin g in p u t p rices fo r 

th e I n d u s tr y S e c to r In d e x e s, it is q u ite p ro b a b le 

th a t co llectio n of p rices fro m b u y e rs m ig h t b e 

u n a v o id a b le in som e in sta n c e s. I n b o th cases, 

b u y e r s ’ p rices ev e n co llected a t lo n g er in te rv a ls 

m ig h t b e u sed a d v a n ta g e o u s ly to sp o t c o m m o d ity 

a re a s in w h ich c o rre c tiv e a c tio n is in d ic a te d . A s 

n o te d earlier, th e B u r e a u ’s c o m m o d ity a n a ly sts 

are u sin g S tig le r a n d K in d a h l d a ta fo r precisely 

th is p u rp o se.

I n te rm s of a lo n g e r ru n a n d m u c h m o re am b L 

tio u s p ro je c t— c o n s tru c tin g a G e n e ra l P ric e In d e x , 

co v e rin g all se c to rs of th e ec o n o m y — p rice d a ta 

fro m th e v e ry im p o r ta n t g o v e rn m e n t se c to r 

p ro b a b ly c a n b e m o st re lia b ly , efficiently, a n d 

eco n o m ically co llected fro m th e p u rc h a sin g g o v e rn ­

m e n t agencies.

The Bureau is no more prejudiced toward sellers’

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a n d a g a in s t b u y e r s ’ p rice s th a n i t is w ed d e d to th e 

th e o ry of a u n iq u e price. I t is c rib b e d , ca b in e d , a n d 

co n fin ed b y th e b a sic eco n o m ic p ro b le m of 

a llo c a tin g scarce reso u rc e s to m e e t m a n y n eed s. □

------- —FOO TNO TES----------

1 The M aking and Use of Index Numbers ( b l s Bulletin

656, 1938), p. 25. (M itchell, of course, gained his greatest

fame in later years for his work on business cycles and on

many other economic and statistical areas, but was a

principal architect of the Wholesale Price Index in roughly

its present form.)

2 Ibid., p. 26.

3 George J. Stigler and James K. Kindahl, The Behavior

of Industrial Prices (New York, National Bureau of

Economic Research, 1970), General Series 90.

4 The Structure of the American Economy (Washington,

National Resources Committee, 1939), pp. 122-152.

5 Ibid., p. 185.

8 George J. Stigler, “Administered Prices and Oligopolistic Inflation,” Journal of Business, January 1962,

pp. 1-13: John M. Blair, “Administered Prices and

Oligopolistic Inflation: A Reply,” Journal of Business,

January 1964, pp. 68-81 (see also Stigler’s comment, pp.

82-83, and McAllister’s comment, pp. 84-86, of the same

issue); Walter Adams and Robert F. Lanzillotti, “The

R eality of Administered Prices,” Administered Prices:

A Compendium on Public Policy (U.S. Senate, Committee

on the Judiciary, Subcommittee on Antitrust and

Monopoly, 1963), pp. 5-21.

7 Government Price Statistics, Hearings before the Subcommittee on Economic Statistics of the Joint Economic

Committee (U.S. Senate, 1961), Pt. I, p. 21.

8 Ibid., p. 71.

8 Ibid., Staff Paper 8, pp. 373-418.

10 Ibid., Staff Paper 9, pp. 419-458.

11 Government Price Statistics, Hearings before the Subcommittee on Economic Statistics of the Joint Economic

Committee (U.S. Senate, 1961), Pt. II, p. 559.

12 Ib id ., p . 560.

13 Ib id ., p p . 6 0 2 -6 0 3 .

“ Ib id ., p . 673.

13 Ib id ., p p . 6 7 2 -6 7 3 .

18 Government Price Statistics, R e p o r t of th e S u b c o m ­

m itte e on E c o n o m ic S ta tis tic s of th e J o in t E c o n o m ic

C o m m itte e (U .S . S e n a te , 1961), p . 8.

17 Improved Statistics for Economic Growth, A C o m ­

p en d iu m of V iew s an d S u g g estio n s F rom In d iv id u a ls,

O rgan ization s, and S ta tis tic s U sers (U .S . S e n a te , J o in t

E c o n o m ic C o m m ittee , S u b c o m m itte e on E c o n o m ic

S ta tistic s, 1965), p . 15.

18 Ib id ., p . 130.

19 Improved Statistics for Economic Growth, C o m m e n ts b y

G o v er n m e n t A g en cies on V iew s a n d S u g g estio n s F ro m

In d iv id u a ls, O rgan ization s, a n d S ta tis tic s U ser s (U .S .

S en a te, J o in t E c o n o m ic C o m m ittee , S u b c o m m itte e on

E co n o m ic S ta tistic s, 1966), p . 48.

20 Government Price Statistics, H ea rin g s before th e S u b ­

c o m m itte e on E c o n o m ic S ta tistic s o f th e J o in t E c o n o m ic

C o m m ittee (U .S . S e n a te , 1966), p . 11.

21 Ib id ., p p . 59 a n d 63.

22 Ib id ., p . 154.

23 Government Price Statistics, R e p o r t of th e S u b c o m m itte e on E co n o m ic S ta tistic s o f th e J o in t E co n o m ic

C o m m ittee (U .S . S e n a te , 1966), p . 8.

24 Ib id ., p. 17.

25 S tig ler an d K in d a h l, op . c it., p . 4.

28 A s q u o te d in press release d a te d J u n e 26, 1970, issu ed

b y th e N a tio n a l B u reau of E c o n o m ic R esea rch , a n n o u n cin g

p u b lica tio n of The Behavior of Industrial Prices.

27 G ard in er C . M ea n s a n d J o h n M . B lair, in th e ir

in d iv id u a l te stim o n y , M idyear Economic Review, H ea rin g s

before th e J o in t E c o n o m ic C o m m ittee (U .S . S e n a te , 1970)

in press.

28 B LS Handbook of Methods for Surveys and Studies

( b l s B u lle tin 1458, 1966), p . 92.

29 M itc h e ll, op . c it., p . 53.

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Recent studies cast

doubt on view that Consumer

Price Index shows upward bias

because of inadequate correction

for product improvement

JACK E. TRIPLETT

M any econom ists believe that quality changes

in goods and services are not adequately taken

into account in the preparation of the Consumer

Price Index (c pi). A s a result, they believe, the

c p i has a systematic and persistent upward drift

which makes the index a questionable indicator

of the course of inflationary price movements.

Products and services probably do tend to

improve in quality as the years go by. It is easy,

therefore, to suppose that the “market basket”

priced by the Bureau of Labor Statistics must

experience a similar change in quality. If not

allowed for in some way, such improvements in

quality would cause the computed price index to

rise too rapidly, which is in contrast to the concept the cpi is supposed to measure: the cost of

acquiring a fixed collection of goods and services.

Thus, the argument goes, price indexes will drift

upward even when no inflation is actually taking

place, and they will give an exaggerated notion of

the speed of inflation when prices are in fact

increasing.

To what extent is the belief that price indexes

are biased upward borne out by existing evidence?

No assessment of the quality error in the c p i as a

whole has yet been made, but a number of investigations have produced estimates of quality

error in individual index components. The present

article is a survey of existing studies, which present contradictory evidence. Some investigators

found upward bias, but others reported that

quality error might be negative—that is, when

the bls failed to correct adequately for quality

changes, it resulted in a price index that rose too

slowly, rather than too rapidly. The key studies are

reviewed in the following section. For convenience

Jack E. Triplett is assistant professor of economics,

Washington U niversity, St. Louis, Mo.

the effects of

quality change

on the CPI

of presentation they are grouped according to

dates and products covered.

Automobiles

B y far the best known empirical work on the subject of price indexes and quality change is Zvi

Griliches’ study of the c pi new automobile component.1 Griliches employed what has come to be

known as the “hedonic technique” for measuring

quality change, an approach also used in a number

of other studies. Briefly, this technique involves

searching for variables or attributes which may

account for quality differences among varieties

of a product selling at the same point in time.

For example, if we look at the automobile market

as a whole, it appears (from analysis of the statistical data, as well as from knowledge of the nature of

automobiles and the behavior of consumers in the

aggregate) that a more powerful engine, other

things equal, is generally preferred to a less powerful one. People differ in how much they are willing

to pay for more power, but there are clear statistical regularities between power ratings and the

price paid for an automobile, after allowing for

differences in size, comfort, economy, and so on.

Regression analysis is used to isolate an “implicit

price” for power (as well as for the other things);

then if the power of a 1970 car exceeds that of the

same car in 1969, the 1969 implicit price for

power can be used to adjust for the value of the

change in power between the 2 years.2 Other

attributes can be allowed for in the same way.

Griliches presented several different estimates

of quality-adjusted automobile price indexes, but

all his quality-adjusted indexes showed that the

new automobile component of the c pi rose too

rapidly. Indeed, for the 1954-60 period, during

which the c pi index of new automobile prices rose

11.3 percent, Griliches’ quality-adjusted index

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numbers actually declined. The decreases he

recorded in his several indexes ranged from 1.0

percent to 26.6 percent. These results were widely

interpreted as strong evidence that the c p i rose

far too rapidly during the inflation of the late

1950’s, and several economists have suggested

that there might not have been any inflation at all.

However, a second study of quality bias in the

c p i new car component reached just the opposite

conclusion. Philip Cagan3 used an entirely

different technique to allow for quality change in

autos: the “vintage price” method.4 When Cagan

compared his quality-adjusted index, for the

years 1954-60, with the c p i component, he found

his own index had risen more than the c p i —16.7

percent compared with 11.3 percent in the c p i —

suggesting “. . . that the c p i is not biased upward

and may even overcorrect for quality improvements in automobiles. How that happened is not

clear.” 6

Since Cagan and Griliches produced nearly

identical estimates of the value of quality change,

most of the difference in their results stems from

their adjusting different price series. Griliches

applied his quality indexes directly to the published c p i auto component. Cagan, on the other

hand, adjusted an index of list prices for the cars

priced for the c p i . If we take Griliches’ quality

figures and apply them to an index of list prices,

we get an estimated 12.7-percent increase in the

price of cars, corrected for quality changes, between 1954 and 1960—not far from the actual c p i

estimate of an 11.3-percent increase, and (like

Cagan’s result) a somewhat greater price increase

than was recorded by the published c p i .

Which is the appropriate basis for adjustment—

list prices or the actual c p i ? There are arguments

either way. Because the c p i always included some

quality adjustments, applying a quality index to

the published c p i new auto component probably

introduces double-counting of quality changes.®

On the other hand, an index based on changes in

list prices may not correspond to the movements

of actual transactions prices, although the longer

the time span, the better the approximation.

In view of the divergence in movement between

the index of list prices and the c p i auto component,

close examination of the c p i during the 1954-60

period seems imperative. The required information

can be extracted from an unpublished b l s memorandum by Thomas W. Gavett, on which the

Table 1. Comparisons of percent changes in price indexes

for automobiles, 1953-60

P e rio d P u b lish e d

CPI

N ew se rie 

action

U n a d ju ste d 

fo r q u a lity 

ch an ge

s of tra n s ­

p rice s

A d ju ste d 

fo r q u a lity 

change

L is t p ric 

(a d ju ste d 

ch a r

G rilic h e s

in d e x e s 

fo r q u a lity 

ges)

Cagan

1 9 5 3 -5 4 ______ 2 .6 0 .9 - 3 . 2 - 0 . 5 ( i)

1 9 5 4 -5 5 .......... - 1 . 7 2 .9 - 4 . 5 - 2 . 3 - 2 . 5

1 9 5 5 -5 6 ............ - . 9 .9 - . 4 2 .2 6 .3

1 95 6 -5 7 ............ 5 .1 1 2 .9 9 .9 4 .7 6 .1

1 9 5 7 -5 8 ______ 4 .2 1 .3 - . 8 3 .2 5 .3

1 9 5 8 -5 9 ............ 4 .2 6 .2 3 .4 4 .5 .4

1 95 9 -6 0 ............ . 1 2 .4 3 .5 - . 2 . 4

1 95 3 -6 0 ______ 1 4 .2 3 0 .3 7 .4 1 1.2 ( i)

1 9 5 4 -6 0 ........... 1 1.3 2 9.1 1 1 .0 1 2.5 16.7

> N o t a v a ila b le .

N O T E S : C u m u la tiv e ch an g e s co m p ute d by ch ain in g .

S O U R C E S : C o lu m n 1: C o m p uted fro m an n u al CPI new a u to m o b ile com p on en t.

C o lu m n 2: B ased on an u n p u b lish e d B L S m em o ran d u m by T h o m a s W. 

G avett.

C o lu m n 3: C o lu m n 2 d iv id e d by an in d e x o f q u a lity , co m p u te d by 

G av e tt u sin g m ate ria l fro m G rilich e s, op. cit. (1961, 1964), 

and sp e cifica tio n s o f ca rs in th e N ew P ric e S e rie s.

C o lu m n 4: Fro m an in d e x o f lis t p ric e s co m p ute d fro m G rilic h e s , op.

cit. (1961), p. 185, ta b le 6, d iv id e d by a q u a lity in d e x ta k en 

fro m G rilic h e s , op. c it. (1964), p. 186, ta b le 8, w ith co rre c ­

tio n o f a c le ric a l e rro r noted by T h o m a s W. G avett. T h e 

co lu m n re co rd s ch an g e s in th e re su ltin g in d e x.

C o lu m n 5: Cagan, op. cit., p. 230, ta b le 5.

following paragraphs are based.

In mid-1954, b l s began to gather information

on the typical or average price concession (discount, or over allowance on trade-ins) allowed by

dealers on the car selected by b l s for pricing.

When price concession information first became

available, it was not “linked” out of the index.

Instead, prices including concessions were compared directly with previous prices (which were,

in effect, list prices). The full amount of the

reported price concession was treated as a price

decrease for the month when data on price concessions were first collected. Because the overall

impact on the index was spread over a period of

time, the error introduced into the index affects it

from'the end of 1953 through 1955.7

In order to have an index for 1953-56 free of the

concession error, we made use of information on

price concessions compiled by Gavett to construct

a new price index for the whole 1953-60 period.8

This is an index of transactions prices, without

quality adjustment, and free (or as nearly free as

it can be made) from the price-concession error

affecting the c p i . The results are shown in the

second column of table 1 . While the published c p i

for cars rose by 14 percent, from 1953-60, the

new price series shows an increase about double

that figure. The next step was to deflate the new

price series by use of a quality index appropriate

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to the cars of the price series. The results, detailed

in column 3 of table 1, are in my opinion the best

estimate that can be made of the true course of

price movement for automobiles for the 1953—60

period.

This index indicates that there was substantial

quality error in the automobile price index during

this period. But, contrary to some economists’

opinion, it also indicates that inflation was real

and not just a product of faulty engineering in

the price indexes. Cagan’s result (column 5 of

table 1), and what I have labeled the “Griliches’

Quality-Ad justed List Price Index,” are both not

far from the mark, though Griliches’ original c p i

comparisons were thrown off by the priceconcession error, just as was the c p i itself.

Around 1960, major revisions were accomplished

in the way quality changes were made in the new

automobile component,9 so one would not expect

the Griliches-Cagan conclusions on quality bias in

the automobile component necessarily to hold

beyond the period they studied. Several more

recent studies 10 have examined automotive components of the price indexes for the period after

1960. All of them agree in finding that automobile

price indexes seem biased downward since 1960,

with the major part of the discrepancy occurring

soon after cost-based quality adjustments were

incorporated into the indexes. (See table 2.)

Although the c p i component declined, the indexes

reported in the studies increased. This group of

studies should be interpreted with caution,

especially since there is the possibility of bias in

both quality measurement techniques employed by

the investigators.11 However, they do strongly

suggest that quality adjustments in the c p i (and

in the w p i, since ad ju stm en ts in both indexes are

based on the same data) may have been too large.

If the studies are correct, one should be wary of

concluding that price indexes are always rising too

rapidly.

Table 2. Percent changes in price indexes for vehicles,

selected periods, 1960-67

A u th o r o r source P e rio d P e rce n t change

A ctu a l CPI (a u to c o m p o n e n t)______ 1960-66 - 7 . 2

T rip le tt (a u to m o b ile s )........... .......... 1960-66 + 9 . 6

D h rym e s (a u to m o b ile s )............. . . . 1961-64 1 + 1 3 .2

H all (p ic k u p tru c k s ) __________ . . . 1961-67 + 8 . 9

i T h is fig u re w as co m p u te d fro m th e la te r v e rsio n o f D h ry m e s' p aper. In th e e a rlie r 

v ersio n , a 7 .0 -p e rce n t d e cre ase w as re p o rted .

S O U R C E : S ee th e s tu d ie s c ite d in fo o tn o te 10.

Studies of refrigerator prices

Appliances offer a fertile field for investigations

into quality change. Two studies have evaluated

quality bias in the c p i refrigerator component.12

The refrigerator index has been falling continuously through most of the postwar period;

interestingly, both studies of refrigerator prices

suggest that the c p i component m ay have declined

too fast.

Burstein computed his indexes from refrigerator

prices in mail-order catalogs. He expressed surprise at finding that his quality-adjusted index fell

(in a period of falling prices) more than an index

which lacked quality adjustments: “Why should

procedures ignoring quality changes impart a

downward bias to a price index? A plausible

explanation is that the prices of the . . . models

chosen for pricing by the Bureau fell relative to

the prices of refrigerators and freezers as a

group.” 13

Burstein’s paper opens up some intriguing questions, but the specific result (that the c p i may be

biased downward) relies exclusively on the behavior of indexes of mail-order prices and these

indexes could be given a different interpretation.

However, Burstein’s interpretation is consistent

with a later study by Dhrymes.14

Using a variant of the hedonic technique

Dhrymes produced a quality-adjusted price index

for refrigerators covering most of the postwar

period. Between 1950 and 1960, Dhrym es’ refrigerator index fluctuates, but shows no clear

trend at all. Over the same period, the c p i refrigerator component declined by over one-third.

After 1960, Dhrymes’ index moves downward at

about the same rate as the c p i , except for a

precipitous and unexplained drop in the final year

of his study. Overall, Dhyrm es’ data are consistent

with downward quality bias in the c p i refrigerator

component.

Although I have reservations about each of the

studies on refrigerators, both point to downward,

not upward, bias in the c p i . In the face of such

evidence, one has less faith that price indexes

always drift upward because of quality change.

Other studies

Although there has been much recent research

on the problem of quality measurement,15 some of

the studies did not present results that can be

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compared with the c p i . A noteworthy group of

studies (unpublished) were carried out within b l s

by Thomas W. G avett.16 Covering automatic

washing machines, men’s suits, and carpets, these

studies are the only ones conducted in conjunction

with close examination of actual pricing and

processing procedures within the indexes for the

products studied. The latter two employed actual

price quotations from the w p i . Condensed portions

of the results are presented in table 3.

The analyses of washing machines and suits

gave quality-adjusted indexes slightly below the

corresponding c p i and w p i components, but their

author judged the differences not statistically

significant, especially since the indexes computed

were, for various reasons, not precisely comparable

with the published indexes with which they are

compared. For the carpet study, the discrepancy

between the quality-adjusted index and the c p i

and w p i components was considerably larger.

All three of the recomputed indexes are consistent with upward quality bias in the respective

components.

Next, we examine a few investigations into

price changes in services. The services components

of the c p i have consistently risen more rapidly

than price indexes for commodities, taken as a

whole. Of major importance are the medical care

components.

Table 3. Comparison of percent changes in CPI and

WPI with percent changes in indexes adjusted for quality

change, various periods, 1958-66

P e rio d N o q u a lity 

a d ju stm e n t"

Q u a lity

a d ju ste d "

C h an g e In 

W PI

C h an g e in 

C P I

1 96 3-64 ____________

A u to m a tic w a sh in g m ach in e s

- 1 . 7 4

- 1 . 4 8

- 1 . 4 6

- 4 . 6 1

0. 11 

- 2 . 23 

- 0 . 3 3 

- 2 . 4 5

- 1 . 3 5 

- 1 . 3 6 

- . 5 8 

- 3 . 2 5

1 96 4-65 ____________

1 96 5-66 _____

1 96 3-66 .......................

1 95 8-66 _____

M e n ’s su its

2 .01 19. 27 22. 07 2 4 .2 5

1 9 5 9 -6 6 ...

C a rp ets

2 .91 - 1 1 . 7 9 3 - 4 . 38 * 1 .2 0

w i l f i t q L n|ityS a d jUs?mefn0trsCarPetS’ C° m PUted fr ° m W P ' P riCe q u o ta tio n s’

? a u to m atic w a sh in g m achin es, h ed o n ic q u a lity a d ju stm e n ts w e re a p p lie d to a 

p ric e in d e x co m p u te d fro m p ric e s ta k e n fro m Consumer's Digest Price Buying 

Directory. F o r m en s s u its an d fo r ca rp e ts, h ed o n ic q u a lity ad ju stm e n ts w e re a p p lie d 

to th e in d e x d e sc rib e d in th e first footn ote.

3 “ S o ft s u rfa c e flo o r c o v e rin g s " co m p o n e n t o f the W PI

4 R ugs, so ft s u rfa c e " co m p o n e n t o f th e C P I (p e rce n t ch an ge fro m 1957-59 ave rag e.) 

S O U R C E : U n p u b lis h e d B L S m e m o ran d u m b y T h o m a s W. G avett.

Table 4. Summary of conclusions of several studies of

price indexes and quality change, various periods 1947-66

A u th o r P ro d u c t o r se rv ic e P e rio d C o n clu sio n

G a v e tt.___________ W a sh in g m a c h in e s ... 1963-66

1958- 66

1959- 66 

1954-61 

1951-65 

1945-64 

1947-64

S lig h t u p w a rd bia s. 

S lig h t u p w a rd bia s. 

U p w a rd bia s. 

U p w a rd b ias. 

D o w n w a rd bias. 

U p w a rd b ia s 

U p w a rd bia s.

G a v e tt__________ _.

G a v e tt________ . _____ C a rp ets

M a rtin _____

S c ito v s k y _______

B a r z e l.. . M e d ic a l S e rv ic e s ____

L a m so n ___ T h e a te r a d m is s io n s .. .

N O T E : “ U p w a rd b ia s " m ean s th e q u a lity -a d ju ste d p rice in d e x co m p u te d in the

A n ? se ess TM an re ^eva n t C P I co m p o n e n t if p rice s w e re ris in g , o r fe ll m ore th a n 

th e C P I co m p o n en t, if p ric e s w e re fa llin g . “ D o w n w a rd b ia s " in d ic a te s th e op p o site 

fin d in g .

It has often been argued that one of the defects

in the c p i medical care components is that they

price units such as “hospital room” and “physician’s fee,” and that the correct transaction unit—

and therefore the appropriate unit for pricing—is

the cost of a cure. It has been alleged, furthermore,

that a move toward pricing the cost of recovery

from an illness would produce a lesser increase

than the present c p i procedures.

Such allegations seemed to be borne out in a

somewhat tentative study which related hospital

costs to the length of stay for particular illnesses.17

Though daily charges, in a sample of hospitals,

rose about the same as the c p i hospital room

component, adjusting for changes in the length of

stay cut the increase in the price index almost in

half. (The years studied were 1954 to 1961.)

However, a second study found just the opposite

result: “In the 14 years from 1951-52 to 1964-65,

the costs of treatment of all five illnesses covered

by the study (with one minor exception) increased

more— some of them substantially more— than

the b l s medical care, price index.” 18 A number of

objections have been raised concerning the

methodology of the Scitovsky study.19 B ut the

debate actually indicates that the appropriate

pricing concept in the medical services area is not

patently obvious. Nor is the concept of quality in

medical care without serious conceptual difficulties, which are intertwined with the problem

of defining the appropriate measurement units for

transactions and output.

In order to get around some of these problems,

Yoram Barzel20 constructed a quality-adjusted

medical price index based on insurance rates (for

“Blue Shield” plans). Between 1945-64, the c p i

“Physician’s Fees” index rose by 85 percent, while

Barzel’s index rose only 66 percent.

A final study on services was concerned with

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motion picture theaters.21 The results are especially questionable, since they are derived

almost exclusively from a sample of theaters in

Seattle which were “unchanged” over the period

1947-64. For what the findings are worth, the

adjusted (for some quality changes) index for

these theaters rose less than either an index of

their actual ticket prices or the c p i theater

admissions index.

Conclusions of the studies noted in this section

are summarized in table 4. Without attempting

to evaluate these studies, most point to upward

bias in price index components.

Conclusions

The empirical studies surveyed in this paper

do not exhaust the investigations that have a

bearing on the problem of quality error in price

indexes. But they show that the index may have

negative as well as positive errors due to quality

changes. The studies of the quality problem are

themselves of uneven quality, so some should

carry more weight than others. Also, a simple

count shows more conclusions of “upward bias”

than of “downward bias.” Nevertheless, those

that show downward quality error indicate that

the widespread view that price indexes always

overstate the degree of inflation may be incorrect.

Notice that these studies do not point to a

positive conclusion: we have not proved that price

indexes are biased either upward or downward;

rather, they establish only that the proposition

that indexes are systematically upward-biased is

not conclusively confirmed by the available evidence. If individual components show both upward

and downward errors, the overall error may go

either way.

The reader may wonder, however, how it is

possible that quality error can cause price indexes

to be biased downward. Clearly downward bias

can result when deterioration in products and

services is not fully allowed for in the indexes.

There are frequent allegations of this, particularly

in services.

B ut it is also quite possible for quality errors to

cause the index to understate price increases even

when the quality of products is improving. The

reason is that b l s does not simply price whatever

products may appear in stores and ignore any

change in quality that may occur. Instead, for

most products there is an attempt to control for

quality differences. This means that the prices

that are compared for the index are not necessarily prices of product varieties which show the

average rate of quality improvement.

