FILE COPY 

CURRENT POPULATION REPORTS 

Consumer Income 

P60-183 

Contributors: 

Paul Ryscavage, Gordon Green, 

Edward Welniak, and John Coder 

U.S. Department of Commerce 

Economics and Statistics Administration 

BUREAU OF THE CENSUS 
CURRENT POPULATION REPORTS 

Consumer Income 

P60-183 

Issued October 1992 

U.S. Department of Commerce 

Barbara Hackman Franklin, Secretary 

Rockwell A. Schnabel, Deputy Secretary 

Economics and Statistics Administration 

J. Antonio Villamil, Under Secretary 

for Economic Affairs 

BUREAUOFTHECENSUS 

Barbara Everitt Bryant, Director 
Economics and Statistics 

Administration 

J. Antonio Villamil, Under Secretary 

for Economic Affairs 

BUREAU OF THE CENSUS 

Barbara Everitt Bryant, Director 

C.L. Kincannon, Deputy Director 

William P. Butz, Associate Director 

for Demographic Programs 

HOUSING AND HOUSEHOLD 

ECONOMIC STATISTICS DIVISION 

Daniel H. Weinberg. Chief 

SUGGESTED CKATION 

U.S. Bureau of the Census, Current Population Reports, Series P60-183, Studies in the 

Distribution of Income, U.S. Government hinting Office, Washington, D.C. 1992 
iii 

Contents 

INTRODUCTION 

An Overview of lncome Trends and Growing lncome Inequality ... . . . . . . . . . . . .. . . . . . . . . .. . . .. . 1 

TABLES 

1. Share of Aggregate lncome Received by Each Fifth of Households: 1967 to 1990.. . 4 

FIGURES 

1. Real Median Family Income, Real Median Household Income, and Gini Indexes for 

Families and Households: 1947 to 1 990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

2. Real Mean Income of Each Fifth of Households: 1973 to 1990.. . . . . . . . . . . . . . . . . . . . . . . 2 

3. Lorenz Curve for U.S. Household Incomes: 1990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

4. Gini Indexes, by Age of Householder: 1 969, 1979, and 1989 . . . . . . . . . . . . . . . . . . . . . . . . . 6 

5. Gini Indexes, by Race of Householder: 1 969, 1979, and 1 989 . . . . . . . . . . . . . . . . . . . . . . . . 6 

6. Gini Indexes, by Type of Household: 1969, 1979, and 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

7. Gini Indexes, by Education of Householder: 1 969, 1979, and 1989.. . . . . . . . . . . . . . . . . . 7 

8. Gini Indexes for Married-Couple Households, by Education of Householder, Age 25 

to 54. .. . .. . ... . .. . .. .. .. . .. . .. . .. . . .. . .. . .. . .. . .. .. . .. ... . ... .. . ... .... .................... 8 

APPENDIX 

Source and Accuracy of Estimates for Studies in the Distribution of lncome . . . . . ... . . . . . .. . . 71 
The Impact of Demographic, Social, and Economic Change on the Distribution of 

lncome 

by Paul Ryscavage, Gordon Green, and Edward Welniak 

I. Introduction.. ............................................................................... 11 

II. Demographic, Social, and Economic Trends ............................................. 12 

Ill. A Standardization Technique .............................................................. 17 

IV. The Results of the Standardization.. ...................................................... 18 

V. Implications ................................................................................. 23 

References .................................................................................. 26 

TABLES 

1. Various Measures of the Household Income Distribution: 1969, 1979, and 1989.. .. 2. Effect on 1989 Median Household lncome of Standardizing for Demographic, 

Social, and Economic Changes Between 1 969 and 1989 ............................. 3. Effect on Shares of lncome (in percent) Received by Each Fifth of Households in 

1989 of Standardizing for Demographic, Social, and Economic Changes Between 

1969 and 1989.. ......................................................................... 4. Effect on 1989 Gini lndex of Standardizing for Demographic, Social, and Economic 

Changes Between 1969 and 1989.. .................................................... 5. Effect on 1989 Poverty Rate (in percent) of Standardizing for Demographic, Social, 

and Economic Changes Between 1969 and 1989 ..................................... 6. Effect on 1989 Median Household lncome of Standardizing for Demographic, 

Social, and Economic Changes Between 1979 and 1 989 ............................. 7. Effect on Shares of lncome (in percent) Received by Each Fifth of Households in 

1989 of Standardizing for Demographic, Social, and Economic Changes Between 

1979 and 1989.. ......................................................................... 8. Effect on 1989 Gini lndex of Standardizing for Demographic, Social, and Economic 

Changes Between 1979 and 1989.. .................................................... 9. Effect on 1989 Poverty Rate (in percent) of Standardizing for Demographic, Social, 

and Economic Changes Between 1979 and 1989 ..................................... 

FIGURES 

1. Households With a Householder Age 45 or Younger as a Percentage of all 

Households: 1969 to 1989 .............................................................. 13 

2. Married-Couple Households as a Percentage of all Households: 1969 to 1989 ....... 13 

3. Percentage of Persons Age 25 and Over that Completed 4 Years of High School or 

More and Completed 4 Years of College or More: 1969 to 1989.. ................... 1 5 

4. Percentage of Households in Which the Householder Worked or Did Not Work 

During the Year: 1975 to 1989 ......................................................... 1 5 

5. Percentage of Married-Couple Households in Which the Wife Was in the Paid Labor 

Force: 1969 to 1989.. ................................................................... 16 

6. Percentage of Nonagricultural Payroll Employment in the Goods-Producing and 

Service-Producing Industries: 1 969 to 1989 ........................................... 16 

APPENDIXES 

A. Standardization Methodology ............................................................. 27 

B. Income Measures .......................................................................... 29 
Factors Affecting Black-White lncome Diierentials: A Decomposition 

by Gordon Green. Paul Ryscavage. and Edward Welniak 

I . Introduction ..................................................................................... II . Factors Affecting Incomes in Black and White Households. 1969 to 1989 ................ Ill . The Standardization Methodology ............................................................ IV . Standardization Results ........................................................................ V . Summary and Directions for Future Research ............................................... References ......................................................................................... 

TABLES 

White Household's Effect on lncome and Poverty of Standardizing for Demographic 

Variables: Age ............................................................................ Black Household's Effect on lncome and Poverty of Standardizing for Demographic 

Variables: Age ............................................................................ White Household's Effect on lncome and Poverty of Standardizing for Social 

Variables: Type of Household and Education ........................................... Black Household's Effect on lncome and Poverty of Standardizing for Social 

Variables: Type of Household and Education ........................................... White Household's Effect on lncome and Poverty of Standardizing for Economic 

Variables: Work Experience and Industry ............................................... Black Household's Effect on lncome and Poverty of Standardizing for Economic 

Variables: Work Experience and Industry ............................................... White Household's Effect on lncome and Poverty of Standardizing for Demographic. 

Social. and Economic Variables ........................................................... Black Household's Effect on lncome and Poverty of Standardizing for Demographic. 

Social. and Economic Variables .......................................................... Black Income and Poverty Standardized by White Demographic Variables: Age ...... Black lncome and Poverty Standardized by White Social Variables: Type of 

Household and Education ................................................................ Black lncome and Poverty Standardized by White Economic Variables: Work 

Experience and Industry .................................................................. Black lncome and Poverty Standardized by White Demographic. Social. and 

Economic Variables ....................................................................... 

FIGURES 

1 . Income Ratios for Black and White Households: 1969 to 1989 ........................ 2 . Living Arrangements of Black and White Households: 1969 and 1989 ................ 3 . Educational Attainment of Blacks and Whites Age 25 and Over: 1969 and 1989 .... 4 . Work Experience of Black and White Family Householders and Unrelated Individuals: 

1969 and 1989 ........................................................................... 5 . Married-Couple Families With Working Wives as a Percent of All Households. by 

Race: 1969 to 1989 ...................................................................... 6 . Type of Industry for Black and White Workers: 1979 and 1989 ....................... 

APPENDIXES 

A . Standardization Methodology ............................................................. B . Income Measures .......................................................................... 
International Comparisons of Earnings Inequality for Men in the 1980's 

by Gordon Green, John Coder, and Paul Ryscavage 

Abstract.. .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 

Measures of Earnings lnequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 

The Data .......... .. ... .... ... ... .. . .. . ... . . . . . . .. . .. . ... . .. .. . .. . . . .. . .. . .. . . . . . . . . . . .. . . . . . .. . . .. 52 

Cross-National Comparisons of Earnings lnequality Measures.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 

Implications of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 

References ......................................................................................... 59 

TABLES 

la. Summary Earnings Measures for Men, Heads of Households, Age 25 to 54 Years, 

Who Worked Year Round, Full Time in Selected Countries: Early-1 980's and 

Mid-1 980's.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

1 b. Summary Earnings Measures for Men, Heads of Households, Age 25 to 54 Years, 

Who Worked Year Round, Full Time in Selected Countries, After Exclusion of the 

Self-Employed: Early-1 980's and Mid-1 980's . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 

2. Percentage Share of Aggregate Wage and Salary Income Received by Men, Heads 

of Households, Age 25 to 54 Years, Working Year Round, Full Time by Deciles for 

Selected Countries: Mid-1 980's . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

3. Measures of Earnings lnequality for Men, Heads of Households, Age 25 to 54, 

Working Year Round, Full Time for Selected Countries: Mid-1 980's . . . . . . . . . . . . . . . . i 56 

4. Shares (in percent) of Aggregate Wage and Salary Income Received by Men. Heads 

of Households, Age 25 to 54, Working Year Round, Full Time, by Quintile for 

Selected Countries: Early-1 980's and Mid-1 980's . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 

5. Changes in Measures of Earnings lnequality for Men, Heads of Households, Age 25 

to 54 Years, Working Year Round, Full Time for Selected Countries: Early-1 980's 

and Mid-1 980's.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 

6. Average Annual Growth Rates in Total Employment for Selected Countries: 

Early-1 980's to Mid-1 980's . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 

Lorenz Curve, Five Nations: Mid-1 980's . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 

APPENDIX 

~ Inequality Measures and Data Sources.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 
vii 

Trends in lncome and Wealth of the Eklerly in the 1980's 

by Paul Ryscavage 

lncome Growth for the Elderly ... The Big Picture. ... ............ ........ .... ..................... 61 

Special Problem Groups Among the Elderly.. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 

The Elderly and Wealth. ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 

Other Aspects of the Elderly's Net Worth.. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 

Conclusions ........................................................................................ 69 

TABLES 

Real Median Household lncome for Elderly Households and All Households: 1979 to 

1989.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Real Median lncome for Elderly Men and Women and All Men and Women (age 15 

and over): 1979 to 1989.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. Distribution of Aggregate Household lncome by Quintile and the Number of Elderly 

Households Represented in Each Quintile: 1979 and 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of Persons With Incomes Below the Poverty Level and the Poverty Rate for 

Elderly Persons and All Persons: 1979 to 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of Aggregate Household lncome by Quintile for Households Wih an 

Elderly Householder: 1979 and 1 989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Real Median lncome of Elderly Families by Type and Elderly Unrelated Individuals by 

Sex: 1979 and 1987 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Poverty Levels and Poverty Rates of Elderly Families, by Type and of Elderly 

Unrelated Individuals, by Sex: 1 979 and 1 989.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of Elderly Family Households, Elderly Unrelated Individuals, and Elderly 

Persons by Their Income-to-Poverty Line Ratio.. 1979 and 1987 . . . . . . . . . . . . . . . . . . . . . Distribution of Net Worth, by Asset Type and Median Valire for Elderly Households 

and All Households: 1988 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Median Value of Household Net Worth, by Monthly lncome Quintile and Median 

Value of Household Net Worth After Excluding Home Equity, by Monthly lncome 

Quintile for Elderly Households and All Households: 1984 and 1988.. . . . . . . . . . . . . . . . Median Value of Net-Worth and Median Value of Net Worth After Excluding Home 

Equity for Elderly Households, by Age and Type of Household: 1984 and 1988 . . . . 

Trends in Real Median lncome in Elderly and All Households: 1979 to 1989 . . . . . . . . . . . . . . . . . 63 
AN OVERVIEW OF INCOME TRENDS AND GROWING 

INCOME INEQUALITY 

INTRODUCTION 

The following collection of papers contains analyses 

relating to the distribution of income. They were motivated by two long-run economic developments: the 

slowdown in the growth of family and household incomes 

in recent yedrs and the growing inequality in the distribution of these incomes. 

From the end of World War ll until the early 1970's 

real incomes (incomes adjusted for changes in consumer prices) of most Americans rose rapidly. Moreover, the increases were spread fairly evenly across the 

income distribution, and income inequality changed very 

little. Between the early 1970's and 1 9901s, however, 

real income growth faltered, and the income gains that 

did occur were among those families and households at 

the upper end of the income distributi0n.l The result 

was a more dispersed and unequal distribution of income. 

These developments, of course, were the cause of 

concern among many groups in the Nation. In the 

media, for example, news accounts of a declining and 

struggling middle class began to appear in the 1980's 

and they became more numerous when the economic 

slowdown of the early 1990's began. Politicians debated 

the economic problems confronting the Nation, while 

researchers tried to uncover the causes of slow income 

growth and growing income inequality. 

Indeed, a significant amount of research has been 

conducted in this area over the years. The papers in this 

collection represent only a very small part of the total 

effort to understand the causes and implications of 

these economic developments. 

The first study, "The Impact of Demographic, Social, 

and Economic Change on the Distribution of Income," 

by Paul Ryscavage, Gordon Green, and Edward Welniak, attempts to identify changes in the Nation's demography, society, and economy that may have been associated with income trends and rising income inequality. 

The second study, "Factors Affecting the Black-White 

lncome Differential: A Decomposition," by Gordon Green, 

Paul Ryscavage, and Edward Welniak, examines the 

differential impact of the above mentioned changes on 

incomes and the income distribution of Black and White 

'Attention to this divergence in income growth was manifested in 

the early to mid-1980's in the debate over the declining middle class. 

For example, see Bob Kuttner, "The Declining Middle," Atlantic, July 

1983, pp. 60-72. 

households. The third study, "International Comparisons of Earnings Inequality for Men in the 198OSs," by 

Gordon Green, John Coder, and Paul Ryscavage, analyzes the rise in wage dispersion in the United States in 

the context of the experience of four other countries. 

And the fourth study, "Trends In lncome and Wealth of 

the Elderly in the 1980'~~" byPaul Ryscavage focuses 

on the economic situation of the elderly in relation to the 

rest of the population during the 1980's. All authors are 

economists or statisticians of the Bureau of the Census. 

Before turning to these studies, however, a brief 

overview of trends in income and income inequality 

during the last 20 years is presented which provides a 

useful framework for their reading. 

LONG-RUN TRENDS IN MONEY INCOMES 

Figure 1 shows the long-run trend in the real median 

money income (in 1990 dollars) of families between 

1947 and 1990 and households between 1967 and 

1990.2 With respect to the trend for families, the two 

distinct periods of income growth alluded to earlier can 

be seen in the figure. Between 1947 and 1973, the 

median rose from $1 6,370 to $33,370, or by 2.7 percent 

a year; between 1973 and 1990, however, the median 

fluctuated with the swings in the business cycle and, at 

$35,353 in 1990, was only about $2,000 higher than it 

was 17 years earlier. 3 Clearly, the income growth of 

families over these two periods was distinctly different. 

20ne of the major sources of data for studies of the income 

distribution is the Current Population Survey (CPS) conducted by the 

Bureau of the Census. Each March, income data relating to the 

previous calendar year are collected from a scientifically designed 

random sample of 60,000 households from around the country. The 

data relate to "money" incomes and data on noncash income, such 

as food stamps, health benefits, rent-free housing, and payments 

in-kind are excluded. For a description of the March CPS, see the 

appendices of U.S. Bureau of the Census, Current Population Reports, 

Consumer Income, Series P-60, No. 174, Money lncome of Households, Familks, and Persons in the United States: 11990. 

3The Consumer Price Index for All Urban Consumers (CPI-U) of 

the Bureau of Labor Statistics (BLS) has typically been used to adjust 

nominal incomes for price inflation. Prior to 1983, however, the 

CPI-U's measurement of shelter costs included changes in the asset 

value of homes, thereby overstating inflation and understating changes 

in real incomes, especially in the late 1970's. To provide researchers 

with a consistent price deflator, the BLS created an experimental price 

deflator called the CPI-U-XI which used a rental equivalence approach 

in the measurement of shelter costs. As a result, this deflator has 

been used to adjust nominal incomes for inflation in the 1987 to 1990 

period; it was extrapolated backyvard to 1947 by the Census Bureau. 

The CPI-U-XI is used throughout the remainder of this overview of 

income trends. 
Figure 1. 

Real Median Family Income, Real Median Household Income, and 

Gini Indexes for Families and Households: 1947 to 1990 

Income Gini index (0.0 to 1 .O) 

$40,000 1 0.500 

Gini index, Families 

Gini index, Households 

Median Familv Income - - 

Median Household Income I. III 

Years 

Figure 2. 

Reai Mean lncome of Each Fifth of Households: 1973 to 1990 

lncome 

$100,000 

$80,000 - 

$60,000 

I I 

Fourth fifth 

/ I I I 

$40,000 -- I 

Third fih 

Second fifth 

$20,000 / I 

I 

Lowest fifth I I I 

$0 . 

1973 1975 1980 1985 1990 

Year 
Real median household incomes also appear in 

figure 1. This statistical series begins in 1967, the first 

time household income data were a~ailable.~ Median 

household income is considerably lower than the median 

for family households, for example, in 1990 the median 

for households was $29,943 and for families $35,353. 

This, of course, reflects the large proportion of individuals living alone or unrelated individuals living together 

that are counted as households. These individuals, on 

average, have lower incomes than family households, 

especially married-couple households. 

Real median household income between 1973 and 

1990 did not grow much and paralleled the trend in 

incomes of families. Indeed, the median for households 

increased by only $835 and stood at $29,943 in 1990. 

Between 1967 and 1973, on the other hand, it increased 

by almost $3,400-from $25,719 to $29,108. Consequently, the poor income performance observed for 

family households in the 1973-90 period was also 

evident among all households. 

INCOME INEQUALITY 

Several methods can be used to measure the amount 

of inequality in the income distribution. The Census 

Bureau uses two of the more common ones, the shares 

of aggregate income received by equal proportions of 

households and the Gini index of income concentrati~n.~ In the shares approach, incomes of households 

are ranked from lowest to highest and then divided into 

equal proportions. Typically, the income distribution is 

divided into fifths or quintiles, but other quantiles could 

be used such as quartiles (quarters), deciles (tenths), or 

even ventiles (twentieths). 

Table 1 contains the shares of income received by 

each fifth of households in each year between 1967 and 

1990. As is shown there, the share received by the 

highest fifth of households is nearly 12 times as large as 

that received by the lowest fifth of hou~eholds.~ Moreover, while the share received by the bottom fifth 

4A household is defined as a person or group of persons occupying a housing unit. A family is defined as a group of two or more 

persons related by birth, marriage, or adoption, and residing together. 

'In addition to those used by the Census Bureau, measures such 

as the coefficient of variation, variance of the natural logarithm of 

incomes, the interquartile range, Theil index, and Atkinson measures 

of inequality have been used in the literature as well. A recent Census 

Bureau study also used a "relative income" concept to measure 

inequality. See John McNeil, U.S. Bureau of the Census, Current 

Population Reports, Series P-60, No. 177, Trends in Relative Imm: 

1964 to 1989, U.S. Government Printing Office, Washington, D.C., 

1991. 

'As Edgar K. Browning has pointed out, the shares received by 

each quintile would be different if different definitions of income are 

used. See Edgar K. Browning, "Inequality and Poverty," Southern 

Economic Journal, Vol. 55, No. 4, April 1989, pp. 819-830. As an 

illustration of his point, see U.S. Bureau of the Census, Current 

Population Reports, Series P-60, No. 176-RD, Measun'ng the Effect of 

Benetits and Tmes on Income and Poverly: 1990, U.S. Government 

Printing Office, Washington, D.C., 1991. 

changed little between 1973 and 1990 (not a statistically significant change), the share received by the 

highest fifth rose from 43.6 to 46.6 percent. The middle 

three-fifths, often used to represent the "middle class," 

experienced a decline in its share from 52.2 to 49.5 

percent. Consequently, it was the highest fifth of households that were experiencing the greatest income growth. 

This link between differential rates of income growth 

and rising inequality can be seen more clearly when 

mean incomes in each quintile are examined over the 

1970's and 1980's. As is shown in figure 2, the real 

mean income of the highest fifth of households increased 

from $73,438 to $87,137 between 1973 and 1990, or by 

19 percent. Real mean incomes grew much slower 

among the other fifths of households. 

The second method for measuring income inequality 

used by the Census Bureau-the Gini index of income 

concentration-is related to the shares of income approach. 

The shares of income received by each fifth of households, or any quantile of households, can be displayed 

graphically in the form of a Lorenz curve, as is shown in 

figure 3. The vertical axis of the chart represents the 

cumulative percentage of aggregate income, while the 

horizontal axis represents the cumulative percentage of 

households ranked by income from lowest to highest. 

The Lorenz curve represents the relationship between 

the two. In other words, if all households received the 

same money incomes-perfect equality-the Lorenz 

curve would be represented by a diagonal emanating 

from the origin of the chart; if only one household 

received all the income-perfect inequdity-the Lorenz 

curve would be represented by the horizontal axis and 

the right hand side of the chart. 

Lorenz curves typically fall between the extremes of 

perfect equality and perfect inequality, as shown in 

figure 3. (The Lorenz curve depicted is based on the 

household income distribution existing in 1990 using 

deciles, or tenths of households.) The Gini index represents the area (A) between the diagonal, or line of 

perfect equality, and the Lorenz curve, as a percentage 

of the total area lying beneath the diagonal (A + 6). In 

other words, the Gini index can vary between 0 and 1: 

therefore, when income inequality rises, the Lorenz 

curve bows further downward and the area (A) between 

it and the diagonal increases in size. The result is that 

the Gini index rises. 

Table 1 and figure 1 show the Gini indexes for the 

household income distribution between the years of 

1967 and 1990. Income inequality, as measured by the 

Gini index, stood at .399 in 1967 and .402 by 1977, a 

change that was not statistically significant. Thereafter, 

however, the index began to rise. By 1983, the index 

was .414, and by 1990 it was .428. Figure 1 also shows 

the trend in the Gini index for the family income distribution. Between 1947 and 1973, the period when family 
Table 1 . Sham of Aggregate Income Received by Each Fifth of Households: 1967 to 1990 

Percent distribution of aggregate income 

Year Number Median Gini 

(thous.) Lowest Second Third Fourth Fifth (1990) ratio 

1990 .............................. 94. 312 3.9 9.6 15.9 24.0 46.6 $29. 943 .428 

1 989 .............................. 93. 347 3.8 9.5 15.8 24.0 46.8 30. 468 .431 

1988 .............................. 92. 830 3.8 9.6 16.0 24.3 46.3 30. 079 .427 

1987 .............................. 91. 124 3.8 9.6 16.1 24.3 46.2 29. 984 .426 

1986 .............................. 89. 479 3.8 9.7 16.2 24.3 46.1 29. 690 .425 

1985 .............................. 88. 458 3.9 9.8 16.2 24.4 45.6 28. 688 .419 

1984 .............................. 86. 789 4.0 9.9 16.3 24.6 45.2 28. 197 .415 

1983 .............................. 85. 290 4.0 9.9 16.4 24.6 45.1 27. 581 .414 

1982 .............................. 83. 918 4.0 10.0 16.5 24.5 45.0 27. 577 .412 

1981 .............................. 83. 527 4.1 10.1 16.7 24.8 44.4 27. 669 .406 

1980 .............................. 82. 368 4.2 10.2 16.8 24.8 44.1 28. 125 .403 

1979 .............................. 80. 776 4.1 10.2 16.8 24.7 44.2 29. 074 .404 

1978 .............................. 77. 330 4.2 10.2 16.9 24.7 44.1 29. 168 .402 

1977 .............................. 76. 030 4.2 10.2 16.9 24.7 44.0 28. 067 .402 

1976 .............................. 74. 142 4.3 10.3 17.0 24.7 43.7 27. 913 .398 

1975 .............................. 72. 867 4.3 10.4 17.0 24.7 43.6 27. 442 397 

1974 .............................. 71. 163 4.3 10.6 17.0 24.6 43.5 28. 197 .395 

1973 .............................. 69. 859 4.2 10.5 17.1 24.6 43.6 29. 108 .397 

1972 .............................. 68. 251 4.1 10.5 17.1 24.5 43.9 28. 545 .401 

1971 .............................. 66. 676 4.1 10.6 17.3 24.5 43.5 27. 377 .396 

1970 .............................. 64. 778 4.1 10.8 17.4 24.5 43.3 27. 640 .394 

1969 .............................. 63. 401 4.1 10.9 17.5 24.5 43.0 27. 828 .391 

1968 .............................. 62. 214 4.2 11.1 17.5 24.4 42.8 26. 844 388 

1967 .............................. 60. 813 4.0 10.8 17.3 24.2 43.8 25. 719 .399 

NOTE: Median household income has been deflated by the CPI.U.Xl . See footnote 3 . 

Figure 3 . Lorenz Curve for U.S. Household Income: 1990 

Percent of Households 
income was rising, inequality, as measured by the Gini 

index, was fairly stable or declining. In the 1973 to 1990 

period, however, when family income grew very little, 

inequality began to rise. 

INCOME INEQUALITY AMONG GROUPS 

Growing income inequality in recent years has not 

been isolated among just one or a few groups. Rather, 

evidence of greater income differences has been observed 

among many groups in society to varying degrees. 

Special income tabulations that were developed for two 

of the income studies contained in this report, provide 

some evidence as to how income inequality changed 

among various groups between 1969,1979, and 1989. 

Figure 4 shows the Gini indexes for four groups of 

households classified on the basis of the age of the 

householder. As shown, increases in inequality were 

particularly sharp among those households in which the 

householder was under 45 years of age. While the 

number of households with a householder under 25 

were not very numerous (5 million in 1989), the Gini 

index for this group rose from .316 to .396 between 

1969 and 1989, or 25 percent. For those householders 

age 25 to 44, the Gini index increased from'.302 to .374, 

or 24 percent. (Implied comparisons are not significant.) 

Inequality also increased among households in which 

the householder was age 45 to 64, but only by about 11 

percent and most of this occurred in the 1980's. And for 

elderly households, those in which the householder was 

65 or over, inequality actually fell between 1969 and 

1989. 

When inequality is examined by race, increases 

between 1969 and 1989 are also observed among 

White and Black households. Figure 5 indicates that the 

Gini index for White households was up by 10 percent 

and for Blacks 1 1 percent (statistical comparisons implied 

here are not statistically significant). For households of 

other races the change in the Gini index was not 

statistically significant. 

Growing income inequality was also observed among 

married-couple households and single-parent households, especially during the 1980's. As shown in figure 

6, the Gini index among the former rose from .331 to 

.368 between 1969 and 1989, or 1 1 percent, and for 

single parent families from ,410 to .448, or 9 percent. 

The trend in inequality for nonfamily households reflected 

that of elderly households. 

Gini indexes increased more among those households in which the householder had less than a college 

education than it did for householders with more education, as is shown in figure 7. The Gini index rose from 

.381 to .418 over the 20-year period, or by 10 percent, 

for those with less than a college education. 

