measuring Produciwity 2/to

In State and Local Government

U.S. Department of Labor 

Bureau of Labor Statistics 

December 1983

Bulletin 2166

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measuring Productivity

On State and Leeal Oewemment

U.S Department of Labor 

Raymond J. Donovan, Secretary

Bureau of Labor Statistics 

Janet L. Norwood, Commissioner

December 1983

Bulletin 2166

For sale by tlie Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402

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Fisk, Donald M.

M easuring p ro d u c tiv ity in s ta te and lo c a l government o

(B u lle tin / U.S. Department o f Labor, Bureau o f Labor 

S t a t i s t i c s ; 2166)

"January 198U."

Prepared by Donald M. Fisk.

B ibliography: p.

I . Government p ro d u c tiv ity —U nited S ta te s —Measurem ent. 2 . S t a t e g o v e rn m e n ts. 3. L o c a l governm ent — 

U nited S ta te s . I . T i t l e . I I . S e rie s: B u lle tin (U nited 

S t a t e s . B ureau o f Labor S t a t i s t i c s ) ; 2166.

JK2^80 .L2^F5T 198U 3 5 0 . 1 ^ 7 ’0973 83-600318

Library of Congress Cataloging in Publication Data

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Pir@fae<§

The Joint Economic Committee of the U.S. Congress, the National Research Council of the National

Academy of Sciences, and the General Accounting Office have all called for further exploration into the

possibility of measuring State and local government

productivity. This bulletin was prepared in response to

the recommendations of these organizations and the

recognition that national productivity indexes are lacking for the 13 percent of the civilian labor force that is

employed by State and local government.

The bulletin reviews past research in the area,

discusses conceptual issues, reviews national data which

could be used to calculate productivity, examines seven

State and local government services, and offers recommendations for future research. While the focus is on

producing national indexes of State and local government productivity, the concepts and procedures are

equally valid for individual governments. The study was

carried out during 1980-82. While some of the

statements and data presented have been superseded by

recent events, the basic conclusions remain unchanged.

Donald M. Fisk prepared this bulletin under the

supervision of Jerome A. Mark, Associate Commissioner for Productivity and Technology. Dagmar Horna

assisted in the research and tabulations, and Rita

Walker typed the manuscript. Charles Ardolini, Chief

of the Division of Industry Studies, Office of Productivity and Technology, and his staff reviewed much of

the research and provided helpful comments. A number

of individuals outside b l s reviewed parts of the

manuscript; their contributions are acknowledged in the

appropriate chapters.

Material in this publication is in the public domain

and, with appropriate credit, may be reproduced

without permission.

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OoimteiniBs

Page

Chapters:

I. Introduction, conclusions, and recommendations................................................................ 1

Types of measures.................................................................................................................. 1

Basic measurement issues..................................................................................................... 2

Services exam ined.................................................................................................................. 3

Current status.......................................................................................................................... 4

A strategy for development................................................................................................... 4

II. Background and uses of productivity measurements............................................................ 7

Research and surveys.............................................................................................................. 7

Decisions and the use of productivity measurements....................................................... 9

The focus ................................................................................................................................. 11

III. Methodological considerations ................................................................................................. 12

The production framework and process............................................................................ 12

The measurement of ou tp uts............................................................................................... 14

The measurement of inputs................................................................................................... 16

Other issues............................................................................................................................... 21

IV. Measuring the measurable: Three case studies........................................................................ 25

Electric utilities........................................................................................................................ 25

Institutional considerations........................................................................................... 25

Research and statistics..................................................................................................... 26

Outputs............................................................................................................................... 28

Labor inputs and employee costs.................................................................................. 30

Productivity ind exes....................................................................... 32

Suggested research............................................................................................................ 34

State alcoholic beverage control operations...................................................................... 34

Institutional considerations........................................................................................... 34

Research and statistics................ 37

Outputs............................................................................................................................... 38

Labor inputs and employee costs.................................................................................. 40

Productivity ind exes....................................................................................................... 41

Conclusions and suggested research ............................................................................ 42

Unemployment insurance................................................................................................... 42

Institutional considerations................................................................... 42

Research and statistics..................................................................................................... 44

Outputs............................................................................................................................... 45

Labor inputs...................................................................................................................... 49

Productivity indexes....................................................................................................... 50

Suggested research............................................................................................................ 51

V. Thinking about the unmeasured: Four case studies.............................................................. 52

Solid waste collection and d isp osal..................................................................................... 52

Institutional considerations................................. 52

Research and conceptual issu e s..................................................................................... 54

Outputs............................................................................................................................... 55

Labor inputs...................................................................................................................... 58

Suggested research............................................................................................................ 59

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Chapter V—Continued

Drinking water supply.............................................. 59

Institutional setting......................................................................................................... 59

Research ............................................................................................................................. 60

Outputs.............................................................................................................................. 62

Labor inputs...................................................................................................................... 64

Suggested research........................................................................................................... 64

Mass transit ............................................................................................................................ 65

Institutional setting......................................................................................................... 65

Research and conceptual issu e s.................................................................................... 67

Outputs.............................................................................................................................. 68

Labor inputs...................................................................................................................... 71

Suggested research........................................................................................................... 72

The Employment Service ..................................................................................................... 73

Institutional considerations........................................................................................... 73

Research and conceptual issu e s.................................................................................... 74

Outputs.............................................................................................................................. 76

Labor inputs...................................................................................................................... 81

Suggested research........................................................................................................... 81

Charts:

1. Basic model of the production process ................................................................................ 12

2. Sophisticated model of the production process ................................................................. 13

Tables:

1. Terminology of government productivity measurement............................................ 2

2. Illustrative matrix for selecting services for computing State and local

government productivity ............................................................................................... 5

3. State and local government employees by function, October 1977............................ 8

4. Decisions and State and local productivity index requirements................................ 10

5. Examples of steps in the production of selected government services..................... 13

6. State and local government expenditures for salaries and wages as

a percent of total expenditures by function, fiscal year 1977 .................................. 17

7. State and local government activities included in three labor

hour m easures...... ........................................................................................................... 18

8. Estimated use of volunteers by State and local government function ..................... 19

9. Four national surveys used to collect State and local government

employment data.............................................................................................................. 21

Electric utilities:

10. Distribution of kilowatt hours sold, customers served, and plant and

equipment owned by type of utility ownership, 1978 ............................................... 26

11. Finances of State and local government electric utilities by type of

government, fiscal year 1977 .............................................................. ......................... 27

12. Weights for calculating output indexes for State and local government

electric utilities by class of service, 1967,1972, and 1977 ........................................ 29

13. Three output indexes for State and local government electric utilities,

1967-78.............................................................................................................................. 30

14. Two employment measures for local government electric

utilities, 1967-78 ............................................................................................................. 31

15. Two employment indexes for private sector electric utilities, 1967-78..................... 31

16. Three employment indexes for State and local government

electric utilities, 1967-78................................................................................................. 32

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Electric utilities—Continued

17. Index of average salaries and wages of local government electric utility

employees, 1967-78......................................................................................................... 32

18. Indexes of output, employees, and output per employee for local government

electric utilities, 1967-78................................................................................................. 32

19. Indexes of output, employees, and output per employee for local and selected

State government electric utilities, 1967-78 ........................................................... 33

20. Weighted indexes of output, employees, and output per employee for 33

large government electric utilities, 1967-78 ............................................................... 33

21. Three productivity indexes for State and local government electric utilities,

1967-78................................................................................................................. 34

22. Average annual rates of change for government and private electric utility

output, labor input, and productivity, 1967-78......................................................... 34

State alcoholic beverage control operations:

23. Type of control of alcoholic beverage sales by S ta te ................................................... 35

24. Distribution of alcoholic beverage control revenue and employees by State,

fiscal year 1977.................................................................................................................. 35

25. Forms and functions of State alcoholic beverage control operations....................... 36

26. Selected data contained in State alcoholic beverage control annual reports........... 39

27. Three gallonage indexes for alcoholic beverage control operations, 1968-78 ......... 40

28. Three employment indexes for alcoholic beverage control operations, 1967-78 .. 41

29. Two full-time-equivalent employment indexes for alcoholic beverage control

operations, 1967-78 ....................................................................................................... 41

30. October earnings for alcoholic beverage control personnel, 1967-78 ..................... 41

31. Three productivity indexes for alcoholic beverage control operations, 1967-78 .. 42

Unemployment insurance:

32. Unemployment insurance programs, 1963-80 ............................................................. 43

33. Unemployment insurance program benefits, selected years, 1965-80 ..................... 43

34. Synopsis of unemployment insurance activities........................................................... 45

35. Distribution of time expended by Unemployment Insurance Service function,

fiscal year 1979............................................ 46

36. Four output indexes for the Unemployment Insurance Service, fiscal years

1963-79....................... 47

37. Selected quality appraisal measures of the Unemployment Insurance Service___ 49

38. Positions in the State Unemployment Insurance Service,

fiscal years 1963-79................ 50

39. Indexes of output, employee positions, and output per employee position,

Unemployment Insurance Service, fiscal years 1963-79.......................................... 50

Solid waste collection and disposal:

40. Local government solid waste collection and disposal expenditures,

fiscal year 1977........................... •............................................................................ ....... 53

41. Refuse materials by kind, composition, and source..................................................... 54

42. Studies of effect of location and frequency of collection on solid waste

collection costs.................................................................................................................. 55

43. Community surveys of solid waste collection............................................................... 58

44. Local government sanitation employment, 1967-80 ................................................... 59

Drinking water supply:

45. Community water systems by size of population, 1979 ............................................... 60

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Drinking water supply—Continued

46. Finances of government-operated water utilities by type of government,

fiscal year 1977........................................................................................................... 61

47. Revenue of State and local government-owned water utilities,

fiscal years 1967-80................................................................... 62

48. Local government water utility employment, 1967-80 ............................................ 64

Mass transit:

49. Finances of government-operated mass transit systems by type of government,

fiscal year 1977........................................................................................................... 66

50. Transit modes (private and public) ranked by passenger trips, revenue,

and miles, 1980 ......................................................................................................... 67

51. Heavy rail operations in the United States, 1980 ...................................................... 67

52. Examples of relationship of quality and level of service to program change,

labor cost, and two output measures........................................................................ 71

53. Availability of employment data by transit mode ............................................:... 72

The Employment Service:

54. Placements per staff year as shown in the President’s budget,

fiscal years 1974-82................................................... 75

55. Comparison of Employment Service transactions and individual placements,

fiscal years 1975-80........................................................................................... 78

56. Agricultural and nonagricultural placement transactions, Employment

Service, fiscal years 1955-79 ................................................................................... 79

57. Five placement transaction time series, Employment Service, fiscal years

1955-79....................................................................................................................... 79

58. Missions and measures of performance of the Employment Service as

suggested by C.K. Fairchild..................... 80

Appendixes:

A. Number of State and local governments and number of employees............................ 82

B. Comparison of Bureau of the Census classification of government functions with

Standard Industrial Classification..................................................................... 84

C. Large electric utilities....................................................................................................... 87

Bibliography ................................................................................................................................... 88

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Chapter I. Introduction, Conclusions,

aond ^®©@mm@rii(dlatn©sii

State and local government employment and expenditures have increased greatly over the past two decades.

In 1960, these governments employed about 6.1 million

workers, or 8.7 percent of the civilian labor force, and

spent about $46.5 billion on the purchase of goods and

services, or 9.2 percent of the gross national product. By

1980, these governments employed 13.4 million, or 12.5

percent of the civilian labor force, and spent $341.2

billion on goods and services, or 13.0 percent of the

gross national product.

Despite the growth and current importance of State

and local governments, no national index of their productivity is maintained such as those calculated for the

private sector or the Federal Government. Nor, apparently, is there a current index for a single State or

local government service.1 State and local government

productivity remains the largest unmeasured sector of

the economy.

Several organizations have recommended, particularly in the 197Q’s, that State and local government productivity be measured. The Joint Economic Committee

of the U.S. Congress, the National Academy of

Sciences, and the General Accounting Office have suggested additional research to measure State and local

government productivity.2 Each group recognized the

problems associated with such an undertaking but

nevertheless believed its importance warranted further

investigation.

Types off measures

Considerable confusion has surrounded discussions

of the basic concepts and procedures used in government productivity measurement.3 Furthermore, as the

1 Government enterprises, such as electric power utilities and water

supply, are included in the private sector productivity indexes

calculated quarterly by bls because their outputs are sold in the

marketplace. Enterprise services are included as a group; they are not

separated by type of government or service.

2 National Research Council, Measurement and Interpretation of

Productivity (Washington: National Academy of Sciences, 1979), pp.

9-10; U.S. Congress, Joint Economic Committee, Productivity in the

Federal Government (Washington: Government Printing Office,

1979), p. 7; and General Accounting Office, The Federal Role in Improving Productivity—Is the National Center for Productivity and

Quality o f Working Life the Proper Mechanism? May 23, 1978, p. 45.

3 Jerome A. Mark, “ Measuring Productivity in Government-Federal, State, and Local,” Public Productivity Review, Mar.

1981, p. 21.

literature has expanded, definitions and concepts have

become more complex. Today, public sector productivity literature variously defines productivity as efficiency,

effectiveness, cost reduction, input-output, management improvement, performance measurement,

methods improvement, systems analysis, work measurement, and program evaluation.4 Because of the confusion over terms, some of the more important definitions

used in this study are presented in table 1. As Jerome A.

Mark notes, however, there is no best or right measure.5

Productivity measurement requires that measures be

shaped for the decision process, which leads to a variety

of types of measures.

In this bulletin, government productivity measures

are assigned to one of three categories based on the type

of output measure. These are: (1) Measures which focus

on operational issues, (2) those which focus on

organizational or program outputs, and (3) those which

are concerned with program consequences.

Operational measures are concerned with the internal

workings or efficiency of the organization. Work

measurement, which deals with resource requirements

under a given technology or set of conditions, is a common operational measure. Intermediate activities, such

as the number of reports produced, number of audits

completed, or the number of samples tested, and utilization measures, such as equipment downtime, are other

types of operational measures. Each is important for

day-to-day management of government.

The second category of productivity measures, direct

outputs, is the final organizational output divided by

the resources used to produce the output. The direct

output productivity measure is the measure most commonly computed for the private sector. Public sector examples of such measures are the “ tons of solid waste

collected per employee hour” for sanitation services and

the “revenue gallons of water sold per employee hour”

for water utilities. These measures are also known as

technical efficiency measures. They do not address the

issue of whether the services should be produced or

relate them to some desired goal.

4 Jesse Burkhead and Patrick J. Hennigan, “Productivity Analysis:

A Search for Definition and Order,” Public Administration Review,

Jan./Feb. 1978, pp. 34-40.

3 The Meaning and Measurement o f Productivity, Bulletin 1714

(Bureau of Labor Statistics, 1971), p. 7.

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Table 1. Terminology of government productivity measurement

Term

Activity

Consequence

Effectiveness

Efficiency

Function

Goal

Impact.

Definition Term

A task performed by an organization to

produce a desired output. Examples include miles driven, trucks serviced, and

meters read. Sometimes described as

Input..............................

workload.

The desired results of government programs or services such as improved

citizen safety, increased longevity, and

reduced infant mortality. Sometimes

Outcome........................

described as impact or outcome.

The degree or extent to which program

goals are met, such as percent of

population served or percent of clients

successfully treated.

Output..........................

The ratio of output to inputs such as work

performed per staff hour or downtime

as a percent of total hours. Includes

Productivity..................

productivity, unit costs, and technical

efficiency.

Productivity index........

A government service such as police, fire,

and education. “Function” and “service” are used synonymously.

Service..........................

A statement which describes what is to be

accomplished by a program, service, or

agency such as “insure a safe and

secure environment.”

Social indicator ............

The long-term effect of a program on a

community of its citizens. Impact and

consequence are used synonymously.

See “Consequence.”

Workload......................

Definition

The resource used by an agency to produce

a function or service. Examples of inputs

are labor, facilities, equipment, and

materials.

Short-term impact or consequence of

government action or outputs, such as

increased income which might come as a

result of job training.

The result of work performed or produced

by an agency. Outputs are what government produces. Examples of outputs are

the number of individuals served, gallons

of revenue water delivered, or tons of

trash collected.

Amount of physical output per unit of input.

Inverse of the change in resources used per

unit of output.

A government function such as police, fire,

or education. The terms “service” and

function” are used synonymously.

A measure of societal well-being such as

longevity or happiness. These measures

are of interest because they are considerations which governments wish to

promote.

A measure of the amount of work performed,

usually an intermediate output, such as

the number of miles driven, or number

of machines serviced. See “Activity.”

The third category, consequences, addresses the issue

of a program’s impact on society and whether that program makes optimum use of resources to achieve its

goals. This type of measure is alternatively known as

outcome, impact, effectiveness, and economic efficiency. Examples of these types of measures are “ deaths

prevented per employee hour” for fire departments and

“jobs created per employee hour” for economic

development agencies. Measures such as these focus on

consumers and consumption whereas operational and

direct output productivity measures are concerned with

production relationships.

Each of these three general types of productivity

measures is important. However, the most common, at

least nationally, is the second type, the direct output or

technical efficiency measure. It is this type of measure

which is most often computed for the private sector and

the one with which this study is primarily concerned.

In many ways, the measurement of productivity in

government and private organizations should be

similar. Both types of organizations produce goods and

services, both compete in the marketplace to purchase

resources, and both use varying combinations of

resources to produce a product or service.

There are important differences, however. Probably

the most important is the absence of a market and a

market price for most government production. Government officials, not the marketplace, decide what to produce.

Basse msasuromesit tssyes

Specification and measurement of output are the

most difficult problems in measuring the productivity of

State and local government. The basic measure of output should be a homogeneous physical unit, with the

unit measure of output related to the unit labor hours

spent in its production. Where a government provides a

single service, as in the case of some of the special

districts—solid waste disposal and drinking water are

examples—the output can be simply a count of the units

of service. However, most governments produce a

number of heterogeneous services, and it is often difficult to even identify the basic services.

Furthermore, most government services are composed of a number of different subservices or products

which also are difficult to identify. For example, the

direct outputs (not consequences) of police and education services are not easily specified. For productivity

measurement, it is necessary to specify homogeneous

service outputs.

In addition, if the output index is to reflect trends accurately, the service units must be homogeneous

through time. In many instances, the scope and dimensions of government services are constantly changing.

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Many transit systems now provide demand response service in addition to regularly scheduled bus service, and

some jurisdictions have added the testing of automobile

emissions to safety inspections. In both cases the service

unit has changed, which requires product differentiation.

Quality and level of service considerations are also

important for productivity measurement because of

their potential impact on the unit of service. To

distinguish service or product shifts from changes in

unit labor requirements, or productivity, outputs or inputs must be adjusted or a new product identified.

Movement of solid waste collection from backdoor to

curbside pickup and improvement of drinking water

quality to conform with environmental standards will

affect unit labor requirements, and reduction of welfare

error rates may affect unit labor requirements.

Selection of the proper measure of output requires a

service-by-service and product-by-product approach.

By dividing a service, it is possible to identify

homogeneous outputs with reasonably stable unit labor

requirements. The difficulties with this approach are a

lack of research to identify the correct units and a lack

of data to make the calculations.

Data to calculate aggregate national, State, and local

government output indexes are generally lacking. The

Federal Government collects some data, particularly in

those areas where it has shared responsibilities, such as

unemployment insurance and drinking water, and some

data are collected by national associations and public interest groups. These statistics are often inaccurate and

incomplete. But more often than not, national statistics

are simply unavailable on State and local government

output.

To collect output data through a regular survey of

State and local governments would be extremely expensive. But even if the decision were made to spend the

necessary funds, there is the fundamental question of

what data to collect. For some services, there is a

reasonably good idea of the data needed on type of output and level and quality of service for productivity

measurement, but for most services this information is

unknown. In short, it makes little sense to establish a

data collection system at this time. However, if a system

is established to collect State and local government output data, input information should be collected

simultaneously.

The most frequently used measure of input is labor.

Constituting over half of all State and local government

operating expenditures, labor is important for public

policy considerations, is easy to calculate compared

with other factors of production, and is the most accessible of State and local government factor inputs. It

is the measure recommended for State and local government productivity calculations.

The preferred labor measure is labor hours. However,

no national statistics are available for labor hours of

State and local governments; few governments even collect such data. A measure often used as a proxy for the

number of hours is the number of full-time-equivalent

employees. Most State and local governments maintain

such statistics. Most governments also have statistics on

the number of employees, a measure widely used in the

private sector.

However, none of the sources of national statistics

are entirely satisfactory for computing individual service labor productivity indexes. National statistics collected through the Bureau of the Census’ Census of

Governments, the Employment Service’s ES-202

report, and the Bureau of Labor Statistics’ Current

Employment Statistics survey (CES-790) are not divided

sufficiently to compute labor productivity indexes for

individual services. In a few instances, Federal Government programs, such as unemployment insurance and

the Employment Service, and professional interest

groups, such as the American Public Transit Association, do collect data on State and local government

employment. However, comparisons of labor data

drawn from these and other sources reveal considerable

discrepancies.

In summary, no single source of labor data on State

and local government is adequate to compute labor indexes. Most contain errors. Construction of viable labor

indexes, either total or for individual services, will require detailed comparison and adjustment of the data.

As with the private sector, cyclical and secular change

can dramatically affect productivity trends. Cyclical

productivity change most often occurs in services such

as unemployment insurance, where inputs cannot be adjusted as rapidly as outputs change. Secular trends are

found in services for which a fundamental change occurs over time, such as electric power. Generally speaking, the greater the number of years included in the productivity index, the more representative the index will be

of long-term trends. Data availability will normally set

the outer bounds for the years measured.

Sen/ices examined

Seven State and local services were selected for this

report from dozens provided by the government. The

more important government services, such as education,

police, and fire, are not included because of conceptual

or data problems.

For three services—electric power, State alcoholic

beverage stores, and unemployment insurance—

illustrative indexes are calculated. For two services

—sanitation and drinking water—productivity was not

calculated because national data are lacking. For the remaining two services—transit and the Employment Service—productivity indexes were not calculated because

of unresolved conceptual and data issues; additional

research is being conducted in these two areas. The

results for each service are briefly noted below.

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Electric power. Considerable research has been conducted into private electric power productivity, and considerable data are collected on private and public

utilities. In 1978, about 2,200 State and local government electric power utilities employed about 66,000

workers. From 1967 to 1978, labor productivity increased an average of 3.0 percent annually, output (kilowatt

hours) 4.0 percent, and labor input 0.9 percent.

State alcoholic beverage sales. Seventeen State governments sell alcoholic beverages. The preferred measure

of output for retail store operations is bottles; for

wholesale operations, cases. A proxy measure, gallons,

was used to calculate government productivity. Between

1967 and 1978, the average annual increase in productivity was 2.5 percent; in output, 3.1 percent; and in

labor, 0.6 percent.

Unemployment insurance. Considerable data are

routinely collected on this joint Federal-State program.

In 1978, about 48,000 State employees operated the program. Two primary measures of output are: (1) Services

to those applying for or drawing benefits and (2) collection of funds from employers. A weighted program output measure shows that, between 1965 and 1978, labor

productivity increased at an annual rate of 1.7 percent,

output at 7.4 percent, and labor input at 5.7 percent.

Solid waste collection and disposal. The preferred

measure of output in this area is tonnage. No national

data are routinely collected on outputs, and input data

are insufficient for productivity calculations. Hence, a

productivity index was not calculated. Local government had about 128,000 employees in this area in 1980.

Drinking water supply. Revenue gallons is the preferred

measure of water utility output. Most utilities routinely

collect gallonage data and some data are collected nationally, but there are so many questions about the accuracy of these data that no index was calculated as part

of this study. In 1980, about 134,000 State and local

employees worked in this area.

Mass transit. Research into mass transit operations and

calculation of outputs, inputs, and productivity have a

long history. For some time, the proper measure of output has been debated. On the one side are the traditionalists who favor passenger miles or a similar

measure. On the other side are the transit operators who

favor an availability or capacity measure such as vehicle

miles. Since 1979, national data have been collected for

both output measures, and for labor inputs.

Employment Service. Considerable research has been

done and data collected routinely on the Employment

Service (e s ), a joint Federal-State operation, e s has

calculated “ labor productivity” for several years but

has been criticized for the measure—placements—and

the resulting measurements. Placements measure outcome more than output. Also, serious questions have

arisen about the accuracy of data used to calculate

“ productivity.”

Current status

These seven services illustrate the problems and opportunities that occur in computing State and local

government productivity indexes. The problems are

substantial and include both conceptual and data issues.

However, the difficulties may not be any worse for

calculating State and local government productivity

than for calculating private sector service industry productivity.6

Both sectors produce many of the same services.

There are literally dozens of such services, ranging from

electric power to alcoholic beverage sales to hospitals to

employment counseling. Not every government service

has its private sector counterpart, but most do.

Furthermore, similar economic forces seem to be at

work. For electric power, the productivity trends in the

two sectors are similar; productivity is affected in both

by economies of scale; and the slowdown in productivity growth is evident in both. For the Unemployment Insurance Service, the fluctuations in output mirror shifts

in the number of unemployed. Also, as in the private

sector, productivity increases rapidly during periods of

increasing unemployment as work increases more rapidly than the number of workers (the opposite occurs during periods of decreasing unemployment). Quality apparently decreases during periods of high workload but

improves as the workload drops. Productivity trends are

dramatically influenced by the beginning and ending i

points of the index as a result of the cyclical fluctuations.

Much of the past discussion on calculating government productivity has been entangled in questions of effectiveness, outcome, and impact. Productivity analysis

in these areas has become entrapped in externalities. As

long as the discussion is restricted to direct outputs, the

solutions are at least as tractable as in the private sector.

This is not to say that productivity can be computed

for every State and local government service. Thorny

problems exist in calculating State and local government

productivity, just as in the private sector. However, it

should be possible to compute State and local government labor productivity for many services.

A strategy for development

Development of State and local government productivity indexes must proceed service by service. A threestep process is suggested for developing each service in6 Jerome A. Mark, “Measuring Productivity in Service Industries,”

Monthly Labor Review, June 1982, pp. 3-8.

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dex: (1) Select the service to be examined, (2) review existing research and data, and (3) calculate the index and

document the result. Once established, the index needs

to be updated annually.

In selecting the service to be examined, two—often

conflicting—criteria need to be weighed. One is the importance of the service judged by its cost or the number

of employees. The other is the difficulty of computing a

viable index. Education, for example, is the most important service in resources used but is also one of the

most difficult areas in which to measure outputs.

Alcoholic beverage sales, on the other hand, is one of

the easier services for measuring outputs but is relatively unimportant in terms of employment. In selecting a

service to be examined, these two criteria need to be

balanced.

A suggested procedure for selecting a service is to

compare the two criteria—importance and difficulty—using relative rankings such as those shown in

table 2. Importance, for example, can be measured by

the number of employees. A three-part ranking is used

in the table: (1) 100,000 employees or less, (2) more than

100,000 but less than 500,000 employees, and (3) more

than 500,000 employees. Difficulty can be assessed by

examining the tangible nature of outputs and the

availability of national output data. A three-part ranking is used: (1) Tangible outputs and national data, (2)

tangible outputs or national data, and (3) neither tangible outputs nor national data. The services shown in the

matrix are those listed by the Bureau of the Census, with

the addition of the Employment Service and the

Unemployment Insurance Service.

Although the classification system is arbitrary and the

assessments illustrative, the matrix demonstrates some

of the opportunities and problems in computing State

and local government productivity indexes. First, computing a national index will not be easy for any important service. Second, national data and/or a tangible set

of outputs exist for most services.

After the service has been selected, an initial reconnaissance will be needed to assess the feasibility of computing an index. This initial review will normally be sufficient to identify which services are good candidates for

calculating productivity. An in-depth investigation, the

next step in the process, will determine whether an index

computation is possible.

Construction of an index requires a detailed review of

research and conceptual issues, development of a list of

potential output measures, evaluation of each measure,

selection of the recommended measure, search for data

to compute the index, development of surrogate or

proxy measures if the data are not available, and, finally, documentation of the results. This bulletin illustrates, for seven services, how documentation might

proceed.

Maintenance of an index requires constant vigilance

and analysis. Changes may occur in the quality and level

of service; intermediate outputs may be contracted out,

which would reduce labor input but not output; and

data series may change.

Table 2. Illustrative matrix for selecting services for computing State and local government productivity

Difficulty of

computing productivity

Importance of service

(number of State and local government employees)

100,000 or less 100,000 to 500,000 500,000 or more

Least difficult—has tangible outputs

and national data................................ Electric power

Unemployment insurance

Sewerage

Airports

Alcoholic beverage stores

Water supply

Mass transit

More difficult—has tangible outputs

or national d a ta ................................. Employment Service Corrections Highways

Aid to families with dependent Sanitation Hospitals

children

Food stamps

Libraries

Housing and urban renewal

Water transportation

Gas supply

Natural resources Police

Higher education

Local schools

Most difficult—has neither tangible

outputs nor national d a ta .................. Fire

Financial administration

Parks and recreation

Health

General control

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After the development of individual indexes, it should

be possible to construct aggregate indexes for functional

segments, such as for the enterprise services. This group

of services warrants early examination.7 This bulletin

examines four such services—alcoholic beverage sales,

electric power, transit, and water supply—which account for about two-thirds of total salary and wage expenditures of enterprise services. Services which fall into

this category would be among the easiest for computing

productivity. They are already included in the National

Income and Product Accounts and in national private

sector productivity calculations.

Income maintenance programs, such as aid to

families with dependent children, food stamps, and

unemployment insurance also are suitable for productivity measurement and functional grouping. The conceptual issues are tractable and data are available for the

7 The Bureau of Economic Analysis of the U.S. Department of

Commerce assigns 15 State and local government services to this

group. These services are largely sold in the marketplace and consequently have a price associated with them.

major ones as a result of the Federal role in funding and

oversight.

Service groupings, such as the enterprise services and

income maintenance programs, could also be used for

comparison and benchmarking. Enterprise group

statistics can be compared with the National Income

and Product Account data. Social insurance programs

(unemployment insurance, the Employment Service,

and other labor programs); utilities (water, gas, electric

power, and transit); and transportation (highways, air,

and water) can be compared with Bureau of the Census

employment groupings.

By using the building block approach, individual indexes and groups of indexes might be combined into appropriate functions, such as public works and public

safety. Eventually it may be possible to develop a national productivity index for State and local government, but this is probably many years away.8

8 A national index does not have to include every service, but, to be

representative, 90 percent of the labor input should be included.

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©huptdir IS. i®©kgr@yinidl ©find! Uidt

®ff Produetiwiity Measurements

About §0,000 State and local governments in the 

United States serve their citizens through a vast variety 

of programs. State employees operate such diverse services as hospitals, universities, forests, hatcheries, 

prisons, lotteries, alcoholic beverage stores, and grain 

elevators. Employees of municipalities, townships, 

counties, school districts, and special districts sweep 

streets, inspect restaurants, manage golf courses, 

operate swimming pools, assess real estate, counsel drug 

addicts, put out fires, direct traffic, and teach students.

The number of employees in each of these areas varies 

dramatically. There were over 1 million State employees 

in higher education in 1977 but less than 16,000 in State 

alcoholic beverage stores, and more than 4.8 million in 

local schools but less than 100,000 in local libraries 

(table 3). Also, the number of paid employees ranged 

from-358,497 (New York City) to 0 for some special 

districts.

While there is great diversity in State and local 

government operations there are, at the same time, 

many similarities. Every State operates an employment 

service, an unemployment insurance service, a food 

stamp program, a university system, a court and penal 

system, and a highway system. And most municipalities 

operate fire, police, sanitation, library, and recreation 

services. Such similarities permit computation of national indexes for State and local government productivity.

Furthermore, the major services account for most 

State and local government employment—education, 

hospitals, police, and highway programs alone account 

for 70 percent (table 3).

Also, the large jurisdictions employ a significant proportion of all State and local government workers. The 

10 largest States account for about 48 percent of all 

State employment; the 10 largest special districts, for 

about 25 percent of special district employment; the 10 

largest municipal governments, about 22 percent of all 

municipal employment; the 10 largest school districts, 

about 12 percent of total school district employment; 

and the 10 largest counties, about 11 percent of all county employment.1

1 See appendix A.

Research and stireays

State and local government output, employment, and 

productivity have been examined by a number of 

economists and members of the research community. 

Several scholars examined aspects of the subject in the 

latter part of the 1960’s, when State and local purchases 

and employment began their dramatic growth.

In 1967, William I. Baumol raised the question of 

why the quality of life apparently deteriorated in urban 

areas when State and local governments spent more and 

more resources to solve problems. He concluded that 

productivity growth was extremely low or nonexistent, 

and furthermore, that little could be done to improve 

the situation. The Baumol thesis was based on a twosector conceptual model of the economy, one sector 

characterized by high technological inputs and a high 

rate of productivity growth and the other having highlabor inputs and a low rate of productivity growth. 

State and local government services, according to 

Baumol, lay mostly in the second sector.2

Bradford, Malt, and Oates examined the Baumol 

thesis from readily available data for health and 

hospitals, education, police, and fire. Their conclusions, which were based primarily on unit cost data, 

generally substantiated the Baumol thesis, that is, rising 

unit costs and decreasing or slowly increasing productivity.3

Robert M. Spann also examined the Baumol 

hypothesis from data for six State and local government 

services—police, fire, highways, general control, financial and administration, and public welfare. His approach, output measures, and conclusions generally 

parallel those of Bradford, Malt, and Oates. However, 

he argued that the lack of productivity growth in State 

and local government was due to bureaucratic influences, not simply to the labor-intensive nature of 

State and local government services. In coming to this 

conclusion, he compared selected private sector productivity measures with public sector statistics. He concluded that private sector services exhibited low, but positive

2 William J. Baumol, “Macroeconomics of Unbalanced Growth:

The Anatomy of Urban Crisis,” American Economic Review, June

1967, pp. 415-26.

3 D.F. Bradford, R.A. Malt, and W.E. Oates, “The Rising.Cost

of Local Public Services: Some Evidence and Reflections,” National

Tax Journal, XXII, No. 2 (June 1969), pp. 185-202.

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Tabte 3. SSat© and local government! employees by function,

October 1077

Function Number

(thousands)

Percent of 

total

Percent of 

State total

Percent of 

local total

T o ta l......................................

State..............................

Local..............................

12,765

3,491

9,275

100

100

100

Education.............................. 6,703 53 43 56

Local schools................ 4,885 38 1 52

Higher education .......... 1,724 14 39 4

Other education............ 94 1 3 -

Hospitals................................ 1,105 9 16 6

Police.................................... 622 5 2 6

Other and unallocable............ 595 5 6 5

Highways.............................. 563 4 7 3

General control...................... 502 4 3 4

Welfare.................................. 369 3 5 2

Local utilities.......................... 324 3 _ 4

Water supply.................. 121 1 - 1

Electric power................ 64 1 - 1

T ransit............................ 127 1 - 1

Gas supply .................... 13 - - -

Financial administration........ 291 2 3 2

Fire protection ...................... 270 2 - 3

Corrections............................ 224 2 4 1

Parks and recreation.............. 217 2 - 2

Health.................................... 212 2 3 1

Natural resources.................. 209 2 5 -

Sanitation.............................. 127 1 - 1

Social insurance.................... 117 1 3 -

Libraries................................

Housing and urban

91 1 1

development...................... 90 1 - 1

Sewerage.............................. 86 1 - 1

Airports.................................. 19 - - -

Liquor stores.......................... 16 - 1 -

Water transportation.............. 14 - - -

Source: 1977 Census of Governments—Compendium of Public

Employment (Bureau of the Census, 1979), pp. 13-14.

gains in productivity, unlike their public sector counterparts, which showed declining productivity.4

Probably the most thorough examination of local 

government productivity to date was one undertaken by 

Ross and Burkhead. In addition to reviewing the 

research of others and setting the conceptual foundations, they attempted to estimate that part of the change 

in local government expenditures which was due to 

shifts in cost and workload and the part which was due 

to shifts in quality and productivity. Their empirical 

work examined education, welfare, police, and fire for 

several of the larger cities in New York State, and 

welfare and education for all local governments in New 

York State. Except for fire protection, they found little 

evidence of increasing local government productivity.5

More recently, Charles R. Hulten used the household 

production function to examine the change in State and 

local government productivity. His model, which captured the consequences (effectiveness) as well as produc4 Robert M. Spann, “Rates of Productivity Change and the

Growth of State and Local Expenditures,” in Thomas E. Borcherding, Budgets and Bureaucrats (Durham, N.C.: Duke University

Press, 1977).

5 John P. Ross and Jesse Burkhead, Productivity in the Local

Government Sector (Lexington, Mass.: Lexington Books, 1974).

tivity (efficiency), found that State and local government probably had a negative rate of growth in productivity between 1959 and 1979, the years for which data 

were available.6

A number of studies of individual State and local 

government functions have focused on a few subject 

areas to identify underlying economic relationships 

rather than develop specific productivity indexes. Solid 

waste collection, mass transit, and water supply, for example, have been extensively investigated, as will be 

discussed later in this study.

Many areas have had little investigation of the basic 

underlying economic relationships and no developmental work on productivity measurement. Services such as 

parks and recreation, elderly day care, animal control, 

and general management have been skipped over entirely or examined only superficially.

Although information on productivity measurement 

in individual State and local governments is sparse, the 

majority of medium- and large-size governments collect 

some data. A 1976 survey found that 65 percent of the 

cities and 50 percent of the counties collected and used 

efficiency-measures.7 Surveys of local governments in 

N orth Carolina and the Denver, Colorado, 

metropolitan area in 1978 found the figure to be over 85 

percent.8 A survey of selected governments, also in 

1978, found that 68 percent of the cities and 47 percent 

of the counties with productivity improvement programs had measurement systems. The same survey 

found that 79 percent of the States with improvement 

programs had some type of measurement system.9 An 

examination of State budgets and discussions with State 

budget officers in 1975 found frequent use of productivity measures although few formal systems.10

These and other studies suggest the following conclusions for State and local government productivity 

measurement. First, State and local governments collect 

considerable data although the information is uneven as 

to function and government. Some functions,, particularly those with tangible products and Federal involvement, are well covered at the individual government level. Second, many studies cover individual service areas. Most are cross-sectional; a few examine time 

series data. Third, several studies of State and local 

government productivity have been made from readily

6 Charles R. Hulten, Productivity Change in State and Local

Governments (Washington: The Urban Institute, 1981).

7 Rockham Fukuhara, The Status of Local Government Productivity (Washington: The International City Management Association,

March 1977).

8 Comparative Performance Measures for Municipal Services

(Raleigh: Research Triangle Institute, December 1978); A Demonstration o f Comparative Productivity Measurement (Denver: Denver

Regional Council of Governments, December 1978).

9 State and Local Government Productivity Improvement: What

is the Federal Role? (Washington: General Accounting Office, Dec. 6,

1978), p. 13.

10 The Status o f Productivity Measurement in State Government:

An Initial Examination (Washington: The Urban institute, 1975).

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available time series data. Although ad hoc and

restricted to a few areas, they have generally concluded

that State and local government productivity has remained stagnant or decreased over the past several

decades.

D©oisI©ns and the use ©f productivity 

measurements

Although State and local government productivity

should be measured for many reasons, the ultimate

reason is better management. Four types of management decisions that could benefit from State and local

government productivity measurement are: Policy formulation at the national level; program management at

the Federal level; policy, planning, and programming in

State and local government; and day-to-day operations

in State and local government. This section discusses the

kinds of decisions to be made, the types of decisionmakers who need the data, and the types of indexes

needed (table 4).

National policy formulation. Productivity indexes are

important tools in forecasting national income, projecting national labor demand, formulating national wage

policies, and assessing the sources of national growth.11

Projected changes in productivity, for example, are

often used in forecasting gross domestic product and in

setting fiscal and monetary policy. These projections

often include estimated productivity changes in the

private sector, but normally assume no change in the

public sector. The bias that results by assuming no

change in government productivity is not known but

could be substantial.12

Labor market projections and analyses often take into account the impact of changes in productivity in the

private sector.13 The demand for labor can be

dramatically affected by such changes. State and local

government productivity indexes could help answer

questions about how changes in State and local government productivity affect the supply of and demand for

labor in the economy, how the national supply of and

demand for labor affect State and local government

productivity, how geographic shifts of the population

affect State and local government productivity, and

how new technology influences State and local government productivity and the demand for labor.

National income policies and guidelines have long

taken into account changes in productivity.14 The 1962

Economic Report of the President laid down guidelines

for noninflationary wage behavior in the private sector,

11 National Research Council, Measurement and Interpretation

o f Productivity (Washington: National Academy of Sciences, 1979).

12 Jerome A. Mark, “Progress in Measuring Productivity in

Government,” Monthly Labor Review, December 1972, pp. 3-6.

13 Methodology for Projections of Industry Employment to

1990, Bulletin 2036 (Bureau of Labor Statistics, 1980).

14 National Research Council, op. cit., p. 27.

and from 1971 to 1974 changes in labor productivity

were considered in allowing or disallowing proposed

price increases. In 1978, wage increases above the

guideline were allowed if it could be demonstrated that

explicit changes in work practices led to increased productivity.15 The general policy for public sector wage increases during this period was to permit the public sector the same average increase as the private sector. This

percent has generally been tied to the estimated increase

in private sector productivity.

A number of studies have sought to measure the impact of productivity on economic growth. Most of these

have been analyses of the private sector.16 Similar research on the public sector would assist decisionmarkers

in formulating national policy. Knowledge concerning

the relationships among State and local government

productivity, the factors of production, research and

development, and economies of scale would be helpful.

Federal program management. In addition to providing a valuable tool for formulating national policy,

State and local government productivity indexes could

help Federal program managers who are interested in

State and local government. Federal managers often

have such an interest because (1) State and local governments often operate Federal programs; (2) the financial

health of these governments directly concerns the

Federal Government and the Nation as a whole.

Most State and local governments operate some

Federal programs. In fiscal 1980, the Federal Government transferred approximately $92 billion to about

65,000 State and local governments in direct Federal

aid. In 1978, 492 different grant programs funnelled

money to State and local governments.17 Although the

number of grant programs and total dollars funnelled to

State and local government have decreased slightly, they

will remain an important consideration, and the Federal

Government has a legitimate concern as to how efficiently these dollars are spent.

Even if State and local governments did not operate

Federal programs, their productivity and how it is

changing would still concern Federal policymakers

because of their importance to the national economy.

State and local government purchases of goods and services accounted for about 13.0 percent of the Nation’s

gross national product or $341.2 billion in 1980. Some

Federal agencies operate programs to improve State and

local government productivity. These programs range

from funding research and development to operating information clearing houses for technical assistance.