In order to establish the direction or the size

of quality error in the indexes, we need to examine

the actual quality errors that creep into individual

components. These errors will be determined, not

solely by the extent and rapidity of quality

change in the marketplace, but also by the

particular marketing arrangements for different

products and by the interaction of these factors

with the mechanisms set up by b l s to try to

control the size of quality errors permitted in

index comparisons. Any extended discussion of

these matters is beyond the scope of the present

paper. Elsewhere it has been shown that even

when the quality of a product is improving rapidly,

the quality errors that get into the index may give

it a downward error instead of the upward bias

so frequently assumed.22 

FOO TNO TES

1 “Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Change,” Staff Paper 3 in

Price Statistics Review Committee, The Price Statistics

of the Federal Government, General Series Number 73

(New York, National Bureau of Economic Research,

1961). Additional results from the study were published

in Zvi Griliches, “N otes on the Measurement of Price

and Quality Changes,” in National Bureau of Economic

Research, Conference on Research in Income and W ealth,

Models of Income Determination, Studies in Income and

Wealth, Volume 28 (Princeton, N .J., Princeton U niversity Press, 1964).

2 This is an intuitive explanation of the hedonic technique. For a complete discussion of concepts and problems,

see Griliches, op. cit. (1961); Jack E. Triplett, “The

Theory of Hedonic Quality Measurement and Its U se in

Price Indexes,” BLS Staff Paper Number 6; Richard

Stone, Quantity and Price Indexes in National Accounts

(Paris, Organization for Economic Cooperation and D evelopment, 1965); and Thomas W. Gavett, “Quality and a

Pure Price Index,” Monthly Labor Review, March 1967,

pp. 1&-20.

3 “ Measuring Quality Changes and the Purchasing

Power of M oney: An Exploratory Study of Automobiles,”

National Banking Review, December 1965, pp. 217-236;

reprinted in Zvi Griliches, ed., Price Indexes and Quality

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Change: Studies in New Methods of Measurement (Cambridge, Harvard U niversity Press, 1971).

4 This technique rests on the hypothesis that prices

for used cars of different ages differ partly because the

older cars have less useful life remaining and partly because newer cars differ in quality from older ones. If a

pure rate of depreciation can be established to allow for

the aging effect, the remainder of the price difference can

be taken as an estimate of the value of quality change.

* Cagan, op. cit., p. 231.

6 An estimate of the value of quality changes made in

the auto component over an extended period of time is

contained in Olga A. Larsgaard and Louise J. Mack,

“ Compact Cars in the Consumer Price Index,” Monthly

Labor Review, M ay 1961, pp. 519-523.

7 I do not wish to convey the impression that concessions

data were introduced into the index in this fashion simply

because the b l s was unaware of its impact. When the

concession information became available, it was argued

within the Bureau that the price after any concession

had always been the appropriate concept for the c p i ,

and that linking concessions would ignore real price

change that occurred before the first reports on dealer

price concessions. That is, it was believed that at some

previous period (perhaps around 1950) cars had actually

sold at list prices. (Indeed, popular reports indicate transactions prices were well above list prices in the immediate

postwar years.) Since these price decreases had been

missed by the c p i , bringing concessions in by direct comparison, it was argued, preserved the validity of the index

in making comparisons, such as for the period 1950-57.

8 It should be emphasized that the computations reported

here are based on G avett’s data, but they are not the constructions produced by Gavett (except where indicated),

nor are they used for the same purposes. Therefore, it

should not be inferred that Gavett is necessarily in agreement with anything expressed in this section.

8 Estimates of the cost of quality changes were obtained

from manufacturers and used as adjustments in both the

c p i and the w p i . See Margaret S. Stotz, “Introductory

Prices of 1966 Automobile M odels,” Monthly Labor Review,

February 1966, pp. 178-181; and Ethel D. Hoover, “The

CPI and Problems of Quality Change,” Monthly Labor

Review, November 1961, pp. 1175-1185.

10 These include (in order of com pletion): Jack E.

Triplett, “Automobiles and Hedonic Quality Measurement,” Journal of Political Economy, M ay-June 1969,

pp. 408-417; Phoebus J. Dhrymes, “On the M easurement

of Price and Quality Changes in Some Consumer Capital

Goods,” American Economic Review, M ay 1967, pp. 501-

518, and “Price and Quality Changes in Consumer Goods:

An Empirical Study,” in Griliches, ed., op. cit. (1971);

Richard J. Olsen, “Some Aspects of Quality Change as an

Economic Variable,” (Rutgers University, unpublished

Ph. D. dissertation, 1968); and (on pickup trucks, a closely

related product entering the w p i but not the c p i ) , Robert E.

Hall, “The Measurement of Quality Change from Vintage

Price D ata,” in Griliches, ed., op. cit. (1971).

11 See Triplett, op. cit. (1969). An elaboration of this

point appears in the full report from which the present

article is condensed.

12 Meyer L. Burstein, “ Measurement of Quality Changes

in Consumer Durables,” The Manchester School, September

1961, pp. 267-279, and Dhrymes, op. cit. (1971). A number

of other studies were carried out on various w p i components

and are discussed in the full report.

13 Burstein, p. 279.

14 Dhrymes, op. cit. (1971).

15 See Zvi Griliches, ed., op. cit. (1971).

18 These are contained in the unpublished Gavett memorandum referred to in the text.

17 Leonard W. Martin, “Pure Price Indexes, Quality

Change, and Hospital Costs,” Proceedings of the American

Statistical Association, Business and Economic Statistics

Section, 1966, pp. 479-487.

18 Anne A. Scitovskv, “Changes in the Costs of Treatment of Selected Illnesses, 1951-65,” American Economic

Review, December 1967, pp. 1182-1195.

19 See, for example, Yoram Barzel, “Costs of Medical

Treatment: Comment,” American Economic Review, September 1968, pp. 936-938.

20 “Productivity and Price of M edical Services,” Journal

of Political Economy, Novem ber-Decem ber 1969, pp.

1014-1027.

21 Robert D. Lamson, “ Measured Productivity and Price

Change: Some Empirical Evidence on Service Industry

Bias, M otion Picture Theaters,” Journal of Political Economy, March-April 1970, pp. 291-305.

22 Jack E. Triplett, “Quality Bias in Price Indexes and

New M ethods of Quality M easurement,” in Zvi Griliches,

ed., op. cit. (1971).

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Technical Note

The Use of Price Indexes

in Escalator Contracts

I n long-term contracts governing wages, rents,

continuous or future delivery of a product,

alimony payments, administration of legacies,

and delivery of a new product for which the seller

has no satisfactory cost estimate, to name a few

examples, changes in the purchasing power of the

dollar pose a problem because they are beyond the

control of the contracting parties. One method

the parties use to protect themselves against unforeseen price change, especially in times of inflation,

is the escalator clause. Essentially, this attempts

to have the transaction price represent constantvalue units as measured by the quantity of goods

and services which a given amount of money will

buy. It usually employs a price index as an

objective means of adjusting the actual price.

One advantage of escalation is that the techniques

and measures used to convert monetary units

into constant-value units are normally mechanical

and, once established, cannot be manipulated by

either party. Another is that it is relatively inexpensive to administer since, after the mechanics

have been agreed upon, very little computing is

required.

This article discusses the techniques of escalation using the two major price indexes published

by the Bureau of Labor Statistics— the Consumer

Price Index (CPI) and the Wholesale Price

Index (W PI). It does not discuss the pros and

cons of using one type of index or data as against

another, or the desirability of escalation in preference to other means of protecting against price

change.1 Both of these are matters for the

contracting parties to decide. But, for those who

are interested in escalation, the article highlights

some of the essential qualities of the data they

may specify in the contract, shows how these

data should be described in the agreement, and

suggests techniques for adjustments.

BLS Data for Escalation Purposes

The Consumer Price Index. The CPI measures

changes in the cost of a list of goods and services

which represents the items important in the

expenditures of urban wage earners and clerical

workers and their families. It does not measure

their actual expenditures or their total cost of

living, both of which include outlays for such

purposes as income taxes, contributions to charity,

and personal insurance— things which the workers

and their families do not “consume.” Nor does

it measure the cost of changes in the manner or

level of living which are typically associated with

changes in income, size of family, the age of

family members, etc. It does, however, measure

changes in the prices of things which the '‘average”

family normally buys for current consumption

and, conversely, the purchasing power of the

dollar spent by workers and their families' as a

group.

The same items are priced month after month,

using specifications to insure that identical qualities are priced, in about 50 cities These cities

represent all urban areas from metropolitan New

York City to communities with as few as 2,500 i

i O th er m eth o d s in clu d e h ed g in g , w h ic h in v o lv e s use of a cou n ter b ala n cin g

tran saction ; cost p lu s con tra cts, w h ich p laces th e risk on o n e o f th e parties;

target or in c e n tiv e con tra cts, w h ich stip u la te th e origin al price an d a fee,

w ith th e fee increased if co sts are d ecreased; an d d eliv ery price co n tracts,

w h ich p ro v id e th a t price w ill b e d eterm in ed b y m a rk et or co st co n d itio n s at

th e tim e o f fu ture d eliv ery .

From the Review of August 1963

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residents. Price trends in each city affect the

United States index according to population.

W ith in ea ch c ity , p ric e ch a n g es fo r th e sa m p le 

g o o d s a n d serv ices a re c o m b in ed w ith w eig h ts 

b a s e d o n th e im p o rta n c e in fa m ily e x p e n d itu re s 

of th e sa m p le ite m s a n d th e re la te d ite m s w h ich 

th e y re p re s e n t. T h u s , if fam ilies m a k e 1 p e rc e n t 

of th e ir o u tla y s fo r m ilk a n d 20 p e rc e n t fo r r e n t, 

a 5 -p e rc e n t rise in re n ts w o u ld h a v e 20 tim e s as 

m u c h effect o n th e in d e x as a 5 -p e rc e n t rise in 

m ilk p rices.

Once the item sample has been determined, it

stays fixed until the next major weight revision2

or until there is clear evidence that an alteration

in the list of goods or services is called for. For

example, as wages and their purchasing power

rise, workers begin to spend proportionately less

for food and other necessities and eventually the

index weights must be revised. Also, new items

must be added from time to time as they become

important— such as television sets and nylon hose.

T h e C P I is p u b lis h e d a b o u t th e 2 5 th of th e 

m o n th fo llo w in g t h a t to w h ich th e in d e x ap p lies 

a n d re fle c ts p rice s co llec te d a t v a ry in g d a te s d u rin g 

th e e n tire m o n th . S e p a ra te in d e x es a re a v a ila b le fo r m o s t of th e la rg e s t cities: fo r N e w Y o rk , 

C h ic a g o , L o s A ngeles, D e tr o it, a n d P h ila d e lp h ia 

o n a m o n th ly b a s is; fo r th e o th e rs o n a q u a rte rly 

b a sis. I n a d d itio n to th e to ta l, o r th e A ll Ite m s 

in d e x , s e p a r a te in d e x es a re c a lc u la te d fo r m a jo r 

c a te g o rie s qf fa m ily sp e n d in g : F o o d , h o u sin g , 

a p p a re l, tr a n s p o r ta tio n , e tc .3

The CPI has been used for many years as a

wage escalator in labor-management contracts.

It is estimated that about 2 million workers are

now covered by such agreements. The index is

also used to a lesser extent to adjust rent payments, royalties, pensions, and alimony payments. *

s T h e se re v isio n s, b ased on d eta iled su r v e y s of w o rk ers’ in co m e s an d ex p en d ­

itu re s, are m a d e a t in te r v a ls o f a b o u t 10 years. T h e n e x t re v isio n is sch ed ­

u le d for c o m p le tio n w ith th e J a n u a r y 1964 in d ex . A s u m m a r y o f th e m ajor

ch a n g es in c id e n t to th e re v isio n w a s p u b lish e d in th e J u ly issu e o f th e R e v ie w ,

p p . 794-795.

8 A m o re d eta iled d escrip tio n o f th e in d ex as cu rren tly ca lcu la ted is a v a ila b le on re q u est.

* O fficial m o n th ly in d ex es are a v a ila b le sep a ra tely for so m e o f th e m ajor

g rou p s o f c o m m o d itie s, as w e ll as for th e to ta l, c o n tin u o u sly sin c e 1890. A

fin er cla ssifica tio n b y su b g ro u p s o f co m m o d ities is a v a ila b le sin ce 1913.

I n 1952, th e th ird le v e l o f cla ssifica tio n — p ro d u ct class—w a s in tro d u ced ;

th e s e h a v e b een ex ten d ed b a c k to 1947.

5 Q u estion n aires w ere se n t to th e 2,700 n a m es on th e m a ilin g list for th e

m o n th ly p ress release a n d to th e 4,200 w h o receive th e d eta iled rep o rt. T h e

n u m b e r o f u sa b le retu rn s to ta le d 3,026.

Recently, an insurance company began issuing

life insurance policies which contain a provision

that benefits will be increased in proportion to the

rises in the CPI.

The Wholesale Price Index. The W PI is a general

purpose index designed to provide a continuous

monthly series showing price changes, singly and

in combination, for all commodities sold in primary

markets of the United States. The index measures the general rate and direction of price m ovements in primary markets and the specific changes

for individual commodities or groups of commodities. It is based on a sample of over 2,100 commodities chosen to represent a wide variety of

commodity specifications and markets. The

prices used in constructing this index are those

which apply at the first important commercial

transaction for each commodity. M ost are the

selling prices of representative manufacturers

or producers or prices quoted on organized exchanges or markets. The basic weights are total

transactions as reported in the latest industrial

censuses.

The index is intended to measure price changes

between two periods of time, excluding the influence of changes in quality, quantity, terms of

delivery, level of distribution, unit priced, or

source of price. To accomplish this, the index

calculations are based on the relative change from

one period to the next in prices of identical or

nearly identical items, as defined by precise

specifications.

T h e b a s ic A ll C o m m o d itie s in d e x is d iv id e d in to 

15 m a jo r g ro u p s a n d a b o u t 80 su b g ro u p s. I n 

a d d itio n , so m e 300 “ p r o d u c t c la ss” in d e x es, 

w h ich g ro u p c o m m o d itie s c h a ra c te riz e d b y sim ila rity of ra w m a te ria ls , p ro d u c tio n processes, o r 

en d u se, a re of p a r tic u la r in te r e s t to u se rs of 

e s c a la to r clau se s.4 T h e B u re a u w ill also, u n d e r 

c o n tra c t, c o n s tru c t in d ex es fo r sp e cia l c o m b in a ­

tio n s of in d iv id u a l series to m e e t th e sp e cific atio n s of th e p a rtie s to a n e s c a la to r a g re e m e n t.

A 1961 s u rv e y of W P I u se rs 5 re v e a le d t h a t 

932 c o m p a n ie s o r in d iv id u a ls u se d th e W P I fo r 

th e e s c a la tio n of sa les o r p u rc h a se c o n tra c ts 

to ta lin g n e a rly $14 b illio n . T h e in d e x es m o s t 

fre q u e n tly sp ecified in e s c a la to r c o n tra c ts a re 

sh o w n o n th e fo llo w in g p ag e .

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N u m b e r o f

contracts

Metals and metal products_________________________ 178

All commodities___________________________________ 124

All commodities other than farm

products and foods_______________________________ 87

Finished steel products_____________________________ 87

Steel mill products_________________________________ 73

Iron and steel______________________________________ 66

Machinery and motive products____________________ 24

Electrical machinery and equipm ent________________ 23

Machinery and equipment—________________________ 19

Structural steel shapes______________________________ 19

Petroleum and products____________________________ 19

Industrial chemicals________________________________ 16

General purpose machinery and equipm ent_________ 15

Chemicals and allied products______________________ 14

Carbon plates______________________________________ 13

Crude petroleum___________________________________ 10

Lumber and wood products_________________________ 10

Specially constructed index for

metals and metal products________________________ 10

Elements of Escalation

There are three major elements in an escalator

clause contract:

1. Establishment of the initial price or rate at

the time of the contract. The escalator clause

protects against radical changes in real costs from

the original estimate; it cannot correct an erroneous or inequitable original price. In fact, if the

original price is incorrect, almost any escalator

clause will exaggerate the error over time.

2. Selection of an appropriate escalating index.

Escalation is usually based on an index which is

assumed to represent the commodity or service

being escalated. The escalator, then, is subject

to any limitations inherent in the escalator index.

The CPI is generally used for escalating wages

and items sold at retail levels; the W PI is more

often used for adjusting prices of raw materials

or production equipment, industrial rents, etc.

3. Procedures for carrying out the escalation.

Six basic points are usually defined in escalator

mechanisms:

A. Identification of the Index To Be Used. The

index used as the escalator should always be

completely identified regardless of whether it is

a widely known index or a special combination of

individual series or categories. Exact title and

the index base period should be indicated. For

example, an adequate identification would be:

The Consumer Price Index, All Items, U.S., 1957-

59= 100, or the Wholesale Price Index, All Commodities (Except harm and Food Products),

1957-59=100, issued by the U.S. Bureau of Labor

Statistics. The indexes are published first in a monthly press release, about 2 weeks later in a detailed

report, and, about a month thereafter, in the

Current Labor Statistics section of the Monthly

Labor Review. The publication to be used should

be specifically named in the contract.

If the contract is based on the W PI, it is safer

to specify whether the preliminary or final index 6

is to be used. The CPI is final on first publication. If an item or group or specially computed

combination of individual W PI series is to be

used, the BLS code or category numbers should

be included in the identification.

In specially computed indexes based on either

the CPI or W PI, the relative weights of items

should be specified. For example, an index for

escalating the price of turbines might assign the

major components of the product the following

relative importances to reflect changes in material

costs:

W eight

B L S C o d e { p e r c e n t )

Carbon steel plate-------------------------- 10-14-26 50

Carbon steel sheet_________________ 10-14-46 20

Electrical sheet____________________ 10-14-50 15

Steel forgings______________________ 10-15-71 7

Copper wire_______________________ 10-26-01

B. Reference Dates of Escalation. The date to which

the index being used as the base of the escalator

refers should always be indicated. The reference

base period is usually not the same as the base

period of the price index or series, for example, the

reference index may be the CPI for March 1962,

stated on the 1957-59 official base period. The

reference date of indexes on which subsequent

changes are to be computed should also be specified. The parties may prefer— for the escalator

base or the subsequent adjustments— to use a

particular month’s index, an annual average, or

an average for 3 months, or 6 months, or 5 years,

or any other period or date, whatever suits their

purpose. In any event, the contract should

specify precisely the reference dates of the indexes

to be used.

« In d ex es are con sid ered p relim in ary for 1 m o n th , or u n til th e Index for th e

m o n th fo llo w in g th e d a te o f reference Is p u b lish e d .

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C. Frequency of Adjustment. It should also specify

the effective dates of adjustments. The parties

may agree that adjustments are to be made

quarterly. For example, if the index goes up

or down by a specified number of points or fraction

of a point by the end of the quarter, the change in

wages, rents, etc., takes place automatically at a

stipulated time. If the index does not change by

at least this amount, then no change is called for

in the wage rate, rent, or product price.

On the other hand, the change in the payment

m ay be required whenever the index reaches a

certain point— 118.0 (1947-49=100), 120.0, etc.—

or when it changes by a specified amount. Thus,

if an increase of 1 cent in a wage rate is called for

whenever the index moves up 0.5 point, the time

interval is immaterial—it may be 1 month or 6,

or 12, etc.

Two factors should be considered in deciding

the frequency of adjustments:

1. Too frequent adjustments may create some

difficulties because of the seasonal or erratic

movements of prices, particularly for farm products and foods. As commodities move up the

processing scale away from raw materials into

more highly fabricated goods, seasonal price

changes tend to become progressively less important. Use of quarterly, semiannual, or annual

average indexes will minimize such periodic

fluctuations and result in a smoother adjustment

pattern.

Conversely, in a period of continuous price

movement in one direction, infrequent adjustments may understate the true change somewhat,

since escalator clauses adjust only for what has

already happened. When prices are rising, payments do not increase as rapidly as the index;

when prices are falling, payments do not

decrease as fast as the index.

2. The time lag between collection of the basic

price data and publication of the indexes does not

permit the contracting parties to time adjustments

to coincide with the occurrence of price changes.

For the CPI and the W PI, 4 to 6 weeks elapse

between the collection and the release of the

index, even in preliminary form. If a final

index is used, then the lag increases by at least a

7 T h e reference b a se for b o th th e C P I an d th e W P I w a s ch a n g ed In 1962

from 1947-49=100 to 1957-59=100. A lth o u g h th e Ind exes are a lso a v a ila b le

o n th e 1947-49 b a se, u sers sh o u ld co n sid er sh iftin g to th e n e w b ase a s soon as

p ra ctica b le.

month. Unless provision is made for this reporting lag, the understatement of the true price

change 'will be intensified when prices are changing

rapidly. In many instances, particularly for

rents, retroactive payments are called for in

order to correct the time lag.

D. The Mechanics of Adjustment. The heart of the

escalator clause is the method of adjustment,

which can be varied in many ways, depending on

the purpose for which the index is to be used and

the wishes of the contracting parties. There are

two basic methods of adjusting payments in

accordance with a price index— one is to apply to

the price (or the wage rate) some multiple of the

percentage change in the index; the other is to

provide that for each specified absolute change in

the index, the price will change by some specified

amount. Either method is satisfactory. However, unless the base index is exactly 100.0, a

change of one index point is not the same as a

change of 1 percent. When the index is greater

than 100, a change of an index point is less than

a change of 1 percent and when the index is less

than 100, a change of a point is more than a

change of 1 percent. Therefore, a clause might

read that prices will change 1 percent for each

1-percent change in the index or that prices will

change a given dollar-and-cents amount for each

1-point change in the index.

M any wage agreements contain a cents-to-point

relationship, requiring that wage rates be upped

by a cent for every change of one-half (or 0.5)

point from the base index. Others condition the

change on 0.6 point, a whole point, etc. Such a

relationship frequently is predicated on the wage

rate-price index relationship at the time of the

agreement. For example, if the base index for

escalation is 120.0 and the average wage is $2.40

per hour, then one index point is equivalent to 2

cents and one-half point equals 1 cent. To maintain this relationship, the parties may agree on a

1-cent wage increase if the index moves up onehalf point. Under this type of agreement, a new

cents-to-point relationship must be calculated

when the BLS changes its index base period,

because rebasing changes the value of an index

point.7 The index-wage relationship can also be

of the percentage type. This is less frequently

used in wage agreements than the cents-to-point

relationship— perhaps because the resulting wage

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rates might result in fractions of a cent or because

the concept is not quite as simple.

For leases and other long-term price agreements,

the percent-of-change technique offers no difficulty. If this method is adopted, the clause

should specify how the change is to be computed;

for example, the parties may decide that an index

change of 12.5 percent is to result in a price adjustment of 12 percent, 12.5'percent, or 13 percent,

or they may work out a schedule of changes, as in

the cents-to-point contracts. As both the CPI

and the WPI are published to one decimal place,

it is desirable that contracts refer to the indexes

in these terms.

E . U p p er a n d Low er L im its o f A dju stm en t. The

escalator clause should specify whether adjustments will be made for index changes in either

direction or only one. If an agreement mentions

increases only, presumably decreases are not

contemplated. Some clauses, on .the other hand,

specify that wages, rents, etc., are to move down

as well as up but are not to drop below a specified

minimum; for example, a wage escalation clause

may call for a 1-cent decrease for every 0.4 index

point down to an index level of 97.8.

F. P rovision fo r R evision o f the Index. The Bureau

occasionally publishes corrections of indexes previously published as final, because of late reports

or errors. The contract should specify whether or

not account is to be taken of such corrections.

For statistical accuracy, the Bureau is committed to keep the composition of the indexes in

line with prevailing conditions. In both the CPI

and the WPI, the BLS revises commodity specifications, adds new products, discontinues obsolete

items, and, from time to time, revises the weighting

structure and reference base.

Escalator mechanisms cannot be controlled by

either pai;ty, so agreements often stipulate a

procedure to follow if the escalator mechanism

changes or disappears. In most cases, this procedure simply states that the original issuing

agency will be sole judge of the comparability of

successive indexes, and that if the agency cannot

supply indexes which are comparable, a named independent authority (such as the dean of the business

school or the head of the economics department in

the State university) will select a method of continuing the contract. When the relationship is

one of cents to point as in a wage contract, the

parties may want to renegotiate. For this reason,

the Bureau gives notice of anticipated changes in

the official indexes at least 6 months in advance.

— F r a n c i s S. C u n n i n g h a m

D ivision of Industrial Prices and Price Indexes

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Fluctuations in 

the rate of change 

of consumer prices 

generally match changes 

in economic activity

GEOFFREY H. MOORE

I n f l a t i o n is characterized by a general and widely

diffused rise in prices and costs. However, all

prices and factors affecting prices do not begin to

rise or fall at the same time. Moreover, prices

do not all move at the same pace. These differences in price behavior have significant consequence. Real wages—money wages adjusted for

price changes—may rise or fall, with vital effects

on the wage earner and his family. Profit margins,

dependent on the difference between prices and

costs, may rise or fall, thereby encouraging or

discouraging expansion of production, hiring of

workers, development of investment plans, or

shifts of resources from one activity to another.

This article sets forth some of the results of a

recent study of the cyclical behavior of prices.1

It describes a new chronology of fluctuations in

the rates of change in the price level, relates these

fluctuations to those in overall economic activity,

examines the extent to which price changes involve

the entire price system, measures the tendencies

of some prices to lead and others to lag, and

sketches the relationship of price cycles to changes

in costs and profits. Recent developments are

touched on wdth a very broad brush.

