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7 Occupational Segregation, Compensating Differentials, and Comparable Worth RANDALL K. FILER In surveying the relative positions of men and women in the labor market, two facts stand out. First, wage differences between the two sexes are substantial. Second, dif- ferences in occupational structure are sig- nificant; men and women are concentrated in different occupations and heavily female occupations tend to be Tower paying. Recent years have seen substantial shifts in both of these factors. Median weekly earnings of fulI-time female workers over age 16 have risen from 61 percent of those for men in 1978 to 71 percent of male earnings in the second quarter of 1987. Thus, in the past 10 years approximately 25 percent of the difference in mate and female earnings has been eliminated. At the same time, although there are methodological difficulties in mea- suring changes in the degree of occupational sex segregation over time, numerous recent studies have documented that the degree of sex segregation in occupations has de- cTined since at least 1970 (and probably since 1960. As one example, Bianchi and iThe methodological difficulties arise from changes over time in the categories into which occupations are classified (England, 1981~. Rytina (1986) found that the index of dis- similarity fell from 67.9 in 1970 to 59.4 in 980.2 Despite these improvements in the rel- ative economic position of women over the past few years, significant differences in occupational (listributions an(l earnings be- tween the sexes persist. There are important policy implications to be derived from an understan(ling of why these differences ex- ist. To the extent that they arise from un- equal opportunities caused by unfair hiring or promotional practices, the economy has failed to make appropriate use of human resources and has created inefficiencies. In this case there is justification for interven- tion to facilitate greater sex equality in the labor market. On the other hancl, to the extent that sex (differences in occupational structure and earnings arise from differences in individual productivity or choices, (le- spite equal labor market opportunity, in- terventions to change either employment 2The dissimilarity index is roughly equal to the percentage of workers who would have to change jobs in order to create an equal distribution of the sexes in all jobs. 153

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154 or earnings patterns would leac] to distor- tions in resource allocation and the creation of inefficiencies. POSSIBLE CAUSES OF SEX DIFFERENCES The observed pattern of the genders being concentrated in different occupations, cou- pled with lower average wages in the oc- cupations that are heavily female, is con- sistent with a number of possible explanations that have been proposed by economists. Those explanations provide the framework for the analysis in this paper. Differences in Productivity It may be that one gender has lower average levels of productivity and has con- centrated in occupations in which it has a comparative advantage. Primary among the factors that may contribute to differences in productivity between typical men and women is past work experience. Previous research has established that between one- quarter and one-half of the gender gap in wages may be due to differences in the extent of previous employment (Corcoran, 1979; Mincer and Ofek, 1982; Mincer and Polachek, 1978; Sandell and Shapiro, 1978~. Physical differences may also contribute to slivering occupational comparative ad- vantages and overall productivity. In one setting, Hoffmann and Hoffmann (1987) found that upper body strength and lifting re- quirements limited women's bidding on and accepting "mate" jobs even though they were actively encouraged to clo so by their employer. Similarly, several authors (e. g., Daymont and Andrisani, 1984; Filer, 1983; Greenfield et al., 1980) have observed that women and men in the labor market have substantially different personality patterns with respect to such characteristics as em- pathy and aggression, which may lead to PAY EQUITY: EMPIRICAL INQUIRIES different job choices an(l, consequently, dif- ferent reward structures.3 Differences in Utility Functions Men and women may make rational choices in the job market based on differences in utility functions that create differing pref- erences for certain types of work and other (luties. For example, some evi:lence in(li- cates that women attach greater importance to various forms of attractive working con- clitions and that men place relatively greater emphasis on incomes (Forgionne and Pe- ters, 1982; Harvey, 1986; Murray an(l At- kinson, 1981~. Such a difference in pref- erences, coupled with the fact that the market forces employers to pay compen- sating differentials to those workers who fill jobs with relatively unattractive working conditions, will, even given equal produc- tivity, result in women being concentrates! in lower paying but otherwise more attrac- tive jobs. Evidence presented in Filer (1985) suggests that such compensating differen- tials may be responsible for up to one- quarter of earnings differences between men and women. Much of the literature regarding (liffer- ences between men ant] women in labor market preferences starts with the fact that there are differences in home duties. Filer (1985) reports that jobs typically held by women are those from which it is easier to take time off for personal reasons and are typically located closer to their homes.4 3~t should be noted that nothing has been said about how these personality differences may have arisen. Some maintain that they are innately linked to bio- logical differences between the sexes; others believe they are the result of childhood conditioning. The reality is probably some combination of these and other sources. 4Such locational differences contribute to wage dif- ferentials because commuting time is a negative char- acteristic that must be compensated for and because, by restricting the opportunity set from which a job may be chosen, individuals who desire to work close

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OCCUPATIONAL SEGREGATION Others (O'Neill, 1983, 1985; Waite ant] Ber- ryman, 1985) have pointed out that female- dominated jobs require less overtime, are less likely to have rotating shifts, ant] are more likely to be part time. All of these findings are consistent with women assum- ing responsibility for child rearing. Perhaps the most frequently advanced reason why Mitering home responsibilities might lead to occupational segregation comes from the fact that women tend to have more discontinuous work histories. This should lead them to choose jobs that require little firm-specific human capital and in which there is relatively little atrophy of skills when not in use. Such a rational investment reason for occupational segregation has been most forcefi~ly advocated by Polachek (1979, 1981, 1985, 1987~. It can also be found in AneshenseI and Rosen (1980), Blakemore and Low (1984), Matthaei (1982), Mincer and Ofek (1982), and Waite and Berryman (19851. Evidence casting doubt on this hy- pothesis has been presenter] by Corcoran et al. (1984), England (1982, 1985), and England et al. (19861. A related but more complex explanation has been advanced by Goldin (1986), who argues that a higher turnover rate for women makes structuring of contracts preventing shirking more dif- ficult ant] therefore results in the concen- tration of women in occupations in which such contracts are less critical. Employers may respond to a greater pro- pensity of women to leave the labor force to home limit their ability to seek out their highest productivity and highest paying match. Occupational segregation will result from the fact that workers who desire employment close to home will only be available to those industries that are well suited to decentralized production in residential areas. Firms requiring large work forces (therefore having to draw workers from a wide geographic area) or not able to locate in residential neighborhoods (due to the need to be in a central place or because of production externalities) will tend to have a disproportionate share of male workers. To the extent that such firms are (as would appear likely) better paying establishments, this segregation will also contribute to wage differentials. 155 by investing less in training women and being less likely to promote women (see Lazear and Rosen, 1989~. Such theories of"statistical discrimination" rest on the inability of employers to distinguish be- tween those women who will remain on the job and those who will leave. They do not, however, explain why women, who presumably know whether they intend to leave their employer to assume respon- sibilities at home, clo not negotiate con- tingent claims contracts insuring employ- ers against lost investments. Finally, Becker (1985) provides a theoretical rationale for why differing home duties will result in men providing greater levels of effort on the job. Discrimination If lower wages for one group are not the result of lower productivity and are not fully compensated by nonwage aspects of the job, the labor market is not in equilibrium and members ofthe group receiving lower wages should move into higher wage occupations. The absence of such equilibrating move- ment (and thus a stability over time in the extent of occupational sex segregation) would suggest that women have been involuntarily lenie(l access to certain occupations (see Bergmann, 1974; Blau, 1984; Madclen, 1975; Stevenson, 1984~. Obviously, such con- scious denial of access to occupations, whether through the actions of employers, other workers, customers, or legislative action, would create occupational segregation.5 This fit is an unanswered question to what extent ob- served patterns of occupational segregation result from impositions from outside the labor market. An obvious example is the law preventing women from serving in combat specialties in the armed forces. Until very recently, many states had protective legislation that limited the exposure of women to hazardous working conditions and restricted the schedules and number of hours women could work. It has been very difficult to develop models of how denial of access can be stable over time and not create