State and local government productivity indexes

15 Economic Report of the President (Council of Economic Advisers, January 1979), p. 81.

16 National Research Council, op. cit., pp. 145-65.

17 The Federal Role in the Federal System: The Dynamics of

Growth (Washington: Advisory Commission on Intergovernmental

Relations, December 1980), p. 8.

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Table 4. Decisions and State and local productivity index requirements

Decision area Decision category Decisionmaker and data user Type of index

National policy formulation........ Gross domestic product

forecasting

Congress, Federal officials, and

academicians

Aggregate national indexes including State and local government

Federal program formulation . . . . Problem identification

Legislative impact analysis

Budget justification and

allocation

Program and organization

design

Congress, Federal officials, and

academicians

National indexes including State

and local government by service area

State and local government

policy formulation .................... Problem identification

Fiscal analysis

Legislative impact

State and local government

officials, Congress, Federal

officials, and academicians

State and local government

indexes by service area

State and local government

operations .................................. Setting goals and objectives

Estimating resource requirements

Budget justification

Cost reduction

Scheduling and control of

operations

Accountability

Motivation for improvement

State and local government

officials and academicians

Individual State and local

government indexes by service area

would help answer such questions as: Where is productivity lagging? What are the sources of the decline? How

can productivity improve? Is additional research needed? Does productivity vary by geographic area? If so,

why? What is the relationship between productivity improvement and the financial strength of State and local

governments? And so forth.

There is considerable discussion of the impact of

Federal legislation and administration on State and

local productivity. In 1979, an estimated 1,259 mandates were in effect, of which 223 were direct orders and

1,036 were conditions of aid.18 Anecdotal examples

abound. One report concluded that well-executed

changes in the Federal grants program would raise State

and local government productivity.19 At this time, little

is known of the impact of Federal legislation and its administration on State and local government productivity.

Some Federal agencies are directly involved in State

and local government operations. The U.S. Employment Service (e s) and the U.S. Unemployment Insurance Service (UIS) are operated by State personnel

but funded by the Federal Government. Budgets to support these operations are prepared by the U.S. Department of Labor and defended before the U.S. Office of

Management and Budget and the Congress. Productivity measures have played an important role in justifying

and defending these budgets. The es includes productivity calculations in its annual budget. The uis uses

18 The Federal Role in the Federal System, p. 4.

19 State and Local Government Productivity Improvement: What

is the Federal Role? pp. 41-51.

detailed work standards to justify its requests for funds

to support State staff.

Productivity statistics are also used to allocate funds

to the States. The e s has used productivity as one explicit variable in its allocation formula, and the UIS had

used projected work and estimated work standards in

making its allocations.

The Federal Government is also directly involved in

the design and organization of some State and local programs. In addition to the e s and uis, the Federal

Government oversees other programs such as the Work

Incentive Program, Food Stamps, and Aid to Families

with Dependent Children. A better understanding of

productivity would help in questions such as whether to

decentralize or centralize services and how to allocate

resources among various activities such as counseling,

testing, and training.

State and local policy formulation. State and local

government policy formulators also could benefit from

productivity statistics in identifying potential problem

areas, estimating fiscal effects, and assessing the impact

of State and local legislation.

Some States assign operating responsibilities to local

government and provide partial funding, as in the case

of the Aid to Families with Dependent Children and

Food Stamps programs. Many States assign the responsibility for road maintenance to local government along

with receipts from gasoline taxes. States have local

government oversight responsibilities, one phase of

which is fiscal solvency.

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Productivity measurement can also play an important

role in State fiscal analyses. The passage of propositions

13 in California and 2-1/2 in Massachusetts led to selective employment cuts, the net effect of which is widely

debated but unknown. Some States, such as North

Carolina, fund many local services. Accurate productivity estimates should result in better fiscal estimates

and help conserve scarce resources. Some cities, most

notably New York, have tied increases in pay to increases in productivity. The measures for assessing

change in productivity are generally lacking.

Finally, State legislation affects the productivity of

local governments just as Federal legislation affects the

productivity of State governments. Most States require

detailed reports from local governments, and some

restrict local government operations. Some States require analyses of the fiscal impact of any new legislation. The effect of new legislation on local government

productivity is not generally known.

State and local government operations. Productivity

measures and data probably find their greatest use today in the day-to-day operations of State and local

governments. The following issues have been among

those suggested:20

Setting goals and objectives. A productivity

measurement system lends specificity to a process

that is usually general in form and substance. Concrete data also show managers and workers how

well goals are met.

Estimating resource requirements. Productivity

measurement helps managers better estimate their

resource requirements. For example, productivity

change should be considered in estimating work

force needs.

20 “ Implementing a Productivity Program: Points to Consider”

(Washington: Joint Financial Management Improvement Program,

March 1977), pp. 20-26.

Budget justification. Projections of resource requirements, including changes in productivity, are

important parts of budget preparation and

justification. Capital projects are sometimes

justified by their potential positive impact on productivity.

Cost reduction. Productivity measurement provides

a base for measuring change and goals.

Scheduling and control o f operations. Productivity

measures provide techniques for scheduling work,

routing crews, monitoring work performed, and

comparing direct labor with indirect.

Accountability. Productivity measurement may

lend credibility to government operations by making managers more accountable and giving the

public a tool with which to assess government

operations.

M otivation fo r im provem ent. Productivity

measurement can also provide documentation for

bonuses, special recognition, group incentives, promotions, and productivity bargaining.

Thefoeus

The four decision areas discussed require, in most

cases, very different types of productivity indexes (table

4). National policy issues normally would require aggregate national indexes. Federal program formulation

and management and State and local government planning and programming would benefit from national,

regional, and State indexes by functional area. State and

local government operations would require functional

indexes by government.

This study focuses on midlevel decisions, Federal program management, and State and local government programming. Conceptual issues and data availability dictate this approach. As functional indexes are developed,

it may be possible to calculate aggregate national and individual government indexes.

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Ghaptnir DDL M©1i[h®dl©l®gS©®D G@[niiid<§[r§i'8i®ms

The methodology underlying the measurement of

State and local government productivity is a fundamental issue on which there is considerable disagreement.

Not even the definitions and terminology are consistently applied. This chapter discusses the basic conceptual

issues and presents the approach used in succeeding

chapters.

The pr@dueS!@n framework and process

The measurement of government output may be approached in two ways: One focuses on welfare aspects

and considers utility functions, indifference curves, and

community satisfaction; the other focuses on production possibilities and considers production frontiers and

comparisons. This study deals with production possibilities. This approach assumes that government production decisions can be modeled through a production

function framework similar to that used in private sector productivity analysis. It requires the general identification of output and inputs but does not require

detailed specification of a production function.1

The basic conceptual model is the following: Government draws on a series of inputs to undertake a series of

activities which result in one or more outputs intended

to produce a series of desirable consequences. Inputs

consist of labor, capital, and purchased materials. Activities are intermediate services or processes. Outputs

are the final goods or services produced by the government. Consequences, which are sometimes known as

outcome and impact, are the intended results of government action.2 A basic model of the production process

is portrayed in chart l . 3

In its more sophisticated form, the model includes the

citizen, who is a producer as well as a consumer, and environmental and community conditions which affect

service production techniques. In this model, consumers

and the environmental setting are necessary parts of the

1 Reino T. Hjerppe, “The Measurement of Real Output of Public

Sector Services,” The Review of Income and Wealth, June 1980, p.

239.

2 For further exposition of this model, see: D.F. Bradford, R.A.

Malt, and W.E. Oates, “The Rising Cost of Local Public Services:

Some Evidence and Reflections” National Tax Journal, Vol. XXII,

No. 2 (June 1969), pp. 185-202; Jesse Burkhead and Patrick J. Hennigan, “Productivity Analysis: A Search for Definition and Order,”

Public Administration Review, Jan./Feb., 1978, pp. 34-40; and Gordon P. Whitaker and others, Basic Issues in Police Performance

(Washington: U.S. National Institute of Justice, 1982), pp. 92-123.

3 Hjerppe, “Measurement of Real Output,” p. 240.

production process, although their importance will depend on the service. They are likely to be much more important in education and policing than in water supply,

although even water supply will be affected by these

considerations. One view of the consummate model is

shown in chart 2.

For some government services, such as sanitation, the

model can be applied in a relatively straightforward

manner. Sanitation organizations use laborers, drivers,

trucks, brooms, gas, and uniforms as inputs. These inputs are deployed to produce a series of activities such

as sweeping streets, emptying litter cans, and picking up

discarded furniture. Outputs, in this case, might be the

trash collected. The consequences should be cleaner

streets and neighborhoods, fewer fire and health

hazards, and, presumably, happier citizens. The more

sophisticated model also includes citizen inputs such as

reporting of missed collections to government

authorities, separating and preparing trash for recycling

and disposal, carrying trash to the curb for pickup, and

environmental concerns such as the topography,

household density, and amount of precipitation.

For police services, inputs would include patrol

officers, police cars, and communication equipment.

Activities would include recruiting and training police

officers, and taking calls from citizens. Outputs might

include the amount of patrolling and the number of arrests. The consequences of these actions should be a

safer community. The sophisticated model would include citizen behavior, citizen reports to police, and

even citizen neighborhood watch activities.

For some services, there is general agreement as to

what constitutes an activity, an output, and a consequence, but for many services these are not always ob12

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vious (table 5). For police and fire services, the intended

consequences and activities are reasonably clearcut but

the outputs are not. For electric power, the activities

and outputs are reasonably clearcut but the intended

consequences are not. Public transit officials, for example, argue that their job is to provide service to a community and its residents. Their output is the operating

transit vehicle; the community’s use of the service is a

consequence. This concept of transit service differs from

the approach of the private sector transit manager, who

would argue that the capacity provided to a city is an activity while use of that capacity is the output.4

Although it may not always be easy to define outputs—or even to draw the line between a consequence

and an output, or between an output and an activity—outputs have to be selected service by service and

organization by organization.

The service and the organizational level will affect the

activity, output, and consequence. The output of one

organization may be an activity of another. Water meter

repair, for example, would be the output of the water

utility repair shop but not of the water utility. The output of a catalog unit of a library would not be the output of the library. This is similar to the intermediate

output issue encountered in private organizations. The

outputs of an organization’s personnel, data processing,

budget, and communications units are inputs for other

parts of the organization.

This study deals with final organizational or government output; that is, service provided to the community

and its citizens. It excludes consequences. Specifically,

we are interested in the rate of change of final government output and the inputs (primarily labor) which are

used to produce the output.

4 Anthony R. Tomazinis, Productivity, Efficiency, and Quality in

Urban Transportation Systems (Lexington, Mass.: D.C. Heath and

Company, 1975); and Gordon J. Fielding and others, Development of

Performance Indicators fo r Transit (Irvine: University of California,

1977).

Table 5. Examples of steps in the production of selected government services

Service or function Activity Output Consequence

Corrections .................................... Clothe inmates

Serve meals

Patrol cell blocks

House offenders Reduce crime

Protect society

Education........................................ Conduct classes

Give tests

Serve meals

Operate school buses

Educate students Increase literacy rate

Reduce unemployment

Fire................................................. Maintain fire trucks

Train firefighters

Put out fires

Rescue citizens

Inspect property for fire

hazards

Reduce fire losses

Reduce fire deaths

Food stamps.................................... Conduct interviews

Conduct audits

Issue stamps Increase nutritional level

Library............................................ Shelve books

Catalog books

Circulate books Increase literacy rate

Street and highway maintenance. .. Maintain trucks

Dispatch trucks

Repair streets Reduce traffic deaths

Reduce travel time

Water supply.................................. Read meters

Repair water mains

Deliver potable water Improve community health

Generate revenue to support

government

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Tlh@ off outputs

The measurement of outputs is the single most

troublesome problem in computing State and local

government productivity. It is often a problem even

after the output has been identified. Some of the difficulties are specific to the government activity; others

are more general in nature. This section is concerned

with the latter. Issues examined are the unit of measure;

the extent of coverage; weighting of outputs; accounting

for quality change; criteria for selecting outputs; and

availability of output data.

Unit of measure. The basic output measure(s) of an

organization should be a homogeneous physical unit.

Furthermore, whenever possible, the measure should be

related to unit labor hours spent in its production.

Because of the problems in defining and measuring

government output, a series of outputs should be examined and tested.5

Street cleaning illustrates some of the issues. Two

commonly used measures of street cleaning output are

cubic yards collected and curb miles swept. The curb

miles swept will be about proportional to labor requirements needed for sweeping. Also, the quality of

service—cleanliness—should be related to the output

and input. On the other hand, cubic yards collected will

not be as closely related to labor inputs. In fact, the inverse is likely: As streets are swept more frequently, the

cubic yards increase but at a much slower rate than

labor inputs. The result is decreasing productivity. Curb

miles is the preferred measure of output in this case.

In the private sector, when physical data are not

readily available, value data are often used as the

measure of output. In such cases, price changes must be

removed from the value data to obtain an index of real

output. Removing the price change facilitates calculation of output where the industry produces and sells a

number of products or where the industry lacks a

discrete, tangible product.

In the public sector, the primary problem with using

price-adjusted value as the measure of output is that, in

most cases, market prices are lacking. Without direct

pricing, estimating output in real terms is impossible.6

Exceptions may be “ enterprise” services, such as water

and electric power, which are sold in the marketplace

much as services are sold in the private sector. They account for about 6 percent of total State and local

government employment. But even for the enterprise

services, value is not always a good measure of output

since prices are administratively determined and many

have little relation to the costs of production. Transit,

5 Jerome A. Mark, “Industry Indexes of Output Per Man-Hour”

Monthly Labor Review, November 1962, pp. 1269-73.

6 John P. Ross and Jesse Burkhead, Productivity in the Local

Government Sector (Lexington, Mass.: Lexington Books, 1974), p.

35.

for example, is heavily subsidized, and the subsidies are

adjusted frequently. Thus, physical measures are

preferable, even when measuring enterprise service productivity.

The second issue is the degree of coverage within each

function. Most State and local governments produce

multiple services. Some of these are relatively easy to

identify. Many sanitation departments, for example,

sweep streets, pick up trash, and remove abandoned

cars, a set of easily identifiable services. In other cases,

the multiple services are not so easily identified. Some

electric utilities, for example, conduct energy audits and

provide recreation services as well as electric power.

There are two approaches to the construction of output indexes for multiple-service organizations. One is to

identify each organizational product. For sanitation,

this might be trash pickup (measured by tons removed),

street sweeping (measured by curb miles swept), and

abandoned car removal (measured by the number of

cars removed). A separate index could be calculated for

each product or service; these, in turn, could be combined into a single sanitation index by using input weights.

The other approach is to identify the dominant output. The index for the dominant or primary output

would represent the entire function. This approach is

valid when secondary outputs are unimportant (at least

when the impact on productivity calculation is

marginal) or when growth in uncovered output would

about parallel the growth in covered output. The tons of

trash removed is probably a reasonably good measure

of sanitation service output, since most cities spend

most of their sanitation resources on household collection. Street sweeping and abandoned car removal are

normally small consumers of resources and may be safely ignored.

Whether single or multiple products are used to

measure organizational output depends on whether the

sample product is representative of total output, the importance of the sample product, data availability, and

the importance of multiple products to decisionmakers.

The dominant output approach normally is used when

the primary product accounts for at least 90 percent of

the labor input.

The third issue is accounting for only that part of the

output actually produced during the output cycle (e.g.,

year). This is not likely to be as significant an issue for

State and local government as it is in the private sector

where the production of a single item, such as an office

building or an airplane, may take several years to complete. Most State and local government service outputs

are started and completed in the same year. If the product is not completed within the year, an estimate must

be made of what part of the final output is produced in

each year so that outputs and inputs match.

The fourth issue in service output is including only the

work the organization actually does, since many services

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are purchased from other governments and private contractors. Government, like private industry, is confronted with “ make or buy” decisions. Many communities have contracts with private firms for 

household trash collection, day care, mental health, 

custodial services, and data processing. Governmentproduced outputs must be separated from contracted 

outputs. In most cases, the separation is relatively 

straightforward if data are available. Where one service 

is provided partly by the government and partly by a 

private contractor, separation of output is more difficult. This mode of operation is becoming increasingly 

popular. Even more troublesome is the contracting for 

intermediate services such as custodial or data processing. A problem arises when inputs reflect the shift from 

public to private or vice versa, but outputs do not. Identifying such shifts is important for calculating trends.

Output weights. Calculation of a multiple-service output index or a single service with multiple outputs will 

require aggregation of individual measurements and indexes. In combining indexes, it is generally preferable to 

use weights—labor weights for labor productivity indexes. However, labor weights are not always available, 

and surrogates are often used. Several different approaches have been used in private sector measurement.7

For construction of most State and local government 

output indexes, labor weights are not only preferable, 

they are necessary. Except for government enterprises, 

most government services do not have unit value 

statistics.

Quality o f service. Quality change is a major concern in 

developing output and productivity indexes. It is particularly important in measuring government output. 

The justification often given for increases in government expenditures is that the quality of service has improved—streets are kept cleaner, snow is removed 

faster, and police respond more rapidly to calls for 

assistance. Conversely, some feel that productivity gains 

are made at the expense of quality. One study of New 

York City concluded that output (quantity) had increased but performance (quality) had deteriorated.8 In the 

police department, 36 of 37 measures of output quantity 

examined increased, but most measures of quality, such 

as the proportion of crimes solved, decreased.

7 National Research Council, Measurement and Intepretation of

Productivity (Washington: National Academy of Sciences, 1979), pp.

68-70.

8 David Greytak, Donald Phares, and Elaine Morely, Municipal

Output and Performance in New York City (Lexington, Mass.: Lexington Books, 1976).

9 See, for example, Frankin M. Fisher and Karl Shell, The

Economic Theory of Price Indices (New York: Academic Press,

1972), and Jack E. Triplett, “Robert Gordon’s Approach to Price

Measurement,” BLS Working Paper 101 (Bureau of Labor Statistics,

April 1980).

There is considerable debate as to what changes in 

quality mean and how they should be handled 

analytically in index construction.9 Of the two general 

approaches, one is based on consumption, the other on 

production. For consumption, a quality change is 

reflected in a change in consumer utility; for production, a quality change is reflected in a change in resource 

requirements. This study is concerned with the latter.

It is helpful to divide production quality changes into 

those which should be reflected in the output indexes 

and those which should'not. The latter type includes 

those arising from events external to the production 

process. For example, waiting time by clients might increase because of an influx of new clients, or mass transit commuting times might decrease because of the 

opening of a new road. Adjustments are not appropriate in such cases.

When production and unit labor requirements 

change, adjustments need to be made in the output index. An example would be a shift from backdoor to 

curbside collection of solid waste, which requires 

citizens to carry their trash to the street, a task formerly 

done by government collectors. In fact, the government 

has introduced a new service or changed the level of service. The production process has been modified and unit 

labor requirements have shifted as a result of this 

change in service.

Some classify changes in quantity or volume of production as quality shifts. From the standpoint of 

citizens, adding branch libraries or recreation centers 

may improve quality since citizens will not have to travel 

so far to a facility. From a production standpoint, they 

are simply an increase in the quantity or volume of production.

One way to adjust for a quality shift is to adjust the 

index. In the example of the shifting from backdoor to 

curbside collection, the input index would be adjusted 

to include the work of the citizen (labor hours) in 

transporting the trash from the back door to the curb. 

The output index would remain the same.

The second approach to a quality shift is to identify 

the new service and create a new index. In this case, the 

new service would be curbside collection; the old service, backdoor collection. The two productivity indexes 

would be linked to create a single index.

The impact of a change in quality on productivity 

measurement can vary depending on the output measure 

chosen. For street cleaning, the more frequent the cleaning, the cleaner the streets. If the output measure is curb 

miles swept, unit labor requirements will remain about 

constant, productivity will remain about constant, and 

the quality of service and output will be about proportional. However, if the measure is cubic yards collected, 

more frequent cleaning will result in a decrease in cubic 

yards collected with each additional cleaning. In other 

words, unit labor requirements will increase, productiviDigitized for FRASER 

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ty will decrease, and quality of service and output will 

not be proportional.

Clearly, quality should be examined function by function. For some functions, the issues and variables,,if not 

the solutions, are straightforward, while for others they 

are complex and certainly not obvious.

Unfortunately, identifying the crucial quality considerations in State and local government services is not 

always easy. Although some research and discussion of 

quality and its measurement have taken place over the 

past decade, little research has been done on the absolute or relative impact of quality change on productivity costs and unit labor requirements. Lacking 

systematic research, the process has to be ad hoc.

The suggested approach for handling quality in State 

and local government productivity measurement is as 

follows:

1. Identify service output.

2. List quality considerations for the output 

measure.

3. Assess each quality factor for its potential impact on unit labor requirements.

4. Create a quality index time series if the impact 

is potentially important.

5. Track the quality index through time.

6. Adjust the input index or link a new productivity index with the old index if the quality index 

changes.

Criteria fo r selecting outputs. Eight criteria are 

presented below for selecting measures of State and 

local government output.10 The first four are essential; 

the last four are desirable.

1. Outputs must reflect the final product (service) o f the organization. To determine productivity, 

the output must be the product or service leaving 

the organization, not the intermediate products. 

Output must reflect the work rather than the consequences of the work.

2. Outputs must be measurable. Absolute (cardinal) numbers are required. Arguments that 

government services cannot be measured usually 

fail to distinguish among the measurement of intermediate products, final outputs, and consequences of government service. Whether or not a 

service can be measured has to be considered function by function.

3. Outputs must be repetitive. Construction of 

an output index requires a repetitive or recurring set

10 For a slightly different list, see Brian Usilaner and Edwin Soniat,

“ Productivity Measurement,” in George Washnis, ed., Productivity

Improvement Handbook (New York: John Wiley, 1981), p. 95.

of services or products. The level and quality of service can change, since they can be adjusted, but the 

basic service must be repetitive.

4. Output data must be accurate and comparable. Much output data currently collected, at 

least at the national level, is incomplete, inaccurate, 

and inconsistent from period to period. Construction of a viable output index requires accurate, 

comparable data. Comparability is more important 

than absolute accuracy in preparing a time series.

5. Output calculations should use existing data

and data collection procedures. Two issues are involved here—whether the records exist in State and 

local government, and whether a procedure currently exists to collect national data. In either case, existing data and data collection procedures should be 

used whenever possible, as new procedures will likely be costly and time consuming.

6. Outputs should be easily understood. An index which is simple and easily understood is most 

likely to be accepted, supported, and used. Esoteric 

measures and complex quality adjustments should 

be avoided.

7. Outputs should be physical measures. The 

lack of a market price for most government services 

precludes use of a deflation procedure when 

physical quantity data are difficult or impossible to 

obtain. Even for services that have a market price, 

such as government enterprises, physical output 

measures are preferable because prices are often 

subsidized and set by administrative decree.

§. Output units should reflect the labor units

spent in their production. Since unit labor weights 

are used in constructing individual service indexes 

and functional groupings, the output measure 

should reflect base-year unit labor requirements.

Availability o f data. There is no single source of national data on State and local government outputs; data 

have to be collected function by function. National data 

are available for a few functions, such as electric power 

and unemployment insurance.

More data are available at the State and local level. 

Many governments routinely prepare statistical tabulations and performance reports from which output indexes can be constructed.

Th@ measurement ©f Snpyts 

This section discusses the number and type of factor 

inputs used to measure productivity, presents the labor 

measures most often used, reviews some of the thorny 

questions surrounding these measures, presents criteria 

which should be used to select inputs, and discusses data 

currently collected that might be used to calculate national labor indexes for State and local government.

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Number and type o f inputs. Productivity measures are 

often characterized by the number and type of inputs. A 

common categorization is in terms of single factor or 

multifactor productivity.11 In reality, there is a continuum of inputs or factors: The number and type of 

resource inputs included should reflect the use to which 

the measure is put.

A single factor productivity measure, the most common type, relates one resource, most often labor, to 

output. However, it does not measure the specific contribution of the factor to output. Rather, it expresses the 

joint effect of interrelated influences, such as management, technology, and regulation, as well as the factor 

input, on overall output.

Multifactor productivity relates two or more inputs to 

output and also reflects the joint effect of many influences. However, it eliminates the impact of the 

substitution of one factor for another on overall production. Examples of multifactor inputs are labor and 

capital; labor, capital, and energy; or labor, capital, 

energy, and materials.

This study focuses on* single factor productivity, 

specifically labor productivity, for several reasons. 

First, labor is of primary importance in public policy 

issues. Salaries and wages constitute about 40 percent of 

all State and local government expenditures and 55 percent of all current expenditures. Its importance varies by 

type of service (tab;e 6). Salaries and wages, in fiscal 

1977, constituted 75 percent of the expenditures for 

police programs but only 9 percent for electric power. 

Fringe benefits would raise these proportions. Second, 

labor is relatively easy to calculate when compared to 

other factors of production. Third, labor data have 

been collected for many years and generally are the most 

accessible of State and local government factor inputs. 

Fourth, labor indexes are calculated for many parts of 

the private sector, for some parts of the Federal Government, and for some foreign countries. State and local 

government labor-based indexes would permit comparisons with these other sectors.

Three ways of defining and measuring labor input for 

State and local government productivity calculations 

are: Number of hours, number of full-time-equivalent 

employees, and number of persons. Hours can be further divided among hours paid, hours at work, and 

hours actually producing output.

Number o f hours. The preferred measure is the number 

of labor hours. Labor input hours should simply be the 

total hours applied during the period for which outputs 

are measured.

Ideally, the labor hour measure would be the actual 

time worked to produce one or more outputs. The usual 

practice, however, because of definitional and data pro11 Multifactor productivity has also been referred to as total factor

productivity.

Table 6. State and local government expenditures for

salaries and wages as a percent of total expenditures by

function, fiscal year 1977

Function Percent

All functions 39

Police............................

Fire ..............................

General control............

Social insurance..........

Financial administration

Education ....................

Corrections..................

Hospitals......................

Libraries ......................

Sanitation ....................

75

74

67

66

64

61

60

58

52

52

Natural resources....................

Transit......................................

Health......................................

Parks and recreation................

Water transport........................

Highways..................................

Airports....................................

Housing and urban development

Water supply............................

Sewerage ................................

46

43

43

41

28

24

24

24

22

15

Gas ..............

W elfare........

Liquor sales . . 

Electric power

12

10

9

9

Source: Computed from data taken from 1977 Census of Govern­ ments—Compendium of Government Finances (Bureau of the Census, 

1979), pp. 29 and 33.

blems, is to use hours paid. That is, labor input normally includes actual hours worked plus time on the job 

which is paid for but not worked, plus paid time off the 

job. Hours on the job but not worked might include 

coffee breaks, training, and washup time. Hours off the 

job but paid might include vacation, holidays, and sick 

leave. Calculations using hours paid tend to understate 

true productivity. If the difference between hours paid 

and hours worked is increasing, labor productivity 

trends are understated.

The converse—hours worked but not paid—also 

needs to be considered. Many people, including 

managers, teachers, and coaches, work hours for which 

they are not paid. These hours should be counted too, 

for if they are increasing (or decreasing), productivity 

trends will be overstated (or understated) if not included.

Many practical problems arise in measuring the hours 

of State and local government workers. Should standby 

hours of police, fire, and public works officials who are 

home but subject to call be ihcluded? Sometimes 

employees are paid for standby time but more often 

they are not. What about employees who are paid by the 

task, such as collecting the trash on a specified route? 

When they finish the task they are permitted to go 

home. Irrespective of the actual hours worked, the 

employees are paid for a fixed, previously agreed upon 

number of hours. Should the hours worked or hours 

paid be included? Should extracurricular activities and

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Table 7. State and local government activities included in

three labor hour measures

Activity Hours

paid

Hours 

at work

Hours

worked

Paid vacation ........................ X

_ -

Paid holidays.......................... X - -

Paid sick leave ...................... X - -

Jury duty................................ X - -

Military leave.......................... X -

Standby'................................ X -

Meal hours2............................ X X

Washuptime.......................... X X -

Rest and coffee breaks.......... X X -

Union business...................... X X -

Training.................................. X X -

Production ............................ X X X

1 Some governments pay for standby time, such as to water supply and 

electric power employees.

2 Some workers, including police officers and firefighters, receive pay for 

meal times.

citizen meetings be counted? Teachers often supervise

activities outside school and attend meetings for which

they may not be paid. How should these hours be

counted? Most of these issues relate to a specific government function or service and should be addressed in that

context.

The relationships among State and local government

activities and hours paid, hours at work, and hours

worked are shown in table 7.

Employee years. Government labor indexes often reflect

the number of employee years or the number of fulltime-equivalent employees, since the number of

employee hours is rarely available. An employee year

commonly equals 2,080 hours (40 hours per week times

52 weeks per year) and includes all paid time including

overtime, vacation, holidays, and sick leave. Part-time

employment is usually computed on a full-timeequivalent base, such as two half-time employees equal

one full-time equivalent. Seasonal employment is also

usually computed this way; for example, four summer

employees equal one full-time equivalent. Overtime can

be handled in the same manner but is more often simply

ignored. A full-time-equivalent index could produce a

rate of change exactly the same as an hours index,

depending on the computational rules. In most instances it approximates the hours index.

Number o f employees. An index of the number of

employees is sometimes computed when data are not

readily available to compute an hours or a full-timeequivalent index. This type of index simply counts the

number of employees who produced the output without

concern for the time each employee worked. It approximates the actual hours expended to produce the outputs. An index of the number of employees will

understate the change in labor input when the number

of hours worked per person increases, such as in overtime, and will overstate the change in labor input when

the number of hours worked per person decreases. The

greatest divergence between an hours index and an

employee index will probably occur when part-time

employment increases or decreases.

Most State and local governments use part-time

employees extensively. In October 1980, they employed

10.3 million full-time and about 3.0 million part-time

workers. Part-time employment made up 23 percent of

the total. Part-time employment varies substantially by

service. In 1980, it was only 3 percent for transit, 5 percent for corrections, and 6 percent for sewerage, but 42

percent for local libraries and 49 percent for higher

education.

Seasonal employment such as for snow removal, leaf

pickup, park maintenance, and swimming pool operation often can create measurement problems when

calculating an index of the number of employees. The

primary problem is the period of coverage. Employee

counts are commonly published for one date such as

December 31 each year, but an employee count on July

30 or October 30 may be quite different because of

seasonal variations. To overcome the problem of

seasonal employment, the preferred approach would be

to use a weekly or monthly average of the number of

employees to calculate the index.

Comparison o f the three approaches. Whether the three

approaches to labor measurement—hours, years, and

employees—would produce markedly different labor

trends in the public sector is not known. In the private

sector, labor trends for hours and employees differ, but

only slightly over the long run.

Several government occupations, especially firefighting, have moved toward a shorter workweek over

the past decade, and at least one State government, New

Hampshire, has shortened the workweek of its

employees. An employee-hour index would reflect such

changes, but indexes of employee years or number of

employees would not. Whether the shift in the hours

worked is sufficiently large to affect a national productivity index is not known.

The three indexes did not vary greatly for the services

examined in this study. Data problems caused more

variation than the type of index. However, in some services, such as fire protection, the type of labor index

may substantially affect labor trends and productivity

calculations.

Volunteers. Volunteers are used by most governments,

although the extent of use varies considerably.12

Volunteers are common in services such as fire, education, hospitals, museums, and recreation but are

12 Martha A. Shulmann, “Alternative Approaches for Delivering

Public Services,” Urban Data Service Reports, Vol. 14, No. 10

(Washington: International City Management Association, October

1982), pp. 8-9.

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relatively rare in others such as public utilities and transit (table 8).

Conceptually, a labor index should include volunteer 

participation. Volunteers contribute to output just as do 

paid employees. The treatment of volunteer labor 

should depend on the projected uses of the productivity 

data. Some uses would require that volunteers be 

separately identified.

However, the way volunteer labor is accounted for 

will, in most cases, be moot. Probably in no more than 

four or five State and local government service areas are 

volunteers even potentially important. If the ratio between paid and volunteer labor remains constant in these 

areas, their inclusion or exclusion will not affect labor 

trends. Further, in the real world of data collection and 

measurement, identifying volunteer labor input or output is extremely difficult. Records on volunteers are 

almost nonexistent in State and local government. Productivity calculations usually include the output 

generated by volunteers but not their inputs. However, 

the decision will need to be made service by service.

Changes in work force composition. Labor is often 

treated as a homogeneous input although clearly it is 

not. Depending on the mix, labor inputs can produce 

very different levels of output. If the mix changes, the 

level of output can be affected. For example, police 

departments increasingly require new recruits to have 

some college education. The rationale behind the requirement is the creation of a police force which can 

better deal with the public and today’s complex society.

Table 8. Estimated use of volunteers by State and local

government function

Function Use of volunteers

Fire protection 1

Parks and recreation |

More than 10 percent of

total employed

Education

Libraries

Less than 10 percent but

more than 1 percent

Airports

Corrections

Electric power

Financial administration

Gas

General control

Health

Highways

Hospitals

Housing and urban development

Liquor stores

Natural resources

Police

Sanitation

Sewerage

Social insurance

Transit

Water supply

Water transportation

Welfare /

*

Less than • percent

Source: Based on discussions with State and local government

experts.

Supposedly, more education will increase police output. 

However, an increase in output from additional education is not an increase in productivity but an improvement in labor input—i.e., a shift in the composition of 

the labor input.

The method generally used to adjust for changes in 

labor force composition is pay differentiation. This requires information on the change in pay by occupation 

through time, data which are not readily available from 

State and local governments. Although often discussed 

in private sector productivity measurement, changes in 

labor force composition are of secondary concern at this 

time for State and local government and are not considered further.

Criteria fo r selecting inputs. Five criteria are suggested 

for identifying input data, specifically labor inputs. 

Criteria which are essential for productivity measurement are presented first, followed by those which are 

desirable.

1. Inputs must match output. Calculation of productivity requires that the resources applied match 

the measured organizational output. For organizations with multiple outputs, like the typical city 

government, this will require careful identification 

of resources used to produce the outputs.

2. Inputs must be measurable. Absolute numbers 

are required.

3. Input data must be accurate and comparable.

Much of the labor data collected on State and local 

government operations is inaccurate and inconsistent from period to period. Comparability is more 

important than absolute accuracy. Data checks and 

analysis must be part of the construction of any index.

4. Input calculations should use existing data. New 

data collection procedures will likely be time consuming and costly to develop and maintain, and 

burdensome for those providing the data. Existing 

data and collection procedures should be used 

whenever possible.

5. Inputs should be easily understood. General acceptance, support, and use of an index are more 

likely if the construction is straightforward and 

easily understood. This is one reason that labor indexes are widely used.

Availability and accuracy o f labor statistics. Every State 

and local government collects labor statistics for use in 

its day-to-day operations. Two types of labor measures, 

the number of full-time-equivalent employees and the 

number of employees, are most common; less common 

is the number of hours. Most State and local govern19

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ments should be able to prepare labor indexes by function and for the government as a whole.

Preparation of national or regional labor indexes is

not as straightforward. Some labor data are collected

and published by function by trade associations, public

interest groups, and Federal agencies. The International

City Management Association, the American Public

Works Association, the American Water Works

Association, and others routinely collect statistics on

public employment for specific functions and

sometimes for government as a whole. Federal agencies

such as the Department of Labor, the Department of

Justice, and the Department of Health and Human Services often collect data on the number of State and local

government employees for the programs they fund and

coordinate.

In addition, there are four sources of national State

and local government employment statistics: The Census of Governments; the Current Population Survey

(c p s ); the Current Employment Statistics survey (c e s

790); and unemployment insurance reports (e s 202).

The characteristics of these surveys are discussed below

and summarized in table 9.

Census o f Governments. The best known and probably

the most widely used national statistics on State and

local government employment are produced by the U.S.

Bureau of the Census. Sample data are collected and

published annually. Every 5 years (years ending in 2 and

7), the Census Bureau takes a complete enumeration

and publishes the results. Statistics are collected and

published on the number of employees (full time and

part time), number of full-time-equivalent employees by

major function, and salaries and wages.

Several problems arise in using these employment

statistics to calculate government productivity. First,

the statistics are for a single month, October, of each

year. Second, the functional classification system used

by Census is very broad—e.g., police, fire, employment

security—probably too broad for productivity calculations. Third, the information is not available until 6 to 9

months after the reference date. Fourth, no information

is collected on hours, whether hours paid, hours worked, or hours at work.

Current Population Survey (c p s) . Data collected by the

Current Population Survey are used primarily to

calculate the monthly employment and unemployment

statistics. A number of other statistics, such as hours

worked and pay, are collected. The two strengths of the

survey for productivity measurement are its timeliness

and the information on hours worked. However, the

CPS has a number of problems for State and local

government productivity measurement. First, it contains no information on services or functions. Second, it

is impossible to separate the employment data by type

of government. Third, the employment counts for State

and local government as taken from the c p s are

markedly different from those obtained from other

sources. In short, the CPS has too many major limitations for productivity calculations to be considered further in this study.

Current Employment Statistics survey (c e s 790). The

CES survey is used to collect data monthly from

establishments in nonagricultural industries and government on the number of employees, average hours worked, and average hourly and weekly earnings. About half

of all State and local government employment is

covered. Employment statistics are broken down into

eight functional areas for State government and into

seven areas for local government. One advantage of the

CES 790 data for productivity measurement is its

timeliness; preliminary data are published about 3

weeks after reporting. Another advantage is that they

include hours worked. However, the CES survey has

several deficiencies. First, statistics on hours are collected for nonsupervisory employees only. Second,

statistics are not available for many government functions. Third, the sample size does not permit presentation of data below the national level. Fourth, and most

important, coding of the data by government function is

very poor.

Unemployment insurance reports (e s 202). Since

January 1978, all State and local government employees

have been covered by unemployment insurance. As a

result, State and local governments record monthly

employment and wages and report the data quarterly to

the U.S. Unemployment Insurance Service. Since the e s

202 is linked to financial reports, it should provide the

most accurate statistics available on the number of persons employed by State and local government, by State,

county, and Standard Metropolitan Statistical Area.

Although the e s 202 reports are comprehensive, they

lack detail. The primary problem insofar as productivity

calculations are concerned is the inadequate division by

function—most employees are assigned to the general

government category. Furthermore, no information is

collected on hours and type of worker. The attraction of

the e s 202 report lies not in its current form but in its

potential if the coding by function were improved.

Data accuracy. Data collected by the Census of Governments and the unemployment insurance reports were

compared with data obtained through personal contacts

with individual governments for three functions—electric utilities, drinking water, and State liquor stores.

Considerable variability was evident. The variance

resulted from differences in categorizing functions, in

assignment of work activities to functions, and from

clerical mistakes.

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Table 9. Four national surveys used to collect State and local government employment data

Characteristic Census of

Governments

Current

Population Survey

Current Employment

Statistics survey

Unemployment

insurance reports

Initial year.......................... Prior to 1967 Prior to 1967 Prior to 1967 1978 for all government

Coverage by government function ................ About 30 major

functions

None Local—8 functions

State—7 functions

None nationally;

Standard Industrial

Classification for a

few States

Coverage by government u n it........................ States, Standard Metropolitan Statistical

Areas, cities,

towns, school

districts

National National States, Standard

Metropolitan Statistical

Areas, counties;

no city data

Definition of employee. . . . All paid persons All employed persons

16 years or older,

paid or unpaid

All paid persons All paid persons

Frequency of survey.......... Annual—October of

each year

Monthly Monthly Monthly—reported

quarterly

Reference period................ Payroll period containing October 15

of each year

Survey week containing

12th of each month

Payroll period containing 12th of each

month

Payroll period containing

12th of each month

Timeliness of publication . . About 9 months 3 weeks 3 weeks (for preliminary data)

About 9 months

Sample size.......................... All governments every

5th year—sample

other years

About 60,000 households About 12,000 governments

All governments

Nonresponse...................... Unknown 3-5 percent About 9 percent None

Benchmark.......................... Quinquennial Census

for sample survey

Decennial Census Quinquennial Census

of Governments

None

Data collected.................... Number of full- and

part-time employees

Monthly earnings in

October

Employment and hours

at work

Annual earnings in

March

Weekly earnings in

May

Employment and hours

for nonsupervisory

employees, full and

part time

Total employment

Total wages

Method of collection.......... Mail survey (some

interviews)

Household interviews Mail survey shuttle

form

Byproduct of administrative reporting

Information on employee

quality.............................. No No No No

Information on volunteers.................................. No No No No

Suvervisory vs. nonsupervisory employees . . No No Yes No

Information on parttime employees .............. Yes No Yes No

Three conclusions are to be drawn from this examination: First, no single data source is likely to be entirely

acceptable. Second, major errors are likely in each data

series. Third, viable labor-based input indexes will require detailed data comparison and adjustment irresspective of which data set is used.

Other Issues

This section discusses productivity comparisons, frequency of measurement, geographic coverage, period

coverage, service definitions, and the productivity index

itself, important issues which were only briefly touched

on in the preceding review.

Productivity comparisons—levels and trends. Underlying all productivity measurement is comparison—comparison through time, comparison of producing units,

or comparison of producing units through time. Most

private sector productivity measurements are time comparisons, such as, “ Productivity increased by x percent

between 1967 and 1982.” Trends are routinely computed for industries, for groups of industries, for individual countries, and for groups of countries.

Similarly, State and local government productivity

trends might be calculated and stated as, “ Municipal

electric power productivity increased by x percent between 1972 and 1978.” Productivity trends could also be

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computed, for example, for sanitation services in New

York City between 1967 and 1978, or for the Unemployment Insurance Service in the Southern States between

1963 and 1978.

Some productivity measures focus on absolute levels:

“ Each employee produces on the average x tons of steel

or y cars.” Local government level measures might be,

“ w tons of trash collected” or “x miles of street swept

per employee.” Comparisons of levels could be made

between jurisdictions or regions, or with the Nation as a

whole.

Trends and levels are complementary. A city service

might have a low level of productivity but a high rate of

change, or vice versa. A true picture of productivity requires examination of both levels and trends. However,

the data and analyses required to compute trends are

much less demanding than those required to calculate

absolute levels. This study focuses on productivity

trends.

Frequency o f measurement. Productivity trends are

normally calculated annually, although some national

estimates are produced quarterly. In view of the cost

and the doubtful benefit of more frequent measurement, State and local governments should probably

focus on annual measurement, at least initially.

Benefits which would accrue from more frequent

measurement depend primarily on the decisions to be

made. Until these decisions can be specified, there is little point in developing other than annual measures. Furthermore, monthly or quarterly productivity

movements might not even be detectable for most State

and local government services.

In addition, seasonal adjustments may have to be

made if quarterly or monthly calculations are to be

useful. This, of course, requires knowledge about

seasonal fluctuations, which is generally lacking.

Finally, some State and local government services,

such as education, have outputs that require more than

one month or one quarter to produce and, thus, to

measure.

Geographic coverage. State and local government as

defined for this study includes the 50 States, the District

of Columbia, and all cities, counties, special districts,

townships, and school districts. There are, in total,

about 80,000 State and local governments.