Reference chronology for prices

A simple yet effective device for studying business cycles is the National Bureau of Economic

Research’s reference chronology of peaks and

troughs in economic activity, created by Wesley

Clair Mitchell. It is a widely used device in tracing

fluctuations in the economy and has imprinted

upon the minds of many economic statisticians the

Geoffrey H. Moore is Commissioner of Labor Statistics,

Bureau of Labor Statistics.

Postwar

price cycles:

a new

chronology

meaning of “shaded areas” in charts of monthly

time series.

In view of the usefulness of such a framework,

it seems sensible to adopt a similar strategy for

studying movements in the price system. To do

so, however, a number of questions have to be

faced. Should the chronology represent peaks and

troughs in the level of prices or in their rate of

change? If the latter, how should the rate of

change be measured? monthly? quarterly? W hat

type of data should be used: unadjusted or seasonally adjusted? W hat index or set of indexes

of prices should be used to construct the chronology? What criteria should be set up to define

the chronology and identify its turning points?

The business cycle chronology was based on the

working definition of business cycles set forth by

Mitchell in his 1927 volume, Business Cycles— The

Problem and Its Setting,2 and later refined by

Arthur F. Burns and Mitchell in their 1946 monograph, Measuring Business Cycles.3 In brief, the

definition applied three criteria to the problem:

the magnitude, the duration, and the diffusion of

fluctuations in economic activity. One inquired

how large the decline or rise in aggregate activity

was, how long it lasted, and how widely it was

diffused over different economic sectors. Turning

points were identified not by referring to a single

aggregate, such as gross national product, but by

determining the consensus among a number of

series, each of which had some claim to represent

or reflect total economic activity.

There is much to be said for developing a price

chronology in a similar manner. Whether it is the

level of prices or their rate of change that is

selected as the ultimate variable, attention should

naturally be focused upon swings that are of substantial size, last more than just a few months, and

are widely diffused throughout the price system.

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It would be convenient to depend upon a single

general price index for this purpose. Unfortunately,

although the idea of an index of the general price

level is an ancient one, there is today no single

widely accepted measure of it. The three leading

candidates would be the Consumer Price Index,

the Wholesale Price Index, and the Gross National

Product Deflator. Each of these has its merits and

deficiencies for the purpose.

The Deflator is quarterly, whereas the other two

indexes are monthly, and other things being equal,

a monthly chronology would be preferred. The

Deflator has the largest economic coverage, but

that also means it includes some dubious elements,

notably in the government sector where the “price”

is really a wage rate. For this reason many consider

the Private g n p Deflator a better price index.

The Deflator is affected not only by changing

prices but also by changing weights, because it is

derived by dividing current dollar g n p by an

estimate of g n p in constant dollars, whereas the

other two indexes use fixed weights and hence

reflect price changes alone.

The Wholesale Price Index, of course, covers

only one part of the price system— commodities,

not services— has some gaps in its industrial

coverage and depends in part upon list prices

rather than actual transaction prices. The Consumer Price Index is the closest approximation of

the three to an actual transaction price index but

is limited to prices paid by urban wage earners’ and

clerical workers’ families. Unlike the other two,

it includes prices for existing goods, such as houses

and used cars, as well as for newly produced goods

and services.

These considerations do not point to a clear-cut

conclusion, except to suggest a real need for a

monthly general price index. Lacking this, I have

based the chronology in this paper upon the Consumer Price Index, using the g n p Deflator and

the Wholesale Price Index, and some of their

principal components (for example, the Private

Deflator and the wpi for industrial commodities)

to provide supplementary evidence. The c p i has

risen almost continuously since 1954, but there

have been sizable fluctuations in its rate of increase, and the chronology identifies these fluctuations. The rate of inflation is, of course, of major

concern. The chronology shows when this rate, as

measured by the c p i , reached high points and low

points since 1947.

To aid us in identifying turning points in the

price cycle, we turned to a National Bureau of

Economic Research computer program recently

developed by Charlotte Boschan and Gerhard Bry.

Essentially, this program reproduces, in an objective and mechanical fashion, most of the choices

of “specific cycle” turning points that used to be

entirely dependent upon the judgment of National

Bureau staff. Of course, it uses criteria that are

similar to those used by the staff. It bases its

choices upon whether the fluctuations in the data

are sufficiently large and long enough to be reflected in various moving averages, but does not

explicitly use any criterion as to the size of a

swing. Despite this, it is rather uncanny in its

ability to detect and identify turning points

independently selected by experts. We used the

turns selected by the computer program in a

large majority of instances. The exceptions were

due to the occasional failure of the program to

mark a large movement because it is too short,

or (more frequently) to mark very small m ovements simply because they last quite long.

After deciding upon the rate of change in prices

as the variable that the chronology would represent, several other decisions remained. First, the

rates of change had to be seasonally adjusted or

derived from seasonally adjusted indexes. During

the past year the Bureau of Labor Statistics has

been reporting the seasonally adjusted rate of

change in the c p i . The seasonal pattern has a

relatively small effect upon the level of the index

(currently the largest and the smallest seasonal

factors are, respectively, 100.12 in July and 99.83

in January and February). Nevertheless, it has a

substantial effect upon rates of change over short

periods. For example, the rate of change from

July 1969 to January 1970 is raised from an annual

H§tte of 5.7 percent to 6.3 percent after seasonal

adjustment, which is equivalent to dividing a

seasonal index of 90 into the unadjusted rate.

This seasonal effect has been powerful enough to

cause the unadjusted July to January rates to be

lower than either the preceding or the following

January to July rates in 4 years out of the past 5.4

Next, it is necessary to determine precisely

how the rate of change is to be measured. The

range of possibilities is wide. The interval over

which change is measured can be as short as 1

month or as long as 12 months or more. Monthly

indexes can be averaged over calendar quarters,

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or over moving 3-month intervals and rates of

change measured between these averages. More

complicated smoothing formulas can be applied.

Generally, month-to-month changes are highly

erratic, so some form of smoothing is desirable.

On the other hand, smoothing formulas can twist

and distort cyclical patterns and timing relationships. After some experimentation I concluded

that the rate of change over a 6-month span met

reasonably well such criteria as smoothness,

simplicity, and limited distorting effects, for the

c pi and most other price and wage series. For

series that are available only in quarterly form,

quarter-to-quarter changes are used. Occasionally

we use changes over 12-month or 4-quarter spans,

when these are the only data available or when

the 6-month or 1-quarter rates are unduly erratic.

Postwar price cycles

Taking into account the foregoing considerations, chart 1 shows the reference chronology,

based upon the rate of change in the Consumer

Price Index, together with rates of change in the

other comprehensive indexes mentioned earlier.

Six contractions in the rate of change are identified:

in 1947-48, 1950-52, 1953-54, 1956-58, 1959-61,

and 1966-67. We have marked a tentative peak in

February 1970. If this peak is confirmed b y data

later this year and in 1971, this will mark the

beginning of the seventh contraction since 1947.

Taking the 23-year period between the 1947 and

1970 peaks, we find that expansions in the Irate of

change lasted 162 months in the aggregate, while

contractions covered 106 months. That is, although

Chart 1. Rates of change in comprehensive price indexes

1947 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 1970

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the Consumer Price Index has been generally

rising during this period, the rate of increase has

declined over long stretches— aggregating nearly

9 years.

The other indexes show broadly similar fluctuations, but with exceptions, especially in the period

1959-64. In terms of these comprehensive indexes,

therefore, the chronology seems to represent

fluctuations that are widely diffused in the price

system. This matter will be examined more

directly later.

During the first three contractions in the rate

of change in the cpi, the rate fell below zero;

that is, the index declined. But the rate barely

reached zero in the next two contractions (1958

and 1961), and did not do so at all in the last one

(1967). Indeed, the level of the rate at its successive low points becomes progressively higher

throughout the period. There is a related tendency

for the declines in the rate to become progressively

smaller. In the first two contractions the rate

dropped 18 and 15 percentage points; in the next

two, 3 and 4% percentage points; and in the last

two, 2 and 2% percentage points. (See table 1.)

However, the high points in the rate have not

become progressively higher, nor have the expansions become progressively larger. If there has

been a rising floor under the rate, there has not

been a rising ceiling also. One possible explanation,

which needs further exploration, is that the rising

importance of services, and the diminishing

importance of foods in family budgets has had the

effect of preventing declines in the rate of change of

the cpi from reaching as low a level in recent

years as they did earlier in the postwar period.

Price cycles and business cycles

How does the price chronology compare with the

business cycle chronology? Four of the price

contractions correspond with the four business

contractions of 1948-49, 1953-54, 1957-58, and

1960-61. But the business expansion of 1949-53

was interrupted by the price contraction of

1950-52, and the long business expansion that

began in 1961 was interrupted by the price

contraction of 1966-67. Each of these interruptions was also characterized by some hesitancy

in business as well. Hence there is a notable

degree of correspondence between the behavior of

the rate of change in the Consumer Price Index

and general economic activity. Since World War

IT, every economic slowdown or actual recession

has been accompanied by a cyclical contraction in

the rate of change in the price level, and cyclical

contractions in the rate of change in the price level

have not occurred at other times.

This is not to say, however, that a business

recession is a necessary condition for a reduction

in the rate of inflation. As already noted, two

such reductions since 1947 have occurred at times

when the economy merely slowed down. Moreover,

several of the declines in the rate of price rise that

Table 1. Comparison of peaks and troughs in the rate of change of the Consumer Price Index (all items) with those for

selected price indexes, 1947-70

Peaks and troughs in the rate of change 

in the Consumer Price Index, all items

Peak or 

trough

Date Rate

(percent)

Peak..

Trough

Peak..

Trough

Peak..

Trough

Peak..

Trough

Peak..

Trough___

Peak...........

Trough___

Peak...........

October 1947.........

November 1948... 

November 1950... 

November 1952...

July 1953................

August 1954..........

July 1956...............

July 1958...............

July 1959...............

March 1961............

January 1966.........

January 1967.........

February 1970«...

13.8

-4 .3

14.3

- 0.6

2.1

- 1.2

4.3 

- 0.2

2.3 0

4.1

1.6

6.7

Median lead ( - ) or lag (+ ), in months.

Lead ( —) or lag (+ ) in months, at turns in the Consumer Price Index, all items

Consumer Price Indexes for— Wholesale Price Indexes for—

Food Other Services * All Industrial Consumer

commodities1 commodities commodities finished goods

0 0 0 0

0 + 3 + 5 0

0 0 - i - 2

+3 -1 7 -1 5 0

- 3 3 +7

+2 - 1 -1 0 - 1

- 3 +5 +7 - 5 -1 1 +17

0 0 + 2 (>) - 8 +1

+10 - 3 - 1 (*) - 5 +7

0 -1 0 + 2 0 - 6 - 1

1 (») + 5 0 +3 - 1

0 <*) +3 - 1 - 3 - 1

0 -1 .5 + 2.5 -0 .5 - 4 -0 .5

GNP implicit 

price deflator

0

+ 5

+ 2

+ 5

+6-10

<s)(>)

+4

+3

+3

+ 3

1 Comparable data not available prior to 1956.

3 Tentative.

3 No timing comparison.

NOTE: Rates of change in the Consumer and Wholesale Price Indexes are computed 

over 6-month spans, centered, seasonally adjusted at annual rate. Rates of change 

in the GNP deflator are computed from quarter to quarter, centered, seasonally adjusted 

at annual rate.

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were associated with business cycle contractions

began well before the contraction in business

activity got under way. The 1947 and 1956 peaks

in the rate of change in the Consumer Price Index

both came about a year before the business cycle

peak, and the 1959 price peak came 10 months

before the business peak. In fact, in 1948, all of

the decline in the rate of change in prices— and it

was substantial— occurred before the recession

began. In 1953, the two peaks coincided. More

often than not, then, the cpi has begun to decelerate while business activity was still expanding.

On the other hand, low points in the rate of

price change have coincided rather closely with

business cycle troughs, at least on three out of four

occasions. The 1948 upturn in the rate of price

change (from a level of minus 4 percent) came 11

months before the business upturn, but the 1954

price upturn coincided with the business upturn,

while the 1958 and 1961 price upturns followed

the business turn by 3 months and 1 month,

respectively. In short, declines in the rate of price

change have typically started earlier and hence

have continued somewhat longer than business

cycle contractions.

It is important to note, however, that the rate of

price change has usually persisted at a low level,

even a negative level, beyond the point of upturn.

Perhaps the most striking finding is that a year

or a year and a half after the business peak the

rates of price change have all been in the vicinity

of zero, plus, or minus 1 percent.

The diffusion of price change

One of the characteristics of business cycles

that W esley Mitchell deemed important, and

which he demonstrated empirically time and

again, was their generality. Mitchell and Burns

wrote in their 1946 volume: “A business cycle consists of expansions occurring at about the same

time in many economic activities, followed by

similarly general recessions, contractions, and

revivals. . . .” Among the many activities are

prices, and we have just seen that the rate of

change in the price level is clearly one of the

participants in the ebb and flow of business cycles.

This observation does not, however, directly

answer the question whether the price chronology

we have constructed reflects widespread, similar

movements among different prices. We can get at

this question by examining diffusion indexes of

prices, for such indexes report how many out of a

given population of prices are rising at a particular

time and how many are falling. In terms of the

popular conception of whether or not the economy

is experiencing inflation, or whether it is getting

worse or better, variations in the degree of generality of price increases are perhaps of more significance than variations in the rate of change in a

price index.

The price diffusion indexes constructed in this

analysis illustrate several propositions. First,

at all times some prices are falling and some are

rising, but the proportions that are in the one

category or the other vary greatly. Second, the

most widespread increases in prices have generally

occurred during the periods marked off as expansions in our price chronology, while the most

widespread reductions in prices have generally

occurred during the contractions. This reflects

the fact that the Consumer Price Index increases

more rapidly at some times than at others partly

because price increases are more widespread at

those times, not only because the increases are

larger.

Third, there are discernible sequences in the

process whereby price changes spread through

the economy: prices of industrial materials take

an early position, wholesale prices of manufactured

goods move somewhat later, and retail prices of

consumer goods and services come still later.

The sequences among those parts of the price

system are so long drawn out, in fact, that on

several occasions (notably during 1957-58) the

most widespread declines in the early moving

prices came almost at the same time as the most

widespread increases in consumer prices. Unless the

sequences in the price system are taken into

account, therefore, one could be misled into

thinking that the cyclical swings in prices are less

general than they are in fact.

Leads and lags

The diffusion indexes depict some of the

sequences in the price system. But we can examine

the matter more thoroughly by referring to the

rates of change in a larger array of price indexes

using the price chronology as a reference frame in

the same way that the business cycle chronology

has been used to study leads and lags in economic

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activities generally. In this manner we can observe

not only the leads and lags of other prices vis-a-vis

the Consumer Price Index, but eflso their leads and

lags with respect to one another.

Looking first at certain major components of

the Consumer Price Index, we find that the

turns in the commodity component match those

in the total index very closely. On five occasions

since 1956 (when the commodity-service grouping

first became available) the turns in the rate of

change in the commodity index and in the total

index came in exactly the same month, while on

the remaining occasion the commodity turn was

1 month earlier. This correspondence is due more

to food prices, whose volatile movements have a

marked effect on both the commodities component and the total, than to commodities other

than food. As for prices of services, their wellknown tendency to lag is apparent. Perhaps less

well known is the fact that the rate of change in

service prices undergoes cyclical movements that

correspond closely, except for the lag, to those in

commodity prices. The lag of service prices behind

commodity prices averages about 3 months.

Turning to wholesale prices, we find that the

total w p i exhibits a slight tendency to lead the

total c p i . That is, it leads on five occasions,

exactly coincides 4 times, and lags only once.

The lead appears to derive more from the industrial commodities in the w p i than from the

farm products, processed foods and feeds component. The latter component, however, matches

the c p i quite closely, and of course compares most

directly with the food price component of the

c p i , which, as we have seen, itself has a dominant

effect on the c p i . The behavior of consumer

prices depends, to an extent most city dwellers

are probably unaware of, on the behavior of farm

prices.

The industrial commodities component of the

w p i has turned before the c p i 9 times since

1948, coincided once, and lagged twice. The

tendency to lead is imparted primarily by the

prices for crude and intermediate materials other

than foods, rather than for finished goods. Prices

for crude materials other than food have led 9

out of 10 turns in the c p i since 1947; the average

lead is about 4 months. This index is similar in

its movements and timing to the weekly spot

market index of industrial materials prices. On

most occasions the turns in the rates of change in

these two materials price indexes have occurred

within a month or two of each other. Prices for

producer finished goods— that is, machinery,

equipment, trucks, office furniture, and so on—

show about as much tendency to lag behind as to

lead the movements in the c p i .

The rate of change in the g n p Deflator is a

lagging indicator in comparison with the rate of

change in the c p i . This is true also of the Private

Deflator, since its turns usually coincide with

those of the total. The Deflators have lagged

behind the turns in the c p i far more frequently

than they have led or coincided with it, and the

average lag has been about 3 months. The reason

for the lag may lie in the fact that personal consumption expenditures— that is, the type of

expenditure reflected in the c p i — constitute less

than two-thirds of total g n p , while the prices for

the two largest elements in the remainder—fixed

investment goods and government services— are

relatively sticky.

Our review of the complex structure of leads

and lags in the price system has merely scratched

the surface of the subject. Very generally, the

discernible sequences in the manner in which

price changes spread through the economy are as

follows: Prices of industrial materials move first.

Wholesale prices of manufactured goods move

somewhat later. Retail prices of foods and other

commodities follow shortly thereafter, and retail

prices of services, such as passenger fares and

medical fees, bring up the rear. In this review, we

have dealt with prices for fairly large groupings of

goods and services and have not dealt at all with

the prices of fixed assets, such as land or buildings,

or the price of labor, or of interest rates. There is

much room for further investigation.

Costs and profits

During the past few years, economists and

statisticians have developed a systematic body of

data that connects the rate of change in the price

level with rates of change in labor compensation,

output per man-hour, labor costs, profits and

other costs per unit of output. From these data,

a fairly clear picture of the general behavior of

costs and profits in the United States emerges.

When prices are relatively stable or declining—

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the bottom of the price cycle— the rate of increase

in output per man-hour is high. It diminishes,

however, as prices rise. Rates of increase in hourly

compensation for workers, on the other hand, are

usually at a moderate level but soon begin to rise,

partly in response to the upward movement of

prices. The rate’of change in unit labor costs is

low and oft n declining during the beginning

phase of price expansion but rises sharply in the

later phase as a result of the opposing movements

of the rates of change in labor compensation and

in productivity. Other unit costs follow a similar

path. The effect of all this on unit profits is to

produce a rapid rise at the start of a price

expansion, but a decline at the end.

The situation is reversed when inflation starts

to subside. When the rate of price increase first

starts down, output per man-hour continues to

grow at lower rates for a while but presently

starts up, contributing to a reduction in physical

costs. N ot long afterward, the rate of increase in

hourly compensation turns down. The output rise

and the compensation slowdown generate a decline

in the rate of increase in unit labor costs and other

unit costs start showing more moderate rates of

increase. In summary, at the start of a price

contraction, increases in total unit costs exceed

those of prices—with unit profits therefore

declining—but the downswing in costs exceeds

that in prices before the bottom of the price

contraction is reached.

How does the current situation in the United

States stack up in terms of the price chronology

we have outlined? As stated earlier in the article,

we have placed a tentative recent peak for the

chronology of prices in February 1970, based upon

the rate of change in the Consumer Price Index.

This is the month when the seasonally adjusted

rate of change over a 6-month interval reached its

highest level in the current upswing, 6.7 percent

per year. (February is simply the central month of

that interval, which runs from November 1969 to

M ay 1970.) Since then, the 6-month rate has begun

to decline, and the most recent observation on it

(covering the period March to September 1970)

is 5.0 percent.

We do not consider this peak to be firmly

established as yet, since the decline has not been

very large or very long. But there is evidence to

support it in the behavior of the Wholesale Price

Index, the Gross National Product Deflator, and

indexes of unit labor costs and unit profits.

Moreover, all of the price diffusion indexes for the

current period have receded from their highs,

which were reached during 1968 and 1969. That is,

fewer prices have been rising in recent months,

and more have been declining. The general trend

has been one of a slowing in the pace of price and

cost inflation, and that is the reason for recognizing

it, at least tentatively, in our chronology of price

change. 

----------FOO TNO TES----------

1 T h is a r tic le is a d a p te d from a p a p er p r e se n ted a t a

co llo q u iu m “ T h e B u sin ess C y cle T o d a y ,” w h ic h w as

sp o n so red b y th e N a tio n a l B u rea u of E c o n o m ic R e se a r c h

in S e p tem b er 1970. T h e fu ll p ap er w ill b e p u b lish e d as

The Cyclical Behavior of Prices (b l s - R e p o r t 384, 1970).

2 W e sley C. M itc h e ll, Business Cycles— The Problem and

Its Setting ( N e w Y ork , N a tio n a l B u reau of E co n o m ic

R esea rch , 1927).

3 A rth u r F . B u rn s a n d W esley C. M itch ell, Measuring

Business Cycles (N e w Y ork , N a tio n a l B u rea u of E c o n o m ic

R esea rch , 1946).

* T h e su b sta n tia l se a so n a l e ffect on th e ra te of ch a n g e

can b e illu stra te d as fo llo w s. T h e in crease in th e sea so n a l

fa c to r fro m 9 9 .8 3 in J a n u a ry to 100.12 in J u ly is 0 .6 p ercen t

a t an a n n u a l rate. If th e in crease in th e u n a d ju ste d in d ex

is a t a 6 -p e r ce n t a n n u a l rate, th e sea so n a l fa c to r a c co u n ts

for a b o u t 10 p e rcen t of th e rise. O f course, it h as an eq u al

a n d o p p o site e ffect on th e in crease from J u ly to J a n u a ry .

T h e u p s a n d d o w n s in th e ra te of in crease th a t are a ttr ib u ta b le to se a so n a l fa cto rs can b e q u ite m islea d in g in ju d g in g

tren d s in th e ra te of in fla tio n . A s th e figures g iv en b elo w

in d ic a te, th e se a so n a lly a d ju ste d ra tes sh o w far m ore

clea rly th e o n se t of in fla tio n in 1965, its in te rr u p tio n in

1967, a n d its c o n tin u a tio n th ereafter, th a n do th e u n ­

a d ju ste d ra tes.

Percent change at annual

rate, c p i , all items

Unadjusted

Seasonally

adjusted

1964— J an uary-J u ly_____ 1.1 0. 6

July-January____ 1.1 1. 6

1965— J a n u a r y -J u ly ___ 2 .4 1. 9

July-January_____ 1.5 2. 0

1966— J a n u a r y -J u ly ___ 4 .2 3. 7

July-January____ 2 .5 3. 1

1967— J a n u a r y -J u ly ___ 3 .2 2. 6

July-January__ _ 3 .6 4. 2

1968— January-July____ 5 .0 4. 4

July-January _ . _. 4 .3 5. 0

1969— January-July____ 6 .7 6. 1

July-January__ _ 5 .7 6. 3

1970— J a n u a r y -J u ly ___ 6 .0 5 .4

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Prices in 1972:

An analysis

of changes

during Phase 2

Price behavior in 1972 was marked by the existence of Phase 2 of the Economic Stabilization Program put into effect by the President on August 15,

1971. Phase 1 of that program consisted of a freeze

of virtually all wages and prices that lasted until

November 13, 1971. Phase 2, which ensued immediately, consisted of a varied program of regulation

ranging from the exemption of prices of certain raw

commodities, particularly farm products, to the imposition of absolute control of the rate of price increase in areas such as medical care. In between

there were regulations governing the extent to which

cost increases could be passed through as price increases and the extent to which profit margins could

rise.

Prices for most major groups of commodities and

services in the Consumer (CPI) and Wholesale

(WPI) Price Indexes rose at a slower rate in 1972

than in the first 8 months of 1971 up to the freeze.

The major exceptions were food prices at retail and

wholesale and the price index for crude nonfood materials, a component of the Industrial Commodities

Index of the WPI.

During 1972 the Consumer Price Index rose 3.4

percent. The annual rate of increase for the 13

months after Phase 2 began in November 1971 was

also 3.4 percent. The analysis of price behavior

throughout this article will be based largely on

movements during the 13 months from November

1971 when Phase 2 began through December 1972.

How the rate of advance during this time period

compares with periods before and during the Phase

1 freeze is shown in table 1 for the CPI and WPI

and their major components. Changes are expressed

at annual rates (all seasonally adjusted except

services).

Joel Popkin is assistant commissioner for Prices and

Living Conditions, Bureau of Labor Statistics. Toshiko

Nakayama, an economist in the Division of Consumer Prices

and Price Indexes, assisted.

Prices of finished goods rose less

at retail than at manufacturers’ level;

spread narrowed between

price increases for services

and those for nonfood commodities

JOEL POPKIN

From December 1969 to December 1970 the rise

in the CPI slowed by 0.6 percentage points. From

December 1970 to August 1971 (up to the start of

the freeze) the rate dropped further by 1.7 percentage points. From the start of the freeze through December 1972, the first 16 months of the Economic

Stabilization Program, the CPI increased at an annual rate of 3.2 percent, a drop of 0.6 percentage points from the pace of the first 8 months of

1971. During the 13 months of Phase 2 through

December of 1972 the rate was down 0.4 percentage points from the rate in the 8 months before the

freeze.