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156 could explain Tower wages for women through one of two mechanisms. "Crowding" of women into a limited number of jobs could artificially increase supply and depress wages (see Bergmann, 1974; Johnson and Solon, 1984~. Alternatively, employers may con- sciously take the sex composition of jobs into account when setting pay levels (see England et al., 1982; Treiman and Hart- mann, 1981~. If discriminatory differences in occupa- tional structures are not being eliminatecl by labor market mobility, some structural barrier must be preventing such movement. This would suggest two possible courses of action. Either the barrierks) to mobility may be removed so that rational mobility cle- cisions on the part of workers will create equality of compensation, or the occupa- tional distribution may be taken as fixed and an attempt made to raise wages in jobs heavily filled by women.6 It is the latter policy that has come to be known as "comparable worth." Advocates of comparable worth call for pay to be administratively set so that differences in wages (or full compensation, including the value of fringe benefits) not based on dif- ferences in productive skills, effort, re- sponsibility, and working conditions are eliminated. An excellent review of the de- velopment and implementation of the con- cept of comparable worth is presented in Weiler (1986). incentives for nondiscriminatory employers (including women themselves) to employ women in the previously denied occupations. Indeed, most models of discrim- inatory actions on the part of employers lead to firm segregation rather than occupational segregation, a result consistent with the finding of Bielby and Baron (1984) that segregation among firms is generally greater than that across occupations. in. .. . . . .. bThese policies are. to a certain extent, mutually exclusive. If policies are enacted to raise wages in women's occupations to a level commensurate with their productivity and working conditions, this reduces the incentive for women to move into jobs previously held by men. PAY EQUITY: EMPIRICAL INQUIRIES ANALYTIC FRAMEWORK This paper investigates the extent to which there exist (differences in the wages paid in various occupations that are not related to levels of effort and responsibility, working conditions, or the productive characteristics of incumbents in them, but which are re- lated to the sex composition of the occu- pation. Much recent work has applied a similar procedure to micro-level data, re- gressing inctiviclual wages on personal char- acteristics and the percentage of women in an individual's occupation.7 Studies such as England (1982, 1985), England et al. (1986), Ferber and Lowry (1976), Jusenius (1977), and Stevenson (1975) have found a negative relationship between the proportion of fe- male workers in an occupation and its av- erage wage. Other studies (Aldrich and Buchele, 1986; England et al., 1982; Fuchs, 1971; Treiman et al., 1984) have used occupations as the unit of analysis, regressing average wages in an occupation (either separately for men and women or combined) on a set of ex- planatory variables as well as the occupa- tion's sex composition. These studies have been han(licapped by their ability to in- clucle, at most, a small subset of the factors encompassed in an occupation's effort, re- sponsibility, working conditions and pro- uctive requirements." Estimation Issues Comparable worth studies traditionally estimate an equation whereby wages (w) are a function of productive characteristics (P), job characteristics (effort, responsibility, anti working conditions) (C), and the sexual composition of the job (F) such that 7See Polachek (1987) for an explanation of why this procedure is unable to distinguish adequately between human capital and occupational sex segregation ex- planations for sexual differences in earnings.

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OCCUPATIONAL SEGREGATION Wi = aPi + bci + cFi + Ui. (1) A number of issues must be addressed in the estimation of this relationship. First, some authors (see Treiman and Hartmann, 1981; Treiman et al., 1984) have regressed average wages for men and wom- en combined in an occupation on its char- acteristics as specified in Equation (11. Such a strategy, however, is inherently incapable of addressing comparable worth issues be- cause it confounds male-female wage dif- ferentials that result from differences in pay according to sex composition with differ- ences due solely to pay differentials within each occupation. Suppose women were paid 75 percent of men's earnings in each oc- cupation no matter what its sex composition. It would still be the case that an occupation that was almost 100 percent female would have an average wage three-quarters of that for an occupation that was almost 100 per- cent mate. Thus, separate equations for men and women must be estimated to determine whether the gender composition of an oc- cupation affects earnings. Second, in a comparable worth study it is especially incumbent on the researcher to be sure that the hedonic wage equations are properly specified. Consider the issue of omitted variables. Suppose that instead of Equation (1) a researcher estimated an equation of the form Wi = CtFi + U i A classic case of omitted variables bias then exists such that c' = c + bEcov~ci' Fi~lvar(Fi)] + atcov(Pi, Fi~lvar(Fi)~. Thus, if P and C are measured so that the expected signs of a and b are positive, and if women have lower levels of the productive attributes or choose to enter occupations with fewer undesirable job characteristics that elicit compensating differentials so that cov(Pi, Fi) and/or cov(Ci, Fi) are neg- 157 ative, c' will be less than c and estimates will overstate the negative impact of the proportion female on the average level of earnings in an occupation. Given that, as was discussed above, gender is likely to be causally related to a number of important characteristics of occupations through pro- ductivity and occupational choice consid- erations, little confidence can be placed on estimates of gender effects derived from equations that do not fully reflect the jobs under analysis. Given the complexity of occupations, this is a daunting task and ought to endow claims for perceived effects of an occupation's gender composition on its wages with a strong dose of agnosticism. Third, comparable worth equations of the form of Equation (1) are in fact hedonic wage or price equations resulting from the interaction of workers' demand for and firms' supply of various job characteristics. As is typical in the compensating differentials lit- erature, this relationship is estimated here using ordinary least squares (OLS). It must be recognized, however, that it is only in special cases that such estimates can be consistent (Epple, 19871. Although some recent empirical work has suggested that the available instruments that could be used to "solve" this problem in the context of compensating differentials studies are so poor as to render it impossible to reject statistically the accuracy of the OLS esti- mate, such a conclusion would raise serious questions were such studies to become the basis for legal actions rather than academic research. Fourth, it is important to recognize that unless the hedonic wage equation is strictly linear in all its regions, there is no reason that returns to various factors should be the same for different groups even if wages for both were determined by a common func- tion. Thus, an interpretation of differences in coefficients between male and female wage equations as indicating structural dif- ferences in how the sexes are treated in the