Data sources are not consistent in their geographic

coverage. Some sources include trust territories as well

as the 50 States, others include only the larger jurisdictions. Whether trust territories are included is probably

not important since they are such a small part of the

total. Focusing on only the larger jurisdictions,

however, will introduce bias into productivity calculations if economies of scale are present. Large municipal

electric power utilities, for example, are growing

somewhat faster than smaller utilities.

When national data are not readily available,

representative data may be collected by sampling. The

sample would have to be balanced for size, geographic

distribution, and any other factors which might affect

productivity. Consistency through time, among

jurisdictions, and between inputs and outputs is crucial

in such cases.

The size and location of a jurisdiction can affect productivity. Some government services, such as water,

sewerage, electric power, and refuse collection, benefit

from economies of scale.13 Other services, such as police

and recreation, evidently do not. Garbage collection

and street repair are affected by topography and

climatic conditions.

Productivity measurements are much more useful to

national, State, and local government decisionmakers if

they are available by location. For some services, such

as solid waste collection, no national output data are

readily available; for others, such as electric power, only

national data are available, and for still others, such as

unemployment insurance, both State and national data

are readily available.

Time period coverage. Two types of time periods need

to be considered in formulating a productivity index: (1)

Number of years to be covered by the index, and (2)

whether the calendar or fiscal year is used.

The number o f years covered by a productivity index,

and the beginning and ending years, can have a marked

effect on the overall rate of change. Generally, the

longer the time span, the less important the beginning

and ending years. Also, a longer time span would normally be more representative of long-term trends.

Cyclical fluctuations can affect long-term productivity trends. Such fluctuations occur when inputs do not

change as rapidly as outputs. Unemployment Insurance

Service (uis) outputs, for example, parallel the

unemployment cycle, and inputs usually lag behind

changes in outputs, as discussed later in this study. The

result is a productivity index which will shift significantly depending on the years included in the index. To

avoid arbitrary cutoff dates, and to reflect long-term

trends more accurately, average annual growth rates

might be calculated from peak to peak, trough to

trough, or midpoint to midpoint.

Sudden shifts in the economy can also influence

calculations of long-term rates of productivity change.

State and local government electric power productivity

dipped markedly with the increase in energy prices in

1973 just as it did in the private sector.

The second time-related issue is whether the calendar

or fiscal year should be used. Most productivity indexes

13 William F. Fox, Size Economies in Local Government Services: A Review (U.S. Department of Agriculture, Economics,

Statistics, and Cooperative Service, 1980).

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are based on the calendar year. The Federal Government’s productivity measurement system, however, uses

the Federal Government’s fiscal year of October

1-September 30.

The question is more complicated for State and local

government. For a single government, or a group of

governments with the same fiscal year, there is no problem. The Bureau of the Census, for example, asks all

State and local governments to use the July 1-June 30

fiscal year in reporting financial data.

However, State and local government fiscal years

vary. The U.S. Department of Transportation collects

data from over 300 transit systems with fiscal years ending on March 31, April 30, June 30, September 30, and

December 31. As discussed in a later chapter, electric

power data are reported by calendar year; Unemployment Insurance Service statistics by the Federal fiscal

year.

The closing month of the “ productivity” year is not

important for trend determinations. The same month

should be used each year, and the inputs and outputs

should cover the same period.

Service specifications and definitions. Definitions of

services unfortunately vary among governments and

through time. Public works, for example, may be

specified as a single unit; may be broken into major

components such as sanitation, water supply, and street

maintenance; or may be divided into subservices such as

solid waste residential collection, street sweeping, street

flushing, and so forth.

For single measurements or studies, definitions can

usually be adjusted to meet analytic needs and data

availability. For preparation of a national, aggregate

productivity index, a formal classification system is

needed.

Most of the summary data in this study are based on

the classification system and definitions of the Census

of Governments (see appendix B). This structure has

been used for years by the Bureau of the Census, State

and local governments, and the research community.

However, even this classification system has several

deficiencies for productivity measurement. First, the

service categories are too broad. Second, governments

differ in the manner in which they structure, and thus

report, their operations. The functions assigned to

police departments or even State alcoholic beverage

control agencies vary from jurisdiction to jurisdiction.

The Standard Industrial Classification (sic) system,

in contrast to the Census of Government system, is very

detailed. It includes all goods and services produced by

private and public establishments. State and local

government is a small part of the sic (appendix B lists

State and local government services included), sic

categories are not widely used in State and local government data collection and analysis.

Both the sic and Census of Governments classification systems should be helpful in structuring productivity analysis, collecting data, and making comparisons.

However, for some services, neither classification

scheme will be satisfactory, and further specification

will be necessary. Appendix B presents an initial attempt

at a cross-classification of these two systems.

The productivity index. This study aims to develop a

procedure for developing two basic types of State and

local government indexes—one, a series of productivity

indexes for individual government services such as fire,

police, water supply, personnel, and purchasing; and

two, an index of total State and local government productivity.

To compute the total State and local government productivity index, output indexes for individual functions

must be developed. After these outputs have been

developed, a total national aggregate State and local

government output index may be computed. The total

State and local government productivity index would be

the sum of the final outputs weighted by their inputs.

The index would not include intermediate outputs.

However, all inputs would be included, intermediate as

well as final.

The mathematics of the productivity index is simple

and straightforward.14 The index should compare the

quantity of service in the current year with the base

year, each weighted by the employee hours expended

per unit produced per unit of labor input in both

periods.

If State and local government produced a single output, the index would simply be the reciprocal of

employee hours spent to produce a unit of output over

the two periods of time or:

1 Lj

Where

Iu = unit employee hour index

Ir = output per employee hour index

Lj = unit employee hours in current period

Lo = unit employee hours in the base period.

14 Bls Handbook o f Methods, Bulletin 2134-1 (Bureau of Labor

Statistics, 1982), p. 102.

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The same ratio would be used for a single service such as

sewerage or water.

Where more than one service is produced in a functional area, such as sanitation, or where several functional areas are combined, a composite index is required:

Q L1

I, = ____

Where

Iu = unit employee hour index

Q0 = quantity in the base period

Lj = labor hours expended in current period

L0 = labor hours expended in the base period.

This particular form, the base-period composite, is used

in the following chapters.

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Chapter 0¥= Msasurimg tlh® Usasuiralbl®"

Thir®© Case Studies

This chapter presents illustrative national productivity measures for three State and local government services—electric power, alcoholic beverage sales, and

unemployment insurance. These three services were

selected for examination because each has a reasonably

well-defined set of outputs, and output and employment

data are available for calculating a productivity index.

Each service is discussed briefly, a synoposis is

presented of past research, potential output measures

and data to calculate output indexes are considered,

labor input data are examined, s'everal productivity indexes are calculated, and suggestions are made for

future research. The specific approach, geographic

area, and time period covered vary by service, depending upon data availability. Though the following examples illustrate how national productivity indexes

might be calculated, they should not be taken as

representative of State and local government outputs,

data availability, or productivity.

El@©trSe Utilities

Electric utilities are a good starting place for an investigation of the feasibility of calculating State and

local government productivity indexes because they are

easily identifiable, they have a measurable set of outputs, and the larger ones report annually to the Federal

Government. Moreover, productivity indexes have long

been calculated for private and cooperative utilities so

there is a large analytical and institutional base of

knowledge on which to build.

Institutional oonsiderations

Of the three basic types of electric utilities—private,

cooperative, and government—the private or investorN ote: This chapter has benefited from the comments of Edwin

Adelman, Charles Ardolini, Horst Brand, Arthur Herman, Clyde

Huffstutler, James Urisko, and Martin Ziegler of the Bureau of Labor

Statistics; Steven Barsby, Consultant; Susan Clark of the National

League of Cities; John Flynn of the New York State Legislative Staff;

Paul Fry of the American Public Power Association; Harry Hatry of

the Urban Institute; John Humphrey of the Unemployment Insurance

Service; James Jarrett of the Council of State Governments; Raymond Long of the National Association of State Budget Officers;

Gary Marshall of the Distilled Spirits Council of the United States;

and Allan Stevens of the Bureau of the Census.

owned utility accounts for about three-fourths of all

production and sales.1 The 250 investor-owned utilities

sell about 78 percent of the kilowatt hours, serve about

76 percent of the customers, and own about 73 percent

of the Nation’s electric plant and equipment (table 10).

The second type consists of Rural Electrification Administration (r e a ) cooperatives, which expanded

dramatically into the rural areas in the 1930’s under the

sponsorship of the Federal Government. Today, about

900 cooperative systems sell about 7 percent of the Nation’s electricity, serve about 10 percent of the

customers, and own about 7 percent of utility plant and

equipment.

The third type, the government-owned utility, sells

about 16 percent of all kilowatt hours, serves about 14

percent of the customers, and owns about 20 percent of

the plant and equipment. The two basic types of

government-owned utilities are Federal and State/local.

The Federal Government is primarily a generator and

wholesaler of electric power. It produces about 11 percent of the Nation’s power and owns about 8 percent of

the plant and equipment, but serves less than 1 percent

of final users.

State and local electric power systems include all

government systems other than those operated by the

Federal Government—State, special district, city, and

county operations. The 2,200 State and local systems,

also called municipal systems, account for about 14 percent of the Nation’s customers, 12 percent of kilowatt

sales, and about 12 percent of electric plant and equipment investment.

In 1977, State and local systems sold about one-third

of their kilowatts to residential users, one-third to industrial/commercial users, and one-third to other users

such as other public power authorities, railroads, and

highway and street lighting authorities. Generating

capacity was divided among steam (52 percent), nuclear

(8 percent), hydroelectric (33 percent), and internal

combustion engine (7 percent).2

The primary factor input into State and local power

operations is capital, which accounted for about 43 per1 Some authorities divide the utilities into two types—privately and

publicly owned. Cooperatives are considered as privately owned.

2 Statistics o f Publicly Owned Electric Utilities in the United

States-1977 (U.S. Department of Energy, Energy Information Administration, 1979), pp. 6-7.

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Table 10. Distribution of kilowatt hours sold, customers

seresd, and plant and equipment owned by typ® ©f utility

ownership, 1078

(Percent)

Type of 

ownership

Kilowatt

hour

sales

Customers

served

Plant and 

equipment 

owned

T ota l.......................... 100.0 100.0 100.0

Private (investor).................... 77.6 76.2 73.3

Cooperative ( r e a ) .......................... 6.6 10.2 7.1

Government.......................... 15.8 13.6 19.6

Federal.......................... 3.6 (') 8.0

State and local .............. 12.2 13.6 11.6

1 Less than 0.05 percent.

SOURCE: Public Power, Jan./Feb. 1980, p. D-3.

cent of all utility expenditures in 1977. Salaries and 

wages accounted for about 9 percent (16 percent of current operating expenditures). Fuel, materials, supplies, 

and purchased power accounted for the remaining 48 

percent (table 11).

State and local systems are scattered throughout the 

United States. The District of Columbia, Hawaii, and 

Montana are the only jurisdictions which have no State 

or local government power system. For the 48 States 

with one or more systems, the average number of 

employees was 1,220 and the average revenue was $146 

million in 1976. California led the list with 9,600 fulltime-equivalent employees and $1,063 million in 

revenue.3

According to the Bureau of the Census, State and 

local utilities had gross revenue of $7,142 million in 

fiscal year 1977. The American Public Power Association (a p p a ) estimates the figure at $7,059 million (excluding Puerto Rico) for calendar year 1977.4 The 19 

largest utilities accounted for 57 percent of total dollar 

sales. The 160 largest reporting units accounted for 

about 95 percent, according to information published 

by the U.S. Department of Energy.5

Kilowatt hour sales to the ultimate customer is the 

statistic most often used to measure electric utility output. In 1978, State and local systems sold 234,478 

million kilowatt hours.6 The 10 largest systems accounted for about 35 percent of the kilowatt sales and 

the 25 largest for about 50 percent. The largest 160 accounted for almost 90 percent.7

The coverage of utility statistics varies. Bureau of the 

Census financial and employment statistics include all 

State and local government electric power operations; 

before 1980 the Census employment statistics included 

only local employees.

3 Number of employees from 1977 Census o f Governments—Compendium o f Public Employment (Bureau of the Census,

1979); revenue from 1977 Census o f Governments—Compendium of

Government Finances (Bureau of the Census, 1979).

4 Public Power, Jan./Feb. 1980, p. 0 -2 .

5 Publicly Owned Electric Utilities—1977, p.16.

6 Public Power, Jan./Feb. 1980, p. D-2.

7 Publicly Owned Electric Utilities—1977, p. 16.

The Federal Energy Regulatory Commission (ferc),

the Energy Information Administration (eia) of the 

U.S. Department of Energy, and the American Public 

Power Association (appa) include all State and local 

government utilities in their statistics, eia, for example, 

defines a “ municipal” power utility as “ a city, county, 

irrigation district, drainage district, or other political 

subdivision or agency of a State competent under the 

law thereof to carry on the business of developing, 

transmitting or distributing power.” 8

The Standard Industrial Classification (sic) system 

also includes all “ companies and systems engaged in the 

generation, transmission and/or‘distribution of electric 

energy for sale.” 9 This definition is used in the b l s data 

and productivity analysis of investor-owned and 

cooperative utilities.

The discussion and calculations that follow focus on 

State and local government electric utilities whether they 

generate, transmit, or distribute power. Utilities in the 

U.S. territories are excluded, however. These are included in the eia and the appa statistics, but not in those 

compiled by Census.

A potential problem, particularly for data collection, 

is the utility that covers more than a single service. Combined electric-gas utilities are common in the private sector, and a separate classification is given to them in the 

sic. The problem is likely to be even more common in 

local government, where utilities may be combined in a 

single agency. In Los Angeles, for example, water and 

power are combined; in Memphis, water, light, and gas 

are combined.

Research amid statistics

Research and statistics abound on electric utility 

operations. This stems from the public’s interest in utility regulation and rate setting, the great debates of the 

1930’s over public vs. private power, and the more recent interest in the safety of nuclear power and the effect of acid rain, all issues that lend themselves to 

economic analysis. Universities, private consulting 

firms, utilities, and government regulators routinely 

study the industry.

Productivity measurement has attracted a moderate 

amount of interest on the part of economists. Jacob 

Gould was one of the first to attempt an electric utility 

productivity index.10 Gould calculated productivity indexes for 1899-1942 for the total electric utility industry, public and private. He measured output of 

kilowatt hours, both unweighted and weighted by class 

of service; and inputs of labor, fuel, and capital.

8 Ibid., p. 1.

9 Standard Industrial Classification Manual (U.S. Office of

Management and Budget, 1972), p. 218.

10 Jacob Martin Gould, Output and Productivity in the Electric and

Gas Utilities—1899-1942 (New York: National Bureau of Economic

Research, 1946).

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Tab!® 11. FSnanees ®? Stat© and local government @!©c?rle utilities by type of government, fiscal year 1077

(Millions)

Government Revenue

Expenditures

Total Capital Interest 

on debt

Current operations

Total Salaries 

and wages Other

Total ...................................... $7,142 $9,313 $3,167 $856 $5,289 $861 $4,428

States ................................................ 377 982 630 175 177 41 136

Municipalities.................................... 5,353 5,377 909 345 4,123 587 3,536

Special districts.................................. 1,273 2,804 1,619 329 856 217 639

Townships.......................................... 127 131 7 2 121 15 106

Counties............................................ 11 19 1 5 12 2 10

N o t e : Because of rounding, detail may not add totals. Finances (Bureau of the Census, 1979), p. 33.

S o u r c e : 1977 Census of Governments—Compendium of Government

Kendrick and Barzel calculated indexes of private

electric utility productivity. Barzel’s work covered

1929-55 for partial and total factor productivity.11 He

derived kilowatt hour class weights through regression

analysis. The Kendrick indexes were for total factor

productivity and covered 1948-69.12 13

Dragonette and Jaynes of b l s calculated and published an index of labor productivity for the electric and gas

industry in 1965 which covered 1932-64.13 This index of

investor-owned and cooperative electric utilities is updated yearly and has recently been divided between gas

and electricity.14 The Bureau also calculates an index of

Federal electric power productivity as part of its Federal

productivity measurement program.15 No index is

routinely published for State or local government electric power productivity.

Much of the research has focused on utility regulation. Recently, it has centered on the role productivity

should play in regulation, particularly in rate setting.

Kendrick discussed the issue in several papers.16 Rodney

Stevenson considered conceptual issues, including partial and total factor productivity, rates of technological

change, econometric modeling, and management

audits. He also presented a private electric utility productivity index. Total factor productivity was estimated

using five factors—capital, labor, fuel, purchased

11 Yoram Barzel, “Productivity in the Electric Power Industry

—1929-1955,” Review o f Economics and Statistics, November 1963,

pp. 395-408.

12 John W. Kendrick, Postwar Productivity Trends in the United

States (New York: National Bureau of Economic Research, 1973).

13 Joseph E. Dragonette and Philip W. Jaynes, “Output Per ManHour, Gas and Electric Utilities,” Monthly Labor Review, January

1965, pp. 34-39.

14 Productivity Measures for Selected Industries—1954-81, Bulletin

2155 (Bureau of Labor Statistics, 1982).

15 Measuring Federal Productivity (U.S. Office of Personnel

Management, 1980).

16 John W. Kendrick, “Efficiency Incentives and Cost Factors in

Public Utility Automatic Reserve Adjustment Clauses,” Bell Journal

o f Economics, Spring 1975, pp. 299-313; and “ Some Productivity

Issues in the Regulated Industries,” in Public Utility Productivity,

Waiter L. Balk, ed., (Albany: New York State Department of Public

Services, 1975), pp. 3-9.

power, and material and supplies.17

Howard Axelrod called for the use of a series of partial productivity ratios targeted to different functions.18

He suggested kilowatts per man hour and total capitalized cost per kilowatt hour for measuring power generation productivity, and the number of bills processed per

man hour and the number of complaints answered per

man hour for assessing customer account productivity.

Research papers by lulo and Pace are also widely

cited in the literature.19 Both authors used econometric

techniques to identify the variables most responsible for

differences in utility unit costs. Neither study found that 

employee hours play a significant role.

Lastly, J.W. Wilson and Associates conducted a 

study for the U.S. National Bureau of Standards which 

focused on helping States improve their electric utility 

regulatory process.20 As part of the study, they reviewed electric utility productivity measurement and 

associated problems.

Several points stand out in the research:

1. The principal measure of output is the 

kilowatt hour, often weighted to reflect the different classes of service.

2. No input measure is dominant. Labor is often 

used, apparently because of the difficulty in 

measuring capital and fuel, the two factors most 

favored in theoretical discussions.

3. The research has focused on private utility 

measurement, particularly as related to regulatory 

issues.

17 Rodney E. Stevenson, “ Regulating for Efficiency in the Public 

Utility Industry” and “Productivity in the Private Electric Utility Industry,” in Public Utility Productivity.

18 Howard J. Axelrod, “Measuring Electric Utility Productivity,” 

in Public Utility Productivity, pp. 57-69.

19 William lulo, Electric Utilities—Costs and Performance

(Pullman, Washington: State University Press, 1961); and Joseph D. 

Pace, “Relative Efficiency in the Electric Utility Industry” (Ann Arbor, University of Michigan, 1970).

20 J.W. Wilson and Associates, The Measurement of Electric Utility

Productivity, Vols. I and II (National Bureau of Standards, 1980).

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4. Little interest has been shown in State and

local government productivity measurement, and

no productivity index has been calculated.

Although no productivity index exists, considerable

data are available on State and local government electric

power operations. The eia, the appa, the Bureau of the

Census, investment firms, and individual utilities all

publish some data in this area. Statistics on the number

of customers, kilowatt hour sales, revenues, number of

generating stations, miles of transmission lines, plant

cost, and allowances for depreciation and amortization

are routinely collected and published. Data which could

be used to construct a State and local government productivity index are reviewed in the following sections.

Outputs

The output measure used most often is kilowatt hours

sold. Other measures are the number of customers,

kilowatt hours generated, percent of capacity used,

generator capacity, dollar sales, and net profit. The

relative strengths and weaknesses of the different

measures are seldom discussed in the literature.

William lulo, one of the few researchers who has examined the different measures, offers four reasons for

using kilowatt hours:

1. The measure is familiar to industry and the

public and has long been used by both.

2. Data are readily available. All utilities collect

and keep, and most report, statistics on kilowatt

hours sold.

3. The kilowatt hour is a standard physical unit

which is not affected by price changes.

4. Kilowatt hours are a rough indicator of the industry’s ability to produce electric energy.

The only argument lulo offers against the use of the

kilowatt hour is that production costs per kilowatt hour

are not always similar though this may be implied.21

The previous chapter listed the criteria used in this

study to select State and local government output

measures. The kilowatt hour satisfies the four essential

criteria and three of the four optional criteria. The only

criterion not met is the one noted by lulo: Kilowatt

hours are not always proportional to the cost (labor

hours) spent in their production.

Production costs vary by class of service. Capital requirements to construct distribution systems for industrial users are normally less than those required to

service residential customers per kilowatt hour

delivered. Similarly, the labor required to maintain and

service industrial/commercial distribution is likely to be

less than that required for residential service.

21 lulo, Electric Utilities—Costs and Performance, p. 30.

Weighting output. Because production costs vary, differentiating or segmenting output to account for the different classes of service is common practice, bls, for example, uses three basic weighted aggregates—residential, commercial/industrial, and other—to estimate

labor productivity of investor-owned utilities.22

Wilson and Associates propose a seven-way break

—residential, commercial, industrial, street and

highway lighting, public railroads and railways, interdepartmental, and sales for resale.23

Weights should reflect the input being measured: A

labor productivity measure should use unit labor inputs,

and a capital productivity measure should use capital input weights. However, appropriate statistics are not

always available, and it has become common practice to

use average price per kilowatt hour for each class of service (revenue divided by kWh’s sold) as the weight. Insofar as labor and capital requirements and costs are

proportional to price differentials, price weights are

useful.

lulo has shown that the relationship between unit cost

and unit revenues is good.24 Both bls and Wilson use

unit revenue as weights, and this procedure is suggested

for State and local government output measurement.

Utility weights have been calculated for 1967, 1972,

and 1977 (table 12). For 1967 and 1972, weights were

calculated for residential, commercial/industrial, and

other. For 1977, additional data made it possible to

divide the commercial/industrial field between small

and large producers.

Quality o f service. Quality of service has not been an

issue for most researchers. Whether this lack of concern

is due to conceptual difficulties, data problems, a feeling that quality is not an important issue, or a combination of factors is not known. Researchers who have

studied the quality issue have singled out the following

as important:

1. Reliability. This factor concerns the number,

length, and duration of supply interruptions. Interruptions can be caused by factors such as weather,

disaster, lack of equipment, or lack of fuel.

Building redundancy into the system increases

reliability.

2. Voltage. Lack of proper equipment or insufficient generating capacity may cause voltage fluctuations which result in damage or malfunction of user 

equipment. Installing additional equipment can 

control voltage fluctuations.

3. Aesthetics. The aesthetic factor most often

discussed is placement of utility lines—above vs.

22 Dragonette and Jaynes, “Output per Man-Hour,” pp. 34-39.

23 Wilson and Associates, Electric Utility Productivity, Vol. II, p. 16.

24 lulo, op. cit.

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Tab!© 12. Weigfals for eaSeis Sating output indexes for State

and local government electric utilities by class of service,

1967, 1972, and 1977

Class of service

Dollars per kilowatt hour

1967 1972 1977

Residential ............................ $0.0149 $0.0163 $0.0293

Commercial/industrial............ .0116 .0132 .0253

Small.............................. n.a. n.a. .0328

Large.............................. n.a. n.a. .0211

Other...................................... .0147 .0168 .0345

n.a. = not available.

S o u r c e : Computed from Statistics of Publicly Owned Electric Utilities in the United States, selected issues (U.S. Department of Energy, Information Administration).

below ground. Placing utilities below ground increases initial costs. Its impact on operating costs is

open to debate.

Adjustments for quality have not been attempted in

this study.

Generation vs. sales. Because State and local government utilities are not closed systems, a problem may

arise in calculating productivity when State and local

government utilities generate and sell power to nonState and nonlocal utilities, and purchase and distribute

power that other utilities generate and transmit. North

Platte, Nebraska, for example, sold 178 million

kilowatts in 1978 to ultimate consumers but generated

no electricity itself. The New York State Power

Authority, on the other hand, generated 34 billion

kilowatt hours in 1978 but sold only 14 billion to

ultimate consumers; it sold the remaining kilowatt

hours to other utilities.

The difference between generation'and sales to the

ultimate consumer becomes a problem when the ratio

between generation and sales is changing, as it is with

State and local government electric power utilities. In

1967, State and local government utilities generated

101,672 million kilowatts and sold 142,928 million

kilowatts to ultimate consumers, a difference of 29 percent. In 1978, 228,645 million kilowatts were generated

and 234,478 million kilowatts were sold, a difference of

about 2 percent. While growth has been considerable in

both generation and ultimate sales, the gap has been

closing.

Although the growth of State and local government

generating capacity vis-a-vis sales to ultimate customers

has been large, the impact on productivity apparently

has been limited. Labor productivity with, and without,

the incremental change in employees working in generation differ by less than 1 percent for large utilities, the

only group for which data are readily available. Any

bias as a result of increasing generation is downward.

That is, productivity gains are understated, since output

is calculated as a function of final sales to' customers

while inputs include all the labor needed to distribute,

transmit, and generate electricity.

Statistics. Statistics on kilowatt hours sold to ultimate

consumers by government utilities have been collected

for many years. Today, eia collects and publishes information annually on approximately 160 of the largest

State and local utilities. The appa also collects and

publishes kilowatt hour statistics on State and local

utilities.

The eia and its predecessor organizations, particularly the Federal Power Commission, have published

statistics on publicly owned utilities since 1946.

However, reporting requirements and tabulation procedures have been modified so that year-to-year summary comparisons are likely to be misleading. Also, the

number of utilities reporting each year depends on the

reporting requirements and whether the utilities abide

by such requirements.

The new eia series starting with 1974 includes those

utilities which have consistently filed annual reports and

have annual operating revenue of at least $5 million.

The new series includes abut 160 utilities and reporting

units. (The old series included 511 utilities; the new

series lists the utilities in Tennessee which purchase their

power from the tva under a single heading, “ tva providers.”)

The eia statistical reports include, in addition to summary data, details for individual utilities on the number

of customers, kilowatt hour sales, revenues, production

expenses, assets, liabilities, profit and loss, generation

capacity, and the number of miles of transmission lines.

Kilowatt hour sales are divided by class of customer so

that unit revenue* weights may be developed.

Insofar as national output measurement is concerned,

a limitation of eia statistics is that they do not indicate

the extent of their coverage. But if one accepts the appa

estimates for total kilowatt hour sales by State and local

utilities, the eia statistics account for about 90 percent.

Another limitation is that the sample excludes the

smaller utilities, which may bias productivity calculations.

The appa collects kilowatt hour sales monthly from

the 29 largest municipal utilities. It uses these statistics,

data collected by eia, and data from the private sector

to estimate the annual sales of kilowatt hours to

ultimate users, appa is the only known source of

published data on total State and local government

sales.

Several problems arise in using appa estimates to

calculate national State and local electric utility output.

First, the figures include kilowatt hours sold in

American Samoa, the Canal Zone, Guam, Puerto Rico,

and the Virgin Islands. Statistics for these five territories

can be subtracted from the total, however, to obtain the

estimated kilowatt sales in the United States. Moreover,

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the Puerto Rico Electric Power Authority is the only

large utility located in the territories, and its output can

be easily removed from the summary statistics, as has

been done in the statistics presented here.

Second, appa statistics are not separated by

class—residential, commercial, highway lighting, and so

forth—or by geographic area. As noted, a class division

is needed to assign unit revenue weights. If that computation is not made, which the preceding discussion

suggested was desirable, this type of information is not

needed. Geographic information is valuable for matching and comparing statistics from various sources.

Third, and more troublesome, the error associated

with the appa estimate is not known since the true

universe is unknown. However, comparison with other

data suggests that they are reasonably good estimates of

the universe.

Output indexes. Three output indexes are presented here

(table 13). Two are based on appa statistics. One is an

index of local and State kilowatt sales to ultimate

customers with statistics for Puerto Rico removed. The

other reflects only local government electric power

kilowatt sales. In addition to Puerto Rico, sales of the

major State authorities have been removed so that the

index would match Census local government employment statistics. In addition, a third output index has

been constructed for 33 large State and local government electric utilities for which data were readily

available.25 The kWh statistics were taken from Federal

Government sources and data provided by the utilities.

The average annual rates of growth for these three indexes are 4.0? 3.7 and 6.0 percent, respectively. The first

two indexes, as expected, are very similar; the large utility index grows about 50 percent faster than the other

two. These statistics will be used later in this section to

calculate electric power productivity.

Labor inputs and employee eosts

The previous chapter listed three labor measures to be

used in calculating State and local government labor

productivity: Number of employees, number of fulltime-equivalent employees, and number of employee

hours. Data to calculate these measures are not always

available.

Sources o f data. The three sources of data on publicly

owned power system employment are: (1) The individual public power systems, (2) the American Public

Power Association (appa), and (3) the Bureau of the

Census.

Although all public power agencies collect and maintain data on employment, and some publish them annually, these statistics are mostly simple counts of the

25 See appendix C for a list of the utilities included.

(1977 = 100)

Table 13. Three output indexes for State and local

government electric utilities, 1967-78

Kilowatt hour sales to ultimate customers

Year Local and 

State

Local

utilities

Large

utilities

1967 ...................................... 63.6 65.2 54.4

1968 ...................................... 69.1 71.6 57.4

1969 ...................................... 75.5 77.8 62.5

1970 ...................................... 81.5 84.2 67.4

1971 ...................................... 87.3 90.4 70.7

1972 ...................................... 88.3 91.6 76.2

1973 ...................................... 90.0 93.0 83.5

1974 ...................................... 91.1 93.8 83.1

1975 ...................................... 91.5 94.6 85.1

1976 ...................................... 96.2 99.0 90.1

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 104.3 103.3 106.2

Average annual percent

change1.............................. 4.0 3.7 6.0

1 Average annual rates of change in this and all subsequent tables are 

based on the linear least squares trend of the logarithms of the index 

numbers.

S o u r c e : Local and State utilities—January issues of Public Power (Puerto Rico statistics are excluded). Local utilities— local and State utility index 

with major State utility sales excluded. Large utilities— compiled from 

Federal and individual utility sources.

number of employees. A review of a number of annual

reports did not find any statistics of the number of 

hours worked, part-time employment, or seasonal

employment. Discussions with government utility officials suggest that in most cases data are readily

available on the number of employees but not on

employee hours.

Appa collects some employee data in its survey of 

utility salaries. The salary survey, initiated in 1957, was

conducted every other year until 1977, when it became

an annual survey. The 1979 survey collected information from 358 State and local utilities. Data collected include the number of permanent employees, salaries of

selected officials, and the number of years since the

nonsupervisory engineers received their degrees. Other

statistics include kWh sales, kWh purchases, and

generating capacity. Data on sales to ultimate consumers are not collected. The appa statistics can help in

data analysis and evaluation but by themselves are not

sufficient to construct a viable labor index.

The Bureau of the Census is the only organization

that routinely collects and publishes time series data on

public power utility employment. It publishes figures on

both total employment and full-time-equivalent

employment. However, several problems arise in using

these data to measure electric utility labor productivity.

First, Census did not include State employees in its

electric utility series until 1980. Since output statistics

include both State and local kilowatt hours, the labor

input series must include both State and local governments. The 1980 Census figures show 3,000 State power

employees; other data suggest that the figure is closer to

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4,000. The important consideration for trend determinations is relative growth, not absolute numbers.

A second problem is that Census employment is collected for only one month—October—of each year.

These statistics do not capture seasonal employment. If

the October/seasonal proportion remains constant, the

October statistics will be satisfactory for trend determinations. A set of October/seasonal statistics is needed

to establish whether there is constancy.

A third problem is that Census statistics are available

for only aggregate and full-time-equivalent employment. No data are collected on the number of hours,

nor are the statistics broken down between operations

and force account (construction) employees.

Another potential problem, discussed briefly earlier,

is the assignment of government personnel to the power

function when, in fact, they work in other or multipleservice areas—e.g., gas, water, and sewerage. Overhead

personnel are a special case. In labor data collected for

this study, however, the inability to allocate personnel

was not a significant problem. Only one of the 33 large

utilities indicated it was difficult to allocate personnel.

Two large utilities not included in the sample also indicated that they were unable to provide the employee

data by function. The extent of the problem for the

smaller utilities is not known, but Census personnel do

not feel that it is significant. Census suggests that when

governments have this type of problem, they should

allocate personnel using revenue figures.

Although statistics are not available to compute an

hours index, other sources suggest that such an index

would parallel the total employee index and the fulltime-equivalent employee index. Census full-timeequivalent and total employment show a high degree of

correlation (table 14). Between 1967 and 1978, both

grew at the same annual rate—0.7 percent. For trend

comparisons, the two measures should not differ a great

deal. Also, private electric utility trends for labor hours

Table 14. Two employment measures for local government 

electric utilities, 1967-78

Year

Number of 

employees 

(thousands)

Full-timeequivalent

(thousands)

Full-timeequivalent as 

a percent of 

number of 

employees

1967 .......................... 59 57 96.6

1968 58 56 96.6

1969 .................................... 55 54 98.2

1970 ...................................... 58 56 96.6

1971 ...................................... 59 58 98.3

1972 ...................................... 59 58 98.3

1973 ...................................... 61 60 98.4

1974 ...................................... 62 60 96.8

1975 ...................................... 62 60 96.8

1976 .............................. 61 59 96.7

1977 .................................... 60 58 96.7

1978 ...................................... 62 60 96.8

S o u r c e : Employment data from Public Employment, annual issues 

(Bureau of the Census).

Table 15. Two employment indexes for private sector 

electric utilities, 1967-78

(1977 = 100)

Year Employees Employee hours

1967 ...................................... 82.3 82.6

1968 ...................................... 84.1 84.6

1969 ...................................... 86.5 87.4

1970 ...................................... 89.8 90.4

1971 ...................................... 90.8 91.2

1972 ...................................... 93.3 94.1

1973 ...................................... 96.5 98.3

1974 ...................................... 98.9 99.7

1975 ...................................... 97.4 96.6

1976 ...................................... 98.0 97.8

1977 ...................................... 100.0 100.0

1978 ...................................... 105.1 107.0

Average annual percent 

change:

1967-78 ........................ 2.1 2.0

1975-78 ........................ 1.3 1.3

SOURCE: Donald M. Fisk, "Pilot Study Measures Productivity of State, 

Local Electric Utilities,’’ Monthly Labor Review, Dec. 1981, p. 46.

and total employment closely parallel each other during

the time period examined here (table 15).

Employment indexes. Three employment indexes were

computed (table 16). The first, based on Census

statistics, is for total local government electric power

employment. The second index attempts to capture

State as well as local government employment by adding

employment for the four largest State systems. This index should approximate total State and local government electric power employment since local governments constitute over 90 percent of electric power

employment and the four State systems constitute over

90 percent of State electric power employment. The

third index, based on statistics provided by the utilities,

reflects employment in 33 large, publicly owned

utilities.

The average annual rates of growth for the three indexes are 0.7, 0.9, and 1.6 percent for the local,

local/selected States, and large utilities, respectively.

The first two indexes are quite close, as expected, but

the third grows at almost twice the rate of the other two.

These indexes are used to calculate the productivity indexes presented later.

Salaries and wages. During 1967-78, employee salaries

and wages increased at an average annual rate of 7.8

percent (table 17). This index, computed from data

taken from Public Employment, an annual publication of

the Bureau of the Census, is for salaries and wages paid

to full-time local government personnel in October of

each year. The criticisms of Census employment

statistics discussed earlier apply equally here-—they include only one month of data and exclude State employment. An additional criticism is that fringe benefits are

not included. This exclusion will bias labor cost trends if

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Table 16. Three employment indexes for Slate and local 

government electric utilities, 1867=78 

(1977 = 100)

Year

Local

utilities

Local and 

selected State 

utilities

Large

utilities

1967 ...................................... 98.1 96.2 86.7

1968 ...................................... 96.5 94.7 87.2

1969 ...................................... 91.5 90.0 89.6

1970 ...................................... 96.5 95.0 92.5

1971 ...................................... 98.1 96.7 96.2

1972 ...................................... 98.1 96.9 96.2

1973 ...................................... 101.5 100.3 98.0

1974 ...................................... 103.1 102.1 99.9

1975 ...................................... 103.1 102.3 99.8

1976 ...................................... 101.5 101.1 99.7

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 103.1 103.7 103.2

Average annual percent

change .............................. .7 .9 1.6

S o u r c e : Local utilities—data to compute index from Public Employment, annual issues. Local and selected State utilities—data for local 

government employees from Public Employment, annual issues; data for 

State government employees provided by individual utilities. Large 

utilities— data to compute index provided by individual utilities.

Table 17. Index of average salaries and wages of local

government electric utility employees, 1967-78

,(1977 = 100)

Year Index

1967.............................................................. 48.6

1968................................................................ 50.3

1969........................................ 55 3

1970........................................................ 58.6

1971................................................................ 62.1

1972............................................................ 66.0

1973........................................ 74.6

1974................................................................ 79.3

1975................................................................ 87.3

1976................................................................ 96.3

1977................................................................. 100.0

1978............................................ .............. 105.6

Average annual percent change:

1967-78.................................................. 7.8

1967-72.............................. 6.6

1973-78.................................................. 7.5

S o u r c e : D a ta to c o m p u te in d e x fro m Public Employment, a n n u a l is s u e s .

the ratio of fringe benefits to salaries and wages is

changing through time.

Prodyetivifiy Snd@x@3

Three illustrative productivity indexes for electric

utilities are presented in this section. One covers local

government; another, local and selected State government; and the third, large, government-owned utilities.

All thfee draw on the data and investigative approaches

presented in the preceding discussion.

Local government index. Local governments sell most

of the kilowatt hours supplied by government to

ultimate customers in the United States; the States play

a relatively minor role. Although precise statistics are

not available, more than 90 percent of total kilowatt

hours and total State and local government employment

result from local operations.

The local government index shows a 51-percent

growth between 1967 and 1978, an annual increase of

3.0 percent (table 18). The average annual increase for

kilowatt hours and employment during 1967-78 was 3.7

and 0.7 percent, respectively. This index is unweighted

since only aggregate kilowatt hour sales are available.

The main question surrounding this index is data accuracy, which was discussed earlier. Kilowatt hour sales

were taken from a p p a statistics, from which State sales

to final customers were removed. IState kWh sales were

taken from State and f p c / f e r c / e ia data, and should

be accurate. The employment statistics were taken from

the Bureau of the Census; the strengths and weaknesses

of that source of data have been noted.

Local and selected State government index. An illustrative index was also calculated for all local and

selected State government utilities. Twenty-one Stateowned utilities were identified. Four—Power Authority

of the State of New York, South Carolina Public Service Authority, Grand River Dam Authority, and Lower

Colorado River Authority—accounted for almost all

employment and sales to ultimate customers. These four

utilities were combined with the local government indexes to arrive at the local and selected State government index. This index should include 99 percent of all

State and local government employment and kilowatt

hour sales to ultimate customers.

The results of these calculations show that local and

selected State government productivity increased by

about 52 percent from 1967 to 1968 (table 19). The

average annual increase in output per employee was 3.0

percent; in kilowatt hours, 4.0 percent; and in

employees, 0.9 percent. The index is unweighted.

Table 18. Indexes of output, employees, and output per

employee for local government electric utilities, 1967-78

(1977 = 100)

Year Output Employees Output per 

employee

1967 ...................................... 65.2 98.3 66.3

1968 ...................................... 71.6 96.7 74.1

1969 ...................................... 77.8 91.6 84.9

1970 ...................................... 84.2 96.7 87.1

1971 ...................................... 90.4 98.3 92.0

1972 ...................................... 91.6 98.3 93.2

1973 ...................................... 93.0 101.7 91.5

1974 ...................................... 93.8 103.3 90.8

1975 ...................................... 94.6 103.3 91.5

1976 ...................................... 99.0 101.7 97.3

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 103.3 103.0 100.0

Average annual percent

change .............................. 3.7 .7 3.0

S o u r c e : O u tp u t, ta b le 13; e m p lo y e e s , ta b le 16; o u tp u t p e r e m p lo y e e , 

c a lc u la te d .

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(1977 = 100)

Table 19. Indexes of output, employees, and output per

employee for local and selected State government electric

utilities, 1967-78

Year Output Employees Output per 

employee

1967 ...................................... 63.6 96.2 66.1

1968 ...................................... 69.1 94.7 73.0

1969 ...................................... 75.5 90.0 83.9

1970 ...................................... 81.5 95.0 85.8

1971 ...................................... 87.3 96.7 90.2

1972 ...................................... 88.3 96.9 91.1

1973 ...................................... 90.0 100.3 89.8

1974 ...................................... 91.1 102.1 89.2

1975 ...................................... 91.5 102.3 89.4

1976 ...................................... 96.2 101.1 95.2

1977 : .................................... 100.0 100.0 100.0

1978 ...................................... 104.3 103.7 100.7

Average annual percent

change .............................. 4.0 .9 3.0

S o u r c e : O u tp u t, ta b le 13; e m p lo y e e s , ta b le 16; o u tp u t p e r e m p lo y e e , 

c a lc u la te d .

Table 20. Weighted indexes of output, employees, and

output per employee for 33 large government electric

utilities, 1967-781

(1977 = 100)

Year Output Employees Output per 

employee

1967 ...................................... 54.4 86.7 62.7

1968 ...................................... 57.4 87.2 65.8

1969 ...................................... 62.5 89.6 69.7

1970 ...................................... 67.4 92.5 72.8

1971 ...................................... 70.7 96.2 73.5

1972 ...................................... 76.2 96.2 79.2

1973 ...................................... 83.5 98.0 85.1

1974 ...................................... 83.1 99.9 83.1

1975 ...................................... 85.1 99.8 85.2

1976 ...................................... 90.1 99.7 90.3

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 106.2 103.2 102.9

Average annual percent

change .............................. 6.0 1.6 4.4

1 See appendix C for list of utilities included.

S o u r c e : O u tp u t, ta b le 13; e m p lo y e e s , ta b le 16; o u tp u t p e r e m p lo y e e , 

c a lc u la te d .

Large, government-owned utility index. A productivity

index was also calculated for 33 of the largest publicly

owned utilities. The two State and 31 local government

systems included account for about 45 percent of all

municipal electric utility employment and 55 percent of

all kilowatt hour sales to ultimate customers. They cannot be considered representative of all governmentowned utilities because of their large size.

Between 1967 and 1978, the index for these 33 utilities

increased by 64 percent, or an annual increase of 4.4

percent (table 20). The average annual increase for

kilowatt hours was 6.0 percent; for employment, 1.6

percent.