In the spring of 1971, there was a sharp decline in

mortgage interest rates which affects the comparisons

made between the first 8 months of 1971 and periods

preceding and following them. If the change in the

CPI in the first 8 months of 1971 is recalculated to

exclude mortgage interest costs and the effect the

elimination of die excise tax on autos had on the

August CPI, the rate of advance is higher, 4.8 rather

than 3.8 percent.1 With these exclusions during January-August of 1971 and the exclusion of mortgage

interest costs from the CPI during Phase 2, the

decline in the rate of advance of the CPI in Phase 2

through December compared to the pre-controls period is slightly more than 1 percentage point. A decline in the rate of advance of the CPI from 1970

to the first 8 months of 1971 still shows up, even

when mortgage interest costs are eliminated from

1970 data, but that decline is much smaller.*

Consumer prices

The pattern of movement in the CPI was varied

throughout 1972. In the first 3 months of Phase 2 it

rose at an annual rate of 4.8 percent. In spring as

food prices fell and price rises for services decelerated, the pace of increase slowed to 2.2 percent in

the 3 months ending in June. In the 3 months

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ending in September, the pace quickened to a rate of

4.6 percent as prices of food began to rise again

sharply and those of nonfood commodities advanced

at a faster rate than in the second quarter. Increases

in the food and nonfood components slowed in the

final 3 months of the year with the result that the CPI

rose at a lower rate of 3.2 percent from September

to December.

Implicit price deflator

Another measure of price change, available quarterly, is the implicit price deflator (IPD) for private

gross national product. From the fourth quarter of

1971 to the fourth quarter of 1972, a period roughly

commensurate with Phase 2, the rise in the IPD was

lower than the 3.9-percent rate from the end (fourth

quarter) of 1970 to the third quarter of 1971, the

quarter in which the freeze was imposed (table 2).

A major factor contributing to this slowdown was a

slackening in the advance in unit labor costs. The

rate of advance for compensation per man-hour fell

to 6.6 percent during 1972 from 7.1 percent during 1971 up to the quarter in which the freeze was

ordered. Moreover, throughout most of 1972 the

rate of increase in compensation per man-hour fell,

reaching a low of 4.4 percent in the third quarter.

Output per man-hour advanced more rapidly during

Phase 2— 4.9 percent— than in 1971, before the

freeze— 4.3 percent. Because of these two factors,

unit labor costs rose at the lowest rate for any fourquarter period since 1965. Since the rise in the deflator exceeded the increase in unit labor costs during

Phase 2 through December 1972, unit nonlabor

costs advanced in 1972, but less rapidly— based on

indications from preliminary data— than in prefreeze 1971.

The deflator for personal consumption expenditures (PCE), a component of the deflator for private

GNP, rose 2.5 percent in 1972. Because both this

deflator and the one for private GNP have moving

weights they are not measures of price change alone,

but both are calculated, alternatively, on a fixed

weight basis. On this basis the deflator for PCE is

more nearly comparable to the fixed weight CPI.

From the fourth quarter of 1971 to the fourth quarter

of 1972, the fixed weight PCE deflator increased less

than the CPI. Much of this difference is attributable

to the treatment of owner-occupied housing in the

two indexes.3 The annual rate of change in the fixed

weight deflator for PCE from the third to the fourth

quarter of 1972 was 2.9 percent as compared

with 3.9 percent between the third and fourth

quarter averages of the CPI.

Wholesale prices

The Wholesale Price Index for all commodities

rose at a faster rate in the 13 months after the freeze

than in the 8 months before it. This occurred as a

result of an acceleration during Phase 2 in price rises

for farm products and processed foods and feeds that

more than offset a deceleration in the rate of

advance for industrial commodity prices. The rate of

Table 1. Changes in Consumer and Wholesale Price Indexes, selected periods 1968-72

[Seasonally adjusted, except services, compound annual rate]

Item

12 months,

Dec. 1968 to

Dec. 1969

12 months,

Dec. 1969 to

Dec. 1970

• months

prior to

Phase 1,

Dec. 1970 to

Aug. 1971

3 months,

Phase 1,

Aug. 1971 to

Nov. 1971

13 months,

Phase 2,

Nov. 1971 to

Dec. 1972

16 months,

Phases 1

and 2,

Aug. 1971 to

Dec. 1972

First

7 months,

Phase 2,

Nov. 1971 to

June 1972

Second

6 months,

Phase _2,

June 1972 to

Dec. 1972

CONSUMER PRICE INDEX

All items........................................................................... 6.1 5.5 3.8 1.9 3.4 3.2 3.1 3.9

Food.......................................................................... 7.2 2.2 5.0 1.7 5.0 4.4 4.0 6.1

Commodities less food............................................. 4.5 4.8 2.9 0 2.5 2.0 2.5 2.5

Services.................................................................... 7.4 8.2 4.5 3.1 3.6 3.5 3.7 3.5

WHOLESALE PRICE INDEX

All commodities............................................................... 4.8 2.2 5.2 -.2 6.6 5.3 5.3 8.1

Farm products and processed foods and feeds___ 7.5 -1.4 6.5 1.1 14.7 12.0 7.6 23.6

Industrial commodities............................................. 3.9 3.6 4.7 - .5 3.5 2.7 4.4 2.6

Selected Stage of Processing Indexes:

Crude materials except food............................ 10.2 4.6 3.3 2.3 10.3 8.8 8.5 12.6

Intermediate materials except food................. 3.9 3.1 6.5 - .7 4.0 3.1 4.8 3.0

Producers’ finished goods................................ 4.6 4.9 3.7 -2.0 2.3 1.5 4.1 .2

Consumers goods except food.......................... 2.9 4.0 2.2 - .4 2.4 1.9 2.9 1.8

Consumer foods................................................ 8.2 -2.5 6.8 .3 8.8 7.1 5.4 12.9

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Table 2. The anatomy of price change

Chang* in quarterly averages

Ken IV-68 to

IV-C9

IV-69 to

IV-70

IV-70 to

111-71 ‘

Phase 1

111-71 to

IV-71 *

Phase 2

1972

IV-71 to

IV-72 *

Phases 1

and 2

IU-71 to

IV-72»*

Deflator: Private 6NP........................................................................................... 5.0 4.8 3.9 1.0 2.7 2.3

Personal consumption expenditures................................................................. 4.8 4.5 3.4 1.2 2.5 2.2

Private construction:

Residential............................................................................................ 6.4 .1 6.5 - .8 6.2 8.1

Nonresidential........................................................................................ 9.4 9.0 11.4 7.2 8.4 4.7

Producers’ durable equipment........................................................................ 3.2 5.1 3.1 -2.9 2.6 1.4

Government purchases of goods and services *................................................. 5.3 8.0 3.5 -.1 5.6 4.4

UNIT COSTS

Total private, all persons

Deflator: Private GNP........................................................................................... 5.0 4.8 3.9 1.0 2.7 2.3

Unit labor costs............................................................................................. 8.2 5.2 2.7 1.0 1.6 1.5

Compensation per man-hour.................................................................... 6.9 6.8 7.1 4.7 6.6 6.2

Output per man-hour.............................................................................. -1.2 1.6 4.3 3.7 4.9 4.6

Unit nonlabor costs....................................................................................... - .9 5.4 6.1 1.1 4.6 3.9

* Compound annual rat*. 1 - preliminary, January 1973 data. ' Excluding services of government employees.

advance in industrial commodities prices during

Phase 2 was slower than before the freeze, but the

slowing was largely due to the deceleration that

began in July. The extent of this deceleration was

influenced by the fact that the Price Commission

approved price increases for new cars that were less

than the value BLS placed on the quality changes in

1973 models. Some analysts feared increases for

industrial commodities, which exceeded 4 percent

until the summer of 1972, portended an acceleration

for the CPI. This did not occur in Phase 2 and for

good reason.

Changes in the CPI, primarily the nonfood commodities component, are directly affected by changes

in the prices manufacturers charge for consumer finished goods. And it is not unusual for the industrial

commodities price index as a whole to rise or fall

more than its consumer nonfood finished goods component when industrial prices are rising or falling.4

This can be seen from chart 1, which compares

changes in the industrial commodities price index

with changes in prices of its component for consumer

nonfood finished goods. The percentage change in

the industrial commodities index from December of

one year to December of the next for each year from

1947 through 1972 is measured along the horizontal

axis. Percentage change data for the consumer nonfood finished goods component of the WPI industrials index for the same years are measured along the

vertical axis.6 The diagonal line represents equal

changes in both series. For the most part, when industrial prices rise (especially 2.0 percent or more)

consumer finished goods prices rise less than industrial prices; when industrial prices decline, consumer

finished goods prices fall less or rise slightly. All but

one of the exceptions— observations above the diagonal when industrial prices are rising and below the

line when they are falling— are years which include

at least in part the recessionary phase of a full or mini

business cycle.6 Price relationships in Phase 2 did

not deviate from those found in past years of expansion. The rate of rise in industrial prices during

Phase 2— 3.5 percent— did not signal an acceleration

in finished goods. They rose at a rate of 2.4 percent.

Commodities: A comparison

Prices of consumer finished goods in the WPI directly affect prices of commodities in the CPI. During the first 13 months of Phase 2, the annual rate

of increase in prices of nonfood commodities in the

CPI was only slightly higher than the advance of

consumer nonfood finished goods in the WPI. And

when prices of houses and used cars are excluded

from the CPI component, retailers’ prices rose less

than manufacturers’ prices during Phase 2.

Historically, retail prices of nonfood commodities

have risen somewhat faster than manufacturer’s

prices of consumer nonfood commodities— about

one-half percent a year on average from 1956

through 1971. Part of the reason is that the retail

component includes used cars and houses that are

not in the wholesale measure. But even if they were

eliminated, retail prices still have risen faster than

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Chart 1. Changes*in Wholesale Price Index for industrial commodities relative to its component for consumer finished

goods excluding foods, 1947-72

WPI: Consumer finished goods 

excluding food

NOTE: Percent changes for year ending in December, except

1947 which is 11-month span (January-December 1947). Phase 2

is represented by 13-month span from November 1971 to December 1972.

wholesale prices for these commodities in all but

3 years from 1956 to 1971.7 The relationship during

Phase 2 is then another exception from the typical

pattern.

Several explanations are worth considering. It may

be that a slackness of demand made it impossible for

retailers to push through to the usual extent increases

in the prices they paid for the commodities they

distribute. But consumer demand was strong in

1972. Or it could be that a slowdown in labor costs

as a result of the slowdown in the rate of wage

increase and the likely increase in sales per manhour in retailing caused profit margins to rise up to

their Phase 2 limit.8 But there is no reason to expect

that the unit labor cost situation was more favorable

for retailers in 1972 than in earlier years in the

1960’s. Or it could be that the growth of the economy in 1972, in bringing about an increase in

sales and a decrease in unit fixed costs, invoked the

profit margin constraint sooner for retailers than for

manufacturers, thus preventing retailers from raising

prices as much as they might have based on past

relationships. There is some evidence for this last

possiblity.

Based on an examination of profit data from IRS,

over past business cycle contractions, the pre-tax

profit margin for the manufacturing sector tends to

fall more in absolute percentage points and frequently in percent than the profit margin for the

retail sector. Historically the profit margin in manufacturing has been higher than in retailing. If profit

margins conformed with this historical experience,

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manufacturers profit margins, on a calendar year

basis, could have risen from the business cycle

trough reached in 1970 by more percentage points

than those for retailers, if not by a greater percentage

amount, in regaining their pre-recession high.9 Thus,

because of the profit margin rule, there may have

been more room for price increases for consumer

nonfood commodities at the manufacturer’s level

than at the retail level, and this may be why retail

prices did not rise as fast as might be expected relative to manufacturers’ prices for nonfood commodities.

Nondurables. Retail prices of nondurables rose

somewhat less than prices of similar goods in the WPI

during Phase 2. This was the first year since both

series became available in 1956 in which retail prices

for these commodities rose less than manufacturers’

prices when the latter were rising. In 3 of the 4 years

from 1968 to 1971, the difference exceeded 1 percentage point. (See table 3.)

The most important rise at retail was for apparel,

Chart 2. Changes in Consumer Price Index for commodities less food relative to services excluding mortgage

interest costs, 1964—72

CPI: Services excluding 

mortgage interest costs

NOTE: Percent changes for year ending in December. Figures

for 1972 are 13-month span ending December 1972.

prices of which increased more than at wholesale.

Retail prices for gasoline and footwear also increased

but at a slower rate than at wholesale. The rise in

footwear prices was of course fueled by the sharp

advance in prices of hides. In 1972, however, the

advance in retail footwear prices was particularly

low, given the substantial increase in hide prices.

Durables. Retail prices for durables rose faster

than wholesale prices for these goods. This faster rise

is mainly attributable to the advance in prices of

houses and used cars. Houses and used cars are not

priced for the WPI and were exempted from regulation during Phases 1 and 2. In 1972 strength in

demand for automobiles spilled over, as it usually

does, to used cars resulting in price rise during Phase

2. The rise began in the spring of 1972 after a period

during which used car prices fell. (The earlier decline

was probably due to the elimination of the 7-percent

excise tax on new cars, which made them cheaper

relative to used cars so a downward adjustment of

used car prices had to occur.) Prices of new cars

and furniture rose less at retail than at the manufacturers’ level.

Services

Over periods of time as long as a year, service

prices excluding mortgage interest costs usually rise

1.5 to 2.5 percentage points faster than prices

of nonfood commodities. Chart 2 at left shows

December-to-December percentage changes from

1964 to 1971 for the nonfood commodities component of the CPI on the horizontal axis and for the

services component excluding mortgage interest costs

on the vertical axis. The diagonal line shows where

the points would lie if services always rose at the

same rate as nonfood commodities’ prices. The point

plotted for Phase 2 (November 1971-December

1972) is one of the two closest to the diagonal line

and represents a departure from past experience.

The only other exception occurred in 1967, in part

a year of economic slowdown.

This departure from past patterns is an important

aspect of price behavior during Phase 2. But its

attribution to Phase 2 controls is not unambiguous.

In certain sectors like medical care and rent, where

direct controls were put in place, there was a sharp

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Table 3. Changes in wholesale and retail prices for consumer goods and services

[Seasonally adjusted (except total services and rent), compound annual rate)

Item

Relative

Importance

Dec. 1971 Index

8 months

prior to

Phase 1,

Dec. 70-

Aug. 71

3 months,

Phase 1,

Aug. 1971 to

Nov. 1971

13 months,

Phase 2,

Nov. 1971 to

Dec. 1972

16 months,

Phases 1

and 2

Aug. 1971 to

Dec. 1972

1972

3 months ending

CPI WPI Mar. June Sept. Dec.

Consumer Price Index................ 3.8 1.9 3.4 3.2 3.6 2.2 4.6 3.2

Consumer goods..................................... 100.0 CPI 3.4 1.4 3.3 3.0 3.8 2.0 5.4 2.3

100.0 WPI 4.1 -1.1 5.0 3.8 2.8 2 5 6.7 5.9

Food................................................ 35.5 CPI 5.0 1.7 5.0 4.4 7.2 0 7.0 5.2

39.2 WPI 6.8 .3 8.8 7.1 3.8 2.7 10.0 15.8

Commodities less food................... 64.5 CPI 2.9 0 2.5 2.0 2.4 2.7 4.1 1.0

60.8 WPI 2.2 - .4 2.4 1.9 2.9 2.5 3.2 - .3

Nondurables less food1.......... 37.8 CPI 3.0 1.4 2.5 2.3 2.4 1.3 3.7 2.3

37.2 WPI 1.8 .4 2.8 2.3 2.5 2.9 3.9 2.8

Apparel, less footwear... 7.5 CPI 2.2 1.7 2.5 2.4 3.0 .3 2.3 4.6

10.7 WPI 1.9 .7 1.9 1.7 .7 1.4 2.1 3.5

Footwear.......................... 2.5 CPI 2.6 3.7 3.2 3.2 2.0 3.6 3.6 4.2

2.1 WPI 5.2 -3.0 9.3 6.9 10.6 21.9 2.2 5.8

Gasoline.......................... 4.7 CPI .1 - .7 2.6 2.0 -5.8 - .4 17.5 .4

5.3 WPI 2.8 3.7 5.7 5.3 5.0 4.0 14.3 4.2

Durables1 ................................ 26.8 CPI 2.5 - .3 2.5 2.0 2.8 3.1 5.9 - 1.0

23.6 WPI 3.0 -3.9 2.1 .9 2.5 2.1 3.4 -5.5

New cars.......................... 3.4 CPI -1.1 -13.3 1.1 -1.8 9.1 3.6 4.0 -14.4

12.7 WPI 4.4 -8.8 2.3 .1 4.3 - 1.0 5.6 -11.4

Furniture.......................... 2.2 CPI 3.0 1.0 1.7 1.6 1.3 1.7 1.3 2.3

2.7 WPI 3.3 .4 2.6 2.2 2.8 2.1 2.8 3.4

-1.2

Appliances, including

radio and TV................ 2.7 CPI 1.3 - .4 .1 0 .8 - .4 - .4 0

3.5 WPI .9 -1.2 -.8 - .9 - .8 - .4 4.0 -5.9

Services................................................. 100.0 _ CPI 4.5 3.1 3.6 3.5 4.4 3.1 3.0 • 3.9

Rent......................................... 13.5 — CPI 4.3 2.8 3.5 3.3 3.8 3.4 3.1 3.7

Household less rent................ 41.1 — CPI 1.9 5.5 4.6 4.8 5.1 4.4 2.6 5.2

Medical care............................ 14.8 — CPI 6.9 1.5 3.3 2.9 2.7 3.9 1.8 7.1

Transportation................... . 14.9 — CPI 8.0 .6 1.9 1.1 0 3.0 4.2 - .6

Other services......................... 15.7 CPI 4.2 3.6 2.8 3.0 2.3 1.6 4.2 3.8

1 Includes commodities not shown separately in table.

reduction in the rate of increase. But there were at

least equally sharp decelerations in other parts of the

service sector in many of which small firms, exempt

from most, if not all, forms of regulation, abound.

Prices of most services had started to slow down

prior to the freeze, even after the effects of the sharp

decline in mortgage interest rates is taken into account. During Phase 2, however, the deceleration

became more marked and characterized almost every

item for which there is an index. The slowdown for

transportation services during Phase 2 was by far

the sharpest among the five major services components. The advance in charges for medical care services slowed by about 40 percent during Phase 2 and

rents by about 20 percent. Physicians fees rose at a

rate not much different from the 2.5 percent limitation (exclusive of cost pass-throughs) placed on

them by the Price Commission. Hospital service

charges advanced well within the 6-percent limit.

The increase for rent is lower than the 4.3 percent in

1971 prior to the freeze; rent increases in Phase 2

were limited to 2.5 percent plus pass-through of selected costs. Rates of increase for household services

less rent (excluding mortgage interest costs) and

other services also declined during Phase 2. In many

parts of these service areas in particular, there is a

prevalence of small firms with 60 or fewer employees, which are generally exempt from regulation.

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Food

Food price increases were of concern virtually

throughout Phase 2. Of the three major CPI components, the food index rose most from November

1971 through December 1972. As is typical for food,

the index displayed considerable volatility. Food purchased in grocery stores, the major part of the total

food index, rose to a peak annual rate for the year of

10.6 percent in the 3 months ending in February and

then declined at a rate of 2.3 percent in the 3 months

ending in May. Fueled by sharp rises in meat prices,

the index accelerated to a rate of 7.9 percent in the 3

months ending in September. In the last 3 months of

1972 the rate continued to rise, but at a somewhat

slower pace. The slowdown was due to fruits and

vegetables, as most other major food components

accelerated. Prices of restaurant meals rose at a rate

of 4.2 percent in the 13 months ending in December. Price rises accelerated throughout the summer

reflecting the rise in meat prices.

As is usual, the pattern of movement in the CPI

food component throughout the year mirrored rises

in food prices in the WPI, but retail food prices rose

less than wholesale, a relationship that usually obtains, especially when wholesale price rises exceed 2

percent.

Industrial commodities

During Phase 2 through December 1972, the

WPI industrial commodities index rose 3.5 percent.

The largest advances were recorded for hides,

skins, leather and related products, and lumber and

wood products. If these two groups are eliminated

from the 13 major product groups comprising the

index, the increase for the 13-month period was

2.9 percent.

By stage of process, the largest rise among industrial commodities was for crude materials. It is

not uncommon for crude materials prices to rise

sharply during periods of rapid cyclical expansion

like 1972. Prices for iron and steel scrap, wastepaper, and coal rose briskly. Worldwide imbalance

between supply and demand was the principal cause

of the large advances in wool and hides prices.

Under the influence of rising crude materials prices,

the index for intermediate materials, supplies and

components except food also rose. Leather and lumber and wood products contributed significantly to

the increase. Increases for finished goods, producer

and consumer (nonfood), were substantially lower

than those at either the crude or intermediate level.

The price advance for producer finished goods in

Phase 2 was markedly slower than the increase of

4.6 percent in 1969 and 4.9 percent in 1970 and the

3.7 percent annual rate in the first 8 months of 1971

before the freeze. 

----------FOOTNOTES----------

1 The mortgage interest cost component of the CPI measures not only changes in mortgage interest rates but also

changes in the size of mortgages, which are related to

changes in house prices.

2 The slowdown in the 8 months of 1971 before the

freeze was due to a deceleration from 1970 in prices of both

nonfood commodities and services (even excluding mortgage

interest costs) which more than offset an acceleration in

food prices.

3 The homeownership segment of the CPI Housing component incorporates prices for major elements which make

up the cost of providing housing services (taxes, insurance,

maintenance, interest rates, and the prices of required capital goods), and thus may be regarded as an attempt to estimate a cost function for housing services. All prices which

enter the index are, in principle, current market prices, so

the homeownership component is a measure of the current

cost of providing housing services. The PCE Housing component approximates the price of housing services for owneroccupied houses by using measured rents for units that are

in fact rented. Although benchmark data on owner-occupied

and rented single-family dwellings are used in computing

the national accounts, the PCE price measure is driven by

the CPI Rent index, which is a comprehensive rent measure,

heavily weighted toward multi-family units.

4 Except for minor adjustments, the industrial commodities

index consists of four major stage-of-production components.

The groups are crude nonfood materials for further processing, which in December 1971 had a relative importance

of 4 percent, intermediate nonfood materials, supplies and

components, 55 percent, producers’ finished goods, 14 percent, and consumer nonfood finished goods, 27 percent.

5 The percentage change for 1947 is the annual rate of

increase from January to December of that year for both

series since the consumer nonfood finished goods index is

not available for December of 1946 or earlier periods.

6 Because the industrial commodities index includes crude

and intermediate goods prices of which affect prices of consumer nonfood finished goods with some lag, it can be

argued that changes in consumer nonfood finished goods

prices should be plotted lagged behind those in industrial

prices. The most important part of that lag has been found

to be short, about one quarter (see Joel Popkin and Paul

Earl, “The Relationship Between the Behavior of Consumer

and Wholesale Prices,” Papers and Proceedings of the

American Statistical Association, Business and Economic

Statistics Section, 1971). If a scatter diagram similar to

chart 1 is plotted lagging the consumer nonfood finished

goods index 3 months behind the industrial commodities

index only one important difference emerges. The point representing 1951, the only nonrecessionary year above the

diagonal line in chart 1 when industrial prices are rising,

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would be plotted below the diagonal line. Thus, the only

exceptions to the general phenomenum that the industrial

commodities price index rises or falls more than its

consumer nonfood finished goods component would be

recessionary or mini-recessionary years. It then becomes

apparent that because crude materials prices usually fall

considerably during recessions, they pull the industrial index

down without affecting prices at subsequent stages of production by anywhere near the same amount.

’ Estimates of the CPI nonfood commodities index excluding used cars and houses are available only back to 1956.

The 3 years that are exceptions are 1960, 1964, and 1965. In

1964, both series rose by the same small amount. In the other

2 years, the WPI for consumer finished goods excluding

food rose faster primarily because of new car prices which

dropped more at retail than at wholesale. In 1965, this was

attributable to the reduction in auto excise taxes which are

included in the CPI, to the extent they are passed through to

consumers, but not in the WPI.

( The profit margin requirement is that price increases

instituted by firms result in a pre-tax profit margin no higher

than that which obtained for a weighted average of the best

two out of the firms three fiscal years ending before August

15, 1971. Based on available data the high was reached in

1968, following the mini-recession of 1967. In 1969 profit

margins appear to have edged downward toward their recession low reached in 1970. The years 1968 and 1969, then,

would on average be the best two years upon which to base

a calculation of the maximum allowable profit margin to

which margins could return in 1972.

•If company A wants to raise its profit margin to 4

percent from 2 percent by raising prices only, and company

B wants to raise its profit margin to 8 percent from 6 percent by raising prices only, then, arithmetically, company

B will find it can raise its prices by a slightly larger percentage in reaching its objective than can company A in

reaching its objective. Thus, even if manufacturers were

able under the profit margin rule only to raise their respective margins by the same number of percentage points as

retailers, manufacturers could raise their prices, holding

other things constant, more than retailers since manufacturers’ profit margins are on average at a higher level than

those of retailers.

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Chapter V. Productivity and Technological Change

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Technical Note

Industry Indexes of

Output Per Man-Hour

J erom e A . M a r k *

T h e i n d u s t r y i n d e x e s of output per man-hour

prepared by the Bureau of Labor Statistics

measure changes in the relationship between the

physical production of an industry and the manhours expended in that production.1 Although

traditionally output per man-hour has been the

measurement most frequently used, discussion of

physical output per man-hour is often simplified

if conducted in terms of its reciprocal: man-hour

requirements per unit of production (unit manhours). Therefore, this form of index is used in

the following description.

For an industry producing a single uniform

product, the unit man-hour index is simply the

ratio of the man-hours required to produce a unit

of product over two periods of time. Symbolically, this ratio may be expressed as follows:

Where I u represents the unit man-hour index, I p

represents the output per man-hour index, and

l t and l0 denote unit man-hours expended in the

current and base periods, respectively.