Among those with some college experience, the Gini 

index was up by only 6 percent (from .346 to .368) and 

for those householders with a college education or 

more, the Gini index rose by only 5 percent, or from .351 

to .370. Both of these changes were not statistically 

significant. 

As has been shown, inequality of incomes has increased 

across many different groups in society, but the amount, 

or degree, of increase has also varibd. One of the 

sharper contrasts was found among married-couple 

households in which the householder was age 25 to 44, 

and had either a college education or a high school 

education or less. Figure 8 shows that income inequality 

in the 1969-89 period, as measured by the Gini index, 

rose dramatically for those households in which the 

householder had a high school education or less-from 

.244 to .298, or 22 percent. In contrast, for those with 

college educations or more, the Gini index increased by 

only 6 percent, or from .281 to .297, a change that was 

not statistically significant. (The comparisons implied 

here were also not statistically significant.) 

The composition of both groups were greatly affected 

by the maturing of the large baby boom cohort born 

between the years of 1946 and 1964. Indeed, in 1969 

the "baby boomers" were only age 5 to 23 years of age 

but by 1989 they were age 25 to 43. As was pointed out, 

for those households in which the householders had 

high school educations or less, income inequality rose 

substantially, while for those with college educations the 

increase jn inequality was less dramatic. Incomes also 

rose much faster for the college educated than for those 

with high school educations or less. For the college 

educated, median household incomes increased from 

$43,255 to $56,654 between 1969 and 1989, or 31 

percent; for those with high school educations or less, 

household incomes rose from $30,560 to $33,026, or by 

about 8 percent. In other words, both the trend in 

income and income inequality were quite different and 

the major distinguishing characteristic between both 

groups was education. 

POSSIBLE CAUSES OF GROWING INCOME 

INEQUALITY 

Growing income inequality has been the subject of 

much research in recent years. Much of this research 

has focused on the changes taking place in the Nation's 

labor market and its wage distribution. This is a reasonable connection since the single largest component of 

household income is earnings derived from the labor 

market. According to this research, real earnings growth 

did indeed slowdown in the 1970's and 1980's relative 

to earlier decades, and greater dispersion in market 

earnings occurred.' One study showed that for men, the 

'The literature in the area of earnings and wage inequaliity is 

voluminous. For a sampling of the earlier work in this area see Peter 

Henle and Paul Ryscavage, "The Distribution of Earned Income 

Among Men and Women, 1958-1977," Month& Labor Review, April 
Figure 4. 

Gini Indexes, by Age of Householder: 1969,1979, and 1989 

Gini Index (0.0 to 1 .O) m1969 111979 1989 

0.6 

Under 25 Age 25 to 44 Age 45 to 64 Age 65 and over 

Age of Householder 

Figure 5. 

Gini Indexes, by Race of Householder: 1969,1979, and 1989 

Gini Index (0.0 to 1 .O) Dl969 1111979 1989 

0.5 1 

White Black Other 

Race of Householder 
Figure 6. 

Gini Indexes, by Type of Household: 1969,1979, and 1989 

Gini lndex (0.0 to 1 .O) 0.6 - HI969 1111979 1989 

Other family households Nonfamily households 

Type of Household 

Figure 7. 

Gini Indexes, by Education of Householder: 1969,1979, and 1989 

Gini Index (0.0 to 1 .O) m1969 m1979 1989 

0.5 

Less than college College, 1 to 3 years 

Education of Householder 

Education represents highest year of school completed. 

College or more 
Figure 8. 

Gini Indexes for Married-Couple Households, by Education 

of Householder: Age 25 to 54 

Gini Index (0.0 to 1 .O) m1969 111979 1989 

0.35 

Less than college 

Education 

Education represents highest year of school completed. 

Gini index rose from .351 to .405 between 1967 and 

1987 indicating a 15 percent increase in the inequality of 

earnings derived from the labor market.8 

One of the demand-side factors that researchers 

have thought to be causing greater wage dispersion has 

been the shift in economic activities away from goodsproduction to service-production.9 Industries involved in 

the production of services typically have been paid 

lower wages and exhibited a larger amount of variation 

in their wage structure relative to those industries engaged 

in the production of goods. It would stand to reason as 

more and more workers entered the service-producing 

1980, pp. 3-10; Martin Dooley and Peter Gottschalk, "Earnings 

.Inequality Among Males in the U.S.: Trends and the Effect of Labor 

Force Growth," Journalof PolHical Economy, Vol. 92, No. 1,1984, pp. 

59-89; and Barry Bluestone and Bennett Harrison, The Great American Job Machine: The Prolifemtion of Low Wage Employment in the 

US. Economy, Report to the Joint Economic Committee of the U.S. 

Congress, December 1986. 

'See Gary Burtless, "Earnings Inequality Over the Business and 

Demographic Cycle," in Gary Burtless, ed., A Future of Lousy Jobs? 

WashinGon, The Brookings Institution, 1990. 

Osee Barry Bluestone and Bennett Harrison in footnote 7. Goods 

producing industries have traditionally been defined as agriculture, 

mining, construction, and manufacturing. The service-producing industries have been defined as transportation, communication, and public 

utilities,'wholesale and retail trade, finance, insurance and real estate, 

business and repair services, personal services, entertainment and 

recreation services, professional and related services, and public 

administration. 

College or more 

of Householder 

industries wage growth would have become slower and 

the structure of wages more dispersed. 

Other researchers have pointed out, however, that 

greater wage inequality has also been observed over 

time in the goods-producing industries as well. For 

example, in manufacturing the Gini index for men who 

worked full time, year round increased from .247 to .286 

between 1978 and 1987.10 Consequently, additional 

explanations have emerged. 

One of the more popular explanations has focused 

on the growing gap in the economic returns to welleducated and poorly-educated workers.11 According to 

this explanation, during the 1980's employers became 

more willing to pay wage premiums for highly skilled 

workers as the supply of these workers began to grow 

more slowly in that decade. Wages for the less-skilled, 

on the other hand, changed very little or even declined. 

A decline in labor demand for these workers, particularly 

in manufacturing industries, resulted in fewer job opportunities for them at relatively high wages. 

l0See Paul Ryscavage and Peter Henle, "Earnings lnequdi 

Accelerates in the 1980's." Month& Labor Review, December 1990, 

pp. 3-16. 

''See Chinhui Juhn, Kevin M. Murphy, and Brooks Pierce, "Wage 

Inequality and the Rise in the Returns to Skill, The Journal of Poltical 

Economy, (forthcoming). 
Immigration patterns and the country's trade deficit of 

the 1980's may have been related to the growing gap in 

wages between educational classes of workers.'* Recent 

immigrants have been poorly educated and have added 

to the pool of native born workers with low education 

levels thereby depressing wages. The trade deficit may 

have also added to the problem since the Nation's 

imports embodied a greater proportion of unskilled labor 

than our exports. In other words, the imports consisted 

of goods that could have been made by low-skilled 

workers in this country but instead were made abroad. 

Declining unionization, according to some researchers, may have also exacerbated the income inequality 

picture in recent years.13 With union influence on wage 

setting practices diminishing, especially in certain highpaying goods-producing industries, the economic prospects for many less-skilled workers darkened in the 

1980's. Such workers have typically received the greatest economic benefits from union membership. 

I Outside the economic realm, certain social and demographic developments have been associated with rising 

income inequality. These perhaps represent the linkage 

between both greater income inequality and greater 

earnings inequality. While the trend in income inequality 

is related to the trend in earnings inequality, it is also 

linked to the number of earners in a household, the 

amounts of income derived from outside the labor 

market, and other factors associated with household 

formation. 

Changes in the age structure of the population and 

changes in household composition have been two 

noneconomic factors mentioned as affecting growing 

income inequality. The maturing of the baby boom 

generation in the 1970's and 1 9801s, as was discussed 

earlier, coincided with the growth in inequality, so it has 

been a logical suspect. Indeed, there was evidence of a 

growing gap in the incomes between younger and older 

workers in the 1970's and early 1980'~~ as the baby 

l2See Frank Levy and Richard J. Murnane, "Earnings Levels and 

Earnings Inequality: A Review of Recent Trends and Proposed 

Explanations," Journal of Economic Literature (forthcoming). 

I3See McKinley Blackburn, David E. Bloom, and Richard B. 

Freeman, "The Declining Economic Position of Less Skilled American 

Men," in A Future of Lousy Jobs2 Gary Burtless, ed., Washington, 

The Brodcings Institution, 1990. 

boom cohort matured.14 However, and has been pointed 

out by a number of researchers, by the mid-1980's the 

baby boom generation was well into their middle years 

and income inequality continued to rise. Furthermore, 

because the baby boom generation spanned so many 

years, there were offsetting effects associated with its 

aging, and by the 1990's its overall impact had been 

neutralized.15 

The evidence with respect to changes in household 

composition are more clear cut. Between 1969 and 

1989 the proportion of all households that were marriedcouple households plunged from 70 percent to 56 

percent. Taking the place of this traditional type of living 

arrangement were single-parent family households and 

nonfamily households, groups in which inequality of 

incomes is greater than among married couples. Increases 

in divorce and separations, births out of wedlock, and 

alternative life-style choices were obviously related to 

the dramatic change in living arrangements that took 

place. 

The consequences of these changes, when combined with the changes that took place in the labor 

market during the 1970's and 1980's (i.e., lackluster 

wage growth), had implications for growing income 

inequality. On the one hand, a larger and larger propQrtion of working wives moved into the work force, in many 

cases to prop up the stagnating earnings of husbands. 

On the other, a smaller proportion of households had 

available to them additional persons to enter the labor 

force. Incomes in single-parent families and nonfamily 

households are much lower, on average, than those of 

married couples in which the wife worked. Obviously, 

the reasons for the recent increases in income inequality in this country are not fully understood. Not only is 

more research needed in understanding the greater 

inequality in the distribution of earnings derived from the 

labor market, but also in understanding the linkages 

between it and rising income inequality among households. We hope the following studies shed some light on 

growing earnings and income inequality in the United 

States. 

14See Frank Levy, DoI/ars and Dreams: lhe Changing American 

Income Distnauton, New York, Russell Sage Foundation. 1987. 

'=See Paul Ryscavage, Gordon Green, and Edward Welniak, "The 

Impact of Demographic, Social, and Economic Change on the Distribution of Income," in this report. 

THE IMPACT OF DEMOGRAPHIC, SOCIAL, AND 

ECONOMIC CHANGE ON THE DISTRIBUTION OF INCOME 

by Paul Ryscavage, Gordon Green, and Edward Welniak 

Presented at the 13th Annual Research Conference of the Association for Public Policy Analysis and Management in Bethga, Maryland, October 26, 1991. Paul Ryscavage is a Senior Labor Economist in the Housing and Household Economtc SWsWS 

Division, Gordon Green is Chief of the Governments Division, and Edward Welniak is Chief of the Income Statistics Branch in the 

Hous~ng and Household Economic Statistics Division, all from the U.S. Bureau of the Census. The views expressed In this paper 

are those of the authors and do not necessarily represent those of the Census Bureau. 

I. INTRODUCTION 

One of the more striking economic developments 

over the past 20 years has been the growth in income 

inequality among American households. During this 

period the percentage of households with incomes 

below the Federal government's poverty thresholds has 

also been persistently high. Both developments took 

place during a time when household incomes, on average, grew very slowly. Between 1969 and 1989, real 

median household income grew by a meager 2 percent, 

or from $28,344 to $28,906.1 

These developments, of course, were in sharp contrast to the trends during the 1950's and 1960's. Incomes 

rose strongly in the immediate post-World War II era and 

the increases were spread more evenly throughout the 

income distribution. Income inequality changed very 

little, and poverty was dramatically reduced. It is for this 

reason, therefore, that the income trends over the last 

two decades have attracted so much attention from so 

many quarters of society. 

At issue, of course, is whether the U.S. economy in 

recent years has been so transformed by world competition and other developments that it can no longer 

assure the ever increasing standard of living American 

households have become accustomed to. This theme 

has been popularized by a number of writers and 

economists during the 1980's. For example, Barry Bluestone and Bennett Harrison (1982) argued that "deindustrialization" was impeding the growth of the middle 

class. Robert Kuttner (1 983) and Lester Thurow (1 984) 

speculated about whether the Nation was becoming 

one of "haves and have nots." And Frank Levy (1987) 

warned that the increasingly unequal distribution of 

incomes was creating "an inequality of prospects" for 

the attainment of the American middle class life style. 

'This increase in nominal incomes between 1969 and 1989 was 

adjusted for inflation by the Consumer Price lndex for All Urban 

Consumers (CPI-U). If it had been adjusted with the experimental 

Consumer Price lndex for All Urban Consumers (CPI-U-Xl), the 

median would have increased by 9.5 percent.'All other changes in 

incomes cited in this paper have been adjusted by the CPI-U-XI. 

One of the common threads running through all of 

their arguments was the belief that the source of the 

greater income polarization was the labor market. Economic forces, whether they be low productivity and 

wage growth, the soaring trade and fiscal deficits, the 

decline in union membership, and so forth, were all 

taking their toll on the traditional jobs of the middle 

class. On the other hand, demographic changes, such 

as the maturing of the baby boom generation, and social 

changes, such as changes in living arrangements, were 

usually given lower importance in the explanation for the 

growth in income inequality among households. 

We believe that the developments occurring in the 

income distribution, whether they involve income levels, 

income inequality, or poverty, are the outcomes of a 

complex assortment of demographic, social, and economic factors. Furthermore, attempting to weight their 

relative importance suggests some preconception of 

the complex interrelationships going on among these 

factors. As is well known, this country's demography, 

society, and economy underwent profound changes 

during the 1970's and 1980's. In this paper, therefore, 

we examine trends in various demographic, social, and 

economic factors and investigate how they may have 

both separately and collectively affected our income 

and poverty measures between 1969 and 1 989. 

We begin in Section II by discussing some of the 

more important demographic, social, and economic 

trends during the last twenty years and their possible 

effects on income levels, inequality, and poverty. In 

Section Ill we present a standardization methodology-a tool of analysis more common among demographers-which allows us to quantify the impact of these 

factors on income measures. Section IV presents the 

results of this standardization technique when 1989 

incomes of households are standardized for demographic, social, and economic changes taking place 

over the 1969-89 period and then standardized for 

demographic, social, and economic changes taking 

place over the 1979-89 period. The last section of the 

paper, Section V, outlines the implications of our results 

for further research into growing income inequality. 
II. DEMOGRAPHIC, SOCIAL, AND ECONOMIC 

TRENDS 

Trends in seven specific demographic, social, and 

economic factors and their possible effects on income 

and poverty measures are discussed in this section. 

Some of these factors are exogenous, that is, household behavior cannot affect them. Others are endogenous, that is, household members could have some 

control over them. Other factors might have been 

included, but given the nature of the methodology and 

our reading of the literature, the trends in these seven 

factors reflect the more fundamental changes that have 

taken place in the country during the 1970's and 1980's. 

A. Demographic 

Demographic trends in this paper refer specifically to 

changes in age structure and race of the population and 

these are considered to be exogenous in the analysis. 

The age structure might be thought of as endogenous, 

however, to the extent that people have some control 

over their fertility levels which in turn affects the age 

structure. Both age structure and racial composition do 

not change much in the short run, but over a longer 

period of time, such as 20 years, their potential effects 

on income and poverty can be large. Both income and 

the incidence of poverty, of course, vary substantially by 

age and race. 

Perhaps the most important influence on the age 

structure during the 1970's and 1980's was the entrance 

of the baby boom generation into adulthood. Between 

1946 and 1964, approximately 75 million babies were 

born and by 1969 the leading edge of this age cohort 

had finished their schooling and was entering the labor 

market (or had already entered). This process would 

continue throughout the 1970's and not be completed 

until almost the end of the 1980's. In other words, it was 

over the last two decades that this unusually large 

cohort of persons entered the labor force, started 

households, and, in general, moved on towards middle 

age. 

Many studies have examined the effects of the baby 

boom generation on relative incomes over the years 

(e.g., Freeman, 1979). There is a consensus that the 

baby boom cohort did have lower relative incomes than 

other cohorts as they entered. Indeed, economic theory 

would suggest that an exogenous shift in labor supply, 

such as the maturing of the baby boom, would depress 

wages. This would also tend to depress income levels 

and most likely increase poverty rates and income 

inequality. But such effects, theoretically, should be 

short-lived as the cohort adjusts through increases in 

their training and the accumulation of work experience. 

Consequently, the baby boom's impact on income, 

poverty, and inequality should have been muted as the 

cohort aged. 

Figure 1 reflects the impact of the baby boom generation on the age structure of households. The proportion of all households with a householder under 45-our 

dividing line between younger and older households-increased fairly rapidly during the 1970's and then began 

to slow down in the 1980'~.~ This trend, of course, is not 

only affected by basic fertility patterns, but also by rates 

of household formation and dissolution. Nevertheless, it 

is clear that the most dramatic shift in the age structure 

of households occurred in the 1970's. 

A second demographic variable examined is race. 

Because of differential fertility patterns, the Black population has been growing faster than the White population.3 Households with Black householders increased 

from 9.5 to 11.2 percent of all households, and the 

increase was relatively constant across the 1969-89 

period. Given the generally lower income levels and 

higher poverty rates for Black households, their trends 

would have had a negative impact on broad income and 

poverty measures over time. 

B. Social 

Two frequently discussed social trends examined are 

changes in living arrangements (or type of household) 

and educational attainment. Both are considered to be 

endogenous since individuals have some control over 

their choice of marital status and education. Income and 

poverty status, of course, vary greatly by household 

type and educational attainment. 

The greater incidence of marital disruptions and 

births out of wedlock, along with the increasing age at 

first marriage in recent years, has had a profound effect 

on the living arrangements of society. The Nation's 

divorce rate climbed steadily from the late 1960's and 

into the 1970's, only to level off in the 1980's. Births out 

of wqdlock more than doubled as a proportion of all 

births between 1970 and 1987 (from 10.7 percent in 

1970 to 24.5 percent by 1987). And the median age at 

first marriage for men went from age 22.5 in 1970 to age 

25.1 by 1986. The resulting impact on the proportion of 

all households composed of married couples over these 

years can be seen in figure 2: In 1969, 70.1 percent of 

all households were made up of married couples, by 

1979 the proportion had dropped to 60.8 percent, and 

by 1989, it was down to 56.0 percent. Both single parent 

families and nonfamily households became considerably more common. These compositional changes took 

place more rapidly in the 1970's rather than the 1980's. 

TThe householder is the person in whose name the home is owned 

or rented. 

=Although the Hispanic population has increased in size as well 

and their income situation is similar to Blacks, they were not explicitly 

accounted for in the standardization because data on this group only 

begin in 1972. While Hispanics may be of any race, most are White. 
Figure 1. 

Households With a Householder Age 45 or Younger As a Percentage 

of All Househblds: 1969 to 1989 

1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 

Year 

Figure 2. 

Married-Couple Households As a Percentage of All Households: 

1969 to 1989 

Percent 

80 

Year 
Married-couple households have much higher incomes 

than other types of households and the impact on 

income levels and the incidence of poverty is obvious. 

The declining proportion of married couple households 

would tend, ceteris paribus, to lower overall income 

levels and raise the poverty rate. 

Educational attainment continued to increase throughout the 1969-89 period as is shown in figure 3. Persons 

age 25 and over with four or more years of college rose 

from 10.7 percent in 1969 to 16.4 percent in 1979 and 

21.1 percent by 1989. The gain in education, therefore, 

occurred throughout the 20-year period. This trend is 

related to the maturing of the baby boom generation. 

Increases in the proportion of persons with college 

educations have raised income levels over time and are 

consistent with human capital theory. However, and as 

has been widely reported, returns to education dropped 

during the 1970's and then increased in the following 

decade (e.g., Juhn, Murphy, and Pierce, 1989). For 

example, in 1968 the ratio of mean incomes of male 

college graduates to males with less than high school 

educations (age 25 to 34) was 1.38; by 1978 it had 

dropped to 1.22, but by 1987 it was up to 1.50. 

C. Economic 

Three economic trends often discussed in the income 

inequality literature are the work experience of householders, the work experience of wives in married-couple 

families, and the industry in which workers are employed. 

Occupation is also discussed, but it is difficult to analyze 

because of changes in the occupational classification 

system between 1970 and 1990. Some of the shifts in 

occupation are certainly related to shifts in industry. 

Each of these three variables is endogenous to the 

extent that a choice is made on the part of the individual 

as to whether or not to work and where to work. On the 

other hand, economic conditions, geographical considerations, and other barriers to employment may seriously constrain the decision to work and where. 

In theoretical terms, the decision to work is based on 

a person's choice between labor or leisure, and his or 

her maximization of utility. When an Individual's reservation wage-or the lowest wage a worker would accept 

to give up one hour of leisure-falls below an offer 

wage, the individual will supply labor. But, of course, 

many other considerations enter into this process. The 

presence of other household members, their earning 

power and need for support, the availability of nonlabor 

income, working conditions, and so on, all are factored 

into the labor-leisure choice. 

As shown in figure 4, the proportion of householders 

with some work experience during the reference year 

has declined slightly over the 1975 to 1989 period. This 

probably reflects the increase in the proportion of 

elderly households as well as the increase in the 

proportion of households headed by women with small 

children. Householders who work full time (35 hours or 

more a week) year round (50 to 52 weeks a year) have 

represented slightly more than half of all householders. 

The decline in this proportion, especially in the early 

1980'~~ reflects the recessions which took place at that 

time. Obviously, fluctuations in economic conditions 

have an impact on income trends and poverty. Burtless 

(1 990), however, has shown that the changing levels of 

unemployment in the last two decades accounted for 

only a small part of the increase in earnings inequality in 

recent years. 

The entrance of women into the labor force, especially married women, has been one of the more dramatic labor force developments in the second half of 

this century. As is shown in figure 5, the proportion of 

married couples with a wife in the paid labor force 

increased from 39.3 percent in 1969 to 49.2 percent by 

1979 and 57.7 percent by the close of the 1980's. The 

somewhat slower increase in the 1980's parallels the 

slightly slower growth in labor force participation of all 

women. 

The impact of working wives on family incomes has 

been the topic of much research. One of the more 

recent contributions has concluded that while wives' 

earnings have become an increasingly important source 

of family income they have not caused greater income 

inequality among married couples (Cancian, Danziger, 

and Gottschalk, 1991). Indeed, wives earnings have 

been an "equalizing" force on the income distribution of 

married couples. 

The more important question for our purposes, however, is the impact of working wives on the income 

distribution of all households. As was pointed out earlier, living arrangements have changed dramatically in 

recent years and between group differences in incomes 

have widened. For example, the ratio of married couples' median income to the median incomes of families 

with a female householder has risen from 2.07 to 2.22 

between 1969 and 1989. Such differences would tend 

to increase income inequality. Consequently, the impact 

of working wives on the household income distribution is 

important. 

The shift in the Nation's industrial structure from a 

goods-producer to a service-producer has been discussed extensively by Barry Bluestone (1 990) and others as a primary factor in the growth of wage inequality, 

and by implication, greater income inequality. This restructuring, in terms of jobs, has been documented many 

times and is shown in figure 6. The proportion of all 

nonagricultural payroll jobs found in the sewice-producing 

industries rose from 65.4 percent in 1969 to 76.6 

percent twenty years later. This gradual shifting has 

been alleged to depress income growth and produce 

greater income inequality because wages tend to be 

lower in service-producing industries and are more 

greatly dispersed. 
Figure 3. 

Percentage of Persons Age 25 and Over That Completed 4 Years of High High school School or More and Completed 4 Years of College or More: 1969 to 1989 

Percent college 

Year 

Figure 4. 

Percentage of Households in Which the Householder Worked or Did 

Not Work During the Year: 1975 to 1989 Householders who worked 

Percent Householders not working 

80 

Year 
Figure 5. 

Percentage of Married-Couple Households in Which Wife Was in 

Paid Labor Force: 1969 to 1989 

Percent 

60 

58 

56 

54 . 

52 

50 

48 

46 

44 

42 

40 

38 

1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 

Year 

Figure 6. 

Percentage of Nonagricultural Payroll Employment in Goods-Producing 

and Service-Producing Industries: 1969 to 1989 Service-producing 

Percent Goods-producing 

Year 
Ill. A STANDARDIZATION TECHNIQUE 

While it is possible to speculate'about the possible 

effects of these demographic, social, and economic 

trends on income, poverty, and income inequality during 

the past two decades, a more rigorc!rs approach is 

required to measure their quantitative importance. A 

standardination methodology, therefore, has been employed 

to do just that.4 Moreover, because some of these 

trends did not continue at constant rates over the 

1970's and 1 98OSs, the methodology has been applied, 

first, to the entire period from 1969 to 1989 and, second, 

to just the 1979 to 1989 period. The standardization 

technique used here, therefore, allows us to address the 

hypothetical question: What would happen to 1989's 

income, poverty, and income inequality measures if 

households had the same set of demographic, social, 

and economic characteristics that existed in 1969 and in 

1 9793 

The methodology discussed below is presented in 

the context of the 1969 to 1989 period, but it is identical 

to that used in the 1979 to 1989 period. Mean household income in 1989 is defined as a weighted average 

where is the overall mean income in 1989, pieg is the 

proportional weight of the ith group in 1989, and Yies is 

its mean income in 1989. Alternatively, we could write 

the formula for standardized mean income as 

where p, is the ith group's proportion ot all households 

I in 1969: An equivalent way to write this equation is 

where Tgg is the overall mean income in 1969 and 

Aii is the change in mean income for the ith group 

between 1969 and 1989. This is a useful formulation for 

a later discussion on changes in income between 1969 

(or 1979) and 1989. Our standardized mean income, - 

Y,;, therefore, is similar to a fixed-weighted price index 

where base period weights, or quantities of goods, are 

used to weight the current prices of the goods. 

4Regression analysis, of course, would also allow one to quantify 

the effects of these demographic, social, and economic factors on 

these broad income measures. The advantage with the particular 

standardization approach we use (as will be seen), however, is that an 

entirely new data file is created which can then be tabulated by any set 

of characteristics and distributional measures. 

Using data files from the March 1970 Current Population Survey (CPS) and March 1980 CPS (and March 

1980 CPS for the 1979-89 standardiiation), sample 

members who were householders and wives have been 

categorized into a matrix of demographic, social, and 

economic characteristics of their households.5 A matrix 

of over 5,000 cells was produced (as indicated in 

appendix A, sparse cells were collapsed). The proportion of persons in each cell for 1970 (and 1980) was 

divided by the proportion for 1990 thus producing a ratio 

for each cell in the matrix. In effect, these ratios, or 

"adjustment factors," reflect the compositional shifts in 

the characteristics that have taken place over time. The 

adjustment factors were then multiplied by the sample 

weight of each sample member falling into a particular 

cell in the 1990 March CPS. In other words, the 1990 

March CPS was reweighted according to the adjustment 

factors. Consequently, a variety of income measures, 

such as the mean and median household income, 

shares of aggregate income by quintile, Gini indexes, 

and poverty rates could then be derived from the 

reweighted file. 

The sets of demographic, social, and economic household characteristics used to create the matrices are 

indicated below: 

Demographic Social Economic 

Age Education Work Experience, Hhldr. 

Under 25 12 years or less Did not work 

25 to 44 1 to 3 yrs., cot. Wkd., RYR1 

45 to 64 16 yrs. or more Less than FTYR1 

65 and over 

Race Household Type Work Experience, Wife 

White Married Cwple Not married couple 

Black Not manied couple Did not work 

Other Nonfamily household Wkd. FWR1 

Less than FTYR1 

Industry 

Did not work 

Goods-producing 

Serv.-proUucing (1) 

Sew.-producing (2) 

'Full-time (35 hours or more week), year-round (50 to 52 weeks a 

year) employment. 

