why do “women’s jobs” have low pay for their educational level?

Carolyn Aman Karlin, Paula England, and Mary Richardson

Why Do "Women's Jobs" Have Low Pay for Their Educational Level?

Jobs held largely by women have low pay for the amount of education they require. Why is this? One hypothesis is that employers prefer to hire men, and all workers prefer better jobs, so that women can get in only when a job 's rewards (relative to its skill requirements) are low enough that men no longer want the job. Thus, wages affect sex composition. Others see the causal arrow to run the other way. In this view, the sex composition of jobs affects the wage that employers offer; employers' biased perceptions lead them to see jobs as less important and less payworthy if they are filled largely by women. These explanations are not mutually exclusive; the causal arrow may run both ways (or neither way). In this paper we use CPS data from 1984 to 1991 and a cross-lag panel model to examine these effects. Jobs are defined with a detailed occupational category within a specific broad industry category. We find that jobs with a higher percentage of females at one point have slower wage growth (or steeper wage decline) for both men and women in the ensuing years. But we find no effect of earlier wage rate on later sex composition. For those interested in reducing gender inequality in earnings, these findings suggest the utility of "comparable worth" policies.

The pay gap between women and men has decreased. The ratio of women's

to men's median annual earnings rose from 60% in 1980 to 74% in 1997 (for

Carolyn J. Aman Karlin earned her Ph.D. in sociology at the University of Arizona and taught at the University of Central Arkansas. Now pursuing a second career, she is studying veterinary medicine at the University of Minnesota.

Paula England is a professor of sociology at Northwestern University and affiliated with the Institute for Policy Research. Her teaching and research focus on gender in the labor market and family, and on integrating sociological, economic, and feminist theories.

Mary Richardson earned her M.A. in sociology at Northwestern University, emphasizing economic sociology and gender issues. She now works as a senior financial analyst at Newark InOne in Chicago.

Direct correspondence to Paula England, Department of Sociology, Northwestern University, 1810 Chi- cago Avenue, Evanston, IL 60208-1330 (p-england @ northwestern.edu).

4 Gender Issues / Fall 2002

full-time, year-round workers). There still remains a sizable gap, and it has been relat ively stable since 1990.1 Most researchers who study the subject

agree that relatively little of the pay gap comes from men and women in the same job with the same seniority receiving different pay, although this does occas ional ly happen. 2 Research shows that predominantly female jobs aver-

age lower pay than male jobs that require about the same amount of education. 3 When we put these facts together, it is clear that the proximate cause of much of the sex gap in pay is the sex segregation of jobs and the low pay of predominantly female j obs . 4 Estimates of the sex segregation index, the per-

centage of women (or men) who would have to change jobs for the job distribution to be equivalent between the sexes, indicate that the index fell from 68 in t970 to 53 in 1990. 5 While segregation has declined substantially, many jobs still are filled largely with one sex or the other, which continues to fuel the sex gap in pay. 6

Despite this widespread agreement that women 's concentration in lower p a y i n g j o b s 7 explains much of the sex gap in pay, researchers differ in their view of why occupational sex composition and pay are linked. There are three

main views here. One view, the "devaluation theory," is associated with policy proposals for "comparable worth. ''8 The theory doesn ' t specify why jobs are

filled by one sex or the other, but focuses on what happens once they are. They claim is that if a job is filled mostly by women, employers start seeing the job as less valuable or less demanding. Somehow, the low status of women "rubs o f f '

on employers ' evaluation of the job, and they set a lower pay level for both men and women in the job than they would have if the identical job were done mostly by men. If this is true, then because of this bias in wage setting, the sex composi t ion of a job has a causal effect on the wage. This is the bias that

comparable worth policies were designed to address. However, such policies have never been legislatively mandated in the United States except in some states for public sector workers only. Such policies would require employers to use some consistent mechanism of job evaluation to set the pay in both male

and female-dominated jobs. Another prominent view of why women's jobs pay less sees the important

type of employer discrimination to be hiring or placement discrimination. This

view is associated with Strober and her colleagues and by Reskin and Roos. 9

The theory does not specify why some jobs pay more than others, but works out logic about how the relative pay levels of jobs would affect their sex com-

position under certain assumptions about employer and worker behavior. The general assumption about employers is that they would prefer to hire men in virtually all jobs (although some authors assume this preference is greatest in

Karlin, England, and Richardson 5

higher-paying jobs); the assumption about workers is that both men and women prefer highly rewarded jobs. If we put these two assumptions together, it fol-

lows that when hiring for high-paying jobs, employers will be able to get men, but when hiring in low paying jobs, they will often have to settle for women even if they prefer men, since men will gravitate first to the high-paying jobs.

In such a process, even if women also prefer high-paying jobs, which these authors generally assume, they will only be able to get the jobs men don ' t want. If this is roughly what is happening, then the pay offered by jobs indirectly causes a change in their sex composi t ion through these processes by

which employers rank potential workers by sex and workers rank jobs. Of course, these writers recognize that educational credentials are crucial for entry into some jobs, so they generally state that it is when jobs pay badly relative to

their educational requirements that employers won ' t be able to get men, and the jobs are likely to end up filled with women. Using Reskin and Roos' ~0 term,

we refer to this as the "queueing" view. Strober and Catanzarite ~ have referred to it as the "relative attractiveness" theory of occupational segregation by gen-

der. These two views of the link between a job's sex composition and its wages

make opposite arguments about which factor causes the other. In the devaluation view, occupational sex composition affects jobs ' wages. In the queueing view, wages affect sex composition. In either case there is sex discrimination,

but the types of discrimination are very different. In the queueing view, the primary issue is hiring or placement discrimination. (Such discrimination has been illegal since the Civil Rights Act of 1964 but may still go on.) In the

devaluation view, gender bias affects which jobs get assigned higher wages, but this form of bias does not violate current law. ~ A central, unresolved issue in research on the sex gap in pay is which way this causal arrow goes, that is, which type of discrimination is the important sex bias in labor markets. Of

course, both could be going on simultaneously, so that the causal arrow runs both ways. ~3 In this paper, we use eight years of national data from the 1984- 1991 Current Population Surveys and cross-lag panel to test for causal effects

in both directions. There is a third view of the association between jobs ' sex composition and