For an industry producing a number of products— the more typical case—the unit man-hours

index is the ratio for two periods of the total hours

expended in the production of a given composite

of products. Indexes of such industries vary with

the composite of products specified and can take

many symbolic forms. Letting q0 and qt represent

base period and current period quantities of a

given product, respectively, two of these forms

are:8

a. Using a current period composite

T _

M 2<Z <lo

b. Using a base period composite

T _

* S g X

An index constructed according to (a) compares

the man-hours expended in the production of the

current composite with man-hours which would

have been required to produce the current composite in the base period. This index thus

eliminates the effects of variations over time in the

relative importance of products on unit man-hours.

Similarly, an index constructed according to (b)

compares the man-hours expended in both periods

in producing the base period composite.

In either form, the index of unit man-hours may

be expressed as the quotient of an index of manhours and an index of production, and this is how

the BLS series is actually constructed.

The man-hours index measures the change in

aggregate man-hours between the base and current periods. The appropriate production index

is the one which compares the quantities of the

various products in the current to the base period,

each weighted by the base period man-hours

expended per unit produced. Thus, this current

period weighted unit man-hour index employs a

•O f th e D iv is io n of P r o d u c tiv ity a n d T ech n o lo g ica l D e v e lo p m e n ts , B u rea u

of L ab or S ta tistic s.

» T h e p rocedu res d escrib ed in th is tech n ic a l n o te w h ich are u sed in d e v e lo p in g th e B L S in d u str y o u tp u t per m an -h o u r in d ex es, su ch as th e tw o

a p p earin g on p p . 1241-1248 of th is issu e, ste m from a n u m b er of w o rk s in

th is field . Im p o r ta n t earlier stu d ie s in clu d e:

W . D u a n e E v a n s a n d Irv in g H . S iegel, “ T h e M e a n in g o f P r o d u c tiv ity

In d e x e s,” J o u rn a l o f the A m e ric a n S ta tistica l A sso cia tio n , M arch 1942, p p .

103-111.

S olo m o n F a b rlca n t, E m p lo y m e n t in M a n u fa c tu rin g , 1899-1989 (N e w Y o rk ,

N a tio n a l B u rea u o f E co n o m ic R esearch , 1942).

H a r ry M agdofT, Ir v in g H . S ieg el, a n d M ilto n B . D a v is , P ro d u ctio n , E m ­

p lo y m en t, a n d P ro d u ctivity in 59 M a n u fa ctu rin g In d u stries, 1919-1986 (P h ila ­

d elp h ia , W o rk s P rogress A d m in istr a tio n , N a tio n a l R esearch P r o ject, 1939)

R ep o rt 8 - 1 ,3 v o ls.

U .8 . D e p a r tm e n t o f L a b o r, B u rea u of L ab or S ta tistic s, T ren ds in O u tp u t

P e r M a n -H o u r a n d M a n -H o u rs P e r U n it o f O u tp u t— M a n u fa c tu rin g , 1989-63

(W a sh in g to n , 1955). B L 8 R ep o rt 100.

U .S . D e p a r tm e n t o f L ab or, B u rea u o f L ab or S ta tistic s, “ T h e M e a su rem e n t

of T re n d s in O u tp u t P e r M a n -H o u r ,” Techniques o f P re p a rin g M a jo r B L S

S ta tistica l S eries (W a sh in g to n , 1955), B L S B u lle tin 1168, C h . 14.

1 T h e first o f th e se , w ith w e ig h ts im p lic itly or e x p lic itly referring to th e

cu rren t p eriod, is o ften referred to as th e “ P a a sc h e ” form; th e seco n d is k n o w n

as th e “ L a sp e y res” form . T h ere are a n u m b e r o f in d ex fo rm u la tio n s such

as th e F ish e r “ I d e a l” in d ex , in w h ic h a co m p ro m ise b e tw e e n b eg in n in g

a n d e n d in g p eriod w e ig h tin g is so u g h t. B L S m ea su res, h o w e v er, fo llo w th e

a b o v e form s.

From the Review of November 1962 236

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base year weighted production index divided into

the man-hours index. Conversely, the base period

weighted unit man-hour index is consistent with

a production index which utilizes current period

weights.8

Unit man-houra Man-houra Production

index index index

1. P aosche fo r m — B ase p erio d weights

Vlolo ' 2 l 0q0

__2Z<g<

t . L aspeyres fo rm — C urrent p erio d weights

r _ _ 2/ <g < . 2 Z <g <

2Zo2o * 2 ltq0

= 2Z,g.

2Z0g0

Methods and Sources

In constructing the indexes of output per manhour and unit labor requirements, the Bureau

follows the concepts outlined wherever possible.

Because available data on output and man-hours

are collected for many purposes, rather than for

specific use in productivity measurement, it is

frequently necessary to make departures and

adopt approximations which vary considerably

by industry.

Production Indexes

Industry production indexes are prepared from

basic product data published by various public

and private agencies, using the greatest amount

of detail available.* 4 The number of physical

units produced is the basic unit of measurement,

but this often leads to questions. For example,

should textile production be measured in linear

yards, square yards, or pounds of the various

types of cloth? The Bureau tries—wherever

there is a choice—to measure output in terms of

the unit which is most nearly proportional to the

unit man-hours spent in its production. In some

industries, production is measured in more than

one way, e.g., in mining industries both tons of

raw ore and tons of recoverable ore are used to

obtain two different series of indexes.

As already indicated, the different units of

products are then combined with base year weights

to derive the production measure. Although the

mathematical form of the production index implies use of unit man-hour weights, most often

this information is not available for individual

products. Other weights are then substituted—

unit labor costs, unit value added, or unit value.

For most industries, the Bureau uses unit values

as weights. Data on unit labor cost and unit

value added are available for only a few industries.

The introduction of substitute weights such as

unit value results in an industry unit man-hour

index which shows the change in man-hours per

dollar of total value, in terms of base year value

per unit for each product. Changes in these

indexes reflect shifts in product “mix.” Thus, a

change can occur in the index without any change

in the unit man-hour requirements for any product

of the industry.

The extent to which error or bias may be introduced by the use of unit value or other substitute

weights is not known. An index weighted with

unit values is equivalent to one weighted with

unit man-hours if the unit man-hour and unu

values of the products are proportional or if there

is no correlation between the relative change in

quantity and value per man-hour.®

There is evidence that unit values are fairly

reliable approximations of unit man-hour requirements for individual products where wages constitute a large proportion of total value of output.

Moreover, the error generated in the production

index by an error in the weights is generally considerably smaller than the error in the weights

themselves. It usually requires a substantial

error in the assumption of proportionality between

1 Unit man-hour data are rarely available for each year for separate products. In general, therefore, Indexes cannot be prepared In this form.

4 The Bureau of the Census of the U.8. Department of Commerce is the 

major source of product statistics for the manufacturing Industries. The 

Bureau of Mines compiles most of the Information for mining, cement, coke, 

and nonferrous metals Industries. Other important Government sources 

Include the U.S. Department of Agriculture, the Fish and Wildlife Service 

of the Department of the Interior, the Interstate Commerce Commission, 

and the Bureau of Internal Revenue. Important sources of trade association 

data Include the Tanners Council, Textile Economics Bureau, National 

Association of Hosiery Manufacturers, National Canners Association, 

Rubber Manufacturers Association, and the American Iron and Steel 

Institute.

1 See Irving H. Siegel, “ Further Notes on the Differences Between Index 

Number Formulas," Journal of tht American Statistical Association, December 1941, pp. 619-524.

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unit value and unit labor requirements to seriously

change the production index level.

Coverage. Production data are rarely available

for the entire output of an industry. However,

for most industries in the series published by the

BLS, the products covered usually represent at

least 70 percent of each industry’s total value of

output.

The quantity data which are available usually

relate only to the primary products of the industry— the set of products accounting for the principal portion of its total output. In addition, these

quantity data are usually only available on a

“wherever made” basis. That is, they include

quantities produced by establishments in other

industries where the products are secondary items

as well as quantities produced by establishments

where they are the principal ones. If the proportion of total production of primary products

made within the industry remains the same, the

production index will reflect industry production

movements.6 Information on secondary products

and on custom contract work is available only

for limited periods and generally then in terms of

dollar value rather than physical quantity.

Although the proportion of an industry’s output

covered by quantity data is usually substantial,

the ratio may vary from year to year.

For the output per man-hour or unit man-hours

index, an additional problem enters with the use

of the man-hours index because reported manhours include the total man-hours of each establishment classified in the industry regardless of

the particular products being produced. Consequently, to assure comparability between manhours and output series, benchmark adjustments

are made whenever a Census of Manufactures is

available. For Census years, the following procedure is used: (1) A physical quantity production

index is derived for the primary products of the

industry wherever made. (2) An index of total

dollar value is computed for these products.

(3) This value index is divided by the physical

quantity index to obtain a price index. (4) The

price index is then used to deflate the total value

of the industry’s production (which includes primary and secondary products). The resultant

industry index is one of physical output with unit •

• S tr ic tly sp e a k in g , th e d eriv ed p ro d u ctio n Index reflects th e In d u stry

p rim a ry p ro d u ct p ro d u ctio n m o v e m e n ts w h ic h In tu r n rep resen t th e o u tp u t

m o v e m e n t for th e en tire In d u stry .

value weights. This method of coverage adjustment assumes that the price movements of the

secondary products of the industry are the same

as the price movements of the primary products.

From time to time, as information becomes

available, products not made in the base year are

introduced into the output measure. The production of these items is added to the composite

of output when they were first produced (if the

data are available) and is counted as “zero” for

the years preceding. Based usually on the relationships to other products in the industry, an estimate of the base year unit man-hour requirements

or substitute weight is introduced.

Alternative Production Indexes. When adequate

annual physical production data are not available,

other types of production measures are constructed. The most commonly used alternative

measure is an index of deflated value of production. To derive this index, data on the industry’s

value of production are divided by an appropriate

industry price index. The resultant index shows

the change in the real value of production between

the base and current period.

In concept, these indexes are identical to those

physical output indexes which employ substitute

unit value weights. Thus, for example

Deflated value Value Price

index index index

Laspey res form I dt 'ZViQ.i .

2 p 0q0 ' 2 p 0qt

_ 2 p 0g<

2 p 0q0

where p t and p 0 represent prices of products in

the industry in the current and base periods,

respectively. This index requires quantities of

all items produced in each year. These data are

not available for the particular industries where

this measure is used, and quantity data are usually

available for the base year only, so that the

deflated value indexes employed usually take the

following forms:

Deflated value Value Price

index index index

Paasche form I dv 2 p 0q0 ' 2 p 0q0

2j><g0

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In either form, the deflated value indexes, in

contrast to physical volume indexes, are constructed from value of production data which

relate to total value of products produced by

the industry only. The price indexes used in

deflating these values, however, are of primary

products derived on a wherever-made basis.

For most industries where deflated value measures are used, only data on value of shipments

are available. In order to derive the deflated

value of production, these data are adjusted for

the net change in the industry’s inventories to

approximate total production.

Another alternative measure used occasionally

by the Bureau is based on the physical volume

of material consumed. A consumption index of

this type provides a satisfactory indicator of production trend in those industries where the

amount of material consumed per unit of output

remains fairly constant.

Man-Hour Indexes

The man-hour indexes measure the relative

change, from the base year, of an unweighted

aggregate of labor input. Man-hours are treated

as homogeneous and additive with no distinction

being made between hours of different groups of

employees. Data on changes in qualitative

aspects of man-hours, such as skill, efficiency,

health, experience, age, and sex of persons comprising the aggregate, are not used and often not

available.

The man-hour data are the total hours expended

by employees in establishments classified in the

industry, including not only those hours spent

on primary activities of the establishment but

also those on the production of secondary products.

Indexes are developed for all employees in each

industry and for production workers. The term

production workers covers working foremen and

all nonsupervisory workers, including leadmen and

trainees, “engaged in fabricating, processing,

assembling, inspection, receiving, storage, handling, packing, warehousing, shipping, maintenance, repair, janitorial and watchmen services,

product development, recordkeeping, auxiliary

production for plant’s own use (e.g., powerplant

operations), and other services closely associated

with the above production activities of establishments.” The term thus includes some indirect

as well as direct plant labor.

Two major sources are used for developing these

series. For most manufacturing industries the

various censuses and annual surveys of manufactures furnish man-hours data for production

workers and employment data for all employees

(including non production workers). For most

private nonagricultural industries (including manufacturing), the Bureau of Labor Statistics

publishes employment and average weekly hours

data for production and nonsupervisory workers

and employment data for all employees. Census

provides data for more industries within manufacturing than BLS.

The two sources differ also in their definitions

of man-hours. The Census data include all hours

at the plant, worked or paid for, and exclude

paid time for vacations, holidays, or sick leave,

when the employee is not at the plant. Overtime

and other premium pay hours are included on

the basis of actual time at the plant. In contrast,

the Bureau of Labor Statistics data include time

for paid vacations, holidays, and sick leave, as

well as plant man-hours. Differences in the data

from the two sources for the same industry

however, may as well be due to sampling and

reporting differences as to differences in definition.

The all employee man-hours estimates which

have been used to derive indexes of output

per all employee man-hours are composed primarily as follows: (a) published data on the

number of production worker man-hours, (b)

published data on non production worker employment, and (c) an estimate of the average annual

hours of nonproduction workers. The two published components for the selected industries

come mainly from two sources, the Bureau of the

Census and the Bureau of Labor Statistics.

Indexes based on Bureau of the Census publications incorporate the concept of hours at work,

excluding vacation time, holidays, disability

time (sick leave), and personal time off; those

based on BLS employment and earnings

statistics cover payroll hours which include

plant hours plus hours of company paid sick

leave, vacations, holidays, etc. Estimates of

nonproduction worker man-hours which would be

consistent with the two concepts of man-hours

were necessary.

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The basic procedure for estimating nonproduction worker man-hours for an industry using

BLS employment and man-horn's was to obtain an

estimate of scheduled weekly hours for nonproduction workers for a given year, and to adjust

that estimate annually with the trend of scheduled

weekly hours for office and other nonproduction

workers in manufacturing industries as found in

various studies by the Department of Labor.

Estimates for industries using Census data involved the additional step of subtracting paid

leave time, and were put on an annual basis by

multiplying the scheduled weekly hours by the

number of 5-day workweeks actually worked in

each respective year.

The most recent and complete estimate of nonproduction worker scheduled weekly hours for

manufacturing was derived from unpublished data

from a survey conducted by the BLS on employer

expenditures for selected supplementary remuneration practices m manufacturing industries.

In this survey, respondents furnished data on the

hours in the standard workweek in 1959 for

executive, professional, and supervisory employees in one group, and for clerical and other

nonsupervisory employees in another group.

Estimates for leave time (vacations, holidays,

etc.) were derived from a variety of sources. The

data on vacations and holiday leave were estimated largely from two Bureau of Labor Statistics bulletins, Wages and Related Benefits, 60

Labor Markets, 1959-60; Employer Expenditures

for Selected Supplementary Remuneration Practices jo r Production Workers in Manufacturing

Industries, 1959 (Nos. 1265-62 and 1308, respectively); and from BLS industry wage studies,

as well as various National Industrial Conference Board studies. Disability and personal

time off were based on Social Security Administration estimates plus data obtained in a Bureau

of Labor Statistics survey. All-manufacturing

estimates were used for vacations, holidays, etc.,

except when additional data permitted individual

industry estimates.

Prior to 1959, information for total manufacturing from the various community wage

surveys conducted by the Bureau was used to

derive the trends for holidays, vacations, and the

scheduled workweek. These trends were then

applied to the 1959 levels.

Limitations

Because of the wide variety of concepts and

definitions of productivity, it is important for the

user of the BLS series to consider the definitions

and terms presented in this description in relation

to their purpose: to provide a measure of the

change in average man-hour requirements for the

physical output of various industries. While this

type of productivity index serves diverse purposes,

it is n o t. strictly appropriate for certain uses.

Some characteristics of the indexes which limit

their use in answering particular questions should

be noted.

First, productivity is measured in terms of labor

input only. This type of index is especially

helpful in estimating labor requirements and analyzing employment fluctuations. It does not

mean, however, that labor is the sole or principal

factor responsible for productivity gains. Nor

can it be assumed that an index of unit fuel,

energy, capital, or total factor requirements

necessarily follows the same trend as man-hour

requirements.

Second, output is measured in terms of completed

goods, weighted generally by unit value rather

than such possible weights as unit labor requirements or cost, or unit value added. BLS index

series do not take into account savings by the

industry in the consumption of materials, fuel, and

power as a result of greater integration, better raw

materials, or changes in product composition.

Third, the indexes do not generally reflect

changes in the quality of an industry’s output.

Data are not generally available for quantifying

changes in the serviceability of various commodities.

Fourth, the indexes are affected by shifts of

product composition among establishments and

workers at different levels of efficiency. The series’

may therefore not be used to represent the trend

for any individual firm or group of workers.

Finally, a small difference between two annual

indexes should not be interpreted as a significant

change. Productivity series are based on production and man-hour data which are subject to

varying margins of error. Recognizing that productivity indexes may be useful even though

approximate, it is important to apply them only

in those areas where they are appropriate.

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Output per man-hour increased

at a lower rate than in 1972;

analysis of 1967-73 period shows

part of the productivity slowdown

is accounted for by employment shifts

J.R. NORSWORTHY AND L.J. FULCO

Pr o d u c t iv it y ro se 3.0 percent in 1973 in the total

private economy, a lower rate than 1972, when it

increased 3.8 percent, and slightly lower than the

3.1 percent average annual rate which has prevailed

over the last two and a half decades. This lower rate

of growth in output per man-hour in 1973 signaled

the end of the period of rapid expansion which

began in 1971.

In the nonfarm sector, output per man-hour increased 3.1 percent, compared to a 4.2-percent

increase in the preceding year. In both 1972 and

1973, nonfarm output per man-hour rose at a faster

rate than did productivity in the total private economy

because of declines in farm output per man-hour.

In manufacturing, growth in output per man-hour,

although substantial, also fell off somewhat in 1973,

rising 4.7 percent compared to 5.4 percent in 1972.

Productivity growth in manufacturing reflected the

continued strength of the output advance in this

sector— 9.7 percent in 1973 and 8.9 percent in 1972

in real terms.

In nonfinancial corporations, output per man-hour

increased 3.5 percent in 1973, compared to a 4.0-

percent increase in 1972. This sector, which includes

all corporations doing business in the United States

except banks, insurance companies, and stock and

commodity brokers, showed an increase in output

of 7.7 percent, compared to a 7.4-percent rise 1

year earlier. (See table 1.)

The index of output per man-hour in the private

sector hit a peak in the first quarter of 1973 of 116.4

but it declined slightly in the subsequent three quarters; the average for the year was 116.2 (1 9 6 7 =

100). In contrast, compensation per man-hour was

highest in the fourth quarter (157.7) reflecting a

continuous rise in compensation through the year.

The interaction of these divergent movements of

J. R. Norsworthy is chief, and L. J. Fulco. an economist,

in the Division of Productivity Research, Bureau of Labor

Statistics.

Productivity and

costs in the

private economy,

1973

output per man-hour and compensation per manhour resulted in an 8.9-percent rise (at an annual

rate) in unit labor costs in the fourth quarter. The

increase for the year 1973 as a whole was 4.7

percent.

Farm slowdown

The decline in output per man-hour in the farm

sector in recent years is a reversal of a long-standing

relationship between productivity growth in the farm

and nonfarm sectors. The following tabulation shows

annual average percent change in output per manhour by major sector:

P eriod Farm Nonfarm

1 9 4 7 -6 8 ................................... 5 .9 2.7

1 9 6 9 -7 3 ................................... .5 3.2

For many years, output per man-hour in the farm

sector, although lower in absolute terms than productivity in the nonfarm sector, grew at a significantly faster pace than the rest of the economy, as

mechanization was introduced and harvesting and

cultivation tasks transferred from manual labor to

mechanical and chemical processes. Thus, output

increased and man-hours declined, producing a rapid

growth of output per man-hour in farming.

However, in both 1972 and 1973, the real net

value of output in the farm sector was less than in the

preceding year, after allowing for the value of

products which are consumed on the farm (such'as

fertilizer and animal feed). Employment and manhours did not decline to the same extent, and thus

output per man-hour declined— 6.8 percent in 1972

and 4.6 percent in 1973. Annual percent changes in

productivity in the farm sector from 1969 to 1973

are shown below:

Measure 1969 1970 1971 1972 1973

O u t p u t .............. . . 3.0 2 .8 4 .9 — 5.2 — 5.9

M an-hours . . . . — 6 .4 — 5.5 — 1.6 1.7 — 1.3

O utput per

m an-hour . . , . 10.1 8.7 6 .6 — 6.8 — 4 .6

From the Review of June 1974 241

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Completion of the recovery

Part of the reduction in the rate of productivity

growth for the private economy is attributed to completion of the business recovery from the 1969-70

economic downturn. A typical recovery pattern from

postwar business slumps is as follows: First, as sales

recover, inventory accumulations are drawn down.

Next, businessmen increase output by more intensely

using the existing work force and capital stock. This

effect sometimes appears as an increase in average

weekly hours, accompanied by a sharp increase in

output per man-hour. Then as output continues t©

increase, the work force is increased. Productivity

continues to grow rapidly. In the latter stages of the

recovery, the increase in labor input (man-hours)

usually continues to grow at a rapid rate for one or

two quarters after the rate of growth in output

slackens; output per man-hour grows more slowly

and may even fall slightly as the work force adjustment is completed.

This same pattern emerges from examination of

Table 1.

the recovery in the private economy since the

1969-70 downturn. Man-hours and output both fell

in 1970, and labor productivity increased very

slowly (1.0 percent). In 1971, output increased by

3.5 percent while man-hours continued downward,

although only slightly. Output per man-hour increased strongly by 4.1 percent. In 1972 the rate of

growth of output increased to 6.5 percent, and manhours rose by 2.7 percent. Thus, productivity grew

somewhat more slowly than in 1971— 3.8 percent—

but still well above the long-term trend rate. In

1973, the completion of the recovery was marked

by the reduced rate of growth of output. After a

first-quarter growth rate of 9.3 percent (seasonally

adjusted annual rate), output in the second and third

quarters increased only 2.6 and 3.6 percent, respectively, while man-hours continued to increase strongly

at annual rates of 3.8 and 3.2 percent. Output per

man-hour fell slightly in the second quarter and

increased only slightly in the third. By the third

quarter of 1973, the recovery was about complete;

the fourth quarter showed the first effects of the

Annual rates of change In productivity and related measures, 1969-73

Sector and measure 1969 1970 1971 2972 1973

1972 1973

1 II III IV 1 II III IV

TOTAL PRIVATE ECONOMY

Output per man-hour................................................. 0.4 1.0 4.1 3.8 3.0 2.1 6.5 3.1 5.2 5.8 -1 .2 0.4 - 0 .8

Output............................................................... 2.8 - . 5 3.5 6.5 6.2 5.4 10.4 5.8 8.4 9.3 2.6 3.6 1.5

Man-hours................... ............................................. 2.3 -1 .5 - . 6 2.7 3.2 3.2 3.6 2.6 3.0 3.3 3.8 3.2 2 3

Compensation per man-hour..................................... 7.6 7.6 7.0 6.8 7.8 9.9 5.9 4.9 7.4 11.3 6 3 7.3 8 1

Unit labor cost_________________ ____ _______ 7.1 6.5 2 8 2.9 4.7 7.6 - . 5 1.7 2.0 5.2 7.6 6.9 8.9

Real compensation per man-hour............................. 2.1 1.5 2.6 3.5 1.5 6.3 2.8 1.2 3.6 5.2 -2 .3 -1 .7 - 1 .6

Implicit price deflator................................................ 4.5 4.8 4.3 2.9 5.4 4.9 1.5 2.8 3.5 5.7 7.5 7.3 8.6

NONFARM SECTOR

Output per man-hour.............................................. - . 1 .7 4.0 4.2 3.1 2.5 5.7 5.6 4.7 5.0 - . 8 1.8 - 1 .9

Output......................................................................... 2.8 - . 6 3.4 7.0 6.6 5.6 11.0 7.0 8.3 9.2 3.9 4.4 - . 1

Man-hours................................................................... 2.9 -1 .3 - . 6 2.7 3.4 3.0 5.0 1.3 3.4 4.0 4.8 2 6 1.9

Compensation per man-hour..................................... 7.0 7.3 7.0 6 9 7.6 9.9 5.2 6.7 6.8 10.7 5.3 8.0 8 4

Unit labor cost.................. ...................................... 7.2 6.6 2 8 2 6 4.4 7.2 - . 5 1.0 2.0 5.4 6.2 6.1 10 5

Real compensation per man-hour............................. 1.6 1.2 2.6 3.5 1.3 6.3 2.0 2.9 3.1 4.6 -3 .3 -1 .1 -1 .3

Implicit price deflator................................................ 4.5 5.0 4.4 2.3 4.1 4.4 1.0 1.9 2.9 4.3 5.9 4.7 7.9

MANUFACTURING

Output per man-hour................................................. 2.5 .6 7.1 5.4 4.7 7.8 7.4 8.6 5.7 3.9 3.8 5.0 - 3 .3

Output......................................................................... 4.3 -4 .9 2/9 8.9 9.7 12.2 15.3 11.4 14.3 10.3 7.7 5.0 1.2

Man-hours.................................................................. 1.8 -5 .4 - 4 .0 3.4 4 8 4.1 7.4 2.6 8.2 6.2 3.8 - 0.0 4.7

Compensation per man-hour............ ....................... 6.3 7.2 7.0 6.3 7.9 11.1 4.9 4.4 6.8 10.8 8.2 9.3 5.4

Unit labor cost........................................................... 3.8 6.6 - . 1 .9 3.1 3.1 -2 .3 -3 .8 1.1 6.6 4.2 4.1 9.0

Real compensation per man-hour............................. .9 1.2 2.6 2.9 1.6 7.6 1.6 .8 3.2 4.7 - . 7 .2 -4 .1

NONFINANCIAL CORPORATIONS

Output per man-hour................................................. 2.1 .9 5.5 4.0 3.5 4.7 2.5 3.6 3.4 7.3 1.0 4.6 - 3 .3

Output......................................................................... 4.6 -1 .4 3.5 7.4 7.7 9.0 8.6 5.1 10.4 11.6 5.0 6.1 .5

Man-hours.................................................................. 2.4 -2 .3 -1 .9 3.3 4.0 4.1 6.0 1.5 6.8 4.0 4.0 1.5 3.9

Compensation per man-hour..................................... 7.3 7.2 7.3 6.8 7.6 11.5 4.1 5.9 4.9 12.1 6.6 9.0 6.0

Unit labor cost............................................................ 5.2 6.3 1.6 2.7 3.9 6.5 1.6 2.3 1.4 4.4 5.5 4.2 9.7

Real compensation per man-hour............................. 1.9 1.2 2.8 3.4 1.3 7.9 1.0 2.2 1.3 5.9 -2 .1 - . 2 - 3 .4

Implicit price deflator................................................ 2.7 4.5 3.1 2.3 3.7 4.9 1.6 2.2 3.2 3.8 4.7 4.0 6.1

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energy problem, as output grew more slowly than

man-hours, and productivity again fell slightly, at an

annual rate of 0.8 percent.

