UI BENEFITS STUDY 

Recent Changes in the Characteristics 

of Unemployed Workers 

August 2009 

Prepared by:

Marios Michaelides 

IMPAQ International, LLC 

Peter Mueser 

University of Missouri and IMPAQ International, LLC 

Submitted to: 

 U.S. Department of Labor 

Employment & Training Administration 

200 Constitution Avenue, NW 

 Washington, DC 20210

Submitted by: 

IMPAQ International 

10420 Little Patuxent Parkway 

Suite 300 

Columbia, MD 21044

 This project has been funded, either wholly or in part, with Federal funds from the Department of Labor, 

Employment and Training Administration under Contract Number DOLJ041A000031. The contents of this 

publication do not necessarily reflect the views or policies of the Department of Labor, nor does mention of trade 

names, commercial products, or organizations imply endorsement of same by the U.S. Government. 
ABSTRACT 

This paper examines how gender, racial, and ethnic differentials in unemployment and 

Unemployment Insurance (UI) receipt have changed over the past 50 years. Our primary 

analysis focuses on the degree to which such differences, in the past 20 years, are explained by 

differences in the industrial and occupational structure of employment. Using Current 

Population Survey (CPS) data, we find that: (1) the female unemployment rate has converged to 

the male unemployment rate since the 1980s, (2) the nonwhite and Hispanic unemployment rates 

are converging to the unemployment rate of the rest of the population, but racial and ethnic 

unemployment rate gaps have not yet been eliminated, and (3) the U.S. labor market has 

experienced significant shifts in its industrial and occupational distribution. 

To examine how gender, racial, and ethnic differences in the unemployment experience are 

affected by differences in the primary industries and occupations in which each group is 

employed, we use the 1992-2007 CPS March supplement data. Our analysis shows that, when 

controlling for industry and occupation differences: (1) women have higher unemployment rates 

than men but are equally likely to receive UI benefits, (2) the racial unemployment rate gap is 

smaller than in earlier years but remains substantial, yet nonwhites are only marginally more 

likely to receive UI benefits than whites, and (3) there is a dramatic convergence in the 

unemployment rates between Hispanics and non-Hispanics, although Hispanics remain less 

likely to receive UI benefits. 
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of Unemployed Workers 

1. Introduction 

In the last 50 years, the gender, racial, and ethnic composition of the U.S. labor force has 

changed dramatically. The labor force participation of women has increased substantially, and in 

the past 20 years, women have become nearly as likely as men to participate in the labor market. 

There has also been a steady increase in the Hispanics’ share of the U.S. labor force. In the face 

of these dramatic changes in the demographic makeup of the labor force, a number of papers 

have addressed the experiences of specific demographic groups. For example, Gorgens (2002) 

examines the labor market experience of female workers; Holzer et al. (2005) does the same for 

black workers. 

There were also important changes in the characteristics of the unemployed and of 

Unemployment Insurance (UI) recipients during this period. Numerous studies focus on the 

structure of U.S. unemployment and the UI system in recent years (Nichols and Needels, 2006), 

including several that provide historical summaries of the UI program (Kletzer and Rosen, 2006; 

Burtless, 2007). 

Yet another important change is the shift in the industrial and occupational distribution of the 

labor market. The U.S. economy has transitioned from a system in which a large share of 

employment was in manufacturing to one in which services play a dominant role. There has also 

been a change in the occupational distribution of the employed population, with the employment 

share of white collar occupations increasing steadily over time. Although changes in the 

industrial and occupational structure of employment are well known and widely documented 

(Groshen and Potter, 2003), essentially no work examines how these interact with the shifting 

demography of the labor force. Such an examination is necessary to understand the new 

employment environment faced by the U.S. labor force. 

This paper focuses on determining the role of the industrial and occupational changes in the U.S. 

labor market in explaining patterns of unemployment across core demographic groups in the past 

20 years. In particular, we examine how the unemployment experience of gender, racial, and 

ethnicity groups has changed over time, and whether such changes are connected to changes in 

the industrial and occupational distribution of the labor force. We also examine the role that 
Recent Changes in the Characteristics Page 2 August 2009 

of Unemployed Workers 

differences in labor market experience between these groups play in explaining overall 

unemployment patterns. 

The paper is organized as follows. In section 2, we provide a brief overview of historical shifts 

in the U.S. labor force and changes in the unemployment rate. In section 3, we discuss the 

growing role of women in the labor market, focusing on differences in the unemployment 

experience between women and males, and on the degree to which these differences are 

connected to the changed industrial and occupational structure of the labor market. In sections 4 

and 5, we focus on the same issues for nonwhite and Hispanic labor force participants, 

respectively. We end with a brief summary and conclusion. 

2. Historic Overview 

A summary of the demographic changes that have occurred in the U.S. labor market in the past 

50 years is presented in the upper portion of Table 1. One of the most important has been the 

dramatic increase in the number of women in the labor force. Women accounted for just 32 

percent of the labor force in 1955, compared to 46 percent in 2005, an increase of nearly 50 

percent. As Table 2 shows, the female experience with unemployment has also changed. Before 

the 1980s, women in the labor force were much more likely to experience unemployment than 

men, but convergence has occurred in the last two decades. 

The proportion of nonwhites in the labor force has gradually increased over the last several 

decades, and, although the unemployment differential has declined, the unemployment rates for 

nonwhites remain appreciably above those for the general population. Perhaps the most striking 

change has been the increase in the Hispanic labor force, which has grown from less than 5 

percent of the total in 1975 to over 13 percent in 2005. Table 2 shows that, although Hispanics 

continue to have higher unemployment rates than non-Hispanics, the gap has gradually declined 

over time. 
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Table 1: Historic Changes in the Composition of the Labor Force 

 Proportion of the Labor Force 

1955 1965 1975 1985 1995 2005 

Female 31.6 35.2 40.0 44.2 46.1 46.3 

Nonwhite 10.7 11.2 11.7 13.5 15.4 18.1 

Hispanic -- -- 4.4 6.7 9.3 13.3 

Industry 

 Manufacturing 

 Trade 

 Service 

 Other 

 Construction 

28.7* 

17.7* 

-- 

53.6* 

-- 

26.0* 

18.6* 

-- 

55.4* 

-- 

23.6 

21.6 

29.4 

18.7 

6.6 

20.1 

21.4 

32.0 

19.5 

6.9 

16.9 

21.5 

36.2 

19.1 

6.4 

11.6 

23.5 

44.8 

13.0 

7.0

Occupation 

 White collar 

 Blue collar 

-- 

-- 

-- 

-- 

-- 

-- 

53.7 

46.3 

57.2 

42.8 

61.5 

38.5

Source: Each number represents the average of Current Population Survey monthly values for the 

referenced period. *Data from the U.S. Department of Commerce, Bureau of Economic Analysis: 

http://www.bea.gov/bea/dn/nipaweb/index.asp. 

Table 1 also provides information on the distribution of the labor force across major industries 

and occupations. The most dramatic change in the industrial structure is the shift from 

manufacturing to services. Between 1955 and 2005, the relative share of the labor force in 

manufacturing declined from 28.7 to 11.6 percent, and the service category increased by a 

similar amount. The shift toward white collar jobs has also been dramatic. 