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158 labor market, although frequently macle, is fundamentally inappropriate. Finally, it must be recognizes! that the estimated impact of gentler composition on wages from Equation (1) overstates the po- tential effect of the adoption of comparable worth policies. The proposals for the United States, unlike the situation in Australia, do not require job comparability to be estab- lished across firms but rather within par- ticular firms. Thus, to the extent that the gentler impact represents different pay policies among firms, it is immune to com- parable worth remedies. The Data This analysis combines data from a num- ber of sources to obtain a more exhaustive portrait of the occupations and their incum- bents than is available from any single data set. Earnings and personal characteristics for each three-digit occupation were derived from the 5 percent sample of the 1980 census.8 Measures of working conditions, effort, and responsibility were obtained fiom the Dictionary of Occupational Titles (U. S. Department of Labor, 1977), the 1977 Qual- ity of Employment Survey (Quin and Staines, 1979), the 1976 Survey of Time Use in Economic and Social Accounts (data tape provided by the Institute for Social Re- search, University of Michigan), and the May 1983 Current Population Survey (data tape provided by Bureau of the Census). Measures of personality were taken from a proprietary data set (leveloped by the author in the mid-1970s (see Filer, 1981~. Union- ization is a 3-year moving average centered on 1980 taken from Kokkelenberg and Sock- ell (1985~. The Duncan measure of occu- pational prestige (see Duncan and Stafford, 1980) and a measure of occupational dead- encledness (Brown, 1982) were added. Fi- 8Thus, the results presented here miss any of the substantial changes in the labor market status of women that have occurred since 1980. PAY EQUITY: EMPIRICAL INQUIRIES nally, since the typical imputation of ex- perience as age minus years of schooling minus six is not particularly useful for wom- en, average levels of experience for women were~ computed for each occupation using the National Longitu(linal Surveys (NLS) of Mature Women ant] GirIs.9 (See notes to appendix table for full data sources.) Since job characteristics were observer! for occupations and not workers, there would be no added variability from attributing these common measures to incTividual-level data. No micro-level data set contains suf- ficient measures of the concepts important for comparable worth to enable indivi(lual- level cross-sectional estimates. The process of integrating the data was complicate`] by the fact that the sources used several distinct sets of occupational codes that had to be mapped into the 1980 census scheme. it When small sample sizes meant that there 9The procedure involved combining all of the waves of both panels into a single data set so that sample sizes would be large enough and then estimating for each census occupation an equation of the form: Ex- perience = f (age, ages, marital status, race, and the year of observation). Since all of the right-hand side variables in these equations are available in the census, the estimated coefficients could be used to obtain a predicted average level of experience for those women actually in any occupation in 1980. In theory, a similar procedure could have been followed for men; however, the improvement that this would offer over the more traditional approximation was judged not to be worth the additional computational effort. iThe Bureau of the Census double-coded a sample of occupations each time the codes were revised, thereby enabling each earlier-year code to be divided among one or more later-year codes according to population proportions. Similarly, the May 1981 Cur- rent Population Survey was double-coded using both 1970 census occupation codes and DOT codes. Thus, the DOT codes and the Filer data (which used DOT codes) could be merged into the 1970 census data. The Institute for Survey Research provided a matching of the codes used in the Time Use Survey with 1970 census codes. Thus, by appropriate chaining, each data set could be bridged into 1980 census codes. The method used was to compute average values for each variable in its "native" code and then to calculate values for the 1980 coding scheme as weighted averages of the native code values.

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OCCUPATIONAL SEGREGATION were too few observations in any three-digit code to enable reliable computation of av- erage values for the characteristics, the av- erage values were computed at the two- digit levelly and then imputed back to the three-digit occupation. The final data set contained over 225 measures of various characteristics of oc- cupations and the workers who held them. i2 There are obvious difficulties with the in- clusion of all 225 available job character- istics. Given that there are only 430 three- ctigit occupations in the final sample, the reduction in degrees of freedom if all pos- sible explanatory variables were included wouIc! be substantial. Therefore, estimated results are reported in the appendix for a specification that maximizes the adjusted R2 of the wage equation. Estimated Wage Equations The 5 percent sample from the 1980 census contains data on 6,490,318 men and 4,986,538 women who were employed for all or part of 1979. The average wage for men in this sample was $9.23 an hour and that for women was $5.87.~3 Thus, women Census provides a set of two-digit codes and their constituent three-digit codes for the 1980 scheme. After a conversation with John Priebe at the Bureau of the Census failed to uncover any similar amalgam- ations for the 1960 and 1970 censuses, they were created by the author following as closely as possible the groupings in the 1980 scheme. Generally, this procedure was invoked if there were fewer than 5 observations in any cell in which averages were com- puted or fewer than 50 observations in the NLS cells in which the equations for imputing female work experience were estimated. t2A complete list of the variables in the data set is available from the author. i3Wages were calculated as labor earnings (the sum of wage or salary income in 1979, nonfarm self- employment income in 1979," and "farm self-employ- ment income in 1979" divided by the product of"weeks worked in 1979" and "usual hours worked per week in 1979"~. Since, for confidentiality reasons, the Census Bureau codes all incomes in excess of $75,000 as $75,000, "top-coded" incomes were replaced by an 159 in the sample earne(l approximately 64 per- cent of what men earned. The first acJjust- ment that needs to be macle recognizes that men and women typically work different numbers of hours.~4 Results are reporter] below only for full-time, full-year workers (those who averaged at least 32 hours of work per week for at least 45 weeks in 19791. RESULTS Table 7-1 reports the results of a number of hedonic wage equations that forcefully illustrate the points macle above in the discussion of estimation considerations. Re- sults are reported for regressions using lev- els of wages as the dependent variable. Both this specification and a semilogarithmic one in which the dependent variable is the natural logarithm of wages are common in the literature. The results for levels of wages are reported because their interpretation is patently obvious. Results using the natural logarithm of wages do not (lifter in any significant way and are available from the author. The first column in Table 7-1 reports the estimated impact on the wages in a job if it were to move from O percent female to 100 percent female as estimated from a combiner] sample of men and women. Col- umns two and three show results for men and women separately. Differences be- tween them and column one represent the extent to which women's lower wages within estimate of the mean income among those earning over $75,000 (calculated by fitting a Pareto distribution to reported incomes for those earning less than $75,000 within each income category and occupation) before total labor incomes and average wages were calculated. i4Census data cannot be adjusted for the fact that the typical longer work week of men means that more of them are likely to have hit statutory limits requiring the payment of overtime. Weller (1986) reports that comparisons of base pay of men and women rather than average hourly wages may eliminate as much as one-third of the apparent gender gap in earnings.