Recent movements in unit labor costs reflected the

cyclical recovery in output per man-hour as well as

rapidly rising compensation per man-hour. In both

1971 and 1972, substantial increases in compensation per man-hour occurred in the first quarter, as

employers’ social security contributions increased.

Except for these first-quarter rises, the rate of increase grew in each quarter throughout 1972 and

1973. Since output per man-hour increased less in

1973, unit labor costs rose more than m proportion

to the rise in compensation. The largest increase in

unit labor costs in 1973— 8.9 percent at an annual

rate— occurred in the last quarter when compensation per man-hour rose at an annual rate of 8.1

percent and output per man-hour fell at an annual

rate of 0.8 percent.

The productivity slowdown since 1966

In the postwar period (1947 -7 3 ), output per

man-hour has grown at an average rate of 3.1 percent a year in the total private economy. However,

in some recent years, the rate of growth of output

per man-hour has been lower. It is possible to estimate the gap between the level of output per manhour today and the level it would have attained had

the productivity slowdown not occurred. In 1969

and 1970, output per man-hour increased 0.4 percent and 1.0 percent, respectively. Although productivity then grew at faster than trend rates for the

next 2 years, 4.1 percent in 1971 and 3.8 percent

in 1972, the entire shortfall was not recovered in

either the total private or in the nonfarm sector. (See

chart 1.)

Output per man-hour measures reflect productivity

changes which occur within the component subsectors as well as man-hour shifts between the subsectors

with different levels of output per man-hour. Conceptually, the annual change in productivity in the

private economy can be separated into two major

parts: A “productivity effect” and a “shift effect.” 1

The productivity effect is the change in output per

man-hour which would have taken place if the proportions of total man-hours remained the same for

each sector, but output per man-hour changed within

each sector. The shift effect is the change in output

per man-hour which would have taken place if output per man-hour remained the same in each sector

and only the shift in man-hours had taken place.2

There is also a third effect which results from the

interaction of the productivity change and the shift

in man-hours. This effect is quite small in each

postwar year.

In 1947, more than 18 percent of all man-hours

in the private economy were expended in agriculture. By 1973, the proportion had fallen to 5.2

percent. Over this period, about 10 percent of the

growth in output per man-hour can be attributed to

the shift of labor from agricultural to nonagricultural

employment because output per man-hour was

higher in the nonfarm sector than in agriculture.

Levels erf output per man-hour and annual proportions of total man-hours engaged in agricultural and

nonagricultural sectors are shown in table 2.

This shift effect was more important in the earlier

part of the period than it has been since 1967, for

Chart 1. Actual and trend levels of output per man-hour,

1968-73

Indexes 1967=100

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example. From 1947 to 1957, the shift accounted for

about 0.5 percent per year increase in productivity

or about one-seventh of the total annual rate of increase; from 1967 to 1973, the shift accounted for

a productivity increase of only about 0.1 percent per

year, about one-thirtieth of the total annual rate of

increase. Because this shift phenomenon explains a

major part of the decline in the annual rate of

productivity increase in the postwar period, it should

be examined in some detail.

Table 3 separates annual productivity changes

since 1947 into these three effects. Two major characteristics of the shift effect are revealed by this

breakdown.

First, the productivity effect is more important

than the other effects over the whole period; that is,

productivity growth within each of the two broad

sectors has been far more important than the effect

of relative shifts in employment between sectors for

the whole postwar period. We can treat column two

in table 3 as a shift-adjusted productivity growth

series.3 Rounding off columns 1 and 2 to the nearest

tenth gives a comparison of the unadjusted index

published by the Bureau of Labor Statistics compared with a shift-adjusted index of output per manhour for the private economy.

Table 2. Output per mar-hour and man-hour proportions

in the farm and nonfarm sectors, 1947-73

Year

Output per man-hour Proportion of total 

private man-hours

Total

private

economy

Nonfarm Farm

Nonfarm

sector Farm

1947....... . 2.29 2.62 0.77 0.82 0.18

1948............ 2.39 2.70 .90 .83 .17

1949_______ 2.47 2.81 .88 .82 .18

1950.............. 2.67 2.98 1.00 .84 .16

1951............ 2.75 3.04 1.00 .85 .15

1952............... 2 80 3.07 1.09 .86 .14

1953_______ 2.92 3.16 1.24 .87 .13

1954_______ 2.99 3.24 1.30 .87 .13

1955............... 3.12 3.38 1.31 .87 .13

1956............... 3.12 3.36 1.37 .88 .12

1957_______ 3.22 3.43 1.45 .89 .11

1958 3 32 3 52 1.60 .89 .11

1959............. 3.43 3.64 1.63 .90 .10

1960_______ 3.49 3.69 1.72 .90 .10

1961:............. 3 61 3.79 1.85 .90 .10

1962............... 3.78 3.97 1.90 .91 .09

1963............... 3 92 4.09 2.07 .91 .09

1964_______ 4.07 4 24 2.10 .92 .08

1965............... 4.21 4.36 2.30 .92 .08

1966....... . 4.37 4.52 2.39 .93 .07

1967_______ 4.46 4.59 2.65 .93 .07

1968_______ 4.59 4.72 2 65 .94 .06

1969_______ 4.61 4.72 2.92 .94 .06

1970............... 4 66 4.75 3.17 .94 .06

1971............... 4 85 4.94 3 38 .95 .05

1972............... 5.04 5.15 3.14 .95 .05

1973............... 5.18 5.30 3.01 .95 .05

Table 3. Productivity growth rates by source, 1948-73

Year

Annual 

percent 

change in 

output per 

man-hour

Percent 

change 

due to 

productivity 

change

Percent 

change 

due to 

shift In 

man-hours

Percent 

change due 

to Interaction: 

productivity 

and shift

1948......................... 4 45 3.85 0.61 —0.015

1949.......... ................... 3.16 3.54 -.3 6 -.0 2 6

1 9 5 0 ....................... 8.10 6.79 1.27 .037

1 9 5 1 ...................... 2.97 1.92 1.01 .029

1952............ .................. 1.95 1.36 .61 -.0 1 8

1953_______________ 4.19 3.45 .76 -.021

1954............................... 2.43 2.49 -.0 6 - 0003

1955........................... 4.40 4.20 .18 .013

1956____ ___________ .18 -.3 2 .51 -.0 1 9

1957_______________ 2.92 2.43 .49 -.0 0 2

1958_______________ 3.08 2.88 .21 -.0 0 7

1959........ ............. ......... 3 58 3.35 .22 .010

I9 6 0 .._____________ 1.60 1.46 .15 -.0 0 3

1961............................... 3.46 3.20 .26 -.0 0 4

1962_______________ 4.70 4.45 .24 .016

1963_______________ 3.62 3.33 .30 -.0 0 7

1964_______________ 3.89 3.64 .24 .014

1965_______________ 3.41 3.17 .24 -.0 0 8

1966_______________ 3.97 3.54 .43 .012

1967_______________ 2 06 1.94 .12 -.0 1 0

1968_______________ 2.89 2.77 .11 .007

1969_______________ .44 .23 .24 .032

1970_______________ 1.03 .95 .09 -Oil

1971_______________ 4.14 4.13 .02 -.0002

1972_______________ 3.78 3.76 .02 .005

1973_______________ 2.93 2.82 • .09 .014

AVERAGES

1948-73.............. .......... 3.21 2.90 0.31 .0014

1967-73.............. ........... 2.47 2.37 .10 .005

1958-66...................... 3.48 3.22 .25 .002

1948-57..................... 3.48 2.97 .50 -.0 0 2

1948-66.......................... 3.48 3.09 .38 -.00002

Second, the importance of the shift effect declines

rather sharply from the beginning of the period to

the end of the period, even though the trend is not

smooth. To clarify this decline in the shift effect,

we have averaged the changes for three periods:

1948-57, 1958-66, and 1967-73. For the period

1948-57, the shift effect averages 0.5 percent per

year; for 1958-66, 0.25 percent per year; and for

1967-73, 0.1 percent per year. Thus, an important

part of the decline in the rate of productivity increase

in recent years is directly traceable to the declining

effect of the shift of labor from agricultural to

nonfarm employment.

There are two key factors in the declining contribution of the shift effect to overall productivity

growth: first, as the nonfarm share of total private

man-hours increases, the effect of shifting a fixed

number of workers from farm to nonfarm diminishes.

Second, although the level of productivity is higher

in the nonfarm sector than in the farm sector, over

the long term, productivity in agriculture has increased faster than in the nonfarm sector. Thus, as

the level of output per man-hour in the farm sector

approaches the higher level in the nonfarm sector,

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the effect of shifting man-hours out of agriculture

will lead to a smaller increase in productivity for the

total private sector. For example, in 1947, nonfarm

output per man-hour was about 3.4 times as high

as in agriculture; by 1973, it was about 1.8 times

the farm level. In these terms, then, a 1-percent shift

in man-hours from agriculture would have only about

half the effect on overall productivity growth in 1973

than the same shift would have had in 1947.

The contribution of the shift effect to productivity

growth in the private economy has declined substantially in recent years, and there is little reason to

suppose that either of the two factors noted above

will be reversed. Consequently, it is more meaningful

to examine the shortfall in productivity growth in

recent years against a yardstick which abstracts from

the effect of the employment shift from agriculture.

To do so, we have constructed a trend line corresponding to a shift-adjusted productivity growth

index which we compare with a conventional 3.1

percent productivity growth trend in chart 2. A e

shift-adjusted growth rate, no;id on the chart as G*,

Chart 2. Output per man-hour in the total private economy, 1947-73

[1947=100]

Ratio scale

Table 4. Analysis of productivity shortfall, 1966-73

Year

Indexes of productivity, total private, 

all persons

Cumulative shortfall 

of actual from—

G*

3.1

percent

trend

Actual G*

3.1

percent

trend

1966............... 100.00 100.00 100.00 0.00 0.00

1967......... . 102.80 103.10 102.06 .74 1.04

1968_______ 105.67 106 30 105.00 .67 1.30

1969_______ 108 61 109.60 105.47 3.14 4.13

1970............... 111.62 113.00 106.55 5.07 6.45

1971_______ 114.70 116 50 110.97 3.73 5.53

1972....... . 117.87 120.11 115.16 2.71 4.95

1973............... 121.12 123.83 118.54 2.58 5.29

NOTE: G* denotes shift-adjusted growth trend, that is, the productivity changes 

which would have occurred had there been no employment shift.

is the average of farm and nonfarm growth rates

weighted by man-hours in each sector.4

The recent productivity slowdown appears less

serious when compared with the shift-adjusted growth

trend5 than with the 3.1-percent average rate of

growth in the total private sector over the period—

the conventional growth trend. (See table 4.) (Similarly, the shift-adjusted growth trend probably provides a more realistic basis for projecting future

productivity movements than the aggregate trend.)

Consequently, part of the recent productivity slowdown can be accounted for by the decreased contribution of the shift of man-hours from agriculture.

There are no readily discernible reasons, however,

for the rest of the slowdown. While employment has

expanded rapidly since 1966, investment has more

than kept pace, so that the capital/labor ratio increased somewhat from 1966 to 1972, the last year

for which data are available. When capital is disaggregated to structures and equipment, the same

pattern holds.6

Employment and man-hours

Employment in the total private economy increased

in 1973 to about 76.2 million, an increase of 3.3

percent over the year before. Both the level and the

percentage increase were the largest in the postwar

period. Man-hours increased 3.2 percent in 1973,

the largest increase since 1959. The man-hour increase was slightly smaller than the rise in employment due to the small decline (0.1 percent) in average weekly hours, to 37.7. (See table 5.)

Employment growth, at least for manufacturing,

must be viewed in the context of the recovery. Em245

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ployment growth was affected by the slowing of economic growth begun in 1969. The largest adjustment

occurred in the manufacturing sector, in which the

employment decline lasted until the third quarter of

1971. The expansion that began then continued

through 1973, and by the fourth quarter, employment in manufacturing equaled the level of the first

quarter of 1969 in this sector.

Costs and prices

Unit labor costs, which are affected by changes

in output per man-hour and compensation per manhour, rose 4.7 percent in the private economy from

1972 to 1973. This increase was larger than those

which occurred in either 1972 or 1971, when the

increases were 2.9 and 2.8 percent, respectively. The

faster rate of cost increase reflected the slower rate of

productivity growth last year, coupled with a faster

rate of increase in compensation per man-hour. The

increase in unit labor costs in the fourth quarter was

greater than the increase in the second or third quarters, because as 1973 drew to a close, productivity

was declining while compensation per man-hour was

increasing.

Real compensation per man-hour grew at a slower

rate in 1973 than the year before, even though money

Table 5. Percent change in man-hours, employment, and

average weekly hours in the private economy, annual rate,

1973

[Change over previous quarter at annual rate]

Item

1973

Annual 1 II III IV

MAN-HOURS

Total private economy______ 3.2 3.3 3 8 3.2 2.3

Nonfarm sector_________________ 3.4 4.0 4.8 2.6 1.9

Manufacturing-. . . .. ______ 4.8 6.2 3.8 - . 0 4 7

EMPLOYMENT

Total private economy______ 3.3 3.1 3.2 3.6 3 6

Nonfarm sector_________ ____ _ 3.5 4.0 3.9 3.3 3.1

Manufacturing__________________ 4.6 5.5 4.6 1.2 4.4

AVERAGE WEEKLY HOURS

Total private economy______ - . 1 .2 .7 - . 4 -1 .2

Nonfarm sector________________ - . 1 - 0 .8 - . 7 -1 .2

Manufacturing_______ ______ ___ .2 1.0 -1 .0 -1 .0 .0

Table 6. Distribution of price changes in the implicit

price deflator, private economy, 1965-73

[In percent]

Percentage point distribution

of price change

Year Price

change

Unit labor Unit Other unit1

costs profits costs

1965-66................................. 2.5 1.7 0.2 0.6

1966-67._________ _____ 2.9 2.2 - . 8 1.4

1967-68_____ ___________ 3.6 2.8 - . 1 .9

1968—69________________ 4.5 4.4 -1 .2 1.3

1969-70________________ 4.8 4.1 -1 .7 2.5

1970-71________________ 4.3 1.8 .6 1.9

1971-72________________ 2.9 1.9 .4 .6

1972-73________________ 5.4 3.0 1.6 .9

1 Includes depreciation, interest, and indirect business taxes.

compensation increased faster. Since real compensation per man-hour takes account of changes in the

Consumer Price Index, the slower rate of growth of

real compensation per man-hour reflects the faster

rise of prices in 1973. Real compensation per

man-hour increased 1.5 percent in 1973 compared

to 3.5 percent in 1972.

Unit nonlabor payments in the private sector increased 6.6 percent in 1973. This measure, which

includes capital consumption allowances, net interest,

indirect business taxes and profits,, had increased 2.8

percent the year before. Profits in nonfinancial corporations increased 17.4 percent from 1972 to 1973,

after increasing 13.7 percent from 1971 to 1972.

Unit profits were also higher, up 9.0 percent in 1973

compared to 5.8 percent in 1972.

The rate of change of the implicit price deflator

in the private economy, which reflects changes in

unit labor costs, unit nonlabor costs and unit profits,

increased in 1973, having slowed in each of the preceding 2 years. The increase in 1973— 5.4 percent—

was the largest annual increase measured. Table 6

shows how the movement of the component parts of

the implicit price deflator interacted to produce the

annual change. In the period beginning in 1965, unit

profits rose little or not at all in every year except

1973, when they registered their largest contribution

to price change since 1955. Thus, the unusual increase in unit profits, added to the continuing increase in unit labor and nonlabor costs, produced the

large increase in the implicit price deflator. □

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FOOTNOTES

1 Algebraically, we can therefore partition the change in

output per man-hour for the private economy as:

APt = A PtA • S V i + A PtN • S V i (productivity effect)

-f- AS»a • P V i + AS»N • PNt-i (shift effect)

+ A SAt • A PtA + A StN • A PtN (interaction effect)

where:

Pt is output per man-hour in the private economy in year t

PtA is output per man-hour in the farm sector in year t

PtN is output per man-hour in the private nonfarm sector

in year t

StA is the share of total private man-hours in the farm sector

in year t

StN is the share of total private man-hours in the nonfarm

sector in year t

t— 1 refers to the value of a variable for the previous year

A is a first difference operator:

APt denotes Pt — Pt-i

A StA denotes StA — S V i

2 Implicit in our analysis is the assumption that each new

worker shifting into the nonfarm sector has the same productivity as the average worker in that sector. To the extent

that new workers enter subsectors with lower labor productivity than the nonfarm average, our procedure overstates

the shift effect.

‘ There are substantial shifts in man-hours during the

postwar period among subsectors of the nonfarm economy

as well. The effects of these shifts are also being investigated.

‘ With this designation, we ignore the interaction effect.

This effect should, properly speaking, be divided between the

shift-adjusted productivity measure and the measure of the

shift effect, so that the two resulting series account for

the total productivity change. The interaction effect is so

small that no allocation procedure would change the values

of the shift-adjusted productivity growth series.

5 A weighted average growth trend can be defined in the

notation of footnote 1 above, as

G = S \ • A PtA + StN • A PtN

For purposes of smoothing and insuring certain desirable

properties in the resulting series, we have constructed a

Divisia index of growth rates, which uses average weights

for each sector based on shares in the current and previous

years:

G* = (SAt - S V i) • A P .a + (StN - SN t-i) • APt"

2 2

•The capital labor ratio increased by 17.4 percent from

1966 to 1972 for an employees-based measure of labor input, and by 21.8 percent for a man-hour measure. For equipment only, the increases were 25.4 percent and 30.0 percent.

The annual rates of increase were in each case greater than

for the period 1958-66. These ratios are based on the Constant Cost II measures of the capital stock from the Bureau

of Economic Analysis, U.S. Department of Commerce.

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Productivity for organizations sampled 

rose at an annual average rate 

of 1.6 percent a year 

from fiscal 1967 through fiscal 1973

CHARLES ARDOLINI AND JEFFREY HOHENSTEIN

T h e e f f ic ie n c y with which the public sector discharges its responsibilities is of paramount importance to the Nation’s economic health. With this

sector accounting for one out of every six jobs in the

economy, the productivity of the public employee

has a significant effect on the tax burden shared by

all citizens. The lack of adequate measures of productivity for this sector has been one of the most

serious data gaps we have had to contend with in our

national productivity statistics. For several years, an

effort has been undertaken by a joint interagency

task force to develop and refine productivity measures for the Federal sector.1 This article presents the

results of the Bureau of Labor Statistics’ current

measurement efforts in this area. About 1.7 million

man-years, or 61 percent of the employment in the

Federal civilian government, are currently covered

by the output per man-year measures developed for

this sector.

Background

Since 1898, when the Bureau first began to measure productivity, coverage has been restricted to the

private sector. The most comprehensive measure of

output per man-hour currently prepared by the Bureau covers the total private economy only.2 General

government has been excluded because the real gross

product for this sector in the national accounts is

defined as base-year wages and salaries of government employees multiplied by changes in government employment.3 The use of this real product estimate for productivity measurement purposes would

imply a zero productivity growth for the government

sector and introduce a bias in an overall measure.

(However, estimates for government enterprises such

as the Tennessee Valley Authority and the BonneCharles Ardolini is chief of the Division of Industry Productivity Studies, Bureau of Labor Statistics, and Jeffrey

Hohenstein is an economist in the same division.

Measuring

productivity

in the Federal

Government

ville Power Administration are included in the private measure because their outputs are sold and

thus they are comparable with the treatment of the

private sector in the national accounts.)

Before 1971, there were only a few isolated efforts

by Federal agencies to measure the productivity of

an entire organization or selected organizational components. This included a landmark effort in 1962

by the U.S. Bureau of the Budget (now the Office

of Management and Budget) to explore the feasibility and usefulness of productivity measurement in

five government organizations. The study demonstrated that productivity measures could be prepared for four of the five organizations covered and

concluded that valid measures could be developed

for many other government activities.

In the fall of 1970, at the behest of the Joint Economic Committee, the General Accounting Office

began to investigate the feasibility of measuring productivity in the Federal Government. Subsequently,

a joint interagency team (composed of the Office of

Management and Budget, the General Accounting

Office, and the Civil Service Commission) was established which surveyed the existing agency measurement systems. This team concluded that these

systems probably could generate the data required

for productivity index construction, and it recommended the establishment of a joint Federal productivity measurement task force which would receive

technical assistance and manpower support from the

Bureau of Labor Statistics.

During fiscal year 1972, the task force collected

data for fiscal years 1967-71 from 114 organizational units4 in 17 agencies representing about 55

percent of the Federal civilian work force. Man-year

coverage was expanded to 60 percent when data were

collected covering fiscal years 1967-72.

In July 1973, the Office of Management and

Budget endorsed the continuation of the project to

measure Federal productivity, and full responsibility

From the Review of November 1974 248

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for collecting input, output, and related information

and for developing the productivity measures shifted

to the Bureau of Labor Statistics. The Joint Financial Improvement Program,5 the General Services

Administration, and the Civil Service Commission

were to study the reasons for productivity change

and ways in which to enhance productivity in the

Federal sector, as well as several related subject

areas.6

In fiscal 1974, the Bureau extended the data base

to fiscal 1973, expanded coverage to include organizational units not previously covered, improved the

quality of some of the input and output data, and

refined the methodological procedures used by the

task force to construct productivity indexes.

Concepts

In a general sense, productivity may be defined as

the relationship of real, that is, physical, 'output

(goods and services) to real input. Productivity

measures can be grouped into two broad classes.

The first expresses productivity as the relationship

of output to one type of input such as labor, capital,

or energy, and the second expresses productivity as

the relationship of output to a combination of inputs. Although the former measures relate output

to one input, they do not measure the specific contribution of the factor to production. Rather they

express the joint effect of a variety of interrelated

influences such as changes in technology, substitution

of one factor for another, utilization of capacity,

skill levels and the efforts of the work force, and

managerial and organizational skills on the use of

the factor in the generation of output.7 It is this

type of measure, output per man-year indexes, that

has been developed for the Federal Government.

Productivity measures for the Federal Government

compare the current output-input relationship with

that of some previous reference period, in this case,

fiscal 1967. These measures reflect the changes

which have actually taken place regardless of the

mission of the organization. In that sense, they must

be distinguished from measures of effectiveness

which provide a means of determining whether an

agency, is proceeding toward its objectives by establishing a relationship between organizational actions

and mission accomplishment. Both types of measures are useful tools: the productivity measures indicate changes in the real cost of producing an agency’s

output and the effectiveness measures determine the

value of an agency’s output to the recipients of its

goods and services. These latter indicators are much

more difficult to develop and attempts have been

limited.

Ideally, a productivity index should relate final outputs to their associated direct and indirect input(s).

And, in fact, the output data included in the overall

sample are final from the perspective of the organization providing the information and from the 16

functional areas within which these data are classified. However, since the outputs of one organization

may be consumed wholly or partially by another

Federal organization in the production of its final

outputs, all output indicators in the Federal sample

may not be final from the perspective of a higher

level organization, for example, the entire Federal

Government. Therefore, the overall statistics do not

represent “Federal productivity” but rather, the

weighted average of the productivity changes of the

measured Federal organizations included in the

sample.

Perhaps this distinction can best be described in

terms of an analogy with the productivity measures

available for the private sector. The Bureau of Labor

Statistics develops productivity indexes for selected

industries, such as steel, as well as for major sectors

of the economy, such as manufacturing. The sector

indexes are “net” measures excluding the intermediate outputs of the component industries within each

sector. Therefore, that portion of the steel industry’s

output consumed by other manufacturing industries

is excluded from the output index for the manufacturing sector. If all manufacturing industries were

measured separately on a “gross” basis and then aggregated, the resulting measure would not represent

the productivity of the manufacturing sector because

of the inherent duplication.

Although it would be desirable to have a net

measure of “Federal productivity,” it is not available

at this time. While many of the measured outputs

are consumed outside of the Federal Government,

sufficient data are not available to determine the

degree of internal consumption. Nevertheless, the

overall indexes do provide some insight into Federal

productivity trends.

Methods used

For an organization producing a single uniform

product or performing a single uniform service, the

productivity index simply measures the change over

time of the ratio of units produced to man-years.

However, most organizations produce several types

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of products. To construct a composite output index,

the several outputs must be combined in some meaningful fashion. This is accomplished by multiplying

the quantity of each product produced each year by

the man-years required to produce one unit of output in the base year. Thus, those products which

require more labor time are given more importance.