These shifts may interact with changes in the labor force demography. In the next sections, we 

discuss historic changes in the composition of the labor force across gender, race, and ethnicity 

groups. We also consider how the unemployment rates of women, nonwhites, and Hispanics 

relate to that of the rest of the population and the extent to which any gaps are explained by 

differences in the employment distributions of these groups across industries and occupations. In 

addition, we examine how the composition of the Unemployment Insurance (UI) recipient 

population corresponds to these changes. 
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of Unemployed Workers 

Table 2: Unemployment Rate, by Demographic Category and Job Type 

 1953-57 1963-67 1973-77 1983-87 1993-97 2003-07 

Population 4.2 4.6 6.7 7.5 5.8 5.2 

Male 

Female 

4.0 

4.8 

4.0 

5.7 

6.1 

7.8 

7.5 

7.5 

5.8 

5.7 

5.3 

5.1 

White 

Nonwhite 

4.1 

8.6 

4.1 

8.6 

6.1 

11.8 

6.5 

14.1 

5.0 

10.0 

4.6 

8.2 

Non-Hispanic 

Hispanic 

-- 

-- 

-- 

-- 

6.6 

9.9 

7.3 

10.9 

5.4 

9.3 

5.0 

6.4 

Industry 

 Manufacturing 

 Trade 

 Services 

 Construction 

 Other 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

7.2 

7.5 

5.1 

11.1 

4.0 

8.2 

7.6 

5.0 

11.6 

4.9 

5.5 

6.6 

4.3 

9.2 

3.7 

6.4 

6.4 

4.3 

7.4 

3.9 

Occupation 

 White collar 

 Blue collar 

-- 

-- 

-- 

-- 

-- 

-- 

3.8 

9.6 

3.4 

7.5 

3.5 

6.7 

Source: Each number represents the average of Current Population Survey monthly values for the 

referenced period. 

3. Women in the Labor Market 

3.1. Historic Changes 

Figure 1 illustrates how the proportions of women in the labor force and the unemployed 

population have changed over time. In order to remove variation during the year, these are 

measured as 12-month moving averages. From the early 1950s to the early 1970s, the proportion 

of women in the labor force increased from just over 30 percent to nearly 40 percent. This 

upward trend continued through the 1970s and the early 1980s before reaching 45 percent in 

1987. Until the 1980s, women were overrepresented in the unemployed population. In 1954, for 

example, women made up 31 percent of the labor force and 35 percent of the unemployed. By 

1970, the unemployment rate gap had increased and women were 37 percent of the labor force 

and 50 percent of the unemployed population. Between 1965 and 1980, female labor force 

participation continued to rise while the female proportion of the unemployed did not exhibit any 

upward trends. By the early 1980s, the two proportions were about equal.
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Figure 1: Labor Force and Unemployment Proportions, Women 

Source: Current Population Survey monthly values, 1948-2007. Values represent 12-month centered 

moving averages. 

The convergence in the unemployment experience of men and women is shown in Figure 2. 

Before 1980, the male unemployment rate was consistently lower than the female one at all 

points during the economic cycle. After 1980, the male and female unemployment rates had 

largely converged, except during the recessionary periods of the early 1980s, the early 1990s, 

and the early 2000s, when male rates were higher, reflecting greater sensitivity to the business 

cycle. The increase in the female unemployment rate between the economic peak in 1980 and 

the 1984 trough was 3 percentage points, compared to the male rate over the same period, which 

increased by more than 5 percentage points. Similar differentials are apparent for 1989-1992 

(1.5 points versus 2.4 points) and 2000-2004 (1.1 points versus 2.1 points). 

It is possible that the convergence of the female and male unemployment rates, as well as the 

relatively low volatility of the female unemployment rate during recessions in the last 20 years, 

is due to differences in the industrial or occupational distributions of the employed population in 

the two groups. It may also result, in part, from the decline in the number of women who were 

new entrants to the labor force in the 1980s and thereafter. We explore these possibilities in the 

next section. 
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of Unemployed Workers 

Naturally, these patterns reflect both differences in the choices made by men and women, as well 

as the direct and indirect effects of employer and customer discriminatory preferences. Although 

we are not able to identify the underlying reasons for differences, these analyses allow us to 

investigate the extent to which differences trace to the types of jobs – occupation and industry –

held by men and women, or exist within a given job type. 

Figure 2: Unemployment Rate, by Gender, 1948-2007 

Source: Current Population Survey monthly values, 1948-2007. Values represent 12-month centered 

moving averages. 

3.2. Female Unemployment Experience between 1992 and 2007 

We use the Current Population Survey (CPS) March supplement for 1992-2007 to generate 

estimates of the unemployment rates by gender. The CPS March supplement includes not only 

the basic CPS monthly data reports (demographic characteristics, industry and occupation 

affiliation, labor force, and employment status), but also income information of the respondents 

for the year prior to the survey. The latter information can be used to determine which 

individuals received UI benefits in the year prior to the survey and the benefit amounts received. 

Figure 3 depicts male and female unemployment rates based on the March CPS data. Here we 

see that the male unemployment rate is relatively higher than in the earlier graph, and, as a result, 

it is above the female rate at all times. This difference is due to the overrepresentation in the 

March data of men in industries that experience slowdowns during winter, most notably 
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of Unemployed Workers 

construction.1 Despite this difference, it is important to note that the patterns of unemployment 

by gender we see in the March data (Figure 3) almost exactly reproduce those in the 12-month 

moving average (Figure 2). 

Figure 3: Unemployment Rate, by Gender, 1992-2007

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

It is interesting to examine whether the gender gap in the unemployment rate reflects in part 

gender differences in labor market experience. For example, if women are more likely than men 

to be new entrants in the labor market (i.e., inexperienced), the overall female-to-male 

unemployment rate gap may be even larger. The importance of this factor is easily explored by 

comparing the overall unemployment rate with that of experienced workers (i.e., those with a job 

prior to becoming unemployed). Whereas the overall unemployment rate is the ratio of the total 

number of unemployed people to the total labor force, the experienced unemployment rate is the 

ratio of the experienced unemployed to the experienced labor force (i.e., the sum of the 

employed plus the experienced unemployed).2

                                                           

1

 In Appendix A, we provide more details of the cyclical variation in the gender unemployment gap. 

2

 Where the distinction between the overall unemployment rate (UR) and the experienced unemployment rate 

(EUR) is either unimportant or obvious, we will refer to them as “unemployment.” Note that whenever 

unemployment is identified by industry or occupation, only the EUR is has meaning. 
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Figure 4: Experienced Unemployment Rate, by Gender, 1992-1997 

               Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

Figure 5: Male – Female Unemployment Rate Gaps

 Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

Figure 4 presents the experienced unemployment rate for men and women. Although the 

experienced unemployment rate is lower than the unemployment rate for both groups by about 

0.6 percentage points, the gender patterns over time are very similar. Figure 5 shows that the 

male-female unemployment rate gap and male-female experienced unemployment rate gap are 

essentially the same over time, confirming the similarity. The conclusion is therefore clear, that 
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of Unemployed Workers 

is, gender differences in labor market experience do not account for the unemployment rate gap 

between men and women at any point between 1992 and 2007.

3.3. Decomposition of the Gender Experienced Unemployment Rate Gap 

Two interesting patterns emerged in the previous section when comparing the female and male 

unemployment experience in the past 20 years. First, the female unemployment rate is lower 

than the male rate, especially during recessions. Second, the female rate is less sensitive to 

changes in the business cycle. These patterns are not explained by gender differences in the 

labor market experience, since the female experienced unemployment rate is also generally 

lower and less volatile than the comparable male rate. In this section, we consider an alternative 

explanation for these patterns, namely, that women may be less likely than men to be employed 

in sectors or occupations with higher unemployment rates and higher sensitivity to changes in the 

business cycle. If so, the female experienced unemployment rate would be lower than that of 

males, even if women do not have lower unemployment rates within an industry or occupation. 

Gender Differences in Industry and Occupation 

Using the industry classifications in the March CPS, we define 17 industry categories. Table 3 

presents the unemployment rate by gender for the period between 1992 and 2007 for each 

industry category, starting with the industry with the highest overall experienced unemployment 

rate (construction) and going down to the sector with the lowest average rate (health care 

services) during that period. 

