THE EFFECTS OF OCCUPATIONAL GENDER SEGREGATION ACROSS RACE

THE EFFECTS OF OCCUPATIONAL GENDER SEGREGATION ACROSS RACE

David A. Cotter Union College

Joan M. Hermsen University of Missouri-Columbia

Reeve Vanneman University of Maryland

The general relationship between occupational gender segregation and earnings inequality is well documented, although few studies have examined the relationship separately by raceiethnicity. This article investigates occupational gender segregation effects across whites, African Americans, Hispanics, and Asians. In addition, we explore two ways in which segregation may affect earnings: (1) by lowering the earnings of workers in female-dominated occupations and (2) by lowering the earnings of all workers in highly segregated labor markets. Our central findings are that both segregation effects contribute to earnings inequality and that the effects are observed quite broadly across racialiethnic groups, although they particularly impact the carnings of African American women.

Women make less money than men in part because they are segregated into Iow- paying predominantly female jobs. The importance of occupational gender segregation is one of the earliest and most thoroughly documented relationships in the gender stratification literature (Hartmann 1976; England, Farkas, Kilbourne, and Dou 2988; Baron and Newman 1990; Reskin and Roos 1990; Sorensen 1990; Blau and Ferber 1992; Macpherson and Hirsch 1995; Tomaskovic-Devey 1995). Recent work has shown that occupational segregation has both contextual and individual effects on the gender pay gap (Cotter, DeFiore, Hermsen, Kowalewski, and Vanneman 1997). Women working in metropolitan areas with more occupational segregation (e.g., Cleveland) are paid less than women working in more gender integrated areas (e.g., San Francisco), even when the gender composition of the individual’s own occupation is held constant.

Given this substantial record of empirical research, it is essential to ask how general the relationship is between ciccupational gender segregation and gender earnings

Authors are listed alphabetically, reflecting equal contributions. Direct all correspondence to David A. Cotter. Department of Sociology, Union College, Schenectady, New York 12308; e-mail [email protected]

The Sociological Quarterly, Volume 44, Number 1, pages 17-36. Copyright 0 2003 by The Midwest Sociological Society. All rights reserved. Send requests for permission to reprint to: Rights and Permissions, University of California Press, Journals Division, 2000 Center St., Ste. 303, Berkeley, CA 947WU23. ISSN 0038-0253; online ISSN: 1533-8525

18 THE SOCIOLOGICAL QUARTERLY Vol. 44/No. 1/2003

inequality. Does occupational segregation hurt the earnings of women of color as well as those of white women? A lingering suspicion about occupational segregation, like some questions about contemporary feminism, is that entry into traditionally male jobs is primarily a concern for white women and that quite different changes are needed to assist women of color. The growing body of intersectionality literature has impressed upon the discipline the need to examine how race, ethnicity, class, and gender jointly structure stratification outcomes (Dill 1983; Glenn 1987; King 1988; Hill-Collins 1990; Brewer 1993; Chow 1996; McCall 2001). We make a step in this direction by investigating occupational segregation effects across four raciayethnic groups for both men and women.

We extend the existing empirical research in two ways First, we examine the segregation effect across several racial/ethnic groups: whites, African Americans, Hispanics, and Asian Americans. Second, we examine two types of segregation effects: how both the gender composition of one’s occupation and the degree of occupational segregation in the local labor market affect earnings. We begin by discussing three theoretical perspectives that are most often used to inform analyses of gender segregation and earnings across racial and ethnic groups.

CONCEPTUAL BACKC RO U N D

Three principal theories have emerged to explain why occupational segregation pro- duces a gender earnings gap. Some identify the cause in labor market “crowding” (restricting the number of jobs for women creates an over supply of female labor; Berg- mann 1974); others in “devaluation” (occupations that are filled by low status workers become low status themselves; England 1992); and others in “human capital” investments (women choose occupations with low returns to experience because they expect intermittent careers; Polachek 1981). None of these theories have specified how the gender segregatiodearnings relationship might differ by race. We extrapolate from the existing theories to offer several alternative hypotheses about racial/ethnic variations in the segregationlearnings relationship. It is important to realize that we are not testing these three theories directly but rather our extrapolation of what these theories might predict about racial and ethnic variations in the segregation effect. It is a tribute to the richness of these theories that they can be used to deduce new and sometimes cornpet- ing hypotheses about the differential effects of gender segregation. We summarize our theoretical hypotheses in Table 1.

Predictions from Segregation Theories

Human Capital Theory

Extending the neoclassical models of economics, human capital theorists contend that gender differences in earnings across occupations are largely attributable to workers’ investments in skills that raise productivity (traits such as education, job training, and work experience). Not all skills are equivalent, as some require consistent upgrading and others are so arcane as to be specific to a particular occupation, industry or even employer (England 1992;Tam 1997). Human capital theorists also posit that, because of family obligations, women exhibit lower levels of attachment to the labor force. Thus women are less likely to invest in training and will seek employment in occupations with

Effects of Occupational Gender Segregation Across Race 19

TABLE 1. HYPOTHESIZED RELATIONSHIPS BETWEEN OCCUPATIONAL SEGREGATION A N D EARNINGS INEQUALITY

Human Capital Crowding Devaluation Theory Theory Theory

Occupational Segregation Efiect- Gender Composition of Occupation

Differences by Race/Ethnicity Greater negative Greater negative Greater negative effect for white ef€ect for women effect for women of color women of

color Occupational Segregation Effect-

Local Labor Market Segregation Differences by Race/Ethnicity No difference or Greater negative Greater negative

greater negative effect for women effect for effect for white of color women of women color

relatively low barriers to entry and exit, low penalties for discontinuous work histories, and less emphasis on firm-specific training. Although such occupations may offer lower wages, they may also offer other “compensating differentials” such as flexible schedul- ing, pleasant working conditions, and reduced responsibilities (Kilbourne, Farkas, Beron, Weir, and England 1994).