their pay that sees neither as causing the other, but sees their association as complete ly or largely spurious. H In this view, favored by some economists, women choose less demanding jobs than men because they prioritize mother-

hood more and money less than men. In this view, "women 's jobs" pay less because they are less demanding or more "mother-friendly." For example, they

may have more flexible hours, or allow a parent to use the phone to check in


with children, or provide childcare. These gender-specific hypotheses are part of a larger theory in neoclassical labor economics known as "compensat ing differentials." The general idea is that, net of human capital, employers have to

pay more for jobs that have nonpecuniary or intrinsic characteristics workers do not like as well. Put statistically, this suggests that if we include the right control variables for job characteristics, the relationship between the percent-

age female employees in a job and its pay will disappear. (Research favoring the compensating differentials view of the lower pay of women's jobs includes Filer, 15 and Macpherson and Hirsch; 16 research disputing this view includes Jacobs and Steinberg, 17 Kilbourne et al., 18 and Budig and England. tg) Our analy-

sis will also attempt to include sufficient controls so that we do not erroneously

assert a causal effect in either direction where none exists. Below we review past research on these questions. Then we discuss the

data set and methods we use. This is followed by a discussion of our findings

and their implications for theory and public policy.

Past Research and Theory on the Relationship Between Sex Composition and Wages

Strober's theory of occupational segregation predicts that occupations with low or declining wages will become feminized. 2~ In her view, patriarchy oper-

ates such that men receive first choice of occupations. Male employers have a vested interest in maintaining the system of male privilege. They fear that a breakdown of male privilege in the working class will lead to a domino effect through the upper class, thereby risking their own positions. Moreover, if they

do not privilege male over female workers in hiring for good jobs, they face the risk of sanctions from employees, customers, and other community members; breaking patriarchal norms is punished, z~ As a result, females are concentrated

in the jobs that males do not want, the least desirable and lowest-paying jobs, which may not be compensated in accordance with their skill demands. Thus, according to Strober's theory, wages affect occupational sex composition since wages largely determine which jobs men will accept.

Strober and Arnold found that in 1950, male bank tellers were not compensated commensurate with their education, and over time the occupat ion

feminized. = They conclude that "men with the requisite education left bank- telling, or failed to enter it, because they found more lucrative jobs in other occupations. ''23 They present evidence that many women entered bank-telling

during World War II, but after the war men did not reclaim their positions, even

though banks gave them opportunity prior to offering permanent positions to


women, because the pay was unattractive compared with alternatives. They do

not consider the possibility that bank-telling was easier to picture as a "female" occupation than the other occupations females took over during the war and that this, rather than low wages, led to more thorough feminization later. 24 If

this is true, and wages were lowered in comparison to comparable jobs because

bank-telling became feminized, Strober and Arnold's interpretation has the causal order wrong. Thus, while their case study is consistent with the view that wages affect sex composition, it is not inconsistent with the possibility that the arrow

goes only the other way. In an argument similar to Strober and Arnold's, Reskin and Roos ' queue-

ing model implies that the wage level of an occupation affects its sex composition. 25 In their view, employers order groups of workers according to their

attractiveness as employees, while workers rank jobs according to their desir-

ability. The preferences of male and female workers are substantially similar in this view; both sexes prefer jobs with higher pay, status, and mobili ty pros- pects. Changes in the labor market affect how far down in their queue employees must go to find a job and employers must go to find workers. A shrinking

job market may force employees to take lower-ranked jobs than they normally would; a tighter labor market may force an employer to hire someone below her/his preferred standard. Reskin and Roos point to a variety of contributing

factors that may cause employers to rank males ahead of females in their labor queues, including force of custom, statistical discrimination, fear of sanctions from male employees , and patriarchy. Since employers generally rank males ahead of females in their labor queues, this propensi ty to dis-

crimination in all jobs, when combined with men's preference for the better jobs, leads to a causal sequence in which jobs with low or declining wages are those in which employers are forced to accept women. Reskin and Roos

ident i fy the decl ine in the re la t ive a t t rac t iveness of some former ly male occupations as causing a change in the location of these male occupat ions in male workers' queues. This allows more women into those occupations be-

cause men leave, fewer new men are recruited, and men fight less hard to keep women out of occupations if they are planning to leave themselves. If the occupations are better than women's previous options, women will seize the op-

portunity to enter them. They provide evidence that "male" occupations which had a large influx of women from 1970 to 1980 offered lower wages or less prestige and autonomy than they had previously. In their view, lower wages (or benefits) were the driving force behind women's entrance into male occupations. While they do not provide a longitudinal statistical test of this view, they


discuss a number of case studies of occupations that appear to support the view. 26

Wright and Jacobs took issue with Reskin and Roos ' contention, at least as regards computer specialists , one of Reskin and Roos ' cases. 27 Wright

and Jacobs used data from the 1980s and showed that men's pay in these

occupations increased relative to pay in other fields requiring the same education, yet women ' s proport ion in the occupat ion increased all during the period. Thus in this case, feminization was not preceded by declines in the

relative pay of the jobs. Four empirical studies have used longitudinal data on a range of jobs to

investigate the causal order between the sex composit ion of occupations and their wages. In a study of college administrators, Pfeffer and Davis-Blake con- c luded that there was ev idence for causa l i ty in both d i rec t ions . 2s Using

1978-1979 and 1983-1984 data from the College and Universi ty Personnel Association's Annual Administration Compensation Surveys, they found evidence consistent with both devaluation and queueing. ~9 Consistent with de-

valuation, salaries decreased in response to increases in the proportion of women administrators in the institution (at least up to a relatively high point of percent female). But they also found, supporting the queueing view, that controlling

for other factors, the change in mean salary between the periods affected the proportion of women employed in 1983. That wages also have an effect on

occupational sex composition could indicate a form of hiring discrimination in which women largely are barred from a lucrative occupation until its wages decline and it becomes less desirable. 3~

Baron and Newman concluded from their study of wage rates in the Cali-

fornia Civil Service from 1979 to 1985 that the increase in female and minority representation had strong negative effects on the relative starting pay of civil service jobs . 31 The devaluation effect was moderated somewhat in recently created jobs and in growing lines of work. They did not test for the reverse

causal order. Snyder and Hudis used a similar design to the analysis here. 32 Using U.S.