The same table reports the employment distribution for men and women in the period between 

1992 and 2007 by each industry category. Over this period, the female employment distribution 

across industries was quite different from the male distribution. For example, only 1 percent of 

women were employed in construction relative to 12 percent of men. Women were also less 

likely to be employed in manufacturing (both durables and non-durables), in the ‘other’ 

industries category (which includes agriculture, mining, fishing, and forestry), and in 

transportation, warehouse, and utilities. 
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Table 3: Employment Distribution by Gender, across Industries: 

Averages of March CPS Supplement Values, 1992-2007 

Experienced 

Unemployment Rate Employment Distribution 

Males Women Males Women 

Construction 11.3 % 7.3 % 12 % 1 %

Food Services 7.1 % 7.8 % 12 % 15 %

Manufacturing – Non-Durables 5.1 % 8.0 % 6 % 4 %

Personal Services 7.8 % 6.9 % 2 % 3 %

Business Support Services 6.9 % 7.4 % 8 % 7 %

Repair and Maintenance 5.9 % 6.1 % 2 % 3 %

Manufacturing – Durables 5.0 % 5.8 % 11 % 5 %

Other Industries 5.7 % 5.2 % 6 % 2 %

Wholesale Trade 3.8 % 5.6 % 5 % 2 %

Information and Communications 4.2 % 5.1 % 3 % 2 %

Transportation, Warehouse, and Utilities 4.2 % 4.7 % 8 % 3 %

Retail Trade 3.6 % 3.8 % 5 % 6 %

Entertainment and Recreation Services 3.7 % 3.5 % 2 % 6 %

Education and Social Services 3.0 % 3.1 % 5 % 12 %

Finance, Insurance, and Real Estate 3.0 % 2.9 % 4 % 5 %

Public Administration 2.9 % 2.8 % 3 % 4 %

Health Care Services 2.3 % 2.6 % 6 % 19 %

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. Industry categories are mutually 

exclusive. 

In contrast, a greater proportion of women were employed in health care services (19 percent 

versus 6 percent) and in education and social services (12 percent versus 5 percent). Women 

were also more likely to be employed in the finance, real estate, and insurance sector and in the 

entertainment and recreation sector. These differences display a clear pattern - women were, 

indeed, less likely to be employed in high unemployment sectors relative to males. 

Figure 6 presents the unemployment rates over time of two sectors in which women are 

significantly overrepresented relative to men (health care services, and education and social 

services), and the unemployment rates of two sectors in which women are significantly 
Recent Changes in the Characteristics Page 11 August 2009 

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underrepresented relative to men (construction, durables manufacturing). In addition to 

substantially higher rates of unemployment, construction and manufacturing display much 

greater volatility than the health care services and education/social services sectors over the 

business cycle. For example, the decline in the unemployment rate between 1992 and 2000 was 

11 percentage points for construction and less than 2 percentage points for health care services; 

comparable figures for 2000 and 2003 were 2.5 percentage points for construction and 1 

percentage point for health care services. 

Figure 6: Experienced Unemployment Rate, Select Industries, 1992-2007 

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007.

Using the occupation classification in the March CPS, we next define five blue collar and seven 

white collar occupation groups. Table 4 presents the average unemployment rate by occupation 

group between 1992 and 2007 and the gender employment distributions across occupations 

during the same period. 
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Table 4: Employment Distribution by Gender, across Occupations: 

Averages of March CPS Supplement Values, 1992-2007 

Experienced 

Unemployment Rate Employment Distribution 

Males Women Males Women

Blue Collar Occupations 7.9 % 7.7 % 53 % 34 %

Construction Workers 11.6 % 15.0 % 10 % 0 %

Farming, Fishing, and Forestry Workers 9.8 % 11.9 % 3 % 1 %

Other Blue Collar Occupations 7.3 % 8.9 % 22 % 8 %

Other Service-Related Occupations 7.3 % 6.8 % 12 % 15 %

Mechanics and Repairers 4.3 % 4.3 % 6 % 0 %

White Collar Occupations 3.1 % 3.7 % 47 % 66 %

Sales Occupations 4.1 % 6.6 % 11 % 13 %

Office and Administrative Support 5.1 % 4.2 % 6 % 25 %

Engineers and Scientists 2.7 % 3.2 % 7 % 3 %

Professional Specialty Occupations 2.7 % 2.9 % 4 % 5 %

CEOs and Managers 2.3 % 2.6 % 13 % 11 %

Health Care Occupations 1.7 % 2.7 % 2 % 11 %

Teachers and Social Workers 1.6 % 1.5 % 3 % 8 %

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. Occupation categories are mutually 

exclusive. 

Employment distributions across occupations for women and men were even more discrepant 

than the industrial distributions. Sixty-six percent of women were employed in white collar 

occupations compared to 47 percent of men. Women were most dramatically overrepresented in 

health care occupations (11 percent versus 2 percent), teachers and social workers (8 percent 

versus 3 percent), and office and administrative support occupations (25 percent versus 6 

percent). Men, in contrast, were more likely to be employed in certain blue collar occupation 

categories. Ten percent of men were construction workers relative to less than 0.5 percent of 

women, while 22 percent of men were employed in the ‘other blue collar occupations’ category 

(which includes laborers, extraction workers, material moving workers, and other low-skill blue 

collar occupations) compared to 8 percent of women. 
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We also see in Table 4 that women were more likely to be employed in occupational groups with 

lower unemployment rates than men. Women were underrepresented relative to men in the two 

occupation categories with the highest unemployment rates (construction workers and farming, 

fishing, and forestry workers), while they were greatly overrepresented relative to men in the two 

occupation groups with the lowest unemployment rates (teachers and social workers, health care 

occupations). Therefore, it is possible that the lower female unemployment rate between 1992 

and 2007 is a reflection of the gender differences in the employment distribution across 

occupations during that period. 

Figure 7: Experienced Unemployment Rate, Blue Collar vs. White Collar Occupations 

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007.

Figure 7 compares the unemployment rate for blue collar and white collar occupations between 

1992 and 2007. This figure shows that blue collar occupations had higher unemployment rates 

during that period and were also more volatile over the business cycle than white collar 

occupations. Since women were much more likely to be employed in white collar occupations 

relative to men, their lower overall unemployment rates, especially during the early 1990s and 

early 2000s recessions, may be partly traced to these occupational differences. 
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of Unemployed Workers 

Decomposing the Experienced Unemployment Rate Gap 

The discussion in the previous section shows that women were less likely to be employed in 

high-unemployment and cyclically sensitive industries and occupations than men during the 

period between 1992 and 2007. To determine whether gender differences in the employment 

distribution in fact affect the gender experienced unemployment insurance gap, we need to take 

one more step – compare the female and male unemployment rates holding employment 

distributions constant. In this section, we describe analytical exercises that enable us to 

determine to what degree differences in the gender employment composition by industry, by 

occupation, and by both industry and occupation, affect gender differences in unemployment. 

Our approach is to adjust the experienced unemployment rate of each gender to reflect the 

employment distribution of across industries and occupations of the other gender. To do so, we 

classify jobs into 184 industry-by-occupation categories (17 industries by 12 occupations). 

Using these classifications, we first construct a measure that shows what the female rate would 

be if the female distribution across jobs corresponded exactly to that of men.3

 The difference 

between this adjusted female unemployment rate ( adj EURF ) and the actual male unemployment 

rate ( EURM ) is one measure of the experienced unemployment rate gender gap after adjusting 

for gender differences in the employment distributions. We also adjust the male rate to the 

female job distribution. Comparison of the adjusted male unemployment rate ( adj EURM ) to the 

actual female unemployment rate ( EURF ) provides an alternative estimate of the gender gap 

after adjusting for differences in the employment distribution. Insofar as there is a positive 

association between male and female unemployment rates across industry-occupation cells, these 

estimates will be similar.4

                                                           

3

 The female unemployment rate can be rewritten as the weighted sum of the female unemployment rate for each 

industry-occupation cell weighted by the proportion of female labor force participants in each cell. Our 

adjusted measure replaces the female proportion for each cell with the male proportion. See Appendix B for a 

formal discussion. 