Regarding race: if the segregation effect is a result of women choosing occupations with lower penalties for intermittent work (a finding unsubstantiated in the sociological literature), then African American and Asian women should show weaker segregation effects since they have had higher and steadier rates of labor force participation than have white women. Latinas do not have higher labor force participation rates than white women, but their age-participation profiles lack the double-maxima “saddle” distribu- tion that is characteristic of white women, which implies a more intermittent work commitment. Therefore segregation effects may be weaker for Latinas as well.

Human capital considerations might make similar predictions for both types of segregation effects, across occupations and across local labor markets, although the inferences are more straightforward for the effects of the gender composition of an individual’s occupation. Since human capital explanations depend primarily on individual-level mechanisms (how individual women choose their occupations), predictions about macrolevel, contextual effects of labor market segregation are more problematic. Additional assumptions must be made about spatial variations in women’s intermittent employment in order to add a contextual dimension to human capital theory. Perhaps the industry mix of some areas leads employers in those areas to have greater concerns for human capital investments and intermittent employment so that there is more segregation. Or the family obligations of women may vary across areas, leading to more intermittency among women and thus a greater perceived need to segregate jobs by gender. In either case, the effects of this higher local-area segregation may be more noticeable for white women for whom intermittency has a higher probability.


Crowding Theory

As with the human capital perspective, the crowding hypothesis begins with basic neoclassical economic concepts of supply and demand. However, crowding theory posits that, because of employment discrimination, women are restricted to a limited subset of occupations. The greater supply of labor in these “female” occupations lowers the overall wage for female labor (Bergmann 1974, 1986). Conversely, since women are restricted from entering male occupations, the lower supply of workers raises wages in these occupations.l

Crowding theory implies differential segregation effects by race. Julianne Malveaux (1990) suggests that crowding disproportionately hurts African American women relative to white women because African American women are crowded into fewer and lower-paying occupations than are white wolaen.White women tend to be in better paying female occupations, are distributed across more occupations (and hence, are less crowded), and are the first women to exit predominantly female occupations for inte- grating occupations. While occupational gender segregation from white men has declined for women of all racelethnic groups (Table 2), white women continue to be the least segregated from white men and African American women the most segregated. Thus, Malveaux speculates that the greater occupational segregation of African Ameri- can women contributes more to the lower earnings of African American women than does occupational segregation to the earnings of white women.

Devaluation Theory

The devaluation hypothesis suggests that it is not the specific characteristics of women or their occupations that account for lower earnings in predominantly female occupations. Instead, it is a matter of “status composition.” These occupations are filled by women, and work assigned to women is held in low esteem by the larger culture. As such, it is perceived by employers and organizations to be of lesser value than comparable work performed by men (England 1992; Tomaskovic-Devey 1995; Reid 1998). This

TABLE 2. GENDER EARNINGS RATIOS AND OCCUPATIONAL SEGREGATION BY RACE A N D HISPANIC ORIGIN

Index of Dissimilarity (D)

1979 1989 %Change 1979 1989 %Change

Earnings Ratio

All women relative to all men

Women by race/ethnicity relative Totals 58 52 -11.5 60 66 +10.0

to white men White 59 52 - 13.5 58 64 +10.3 African American 66 60 -10.0 55 59 +7.2 Hispanic 61 56 -8.9 51 54 +5.9 Asian 63 56 -12.5 65 71 +9.2

Source: USBC (1993b).

Effects o f Occupational Gender Segregation Across Race 21

perception of lessened value is translated into lower wages for all individuals working in these “devalued” occupations.

While sexism devalues female-dominated occupations, both sexism and racism lead occupations dominated by minority women to be devalued even further.Thus segregation effects on earnings may be even stronger for minority women than for white women.

As shown in Table 1, the crowding and devaluation theories lead to the same hypotheses: women of color are particularly disadvantaged by working in predominantly female occupations and in gender-segregated labor markets. These perspectives differ, however, in their explanations for why the hypothesized patterns emerge. Human capital theory posits a different relationship whereby white women should be particularly disadvantaged from working in a predominantly female occupation as compared to women of color. Human capital theory is potentially less useful for understanding macro-oriented processes such as occupational segregation in local labor markets Thus we expect no differences by race/ethnicity in the effect of labor market occupational segregation on earnings inequality.