Census Data from 1950, 1960, and 1970, and a cross-lag panel model (ex-

plained below), they assessed causal relationships be tween sex composi t ion and white males' wages in detailed occupations (they did not consider women's wages). They found that the proportion female had a negative effect on male

median income, whi le income did not have a s ign i f ican t e f f ec t on an occupation's proportion female. Thus, their analysis supported the devaluation

more than the queueing view.


Catanzarite uses Current Population Survey data and a panel model to test for pay deterioration to white males' wages in detailed occupations from 1971- 1981 and 1982-1992. 33 (Her modeling strategy is similar to ours except that

she only examines effects of sex composit ion on wages, not vice versa.) She finds that the proportion white female and the proportion black male in an

occupation had a negative effect on male median income in the 1970s and that the propor t ion black female had a negat ive e f fec t in the 1980s. She does not test the reverse causal order in this paper, but notes that she does not

find the reverse effect (pay affecting sex composition) in unpublished work in

progress. Neoclassical economists suggest that the correlation between wages and

sex composition may not be causal in either direction, but spurious, owing to a third factor. In their view, the wages of an occupation are determined either by the human capital required of incumbents (given that they have to pay for

human capital, employers will not pay more for more human capital if it does not repay them to do so) or by compensating differentials. The theory of compensating differentials refers to the fact that employers have to pay a premium

to get workers to enter jobs with nonpecuniary disamenities, such as dangerous or unpleasant working conditions. Correspondingly, jobs with pleasant working conditions can be filled for less. Thus, economists hypothesize that a pos-

sible explanation for the pay gap between "male" and "female" jobs is their

skill demands or their working conditions. In the absence of discrimination, female occupations would pay less than

male occupations if they required less human capital, the skills and training that contribute to productivity, or if the working conditions were more pleasant

than those of male occupations. One of the amenities especia l ly re levant to women might be the "mother-fr iendl iness" of jobs. Empirical research has found that d i f ferences in skill demands and disameni t ies of j obs explain

part of the difference in pay between female and male jobs, but net of these factors, a portion of the difference in pay still co-varies with occupational sex compos i t ion . 34 Budig and England find no evidence that women select

female jobs because they are mother-fr iendly; mothers are no more likely than non-mothers to work in "female" jobs. While there is a wage penalty

for motherhood, it is not associa ted with any part icular j ob character is t ic they were able to measure, other than working part-time. 35 However, a limi-

tation of their analysis as well as ours is that we lack good measures of the

mother-fr iendliness of occupations. This highlights the importance of controlling for occupations ' prior wage, which will indirectly control for these

unmeasured job characteristics to the extent that they are relat ively stable.


Since some women ' s occupations are concentrated in low-paying (e.g., service) industries, 36 it will be important to hold constant the industrial mix of

occupations.

Data and Methods

To ascertain the causal order between occupational wage and sex composition, we analyzed data from March extracts of the Current Population Survey: Annual Demographic File for year-pairs from 1984 to 1991. Using extracts

from the CPS that merged relevant household and family information onto person records, we selected records to include all civilian workers aged 16 or older, in the rotation groups that were asked earnings questions. The units of analysis are occupation-industry categories (hereafter referred to as INDOCCs

or jobs). That is, we cross-tabulated detailed (three-digit) 1980 Census occupational categories with major (one-digit) 1980 industry code. Each cell was an INDOCC. Person-record data for full-time workers were used to calculate the sex compos i t ion and a sex-speci f ic mean on each other var iable for each

occupation-industry category. Part-time workers were included only to create a variable for the proportion of workers in the INDOCC who were employed full-time. These cell means and percents were then output to a file with INDOCC

as the unit of analysis. We conducted analyses for each year-pair from 1984 to 1991, using 1991 as an endpoint in each case. We did not use the 1990-1991 year-pair, both because one year seems too short a lag for the processes we are modeling, and because the CPS samples some of the same people in adjacent

year pairs, rendering the samples of adjacent years nonindependent. For each set of analyses, we used the subset of INDOCCs for which there was at least one male and one female with non-missing information on each of the variables for the two years used in the analysis. (In sensitivity tests not shown, we

eliminated all INDOCCs below 5 or 10 in either of the years; while some significant coefficients lost significance, they retained the same sign and approxi-

mately the same magnitude in most cases.) We use INDOCC as the unit of analysis to control for industry and to

partially control for the characteristics of the occupations examined. Although there may be qualitative changes in some of the occupations from an earlier year to 1991, crossing occupation with industry provides more detail, thereby minimizing the differences. This also controls for changes in occupations that

would occur if, over time, an occupation expands within one industry while contracting in another. Including industry in the unit of analysis holds constant industrial characteristics hypothesized by economic segmentation theorists to


affect wages and sex composition, insofar as the one-digit level of detail cap-

tures these characteristics.

Cross-lag panel ordinary-least-squares regression models were used to

estimate causal effects of wages and sex composition of an INDOCC in each

direction. Regressions were run separately by sex to determine if the effects of

wage on sex composition or vice versa were different for males than females,

and to ensure that sex differences in average variable values within an INDOCC

did not affect coefficients for sex composition. Two regressions were run for

each sex, one with sex composition in the later year as the dependent variable

and one with wages in the later year as the dependent variable, for a total of

four basic regressions per year-pair. Regressions contained the same control

variables, but differed in their dependent variable and weights. 37

The basic design is to predict (1) wage at T 2 from sex composition at T t,

controls at T~ and T 2, and a control for the dependent variable, wage, at Tj and

to predict (2) sex composition at T 2 from wage at T~, controls at Tj and T 2, and a control for the dependent variable, sex composition, at T~.

The inclusion of the lagged dependent variable as a control is the key

modeling strategy here designed to minimize omitted variable bias. This vari-

able can be "interpreted as a proxy for causal paths linking the dependent vari-

able at T~ to T 2 through variables that are omitted from the model. ''38 The idea

is that any relatively stable omitted variables that are correlated with both the

independent and dependent variable of interest will already have had their ef-

fect on the outcome, and by controlling for the dependent variable at an earlier

time, one nets out their effect. While this method is not perfect, especially in the presence of measurement error, 39 it goes a long way toward letting us assess

causal effects in both directions.