4 The method requires modification for those industry-occupation cells that only contain men (for adj EURF ) or only 

contain women ( adj EURM ). In such cases, we use the unemployment rate that is available. For example, if no 

women are in the relevant cell, we use the male unemployment rate where we would normally use the female 

rate. Given the relatively small weight that applies to such cells, the exact method used in dealing with them 

does not affect the results. 
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of Unemployed Workers 

Figure 8 presents the output of this exercise. The gap between adj EURF (grey dashed line) and 

EURM (black solid line) was around 1 percent between 2000 and 2007, even during the trough 

of the 2002 recession. This indicates that if women had the same industry-occupation 

employment distribution as did men, the female unemployment rate would be substantially 

higher than the male rate between 1992 and 2007. Therefore, the lower observed female 

experienced unemployment rate actually hides higher within-cell unemployment rates for 

women.5

Figure 8: Experienced Unemployment Rate, by Gender, 

Actual and Adjusted for Industry-Occupation

Source: Authors’ tabulation of the CPS March Supplements, 1992-2007. 

This result also holds if we compare adj EURM (black dashed line) with EURF (grey solid line). 

Figure 8 shows adj EURM is everywhere below EURF , showing that women have higher average 

unemployment rates than men across industry-occupation categories when weighted according to 

the female distribution. Our conclusion from the two measures is therefore the same: female 

                                                           

5

 We observe that, in 1997 and 1998, the female adjusted unemployment appears to depart from the overall pattern, 

displaying a much larger adjustment increment than at other points. In part, this reflects the instability of our 

adjustment procedures. Several cells with very small numbers of women relative to men have high levels of 

female unemployment during this period. 
Recent Changes in the Characteristics Page 16 August 2009 

of Unemployed Workers 

unemployment is reduced by the kinds of job women hold. If women held the same jobs as men, 

the female experienced unemployment rate would be higher than the male rate. 

The lower volatility of the female experienced unemployment rate relative to the male 

unemployment rate can also be explained in part by the fact that women were less likely to be 

employed in cyclical sectors or occupations. Figure 8 shows, for example, that between 2000 

and 2003, adj EURF increased by 1.5 percentage points, EURF by 1.2 percentage points, and 

EURM by 2 percentage points. These numbers indicate that about a third of the volatility 

difference between EURF and EURM is explained by gender differences in the employment 

distribution. When we compare the volatility of adj EURM and of EURF , this also implies that a 

significant portion of the difference in cyclical sensitivity between male and female experienced 

unemployment rates is explained by gender differences in the industry-occupation employment 

distribution. 

3.4. Women and the Unemployment Insurance Program 

Workers who experience unemployment and fulfill certain criteria may apply for and receive UI 

benefits.6 In this section, we examine the degree to which female workers use the UI program 

and whether female use of the UI program differs from that of male use of the program. We also 

examine how industry and occupation affiliations affect differences in participation in the UI 

program between women and men. 

The most suitable publicly available data for characterizing the general trends of UI receipt over 

time is the CPS March Supplement. In this data, respondents are asked to list their income 

sources for the year prior to the survey, including whether they received UI benefits and the 

amount received. We use the information on UI receipt to calculate the number of UI recipients 

by gender in the prior year. We then construct the receipt rate, by dividing the total number of 

                                                           

6

 UI eligibility criteria vary by state. Federal legislation specifies that the program must be designed to 

serve those who have lost their jobs through no fault of their own and are available for work. In all 

states, recipients must meet certain minimum requirements in terms of their prior employment and 

earnings, and satisfy rules regarding job separation. The duration of UI benefits for a given individual 

varies by state, but the maximum duration is, in most cases, 26 weeks. 
Recent Changes in the Characteristics Page 17 August 2009 

of Unemployed Workers 

individuals receiving UI at some point in the prior year by an estimate of the labor force.7 In the 

analysis that follows, we use the UI receipt rate constructed using the CPS March Supplement to 

describe changes in UI receipt over time, by gender. 

Another data source that can be used to conduct this type of analysis is the Benefit Accuracy 

Measures (BAM) data, a U.S. Department of Labor, Employment and Training Administration 

(DOL/ETA) dataset. The advantage of the BAM data, which is not publicly available, is that it 

reports the demographic and employment characteristics of a nationally representative sample of 

UI recipients for each week between 1988 and 2007. This enables the construction of consistent 

estimates of UI receipt, by gender and other characteristics, on a weekly basis for the past 20 

years. With the support of DOL/ETA, we are currently in the process of producing a 

complementary research paper, which uses the BAM data to assess: (1) the characteristics of UI 

recipients (e.g. gender, race, and ethnicity), (2) how these change over time, and (3) how any 

changes are related to changes in the structure of the economy and the business cycle. 

Figure 9 presents the receipt rate by gender over time. Comparing the receipt rate between 

women (grey solid line) and men (black solid line), we see that female labor force participants 

were generally less likely their male counterparts to receive UI benefits. Looking at the two 

recessions, we see that 5.3 percent of female labor force participants in 1992 received UI benefits 

compared to 8.5 percent for men, and 4.2 percent of women in 2003 received UI benefits 

compared to 6 percent of men. Although the receipt rate was countercyclical for both genders, 

male receipt was also significantly more volatile. During the period of economic growth of the 

1990s, the rate declined by 5.5 percentage points for men, in contrast to only 2.4 percentage 

points for women; in the early 2000s, the growth in the unemployment rate was 3 percentage 

points for men but only 1.2 points for women. 

                                                           

7

 Labor force in the previous year is estimates by adding the number of individuals receiving either earnings or UI 

benefits in that year. Labor force participants who were never employed during the year are therefore omitted. 

Note also that the receipt rate constructed for our purposes differs from a measure of UI participation at a single 

point in time. 
Recent Changes in the Characteristics Page 18 August 2009 

of Unemployed Workers 

Figure 9: Unemployment Insurance Receipt Rate, by Gender, 

Actual and Adjusted for Industry-Occupation 

 Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

Interestingly, gender differences in the UI receipt rate correspond, at least roughly, to gender 

differences in unemployment described above. Therefore, part of the difference between male 

and female UI receipt rates may be a product of gender differences in the industry and 

occupation distribution. The grey dashed line in Figure 9 shows that, if we adjust the female UI 

receipt rate to the male industry and occupation distributions, the adjusted female receipt rate 

generally exceeds the actual male receipt rate. The lower observed receipt rate for women is 

therefore more than explained by the types of jobs they hold. Adjusting the male UI receipt rate 

(black dashed line) provides a similar conclusion, with the difference between male and female 

UI receipt rate eliminated. 

The greater cyclical variability for men is also explained in part by industry and occupation. We 

see that, when we adjust the female UI receipt rate for job type cyclical sensitivity increases, 

although the trough-to-peak variation is still lower for women than it is for men. Similarly, the 

adjusted male receipt rate has lower cyclical sensitivity than the actual male receipt rate. Either 

adjustment yields a pattern for which the peak-to-trough change is an approximate compromise 

between the male and female patterns. Hence, roughly speaking, about half the gender 

difference in the cyclical sensitivity of the UI receipt rate is explained by differences in the 

industry and occupation of the jobs held by those two groups.
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of Unemployed Workers 

3.5. Summary 

Female and male workers are employed in very different kinds of jobs and this has a large 

impact on their unemployment experiences. Since women are disproportionately in jobs where 

unemployment is lower, the difference in their distribution across jobs reduces their overall 

unemployment by 1-2 percentage points. Adjusting for this difference reveals that, within a job 

type in a given industry and occupation, the female unemployment rate is in fact somewhat 

higher than the male unemployment rate. Similarly, differences in industry and occupation by 

gender impact the UI receipt level. Although female UI receipt rates are below those for males, 

those differences disappear, and are to some degree even reversed, when industry and occupation 

are taken into account.