EMPIRICAL LITERATURE

Micro-Level Segregation Effects

Four empirical analyses have tested whether being in a female occupation lowers the earnings of both white women and women of color. Paula England, George Farkas, Bar- bara Stanek Kilbourne, and Thomas Dou (1988) report negative gender composition effects on both white and black women’s earnings using the National Longitudinal Sur- vey (NLS) data for 1968-1980; the effect for black women is slightly larger than the effect for white women, but they do not test for statistical significance. Elaine Sorensen (1989), using 1983 Current Population Survey (CPS) data, also finds a slightly more negative effect of the proportion female for women of color (b = -.209) than for white women (b = -.151). Lori Reid (1998), using National Longitudinal Survey of Youth (NLSY) data, finds similar effects of high female concentrations on the earnings of white and black women but smaller coefficients for Latinas. Paula England, Karen Christopher, and Lori Reid (1999), also using the NLSY, show that 19 percent of white femalelwhite male and 26 percent of the Latina/Latino earnings gaps are due to occupational gender segregation; however, the pattern is less clear when comparing the earnings of African American men and women as the effect ranges from 0 percent to 53 percent. Thus some, but not all, previous studies provide support for differential segregation effects by race although the size of the difference varies substantially from one study to the next, and the difference is rarely large.

Macro-Level Segregation Effects

The four studies reviewed above all investigate the effect on individual earnings of the gender composition of the individual’s own occupation. But we now know that occupational segregation in a labor market has contextual effects that are felt even for those women who are not working in predominantly female occupations (Cotter et al. 1997). According to this reasoning, all women benefit from working in a gender-integrated labor market-not only those women who enter previously male-dominated occupations

22 THE SOCIOLOGICAL QUARTERLY Vol. #/No. 1 /2003

like building trades and medicine but also those women remaining in traditionally female work. Tests of the segregatiodearnings relationship that do not account for the contexxtual effect of occupational gender segregation on earnings inequality miss an important path by which occupational segregation contributes to earnings inequality (see McCall2001 for an example).

No studies have yet investigated how general this contextual segregation effect is across racial and ethnic lines. The one study of local area labor market segregation on the gender earnings inequality (Cotter et al. 1997) did not disaggregate the relationship by race. Nevertheless, changes over time suggest the macrolevel relationship between occupational integration and women’s earnings has been quite broadly shared across racialiethnic boundaries. The ratio of women’s to men’s median earnings among full- time, year-round workers increased from 57 percent in 1973 to 74 percent in 3 997 (U.S. Census 1998). Occupational integration also improved in the last quarter of the century (Bianchi and Rytina 1986; Weeden 1998; Wells 1999). Both increases were experienced by women of color as well as by white women (Cotter et al. 1995; Spain and Bianchi 1996). Table 2 shows that African American and Hispanic women have a larger earnings gap with white men and are more occupationally scgregated than are white women while Asian women have a slightly larger earnings ratio and are less segregated. But the important point is that all improved in the 1980s.

METHODS

We ask two primary questions in this article: Are the earnings of women of color as well as those of white women hurt by occupational segregation? Is occupational gender segregation associated with the gender earnings gap for women of color as well as for white women? We test both the contextual and the individual-level aspects of each of these segregation/earnings relationships. First, in a conventional individual-level earnings model, we focus on whether the impact of an individual’s occupational gender composition varies by race/ethnic group and gender. Second, in a (simultaneous) metropolitan- level (hereafter MA) analysis, we explore whether occupational gender segregation in the local labor market has differential impacts according to racialiethnic groups and gender. These two levels of analysis require use of a multilevel design.

Data Sources

Individual Data

We combine data from the 1 percent and 5 percent 1990 Public Use Microdata Samples (PUMS) (USBC 1993b) to construct a sample of white, African American, Hispanic, and Asian men and women, age 25-54, who, in 1989, worked at least four weeks for at least four hours a week and had positive carnings The resulting sample includes 4,073,006 individuals.

Metropolitan Areas

We compare the occupational segregation effect on gender earnings inequality across 261 metropolitan areas (MAS) that follow the June 30,1993 definitions (USBC 1993a).

Effects o f Occupational Gender Segregation Across Race 23

Metropolitan area labor markets are an appropriate unit of analysis. Gender inequalities vary across these areas more than they vary nationally over time (Lorence 1992)? Since we include the entire population of MAS, statistical significance is also less of a concern at this level as we are not generalizing to any larger population. Nevertheless, we report significance tests as a guide to which rcsults might have been obtaincd through chance fluctuations alone.

Variables

Dependent Variable

We estimate hourly earnings based on annual earnings in 1989, weeks worked in 1989, and number of hours usually worked per week. We take the logarithm of hourly earnings in order Lo estimate proportional rather than absolute dollar differences in earnings levels. We also compute the analysis for the logarithm of total annual earnings among workers who worked full-time year-round; the results are essentially similar so we do not report them here.

RacelGender Groups

We divide the sample into eight groups based on gender and four racial-ethnic categories. All Hispanics of whatever race comprise one group; the remaining population is divided into whites, African Americans, and Asian Americans. Because of small sample sizes, Native Americans and people who checked “other” for race were excluded from the analysis unless they were Hispanic.

Depending on the analysis in question, we use different comparison groups. First, in an analysis to determine if the “earnings levels” of all groups of women and men are negatively affected by occupational segregation, we estimate a model with no comparison group, providing eight separate gender composition slopes, and test whether each slope is significantly different from zero. Next, to test whether occupational gender segregation has an effect on the “relative wages” of women (as compared to white men), we conduct an analysis where the comparison group for the segregation effect is white men. Although not the focus of our analysis, the effects of occupational gender segregation on the earnings levels and gaps of African American men, Latinos, and Asian men are also presented.