Our analyses focus on the effects of both sex composition and wage at T~

on each of these two variables at T 2. The basic format of the equations is:

WAGET2 = bo + b~ WAGE vl + b2 PROPORTION FEMALEvl +

ZNi=3 (b i Control Variables ,~ + T2)

and

PROPORTION FEMALEv2 = c o + c I WAGEa. 1 + c 2 PROPORTION FEMALET~ +

ENd=3 (c~ Control VariablesT~ § T2)

where WAGE is sex-specific--the mean hourly wage in 1991 dollars for fe-

male or male full-time workers in the INDOCC. PROPORTION FEMALE is the

proportion female among full-time workers in the INDOCC. In this model, the


cross-lag coefficients can be interpreted as the effect of the earlier year 's sex composition on change in wage between the earlier and later year (b2), and the earlier year's wage on change in sex composition between the earlier and later

year (c2).4~ The other control variables used in the regressions include T~ and T 2

sex-specific variables created from the CPS extract that measure an INDOCC's mean worker characteristics or job characteristics believed to affect wages or

sex composition. The use of INDOCCs as the units of analysis with T~ and T z data partially controls for job characteristics. The variables incorporating worker characteristics, which indicate the mean level of human capital of those in an

INDOCC, include INDOCC workers' average education (in years) and average age (in years). The CPS does not provide information on work experience, training, or job tenure, so age is used as a rough proxy for experience.

Other control variables for characteristics of workers within an occupation that have been found to affect wages and are sex-related include proportion of full-time workers (with full-time defined as working 35 hours or more), proportion of workers who are union members, and proportion of workers who

are married. Since race composition could also affect wages, controls for the proportion of workers who are white, and proportion of workers who are black

were included for T~ and T 2 in the equations. Controls tapping job characteristics incorporate both industrial and physical location. They include the proportion of INDOCC employees working in the private sector, the proportion of an

INDOCC's employees located in the Midwest, Northeast, and South, and the proportion of employees living in or near a central city. Given the benefits of the cross-lagged panel model discussed above, other unmeasured occupational

characteristics should not produce significant omitted variable bias to the extent that they are relatively stable over time.

The equations predicting wage at T 2 do not contain proportion female at T2, nor do the proportion female equations contain wage at T 2. Both equations assume that there is no contemporaneous causation between WAGE and PRO-

PORTION FEMALE at time T 2. This assumption is sound, as it is unlikely that wages instantly respond to changes in sex composition and/or that sex composition instantly responds to changes in wages. Instant responsiveness would imply

that organizations continuously reassess pay scales in response to even tiny changes in sex composition, and/or that many workers enter and leave occupations within a short time in response to wage changes; this seems unlikely.

Regressions were run weighted by the number of females (males) in the INDOCC at Tj. The alternative strategies of weighting them by the number of

females (males) in the INDOCC at T 2 produced no qualitative differences in


findings, so we report only those weighted by Tj number of men or women. The rationale for weighting is that if we want a picture of how forces affect the

average worker, then occupations with more workers need to have a correspondingly larger impact on the analysis. However, our basic conclusions re- main if unweighted regressions are used (results not shown).

Results: Does Sex Composition Affect Wage, Wage Affect Sex Composition, Both, or Neither?

Our focus is on ascertaining whether there are causal effects of occupational sex composition on wages or vice versa. Table 1 presents the coefficients of most interest from all the regressions--the effects of sex composition at T 1 on

wages at T 2, and of wages at T 1 on sex composition at T 2, always controlling for the dependent variable at T~ (and the other control variables at both time periods).

Wages at TI is a significant predictor of 1991 sex composition in only two of the six male regressions, and in none of the six female regressions. This

indicates that occupations' wages generally do not affect their changes in sex composition. Thus, we do not find support for the idea that low (declining) wages lead jobs to become more feminized. Indeed, in the two cases out of six

where lagged male wage had a significant effect on later percent female of occupations, it was pos i t ive-- in the opposite direction to Reskin and R o o s 41

and Strober ' s p red ic t ion? 2 But the main finding is no effect of earlier wage levels on later sex composition.

Results from the wage regressions in Table 1 provide support for a causal effect of sex composition in one year on the wage level of the occupation in a

later year (1991). All signs are negative, indicating that when occupations have a higher proportion of female employees in the earlier year, the wages offered to either men or women later in 1991 are lower. The models can also be interpreted to mean that occupations with an higher initial percent female had lower

wage growth (or greater wage decline) than occupations with a lower initial percent female. For the female wage models, sex composition is negative and significant for four out of six year-pairs (all except 1989-1991, which is nega-

tive and nonsignificant, and 1986-1991, which is also negative and almost significant at p=.0528). The magnitude of the significant effects is such that if

an occupation changed from 0% to 100% female, wages would go down by about $1.30 to $1.50 an hour. Given that the average of women's median wages in most of these years was below $10 an hour, this is not a trivial effect.

For models predicting male wages, all signs are negative also, but only three are significant. For these year-pairs, all at least five years apart, the wage


penalty is of a somewhat larger magnitude than for women. It is such that if a man moved from an occupation that was 0% female to one that was 100%

female, his wage would fall between $1.45 and $2.25 an hour. Given average median male wages in the neighborhood of $10 to $12 an hour, this is not trivial. We experimented with a nonlinear effect (including proportion female and its square) and found that for men, the squared terms were significant in

four of the specifications. Thus, Table 1 presents the coefficients for linear and nonlinear specifications for the effect of proportion female on wages for men. (Only linear specifications are presented for women, since the squared terms were never significant for them.) For men, where the negative effect of propor-

tion female on wage was nonsignificant in the later year-pairs in the linear models, the nonlinear specification shows significant effects of proportion female and its square. The coefficients of sex composition and sex composition

squared for male year-pairs from 1986-1991 to 1989-1991 indicate that as the proportion female of occupations increase, male wages decrease up to a point beyond which the effect reverses and male wages actually go up as proportion female continues to rise. However, Table 1 shows that the inflexion points where

the negative effect of proportion female turns positive are always between 60% and 70% female. In 1991, only about 12% of men worked in occupations that were more than 60% female. 43 Thus, for the range of sex composition in which

the vast majority of men work, these models suggest that they lose pay if the proportion female of their jobs increases. Moreover, even in the range beyond the curve's inflection point where the predicted wages increase as percent female continues upward, they never get back up to as high a level as those

predicted when the occupation is all male. (Percent female would have to be about 250%, out of the existing range, for this to happen.)