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160 PAY EQUITY: EMPIRICAL INQUIRIES TABLE 7-1 Estimated Coefficients of Gender Composition on Wages, All Full-Time, FulI-Year Workers Estimated Coef~cienta (Standard Error) Men and Women Women Men Equation Specification Combined Only Only Gender composition only - 4.41 - 1.73 - 2.32 (.38) (.25) (.42) Gender composition and - 3.30 - 1.92 - 1.38 demographic variables (.30) (.19) (.32) Gender composition, demographic - 3.13 - 1.59 - 1.29 variables, and union coverage (.30) (.19) (.32) Gender composition, demographic -1.70 0.30 0.31 variables, union coverage, (. 31) (.24) (.32) effort, responsibility, and working conditions Maximum R2) Gender composition, demographic - 1. 35 - 0.55 0.18 variables, union coverage, ef- (.50) (.37) (.53) fort, responsibility, and work- ing conditions (all variables) aThe estimated impact of an occupation shifting from entirely male to entirely female composition. Multiply by the difference in the femaleness of the job held by the typical woman and that held by the typical man to obtain the estimated gender contribution to the gross wage differential. occupations bias the gender effect when it is estimated using a combined sample. To calculate the impact of gender composition on wage differentials, one must calculate the change in wages that would occur if each occupation exactly mirrored the pro- portion female in the work force. Forty-two percent of the workers in the 5 percent census sample were women. The average man was in a job that was 23 percent female and the average woman was in a job that was 68 percent female. Adding Demographic and Personal Characteristics The first row of Table 7-1 shows the estimated effect of a job's being 100 percent female on the wages of full-time workers in that job if no other characteristics of either the worker or the job are taken into account. The most standard adjustment is to rec- ognize that men and women do not, on average, bring the same levels of productive attributes to the labor market. Census data provide a limited set of personal and de- mographic characteristics that may capture these productivity differences and can be included in the regression. Among them are racial group, marital and citizenship status, education, and crude measures of the type of employer. In addition, estimated actual work experience (as discussed above) was included in this specification. Results from this estimation are reported in the second row of Table 7-1. Increased female- ness of an occupation still implies signifi- cantly lower wages after controlling for these characteristics. Moving from being 100 per- cent male to being entirely female would, according to these estimates, bring a re- duction of $1.92 an hour in average wages for women and $1.38 an hour for men. The relationship is significant for both sexes, although, unlike results found by other re- searchers, it appears to be stronger for women than for men. It is often asserted that one reason women earn less than men is their lower partici- pation in unions that obtain higher than

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OCCUPATIONAL SEGREGATION competitive wages. The third row of Table 7-1 reports estimated gentler coefficients for an equation including personal character- istics and the proportion of the occupation covered by a collective bargaining agree- ment. There is some support for the hy- pothesis that lack of female participation in unions contributes to the estimated gender impact on wages. When unionization is added, the gentler impact in the female equation falls by almost 20 percent. Due to space considerations, coefficients on other variables are not reported in full. is In general, however, they are as expected. Education effects are somewhat stronger than found in micro-level studies. An ad- clitional year of schooling is estimated to result in between 51 and 76 cents an hour in additional earnings. Estimated union ef- fects are consistent with those from micro- leve} studies. The estimated benefits to join- ing unions are substantially greater for wom- en than for men. This raises the question of why women's unionization rates have traclitionally been lower than men's. The answer may lie in discrimination within unions, higher costs to women in joining unions, or the fact that women must am- ortize organizational costs over shorter ex- pectec] periods of job tenure. No relationship was found between the imputed average level of experience of wom- en in an occupation and its wages. For men, a positive relationship was found. These findings provide at least partial support for the Polachek analysis noted above. Results regarding the impact of the ethnic com- position of an occupation on its wages will be discussed below. Adding the Full Set of "Comparable Worth" Factors Even after the addition of census de- mographic characteristics, the effect of the proportion female in an occupation on its They are available from the author. 161 average wages remains substantial. We can turn now to the extent to which this is a statistical bias resulting from the omission of significant characteristics that are cor- related with the proportion female in an occupation but which advocates of compa- rable worth recognize as compensable in their own right, such as a job's working conditions ant] levels of effort and respon- sibility. The results of two versions of the "com- plete" comparable worth specification are presented. The fourth row of Table 7-1 contains estimates ofthe gentler impact from an equation constrained to include the vari- ables discussed in the previous section plus the job characteristics that maximized the adjusted R2 of the linear hedonic wage equa- tion for men and women combined (since there were slight differences in the set that was entered for the sexes separately). t6 The actual estimates for this equation are pre- sented in the appendix. The reader is cau- tioned, however, that where there are sev- eral measures relating to any comparable worth factor, patterns of multicollinearity make interpretation of any one coefficient impossible. It is only the effect of the full set taken jointly that has meaning. What is of interest here is not the coefficients in and of themselves (for a discussion of their meaning see Filer, 1987), but rather the impact that their inclusion has on estimates of the i6As an alternative method of data reduction, factor analysis was tried on the full set of raw variables. Even when rotated in several alternative ways, however, a large number of factors were required to capture even a moderate portion of the complexity in the data. Given the pattern of loadings on the factors, it proved difficult to assign any meaningful interpretation to them. An attempt was then made to group the variables on an ad hoc basis into 25 distinct sets based on what they apparently measured and then extract factors only within each set. Results from this experiment were highly ungratifying, and the resulting factors were of little use in explaining average wages in occupations. Thus, the decision was reached to retain variables in their raw form and reduce the number of variables used through a stepwise procedure.