The kWh’s were taken from f p c / f e r c / e ia publications and were weighted for class of service—residential, commercial/industrial and other—as discussed.

There is very little difference between the weighted and

unweighted indexes.

The employment data used in the index reflect the

average number of employees. These statistics, for the

most part, were provided by the individual utilities, and

were checked against Census data insofar as possible.

Comparison o f productivity indexes. The local and

local/State indexes show much the same productivity

growth between 1967 and 1978; both increased at exactly the same average annual rate—3.0 percent (table 21).

Similar trends are to be expected for these two indexes,

since over 90 percent of local/State government electric

utility kWh sales and employment are those of local

government.

The 33 large utilities, which are a subset of the

local/State government index, showed a markedly

faster rate of productivity increase—4.4 percent—between 1967 and 1978. This, too, is to be expected since

economies of scale are important in electric power

generation and distribution.

All three indexes showed a dramatic drop in output

and productivity in 1973-74, in response to the oil embargo and the recession. Also, productivity growth

peaked in 1969 in all three indexes.

Although uncertainty surrounds some of the data used to calculate the indexes, the output index, the three

input indexes, and the three productivity indexes move

together quite well, and any differences, such as with

the large utility index, are easily explained. Thus, even

though the precise increase in local and State government electric utility labor productivity over the past

decade may be open to question, the general movement

is clear.

Comparison with private utility index movements. Indexes that BLS routinely calculates for investor-owned

and cooperative electric utilities grew somewhat faster

than those for State and local government utilities during 1967—78 (table 22.) However, this may be a function

of size. Large public system output and productivity

grew faster than that of private utilities.26

Productivity growth slowed in both the public and

private sectors in the early 1970’s. Between 1967 and

1972, private utility growth dropped from 5.5 percent to

2.4 percent. The comparable figures for State and local

governments were from 6.7 percent to 2.8 percent.

In neither government nor private electric utilities did

productivity increase fast enough to offset the increase

in salaries and wages. The result was an increase in

average kWh unit cost, particularly in the latter part of

the period. In State and local governments, unit cost in26 The average number of employees in State and local government

utilities was about 30 per system; in the private sector, 435; and in

large public systems, 925.

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(1977 —100)

Table 21. Three productivity indexes for State and local

government electric utilities, 1967-78

Local Local

utilities

Local and 

selected State 

utilities

Large

utilities

1967 ...................................... 66.3 66.1 62.9

1968 ...................................... 74.1 66.4 66.0

1969 ...................................... 84.9 83.9 69.9

1970 ...................................... 87.1 85.8 73.0

1971 ...................................... 91.9 90.2 73.6

1972 ...................................... 93.2 91.1 79.4

1973 ...................................... 91.5 89.8 85.3

1974 ...................................... 92.8 89.2 83.3

1975 ...................................... 91.5 89.4 85.4

1976 ...................................... 97.3 95.2 90.6

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 100.0 100.7 103.1

Average annual percent

change .............................. 3.0 3.0 4.4

S o u r c e : L o c a l u tilitie s , ta b le 18; lo c a l a n d s e le c te d S ta te u tilitie s , ta b le 19; 

la rg e u tilitie s , ta b le 2 0 .

Table 22. Average annual rates of change for government 

and private electric utility output, labor input, and 

productivity, 1967-78 

(Percent)

Type of utility Output

(kwh)

Labor input 

(number of 

employees)

Productivity 

(output per 

employee)

Local government utilities. . . . 3.7 0.7 3.0

Local and selected State

government utilities .......... 4.0 .9 3.0

Large government utilities ... 6.0 1.6 4.4

Private utilities ...................... 5.8 2.1 3.7

SOURCE: Local government, table 18; local and selected State government, table 19; large government, table 20; private, Donald M. Fisk, “ Pilot 

Study,” p.46.

creased 7.8 percent annually in 1967-78. Between 1967

and 1972, the rate of increase was 6.6 percent or about

the same as output per employee. But between 1973 and

1978, the average annual increase was 7.5 percent while

the rate of increase in output per employee dropped to

2.8 percent per year.

For private utilities, the average annual increase was

9.4 percent. Between 1967 and 1972, the rate of increase

was 6.7 percent, about the same as government and

slightly ahead of the 5.5-percent increase in output per

employee. However, between 1973 and 1978 the rate of

salary and wage increase jumped to 8.7 percent while

the increase in output per employee dropped to 2.4 percent.

Suggested research

Further analysis of State and local government electric productivity should focus on multifactor productivity. The Bureau of the Census estimates that in fiscal

year 1977 labor accounted for only 9 percent of State

and local government utility expenditures. Capital ex27 Stevenson, in Public Utility Productivity. These percentages undoubtedly have shifted in recent years as fuel costs have increased.

penditures were estimated at 43 percent. A 1973 study of

investor-owned utilities found labor to be 15 percent

and capital 48 percent of total expenditures.27 For a better understanding of productivity and cost movements

in the electric power industry, it would be necessary to

examine capital, fuel, and other factor inputs.

Stats Aicoholie Bs^srags

Control Operations

All States and many local governments regulate

alcoholic beverage sales. States license sellers, tax sales,

regulate advertising, set the legal age for purchase of

beverages, and establish the hours and days of sale. In

addition, about one-third of the States operate retail or

wholesale alcoholic beverage stores. These operations

are the focus of this section.

Alcoholic beverage operations have not captured the

attention of productivity measurement specialists,

unlike electric power production. Productivity

measurements do not exist for either the private or

public sector. However, the service is straightforward,

at least on the surface; the outputs are tangible; and

data are routinely collected on many aspects of State

alcoholic beverage operations.

Institutional eonsidorations

States which operate their own stores using government employees are known as control or monopoly

States. Those States which use private sellers are known

as license States. There are 18 control States and 33

license States including the District of Columbia (table

23).

Although States are often divided into these two

groups, there is a broad spectrum of institutional arrangements, from almost completely private operation

to total government control and operation. These can be

grouped into the following fairly distinct categories:

1. Private retail and wholesale operations, in effect in more than half of the States.

2. Private retail and government wholesale, as in

Mississippi and Wyoming.

3. Private and government (municipal) retail and

private wholesale, as in Minnesota.

4. Government (city and county) retail and

private wholesale, as in North Carolina.

5. Government and private agency retail and

government wholesale, as in Ohio.

6. Government retail and wholesale, as in

Alabama and Virginia.

This study focuses on those States included under 2,

5, and 6 above. North Carolina and Minnesota are not

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Table 23. Type of control of alcoholic beverage sales by State

S ta te

S ta te

o p e ra tio n s

(c o n tro l/

m o n o p o ly )

P riv a te

o p e ra tio n s

(lic e n s e )

S ta te

S ta te

o p e ra tio n s

(c o n tro l/

m o n o p o ly )

P riv a te

o p e ra tio n s

(lic e n s e )

A l a b a m a .............................................................. X

A la s k a .................................................................... - X

N e b r a s k a ............................................................... X

A r iz o n a ................................................................. X

N e v a d a .................................................................

_ X

A r k a n s a s ...............................................................

C a lif o r n ia .............................................................. -

X

X

N e w H a m p s h ire .............................................

N e w J e r s e y .........................................................

X

X

C o lo r a d o .............................................................. X

N e w M e x ic o ...................................................... _ X

C o n n e c t ic u t ......................................................... “ X

N e w Y o r k ............................................................... - X

D e la w a r e .............................................................. “ X

N o rth C a r o lin a ................................................... X

-

D is tr ic t o f C o lu m b ia ........................................ X

N o rth D a k o ta ...................................................... _ X

F lo r id a .................................................................... ~ X

O h io .......................................................................... X -

G e o r g ia ................................................................. - X

O k la h o m a ............................................................ X

H a w a ii.................................................................... — X

I d a h o ....................................................................... X -

P e n n s y lv a n ia ...................................................... X -

I lli n o i s .................................................................... R h o d e I s la n d ......................................................

- X

In d ia n a ................................................................. “ — S o u th C a r o lin a ................................................... - X

Io w a ....................................................................... X

S o u th D a k o ta ...................................................... - X

K a n s a s ................................................................. “ X

T e n n e s s e e ............................................................ - X

K e n tu c k y .............................................................. “ X

T e x a s ....................................................................... _ X

L o u is ia n a .............................................................. - X

U ta h ....................................................................... X

_

M a in e .................................................................... X —

M a r y la n d ..............................................................

- X

V irg in ia ................................................................. X

M a s s a c h u s e tt s ................................................ - X

W a s h in g t o n ......................................................... X -

M ic h ig a n ............................................................... X —

M in n e s o ta ............................................................ - X

W is c o n s in ............................................................ X

M is s is s ip p i............................................................

M is s o u r i.................................................................

X

X

W y o m in g ............................................................... X

discussed because local rather than State personnel

operate stores, and data are not readily available.

The 17 control States (18 including North Carolina)

account for about 30 percent of the U.S. population and

25 percent of the spirits sold in the United States. In

1977, State alcoholic beverage sales totaled over $2

billion.

Five States accounted for 65 percent of the revenue

and 70 percent of the employment in fiscal 1977 (table

24). Pennsylvania alone accounted for 20 percent of the

revenue and 31 percent of the employment.

Most State alcoholic beverage control commissions

are responsible for four functions—wholesale sales,

retail sales, enforcement, and licensing (table 25). Each

of the 17 control State commissions operates wholesale

or retail alcoholic beverage facilities. Sixteen of the 17

State commissions license others, such as wineries and

restaurants, to sell alcohol. Fourteen of the 17 are

responsible for enforcement of State alcoholic beverage

laws and regulations; three States assign the responsibility to their departments of public safety.

This investigation includes wholesale as well as retail

sales. Two of the 17 States operate only wholesale

operations; the other 15 operate a combination of

wholesale and retail. All of the 15 State retail operations

sell spirits; 13 of these sell wine as well as spirits, and

5 also sell beer. Spirits account for about 75 percent of all

gallons sold in State stores, wine about 25 percent, and

beer less than 0.01 percent. Ten of the 15 States that

operate retail stores use agency (private) stores to augment their operations.

The agency arrangement is an important issue in

calculating productivity. Usually, private retail merchants operate agency outlets in addition to their normal sales. Control States have long used agents to serve

the population of sparsely populated areas where a

“ full-service” State store could not be justified. Ohio,

for example, permits agents only in towns with a

population of less than 10,000. Agents are usually paid

Table 24. Distribution of alcoholic beverage control revenue

and employees by State, fiscal year 1977

(Percent)

State Revenue Employees

All control States........ 100 100

Pennsylvania.......................... 20 31

Michigan................................ 15 4

Ohio ...................................... 14 13

Virginia.................................. 8 11

Washington............................ 8 7

Oregon.................................. 5 2

New Hampshire.................... 5 3

Alabama................................ 5 7

Io w a ...................................... 5 5

Mississippi ............................ 3 1

West Virginia.......................... 3 6

Montana................................ 2 3

Utah ...................................... 2 1

M aine.................................... 2 2

Idaho...................................... 1 2

Vermont ................................ 1 1

Wyoming................................ 1 1

S o u r c e : Computed from 1977 Census of Governments—Compendium of Government Finances (Bureau of the Census, 1979), p. 39; and 1977 Census of Governments—Compendium of Public Employment (Bureau 

of the Census, 1979), pp. 33-83.

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a percent of their gross sales, although some States pay

a fixed fee or negotiate a price with the individual merchant. In all cases, prices of alcoholic beverages are set

by the State.

Several States have begun to substitute agents for

State stores. Oregon, for example, has reduced the

number of State stores from 20 in 1976 to 6 in 1980, and

would have used agents entirely except for the intervention of the State legislature. Maine, Montana, and Utah

have also substituted agents for State-operated stores.

Utah uses three different forms of agency arrangements. In the first, agents operate beverage stores

just as they might a State store, but do not hire State

employees. In the second, merchants contract to sell

alcoholic beverages in addition to their regular product

lines. In the third, resort or hotel owners operate an

agency store as a convenience to their guests, usually at

no cost to the State.

Synopsis of State operations. Alcoholic beverage operations vary markedly from State to State. A brief description of operations in each control State as of 1980

follows.

Alabama. The Alabama Alcoholic Beverage Control Board is responsible for sales, licensing, and enforcement within the State. It operates approximately 130 retail stores and a warehouse. Enforcement activities, which require about 100 of the

1,000 State Control Board employees, include drug

abuse as well as alcohol control activities.

Idaho. The State Liquor Dispensary operates approximately 90 State stores and one warehouse and

oversees the operation of about 45 agency stores.

Agency stores are licensed to sell spirits and wine,

primarily in locations where sales would not warrant operation of a State store. Licensing and enforcement of liquor laws and regulations are handled by another part of the State government.

Iowa. The Iowa Beverage Control Council operates

approximately 215 State retail stores and a

warehouse. The stores sell spirits, wine, and beer.

The Council also licenses on-premise alcoholic

beverage sales. Enforcement is the responsibility of

the Department of Public Safety.

Maine. The Bureau of Alcoholic Beverages, part of

the Department of Finance and Administration,

operates about 70 State stores and oversees the

operation of about 50 agency stores. Licensing is

part of the State store operation, but enforcement is

handled by another part of the State government.

Michigan. The Liquor Control Commission of the

Department of Commerce oversees the operation of

76 State stores and about 3,300 agency stores. State

stores provide a combination o f wholesale and

retail services; 99 percent of their operation is

wholesale. Licensing and enforcement are also

handled by the Commission.

Mississippi. The Alcoholic Beverage Control Commission of the State Tax Commission handles

wholesale spirit and wine sales, and alcoholic

beverage licensing and enforcement. Private stores

handle retail sales.

Montana. The Montana Department of Revenue

operates approximately 115 retail stores and

oversees the operation of 30 agency stores. The

Department also handles alcoholic beverage licensing and enforcement.

New Hampshire. The New Hampshire Liquor

Commission operates about 70 retail stores and one

Table 25. Forms and functions of State alcoholic beverage control operations

State

Scope of operations Type of sales Type of retail sales Type of beverage sold

Sales Licensing Enforcement

Wholesale

only

Wholesale 

and retail

State

store Agency Spirits Wine Beer

Alabama ...................... X X X X X X X

Idaho............................ X - - - X X X X X -

Iowa.............................. X X - - X X - X X X

Maine............................ X X - - X X X X X -

Michigan...................... X X X - X X X X X -

Mississippi.................... X X X X - - - X X X

Montana ...................... X X X - X X X X X -

New Hampshire............ X X X - X X - X X -

Ohio.............................. X X X - X X X X - -

Oregon.......................... X X X - X X X X - -

Pennsylvania................ X X X - X X - X X -

Utah.............................. X X X - X X X X X X

Vermont........................ X X X - X X X X X X

Virginia.......................... X X X - X X - X X -

Washington.................. X X X - X X X X X X

West Virginia................ X X X - X X X X X -

Wyoming...................... X X X X - - - X X -

S o u r c e : Distilled Spirits Council of the United States, Summary of State Outlets for the Sale of Distilled Spirits-1978, 1979; National Alcoholic

Laws and Regulations Relating to Distilled Spirits, 1977; and Retail Beverage Control Association, Inc., Yearbook 11th Edition, 1978.

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warehouse. The Commission is responsible for

licensing and enforcement as well as sales. Sale of

alcoholic beverages provides about 18 percent of

total State revenues.

Ohio. The Department of Liquor Control operates

about 290 retail liquor stores and is responsible for

another 125 agency stores. The Commission is also

responsible for licensing and enforcement.

Oregon. The Oregon Liquor Control Commission

relies heavily on agency stores for retail alcoholic

beverage sales. There are approximately 175 agency

stores and 6 State stores. The Commission is

responsible for licensing and enforcement.

Pennsylvania. The Pennsylvania Liquor Control

Board is responsible for licensing, enforcement,

and sales. Pennsylvania runs the largest State

alcoholic beverage control operation in the United

States, with approximately 725 State retail stores.

The stores sell spirits and wine.

Utah. The Utah Liquor Control Commission

handles sales, licensing, and enforcement. There are

about 30 State stores and 74 agency stores which sell

spirits, wine, and beer.

Vermont. The Department of Liquor Control is

responsible for spirit, wine, and beer sales, as well

as licensing and enforcement. Both State-operated

stores and agency stores are used.

Virginia. The Virginia Alcoholic Beverage Control

Commission is responsible for sales, licensing, and

enforcement. About 260 State stores sell spirits, or

wine and spirits. Wine and beer are also sold

through private distributors.

Washington. The Washington Liquor Control

Board oversees alcoholic beverage sales, licensing,

and enforcement. At one time it operated its own

bottling plant. Today, it operates 6 warehouses and

about 140 retail stores, and monitors the operation

of another 180 agency outlets. These outlets are

managed by State employees—1 per outlet—who

share in the profit from their sales. Beer, wine, and

spirits are sold in State and agency stores.

Wyoming. The Wyoming Liquor Commission administers licensing, enforcement, and wholesale

sales. Private vendors handle retail sales.

In addition to these 17 States, four States permit or

require local government sales. North Carolina requires

local government sales if alcohol beverages are sold for

use off-premise. Local governments operate about 225

county stores and 120 city stores in the State.

Maryland, Minnesota, and South Dakota permit

local option. Minnesota has a combination of private

and municipal liquor stores. As of October 1978, there

were 567 private and 113 municipal stores. South

Dakota also permits local jurisdictions to operate their

own stores.

Research amd statistics

Little research or even descriptive writing has been

done on alcoholic beverage store operations, either

State or private. Academic research has focused almost

entirely on tax issues, such as the incidence of the liquor

tax and its impact on consumption. The principal

references to State store productivity found during this

investigation were in State annual reports.

Statistics on alcoholic beverage operations are collected under a variety of definitions. The Standard Industrial Classification Manual defines alcoholic

beverage stores as:

“Establishments primarily engaged in the retail sale of

packaged alcoholic beverages such as ale, beer, wine and

whiskey, for consumption off premises.”28

The Bureau of the Census uses two slightly different

definitions for alcoholic beverage operations. For financial transactions, Census defines a “liquor store” as an

alcoholic beverage distribution facility “ operated by 17

State governments and by some counties and small

municipalities in a few States.” “ Liquor store” expenditures consist of purchases of “ beverages for resale and

provision and operation of liquor stores. Excludes expenditure for law enforcement and licensing activities,

which are classed under general expenditures.” 29

For employment statistics, Census limits its definition

to the “ administration and operation of retail liquor

stores operated by State governments.” 30 Statistics collected under this heading apparently include wholesale

as well as retail operations; many control States are

unable to separate licensing and enforcement personnel

from State store personnel. The Census employment

statistics do not include any local government personnel.31

The Distilled Spirits Council of the United States

(d is c u s ) collects and publishes statistics by State on

sales of spirits, wine and beer, gallons sold, revenue

generated, specific revenue source, and number of retail

outlets.32 Many statistical series date from the end of

Prohibition.

The National Alcoholic Beverage Control Association (nabca), the association of State control officials,

collected and published statistics on store operations in

its annual Yearbook from 1973 and 1977. These includ28 Standard Industrial Classification Manual, p. 272.

29 Government Finances, p. 630.

30 Public Employment, p. 462.

31 Rough calculations suggest that local governments have about

2,500-3,000 employees in alcoholic beverages sales and operations.

32 See Public Revenues from Alcohol Beverages, Annual

Statistical Review, and Retail Outlets fo r the Sale o f Distilled Spirits,

all published annually by the Distilled Spirits Council of the United

States.

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ed the total number of personnel, enforcement and

licensing facts, the number of State stores and store personnel, and revenue raised through State stores, n a b c a

ceased collecting and publishing these statistics in 1977.

Na b c a still collects and summarizes each month

statistics on the number of sales by case and by bottle

size in control States, d is c u s uses these statistics to

compute and publish the number of gallons sold.

In addition, each control State publishes an annual

report on its operations, ranging from a few summary

pages of financial information to detailed reports on all

phases of the State’s operations, including the number

of employees, gallons and cases sold, and financial information on each store (table 26). Two States, Idaho

and Michigan, present labor productivity statistics in

their annual reports.

Oytpyts

This section discusses how State alcoholic beverage

store outputs might be measured, and presents several

illustrative indexes for wholesale and retail sales. As indicated earlier, many alcoholic beverage control

authorities are responsible for enforcement and licensing as well as wholesale and retail sales. Enforcement

and licensing, which account for less than 10 percent of

beverage commission employees, would require a different set of output measures; they are not considered in

this review.

This investigation focuses on sales of spirits and

wines. Although several States sell beer, beer sales are

such a small part of State sales—less than 0.01

percent—they can be safely ignored in any output

calculations.

Sales of alcoholic beverages are commonly measured

in one of five ways—dollars, customers, bottles, cases,

or gallons. A brief discussion of each measure follows.

Dollar sales. Dollar sales figures measure the final

organizational output, and are readily available, easily

understood, and repetitive. Every State collects dollar

sales data, and many publish them annually. Furthermore, the Census Bureau collects these statistics and has

published them annually since 1960, for each State and

in total.

The difficulty with using dollar sales as a measure of

output is that it is not a good measure of* the base-year

unit labor required to move and sell the product. This is

true for retail trade in general, where manufacturing

costs are a large part of the retail cost. It is particularly

true for alcoholic beverages, where taxes and mark-ups

are a significant part of the sales price. One expert

describes the situtation as follows:

“ Control States adopt a variety o f postures with regard

to earning revenues via taxes or mark-ups, depending on

enabling legislation. States are interested in generating a

certain amount o f revenue per gallon, and can arrive at

that amount through any mix o f taxes and mark-ups.

Some States depend relatively heavily on mark-ups;

others on taxes. Since mark-ups usually can be changed

administratively whereas tax changes require legislation,

there is a tendency to adjust mark-ups rather than taxes.

Thus, in concept, mark-ups do not serve the same purpose in control States as they do in the private sector.” 33

An additional problem, common to most revenue indexes, is that price and revenue usually reflect changes

in input costs, including labor. If deflated revenue is used as the measure of output, it is necessary to adjust for

change in input price.

Prices of alcoholic beverages vary markedly by product, but this variance seems unrelated to labor requirements. Pennsylvania, for example, reported that

wine sales accounted for 40 percent of the gallons sold

in 1972, but only 17 percent of the revenue generated.

Furthermore, sales have shifted from higher priced to

lower priced distilled spirits and from distilled spirits to

wine over the past several years.34

In addition, dollar sales must be adjusted to reflect

price changes, which vary according to whether wine or

spirits are examined. Between 1967 and 1978, wine

prices increased over 75 percent while spirit prices increased less than 25 percent.

In short, dollar sales are a poor measure of State

alcoholic beverage operations for calculating labor productivity.

Customers. The main virtue of using the number of

customers is that it is a physical measure. Also, it is easily understood and a repetitive unit.

It has two shortcomings, however. First, stores stock

and sell bottles or cases; the number of customers is only a proxy for the number of bottles or cases sold. Unit

labor requirements more closely correlate to bottles and

cases than to the number of customers. Second, data to

calculate this measure are not readily available, at least

nationally. Only one State report examined during this

investigation included data on the number of customers.

Bottles. Probably the best measure of labor effort in

retail alcoholic beverage sales is the number of bottles

sold. Most retail spirit and wine sales are made by the

bottle, and shelves are replenished bottle by bottle. In

addition, this measure is easily understood.

Several problems arise in using bottles as the measure

of output. First, bottles range from miniatures to 4

liters, and different size bottles require different

amounts of effort to stock and sell. The impact of these

differences is not known but it could be substantial.

Furthermore, the average bottle size has increased over

the past several years. Also, bottle statistics are not

published nationally, and only two States included the

33 Personal communication from Steve L. Barsby, dated August

11, 1981.

34 Ibid.

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Table 26. Selected data contained in State alcoholic beverage control annual reports

State Dollar

sales

Gallons

sold

Cases

sold Employment Other

Alabama...................................................................... X X X X

Idaho .......................................................................... X - - X Bottles sold

Iowa............................................................................. X X _ X _

Maine.......................................................................... X X Bottles sold 

and number 

of customers

Michigan .................................................................... X X X X Bottles sold 

and bottles 

sold per clerk

Mississippi.................................................................. X - - - _

Montana....................................................................... X - X _ _

New Hampshire.......................................................... X - - _ _

Ohio............................................................................. X X -

X _

Oregon......................................................................... X - - - _

Pennsylvania.............................................................. X - - - _

Utah............................................................................. X X - _ _

Vermont....................................................................... X X X X _

Virginia......................................................................... X X X X _

Washington................................................................ X X X X _

West Virginia.............................................................. X - X

- -

Wyoming..................................................................... X - - - -

statistics in their annual reports. However, these data

are available in unpublished form nationally.

Cases. Another commonly used measure of output is

the number of cases sold. It is also easily understood.

For warehouse operations, it is probably better than

bottles as a measure of output and unit labor requirements.

About half (8 of 17) of the State annual reports included statistics on the number of cases sold. No

published national statistics were found but they are

available in unpublished form.

Gallons. Probably the most common physical output

measure is gallons. The trade associations calculate it

annually and more than half (9 of 17) of the control

States carry the statistic in their annual reports. Comparable data are available for a number of years.

Although not as good a measure of unit labor requirements as bottles or cases, it is probably an adequate surrogate.

Preferred measure. Experts suggest that the best output

measure for computing State alcoholic beverage retail

productivity is the number of bottles sold. Probably the

best measure of wholesale operations is the number of

cases.

The most readily available physical statistic, however,

is gallons sold; bottle and case statistics are compiled for

spirits although they are not published. Bottles, cases,

and gallons are highly correlated, and output trends

constructed for these three measures would probably be

similar, at least in the short run. Data are not generally

separated between retail and wholesale movements.35

35 Statistics for Michigan, which publishes data on all three

measures, show a correlation of .91 between cases and bottles, .89 between bottles and gallons, and .99 between cases and gallons for 1978.

Gallonage indexes. Retail gallonage spirit sales are

available by State and year from d is c u s ; wine gallonage

sales are available by State and year from the Wine Institute. Also, as noted, the majority of States publish

statistics on gallons sold. These three sources of data

have been used to prepare three illustrative output indexes—all States, the five largest States, and wholesaleonly States (table 27).

Total State gallonage sales increased about 40 percent

between 1967 and 1978, an average annual increase of

3.1 percent. Gallons sold rose every year, but the rate of

increase dropped throughout the period: 4.3 percent

from 1967 to 1970, 3.3 percent from 1971 to 1974, and

2.0 percent from 1975 to 1976.

Gallon sales of the five largest control States—Pennsylvania, O hio, V irginia, W ash in gton , and

Alabama—increased about 30 percent, an average annual increase of 2.3 percent between 1967 and 1978.

Like total sales, the rate of increase decreased

throughout the period.

G allons sold by the three w h o lesa le-o n ly

States—Michigan, Mississippi, and Wyoming—increased 52 percent between 1967 and 1978, an annual increase of 3.7 percent.36 Gallons sold increased every year

but again the rate of increase decreased over the period.

Agent sales need to be removed from the retail sales

statistics insofar as possible because they create difficulties in productivity calculation. Ten of the 17 control States use agents (non-State employees) to sell

alcoholic beverages.

The procedure used to remove agent sales was to

calculate the ratio of dollar sales by agents to total

dollar sales and to apply that ratio to total gallons sold.

In 6 of the 10 States using agents, data were readily

available for this computation. In four States—Idaho,

36 Michigan is primarily a wholesale State; less than 1 percent of its

gallonage sales are retail sales.

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(1977=100)

Table 27. Three gallonage indexes for alcoholic beverage

control operations, 1967-78

Year All States Five largest 

States

Wholesaleonly States

1967 ...................................... 73.3 97.5 69.1

1968 ...................................... 76.3 82.8 71.8

1969 ...................................... 80.0 86.3 76.4

1970 ...................................... 82.9 87.8 80.9

1971 ...................................... 86.1 90.2 82.7

1972 ...................................... 89.9 93.2 89.5

1973 ...................................... 94.2 97.9 92.4

1974 ...................................... 94.6 97.2 93.9

1975 ...................................... 97.4 99.9 94.8

1976 ...................................... 98.5 99.4 97.7

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 103.4 102.4 105.0

Average annual percent

change .............................. 3.1 2.3 3.7

S o u r c e : State annual reports, unpublished data provided b y individual 

control States, and published reports of Wine Institute and Distilled Spirits 

Council of the United States.

Montana, Utah, and West Virginia—agent sales data

were not readily available for the period examined and

the calculations could not be made. However, these four

States accounted for only about 8 percent of total State

sales; hence, including agent sales for these four States

is unlikely to affect the output index significantly.

Labor inputs and ©mployse eosts

The number of employees, number of full-timeequivalent employees, and number of hours are the input measures suggested in the previous chapter to

calculate State and local government labor productivity.

The three sources of such data for alcoholic beverage

control are: (1) The Bureau of the Census, (2) individual

State authorities, and (3) the National Alcoholic

Beverage Control Association (nabca). Since NABCAno 

longer collects and publishes these data, as noted

earlier, nabca statistics are of use only in constructing

historical time series.

The Census figures are collected annually as part of

the survey of government employment. This survey’s

overall strengths and weaknesses were discussed earlier.

However, the State alcoholic beverage control data have

several specific weaknesses. First, agencies apparently

report all their personnel, which usually include enforcement and licensing staff as well as store and

warehouse staff. One State, as noted earlier, assigns

drug control operations to its enforcement staff. To

match employment with sales, enforcement and licensing personnel must be separated, which is not possible

with Census information. Second, State liquor stores

use part-time, intermittent, and seasonal employees extensively. Census statistics, collected each October, miss

the peak holiday (November and December) sales and

staffing period. If the ratio of October to holidayperiod sales remains constant, the October statistics will

be satisfactory for trend analysis. A set of ratios is needed to determine this.

The other principal source of employment data is the

State alcoholic beverage agencies themselves. As noted

earlier, all agencies publish annual reports; about half

include statistics on the number of staff by function.

Several include statistics on salaries, the number of

supervisors, and part-time and full-time employment.

However, none provides information on hours worked,

although several provide statistics on full-time

equivalency.

The primary problems in using State agency reports

to calculate a labor index are inconsistency through time

and incomplete information on coverage and methods

of derivation. Individual State data series will appear in

several annual reports, disappear for several years, and

then reappear. Sometimes yearend figures are

presented; other times full-time equivalency is included.

Employment indexes. Labor indexes were prepared for

the total number of employees, total full-timeequivalent employees, and total hours paid. State and

Census data were used to make the computations. In

each case, State license and enforcement personnel have

been removed from the figures.

The total number of employees, part time and full

time, was taken from unpublished Census Bureau

statistics and adjusted with State data when available

and appropriate. For example, the 8-percent jump in the

Census figure in 1973 was directly attributable to one

State, Pennsylvania. This statistic was at odds with data

provided directly by the State as part of this study, and

the Census statistics were adjusted accordingly. Census

employee counts have been available only since 1970.

Statistics for 1967-69 were taken from State data or

Census full-time-equivalent data when State statistics

were unavailable.

The total number of full-time-equivalent employees

was taken from published and unpublished State data,

augmented when necessary by Census data.

The third set of data is for hours paid for State

alcoholic beverage control employees.37 These data were

provided by the individual States or were calculated

from information provided by the State (average hours

worked) and the Bureau of the Census (full-timeequivalent employment). Pennsylvania, which employs

about 30 percent of all State and alcoholic beverage personnel, provided statistics on hours paid. Ten States

(about 40 percent of the employees) provided full-time

equivalent data and the average hours paid per

employee per year. The remaining six States (about 30

percent of the employees) were unable to provide the requested data so Census data and average workweek

hours were used to estimate total paid hours.

37 No State could supply a time series of actual hours worked or

hours at work. Several States had limited recent data.

40

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Tabs® 2®. Three employment indexes for aleoholic beverage

control operations, 1967-78

Year Number of 

employees

Full-timeequivalent

employment

Hours paid

1967 ...................................... 93.8 92.7 92.6

1968 ...................................... 96.5 94.9 94.8

1969 ...................................... 99.6 98.3 98.1

1970 ...................................... 100.3 102.0 101.9

1971 ...................................... 100.4 100.4 100.2

1972 ...................................... 100.5 101.6 101.4

1973 ...................................... 99.2 100.2 100.2

1974 ...................................... 98.7 99.6 99.5

1975 ...................................... 100.9 100.8 100.7

1976 ...................................... 101.4 100.9 100.9

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 100.3 98.5 98.5

Average annual percent

change .............................. .4 .4 .4

SOURCE: Number of employees— 1967-69, State-provided data or 

statistics published by Bureau of the Census; 1970-78, Bureau of the Census 

computer printout. Full-time-equivalent employment—State and Census 

data. Hours paid—State data or computed from State-provided data 

augmented by Census data.

The total change and the- average annual rate of

change for the 1967-78 period were similar for the three

indexes (table 28). The number of employees increased

6.5 percent, full-time-equivalent employment increased

5.8 percent, and hours paid increased 5.9 percent. The

averge annual change was 0.4 percent for each index.38

Full-time-equivalent employment indexes were also

calculated for the five largest States and the wholesaleonly States for the 1967-78 period (table 29). The index

for the five largest States—Pennsylvania, Ohio,

Virginia, Washington, and Alabama—increased a total

of about 5 percent, or 0.3 percent annually. The three

wholesale-only States—Michigan, Mississippi, and

Wyoming—decreased about 2 percent, or 0.8 percent

annually.

Salaries and wages. The average annual increase in

Table 29. Two full-time-equivalent employment indexes for 

alcoholic beverage control operations, 1967-78

(1977 = 100)

Year Five

largest States

Wholesale-only

States

1967 ...................................... 92.3 106.4

1968 ...................................... 95.5 101.5

1969 ...................................... 99.3 106.0

1970 ...................................... 103.9 108.4

1971 ...................................... 102.7 107.2

1972 ...................................... 102.6 105.5

1973 ...................................... 99.7 105.5

1974 ...................................... 99.4 93.7

1975 ...................................... 100.5 94.7

1976 ...................................... 100.1 94.8

1977 ...................................... 100.0 100.0

1978 ...................................... 97.4 104.4

Average annual percent

change .............................. .3 -.8

S o u r c e : Bureau of the Census and State data.

Table 30. October earnings for alcoholic beverage control 

personnel, 1967-78

Year Dollars Index

(1977 = 100)

1967 ...................................... $ 493 52.0

1968 ...................................... 510 53.7

1969 ...................................... 526 55.4

1970 ...................................... 590 62.2

1971 ...................................... 585 61.6

1972 ...................................... 642 67.7

1973 ...................................... 735 77.5

1974 .................................... ,. 788 83.0

1975 ...................................... 818 86.2

1976 ...................................... 895 94.3

1977 ......................................

1978 ......................................

949 . 

1,031

100.0

108.6

Average annual percent 

change .............................. 7.3

S o u r c e : D o lla rs — P u b lic E m p lo y m e n t, a n n u a l is s u e s ; in d e x — c o m ­

p u te d .

salaries and wages (excluding fringe benefits) was

calculated for 1967-78. These statistics, which are collected and published by Census, are for October of each

year. They show that during 1967-78 salaries and wages

more than doubled, or increased 7.3 percent annually

(table 30).

Prodye^svity indexes

This section presents three productivity indexes

calculated from data presented and discussed in the

preceding sections. All use gallons as the output

measure and full-time-equivalent employment as the

measure of labor input. Separate indexes are presented

for the total, for the five largest States, and for the

States which operate wholesale-only operations.

The results of these calculations show that labor productivity for total State alcoholic beverage control

operations increased 33 percent between 1967 and 1978,

or 2.7 percent annually (table 31). The rise was fairly

constant throughout the period.

The figures for the five largest States generally

parallel the statistics for all States, which is to be expected since they account for about 70 percent of all

employment. From 1967 to 1978, large-State productivity increased 22 percent, or 2.0 percent annually.

Productivity in the three wholesale-only States

—Michigan, Mississippi, and Wyoming—increased at a

much more rapid rate than in the control States as a

whole or in the large States. Labor productivity for the

1967-78 period increased 55 percent for full-timeequivalent employment, or 4.6 percent annually.

None of the three productivity indexes, including the

wholesale-only index, grew as rapidly during this period

as State employee earnings, which increased 7.3 percent

38 A comparison was also made between data presented here and

data without adjustments for enforcement and licensing personnel

and obvious errors. Unadjusted data rose 0.2 percent a year compared

with 0.4 percent presented here. The absolute numbers differed by

about 8 percent.

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(1977 -100)

Table 31. Three productivity indexes for alcoholic beverage

control operations, 1967-78

Year Total Five largest 

States

Wholesaleonly States

1967 ............................ 79.1 86.1 64.9

1968 ........................ 80.4 86.7 70.7

1969 .................................. 81.4 86.9 72.1

1970 ...................................... 81.3 84.5 74.6

1971 .............................. 85.5 87.8 77.2

1972 .................................... 88.5 90.8 84.8

1973 ...................................... 94.0 98.2 87.6

1974 .................................... 95.0 97.8 100.2

1975 ...................................... 96.6 99.4 100.1

1976 ...................................... 97.6 99.3 103.1

1977 ....................................... 100.0 100.0 100.0

1978 ...................................... 105.0 105.1 100.6

Average annual percent 

change ...................... 2.7 2.0 4.6

SOURCE: C o m p u te d fro m d a ta in ta b le s 2 7 , 2 8 , a n d 2 9 .

annually. The net result was an increase in unit salary

and wage cost.

Conclusions and suggested research

Calculation of a national State alcoholic beverage

store productivity index should be a straightforward

operation. Although no national index exists today,

several States calculate their own labor productivity. All

States collect data that could be used as the base from

which to build a national productivity measure.

The preferred output measure has not been discussed

in the literature. Several State officials have suggested

that the number of bottles is the preferred retail measure

and the number of cases the preferred wholesale measure. The national data which are most readily available

are the number of gallons sold, which is a good proxy

for bottles and cases. Insofar as inputs are concerned,

Census collects and publishes annually full-timeequivalent employment statistics which can be used to

calculate labor trends.

Several illustrative productivity indexes were

calculated from available data. In general, they track

quite well and match intuitive judgment. The summary

index, which uses gallons as the output and full-timeequivalent employment as the input, shows labor productivity increasing at an annual rate of 2.7 percent between 1967 and 1978. Gallons sold increased 3.1 percent

annually while employment increased 0.4 percent annually.

Topics for further research have been noted and

discussed. They include substituting bottles and cases

for gallons as the measure of output; separating

wholesale from retail operations; more accurately identifying and removing agent sales from the totals; further

analyzing the impact of licensing and investigation on

productivity; and including local government alcoholic

beverage operations in the index.

U n e m p lo y m e n t Sr?suram©@

The Unemployment Insurance Service (uis) presents a

very different set of productivity measurement issues

from those encountered in electric utility operations or

alcoholic beverage control sales. The uis provides a service without a market price, one for which demand fluctuates greatly. It typifies State and local government income maintenance programs in that it covers every State

and is jointly administered by the Federal and State

governments.

SnstituttosiaS considerations

Unemployment insurance was established by the

Social Security Act of 1935. The intent of the act, insofar as unemployment insurance was concerned, was

to provide financial security for the majority of the Nation’s workers during times of temporary unemployment.

Coverage. About 97 percent of all workers are covered

by unemployment insurance today. The only employees

not covered are the self-employed, domestic employees

earning less than $1,000 per quarter, and agricultural

workers who work on farms with 10 or fewer employees

which have a payroll of less than $20,000 per quarter.

The number of beneficiaries fluctuates substantially,

depending on economic conditions. In mid-1980, about

4.2 million individuals were drawing unemployment insurance benefits.39 In 1968, the figure was 1.1

million; in 1976, 3 million. In 1977, total paid

unemployment insurance benefits were about $13

billion; in 1980, they were almost $19 billion.

Program description, uis provides coverage through a

series of programs. In 1981, these included the regular

State, Federal, and veterans programs which provide up

to 26 weeks of benefits. States with high unemployment

provide another 13 weeks of benefits through the extended benefit program.

The individual States fund the regular State program.

The Federal Government covers the cost of the regular

Federal civilian and military programs. The extended

benefit program is financed jointly (50-50) by the

Federal and State governments. Each State sets its own

level of benefits. Each State also sets the waiting period,

if any, the qualifying wage, dependent allowances, and

other requirements.

In addition to the regular unemployment insurance

programs, several special programs provide assistance

under unique situations, such as natural disasters, trade

dislocations, and deregulation. These programs provide

stipends, in addition to normal unemployment benefits,

39 “Unemployment Insurance Claims” (Employment and Training

Administration, July 10, 1980).

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for relocation and retraining. They are funded by

general Federal tax receipts.

Uis programs are born in response to economic and

political conditions of the times. Between 1963 and

1980, 10 different uis programs were in operation (table

32). Some, such as the regular State program, operated

throughout the period. Others, such as the temporary

compensation program, operated for only a brief time.

Size and scope. The size and the scope of the programs

vary substantially. Some are broad-based and operate in

every State. Others, such as the National Redwood Park

program, are tailored to a small geographic area or

clientele.

The importance of a program can vary considerably,

depending on the time period examined (table 33).

Trade readjustment allowance, for example, began as a

small program in the mid-1970’s but expanded rapidly

in the latter part of the decade. In 1975, $38 million of

benefits were paid; in 1980, $2.1 billion were paid. The

program has since been cut substantially.

The regular State program is the backbone of the uis.

It normally accounts for 90-95 percent of all benefit expenditures, although it accounted for only 55 percent in

1976 when a number of temporary programs were in

force.

Programs studied. This study focuses on the traditional

uis programs. These include the regular State program,

the Federal employee program, the ex-serviceman program, extended benefits, temporary compensation,

Federal supplemental benefits, and special unemployment assistance. Excluded from this study are special or

nontraditional programs, including disaster unemployment assistance, trade readjustment allowance, and the

National Redwood Park program, which each require a

unique set of output measures. Also, the railroad

Table 32. Unemployment insurance programs, 1963-80

Program Period

Regular State program.............................. 1935 to present

Unemployment compensation for

Federal employees.................................. 1955 to present

Unemployment compensation for

ex-servicemen.......................................... 1958 to present

Extended benefits...................................... 1970 to present

Temporary compensation ........................ 1972-73

Disaster unemployment assistance.......... 1972 to present

Trade readjustment allowance.................. 1972 to present

Federal supplemental benefits.................. 1975-78

Special unemployment assistance ....... 1975-78

National Redwood Park............................ 1978-84

Source: Unemployment Compensation: Final Report, (National

Commission on Unemployment Compensation, 1980).

(M illio n s )

Table 33. Unemployment insurance program benefits,

selected years, 1965-80

Program 1965 1970 1975 1980

T ota l.......................... $2,283 $4,158 $19,362 $18,790

Regular programs:

State .............................. 2,166 3,847 13,239 14,486

Federal.......................... 50 76 158 129

Veterans........................ 67 199 387 294

Extended benefits:

State ..............................