Thus, overall, our findings show that wage levels in one year do not affect the sex composit ion of jobs in a later year. However, sex composi t ion does exert a causal effect on wages. Generally, the higher the percent female in an occupation, the less men's or women ' s wages in the occupation grow over

time, or the more they decline. Our panel-data evidence for this causal effect is substantially stronger than that offered by cross-sectional data. We see this as evidence that employers are influenced by the sex composition of jobs when they set wages.

Discussion: Why Do Women's Jobs Pay Badly?

Why do women's jobs pay badly for their educational level? Our analysis suggests that one reason is that employers respond to the sex composit ion of


Table 1 Coefficients from Cross-Lag Panel Models for Effects of

Occupations' Wage on Later Sex Composition and Sex Composition on Later Wage for Six Year-Pairs, 1984-1991

Linear Effect of Proportion Female at T1 on Wage at T2

Linear Effect of Wage at T1 on Proportion Female at T2

T1 T2

1984-1991 -1.438"*

1985-1991 -1.517"*

1986-t99t -.828

1987-1991 -1.326"*

1988-1991 -1.432"*

1989-1991 -.495

Models Involving Women's Wage N

-.005 N=314

-.001 N=335

-.000 N=348

-.004 N=350

-.004 N=371

.001 N=348

Models Involving Men's Wage

Nonlinear Specification of Prop. Female Effects Proportion Proportion Inflection

Female Female 2 Point

1984-1991 -2.249* .005* N=314

1985-1991 -1.951"* .007** N=335

1986-1991 -1.464" -8.375** 5.964** .702 -.002 N=348

1987-1991 -.933 -6.391" 4.732* .675 .003 N=350

1988-1991 -.529 -6.040* 4.787* .619 .002 N=371

1989-1991 -.906 -7.930** 6.131"* .647 .000 N=348

* p<.05 ** p<.01 (two-tailed tests)

Note: Wages for all years are expressed in 1991 dollars, using the Consumer Price Index. Models are weighted by the number males (male models) or females (female models) in 1991. Although only coefficients for Proportion Female (and its square for some specifications) and Wage are shown, the models for each year-pair also include controls for the dependent variable at TI, and T1 and T2 Mean Age, Proportion Private Organization, Mean Education, Proportion Full-Time Workers, Proportion Black, Proportion White, Proportion in Midwest, Proportion in Northeast, Proportion in South, Proportion in Central City, Proportion Union Members, and Proportion Married. Nonlinear effects of sex composition are shown where statistically significant. They were never significant for women. Models for 1990-1991 year-pair not included because consecutive CPS years use some of the same respondents.

jobs and assign lower wages if jobs are more heavily female. Our longitudinal analysis of the relationship between the sex composition of a job (operationalized here as occupation-industry cell) and its wages has shown that a higher propor-

tion female in an earlier year is associated with lower wages in a later year (net of the wage in the early year and other controls). Thus, the results support the devaluation view of labor market gender discrimination. 44


Neoclassical economic theory attributes the sex gap in pay to men and

women's differential distribution across jobs in the labor market, arguing that

differences in the jobs held by the sexes account for the sex gap in pay. Ac- cording to this theory, this pay gap is due to the different skills or disamenities

that are demanded by the jobs held by men and women. If women dispropor- tionately occupy lower-skilled jobs with fewer disamenities, this would explain

some of the gap. However, our analysis suggests that it does not explain it all. Our modeling strategy assessed whether a job pays less than the same job did previously if its percent female goes up. Making the wages relative to the previous

wage in the same job (through controlling for the lagged wage) goes a long way to making sure that skill levels and disamenities are held constant in the compari- sons while sex composition is varied. (And if educational requirements change,

this should be netted out by our controls for educational levels of workers.) Our results do not support the queueing view of Strober and colleagues 45

and Reskin and Roos. 46 Those views suggest that employers prefer men, and that men get the jobs that pay well relative to their educational requirements,

and that women's integration of "male" jobs generally occurs only in male jobs that are losing in relative reward level. This would imply a causal effect of

wages on sex composition. We never find coefficients consistent with this. Our negative finding does not necessarily negate the possibili ty of hir ing/placement discr iminat ion against women. We suspect that such d iscr iminat ion

does exist, although it probably has declined. But if our modeling strategy is believable, as we think it is, then some part of the queueing view must be wrong. Either (1) there is no hiring discrimination (we doubt this), or (2)

employers ' discrimination against women seeking to enter male jobs is neither uniform across jobs nor greatest in the most rewarding positions, or (3)

men do not choose jobs by their reward level, or (4) men do not encourage employers to discriminate most when their jobs are most rewarding, or (5) men

do not leave jobs when their rewards are declining, or (6) women do not enter male j obs as their rewards decline. Future research should try to isolate what part of the interrelated proposit ions in this theoretical view is leading

to the incorrect prediction. Alternatively, researchers may discover a supe- rior way to model causal dynamics with longitudinal data that will produce

a different conclusion. For now, our provisional conclusion is that the queueing prediction that low (or declining) wages produce feminization of fields is

not supported. What are the policy implications of our findings? The Civil Rights Act of

1964 makes hiring discrimination illegal, but it does nothing to remedy the kind of gender bias, devaluation of female work, for which we find evidence in


this study. Of course, anything that would contribute to the integration of occupations would also reduce the pay gap, whether that was a reduction of the hiring discrimination illegal under 1964, or changes in supply-side gendered processes that lead to different occupational outcomes. But absent large num- bers of women moving to "male" and men moving to "female" occupations, the gender pay gap is unlikely to be remedied under current policy. Compa- rable worth policies would directly attack the ability of employers to respond to the sex composition of jobs when they set wages. Since our findings suggest that employers engage in this bias, such a prohibition, if it could be success- fully enforced, would help reduce the sex gap in pay.