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162 elect of gender composition on wages in an occupation. Finally, the last row of Table 7- 1 reports the result when all 225 job char- acteristics were entered into the wage equa- tion. The impact of adjusting for compensable job characteristics is striking. Once com- pensating differentials for a iob's effort re- sponsibility, fringe benefits, and working conditions are taken into account there is no significant relationship between an oc- cupation's gender composition and its wages for either men or women. What appears to be an effect in the combined equation results from lower wages for women within each occupation, which, to the extent that they represent other than legitimate compen- sation practices, can be addressed by equal r ., . employment laws but which are immune to comparable worth adjustments. Some of the results for other variables in this equation are worthy of note. For a more thorough analysis, the reader is referred to Filer (1987~. The pattern of the census vari- ables remains the same. There is an ap- proximately 25 percent reduction in the estimated impact of education on wages, although this coefficient is still highly sig- nificant. Examining comparable worth con- cepts such as effort and responsibility is complicated by the fact that there are several related ant] highly intercorrelated measures of each. When taken as a group (say by assuming a change of one standard! deviation in each), the results suggest that occupations requiring more effort or responsibility, ex- posure to worse working conditions, or long- er commutes pay higher wages for both men and women; while those with higher levels of fringe benefits, more interaction with other people, or employment in smaller establishments can pay lower average - ~ - wages. i7 PAY EQUITY: EMPIRICAL INQUIRIES Results for Other Ethnic Groups It is important to remember that the principle behind comparable worth laws shouIc! apply to all groups who might have been the victim of discrimination, not just to women. Thus, the question to be asked is whether there is a significant relationship between the average wages in an occupation ant] the proportion of that occupation filled by ethnic groups, such as blacks, Hispanics, and Asians. Table 7-2 reports the results of such es- timates for a sample that includes all work- ers. i8 Once job characteristics are controlled for, the estimated ethnic impacts are not significant. Before controlling for the nature ot the JOUS and any compensating differ- entials but after standardizing for average levels of productive attributes, it would appear that occupations held by blacks and Asians paid higher wages than those held by whites. The reality, however, is that these groups have entered better paying but otherwise less attractive jobs. Thus, with respect to other protected groups, as with women, there is no evidence that there are compositional effects on wages that could be remedied by the adoption of comparable worth policies. i7Fringe benefits are one of the clearest cases for which compensating differentials theory suggests that wages will adjust to job characteristics. For example, a job that provides health coverage can attract workers tion. at a lower wage than one that does not provide health benefits since workers will not have to pay for them out of pocket. Although one might alternatively add fringe benefits to wages to obtain a measure of full compensation, there are difficulties with this approach. Since fringe benefits are typically offered as a take-it- or-leave-it package, there can be no assumption that any given worker values them at their full cost to the employer (e.g., consider the value of maternity leave to a single man). Thus, the value of a fringe benefit package to employees is best established not by ac- counting costs but rather by the wage reductions workers are willing to accept in order to obtain the package. i8Thus, as was discussed above, these results include both the impact of the ethnic composition of an occupation and any within-occupation discrimina-

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OCCUPATIONAL SEGREGATION 163 TABLE 7-2 Estimated Coefficients of Ethnic Group on Wages, All Full-Time, Full-Year Workers Estimated Coefficienta (Standard Error) Equation Specification Blacks Hispanics Asians Ethnic composition only - 19. 39 - 39.07 33.64 (1.75) (3.69) (9.79) Ethnic composition and 3.47 0.69 24.05 demographic variables (1.30) (3.84) (7.21) Ethnic composition, demographic 2.72 1.12 26.08 variables, and union coverage (1.32) (3.82) (7.22) Ethnic composition, demographic .88 - 6.05 4.41 variables, union coverage, (1.15) (3.03) (5.36) effort, responsibility, and working conditions (maximum R2) Ethnic composition, demographic .27 2.50 2.16 variables, union coverage, (1.64) (4 90) (8.26) effort, responsibility, and working conditions (all variables) aResults are from an estimating equation that combines men and women. Changes in Sex Composition To what extent are mobility patterns of women consistent with a labor market mov- ing toward an equilibrium resulting in equality of wages? Women can be expected to enter those jobs for which pay is greatest for women, no matter what the extent of any (liscriminatory pay gap in that occu- pation. Women can also be expected, all else being equal, to enter those jobs for which there is the least degree of penalty for being female. The size of such a potential gap can be estimated by comparing the actual earn- ings of women in an occupation with the earnings they would be predicted] to have if they were rewarded in the same manner as men (the sum of the average levels of independent variables for women times the coefficients for men). If these predicted earnings are equal to their actual earnings, women in the occupation are being exactly compensated for their productive attributes, effort, responsibility, and working condi- tions faced. If predicted earnings exceed actual earnings, women are being un(ler- compensated in that occupation (i.e., there is a discriminatory gap). The greater the gap, the greater shoul(1 be the incentive for women to move out of that occupation and into ones in which they do not face such a disadvantage. Finally, if pre(lictecl earnings are lower than actual earnings, the incentive goes in the opposite direction, the discrim- inatory gap favors women, and women shouIc! esire to enter the occupation. Thus, if occupational mobility is serving to equalize wages between men and women, a positive relationship between actual wages pai(1 to women in an occupation and the movement of women into that occupation shouIcl be fount] as well as a negative re- lationship between the gap between pre- dicted and actual wages ant! the rate of growth offemale employment. Both ofthese results are seen. The correlation between the percentage increase in the proportion of workers in an occupation who were wom- en between 1970 and 1980 and its average wage in 1980 was .24, a result that is sta- tistically significant at a better than .0001 conficlence level. Similar results are foun when correlating growth in the percentage