Gender Composition of Occupations

We measure the proportion female of each three-digit occupation code as reported in the 1 Y Y O census (USBC 1992). This method is comparable to the way occupational segregation effects have been estimated in most research over the last two decades (e.g., England 1982; Sorensen 1989). Negative coefficients are expected: the more “female” the occupation, the lower the earnings. Given our interest in how this gender composition effect varies across race and gender. we calculate interaction effects between percentage female for the four racial ethnic women’s groups and the three minority men’s groups.


MA Occupational Gender Segregation

In addition to the usual occupational gender composition effects, we are also interested in how segregation in the local metropolitan area labor market affects earnings. Levels of occupational gender segregation in a labor market are usually measured by the dissimilarity index, D (Duncan and Duncan 1955). The dissimilarity index enables compa- rability with other studies and is readily interpreted as the percentage of workers of either gender who would have to change occupations for the two occupational distributions to match. The index can be computed for each MA from county-level detailed occupational distributions (USBC 1992) .3

Individual-Level Control Variables

Our earnings models include controls for education, potential work experience and its square, marital status, number of children, hours usually worked per week, immigrant status, citizenship status, and English proficiency. We also include controls for the specific vocational preparation (SVP) and general education (GED) requirements of the

TABLE 3. MEANS, STANDARD DEVIATIONS, A N D RANGES OF PRINCIPAL VARIABLES IN THE ANALYSES

Standard Mean Deviation Minimum Maximum

MA-Level Variables (N = 261) Occupational segregation

Individual Variables (N = 4,073,006) Hourly earnings (log) White women African American women Hispanic women Asian American women White men African American men Hispanic men Asian American men Females in occupation (Yo) Years of school completed Experience (potential) Experience squared Log hours worked per week # children in household Widowed, divorccd, separated Never married Immigrant Citizen Difficulty at English Occupation: General Educational Development Occupation: Specific Training Inverse Mills Ratio

.50

2.35 .37 .05 .04 .02 .42 .04 .05 .02 .46

13.62 17.93

396.82 3.66 .Y8 .I7 .18 .12 .94

1.09 3.86

26.27 .96

.03

.7 1

.48

.22

.19

.13

.49

.20

.21

.I3

.31 2.95 8.93

358.79 .34

1.19 3 9 .39 .33 .25 .42 3 7

20.34 .42

.40

0 0 0 0 0 0 0 0 0

0 0 0 1.38 0 0 0 0 0 1 1.6 1.1 0

.01

.61

8.86 1 1 1 1 1 1 1 1

20 48

2304 4.60

18 1 1 1 1 4 6.0

105.3 1.96

.99

Source: USBC (1993b).


individual’s occupation (Tam 1997; England, Hermsen, and Cotter 2000). Finally, we correct for selection bias with a conventional two-step procedure (Heckman 1979). The descriptive statistics for these variables are reported inTable 3. The operational definitions of these variables can be found in Appendix 1. In the multilevel analyses, each of these variables is centered at its mean. Furthermore, in the analyses we include interaction terms to account for possible gender differences in the effects of the control variables4

MA-Level Control Variables

We have incorporated MA-level controls for region and racial/ethnic composition as we expect both impact the racial-ethnic patterning of the segregation effect. The MA- level control variables are aggregated from county-level data; definitions are found in Appendix 1.

Multilevel Design

We use a multilevel design (Bryk and Raudenbush 1992) to estimate the MA-level effect of local labor market segregation on individual earnings The multilevel models incorporate in a single design a standard microlevel earnings function and macrolevel equations that predict the coefficients in the earnings function. In effect, the microlevel earnings equation is estimated separately for each of the 261 MAS, and the race*gender coefficients for each MA become the dependent variables in the MA-level analysis

The full multilevel model for analyzing earnings levels (i.e., the no intercept model) is:

8 16 NI

wi, = 2 ~ i a * ~ x ~ j i a + 2 ~ k a * ~ x ~ j i o * ( % ~ i , - w,)+ 2 ~ l a * ( x r , a - F.> + era;

( 1 4 ] = I k = j f 8 1 = 1

P k a = Y ~ o ; P l a = Y,o (Ic> where wia is the logarithm of hourly earnings of individual i in MA a; pju are the estimated earnings, adjusted for the covariates, for the eight race-gender groups in MA a; RxGji, is the vector of eight race and gender dummy variables for individual i in MA a; pku is a vector of eight individual-level coefficients for the effects of occupational gender composition for each of the eight race and gender groups; %Fia is the percentage female in individual i’s occupation; % F.. is the average percentage female in all occupations across all MAS; PI , is a vector of individual-level coefficients for control variables XIia (e.g., education); XI,, is a vector of grand means of the individual-level control variables; and e , is the error term for individual i in MA a.