Notes

1. National Committee on Pay Equity, "The Wage Gap," [online], World Wide Web [cited 1999]: http://www.feminist.com/wagegap/htm

2. Trond Petersen and Laurie A. Morgan, "Separate and Unequal: Occupation-Establishment Sex Segregation and the Gender Wage Gap," American Journal of Sociology 101 (2) (1995): 329-365.

3. Paula England, Comparable Worth: Theories and Evidence (Hawthorne, NY: Aldine de Gruyter, 1992); Elaine Sorensen, Comparable Worth: ls It a Worthy Policy? (Princeton, N J: Princeton University Press, 1994); R. J. Steinberg, "Comparable Worth in Gender Studies," in International Encyclopedia of the Social & Behavioral Sciences, 2393-2397, ed. Neil J. Smelser and Paul B. Baltes (Elsevier Press, Vol. 4, 2001 ).

4. The other major factor in the pay gap is men's greater number of years of job experience, arising largely because women sometimes drop out of paid employment to rear children, while men seldom do. This part of the pay gap is both within and between occupations. It is diminishing over time as women's employment is becoming more continuous. Allison Wellington, "Changes in the Male- Female Wage Gap, 1976-1985," Journal of Human Resources 28 ( 1993): 383-411 ; Allison Wellington, "Accounting for the Male/Female Wage Gap Among Whites: 1976 and 1985," American Sociological Review 59 ( 1994): 839-884.

5. Francine D. Blau, "Trends in the Well-Being of American Women, 1970-1995," Journal of Economic Literature 36 (1998): 112-165.

6. Blau. 7. Sociologists generally use the term "job" to refer to a detailed occupational title within a

specific organization. Occupations are job titles in general-enough use that they can be assigned in national survey data sets (e.g., titles such as secretary, doctor, carpenter, librarian). We seldom have data linking wages with the sex composition of jobs. Most research uses occupations for which good national data are available, recognizing that much detail is lost by doing this. For an argument that it is preferable to use job data, see Tomaskovic-Devey, Donald, Gender and Racial Inequality at Work: The Sources and Consequences of Job Segregation (Ithaca: ILR Press, 1993). For unusual examples of analyses using job level data, see Petersen and Morgan; Tomaskovic-Devey; and Julie A. Kmec, "Minority Job Concentration and Wages," Social Problems 50 (2003): 38-59. This analysis will use occupation-industry cells.

8. England, Comparable Worth: Theories and Evidence; Steinberg; Sorensen. 9. Myra H. Strober, "Toward a General Theory of Occupational Sex Segregation: The Case of

Public School Teaching," in Sex Segregation in the Workplace: Trends, Explanations, Remedies, 144- 156, ed. Barbara E Reskin (Washington, D. C.: National Academy Press, 1984); Myra H. Strober and Carolyn L. Arnold, "The Dynamics of Occupational Segregation Among Bank Tellers," in Gender in the Workplace, 107-148, ed. Clair Brown and Joseph A. Pechman (Washington, D. C.: Brookings Institu- tion, 1987); Myra H. Strober and Lisa M. Catanzarite, "The Relative Attractiveness Theory of Occupa-


tional Segregation by Gender," in Women "s Employment, Special Issue of Beitrgige zur Arbeitsmarkt- und Berufsforschung (Labor Market and Occupational Research), Germany 179 (1994): 116-139; Barbara E Reskin, and Patricia Roos, Job Queues, Gender Queues: Explaining Women's Inroads into Male Occupations (Philadelphia: Temple University Press, 1990).

10.Reskin and Roos. 11. Strober and Catanzarite. 12. England, Comparable Worth: Theories and Evidence, Chapter 5. 13. Indeed, authors advocating the queueing view recognize the possibility of devaluation occur-

ring during and following occupational feminization, and authors advocating the devaluation view recognize that there may be hiring/placement discrimination.

14. Randall Filer, "Male-Female Wage Differences: The Importance of Compensating Differen- tials," Industrial and Labor Relations Review 38(3) (1985): 426-437; Filer, Randall, "Occupational Segregation, Compensating Differentials, and Comparable Worth," in Pay Equity. Empirical Inquiries, 153-170, ed. Robert T. Michael, Heidi I. Hartmann, and Brigid O'Farrell (Washington, DC: National Academy Press, 1989); Randall Filer, "Compensating Differentials and the Male-Female Wage Gap: A Comment," Social Forces 69(2) ( 1990): 469-474; David A. Macpherson and Barry T. Hirsch, "Wages and Gender Composition: Why Do Women's Jobs Pay Less?" Journal of Labor Economics 13(3) (1995): 426-471.

15.Filer, "Male-Female Wage Differences: The Importance of Compensating Differentials"; Filer, "Occupational Segregation, Compensating Differentials, and Comparable Worth"; Filer, "Com- pensating Differentials and the Male-Female Wage Gap: A Comment."

16. Macpherson and Hirsch. 17.Jerry A. Jacobs and Ronnie J. Steinberg, "Compensating Differentials and the Male-Female

Wage Gap: Evidence from the New York State Comparable Worth Study," Social Forces 69(2) ( 1990): 439-468; Jacobs and Steinberg, "Compensating Differentials and the Male-Female Wage Gap: A Re- ply," Social Forces 69(2) (1990): 439-468.

18. Barbara Kilbourne, Paula England, George Farkas, Kurt Beron, and Dorothea Weir, "Returns to Skills, Compensating Differentials, and Gender Bias: Effects of Occupational Characteristics on the Wages of White Women and Men," American Journal of Sociology 100 ( 1994): 689-719.

19. Michelle J. B udig and Paula England, "The Wage Penalty for Motherhood," American Socio- logical Review 66 (2001): 204-225.

20. Strober. 21. Strober and Arnold. 22. Strober and Arnold. 23. Strober and Arnold, 121. 24. Strober and Arnold acknowledge that after bank-telling was largely feminized, its compensa-

tion commensurate to education continued to fall for both men and women, and suggest that devaluation of feminine work is a likely causal factor. However, they think that the low reward level preceded the feminization.

25.Reskin and Roos. 26.While they emphasize diminishing rewards as the major reason that jobs feminize, this is not

the only factor they discuss. They also discuss examples of highly skilled, quickly growing occupations where employers turned to women because of a lack of men with the appropriate credentials. In these cases, a shortage of men rather than lower wages may drive women's entry.