In the Federal sample, over 850 output indicators

have been aggregated by the Bureau of Labor Statistics into output indexes by combining the quantities of each type of output by their respective actual

or estimated fiscal 1967 labor requirements. These

weights were constructed from the detailed output

and input data provided by each participating organization. Output indexes were constructed by dividing

the weighted output quantities each year by the

weighted output quantity in the base year.

The nature of the indicators varied substantially.

They included such diverse items as trademarks disposed, tank repairs, weather observations, square

feet of buildings cleaned, electrical power generated,

and deportable aliens located. The output volume

ranged from several hundred units completed per

year (for example, river basin studies) to billions

(such as mail delivery items).

Man-year indexes were developed by dividing the

man-year aggregates for each year by the aggregate

in the base year. As in all input measures used by

the Bureau of Labor Statistics to develop productivity indexes, man-years are considered homogeneous and additive. Adequate information is not

available to separately weight the various types of

labor. Each man-year reflects the regularly scheduled time, overtime, and leave time of all full-time,

part-time, or intermittent employees. A man-year

is equivalent to one individual paid for 40 hours a

week, 52 weeks a year.

Output indexes were divided by corresponding

man-year indexes to derive the productivity measures for the overall measured sample and for 16

major functional activities, such as procurement and

supply, general support, and training and education.

Although indexes were also constructed for each of

approximately 200 organizational units, these will

not be published. Rather, they are returned to each

organization for its own use (for example, to stimulate further examination of the causes of productivity

change within each organization) and are used by

BLS as one method of validating the basic data

(that is, by examining the reasonableness of the derived trends).

Data on personnel compensation (wages and

fringe benefit costs) also were submitted to the

Bureau of Labor Statistics. Compensation indexes

were constructed as described for man-years.

Slightly more than 41 percent of the organizational units were unable to provide any data as far

back as fiscal year 1967. Under certain circumstances, simply adding new information as it became

available to the existing data base would distort the

various calculations. Consequently, as additional

data on output, man-years, and compensation became available, they were linked to the existing data

series. The linked series provided the basis for constructing indexes of output per man-year, unit labor

costs, unit labor requirements, and related information.

Productivity trends

Productivity for the total measured sample rose

at an average annual rate8 of 1.6 percent a year

during fiscal 1967 through fiscal 1973. This increase

reflected an average increase in output of 1.9 percent a year coupled with a modest rise in the manyear resources consumed— 0.3 percent a year. (See

chart 1 and tables 1 and 2.) Within each of these

series, the year-to-year percent changes varied substantially, with the largest productivity gain of 4.3

percent occurring in fiscal 1973. In this year, output

rose about 3.5 percent while man-years declined

0.8 percent, continuing the downward trend in manyears started in fiscal 1971.

Since the overall rate masks the divergent trends

of its components, data for the 200 organizations

have been classified into 16 functional areas of government activity. (For definitions of these areas, see

exhibit 1.) Some of these categories, such as standard printing and power, are more homogeneous than

others, such as citizens’ records and specialized manufacturing.9 Nevertheless, these categories do provide insight into the trends for the major functional

areas underlying the overall sample.

For these 16 groups, average annual changes

ranged from— 2.4 percent for standard printing

services to 5.8 percent for power and general support

activities. Two of the 16 functions— printing and

health care— showed annual average productivity declines of 2.4 and 0.7 percent, respectively.

Analysis of the printing plant trend indicates easier

jobs are being contracted out to commercial firms

while the government printers are doing the more

difficult jobs, thus creating a downward bias in the

measure. This example reflects the quality problem,

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Chart 1. Indexes of output per man-year, output, and

man-years, total measured sample, fiscal years 1967-73

or more specifically, the product mix problem in

measuring productivity. If the output data were

available in sufficient detail to allow quantification

of easy versus difficult jobs, for example, the effects

of these shifts could be removed from the measure.

Similarly, the output per man-year decline in health

care might be misleading because the current measures do not properly reflect the increase in quality

of medical care. Federal medical managers have reported an increase in staff per patient and advances

in medical technology— for example, open heart

surgery and kidney dialysis— which have increased

the scope and cost of diagnostic and treatment procedures during this period.

Each of the functional indexes exhibit a wide

range in year-to-year movements, with six of the

functional groups declining in 1973.

Productivity increased at an average annual rate

of 2.4 percent a year from fiscal years 1968-73 in

those government units engaged in the overhaul and

repair of heavy equipment. Modernization of plant

and equipment was one of the major factors underlying this productivity gain.

As indicated earlier, indexes also have been developed for the 200 organizations providing output

and input data to the Bureau of Labor Statistics.

These organizations ranged in size from less than

100 man-years to hundreds of thousands of manyears. In 1973, the number of organizations showing productivity increases was slightly greater than

the number showing declines, 103 compared to 97.

However, for the 6-year period, nearly three-fourths

of the organizations showed productivity increases,

and about half of these increases exceeded 4 percent

per year. The following tabulation shows the distribution of average annual percent changes in output per man-hour for fiscal years 1967-73 among

the 200 organizations:

Number of Percent change in

organizations output per man-year

Less than—

2 ........................................... — 10

I ....................................................... — 8 to —10

6 ....................................................... — 6 to — 8

17 .................................................... — 4 to — 6

II ....................................................... — 2 to — 4

18 ....................................................... 0 to —2

41 ....................................................... 0 to 2

More than—

36 ....................................................... 2 to 4

28 ....................................................... 4 to 6

12 ....................................................... 6 to 8

6 ....................................................... 8 to 10

22 ........................................... 10

Unit labor costs

Over the 6-year period, compensation rose at an

average rate of 8.8 percent per year, with year-toyear changes ranging from 4.9 percent in 1968 to

11.7 percent in 1970 and 1971.

The associated average increase in productivity of

1.6 percent a year was not sufficient to maintain a

stable level of unit labor costs. Consequently, unit

labor costs rose 7.1 percent a year over this period.

Unit labor costs will rise to the extent that changes

in average compensation are not offset by increases

in productivity. The smallest gain in unit labor costs

of 1.8 percent occurred in fiscal 1973, when the

average compensation gain of 6.3 percent was partially offset by a 4.3-percent increase in productivity.

There was wide variation in unit labor cost trends

among the functional areas, ranging from a 0.1-

percent average annual increase in general support

to 11.5 percent a year in standard printing. (See

table 3.) In general, the dispersion in the changes

in unit labor costs among the various functions was

almost entirely a reflection of the variation in the

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Table 1. Functional and summary indexes of output per man-year, measured portion of the Federal civilian government,

fiscal years 1967-73

[Fiscal 1967=100]

Fu -tional groupings

Fiscal year Rata of

change,

fiscal years

1967 1968 1969 1-70 1971 1972 1973 1967-73

Citizens’ records_____________ ____ _________ 100.0 100.5 103.7 106.7 111.0 116.7 120.0 3.3

Reference services__________ ________ ______ 100.0 102.1 110.7 111 2 111 3 114.2 109.2 1 8

Loans and grants___________________ _________ 100.0 105.0 109.4 121.5 135.1 138.1 129.5 5.6

Training and education................................................ 100.0 101.0 99.8 97.5 102.8 100.3 101.1 .2

Agriculture and natural resources_______________ 100 0 101.6 101.2 102.4 108.0 110.8 111.5 2.0

Regulation_______ _______ ___________________ 100.0 106 6 114.7 119.6 117.2 116.9 123.0 3.0

Medical services______________________ ______ 100 0 102.1 102.4 101 4 100.7 95.3 98.8 - . 7

Power____________________________ _________ 100.0 103.7 123 9 131.9 131.9 138 5 136 5 5 8

Transportation___ ____ ___ __________________ 100.0 110.9 117.7 119.6 113 4 116.7 123 1 2.5

Postal service................................. ............................. 100.0 98.3 98 9 99 4 101.6 102.7 108.4 1.3

Specialized manufacturing_____ _______________ 100.0 106.5 110.6 120.7 139.8 132 9 128.1 5.2

Standard printing____________________________ 100.0 98.4 94.4 93.1 87.8 87 2 88.7 -2 .4

Procurement and supply________ _____________ 100 0 103.7 114.4 107.7 107.1 106.6 107.6 .7

Overhaul and repair of equipment___ ___________ (*) 100.0 98.7 103.4 110.8 110.6 108 5 *2.4

Maintenance of facilities______________________ 100.0 102 3 102.6 108 2 110.1 108 2 105.5 1.2

General support services______________________ 100.0 106.3 110.1 114 8 128.2 132 4 138.7 5 8

Total of functional groupings_____________ 100 0 100 8 102 9 104.0 105.7 106.5 111.1 1.6

1 Index fiscal 1968=100.

rates of productivity growth. There was a great deal

of homogeneity in average yearly compensation

changes among the organizations in the functional

areas. Almost all of the functions experienced

changes in average yearly compensation within plus

or minus 2 index percentage points of the 8.8-percent average change for the overall sample. Thus,

in general, there was a strong inverse relationship

between the functions’ productivity and unit labor

cost experience.

Standard printing and medical services, the only

functions with declining productivity, experienced

the greatest average increases in unit labor costs—

11.5 percent and 9.3 percent, respectively. In contrast, the power, loans and grants, specialized manufacturing, and general support functions, all of which

had average productivity gains of over 5 percent,

had the smallest increases in unit labor costs.

Measurement problems

Several problems in measuring productivity in the

Federal sector arise primarily because most Federal

activities are service-oriented. It is often difficult

to define and quantify the outputs of government

organizations since they usually do not produce

clearly specified physical products such as those in

the goods-producing sectors of the private economy.

A recurring concern of many Federal managers

has been that some reported outputs do not reflect

changes in output quality. The definitions of changes

in quality of output in general are somewhat ambiguous. For purposes of labor productivity measure­

* Rate o’ changa is for fiscal years 1968-73.

ment, changes in output quality refer to changes in

the characteristics of the output which reflect an

altered production process with different base-period

labor requirements for producing the output. For

these purposes, changes in output characteristics

which affect the value of the output to the user but

which do not reflect an altered production process

or different base-period labor requirements do not

require special treatment.

If productivity indexes are derived from output

Table 2. Functional and summary average annual rates

of change for output per man-year, unit labor costs,

compensation per man-year, output, and man-years, fiscal

years 1967-73

Functional groupings

Output

per

manyear

Unit

labor

costs

Compensation

per manyear

Output Manyears

T o ta l o f fu nctio n 1 

g ro u p in g s_____________ 1 .6 7.1 8 .8 1 .9 .3

C itiz e n s ’ re co rd s .............. ............. 3 .3 4 .6 8 .1 4 .6 1 .2

R eference s e rv ic e s _____________ 1 .8 5 .6 7 .5 6 .1 4 .2

Lo a ns and g r a n ts ........................ 5 .6 2 .6 8 .4 8 .2 2 .5

T ra in in g and e d u c a tio n ________ .2 8 .1 8 2 2 .7 2 .5

A g ric u ltu re and n atural 

re so u rc e s .................................... 2 .0 6 .0 8 .2 .1 - 1 . 9

R e g u la tio n ______________ _______ 3 .0 4 .8 7 .9 5 .3 2 .2

M e d ica l s e rv ic e s _______________ - 7 9 .3 8 .5 1.1 1 .8

P o w e r__________________________ 5 .8 2 .3 8 .3 1 1.6 5 .5

T ra n sp o rta tio n ...................... ......... 2 .5 8 .7 1 1.4 4 .4 1 .8

Po stal s e rv ic e __________________ 1 .3 7 .5 8 .9 1 .6 .3

S p e cia lize d m a n u fa c tu rin g ____ 5 .2 1 .5 6 8 7 .2 1 .9

S ta nd ard p r in t in g . . . .................. - 2 . 4 11 5 8 8 - 4 1 - 1 7

P ro c u re m e n t and s u p p ly ............ .7 6 .9 7 .7 - 5 . 1 - 5 . 8

O v erh au l and re p a ir of 

e q u ip m e n t1_________________ 2 .4 5 .7 8 .2 - 3 . 4 -5 .7

M a in ten an ce o f fa c ilitie s _______ 1.2 7.0 8 .3 -1 .4 - 2 .6

G en eral su p p o rt se rv ic e s ............ 5 .8 .1 5 .9 - 2 . 4 -7 .7

1 Rate of change is for fiscal years 1968-73.

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measures which have not been adjusted for changes

in output quality, they will reflect both real changes

in efficiency and apparent changes resulting simply

from alterations in the basic characteristics of the

output. Thus, when quantifying outputs, it is necessary to identify changes in output quality and to

adjust for these changes in order to obtain a meaningful measure of productivity.

Another important problem to be solved in measuring Federal productivity is that of quantifying outputs in sufficient detail to adjust for shifts in types

of outputs. If the output units represented by one

output indicator are not homogeneous and if over

a period of time the proportion changes between

those units that are more labor intensive and those

that are less labor intensive, the resulting output per

man-year measure will reflect this change as well as

productivity changes.

Outputs which take many months or, in some

cases, years to complete also present difficult measurement problems. Quantifying such outputs only

in the year they are completed often produces inaccurate and erratic output indexes. In order to

obtain a meaningful measure, organizations must

either estimate what proportion of these long-term

outputs were produced in each year or break these

outputs down into component parts, each of which

is completed in a relatively short time.

Closely related to the problem of quantifying output are the difficulties which emerge when the Federal Government contracts out work. In these cases,

the final output measure for a government agency

may reflect not only the results of the activities of

government employees but also those of the private

contractors. It is important in these cases to deterEXHIBIT 1. Definitions of functional areas

The following definitions briefly describe the

nature of the organizations classified within each

function:

1. Citizens records— Organizations maintaining

records of government obligation to citizens

and vice versa, also criminal and demographic records.

2. Reference services— Organizations maintaining

library or publications services, or developing

statistical information for the public.

3. Loans and grants— Organizations making research grants and various business loans.

4. Training and education— Organizations overseeing training, education, and social welfare

programs.

5. Agriculture and natural resources— Organizations responsible for overseeing and protecting natural resources.

6. Regulation— Organizations responsible for enforcing Federal statutes in such areas as interstate commerce, immigration, taxation, and

labor.

7. Medical services— Organizations operating hospitals, clinics, or public health programs.

8. Power— Organizations responsible for generation, sale, or transmission of electric power.

9. Transportation— Organizations responsible for

operating U.S. canals, and maintaining safe

conditions in U.S. airways and waterways.

10. Postal service— Organization responsible for

delivering the mail and providing other

services, such as mail insurance and money

orders. *

11. Specialized manufacturing— Organizations involved in manufacturing-type activities, such

as printing currency or maps and fish production.

12. Standard printing — Organizations printing

standard text or statistical documents.

13. Procurement and supply — Organizations responsible for purchasing and distributing

supplies.

14. Overhaul and repair of equipment— Organizations responsible for upkeep of major military equipment.

15. Maintenance of facilities— Organizations responsible for the operation and upkeep of

Federal buildings or installations.

16. General support services— Organizations performing overall administrative and supportive

activities such as personnel, automatic data

processing, and budget.

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mine which output is exclusively associated with

government employees since the input measure is

limited to them. For example, the Manpower Administration administers contractor-operated job

training programs. Here the outputs of the contractors should not be used unless a strong case can be

made that the workloads of the Federal employees

depend on and remain proportional to the workloads

of the contractors. Attempts were made to adjust

for this problem in this study.

The increasing use of the regional generalist, who

divides his or her time among several program areas,

has made it difficult to accurately allocate manpower

to each individual output. This manpower allocation

is crucial in developing proper weights for combining the outputs. Further, reorganizations have

made it difficult to obtain consistent output and

man-year data and to continue measuring relatively

stable organizational units.

The Bureau has attempted to resolve these and

other problems to the extent possible. Much work

still remains to be done, and the Bureau will be concentrating on improving the output indicators.

Future plans

During the current fiscal year, the Bureau of Labor

Statistics will extend the data series to include fiscal

1974 information. In addition, the Bureau will be

introducing fiscal 1972 unit man-year weights which

will be used to combine output quantities from 1972

forward.

Some efforts will be devoted to expanding the coverage in the Federal sector. As mentioned earlier,

46 agencies covering about 61 percent of Federal

employees are presently in the sample. It is believed

that this coverage can be expanded perhaps to a

maximum of 70 percent of Federal employees. It is

doubtful if measures can ever be developed for areas

such as the research activities in the National Aeronautics and Space Administration and the Atomic

Energy Commission, parts of the Defense Department, and parts of the State Department.

Therefore, most work will be concentrated on improving the output indicators reported. This will involve attempting to develop indicators more representative of the results of the agency’s activities in

terms of better definition and greater product detail

than some of those presently included.

On the input side, some attention will be devoted

to exploring ways of taking into account changes in

the characteristics of the Federal work force. In the

present set of measures, all man-years are treated

as homogeneous and additive. It would be desirable

to determine what effect shifts in the composition of

the work force have on the changes in output per

unit of labor input.

In addition to the continuation of productivity

Table 3. Functional and summary indexes of unit labor costs for the measured portion of the Federal civilian government, fiscal years 1967-73

[Fiscal 1967 = 100]

Functional groupings

Fiscal year Rate of

chance

fiscal years

1967 1966 1969 1970 1971 1972 1973 1967-73

Total of functional groupings........................ 100.0 104.1 109.5 120.9 132.9 141.9 144.5 7.1

Citizens' records.................................................. 100.0 103.0 107.6 119.9 124.3 127.1 126.4 4.6

Reference ssrvices................................................ 100.0 100.8 100.3 113.2 122.1 126.3 134.0 5.6

Loans and grants................................................. 100.0 102.6 105.6 108.2 104.9 108.5 122.7 2 6

Training and education............ ............................ 100.0 105.8 113.1 129.7 135.2 147.4 155.7 8.1

Agriculture and natural resources.......................... 100.0 104.8 113.2 127.8 132.4 134.7 138 4 6.0

Regulation.......................................................... 100.0 98.1 101.3 109.8 118.6 125.0 125.1 4 .6

Medical services.................................................. 100.0 102.8 107.3 123.2 135.5 155.9 159.7 9.3

Power................................................................. 100.0 101.8 93 9 94.4 104.2 107.6 115.4 ’ .3

Transportation..................................................... 100 0 95 3 100 0 115.2 134.7 142.2 151.6 8.7

Postal service..................................................... 100 0 106.7 113.9 123.1 138.0 147.0 148.7 7.5

Specialized manufacturing.................................... 100.0 100.9 104.4 104.1 96.3 105.4 114.4 1.5

Standard printing................................................ 100.0 107.0 117.8 132.8 155.0 173.5 181.9 11.5

Procurement and supply...................................... 100.0 102.0 94.9 114.4 127 6 135 5 139 8 6 9

Overhaul and repair of equipment.......................... (*) 100.0 119.4 123.5 128.9 130.4 138.9 *6.7

Maintenance of facilities....................................... 100.0 104.5 106.7 108.5 121.3 136.5 150.2 7.0

General support services....................................... 100.0 97.5 93.4 103.6 101.3 102.1 95.7 .1

> Index, fiscal 1968=100. * Rate of change it for fiscal years 1961-73.

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measurement, the Bureau of Labor Statistics will

analyze causal factors underlying the productivity

change. The Joint Financial Management Improvement Program also will continue to concentrate on

the analysis of causal factors.

One conclusion reached in the effort to measure

Federal productivity was that if adequate measures

could be developed for this sector, the knowledge

and insights gained would be applicable to State and

local government productivity measurement. However, more developmental work needs to be completed before productivity of State and local governments, which represents about 80 percent of total

civilian government employment, can be measured.

■FOOTNOTES1 More than a dozen reports covering Federal productivity

measurement and enhancement have been prepared since

June 1972.

a In addition, the Bureau’s productivity measurement program includes major sectors of the economy such as private

nonfarm and manufacturing and a group of about 50

selected industries.

“See Jerome A. Mark, “Progress in measuring productivity in government,” M onthly L abor R eview , December

1972, pp. 3-6.

4 These units fall within the categories of general government and government enterprises.

* The Joint Financial Management Improvement Program

is a joint undertaking of the Office of Management and

Budget, the General Accounting Office, the Treasury

Department, the General Services Administration, and the

Civil Service Commission, working in cooperation with

each other and with each of the operating agencies to promote improved financial management on a governmentwide

scale and in individual agencies.

8 For results of those studies, see Joint Financial Management Improvement Program, R eport on Federal Productivity, Volum e 1, June 1974.

7 For a more detailed discussion of the concepts and

measurement of productivity, see The M eaning and M easurem ent of P roductivity, Bulletin 1714 (Bureau of Labor

Statistics, September 1971).

*A11 average annual rates of change are based on the

linear least squares trends of the logarithms of the index

numbers. Different rates may result from using the compound interest formula.

B For a listing of the organizations participating in this

study, and other related information, see R eport on Federal

Productivity, Volum e 1.

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Modernization

and manpower

in textile

mills

O u t p u t p e r m a n - h o u r has been increasing in the

textile industry, an improvement which is largely

associated with the adoption of technological advances. Nevertheless, the average mill is still highly

labor intensive, and its equipment is outmoded relative to other industries. Outlays for more modern

equipment are increasing, but the investments

planned for the next several years are still substantially below the peak outlays of the 1960’s and may

not be large enough to enable the industry to maintain a favorable competitive position. Overall employment may not change appreciably by 1980, but

job content and occupational requirements will continue to be affected by changing technology.

This article analyzes technological developments

of the industry, emphasizing the effect on manpower

of these changes. Some indicators of technological

progress as well as the industry’s efforts to increase

investment in equipment and research will also be

presented. Before examining these developments,

some of the characteristics of the industry will be

discussed.

Industry profile

Concentrated largely on the east coast, the textile

industry, with more than 7,300 mills, is the ninth

largest employer in the country. The industry consists basically of three stages of manufacture— the

manufacture of yarn and thread; the manufacture

of fabric, some types of clothing, and carpets; and

the finishing processes. Although many of the larger

mills engage in all the stages of manufacture from

raw material to finished product, the great majority

of establishments are small single-unit organizations

which produce only one intermediate product, which

Rose N. Zeisel is an economist in the Division of Technological Studies, Office of Productivity and Technology,

Bureau of Labor Statistics.

Technological changes 

are altering skill requirements, 

but modernization lags 

behind other industries

ROSE N. ZEISEL

has either been commissioned or is sold to another

manufacturer.1 The industry is thus seriously fragmented, with a large number of small mills for which

it is difficult to finance new equipment. These mills

may be vulnerable to economic setbacks if their

ability to compete effectively with domestic or foreign goods is eroded by obsolete equipment. To

some extent, however, the effects of fragmentation

are being offset by the growth of larger vertically

integrated companies.

Labor intensity. In spite of a decline over the decade,

the industry is still highly labor intensive relative to

other manufacturing industries. In the 10 years ending in 1971, the ratio of payroll to value added—

a measure of labor intensity— averaged 0.54 compared with 0.48 in all manufacturing. With the exception of the furniture and leather industries, textiles had the highest payroll-value added ratio of the

20 major manufacturing industries in 1971. Considerable variation, however, exists within the industry, with carpet mills on the low side and weaving

mills on the high side of the average.

Labor force. Nearly a million people are employed

in textile mills, and some characteristics are distinctive to the industry. In spite of a steady downtrend

in the last decade, production workers constitute a

very high proportion of textile employees (88 percent) compared with all manufacturing (72 percent).

About half the employees are women, compared with

28 percent in all manufacturing, and in some sectors

of the industry, such as knitting, the proportion of

women is considerably higher. Black workers have

made significant gains in recent years. About 115,-

000 black workers were employed in textile mills in

1970 compared with 45,000 in 1960, more than

2 Vi times as many.

The work force is immobile relative to other industries. It consists largely of semiskilled workers

From the R eview of June 1973 256

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and persons with comparatively low educational

attainment. Moreover, the mills are generally located

in small nonurban communities where opportunities

for other employment are limited, and several family

members may work in the same mill. In addition

to the personal and locational characteristics which

tend to limit textile workers’ mobility, the skills

acquired in the mill generally are not transferable.

Changing markets. Changes in technology, in demography, and in import penetration in the 1960’s reshaped the industry. Of particular importance was

the dramatic growth of synthetic fibers, which resulted in uneven growth patterns among the industry

sectors. While cotton broadwoven production did

not rise appreciably over the decade, output of synthetic broadwovens increased very sharply. Changing technology also resulted in new processes, new

products, and new markets. For example, new knit

goods products are causing considerable dislocation

in the industry as inroads are being made into traditional woven goods markets. At the same time,

demographic changes shifted markets to the teenage,

fashion-conscious groups and more recently to the

young marrieds interested in practical, moderately

priced household goods. For example, carpeting—

primarily synthetic, tufted— experienced the sharpest

rise in output of all the major sectors.

Imports have also seriously affected some sectors

of the industry, although international agreements

since the mid-1960’s have been a moderating influence. Imports of all textile products constituted

about 9 percent of domestic consumption in 1971,

compared with 4 percent a decade earlier. The cotton import-consumption ratio, at about 12 percent

in 1971, was more than double the ratio a decade

earlier. Manmade fiber textile imports increased

more than fourfold in the last 5 years, to about 7

percent of domestic consumption in 1971. The wool

ratio stood at 24 percent, and for many individual

textile products the ratio was even higher.

Technological lag

In general, the American textile industry has not

been in the forefront of technological advance in

spite of many machine improvements and innovative

fiber use. Many reasons can be cited. After the war,

the necessity to rebuild European mills and the

growth of the industry in Asia provided mills in those

countries with the incentive to invest in new technology, while at the same time American mills were

operating below capacity. But basically, the fragmented organization of the textile industry, severe

cyclical business patterns, low profit margins of the

average mill, and historical conservatism to longterm debt have tended to discourage basic research

and long-range investment for new technologies.2

This cautious adoption of new technologies by the

average mill has been accompanied by a lag in technological development by the American machinebuilding industry. Most textile machine companies

in the United States have neither the financial resources nor the productive capacity to develop new

technologies. As a result, machine manufacturers

have largely devoted themselves to improving existing mill machinery rather than promoting fundamental redesign. Also, they have tended to become

job-order shops producing what their customers request.3 For example, machine companies are still

manufacturing picker machine parts for early 1900

models. A few parts are actually interchangeable

with current models because basically the picking

process has not changed very substantially. At the

same time newer technology which eliminates the

necessity for a separate picking operation has been

installed in fewer than 50 mills.