Although most of these variables are self-explanatory, 

the categories of goods-producing and se~ce-producing 

industries require explanation. The goods-producing 

industries are composed of agriculture, mining, construction, and manufacturing. Service-producing industries 

(1) are composed of transportation, communication, 

and public utilities, wholesale trade, finance, insurance 

Characteristics from the March CPS are as of the survey date, but 

income statistics refer to amounts received in the previous calendar 

year. A standardization technique of this kind was used by Green and 

Welniak (1 982) to examine the effects of changing family composition 

on income differentials. Standardization techniques have also been 

popular for examining the effects of population changes on the 

Nation's unemployment rate. For an example of this work see Flaim 

(1 990). 
and real estate, public administration, and professional 

and related services. Service-producing industries (2) 

are composed of retail trade, personal services, business and repair services, and entertainment and recreation services. Annual earnings tend to be higher in the 

first service category than in the second, and this is the 

reason the two groups were treated separately. 

The resulting fixed-weight mean income for 1989, by 

definition, reflects only "noncompositional" changes 

that have taken place between 1969 (or 1979) and 

1989. That is, only incomes have been allowed to 

change. It is the amount of change in income that would 

have taken place if the world had stayed as it was in 

1969 (and 1979). But, it is not a pure measure of the 

change that would have occurred because of noncompositional reason^.^ If the composition of the population 

actually remained the same as in 1969 (and 1979), for 

example, this could have affected the supply of workers 

in different jobs, which could have affected wages if 

there were not corresponding shifts in demand to offset 

these trends. Under such circumstances, actual incomes 

would be different than the ones shown in the fixed 

weight measure. Such interactions are not accounted 

for in the standardization procedure used here. This can 

be shown to be the case when the change in mean 

household incomes between 1969 (or 1979) and t 989 

is decomposed. Thus, in the case of the 1969-89 

standardization 

where the 7s represent mean income in 1969 and 

1989, the pi's represent the proportion of the ith group, 

the yi's, the mean income of the same group, and A 

represents the change in either pi or ii between 1969 

and 1989. The change in incomes from 1969 to 1989 (or 

any years) consists of three components: The first 

term-the change in income if composition had remained 

unchanged and incomes for the groups had changed as 

they did-is the basic component of the fixed weight 

mean household income; the second is the change that 

would have taken place if only the composition had 

changed and incomes remained the same as they were 

in 1969; and the third is the interaction between changes 

in composition and changes in incomes. Only if the third 

term is zero or small can the first term be assumed to be 

a measure of the change that would have occurred 

because of noncompositional reasons.' 

Other limitations involving standardization techniques 

exist. First, standardizations are inherently static analyses, as the above discussion implies. In a sense, the 

'See Antos, Mellow, and Triplett (1 979) for an excellent discussion 

of this point in the context of a weighted unemployment rate. 

'The size of the interaction term was estimated and found to be 

about 14 percent of the size of the first term of the equation, in other 

words, relatively small. 

analysis moves from one equilibrium position to another, 

without any regard to the dynamics, or process going on 

between positions. The result is necessarily a partial 

equilibrium, not a general equilibrium, because the 

interactions have not been accounted for. 

Another limitation concerns the selection of the base 

year in which the income measures are standardized to, 

or fixed.8 Clearly, the choice is arbitrary and results 

could differ if different years had been selected. 

One last limitation is that the selection of characteristics is also arbitrary, even within broad demographic, 

social, and economic categories. The number of characteristics selected is constrained by the size of the 

CPS. Although the CPS is a fairly large household 

survey (approximately 60,000 households), controlling 

for too many household characteristics would be impractical. It would be possible to control for many more 

characteristics if data from the decennial census were 

used instead. 

The major advantage of using our standardization 

technique is that a reweighted data file for 1989 is 

created which refkcts the demographic, social, and 

economic characteristics of households in both 1969 

and 1979. Not only can mean incomes be derived, but 

other distributional measures as well. In addition, it is 

possible to isolate the effects of demographic, social, 

and economic factors, both individually and collectively, 

on income and poverty measures. 

IV. THE RESULTS OF THE STANDARDIZATION 

Table 1 shows the actual real median household 

income levels, shares of aggregate income received by 

quintiles of the household income distribution, the Gini 

index, and the poverty rate for households in 1969, 

1979, and 1989 (see appendix B for a technical discussion of these measures). As was summarized at the 

outset, the trends in these data suggest that real income 

growth over this period was minimal, income inequality 

had increased, and the incidence of poverty showed 

very little improvement. 

The data presented in Tables 2 to 5 reveal what the 

income measures would have been in 1989, if the 

various demographic, social, and economic characte~- 

istics of the Nation's households had not changed 

between 1969 and 1989. Tables 6 to 9 reveal what the 

situation would have been if these characteristics remained 

unchanged between 1979 and 1 989. 

'Aside from the fact that the year 1969 provides a significant 

amount of time to allow for characteristics to change, another reawn 

exists for selecting 1969. It represents a point in time when significant 

changes were about to occur in the country: the baby boom generation was on the threshold of adulthood, changes in social behavior 

were soon to intensify (i.e., divorce, births out of wedlock), and the 

economy was shortly to enter a period of stagflation. 
A. The 1969 to 1989 Period 

As is shown in table 2, the changes in the age 

distribution of householders had a negative effect on 

the real median household income in 1989. In other 

words, the median would have been $29,170, or $264 

more than it actually was, if the age distribution had not 

1 shifted. 

This relatively small effect of age on income may 

appear odd in light of the 75 million or so young persons 

who came to maturity over this time period. However, it 

may very well be the case that their movement into 

adulthood had offsetting effects on this income measure. That is, as the leading edge of the cohort moved 

into their middle-age years, rising incomes associated 

with greater work experience and maturity offset the 

downward pressure of the relatively low incomes of 

younger members of the cohort. Other writers have also 

showed that demographics explained little of the increase 

in income inequality during these years (e.g., Bradbury, 

1986). 

The more rapid growth in the number of Black 

households relative to White households also had a 

small negative effect on overall median household 

income. In its absence, the median would have reached 

$29,100 in 1989, or $1 94 more than the actual median 

in that year. 

Changes in social characteristics of households, 

specifically, changes in types of households and educational attainment of householders, produced two dramatic effects. However, the effects were in opposite 

directions. Household compositional changes have frequently been cited as responsible for much of the 

slower increase in income and growing income inequality. Controlling solely for this factor confirms this point. 

Real median household income would have been $3,226 

more than was officially reported for 1989 if household 

composition had not changed. By implication this means 

that the declining proportion of married couple households and rising proportions of single parent families 

and nonfamily households had a large negative impact 

on the Nation's median income level in 1989. 

On the other hand, continued increases in educational attainment had a positive effect on real median 

household income over this 20 year period. As is shown 

in table 2, the standardized median would have only 

been $25,896 in 1989, $3,010 below the actual median, 

had not educational attainment continued to increase. 

The impact of human capital accumulation on the part of 

householders is clearly demonstrated by this result. 

Standardizing for compositional shifts in the economic variables-the work experience of householders, 

the work experience of wives in married couple families, 

the industries employing the householders-resulted in 

substantial negative effects on the median income level 

between 1969 and 1989. In each instance, median 

household income would have been much higher-over 

$30,000- if the shifts had not occurred. 

These economic effects appear reasonable for the 

work experience of householders and the industry of 

their employment based on the trends in these factors 

discussed earlier. Proportionally fewer householders 

over this period were in the paid labor force and the 

composition of the labor force was shifting towards the 

lower-paying service industries. For the wives in married 

couples, however, the result may appear more punling 

and, indeed, counter-intuitive. It would seem that more 

working wives should increase the median income level, 

or in other words, have a positive influence on income. 

This apparent inconsistency becomes more plausible 

when one considers the increase in working wives in the 

context of the shifts that took place in the composition 

of households. Living arrangements changed dramatically in the 1970's and 1980's, as reflected by the 

decline in the proportion of married couple families as a 

percent of all households. Households with working 

wives in them actually changed very little as a proportion of all households in this period. 

When the effect of working wives is examined only in 

the context of married couple families, the expected 

positive effect can be observed. If wives had not 

entered the labor force to the extent they had, the 

median income of married couples would have only 

been $35,645, instead of the actual median of $38,664. 

Overall, the effect on real median household income 

of the trends in these seven demographic, social, and 

economic variables was to retard its growth somewhat. 

Had these compositional shifts not taken place between 

1969 and 1989, the real median household income in 

1989 would have been $29,146, or $240 more than the 

official level. 

The effects of the changes on the income shares 

received by each quintile, the Gini index, and the 

poverty rate are presented in Tables 3 to 5. After 

standardizing only for changes in the age distribution, 

the shares of income received by each fifth of households are virtually the same as they actually were in 

1989. The one small exception is the effect of age on 

the share of income going to the highest fifth. It would 

have become 47.0 percent compared to the actual level 

of 46.7 percent (table 3), and this accounts for the fact 

that income inequality, as measured by the Gini index, 

would have been even greater in 1989 than it actuallywas (table 4). The changing age distribution, however, 

had no effect on the poverty rate (table 5). 

The shifting racial composition of households had 

very little impact on the shares of income received by 

each fifth of households as well as the degree of 

inequality in the income distribution. It did, however, 

(and as might be expected) tend to push the overall 
Table 1 .Various Measures of the Household lncome Distribution, 1969, 1979, and 1989 

(Incomes in 1989 dollars) - - 

Measure 1989 1979 1969 

Total households (in thous.). ...................................... 93,347 80,776 63,401 

Median household income. ....................................... $28,906 $28,115 $28,344 

Share of income received by (in percent): 

Lowest fifth .................................................. 3.8 4.1 4.1 

Second fifth. ................................................. 9.6 10.2 10.9 

Third fifth .................................................... 15.9 16.8 17.5 

Fourth fifth ................................................... 24.0 24.7 24.5 

Highest fifth.. ................................................ 46.7 44.2 43.0 

Gini index.. ..................................................... .429 .404 .391 

Poverty rate (in percent). ......................................... 12.2 12.0 14.1 

Table 2. Effect on 1989 Median Household lncome of Standardizing for Demographic, Social, and 

Economic Changes Between 1969 and 1989 

(1989 incomes standardized to 1970 composition, in 1989 dollars) 

1989 median income 

Standardizing variable Absolute difference 

Actual Standardized (actual-standardized) 

ALL VARIABLES .............................................. $28,906 $29,146 -8240 

Age ............................................................ 28,906 29,170 -264 

Race ........................................................... 28,906 29,100 -194 

Type of household. .............................................. 28,906 32,132 -3,226 

Education ....................................................... 28,906 25,896 3,010 

Work experience of household head ............................... 28,906 30.469 -1,563 

Work experience of wife.. ........................................ 28,906 30,124 -1,218 

Industry shifts ................................................... 28,906 30,669 -1,763 

Table 3. Effect on Shares of lncome (in percent) Received by Each Fifth of Households in 1989 of 

Standardizing for Demographic, Social, and Economic Changes Between 1969 and 1989 

(1 989 incomes standardized to 1970 composition) 

Standardizing variable Total fifth 

Actual 1989 shares. ........................ 100.0 

After standardizing: 

ALL VARIABLES.. ....................... Age. ...................................... Race. ..................................... Type of household ......................... Education ................................. Work exper. of head.. ...................... Work exper. of wife. ........................ Industry of head.. .......................... 

Lowest fifth 

poverty rate higher in 1989 than it would have been 

(1 2.2 vs. 1 1.8 percent). Black households, of course, 

are disproportionately represented among all poor households. 

The mixed effects of household compositional shifts 

and educational shifts are clearly seen in the data. With 

respect to the influence of changing living arrangements, Tables 3 and 4 show that income inequality 

would have been much lower had not these shifts taken 

place. The lowest fifth of households would have received 

Second fifth Third fifth Fourth fifth Highest fifth 

9.6 15.9 24.0 46.7 

a bigger part of aggregate income and the highest fifth 

would have received much less. The Gini index would 

have only risen to .411 instead of the .429 that it actually 

did reach. In addition, table 5 indicates that the poverty 

rate would have risen much less over the 20 year 

period-it would have reached only 10.1 percent instead 

of 12.2 percent. 

As was indicated above, the increase in education 

among householders, on the other hand, had a very 

strong positive effect on income growth. With respect to 
income inequality, as reflected by the income shares 

and the Gini index, it appears that the trend had very 

little impact. The same level of inequality would prevail 

if the education distribution of householders were the 

same in 1989 as it was in 1969. In terms, of poverty, 

however, the education effect is very apparent as it was 

in terms of income levels. As shown in table 5, the 

poverty rate, standardized for educational changes, 

would have been 14.2 instead of the actual 12.2 percent. 

All three economic variables tended to exacerbate 

the trend toward greater income inequality. In each 

instance--the work experience of the householder, the 

work experience of the wife, and the industrial attachment of the householder-the share of income received 

by the lowest quintile of households would have been 

higher and the share received by the highest quintile 

would have been lower had not these shifts taken place 

(table 3). Moreover, the Gini index, instead of .429 in 

1989, would have been .419 if there had been no 

change in the work experience of householders, .420 if 

there had been no change in the work experience of 

working wives, and .415 if there had been no change in 

the industrial attachment of the householders (table 4). 

(It should be pointed out, however, that in the context of 

married couples only, the effect of the work experience 

of working wives did have an "equalizing effect" on the 

married couples' income distribution, as others have 

shown (Cancian, Danziger, and Gottschalk, 1991). The 

Gini index for married couple families would have risen 

to .384 instead of the .368 level that it actually reached.) 

Table 5 also shows that these three economic variables 

also had a fairly strong impact on the poverty rates, in 

that, in each case, the incidence of poverty would not 

have risen to the extent that it did in 1989. 

When all of these demographic, social, and economic factors are combined, it can be shown that over 

this 20 year period they had a profound impact on the 

income distribution. As shown in table 3, the share of 

income received by the lowest fifth of households would 

have been 4.3 percent compared to the 3.8 percent that 

was actually received; the share received by the highest 

fifth would have only been 45.3 percent compared to 

the actual 46.7 percent. The Gini index would have 

increased only half as much as it did-from .391 to .410, 

instead of .429 (table 4). And the poverty rate would 

have fallen to 11.7 percent instead of 12.2 percent 

(table 5). 

6. The 1979 to 1989 Period 

As was mentioned earlier, some of the demographic, 

social, and economic trends between 1969 and 1989 

did not occur at constant rates over this period. The rise 

in the proportion of householders age 45 or younger 

began to slow down or level off in the 1980's, the 

decline in the proportion of households that were married couple households appeared to ease in the 1980's, 

and the increase in the proportion of working wives in 

married couple households was a little slower in the 

1980's relative to the 1970's. Such slowdowns in the 

"rates of change" of these trends suggest that the 

standardization exercise be applied to the decade of the 

1980's. 

Table 6 shows that, indeed, the effects of the trends 

on income were different in the 1980's then over the 

entire 20 year period. Indeed, the effect of the changing 

age distribution in the 1980's now had a positive influence on real median household income. In other words, 

a significant proportion of the baby boom generation 

had now moved well into their middle years, years in 

which household incomes begin to rise. Median household income would have been nearly $300 less than it 

actually was had not the age distribution changed. On 

the other hand, the effect of race continued to exert a 

small negative effect on income in the 1980's as it had 

in the 1969-89 period. 

The impact of changing living arrangements continued to have a negative influence on median household 

income in the 19801s, but not to the same extent it did 

over the 20-year period. Median income would have 

been nearly $1,200 higher if the shifts away from 

married couple households had come to a halt. Education continued to have a strong positive influence on the 

income trend-the median income level would have 

been almost $1,500 lower had not this trend continued 

in the 1980's. In fact, while changing household composition had the largest effect of all seven factors 

studied in the 1969-89 period, education had the largest 

single impact in the 1979-89 period. 

Among the economic factors, both changes in the 

work experience of the householder and changes in the 

work experience of the wife were much less important 

influences on the overall trend in household income. 

However, of continuing importance was the shift in the 

industrial attachment of the householder. It continued to 

have a negative influence and the median household 

income level would have been $744 higher than it 

actually was had householders remained in the same 

industries they were employed in 1979. 

On balance then, the positive influences of education 

and age were sufficient to offset the continued negative 

influences of changes in living arrangements and industrial attachment of householders. Overall, the changes 

in demography, society, and the economy in the 1980's, 

as reflected in the trends under examination, were 

operating in a favorable direction as far as the median 

household income level was concerned. Real median 

household income would have been $881 lower in 1989 

had not these changes occurred. 

Examination of the income shares and Gini indexes 

in Tables 7 and 8, respectively, shows what impact the 

demographic, social, and economic trends had on income 
Table 4. Effect on 1989 Gini Index of Standardizing for Demographic, Social, and Economic Changes Between1989and1989 

(1 989 incomes standardized to 1970 composition) 

Table 5. Effect on 1989 Poverty Rate (in percent) of mndarditing for Demographic, Social, and Economic 

Changes Between 1969 and 1989 

(1989 incomes standardized to 1970 composition) 

I 

Absolute diierence 

(actual - standardized) 

.019 

-.003 

.002 

.018 

-.001 

.010 

.009 

.014 

Standardizing variable 

ALL VARIABLES .............................................. Age ............................................................ Race ........................................................... Typeof household ............................................... Education. ...................................................... Work exper. of householder.. ..................................... Work exper. of wife .............................................. Industry of householder .......................................... 

I 1989 poverty rate I 

Standardizing variable 

1989 Gini index 

I Absolute diierence I Standardized (actual - standardized) 

Actual 

.429 

.429 

.429 

.429 

.429 

.429 

.429 

.429 

Standardized 

.410 

.432 

.427 

.411 

.430 

.419 

.420 

.415 

Table 6. Effect on 1989 Median Household Income of Standardizing for Demographic, Social, and 

Economic Changes Between 1979 and 1989 

ALL VARIABLES .............................................. Age ............................................................ Race ........................................................... Type of household.. ............................................. Education. ...................................................... Work exper. of householder.. ..................................... Work exper. of wife .............................................. Industry of householder .......................................... 

(1 989 incomes standardized to 1980 composition, in 1989 dollars) 

1989 median income 

12.2 

12.2 

12.2 

12.2 

12.2 

12.2 

12.2 

12.2 

Standardizing variable 

Actual Standardized 

ALL VARIABLES .............................................. $28,906 $28,025 

Age ............................................................ 28,906 28,618 

Race ........................................................... 28,906 28,990 

Type of household. .............................................. 28,906 30,059 

Education ....................................................... 28,906 27,422 

Work exper. of householder.. ..................................... 28,906 28,859 

Work exper. of wife .............................................. 28,906 28,987 

Indusky of householder .......................................... 28.906 29,650 

Absolute difference 

(actual - standardized) 
inequality after standardizing. On first glance, income 

shares do not seem to have been very much affected. 

However, shares that would have prevailed had not 

changes in household composition and industrial attachmen? occurred do appear to be more equally distributed 

then is the case for other trends. Indeed, table 8 

indicates that the Gini indexes would have only reached 

.424 in both cases, instead of the actual .429 in 1989. 

Nevertheless, when all the trends are accounted for 

in the standardization, the degree of income inequality 

would not have been much different than actually 

resulted in 1989. The Gini index in 1989, after standardizing would have been .426 compared to .429. In other 

words, collectively, these factors appeared to have had 

little effect on the overall trend toward growing income 

inequality in the 1980's. As we have seen, in the 

1969-89 period, however, they accounted for roughly 

half of the increase in the Gini index. 

Table 9 presents the effect on the poverty rate after 

standardizing 1989 incomes for demographic, social, 

and economic changes that took place during the 

1980's. As was shown earlier, these changes had a 

positive effect on the trend in real median household 

income in that decade, or in other words, had not the 

changes taken place, median income would have been 

lower than it actually was. It follows, therefore, that the 

poverty rate would have been higher had not these 

changes taken place. Indeed, the overall poverty rate 

would have reached 12.7 percent instead of the actual 

12.2 percent. Both the favorable shifts in the age 

distribution and the educational distribution were sufficiently powerful enough to offset the unfavorable shifts I in living arrangements and the industry employment of 

1 the householders. 

V. IMPLICATIONS 

One of the major findings of this research is quite 

evident: the changes taking place in the seven demographic, social, and economic factors examined had a 

much different impact on income trends over the entire 

1969-89 period than they did over the 1979-89 period. In 

the first instance, they tended to retard income growth 

and increase the poverty rate, while generating more 

income inequality. In the second instance, they tended 

to promote income growth, reduce the poverty rate, and 

have a relatively benign effect on income inequality. The 

major implication of this finding is that it was in the 

1970's that changes in the characteristics of households had their most "unfavorable" impact on income 

trends and the shape of the income distribution. 

Other implications, however, flow from the findings. 

As was indicated, during the 1969-89 period changes in 

living arrangements and changes in economic characteristics of the households had the greatest negative 

influence on income trends. But even in the 1979-89 

period, when the overall effect of all the factors was 

positive, changes in living arrangements and changes in 

the industrial attachment of the householder were exerting a downward pressure on income trends and exacerbating income inequality and poverty. Median household income would have been higher and the Gini index 

and poverty rate would have been lower if these specific 

changes had not occurred. In other words, these findings provide useful "signposts" as to what areas should 

be further investigated in explaining changes in broad 

income measures. 

Another implication of the findings is that education 

has a strong positive influence on income trends and 

poverty rates. This was the case in both the 1969-89 

period and 1979-89 period. In fact, increases in the 

educational attainment of householders (along with 

changes in the age distribution) were powerful enough 

in the 1980's to offset the negative influences of changes 

in household compositions and the industrial attachments of householders.Q 

Last, the effect of the changes in the age distribution, 

specifically, the maturing of the baby boom generation, 

was shown to exert both negative and positive effects 

on income trends. For the entire 1969-89 period, a 

relatively small negative effect was observed on the 

median income of households. In the 1979-89 period, 

on the other hand, the effect was in the positive 

direction reflecting the aging-and rising income levels 

-of this large cohort. By implication, this suggests that 

the baby boom generation had a significant negative 

impact on income trends in the 1970's. 

The results of these standardization exercises also 

suggest that more research is needed to understand the 

causes of rising income inequality in the 1980's. The 

standardization for the 1969-89 period accounted for 

about half of the increase in the Gini index over these 

two decades, while the standardization for the 1979-89 

period could account for much less. In the latter case, 

evidence was found that changes in household compositions and in the industrial attachment of householders 

may be contributing to the increase, but the evidence 

was indirect at best. Consequently, these two factors, 

their interrelationship, and other aspects associated 

with the choice of industrial employment and living 

arrangements require further investigation. 

Standardization exercises, such as this one, are only 

first approximations as to the possible effects of particular demographic, social, and economic factors on 

broad income measures. This is inherent in the nature of 

qhe powerful effect of education was also observed when the 

original standardization exercise for the 1969-89 period was repeated, 

but after excluding the education variable. As was shown in table 2, 

after standardizing the incomes for the seven factors, median household income would have been $29,146 in 1989 compared to the actual 

$28,906 estimate, or $240 more. When the education variable is 

excluded, or in other words, letting its effect on income be felt, the 

standardized median would rise to $31,123, or $2,217 more. 
Table 7 . Effect on Shares of Income (In percent) Received by Each Fifth of Households in 1989 of 

Standardizing for Demographic. Social. an4 Economic Changes Between 1979 and 1989 

(1 989 incomes standardiied to 1980 composition) 

Standardiing variable 

Actual 198s shares ......................... 

After standradiing: I 

ALL VARIABLES ......................... Age ....................................... Race ...................................... Type of household ......................... Education ................................. Work exper . of householder ................. Work exper . of wife ......................... Industry of householder ..................... 

Total fifth I Lowest fifth 1 Second fifth 1 Third fifth I Fourth fifth I ~igtest fifth 

I Table 8 . Effect on 1989 Gini Index of Standardizing for Demographic. Social. and Economic Changes Between 1979and 1989 

(1 989 incomes standardized to 1980 composition) 

1989 Gini index 

Standardizing variable Absolute difference 

Actual Standardized (actual . standardized) 

ALL VARIABLES .............................................. . 429 . 426 . 003 

Age ............................................................ . 429 . 431 -.002 

Race ........................................................... . 429 . 428 .001 

Type of household ............................................... . 429 . 424 .005 

Education ....................................................... . 429 . 430 -.001 

Work exper . of householder ....................................... . 429 . 429 

Work exper . of wife .............................................. . 429 . 427 .002 

Industry of householder .......................................... . 429 . 424 . 005 

Table 9 . Effect on 1989 Pove Rate (in percent) of Standardizing for Demographic. Social. and Economic 

Changes Between 19 r 9 and 1989 

(1 989 incomes standardized to 1980 composition) 

1989 poverty rate 

Standardizing variable Absolute difference 

Actual Standardized (actual . standardized) 

ALL VARIABLES .............................................. 12.2 12.7 -0.5 

Age ............................................................ 12.2 12.5 -0.3 

Race ........................................................... 12.2 12.0 0.2 

Type of household ............................................... 12.2 11.4 0.8 

Education ....................................................... 12.2 13.0 -0.8 

Work exper . of householder ....................................... 12.2 12.2 

Work exper . of wife .............................................. 12.2 11.9 0.3 

Industry of householder .......................................... 12.2 11.7 0.5 
I 

25 

I 

the methodology. The methodology may not be suffi- procedures are required for refining these first approxiciently sensitive to the interaction of compositional and mations and uncovering the causes of growing income 

noncompositional shifts. For example, other changes inequality. Such procedures permit a more detailed 

taking place in the country, such as, changes in corn- specification of economic behavior (which would include 

puter technology, international trade, and employment those captured in the interaction term of a standardizain trade-sensitive industries, are not easily accommo- tion). Decomposing a Theil or Atkinson inequality index 

into various behavioral characteristics might be a useful dated in a standardization methodology. More rigorous next step in untangling effects. 

I 
REFERENCES 

Antos, Joseph, Mellow, Wesley, and Triplett, Jack E., 

"What is a Current Equivalent to Unemployment Rates 

of the Past?" Monthly Labor Review, Vol. 102, No. 3, 

March 1979, 36-46. 

Bluestone, Barry, and Harrison, Bennett, The Deindustrialkation of America, New York: Basic Books, 1982. 

Bluestone, Barry, "The Impact of Schooling and Industrial Restructuring on Recent Trends in Wage lnequality 

in the United States," Papers and Proceedings of the 

American Economic Association, Vol. 80, No. 2, May 

1990, 303-307. 

Burtless, Gary, "Earnings lnequality Over the Business 

and Demographic Cycles," in A Future of Lousy Jobs 

(edited by Gary Burtless), Washington, D.C.: The Brooking~ Institution, 1990, 77-1 17. 

Bradbury, Katharine L., "The Shrinking Middle Class," 

New England Economic Review, September/October 

1986, 41 -55. 

Cancian, Maria, Danziger, Sheldon, and Gottschalk, 

Peter, "Working Wives and Family lncome lnequality 

Among Married Couples," paper prepared for the Jerome Levy Economics Institute project on Economic 

Inequality at the Close of the Twentieth Century, January 1991. 