- 34 2,492 1,697

Federal.......................... - 1 44 14

Veterans........................ - 1 75 30

Federal supplemental 

benefits:

State .............................. - - 2,133 -

Federal.......................... - - 59 -

Veterans........................ - - 55 -

Special unemployment

assistance.......................... - - 670 -

Trade readjustment allowa n ce .................................. - - 38 2,138

Disaster unemployment

assistance.......................... “ 3 2

SOURCE: U n p u b lis h e d d a ta fro m U n e m p lo y m e n t In s u ra n c e S e rv ic e , S e p t. 

24, 1981.

unemployment insurance program, which is sometimes

included in unemployment insurance statistics and

discussions, is not included since it is not financed or administered by uis or the State governments.

The regular uis program has two primary activities:

Making payments to unemployed workers, and collecting money from employers. Making payments to

unemployed workers includes activities such as registration, establishment of eligibility, issuance of checks,

and hearing appeals. Collection of funds from

employers includes monitoring and auditing employers’

contributions, auditing employers’ books, and capturing money due from delinquent accounts.

Financing, uis programs are financed through three

sources—a Federal payroll tax, a State payroll tax, and

general Federal revenue. The Federal payroll tax, which

is set at 0.7 percent on the first $6,000 of annual wages

paid to each employee, is levied on the employer. These

funds are used to administer the program, finance extended benefits, and maintain a reserve from which

States may borrow if their reserves are inadequate to

pay beneficiaries.

A State payroll tax is also assessed against each

employer. These assessments are used to pay regular

benefits. Each State establishes the tax level and the

base against which it is levied. Taxes currently range up

to 7.5 percent on the first $6,000-$10,000 of annual

wages paid to each employee.

The third source of financial support is general taxes.

These are used to finance Federal and veterans

unemployment insurance and special programs, such as

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trade readjustment and disaster assistance.

Most uis funds are used to pay beneficiaries. Administrative or operating expenditures account for only

5-10 percent of total expenditures, depending on the

year. About 80 percent of the administrative funds are

for wages, salaries, and fringe benefits.40 The remaining 20 percent are for items such as mail, computer lease

and purchase, and building rental and operation.

Definitions. “ Unemployment insurance” is the term

most commonly used when discussing the program.

“ Unemployment compensation” and “unemployment

assistance” are also used, although in a strict technical

sense the terms are quite different. The three terms are

used interchangeably here.

Neither the Census of Governments nor the Standard

Industrial Classification (sic) system separately identifies the uis program. Thus, it is impossible to rely on

either of the two systems most often used to collect and

categorize State and local government statistics. Alternative sources of data will be discussed later.

The Census of Governments includes the uis program

under the general category of Income Security. The sic

Manual assigns the program to Industry 9441, “ Administration of Social, Manpower, and Income

Maintenance Programs.” 41 The program is lumped

with equal employment opportunity offices, public

welfare administration, and workers’ compensation offices. The major group title is, “ Administration of

Human Resources Programs.” Local employment service offices are assigned to Industry 7361, Employment

Agencies, which are part of Business Services, Major

Group 73.

R@s@areh and sflatisflies

Surprisingly little formal research has been published

on unemployment insurance operations in view of the

size of the program and the massive amounts of data

collected and published. Most published research has

focused on actuarial issues, such as financial solvency,

and program issues, such as the impact of benefit levels

on the willingness to work.

The cost model, uis has funded some research on administrative issues. Most notable is the development of

a cost model to establish budget needs, allocate money

to the States, undertake cost comparisons, and identify

cost-effective procedures.

The cost model divides the ui process into six activities: Initial claims, weeks claimed, nonmonetary

determinations, appeals, wage records, and tax functions (table 34). The labor required for each activity

varies, sometimes significantly, by workload mix. For

40 Unemployment Compensation: Final Report (National Commission on Unemployment Compensation, 1980), p. 128.

41 Standard Industrial Classification Manual, 1972, p. 340.

example, in 1981 an average of 53 minutes was required

to process an initial State claim (uc). An initial Federal

claim (u c f e) took twice as long, or 105 minutes. The

overall weighted average was about 60 minutes since the

majority of claims are State claims.

The time per unit also varies by State. One State processed its initial claims on the average in 38 minutes.

Another State required 67 minutes.

Uis uses such statistics, updated annually, to set State

ui budgets and allocate funds.

For the purposes of this study, cost-model dollar and

time expenditures are grouped into three basic functions: Beneficiary payments, finance operations, and

support (table 35). Beneficiary payments, which account for about 75 percent of the labor time input, include activities such as initial claims, weeks claimed,

and appeals. Finance or tax functions account for about

17 percent. Support or overhead accounts for about 26

percent of the labor input.

Other studies. The cost-model work has spawned a

number of internal uis studies which have particular

relevance for productivity computation.42 Some of

the more important findings include:

1. The time to process a claim varied significantly, depending on the workload mix. A veteran or

Federal claim took significantly longer to process

than did a regular intrastate claim.

2. Internal office procedures substantially influenced unit processing time.

3. Larger States required less unit labor.

4. The more heavily urbanized a State, the more

efficient its uis operations.

5. The greater the percent of employers (not

employees) added to and subtracted from the rolls,

the greater the unit labor requirements.

6. Unit labor requirements differed greatly from

State to State for the same functions. For initial

claims, unit labor requirements varied by 368 percent.

7. A number of procedural changes would improve UIS productivity, including more extensive

use of computers and paying recipients biweekly.

Statistics. The Federal Government, as the primary funding and coordinating agency, requires dozens of

42 “Development and Utilization of the Cost Model Management

System in the Unemployment Insurance Program” (Unemployment

Insurance Service, May 1979), pp. 40, 76-87; “Report on the Analysis

of Initial Claims and Wage Record Activities by the Operational Improvement and Cost Equalization Project” (Unemployment Insurance Service, 1979), p. 4; and Millions Can be Saved by Improving

the Productivity o f State and Local Governments Administering

Federal Income Maintenance Assistance Programs (General Accounting Office, June 5, 1981).

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Table 34. Synopsis of unemployment insurance activities

Activity Synopsis Activity Synopsis

Initial claims.................... The process whereby individuals file ap- Nonmonetary determi- employer or employee, or from new

plications for unemployment insurance. Initial claim activities include

completion and review of the application, monetary determinations, and

monetary redeterminations. The

number of initial claims directly

reflects uis coverage and the rate of

unemployment. Between 1963 and

nations—Continued information such as a new Federal or

State statute or court ruling. Common

reasons for determinations include

refusal to accept an offer of work and

nonavailability for work. There were

about 10 million nonmonetary determinations and redeterminations in 1979.

1979, the number of initial claims filed

ranged from 10 million (1969) to 30

million (1975 and 1976).

Appeals.......................... Processes that give the former employee or

employer the right to challenge a claim

ruling. All States have hearing officers or

Weeks claimed................ The number of weeks for which unemployment insurance payment is requested

by those filing claims. The tasks under

this heading include certification of

eligibility, periodic client interviews to

verify job search, and the processing

referees to hear appeals, investigate the

circumstances, and hand down written

decisions. In fiscal 1979, there were

about one million appeals—lower,

higher, intrastate, and interstate.

and paying of benefits (i.e., writing

checks). The number of weeks claimed

is somewhat larger than the weeks compensated (or checks written) since

about 20 percent of the claims are

disallowed. Between 1963 and 1979,

the number of weeks claimed ranged

from 55 million (1969) to 292 million

Wage records................ Processes required to maintain and update

employee earnings files. Computerized

records are maintained by 38 States and

the District of Columbia. Normally, uis

updates wage records quarterly, uis

measures the workload by the number of

updated wage records.

Nonmonetary determi-

(1976). Tax functions................ All activities involved in collecting money

from employers to support uis benenations.......................... Exam inations, determ inations, and

redeterminations of whether an individual is eligible to draw benefits.

Determinations result from a protest

by an interested party, such as a former

ficiary payments. Activities include new

account registry and discovery, accounting, auditing, and delinquent account

follow-up. The work in this area has

grown steadily since 1963.

routine reports from the States and, in turn, prepares a

number of summary reports. State governments, as

operating agencies, collect statistics from their own offices and contributing employers, many of which they

summarize, analyze, and publish.

The basis for most of these data is the State Employment Security Agency (s e s a ) accounting system. Over

40 SESA reports are prepared routinely in five general

categories—time distribution, property, appropriation,

general ledger, and activity expenses.43 In addition to

the accounting information and reports, there are about

two dozen program reports and one dozen financial

reports.44

Specific output and input statistics are discussed in

the following two sections.

Outputs

Selection of the preferred uis output measure is not as

straightforward as it might first appear. No research on

the question was uncovered during this investigation.

43 “ Sesa Accounting System Accounting Manual—Report

Utilization Guide” (Employment and Training Administration, October 1978).

44 Summary o f Employment Security Statistical Reports-August

1977 (Employment and Training Administration, 1977).

The following discussion briefly reviews seven possible

output measures:

Number of employees covered

Number of employers covered

Number of beneficiaries

Number of compensation weeks

Composite benefit/finance index

Composite program index

Composite functional index.

Number o f employees covered. The number of

employees covered is simply a count of the people who

have earned wages in jobs covered by unemployment insurance. This type of measure is often used by the

private insurance industry; their measure of output is

the number of policies sold. The arguments for using

the number of employees covered as the output measure

for uis include the following: (1) It is measurable, (2) it

has been calculated for a number of years, (3) it is a

physical measure, (4) it is easily understood, and (5) it is

supported by good data.

The primary argument against using this measure is

that it is not a measure of the final product of the uis.

There is apparently little correlation between the

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Table 35. Distribution of time expended by Unemployment

Insurance Service function, fiscal year 1979

Function Percent

Total.................................................... 100

Benefits.......................................................... 57

Initial claims .......................................... 17

Weeks claimed ...................................... 19

Nonmonetary.......................................... 11

Appeals ................................................... 8

Wage records........................................ 2

Finance........................................................... 17

Support........................................................... 26

S o u r c e : Unpublished d a ta from Unemployment Insurance Service.

number of persons covered and the resources required

to operate the uis.

Number o f employers covered. The number of

employers covered is simply a count of the businesses,

firms, and organizations which have one or more individuals who are covered by unemployment insurance.

It could be divided by size of firm, type of firm, or other

characteristics. General Motors, New York City, and

the State of California are examples of covered

employers. Arguments for and against using the number

of employers as the uis output measure are essentially

the same as those noted in the preceding discussion.

In the finance function of uis, the number of

employers and unit labor requirements should be closely

related. This avenue will be explored further in a later

section.

Number of beneficiaries. The number of beneficiaries is

the number of individuals who draw unemployment

benefits during a given period. The basic measure has

several variations: The number of different persons (one

person may draw unemployment benefits several times

during a year); the number of different times a person

is assisted during a year; and the number of claims.

Claims and beneficiaries, though closely related, are not

synonymous; individuals can file claims but do not

become beneficiaries until the claim is approved and a

check is written.

All these outputs are related to work performed, are

measurable, repetitive, and easily understood. Also,

data are available to calculate an index.

A criticism of this measure is its failure to take into

account the length of time an individual draws

unemployment insurance, and this time does vary. During periods of high unemployment, the average time increases as tasks associated with the maintenance of a

person on unemployment compensation (check writing,

recertifications, and appeals) increase. This measure of

output does not consider these additional labor requirements.

Number of compensation weeks. A measure which

takes into account the length of time unemployment insurance is drawn, as well as the number of people drawing insurance, is the number of compensation or

beneficiary weeks. This measure is the number of people

drawing unemployment insurance each week during a

given period, such as a month or a year.

Variations of the number of compensation weeks

which would produce time series that parallel the basic

measure are: (1) Average weekly number of beneficiaries for a year, or the total divided by 52; and (2) the

average weekly insured unemployed, which includes all

persons reporting at least 1 week of unemployment during the reporting period. A person may report being

unemployed but be refused unemployment insurance or 

drop out of the program before receiving a check.

The number of compensation weeks is sometimes suggested as the output measure for unemployment insurance. It is measurable, repetitive, accurate, comparable through time, and is not affected by different

State unemployment insurance levels. Also, it is an easily understood, unitary measure, and data exist to

calculate an output index for a number of years. The

primary argument against this measure is that compensation is only part of uis operations; the other part,

financing the program, is not covered by this measure.

Despite its faults, the number of compensation weeks

has been used to calculate an output index. These computations show that in 1963 uis paid about 86 million

weeks of compensation to the unemployed. The figure

dropped to 49 million in 1969, jumped to 260 million in

1976, and declined to 109 million by 1979. The average

annual change between 1963 and 1979 was 7.1 percent.

Benefit/finance index. A good case can be made for

dividing uis outputs into two distinct parts. One would

focus on service to the unemployed, that is, those applying for and drawing benefits. The other would focus on

finance operations.

Service to the unemployed is a function of the number

applying for and drawing ui. One measure of

beneficiary output is the number of compensation

weeks paid, already discussed. A better measure would

take into account the different unit labor requirements

needed to process those applying for and those drawing

ui. Separate indexes have been calculated for each. Between 1963 and 1979 the average annual percent change

in compensation weeks was 7.1 percent, as already

noted, and for initial claims, 4.6 percent.

Finance operations, the second part, have the objective of ensuring the integrity of employer tax payments.

These operations are a function of the number of

employers, not the number unemployed. If uis benefits

and operations were funded from general revenue, the

tax collection function and the supporting State staff

would not exist.

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The output measure suggested for finance operations 

is the number of employers. It is tangible and 

straightforward, and accurate data exist to make the 

calculations. The measure is not subdivided by size and 

type of business, since base-year labor requirements are 

not readily available for such a division.

Finance output has increased fairly constantly since 

1963. The average annual increase between 1963 and 

1979 was 4.8 percent.

The three indexes that make up the benefit/finance 

index have been combined using annual labor weights. 

This index shows an average annual growth of 5.4 percent between 1963 and 1979.

Program index. An even better measure of uis output 

would be one that maintained the separation between 

benefit and finance but took into account the differences in program unit labor requirements in the base 

year for initial claims, the area where unit labor requirements vary by program. As indicated earlier, in 

1981, the time required to process the basic State initial 

claim was 53 minutes; the unemployed Federal worker, 

105 minutes; and the unemployed veteran, 70 minutes.

An index weighted by prograpi was calculated. Other 

than for the initial claim output, the index is the same as 

the benefit/finance index presented earlier. That is, the 

index has three outputs—initial claims weighted by program unit labor requirements, weeks compensated 

(unweighted), and tax (unweighted). These three output 

indexes are combined by using labor weights to arrive at 

a single output index. The average annual increase in the 

program index between 1963 and 1979 was 5.3 percent.

Function (activity) index. Another approach to measuring uis output is to focus on activities or functions. The 

six basic functions were described at some length earlier 

in this paper. Separate indexes were calculated for each 

function, which have, in turn, been combined by labor 

weights to form a single index. The functions were not 

weighted by program.

The primary arguments in favor of using a functional 

index are its familiarity and the ready availability of the 

data. The data should be reasonably accurate and comparable through time. The primary argument against 

the functional index is that it measures functions and activities in several instances, not final outputs.

The functional index shows an average annual rate of 

change of 6.2 percent between 1963 and 1979.

Comparison o f output indexes. Seven measures of ui 

output have been discussed in this section. Indexes have 

been calculated for four measures: Compensation 

weeks, functions (activities), benefit/finance, and program.

Comparison of trends for the four indexes for 

1963-79 shows that the compensation week iijdex grew

(1977 = 100)

Table 3®. F@ur output Indexes for th® Unemployment

Insurance SerwSe®, fiscal years 1983=79

Year

Compensation

weeks

Function

(activity)

Benefit/

finance Program

1963 ...................................... 45.3 48.1 53.2 54.3

1964 ...................................... 42.2 46.7 50.5 51.6

1965 ...................................... 35.5 42.6 46.6 47.5

1966 ...................................... 28.0 38.6 42.4 43.2

1967 ...................................... 27.4 38.8 43.7 44.5

1968 ...................................... 28.2 38.6 43.2 44.0

1969 ...................................... 26.0 37.8 42.0 42.7

1970 ...................................... 33.9 42.5 48.9 49.8

1971 ...................................... 58.0 57.5 62.3 63.0

1972 ...................................... 61.7 59.1 64.7 65.5

1973 ...................................... 46.7 56.9 60.2 60.9

1974 ...................................... 48.9 60.5 64.9 65.7

1975 ...................................... 102.0 90.5 105.6 106.2

1976 ...................................... 137.4 108.7 121.2 119.6

1977 ...................................... 100.0 100.0 100.0 100.0

1978 ...................................... 68.0 84.9 79.4 80.2

1979 ...................................... 57.8 80.6 76.1 77.4

Average annual percent 

change:

1963-79 ........................ 7.1 6.2 5.4 5.3

1966-78 ........................ 13.3 9.7 8.8 8.6

1966-79 ........................ 10.9 8.7 7.5 7.4

1969-79 ........................ 10.3 9.4 7.7 7.6

1973-79 ........................ 4.7 6.7 3.8 3.9

SOURCE: Computed from unpublished data provided by the Unemployment Insurance Service.

most rapidly (7.1 percent per year), followed by the 

function index (6.2 percent), the benefit/finance index 

(5.4 percent), and the program index (5.3 percent) (table 

36). In other words, there is very little difference between the program index and the benefit/finance index, 

and not a great deal of difference between these two and 

the function (activity) index. The compensation week 

index grew at a higher rate than the other three; because 

finance activities are not explicitly considered and no 

provision is made for separating intake and continuing 

payments, it is not considered further.

The rate of growth of the program, benefit/finance, 

and function indexes varies depending on the period examined. Rates of growth have been calculated for five 

different periods. They are:

1. 1963-79—the entire period,

2. 1966-78—the period covered by the productivity

index presented later in this study,

3. 1966-79—three uis cycles,

4. 1969-79—two uis cycles, and

5. 1973-79—one uis cycle.

Four points stand out in examining the rates of 

change in these different periods. First, the period can 

dramatically affect the rate of growth of the three indexes. In fact, the rate of change depends more on the

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period chosen than the index chosen.

Second, there is very little difference between the rate 

of change of the benefit/finance and program indexes, 

regardless of the period.

Third, the benefit/finance and program indexes 

generally move in concert with the function index. The 

function index generally increases at a more rapid rate.

Fourth, the cyclical highs and lows generally fall in 

the same years for each of the three indexes.

To summarize, the benefit/finance, program, and 

function indexes move in much the same manner, and 

there is virtually no difference between the first two.The 

program index is preferable from the conceptual standpoint.

Output data. Although there is no lack of data for computing output indexes, several potential problems exist 

in using the data. First, because of the cyclical nature of 

uis operations, the period covered by outputs must 

match that covered by inputs. The output information is 

for the calendar year or the fiscal year, or both, depending on the source. Because the labor information in this 

study is summarized by fiscal year, output information 

and indexes are by fiscal year.

A special problem with federally collected fiscal year 

data is how to handle the shift in the beginning of the 

fiscal year in 1976 from July 1 to October 1. The indexes 

presented here simply delete that period from both outputs and inputs.

Another potential problem is inconsistent coverage of 

the statistics. Some output series include only State programs, others include State and Federal uis programs, 

and some include State, Federal uis, and railroad 

unemployment insurance.

Third, most ui statistics, including those presented 

here, include the trust territories. An index of “ State 

only” uis output could be computed with some additional effort. The trust territory statistics have not been 

removed from the statistical series presented here since 

they account for only about 0.05 percent of uis 

resources and are unlikely to have much effect on productivity calculations.

Quality o f service. The issue of quality of uis service is 

important, complex, and often discussed. The uis has 

collected statistics on quality for decades and today collects statistics on 35 different quality variables, which it 

summarizes and reports annually.

The question for this study is: How does quality affect productivity measurement and, particularly, how 

does quality change affect productivity trends? To affect productivity trends, two conditions must exist: (1) 

The quality attribute must affect base-year unit labor 

weights and (2) quality must be changing. Some quality 

characteristics, such as courtesy and helpfulness of uis 

staff, are important to uis managers but probably do 

not affect unit labor requirements and thus do not

directly concern this in v estig atio n . O ther 

characteristics, such as the error rate, could affect unit 

labor requirements through time and thus need to be examined. Assessment of the impact of quality change on 

productivity trends requires two kinds of information—a time series on quality and the relationship between the quality characteristic and unit base-year labor 

requirements.

Since 1975, the uis has measured quality in ten areas, 

and has established minimum standards (desired levels 

of achievement) for 9 of the 10 (table 37). Data are collected, tabulated, and published by State for 35 different measures. States conduct self-appraisals for two 

consecutive years. Every third year, staff from the 

Federal Government and other States conduct appraisals. With this information it is possible to trace 

(since fiscal 1977) how uis quality is changing by 

State.45

This study examined trends for 7 of the 35 quality 

measures: Timeliness of higher and lower appeals, 

promptness of intrastate and interstate payments, 

promptness of status determinations, percent delinquent employers, and percent employers audited. Data 

on appeals are available since the 1930ss; data on the remaining areas, since the mid-1970’s.

These seven time series show the following trends: 

Two series—timeliness of high-level appeals and of intrastate payments—show little change; three series 

—timeliness of lower level appeals, promptness of intrastate payments, and promptness of status determinations—show quality improvement; and two series—percent deliquent employers and percent employers 

audited—show quality deterioration.

Although these statistics suggest no general shift in 

uis quality, the data are too incomplete to draw hard 

and fast conclusions.

The four time series that include the 1974-75 recession do suggest that, during that period of increased 

workload and increased production, the quality of service may have dropped temporarily. Both the timeliness 

of appeals (lower and higher) and promptness of 

payments (intrastate and interstate) deteriorated during 

the recession. After the recession, quality returned to its 

previous level,, These brief dips in quality evidently did 

not affect long-term productivity trends.

Uis has examined the relationship between quality 

and workload on several occasions but has been unable 

to associate shifts in quality with changes in unit labor 

requirements and productivity. This may be due to insufficient change in quality variables, insufficient data, 

inadequate analytic techniques, or simply the absence of 

such relationships. The impact on productivity of quality of uis service remains an area for future discussion 

and analysis, particularly as additional time series data

45 “Unemployment Insurance Quality Appraisal Results for FY

1981” (Employment and Training Administration, May 1981).

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Table 37. Selected quality appraisal measures of the Unemployment Insurance Service

Area Quality characteristic Typical measure Federal

standard

Initial claims .................................. Performance (interstate, intrastate) Number of undetected issues per 100 cases Yes

Promptness (interstate, intrastate) Percent payments within “x” days Yes

Weeks claimed................................ Performance (interstate, intrastate) Percent weeks claimed affected by undetected

issues

Yes

Nonmonetary determinations .... Performance Percent acceptable errors Yes

Promptness Percent issues resolved within “x” days Yes

Combined wage claims.................. Promptness Percent forms processed within “x” days No

Appeals............................................ Performance Percent appeals scoring 80 or above Yes

Promptness Percent discussions issued within “x” days Yes

Status determinations.................... Performance Percent acceptable cases Yes

Promptness Percent determinations within 180 days Yes

Employer accounts........................ Promptness Percent of monies deposited within “x” days Yes

Field audits...................................... Penetration Percent employers audited Yes

Report delinquency........................ Promptness Percent reports delinquent Yes

Collections...................................... Performance/promptness Percent delinquent employers with payments within 150 daysYes

Source: Adapted from data in “Unemployment Insurance Quality

Appraisal Results for FY 1980” (Employment and Training Administration, May 1980).

are collected and national indexes are calculated.

Indexes in this paper have not been adjusted to account for quality shifts. However, future adjustments

may be needed, particularly in fraud identification and

control, for which the uis has created separate administrative units. Two quality measures—initial claims

performance and weeks claimed performance—should

reflect the increased emphasis that the UIS is giving to

this area.

Labor inputs

The uis program is extremely labor intensive. About

80 percent of all administrative funds go to pay

employee salaries and benefits. All operating personnel

are State employees; no local employees are involved.

About 200 uis Federal employees oversee and coordinate State activities, but they are not included in this

study. In any case, they constitute less than 0.05 percent

of all uis employment.

Three labor measures are recommended for

calculating State and local government labor productivity—all employees, all employee hours, and number

of full-time-equivalent employees (see chapter III).

The only nationwide uis employee statistic routinely

collected is the number of positions—analogous to the

number of full-time-equivalent employees. The Federal

Government uses these statistics, which are available by

activity (e.g., initial claims, appeals, and tax) by State,

to budget for uis programs, allocate funds to the States,

and account for funds allocated. Statistics on hours

worked are collected by State but not for the Nation. No

counts are made of the total number of employees.

The employee figures used here are the number of uis

positions reported by State labor officials to the U.S.

Department of Labor. The statistics relate to positions

used, not positions budgeted. The trend computed from

the number of State positions should approximate an

hours trend if it were possible to compute that index.

However, it probably would differ from the trend of the

total number of employees since States use many intermittent employees.

Data on positions are available by State, by function,

and for the total uis since 1963. They show that the total

number of positions increased from about 35,000 in

1963 to 45,000 in 1979, or 27 percent (table 38). The

numbers fluctuate considerably, ranging from 26,567 in

1967 to 57,321 in 1976. The average annual rate of

change between 1963 and 1979 was 3.7 percent.

The next section of this study examines three time

periods: 1963-79, 1966-78, and 1972-79. The average

annual rate of growth of positions in these three periods

was 3.7, 6.6, and 5.6 percent, respectively.

These statistics cover the basic unemployment insurance program, including the regular State program,

Federal and veterans programs, extended benefits, supplemental benefits, special unemployment assistance,

and temporary compensation. Positions for trade readjustment allowance and disaster unemployment

assistance have been removed from the totals since these

two programs are not included in the outputs. The

number of positions dealing with disaster is extremely

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Table 38. Positions in the State Unemployment Insurance 

Service, fiscal years 1963-79

Y e a r N u m b e r

In d e x

( 1 9 7 7 - 1 0 0 )

1 9 6 3 ...................................................... 3 5 ,1 4 6 6 3 .7

1 9 6 4 ...................................................... 3 2 ,9 4 6 5 9 .7

1 9 6 5 ...................................................... 3 0 ,9 2 1 56.1

1 9 6 6 ...................................................... 2 8 ,4 8 4 5 1 .6

1 9 6 7 ...................................................... 2 6 ,5 6 7 4 8 .2

1 9 6 8 ...................................................... 2 7 ,4 8 3 4 9 .8

1 9 6 9 ...................................................... 2 7 ,0 6 5 49.1

1 9 7 0 ...................................................... 2 8 ,4 8 9 5 1 .7

1971 ...................................................... 3 2 ,7 2 0 5 9 .3

1 9 7 2 ...................................................... 3 7 ,7 9 9 6 8 .5

1 9 7 3 ...................................................... 3 5 ,1 3 7 6 3 .7

1 9 7 4 ...................................................... 3 2 ,7 1 1 5 9 .3

1 9 7 5 ...................................................... 4 4 ,5 2 8 8 0 .7

1 9 7 6 ...................................................... 5 7 ,3 2 1 1 0 3 .9

1 9 7 7 ...................................................... 5 5 ,1 5 2 1 0 0 .0

1 9 7 8 ...................................................... 4 8 ,2 0 5 8 7 .4

1 9 7 9 ...................................................... 4 4 ,5 4 6 8 0 .8

A v e ra g e a n n u a l p e rc e n t 

c h a n g e :

1 9 6 3 -7 9 .................................. 3.7

1 9 6 6 -7 8 .................................. - 6 .6

1 9 7 2 -7 9 .................................. - 5 .6

N o t e : E x c lu d e s d is a s te r a n d tra d e re a d ju s tm e n t p e rs o n n e l.

SOURCE: Unpublished data from Unemployment Insurance Service except 

for fiscal 1975, which was taken from the fiscal 1977 Federal Budget, Appendix, p. 517.

small, usually two or three per year, and would not

make any marked difference in productivity calculations. However, the positions supporting trade readjustment could have a significant effect if included. In the

mid-1970’s, there were fewer than 100 such positions; in

1980, the figure had climbed to almost 2,000. With 1981

Federal legislation, the number has dropped dramatically.

Productivity indexes

This section presents national uis productivity trends

drawing on the output and input indexes from preceding

sections. Productivity trends for six States are compared

with the national index. (All average annual rates of

change are based on the linear least squares trend of the

logarithms of the index numbers.)

National index. The preferred measure of output, at

least from the conceptual standpoint, is the programbased index. Between 1963 and 1979, program output

increased at an average annual rate of 5.3 percent. During the same period, employee positions increased at an

annual rate of 3.7 percent and output per employee, or

productivity, increased at a rate of 1.5 percent (table

39). -

The average annual rate of change is extremely sensitive to the period examined, as has been noted earlier.

For example, productivity increased at an annual

average rate of 7.2 percent between 1972 and 1976 but

decreased at a rate of 0.7 percent between 1972 and

1979.

In selecting time periods, it is preferable to focus on

complete cycles, that is, from trough to trough or peak

to peak or midpoint to midpoint. The 1963-79 troughs

were 1966, 1969, 1972, and 1978; the peaks were 1964,

1967, 1971, and 1975. The period examined here is

1966-78, which includes three cycles.

Between 1966 and 1978, outputs increased at an

average annual rate of 8.6 percent; inputs increased at a

rate of 6.6 percent; and output per employee at 1.9 percent.

Output per employee (productivity) increases rapidly

during periods of increasing .work for uis (higher

unemployment) and falls during periods of decreasing

work (falling unemployment). For example, during the

1974-75 recession, output per employee increased 34

percent. In the subsequent two years it fell 26 percent.

Apparently it is difficult to add staff as rapidly as the

work increases. Conversely, management is reluctant to

reduce staffing as rapidly as the work diminishes. This

pattern is like that found in the private sector.

Two other points should be noted: For each of the

three cycles, the relative increase in output was larger

than the relative increase in employees—hence the increase in productivity. In addition, the longer the period

examined, the more stable the index and the less important the individual cyclical fluctuations. As additional

years are added to the index, the results should become

more and more stable; however, results will be influenced by the magnitude of the fluctuations.

Table 39. Indexes of output, employee positions, and 

output per employee position, Unemployment Insurance 

Service, fiscal years 1963-79

(1977 =-100)

Year Output Employee

positions

Output per 

employee 

position

1963 ...................................... 54.3 63.7 85.2

1964 ...................................... 51.6 59.7 86.4

1965 ...................................... 47.5 56.1 84.7

1966 ...................................... 43.2 51.6 83.7

1967 ...................................... 44.5 48.2 92.3

1968 ...................................... 44.0 49.8 88.4

1969 ...................................... 42.7 49.1 87.0

1970 ...................................... 49.8 51.7 96.3

1971 ...................................... 63.0 59.3 106.2

1972 ...................................... 65.5 68.5 95.6

1973 ...................................... 60.9 63.7 95.6

1974 ...................................... 65.7 59.3 110.8

1975 ...................................... 106.2 80.7 131.6

1976 ...................................... 119.6 103.9 115.1

1977 ...................................... 100.0 100.0 100.0

1978 ...................................... 80.2 87.4 91.8

1979 ...................................... 77.4 80.8 95.8

Average annual percent 

change:

1963-79 .......................... 5.3 3.7 1.5

1966-78 .......................... 8.6 6.6 1.9

S o u r c e : Tables 3 6 and 3 8 .

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State trends. Additional insights can be gleaned by examining individual State trends, which often move quite

differently from national trends. Some differences are

due to economic conditions and institutional situations,

but many reflect management processes and procedures. Management processes probably account for

the greatest differences in uis productivity.

Productivity trends have been computed for six States

selected to display a range of institutional arrangements

and productivity trends-—Alabama, Illinois, Kentucky,

Pennsylvania, Texas, and Virginia. They are illustrative, not representative, of the States.

The State indexes differ from the national index in

several respects. First, labor data to compute the program index were not readily available, so the

benefit/finance measure, which parallels the program

index quite closely, was used for output. Second, the

base index year was shifted to 1972 for ease of comparison. Third, and most important, the time period

covered was restricted to 1972-79. State data were not

readily available before fiscal 1972.

The results of these computations show that the

average annual change in output per employee ranged

from 4.7 percent for Virginia to -3.1 percent for Texas,

as follows:

Annual percent

change, 1972-79

Virginia...................................... + 4.7

Kentucky.................................. + 2.6

Illinois........................................ +1.9

Alabama.................................... +1.2

Pennsylvania............................ - 1.7

Texas.......................................... - 3.1

These trends generally support the conclusions

drawn from the national UIS productivity data. First,

those States with the most rapid growth in output have

the highest rate of productivity growth. Second, the increase in employee input generally follows the increase

in output. And third, the employee growth peak does

not generally reach the peak in output. The exception in

this case was Texas.

Comparison o f State and national indexes. A national

productivity index can be useful as a norm or benchmark for assessing State productivity progress. To

make the comparison, it was necessary to recompute the

national index presented earlier by the same procedures

used to compute the six State indexes. That is, the new

or truncated national index uses the shortened time

period (1972-79), the benefit/finance output measure,

and the 1972 base year.

The new national index does mirror the national index presented earlier: The average annual trend in output per employee is exactly the same for the two indexes

(-0.7 percent) for the 1972-79 period even though individual years do not match precisely.

In four States—Virginia, Kentucky, Illinois, and

Alabama—productivity grew faster than in the total

uis. In two States—Pennsylvania and Texas—productivity declined. Both outputs and employees increased in

the total uis and in each of the six States. Outputs increased 4.9 percent and employees 5.6 percent.

Employee growth was smaller for the Nation than in

any of the six States examined, and output was smaller

than in five of the six States.

These statistics are presented for illustrative purposes

only. The truncated national index is not representative

of long-term trends. Also, it is doubtful that the State

trends presented here are representative of long-term

State trends.

Sugg©st@d research

A number of extensions to the work presented here

are possible and desirable. Probably the most useful extension would be to compute trends for each State by

methods similar to those presented for the six States.

This would provide each State a means with which to

compare its progress. The uis routinely collects data

which could be used to make such calculations.

Other potentially useful extensions would be the

preparation of an index of the number of uis employees

and comparisons of the index of positions with an index

of hours paid. Additional research is needed into quality

factors and their possible impact on unit labor requirements, particularly as additional quality data

become available. Also, it should be possible with little

additional effort to separate trust territory statistics

from State statistics. Finally, the uis is a service which

lends itself to the computation of absolute levels of productivity in contrast to the productivity trends examined

here. The feasibility and implications of making such

calculations should be addressed.

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Chapter V. Thinking Afb®ut 

the Unmeasured: Four 

Gas® Studies

This chapter examines the possibility of calculating

productivity indexes for four services—solid waste collection and disposal, drinking water supply, mass transit, and the Employment Service. Each service presents

a different set of issues and problems. Some are simple

problems of data availability, some are conceptual problems, and some combine conceptual and data problems. In no case has a productivity index been calculated.

These four services were selected because of past conceptual and analytical work in the area. Each illustrates

some of the problems encountered in measuring State

and local government productivity, but should not be

construed as being representative of all the problems

that might be encountered.

The investigative approach for each service is similar

to that used in the preceding chapter: Institutional considerations and past research are reviewed; possible

measures of service output are examined, the preferred

measure is selected, and data needed to compute the

measure are reviewed; labor input issues and data are

discussed; and recommendations for future research

and data collection are presented.

Solid W aste Collection and Disposal

Sanitation service, particularly solid waste collection,

is the government service which is used most often to illustrate productivity measurement issues. Its

straightforward nature, tangible outputs, private sector

counterparts, and past research make it a useful example for analysis and examination. Even though the

analytic and institutional base of knowledge underlying

the service is fairly large, no productivity index, or even

time series data with which to build one, has been produced.

Note: This chapter has profited from the comments of Charles

Ardolini, Gary Burdette, Richard Carnes, and Arthur Young of the

Bureau of Labor Statistics: Robert Clark of the Environmental Protection Agency; George Craft of the American Water Works Association; John Greiner and Harry Hatry of The Urban Institute; Norman

Paulhus of the Department of Transportation; and Allan Stevens of

the Bureau of the Census.

Institutional ©©nsId@rati<o>ms

Although the Federal Government and the States set

standards, local government is responsible for day-today operations of solid waste collection and disposal.

Most local governments are deeply involved as service

providers or as regulators. Private firms and voluntary

organizations are also active in the solid waste field.

Few cities are served by a single provider. One investigation identified 12 different types of solid waste collection, ranging from total private to total public operations.1 A combination of providers is the norm for most

jurisdictions; for example, private service for commercial establishments, government service for private

residences, and recycling by voluntary associations.

Local governments collect solid waste from about

two-thirds of the Nation’s residents. They serve the

residents of most medium- and large-size cities.

Private firms serve the other one-third of the Nation’s

residents, but two-thirds of the Nation’s cities; private

companies serve most small cities.2 According to a 1966

survey, private firms collected about 36 percent of the

Nation’s residential solid waste.3

Voluntary associations, such as the Boy Scouts and

neighborhood recycling centers, are primarily concerned with resale items, such as newspapers, bottles, and

aluminum cans. These organizations, which play a

relatively minor role in this field, are not considered further.

Expenditures. This discussion focuses on local government, which spent $2.3 billion on solid waste collection

and disposal operations or about 1 percent of all State

and local government expenditures in 1977 (table 40).

Approximately 133,000 local workers or 1 percent of all

State and local government employees were in this field.

For the average city, solid waste collection and disposal

constitute about 9 percent of its budget.4

1 E. S. Savas, The Organization and Efficiency o f Solid Waste

Collection (Lexington, Mass.: Lexington Books, 1977), p. 29.

2 Ibid., pp. 63-64.

3 R. J. Black and others, 1968 National Survey o f Community

Solid Waste Practices: An Interim Report (Cincinnati: U.S. Bureau of

Solid Waste Management, 1968).

4 E. S. Savas and Barbara J. Stevens, “ Solid Waste Collection,”

in George J. Washnis, ed., Productivity Improvement Handbook

(New York: John Wiley, 1981), p. 592.

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Solid waste services, as a group, are fairly labor intensive. Salaries and wages account for about 52 percent of

all sanitation expenditures and 58 percent of current

operating expenditures (table 40). Inclusion of fringe

benefits would raise the latter figure to about 65 percent. A 1970 study found that labor-related costs accounted for 88 percent of all solid waste expenditures.5

A 1975 study of Connecticut cities found that 73 percent

of collection costs were for labor.6 A more recent study

estimated salaries, wages, and fringe benefits at 69 percent.7

Solid waste operations are commonly divided between collection and disposal. One 1968 study estimated

that 85 percent of funding supported collection, 15 percent supported disposal. Another study, at about the

same time, estimated the ratio at 80-20.8 A breakdown

of Detroit’s solid waste expenditures in 1969 showed

that 69 percent went for residential collection, 6 percent

for street cleaning, 4 percent for snow removal, and 22

percent for disposal operations.

For most governments, solid waste collection is

residential collection. Collection may also encompass

street sweeping, dead animal pickup, abandoned vehicle

removal, leaf collection, snow removal, and bulky waste

pickup (e.g., stoves and refrigerators). Some governments also collect commercial and industrial waste. At

one time most jurisdictions collected garbage and trash

separately; today they are usually combined.

Solid waste disposal is primarily disposal at a sanitary

landfill or a dump. A 1966 survey found that cities with

a population of over 25,000 used landfill mostly (79 percent), incineration some of the time (20 percent), and

composting infrequently (1 percent). A 1967 survey

found that 92 percent of the solid waste was disposed of

at landfills and 8 percent was burned at incinerators.9 A

1974 survey found that landfills accounted for 85 percent of the disposal operations.10 Resource recovery and

recycling are much discussed but apparently play a small

role in municipal disposal operations.

Definitions. A variety of terms are used to describe

sanitary services, including solid waste, refuse, trash,

rubbish, and garbage. Solid waste is used most often today. This report uses them interchangeably. Definitions

5 National Center for Resource Recovery, Municipal Solid Waste

Collection (Lexington, Mass.: Lexington Books, 1973), p. 42.

6 Peter Kemper and John M. Quigley, The Economics of Refuse

Collection (Cambridge, Mass.: Ballinger Publishing Company, 1976),

p. 109.

7 Columbia University Graduate School of Business, Evaluating

Residential Refuse Collection Costs: A Framework fo r Local Government (Washington: Public Technology, Inc., 1978), p. 18.

8 Black, 1968 National Survey.

9 Fred N. Rubel, Incineration o f Solid Wastes (Park Ridge, N.J.:

Noyes Data Corporation, 1974).

10 Eileen Berenyi, “Solid Waste Disposal,” In George J. Washnis,

ed., Productivity Improvement Handbook (New York: John Wiley,

1981), p. 629.

Table 40. Local government solid waste collection and

disposal expenditures, fiscal year 1977

Type of expenditure Dollars (millions)

Total.................................................... $2,374

Current operation .......................................... 2,126

Salaries and wages................................ 1,239

O ther....................................................... 888

Capital outlay................................................... 247

Construction.......................................... 129

O ther....................................................... 119

Note: Because of rounding, detail does not add to totals.

Source: 1977 Census of Governments—Compendium of Government

Finances (Bureau of the Census, 1979), p. 30.

which are used by others include the following:

The Bureau of the Census uses the term “ sanitation

services,” which it defines as follows:

“Refuse collection and disposal, operation of sanitary

landfills and street cleaning activities.” 11

Snow and ice removal are not included.

The Standard Industrial Classification Manual

assigns solid waste systems to one of two codes, depending on whether disposal is part ofthe process. Code

4212 is used for collection without storage; code 4953 is

for refuse with disposal. The precise definitions are the

following:12

Code 4212 Local trucking without storage:

“Establishments primarily engaged in furnishing

trucking or transfer services without storage, in a

single municipality, contiguous municipalities, or a

municipality and its suburban areas.”

Code 4953 Refuse systems:

“Establishments primarily engaged in the collection

and disposal of refuse by processing or destruction. Includes:

Acid waste, collection and disposal

Ashes, collection and disposal

Dead animal disposal

Dumps, operation of

Garbage: collecting, destroying, and

processing

Incinerator operation

Radioactive waste materials, disposal

Refuse systems

Rubbish collection and disposal

Street refuse systems

Waste materials, disposal at sea.”

The American Public Works Association (a p w a )

defines solid waste as garbage, rubbish, ashes, bulky

wastes, street refuse, dead animals, abandoned vehicles,

11 1977 Census o f Governments—Compendium of Public Employment (Bureau of the Census, 1979), p. 461.

12 Standard Industrial Classification Manual (Office of Management and Budget, 1972), pp. 224 and 238.

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Table 41. Refuse materials by kind, composition, and source

Kind of refuse Composition Source

Garbage..................

Rubbish.................

Ashes .....................

Street refuse............

Dead animals..........

Abandoned vehicles

Industrial wastes . .