27. Rosemary Wright and Jerry A. Jacobs, "Male Flight from Computer Work: A New Look at Occupational Resegregation and Ghettoization," American Sociological Review 59 ( 1994): 511-536.

28. Jeffrey Pfeffer and Alison Davis-Blake, "The Effect of the Proportion of Women on Salaries: The Case of College Administrators," Administrative Science Quarterly 32 (1987): 1-24.

29. Their modeling strategy was similar to ours, except that they controlled for a predicted score on the dependent variable in the earlier year rather than the observed score, as we do, and they put their independent variable of interest in as a change score. It is also important to note that their unit of analysis is not an administrative job or occupation, but an entire university or college, for which they take an average of all administrative salaries for men and women.

30. Pfeffer and Davis-Blake.

KarIin, England, and Richardson 19

31. James Baron and Andrew Newman, "Pay the Man: Effects of Demographic Composition on Prescribed Wage Rates in the California Civil Service," in Pay Equity: Empirical Inquiries, 107-130, ed. Robert Michael, Heidi Hartmann, and Brigid O'Farrell (Washington, DC: National Academy Press, 1989). The prescribed monthly starting wage in a job class was used rather than an average of the actual wages paid.

32. David Snyder and Paula M. Hudis, "Occupational Income and the Effects of Minority Com- petition and Segregation: A Reanalysis and Some New Evidence," American Sociological Review 41 (1976): 209-234.

33.Lisa Catanzarite, "Race-Gender Composition and Occupational Pay Degradation," Social Problems (forthcoming).

34. Sorenson; England et al., "'Explaining Occupational Sex Segregation and Wages: Findings from a Model with Fixed Effects"; Jacobs and Steinberg; England; MacPherson and Hirsch focus on the compensating differentials view and how much controls reduce the effect of occupational percent female on wages. However, even after controls, most of their models still find some dampening effect of percent female on wages.

35. Budig and England. 36. E. M. Beck, Patrick M. Horan, and Charles M. Tolbert II, "Stratification in a Dual Economy:

A Sectoral Model of Earnings Determination," American Sociological Review 43 (1978): 704-720; Randy Hodson and Paula England, "Industrial Structure and Sex Differences in Earnings," Industrial Relations 25(1) (1986): 16-32; James E. Coverdill, "The Dual Economy and Sex Differences in Earn- ings," Social Forces 66(4) (1988): 970-993; Paula England, Karen Christopher and Lori R. Reid, "Gender, Race, Ethnicity and Wages," in Latinas and African American Women at Work, ed. Irene Browne (New York: Russell Sage, 1999).

37. Steven E. Finkel, Causal Analysis with Panel Data (Thousand Oaks, CA: Sage Publications, 1995).

38. Finkel, 9. 39. Paul Allison, "Change Scores as Dependent Variables in Regression Analysis," in Sociologi-

cal Methodology, 93-114, ed. C.C. Clogg (Oxford: Basil B lackwell, 1990). 40. Allison; Finkel. 41.Reskin and Roos. 42. Strober. 43.Calculated from 1991 Current Population Survey data by occupation. 44. One alternative interpretation consistent with our findings is that occupational deskilling acts

as an intervening variable between feminization and wages. That is, feminization of an occupation may lead employers to lower its skill level, and then lower wages accordingly. The use of control variables for incumbents' human capital partially controls for the change in the skill level of an occupation, as long as incumbents in female jobs are not systematically overqualified for their jobs. However, since the controls (years of education and age as a proxy for experience) are imperfect, we cannot rule out the possibility that occupational deskilling acts as an intervening variable. This would entail a causal effect of sex composition on wages, but not via devaluation.

45. Strober; Strober and Arnold; Strober and Catanzarite. 46.Reskin and Roos.

References

Allison, Paul. "Change Scores as Dependent Variables in Regression Analysis." In Sociological Meth- odology. (Pp. 93-114.) Edited by C.C. Clogg. Oxford: Basil Blackwell, 1990.

Baron, James, and Andrew Newman. "Pay the Man: Effects of Demographic Composition on Pre- scribed Wage Rates in the California Civil Service." In Pay Equity: Empirical Inquiries. (Pp. 107- 30.) Edited by Robert Michael, Heidi Hartmann, and Brigid O'Farrell. Washington, DC: National Academy Press, 1989.

Beck, E. M., Patrick M. Horan, and Charles M. Tolbert II. "Stratification in a Dual Economy: A Sectoral Model of Earnings Determination." American Sociological Review 43 (1978): 704-20.

Blau, Francine D. "Trends in the Well-Being of American Women, 1970-1995." Journal of Economic Literature 36 (1998): 112-65.

20 G e n d e r I s s u e s / F a l l 2 0 0 2

Budig, Michelle J., and Paula England. "The Wage Penalty for Motherhood." American Sociological Review 66 (2001): 204-25.

Catanzarite, Lisa. "Race-Gender Composition and Occupational Pay Degradation." Social Problems 50 (2003): 14-37.

Coverdill, James E. "The Dual Economy and Sex Differences in Earnings," Social Forces 66, no. 4 (1988): 970-93.

England, Paula. Comparable Worth: Theories and Evidence. Hawthome, NY: Aldine de Gruyter, 1992. England, Paula, Karen Christopher, and Lori R. Reid. "Gender, Race, Ethnicity, and Wages." In Latinas

andAfrican American Women at Work, Edited by Irene Browne. New York: Russell Sage, 1999. England, Paula, George Farkas, Barbara Stanek Kilbourne, and Thomas Dou. "Explaining Occupa-

tional Sex Segregation and Wages: Findings from a Model with Fixed Effects." American Sociologi- cal Review 53 (1988): 544-58.

Filer, Randall, "Male-Female Wage Differences: The Importance of Compensating Differentials." In- dustrial and Labor Relations Review 38, no. 3 (I985): 426-37.

Filer, Randall. "Occupational Segregation, Compensating Differentials, and Comparable Worth." In Pay Equity. Empirical Inquiries. (Pp. 153-70.) Edited by Robert T. Michael, Heidi I. Hartmann, and Brigid O'Farrell. Washington, DC: National Academy Press, 1989.