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166 Matthaei, Julie A. 1982 An Economic History of Women in the Amer- icas: Women's Work, the Sexual Division of Labor and the Development of Capitalism. New York: Shocken Books. Mincer, Jacob, and Haim Ofek 1982 Interrupted work careers: Depreciation and restoration of human capital. Journal of Hu- man Resources 17 3-24. Mincer, Jacob, and Solomon Polachek 1978 An exchange: Theory of human capital and the earnings of women: women's earnings reexamined. Journal of Human Resources 13 118-134. Murray, Michael A., and Jon Atkinson 1981 Gender differences in the correlates of job satisfaction. Canadian Journal of Behavioral Science 13 44-52. O'Neill, June 1983 The Determinants and Wage Effects of Oc- cupational Segregation. Washington, D.C.: The Urban Institute. 1985 Role differentiation and the gender gap in wages. Pp. 50-75 in Laurie Larwood, Ann H. Stromberg, and Barbara A. Gutek, eds., Women and Work. Vol. 1. Beverly Hills, Calif.: Sage. Polachek, Solomon 1979 Occupational segregation among women: Theory, evidence and a prognosis. Pp. 137- 157 in Cynthia B. Lloyd, Emily S. Andrews, and Curtis L. Gilroy, eds., Women in the Labor Market. New York: Columbia Uni- versity Press. 1981 Occupational self-selection: A human capital approach to sex differences in occupational structure. Review of Economics and Statistics 63 60-69. 1985 Occupational segregation: A defense of hu- man capital predictions. Journal of Human Resources 20 437-440. 1987 Occupational segregation and the gender wage gap. Population Research and Policy Review 6 47-67. PAY EQUllrY: EMPIRICAL INQUIRIES Quin, R. P., and G. L. Staines 1979 The 1977 Quality of Employment Survey: Descriptive Statistics with Comparison Data from the 1969-70 Survey of Working Con- ditions and the 1972-73 Quality of Employ- ment Survey. Ann Arbor: Institute for Social Research, University of Michigan. Sandell, Steven H., and David Shapiro 1978 An exchange: Theory of human capital and the earnings of women: A reexamination of the evidence. Journal of Human Resources 13 103-117. Stevenson, Mary Huff 1975 Relative wages and sex segregation by oc- cupation. Pp. 175-200 in Cynthia B. Lloyd, ea., Sex, Discrimination and the Division of Labor. New York: Columbia University Press. 1984 Determinants of Low Wages for Women Workers. New York: Praeger. Treiman, Donald J., and Heidi I. Hartmann, eds. 1981 Women, Work, and Wages: Equal Pay for Jobs of Equal Value. National Research Coun- cil, Committee on Occupational Classification and Analysis. Washington, D. C.: National Academy Press. Treiman, Donald J., Heidi I. Hartmann, and Patricia A. Roos 1984 Assessing pay discrimination using national data. Pp. 137-154 in Helen Remick, ea., Comparable Worth and Wage Discrimina- tion: Technical Possibilities and Political Real- ities. Philadelphia: Temple University Press. U. S. Department of Labor 1977 Dictionary of Occupational Titles. 4th ed. Washington, D.C.: U.S. Government Print- ing Office. Waite, Linda J., and Sue E. Berryman 1985 Women in Nontraditional Occupations. Santa Monica, Calif: Rand. Weiler, Paul 1986 The uses and limits of comparable worth. Harvard Law Review 99 1728-1807. The appendix follows.

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OCCUPATIONAL SEGREGATION APPENDIX Estimated Results Inclucling Personal anc] tote Characteristics Parameter Estimates Parameter Estimates (Standard Errors) for (Standard Errors) for Meanb (Standard Variablea Women Men Deviation) PERSONAE CHARACTERISTICS Proportion female .30 .31 .31 (.24) (.32) (.28) Proportion black 2.34 .45 .09 (.74) (1.29) (.06) Proportion Hispanic 3.01 - 5.03 .04 (1.82) (3.21) (.03) Proportion Asian 6.11 - 3.13 .01 (3.33) (5.15) (.01) Proportion other nonwhite ethnic - 2.40 10.67 .01 group (3.21) (8.77) (.01) Proportion noncitizens 3.02 - 6.30 .03 (2.85) (4.74) (.03) Proportion having English - 7.06 .42 .02 difficulty (4.41) (5.06) (.02) Proportion married .95 - 1.62 .63 (.53) (1.40) (.13) Proportion having work- 1. 83 - 12.61 .06 influencing disability (1.79) (4.05) (.02) Proportion employed by federal .80 -.38 .05 government (.37) (.47) (.11) Proportion employed by state and - .76 - 1.52 .12 local governments (.28) (.37) (.20) Proportion self-employed - .90 .42 .08 (.51) (.55) (.12) Proportion working part-time - . 06 - .002 .11 (.41) (.006) (.12) Average years of education .51 .76 12.39 (.07) (.09) (1.84) Average years of work experiences .008 .16 16.76 (.02) (.03) (4.86) Proportion in a union (Kokkelen- 1.40 .55 .27 berg and Sockell, 1985) (.29) (.41) (.20) Average number of children in - .41 1.66 1.12 workers' homes (.21) (.57) (.19) FRINGE BENEFITS Average number of vacation days - . 03 - .13 13.93 (Time Use Survey) (.01) (.02) (4.28) Proportion having a K401 plan . 57 - 2.60 .08 (CPS) (.67) (.85) (.07) Proportion having employer- - .22 - 1.15 .71 provided medical insurance (.45) (.58) (.20) (QES) Proportion eligible for paid mater- - 1. 19 - 2.65 .07 nity leave (female workers (. 65) (.84) ( 09) only) (QES) Proportion having employer- .76 1.81 .59 provided life insurance (QES) (.49) (.62) (.22) Proportion having employer- - .62 - 2.59 .63 provided retirement program (.44) (.56) (.21) (QES) 167 Continued

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168 APPENDIX Continued PAY EQUITY: EMPIRICAL INQUIRIES Parameter Estimates Parameter Estimates (Standard Errors) for (Standard Errors) for Meanb (Standard Variablea Women Men Deviation) Proportion eligible for profit- .13 1.73 .44 sharing stock option or thrift (.37) (.47) (.18) plans (QES) EFFORT MEASURES Proportion of workday not work- - 8.20 - 31.58 .16 ing (Time Use Survey) (2.88) (3.80) (.03) Proportion of workday "goofing 21.72 63.83 .05 off"(Time Use Survey) (6.63) (8.63) (.01) Anchored work effort 2.31 5.18 .55 (Duncan and Stafford, 1980) (.90) (1.17) (.07) Worker determines pace - .53 - 1.54 3.78 (1-5 scale, QES) (.21) (.26) (.29) Enough time to do work -.37 -.48 3.04 (1-4 Scale, QES) (.27) ( 34) (.25) Worker not asked to do "exces- .21 .72 2.84 sive" amounts of work (1-4 (.21) (.26) (.26) scale, QES) Self-reported effort .29 - .84 3.66 (1-4 scale, QES) (.35) (.45) (.17) Extra effort required .03 .79 3.42 (1-4 scale, QES) (.26) ~ 33) (.23) REsPoNs~s~TY MEASURES Many people affected -.31 -.73 4.16 (1-5 scale, QES) (.19) ( 25) (.33) Have a lot of say about work .30 .31 3.31 (1-5 scale, QES) (.18) (.22) (.58) Worker feels personally respon- .35 .46 4.33 sible for work (1-5 scale, (.31) ( 40) (.20) QES) PERSONAL RELATIONS FACTORS Average "friendliness' of workers - .11 - .14 16.48 in occupation (1-30 scale, (.04) (.05) (1.27) Filer, 1981) Co-workers take a personal inter- .10 -.34 2.88 est in respondent (1-4 scale, (.22) (.28) (.32) QES) Job requires a preference for .17 .22 .51 dealing with things and oh- (.15) (.19) (.46) jects (DOT) Job requires a preference for .22 -.40 .27 contact with people (DOT) (.16) (.20) (.42) Job requires a temperament for .31 .98 .38 dealing with others (DOT) (.17) (.22) (.46, PHYSICAL WORKING CONDITIONS Proportion exposed to risk of 1.22 1.82 .22 attack by people or animals (.34) (.43) (.18) (QES) Proportion exposed to dangerous .62 .88 .18 work methods (QES) (.44) (.58) (.15)