4. Nonwhites in the Labor Market 

4.1. Historic Changes 

Figure 10 presents the proportion of nonwhites in the labor force and in the unemployed 

population between 1954 and 2007. In the 1950s, nonwhites accounted for 11 percent of the 

labor force, a proportion that did not change much through the mid-1970s.8 After 1975, there 

was a steady increase in the percentage of nonwhites until, by 2007, 18.5 percent of the labor 

force were nonwhites. We observe a similar pattern for the nonwhite proportion of the 

unemployed. Between the early 1950s and the mid 1970s, the nonwhite proportion of the 

unemployed was around 20 percent. This proportion increased steadily to 2007, when 28 percent 

of the unemployed were nonwhites. 

                                                           

8

 Nonwhites are mostly black, but include other races as well (Asians, American Indians, and those who identify 

with more than one race). Hispanics are discussed later since they are not classified as a racial group in the CPS 

or in this paper. 
Recent Changes in the Characteristics Page 20 August 2009 

of Unemployed Workers 

Figure 10: Labor Force Participation and Proportions, Nonwhites 

Source: Current Population Survey monthly values, 1948-2007. Values represent 12-month 

centered moving averages. 

There are two other interesting patterns here. First, nonwhite labor force participants are 

overrepresented among the unemployed compared to white labor force participants. Second, 

changes over time in both the labor force and the unemployed populations correspond closely 

between 1954 and 1974. Between 1974 and 2007, however, the nonwhite proportion of the 

unemployed increased less rapidly than the percentage of nonwhites in the labor force. This is 

also reflected in a decline in the unemployment gap between nonwhites and whites (see Figure 

11). However, nonwhites are still overrepresented in the unemployed population. In the 

following sections, we investigate the degree to which these differences, and changes in the last 

two decades, can be explained by differences in the industry and occupation distributions 

between whites and nonwhites. 
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of Unemployed Workers 

Figure 11: Unemployment Rate, by Race, 1954 - 2007 

Source: Current Population Survey monthly values, 1948-2007. Values represent 12-month 

centered moving averages. 

4.2. Unemployment Experience of Nonwhites between 1992 and 2007 

In turning to our analysis for the period 1992-2007 based on the CPS March supplement, we 

consider the extent to which racial differences in unemployment are explained by differences in 

the levels of experience by race and differences in the industrial or occupational distribution. 

We first address the issue of whether racial differences in the number of unemployed new 

entrants into the labor force are of concern. Figure 12 shows that the racial gap for the 

unemployment rate and the experienced unemployment rate are very similar. Only in the period 

2002-2005 do the two measures differ by as much as a third of a percentage point. It appears 

that, during this period, nonwhite new entrants were relatively less likely to be unemployed than 

white new entrants. Although this means that the experienced unemployment rate racial gap was 

somewhat larger during those years than the unemployment rate racial gap, the pattern did not 

change. It is quite clear that both unemployment rate gaps display an overall declining trend, 

supportive of the view that racial unemployment experiences are converging. If this trend 

continues over the next two decades, it is possible that the difference in the unemployment rates 

between the two groups may be largely eliminated. 
Recent Changes in the Characteristics Page 22 August 2009 

of Unemployed Workers 

Figure 12: Nonwhites - Whites Unemployment Rate Gaps

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

4.3. Decomposition of the Racial Experienced Unemployment Rate Gap 

We now examine the extent to which racial differences in the industrial and occupational 

distribution are partly responsible for differences in the unemployment experience by race. As 

we noted in previous sections, unemployment rates were higher and more sensitive to the 

business cycle in certain industries (e.g., construction) and occupations (e.g., blue collar). If 

nonwhites were more likely than whites to be in such industries or occupations, this could 

explain some of the racial differences in the levels and volatility of the unemployment rate. 

Table 5 presents the nonwhite and white employment distributions across industries, showing 

that the two distributions were marginally different from one another. Whites were more likely 

than nonwhites to be employed in construction (7 and 4 percent, respectively) but were equally 

likely to be employed in the remaining high-unemployment sectors. 
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of Unemployed Workers 

Table 5: Employment Distribution by Race, across Industries: 

Averages of March CPS Supplement Values, 1992-2007

Experienced 

Unemployment Rate Employment Distribution

 Whites Nonwhites Whites Nonwhites 

Construction 10.2 % 17.5 % 7 % 4 % 

Food Services 6.6 % 12.3 % 14 % 14 % 

Manufacturing – Non-Durables 5.6 % 9.1 % 8 % 6 % 

Personal Services 6.7 % 10.1 % 2 % 2 % 

Business Support Services 6.3 % 11.5 % 8 % 9 % 

Repair and Maintenance 5.2 % 8.8 % 3 % 3 % 

Manufacturing – Durables 4.8 % 7.9 % 5 % 7 % 

Other Industries 5.2 % 8.8 % 5 % 3 % 

Wholesale Trade 4.0 % 6.9 % 4 % 2 % 

Information and Communications 4.1 % 6.1 % 3 % 2 % 

Transportation, Warehouse, and Utilities 3.7 % 6.3 % 5 % 6 % 

Retail Trade 3.3 % 6.1 % 6 % 5 % 

Entertainment and Recreation Services 3.2 % 4.8 % 3 % 5 % 

Education and Social Services 2.6 % 5.7 % 8 % 8 % 

Finance, Insurance, and Real Estate 2.6 % 5.2 % 5 % 4 % 

Public Administration 2.2 % 5.2 % 3 % 5 % 

Health Care Services 2.1 % 4.5 % 12 % 13 % 

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. Industry categories are 

mutually exclusive. 

A comparison of the occupation distributions for nonwhites and whites (Table 6) shows that 

these were also very similar. Nonwhites were slightly less likely to be employed in white collar 

occupations (57 percent versus 60 percent). Nonwhites were underrepresented in lowunemployment occupation groups, such as CEOs and managers and sales occupations, but they 

were overrepresented in others, such as health care and office and administrative support. 
Recent Changes in the Characteristics Page 24 August 2009 

of Unemployed Workers 

Table 6: Employment Distribution by Race, across Occupations: 

Averages of March CPS Supplement Values, 1992-2007 

Experienced 

Unemployment Rate Employment Distribution 

Whites Nonwhites Whites Nonwhites 

Blue Collar Occupations 7.2 % 11.2 % 40 % 43 %

Construction Workers 10.9 % 18.7 % 6 % 4 % 

Farming, Fishing, and Forestry Workers 9.5 % 18.6 % 2 % 1 % 

Other Blue Collar Occupations 7.1 % 10.5 % 15 % 18 % 

Other Service-Related Occupations 6.2 % 10.3 % 13 % 17 % 

Mechanics and Repairers 4.1 % 6.5 % 4 % 3 % 

White Collar Occupations 3.1 % 5.7 % 60 % 57 %

Sales Occupations 4.6 % 10.2 % 12 % 10 % 

Office and Administrative Support 3.9 % 6.9 % 14 % 16 % 

Engineers and Scientists 2.7 % 3.5 % 5 % 5 % 

Professional Specialty Occupations 2.6 % 4.2 % 4 % 4 % 

CEOs and Managers 2.3 % 3.1 % 13 % 9 % 

Health Care Occupations 2.1 % 4.3 % 6 % 8 % 

Teachers and Social Workers 1.3 % 2.8 % 6 % 5 % 

Source: Authors’ tabulations of the CPS March Supplement 1992-2007. Occupation categories are mutually 

exclusive. 

Perhaps the most interesting patterns in Tables 5 and 6 are in the unemployment rate by race 

within each category. In all 17 industries and all 12 occupational categories, the nonwhite 

unemployment rate is appreciably higher than the white level. Although the proportions across 

race employed in high- and low-unemployment jobs are similar, the level of unemployment is 

uniformly higher for nonwhites. This is in marked contrast to the comparable tabulations by 

gender, where women had higher experienced unemployment rates in some jobs while men had 

higher rates in other jobs. 

We now perform a set of unemployment rate decomposition exercises similar to those described 

in the prior section of this paper for gender. Figure 13 shows that adjusting the nonwhite 

experienced unemployment rate for the white industry-occupation distribution does not explain 

the gap or the differences in volatility of the experienced unemployment rate across races. 
Recent Changes in the Characteristics Page 25 August 2009 

of Unemployed Workers 

Adjusting the white rate to the nonwhite distribution, similarly, has essentially no impact. 