Demolition wastes.

Construction wastes

Special wastes ........

Sewage treatment .

Wastes from preparation, cooking, and serving of food; market

wastes from handling, storage, and sale of produce

Combustible: Paper, cartons, boxes, barrels, wood, excelsior, tree

branches, yard trimmings, wood furniture, bedding, dunnage

Noncombustible: Metal cans, metal furniture, dirt, glass, crockery, materials

Residue from fires used for cooking and heating and from on-site

incineration

Sweepings, dirt, leaves, catch basin dirt, contents of litter receptacles

Cats, dogs, hordes, cows

Unwanted cars and trucks left on public property

Food processing wastes, boiler house cinders, lumber scraps,

metal scraps, shavings

Lumber, pipes, brick masonry, and other construction materials

from razed buildings and other structures

Scrap lumber, pipe, other construction materials

Hazardous solids and liquids: Explosives, pathological wastes,

radioactive materials

Solids from coarse screening and from grit chambers; septic tank

sludge

Households, restaurants, institutions,

stores, markets

Streets, sidewalks, alleys, vacant lots

Factories, power plants

Demolition sites to be used for new

buildings, renewal projects, expressways

New construction, remodeling

Households, hotels, hospitals, institutions, stores, industry

Sewage treatment plants; septic tanks

Source: American Public Works Association, Municipal Refuse

Disposal (Chicago: Public Administration Service, 1970), p. 13.

construction and demolition wastes, industrial refuse, 

special wastes, animal and agricultural wastes, and 

sewage treatment residues (table 41.)

The following discussion includes all solid waste collection activities—whether identified as trash or garbage, residential or commercial, street sweeping, dead 

animal removal, or leaf pickup—and all government 

disposal operations. The crucial factor for this study is 

that local government personnel provide the service.

R®s®ar©Bi and ©@ffi©®pty©l ossy®s

Considerable research has been done on sanitation 

services, particularly solid waste collection. Much of it 

focuses on technical issues such as crew size, truck 

routing, work rales, vector control, and equipmentlabor mix. Also, a modest amount of research has been 

done on broad technical and economic issues such as 

cost functions, quality, effectiveness, and economies of 

scale.13

By contrast, little research has been done on the 

economics of disposal. Most disposal research has 

focused on environmental concerns, with a modest

13 Savas, Organization and Efficiency, pp. 107-110.

amount of analysis of recycling and resource recovery 

issues.

Research on collection falls into three general 

categories—technical, geographic, and policy. Many 

studies have dealt with technical issues such as routing 

procedures, track size, crew size, aging of equipment, 

use of transfer stations, and crew incentive systems and 

their relationship to unit costs and productivity. These 

factors are not the concern of this study although they 

obviously affect labor productivity.

Geographic and community considerations, such as 

precipitation, topography, collection density, and composition of solid waste also affect collection productivity. For example, studies show that costs for cities which 

have hilly terrain average about 15 percent more than 

costs for cities which are relatively flat.14 Management has little or no control over geographic factors, 

which are not considered further here.

14 Johns Hopkins University, Mathematical Modeling o f Solid

Waste Collection Policies, Public Health Publication 2030 (U.S.

Public Health Service, 1970); Ronald A. Perkins, “ Satellite Vehicle

Systems for Solid Waste Collection, Evaluation and Application”

(Cincinnati: U.S. Environmental Protection Agency, 1971); and

University of California, An Analysis o f Refuse Collection and

Sanitary Landfill Disposal (Berkeley: University of California, 1952).

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Local government officials control policy issues, such 

as the level of service, which are subject to modification, vary from jurisdiction to jurisdiction, and affect 

unit labor requirements. The point and frequency of 

pickup are two important policy issues which affect output and need to be considered in calculating productivity. Shifts from curb or alley to backyard pickup can increase collection costs from 25 to 100 percent, according 

to studies (table 42). Shifting pickup of residential solid 

waste from once to twice a week will increase costs from 

19 percent to 74 percent.

Economies of scale of operations can be important in 

constructing a productivity index. Evidence is conflicting for solid waste. Several studies have not found any 

economies of scale13; others have found some.16 One of 

the most recent studies found increasing economies 

(decreasing unit costs) in cities with a population of 

under 20,000; cities with a population of 20,000-50,000 

probably had decreasing costs, although the evidence 

was not conclusive; and cities with a population of more 

than 50,000 showed no evidence of changing unit cost.17 

A review of six different studies in this area concluded 

that economies occur in the smaller cities but not in 

medium-size or larger jurisdictions.18

There is general agreement that major economies of 

scale exist in landfill operations, although few studies 

have specifically addressed the issue. One found large 

decreases in unit costs until the 100,000 tons per year 

figure was reached (residential population of about 

100,000).19 Beyond this point, the costs continued to 

decrease but at a lesser rate.

The amount of economic research on solid waste has 

declined rapidly since the mid=197Q5s; today, little is being done outside environmental concerns. However, the 

analytic groundwork has been well laid for productivity 

calculations.

Several points stand out in the research:

1. The measures of output used most frequently are 

tons, cubic yards, and residents served.

2. The input measures most often used are labor 

hours or number of employees.

3. Almost all quantitative research has been cross 

sectional.

4. Calculation of a solid waste productivity index 

needs to take into account level-of-service issues, 

such as frequency of service and point of collection. These areas have experienced major 13*679

13 Werner Z. Hirsch, “ Cost Functions of an Urban Government

Service: Refuse Collection,” Review o f Economics and Statistics,

February 1965, pp. 87-92.

16 Dennis Young, How Shall We' Collect the Garbage?

(Washington: The Urban Institute, 1972).

17 Columbia University, Evaluating Collection Costs, p. 58.

10 William F. Fox, Size Economies in Local Government Services: A

Review (U.S. Department of Agriculture, 1980), p. 24.

19 Thomas I. Sorg and H. Lanier Hickman, Sanitary Landfill Facts

(U.S. Department of Health, Education, and Welfare, 1970).

Table 42. Studies ©f effect of location and frequency of

collection on solid waste collection costs

Investigator or author Basis of 

comparison

Change in cost 

(percent)

Location: Shift from 

curb/alley to backdoor 

or backdoor to curb/alley

Hirsch.................................... Statistical 100

University of California.......... Statistical and 

time/motion 

study

65

Citizens Budget ( N Y C )................. ? 60

Rawn...................................... ? 26-48

Ralph Stone............................ Statistical 37

Flintoff/M illard........................ ? 30-45

Clark/Gillean.......................... Statistical 100

Kemper/Quigley.................... Statistical 40

Savas .................................... Statistical 25-38

Frequency: Shift from 

once to twice weekly or 

twice to once weekly

University of California.......... Statistical 67

Los Angeles County .............. ? 50

Perkins (dollars) .................... Statistical and 50

(hours)...................... simulation 74

Rogers/Bellenger.................. Time/motion 50

Kemper/Quigley.................... Statistical 50

Savas .................................... Statistical 19-25

S o u r c e : C o m p ile d fro m re fe re n c e s lis te d in b ib lio g ra p h y .

shifts over the past two decades, and such shifts 

are likely to continue.

Outputs

P ro d u ctiv ity m easures should reflect an 

organization’s physical output. The measure most often 

used to compute solid waste output is tons collected and 

disposed. Other measures frequently used are cubic 

yards collected and disposed and residential or 

household units served. Private collectors often base 

their charges on the number of containers emptied. The 

relative strengths and weaknesses of the different 

measures are seldom discussed. The following examines 

these issues, quality considerations, and the data which 

could be used to calculate a national index.

Solid waste collection. Tonnage is the measure most 

often used to track and analyze solid waste collection 

output. This is a good measure of work performed, particularly in residential and commercial collection, and it 

is familiar to the industry. Also, it is a physical unit not 

affected by price level changes. Most large governments 

keep tonnage statistics.

Two reasons sometimes given for not using tonnage 

as the measure of output are: 1) It is not a particularly good measure of work performed for some of the 

secondary sanitary services such as street sweeping, 

snow removal, leaf collection, and dead animal pickup. 

These services, however, account for a small portion of 

sanitation resources in most cities. 2) Tonnage statistics

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are not collected by all governments, particularly 

smaller ones.

Cubic yards is often used to measure output in 

jurisdictions which do not weigh their refuse. Like tonnage, cubic yards is not affected by price change.

The major argument against using cubic yards as an 

output measure is its instability or variability. That is, 

most trash is compacted as it is collected, and the rates 

of compaction differ markedly from jurisdiction to 

jurisdiction, and even from truck to truck. Uncompacted household wastes usually weigh 200-250 pounds 

per cubic yard. Wastes compacted in a collection vehicle 

usually weigh: 550-750 pounds per cubic yard, and landfill wastes are usually compacted at a density of 

750-1,000 pounds per cubic yard.20 Compaction rates 

directly affect resource requirements—fewer trips are 

made to the landfill and thus fewer labor hours and 

other resources are required. Furthermore, compaction 

rates have improved over time, which would bias output 

trends. The problem is further compounded by secondary services such as street sweeping and dead animal 

pickup. In short, solid waste collection experts fed that 

cubic yards is not a very good measure of output for 

productivity calculations.

The number o f residential units served is used by 

many jurisdictions as a measure of output. It is a 

physical unit unaffected by price change, and data to 

support this measure are normally available and should 

be quite accurate. However, it is not a very good 

measure of the work performed since volume and 

weight differences are not reflected. Refuse generation 

varies considerably by household, and the underlying 

variables are not stable through time. That is, the 

number of individuals per residential unit has decreased 

while the amount of refuse generated per person has increased. Also, some sanitation departments serve commercial and industrial businesses as well as residential 

units. Nor can the measure be used to track secondary 

sanitation services such as street cleaning, leaf removal, 

and dead animal pickup.

Finally, the number o f containers emptied is 

sometimes used as a measure of output, particularly by 

private collectors. However, it is not a very good 

measure for local government. Containers vary in size 

and weight, and average container size and weight have 

changed through time. Also, most jurisdictions lack 

statistics on the number of containers.

Level o f service. Level-of-service factors, such as 

point of residential collection pickup and frequency of 

pickup, can markedly affect unit costs and unit labor requirements (table 42). If levels remain constant through 

time, the point becomes moot for productivity trends. If 

the levels are changing, they must be considered.

20 John Reindi, “ Interrelationships Within the Solid Wastes

System” Solid Waste Management, April 1977, p. 23.

Evidence is skimpy on the level and rate of change of 

solid waste collection. Cross-sectional studies conducted 

over the past two decades suggest that the frequency of 

service has remained fairly stable but pickup has shifted 

from yard to alley or street. Calculations of productivity 

trends need to take such shifts into consideration.

Two ways to handle a shift from backyard to curbside 

collection are: 1) To include total resource inputs, not 

just those provided by government. The time residents 

take in setting out the solid waste and carrying back the 

cans would need to be included in the computations. 2) 

To differentiate the services, in this example backyard 

and curbside pickup, by using labor weights or by partitioning the data. This approach is recommended here.

Quality factors. Quality factors, such as missed collections, spillage, collector noise, and damage to 

residential property, are sometimes considered in productivity discussions. As noted earlier, this study includes only attributes which affect unit labor requirements. Since solid waste collection quality factors 

probably would have little impact, no adjustment is suggested here.

Secondary services. In addition to traditional residential service, some sanitation departments remove abandoned automobiles, empty city park refuse cans, pick 

up dead animals, collect leaves, and sweep streets. To 

measure output, these services may be handled in 

several ways.

One approach is to remove secondary service outputs 

and inputs from productivity calculations. That is, the 

tons of refuse from parks and tons of sweepings from 

streets would not be included in outputs, and individuals who performed these services would not be included in inputs. However, data are rarely available to 

make such calculations.

Another way to handle secondary services is simply to 

include them as part of the primary service. That is, no 

distinction would be made between tons collected from 

the park, on the street, or from residential units, and no 

distinction would be made in labor requirements. The 

rationale behind this approach is that most secondary 

services account for only a small proportion of sanitation department resources and are unlikely to affect 

overall productivity irrespective of how they are handled. The argument against this approach is that some 

secondary services, such as street cleaning, are important in some cities and their productivity may change at 

a different rate from that of residential collection. Also, 

tonnage and cubic yards are not very good measures of 

many secondary service outputs.

The preferred approach in measuring secondary service productivity is to develop separate output and input 

measures for each service, and to combine them with 

proper weights to calculate the solid waste collection in56

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dex. Examples of secondary services and suggested outputs are:

Abandoned cars - 

Dead animals 

Leaves

Refuse cans 

Street cleaning -

number of cars towed 

number of carcasses collected 

number of cubic yards 

removed

number of cans emptied 

number of curb miles swept

The primary problem with this approach is data

availability.

Finally, some jurisdictions use sanitation equipment

and personnel for services normally assigned to other

areas. An example is snow removal, often included as

part of street operations. In computing a sanitation productivity index, these services, both outputs and inputs,

should be removed from the calculations. If they constitute a small part of total sanitation resources, they

can be ignored.

Solid waste disposal The factors that are most often used to measure disposal outputs are weight (tonnage) and

volume (cubic yards). The arguments advanced for and

against these measures are essentially the same as those

discussed earlier. The measure that is suggested for use

is weight (tonnage). To quote one expert:

minimally acceptable set. Both use tonnage as the basic

recommended measure of output. The idealized set of

measures is presented first. Tasks as well as measures

are listed.

Collection

Residential—tons collected (small container

collection)

Once-a-week pickup

Backdoor

Curbside/alley

Two or more pickups per week

Backdoor

Curbside/alley

Commercial and school—tons collected (large

container pickup)

Qnce-a-week pickup

Two or more pickups per week

Secondary services

Abandoned car removal—cars removed

Bulk collection—tons collected

Dead animal removal—carcasses removed

Leaf removal—cubic yards removed

Street cleaning—curb miles cleaned

Disposal

Landfill—tons buried

Resource recovery—tons recovered

Other—tons disposed

“In many respects weight data are of limited value; after 

all, when we design a landfill we are concerned about 

volume, not weight. However, weight is an easier 

measurement to take since volume measures will depend 

upon how much the wastes have been compressed, and 

whether or not it is a dense material, such as foundry 

sand, or a very light material, such as waste foam 

rubber.”21

Most solid waste is buried. Although precise figures

are lacking, about 80 percent of municipal solid waste is

disposed of in landfills. The remaining 20 percent goes

to resource recovery operations such as composting,

materials recovery, and energy recovery, or is dumped

into the ocean. Because unit labor requirements vary by

disposal process, and the mix between the various processes may be changing, separation of different processes is important in computing productivity trends.

Because disposal tonnage does not often equal collection tonnage, the two should be measured separately. In

most communities, the private collector dumps at the

community disposal site. In others, the sanitation

department may dump at private sites or at sites

operated by other governments. Regional disposal sites

are quite common.

Data are not always collected and maintained by local

government in the detail needed to compute the idealized measures. Furthermore, for reasons discussed

earlier, idealized measures may not yield a more accurate productivity index than a less comprehensive set

of measures and data. Therefore, a set of minimum

measures- is presented below for which data should be

readily available in most local governments. All services

are included, but without the detailed product division

presented in the idealized measures.

Collection

Pickup—tons collected

Once-a-week pickup

Backdoor

Curbside/alley

Two or more pickups per week

Backdoor

Curbside/alley

Secondary services—tons collected

Disposal

Landfill—tons buried

All other—tons disposed

Recommended measures. Two sets of output measures

are presented here, one an idealized set and one a

21 Reindi, “Interrelationships,” p. 23.

For most Jurisdictions, government collection tonnage and government disposal tonnage will differ

because of the use of multiple collectors—private and

nonprofit as well as government—and multiple disposal

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site operators—private and government. Rarely does a

government collect all the solid waste that is dumped at

its disposal site. Some governments do not even operate

disposal sites, and some that operate disposal sites do

not collect any solid waste with their own employees.

Because of the use of multiple suppliers, tonnage must

be separated between collection and disposal. Also,

statistics should indicate the point of pickup, frequency

of collection, and type of secondary service.

Availability o f data. As noted earlier, data needed to

calculate a national index are not routinely collected. A

number of special studies have collected cross-sectional

data, some of which have focused on a small geographic

area; others have sampled jurisdictions throughout the

United States.

At least 11 surveys have been made of community

solid waste collection practices since 1900 (table 43).

The largest of these, at least in number of communities

contacted, was the 1968 Public Health Service survey.

This survey collected data from over 6,000 communities

located in 38 States and covered about 46 percent of the

U.S. population. Data were collected on many aspects

of solid waste activities including total tonnage or cubic

yards collected and disposed, number of abandoned

automobiles removed, and type of disposal activity.

Three States were asked to assess the quality of the information collected. They gave good marks to institutional information but poor marks to tonnage and yardage statistics.

Probably the most comprehensive solid waste collection survey was one in 1975 by Columbia University,

which was a stratified random sample of over 1,300

jurisdictions. The sample was carefully chosen and the

survey rigorously administered. Data were collected

from each government on institutional arrangements;

type of financing; contractual forms; outputs, including

tons and cubic yards; and inputs, including costs, labor,

and type of equipment.

Most of the surveys leave much to be desired. The

response rates are poor, and definitions and cut-off

levels are unclear.22 None of the surveys, including the

Columbia study, collected time series data.

While national time series data are lacking to compute a solid waste output index, most large and

medium-size jurisdictions do collect and record tonnage

data.23 Some communities, particularly the smaller communities, do not weigh their solid waste, but many keep

statistics on the estimated number of cubic yards or the

number of trucks unloaded, statistics which can be used

to estimate tonnage. Although the percentage of local

governments that weigh solid waste is not known, the

22 Savas, Organization and Efficiency, p. 36.

23 Harry P. Hatry and Donald M. Fisk, The Challenge o f Productivity Diversity; Measuring Solid Waste Collection Productivity, Part

II (Washington: The Urban Institute, 1972), pp. 11-12.

Table 43. Community surveys of solid waste collection

Survey Date

Number of 

communities 

responding

Response

rate

(percent)

Massachusetts Institute of

Technology.................................. 1902 161 ?

Municipal Index.............................. 1929 667 ?

American Public Works

Association (apwa) ....................... 1939 190 ?

Ap w a ................................................ 1955 908 38

Ap w a ................................................ 1964 995 32

Public Health Service...................... 1968 6,259 ?

Ralph Stone.................................... 1969 234 ?

Public Works ................................ 1973 1,630 31

Ap w a ................................................ 1973 661 51

International City Management

Association ................................ 1974 1,092 48

Columbia University........................ 1975 1,377 100

Source: E.S. Savas, The Organization and Efficiency of Solid Waste

Collection (Lexington, Mass.: Lexington Books, 1977), p. 36.

consensus among experts is that most large governments

weight their waste, and hence that the major portion of

local government waste is weighted.

Labor inputs

Three labor measuures are suggested for calculating

local government labor productivity: Number of

employees, number of full-time-equivalent employees,

and employee hours. For sanitation, these data should

be available by function and subfunction—collection,

disposal, abandoned car removal, and so forth.

Only two sources of time series data exist on local

government sanitation employees: (1) The Bureau of the

Census and (2) the individual municipalities.

The Census Bureau is the only organization that

routinely collects and publishes national time series data

on sanitation employment (table 44). These data show

128.000 local government employees in 1980, of whom

119.000 worked full time and 9,000 part time. Full-timeequivalent employees numbered 121,000. The peak

employment year was 1977.

A number of problems, in addition to those noted in

chapter III, result from using Census data. First, the

data are not separated by function or subfunction or

between primary and secondary tasks. Labor data need

to be divided at least between collection and disposal;

separation by frequency of collection and point of

pickup, and between landfill and other techniques of

disposal, also would be helpful.

Nor are Census data available for hours worked,

hours at work, or hours paid. Hours worked is important for solid waste collection because some governments permit employees to go home as soon as they

have completed their assigned task. The task system

enables many workers to receive pay for 8 hours but to

work less time. If program operations remain constant

through time, then the task system would have no effect

on productivity trends—that is, the relationship between the time paid and the time worked would remain

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Table 44. Local government sanitation employment, 1967-80

(Thousands)

Year Total employment Full-time-equivalent

employment

1967 . . 125 118

1968 . . 125 118

1969 . . 127 120

1970 .. 133 125

1971 .. 128 121

1972 . . 125 120

1973 .. 130 124

1974 .. 130 124

1975 . . 128 121

1976 . . 127 121

1977 . . 133 127

1978 . . 131 124

1979 . . 132 123

1980 .. 128 121

SOURCE: P u b lic E m p lo y m e n t, a n n u a l is s u e s (B u re a u o f th e C e n s u s ).

constant from year to year. Such information is not

available.

Another problem with Census statistics is that they

are collected only for October of each year. Seasonal

help may be an important factor in sanitation services.

The seasonal variance in labor employed is not known

but one study estimated a 20-percent average variance in

tonnage collected.24 A variance of this magnitude would

suggest the need for additional labor input during increased tonnage periods. If the October/seasonal proportion remains constant, the October statistics would

be satisfactory for the determination of the trends. A set

of October/seasonal statistics is needed to determine

whether there is constancy.

The only other regular source of local government

sanitation employment statistics is local government

itself. Some sanitation agencies publish annual reports

which include employment statistics, and most governments include employment statistics in their annual

budgets, usually divided between collection and disposal

and often between primary and secondary collection

services.

Suggested research

Data collection is the next step in computing a sanitation productivity index. Two separate approaches are

possible: 1) A census of all jurisdictions, and 2) a

stratified, random sample which might include all large

governments and a sample of medium-size and small

governments. The latter approach would be less expensive and would still produce satisfactory information

for a national index. It would not permit examination of

small geographic areas (depending on the sample size).

Also, a procedure would have to be developed to collect

the data regularly if the index was to be kept current.

Analysis of productivity levels, as contrasted with

24 National Center for Resource Recovery, Municipal Solid Waste

Collection, p. 13.

trends, can provide an important additional dimension

to productivity measurement. With a unitary measure

of output, such as tons, computation of productivity

levels should be possible, and would be helpful for individual government managers.

Drinking W ater Sypply

Water supply, like solid waste collection and disposal,

is a service with a tangible set of outputs and a welldefined set of inputs. The research community has examined this service in some depth and, unlike solid

waste services, some time series data have been collected.

Institutional setting

Of the several hundred thousand public water systems

in the United States,25 about 60,000 are community

systems which serve 25 or more year-round residents or

which have 15 or more connections serving permanent

residents.26 Community water systems serve most of the

American public, an estimated 192 million in 1976.27

Most water utilities (76 percent) serve fewer than

1,000 people (table 45). However, the large systems provide most of the drinking water in the United States.

Eight percent of the systems serve about 80 percent of

the population; at the other end of the scale, 80 percent

of the systems serve 8 percent of the population.28

More systems are publicly owned than privately owned (56 percent vs. 44 percent). Publicly operated systems

serve more than four-fifths of the population (84 percent vs. 16 percent).29 Publicly owned systems tend to be

located in large metropolitan areas; privately owned

systems in rural areas. Public systems tend to provide

more extensive water treatment than private systems,

which often provide no treatment.

Water utilities operated by local governments in every

State and the District of Columbia have over 120,000

employees. The average number of public employees

per State is about 2,200. California leads the list with

almost 15,000 employees and revenue of $766 million

(fiscal 1977). Vermont is last with 139 employees and $6

million in revenue.

In fiscal 1977, government water utilities spent about

$6.4 billion, or 2 percent of all State and local government expenditures.30 Salaries and wages of water utility

25 Small System Water Treatment Symposium (U.S. Environmental Protection Agency, September 1979), p. 2.

26 The number of community water systems is open to debate. In

1975, the figure was estimated at 40,000; in 1976, at 35,000; in 1979 at

58,000; and in 1980 at 61,000. epa staff suggest that the most recent

figures may have some double counting.

27 Temple, Barker, and Sloane, Inc., Survey of Operating and

Financial Characteristics o f Community Water Systems (U.S. Environmental Protection Agency, April 1977), p. II-4.

28 Ibid.

29 Ibid., p. II-5.

30 1977 Census o f Goverments—Compendium of Government

Finances (Bureau of the Census, 1979).

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Tab!® 45. Community water systems by size off

population, 1979

Population Number of 

systems Percent of total

T ota l.......................... 58,379 100

Less than 1 00........................ 21,468 37

100-999 ................................ 22,907 39

1,000-4,999 .......................... 9,221 16

5,000-9,999 .......................... 1,915 3

10,000-100,000 .................... 2,599 4

More than 100,000 ................ 269 1

S o u r c e : Small System Water Treatment Symposium, (U.S. Environmental Protection Agency, Sept. 1979), p. 1.

workers accounted for about I percent of all State and 

local government salary and wage expenditures; capital 

expenditures accounted for about 4.5 percent of all 

State and local government capital expenditures.

The primary factor input into water systems operated 

by State and local governments is capital, which accounts for 32 percent of all water supply expenditures 

(fiscal 1977). Salaries and wages (without fringe 

benefits) account for 22 percent (39 percent of current 

operating expenditures).31 Interest on debt, materials 

and supplies, energy, and purchased water account for 

the remaining 46 percent (table 46).

Utility operations are frequently broken into four 

functions—acquisition, treatment, distribution, and 

overhead. Acquisition, normally a small part of the cost 

of water, includes all operations before treatment, such 

as storage and transport to the treatment facility. Treatment, as the word implies, Includes any purification of 

water before distribution, a relatively small part of utility operations. Distribution includes all operations after 

treatment such as storage and transmission of water to 

the ultimate customer. Overhead includes all administrative and customer services required to manage a 

utility. Overhead and distribution account for the 

largest portion of utility expenditures.

Until 1980, Census public employment statistics 

defined water supply as “ local government activities 

associated with the production or acquisition and 

distribution of water to the public.” 32 In other words, 

employment statistics included only local government 

employees. The Census finance statistics, on the other 

hand, included State as well as local government expendditures.33 Exclusion of State employees before 1980 is 

probably not important since they accounted for less 

than 0.05 percent of total State and local government 

salaries and wages for water supply workers (table 46). 

Only three StMes—Massachusetts, Nevada, and New 

Hampshire—operate utilities supplying drinking water.

31 An e p a study of 12 large water utilities found that labor costs accounted for 42 percent of the utilities’ operating cost. See James I.

Gillean and others, The Cost o f Water Supply and Water Utility

Management, Contract Report 68-03-2071 (Cincinnati: (U.S. Environmental Protection Agency, 1977), p. 9.

32 Public Employment, p. 462.

33 Government Finances, p. 10.

The Standard Industrial Classification Manual

assigns water supply to Industry Group 494 and Industry 4941. The definition presented in the Manual for 

4941 is the following:

“Establishments primarily engaged in distributing 

water for sale for domestic, commercial, and industrial 

use. Systems distributing water primarily for irrigation 

service are classified in Industry 4971.” 34

Agencies that administer water quality, including 

regulation, research, and planning, are “-assigned to code 

9511.

Research

Considerable research has been done on drinking 

water supply. Although most of this research has dealt 

with the environment and .public health, economic 

issues also have been investigated. Economic research 

helps answer questions important for productivity 

calculation, such as relationships among the factors of 

production, the relationship between costs of production and pricing, the role of size in production costs, the 

proper measure of output, and the effect of quality 

change on costs.

Several investigators have done research on production and cost functions, but the results have not been entirely satisfactory. In the 1960’s, Ford and Warford attemped to derive a total cost function for the water supply industry of England and Wales.35 Hines attempted a 

similar analysis with data from Wisconsin, and Goddard did likewise with data from Cincinnati.36 Although 

the industry product was relatively homogeneous, Ford 

and Warford noted that production conditions were 

quite dissimilar. They concluded that either the differences in technology or sources swamped the independent variables or production costs had to be broken 

down further.

Studies of individual parts of the production process 

have explained somewhat more successfully cost variations among systems. Studies by Orlob and Lindorf, 

Koenig, and Hinomoto were all reasonably successful in 

explaining the differences in treatment costs.37 Orlob 

and Lindorf studied cost of operation as a function of 

average daily treatment (in millions of gallons) for 32 

treatment facilities. Koenig expanded the methodology 

of Orlob and Lindorf to examine specific treatment 

costs. His correlation coefficient for the average cost 

relationship was .77. Hinomoto, using Koenig’s data,

34 Standard Industrial Classification Manual, p. 238.

35 J.L. Ford and J.J. Warford, “Cost Functions for the Water Industry,” Journal o f Industrial Economics, November 1969, pp. 53-63.

36 Lawrence G. Hines, “The Long Run Cost Function of Water

Production for Selected Wisconsin Communities” Land Economics,

Vol. 45, February 1969, pp. 133-40; Haynes G. Goddard and others,

Planning Water Supply: Cost-Rate Differentials and Plumbing Permits (Cincinnati: U.S. Environmental Protection Agency, 1978).

37 See Goddard, Planning Water Supply, pp. 20-27, for a description of these studies.

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Table 46. Finances of government-operated water utilities by type of government, fiscal year 1977

(Millions)

Government Revenue

Expenditures

Total Capital Interest 

on debt

Current operations

Total Salaries 

and wages Other

Total ...................................... $4,995 $6,381 $2,047 $786 $3,547 $1,395 $2,152

States ................................................ 6 21 3 6 13 8 5

Municipalities.................................... 3,823 4,306 1,261 475 2,570 1,085 1,485

Special districts.................................. 772 1,413 518 206 688 194 494

Townships.......................................... 139 187 44 20 124 47 77

Counties............................................ 256 453 221 79 153 63 90

Note: Because of rounding, detail may not add to totals.

estimated unit and total costs for a plant of a given

capacity operating at a given rate. His estimating equations used seven resource inputs: Capital, chemicals,

pumping energy, heating energy, labor, maintenance

and repair, and miscellaneous. None of these three

studies explicitly considered economies of scale which

were evident in the derived equations.

A number of studies have been done on economies of

scale in drinking water supply. A study by Clark of

water treatment costs notes that the unit cost water

treatment curve changes dramatically between the

ranges of 0 - 2.5 million gallons per day (MGD), 2.5 - 20

m g d , and more than 20 m g d .38 Another study by Clark

found that in 42 municipal utilities large-system unit

costs were about half those of small systems. Although

statistics were not presented for the relationship between labor and output, he noted that they would

parallel the decreasing cost relationship.39

An e p a study of 70 investor-owned utilities showed

economies of scale as measured by unit costs and the

number of employees for the small and medium-size,

but not large systems.40

William Fox examined five studies of economies of

scale in water utilities.41 Each showed increasing

economies. Two studies used the quantity of water sold

as the measure of output. Another used the number of

users. The other two studies used both quantity and

customers/population.

The water supply industry is extremely capital intensive as measured by the investment per dollar of

revenue. According to one study, water utilities require

$6-10 of capital investment for each dollar of revenue.

38 Robert M. Clark, “ Small Water Systems: Role of Technology,’’

Journal of the Environmental Engineering Division, American Society of Civil Engineers, February 1980, pp. 19-35.

39 Robert M. Clark, “The Safe Drinking Water Act: Its Implications for Planning,” in David Holz and Scott Sebastian eds.,

Municipal Water Systems: A Challenge for Urban Resource Management (Bloomington: Indiana University Press, 1978), pp. 117-37.

40 “ Comparisons of Cost, Manpower Utilization, and Flow in

Operation and Maintenance of Investor Owned Water Companies and

Municipal Waste Water Systems” (U.S. Environmental Protection

Agency, June 5, 1979), unpublished report.

41 Fox, Size Economies, pp. 26-30.

Source: Government Finances, p.33.

Comparable figures for airlines, railroads, telephone

companies, and electric utilities are $1, $2, $3, and $4

respectively.42

Although there is no national index of water supply

productivity, Clark has calculated labor efficiency

measures for a sample of 12 utilities.43 Using revenue

gallons as the output measure, he found that over a

10-year period labor hours per million gallons decreased

(an increase in labor efficiency) but not sufficiently to

offset increasing labor costs. The net result was that

dollar cost per million gallons increased.

Water quality has long been an important issue, and

the amount of capital investment is partly a function of

the treatment required to ensure a safe water supply.

Operating cost, including labor input, is also a function

of the amount of treatment required . The exact relationships depend on a number of factors such as the type

and amount of pollutants in the influent, difficulty in

removing the pollutants, alternative sources of supply,

and available technologies to remove the pollutants.

One analysis found that removing five different types of

pollutants increased unit operating costs from 1 to 700

percent depending on the process used.44 The importance of labor was not specifically considered. In

another study, examination of an exchange process to

remove nitrates from a utility’s water supply showed

that labor cost accounted for about half the increase in

annual operating cost.45 A study of 67 water utilities in

the Cincinnati area showed that increasing the quality of

water to a “ good” level could substantially affect the

costs of production for many utilities, particularly

smaller ones.46

42 Temple, Barker, and Sloane, Community Water Systems, p. II-8.

43 Robert M. Clark, “ Labor Wage Rates, Productivity, and the

Cost of Water Supply” Journal o f the American Water Works

Association, July 1979, pp. 364-68.

44 Clark, “ Small Water Systems,” pp. 26-27.

45 Robert M. Clark, “Water Supply Regionalization: A Critical

Evaluation,” Journal o f the Water Resources Planning and Management Division, American Society of Civil Engineers, September 1979,

p. 286.

46 Robert M. Clark and Haynes G. Goddard, “ Cost and Quality of

Water Supply” Journal o f the American Water Works Association,

January 1977, pp. 13-15.

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Several important conclusions for productivity

measurement emerge from the research:

1. Production conditions are dissimilar.

2. There are marked economies of scale in the

production process, particularly in treatment.

3. The industry is capital intensive, but labor

plays an important role.

4. The function has a relatively homogeneous

product, although water quality factors differentiate the product.

O utputs

Four common measures of water utility output, one

monetary and three physical, are: Sales adjusted for

price changes, number of customers, number of connections, and number of gallons. The strengths and

weaknesses of each measure are briefly enumerated

below.

Dollar sales. The primary virtue of dollar sales is its

ready availability. It is the only output measure for

which national data are available for a number of years

(table 47). The Bureau of the Census collects and

reports these data as part of its annual survey of government finances.

A primary problem in using dollar sales as a measure

of output is adjusting for price level changes. The price

deflator most often used is the joint water-sewerage

deflator of the Consumer Price Index, which is not entirely satisfactory for adjusting drinking water sales.

First, the CPI reflects changes in sewerage as well as

water supply and it is not known whether water and

sewerage price changes move at the same rate. Second,

governments turn to price increases to discourage the

use of water and to increase utility profits. Whether

deflated revenue any longer adequately reflects changes

in physical output is debatable. Third, no adjustments

are made for quality changes for either water or

sewerage, and both have improved greatly since the Safe

Table 47. Revenue of State and local government-owned 

water utilities, fiscal years 1967-80

(Millions)

Year Unadjusted Deflated1

1967 ...................................... $2,187 $4,575

1968 ...................................... 2,313 4,617

1969 ...................................... 2,464 4,606

1970 ...................................... 2,687 4,665

1971 ...................................... 2,980 4,671

1972 ...................................... 3,165 4,781

1973 ...................................... 3,463 4,954

1974 ...................................... 3,712 5,016

1975 ...................................... 4,142 5,095

1976 ...................................... 4,463 4,948

1977 ...................................... 4,995 4,995

1978 ...................................... 5,512 4,952

1979 ...................................... 6,250 5,365

1980 ...................................... 6,766 5,426

1 Water/sewerage index of the Consumer Price Index used to deflate 

revenue dollars; 1977 ^base year.

S o u r c e : Statistical Abstract of the United States, selected years 

(Bureau of the Census).

Drinking Water Act was passed in 1974. In short,

physical outputs are preferred for productivity calculations.

Population served. This measure simply counts the

number of people served and assumes that the average

quantity of water used per person remains constant.

Research shows a good correlation between the water

used and the number of people served. Population

figures are often used to plan the amount of water a

community will need. This statistic is kept or can readily

be produced by most water utilities (number of residential connections times average household population).

Most national surveys of water utilities collect these

data.

There are two arguments against using this as a

measure of output. First, most water utilities serve commercial and industrial users as well as residential

customers. If the community has a large number of

nonresidential users, or a few that consume a large

quantity of water, population is not a particularly good

measure of output. A recent study estimated that

residential units made up 90 percent of the billings of

U.S. water systems but accounted for only 60 percent of

the water delivered. If the proportion of residential to

nonresidential units remained constant through time,

the measure would be satisfactory for trend determinations, but the ratio is not known. Considerable pressure

has been exerted in recent years for industrial and commercial users to restrict their use of water through

recycling.

The second argument against this measure of output

is that sometimes population does not correlate well,

particularly over the short run, with the quantity of

water used. Variations in temperature and rainfall will

affect water usage, and a population measure will not

reflect these changes.

Connections. The number of connections is the number

of residential, industrial, and commercial hookups. It is

a surrogate measure for the water produced and

delivered to users. This measure of output, which has

many of the same strengths and weaknesses as the

population measure, has one distinct advantage: Almost

every water system should have accurate data readily

available on the number of connections. This information is certainly more accurate than a population count,

although population is probably a better estimator of

water use.

Revenue gallons. This measure is the number of gallons

purchased by consumers. Revenue gallons are similar

but not identical to gallons treated, gallons pumped, or

gallons produced, which are sometimes used as output

measures. Revenue gallons do not include water

pumped but lost through leaky water mains and open

hydrants. Revenue water reflects actual sales.

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Another reason for focusing on sales to final

customers is interutility water sales. The magnitude of

resales is not known, but focusing on sales to ultimate

users minimizes the problem of double counting in a national index. This approach is similar to that used in

electric utilities as discussed in the previous chapter.

The primary argument against using this measure is

data availability. There are no comprehensive national

statistics and, in some cases, no individual utility

statistics on revenue gallons. Furthermore, some water

systems do not collect statistics on the quantity of

revenue water sold. New York City, for example, which

does not meter most of its sales, has no record of the

number of gallons purchased by its customers. Even

those cities which have a policy of metering, such as

Boston, New Orleans, and Washington, D.C., do not

meter all sales.47 However, most large and medium-size

water systems do meter most sales.

The use of gallons as the output measure also implies

that production input requirements (unit labor requirements) vary by gallon when, in fact, they do not.

Water treatment has economies of scale, particularly for

small and medium-size utilities. Most water systems set

graduated rates based upon the amount of water used—

the greater the amount of water purchased, the cheaper

the unit price.

An analogous situation exists in electric power production and sales—the greater the amount of power used, the cheaper the unit price. Electric power productivity calculations often take into account decreasing production costs (unit labor requirements) by separating

users into classes and weighting output accordingly.

Quality considerations. Drinking water quality concerns

every water utility. Of the dozens of water quality attributes, some affect consumer utility, some affect production costs. This discussion deals with water pressure

and with health.

Adequate water pressure is an important consideration in drinking water supply. Inadequate pressure can

result in minor inconveniences, such as improperly

working washing machines and dishwashers, or major

effects, such as backsiphonage, contamination of the

water supply, and reduced firefighting capability. A

number of factors affect water pressure. Most important are the design and construction of the system.

Operation and maintenance of the system are less important and less significant for labor productivity and

are not considered further here.

The health issue is often raised in discussions of

drinking water supply. Dozens of pollutants can affect

water quality and community health, and their removal

can increase production costs, as noted above.

47 Additional Federal Aid for Urban Water Distribution Systems

Should Wait Until Needs are Clearly Established' (General Accounting Office, November 24, 1980), p. 30.

Most jurisdictions, particularly medium-and largesize ones, carefully monitor and control water quality.

Most State governments, with the assistance of the Environmental Protection Agency, collect and tabulate

statistics on how well drinking water quality standards

are met. This information is available by system, by

State, and nationally since 1978. e p a estimates that

13,600 water systems, private and public, do not meet

one or more drinking water quality standards.48 Small

systems account for the majority of the problems.

The important issue insofar as productivity indexes

are concerned is how quality has changed through time.

Additional research is needed into the area, particularly

the effect on unit labor requirements. As a first step,

utilities should be divided into two groups: Those

meeting and those not meeting e p a standards. Labor

weights could be used to combine the indexes for a national index.

Recommended measure. The recommended output

measure to compute a national water supply productivity index is revenue gallons, the measure used by most

economic analysts and water utility managers. The output should be weighted by type of customer— residential, commercial, and industrial—since unit labor requirements vary by type. Unit labor requirements are

the preferred weight, but price may be a satisfactory

surrogate. Data and outputs should be separated between those which meet e p a standards and those which

do not. If sample data are used, outputs should be

weighted by size of utility—e.g., small (less than 2.5

million gallons per day), medium (2.5 - 20.0 million

gallons per day), and large (more than 20 million gallons

per day).

Availability o f data. No comprehensive set of statistics

is available to compute a revenue gallon output index

for State and local government water utilities at this

time. Some data may be obtained from several ad hoc

surveys and two ongoing data collection activities.

e p a ’s Federal Regional Data System, the largest

ongoing data collection system, collects data annually.

This system, which was initiated in fiscal 1978, monitors

compliance with Federal drinking water standards.

However, data are also collected on operations, including gallonage, population served, number of

meters, and type of customers served. Whether' the

gallonage data are revenue water, treated water,

pumped water, or some other gallonage measure is not

known. Most likely they are a combination of these.

Possibly there is some double counting. As e p a works

with the States to improve data and reporting, this

system should be more useful in calculating water supply output, e p a does not collect input data, unfortunately.

48 States’ Compliance Lacking in Meeting Safe Drinking Water

Standards (General Accounting Office, March 3,1982), p. 11.

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Probably the most accurate data on community water 

supply operations are available from those e p a collects 

from 78 utilities for research purposes. The data are collected in the field, and the time period covered ranges 

from 6 to 15 years, depending on the utility. The data, 

which reflect fiscal years, include revenue gallons, labor 

hours, costs of operation, population served, and 

population density. These sample data, however, may 

not be representative of U.S. community drinking water 

systems. For example, all 78 utilities meet e p a quality 

standards. Data from the e p a 78-system series can be 

useful, but they are not sufficient for computing a productivity index.

e p a also collected data on operations from 1,000 

utilities in 1976. This Community Water System data 

base includes statistics on population served, connections by type, sources of water, treatment, gallons 

delivered, and revenue for one year. Although the data 

are useful for cross-sectional analysis, they are of no use 

in computing a revenue gallons output index.

Revenue gallonage statistics are available from 

several of the American Water Works Association 

(a w w a ) surveys. However, a w w a statistics cover only 

selected years, they are a sample of the large water supply utilities, and the number of utilities varies from sample to sample. Although the a w w a statistics can help in 

preparing an output index, they are not sufficient.

Labor inputs

Chapter III discussed three labor measures for 

calculating State and local government productivity: 

Number of employees, number of full-time-equivalent 

employees, and number of employee hours.

The foui; sources of data on local government water 

system employment are: (I) The Bureau of the Census, 

(2) the American Water Works Association, (3) e p a ,

and (4) the individual utilities.