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OCCUPATIONAL SEGREGATION APPENDIX COntinUeC/ 169 Parameter Estimates Parameter Estimates (Standard Errors) for (Standard Errors) for Meanb (Standard Variablea Women Men Deviation) Proportion working both indoors -.17 -.27 .20 and outdoors (DOT) (.12) ~ 16) (.34) Proportion exposed to wet or .36 .65 .07 humid environment (DOT) (.20, (.25) (.21) Proportion exposed to noise or .26 -.15 .24 vibration (DOT) (.13) (.17) (.36) Proportion exposed to hazardous - .23 - .20 .21 conditions (DOT) (.14) ( 18) (.34) INTELLECTUAL CHALLENGE Job requires a preference for .05 .30 .34 routine activities (DOT) (.13) (.18) (.42, Job requires a preference for -.38 -.52 .05 abstract activities (DOT) (.23) (.28) (.20) Average level of "thoughtfulness" - .02 - .09 19.72 of workers in job (1-30 scale, (.04) (.05) (1.12) Filer, 1981) WORKERS SKILLS Job requires worker to learn .59 1.20 4.04 new things (1-5 scale, QES) (.16) ( 20) (.54) Level of spatial aptitude required .0004 .004 42.72 (population percentile, DOT) (.002) (.004) (20.89) Level of finger dexterity required .0024 .004 36.65 (population percentile, DOT) (.003) (.004) (16.50) Requirement for math ability .17 .14 2.76 (1-6 scale, DOT) (.06) (.08) (1.21) WORKERS Goads ARE MAINLY MONETARY Workers' lack of interest in .14 .21 8.73 power (1-11 scale, Filer, (.06) ( 08) (.73) 1981) Workers' lack of interest in - .19 - .26 2.90 family life (1-11 scale, Filer, (.07) (.09) (.60, 1981) Workers' lack of interest in com- .18 .26 8.01 munity activities (1-11 scale, (.07) (.09) (.63) Filer, 1981) Workers' lack of interest in job's - .04 - .18 6.35 contribution to society (1-11 (.05) (.07) (.82) scale, Filer, 1981) Workers mainly working for .43 .57 3.04 money (1-5 scale, QES) (.17) (.22) ~ 57) Workers have a lot invested in .05 .84 2.98 their job (1-5 scale, QES) (.14) (.18) (.39) LABOR MARKET CONDITIONS Worker is afraid of what might -.33 -.56 3.18 happen if he quit job (1-5 (.14) ( 18) (.40) scale, QES) Worker reports shortage of similar -.62 -.70 .36 jobs in area (QES) ( 31) (.40) (.16) Continued

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170 APPENDIX Continued PAY EQUITY: EMPIRICAL INQUIRIES Parameter Estimates Parameter Estimates (Standard Errors) for (Standard Errors) for Meanb (Standard Variablea Women Men Deviation) OTHER CHARACTERISTICS OF INTEREST Average travel time to work .06 .09 23.37 (in minutes, census) (.01) (.01) (4.75) Proportion working in establish- -1.03 . - 1.15 .57 ment of less than 100 (CPS) (.23) ~ 30) (.22) Deadendedness of job .68 1.68 .61 (Brown, 1982) (.33) (.43) (.16) Worker has freedom to decide - .26 - .38 3.24 what to do on the job (1-5 (.14) (.18) (.59) scale, QES) Job has aggravated illness or - 1.09 - 2.98 .05 physical condition (QES) (.90) (1.14) (.06) Intercept 3.69 - 2.57 (2.68) (3.37) Adjusted R2 . 81 .89 NOTE: The dependent variable is the average wage for all full-time, full-year workers in each occupation. aVariables are from a 1980 Bureau of the Census data tape (5 percent sample) unless otherwise indicated. Other data sources are either keyed to the Reference list or identified as follows: CPS = Bureau of the Census, Current Populaton Survey, May 1983 (data tape). DOT = U. S. Department of Labor (1977~. QES = Quality of Employment Survey (Quin and Staines, 1979~. Time Use Survey = Survey of Time Use in Economic and Social Accounts, 1975-1976. Raw data tapes released by the Institute for Social Research, University of Michigan, Ann Arbor. bFor characteristics of occupations, means are the same for men and women in that occupation. For average characteristics of workers, the means presented are weighted averages of means for men and women in the occupation. CCalculated for men as (age - schooling - six) from the 1980 census and for women as the mean predicted value using characteristics from the 1980 census and estimates performed using actual work experience from the National Longitudinal Survey.

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Commentary JAMES P. SMITH My comments a(ldress, first, the paper by Parcel and then the paper by Filer. Parcel's analysis is a replication of an earlier one on a similar theme using the 1970 census. Her current paper uses the 1980 census and deals with the basic empirical question posed in comparable worth re- search: Is the wage rate related to the proportion of women in an occupation? The dependent variable throughout is mean oc- cupation-leve} earnings, converted to an annual full-time earnings equivalent. In Par- cel's framework, occupational incomes re- flect four types of factors: an occupation's intrinsic "worth," labor market conditions specific to an occupation, the human capital of the occupation's incumbents, and aspects of social organization that are not reflected in productivity differences. Using a match of 1980 census data with data from the Dictionary of Occupational Titles (DOT) and factor analysis, Parcel de- rived five variables to measure worth. Oc- cupational labor market conditions are mea- sured by several variables, such as the occupation-specific unemployment rate, the reserve labor market pool (defined as the number of workers in that occupation who 171 are out of the labor force divicled by the experienced civilian labor force), and the number of government employees in an occupation. Parcel argues that, at least to some extent, there are distinct occupation- specific labor markets. In essence, skill clif- ferentiation produces labor market seg- mentation along occupational lines. The variables relater] to the quality of labor supplied are standard human capital ones- eclucation an(1 years of labor market ex- perience. The most important thematic concept, especially for comparable worth, is the vari- ables put under the hea(ling "dimensions of social organization." Parcel argues that these dimensions influence wage levels in- lepenclent of productivity. Among these social organization variables, the paper in- clucles "the comparable worth variable"- the percentage of workers who are women. The paper also extends this concept to other ethnic an(1 racial groups, adding the per- cents black, Hispanic, and Asian as vari- ables. The analysis also explores marital status effects. The maintained hypothesis is the higher the proportion of men in an occupation, the higher the wages. The op-