Flaim, Paul O., "Population Changes, the Baby Boom, 

and the Unemployment Rate," Monthly Labor Review, 

Vol. 11 3, No. 8, August 1990, 3-1 0. 

Freeman, Richard B., "The Effect of Demographic 

Factors on Age Earnings Profiles," Journal of Human 

Resources, Vol. 14, Summer 1979, 289-31 8. 

Green, Gordon, and Welniak, Edward, Changing Family 

Composition and lncome Differentials, Special Demographic Analyses, CDS-80-7, Washington, D.C.: U.S. 

Bureau of the Census, August 1982. 

Hurd, Michael D., "Research on the Elderly: Economic 

Status, Retirement, and Consumption and Saving," 

Journal of Economic Literature, Vol. 28, June 1990, 

565-637. 

Juhn, Chinui, Murphy, Kevin M., and Pierce, Brooks, 

"Wage lnequality and the Rise in the Returns to Skill," 

version of a paper presented at a meeting of the Allied 

Social Science Association, December 1989. 

Kuttner, Robert, "The Declining Middle Class," Atlantic, 

July 1983, 60-72. 

Levy, Frank, Dollars and Dreams: The Changing American lncome Distribution, New York: Russell Sage Foundation, 1987. 

Thurow, Lester,"The Disappearance of the Middle Class", 

New York Times, February 5, 1984, Section 5, p. 2. 
APPENDIX A. STANDARDIZATION METHODOLOGY 

The standardization technique used in this paper 

involved adjusting sample weights on a current microdata set to reflect past demographic, social, and economic characteristics of households. Weighting adjustment factors are merely ratios derived by comparing 

past characteristics of households to current ones. The 

basis of adjustment for this paper is the March 1990, 

March 1980, and the March 1970 Current Population 

Surveys(CPS) microdata sets. 

Weighting adjustment factors were derived from the 

set of demographic, social and economic variables 

shown in table A-1. 

Table A-1 . Variables Used to Classify ~oursaholds' 

Variable 

Under 25 years 

25 to 44 years 

45 to 64 years 

65 years and over 

Race White 

Black 

Other 

Social: 

Education 

Household type 

Economic: 

Less than college 

I to 3 years college 

4 or more yeam college 

Maniedcouple households 

Other family households 

Nonfamily households 

Work experience (householder) Did not work 

Year-round, full-time 

Other work experience 

Work experience (wife) Not a wife 

Did not work 

Year-round, full-time 

OtFer work experience 

Industry Did not work 

Goods producing 

se~i~~ssctor1~ 

~ervicesector2~ 

'Age, race, education, and industry variables are chwcteristics of 

the householder. In married-couple households, the husband was 

always designated the householder. 

2Goods producing industries include: agriculture, forestry, fisheries, mining, construction, and manufacturing. 

3Sewice sector 1 industries include: transportation, communication, public utilities, wholesale trade, finance, insurance, real estate, 

professional and related services, and public administration. 

4Service sector 2 industries include: retail trade, business and 

repair senices, personal services, and entertainment and recreation 

services. 

These variables, when cross-classified with each 

other, produce a 5,184 cell matrix into which all house-. 

holds from the March 1990 CPS, March 1980 CPS, and 

the March 1970 CPS were categorized and tallied. 

These weighted tallies were used to compute weighting 

adjustment factors that were applied to the March 1990 

CPS microdata. The adjustment factors (in the context 

of the March 1970 and March 1990 CPS) can be 

expressed as 

Pi70 Fi= - 

 pis^ 

where 

4 51 84 

Pi= -i and T= 4 

i= 1 

for each of the years. t is the number of households in 

cell i. T is the total number of households for all cells in 

the matrix. P is the proportion derived by dividing t by T. 

F is the adjustment factor derived by dividing 

Pi7o by Pi,. The adjustment factors are then applied to 

weights on the March 1990 CPS. 

It is important to realize that not all of the 5,184 cells 

of the matrix necessarily contain any data. In fact, only 

1,156 cells contained one or more sample cases in 

either year. Table A-2 shows the number of cells 

containing data for each of the classification variables 

individually and in combination. (Usable cells in the 

standardization using the March 1980 CPS and March 

1990 CPS were of about the same magnitude.) 

Table A-2. Total and Usable Cells for Classification 

Variables 

Variable 

Economic ........................... Social. .............................. Demographic ........................ Economic-Social ..................... Ewnomic--Demographic. .............. Social-Demographic .................. Economic-Social-Demographic. ........ 

Total cells I Usable cells 

Cells not containing any observations were either 

unlikely combinations or in some cases, impossible 

ones. For example, in the economic variables, householders or wives who did not work obviously could not 

be assigned to a particular industry. The variables were 
classified in the manner selected in order to be able to 

assign all households to each group in a mutually 

exclusive and exhaustive way. 

To reduce the impact of high sampling variability in 

some of the more sparse cells of the matrix, a cQunt of 

the number of sample cases within each cell was kept 

and only cells with 5 or more sample cases in both years 

were used to compute a weighting adjustment factor. 

Cells with fewer than 5 sample cases in either year were 

given a factor derived by collapsing the detail used to 

define the cell. The process of collapsing continued until 

a factor could be derived based on 5 or more sample 

cases. Although none of the demographic and social 

variables needed to be collapsed, about 23 percent of 

the economic variables were collapsed (approximately 

the same proportion were collapsed in the 1979-89 

standardization). Additional collapsing was done for 

combinations of these variables that did not have at 

least five sample cases in both years. 
APPENDIX B. INCOME MEASURES 

Household income. lncome summary measures presented in this paper are limited to money income before 

payments of Federal, State, local, or Social Security 

taxes and before any other types of deductions, such as 

union dues and Medicare premiums. Total money income 

is the sum of the amounts received by all member of the 

housing unit age 15 years and over. lncome sources 

include: wages and salaries, self-employment income, 

Social Security, Supplemental Security income, public 

assistance, interest, dividends, rent, royalties, estates or 

trusts, Veterans' payments, unemployment and workers' compensations, private and government retirement 

and disability pensions, alimony, child support and any 

other source of money income which are regularly 

received. Capital gains (or losses) and lump-sum or 

one-time payments such as life insurance settlements 

are excluded. 

Median income. The median income is the amount 

which divides the income distribution into two equal 

groups, one having income above the median, and the 

other having incomes below the median. All medians in 

this paper were derived from grouped data using linear 

interpolation. The income intervals used were $2,500 

groupings beginning with under $2,500 and ending with 

an interval of $100,000 and over. 

Gini ratio. The Gini ratio (or index of income concentration) is a statistical measure of income equality 

ranging from 0 to 1. A measure of 0 indicates prefect 

equality, i.e., all households having equal shares of 

income; a measure of 1 indicates perfect inequality, i.e., 

one household has all the income and the rest have 

none. Thus, higher levels of the Gini ratio indicate higher 

levels of income inequality. The Gini ratio is derived by 

calculating the ratio of the area between a Lorenz curve 

and a diagonal to the area below the diagonal. As 

shown in figure 1, the Gini ratio can be expressed as: 

A area between Lorenz curve and diagonal G= - - 

A+B - area under diagonal 

Lorenz Curve 

Percent of Aggregate lncome 

0 10 20 30 40 50 60 70 80 90 100 

Percent of Households 

All Gini ratios presented in this paper were derived 

from grouped data. Basically, data in $2,500 intervals 

were interpolated to produce 100 percentile groupings. 

Mean incomes were then estimated for each percentile 

by either linear or Pareto interpolation. From these data, 

a Lorenz curve was developed and the area below the 

curve was estimated using trapezoidal approximation. 

lncome shares. lncome shares are ratios of aggregate 

income for selected groups to the overall aggregate 

income of all groups and expressed as percentages. 

For this paper income shares are presented by income 

quintile. Quintiles are derived by ranking households by 

income and then dividing them into five groups with 

each group containing 20 percent of households. Aggregate income is then calculated for each of the five 

groups and expressed as a percentage of the total 

population aggregate income. All income shares presented in this paper were derived from grouped data. 

Poverty definition. Official poverty estimates are based 

on money incomes of families and unrelated individuals. 
Families and unrelated individuals are considered to be 

in poverty if their annual incomes fall below Federally 

established poverty thresholds. Weighted average poverty thresholds are displayed in table B-1. 

Table B-1. Weighted Average Poverty Thresholds 

in 1989 

Poverty estimates in this paper are not official. Currently, no official poverty definition for households exists. 

The poverty definition presented in this paper is an 

adaptation of the current official poverty definition applied 

to the household concept. The poverty status of a 

household was determined by the poverty status of the 

primary family or primary unrelated individual residing in 

the housing unit. Furthermore, the poverty status of the 

primary family or individual for the year 1969 and 1979 

was determined by applying current criteria for poverty 

instead of the criteria in place at that earlier time. This 

involved the use of a condensed set of poverty thresholds currently in place and deflating them to a 1969 and 

1979 standard of living level as measured by the 

Consumer Price Index for All Urban Consumers (CPI-U, 

1 982-84 = 1 00). 

Size of family unit 

One person (unrelated individual) ............... Two persons. ................................. Three persons ................................ Four persons ................................. Five persons. ................................. Six persons. .................................. Seven persons.. .............................. Eight persons ................................. Nine persons or more. ......................... 

Threshold 

$6,311 

8,076 

9,885 

12,675 

14,990 

16,921 

19,162 

21,328 

25,480 
FACTORS AFFECTING BLACK-WHITE INCOME 

DIFFERENTIALS: A DECOMPOSITION 

by Gordon Green, Paul Ryscavage, and Edward Welniak 

Presented at the 66th Annual Conference of the Western Economic Association International in Seattle, Washington, June 30, 1991. Gordon Green is Chief of the Governments Division, Paul R scava e is a Senior Labor Economist in the Housing and Household Economic Statistics Division, and Edward Welniak is &if o? the Income Statistics Branch in the Housing and 

Household Economic Statistics Division, all from the U.S. Bureau of the Census. The views expressed in this paper are those of 

the authors only and do not represent those of the Census Bureau. 

I. INTRODUCTION formidable task. Analytical models have yet to be developed which capture the complexity of the interactions 

Real incomes of American households during the last among these demographic, social, and exnomic changes. 

two decades grew much slower than in the 25 or so Researchers have had to rely on more indirect methods 

years immediately following World War 11. The conse- and apply them to aspects of the Black situation- The 

quences of this slowdown have been associated with a literature, consequently, contains numerous studies about 

variety of economic developments in recent years. the influence of the labor market or welfare System Or 

These range from concerns over the economic prob- family situation on the economic status of Blacks. 

Despite the formidability of the task, we believe it is [ems of the young and unskilled to more general Ones possible to provide some wevidence,,, in a very broad about the ProsPec~ for the American standard of living. sense, as to what factors may be responsible for the 

Another area Of has been the status slower economic improvement of Blacks in recent years. of Black Americans. In this paper, we use a standardization procedure to 

Blacks made great economic progress over the last identify those demographic, social, and economic facfifty years, but the rate of progress slowed in the 1970's tors that may have influenced the Black-White income 

and 1980's. According to Smith and Welch (1 9861, the ratio, and other income measures, over the 1969-89 

Black-White family income ratio rose from 41 percent in period. ~lthough such techniques are mechanical by 1940 to 61 percent by 1970, but thereafter hardly nature, they are useful for approximating the effects of 

changed at all. Indeed, the poverty rate for Blacks in compositional changes on economic measures, as Sawhill 

1989 was about three times as great as that for Whites, (1 988) points out. The evidence we present, therefore, 

much as it was twenty years earlier. Evidence of this may be helpful to researchers in the development of 

faltering progress was documented in the recent report more appropriate models for understanding the dynamic 

of the National Academy of Sciences on the social and process of economic change among Blacks. 

economic status of Blacks (Jaynes acd Williams, Jr., In Section II of the paper, recent trends in the 

1990). And one economist has recently suggested that Black-White income ratio are presented as well as 

by some indicators, Blacks may have even lost ground trends in demographic, social, and economic characterin recent years (Aaron, 1990). istics of Blacks and Whites that may have affected their 

The slowdown in the rate of economic improvement income levels and poverty rates. The standardization 

methodology used in the analysis is presented in Sec- of Blacks has been difficult to explain. But there is a tion The results from two standardization exercises belief it is related to the complex assortment of eco- and their impact on the Black-White income differential 

nomic, and changes that took and other measures are analyzed in Section IV: First, place in the countv over the last 20 years. Economic those obtained from standardizing 1989 incomes of activity in this period was punctuated by periods of Blacks and Whites for certain household characteristics 

recession, inflation, and recovery, all at a time when as they existed in 1969; and second, standardizing 1989 

profound changes were taking place in the Nation's Black household incomes by certain characteristics of industrial StWCtUre and labor market. Household living White households as they existed in 1989. Section V of 

arrangements changed dramatically, partly in reSPOnse the paper contains a summary of the findings and the 

to increases in divorce, separations, and births out of directions for further research we believe they suggest. wedlock. And the baby boom generation, born between 

the years of 1946 and 1964, moved into adulthood, II. FACTORS AFFECTING INCOMES IN BLACK 

placing an added strain on the country's social struc- AND WHITE HOUSEHOLDS, 1969-89 

ture. Black-White income differentials, or ratios, have been 

Untangling these changes and then assessing their used widely in the literature as measures of the ecoimportance for the economic situation of Blacks is a nomic gap separating the races (e.g., Gwartney, 1970). 
A variety of these income ratios can be calculated from 

the income statistics of the March Current Population 

Surveys (CPS) over the post-World War II period. 

Figure 1 shows the Black-White income ratio for all 

households and married couple households in the 1969-89 

period. Clearly, the ratio for all households has changed 

very little over the last two decades, hovering around 

the .60 mark. This trend stands in sharp contrast to that 

based on income of Black and White "families" in the 

1947-69 period, which is not shown; it increased from 

.51 to .63 reflecting the relatively faster growth in the 

incomes of Blacks and other races.' 

The ratio for all households does not reveal the 

differences in income ratios that exist on the basis of 

household and family type. For example, the differential 

for married-couple families is much higher than it is for 

all families. And more importantly for our purposes, the 

trend in the Black-White income ratio for marriedcouples, as depicted in figure 1, has improved during the 

1970's and 1980's. Real median family income among 

Black married couples increased by 33 percent (from 

$23,066 to $30,650), but for White married-couples the 

increase was about 22 percent (from $32,231 to $39,208).2 1 This development, as well as others, has prompted 

researchers over the years to include - noneconomic 

factors, along with economic factors, in their attempts to 

explain the slowdown in the economic progress of 

Blacks. 

The following discussion, therefore, reviews some of 

the more important demographic and social-as well as 

economic trends-that may have affected Black and 

White incomes and the income ratio over the last 20 

1 years or so. The first to be examined is a demographic 

factor, specifically the age structure of Blacks and 

Whites. It is well known that Blacks have higher fertility 

, and mortality rates, and shorter life expectations. As a 

1 result, their population is younger on average than the 

White population (in 1988 the median age of Blacks was 

27.5 compared to 33.3 for Whites). In addition, a "baby 

boom" among Blacks occurred in the years after World 

War II just as it did among Whites. 

As a consequence of these demographic changes, 

both Black and White households during the 1969-89 

period experienced an increase in the proportion of 

persons who were householders under age 45.3 For 

Blacks, the proportion of householders under 45 in 1969 

was 49 percent and by 1979 it had risen to 53 percent. 

For Whites, the comparable proportions went from 43 to 

'Reflecting changing social customs and improvements in income 

data collection over the years, the Census Bureau made changes in 

the race and household type classifications. It was only in the 1960's 

and 1970's that income data for Black households became available. 

In addition, data became available for Blacks separately (not grouped 

with other races). 

2Nominal incomes have been adjusted for inflation by the Consumer Price lndex for All Urban Consumers (CPI-U-Xl). 

%e householder is the person in whose name the home is owned 

or rented. 

47 percent. But in the 1980's these proportions increased 

much less, the Black's reaching 55 percent and the 

White's, 48 percent. Another changing aspect of the 

age structure has been the slow but steady increase in 

the proportion of householders age 65 or over. Whites 

have a somewhat higher proportion of such households 

than Blacks, but both increased over the 1969-89 

period. Since both younger and older households have 

relatively low incomes, the effect of these age structure 

changes on the broad income averages for both race 

groups was to depress them. 

The two social trends examined here are the type of 

household, or living arrangements, of Blacks and Whites 

and the educational attainment of each group. Significant changes took place in the living arrangements of 

Blacks and Whites during the period under examination. 

As is popularly pointed out in the media, the traditional 

"Ouie & Harriet" type family faded rapidly in importance during this period, and especially so for Blacks. As 

shown in figure 2, the proportion of all Black households 

that consisted of married couples dropped from 56.7 

percent to to 35.7 percent between 1969 and 1989. This 

is why some economists have suggested that examining the economic status of Black married couples is not 

as meaningful in judging the overall economic progress 

of Blacks. For Whites, the proportion of all households 

that were married couples shrunk from 73 percent to 59 

percent, not as sharp a drop as for Blacks. 

Analyses of the changes in the structure of Black 

households, of course, are becoming more numerous. 

One of the more recent studies has found that declines 

in Black marriages have been far more important than 

changes in divorce or separation in explaining the 

decline in married couples (Ellwood and Crane, 1990). 

For Whites, in contrast, it appears that marriages and 

separations were critical factors. 

The complement of this development, of course, has 

been the surge in other family households (primarily 

headed by women) and nonfamily households where 

incomes tend to be less than in married couples. Also 

shown in figure 2 are the increases in these types of 

households for Blacks and Whites. In 1969, the proportion of all households that were other family households 

rose from 25 to 35 percent, while for Whites the 

increase was only from 9 to 12 percent. Nonfamily 

households also increased in prevalence for both races. 

The implication for broad income measures, such as the 

median, is fairly evident. 

Both races continued to show improvement in educational attainment. Indeed, although Blacks still lag 

behind Whites, the former had somewhat stronger gains 

over the 1970's and 1980's than the latter. Figure 3 

shows the proportion of Blacks and Whites age 25 and 

over with 4 years of high school or more and 4 years of 

college or more. In terms of both measures, Blacks 

appeared to double their proportions of persons with the 

amounts of education indicated, compared to much 

smaller gains for Whites. This differential in the acquisition of human capital, of course, should have helped 

narrow Black-White income differences, if everything 

else remained the same. 

Three economic trends were investigated: the work 

experience of householders, the work experience of 

wives in married-couple families, and the movement of 

workers from goods-production to service-production 

jobs. Much has been written about the employment 

problems of Black workers over the past 20 years and, 

in particular, Black men. The work of Smith and Welch 

(1986), alluded to at the outset, suggests that the 

Black-White "wage" gap changed little over the last two 

decades and, indeed, may have widened for younger 

men. Jaynes (1 990) believes that the recent shifts in the 

supply-demand relationship for low-skilled workers stemming from broader economic developments have had a 

significant effect on the real wages of Blacks vis-a-vis 

Whites since a greater proportion of the former have a 

high school education or less. And O'Neill (1990) has 

concluded that even though Blacks continue to advance 

in terms of educational attainment, the increasing premiums placed on high skill levels makes "...differences 

in school quality, family and socioeconomic background ..." 

more important for economic outcomes than in the past. 

While our analysis does not address the Black-White 

wage gap per se, it does focus on labor supply changes 

that occurred between 1969 and 1989 among Black and 

White householders and wives. Obviously, market wages 

as well as many other factors contribute to labor supply 

decisions which in turn affect household income, or 

outcomes. Figure 4 displays proportions of Black and 

White householders and unrelated individuals who worked. 

(In 1969 the Nation's unemployment rate was 3.4 

percent and in 1989, 5.2 percent.) The first item of 

interest is that smaller proportions of Black and White 

householders were working in 1989 than in 1969, but 

the decline was substantially greater among Blacks. 

Second, among Black and White unrelated individuals 

the proportion with some work experience increased 

among Whites but decreased for Blacks. While 1969 

and 1989 are separated by two decades when many 

demographic, social, and economic developments were 

taking place that ultimately determined these changes, 

this basic work experience profile is revealing. 

For both racial groups, the proportion of working 

wives among all married-couple families (as of March of 

each year) has increased between 1 969 and 1 989 (from 

38 to 57 percent among Whites and from 53 to 64 

percent for Blacks). However, when the proportion is 

calculated relative to all households, as shown in figure 

5, a different picture is presented. In 1969,29 percent of 

all Black households contained a working wife, but 20 

years later the proportion had dropped to 23 percent. In 

contrast, for Whites the comparable proportion rose 

slightly from 28 to 34 percent. Consequently, the greater 

changes taking place in the living arrangements of 

Blacks have offset the positive effect of wives' earnings 

on their overall household income average. 

The movement of jobs from goods-producing industries to service-producing industries in recent years has 

also been the focal point of much analysis because of 

its presumed importance for earnings inequality. While 

industrial distributions by race for as far back as 1969 

were not tabulated (except in the decennial census), 

data for 1979 and 1989 were available. As shown in 

figure 6, shifts from goods-producing to service-producing 

industries occurred among both Black and White workers, and by similar amounts. However, it is also interesting to note that a greater proportion of Blacks are 

employed in the service-producing industries where 

wages are not only lower but more greatly dispersed. 

Ill. THE STANDARDIZATION METHODOLOGY 

The standardization methodology used in this paper 

allows us to obtain a first approximation of the effects of 

changes in particular demographic, social, and economic characteristics on the incomes of Black and 

White households-and the resulting income ratio. Techniques of this kind have been used for examining the 

effects of population changes on the Nation's unemployment rate (Flaim, 1990). In addition, the technique 

used here was also used by Green and Welniak (1 982) 

to examine the effects of changing family composition 

on income differentials between Black and White families. 

The conceptual framework for the methodology is as 

follows. Mean household income in 1989, whether for 

Black or White households, is defined as a weighted 

average 

where is the overall mean income in 1989, pieg is the 

proportional weight of the ith group in 1989, and yie9 is 

its mean income in 1989. Alternatively, we could write 

the formula for standardized mean income as 

where the p is the ith group's proportion of all households in 1969. An equivalent way to write this equation 

is 

n 

where is the overall mean income in 1969 and AY~ is 

the change in mean income for the ith group between 

1969 and 1989. This is a useful formulation for a later 
Figure 3. 

Educational Attainment of Blacks and Whites Age 1969 25 and Over: 1969 and 1989 

Percent 11 1989 

80 

4 years of high 4 years of college school or more or more 

White 

4 years of high 4 years of college school or more or more 

Black 

Figure 4. 

Work Experience of Black and White Family Householders 1969 and Unrelated Individuals: 1969 and 1989 

Percent 1989 

90 

Family house- Unrelated 

holders individuals 

White 

Family house- Unrelated 

holders individuals 

Black 
1969 1971 1 973 1975 1977 1 979 1981 1983 1985 1987 1989 

Year 

Figure 6. 

Type of Industry for Black and White Workers: 1979 and 1989 

Percent 

Goods-producing Service-producing 

White 

Goods-producing Service-producing 

Black 
discussion on changes in income between 1969 and 

1989. Our standardized mean income for 1989, or Ti0, 

therefore, is based on a fixed weight, or Laspeyres 

index, where base period weights, or in our case, the 

proportions of persons with certain characteristics in 

1969, are used to weight the current incomes of groups. 

Using data files from the March 1970 Current Population Survey (CPS) and March 1990 CPS, sample 

members who were householders and wives were categorized into a matrix of demographic, social, and 

economic characteristics of their households.4 The characteristics used to create the matrices are indicated 

below: 

Demographic Social Economic 

Age Education Work Experience, Head 

Under 25 12 years or less Did not work 

25 to 44 1 to 3 yrs., col. Wkd., FTYR' 

45 to 64 16 yrs. or more Less than FIYR1 

65 and over 

Household Type Work Experience, Wife 

Married couple Not married couple 

Not married couple Did not work 

Nonfamily hhld. Wkd. FTYR1 

Less than FlYR 

Industry 

Did not work 

Goods-producing 

Sew.-producing (1) 

Sew.-pr~d~cing (2) 

'Full-time (35 hours or more a week), year-round (50 to 52 weeks 

a year) employment. 

Although most of these variables are self-explanatory, 

the goods-producing and service-producing industries 

require definition. Goods-producing industries are composed of agriculture, mining, construction, and manufacturing. Service-producing industries (1) are transportation, communication, and public utilities, wholesale 

trade, finance, insurance, and real estate, public administration, and professional and related services; serviceproducing industries (2) are retail trade, personal services, business and repair services, entertainment and 

recreation services. Annual earnings tend to be higher 

in the first service category than in the second and this 

is why the two service categories were treated separately. 

Matrices of over 1,700 cells were produced for each 

racial group (see appendix A for a full description of the 

methodology). The proportion of persons in each cell for 

1970 was divided by the proportion for 1990, thus 

producing a ratio for each cell in the matrix. The ratios 

represent the compositional shifting that took place 

among Black and White households between 1969 and 

1989. These ratios, or adjustment factors, were then 

4These characteristics from the March CPS are as of the survey 

date, but the income data relate to amounts received in the previous 

calendar year. 

multiplied by the sample weight of each sample member 

falling into a particular cell in the March 1990 CPS. This 

reweighting of the data file, therefore, allowed for the 

calculation of a variety of income measures such as the 

mean and median household income, shares of aggregate income by quintile, poverty rate, and Gini indexes.5 

In addition, it is possible to isolate the effects of 

demographic, social, and economic factors, both individually and collectively, on income measures. 

The standardized income measure that is derived for 

Black and White households must be interpreted with 

care. The "fixed weighted" mean income for 1989 

represents only the noncompositional changes that 

took place between 1969 and 1989. But it is not a pure 

measure of the change that would have occurred because 

of noncompositional reasons. If the composition of the 

population actually remained the same as in 1969, this 

would affect the supply of workers in different jobs, 

which could affect wages if there were not corresponding shifts in demand to offset these trends. Under such 

circumstances, actual incomes could be different than 

the ones derived from the fixed weight measure. This 

interaction would not be captured in the standardization; 

this can be shown with the following definition: 

Thus, the Y's represent mean income (of either White 

or Black households) in 1969 and 1989, the p's represent the proportion of the ith group, the y's their mean 

incomes, and A represent the change in either pi or ii 

between 1969 and 1989. As can be seen in the expression, the actual change in incomes between 1969 and 

1989 consists of three components: The first term is the 

change in income if composition had remained unchanged 

and incomes for the groups had changed, or basically, 

the fixed weight mean income; the second term is the 

change that would have taken place if only the composition had changed and incomes remained the same as 

they were in 1969; and the third term is the interaction 

between changes in composition and changes in incomes. 

Only if the third term is zero or small can the first term be 

assumed to be a measure of the change that would 

have occurred because of noncompositional reasons. 

Since the interaction term has not been estimated but 

very likely is nonzero, the standardization results should 

be viewed as first approximations as to the true effect of 

compositional shifts on income measures.= 

Other limitations of the standardization methodology 

exist. First, it is basically a static analysis as the above 

discussion implies. The results are necessarily partial 

'The Gini index of income concentration, the measure of income 

inequality used here, is discussed in appendix B. Some Gini indexes 

(that have not been standardized) presented in this paper may differ 

slightly from published indexes because of differences in the way they 

were calculated. 