Clearly, the difference in the experienced unemployment rate by race cannot be attributed to 

racial differences in the employment distribution across industry and occupation. 

Figure 13: Experienced Unemployment Rates, by Race, 

Actual and Adjusted for Industry-Occupation Adjustments

 Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

4.4. Nonwhites in the Unemployment Insurance Program 

Since nonwhites had higher experienced unemployment rates between 1992 and 2007 than 

whites, we might expect that they would also be more likely than whites to receive UI benefits. 

In this section, we examine differences in UI receipt rate by race. Figure 14 shows that the 

nonwhite and white receipt rates (solid grey and solid black lines, respectively) were very similar 

through the 1990s, but beginning in 2000, the nonwhite rate was higher by about half a 

percentage point. 

This reflects two opposing factors. On one hand, nonwhites had consistently higher experienced 

unemployment rates during that period. As we showed above, new entrants do not contribute to 

this difference, so nonwhite unemployment is higher exclusively because of the racial difference. 

Since UI is only available to the experienced labor force, we might expect higher unemployment 

among experienced nonwhites to lead to greater levels of UI receipt. On the other hand, 

however, even in the same industries and occupations nonwhites suffer greater employment 
Recent Changes in the Characteristics Page 26 August 2009 

of Unemployed Workers 

instability and lower earnings, making them less likely to meet states’ minimum earnings or 

employment requirements. They may also be more likely to separate from jobs under 

circumstances that make them ineligible for benefits. 

Figure 14: Unemployment Insurance Receipt Rate, by Race 

Actual and Adjusted for Industry-Occupation

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007.

The dashed lines show that adjustments for industry and occupation - whether applied to whites 

or nonwhites - have no important role in explaining UI benefit receipt. It is interesting to note, 

however, the shift in the nonwhite receipt rate relative to the white receipt rate after 2000. This 

indicates that during and after the early 2000s recession, nonwhites became more likely to 

receive UI benefits than whites. The higher UI receipt rate for nonwhites after 2000 is consistent 

with the fact that nonwhites had higher unemployment rates than whites during that period. 

Since the racial unemployment rate gap after 2000 was about 2 percentage points and the UI 

receipt rate gap was less than 1 percentage point, it is still true that unemployed nonwhites were 

less likely to receive UI benefits than unemployed whites. 

4.5. Summary 

Since the early 1990s, the gap between nonwhite and white unemployment rates has declined, 

but it remains substantial, in the range of 2 to 3 percentage points. However, nonwhite use of UI 
Recent Changes in the Characteristics Page 27 August 2009 

of Unemployed Workers 

benefits, as a proportion of the labor force, is very similar to that of whites. Differences in the 

industrial and occupational distributions of employment by race are very modest, and they have 

no role in explaining the observed differences. 

5. Hispanics in the Labor Market 

5.1. Historic Changes 

The Hispanic population in the U.S. has increased dramatically over the last 30 years. Figure 15 

shows that, in the early- to mid-1970s, Hispanics accounted for only 4.5 percent of the labor 

force. This figure had increased to 14 percent by 2007. 

Figure 15: Labor Force and Unemployment Proportions, Hispanics 

Source: Current Population Survey monthly values, 1948-2007. Values represent 12-month centered 

moving averages. 

As Figure 15 shows, the Hispanic proportion of the unemployed was higher than the Hispanic 

proportion of the labor force in all years between 1973 and 2007. In the 1980s and 1990s, the 

Hispanic proportion of the unemployed grew at a faster pace than the Hispanic proportion of the 

labor force. But the proportion of Hispanic unemployed in the 2000s exhibited no upward trend, 

despite the continued growth of the Hispanic labor force. 
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of Unemployed Workers 

This pattern is reflected in Figure 16, which shows that the Hispanic unemployment rate was 

higher than the non-Hispanic unemployment rate during this period. However, this gap has 

decreased over time, as is illustrated clearly by considering three periods of recession: the early 

1980s, the early 1990s, and the early 2000s. In the early 1980s recession, the unemployment rate 

gap for Hispanic workers relative to non-Hispanics was 4.9 percentage points, At the trough of 

the early 1990s recession, that gap was marginally lower, at 4.3 percentage points. But in the 

early 2000s recession, the gap was distinctly lower, at only 2 percentage points. 

In addition, the volatility of the Hispanic unemployment rate has declined over this period. The 

trough-to-peak unemployment rate decline between 1992 and 2000 was 4 percentage points for 

Hispanics and 3 percentage points for non-Hispanics. The subsequent peak-to-trough increase in 

the unemployment rate, between 2000 and 2003, was around 1.6 percentage points for both 

groups. 

If we compare the unemployment patterns observed here with those discussed in the gender 

section, we find some striking similarities in how the position of women and Hispanics has 

changed in the labor market. Hispanics display patterns in the early 2000s that are similar to 

those of women in the 1970s. The female labor force proportion increased steadily during the 

1970s, whereas the female proportion of the unemployed was more or less constant. These 

patterns reflect the decline in the relative unemployment rate for women. In the 2000s, we see 

that, as the Hispanic labor force proportion continued to increase, the proportion of Hispanics 

among the unemployed remained steady, reflecting a similar convergence between Hispanic and 

non-Hispanic unemployment rates. 
Recent Changes in the Characteristics Page 29 August 2009 

of Unemployed Workers 

Figure 16: Unemployment Rate, by Ethnicity 

Source: Current Population Survey monthly values, 1948-2007. Values represent 12-month centered 

moving averages. 

5.2. The Unemployment Experience of Hispanics between 1992 and 2007 

Unemployment differences between Hispanics and non-Hispanics based on the CPS March 

Supplement data correspond closely to those reported in Figure 16. Between 1992 and 2007, 

Hispanics had higher unemployment rates than non-Hispanics, a gap that declines over time, as 

displayed in Figure 17, for both the unemployment rate and the experienced unemployment rate. 

In the early 1990s, the experienced unemployment rate gap was over 3.5 percentage points, 

whereas it declined to less than 1.5 percentage points after 2004. Since 2000, the ethnicity 

experienced unemployment rate gap was about a quarter of a percentage point higher than the 

overall ethnicity unemployment rate gap. This suggests that new labor force entrants are less 

important among unemployed Hispanics than they are among other unemployed workers. 

Despite this difference, it is clear that limiting consideration to the experienced labor force 

makes little difference in comparing Hispanics and non-Hispanics. 

 
Recent Changes in the Characteristics Page 30 August 2009 

of Unemployed Workers 

Figure 17: Hispanic – Non-Hispanic Unemployment Rate Gaps 

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

It appears that the unemployment experience of Hispanics is gradually becoming more like the 

experience of non-Hispanics. In the next section, we examine whether these relationships persist 

after controlling for differences in the industrial and occupational employment distributions of 

the two ethnicity groups. 