For the MA-level equations (lb), each of the eight Pi, race/gender coefficients are treated as “dependent variables.” We are interested in the characteristics of MAS where these coefficients are high or low, that is, where do African American women have better earnings? L)STAT,* is the adjusted index of dissimilarity measuring occupational segregation in MA a; yjm is a vector of macrolevel coefficients for the effects of macrolevel control variables Z,, (e.g., region) on the microlevel coefficients Pi,; z. is a vector of rn grand means of the macrolevel control variables; and uj, is the macrolevel

26 THE SOCIOLOGICAL QUARTERLY Vol. 44/No. 1 /2003

error term for coefficient Pia in MA a. We are especially interested in the eight effects (y jJ of macrolevel segregation (DSTAT; ) on each race/gender group's earnings (pi,). The effects of the remaining individual-level variables are assumed to be constant across MAS (lc). The analysis of the earnings gaps from white men's earnings use similar models, except they include an intercept, a "main" effect of occupational gender composition, and only seven race/gender groups, omitting white men. The earnings levels and earnings gaps models are equivalent models; the advantage of separate calculations is to estimate standard errors for both the absolute levels of earnings and the differences from white men.

RESULTS

Effects of Gender Composition of Occupations

Our first question is whether occupational gender segregation does in fact hurt the earnings levels of all women, not simply white women.To answer this question, we estimate a model with no intercept where the coefficients for the eight gender composition effects indicate whether each group's slope differs from zero. These results are reported in model l a in Table 4. (For a complete reporting of result% see Appendix 2.) The first result to notice is that working in a predominantly female occupation lowers everyone's

TABLE 4. REGRESSION COEFFICIENTS OF OCCUPATIONAL SEGREGATION MEASURES FOR GENDERACE/ETHNIC GROUPS

African White American Asian

Women Women Latinas Women

Gender Composition of Occupation Model la: Earnings levels (comparison is 0) -0.206* -0.231* --0.200* --0.125" Model 2a: Earnings gap (comparison is white men) -0.058" -0.082" -0.052" 0.023

Model lb: Earnings levels (comparison is 0) -0.674" -0.873" -0.121 0.408 Model 2b: Earnings gap (comparison is white men) -0.922* -1.121* -0.368' 0.158

Occupational Segregation of Labor Market

African White American Asian Men Men Latinos Men

Gcndcr Composition of Occupation Model la: Earnings levels (comparison is 0) -0.148* -0.193" -0.204* -0.324" Model 2a: Earnings gap (comparison is white men) -0.148* -0.045* -0.055" -0.175"

Model 1 b: Earnings levels (comparison is 0) 0.248 0.389' 0.431' 1.268" Model 2b: Earnings gap (comparison is white men) 0.248 0.144 0.181 1.019

Occupational Segregation of Labor Market

Source: USBC (1993). Note: N = 4,073,006. 'p < .10; * p < .05.


earnings: the coefficients for the eight gender composition effects are all negative and significant. The segregation effects for white women and Latinas are comparable (6 =

-0.206 and -0.200, respectively) while the penalty for working in a female dominated occupation is slightly greater for African American women (p 1 -0.231). Although Asian women’s earnings are lower when working in a predominantly female occupation, the disadvantage is less than that of other women as noted by the smaller negative coefficient (p = -0.125). In sum, the results show that the earnings of all workers- women, men, whites, African Americans, Hispanics, and Asians-are lower in predominantly female occupations. Therefore, occupational gender segregation is a condition that should be of interest to all workers.

A second concern we have is whether working in a predominantly female occupation contributes to the “gender wage gap” for women of color as it does for white women. Model 2a (Table 4) tests whether the gender composition of one’s occupation is related to the earnings gap with white men. Working in a predominantly female occupation does contribute to the gender earnings gap for white women (p = -O.OSS), African American women (p = -0.082), and Latinas (p = -0.052). In an analysis not reported here, we find that these coefficients are not statistically different; therefore, we conclude the occupational gender segregation hurts the relative earnings of white women, African American women, and Latinas equally. However, the gender composition coefficient for Asian women is positive and not significant (p = +0.023) indicating that occupational gender segregation contributes little to the gender earnings gap between Asian women and white men.

In these data, there is only limited support for the inferences drawn from the crowding and devaluation hypotheses, that the gender composition of one’s occupation disproportionately penalizes women of color. The findings also fail to fully support the prediction from human capital theory that white women would be particularly disadvantaged working in a predominantly female occupation. In sum, while all women’s earnings are lower when working in predominantly female occupations, there is no consistent pattern of disadvantage to suggest that the earnings of women of color are uniformly more negatively affected by occupational gender segregation than are those of white women. Furthermore, working in a predominantly female occupation contributes equallji to the earnings gap with white men for three of the four groups of women, the one exception being Asian women for whom the gender composition of their occupation is unrelated to the gender earnings gap.

Contextual Effects of Occupational Gender Segregation in the MA labor Market

In this section, we shift our focus from the gender composition of occupations to the gender segregation of local labor markets. In doing so we address the same questions posed earlier: Does the degree of occupational gender segregation in the local labor market affect the earnings levels of all groups of women‘? Is the effect of occupational gender segregation in a local labor market on the gender earnings gap with white men consistent for all groups of women?

We begin by examining the effect of occupational gender segregation on earnings levels. The results are reported in the model l b (Table 4). As shown, the coefficients for occupational gender segregation are negative and significant for white women (y =


-0.674) and African American women (y = -0.873), indicating that working in a gender-segregated labor market hurts the earnings levels of these women. Since the gender composition of one’s own occupation is controlled in these models, the negative effect of local labor market segregation applies to women employed in both predominantly female and predominantly male occupations. Simply working where occupations are more gender segregated decreases the earnings for these two groups of women. However, the gender segregation of the local labor market has no significant effect on the earnings levels of Latinas and Asian women.