While the lag in the application of technology is

not new, it is particularly important right now because advanced concepts in technology are being

developed and accepted in other countries. For example, open-end spinning, an innovation developed

in Czechoslovakia, is now manufactured by more

than a dozen international machine companies, none

of which are American. (One U.S. manufacturer is

believed to be developing another system of openend spinning.) Exhibited commercially only 7 years

ago, adoption of these machines has been relatively

rapid in foreign countries, but in the United States

it is limited to evaluation by a few mills.

Major developments. Technological advances which

significantly affect productivity growth can be classified roughly into three types. The first involves modifications to conventional machines affecting speed,

capacity, automaticity, cleaning, and materials

transfer. These changes account for most of the

improvement in basic textile manufacture in the

1960’s. In general, they have been adopted by the

average mill. The second group includes new technologies which, however, can be tied into the existing machinery. These innovations, such as chute-fed

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carding and winding on the loom, have been adopted

by larger modernized mills, but not by the average

mill. The third group includes more radical innovations, such as open-end spinning, which constitute a

change in process and which would involve replacing

operative machinery. In general, such new technologies have been restricted to a few leading mills, particularly in the yam and weaving sectors, although

in a few sectors (such as carpeting) very radical

process innovations (such as tufting) have been

fully accepted. Some of the major innovations of the

last few years, their manpower impact, and rate of

diffusion are presented in table 1. In this section,

only selected developments in fiber use, spinning, and

weaving will be discussed as examples of the industry’s technological problems.

Fiber technology. The textile industry has shown

great flexibility in its adaption to synthetic fibers.

While in 1960 natural fibers accounted for over 70

percent of all fibers consumed, today synthetic fibers

hold almost this proportion of the total. Although

there are no definitive data, the shift from natural to

synthetic fiber has been an important factor in the

industry’s productivity growth. In general, manmade

fibers require less labor per unit of output because

they do not need the cleaning and other preparatory

work necessary for natural fibers. Moreover, certain

types of synthetic fibers can actually bypass the whole

spinning process in the preparation of some fabrics.

Yarn manufacture. In general, yam is still manufactured on a series of discrete machines, very similar

to the process half a century ago, although a continuous yarn system has been adopted by a small

number of mills. Nevertheless, numerous machine

improvements and auxiliary attachments (greater

speed and capacity, automatic sensors, integration of

processes, and improved materials handling) have

continually increased mill productivity. For example,

one yarn mill built in 1970 reported operating 175

spindles per employee, compared with a roughly

similar mill opened by the same company in 1964

with 110 spindles per employee.

Probably the major breakthrough in spinning

since the introduction of the current system of ring

spinning is the implementation of the process known

as open-end spinning, mentioned earlier. It combines

Table 1. Major Innovations in the textile industry, 1965-72

Innovation Description and impact Diffusion

Texturizing................................................ Heat sets a crimp in synthetic fiber to provide bulk, an additional process on specialized machines. Stimulated growth of knitting sector.

Commercially started mid-(960's; 

very rapid growth since then.

Continuous yarn system.......................... Integrates several processes, eliminating conventional picker machines and the lap rolls. 

Reduces unit requirements for machine operators and material handlers.

Installed experimentally in several mills 

in mid-1960's; adoption very limited 

since then.

Direct-feed carding___________ _____ Eliminates picking process and associated manpower....................... . . .. Installed in relatively few mills. 

Open-end spinning. Installed for evaluation in several mills. ................................ Integrates roving, spinning, and winding. Can produce 2 or more times the output of the conventional spindle.

Spinning attachments________ ______ Automatic doffing (unloading) machines reduce unit requirements for doffer operators.. 

Automatic devices for piecing (tying) broken yarn. Reduces unit requirements for spinners___

Automatic doffers installed in a few 

mills experimentally in mid-1960's; 

still limited to 1 percent of spindles in 

early 1970’s.

Automatic piecing devices not available commercially.

Winding attachments and integration__ Automatic creeling (loading) of machines; automatic tying-in of yarn ends. Reduces unit 

requirements for operators.

Integration of filling winding with weaving. Eliminates separate process and associated handling.

Automatic creeling and tying-in machinery installed in only a few of the 

newest mills.

Winding on the loom installed only'in 

largest mills in mid-1960's. Now installed on 30 percent of looms.

Shuttleless loom............................. ......... Operates at faster speeds and requires fewer auxiliary operations than shuttle looms. Can 

produce about 50 percent more cloth than the average shuttle loom per hour.

Installed in only a few mills in mid1960’s. Now account for 7 percent of 

all looms.

New knitting machines.......................... . New machines operate at faster speeds and are more automated. Electronic pattern making 

devices reduce pre-knit time. Reduces unit labor requirements and permits greater flexibility 

in design change.

Very substantial growth of new machines in last 5 years. Electronic 

patternmaking being rapidly adopted.

Continuous computerized finishing____ Integrates dyeing and finishing techniques and incorporates computerized instrumentation. 

Reduces unit labor costs and improves quality.

Very substantial growth.

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into one process the three separate operations of

roving, spinning, and winding. Output per machine

hour may be two or more times that of the conventional machine.4 However, investment in the openend system in this country has been delayed by the

advances which have been made in the conventional

process, some drawbacks inherent in this newer system, and the high cost of replacement.

Weaving. American mills continue overwhelmingly

to use the conventional weaving method of shuttle

looms, but those used are faster, wider, and more

automated than 5 years ago. Improvements such as

laborsaving warp-tying methods and winding attachments are also greatly reducing unit labor requirements. Adoption of the new shuttleless looms,

which has been relatively rapid in some foreign mills,

is still very limited in the United States, although it

is generally acknowledged that these looms permit

increased speed, reduced maintenance, less noise,

and fewer preparatory processes. According to one

company’s report, production was maintained at the

same level with 40 percent fewer shuttleless looms

than with conventional looms. This would require

less floor space and 30 percent less labor.5 Other

reliable industry reports generally confirm this potential increase in productivity with the shift to

shuttleless looms. Although the cost of replacing

operative shuttle looms must first be justified, interest

in shuttleless weaving by the larger mills is expected

to increase substantially.

The competitive position of weaving mills is being

tested by the advance in knitting technology. Following Europe’s lead by about 5 years, technological

changes in knitting and knitting yarn have stimulated

phenomenal output growth and greatly reduced unit

labor requirements. Warp knit machines, now gaining broad acceptance in traditional woven-goods

markets, can produce about twice the quantity of

cloth per hour as the double-knit machine does.

Electronic knitting in which construction of goods

is controlled by electronic impulses has been relatively well accepted, and now electronic pattern

scanners can reduce preknitting preparation time and

permit more rapid adoption of fashion changes.

Effect on employment and occupations

While technological advances are generally considered beneficial to economic growth, the measureable effect on employment in any one industry is not

always clear. In general, if the rise in output per

man-hour is not accompanied by an equivalent or

larger increase in output, employment (total hours

worked) will decline. In hosiery, the only sector for

which BLS productivity measures are available, the

rate of increase in output from 1960 to 1971 fell

slightly behind the growth rate of output per manhour, and man-hours and employment declined

slightly.6 On the other hand, in the case of carpeting

in which technological changes greatly increased output per man-hour, employment nevertheless rose

very sharply because of the more than commensurate

gain in output. The outlook for employment depends

therefore on the relationship of output changes to

productivity changes. A projection by BLS for 1980

consistent with a 4-percent unemployment rate shows

that employment in the textile industry may not

change appreciably from the current level.7

Job content. The impact of technological advance

on job content is also difficult to assess. The “mix”

of duties performed by the operator— the relative

time allotted to each duty— is changing and, in some

cases, one or more of the duties are being eliminated

entirely. As shown earlier, many traditional manual functions requiring dexterity and skill are being

replaced by electronic detection and automatic repair

devices. Consequently, considerably more of the

operator’s time (card tender, spinner, and others) is

now spent patrolling the machine to detect malfunctions than was the case a decade earlier, and this

trend will continue. A very sizable reduction in time

spent for repairing broken yarn is expected when a

new device, the automatic yarn piecer, is adopted.

The change in skills required can be seen in a very

rough comparison of a ring spinning operator’s time

in the most modern mill in the 1960’s and in the

1980’s. As estimated by industry experts, the approximate percent distribution of time spent on each

task may be, roughly, as follows:

Task 1960’s 1980’s

Total .................... . . . . 100 100

Repairs y a r n .................. ____ 49 3 0 -3 5

Creels or lo a d s............ ____ 12 5 -1 0

C le a n s.............................. 10 5 -1 0

Patrols ........................... .... 27 4 0 -4 5

M iscellaneous ............. 2 10

There is some question whether these changes

raise skill requirements. For some jobs, such as loom

fixer, a high degree of skill has always been necessary, but newer more sophisticated instrumentation

may require more skill and more technical knowl259

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edge. For some semiskilled operators, however,

there would appear to be a downgrading of skill when

the new machine or accessory equipment is so automated as to actually require less training and less

experience. For example, the required level of skill

of the spinner will be lowered with the use of automatic piecing devices. The shuttleless loom requires

less training by weavers than does the shuttle loom.

At the same time, however, it can be said that the

operator’s responsibility increases as these machines

become more continuous and more automatic, because the cost of downtime (when the machine is

not working) becomes more costly.

Occupational structure. In addition to changing the

job content, advancing technology is altering mill

requirements for various occupations. In general,

long-term trends show a decline in the proportion

of blue-collar workers in the mill and in the industry, with the possible exception of skilled workers. As

machine speed, capacity, and automaticity increases,

for example, fewer semiskilled operatives, such as

spinners and weavers, are required for the same production. Similarly, unit requirements for laborers are

being sharply reduced in all modernized mills as materials handling is more mechanized or two operations are combined. In contrast, skilled workers

such as mechanics and fixers are becoming more essential as textile machines increase in complexity

and continuity. New technologies, such as computer

processing and pollution control devices, may also

require more technically trained personnel than the

textile industry has generally required. Among whitecollar workers, every major group— professional and

technical, managers and officials, clerical and sales—

has been increasing as a proportion of total employment.

Changes in occupational distribution in the industry reflect shifts in the importance of subindustries,

changes in management organization, and the availability of labor, as well as adoption of technological

advances. Comparison of Census data for 1970 and

Bureau of Labor Statistics projections for 1980 show

the following occupational changes (in percent):

Occupation 1970 1980

Total .............................. 100.0 100.0

White collar................... 14.9 20.7

Blue collar..................... 85.1 79.3

Craftsmen, foremen 11.5 11.4

Operatives ............. 68.2 63.7

Laborers................. 3.9 3.0

Service workers . . . 1.5 1.2

Worker adjustments. While technology advances are

considered beneficial to the worker in the long run,

short-term adjustments, particularly to workload

changes, continue to be an area of major concern.

Since only about one-fourth of all textile workers are

in mills covered by labor-management agreements,

provisions for easing such adjustments are made

largely at management’s discretion.

Efforts to significantly increase unionization have

not been successful. Contraction of the Northern

textile industry since World War II seriously depleted

union membership, and Southern mills have, in general, been resistant to organization. Management’s opposition to unionization has been made possible, in

part, because of the availability of rural farm labor,

although less so than in former years. In addition,

while the traditional mill town no longer exists, the

concept of paternalism continues, and this relationship between worker and management does not encourage union participation. The workers’ attitudes

which play a large part in their failure to organize

are associated with their rural background and low

level of education.8

Productivity growth

Although approved BLS measures of productivity for the industry are not available, a rough indication of productivity changes can be derived by examining output data developed by the Bureau of

Labor Statistics and the Federal Reserve Board and

man-hours data gathered by the Census Bureau. Increases in output per man-hour in the 10 years ending

in 1971 are suggested by the substantial average annual rise in output, 4 Vi to 6 percent, compared with

the moderate rise in man-hours of less than 1 percent

annually. Most of the improvement occurred in the

first half of the decade as output rose to near-capacity

levels. In addition, the improvement in output per

man-hour during the last decade reflects a shift to

more capital-intensive sectors, particularly knitting

and carpet production, and a shift to greater use of

the manmade fibers which have lower unit labor requirements than do natural fibers.

For individual sectors of the industry, an examination of various measures of output and man-hour

data in the 1960-70 period (the latest date for which

complete data are available) suggests considerable

variation in productivity growth. For synthetic

broadwovens, growth is suggested by an average

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annual rise of about 10 to 11 percent in output and

of 6 V2 percent in man-hours. The annual growth

rate of cotton broadwoven output was negligible,

or may have even declined, but an average decline

of almost 4 percent in man-hours annually indicates

an advance in output per man-hour. In the knitting

sector, the improvement in output per man-hour

appears to have been substantially greater than in

broadwovens as output rose an average of 8.5 to 9

percent annually, while man-hours increased about

2 percent a year on the average. In the carpet industry, production increased an average of 12-14

percent annually, and man-hours rose 5.5 percent

annually— suggesting an even more rapid gain in

output per man-hour than in the knitting industry.

In almost all sectors, the improvement in output per

man-hour was considerably greater in the first half

of the decade than in the second.

For the hosiery sector, the Bureau of Labor Statistics index of productivity (output per man-hour)

shows a rapid increase between 1961 and 1971 of

an average annual rate of 6.6 percent compared with

4.1 percent in the previous 10 years. This high rate

of growth, which is not likely to continue in the

1970’s, accompanied a sharp increase in output and

rapid technological changes associated with the shift

to pantyhose.

Best plant practice. While no general conclusions

can be drawn, some indication of the potential productivity level for each subindustry is suggested by

the difference between the productivity levels of the

most efficient plants and the subindustry average.

Comparative data are presented in table 2 on value

added per production worker man-hour in 1967

for the “more efficient” and “less efficient” plants

in several sectors of the industry.9 (Although it has

limitations, value added per man-hour is used here

as an approximate indicator of productivity.) For

purposes of this report, the “more efficient” mills

are defined as those which fall into the highest quartile of the ranking of plants by value added per production worker man-hour; the “less efficient” are

those in the lowest quartile.

In the industry sectors for which 1967 data are

available, value added per production worker manhour in the more efficient mills ranged from almost

two and a half to more than five times greater than

in the less efficient mills. The differences were smallest in the yarn (except wool) mills and in the cotton

weaving mills; the largest variance was in the tufted

carpet sector. Although these data are not fully

comparable with available 1958 data, it appears that

the difference between the more efficient and the

less efficient plants has broadened since 1958 in several sectors, including tufted carpets and knit fabrics. In the yarn and cotton broadwoven mills, the

efficiency difference was about the same in 1967

as in 1958.

Although the wide range in productivity levels

within an industry sector may reflect differences in

management, labor, and other factors, one of the

important keys, judging by Census data, appears to

be capital outlays. Expenditures for plant and equipment per employee by the more efficient mill in

1967 were larger than outlays by the less efficient

mill or the average mill in almost every sector shown

in table 2. The 1958 study generally confirms these

findings.

Capital expenditures. Following a decade of low

investment and general contraction, the textile industry sharply increased capital outlays in the 1960’s.

A booming economy, sharply improved textile demand assisted by trade agreements, revised depreciation rates, and investment tax adjustments encouraged mills to invest in new equipment and reduce

the level of technologically outmoded facilities. Expenditures for plant and equipment rose from $330

million in 1961 to a peak of $820 million in 1966,

according to the U.S. Department of Commerce.

Table 2. Value added and capital expenditures, ratios

of “ more efficient" to “less efficient” plants and to average plant 1967

Industry sector

Value added per 

production worker 

man-hour

Capital

expenditures per 

employee

“ More 

efficient” 

to “ less 

efficient” 

plants

“ More

efficient”

to

average

plant

“ More 

efficient” 

to “ less 

efficient” 

plants

“ More

efficient”

to

average

plant

Weaving, cotton_______ _____ _ 2.4 1.5 0.9 1.2

Weaving, narrow fabric............... 3.5 1.8 3.3 2.3

Hosiery, women’s, except socks.. 2.8 1.6 1.7 .8

Knit outerwear______________ 4.0 2.1 1.7 1.4

Knit fabric.................................... 4.9 2.2 2.7 1.9

Carpets, tufted______________ 5.2 1.9 1.3 1.3

Yarn mills, except wool_______ 2.4 1.4 1.3 1.3

Throwing and winding________ 4.5 2.0 5.1 1.3

NOTE: Establishments in each sector were ranked by the ratio of value added per 

production worker man-hour. The “more efficient” establishments are those in the 

highest quartile; the "less efficient” are those in the lowest quartile.

SOURCE: Based on unpublished Census data prepared for the Nat onal Commission 

on Productivity.

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However, from 1966 to 1971, as capacity utilization dropped sharply, capital expenditures declined

25 percent. (See chart 1.) This was contrary to the

pattern in all manufacturing, in which expenditures

remained stable and then rose in spite of a sharp

drop in capacity utilization. Since textile machinery

prices rose about 24 percent in those 5 years, real

capital spending declined considerably more sharply

than the dollar figures would indicate. It should be

noted that this was the pattern primarily of the basic

spinning and weaving mills. Knitting and carpet

mills increased capital outlays very significantly in

Table 3. Indicators of technological change, 1951-71

Average annual rate of change 1

Indicator

1951-61 1961-71 1961-66 1966-71

Payroll per unit of value added. ______ -1 .3

.6

-0 .5

4.5

.3

-1 .5

18.7

1.9

0.2

- 5 .0

-2 .6

Capital expenditures per production 

worker.. . ..........

Research and development per sales 

dollar2 ...............................

1 Linear least squares trends method.

2 Of textile and apparel companies which have research and development programs.

SOURCE: Bureau of Labor Statistics; Bureau of the Census; Bureau of 

Economic Analysis; National Science Foundation.

Chart 1. Investment decline in the textile industry in the

late 1960's

As excess capacity developed. ..

investments dropped sharply. ..

Index (1967=100)

and the proportion of obsolete plant 

and equipment remained high.

Percent

1962 1968 1970 1972

1 Data are for December of the year.

SOURCES: U.S. Department of Commerce and McGraw-Hill Publications 

Co., Economics Department.

the last half of the 1960’s. Although expenditures

for the industry as a whole increased in 1972, they

were still considerably below the 1966 peak.

These investments were insufficient to substantially reduce technologically outmoded facilities. A

recent survey of technological obsolescence in large

textile companies revealed that 20 percent of plant

and equipment was still technologically outmoded in

1972 compared with 29 percent in 1962.10 Only the

transportation equipment industry (excluding autos

and airlines) had a larger proportion of outdated

equipment in 1972.

Anticipating a more favorable economic climate,

the industry plans to increase spending to $640 million in 1975, about 14 percent above the 1970 outlay.1 1 This expenditure, however, would be 22 percent below the 1966 peak. Considering the increase

in machinery prices, and the degree of technological

obsolescence, this effort may be insufficient to modernize the industry.

Funds for research and development. Outlays for

research and development of new products and processes totaled $58 million in 1971, nearly double the

outlay 10 years earlier. 12 However, these investments are a very small percentage of sales of companies performing research and development and

represent only the very largest companies. (See

table 3.) Moreover, these activities have been cut

back sharply in recent years, and as far as is known,

one or two mills are now doing basic research, and

only a few mills are doing developmental work. To

a large extent, the industry relies on research carried out by synthetic fiber producers, machine manufacturers, the Federal Government, and foreign manufacturers. However, industry plans for 1975 call

for peak outlays of $81 million or 40 percent above

1970.13

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Although industry outlays for new plant and

equipment and for research and development appear to be relatively insufficient at this time, it is

possible that with a strong economic recovery in the

1970’s, textile mills will adopt the level of technology

necessary to assure a favorable competitive position.

-----------FOOTNOTES----------

l For more background information on the textile industry, see Technology and Manpower in the Textile Industry

of the 1970’s, Bulletin 1578 (Bureau o f Labor Statistics,

1968).

* Thomas Stanback, Jr., Tax Changes and Modernization

in the Textile Industry (N ew York, National Bureau of

Econom ic Research, 1969).

•P aul W. Strassman, ‘T extiles,” Risk and Technological

Innovation (Ithaca, N .Y ., Cornell University Press, 1959).

•John F. Dulken, “N ew Horizons in Spinning,” Modern

Textiles, August 1971, p. 7.

8 “Shuttleless Loom s: The W eaver’s W eapon,” Textile

World, August 1972, pp. 4 7-48.

*A n official productivity index is available only for this

sector o f the textile industry. For this and other official

indexes, see Indexes of Output Per Man-Hour, Selected Industries, Bulletin 1758 (Bureau o f Labor Statistics, 1972).

7 The U.S. Economy in 1980: A Summary of BLS Projections, Bulletin 1673 (Bureau o f Labor Statistics, 1970).

The projections will be updated in a forthcoming publication.

•R obert Blauner, Alienation and Freedom, The Factory

Worker and His Industry (C hicago, University o f Chicago

Press, 1964), pt. 4.

•B ased on unpublished Census data prepared for the

National Commission on Productivity. For 1958 data, see

Technology and Manpower in the Textile Industry of the

1970’s, p. 31.

10 How Modern is American Industry (N ew York,

M cGraw-Hill Publications, Econom ics Department, 1972).

11 Business Plans for New Plants and Equipment, 1972-75

(N ew York, M cGraw-Hill Publications, Econom ics Department, 1972).

11 Research and Development in Industry (W ashington,

National Science Foundation, April 1972). Data include

apparel industry.

18 Business Plans for Research and Development Expenditures, 1972-75 (N ew York, M cGraw-Hill Publications, E conomics Department, 1972).

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Output per man-hour tripled in

past two decades, largely owing to

new technologies and high demand,

but the gains have tended to narrow

somewhat in recent years

HORST BRAND

O u t p u t pe r m a n -h o u r in telephone communications 1 more than tripled over the past two decades,

rising 6.5 percent a year, substantially above the

rate for the private economy as a whole. Gains were

high throughout most of the period, but tended to

be somewhat higher in the 1950’s, averaging 6.9

percent annually. Throughout both the 1950’s and

1960’s, output expanded rapidly. However, savings

in man-hours, made possible particularly by direct

dialing, came more slowly during the latter decade,

partly because they were offset by increasing manpower requirements for maintenance and repair and

business services. Thus, between 1960 and 1972,

productivity advanced at an average annual rate of

only 5.2 percent.

With the exception of 1970, gains in output per

man-hour have tended to slacken somewhat in recent

years, in part reflecting fewer opportunities for improving upon the high levels of productivity already

achieved in central office and transmission plants, as

well as the complexities in improving upon such

labor-intensive processes as installation, maintenance, and repair.2

The business slowdown of the early 1970’s was

also a factor retarding the rate of gain in output per

man-hour. In 1970, productivity rose by little more

than 1 percent, the lowest annual increase for any

year of the period under study. Reduced productivity

growth then was associated mainly with relatively

slower output growth. In 1971, too, output growth

was comparatively slow; the strong recovery in productivity in that year was due in part to a drop in

man-hours. Marked slowdowns in output growth

were also experienced in earlier periods of business

contraction. For example, in 1958 and 1961, both

recession years, output rose by around 6 percent,

Horst Brand is an economist in the BLS Office of Productivity and Technology. This study was conducted with the

financial support of the National Commission on Productivity.

Productivity

in

telephone

communications

compared with the two-decade average annual rate

of output increase of just over 8 percent; in 1970,

output rose by less than 7 percent. The impact of

reduced output growth on productivity in earlier

slowdowns was smaller, however, since man-hours

declined rather sharply then. By contrast, man-hours

rose strongly in 1970. (See chart 1.)

Output and demand

Productivity gains in telephone communications

have been closely linked to expansion in output,

which almost quintupled between the early fifties

and the early seventies. (See table 1.) The wide

availability of telephones has of course spurred the

Table 1. Indexes of output per man-hour and related

data, telephone communications, 1951-72

[1967=100]

Year

Output per 

employee 

man-hour

Output Man-hours

1951_____________ _____ _________ 36.9 29.3 79.4

1952_______ ________________ _ 37.7 31.1 82.4

1953____________________________ 38.5 33.0 85.7

1954______________ ____ _______ 41.1 35.3 85.8

1955_______________________

43.8 38.7 88.3

1956..._____________ _____ ______ 45.1 42.2 93.6

1957_____________________ ______ 49.4 46.7 94.5

1958............... ....................................... 55.7 49.4 88.7

1959__________________________ 61.8 54.1 87.5

1960.............................................. 65.6 57.9 88.2

1961_______________________ ____ 71.5 61.6 86.2

1962............................. ...................... 76.4 66.1 86.5

1963............................................ 81.8 70.8 86.5

1964............................ 85.3 76.4 89.6

1965___________________ _______ 89.1 83.5 93.7

1966_________ __________________ 93.0 92.2 99.1

1967______________ .. 100.0 100.0 100.0

1968____________________ ____ _ 106.2 108.1 101.8

1969_____________ _____________ 108.4 119.9 110.6

1970........ .............................................. 109.6 127.9 116.7

1971................................................. 1187 134.4 113.2

1972*.................................... ................ 123.2 146.9 119.2

Average annual rates of change (percent)

1951-72.................................................. 6.5 8.1 1.6

1960-72.................................................. 5.2 8.3 3.0

1 Estimated.

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Federal Reserve Bank of St. Louis
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http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis
Digitized for FRASER 

http://fraser.stlouisfed.org/ 

Federal Reserve Bank of St. Louis