'For an excellenct discussion of these points in the context of a 

weighted unemployment rate, see Antos, Mellow, and Triplett (1979). 
equilibrium positions since interactions have not been 

captured. Second, the selection of the base year is 

arbitrary and the results would differ if years much 

earlier or later had been selected. We selected 1969 

since it provides a significant amount of time for demographic, social, and economic characteristics to change, 

but also because it was around this time that the 

Black-White income ratio stopped rising. And third, the 

selection of characteristics is also arbitrary and constrained by the size of the CPS. It would have been 

possible to control for many more characteristics if data 

from the decennial census had been available. 

IV. STANDARDIZATION RESULTS 

Two sets of standardization results are presented in 

I this section. First, we examine the effects on the 1989 ' Black-White income ratio of standardizing Black and 

I 

White household incomes for demographic, social, and 

economic changes over the 1969-89 period. Separate 

effects as well as combined effects will be presented. 

And second, the effects of simply applying the 1989 

demographic, social, and economic characteristics of 

White households to the 1989 incomes of Black households are discussed. This second standardization, consequently, does not involve any inter-temporal compar- 1 ison. 

A. Standardizing Black and Whlte 1989 

Household Incomes. 

The real median income in 1989 for Black households was $18,146 compared to $30,413 for White 

households. This yielded a Black-White income differential of 597. Twenty years earlier the comparable 

differential was .604. Incomes for both grew very slowly, 

but even more slowly among Black households. 

Demographic. Table A1 and A2 shows what happens 

to the 1989 median household incomes of both races 

when they are adjusted for changes in the age structure. 

As shown there, the effects of age are not very strong, 

although slightly stronger for Whites than Blacks. The 

resulting Black-White income differential would be 59, 

not much different than the actual differential. The 

effects on poverty rates and Gini indexes of these age 

structure changes were also negligible. 

Social. As would be expected, the dramatic shifts in 

living arrangements that took place over the last 20 

years had a profound impact on the standardized incomes 

of both Black and White households. Table 82 shows 

that the median income of Black households would 

have been about 15 percent higher if the same composition of household types existed in 1989 as in 1969. 

The median income of White households, shown in 

table 81, would have been 10 percent higher. The 

resulting effect on the income differential, therefore, is 

that it too would have been somewhat higher-.63 

instead of .60. Also displayed in these tables is the 

effect of changes in household type on the races' 

income distributions, Gini indexes, and poverty rates. 

For both Whites and Blacks, income inequality and 

poverty would have been less had not household compositions changed. 

The effects of standardizing incomes for changes in 

education are also what would be expected. The accumulation of human capital during the 1969-89 period 

continued and demonstrated its powerful effect. Median 

incomes for both White and Black households would 

have been less, almost to the same extent, if educational distributions had remained fixed in 1969. Consequently, the effect on the income differential was insignificant. 

When the effect of changes in living arrangements is 

allowed to interact with the changes in education, there 

appears to be very little effect on White household 

incomes, but a fairly significant effect on Black household incomes. In other words, the effect of changes in 

household vpes appears to dominate the interaction. 

As a result, standardizing 1989 incomes for these two 

social changes would result in the Black-White income 

differential rising to .63. 

Economic. Economic factors, of course, have been of 

primary importance in understanding the income changes 

taking place among Black households. Unemployment, 

low productivity, employment in low-wage' jobs, and 

nonparticipation in the labor force, are just some of the 

labor market problems thought to be directly related to 

the Blacks' low-income problem. 

Tables C1 and'C2 examine the effects on the 1989 

Black-White income differential of changes in the work 

experience of the householder, changes in work experience of wives, and changes in the industry of employment of householders. As was shown earlier, declines in 

the proportion of Black family householders and unrelated individuals who had some work experience over 

the course of a year have been somewhat greater than 

for Whites. This corresponds to the greater amount of 

structural change taking place in Black households than 

White households; nonfamily households and singleparent families became considerably more common 

among Blacks than Whites. To the extent that the 

householders of these household types are less likely to 

be working or working full time year round would imply a 

greater impact on Black incomes than White incomes. 

This is confirmed in the standardization. White incomes 

would have risen by 4.5 percent (table C1) had their 

householder's work experience remained unchanged 
Table A1.White Household's Effect on lncome and Poverty of Standardizing for Demographic Variables: Age 

I (1 989 incomes standardized to 1970 composition, in 1989 dollars) 

I 1989 Income I 

Measure 

Actual Standardiied for age 1969 Income 

Number of households (000). ..................................... 80,143 80,143 56,248 

Median household income ........................................ $30,413 $30,693 $29,581 

I Share of income received by: 

Lowest fifth ..................................................... 4.1 4.1 4.3 

Second f iff h ..................................................... 9.9 9.8 11.3 

Third fifth ....................................................... - ,...a 16.0 15.9 17.6 .. - - rmm ...................................................... Highest fifth.. ................................................... . . I Gin1 tndex.. ..................................................... I Poverty rate.. ................................................... 

Table A2. Black Household's Effect on lncome and Poverty of Standardizing for Demographic Variables: Age 

I (1989 incomes standardized to 1970 composition, in 1989 dollars) 

Measure - 

Actual Standardired for age 1989 income 

Number of households (000). ..................................... 10,513 10,513 6,053 

Median household income. ....................................... $18,146 $1 8,062 $1 7,880 

I Share of income received by: 

Lowest fifth ..................................................... - . -- 3.2 3.1 3.9 

h ..................................................... 8.0 7.9 9.8 

I fifth ....................................................... 15.2 15.1 16.6 I secOna m 

Third 

Fourth fifth.. .................................................... ..... -... I ntgnesrrm ..................................................... 48.7 48.9 44.5 

Gini index.. ..................................................... .459 .462 .410 

Poverty rate.. ................................................... 29.7 29.9 32.7 

Table B1. White Household's Effect on lncome and Poverty of Standardizing for Social Variables: Type of Household and Education 

1 (1989 incomes standardized to 1970 composlon, in 1989 dollars) I 

Measure 

Number of households (000). ..................... Median household income. ....................... 

Share of income received by: 

Lowest fifth ..................................... Second fifth.. ................................... Third fifth.. ..................................... Fourth fifth.. .................................... Highest fi. ..................................... Gini index.. ..................................... Poverty rate. .................................... 

1989 income 

Standardized forType of 

Actual household Education Both 1969 income 

80,143 80,143 80.1 43 80.1 43 56,248 

$30,413 $33,480 $27,181 $30,315 $29,581 

4.1 4.5 4.1 4.5 4.3 

9.9 10.5 9.8 10.4 11.3 

16.0 16.4 16.0 16.4 17.6 

23.8 23.7 24.1 24.0 24.3 

46.2 45.0 46.0 44.7 42.5 

.420 .403 420 403 .383 

9.8 8.2 11.4 9.6 12.0 
Table 82. Black Household's Effect on Income and Poverty of Standardizing for Social Variables: Type of 

Household and Education 

(1 989 incomes standardized to 1970 composition, in 1989 dollars) 

Table C1. White Household's Effect on Income and Poverty of Standardlzlng for Economic Variables: 

I Work Experience and Industry 

1969 income 

6,053 

$1 7,880 

3.9 

9.8 

16.6 

25.1 

44.5 

.410 

32.7 

Measures 

Number of households (000). ..................... Median household income.. ...................... 

Share of income received by: 

Lowest fifth ..................................... Second fifth. .................................... Third fifth ....................................... Fourth fifth. ..................................... Highest fih ..................................... . . Gin1 index.. ..................................... Poverty rate. .................................... 

( Share of income received by: I I I I I I 

(1 989 incomes standardized to 1970 composition, in 1989 dollars) 

Lowest fifth. ............................... Second fifth ............................... 

................................ i Third fifth.. 

Fourth fiih ................................ Highest fifth ............................... I Gini index ................................. Poverty rate ............................... 

1989 income 

- - - - -- - - 

I Table C2. Black Household's Effect on income and Poverty of Standardlzlng for Economic Variables: Work 

I Experience and lndustry 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

1969 income 

56,248 

$29,581 

Measure 

Number of households (000) ................ Median household ilicome. .................. 

(1 989 incomes standardized to 1970 composition, in 1989 dollars) I 

Standardized for-- 

1989 income 

Actual 

80,143 

$30,113 

- 

Both 

10,513 

$1 9,021 

3.4 

8.5 

15.6 

25.2 

47.3 

.443 

28.2 

Type of 

household 

10,513 

$20,931 

3.3 

8.7 

15.8 

25.2 

47.0 

441 

25.7 

1969 income 

6,053 

$1 7,880 

3.9 

9.8 

16.6 

25.1 

44.5 

.410 

32.7 

Measure 

Number of households (000) ................ Median household income. .................. 

Share of income received by: 

Lowest fifth. ............................... Second fifth ............................... Third fifth.. ................................ Fourth fifth ................................ Highest fifth ............................... Gini index ................................. Poverty rate ............................... 

Education 

10,513 

$16,288 

3.2 

7.9 

14.9 

24.9 

49.1 

.462 

32.8 

Standardized for - 

1989 income 

All three 

80,143 

$32,520 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

Industry 

80,143 

$31,946 

Work experience of 

householder 

80,143 

$31,773 

Work experience of wife 

80,143 

$31,302 

Standardized for-- 

Work experience of 

householder 

10,513 

$19,681 

3.3 

8.6 

15.6 

25.0 

47.5 

.446 

26.9 

Industry 

10,513 

$1 9,659 

3.3 

8.6 

15.6 

25.0 

47.5 

.445 

26.9 

Work experience of wife 

10,513 

$19,219 

3.4 

8.7 

15.6 

24.9 

47.3 

.442 

27.2 

All three 

10,513 

$1 9,057 

3.2 

8.5 

15.6 

25.2 

47.5 

,447 

28.8 
between 1969 and 1989, but Black incomes would have 

risen by 8.5 percent (table C2). This further suggests 

that the income differential would have risen to almost 

.62. 

Given the great increase in the participation of wives 

in the labor market over the last two decades, the 

expectation would be that median incomes for both 

Black and White households would have been lower if 

this had not occurred. The standardization results show 

just the opposite. This is because in standardizing for 

this effect, "nonmarried-couple households" had to be 

included as well. Since these types of households grew 

more common over the 1969-89 period and their average incomes are lower than married-couple households, they tended to drag down the overall median 

income. Therefore, the standardized result for working 

wives contains a "type of household" effect which 

apparently dominates the working wives effect. 

When we examine the working wife effect purely 

within the context of married couples, however, the 

expected result appears, as is shown in the text table 

below. 

Thus, the increase in working wives had a significant 

effect on the income levels of Black and White marriedcouple families. 

The last economic factor accounted for was the 

employment shifts from goods-producing to serviceproducing industries that occurred over the last two 

decades. This shift appeared to have a bigger effect on 

Black household income than White household income. 

The Black-White income differential would have increased 

to about .62. 

When these three economic factors are combined, 

the impact on White household income is even greater 

than the individual effects. Their median income would 

have risen by almost 7 percent. This probably occurs 

because of two reasons: First, many of the householders shifted into service-producing industries where wages 

were generally lower and, two, the "type of household" 

effect, which was embodied in the working wives standardization, dominated the effect of working wives. On 

the other hand, the median income for Black households would rise by only 5 percent, much less than their 

separate economic effects. This is probably because a 

relatively smaller proportion of Black householders shifted 

from goods-producing jobs to service-producing jobs 

simply because there weren't as many of them in 

Black.. ............................. White ............................... B/W income ratio .................... 

goods-producing jobs in the first place. All of these 

economic effects appear to offset one another when the 

1989 standardized Black-White income differential is 

calculated-it would have been .586 compared to the 

actual .597. 

Combining Demographic, Social, and Economic Factors. The standardization procedure allows us to combine these factors by pairs as well as let all three factors 

interact simultaneously. Tables Dl and D2 present the 

resulting 1989 standardized incomes on these bases. 

In percentage terms, the pair-wise comparisons find 

standardized Black household incomes rising more than 

White household incomes suggesting that the compositional changes had a greater effect on the former than 

the latter. The resulting income ratios would range from 

.60 (the social-economic effects) to .64 (the demographiceconomic effects). 

When all of the factors are combined, the results 

suggest that shifts in the demographic, social, and 

economic characteristics of Black households between 

1969 and 1989 were considerably more important than 

shifts taking place for White households. Real median 

income among Whites would have been virtually unchanged 

at about $30,400, while among Blacks it would have 

risen from $18,146 to $19,596, or 8 percent. The 

Black-White income differential would have reached 

.65. 

In addition, while the effect on the median income 

level and poverty rate for White households would have 

been very small, their income distribution would have 

become somewhat more equal as measured by the Gini 

index. For Black households, however, inequality and 

poverty would have been lessened as well. The Gini 

index for Blacks would have been .431 instead of .459 

and the poverty rate 26.3 percent instead of 29.7 

percent. 

Married-couple families 

B. Standardizing 1989 Black Incomes by 

White Household Characteristics 

Actual 1989 

Income 

$30,819 

39,351 

.78 

Another hypothetical question can be answered using 

the standardization technique: How would the various 

income measures for Black households change if Black 

households had the same characteristics as White 

households? Such an exercise was carried out using the 

1990 CPS income data. 

This standardization was conducted by assigning 

White demographic, social, and economic characteristics (the same characteristics used earlier) to Blacks 

and recalculating overall Black household income. The 

standardized mean household income for Blacks, YB, 

therefore, can be expressed as: 

Standardized 1989 

Income 

$26,756 

36,332 

.74 
1 Table D2. Black Household's Effect on Income and Poverty of Standardizing for Demographic, Social, and 

Economic Variables 

(1 989 incomes standardized to 1970 composition, in 1989 dollars) 

I 

Table Dl. White Household's Effect on Income and Poverty of Standardizing for Demographic, Social, and 

Economic Variables 

(1 989 incomes standardized to 1970 composition, in 1989 dollars) 

Measure 

Number of households (000) ..............,. Median household income. .................. 

Share of income received by: 

Lowest fifth ................................ Second fifth ............................... Third fifth .................................. Fourth fifth ................................ Highest fifth ............................... Gini index ................................. Poverty rate ............................... 

where pWi are the proportions of White households with 

the various characteristics in 1989 and 2 are the mean 

incomes actually received by Blacks in those groups in 

1989. 

The results of standardizing Black incomes according to the age structure of White households is shown in 

table E. Black median household income would fall from 

$18,146 to $17,350, or by 4.4 percent. The drop in 

income is probably due to the large proportion of elderly 

White householders. Little change would take place in 

the income distribution or in the percentage of Black 

households that were paor. 

Applying the household type characteristics of White 

households and the White householders' educational 

characteristics to Black households has the expected 

Measure 

Number of households (000) ................ Median household income. .................. 

Share of income received by: 

Lowest fifth. ............................... Second fifth ............................... Third fifth.. ................................ Fourth fifth ................................ Highest fifth ............................... Gini index ................................. Poverty rate ............................... 

effects. Table F shows that if Blacks had the same type 

of living arrangements as Whites, the Black median 

household income in 1989 would have been $22,236 

instead of $18,146. If they had the White's educational 

characteristics, incomes would have increased to $20,884. 

When both sets of characteristics are combined, the 

effect on Black median household income is even 

greater than the separate effects. Their income would 

have reached $24,561 and the Black-White income 

ratio would have been .81 in 1989. A strong effect is 

also observable in the Black household income distribution: The distribution would be more equally distributed with the second and third quintiles receiving a 

greater share of the aggregate income and the poverty 

rate for Black households would have been 19.8 percent instead of the actual 1989 rate of 29.7 percent. 

1989 income 

Actual 

80,143 

$30,413 

4.1 

9.9 

16.0 

23.8 

6.2 

.420 

9.8 

1989 income 

Standardized for-- 

Demographic, 

Demographic, Demographic, Social, social, 

social economic economic economic 1969 income 

80,143 80.1 43 80,143 80,143 56,248 

$30,721 $32,018 $30,703 $30,384 $29,581 

4.5 4.3 4.6 4.5 4.3 

10.4 10.1 10.6 10.5 11.3 

16.5 16.0 16.5 16.4 17.6 

24.0 23.4 23.7 23.7 24.3 

44.6 46.1 44.6 45.0 42.5 

.402 .416 .399 .404 .383 

9.5 9.4 9.8 10.0 12.0 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

Standardized for-. 

Demographic, 

Demographic, Demographic, Social, social, 

social economic economic economic 1969 income 

10,513 10,513 10,513 10,513 6,053 

$19,236 $20,437 $18,519 $1 9,596 $1 7,880 

3.4 3.4 3.4 3.5 3.9 

8.7 9.0 8.7 9.2 9.8 

15.9 15.8 15.8 16.0 16.6 

25.3 24.9 25.3 24.9 25.1 

46.7 46.9 46.8 46.5 44.5 

.438 .437 .439 .431 .410 

28.0 25.2 29.4 26.3 32.7 
Table E. Black Income and Poverty Standardized by White Demographic Variables: Age 

(1 989 income) 

Standardizing 1989 Black incomes for the economic 

characteristics of White households has a somewhat 

less strong effect on Black incomes than the social 

characteristics. Nevertheless, as is shown in table G, 

median incomes rise, ranging from 8 percent in the case 

of the industry standardization to 17 percent for the 

standardization involving the work experience of the 

householder. The combined effect of all three economic 

variables is again stronger than the individual effects. 

The median income of Black households rises to $22,213 

in 1989, not quite as high as the effect of the social 

Measure 

Number of households (000). ..................................... Median household income ........................................ 

Share of income received by: 

Lowest fifth ..................................................... Second fifth.. ................................................... Third fifth ....................................................... Fourth fifvl.. .................................................... Highest fifth. .................................................... Gini index. ...................................................... Poverty rate. .................................................... 

Table F. Black Income and Poverty Standardized by White Social Variables: Type of Household 

and Education 

(1989 income) 

variables. Here too the Black income distribution becomes 

more equally distributed and the poverty rate would 

drop to 22.6 percent. 

Table H contains the results of the standardizations 

when pairs of demographic, social, and economic variables are combined and when all characteristics are 

combined. All of the pair-wise combinations result in 

increases in median income, reductions in inequality, 

White 

80,143 

$30,413 

4.1 

9.9 

16.0 

23.8 

46.2 

.420 

9.8 

Black 

Measure 

Number of households (000). ..................... Median household income. ....................... 

Share of income received by: 

Lowest fifth ..................................... Second fifth. .................................... Third fifth.. ..................................... Fourth fifth.. .................................... Highest fih. .................................... Gini index.. ..................................... Poverty rate.. ................................... 

and declines in poverty rates. Overall, the median Black 

household income would rise to $23,648 and the BlackWhite income ratio would have been .78. The amount of 

inequality in the Black income distribution, as reflected 

by the Gini index, would be reduced to almost the same 

extent as the White's (Gini index equal to .420 for 

Whites and .428 for Blacks). And the Black poverty rate 

would have dropped by 10 percentage points to 19.0 

percent. 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

V. SUMMARY AND DIRECTIONS FOR FUTURE 

RESEARCH 

Standardized by 

White age 

10,513 

$1 7,350 

3.2 

7.8 

14.8 

24.9 

49.3 

.464 

30.0 

White 

80,143 

$30,413 

4.1 

9.9 

16.0 

23.8 

46.2 

.420 

9.8 

A central finding of the 1969-89 standardization 

exercise is that demographic, social, and economic 

changes have had a greater effect on Black household 

incomes than White household incomes. Our estimate 

of what the Black-White household income differential 

would have been in 1989, if the changes had not taken 

place, was .65. This compares to the actual differential 

of .60 in 1989. 

Black 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

Standardized by-- 

Type of 

household 

10,513 

$22,236 

3.4 

9.0 

16.1 

25.3 

46.2 

.433 

22.3 

Education 

10,513 

$20,884 

3.1 

8.3 

15.4 

24.6 

48.4 

.456 

25.7 

Both 

10,513 

$24,561 

3.3 

9.2 

16.1 

25.2 

46.1 

.431 

19.8 
Table G. Black Income and Poverty Standardized by White Economic Variables: Work Experience and 

Industry I 

(1 989 income) 

Measure 

Number of households. ..................... Median household income. .................. 

Share of income received by: 

Lowest fifth. ............................... Second fifth ............................... Third fifth.. ................................ Fourth fih ................................ Highest fifth ............................... Gini index ................................. Poverty rate ............................... 

Some of the more significant individual factors affecting Black incomes were related to both social and 

economic factors. The changes in the types of households Blacks live in appeared to have a major impact on 

their incomes. Similarly, the change in the work experience profiles of Black householders, which is no doubt 

related to changes in living arrangements, had a strong 

effect on their income levels. Both factors appeared to 

be important for lowering their poverty rates and levels 

of income inequality. 

I 

Table H. Black Income and Poverty Standardized by White Demographic, Social, and Economic Variables 

(1989 income) 

But a lot of the income gap between Black and White 

households has yet to be explained. Even when the 

1989 incomes of Blacks are standardized on the basis 

of White characteristics the income differential rises 

only to .78. Other factors clearly are at work, factors not 

accounted for by the standardization. 

White 

80,143 

$30,413 

4.1 

9.9 

16.0 

23.8 

46.2 

.420 

9.8 

As stated in the introduction, standardizations are 

only first approximations of the possible effects of 

various compositional shifts occurring in the country's 

demography, society, and economy. The results of the 

standardizations can tell us, in general terms, what 

these shifts have meant for our various income measures of Blacks and Whites. However, they leave us in 

the dark regarding the processes of change. For this we 

need more sophisticated measuring devices and data 

bases. Our findings suggest that the relationship between 

household type and the work experience of the householder requires further examination. A more detailed 

specification of these variables along with a more 

powerful methodology and a longitudinal data base 

might shed additional light on the recent trends in the 

Black-White income differential. 

i 

! Measures 

Number of households~O00) ................. Median household income. .................. 

1 Share of income received by: 

Lowest fifth.. .............................. 

~ Second fifth ............................... Third fifth.. ................................ Fourth fnth ................................ 1 Highest fifth ............................... Gini index ................................. I Poverty rate ............................... 

Black 

White 

80,143 

$30,413 

4.1 

9.9 

16.0 

23.8 

46.2 

.420 

9.8 

Black 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

Actual 

10,513 

$18,146 

3.2 

8.0 

15.2 

25.0 

48.7 

.459 

29.7 

Standardized byStandardized by-- 

All three 

$22,213 

3.6 

9.5 

16.2 

25.1 

45.6 

.422 

22.6 

Demographic, 

social, 

economic 

10,513 

$23,648 

3.6 

9.3 

15.9 

25.0 

46.2 

.428 

19.0 

Industry 

10,513 

$1 9,595 

3.3 

8.6 

15.6 

25.0 

47.5 

.445 

27.0 

Work eXperience of 

householder 

10,513 

$20,502 

3.3 

8.8 

15.9 

25.0 

47.0 

.440 

25.5 

Social, 

economic 

10,513 

$23,204 

3.4 

9.2 

15.9 

25.1 

46.4 

.432 

20.5 

Demographic, 

social 

10,513 

$23,590 

3.3 

8.8 

15.8 

25.2 

47.0 

.440 

20.4 

Work experience of wife 

10,513 

$21,300 

3.5 

9.2 

16.0 

25.0 

46.3 

.431 

24.2 

Demographic, 

economic 

10,513 

$22,458 

3.9 

9.6 

16.2 

25.0 

45.4 

.418 

20.4 
REFERENCES 

Aaron, Henry J., "Symposium on the Economic Status 

of African Americans," Journal of Economic Perspec1 tives, Vol. 4, No. 4, Fall 1990, 3-7. 

Antos, Joseph, Mellow, Wesley, and Triplett, Jack E., 

"What is a Current Equivalent to Unemployment Rates 

of the Past?" Monthly Labor Review, Vd. 102, No. 3, 

March 1979, 36-46. 

Ellwood, David T., and Crane, Jonathan, "Family Change 

Among Black Americans: What Do We Know?" The 

Journal of Economic Perspectives, Vol. 4, No. 4, Fall 

1990, 65-84. 

I 

Flaim, Paul O., "Population Changes, the Baby Boom, 

and the Unemployment Rate," Month& Labor Review, 

1 Vol. 1 13, NO. 8, August 1990, 3-1 0. 

Green, Gordon, and Welniak, Edward, Changing Family 

Composition and Income Differentials, Special Lbmographic Analyses, CDS-80-7, Washington, D.C.: U.S. 

1 Bureau of the Census, August 1982. 

Gwartney, James, "Discrimination and lncome Differentials," me American Economic Review, Vol. 60, No. 3, 

June 1970,306-408. 

Jaynes, Gerald David, "The Labor Market Status of 

Black Americans: 1939-1 985," The Journal of Economic Perspectives, Vol. 4, No. 4, Fall 1990, 9-24. 

Jaynes, Gerald David, and Williams, Jr., Robin M., A 

Common Destiny: Blacks and Amen'can Society, Washington, D.C.: National Academy Press, 1989. 

O'Neill, June, "The Role of Human Capital in Earnings 

Differences Between Black and White Men," The Journal of Economic Perspectives, Vol. 4, No. 4, Fall 1990, 

25-45. 

Sawhill, Isabell, "Poverty in the U.S.: Why Is It So 

Persistent?" Journal of Economic Literature, Vol. 26, 

NO. 3, 1988, 1073-1 11 9. 

Smith, James P. and Welch, Finis R., Clming the Gap: 

Forty Years of Economic Progress for Blacks, Santa 

Monica, CA: The RAND Corporation, R-3300-DOL, 1986. 

APPENDIX A. STANDARDIZATION METHODOLOGY 

The first standardization exercise used in this paper 

involved adjusting sample weights of Black and White 

households on a current microdata set to reflect past 

demographic, social, and economic characteristics of 

households. Weighting adjustment factors are merely 

ratios derived by comparing past characteristics of 

households to current ones. The basis of the adjustment for this paper is the March 1990 and March 1970 

Current Population Surveys (CPS) microdata sets. 

Weighting adjustment factors were derived from the 

set of demographic, social, and economic variables 

shown in table A-1. 

Table A-1 . Variables Used to Classify ~ouseholds' 

Variable Definition 

Demographic: 

*W under 25 years 

25 to 44 years 

45 to 04 years 

65 years and over 

Social: 

Education 

Hhld. type 

Less than college 

1 to 3 years college 

4 or more years college 

Married-couple households 

Other family households 

Nonfamily households 

Economic: 

Work experience 

Hhldr. Did not work 

Year-round, full-time 

Other work experience 

Wife 

Industry 

Not a wife 

Did not work 

Year-round, full-time 

Other work experience 

Wd not work 

Goods producing2 

Wice sector j3 

Service sector 2' - 

'Age, education, and industry variables are characteristics of the householder. In married-couple households, the husband was always designated the 

householder. 

2Goods-producing industries include agriculture, forestry, fisheries, mining, construction, and manufacturing. 

'Service sector 1 industries include transportation, communication, public utilities, whplssale Wade.. finance, insurance, reel estate, professha1 and 

related semces, and pubb admmistration. 

:Service sector 2 industries include retail trade, business and repair 

sernces, Personal services, entertainment and recreation seivices. 

These variables, when cross-classified with each 

other, produce a 1,728 cell matrix (for Blacks and 

Whites separately) into which households from the 

March 1970 CPS and March 1990 CPS were categorized and tallied. These weighted tallies were then used 

to compute weighting adjustment factors that were 

applied to the March 1990 CPS microdata. 

The adjustment factors can be expressed as: 

where 

ti 1728 

Pi = 7 and T = ti 

i=l 

for each of the years. t is the number of households in 

any given cell i, T is the total number of households for 

all cells in the matrix, and P is the proportion derived by 

dividing t by T. F is the adjustment factor derived by 

dividing Pi,o by Pi,. The adjustment factors .are then 

applied to weights on the March 1990 CPS. 