Our next question asks whether the degree of occupational segregation in the local labor market affects the gender gap in earnings for both women of color and white women. Turning to the results shown in model 2b, we find that more occupational segregation is associated with a greater gender earnings gap for white women (y = -0.922), African American women (y = -1.121), and Latinas (y = -0.368). However, working in a segregated labor market does not significantly affect the gender earnings gap for Asian women relative to white men. In an analysis not shown here, we find that the occupational segregation effect for African American women (y = -1.3 21) is significantly greater than that of white women (y = -0.922) while the effect for Latinas (y =

-0.368) is significantly less than that for white women (y = -0.922). Devaluation and crowding theories suggest that the greater occupational gender seg-

regation in a local labor market should lower the earnings and earnings gaps of women of color while human capital theory suggests there will either be no difference or white women will be particularly disadvantaged. Again, as with the microlevel gender composition effect, no consistent support is found for the three hypotheses. The earnings levels of African American women are markedly lower when working in gender-segregated labor markets, a finding consistent with the devaluation and crowding hypotheses. Yet the earnings levels of Latinas and Asian women are not significantly affected by working in a segregated labor market, suggesting support for the human capital thesis. And while occupational gender segregation has a greater effect on the earnings gap of Afri- can American women than on that of white women (a finding in support of the devaluation and crowding theories) the earnings gaps for Latinas and Asian women are less affected than those of white women, a finding suggesting human capital theory may be partially correct.

DISCUSSION

We began by asking, to what extent does occupational gender segregation have comparable effects across racdethnic lines on earnings levels and gender earnings gaps? We examined two types of occupational segregation effects: an individual-level effect that results from working in a predominantly female occupation and a contextual effect that results from working in a local labor market that is more gender segregated. Both effects are broadly important for women of all racial groups. In other words, all women stand to benefit from occupational integration. For all women, higher earnings result from working in an occupation that has been predominantly male, and for most women, higher earnings also result from working in a local labor market that is not so gender segregated.

Our second research question comes from inferences drawn from crowding theory, the devaluation thesis, and human capital theory. All three explanations for the segregation/


earnings relationship may imply differential effects for women depending on racial/ ethnic group. The findings offer only limited support for the theoretical hypotheses posed by these theories. Devaluation and crowding theories suggest that the earnings of women of color, as compared to white women, are disproportionately hurt by segregation. This is the case for African American women. Greater occupational gender segregation, whether of the woman’s own occupation or of her labor market, does have a greater negative impact on African American women’s earnings than on the earnings of white women. However, the findings for Latinas and Asian women are not consistent with the inferences drawn from the devaluation and crowding hypotheses. First, there are no significant differences in the gender composition of occupation effect comparing Latinas and white women. There are significant differences between these two groups in the effect of labor market segregation, but the effect is opposite that expected by the devaluation or crowding hypotheses. Second, Asian women working in predominantly female occupations, while disadvantaged for working in predominantly female occupations, are penalized less than are white women. In addition, while the gender wage gap for white women is greater in segregated labor markets, the gender wage gap for Asian women is not related to labor market segregation. These findings for Asian women lend support to the human capital hypothesis that white women will be penalized more than, or the same as other women, for working in a predominantly female occupation.

What does this all mean? Occupational gender segregation, whether at the occupation level or at the labor market level, does contribute to earnings inequality for the four groups of women we examined. On this all the theories are correct, although they differ on the reasons for this relationship. Yet the interactive effects of gender with race/ ethnicity are more complicated. African American women are the only women of color who are particularly penalized relative to white women by occupational gender segregation. The segregation effects for Latinas and Asian women are no greater than for white women. Thus the devaluation and crowding hypotheses that assume a multiplicative segregation penalty for women of color may be overstating the case. Rather, the earnings of Latinas and Asian women are lower because of occupational segregation and because of race/ethnicity, but not the joint effect of both.The micro- and macroprocesses of how occupational gender segregation impacts earnings are similar for all women-that is, segregation is a gender phenomenon more so than an interactive race/gender phenomenon.

The findings for African American women suggest a unique dynamic may be operat- ing to produce the stronger segregation effects we found. It is not simply being a woman of color, but rather an African American woman, that leads to lower earnings when working in a predominantly female occupation or in a gender-segregated labor market. We offer a few possible reasons for our findings, each of which requires additional research.

First, African American women may face more intense racism than do Latinas and Asian women. Greater racism may lead to a greater devaluation of African American women’s work than the work of other women of color. Second, as we reported earlier, African American women are more occupationally segregated from white men than are other women. This greater crowding of African American women may translate into a greater penalty for working in female-dominated jobs as well as gender-segregated labor markets. Another possible reason for the disproportionate impact of segregation on the earnings of African American women is that African Americans tend to be concentrated in more highly segregated labor markets as measured by the relatively high


correlation between the percentage African Americans in an MA and occupational segregation in an MA ( r = +.533). Hispanics are not as concentrated in segregated labor markets (Y = +.364) nor are Asians ( r = - .055). Lastly, it may be that the greater ethnic and class differentiation among Hispanics and Asians masks segregation effects when one is predicting average earnings. That is, some Hispanic and Asian subgroups of women may not be as devalued or crowded as other Hispanic and Asian subgroups, resulting in a diminished segregation effect for these groups. In addition, a greater share of Asian women are employed in professional and managerial occupations (USDL 1998), while a greater share of Hispanic women are employed in blue-collar occupations (USDL 2000), suggesting potentially greater polarization in the earnings of Hispanic and Asian women as compared to African American women that might then result in smaller effects when fitting to an average earnings model. Future research should clarify how occupational segregation affects the earnings of these more detailed ethnic and class groups.