5.3. Decomposition of the Ethnicity Experienced Unemployment Rate Gap 

Table 7 summarizes the employment distribution across industries for Hispanic and nonHispanic workers. Hispanics were slightly more likely to be employed in high-unemployment 

sectors like construction, food services, and nondurable manufacturing over the period, and less 

likely to be in low-unemployment sectors like education and social services, and health care 

services. Nonetheless, the industry distributions across the two ethnicity groups were not very 

different. 
Recent Changes in the Characteristics Page 31 August 2009 

of Unemployed Workers 

Table 7: Employment Distribution by Ethnicity, across Industries: 

Averages of March CPS Supplement Values, 1992-2007 

Experienced 

Unemployment Rate Employment Distribution 

NonHispanics Hispanics NonHispanics Hispanics 

Construction 11.0 % 10.6 % 7 % 9 % 

Food Services 7.5 % 7.8 % 13 % 16 % 

Manufacturing – Non-Durables 5.5 % 9.0 % 4 % 8 % 

Personal Services 7.3 % 6.9 % 2 % 2 % 

Business Support Services 6.7 % 9.5 % 8 % 9 % 

Repair and Maintenance 5.6 % 6.7 % 2 % 4 % 

Manufacturing – Durables 4.9 % 7.2 % 8 % 8 % 

Other Industries 4.1 % 12.6 % 4 % 5 % 

Wholesale Trade 3.8 % 8.2 % 4 % 4 % 

Information and Communications 4.2 % 5.8 % 3 % 2 % 

Transportation, Warehouse, and Utilities 4.1 % 5.0 % 5 % 5 % 

Retail Trade 3.6 % 4.7 % 6 % 5 % 

Entertainment and Recreation Services 3.6 % 3.7 % 4 % 3 % 

Education and Social Services 2.9 % 4.9 % 9 % 6 % 

Finance, Insurance, and Real Estate 2.8 % 4.2 % 5 % 3 % 

Public Administration 2.8 % 3.5 % 4 % 2 % 

Health Care Services 2.4 % 3.7 % 13 % 9 % 

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. Industry categories are mutually 

exclusive. 

However, the occupational distribution of Hispanics was dramatically different from that of nonHispanics. Table 8 shows that Hispanics were much less likely than non-Hispanics to be 

employed in white collar occupations, with only 41 percent of Hispanics employed in white 

collar occupations compared to 63 percent of non-Hispanics. In fact, Hispanics were 

underrepresented in all white collar occupation categories relative to non-Hispanics. In contrast, 
Recent Changes in the Characteristics Page 32 August 2009 

of Unemployed Workers 

they were overrepresented in most blue collar jobs, especially the service-related occupations 

category (19 percent compared to 13 percent for non-Hispanics) and the ‘other blue collar 

occupations’ category (23 percent compared to 14 percent for non-Hispanics). 

Table 8: Employment Distribution by Ethnicity, across Occupations: 

Averages of March CPS Supplement Values, 1992-2007 

Experienced 

Unemployment Rate Employment Distribution 

NonHispanics Hispanics NonHispanics Hispanics

Blue Collar Occupations 7.6 % 8.9 % 37 % 59 % 

Construction Workers 12.1 % 10.8 % 5 % 9 % 

Farming, Fishing, and Forestry Workers 8.1 % 16.6 % 2 % 4 % 

Other Blue Collar Occupations 7.4 % 8.7 % 14 % 23 % 

Other Service-Related Occupations 7.0 % 7.2 % 13 % 19 % 

Mechanics and Repairers 4.2 % 5.6 % 4 % 4 % 

White Collar Occupations 3.3 % 5.2 % 63 % 41 % 

Sales Occupations 5.1 % 7.5 % 12 % 9 % 

Office and Administrative Support 4.1 % 5.9 % 15 % 14 % 

Engineers and Scientists 2.8 % 3.6 % 5 % 2 % 

Professional Specialty Occupations 2.7 % 4.4 % 5 % 2 % 

CEOs and Managers 2.4 % 3.3 % 13 % 7 % 

Health Care Occupations 2.4 % 3.3 % 7 % 4 % 

Teachers and Social Workers 1.4 % 2.4 % 6 % 3 % 

Source: Authors’ tabulations of the CPS March Supplement 1992-2007. Occupation categories are 

mutually exclusive. 

In most cases, the Hispanic unemployment rates were higher than those for other workers. 

Among construction workers, in contrast, Hispanics had slightly lower unemployment rates than 

others, and unemployment rates by ethnicity were essentially the same in other service-related 

occupations. 

In order to isolate the effect of industry and occupation distribution on the ethnicity experienced 

unemployment rate gap, we produce the same decomposition exercise described in previous 
Recent Changes in the Characteristics Page 33 August 2009 

of Unemployed Workers 

sections. Table 18 presents the actual experienced unemployment rate and the same rate adjusted 

for industry-occupation, by ethnicity. The adjusted Hispanic rate (grey dashed line) is 

consistently 1 percentage point lower than the actual Hispanic rate (grey solid line), indicating 

that ethnicity differences in the industry-occupation distribution account for about one 

percentage point of the ethnicity experienced unemployment rate gap. 

Between 1992 and 1998, between 40 and 60 percent of the ethnicity experienced unemployment 

rate gap was explained by differences in the employment distributions across industries and 

occupations between the two groups. After 1998, more than 70 percent of the gap was explained 

by such differences, and after the end of the early 2000s recession, the industry-occupation 

adjusted Hispanic rate was identical to the non-Hispanic rate. We conclude that, although 

differences in the employment distributions cause the Hispanic experienced unemployment rate 

to be higher, this effect has not changed over time, even as the ethnicity gap in that rate has been 

declining. 

Looking at the adjustment of the non-Hispanic experienced unemployment rate for the Hispanic 

distribution provides a very similar conclusion, although the impact of the adjustment was 

slightly smaller in the beginning of the period. This confirms our conclusion that all remaining 

differences between Hispanics and non-Hispanics in average unemployment after the recession 

of the early 2000s are explained by industry and occupation distributional differences. 
Recent Changes in the Characteristics Page 34 August 2009 

of Unemployed Workers 

Figure 18: Experienced Unemployment Rate, by Ethnicity, 

Actual and Adjusted for Industry-Occupation 

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007. 

5.4. Hispanic Workers and the Unemployment Insurance Program 

Figure 19 presents the probability of receiving UI benefits for Hispanic and non-Hispanic labor 

force participants between 1992 and 2007. As we might expect, the UI receipt rate for Hispanics 

was higher than that for non-Hispanics in the early 1990s, a period during which the Hispanic 

experienced unemployment rate was also higher. However, during that period, the Hispanic 

EUR was considerably higher than that for non-Hispanics (4 percentage points according to 

Figure 17), a difference that it is not reflected by the size of the receipt rate gap, which is 

generally less than 1 percent. 

Following 2000, the receipt rate difference disappears, and after 2006, non-Hispanic labor force 

participants became slightly more likely to receive UI benefits than Hispanics. A possible 

explanation is that some Hispanic labor force participants may not be familiar enough with the 

U.S. labor market to know that they could be eligible to receive UI benefits if they become 

unemployed. Another possible explanation is that unemployed Hispanics are on average less 
Recent Changes in the Characteristics Page 35 August 2009 

of Unemployed Workers 

likely to be eligible to receive UI benefits compared to unemployed non-Hispanics, due to either 

work history or citizenship status. 

The dashed lines in Figure 19 show how adjusting for the industry and occupational distribution 

of employment influences the UI receipt rate. Adjusting the Hispanic UI receipt to the nonHispanic employment distribution shows that until the late 1990s, the entire difference in UI 

receipt was explained by industry and occupation. Since Figure 18 shows that the adjusted levels 

of Hispanic experienced unemployment rate were above those for others during this period, this 

implies that unemployed Hispanics were less likely to receive UI benefits. By the end of our 

period, we see that UI usage rates for Hispanics adjusted for industry and occupation were 

appreciably below those for non-Hispanics. We also see that when we adjust the non-Hispanic 

UI receipt rates for the Hispanic employment distribution, the rate was appreciably higher than 

those observed for Hispanics. Both results confirm the view that, within occupation-employment 

cells, Hispanic UI receipt rates were 1 to 1.5 percentage points lower than those of nonHispanics. 

Figure 19: Unemployment Insurance Receipt Rate, by Ethnicity, 

Actual and Adjusted for Industry-Occupation

Source: Authors’ tabulations of the CPS March Supplement, 1992-2007.
Recent Changes in the Characteristics Page 36 August 2009 

of Unemployed Workers 

5.5. Summary 

In the last 16 years, as the Hispanic proportion of the labor force has increased, and their 

unemployment rate has tended to converge to that of the overall labor force. Over the same 

period, Hispanics were more likely to be employed in blue collar occupations and in industries 

with higher unemployment rates. Controlling for differences in the industrial and occupational 

employment distribution, we find that a substantial portion of the ethnicity experienced 

unemployment rate gap is attributable to those differences. After the early 2000s recession, 

those differences fully explain the relatively modest remaining Hispanic/non-Hispanic gap. 