The Bureau of the Census is the only organization 

that regularly collects and publishes a comprehensive set 

of time series data on employment in local water 

utilities. The data show that these utilities had about

134,000 employees in 1980, a 17-percent increase since 

1967 (table 48). About 11 percent were part-time 

employees in 1980. Census statistics do not include State 

employees until 1980, which is probably not a limitation 

for calculating a national index because of the small 

number of State employees—fewer than 1,000. A more 

serious problem with the Census statistics is that only 

aggregates are collected and presented.

No information is collected on force account (construction) employees. The number of such workers, and 

the extent to which the number has changed during the 

past decade, is not known. This would be an important 

issue for measuring labor productivity trends if the ratio 

between construction and operating employees had 

changed substantially. Also, Census statistics are not

(T h o u s a n d s )

Table 48. Local government water utility employment,

1967-80

Year Total employment Full-time-equivalent

employment

1967 ...................................... 115 108

1968 ...................................... 114 107

1969 ...................................... 116 108

1970 ...................................... 117 110

1971 ...................................... 116 108

1972 ...................................... 114 108

1973 ...................................... 121 113

1974 ...................................... 129 115

1975 ...................................... 129 115

1976 ...................................... 129 115

1977 ...................................... 131 118

1978 ...................................... 134 121

1979 ...................................... 132 120

1980 ...................................... 134 122

S o u r c e : P u b lic E m p lo y m e n t, a n n u a l is s u e s .

divided by type or size of utility, which is useful in 

.developing and applying unit labor weights.

Finally, the assignment of government personnel to 

water supply operations when they work in other 

areas—e.g., sewerage, gas, and/or electric power—is a 

potential problem. The Bureau of the Census suggests 

that governments allocate personnel among functions 

using revenue figures. Statistics from larger electric 

power utilities, as discussed in the preceding chapter, 

suggest that this may not be a problem, particularly for 

trend calculations.

The only other time series on employment is e p a ’ s

78-utility sample. It includes detailed statistics for each 

utility on the number of personnel and hours paid, and 

includes output as well as employment. The primary 

deficiency of the information, insofar as this study is 

concerned, is that it may not be representative of the 

universe. Also, data collection is not timely; some data 

are collected 4 or 5 years after the fact. However, this 

information, collected through field visits, is probably 

the most accurate available.

The American Water Works Association also 

periodically collects statistics on employment, wages, 

salaries, and fringe benefits. The problem with the 

a w w a statistics is their sporadic nature and inconsistent 

sample. Although these statistics may help in analyzing 

employment data, they are not sufficient to compute a 

labor index.

The only other sources of personnel statistics are the 

individual utilities.

Suggested research

An accurate, representative set of output and input 

data is the first requirement for calculating an index of 

productivity for State and local water supply. Analysis 

and research should also be conducted in other areas. 

Quality of service, particularly water quality and its effect on unit labor requirements, warrants further in64

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vestigation. There is some research in the area but much

remains to be done.

Another area for research is multifactor productivity.

The Bureau of the Census estimates that salaries and

wages (without fringe benefits) accounted for only 22

percent of State and local government water supply

utility expenditures in fiscal 1977. Capital accounted for

32 percent, and interest on debt, materials and supplies,

energy, and purchased water the remaining 46 percent.

To better analyze shifts in productivity, all major factor

inputs and their interrelationships need to be examined.

Analysis of levels of productivity, as opposed to trend

analysis presented here, can provide an important additional dimension. With a unitary measure of output,

such as revenue gallons, computation of productivity

levels should be possible. Comparisons among jurisdictions that take into account differences in law,

geography, topography, hydrology, weather, population size and density, location of water, and water quality will be more difficult to prepare.

Hass Tramsot

Although mass transit, like sanitation and drinking

water services, has been the subject of considerable

research and analysis, no national productivity index is

currently calculated. Unlike the other two services, data

are routinely collected on outputs and inputs, and computation of a productivity index should be relatively

straightforward. The problem, as with so many State

and local government services, is to know what measure

of output to use.

Institutional sotting

Interest in measuring State and local government

transit productivity is a fairly recent phenomenon. Until

World War II, most transit systems were privately owned. In 1950, only 26 systems or less than 2 percent of all

transit systems in the United States were publicly owned. By 1980, 55 percent of the systems were public.

More importantly, public systems carried the vast majority of passengers. The private systems were mostly

small operations. State and local government systems

delivered 94 percent of the industry’s passenger trips,

operated 93 percent of the vehicle miles, and owned or

leased 90 percent of the vehicles.49

The shift from private to public systems has been particularly rapid since the mid-1960’s. In 1968, 65 percent

of the industry’s passenger trips were public; in 1978, 93

percent. In 1968, 56 percent of all vehicle miles were

public; in 1980, 93 percent. In 1968, 55 percent of all

vehicles were publicly owned; in 1980, 90 percent. This

49 American Public Transit Association, Transit Fact Book—1981

(apta, 1981), p, 43.

shift from private to public systems continues, although

obviously not at the same rate of speed.

Even today, institutional arrangements are mixed,

varying from total private to total public, including:

1. Private ownership and operation, such as the

South Carolina Electric and Gas Company, which

operates the buses in Charleston, South Carolina.

2. Private ownership and operation with a government subsidy, such as the Southern Pacific

commuter operation to and from San Francisco.

3. Joint ownership with private operation. An example of this type is the Massachusetts Bay

Transportation Authority, which owns the

commuter cars while the Boston and Maine

Corporation, a private corporation, provides the

tracks and service.

4. Public ownership with private operation, such as

the commuter railroads in and around Chicago.

5. Public ownership, private management with

government employees. An example is the a t e

Management and Service Company of Cincinnati,

which manages 49 municipal bus systems throughout the United States. Approximately 125 systems

of this type operate in the United States today.

6. Public ownership and management, as in

Washington, D.C.

The following discussion focuses on types (5) and (6),

those in which government employees operate the

system.

State and local government transit systems operate in

44 States. New York, with over 39,000 State and local

government employees and almost $2 billion in expenditures (fiscal 1977), is the most deeply involved. The

large systems are heavily concentrated in urban areas,

particularly in the Northeast. The large systems

dominate production; the 13 largest systems, for example, account for about 65 percent of all transit employment.

In fiscal 1977, State and local transit systems spent

approximately $5.6 billion, or 1.7 percent of all State

and local government expenditures.50 Their expenditures for salaries and wages accounted for about 2

percent of all State and local salary and wage expenditures; their capital expenditures, for about 3.7 percent

of State and local capital expenditures.

Passenger fares cover about one-third of all transit

expenditures. Transfer payments and gasoline, sales,

and property taxes support the other two-thirds. In

fiscal 1977, the Federal Government provided $1.9

billion in transfer payments for mass transit, 24 percent

of all State and local government expenditures in this

area.

The primary factor input into transit operations is

labor. Salaries and wages account for 43 percent of all

expenditures (65 percent of current operating expen50 Government Finances, pp. 29 and 33.

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Table 49. Finances of government-operated mass transit systems by type of government, fiscal year 1977

(Millions)

Government Revenue

Expenditures

Total Capital Interest 

on debt

Current operations

Total Salaries 

and wages Other

Total ...................................... $1,991 $5,598 $1,681 $250 $3,668 $2,391 $1,277

S tates................................................ 308 628 109 _ 520 290 230

Municipalities.................................... 941 1,811 433 89 1,288 966 322

Special districts.................................. 703 3,052 1,124 157 1,770 1,074 696

Townships.......................................... 1 - - - ■ - - -

Counties............................................ 38 107 15 2 89 51 38

Dash = zero or rounds to zero.

Note: Because of rounding, detail may not add to totals.

ditures) (table 49). Inclusion of fringe benefits would

further increase this percentage. Capital expenditures

account for 30 percent, and interest, fuel, materials,

supplies, and the like account for the remaining 27 percent.

The Bureau of the Census reported 87,000 public

transit employees in 1968. By 1978, the figure had increased to 125,000 and by 1980 to 172,000, or about 1

percent of all State and local government employees.

Although some of this increase reflects redefinition of

terms and new data collection procedures, employment

growth has been real and rapid. The vast majority of the

172,000 State and local,government employees work for

local government (92 percent), and most are full-time

employees (97 percent).51

Public transit can be divided into nine different types

(modes): Bus, heavy rail such as subways, commuter

rail, light rail such as streetcars, trolley coach, urban

ferry boat, cable car, inclined plane, and paratransit.

The first two modes account for aboilt 95 percent of all

passenger trips.

The motor bus was the most important mode in 1980

in the number of passenger trips (67 percent), operating

revenue (60 percent), and vehicle miles (74 percent)

(table 50). About 60,000 urban buses and vans were in

use in the United States in 1980. These vehicles logged

over 5.7 billion passenger trips and approximately 23

billion passenger miles. Over 85 percent of the fleet of

urban buses were either owned or leased by government.

Heavy rail or subway is the next most important

mode of public transit. In 1980, 11 heavy rail systems,

all publicly owned and operated, logged 2.3 billion

passenger trips for an estimated 10.6 billion passenger

miles (table 51).

Eighteen commuter railroads logged about 285

million passenger trips and 5.9 billion passenger miles in

1980. All were government owned or received government subsidies. Amtrak, for example, provided service

for Los Angeles, San Diego, and Washington, D.C.,

and the Baltimore and Ohio provided service to

51 Public Employment in 1980 (Bureau of the Census, 1981).

Source: G o v e rn m e n t F in a n c e s , p. 33.

Baltimore and Washington, with State of Maryland

subsidies. Since these were not operated by government

employees, they are not included in this study.

Only nine streetcar systems were still operating in the

United States in December 1980. These systems

operated about 1,000 cars which traveled about 380

million passenger miles.

Five trolley coaches using 825 cars traveled almost

200 million passenger miles during 1980.

Sixteen ferry boat establishments which operated 68

boats traveled almost 340 billion passenger miles in

1980. Eight of the 16 systems were publicly owned and

operated.

During 1980, one publicly owned and operated cable

car system and five inclined planes were in operation.

None are sufficiently important to warrant special investigation in national productivity calculations.

Paratransit includes services such as dial-a-ride,

subscription bus service, airport limousines, and jitneys.

They provide variable-route or demand response service. The latter type of service and costs of production

are very different from the fixed route and time operations. Paratransit operations are not included in this

discussion.

The focus here is on regularly scheduled transit operations. No distinction is made between “ transit” and

“mass transit.”

The Standard Industrial Classification Manual

defines transit (SIC 4111) as follows:52

“Establishments primarily engaged in furnishing local 

and suburban mass passenger transportation over 

regular routes and on regular schedules. Such 

transportation may involve use of one or more modes 

of transportation. Establishments primarily engaged in 

furnishing passenger transportation by automobile or 

bus to, from or between airports or rail terminals over 

regular routes are included in this industry.”

The Bureau of the Census and the American Public

Transit Association use analogous definitions.53

52 Standard Industrial Classification Manual, p. 221.

53 Public Employment, p. 462.

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Table 50. Transit modes (private and public) ranked by 

passenger trips, revenue, and miles, 1980

(Percent)

Mode Passenger

trips

Operating

revenue

Vehicle

miles

T ota l.............................. 100 100 100

Motor b u s .............................. 67 60 74

Heavy rail (subway)................ 27 23 17

Commuter rail........................ 3 14 7

Light rail (streetcar)................ 1 1 1

Trolley coach ........................ 1 1 1

Ferryboat.............................. 1 1 -

Cable car................................ - - -

Inclined plane........................ - - -

Paratransit ............................ - - -

Dash = not reported.

S o u r c e : Transit Fact Book— 1981, (American Public Transit Association), pp. 40-41.

Table 51. Heavy rail operations in the United States, 1980

Authority Location

Chicago Transit Authority...................... Chicago

Greater Cleveland Regional Transit

Authority....................................... Cleveland

Massachusetts Bay Transportation

Authority........................................ Boston

Metropolitan Atlanta Rapid Transit

Authority........................................ Atlanta

Municipality of Metropolitan Seattle . ... Seattle

New York City Transit Authority............ Brooklyn

Port Authority Trans-Hudson Corporation ................................................. New York

Port Authority Transit Corporation

of Pennsylvania and New Jersey.......... Camden

San Francisco Bay Area Rapid Transit

District.................................................... Oakland

Southeastern Pennsylvania Transportation Authority .............................. Philadelphia

Washington Metropolitan Area

Transit Authority.................................. Washington

S o u r c e : Transit Fact Book— 1981, p . 1 6 .

Research and conceptual issues 

The measurement of transportation productivity has

a long history. The Bureau of Labor Statistics has

calculated private-sector indexes for railroads, intercity

trucking, intercity buses, air transportation, and

petroleum pipelines for a number of years.54 These

measure final output by revenue traffic units. Two output measures are used for railroads (sic 401). One,

revenue traffic units, the preferred measure, is a

weighted composite of freight ton-miles and passenger

miles. Freight ton-miles are adjusted for changes in

commodity mix. Freight and passenger miles are combined using unit revenues. The second measure, car-mile

54 See, for example, Productivity Measures for Selected Industries,

1954-81, Bulletin 2155 (Bureau of Labor Statistics, 1982).

productivity, is an aggregate of loaded and empty car

miles for both freight and passenger service. For intercity trucking (sic 213), the output measure is tonmiles for six classes of service weighted by the number

of employees. As for railroads, outputs are adjusted for

changes in commodity mix. The ouput measures for intercity buses (Sic 4111, 4131, 414) are passenger miles

and deflated freight revenue. The air transport industry

(Sic 4511) output is measured by revenue passenger

miles, freight ton-miles, express ton-miles and mail tonmiles. They are combined using unit revenue weights.

Lastly, petroleum pipeline (sic 4612 and 4613) output

reflects barrel miles (a barrel mile is one barrel of

petroleum moved one mile).

A number of studies have been made of private sector

transportation productivity. John W. Kendrick completed a detailed study of U.S. air, pipeline, waterway,

intercity bus, intercity motor trucking, and local

passenger transit productivity in 1966.55 He developed

indexes for outputs, inputs, and partial and total factor

productivity from 1948 to 1964. The measure for

passenger output was either the number of passengers or

passenger miles. The measure for employee input was

hours worked, estimated from data on the number of

employees and average hours worked per employee.

Deakin and Seward completed a study of United

Kingdom bus, railway, truck, boat, airport, and port

productivity in 1969. Indexes were calculated for outputs, labor and capital inputs, and productivity change.

Outputs were measured in either ton-miles or passenger

miles. For each mode, outputs were separated by type of

transport and weighted. The labor input measure was

adjusted for age and sex to account for composition

changes.56

Scheppach and Woehlcke published a study in 1975

which demonstrated how productivity measures might

be used in transportation regulation. Three

modes—rail, air, and trucking—were reviewed. The

output measures were traditional ones. For rail

passenger service they used the number of passengers,

passenger miles, and passenger revuenue.57

The shift from private to public mass transit has been

accompanied by a number of studies of public transit

productivity, efficiency, effectiveness, and impact. One

study by Anthony R. Tomazinis addressed performance

measurement from the perspective of four different

potential actors—operator, user, society, and government.58 Dozens of measures were examined. Important

55 John W. Kendrick, “Productivity Trends in U.S. Transportation

Industries,” unpublished paper, 1966, as cited in Scheppach and

Woehlcke, Transportation Productivity (Lexington, Mass.: Lexington

Books, 1975).

56 B.M. Deakin and T. Seward, Productivity in Transportation

(Cambridge, England: Cambridge University Press, 1969).

57 Raymond C. Scheppach, Jr., and L. Carl Woehlcke, Transportation Productivity (Lexington, Mass.: Lexington Books, 1975).

58 Anthony R. Tomazinis, Productivity, Efficiency, and Quality in

Urban Transportation Systems (Lexington, Mass.: D.C. Heath and

Company, 1975).

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conclusions were that performance should be divided

between efficiency and effectiveness, and that productivity should be measured by efficiency. Efficiency is

concerned with what is provided, and effectiveness is

concerned with what is consumed. Services provided include vehicle hours, vehicle miles, and seat miles; services consumed include the number of passengers and

passenger miles.

Gordon Fielding, among others, used the efficiency

and effectiveness concept to measure transit system productivity.59 Fielding defined efficiency as “ doing things

right” and effectiveness as “ doing the right things.” He

examined a series of measures, collected data from a

number of transit systems, and compared the systems.

His efficiency measures were divided into three

types— capital utilization, operating expense per produced output, and labor productivity. The labor productivity measure recommended by Fielding was

revenue vehicle hours per employee.

A study of the performance of urban bus systems by

Sinha and Jukins examined five labor productivity

measures: Vehicle miles per employee, vehicle miles per

driver, vehicle miles per driver hour, vehicle hours per

employee, and vehicle hours per driver.60 Vehicle miles

per employee was their recommended measure.

Another study, of bus operations in different systems

in 1960-74, found that the number of buses and

employees remained relatively constant over the time

period while patronage decreased. Almost every productivity index examined showed a decline.61

These are only a few of the many studies of public

sector transit productivity, some of which are analytic,

some conceptual, and some simply descriptive. Government officials have generally followed the recommendations of these studies and separated efficiency and effectiveness m easurem ent.62 Consequently, a real 

dichotomy has developed between private and public

sector transit productivity measurement over the past

decade. Private sector measurement has focused on

final outputs, such as passenger miles, while public sector measurement has focused on capacity measures,

such as vehicle hours or vehicle miles. Capacity

measures are thought to capture, at least partially,

social objectives reflected in numerous administrative

and legal requirements.

59 See, for example, Gordon J. Fielding, Roy E. Glauthier, and

Charles A Love, Development o f Performance Indicators for Transit

(Irvine: University of Califorina, Institute of Transportation Studies,

1977); and G.J. Fielding and Roy E. Glauthier, Distribution and

Allocation of Transit Subsidies in California (Irvine: University of

California, September 1976).

60 Kumares C. Sinha and David P. Jukins, Definition and Measurement of Urban Transit Performance (West Lafayette, Ind.: Purdue

University, December 1978).

61 Wells Research Company, Trends in Bus Transit Operations,

1960-74 (U.S. Department of Transportation, 1977).

62 Transit System Productivity (Washington: Urban Consortium,

1976, revised 1978); Proceedings o f the First National Conference on

Transit Performance, (Washington: Public Technology, Inc., 1978);

and Eckart Bennewitz, “Mass Transit,” in George J. Washnis, ed.,

Productivity Improvement Handbook, pp. 771-72.

Outputs

Physical quantities are the preferred measure of output for government productivity calculations. Dozens

of such measures exist for public transit, and, as noted

earlier, there is considerable debate over which measure

best describes transit output. The measures most often

recommended include:

1. Revenue vehicle hours

2. Vehicle miles

3. Seat miles

4. Number of passengers

5. Passenger miles

6. Passenger revenue.

Analysts of private sector transit productivity tend to

focus on the last three measures in assessing outputs.

Public sector transit analysts and managers use all

measures but tend to focus on the first three for productivity calculations. Strengths and weaknesses of each of

these measures are analyzed at length in the literature.63

Each measure is briefly reviewed here.

Revenue vehicle hours. Revenue vehicle hours (rvh ) is

the single output measure that most transit managers

prefer for productivity analysis. Revenue refers to the

hours a vehicle is in service and capable of generating

revenue rather than the amount of revenue actually

generated, r v h does not include the hours spent traveling to and from storage facilities, other deadhead travel,

and layover time. Revenue vehicle hours is a measure of

transit availability for a community. A bus could travel

a route for 8 hours without any passengers but still

generate 8 revenue hours.

Arguments in favor of this measure are:

1. It is a good measure of the costs of production.

A recent study showed that 54 percent of the variation in operating costs among transit systems was

explained by this factor.64

2. It is a good surrogate measure for the service

provided to a community. If the hours of service

are extended, revenue vehicle hours increase. If

service is cut, they decrease.

3. It is a measure over which managers have good

control.

4. It encourages reduction of nonproductive use of

vehicles such as deadhead and layover time.

5. Most transit systems have information to calculate

this measure, although it has not been collected

nationally until recently.

The principal argument against using revenue vehicle

hour is that it is a measure of capacity rather than use.

An increase in revenue vehicle hours does not necessarily lead to an increase in the number of passengers car63 For example, see Fielding, Performance Indicators.

64 Ibid., p. 12.

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ried or revenue collected. It is not a measure of final

output.

Vehicle miles. This is a measure of total distance traveled by revenue vehicles. Some vehicle mile measures include deadhead and revenue miles; others focus only on

revenue vehicle miles. The arguments for and against

using vehicle miles as an output measure are similar to

those already discussed under revenue vehicle hours. In

addition, revenue vehicle hours explained 54 percent of

the variation in operating cost; vehicle miles explained

only 28 percent.65

Seat miles. A measure closely related to vehicle miles is

the number of seat miles, which are the total vehicle

miles on passenger-carrying routes multiplied by the

seating capacity. Seat miles is a better measure of transit

capacity than vehicle miles, although it ignores standee

capacity, which is important in many communities with

heavy rash hour traffic. Like revenue vehicle hours,

data should be readily available in most transit systems

to calculate this measure. However, no national time

series data are available.

Number of passengers. A measure of transit use, in contrast to capacity provided, is the number of passengers.

The basic measure is a simple count of all passengers

using a transit system. The measure is sometimes divided by type of passenger—paying, nonpaying, school

child, reduced fare, elderly, and so forth. A special issue

is how to count passenger transfers. Some systems count

transfers as additional passengers; others do not.

The basic strength of the measure is its focus on

usage, or final output. As Fielding noted, “ for the

typical transit system, increased patronage from one

year to the next is much more significant than any other

financial or operating statistic.” 66 Another study noted

that “ service performance must ultimately be measured

by the number of riders attracted.” 67 Most transit

systems keep statistics on the number of passengers. The

American Public Transit Association has national

statistics on total passengers, and revenue passengers

for private and public systems.

Two basic arguments are advanced against using total

passengers as the measure of transit output. First, it

does not consider the numerous legal and administrative

mandates such as assisting the handicapped, serving

low-income riders, reducing air pollution, and conserving energy, a point often raised by transit officials and

academicians.68 This argument applies equally to all

consumption-based measures of transit output. Second,

65 Ibid.

66 Ibid., p. 32.

67 Massachusetts Bay Transportation Authority and Tidewater

Transportation District Commission, Bus Service Evaluation Procedures: A Review, 1979, p. 23.

68 John R. Meyer and Jose A. Gomez-Ibanez, Measurement and

Analysis o f Productivity in Transportation Industries (Cambridge,

Mass.: Harvard University, 1975).

this measure provides no information about the length

of the ride.

Passenger miles. Passenger miles is probably the most

widely used physical output measure of private sector

passenger transportation productivity. This measure is

better than a simple count of the number of passengers

since it takes into account the length of the ride.

Passenger miles are normally defined as the number of

miles traveled by all paying passengers in a set time

period. One passenger traveling one mile is one

passenger mile. The private sector studies of b l s , Kendrick, Deakin and Seward, and Scheppach and

Woehlcke cited earlier all use passenger miles in one

form or another.

Three arguments are normally advanced against

passenger miles as the measure of output:

1. As with the “ number of passengers” measure,

it does not reflect many mandated operations.

2. Studies show that passenger miles are not closely

correlated with the costs of production or unit

labor hour requirements.

3. Data to calculate passenger miles are not generally

available, are difficult to collect, and are not very

accurate when available. A primary problem is

estimating how far passengers ride in fixed-fare

systems. If the length of the average passenger

trip remains constant through time, which is probably a reasonable assumption in the short run,

then passenger miles and number of passengers

would result in the same index.

Passenger revenue. This measure is the total revenue collected from passengers. Passenger revenue is sometimes

known as “ farebox revenue.” This measure is available

in every system that collects fares, and it is available nationally. For those systems that cover costs through

fares, it reflects transit usage.

For several reasons, passenger revenue is not a particularly good measure of output for productivity

measurement. First, passenger revenue makes up only

about one-third of total national transit revenue (subsidies make up the difference). Second, it has been

decreasing as a percent of total revenue for a number of

years. Furthermore, nonpaying passengers are often an

important user group. Every system has some, and some

systems have many. A few systems charge no fares

whatsoever. Also, farebox revenue is a function of administered fares, which may not relate‘directly to the

cost of providing the service and to unit labor requirements.

Preferred measures. The line has been drawn over the

past decade between measuring public and private service output. Private output is measured by the traditional “ passenger mile.” Public sector transit service

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has focused on multiple measures. Transit managers

and many researchers prefer capacity measures such as

revenue vehicle hours for calculating productivity. The

strengths and weaknesses of the two approaches are well

documented.

Output and productivity trends vary depending on

which approach is used.69 Actually, the two approaches

are complementary—one focuses on use, the other on

availability. Both approaches should be used as part of

future research in this area.

The preferred measures are passenger miles and revenue vehicle hours. The two measures should be

calculated and weighted by transit mode. Nine modal

divisions, noted earlier, are preferable but two divisions

(bus and rapid rail) should be adequate for national output indexes.

Quality and level of service. Quality and level of service

are important considerations in every transit operation.

Travel time, reliability, comfort, and frequency of

operation are all important dimensions of transit output. Chapter III suggested that such factors need to be

explicitly considered and adjustments made whenever

they affect base-year unit labor requirements. Unit

labor requirements will be affected by three factors: (1)

The relationship of quality and level of service to total

labor requirements; (2) the unit output measure; and (3)

the magnitude of the shifts.

Quality and level of service must be taken into account when a change markedly affects labor requirements. This is not to say that the attribute is unimportant when it does not affect labor requirements, only

that it need not be considered in productivity calculations. For example, employee courtesy is an important

quality attribute, to which the public and transit

authorities alike are sensitive. To improve employee

courtesy, transit managers sponsor “ Driver of the

Month” awards and courtesy training, and, when all

else fails, disciplinary action. Important as these programs might be to a transit manager, they are relatively

unimportant for unit labor requirements and need not

be considered in transit productivity calculations.

The second factor to be considered is the output

measure used to calculate productivity. The quality and

level of service attribute can affect unit labor requirements for some measures but not others. For example, frequency of service plays a major role in unit

labor requirements. Systems which provide 24-hour service usually have very different unit labor requirements

from those which provide only rush hour service.

Changing the level of service will likely have a major effect on unit labor requirements for output measures

such as passenger miles, but will have little effect on the

revenue vehicle hour measure.

The third point is that, for trend computation, ad69 Ibid., p. 24.

justments are not needed as long as quality and level of

service remain constant or approximately constant.

These attributes need to be followed through time.

Six quality and level of service attributes are often

cited in the literature. Table 52 presents examples of

how they might change and their likely impact on total

and unit labor requirements for two output measures.

Frequency of service seems to affect unit labor requirements the most. At the very least, it needs to be

tracked through time. Three levels are suggested: (1)

Rush hour only, (2) rush hour and limited service up to

12 hours per day, and (3) 12 hours or more of service per

day.

Availability of data. Until recently, the only regular

source of national transit statistics has been the

American Public Transit Association (a p t a ). a p t a

has regularly collected a wealth of statistics from transit

operators. It publishes the Transit Fact Book and the

Operating Statistics Report annually. Some of the data

series go back to the turn of the century.

The a p t a statistics cover the entire transit field,

private and public. They include outputs, as noted

earlier, including revenue and passenger miles, and information is available on individual systems and for the

total industry for the major transit modes.

The primary problem with the a p t a statistics for

calculating government output is the difficulty in

separating private from public operations and the impossibility of assessing the error associated with the

statistics, a p t a statistics are provided by a p t a members

and reporting is strictly voluntary. Less than half the

transit operators are a p t a members and some of these

do not report. The statistical bias associated with the

data is not known.

The availability of transit data has improved greatly

with the implementation of Project f a r e (Uniform

Financial Accounting and Reporting Elements), commonly known as Section 15.70 Section 15 reports provide

detailed statistics on all transit systems, private and

public, that receive Federal financial support, which includes most systems in the United States. The initial

reports were submitted in 1979, and annual reporting is

required. Statistical reports are available on outputs, inputs, operations, community characteristics, personnel,

finances, and so forth, by mode, by system, and by

geographic area. Statistics are available on revenue vehicle hours and passenger miles, the two measures recommended in this report for further examination. The

primary problem with Section 15 data is its newness.

In addition to these national data bases, every transit

system collects and maintains output statistics, and

many also publish them. Some States, including

California and Michigan, collect data from individual

70 “Urban Mass Transportation Industry Uniform System of Accounts and Records and Reporting System” (U.S. Department of

Transportation, Urban Mass Transit Administration, 1977).

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Table 52. Examples of relationship of quality and level of service to program change, labor requirements, and two output

measures

Attribute Illustrative

program change Impact on total

Impact on unit labor

requirements of two

output measures

labor requirements Revenue

vehicle hours

Passenger

miles

Employee courtesy (such as operator Increased employee training Small Small Small

demeanor)

Increased monitoring of complaints Small Small Small

Driver-of-the-month awards Small Small Small

Safety (such as number of accidents) Increased safety training Small Small Small

Increased maintenance Small Small Small

Increased monitoring and discipline Small Small Small

Reliability (such as percent runs com- Increased maintenance Small Small Small

pleted)

Increased monitoring of routes Small Small Small

Replace old equipment Small Small (probably

reduce)

Small (probably

reduce)

Travel time (such as average commut- Cut number of stops Small Small Small

ing time)

Add express buses/trains Large Small Unknown

Comfort (such as adequacy of heat) Increased repair of equipment Small Small Small

Frequency of service (such as rush

hour and non-rush hour)

Shift equipment Small Small Unknown (probably

small)

Add equipment Large Unknown (probably small)

Unknown (probably

large)

Add service hours Large Small Unknown (probably

large)

operators and prepare regular transit operating reports.

Although no single source of statistics is available to

calculate a mass transit output index, Section 15, apta,

and individual system data should provide sufficient

data to build a national index, and Section 15 data

should be adequate to update the index once calculated.

Labor inputs

Transit operations are labor intensive. Salaries and

wages made up 43 percent of all transit expenditures in

fiscal 1977 and 65 percent of current operating expenditures.71 When fringe benefits are added, the figures

rise substantially. One study calculated that salaries,

wages, and benefits accounted for §2-87 percent of

operating expenditures.72 Another study found that they

accounted for 73 percent of operating expenditures.73 In

1980, according to the Bureau of the Census,- 159,000

local and 13,000 State employees, a total of 172,000,

71 Government Finances, p. 33.

72 U.S. Urban Mass Transit Administration, “Transit Operating

Performance and the Impact of the Section 5 Program,’’ November

1976, p. 23.

73 American Public Transit Association, Transit Fact Book—1981,

p. 48.

worked in transit systems. The number of full-timeequivalent employees was 169,000.

Chapter III listed three labor measures to be used in

calculating State and local government productivity—all employees, all employee hours, and the

number of full-time-equivalent employees. The three

sources of yearly time series data on the total number of

public transit employees are the Bureau of the Census,

the American Public Transit Association, and the U.S.

Department of Transportation (Section 15 reports).

The Bureau of the Census reports total employment,

full time and part time, and full-time-equivalent

employment. Census statistics have several problems for

calculating a labor index. First, Census included only

local government employment until 1980, when State

figures were added. Second, data are not separated by

mode of transportation. Information by mode is

necessary because of the apparent differences in productivity and the shift in employment among modes—e.g.,

fewer buses and more subways.

Like the Bureau of the Census, the American Public

Transit Association has collected and published

statistics on transit employment for years. The Census

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and APT A statistics vary dramatically, particularly in the

early years. Several reasons account for the differences.

First, a p t a figures include both public and private

employment; Census figures, only public employment.

Second, a p t a figures reflect estimated employment for

six transit modes—bus, subway, trolley, streetcar, inclined plane, and cable car (commuter rail, ferry boat,

and automated guide way are excluded). Census

statistics, on the other hand, reflect employment in transit agencies as defined by the responding government.

Third, a p t a figures include Puerto Rico; Census does

not. Neither Census nor a p t a separates employment by

mode.

The Section 15 reporting system provides some data

not available from the other two systems. It provides information on the number of full-time:equivalent

employees by mode and by system. It also separates

employees by class (maintenance, transport, and general

administration) and collects information on salaries,

wages, and benefits by mode.

Section 15 reports have five problems for productivity

calculations. First, the few years of data available are

useful for benchmarking but are not sufficient to compute a labor index. Second, statistics are not collected

on the number of employees or employee hours.

However, an index of employee hours should parallel

the full-time-equivalent index. Third, Section 15 reports

do not include all public transit systems, but they do

cover about 95 percent. Fourth, the reports include

private as well as government-owned systems. Fifth, no

distinction is made between government and private

employees.

None of the three sources collects employee hours.

Because of overtime, these could be important in

calculating transit productivity. Split shifts and overtime are common among transit employees and the problems they create for transit operations and staffing are

widely discussed. Although hourly figures are not

available, full-time-equivalent employment statistics of

Section 15 reports should be good surrogate measures.

So far as is known, none of the three sources includes

employment on commuter rail systems. However, this

information is reported to the Interstate Commerce

Commission and also to the Department of Transportation, which plans to publish it in the future.

Table 53 summarizes the availability of employment

data.

Suggested research

The next step should be to attempt to calculate a

series of productivity indexes using the available summary data, augmented, where necessary, with individual

system data. Initial calculations should focus on the two

recommended measures, passenger miles and revenue

vehicle hours, for bus and rapid rail. Level of service

and quality need to be addressed. In addition to national trends, it should be possible to construct trends

by geographic area, by size of system, and by mode.

Statistics on the level of productivity would provide

important additional information, and with a unitary

Table 53. Availability off employment data by transit mod®

Labor coverage

Mode Number of employees (full time,

part time, seasonal) Full-time-equivalent Hours

Multimode...................................... Full time and part time available

from Census for October each

year. No statistics available on

seasonal workers.

Available from Census for Oct- Information not currently available. Some operator statistics

collected by American Public

Transit Association (apta). No

plans to collect data.

tober each year. Section 15

provides information; see

form 404.

B u s................................................. Information not currently available by mode.

Section 15 provides information; see form 404.

Some operator statistics collected

by apta. No plans to collect additional data.

Heavy rail....................................... Information not currently available by mode.

Section 15 provides information; see form 404.

Operator statistics collected by

apata. No plans to collect additional data.

Commuter rail................................ Information reported to Interstate Commerce Commission

(icc) and Department of

Transportation (dot).

Can be computed from information reported to icc and

DOT.

Information reported to icc and

DOT.

Other:

Light rail

Trolley

Ferry boat

Cable car

Inclined plane

Information not currently available by mode.

Section 15 provides information; see form 404.Information not currently available. No plans to collect data.

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output measure, such as revenue hours, it should be 

possible to compute productivity levels with little additional work. Meaningful comparisons among jurisdictions will be much more difficult because of differences 

in laws, topography, weather, population density, road 

networks, and congestion.

Data are available to permit examination of multifactor productivity. However, this will require considerable 

additional work, and probably is not warranted given 

the importance of labor.

Tb& EmpBoyment Ssrw fe

Federal and State governments have been concerned 

for a long time about measuring Employment Service 

(es) productivity. Like sanitation, drinking water, and 

mass transit, considerable research has been conducted 

and much information has been collected. Unlike the 

other service areas, “ productivity” has been routinely 

calculated for the ES. However, there is considerable 

unhappiness with this work and the resulting productivity measurements.

Institutional considgrations

ES was first established during World War I to recruit 

defense workers.74 The Service languished during the 

192Q’s but was given new life by the Wagner-Peyser Act 

of 1933 and the Social Security Act of 1935. The Social 

Security Act, which established the unemployment insurance program, called for a work test as a condition 

for receiving unemployment payments, and ES was 

assigned responsibility for administering that test. ES

was also deeply involved in operating referral service for 

work relief programs. In the 194Q’s and 1950’s, ES first 

recruited and referred workers to the defense program, 

and later helped veterans and defense workers return to 

civilian employment. The emphasis of es shifted in the 

196Q’s to assisting the disadvantaged and administering 

registration required of welfare recipients. The 1970’s 

saw a return to more traditional labor exchange operations, although the registration function (work tests) remained.

es is a joint Federal-State operation; local government is not involved. The Federal Government is 

responsible for setting procedures, standards, and 

guidelines to operate the system. The States operate the 

service. In 1980, about 2,600 es offices and 30,000 State 

employees worked on traditional ES activities authorized 

by the Wagner-Peyser Act. An additional 14,000 State 

employees worked on es responsibilities under afdc

and food stamp programs, and 6,000 other State 

employees worked under other Federal labor 

contracts.75 More recently, these numbers have been 

reduced as programs have been terminated.

74 Employment and Training Report of the President, 1977, p. 71.

75 Ibid., p. 73.

This study focuses on traditional services, which include:

1. Interviewing jobseekers

2. Identifying job openings

3. Matching job applications with openings and

referring qualified applicants to employers

4. Counseling applicants

5. Testing applicants

6. Preparing and distributing labor market

information.

An interview, the first step for a client entering the ES 

process, sheds light on a client’s job skills, knowledge, 

and interests. In fiscal 1979, there were over 15.5 million 

new and renewal applications.

Identification of job openings or job development, 

the next step, is an employer-focused function. The 

primary es activity is contacting employers to obtain job 

listings. These contacts consist of personal visits, 

telephone calls, mail, and promotional activities. Job 

listings are entered into computerized job banks daily. 

In fiscal 1979, more than 1.8 million employers were 

contacted and 9.0 million job openings were listed.

The next step in the process is job matching and referral to employers. In fiscal 1979, ES made 8.2 million 

referrals which resulted in 4.5 million placements. ES

centers are installing computers throughout the country 

to assist in this process.

Counseling is available to those who need assistance 

in choosing a field of work, who wish to change their 

occupation, or who have difficulty in holding a job. In 

fiscal 1979, over 1 million applicants received job 

counseling.

Applicants who do not have a trade or occupation, or 

who wish to change occupations, may take general aptitude, specific aptitude, or general interest tests, es,

which tested about 800,000 applicants in fiscal 1979, has 

an active program to develop and revalidate tests.

Labor market information is needed to support many 

es activities. Information is routinely developed on the 

number of job openings and the characteristics and 

number of workers seeking jobs. The national es office 

develops and maintains aids such as the Dictionary of

Occupational Titles, Handbook o f Occupational

Keywords, and special handbooks such as the Health

Careers Guidebook.

A 1974 study estimated the es staff time spent on each 

of four functions—intake, counseling, referral, and 

labor market information—as follows:76

76 Neil S. Weiner, John H. Powel, and C. Michael Rahm, The

United States Employment Service: A Conceptual Model of Outputs,

Values and Illustrative Estimations, Vol. II, (Arlington: Boeing Computer Services, 1976), p. B-9-13.

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Function Percent of staff time

Total.................................................... 100

Referral ...................................................... 61

Intake......................................................... 25

Counseling................................................. 8

Labor market information........................ 6

An analysis which followed a sample of applicants

through the various activities in fiscal 1974 showed that,

for every 100 applicants,

44 dropped out after intake;

4 dropped out after counseling/testing;

2 entered training;

21 dropped out after job referral; and

29 found jobs.77

In other words, about 29 percent of the referrals led

to jobs. The stability of these proportions through time

is not known but fiscal 1979 showed exactly the same

placement rate.78

In addition to the traditional labor market activities,

es is responsible for enforcement of work test requirements and three compliance activities. Enforcement activities are the reqistration and monitoring of

unemployment insurance and food stamp and welfare

recipient activities. The three compliance activities are

immigration service certification, farm worker and

wage standards certification, and worker complaint

referral.79 Immigration certification requires that es attest that immigrants do not take jobs which unemployed

Americans could fill and that they are paid the prevailing wage for that job. ES local offices take the applications; regional offices determine employability. Less

than 2 percent of the es budget is allocated to this activity.

The es also certifies that the employer provides adequate housing and pays prevailing local wages to foreign

and migrant workers. These activities are now a

“ relatively insignificant proportion of the ES

budget.” 80

The third activity—worker complaints—involves forwarding complaints that arise in the workplace, such as

working conditions, pay, and discrimination, to other

government agencies. This is a minor activity of ES.

Funding for the traditional es program comes from a

tax on employers (Federal Unemployment Tax

Act—futa). Funding for nontraditional es programs,

such as compliance and enforcement, comes from

general tax revenue.

The Department of Labor allocates funds to individual States to support es program staff; administrative, supervisory, and technical (as&t) staff;

labor market information; special projects; and nonper77 Ibid., p. 32.

78 Report o f the President, 1980, p. 58.

79 Weiner and others, A Model of Outputs, pp. 62-64.

80 Ibid., p. 64.

sonnel services such as space, utilities, computers, and 

travel. In fiscal 1980, about $742 million went to support about 25,000 basic program staff; $44 million went

to support about 2,400 individuals in the AS&T function;

and about $127 million went for nonpersonnel services.

Labor market information services received about $32

million from several sources; special projects, enforcement, and worker protection received $26 million.81

Labor, the primary factor input into ES programs, consumes about 85 percent of the budget.82

For several years, es has routinely calculated

placements per staff year, a measure of productivity.

Recently, these statistics have been included in the President’s annual budget (table 54). The basis for these

calculations is not known but it certainly varies from

year to year. The figure for fiscal year 1978, for example, is 201, 238, or 265 depending on whether the figure

is taken from the 1979, 1980, or 1981 budget.

es uses two basic data systems to track its operations

today: One collects program information; the other collects resource and cost accounting information. The

Employment Security Automated Reporting System

(esars) has collected basic program information since

the early 1970’s. The State Employment Security Agency (sesa) Accounting System has collected resource data

since 1970. Both systems collect data from local es offices. Both program and resource data have been 

available since the late 1930’s but not in the depth of

coverage that is available from the esars and sesa Accounting Systems.

The Bureau of the Census does not separately identify

es operations in its statistics, es operations are included

under the general heading of Employment Security,

which includes all State labor activities.

The sic Manual assigns ES operations to Industry

7361, Employment Agencies. This category includes:

“ Establishments primarily engaged in providing employment services except theatrical employment agencies (Industry 7922) and motion picture casting bureaus (Industry

7819). Establishments classified here may assist either

employers or those seeking employment.” 83

State administrative offices apparently are assigned to

Industry 9441, Administration of Social, Manpower,

and Income Maintenance programs.

•Most States and the Federal Government currently

refer to local ES operations as the Job Service.

Research and conceptual issues

Considerable research has been done on es operations and management, including productivity measurement. A recent study reviewed 27 different papers which

81 Charles K. Fairchild, A Performance and Needs Based

Methodology for Allocating Employment Service Grants, (Cambridge, Mass.: Abt Associates, 1980), pp. 12-13.