Attention to potential racial/ethnic variations in the segregation/earnings relationship sensitizes us to the differences among women, but it is still true that occupational segregation hurts all women’s average earnings and most women’s earnings relative to white men. Perhaps, the patterns that have been observed in the results will be useful to encourage occupational segregation theorists to consider more explicitly if and why, segregation might impact the earnings and earnings gaps of different types of women in ways that are not captured in these results.

ACKNOWLEDGMENTS

Financial support for this research was provided by grants from the National Science Foundation (SBR-9422546, SBR-9870949, SBR-9870980, and SBR-9871204). Brenda Kowalewski and JoAnn DeFiore provided important input at an early stage of this work. We also thank Seth Ovadia for his comments and Matt Bramlett, David Consiglio, and Cindy Larison for their programming assistance. This paper was presented at the 1998 annual meeting of the American Sociological Association in San Francisco.


APPENDIX 1. DEFINITIONS OF VARIABLES

Variable Data Source Definition

Macrolevel measures Occupational

Region gender segregation

Percent Black Percent Hispanic Percent Asian

American

Individual-level variables Earnings

Gender*race/ethnicity

Marital status

Number of children Education Experience (potential) Citizenship status

Immigrant status

English difficulty

Hours worked

Proportion female of

Occupation: general occupation

educational development

training time Occupation: specific

1990 Census EEO tables 1990 STF3C

1990 STF3C 1990 STF3C 1990 STF3C

1990 PUMS

1990 PUMS

1990 PUMS

1990 PUMS 1990 PUMS 1990 PUMS 1990 PUMS

1990 PUMS

1990 PUMS

1990 PUMS

1990 Census EEO

England and Kilbourne 1988

England and Kilbourne 1988

See Cotter et al. 1997

Three dummy variables for North Central, South, and West (Northeast is the excluded category)

Percent of population non-Hispanic Black Percent of population Hispanic, any race Percent of population non-Hispanic Asian

American

Logarithm of estimated hourly wages from annual wage and salary income plus self- employment income divided by the number of weeks worked and the usual hours worked per week

Eight dummy variables: non-Hispanic white women, non-Hispanic black women, Hispanic womcn, non-Hispanic Asian women, non-Hispanic white wen, non- Hispanic black men, Hispanic men, and non-Hispanic Asian men. (Because these ten categories exhaust the possibilities in this sample, the model is fit without an intercept .)

Two dummy variables: formerly married (divorced, separated, widowed, and married- spouse absent) and never married (currently married, spouse present, is the excluded category.)

Number of children in the household Number of years of school completed Age minus years in school minus 6 Dummy variable coded 1 if a U.S. citizen by

birth or naturalization; coded 0 if not a citizen of the US.

Dummy variable coded 1 if immigrated to the U.S. in any year prior to 1990; coded 0 if riot an immigrant

Ordinal variable ranging from 1 to 4 with 1 equal to speaks English very well and 4 equal to speaks no English at all

Log of the number of hours usually worked per week in 1989

Proportion female of all employed in the occupation in 1990

Six-point scale measuring occupation’s educational requirements for reasoning, mathematical, and language skills

Number of months of training time required for an occupation

APPENDIX 2. REGRESSION COEFFICIENTS FOR INDIVIDUAL LEVEL CHARACTERISTICS

Model l a Earnings Model 2a Levels Earnings Gaps

Raceigender dummy variables White male" (intercept) White femaled African American female" Hispanic femaled Asian femalea African American maled Hispanic male" Asian male"

% female in occ. (white male) White female * YO female in occ. African Amer. female * % female in occ. Hispanic female * % female in occ. Asian female * Yo female in occ. African Amer. male * % female in occ. Hispanic male * YO female in occ. Asian male * % female in occ.

Years of school completed Experience (potential) [Experience ( p ~ t e n t i a l ) ] ~ (+ 100) Hours worked (In) Full-time status Weeks worked Number of children Formerly married Never married Immigrant Citizen English difficulty Occupation: GED (Occupation: specific training (+ 100)

Sex * education Sex * experience Sex * experience-square (-+loo) Sex * log hours worked Sex * full-time status Sex * weeks worked Sex * number of children Sex * formerly married Sex * never married Sex * immigrant status Sex * citizenship status Sex * English proficiency Sex * occupation GED Sex * occupation specific training (+ 100)

Gender composition of the occupation separately

Control variables

Gender interactions with control variablesb

In verse Mills ratio

2.338*** 2.121*** 2.155*** 2.154*** 2.169*** 2.276*** 2.316*** 2.365***

- .14Y*** -.206*** - .231*** -.200*** - .125*** -.I 93*** - .204*** -.324**"