The second standardization exercise involved adjusting sample weights of Black households on the March 

1990 CPS microdata set to reflect the demographic, 

social, and economic characteristics of White households at that time. The same variables were used in the 

procedures as described above and adjustment factors 

were also derived as above except the adjustment 

factors are 

where Piwhite is the proportion of White households in 

cell i and PiBla& is the comparable proportion of Black 

households. It is important to realize that not all of the 

1,728 cells of the Black and White matrices necessarily 

contain any data. In fact, only 472 cells contained one or 

more sample cases. Table A-2 shows the number of 

cells containing data for each of the classification 

variables individually and in combination. 
Table A-2. Total and Usable Cells tor Classification 

Variables 

Variable Total cells Usable cells 

Economic ........................... 48 36 

Social. .............................. 9 9 

Demographic ........................ 4 4 

EconombSocial ..................... 432 133 

Economic-Demographic ............... 1 92 1 36 

Social-Dem~gra~hk .................. 36 36 

Demo.-Sot.-Econ. .................... 1,728 472 

Cells not containing any observations were either 

unlikely combinations or in some cases impossible 

ones. For example, in the economic variables, householders or wives who did not work obviously could not 

be assigned to a particular industry. The variables were 

classified in the manner selected in order to be able to 

assign all households to each group in a mutually 

exclusive and exhaustive way. 

To reduce the impact of high sampling variability in 

some of the more sparse cells of the matrix, a count of 

the number of sample cases within each cell was kept 

and only cells with 5 or more sample cases in both years 

were used to compute a weighting adjustment factor. 

Cells with fewer than 5 sample cases in either year were 

given a factor derived by collapsing the detail used to 

define the cell. The process of collapsing continued until 

a factor could be derived based on 5 or more sample 

cases. Although none of the demographic and social 

variables needed to be collapsed, approximately 14 

percent of the economic variables were collapsed. 

Additional collapsing was done for combinations of 

these variables that did not have at least five sample 

cases in both years. 
APPENDIX B. INCOME MEASURES 

Household income. lncome summary measures presented in this paper are limited to money income before 

payments of Federal, State, local, or Social Security 

taxes and before any other types of deductions, such as 

union dues and Medicare premiums. Total money income 

is the sum of the amounts received by all member of the 

housing unit age 15 years and over.' lncome sources 

include: wages and salaries, self-employment income, 

Social Security, Supplemental Security income, public 

assistance, interest, dividends, rent, royalties, estates or 

trusts, Veterans' payments, unemployment and workers' compensations, private and government retirement 

and disability pensions, alimony, child support and any 

other source of money income which are regularly 

received. Capital gains (or losses) and lump-sum or 

one-time payments such as lie insurance settlements 

are excluded. 

Median income. The median income is the amount 

which &vides the income distribution into two equal 

groups, one having income above the median, and the 

other having incomes below the median. All medians in 

this paper were derived from grouped data using linear 

interpolation. The income intervals used were $2,500 

groupings beginning with under $2,500 and ending with 

an interval of $100,000 and over. 

Gini ratio. The Gini ratio (or index of income concentration) is a statistical measure of income equality 

ranging from 0 to 1. A measure of 0 indicates prefect 

equality, i.e., all households having equal shares of 

income; a measure of 1 indicates perfect inequality, i.e., 

one household has all the income and the rest have 

none. Thus, higher levels of the Gini ratio indicate higher 

levels of income inequality. The Gini ratio is derived by 

calculating the ratio of the area between a Lorenz curve 

and a diagonal to the area below the diagonal.2 As 

shown in figure 1, the Gini ratio can be expressed as: 

A area between Lorenz curve and diagonal GI=-= 

A+B area under diagonal 

'Income data for 1969 were cailected for ail persons age 14 years 

and over. 

2The Lorenz Curve is obtained by plotting the cumulative percent 

of househokls against the cumulative percent of aggregate income. 

Lorenz Curve 

All Gini ratios presented in this paper were derived 

from grouped data. Basically, data in $2,500 intervals 

were interpolated to produce 100 percentile groupings. 

Mean incomes were then estimated for each percentile 

by either linear or Pareto interpolation. From these data, 

a Lorenz curve was developed and the area below the 

curve was estimated using trapezoidal approximation. 

lncome shares. lncome shares are ratios of aggregate 

income for selected groups to the overall aggregate 

income of all groups and expressed as percentages. 

For this paper income shares are presented by income 

quintile. Quintiles are derived by ranking households by 

income and then dividing them into five groups with 

each group containing 20 percent of households. Aggregate income is then calculated for each of the five 

groups and expressed as a percentage of the total 

population aggregate income. All income shares presented in this paper were derived from grouped data. 

Poverty definition. Official poverty estimates are based 

on money incomes of families and unrelated individuals. 

Families and unrelated individuals are considered to be 
in poverty if their annual incomes fall below Federally 

established poverty thresholds. Weighted average poverty thresholds are displayed in Table 8-1. 

Table 8-1. Weighted Average Poverty Thresholds 

in 1989 

Size of family unit Threshold 

One person (unrelated individual) ............... $6,311 

Two persons.. ................................ 8,076 

Three persons ................................ 9,885 

Four persons ................................. 12,675 

Five persons.. ................................ 14,990 

Six persons.. ................................. 16,921 

Seven persons.. .............................. 19,162 

Eight persons. ................................ 21,328 

Nine persons or more.. ........................ 25,480 

Poverty estimates in this paper are not official. Currently, no official poverty definition for households exists. 

The poverty definition presented in this paper is an 

adaptation of the current official poverty definition applied 

to the household concept. The poverty status of a 

household was determined by the poverty status of the 

primary family or primary unrelated individual residing in 

the housing unit. Furthermore, the poverty status of the 

primary family or individual for the year 1969 and 1979 

was determined by applying current criteria for poverty 

instead of the criteria in place at that earlier time. This 

involved the use of a condensed set of poverty thresholds currently in place and deflating them to a 1969 and 

1979 standard of living level as measured by the 

Consumer Price Index for All Urban Consumers (CPI-U, 

1 982-84 = 1 00). 
INTERNATIONAL COMPARISONS OF EARNINGS 

INEQUALITY FOR MEN IN THE 1980's 

by Gordon Green, John Coder, and Paul Ryscavage, U.S. Bureau of the Census 

Gordon Green is Chief of the Governments Division of the U.S. Bureau of the Census. John Coder is a statistician economist 

and Paul Ryscavaqe is a labor economist, both in the Housing and Household Economic Statistics Division of the U.S. Bureau of 

the Census. The vlews expressed in this paper are those of the authors and do not necessarily represent those of the Census 

Bureau. This paper was previously published in Tbe Review of Income and Wealth, March, 1992. 

ABSTRACT 

This paper presents a comparative analysis of earnings inequality during the 1980's among prime age men 

who headed households and worked year round full 

time from five industrialized countries-Canada, Sweden, Australia, West Germany, and the United States. 

The data were obtained from the Luxembourg Income 

Study (LIS) database, a multinational collection of microdata sets from various countries which have been 

assembled for the primary purpose of making crossnational comparisons of economic and social wellbeing. The results of the comparison indicated that 

during the mid-1980'~~ the United States had the most 

unequal distribution of earnings and Sweden the least 

unequal. Between the early-1 980's and mid-1 9803, 

however, the earnings distributions in all five countries 

showed evidence of becoming more unequal, and especially so in the United States, Canada, and Sweden. 

INTRODUCTION 

Growing inequality in the distribution of labor market 

earnings in the United States has become one of the 

more popular research topics in labor economics in 

recent years. Researchers in the early 1980's first 

observed rising earnings inequality among men (e.g., 

Henle and Ryscavage, 1980; Plotnick, 1982;). Since 

that time many papers, articles, and books have been 

written about the increase in earnings inequality during 

the 1980's and the possible explanations for the development (Levy and Murnane, 1991). 

The analytical framework for explaining the greater 

dispersion in the earnings distribution has typically 

involved identifying the sources-or groups of workers- 

-responsible for the growing inequality. Initially, an 

analysis of variance approach was suggested (Dooley 

and Gottschalk, 1982), but other decomposition techniques have been used. Sources of change in inequality 

measures are typically decomposed into those generated between and within specific groups of workers. 

These groups are typically defined on the basis of 

human capital attributes, such as age, experience, and 

education. 

This new literature, of course, focuses almost exclusively on increasing earnings inequality in the U.S. over 

the last 20 years or so, with particular emphasis on its 

acceleration in the 1980's. In this paper, trends in 

earnings inequality during the 1980's for men from five 

industrialized countries-Canada, Sweden, Australia, 

West Germany, and the U.S.-are presented. While our 

analysis does not involve decomposing changes in 

inequality, it may prove useful in understanding the 

changes occurring in the U.S. If a pattern of rising 

inequality is observed in other developed nations, it may 

be that the same phenomena causing it there is also at 

work in the U.S. (e.g. skill-biased technological changes). 

Or if there has been no change in earnings inequality in 

these other nations, the reason for growing inequality in 

the U.S. may be due to problems unique to the U.S. (e.g. 

import trade imbalances). 

Analyses of changes in the inequality of earnings 

distributions in other countries are rare.' Some related 

evidence, however, was made available by the Organisation for European Co-operation and Development 

(OECD) in their Employment Outlook for 1987 (OECD, 

1987). The OECD examined earnings differentials between 

non-manual and manual workers in 16 of its member 

countries over the last two or three decades. Their 

analysis revealed that in 8 of the 16 countries studied, 

the trend in the differentials since 1980 was upward. 

These countries were Canada, Denmark, West Germany, Italy, the Netherlands, Norway, the United Kingdom, and the United States. Although the OECD data 

are only suggestive at best (given comparability problems, the aggregative nature of the data, and so on), 

they may reflect underlying changes taking place in the 

earnings and wage distributions of these countries. 

This paper begins with a brief discussion of the 

various measures of inequality used in the comparison, 

followed by a section which discusses the data from the 

Luxembourg Income Study (LIS) that was used in the 

analysis. The next section presents the results of the 

comparison and consists of first determining how much 

'Cross-national comparisons of "income" inequality, however, are 

more common. For example, see O'Higgins, Schmaus, and Stephenson (1989). 
inequality existed in each nation's earnings distributions 

I in the mid-1980's and second, whether or not inequality 

changed in these countries between the early-1 980's 

and mid-1 980's. The final section summarizes the findings and discusses their implications for further research. 

MEASURES OF EARNINGS INEQUALITY 

I Numerous indices exist for measuring the degree of 

inequality in an earnings distribution. They range from 

simple measures like the share of aggregate earnings 

received by each quintile, the coefficient of variation, 

and the variance of the natural logarithm of earnings, to 

more complex measures such as the Gini, Theil, Atkinson measures, and generalized entropy indices. All 

have different mathematical constructions and can lead 

to different assessments concerning the degree of 

inequality (Slottje, 1989). For this reason, multiple measures of inequality are examined in our cross-national 

comparison of earnings inequality in the interest of 

robustness. 

Four of these measures of inequality deserve discussion since they have particular properties that some 

readers may not be aware of.2 The variance of the 

natural logarithm is a popular measure of inequality but 

does not always satisfy the "principle of transfers." 

When income is transferred from a high paid worker to 1 a less highly paid worker, earnings inequality should be 

reduced, however, in some instances this inequality 

measure can produce the opposite finding. In addition, 

this measure is particularly sensitive to changes in 

earnings levels in the lower end of the distribution. 

The Gini index, while always satisfying the principle of 

transfers, is more sensitive to changes in the middle of 

the earnings distribution rather than the tails. This is 

because it is derived from the Lorenz curve which 

expresses the relationship between the cumulated percentage of aggregate earnings and cumulated percentage of earners. An increase or decrease in earnings in 

the middle of the distribution will have a greater impact 

on the measure than a similar change at either end 

I since there are more earners in the middle ranks. 

The Theil index also satisfies the principle of transfers but is also most sensitive to movements within the 

I middle of the distribution. Its primary advantage in 

analyses of inequality is its property of decomposition: 

I Overall inequality can be decomposed into "between" 

and "within" groups comprising the distribution. 

Both the Gini and Theil indices, however, have a 

common disadvantage. if they are derived from distributions with intersecting Lorenz curves, that is, curves 

showing the relationship between the cumulated percentage of earnings and the cumulated percentage of 

earners, meaningful comparisons of the indices become 

problematic (Braun, 1988). This is commonly referred to 

as Lorenz dominance. 

'Their mathematical constructions are given in the Appendix. 

The Atkinson measures were developed to wercome this problem. Basically, the Atkinson measures 

allow one to shift the "weight" given to the middle ranks 

of the distribution to either the lower or upper ends of 

the disttibution. The researcher can specify the degree 

of sensitivity to transfers within the distribution. In this 

paper three different values of "epsilon," or the weight, 

were chosen--0.5,0.8, and 1.5. As the value of epsilon 

rises, the measure becomes increasingly sensitive to 

inequality among low earners. 

THE DATA 

The data for our comparison have been obtained 

from the Luxembourg Income Study (LIS), a multinational collection of microdata sets from various countries (Coder, Rainwater, and Smeeding, 1988). All of the 

data were collected in household surveys or surveys of 

administrative systems (Sweden) by institutions in countries participating in LIS. Each survey used different 

questionnaires, collection, and processing techniques, 

and differences also existed in population universes, 

variable definitions, and response rates. To the extent 

possible, however, definitions of income sources and 

family and household characteridics have been placed 

on a common foundation. 

As of mid-1 990, the LIS database consisted of microdata sets for 14 countries.3 The countries of Canada, 

Australia, Sweden, West Germany, and the United 

States were selected for comparison because it is only 

for these countries that data were available 'covering 

two different periods of time in the 1980's. We refer to 

these periods as the early 1980's-and mid-1 980% the 

former comprising 1979 to 1981 and the latter 1984 to 

1987.4 The following are the specific years used for 

each country: 

Early-1 980's Mid-1 980's 

Canada 1981 1987 

Australia 1981 1985 

Sweden 1981 1987 

West Germany 1981 1984 

United States 1979 1 986 

The universe initially selected for examination was 

adult men age 25 to 54 who headed households, 

worked year round, full time, and received no social 

insurance pension or private pension. This universe was 

chosen since the objective of the analysis was to focus 

on, as closely as possible, distributions of labor income 

which reflected standardized units of labor input (e.g., 

an hourly wage rate). In other words, by identifying a 

3The countries are Luxembourg, Italy, Poland, France, Israel, 

Netherlands, Norway, Swikerland, the United Kingdom, Canada, 

Australia, Sweden, West Germany, and the United States. 

4A brief description of the surveys from each country is contained 

in the Appendix. 
I universe fully committed to the work force it was possi- Other potential problems concerned the upper tail of 

ble to minimize the confounding effect of differences in the distribution where data problems are often concenannual hours worked on annual wage and salary earn- trated. One of these problems concerns "topcoding." In 

ings.5 some countries, such as the United States, earnings in 

1 In defining the universes for analysis, however, a 

number of potentially troublesome comparability problems were encountered.= The most important related to 

the lower tail of the distributions where sampling and 

data collection problems tend to be most conspicuous. 

Since the universe being analyzed was composed of 

prime-age men, who were household heads and work 

year round, full time, it would be expected that the lower 

tail of the earnings distribution would be truncated at a 

"minimum wage" level or its equivalent. As shown in 

Table la, unreasonably low amounts of earnings for 

men were observed for Sweden at the 1st percentile of 

the distribution; in addition, particularly low levels were 

also observed at the 1st percentile for Canada and 

Australia. After investigating the data for Sweden in 

some detail it was found that this problem was caused 

by self-employed workers (farmers and other entrepreneurs) who had also received small amounts of wage 

and salary income. This finding was suggestive of a 

more general problem which applied to the other countries, that is, year-round, full-time "self-employed" workers with wage and salary income from other jobs. 

qhere is, no doubt, variation across countries in the definition of 

year-round, full-time employment. For example, in the United States 

the definition was 50 or more weeks of employment at 35 hours or 

more a week. In Sweden, on the other hand, the definition is 1,872 

hours of employment or more during the year. In Canada, Australia, 

and West Germany precise details concerning the definition of 

year-round, full-time employment was not provided in the dataset, 

although variables identifying year-round, full-time workers were pro- 

' vided. 

'All of the microdata were weighted using sample weights and all 

the data presented, therefore, are weighted estimates. 

excess of certain levels may be topcoded, that is, all 

amounts higher than the specified limit are reduced to 

the limit before the data are released to the public. This 

practice is a means of preserving the confidentiality of 

survey respondents. However, it does introduce a bias 

into the data and affects measures of inequality. The 

presence of topcoding is clearly evident in the data for 

the U.S. shown in Table la where the topcode in 1979 

was $50,000 and in 1986, $100,000. In the other 

countries it is not so evident given the differences 

between the maximum value of wage and salary earnings reported and the earnings at the 99th percentile. 

While no adjustments were made for the problem of 

topcoding, it was possible to adjust the data for those 

year-round, full-time self-employed workers with wage 

and salary income.' In each country, they were excluded 

from the universe of male heads of households, age 25 

to 54, who worked year round, full time. The results of 

this exclusion on the distributions can be seen in Table 

1 b. 

The adjusted data in Table 1b also provide some 

preliminary evidence as to how these earnings distributions changed during the 1980's. The mean-to-median 

ratio for each country rose during the 1980's indicating 

that the mean in these distributions were being "pulled 

up by increasingly high earnings values (Col. H). The 

highest-to-lowest decile ratio increased in all countries 

except Australia, indicative of greeter dispersion in the 

'Another procedure for reducing "survey noise" and data collection and processing problems is simply to censor the distributions at 

the lower and upper ends (e.g., 1st and 99th percentiles). We rejected 

this procedure on the grounds of its arbitrariness. 

Table la. Summary Earnings Measures for Men, Heads of Households, Age 25 to 54 Years, Who Worked 

Year Round, Full Time in Selected Countries, Early-1980's and Mid-1980's 

- 

(All numbers in currency of specified country) 

Decile* Percentile* In percent 

Countrylyear G 

A B C D E F max. H I J K 

median mean low high 1st 99th value BIA DIC EIA FIA 

US79.. .................... 18,700 20,079 9,750 32,000 3,000 50,000 50,000 107 328 16 267 

US86 ...................... 27,500 30,848 13,000 50,600 5,200 100,000 100,000 112 389 19 364 

SW81 ..................... 81,900 87,185 55,808 126,272 1,807 238,212 700,000 106 226 2 291 

SW87 ..................... 129,800 140,629 86,607 207,963 924 387,609 1,800,000 108 240 1 299 

CN81. ..................... 23,510 24,761 13,501 37,539 4,590 57,835 105,000 105 278 20 246 

CN87.. .................... 31,410 33,398 14,430 52,210 1,765 95,122 260,000 106 362 6 303 

AS81 ...................... 17,490 18,292 10,971 27,213 2,224 42,000 170,000 105 248 13 240 

AS85.. .................... 23,290 24,763 14,088 37,190 1,318 59,352 200,000 106 264 6 255 

WG81 ..................... 36,700 40,071 25,680 58,680 10,000 88,300 200,000 109 229 27 241 

WG84 ..................... 41,200 46,173 28,900 69,300 18,000 107,900 236,000 112 240 44 262 

Figures for deciles and percentiles are upper limits of the gpecified group. 

NOTE: U.S. = United States, 1979 and 1986; SW = Sweden, 1981 and 1987; CN = Canada, 1981 and 1987; AS = Australia, 1981 and 1985; 

WG = West Germany, 1981 and 1984. 
Table 1 b. Summary Earnings Measures for Men, Heads of Households, Age 25 to 54 Years, Who Worked 

Year Round, Fuii Time in Selected Countries, After Exciuslon of the Self-Employed, Early-1980's and Mid-1980's 

(All numbers in currency of specified country) 

Country/year 

US79.. .................... US86.. .................... SW8l ..................... SW87 ..................... CN81.. .................... CN87.. .................... AS81 ...................... AS85.. .................... WG81 ..................... WG84 ..................... 

distributions (Col. I). The 1st percentile-to-median ratio interesting question as to whether or not the distribu- 

(Col. J) reflects the spread or distance between the tions changed over this period of time. Here, our expecbottom and middle of each distribution. This measure tations are less certain, although the preliminary evishows that this distance increased dramatically in West dence presented above Suggests changes have indeed 

Germany and slightly in Australia and Sweden. In Can- taken place. 

ada and the United States the change in the distance 

A 

median 

18,750 

27,950 

84,700 

135,900 

23,610 

32,760 

17,510 

24,000 

36,880 

41,600 

between these two points of the distribution was not Inequality the ~id-~~8~,~ very large. The 99th percentile-to-median ratio (Col. K) 

Figures for deciles and percentiles 

NOTE: U.S. = United States, 1979 ar 

WG = West Germany, 1981 and 1984. 

B 

mean 

29,118 

31,140 

93,149 

152,379 

24,922 

35,094 

18,429 

25,601 

40,104 

46,508 

reflects the gap in the distribution between the median 

and the top of the distribution. In each country, the 

distance between these two points increased from the 

early-1 980's to the mid-1 980's, with the largest increases 

occurring in the United States and Canada. Consequently, in each country there was preliminary evidence 

that distributions of earnings of prime-age men who 

headed households and were fully committed to the 

labor force had become more unequal during the 1930's. 

are upper limits of the specified group. 

j 1986; SW = Sweden, 1981 and 1987; CN = Canada, 1981 and 1987; AS = Australia, 1981 and 1985; 

In percent 

CROSS-NATIONAL COMPARISONS OF 

EARNINGS INEQUALITY MEASURES 

The results of the comparative analysis of earnings 

inequality in Canada, wede en, Australia, West Germany, and the United States is presented in two parts. 

First, various earnings inequality measures are presented for these countries as of the mid-1980's which 

address the question as to whose distribution was the 

most unequal and whose was the most equal. Naturally, 

differences are expected given the differences in each 

countries' economic structure (e.g., industrial composition, extent of unionization, compensation practices) 

and other factors that influence the shape of the 

earnings distribution. Second, we compare these mid1980's earnings inequality measures to their counterparts as of the early 1980's and address the more 

G 

m~. 

value 

50,000 

100,000 

700,000 

1,800,OOO 

105,000 

260,000 

170,000 

200,000 

200,000 

236,000 

K 

FIA 

267 

358 

286 

294 

246 

299 

240 

250 

239 

253 

Table 2 shows the share of aggregate wages and 

salaries received in each decile by men who were heads 

of households and worked year round full time in 

Canada, Sweden, Australia, West Germany, and the 

United States in the 1984-87 period. The distributions 

were arrayed (from left to right) on the basis of which 

country had the smallest proportion of aggregate earnings in it's lowest decile. In other words, in which country 

did workers at the bottom of the earnings distribution 

receive the smallest share of total earnings? As shown 

in the Table, in the U.S. the lowest decile of men 

received only 3.0 percent of all earnings, followed by 

Canada at 3.4 percent. Australia's and West Germany's 

lowest deciles, respectively, received the next largest 

shares, with Sweden's lowest decile, on the other hmd, 

obtaining 5.9 percent. 

Turning to the shares received by the highest deciles 

in each country produces a somewhat different ranking 

of countries. Once again the U.S. would occupy the first 

position since it's highest decile of earners received 

23.3 percent of the aggregate followed by Canada 

whose highest decile received 20.7 percent. Thereafter, 

the ranking changes. Sweden's 19.4 percent share 

would now occupy the third position (if the table were 

rearranged), followed by Australia and West Germany 

with shares of 19.0 and 18.9 percent, respectively. 

Indeed, careful inspection of these distributions reveals 

that it is diiicult to compare the degree of inequality in 

J 

E/A 

I6 

17 

48 

51 

20 

18 

15 

21 

27 

50 

H 

B/A 

107 

111 

110 

112 

106 

107 

105 

107 

109 

112 

Dedle* 

I 

D/C 

327 

400 

205 

208 

274 

303 

247 

242 

228 

238 

C 

low 

9,800 

13,000 

64,000 

103,400 

13,730 

17,485 

11,020 

15,588 

25,730 

29,300 

Percentile* 

D 

high 

32,000 

52,000 

131,500 

215,400 

37,640 

53,000 

27,270 

37,672 

58,570 

69,800 

E 

1st 

3,000 

4,806 

40,300 

69,324 

4,691 

5,810 

2,709 

5,118 

10,000 

20,800 

F 

99th 

50,000 

100,000 

242,025 

399,594 

58,058 

97,985 

42,013 

59,962 

88,300 

105,104 
Lorenz Cuwes, Five Nations: Mid-1980's 

Table 2. Percentage Share of Aggregate Wage and 

Salary Income Received by Men, Heads of 

Households, Age 25 to 54 Years, Working Year Round, Full Time, by Deciles for 

Selected Countries, Mid-1980's 

countries can be seen to intersect (see Figure). Consequently, to completely answer the question as to whose 

distributions were the most equal and unequal, more 

sophisticated measures of earnings inequality must be 

used. 

Table 3 presents a ranking of the five countries, with 

respect to earnings inequality, using several earnings 

inequality measures, specifically, the variance of the 

logarithm of annual earnings, the Gini and Theil indices, 

and three Atkinson measures. The table indicates that 

regardless of measure, the U.S. distribution of earnings 

showed the highest level of inequality in it in the 

mid-1 980's. The country with the second most unequal 

distribution among these five countries was Canada. 

Although these results were anticipated from the share 

analysis above, these earnings inequality measures 

quantify how much more unequal the U.S and Canadian 

distributions are from one another as well as the other 

countries. 

Decile 

Lowest. . . . . . . . Second . . . . . . . Third. . . . . . . . . . Fourth . . . . . . . . Fifth . . . . . . . . . . Sixth . . . . . . . . . . Seventh ....... Eighth.. . . . . . . . Ninth .......... Highest ....... 

Percent of aggregate earnings 

80 - - 

- 

- United States 1986 

- West Germany 1984 

- - - - - - - Canada 1987 70 - - - - Sweden 1985 --- Australia 1987 

Percent of earners 

NOTE: United States, 1986; Canada, 1987; Australia, 1985; West 

Germany, 1984; Sweden, 1987. 

U.S. 

3.0 

4.9 

6.1 

7.2 

8.3 

9.5 

10.7 

12.2 

14.8 

23.3 

Note: Men, age 25 to 54, heads of households, year around, full time. 

the distributions of Australia, West Germany, and Sweden. For example, if the criteria for judging which 

distribution was more unequal was based on the shares 

of earnings received by the lowest and highest deciles, 

an unambiguous ranking could not be arrived at. In the 

first instance, Sweden's distribution would be considered the most equal, but in the second it would be West 

Germany's. Furthermore, the Lorenz curves for these 

Canada 

3.4 

5.7 

6.8 

7.9 

8.8 

9.9 

10.9 

12.1 

13.8 

20.7 

For Australia, West Germany, and Sweden, the earnings inequality measures provide a somewhat clearer 

picture of whose distribution was the most unequal and 

most equal than the share analysis did. All of the 

measures indicate that Australia's was the most unequal 

followed by West Germany and then Sweden. The 

readings from the Gini and Theil indices, of course, 

should be viewed cautiously since Lorenz dominance 

Australia 

4.4 

6.6 

7.4 

8.2 

8.9 

9.8 

10.6 

11.7 

13.4 

19.0 

west 

Germany 

5.4 

6.7 

7.1 

8.3 

8.5 

9.4 

10.3 

11.7 

13.7 

18.9 

Sweden 

5.9 

7.1 

7.6 

8.1 

8.7 

9.2 

10.0 

11.1 

12.9 

19.4 
Table 3. Measures of Earnings Inequali for Men, Heads of Households, Age 25 to 54, Working Year 

Round, Full Time for Selected t ountries: Mid-1$8o's 

was present in the these distributions. Each of the 

Atkinson measures produced the same ranking of countries, although the magnitude of the differences varied 

between the measure which gives more weight to the 

low end of the distribution and the measure which 

weights the upper end more heavily. 