82 Ibid., p. 39.

83 Standard Industrial Classification Manual, p. 304.

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Table 54. Placements per staff year as shown in the 

President’s budget, fiscal years 1974-82

Fiscal year of President’s budget

measured 1977 1978 1979 1980 1981 1982

1974 .................... 225 _

1975 .................... 211 - - - - -

1976 .................... 208 217 - - - -

1977 .................... 224 225 225 - - -

1978 .................... - - 238 265 201 -

1979 .................... - - 241 ’229 ’207 207

1980 .................... - - - ’238 ’190 ’186

1981.................... - - - - '297 ’177

1982 .................... - - - - - ’212

1 Estimate.

Source: Appendix to President's budget, selected years.

examined and discussed ES productivity.84 Most were

published between 1975 and 1978. The authors assigned

the 27 papers to one of four categories—organizational

productivity, service category productivity, client effectiveness, and labor market effectiveness. In other

words, two categories dealt with efficiency and two with

effectiveness. Although this report focuses on efficiency

(productivity), the papers dealing with effectiveness are

briefly noted first.

Labor market effectiveness. Labor market effectiveness

is defined as the impact of es on the economy as a

whole, such as its effect on the gross national product

and the unemployment rate. Basil Moore of the U.S.

Bureau of the Budget examined this issue in 1966.85

Although his purpose was to develop an analytical

framework, he examined data and made simple calculations. His basic output measure was the difference between the length of unemployment of those registered

with ES and the length for all unemployed persons.

Ten years later, Donald Frey completed a conceptual

examination of the same general issue.86 87 Factors investigated were the duration of unemployment, job turnover, and deterrent effects of es operations. Potential

data sources were reviewed but no attempt was made to

calculate the effect of es.

Client effectiveness. Client effectiveness, the second

category of studies examined in the 27 papers, focused

on how clients themselves, rather than the Nation as a

whole, benefited from es operations.

A study by Arnold Katz examined the impact of es

labor exchange programs on applicant unemployment

84 David W. Stevens and others, “ Specification and Measurement

o f Productivity in the uses,” draft report (Employment Service,

December 1980).

05 Basil Moore, “A Benefit-Cost Analysis of the United States

Employment Service” (Bureau of the Budget, November 1966).

36 Donald E. Frey, A Methodology for Measuring the Impact o f the

United States Employment Service (Winston-Salem: Wake Forest

University, 1976).

87 Arnold Katz, Exploratory Measures o f Labor Market Influences

o f the Employment Service (University of Pittsburgh, 1978).

Jacob Benus, focused on the duration of unemployment

and on earnings.88 His initial conceptual study was

followed by a pilot study.

Service category productivity. Service category productivity focuses on the efficiency with which specific es

services, such as counseling, testing, and job development, are handled.

Fred Englander undertook three studies for the New

Jersey es to identify the net influence of es on job 

placements. One studied the factors affecting the

number of job openings.89 Another looked at the impact

of external, internal, and demographic considerations

on placements. The third examined the method es used

to allocate funds to State employment services.

A study by Neil Weiner used the general productivity

function to estimate the social return of ES.90 Weiner

divided es direct outputs between the labor market and

compliance requirements. Time accounting statistics

were used to estimate the resource flows.

Mark Chadwin and his associates used an institutional approach to examine service category productivity.91 They studied the organizational characteristics

which produced good performance in State es agencies.

Their productivity measure was individuals placed (not

transactions) per staff year.

Charles K. Fairchild examined the development of

performance standards, both for placement and

placement-support functions.92 He developed suggested

standards and procedures for evaluating output and

allocating funds to the States and within the States to ES

offices. Four output measures were considered: Individuals placed per staff year, placements per staff

year, percent of openings filled, and percent of applicants placed. Fairchild concluded that placements per

staff year was the preferred output measure for productivity calculations.

Organizational productivity. Three organizational or

composite productivity studies were identified and

reviewed in the 27-study review. One was a General Accounting Office review of ES operations in 1978 which

88 Jacob Benus and others, Use o f an Experimental Design in

Assessing the Impact of the United States Employment Service (Menlo

Park, Calif.: Stanford Research Institute, September 1976).

89 Fred Englander, “Factors Affecting the Receipt of Job Openingsby the State Employment Service Agencies and by Local Employment

Service Offices in New Jersey;” “The Impact of Demographic, Internal and External Factors on the Placement Performance and Staffing

of the Local Offices of the New Jersey Employment Service;” and

“The Impact of Demographic, Internal and External Factors on the

Placement Performance of State Employment Service Agencies”

(Trenton, N.J.: Department of Labor and Industry, May 1977).

90 Weiner and others, A Model o f Outputs.

91 Mark L. Chadwin and others, The Employment Service: An Institutional Analysis (Employment and Training Administration,

1977).

92 Charles K. Fairchild, Development of Performance Standards

fo r Job Placement and Support Services o f the Public Employment

Service (New York: E.F. Shelley and Company, August 1975).

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examined both placement and job development functions.93 The primary performance statistics were

placements made and jobs filled. The accuracy of placement statistics also was considered. The findings will be

discussed later.

Another study, by C. Meike, focused on productivity

of individual State es agencies.94 It identified outputs,

developed quantification procedures, and combined the

answers into an overall measure of productivity. The

report used four categories of outputs: Applicant process, employer process, placement process, and mandated outputs. A value-added approach was proposed.

The third study, by H. Kaitz, examined relationships

among internal factors, external factors, and output.95

Placements per staff year was his preferred measure of

productivity.

Two other research reports not covered in the Stevens

draft report bear mentioning. One is a 1979 investigation of es productivity by Thorpe and Toikka which used a production function approach.96 Three slightly different measures of output were examined: Placements

of 3 to 150 days, called permanent placements; total

placements less subsidized (ceta) placements; and total

placements (agricultural and nonagricultural). The last

measure was dropped because the regression results

were so “poor.”

Thorpe and Toikka tested a number of independent

variables. Significant variables were the number of ES

staff years, the size of the civilian labor force, the percent of workers unionized in nonagricultural industries,

the percent of applicants who were unemployment insurance claimants, and the percent of applicants who

were economically disadvantaged. Coefficients of all

the independent variables had the expected sign. The

unadjusted 'coefficient of determination for the

Thorpe/Toikka equations ranged from .93 to .96. The

coefficients ranged from .92 to .94 for two

variables—staff years and civilian labor force. The data

used by Thorpe and Toikka were Title III grants for

1977 only.

A Fairchild study examined the feasibility of restructuring the es grants mechanism using performance and

needs as the criteria for distributing funds to the

States.97 His investigation is of interest for this study for

two reasons. First, he separated ES goals into five missions or parts—basic labor exchange, supplemental services, employer technical services, labor market information, and compliance/enforcement—and assigned

93 The Employment Service—Problems and Opportunities for Improvement (General Accounting Office, 1977).

94 C. Meike and others, sesa Productivity Measurement System

(Vienna, Va.: Analytic Systems, September 24, 1976).

95 Employment Service Performance Handbook for Local Offices

(Rockville, Md.: westat, Inc., 1979).

96 Charles O. Thorpe, Jr. and Richard S. Toikka, Determinants o f

State Employment Service Productivity (Washington: The Urban Institute, March 1979).

97 Fairchild, Methodology fo r Allocating Employment Service

Grants.

measures of performance for four of the missions. Second, he examined two production functions for the

basic labor exchange mission. Both used total individuals placed as the output measure. Staff years was

the primary factor input.

External and internal factors were examined. The

significant independent variables in the first production

function were: Staff years, civilian labor force, population density, number of new and renewal applicants,

youth as a percent of all applicants, claimants as a percent of all applicants, the year, and the es region. Fairchild analyzed 5 years of ES data, State by State. His

equations explained 97.5 percent of the variance of “ individuals placed,” the dependent variable. An

estimating equation was also developed from 1979 data.

This equation dropped three variables: Number of new

and renewal applicants, the year, and the es region.

The research on es productivity is extensive, as this

brief review indicates, but several, basic conclusions

emerge:

1. Most productivity research has used placements as

the preferred measure of output. However, there

are a number of variations on the basic theme.

2. Production function research, using the number of

placements as the output, has been fairly successful in explaining variance about the dependent variable.

3. Externalities play an important role in exploring

placement variation.

Outputs

Satisfactory measurement of output is the fundamental issue in calculating es productivity. Two basic outputs—placements and services—are examined here.

In many respects, the issues and concerns that surround the measurement of ES outputs are similar to

those that are found in private sector employment

measurement: Both government and private firms

counsel, test, and assist individuals in obtaining employment (placements). There is one important difference,

however. Although a bundle of services may translate

into dollar output for private firms, es output requires a

physical measure.98

Placements. The number of placements is the measure

almost always used today for es outputs, es has collected statistics on the number of placements since 1938;

it tracks the number of placements monthly; and it includes the number of placements as part of its annual

budget justification.

For es records, a placement occurs each time an

98 Employment services in the private sector generally are priced

in two ways: (1) Payment is contingent on the individual being placed

in a job; without placement there is no payment. (2) Payment is made

for a bundle of services such as testing, counseling, referrals, resume

preparation, and the like. The price in this case is for a bundle of services and is not contingent on job placement.

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employer hires an applicant who is referred. For a placement to be recorded, five steps must take place:

1. A job order form must be prepared before 

referral;

2. Arrangements must be made with the employer for 

the referral of an individual or individuals;

3. The individual must not have been specifically 

requested by the employer;

4. A reliable source, preferably the employer, must 

verify that the individual entered on the job; and

5. The placement must be recorded on appropriate es

forms."

The basic argument in favor of using placements to 

measure output is that placing individuals in jobs is the 

role of es. Counseling, operation of the job bank, 

registering workers, and preparing labor market 

analyses all support the basic service of getting jobs for 

people.

Furthermore, placements are measurable, repetitive, 

and easily understood. They are physical measures, and 

data to make the measurements are readily available.

The three principal arguments against the use of 

placements are: (1) ES has responsibilities in addition to 

its labor market mission; (2) externalities have a major 

effect on placements; and (3) much of the placement 

data are questionable. This last point will be discussed 

later in this section.

Regarding the first point, es has been assigned additional missions from time to time. In 1977, it was involved in administering 21 laws, 11 Executive Orders, 

and 14 agreements with Federal agencies.9 100 These 

responsibilities ran the gamut from certifying aliens for 

work in the United States to checking the adequacy of 

housing for migrant v/orkers.

Fairchild, as noted, identified five separate ES missions or functions. Although Fairchild did not estimate 

es resources for each mission, labor exchange is clearly 

the largest.

A more troublesome issue than multiple es missions 

in using placements as the output measure is the externality issue, or the problem of separating placements 

from other considerations. When the economy is booming and employment is high, placing individuals in jobs 

is relatively easy, but in a declining economy with high 

unemployment, placing individuals is difficult. Other 

considerations, such as the skills requested, availability 

of a skilled work force, and size of the labor market, 

also influence the placement rate, es placements are 

probably influenced more by factors external to es

operations, particularly the state of the economy, than 

by internal considerations. For this reason, placements 

are not a very good measure of organizational outputs.

99 Glossary o f Program Terms and Definitions (Employment and

Training Administration, 1978).

100 General Accounting Office, The Employment Service, p. 2.

According to John P. Campbell:

“The State’s unemployment rate consistently yields 

the highest correlation (negative) with placements. Adding a measure of new hires and a measure of the 

percentage of the work force in lower level jobs boosts 

the total variance accounted for to 40 to 45 percent of 

the total variance in placements. Adding certain additional independent variables to the equation consistently increases the variance accounted for to 60 to 

70 percent of the total.” 101

Studies by Thorpe/Toikka and Fairchild, discussed 

earlier, found external variables to be crucially important in explaining placement variance. The only ES input 

that was statistically significant was the amount of labor 

input.

Variations on the theme. Part of the controversy surrounding the use of placements as the es output measure 

stems from the different ways in which the term is used. 

es makes three important distinctions: Transactions vs. 

individuals; agricultural vs. nonagricultural; and length 

of time the placement lasts—3 days or less, more than 3 

but less than 150 days, and 150 days or more. Each is 

briefly reviewed below.

Transactions vs. individuals. Simply stated, a placement transaction takes place when an applicant is hired. 

The placement may last from 1 hour to many years; one 

individual can be placed several times each year.

Researchers and some es administrators feel that the 

number of individuals placed is a better measure than 

the number of transactions for two reasons: First, the 

time required to place an individual is more stable than 

the time required to complete a transaction. That is, a 

new applicant requires considerable time; a registered 

applicant taking an intermittent job requires little time. 

The primary effort is initial registration, counseling, 

and testing. No research has been found during this 

study which shed light on the time required for an individual placement vis-a-vis a transaction placement.

Second, focusing on individuals encourages longer 

term placements and benefits the client. This thesis, 

though valid, is not a productivity argument and will 

not be pursued further.

es believes that both individual and transaction 

placements are important, and both should be 

calculated and used. Comparison of the two statistics, 

which es has published since 1975, suggests that they 

move together (table 55). Between 1975 and 1980, individual placements rose by 15.7 percent while transaction placements increased by 15.1 percent. The ratio of 

individual to transaction placements varied between 67 

and 71 percent between 1975 and 1980. Thus, in 

calculating productivity trends, it may not make a great 

deal of difference which measure is used.

101 John P. Campbell, “Comments,” in Stevens, “ Specification

and Measurement o f Productivity in the uses, ” p. 188.

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Table 55. Comparison of Employment Service transactions

and individual placements, fiscal years 1975-S0

Year Transactions Individual

placements

Individual 

placments as 

a percent of 

transactions

1975.............................. 4,670,610 3,137,542 67

1976.............................. 4,936,222 3,367,007 68

1977.............................. 5,283,715 3,732,152 71

1978.............................. 6,015,728 4,213,423 70

1979.............................. 6,206,674 4,180,635 67

1980.............................. '5,376,871 '3,630,930 68

1 Excludes New Jersey and Puerto Rico. 

Source: Employment Service staff.

Agricultural vs. nonagricultural. es has traditionally 

divided its transaction placements between agricultural 

and nonagricultural; at one time the ES budgeted for 

these separately. In I960, agricultural placements accounted for 62 percent of total es placements. By 1970, 

placements were evenly divided, but by 1979, 

agricultural placements made up only 6 percent of the 

total (table 56).

When agricultural placements were an important part 

of its work, e s divided them into four types—crew, 

pool, individual, and other. Crew placements resulted 

from interview and selection of a crew leader to recruit 

individual workers. Pool placements were made from 

applicants who gathered at an assembly point each day. 

No interviews were conducted and the work period was 

1 day. Individual placements were those which placed a 

single individual in a farm job, a process that was 

similar to nonagricultural placement. Other placements 

were primarily family groups hired for no longer than 1 

day. Most agricultural placements were of the crew and 

pool types.

Several factors were responsible for the decline in 

agricultural placements. First, demand lessened as 

mechanization increased. Second, the Bracero Program 

was terminated; this program brought Mexican labor to 

American farms. Third, the Judge Richey decision in 

1974 required that agricultural workers be provided 

with the same services as other workers. Fourth, the 

Farm Service Bureau was merged into the e s in the 

1960’s.

Today no distinction is made and no productivity 

calculated for the different types of agricultural placement. However, anyone preparing a productivity index 

that covers the period when agricultural placements 

were an important part of e s ’s work dearly needs to 

consider the issue of agricultural placements.

The 1968 Senate budget hearings noted that 

agricultural placements per staff year for three States 

were in the thousands; nonagricultural placements were 

in the hundreds. Mississippi reported 7,896 agricultural 

placements per e s employee.102 Clearly a “ pool” or

102 Labor-Health, Education, Welfare Appropriation for Fiscal

Year 1968 (U.S. Senate, Subcommittee of the Committee on Appropriations, 90th Congress, 1st session, 1967, Government Printing

Office) p. 121.

“ crew” placement requires less labor input than an individual placement.

Number o f days per placement. Since the mid-1970’s, 

e s has collected and published placement statistics by 

job duration—less than 3 days, 3 to 150 days, and more 

than 150 days. This division is one attempt to identify 

the quality of placement, the rationale being the longer 

the placement, the better off the client. In other words, 

this is a client effectiveness issue.

The question for productivity measurement is 

whether unit labor requirements vary significantly by 

placement duration. If they do, some type of division or 

weighting is needed. No research was uncovered which 

shed light on this subject. The issue is not considered 

further here although the subject needs further 

research.103

Placement data. Transaction placement data are 

available from 1938 on and can be found in a number of 

places. The data series do not always agree, particularly 

in recent years, but these differences are usually explainable, as discussed later. Five series were identified 

during this investigation: Selected Department of Labor 

annual reports before 1979, the Department' of Labor 

annual report for 1979, Historical Statistics, budget 

data, and Employment Security Automatic Reporting 

System statistics (table 57).

A more serious concern is the accuracy of the placement data. The General Accounting Office (GAO) in 

1975 conducted the best known of the various studies on 

the issue. This study examined statistics collected in 

the field and also reported on 1974 and 1975 studies of 

the same issue by the Department of Labor. The 1974 

Labor study found in a sample or four States that 15 

percent of the claimed placements did not take place. 

The 1975 Labor study found in a sample of five States 

that between 14 and 20 percent of the claimed 

placements in seven e s offices did not take place. The 

g a o found in its own sample that 44 percent of those 

recorded as placed in a job claimed that they did not get 

a job or obtained it through some mechanisn other than 

the es referral.104 Although the Department of Labor 

disagreed with g a o methodology and the magnitude of 

the error identified, it did not dispute the fact that 

claimed placements often did not take place.

Four interrelated types of error occur in the data 

series: Coverage errors, definitional errors, data collection errors, and fraud. Probably the largest error comes 

from definitional issues, although no study has examined the relative importance of each type of error.

103 Job duration statistics as reported by the es are “expected

duration” of the job as reported by the employer, not actual duration

on the job. Research shows expected duration statistics to be extremely inaccurate. See “Michigan Placement Follow-up Demonstration

Project” (Silver Spring, Md.: Macro Systems, Inc., March 17, 1981).

104 General Accounting Office, The Employment Service, p.ll.

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Table Si. Agricultural and nonagricuSSural placement

transactions, Employment Service, fiscal years 1955-791

(Thousands)

Year Agricultural Nonagricultural

1955 .........................., .......... 8,992 5,536

1956 ...................................... 9,249 6,174

1957 ...................................... 9,002 5,976

1958 ...................................... 8,710 5,236

1959 ...................................... 9,615 5,704

1960 ...................................... 9,747 6,083

1961 ...................................... 9,004 5,591

1962 ...................................... 9,029 6,506

1963 ...................................... 7,924 6,632

1964 ...................................... 7,125 6,454

1965 ...................................... 6,047 6,330

1966 ...................................... 4,339 6,587

1967 ...................................... 4,113 6,142

1968 ...................................... 4,573 5,760

1969 ...................................... 4,865 5,524

1970 ...................................... 4,550 4,604

1971 ...................................... 3,264 3,597

1972 ...................................... 2,715 3,610

1973 ...................................... 2,105 4,517

1974 ...................................... 1,758 4,913

1975 ...................................... 1,498 4,274

1976 ...................................... 594 4,641

1977 ...................................... 314 4,970

1978 ...................................... 340 5,675

1979 ...................................... 363 5,844

' Includes Puerto Rico.

SOURCE: 1955-76, selected annual reports of Department of Labor; 

1977-79, Employment Service staff.

Table 57. Five placement transaction time series,

Employment Service, fiscal years 1955-79

(Thousands)

Year

Department of 

Labor 

annual 

reports 

(selected 

issues)

Department of 

Labor 

annual 

report 

for 

1979

Historical 

Statistics 

of Employment 

Security 

Activities, 

1938-66

President's 

budget 

and appendixes, 

selected 

issues

Employment'

Security

Automated

Reporting

System

1955 .................... 14,528 14,528

1956 .................... 15,422 - 15,422 - -

1957 .................... 14,978 - 14,960 - -

1958 .................... 13,946 - 13,946 - -

1959 .................... 15,319 - 15,319 - -

1960 .................... 15,830 _ 15,830 _ _

1961.................... 14,595 - 14,595 - -

1962 .................... 15,534 - 15,534 - -

1963 .................... 14,556 - 14,556 - -

1964 .................... 13,579 - 13,579 - -

1965 .................... 12,377 _ 12,377 - -

1966 .................... 10,926 10,892 10,926 - -

1967 .................... 10,255 10,255 - 10,476 -

1968 .................... 10,332 10,332 - 10,323 -

1969 .................... 10,389 10,389 - 10,337 -

1970 .................... 9,154 9,154 - 9,144 -

1971.................... 6,860 6,860 - 6,128 -

1972 .................... 6,325 6,325 - 6,325 -

1973 .................... 6,622 6,622 - 6,738 -

1974 .................... 6,672 6,672 - 6,127 5,159

1975 .................... 5,772 5,772 _ 5,662 4,671

1976 .................... 5,234 6,918 - 4,645 4,936

1977 .................... 3,808 5,932 - 5,250 5,284

1978 .................... 6,015 6,632 - 6,015 6,016

1979 .................... - 6,755 - 6,207 6,207

Some types of coverage error already have been 

discussed. Program coverage is probably the most important. Traditional and nontraditional program 

placements are sometimes commingled. For example, 

placement data series include placements of food stamp 

and a f d c recipients as well as traditional placements 

before 1971. Any productivity series covering these 

earlier years needs to make allowance for this change.105

The time period covered is another potential source of 

error. Placement data are reported and published monthly and summarized by calendar and fiscal year. At 

times the period being examined is not made clear. 

Geographic coverage also may be inconsistent in some 

data series.

The second reason for data error, probably the largest 

single reason and major issue for this study, is the 

definitional issue: What constitutes a placement? As 

noted earlier, e s has very explicit rales, but they are not 

always followed despite the validation routines.

The third type of error is in data collection. Problems 

do occur in keypunch, clerical, and computer operations. For example, the State of Washington was not included in national statistics in 1973 because the data 

were lost. The magnitude of this type of error is not 

known but probably is not large. Where such error exists, it probably does not bias transaction statistics.

The fourth reason for error is outright fraud. Because 

of the pressure to produce placements, some individuals 

have falsified records, the most well-known case occuring in West Virginia. Although newsworthy, fraud is 

probably not an important source of error in placement 

statistics, according to ES staff.

The relevant issue for productivity analysis is not 

placement error or even bias, but how the statistics vary 

through time. A constant bias—30 percent underreporting, for example—would have no major effect on output trends. However, If the magnitude of the bias 

changed, the output trend would be affected.

The ability to identify and correct error varies by type 

of error. Coverage error is probably the easiest to treat. 

Elimination of this error requires, first and foremost, a 

good understanding of es programs and data series.

Definitional error is probably the most difficult one 

to deal with. Studies show that it can be significant, 

although discussions with es staff suggest that the data 

are becoming more accurate and less biased. If so, the 

change in placements and productivity will be 

understated, but whether the error and bias are changing and, if changing, by how much, is not known.

Data collection errors are probably not large and probably do not produce bias. The fourth type of error, 

fraud, is probably not important, as noted.

es has recently embarked on a major program to 

validate placement and other e s a r s data. One of the

105 U.S. Congress, House of Representatives, Committee on

Government Operations, Operation o f the U.S. Employment Service,

(Government Printing Office, 1976), p. 305.

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tasks is validation of placement statistics in selected

States. Although the study is not complete, results thus

far suggest major errors (overstatements of the number

of placements) similar to those noted here.106

Services and activities. Another type of measure which

has been used to measure e s performance is the service

unit or activity measure. This includes factors such as

counseling, testing, employer visits, referrals, and the

like. During the 1950’s and early 1960’s, ES budgeted

and accounted for funds based on the services to applicants and employers.

es service or activity measures have several attractive

attributes. They are physical, measurable, repetitive,

and, for.many, data are available since 1936. Most important, they are measures of work performed which

are largely unaffected by external forces such as the

strength of the economy and the labor market.

Although activity data are readily available, their

completeness and accuracy are not known. Many data

problems noted earlier for placements may apply equally here.

However, the principal argument against using service counts is that they are measures of work performed, not outputs. Knowing the number of people interviewed, number of follow-ups, and number of file

checks does not tell much about the basic output of the

ES—placing people in jobs. This reasoning, of course,

led to the use of placements as the measure of output.

Mission-based outputs. Neither services provided nor

placements made are entirely satisfactory measures of

es output. Placements are outcomes; services are work 

performed or activities.

Fairchild recently examined es missions (goals) and

suggested a series of measures for four missions (table

58). For compliance and enforcement, the number of

registered recipients; for employer technical services,

the number of employers assisted and visited; for supplemental services, applicants registered and number

served; for basic labor exchange, placements. Except

for the basic labor exchange, his recommendations are

measures of output. Futhermore, they are physical,

measurable, and repetitive, and data should be readily

available. They could be combined into a single index by

using labor weights.

The problem remains of how to measure basic labor

exchange outputs. The basic es role in the labor ex ­

change is to refer individuals. Statistics are available

on the number of referrals but this type of output

measure opens the door to numerous types of statistical

106 In a recent survey, The Urban Institute, using intensive

telephone follow-ups of employers and employees, found the

overstatement to be only 2-5 percent, a dramatic improvement over

the figures noted here (Personal correspondence from John Greiner,

September 30, 1982.)

mischief. Additional investigation is needed; the concluding section of this discussion suggests a fourpronged approach.

Quality and level of service. All parts of ES operations,

and all measurements of productivity, are heavily entwined in quality and level of service considerations. As

noted in other parts of this report, the focus here is on

how quality and level of service issues affect unit labor

requirements and productivity. Many, such as the

helpfulness of employees, are important from the

citizen’s standpoint but have little or no impact on unit

labor requirements.

Many of the quality and level of service factors which

could affect unit labor requirements and productivity

already have been identified. These include the distinction between agricultural and nonagricultural

placements, the four types of agricultural placements

used in the 1960’s, and the difference between shortterm or intermittent and long-term placements. On the

other hand, services (counseling and testing, for example) and the process used to produce these services vary

by jurisdiction.

Table 58. Missions and measures of performance ©f the 

Employment Service as suggested by C.K. Fairchild

Mission Measure of performance

Basic labor exchange...................... Individuals placed

Total

From target groups

Type of job placements

Nonagriculture

High wage

Long term

By occupation

Placement transactions

Openings filled by type of job

Performance increase on

measures listed above

Supplemental services............. Applicants registered

Total

Target group

Number served

Any service

Counseling, testing

Referral to job

Referral to other service

Employment technical services . .. Number of employers assisted

Number of employer visits

Compliance and enforcement . ... Number of UI, food stamp, and

welfare recipients registered

with Employment Service

Labor market information............ None proposed

Source: Charles K. Fairchild, A Performance and Needs Based

Methodology for Allocating Employment Service Grants: Final

Report (Cambridge, Mass.: ABT Associates, 1980), p. 9.

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Output indexes need to take into account level and

quality of service when they affect unit labor requirements. However, the information is relatively

sparse. No quality rating system exists such as for the

Unemployment Insurance Service. Nor has research

been done at the national level. Calculation of an es

productivity index requires a systematic examination of

the effect on unit labor requirements of changes in

quality and level of service.

Labor inputs

Labor dominates es resource inputs. As noted earlier,

about 85 percent of the ES budget goes for labor.

Although Federal employees provide oversight, State

government employees manage and operate the programs. No local government employees are involved.

This discussion is restricted to those State employees

who work in the traditional ES program; other sources

fund ES-related operations, such as food stamp and

a f d c certification. Thirty thousand State positions were

funded from 1960 to 1980 to support the traditional

program.

The three labor measures recommended for consideration in calculating State and local government productivity in chapter III were all employees, all employee

hours, and number of full-time-equivalent employees.

The best source of es labor data is the State Employment Security Agency (sesa) accounting system, the

system used for internal office operations as well as

State and Federal management. This system was

automated in 1978 and reports are currently generated

daily, weekly, monthly, quarterly, and yearly, according to the need.

Insofar as labor statistics are concerned, data are collected on the hours worked, number of persons, and the

number of positions (full-time-equivalent employment).

However, the only statistics available nationally are the

number of positions.

Statistics on the number of hours are summarized by

each State and could probably be obtained from each

State. But since the number of positions is computed

from the number of hours, any position index would be

identical to the hours index.

Data on the total number of employees are more difficult to generate; no national or State count is

available. Each State has a master file of employees

from which these data could probably be summarized;

the effort required to make such a count is not known.

However, such a count would not be very meaningful

since ES personnel often work part of a day on one program, such as food stamps or unemployment insurance,

and another part of the day on another program.

Position data are readily available by State and for

the total e s . They are of sufficient detail for use in computing labor requirements for any of the three output

measures—placements, service and activity, or mission—discussed in the preceding section.

A potentially confounding issue is the use of

volunteer and “ free” staff. Some e s offices evidently

use many retired aides, Public Service employees (in

past years), work experience interns, and others not

charged under the se sa accounting system.107 Their

overall importance to e s and to ES productivity calculations is not known. This area needs investigation if

an e s productivity index is to be calculated.

Suggested research

The primary problem in measuring e s productivity

is specification of output. The research strategy suggested is a detailed examination and comparison of four

different approaches. One would focus on placements

to measure output with adjustment for changes in the

level and quality of service. The second approach,

which has been thoroughly investigated, would focus on

placements with adjustments for externalities. The third

approach would attempt to compute a weighted activity

index. Finally, research should pursue the mission output measures along the lines suggested by Fairchild. In

each case, accuracy and data verification need to be

considered carefully to avoid problems noted earlier.

Suggesting additional research for an area that has

already been examined on numerous occasions may

seem peculiar. However, as a recent review of the

research noted, most research has been narrowly focused.108 Several approaches should be compared to

resolve the problems.

If the output issue can be solved, or at least resolved,

then computing a national e s productivity index and individual State indexes should be relatively straightforward. Also, computation of absolute levels of productivity, as contrasted with productivity trends, may be

feasible. Such computations can provide additional insight into how productivity varies and how it can be improved. Finally, this discussion has focused primarily

on traditional labor programs, which in the past have

employed about two-thirds of the e s staff. Other e s programs, such as welfare certification, warrant investigation too.

107 Personal correspondence from John Greiner, September 30,

1982.

108 Stevens and others, “Specification and Measurement of Productivity in the uses.”

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AppsndlSx Wymfe@r ®f Stat® and! 

L@ea! Governments and!

Numb@r of Employ®®®

Or e difficulty in collecting data from State and local

governments is the number of governments that must be

contacted. Tables in this appendix show, for each level

of government, the number of governments and the

number of employees. Data are presented separately for

States, municipalities, school districts, counties, special

districts, and townships. The class sizes in each table

reflect the way the data are presented by the Bureau of 

the Census, the source of the data.

Often, a few large jurisdictions account for a sizable

proportion of the total employment for that level of 

government. Ten States, for example, account for 

almost half of all State government employment.

Table A-1. State governments and State government employment, 1979

(Cumulative)

States Employees Percent of total 

employment

10 ...................................................... 1,860,239 48

20 ...................................................... 2,735,609 71

30 ...................................................... 3,338,541 86

40 ...................................................... 3,668,875 95

Total, 50 ................................ 3,869,282 100

S o u r c e : C o m p u te d fro m d a ta in Public Employment in 1979(B u re a u o f th e C e n s u s , 1 9 8 0 ), ta b le 8, p. 14.

Table A-2. Municipal governments and municipal government employment, 1977

(Cumulative)

Municipalities Employees Percent of total 

employment

6 ........................................................ 522,088 20

24 ...................................................... 820,905 31

46 ...................................................... 988,173 38

64 ...................................................... 1,089,363 42

163 .................................................... 1,516,887 58

908 .................................................... 2,024,355 77

Total, 18,878 .......................... 2,623,271 100

S o u r c e : Computed from data in 1977 Census of Governments—Compendium of Public Employment

(Bureau of the Census, 1979), table 18. p. 343.

Table A-3. School systems and school employment, 1977

(Cumulative)

Districts and 

systems Employees Percent of total employment

10 ...................................................... 622,046 12

20 ...................................................... 886,520 17

40 ...................................................... 1,199,377 23

618 .................................................... 2,302,840 44

1,639 .................................................. 3,178,251 61

3,533 .................................................. 4,004,456 76

Total, 16,489 .......................... 5,242,028 100

S o u r c e : C o m p u te d fro m d a ta in Public Employment, ta b le 2 1 , p. 4 0 5 .

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Table A-4. County governments and county government employment, 1977

(Cumulative)

10 . . 

20 . . 

40 . . 

181 . 

343 . 

679 

1,274

Counties Employees Percent of total 

employment

198,063 

303,325 

449,189 

866,167 

1,043,746 

1,285,601 

1,500,970

11

17

26

49

59

73

85

Total, 3,040 1,761,242 100

S o u r c e : C o m p u te d fro m d a ta in Public Employment, ta b le 17, p, 3 1 8 .

Table A-5. Special districts and special district employment, 1977

(Cumulative)

Districts Employees Percent of total 

employment

10 ...................................................... 98,555 25

20 ...................................................... 138,274 34

40 ...................................................... 182,831 46

499 .................................................... 274,257 68

910 .................................................... 309,746 77

1,724 .................................................. 343,797 86

2,550 .................................................. 359,309 89

Total, 26,010.......................... 401,880 100

SOURCE: Computed from data in Public Employment, table 22, p. 423.

Table A-6. Township governments and township employment, 1975

(Cumulative)

Townships Employees Percent of total 

employment

961 .................................................... 213,260 59

1,831.................................................. 262,187 73

Total, 16,827 .......................... 360,763 100

S o u r c e : Computed from data in Public Employment, table 19, p. 344.

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Appendix B. ComparosQoi ©fi

®(f tlh© Oiiniiys Classification ©f

Government Fmetfens witth)

Standlard! Industrial Classification

Government function

(Bureau of the Census)

Description SIC industry

Airports................................... Operation and support of publicly operated

airport facilities.

4582 Airports and flying fields

4583 Airport terminal services

Corrections............................. Activities pertaining to the confinement and

correction of adults and minors convicted

of criminal offenses. Pardon, probation,

and parole activities are also included.

8361 Juvenile correctional homes

8399 Social services—parole, probation

9223 Correctional institutions

Electric power......................... Local government activities associated with

the production or acquisition and distribution of electric power to individual consumers.

4911 Generation, transmission, or

distribution

Financial administration........ Activities concerned with tax assessment and

collection, custody and disbursement of

funds, debt management, administration of

trust funds, budgeting, and other governmentwide financial management activities.

This function is not applied to school district or special district government.

9311 Tax assessors, budget agencies

Fire protection......................... Local government fire protection and prevention activities plus any ambulance, rescue, or other auxiliary services provided by

the fire protection agency.

9224 Fire departments, fire prevention offices

Gas supply............................... Local government activities associated with

the acquisition and distribution of gas supplies to individual consumers.

4924 Natural gas distribution

General control....................... Judicial, legislative, and governmentwide

administrative agencies of governments.

Includes planning and zoning activities,

central personnel, and administrative services, the office of chief executive, legislative activities, and court and courtrelated activities. This function is not applied to school district or special district

government.

7374 Data processing

9111 Executive offices

9121 Legislative assemblies

9131 Executive and legislative offices

combined

9199 Personnel agencies and personnel boards

9211 Courts

9222 Legal counsel and prosecution

9229 Public safety not elsewhere classified

9532 Zoning boards

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Appendix B. Comparison of Bureau of the Census Glassification of Government Functions with

Standard Industrial Classification=Continued

Government function

(Bureau of the Census) Description

Health Administration of public health programs,

community and visiting nurse services, immunization programs, drug abuse rehabilitation programs, health and food inspection

activities, operation of outpatient clinics,

and environmental pollution control activities.

8081

9431

9641

Higher education State and local government degree-granting

institutions which provide academic training

above grade 12.

8221

8222

Highways Activities associated with the maintenance

and operation of streets, roads, sidewalks,

bridges, tunnels, toll roads, and ferries.

Also includes snow activities.

1611

1622

4452

4784

Hospitals Government-operated medical care facilities

which provide inpatient care.

8062

8063

SIC industry

Outpatient care facilities

Public health agencies, environmental health and immunization programs

Food inspection

Colleges

Juinor colleges

Highways and street constructruction

Bridge, tunnel, and elevated

highway construction

Ferries

Operation of toll roads and

bridges

General medical hospitals

Mental hospitals

Housing and urban renewal. .. The operation of housing and redevelopment

projects and other activities to promote

or aid housing and community revewal.

6513 Operators of apartment buildings

9531 Housing agencies

9532 Community development agencies

Libraries

Liquor stores

Local schools

Natural resources

Libraries operated by local governments for

use by the general public. School and law

libraries are excluded; they are included in

the “local schools” or “higher education”

and “general control” categories, respectively.

Administration and operation of retail liquor

stores operated by State governments.

All activities associated with the operation of

public elementary and secondary schools

and locally operated vocational-technical

schools. Special education programs operated by elementary and secondary school

systems are also included, as are all ancillary services associated with the operation of

schools, such as pupil transportation and

food service.

Activities primarily concerned with the conservation and development of natural resources

—forest fire prevention and control, irrigation, drainage, land and forest reclamation, fish and game preservation and control, soil conservation, forestry, agricultural

aids and research, agriculture development

and inspection, and mineral resources activities.

8231 Libraries

5182 Liquor—wholesale

5921 Liquor and beer—retail

8211 Elementary schools, secondary

schools, and vocational high

schools

4151 School buses

0851 Forest management and services

0921 Fish hatcheries

0971 Operation of game preserves

1629 Flood control projects

4971 Irrigation system operations

9512 Soil conservation

9631 Irrigation districts

9641 Agriculture extension services

Other education State government activities relating to the supervision and regulation of public and private elementary and secondary schools;

programs and institutions for the training

of blind, deaf, and other handicapped persons; and vocational rehabilitation programs.

8249 Vocational schools other than

high schools

9411 Administration of educational

programs

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Appendix B. Comparison of Bureau of the Census Classification of Government Functions with

Standard Industrial Classification—Continued

Government function

(Bureau of the Census) Description SIC industry

Parks and recreation................ Local government activities which include

the operation and maintenance of parks,

playgrounds, swimming pools, public beaches, auditoriums, public golf courses, museums, marinas, botanical gardens, and zoological parks. State government park and

recreation activities are included in the “natural resources” function.

0782 Lawn and garden services

4469 Marinas

7992 Golf courses

7999 Swimming pools and beaches

8411 Museums, noncommercial

8421 Botanical gardens and zoological gardens

9512 Recreational program administration

Police protection..................... All activities concerned with the enforcement

of law and order, including coroners’ offices, police training academies, investigation bureaus, and local jails, “lockups,” or

other detention facilities not intended to

serve as correctional facilities.

9221 Police departments

9223 Jails

Sanitation other than sewerage. Refuse collection and disposal, operation of

sanitary landfills, and street cleaning activities.

4212 Garbage and refuse collection

4953 Refuse systems

4959 Sanitary services not elsewhere

classified

9511 Sanitary engineering agencies

9631 Sanitary districts

Sewerage ................................. Provision, maintenance, and operation of

sanitary and storm sewer systems and sewage disposal and treatment facilities.

1623 Sewerage collection and disposal

line construction

1629 Sewage treatment plant construction

4952 Sewerage systems

7699 Sewer cleaning and rodding

9511 Sanitary engineering agencies

Social insurance....................... Administration and conduct of social insurance programs. For State governments and

the government of the District of Columbia,

these activities include unemployment compensation and worker compensation programs, and work/study programs.

7361 Employment agencies

8331 Job training

9441 Unemployment insurance offices

Transit..................................... Activities relating to the operation and maintenance of public mass transit systems (e.g.,

bus, subway, surface rail, and street railroad systems). Elementary and secondary

school transportation systems are included

in the “local schools” function.

4111 Busline operation and subway

operation

4119 Local passenger transportation

not elsewhere classified

Water supply........................... Local government activities associated with

the production or acquisition of water and

distribution to the public.

4941 Water supply

Water transportation.............. The provision, operation, and support of

canals and other waterways, harbors, docks,

wharves, and other related marine terminal

facilities.

4463 Docks, terminal operation

4464 Canal operation

Welfare..................................... Activities such as the administration of various public assistance programs for the

needy, operation of homes for the elderly,

indigent care institutions, and programs

which provide payments for medical care

and other services for the needy. Health

care and hospital services provided directly

by a government, however, are included in

the “health” and “hospitals” functions

rather than here.

8321 Public welfare services

8351 Day care services

8361 Residential care

8399 Social service information exchanges

9441 Public welfare administration

So u r c e: 1977 Census of Governments—Compendium o f and Standard Industrial Classification Manual, 1972 (Office

Public Employment (Bureau of the Census, 1979), pp. 459-62, of Management and Budget, 1972).

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Appendix CL Isrg® Electric UtSiutnss

Data for the following State and local government utilities were combined to compute the productivity index for “ large utilities” in chapter IV. Utilities are ranked in order of size (kilowatt

hour sales to ultimate customers).

1. Los Angeles Water and Power Department, Calif.

2. New York Power Authority, N.Y.

3. Memphis Light, Gas, and Water, Tenn.

4. Salt River Power, Ariz.

5. Nashville Electric Service, Tenn.

6. Seattle City Light, Wash.

7. San Antonio City Public Service, Tex.

8. Jacksonville Electric Authority, Fla.

9. Sacramento Municipal District, Calif.

10. Chattanooga Electric Power, Tenn.

11. Omaha Public Power District, Nebr.

12. Snohomich County Public Utility District, Wash.

13. Tacoma City Light, Wash.

14. Austin Electric Department, Tex.

15. Cowlitz County Public Utility District, Wash.

16. South Carolina Public Service Authority, S.C.

17. Clark County Public Utility District, Wash.

18. Eugene Water and Electric Board, Oreg.

19. Orlando Utilities Commission, Fla.

20. Nebraska Public Power District, Nebr.

21. Lansing Board of Water and Light, Mich.

22. City of Colorado Springs, Colo.

23. Lincoln Electric System, Nebr.

24. Santa Clara Municipal Electric Department, Calif.

25. Imperial Irrigation District, Calif.

26. Grays Harbor County, Wash.

27. Garland Power and Light Department, Tex.

28. Grant County Public Utility District, Wash.

29. Benton County Public Utility District, Wash.

30. Modesto Irrigation District, Calif.

31. Central Lincoln Public Utility District, Oreg.

32. Tallahassee Electric Department, Fla.

33. Lakeland Electric Department, Fla.

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@©neral

A Demonstration of Comparative Productivity Measurement. Denver: Denver Regional Council of

Governments, December 1978.

Baumol, William J., “ Macroeconomics of Unbalanced

Growth: The Anatomy of Urban Crisis,” American

Economic Review, June 1967, pp. 415-26.

Bradford, D.F.; Malt, R.A.; and Oates, W.E., “ The

Rising Cost of Local Public Services: Some

Evidence and Reflections,” National Tax Journal,

XXII, No. 2 (June 1969), pp. 185-202.

Burkhead, Jesse and Hennigan, Patrick J., “ Productivity Analysis: A Search for Definition and Order,”

Public Administration Review, Jan./Feb. 1978, pp.

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