.055***

.017*** - .032** * - 531***

.329***

.003***

. O M * * * -.082*** - .130*** -.015***

.053*** -.048***

.145***

.044***

- .011*** -.010***

.012***

.237* * * -.060***

.001* * * -.026***

.063***

.102***

.032***

.003 ***

.041***

.072*** -.185***

.155***

2.338*** - .218*** -.182*** -.183*** -.169*** -.062*** -.022**

.027*

-.149*** --.058*** - .082*** - .052***

.023* -.045*** - .056*** -.175***

.055***

.017*** - .032* * * - .S31***

.329***

.003***

.018*** - .082* * * -.130*** - .015***

- .048*** .053***

.145***

.044***

- .011*** -.010***

.012***

.237* * * - .060***

.001*** - .026***

.063***

.102***

.032***

.001***

.041* * *

.072***

.155*** -.185***

Source: USBC (1993). No'tes: People working in 1989 in a Metropolitan area; N = 4,073,006. a The race*gender dummy variables are modeled as random coefficients across MAS. The coefficients here are the predicted values for an MA with mean characteristics on the other variables in the higher-level model.

* p .: .05; **p < .01; ***p < .001. Men = 0. Women = 1.


APPENDIX 3. REGRESSION COEFFICIENTS FOR M A LEVEL VARIABLES

African White American Asian

Women Women Latinas Women

Model l b : earnings levels interccpt

Occupational segregation Region: North Central Region: South Region: West YO African American % Hispanic % Asian

Model 2b: earnings gaps intercept

Occupational segregation Region: North Central Region: South Region: West % African American YO Hispanic % Asian

2.121*** -0.674*** -0.095*** -0.141*** -0.089***

0.283*** 0.126** 0.637+

-0.217*** -0.922*** -0.045***

0.004 -0.025** -0.074* -0.001 -0.003

White Men

2.157*** -0.873*** -O.lOY*** -0.202*** -0.155***

0.087 0.074 0.321

-0.181*** -1.121*** -0.060*** -0.057*** -0.091*** -0.271*** -0.053 -0.320***

African American

Men

2.151*** -0.121 -0.059* -0.105***

0.006 0.037 0.023 0.41 8

-0.188*** -0.368+ -0.008

0.040* 0.070***

-0.320*** -0.104** -0.223***

Latinos

2.168*** 0.408

- 0.116*** -0.117*** -0.066* -0.267* -0.013

0.51 7

-0.170*** 0.158

-0.065* 0.027

-0.002 -0.625*** -0.141* -0.122*

Asian Men

Model l b : earnings levels intercept

Occupational segregation Region: North Central Region: South Region: West % African American % Hispanic % Asian

Model 2b: earnings gaps intercept

Occupational segregation Region: North Central Region: South Region: West % African American YO Hispanic % Asian

2.338*** 0.248

-0.050* -0.145*** -0.064**

0.358*** 0.127** 0.640'

2.338*** 0.248

-0.050* -0.145*** - 0.064**

0.358*** 0.127** 0.640'

2.276*** 0.389 +

- 0.060* * -0.164*** -0.062*

0.034 -0.113*

0.450

-0.063*** 0.144

-0.009 -0.018

0.002 -0.323*** -0.240*** -0.188**

2.313*** 0.431' 0.021

-0.075** 0.023 0.101

-0.119** 0.538'

-0.025*** 0.181 0.070*** 0.070*** 0.876***

-0.257*** -0.246*** -0.101+

2.353*** 1.268**

-0.001 -0.068* -0.060 -0.259'

0.040 0.453**

0.014 1.019** 0.049 0.076* 0.003

-0.617*** -0.087 -0.186


NOTES

1. An alternative, complementary, crowding explanation of why gender segregation lowers female but not male earnings relies on the “reserve army” of female labor (i.e., housewives) available to fill female occupations but not available in such abundance for male occupations. Thus a smaller earnings increase is needed to increase the supply of workers to female occupations than to male occupations. Thus, uncompensated wage elasticities for labor force entry have historically been higher for women than for men (Goldin 1990).

2. This design omits nonmetropolitan areas, which contain one-fifth of the 1J.S. population. Earlier research shows that gender inequalities in nonmetropolitan areas resemble those in metropolitan areas (Cotter e t al. 1996).

3. The dissimilarity index has an important disadvantage when comparing MAS. When a sample is small (e.g., for Enid, Oklahoma) and we use the detailed three-digit occupation codes, random fluctuations alone can produce a high index (Cortese, Falk, and Cohen 1976). In order to use the detailed occupation codes, we therefore calculate an adjusted dissimilarity index, DSTAT”. to measure occupational segregation (following Cotter et al. 1997). The adjusted measure can be interpreted as the percentage of workers of either sex who would have to change occupations in order for the two distributions not to differ by any more than would be expected by chance. Across 261 MAS, the mean adjusted D statistic is 49.8; this varies from a low of 40 in Columbia, Missouri to a high of 61 in Houma, Louisiana.

4. To avoid over complicating the models, we interact the individual-level control variables only with a single gender dummy variable rather than with each of the race*gender dummy variables. Wc have centered each of these individual-level variables before calculating interaction variables so the eight race*gender groups are compared at the national mean of all the control variables. Thus our interpretation of these race*gender coefficients and of their variation across MAS is not affected by these control variables.

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THE EFFECTS OF OCCUPATIONAL GENDER SEGREGATION ACROSS RACE

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