Filer, Randall. "Compensating Differentials and the Male-Female Wage Gap: A Comment." Social Forces 69, no. 2 (1990): 469-74.

Finkel, Steven E. Causal Analysis with Panel Data. Thousand Oaks, CA: Sage Publications, 1995. Hudson, Randy, and Paula England. "Industrial Structure and Sex Differences in Earnings." Industrial

Relations 25, no. 1 (1986): 16-32. Jacobs, Jerry A., and Ronnie J. Steinberg. "Compensating Differentials and the Male-Female Wage

Gap: Evidence from the New York State Comparable Worth Study." Social Forces 69, no. 2, (1990): 439-68.

Jacobs, Jerry A., and Ronnie J. Steinberg. "Compensating Differentials and the Male-Female Wage Gap: A Reply." Social Forces 69, no_ 2, ( 1990): 439-68.

Kilbourne, Barbara, Paula England, George Farkas, Kurt Beron, and Dorothea Weir. "Returns to Skills, Compensating Differentials, and Gender Bias: Effects of Occupational Characteristics on the Wages of White Women and Men." American Journal of Sociology 100 ( 1994): 689-719,

Kmec, Julie A. "Minority Job Concentration and Wages." Social Problems 50 (2003): 38-59 Macpherson, David A., and Barry T. Hirsch. "Wages and Gender Composition: Why Do Women's Jobs

Pay Less?" Journal of Labor Economics 13, no. 3m (1995): 426-71. National Committee on Pay Equity. "The Wage Gap." [online]. Available from World Wide Web [cited

1999]: http://www.feminist.com/wagegap/htm Petersen, Trond, and Laurie A. Morgan. "Separate and Unequal: Occupation-Establishment Sex Segre-

gation and the Gender Wage Gap." American Journal of Sociology 101, no. 2, (1995): 329-65. Pfeffer, Jeffrey, and Alison Davis-Blake. "The Effect of the Proportion of Women on Salaries: The Case

of College Administrators." Administrative Science Quarterly 32 (1987): 1-24. Reskin, Barbara F., and Patricia Roos. Job Queues, Gender Queues: Explaining Women's lnroads into

Male Occupations. Philadelphia: Temple University Press, 1990. Snyder, David, and Paula M. Hudis. "Occupational Income and the Effects of Minority Competition and

Segregation: A Reanalysis and Some New Evidence." American Sociological Review 41 (1976): 209-34.

Sorensen, Elaine. Comparable Worth: Is It a Worthy Policy ? Princeton, N J: Princeton University Press, 1994.

Steinberg, R. J. "Comparable Worth in Gender Studies." In International Encyclopedia of the Social & Behavioral Sciences, edited by Nell J. Smelser and Paul B. Baltes. Oxford: Cambridge University Press, Vol. 4 (2001): 2393-97.

Strober, Myra H. "Toward a General Theory of Occupational Sex Segregation: The Case of Public School Teaching." In Sex Segregation in the Workplace: Trends, Explanations, Remedies. (Pp. 144- 56,) Edited by Barbara E Reskin, Washington, DC: National Academy Press, 1984.

Strober, Myra H., and Carolyn L. Arnold. "The Dynamics of Occupational Segregation Among Bank Tellers." In Gender in the Workplace. (Pp. 107-48.) Edited by Clair Brown and Joseph A. Pechman. Washington, DC: Brookings Institution, 1987.

Kar l in , E n g l a n d , a n d R i c h a r d s o n 21

Strober, Myra H., and Lisa M. Catanzarite. "The Relative Attractiveness Theory of Occupational Segre- gation by Gender." In Women's Employment, Special Issue of Beitriige zur Arbeitsmarkt- und Berufsforschung. (Translation from German: Labor Market and Occupational Research.) 179 (1994): 116-139.

Tomaskovic-Devey, Donald. Gender and Racial Inequality at Work: the Sources and Consequences of Job Segregation. Ithaca, NY: ILR Press, 1993.

U.S. Department of Commerce, Bureau of the Census. Current Population Survey: Annual Demo- graphic File, 1983-1991 [Computer file] Washington, DC: U.S. Department of Commerce, Bureau of the Census.

Wellington, Allison J. "Changes in the Male-Female Wage Gap, 1976-1985." Journal of Human Re- sources 28 (1993): 383-411.

Wellington, Allison J. "Accounting for the Male/Female Wage Gap Among Whites: 1976 and 1985." American Sociological Review 59 ( 1994): 839-84.

Wright, Rosemary and Jerry A. Jacobs. "Male Flight from Computer Work: A New Look at Occupa- tional Resegregation and Ghettoization." American Sociological Review 59 ( 1994): 511-36.


Appendix Table: Means and Standard Deviations for Female and Male Incumbents of INDOCCs, 1991

Mean Std Dev Females 1991 Wages (in 1991 Dollars) 1991 Proportion Female 1991 Mean Age (in Years) 1991 Proportion Private Org. 1991 Mean Education (Years) 1991 Proportion Full-Time 1991 Proportion Black 1991 Proportion White 1991 Proportion Union Members 1991 Proportion in Midwest 1991 Proportion in Northeast 1991 Proportion in South 1991 Proportion Central City 1991 Proportion Married

Males 1991 Wages (in 1991 Dollars) 1991 Proportion Female 1991 Mean Age (in Years) 1991 Proportion Private Org. 1991 Mean Education(Years) 1991 Proportion Full-Time 1991 Proportion Black 1991 Proportion White 1991 Proportion Union Members 1991 Proportion in Midwest 1991 Proportion in Northeast 1991 Proportion in South 1991 Proportion Central City 1991 Proportion Married

9.52 3.71

.61 .25 38.07 4.69

.82 .31 12.70 1.99

.76 .21

.13 .14

.83 .15

.15 .18

.23 .13

.23 .14

.35 .18

.75 .16

.55 .16

12.82 4.73 .33 .22

38.79 4.93

.82 .34 13.06 2.03

.91 .14

.08 .09

.89 .10

.24 .23

.24 .13

.27 .14

.28 .13

.76 .16

.67 .18

Note: INDOCCs are weighted by number of men or women in them in 1991.

why do “women’s jobs” have low pay for their educational level?

Documents