Earnings Inequality: Early-1980's vs. 

Mid-1980's 

Atkinson 

(1.5) 

.341 (US) 

.185(CN) 

.146(AS) 

.097(WG) 

.082(SW) 

Among the many factors that can influence changes 

in a Nation's distribution of earnings are changes in the 

health of the economy. Although measures of earnings 

inequality for these countries are compared over somewhat different time periods, the economies of these 

countries in the early-1 980's and mid-1 980's were, 

generally speaking, in similar phases of the business 

cycle (OECD, 1990). In each country, the mid-1980's 

represented years of economic growth and recovery 

from recessions in the early 1980's. Gross domestic 

products were rising and inflation had moderated relative to the early 1980's. In the early 1980's, on the other 

NOTE: US = United States, 1986; CN s: Canada, 1987; AS = Australia, 1985; WG = West Germany, 1984; SW = Sweden, 1987. 

Atkinson 

(0.8) 

.120((US) 

.09l (CN) 

.067(AS) 

.053(WG) 

.049(SW) 

The data presented in Table 4 show the percentage 

share of aggregate wages and salaries by quintile for 

men age 25 to 54 who headed households and worked 

full time, year round in Canada, Sweden, Australia, West 

Germany, and the U.S. in the early-1980's and mid1980's.8 In each country there is evidence to varying 

degrees of a greater concentration of earnings in the 

upper quintiles of the earnings distributions. For the 

U.S., Canada, and Sweden, the share of earnings 

received by each of the lowest three quintiles declined 

while the share for the top quintiles increased. In the 

U.S., for example, the share of aggregate wage and 

salary earnings received by the top one-fifth of earners 

increased from 35.0 percent to 38.1 percent, or 3.1 

percentage points. Canada had a 2.1 percentage point 

increase in the share going to the highest earners. The 

increase in the share going to Sweden's top earners, 

however, was only 0.7 percentage points. 

In the case of Australia and West Germany, the 

patterns of change in the distributions were somewhat 

diierent than in the other countries. In Australia, the 

Atkinson 

(0.5) 

,074(US) 

.057(CN) 

.042(AS) 

.034(WG) 

.032(SW) 

Rank 

1 ................ 

2 ................ 

3 ................ 

4 ................ 

5 ................ 

Gini 

.298(US) 

.253(CN) 

.212(AS) 

.204(WG) 

.I 9O(SW) 

Ln Y 

.453(US) 

.280(CN) 

.21 O(AS) 

.133(WG) 

.111(SW) 

hand, economic growth was leis robust and inflation eQuintiles instead of deciles are examined here since the changes more problematic as each country was in or about to in this unit of msurement over time are larger and can be more 

enter a period of economic slowdown. easily seen. 

Table 4. Shares (In percent) of Aggregate Wage and Salary Income Received by Men, Heads of Households, Age 25 to 54, Working Year Round, Full Time, by Quintlle for Selected Countries, Early- 1980's and Mid-1980's 

Theil 

.I 49(us) 

.I l6(CN) 

.087(AS) 

,071 (WG) 

,071 (SW) 

I 

Country/year ~ 

United States 

I 1986 .................................... 1979 .................................... 

I Canada 

1987 .................................... 1981.. .................................. 

I Sweden 

1987 .................................... 1981 .................................... Australia 

1985. ................................... 1981 .................................... West Germany 

1984. ................................... 1981 .................................... 

Total 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

100.0 

Quintile 

Lowest 

7.9 

8.9 

9.1 

10.1 

13.0 

13.2 

11.0 

10.8 

12.2 

12.0 

Fourth 

22.9 

22.9 

23.0 

23.0 

21.1 

21.0 

22.3 

22.7 

22.0 

21.8 

Highest 

38.1 

35.0 

34.5 

32.4 

32.3 

31.6 

32.4 

31.6 

32.6 

31.9 

Second 

13.3 

14.6 

14.7 

15.5 

15.7 

16.1 

15.6 

15.9 

15.4 

16.0 

Third 

17.8 

18.6 

18.7 

19.0 

17.9 

18.1 

18.7 

19.0 

17.9 

18.3 
I share received by the highest fifth of earners increased 

and the share increased (slightly) for the lowest fifth of 

earners as well. A similar pattern of change was evident 

in West Germany, except that the share also rose 

slightly in the fourth quintile. In other words, in these two 

countries the middle quintiles received slightly smaller 

shares of aggregate earnings. It is also important to 

note that Lorenz curves for the early-1980's and mid1980's in both Australia and West Germany crossed at 

the lower end of the distributions. 

The above share analysis strongly suggests that 

earnings of the highest paid men in each of the countries examined were becoming more concentrated in 

the 1980's. This development was particularly acute in 

the United States and Canada. Nevertheless, some 

change towards greater earnings inequality was detected 

in Sweden, Australia, and West Germany as well. More 

sophisticated measures of inequal'i, however, are required 

to confirm this finding. 

All of the inequality measures for Sweden registered 

increases as well, but they were not as large as for the 

North American countries. They ranged from as little as 

4 percent (Atkinson, 1.5 epsilon) to 13 percent (the Theil 

index). Interestingly, the increases in both the variance 

of the logarithm of annual earnings and the Atkinson 

measure (1.5 epsilon)-two measures sensitive to the 

low end of the distribution-were on the low side of this 

range reflecting the small change that occurred in the 

share of aggregate earnings received by the lowest 

quintile. 

In Australia four of the six measures increased and in 

West Germany three measures moved higher. The 

increases for both were generally smaller than in the 

other countries (except the Theil measure for Australia). 

It should be remembered, however, that the span of 

years comprising the comparisons for these two countries is relatively short. Again, the measures sensitive to 

the bottom of the distribution-the variance of the 

logarithm and the Atkinson measure (1.5 epsilon)-registered declines in inequality reflecting the share increases Tab'e the iwquality measures that were at the bottom of the distributions discussed earlier. previously discussed in connection with the question of 

which country had the most and least amount of inequality in its distribution. This Table, however, now IMPLICATIONS OF RESULTS 

includes the values of these measures for the early 

1980's, and the percentage change in the measures 

between the early-1 980's and mid-1 980's. As might be 

expected from the share analysis, all of the measures 

indicate a strong increase in inequality in the United 

States and Canada. The percentage increases in the 

measures for the United States ranged from 16 percent 

(the Gini index) to 34 percent (Atkinson, 1.5 epsilon); for 

Canada, they ranged from 14 percent (the Gini index) to 

38 percent (the Theil index). 

The growth of earnings inequality in the U.S. in recent 

years has been well-documented and the subject of 

much concern in both the research and policy communities. One of the concerns has been whether or not the 

millions of jobs created in the U.S. during the 1980's 

were primarily of the "low paying, low productivity" 

variety. Another related concern, of course, is how 

much of the growing earnings inequality was responsible for the growing inequality of incomes among families 

and households. 

Table 5. Changes in Measures of Earnings Inequality for Men, Heads of Households, Age 25 to 54 Years, 

Working Year Round, Full Time for Selected Countries, Early-1980's and Mid-1980's 

Atkinson Atkinson Atkinson I (0.5) (0.8) (1.5) Country/year 

United States 

1986. .................................... 1979. .................................... Percent change ............................ Canada 

1987 ..................................... 1981 ..................................... Percent change ............................ Sweden 

1987. .................................... 1981 ..................................... Percent change ............................ Australia 

1985. .................................... 1981 ..................................... Percent change. ........................... West Germany 

1984. .................................... 1981 ..................................... Percent change ............................ 

Ln Y 

.453 

.3!% 

28.0 

'280 

.225 

24.4 

,111 

.lo5 

5.7 

.210 

.234 

-9.3 

.I33 

.I62 

-1 7.9 

Gini 

.298 

.258 

15.5 

.253 

.222 

14.0 

.I90 

.I80 

5.6 

.212 

.208 

1.9 

.204 

.I95 

4.6 

Theil 

.149 

.I11 

34.2 

.ll6 

.084 

38.1 

.071 

.063 

12.7 

.087 

.075 

16.0 

.071 

.068 

4.4 
The results presented above suggest that the U.S. 

was not the only industrialized country during the 1980's 

to experience an increase in earnings inequality among 

prime age men who head families that were fully committed to the labor force. It was shown that the wage 

and salary earnings distributions for similar men from 

Canada and Sweden became more unequal as well. In 

addition, there was evidence that inequality was growing for these groups of men in Australia and West 

Germany. These findings may be further evidence of 

underlying structural changes taking place in the distributions of labor market incomes in many developed 

countries. 

Table 6. Average Annual Growth Rates in Total 

Employment for Selected Countries, 

Early-1 980's to Mid-1 980's 

Clearly, the "job quality" issue as a cause of growing 

earnings inequality in the U.S. loses credibility when 

Country 

United States.. ...................... Canada ............................. Australia ............................ Sweden. ............................ West Germany. ...................... 

rates of employment growth in these other countries are 

compared to the U.S. experience. Each country had 

quite different employment experiences during the periods in which inequality was rising, as shown in Table 6 

(OECD, 1990). In the U.S. and Canada, the annual 

average rate of growth was in the 1.3 to 1.5 percent 

range and in Australia it was 1.1 percent. In Sweden 

employment growth was meager, while in West Germany it was declining. 

The fact that rising earnings inequality occurred in 

other industrialized countries with different job creation 

experiences suggests that rising inequality may be 

related to more general phenomena occurring across 

nations. One possibility involves changing technologies. 

Computer and communication technologies have changed 

dramatically in recent years and these may have altered 

the demand for different skill classes of labor. Production processes may have been so altered that companies and factories now require more highly skilled and 

educated workers, while the demand for less welltrained labor associated with older production processes has declined. At the same time these shifts in 

demand have taken place, the supply of workers in 

various skill classes may have changed less rapidly 

thereby increasing inequality in the earnings distribution. 

This, of course, is speculation. Nevertheless, evidence 

now exists that the phenomenon of growing earnings 

inequality may have an international dimension. 

Period 

1979-86 

1981 -87 

1981 -85 

1981-87 

1981-84 

Average 

annual 

growth rate 

1.49 

1.28 

1.08 

.43 

-77 
I REFERENCES 

I Braun, Denny, "Multiple Measurements of U.S. lncome 

Inequality," The Review of Economics and Statistics, 

Vol. 70, NO. 3, 398-405, August, 1988. 

Coder, John, Lee Rainwater, and Timothy Smeeding, 

"LIS Information Guide," LIS-CEPS Working Paper No. 

7, Walferdange, Luxembourg, rev. November, 1988 

Dooley, Martin and Peter Gottschalk, "Does a Younger 

Male Labor Force Mean Greater Earnings Inequality?" 

Monthly Labor Review, Vol. 105, No. 1 1,42-45, November, 1982. 

Henle, Peter and Paul Ryscavage, "The Distribution of 

Earned lncome Among Men and Women, 1958-1 977," 

Monthly Labor Review, Vol. 103, No. 4, 3-10, April, 

1980. 

Levy, Frank and Richard J. Murnane, "Earnings Levels 

and Earnings Inequality: A Review of Recent Trends 

and Proposed Explanations," Journal of Economic Literature, (forthcoming). 

Plotnick, Robert D., "Trends in Male Earnings Inequality," Southern Economic Journal, Vol. 48, No. 3, 724- 

732, March, 1982. 

O'Higgins, Michael, Guenther Schmaus, and Geoffrey 

Stephenson, "lncome Distribution and Redistribution: A 

Microdata Analysis for Seven Countries," The Review 

of Income and Wealth, Series 35, No. 2, 107-1 31, June, 

1989. 

Organisation for Economic Co-operation and Development, The OECD Employment Outlook, Paris: OECD, 

September, 1987. 

Organisation for Economic Co-operation and Development, OECD Economic Outlook, Paris: OECD, June, 

1990. 

Slottje, D. J., The Structure of Earnings and the Measurement of lncome Inequality in the US., North Holland, Amsterdam, 1989. 
APPENDIX. INEQUALITY MEASURES AND DATA SOURCES 

INEQUALITY MEASURES 

The mathematical construction of four inequality measures used in this paper are discussed below. 

Variance of the Natural Logarithm of Annual 

Earnings 

This is a popular measure of relative earnings dispersion because earnings distributions are approximately 

lognormal and the lognormal distribution has particular 

properties conducive for analysis. The measure is written as 

Z (In yi - In Y)* is1 

Var In Y = n 

where In yi is the natural logarithm of person i's annual 

earnings, In y, is the mean of annual earnings, and n is 

the number of persons with earnings. 

The Gini Index or Coefficient of lncome 

Concentration 

The Gini index is also a popular measure of inequality. It can be written as 

where fi is the proportion of earners in interval i and pi is 

the proportion of total earnings received by earners in 

interval i and all lower intervals. 

Theil's "Entropy" Index of Inequality 

The Theil index can be written as 

where yi is the annual earnings of the ith earners, y the 

mean annual earnings, and n, the number of earners. 

Atkinson's Measures of Inequality 

The family of Atkinson measures are constructed as 

with the similar notation found in the other measures, 

except for the E, or epsilon. AS the value of the epsilon 

rises, the measure becomes increasingly sensitive to 

inequality among low earners. Low values of epsilon 

produce results similar to the Gini index. 

DATASOURCES 

The following is a brief description of the household 

surveys which were the source of the earnings data. 

Household sample sizes do not necessarily reflect the 

size of the original sample in all cases, but rather the 

number of households comprising the country's microdata set in the LIS database. 

Australia 

The Australian data for both 1981 and 1985 were 

obtained from The lncome and Housing Survey. The 

sample size in 1981 was 15,985 households and in 

1985, 7,560. 

Canada 

Earnings data for Canada were obtained from The 

Survey of Consumer Finances and refer to 1981 and 

1987. Sample sizes for the LIS data base were 15,136 

and 10,999. 

West Germany 

Data for West Germany for the year 1981 were taken 

from the 1981 German Transfer Survey and based on a 

household sample size of 2,727. The German Panel 

Survey (Wave 2) was the source of the data for 1984 

and 5,174 households from it comprised the LIS data 

base. 

Sweden 

The Swedish lncome Distribution Survey in both 

1981 and 1987 was the source of the data for Sweden. 

The LIS database used data from 9,625 households in 

1981 and 9,421 households in 1987. 

United States 

The data for the United States come from the Work 

Experience and lncome Supplement to the March Current Population Survey and relate to the years 1979 and 

1986. In 1979, the sample consisted of 15,225 households and in 1986, 13,707. 
TRENDS IN INCOME AND WEALTH OF THE ELDERLY IN 

THE 1980's 

by Paul Ryscavage 

Presented at the 37th Annual Meeting of the American Society on A ing in New Orleans on March 18, 1991. Paul Ryscavage is a Senior Labor Economist in the Housing and Household Economic gatistics Division of the US. Bureau of the C~WS. The 

views expressed in this paper are attributable to the author and do not reflect those of the Census Bureau 

The 1980's contained one of the longest periods of 

economic expansion in recent history. The economy, 

after experiencing back-to-back recessions in the opening years, grew (as measured by the gross national 

product) between 1983 and 1989 at an average rate of 

almost 4.0 percent a year. Millions of jobs were created, 

inflation was held in check, and prices on the stock 

market soared to all time highs. But as is frequently 

pointed out today, the expansion was not without its 

flaws. The Federal government's fiscal deficit mushroomed as did the Nation's trade deficit. And one of the 

more disturbing developments was that not all groups in 

society shared equally in the benefits of the economic 

growth. Income inequality among households increased 

during the 1980's and the incidence of poverty was 

reduced only slightly. 

Because not everyone benefited from the prosperity 

of the 1980's, it is of interest to find out how the elderly, 

or persons age 65 and over, fared in these years. After 

all, unlike the nonelderly most of the elderly are retired 

and are somewhat removed from the day-to-day workings of the economy. Did they share in the general rise 

in economic well-being or were they left behind? 

This question takes on added significance, today, in 

the decade of the 1990's. In the second half of 1990 the 

economy slumped into a recession and, at the same 

time, the budget and trade deficits continued to impact 

on the country's economic performance. Furthermore, 

the financial costs of the Persian Gulf war and the 

savings and loan crisis represent an additional strain on 

the economy. Where the elderly stood economically as 

they left the 1980's and entered the 1990's becomes 

even more important, if the recession deepens, the 

deficits fail to shrink, and the other economic burdens 

are not resolved. A second and more difficult question, 

therefore, might be asked: Are the elderly prepared for 

"hard times" if indeed the economic climate worsens? 

Comprehensive answers to these two questions are 

difficult since the economic well-being of the elderly, or 

any group, is so multifaceted.1 In the following pages, 

'For a comprehensive review of the research on the elderly, 

including their economic status, see Michael D. Hurd, "Research on 

the Elderly: Economic Status, Retirement, and Consumption and 

Saving." Journal of Economic Literature, June 1990, pp. 565-637. 

however, we examine two aspects of the economic 

expansion of the 1980's which have been discussed 

frequently in the media-income growth and wealth 

accumulation-and assess what they meant for the 

elderly in the previous decade. In so doing, implications 

of these developments for the current decade will 

emerge. Data from Census Bureau surveys are used in 

this assessment. 

Income Growth for the Elderly-The Big 

Picture 

Real incomes (incomes adjusted for price changes) 

of most Americans began to grow again in the 1980's, 

after periods of economic recession and rampant inflation in the 1970's and early 1980's. The real annual 

median household income of all households rose by 

10.5 percent between 1982 and 1989.2 Previous to this, 

income growth had been erratic at best, affected by the 

country's sluggish economic performance. What happened to the incomes of the elderly? 

Perhaps the best source of information to answer this 

question, or for that matter any question relating to the 

income of specific population groups, is the March 

Current Population Survey (CPS) conducted by the 

Census Bureau. This is a sample survey of approximately 60,000 households throughout the country and 

is also the source of the Nation's statistics on poverty, 

labor force activity, and other socio-economic characteristics of the population. 

The CPS allows one to examine income trends in a 

variety of ways and for a variety of groups. For example, 

we can examine the median or mean income of households with an elderly householder, or we can look at the 

incomes of all elderly persons living in households.3 In 

addition, the CPS allows us to examine different groups 

*All nominal income data cited in this paper can be found in various 

editions of Current Population Reports, Series P-60, U.S. Bureau of 

the Census, USGPO, Washington, D.C. Real annual income data are 

in terms of 1989 dollars and have been adjusted for inflation by the 

Bureau of Labor Statistics' Consumer Price Index (CPI-U-XI). 

'A householder is a person in whose name the residence being 

surveyed is owned or rented. 
among the elderly. The elderly, of course, are a heterogeneous group. Many are very old and live alone, 

others may have just retired and live as married couples, 

and still others may live as single individuals in their 

son's or daughter's family. 

The CPS income data, however, do have their limitations. These limitations become particularly troublesome when the emphasis of the analysis is on income 

comparisons between groups, and issues of "wellbeing" are being examined. One limitation is that the 

CPS excludes the value of certain "noncash" income 

items, such as food stamps, Medicare, Medicaid, employerprovided health insurance and pension plans. Some of 

these items are important to the elderly. A second 

limitation is that the CPS income data relate to incomes 

before taxes. Obviously, a "disposable income" concept would be more desirable for determining the elderly's resources for immediate consumption. A third 

limitation concerns income underreporting in the CPS. It 

is well known that some income items, such as income 

from certain assets, are underreported by survey respondents. And a last limitation concerns the adjustments to 

the reported income data which should be made for 

"economies of scale" in households of different sizes 

and compositions. All of these limitations are important 

to the extent that they have differential effects on 

population groups. The emphasis of the analysis in this 

section of the paper, however, is the trend in income for 

the elderly relative to the population as a whole. While it 

is recognized that if the above limitations were accounted 

for the quantitative results would be somewhat different 

from what is presented, it is most likely that the basic 

relationship between income trends would not be much 

different. 

Median Incomes of Households and Persons. Table 

1 shows the real annual median incomes of all households in the country and elderly households between 

1979 and 1989. In 1989, the median income of the 93.3 

million households in the country was $28,906, compared to $1 5,571 for the 20.2 million elderly households. 

When the trends in incomes for all households and 

elderly households are examined over this period some 

interesting patterns emerge. 

Between 1979 and 1989, the real median income of 

elderly households increased by 19.5 percent comapred 

to a 4.8 percent increase in the real median income of 

all households. Between 1982 and 1989, however, 

there was no significant difference in the rates4 This, of 

'Statistical significance is a concept concerning the amount of 

confidence we have in an estimate derived from a sample. Confidence 

in an estimate is expressed in terms of a confidence interval. In the 

case above, if all possible samples were surveyed under the same 

conditions, statistically significant changes in incomes would occur in 

90 percent of the samples. Unless, otherwise stated, all statistical 

comparisons in this paper have been tested for statistical significance 

at the 10 percent level. 

Table 1. Real Median Household lncome for 

Elderly Households and All Households: 

1979 to 1989 

(In 1989 dollars) 

Elderly Total 

households households 

Year Index Index 

(1979 = (1979 = 

Income 100.0) Income 100.0) 

1989 ............. $15,771 119.5 $28,906 104.8 

1988.. ........... 15,642 118.5 28,537 103.5 

1987 ............. 15,765 119.4 28,447 103.1 

1986 ............. 15,664 118.6 28,168 102.1 

1985 ............. 15,274 115.7 27,218 98.7 

1984 ............. 15,275 115.7 26,751 97.0 

1983 ............. 14,905 112.9 26,167 . 94.9 

1982.. ........... 14,321 108.5 26,163 94.9 

1981 ............. 13,629 103.2 26,251 95.2 

1980.. ........... 13,230 100.2 26,683 96.7 

1979.. ........... 13,203 100.0 27,583 100.0 

Percent change in 

income: 

1982-89. ......... 10.1 (NA) 10.5 (NA) 

1979-89. ......... 19.5 (NA) 4.8 (NA) 

Note: Median household incomes have been adjusted for inflation 

using the Bureau of Labor Statistics' CPI-U-X1. 

Source: U.S. Bureau of the Census, Current Population Survey. 

course, was a period of economic recovery. It was in the 

1979 to 1982 period-years containing two recessionsthat the median for all households declined while the 

median for the elderly increased (figure). This illustrates 

the elderly's greater insulation from downswings in 

economic activity. 

Table 2 contains data for all persons age 15 and over 

and elderly persons by sex. (Differences in income 

when measured on a person basis are smaller than 

when measured on a household basis.) During the 1982 

to 1989 period real median annual incomes for elderly 

men and women and for men of all ages rose by similar 

rates-10 percent. For women of all ages, real incomes 

rose by 26 percent, no doubt reflecting the gains made 

by women in the job market during these years. It should 

be noted that even during the early part of the decade--1 979 

to 1982-women's incomes continued to rise faster 

than the other groups. 

Trends in lncome Inequality. Although real household incomes, on average, grew in the 19809s, the 

Census Bureau reported that income inequality had 

increased as well. How were the elderly affected? 

A frequently used measure of income inequality is the 

share of "aggregate" income received by each quintile 

of the population, or in this case households. What this 

involves is a ranking of all households on the basis of 

income and then dividing this distribution into five equal 

parts or quintiles. As shown in table 3, the overall share 

of aggregate income received by the highest quintile in 
Trends in Real Median Income in Elderly and All Households: 

1979 to 1989 (1 979=100) 

Percent 

1979 1980 1981 1982 1983 1984 1 985 1986 1987 1988 1989 

Year 

Note: Elderly householders are age 65 years or more. 

Source: U.S. Bureau of the Census, Current Population Survey 

change. This share increase for the richest households 

Table 2' Women Income 'Or Men and occurred while households in the middle three quintiles and All Men and Women (age 15 

and over): 1979 to 1989 and the lowest quintile experienced slight declines. 

(In 1989 dollars) 

Elderly persons All persons 

Year 

Men Women Men Women 

1989.. ........... $13,107 $7,655 $19,893 $9,624 

1988 ............. 13,072 7,445 19,819 9.31 2 

1987 ............. 13,019 7,527 19,414 9,054 

1986 ............. 13,061 7,269 19,363 8,610 

1985.. ........... 12,561 7,275 18,797 8,317 

1984 ............. 12,472 7,185 18,618 8,197 

1983 ............. 12,320 7,111 18,253 7,974 

1982 ............. 11,917 6,959 18,094 7,636 

1981.. ........... 11,248 6,547 18,542 7,512 

1980 ............. 11,062 6,367 18,879 7,413 

1979.. ........... 10,780 6,302 19,738 7,293 

Percent change in 

income: 

1982-89. ......... 10.0 10.0 9.9 26.0 

1979-89. ......... 21.6 21.5 0.8 32.0 

Note: Median incomes have been adjusted for inflation using the 

Bureau of Labor Statistics' CPI-U-XI . 

Table 3 also shows the number of elderly households 

in these quintiles in 1979 and 1989 and the fact that in 

both years mote than 1 out of every 3 elderly households was in the lowest quintile of the Nation's income 

distribution. It also shows the relatively small number of 

elderly households in the top part of the income distribution. 

The tabulation below looks at these data a different 

way. It shows the proportion of elderly households in the 

lowest quintile, the middle three quintiles, and the top 

quintile of the Nation's income distribution in 1979 and 

Proportion of Elderly Households 

in 

Year 

Lowest Middle Highest 

quintile quintiles quintile 

1989. ......................... 41.6 19.2 8.7 

1979 .......................... 46.2 16.7 5.9 

Source: U.S. Bureau of the Census, Current Population Survey. 1989. Although the elderly are heavily represented in 

the lowest of the income distribution, between 1979 

1979 was 44.2 percent, but 10 year later the proportion and 1989 proportionally fewer of the elderly were in the 

had increased to 46.8 percent, a statistically significant lowest quintile and proportionally more were in the 
Table 3. Distribution of A (Iregate Household 

Income, by Quint 9 le and the Number of 

Elderly Households Represented in Each 

Quintlle: 1979 and 1989 

I NA Not applicable. Source: U.S. Bureau of the Census, Current Population Survey. 

middle and highest quintiles of the distribution. In other 

words, the elderly, while still very common at the bottom 

I of society's income ladder, made progress in climbing to 

higher rungs of that ladder during the 1980's. 

Also shown in table 3 are the Gini indexes, or the 

shorthand measures of how much inequality exists in 

the country's income distribution.. If each household 

had the same income-perfect equality-this index 

would have a value of 0.0; however, if one household 

received all the income--perfect inequality-then the 

Gini index would be 1 .O. In other words, the measure is 

bounded between 0 and 1. The Gini index for the U.S. 

household income distribution rose from .404 to .431 

between 1979 and 1989, a statistically significant increase. 

According to the Census Bureau, the rate of increase 1 was twice as great as the increase during the 1 97OYs, so 

I during the 1980's there was an acceleration in income 

inequality. 

Year 

AGGREGATE INCOME 

OF ALL HOUSEHOLDS 

(in percent) 

1989 .................. 1979 .................. 