These numbers suggest that Hispanic labor force participants are becoming more and more alike 

in terms of their unemployment experience to their non-Hispanic counterparts. UI benefit receipt 

rates are also converging for Hispanics and non-Hispanics, but here we see that once we control 

for industry and occupation, important differences remain. 

6. Conclusion 

Our analyses show that as the female proportion of the labor force has increased, the female 

unemployment rate has converged with the male unemployment rate. This may be an indication 

that gender differences in the employment and unemployment experiences are disappearing over 

time. However, we find that, between 1992 and 2007, there were substantial gender differences 

in the industry and occupation employment distribution, and that these differences had an 

important effect on the gender unemployment rate gap. In fact, when we adjust for such 

differences, the female unemployment rate is higher than the male unemployment rate. 

Our analysis also indicates that women were appreciably less likely than men to receive UI 

benefits in the past two decades. This difference in the UI receipt rates is fully explained by 

differences in industry-occupation distribution, implying that men and women in the same kinds 

of jobs were equally likely to receive UI benefits. Even so, it is necessary to recognize that men 

and women continue to be distinct populations with different experiences in the labor market and 

in the UI system. 
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of Unemployed Workers 

Racial differences in the unemployment are of longstanding importance. Even the most casual 

reference to unemployment and UI statistics reveals that nonwhite levels of unemployment 

remain substantially above those for whites. Despite this conclusion, our analyses provide some 

evidence of convergence. In the last decade, the nonwhite increment in unemployment rates has 

declined somewhat; although differences remain large, they are less dramatic than they were two 

decades ago. In addition, although nonwhites were more likely to experience unemployment 

than whites, the UI receipt rates between the two groups were not very different. This indicates 

that unemployed nonwhites were less likely than unemployed whites to receive UI benefits. 

The story of Hispanics is more clearly one of convergence. In the face of growth in the number 

of Hispanics in the labor market, the unemployment rate gap between Hispanics and nonHispanics has declined dramatically. When we adjust for differences in the industry-occupation 

distribution, we find that the Hispanic unemployment rate in the last several years is essentially 

the same as the non-Hispanic unemployment rate. We do find that Hispanics are somewhat less 

likely to receive UI benefits, although differences are fairly modest. Overall, the evidence 

suggests that, at least in terms of unemployment patterns, Hispanic labor force participants are 

becoming increasingly similar to non-Hispanic labor force participants. 
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of Unemployed Workers 

REFERENCES

Burtless Gary (2007), “Trends in the Structure of the Labor Market and Unemployment: 

Implications for U.S. Unemployment Insurance,” Paper presented at the Association for 

Public Policy Analysis and Management meetings (November 2007). 

Gorgens Tue (2002), “Reservation Wages and Working Hours of Recently Unemployed U.S. 

Women,” Labour Economics, Vol. 9, No. 1, pp. 93-123. 

Groshen Erica L., Simon Potter (2003), “Has Structure Change Contributed to a Jobless 

Recovery?” Federal Reserve Bank of New York, Vol. 9, No. 8, pp. 1-7. 

Holzer Harry J., Paul Offner, and Elaine Sorensen (2005), “What Explains the Continuing 

Decline in Labor Force Activity among Young Black Men?” Labor History, Vol. 46, No. 1, 

pp. 37-55. 

Kletzer Lori G., Howard Rosen F. (2006), “Reforming Unemployment Insurance for the TwentyFirst Century Workforce,” Discussion Paper 2006-06. 

Nicholson Walter, and Karen Needels (2006), “Unemployment Insurance: Strengthening the 

Relationship between Theory and Policy,” Journal of Economic Perspectives, Vol. 20, No. 3, 

pp. 47-70. 
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of Unemployed Workers 

APPENDIX A: CYCLICALITY OF THE 

UNEMPLOYMENT RATE 

Table A: Cyclicality of the Gender Unemployment Rate Gap, 1992-2007 

Quarter 1 

(Jan-Mar) 

Quarter 2 

(Apr-Jun) 

Quarter 3 

(Jul-Sep) 

Quarter 4 

(Oct-Dec) 

Female UR 5.52 (.91) 4.96 (.84) 5.81 (.86) 5.10 (.91) 

Male UR 6.39 (1.32) 5.38 (1.20) 5.29 (1.05) 4.87 (.95) 

Gap -0.87 (.52) -0.42 (.42) 0.53 (.28) 0.23 (.27) 

Nonwhite UR 9.17 (1.84) 8.79 (1.94) 8.99 (1.60) 8.38 (1.67) 

White UR 5.18 (.99) 4.60 (.90) 4.58 (.81) 4.33 (.80) 

Gap 3.99 (.92) 4.20 (1.11) 4.41 (.90) 4.05 (.93) 

Hispanic UR 7.12 (1.66) 7.32 (1.52) 7.18 (1.53) 7.96 (1.70) 

Non-Hispanic UR 4.90 (.84) 4.91 (.69) 4.57 (.71) 5.24 (.75) 

Gap 2.22 (1.18) 2.41 (1.17) 2.60 (1.15) 2.72 (1.24) 

 Source: CPS Monthly Surveys, 1992-2007. Standard deviations are reported in parenthesis. 

Figure A2: Cyclicality of the Unemployment Rate, Construction 

 Source: CPS Monthly Surveys, 1992-2007. Standard deviations are reported in parenthesis. 
Recent Changes in the Characteristics Page 40 August 2009 

of Unemployed Workers 

APPENDIX B: DECOMPOSITION OF THE 

UNEMPLOYMENT RATE

Let U g be the number of experienced unemployed workers for gender g (g = f, m) and Eg be the 

number employed. The EUR for gender g is: 

g g

g

g U E

U

EUR

+ = (1) 

Let Ug ,i be the number of unemployed workers in industry-occupation cell i of gender g, and 

Eg,i the number of employed workers. Using equation (1), we can re-write the total EUR as: 

∑ ∑( )⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

+

+

+ = i

i

g i g i

g i g i

g i g i

g i

g U E

U E

U E

U

EUR

, ,

, ,

, ,

, (2) 

The first term in the summation,

g i g i

g i

U E

U

, ,

,

+

, represents the unemployment rate for gender g in 

industry-occupational cell i. The second term, ∑( ) +

+

i

g i g i

g i g i

U E

U E

, ,

, , , is the ratio of the experienced 

labor force for gender g, cell i, to the total experienced labor force for gender g. This represents 

the proportion of the experienced labor force for gender g that is in cell i. Equation (2) 

represents the unemployment rate for gender g as a weighted sum of the unemployment rate by 

industry-occupation cell, where the weights are the cell proportions. 

The gap between the male and female unemployment rate is a function of cell-specific 

unemployment rates, and the employment distributions. In order to determine what effect 

differences in gender industry and occupation composition have on the observed unemployment 

rate gap, we have to isolate the effect of the distribution. Writing the female unemployment rate 

as: 

( ) ∑ ∑ ⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

+

+

+ = i

i

F i F i

F i F i

F i F i

F i

F U E

U E

U E

U

EUR

, ,

, ,

, ,

, (3) 
Recent Changes in the Characteristics Page 41 August 2009 

of Unemployed Workers 

We may adjust the female unemployment rate to the male distribution by applying the male 

industry weights to equation (3). The adjusted measure is then written as:

( ) ∑ ∑ ⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

+

+

+ = i

i

M i M i

M i M i

F i F i

ADJ F i

F U E

U E

U E

U

EUR

, ,

, ,

, ,

, (4) 

Note that we can use the same methodology to adjust the male unemployment rate according to 

the female industry and occupation distribution. We use the same approach in adjusting the UI 

receipt rates, and adjusting rates for nonwhite and Hispanic workers.