ELDERLY HOUSEHOLDS 

(in thousands) 

1989 .................. 1979.. ................ 

Poverty. Table 4 presents the trend in poverty for the 

elderly during the 1980's. In terms of the total population'in 1989,31.5 million persons were considered poor 

based on the Federal government's poverty definition, 

or 12.8 percent of the population. This poverty rate was 

slightly higher than that in 1979 (1 1.7 percent), but 

considerably lower than the rate in 1983 (1 5.2 percent) 

which reflected the economic downturn experienced in 

the opening years of that decade. 

Total 

100.0 

100.0 

20,156 

16,544 

Distribution by quintile 

'The Gini index can be written as 

Bottom 

3.8 

4.1 

7,759 

7,457 

where f is the proportion of income recipients in interval i and y, is the 

proportion of aggregate income received by the recipients in interval 

i and all lower intervals. 

Among the elderly, 3.4 million persons had incomes 

below the poverty line in 1989, or 1 1.4 percent of all the 

elderly. This rate was well below that for the Nation as 

a whole and was well below the elderly's rate in 1979 of 

15.2 percent.6 The economic downturn of the early 

1980's had less of an impact on the elderly than the 

population primarily because a smaller proportion of the 

elderly are in the work force and susceptible to periods 

of unemployment. 

Indeed, the improvement in the poverty picture for 

the elderly during the 1980's stands out in sharp contrast to that of the nonelderly. The tabulation below 

shows that over the 10 year period the incidence of 

poverty worsened for nonelderly persons between the 

ages of 18 and 64 and for persons under age 18 (the 

majority of whom were children).' 

Mile 

three 

49.3 

51.7 

10,778 

8,118 

These differential changes in poverty rates, of course, 

reflect the changing composition of the poverty population. The elderly's share of it in 1979 was 14.1 percent 

and by 1989 it was 10.7 percent. 

Top 

46.8 

44.2 

1,619 

969 

Group 

Total population.. .................... Persons under 18 ................. Persons 18 to 64. ................. Persons 65 and older.. ............ 

Special Problem Groups Among the Elderly 

Gini 

index 

.431 

.404 

(NA) 

(NA) 

The foregoing suggests that the elderly, on average, 

did quite well in the 1980's relative to the population. it 

should be remembered, however, that their average 

income is still far below that for all households in the 

Nation (46 percent below) 8 and that 2 out of every 5 

households in the lowest income quintile are elderly 

households. Furthermore, not everyone within the elderly population shared equally in the income gains of the 

1980's. 

Percentage poor in 

6According to the Bureau of the Census, the rate in 1989 for the 

elderly would have been 5.1 percent and 8.9 percent for the Nation if 

the value of certain noncash benefii were included and other 

adjustments to the poverty definition had been made. See "Measuring 

the Effect of Benefits and Taxes on Income and Poverty: 1989," 

Current Population Reports, Series P-60, No. 169-RD, U.S. Bureau of 

the Census, USGPO, Washington, D.C. 1990. 

'Just as for the total population, poverty rates increased for 

persons aged 18 to 64 and persons under 18 during the early 1980's 

as a result of the economic slowdown in those years, and then the 

rates declined as the recovery began. 

Poverty rates in 1989 for the groups shown here would be much 

lower if data on taxes, capital gains, and the value of certain noncash 

benefits were included in the CPS income concept. See "Measuring 

the Effect of Benefits and Taxes on Income and Poverty: 1989," 

Current Population Reports, Series P-60, No. 16QRD, U.S. Bureau of 

the Census, USGPO, Washington, D.C., 1990. 

'Research has shown that this income differential is smaller if the 

CPS income were "adjusted" for some of the limitations discussed 

earlier. See Michael D. Hurd, "Research on the Elderly," cited in 

footnote 1, above. 

1979 

11.7 

18.4 

8.9 

15.7 

1989 

12.8 

19.6 

10.2 

11.4 
Table 4. Number of Persons With Incomes Below Table 5. Distribution of Aggregate Household the Poverty Level and the Poverty Rate Income, by Quintile for Households With 

for Elderly Persons and All Persons: 1979 an Elderly Householder: 1979 and 1989 

to 1989 (In percent) 

Elderly persons All persons 

Years Number Rate Number Rate 

(thous.) (percent) (thous.) (percent) 

1989. ......... 3,369 11.4 31,534 12.8 

1988. ......... 3,481 12.0 31,745 13.0 

1987. ......... 3,563 12.5 32,221 13.4 

1986. ......... 3,477 12.4 32,370 13.6 

1985. ......... 3,456 12.6 33,064 14.0 

1984. ......... 3,330 12.4 33,700 14.4 

1983. ......... 3,625 13.8 35,303 15.2 

1982. ......... 3,751 14.6 34,398 15.0 

1981 .......... 3,853 15.3 31,822 14.0 

1980. ......... 3,871 15.7 29,272 13.0 

1979. ......... 3,682 15.2 26,072 11.7 

Source: U.S. Bureau of the Census, Current Population Survey. 

Table 5 shows the distribution of aggregate income 

by quintiles for elderly households in 1979 and 1989, 

just as was shown for all households in table 3. The first 

thing to note is that when measured by the Gini index, 

the elderly have a somewhat more unequal distribution 

of income than society as a whole. Their Gini index in 

1989 was .467. But, more importantly, even among the 

elderly there was some evidence of an increase in 

inequality over 1979-89 period. According to the Gini 

index, inequality in the elderly's distribution rose from 

.446 to .467 (the increase, however, was not statistically 

significant).Q Some groups among the elderly were left 

behind also. Who were they? 

Elderly Women Living Alone. There are a number of 

ways to answer this question. One way is to look at the 

data by the elderly's living arrangements and marital 

status. (Unfortunately, the only published data available 

from the Census Bureau at the time of this writing was 

for 1987. Nevertheless, these data are useful.) 

Table 6 shows that real median annual incomes 

between 1979 and 1987 rose by only 12.9 percent for 

elderly female unrelated individuals, or from $6,966 to 

$7,863. For female family householders in which the 

husband was absent, incomes rose but the increase 

was not statistically significant. These are sizable groups 

when dompared to all elderly families and unrelated 

individuals. The increases in their income levels were far 

a('he change in the Gini index for elderly households was on the 

borderline of statistical significance. Other researchers have observed 

growing inequality of income among elderly households during the 

1980's. See Daniel 6. Radner, "Changes in the Incomes of Age 

Groups, 1984-89," Social Security Bulletin, Vol. 54, No. 12, December, 1991, pp. 2-1 8. 

Quintile 1979 1989 

Total. ................ 100.0 100.0 

Lowest. ................ 4.8 4.5 

Second ................ 9.0 8.5 

Third. .................. 14.3 13.5 

Fourth ................. 22.0 21.7 

Highest ................ 50.0 51.9 

Mean .................. $11,144 $23,452 

Gini index .............. .446 .467 

Source: U.S. Bureau of the Census, Current Population Survey. 

below the increases experienced by elderly married 

couple families. 

The relatively poor income growth performance of 

elderly women during the 1980's is also reflected in their 

poverty statistics. As shown in table 7, while their 

poverty rate fell from 30.4 to 23.4, it still remains very 

high. And the economic situation for elderly Black 

women who are poor has been particularly intractable. 

The poverty rate for the 371,000 Black elderly women in 

1989 was 59.8 percent; in 1979 the comparable figure 

was 64.8 percent, not significantly different. The vast 

majority of these women are widowed and live alone. 

Clearly, the income picture for elderly Black women did 

not brighten much in the 1980's. 

Table 6. Real Median lncome of Elderly Famllles, 

by Type and Elderly Unrelated Individuals, 

by Sex: 1979 and 1987 

(In 1987 dollars) -- 

1979 1987 

Type of family and sex Nmber Number 

(thous.) Income (thous.) Income 

FAMILIES 

Total ................... 8,792 $17,362 10,502 $20,813 

Married-couples ........... 7,248 17,330 8,631 20,996 

Male householder, no wife. . 304 18,606 391 24,946 

Female householder, no 

husband.. ............... 1,240 17,235 1,479 18,761 

UNRELATED INDIVIDUALS 

Total ................... 7,656 7,141 9,330 8,205 

Male ..................... 1,666 7,961 2,158 9,584 

Female.. ................. 5,990 6,966 7,173 7,863 

Source: U.S. Bureau of the Census, Current Population Survey. 

The Elderly "Slightly Above" the Poverty Line. Another 

way to find out who was left behind is to look at how far 
Table 7. Poverty Levels and Poverty Rates of 

Elderly Families, by Type and of Elderly 

Unrelated Individuals, by Sex: 

1979 and 1989 

Source: U.S. Bureau of the Census, Current Population Suwey. 

Type of family and sex 

FAMILIES 

Total.. ................. Married couples.. ......... Male householder, no wife. . Female householder, no 

husband .................. 

UNRELATED INDIVIDUALS 

Total ................... Male ..................... Female.. ................. Black.. ................... 

above and below the poverty line the elderly were 

located in 1979 and 1987. (Again, we must rely on data 

for 1987.) To do this the ratio of income to the poverty 

line is computed for elderly householders and unrelated 

individuals.10 

As was indicated earlier, income levels of the elderly 

rose during the 1980's but at different rates for different 

groups of the elderly. The data in table 8 show that the 

proportions of elderly family householders with income 

to poverty line ratios of under 2.0 (or incomes below 

twice the poverty line) declined between 1979 and 

1987. Moreover, the proportion with income to poverty 

ratios of 2.0 or more rose from 62 to 70 percent of all the 

elderly. 

But when we examine the data for elderly unrelated 

individuals, the improvement is less impressive, especially just slightly above the poverty line. The proportion 

of unrelated individuals with income-to-poverty line ratios 

of between 1.26 and 2.00 was not significantly different 

during the 1980's, vis-a-vis the situation for all elderly 

households. Almost 25 percent of all elderly unrelated 

individuals were located in this part of the distribution in 

both 1979 and 1987. This lack of identifiable change 

was particularly obvious among elderly women who 

were unrelated individuals (most of whom live alone), as 

is shown in table 8. These are probably persons with 

Social Security income as their only source of income. 

'"The poverty index, of course, varies by family size, number of 

children, and age of householder for one or two person households. In 

1987, the poverty line for an elderly unrelated individual was $5,447; 

for an elderly two-person household, it was $6,865. 

In contrast, the proportions of elderly unrelated individuals located below 1.25 of the poverty line did decline 

significantly between 1979 and 1987, while the proportion above 2.0 of the poverty line increased. In consequence, the general improvement in the income situation of the elderly was not necessarily shared by groups 

with incomes slightly- above their poverty lines. 

1979 

The Elderly and Wealth 

Number 

(thous.) 

797 

594 

35 

168 

2,243 

421 

1,822 

295 

1989 

Another important dimension of the economic wellbeing of the elderly is their wealth or net worth. While 

the incomes of the elderly are lower than the nonelderly, 

on average, their net worth is much higher simply 

because they have had a longer period of time to 

accumulate assets. This wealth is an important source 

of income and security for the elderly. 

There is a general impression that asset holdings 

appreciated across society during the 1980's. Real 

estate values and stock market prices did rise, while 

investment opportunities abounded given the expansion 

in corporate indebtedness. Of interest, of course, is 

what happened to the asset position of the elderly 

during this period. 

The Census Bureau's Survey of Income and Program 

Participation (SIPP) periodically surveys households about 

their economic well-being, including wealth holdings or 

net worth.11 The Census Bureau defines net worth as 

the value of assets minus liabilities. Specifically, interestearning assets, stocks and mutual fund shares, real 

estate, own businesses or professions, mortgages held 

by sellers, and vehicles, minus debts sedured by any 

asset, credit card or store- bank loans, and other 

unsecured debts equals net worth. The Bureau recently 

released the results of their wealth survey for 1988 and 

it is possible to compare it to the results of the same 

survey in 1984. 

Rate 

(percent) 

9.1 

8.2 

11.7 

13.5 

29.3 

25.3 

30.4 

64.8 

Number 

(thws.) 

703 

495 

28 

180 

2,166 

385 

1,780 

371 

What the Elderly Have. Before we examine what 

happened to the net worth of the elderly in the 1980's it 

would be useful to take an inventory of their assets as 

compared to the rest of the population. The total value 

of the elderly's net worth, of course, is twice as high as 

that of all households-the median net worth for the 

elderly was $73,471 compared to $35,752 for all households. Table 9 displays the distribution of net worth in 

1988 by asset type for all households in the population 

as well as those households with elderly householders. 

Also shown are the median values of these assets. 

Rate 

(percent) 

8.6 

5.6 

7.8 

12.2 

22.0 

17.3 

23.4 

59.8 

"See Household Wealth and Asset Ownership: 1988, Current 

Population Reports, Series P-70, No. 22, U.S. Bureau of the Census, 

USGPO, Washington, D.C., 1990, and Household Wealth and Asset 

Ownership: 1984, Current Population Reports, Series P-70, No. 7, U.S. 

Bureau of the Census, USGPO, Washington, D.C., 1986. 
Table 8. Distribution of Elderly Family Households, Elderly Unrelated Individuals, and Elderly Persons by Their Income-to-Poverty Line Ratio, 1979 and 1987 

(In percent) 

Source: U.S. Bureau of the Census, Current Population Survey. 

Ratio 

Total ............................ 

Less than .50 ...................... .50-1.00 ........................... 1.01-1.25 .......................... 1.26-1.50. ......................... 1.51 -2.00 .......................... 2.00 or + .......................... 

Table 9. Distribution of Net Worth, by Asset Type and Median Value for Elderly Households and Ail 

Households: 1988 

1 /Includes savings accounts, money market deposit accounts, certificates of deposit, and interest earning checking accounts. 

2/lncludes money market funds, government securities, corporate or municipal bonds, and other interest earning assets. 

Source: U.S. Bureau of the Census, Survey of Income and Program Participation. 

Asset type 

Total .................................................. 

Interest earning assets' ................................. Other interest earned2 .................................. Checking accounts ..................................... Stocks, mutual funds.. .................................. Home equity ........................................... Rental property equity. .................................. Other real estate equity ................................. Vehicles. .............................................. Business equity ........................................ U.S. savings bonds ..................................... IRA/Keogh ............................................ Other financial investments. ..... .-, ...................... Unsecured assets ...................................... 

As is well known, the single most important component of the average household's assets is the equity in 

the home43 percent of total net worth. This is also 

true for the elderly, although to a slightly lesser extent. 

lnterest earning assets, however, are a very important 

element of the elderly's net worth vis-a-vis the population in general. Almost 25 percent of the elderly's net 

worth is comprised of passbook savings accounts, 

money market deposit accounts, certificates of deposit, 

and other interest earning assets held at financial 

institutions. Only 14 percent of all households' net worth 

is comprised of these assets. 

Elderly households Elderly persons 

Changes In the Elderly's Net Worth, 1984 to 1988. 

1979 

100.0 

2.4 

12.7 

9.6 

8.2 

15.3 

51.9 

1979 

100.0 

1.6 

7.4 

6.2 

6.9 

15.7 

62.1 

elderly households by monthly income quintiles, attempts 

to answer to that question. (As with the income data, net 

worth data are presented in "real" terms.) l2 

The table shows that despite the economic expanison of the 1980's, wealth did not change. The real 

median net worth of all households in the country was 

about $36,000 in both 1984 and 1988. (The difference 

shown in the table was not statistically significant.) For 

the elderly, however, median net worth rose from $68,600 

to $73,471, a statistically significant increase. In addition, the elderly in the third and highest monthly income 

quintiles also experienced statistically significant increases 

in their net worth. Net worth increased from roughly 

$110,000 to $142,000 in the third quintile and from 

1987 

100.0 

1.9 

10.3 

8.0 

7.2 

13.1 

59.4 

1987 

100.0 

1.4 

5.7 

4.5 

5.7 

12.8 

69.8 

Elderly unrelated 

individuals 

Elderly households 

So did "wealth" increase during the 1980's and what 

'The net worth data for 1984 are expressed in terms of 1988 happened to the net worth? Table which dollars having been adjusted for inflation by the Bureau of Labor 

shows the median net worth for all households and Statistics' Consumer Price Index (CPI-U). 

1979 

100.0 

4.0 

25.3 

17.1 

11.7 

14.3 

27.7 

Elderly female 

unrelated individuals 

All households 

Total (percent) 

100.0 

22.4 

6.8 

0.5 

8.2 

40.4 

6.7 

2.6 

3.1 

3.0 

0.6 

2.8 

3.5 

-0.5 

1987 

100.0 

2.9 

21 .I 

15.0 

11.0 

13.8 

36.1 

1979 

100.0 

3.8 

26.5 

17.8 

11.7 

14.2 

25.8 

Total (percent) 

100.0 

14.1 

4.2 

0.6 

6.5 

43.1 

7.9 

4.3 

5.8 

8.8 

0.6 

4.2 

3.0 

-2.9 

Median value 

$73,471 

14,560 

24,080 

605 

11,017 

55,447 

45,075 

17,417 

3,834 

10,662 

1,430 

12,155 

26,891 

(NA) 

1 987 

100.0 

3.0 

22.4 

15.4 

11.7 

13.7 

33.8 

Median value 

$35,752 

3,494 

10,911 

487 

4,510 

43,070 

37,439 

18,064 

4,416 

10,446 

546 

9,016 

16,204 

(NA) 
Table 10. Median Value of Household Net Worth, by Monthly lncome Quintile and Median Value of Household Net Worth After Excluding Home Equity, by Monthly lncome Quintile for Elderly Households and All Households: 1984 and 1988 

(In 1988 dollars) 

Source: U.S. Bureau of the Census, Suwey of lncome and Program Participation. 

Year 

NET WORTH 

Elderly Households 

1988 ...................................... 1984 ...................................... 

All Households 

1988. ..................................... 1984 ...................................... 

NET WORTH (excluding home equity) 

Elderly Households 

1988. ..................................... 1984. ..................................... 

All Households 

1988. ..................................... 1984 ...................................... 

$274,000 to $343,000 in the highest. Only about 17 Table Md,an Value of Net Worm and Median percent of all elderly households fall in the third quintile Value of Net Worth After Excluding and only 8 percent in the highest. So, it was only in Home Equity for Edlerly Households, by these relatively small groups of the elderly population Age and Type of Household: 

that gains in net worth were registered. 1984 and 1988 

Also shown in table 10 is-the median net worth (In 1988 dollars) 

position of the elderly and the population as a whole, 

excluding the equity in the home. Obviously, these 

medians are much lower reflecting the importance of 

the home in an elderly household's net worth. What is of 

interest for the elderly is that, although the medians had 

increased between 1984 and 1988, none of the changes 

were statistically significant. 

Total 

$73,471 

68,600 

$35,752 

37,012 

$23,856 

21,557 

$9,840 

8,800 

Other Aspects of the Elderly's Net Worth 

As indicated above, while the net worth position of 

the elderly did increase "on average" during the heart of 

the 19809s, it was not a uniform increase across all 

income groups. In general, the improvement was found 

among those in the middle and upper monthly income 

brackets of society. But as was also pointed out earlier, 

the elderly are more commonly found in the lower 

portion of the income distribution. 

Table 11 shows two other dimensions of the increase 

in the real net worth of the elderly between 1984 and 

1988. First, the increase was concentrated among 

those households in which the householder was 70 to 

74 years of age. Real net worth rose from $68,500 to 

$82,111. Changes for the other age groups were not 

statistically significant. The same pattern was observed 

when home equity was excluded from net worth: only 

Monthly income quintile 

Source: U.S. Bureau of the Census, Suwey of lncome and 

Program Participation. 

Highest 

$343,015 

273,982 

111,770 

98,411 

208,789 

185,844 

40,688 

35,744 

among those elderly households in which the householder was 70 to 74 did net worth increase. (Table 11 

also shows how net worth drops after age 74-from 

roughly $82,000 to $61,000. Those householders age 

75 and over are a growing proportion of the elderly.) 

A second dimension of the table, net worth by type of 

household, also illustrates the rather narrowly based 

increase in net worth for the elderly. Only among elderly 

married couple households did real median net worth 

increase. It rose from $98,128 in 1984 to $124,419 in 

Fourth 

$201,562 

194,876 

46,235 

49,947 

100,480 

97,677 

14,376 

12,406 

Bottom 

$25,220 

25,088 

4,324 

5,130 

3,536 

3,193 

1,152 

1,112 

Age and type of household 

AGE 

65 and over. .............. 65 to 69.. ................ 70to74 .................. 75 and over.. ............. 

TYPE OF HOUSEHOLD 

Married couple ............ Male householder ......... Femalehouseholder ....... 

Net worth 

(excluding 

home equity) 

Second 

$76,050 

73,814 

19,694 

21,248 

28,168 

26,200 

5,454 

5,320 

1984 

$21,557 

24,757 

21,321 

19,469 

39,270 

14,448 

11,622 

Net worth 

Third 

$1 41,811 

109,998 

28,044 

29,459 

57,026 

51,827 

8,418 

7,938 

1988 

$23,856 

27,482 

28,172 

18,819 

45,890 

15,914 

10,693 

1984 

$68,600 

75,992 

68,514 

62,865 

98,128 

47,504 

48,386 

1988 

$73,471 

83,478 

82,111 

61,491 

124,419 

48,883 

47,233 
1988. These types of households, of course, make up 

less than half of all elderly households and are comprised of 65 to 74 year olds. For single male and female 

householders, on the other hand, little change was 

observed in their net worth positions. This pattern was 

also evident after the exclusion of home equity. (The 

table also demonstrates how much lower wealth holdings are for these single householders, especially women, 

as compared to married couple households.) 

Conclusions 

In general, the 1980's were relatively good years for 

the eldedy, at least in terms of income growth and 

increases in net worth or wealth. Broad measures of 

income and net worth for elderly households increased 

more than they did for all households. However, as was 

true concerning the entire population, the gains in 

income and wealth associated with the economic expansion of the 1980's were not shared by all elderly 

households. 

Growth in real income was weakest for elderly single 

householders, especially women, and those elderly 

households slightly above the poverty line. The situation 

was particularly acute for elderly Black women living 

alone-a group whose poverty rate changed very little 

in the decade. Elderly married couple households, on 

the other hand, appeared to have fared best during the 

decade. Income inequality became somewhat more 

acute for the elderly as well. 

Although data on net worth, or wealth, are not as 

readily available as income data, survey data for 1984 

and 1988 provide two points in time by which we might 

obtain a glimpse of what was happening in the decade. 

The data showed that for all households there was very 

little change, and only among the highest income households did median net worth increase. In contrast, among 

elderly households net worth increased and was up 

particularly for middle income and high income elderly 

households. The increase was confined generally to 

married couple households and those with householders aged 70 to 74. 

As the elderly left the 1980's and entered the 1 99OYs, 

therefore, it appears that, on average, they were better 

off, at least with respect to income and wealth, then 

when they entered the 1980's. But the 1990's began on 

less than a sanguine note-recession, indebtedness, 

and unexpected costs. For those among the elderly who 

did not share in the benefits of the economic expansion 

of the 1980's, the years immediately ahead may produce considerable economic stress. Indeed, these groups 

would appear to be the most economically vulnerable. 

Source and Accuracy of Estimates for Studies in the 

Distribution of lncome 

SOURCE OF DATA 

Most estimates in this report come from data obtained 

in March of 1970 through 1990 in the Current Population 

Survey (CPS). The Bureau of the Census conducts this 

survey every month, although this report uses only 

March data for its estimates. The March survey uses 

two sets of questions, the basic CPS and the supplement. The basic CPS collects labor force data for the 

civilian noninstitutional population. In March, supplemental data is collected on money income received during 

the previous calendar year. 

Some estimates in this report come from data obtained 

in the 1984, 1986, and 1987 Survey of lncome and 

Program Participation (SIPP) topical module, Assets 

and Liabilities. The 1984 data were collected during the 

fourth interview of the 1984 panel. The 1988 data were 

collected during the seventh interview of the 1986 panel 

and the fourth interview of the 1987 panel. 

Another source of data for this report is the Luxembourg lncome Study (LIS). The LIS is a multinational 

collection of microdata sets from various countries. All 

of the data were collected in household surveys or 

surveys of administrative systems by institutions in 

countries participating in LIS. 

I Survey Estimates. The estimation procedures used 

for CPS and SIPP data inflate weighted sample results 

to independent estimates of the civilian noninstitutional 

population of the United States by age, sex, race, and 

Hispanichon-Hispanic categories. These independent 

estimates are based on statistics from decennial censuses; statistics on births, deaths, immigration, and 

emigration; and statistics on the size of the Armed 

Forces. The estimation procedure for 1980 through 

1990 data used independent estimates based on the 

1980 decennial census; 1970 through 1979 data used 

independent estimates based on the 1970 decennial 

census. This change in independent estimates had 

actual changes in the population. These differences 

could be disproportionately greater for certain population subgroups than for the total population. 

ACCURACY OF ESTIMATES 

Since the CPS and SIPP estimates are based on 

samples, they may differ somewhat from the figures 

from a complete census using the same questionnaires, 

instructions, and enumerators. There are two types of 

errors possible in an estimate based on a sample 

survey: sampling and nonsampling. The standard errors 

provided in most Current Population Reports primarily 

indicate the magnitude of the sampling errors. They also 

partially measure the effect of some nonsampling errors 

in response and enumeration but do not measure any 

systematic biases in the data. Bias is the difference, 

averaged over all possible samples, between the estimate and the desired value. The accuracy of a survey 

result depends on the net effect of sampling and 

nonsampling errors. Particular care should be exercised 

in the interpretation of figures based on a relatively 

small number of cases or on small differences between 

estimates. 

Nonsampiing Variability. As in any survey work, the 

results are subject to errors of response and nonreporting in addition to sampling variability. Nonsampling 

errors can be attributed to many sources, e.g., inability 

to obtain information about all cases in the sample, 

definitional difficulties, differences in the interpretation 

of questions, inability or unwillingness on the part of the 

respondents to provide correct information, inability to 

recall information, errors made in collection such as in 

recording or coding data, errors made in processing 

data, errors made in estimating values for missing data, 

and failure to represent all units with the sample (undercoverage). 

relatively little impact on summary measures, such as 

medians and percent distributions, but did have a sig- Comparability of Data. Data obtained from sample 

nificant impact on levels. For example, use of the 1980 surveys and other sources are not entirely comparable. 

based population controls resulted in about a 2 percent This is due largely to differences in interviewer training 

increase in the civilian noninstitutional population and in and experience and in differing survey procedures. This 

the number of families and households. Thus, estimates is an additional component of error that is not reflected 

of levels for 1980 and later will differ from those for in the standard errors. Therefore, caution should be 

earlier years by more than what could be attributed to used in comparing results among these sources. 
Sampling Variability. Standard errors are primarily Some statements may contain estimates followed 

measures of sampling variability, that is, of the varia- immediately by another number in parenthesis. For 

tions that occur by chance because a sample rather those statements one needs only to add to and subtract 

than the entire ~~p~l~~~~n is suweyed- Standard errors from the estimate that number to calculate upper and 

are not given in rep0rt because of the wide range Of lower bounds of the QO-percent confidence interval. For topics included and the wide variety of data sources. 

Standard errors may be found in the publications that example, if a statement contains the phrase "grew by 

are noted at the end of each section or by contacting 1.7 ('1.0) percent," the QO-perCent confidence inte~al 

John Coder, Gordon Green, Paul Ryscavage, or Edward for the estimate, 1.7 percent, would be from 0.7 percent 

Welniak. to 2.7 percent.