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172 posite prediction is advanced for the per- centage of married women. The main emphasis of the paper, of course, concerns the variables measuring the de- mographic composition of the workers in an occupation. The comparable worth vari- able, percent female, has the predicted neg- ative coefficient. Moreover, the magnitude of the effect is not small. An increase of 10 percentage points in the proportion female would reduce average earnings by $710. The other demographic variables also matter. For example, a large proportion of married men increased incomes, but at least in this specification the proportion of mar- ried women was not statistically significant. The proportion who were black had no effect on wages, but the fraction who were Spanish speaking reduced incomes and the propor- tion of Asians in the occupation increased salaries. In combination, Parcel interprets her re- sults as indicating not only that dimensions of social organizations are important, but that there exists a richer array of these dimensions than simply the fraction of the workers in a job who happen to be women. My criticisms of Parcel's paper relate to the interpretation of the factors, the worth variables, and the social organization vari- ables. Some of the differences in interpre- tation may simply reflect disciplinary dif- ferences between Parcel and me. For example, instead of arguing that the oc- cupation-specific unemployment rate (at least its Tong-term level) is a signal of excess supply, it seems more plausible to me that the causation runs in reverse. A number of studies show that Tow-skill and low-wage workers exhibit less attachment to their jobs and to the labor market in general. In this view, low wages are associated with high levels of job turnover and, consequently, high unemployment rates. With respect to the worth variables, this study, like many others in this area, does not offer much condolence that occupational worth is being adequately measured' by these proxies. For example, the additional ex- PAY EQUITY: EMPIRICAL INQUIRIES planatory power addecT by the "worth" vari- ables is modest incleectan increase in acI- justed R2 of only 1 percentage point. This finding is consistent with micro-level studies that show that only 1 to 5 percent of the variance in wages is explained by DOT- (lerive(l worth measures. Similarly, the magnitude of the effects estimated for these variables is quite small. A difference of one standard deviation in working conditions or substantive complexity, for example, raises wages by only $145. These results cause me to question seriously what seems to be an implicitly maintained hypothesis. Even putting aside whether the concept of in- trinsic worth of an occupation has any mean- ing, can we seriously claim that we have meaningfully controlled for differences in the worth of an occupation? My question is not a criticism of Parcel's work. Her construction of these worth vari- ables was (lone with skill and goo(1 sense. My question instead is a more generic crit- icism of the research area as a whole. My concerns with the social organization (or comparable worth variables) are also generic in that I have no particular (lifficulty with the way Parcel con(lucte(1 her analysis. It's the interpretation placed on the results that troubles me. Does a variable such as the proportion of workers in an occupation who are women really a(l(1 anything to our knowle(lge about how wages are set in labor markets or whether there is discrimination against any minority group? Although the inclusion of such variables has become stan- lar(1 (especially in papers centering around comparable worth), I am puzzle(1 about its meaning. Let me illustrate with an example. Con- sider two wage (distributions, one for men and one for women. Although the distri- butions overlap, men are paid significantly more than women. All we know are the (distributions, an(l for the moment we don't know what causes the wage (distributions to differ. Now let's consider drawing lines sep- arating segments of the distribution. Ob- viously, if I run a regression of the mean

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COMMENTARY wage in each segment on the proportion of women in the segment, I would get a whop- ping negative coefficient. If I call the seg- ments occupations, I have the comparable worth regression. But have I learned anything from this regression about why men anc] women are paid clifferently? Any difference in the dis- tributions guarantees this result, and I have learned nothing more than what I started with mate wage distributions lie above female wage distributions. Now, of course, we can control for other factors that contribute to sex disparities in wages, such as the worth and skill variables Parcel includes. Our wage distributions would obviously converge. But we also know that, in the end, the conditioned wage distri- butions will still indicate that male wage distributions exceed those of women. Given that, the same generic question can be raised: What do we learn about the causes of the remaining sex (lisparity by inclucling a variable measuring the proportion female? As long as male wage distributions lie above women's (a fact that nobody disputes), the average wage (for women and men, sepa- rately or together) will be correlated with proportion female. In one way, Parcel's paper may put an exclamation mark on my point. Her results indicate that an increasing proportion of Asians in an occupation raises wages. No one will seriously argue that occupations are more highly valued because they have more Asians in them. If not, are we going to be asymmetric in our interpretation of these demographic variables? If a protected minority group has a positive coefficient on a comparable worth variable, is it unob- served components of skill? And if it is a negative one, is it discrimination? Filer's paper) deals with this same ques- iMy comments are based on Filer's presentation of an earlier version of his paper at a Pay Equity workshop in September 1987 and have been somewhat revised to take into account some of the revisions Filer made . . . in nls paper. 173 tion, which has become the central empir- ical question posed in research on compa- rable worth: Is the wage related to the sexual composition of an occupation after you con- trol for other relevant factors, including the productivity of incumbents, the amount of effort and responsibility involved, working conditions, and tastes? Filer argues that a major shortcoming of previous work on this topic is that each existing micro-level data set contains only a subset of these relevant other factors that may matter. He tries to overcome this short- coming (and he does so with a vengeance) by obtaining occupation-level data from a variety of sources. These data are subse- quently merged, and the analysis is per- formed on the merged data set. For ex- ample, mean earnings and the characteristics of the incumbents in an occupation are derived from the 1980 (lecennial census; measures of working conditions, effort, and responsibility are derived from a variety of sources, inclu(ling the DOT, the Quality of Employment Survey, the Current Popu- lation Survey, ant] the Michigan Time Use study; a measure of personality is derived from his own research; and so on. The end results were 400-o(ld occupational groups and 225 potential covariates. The thematic regression in Filer's paper involves regressing the average occupation- specific wage on an ever-expanding list of variables. One variable that remains throughout is the proportion of workers in the occupation who are women. Indeed, the main exercise being performed is to track the estimated coefficient on the pro- portion female to see if it (1) goes away or (2) withstands the onslaught. The correct answer is (2), but it does get a little batterer] in the battle. Filer sum- marizes his results very nicely in his Table 7-1. Starting with a coefficient of $4.41 for full-time, fulI-year workers (moving from an occupation with no women to one that says "la(lies only"), the effect becomes progres- sively smaller. It falls to $3.30 when census- clerived productivity variables (eclucation,

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174 PAY EQUITY: EMPIRICAL INQUIRIES experience, etc.) are added; falls to $1. 70 vices a firmer foundation (in terms of prior when all variables are added subject to a statistical sicle condition of maximizing ad- justec] R2; and to $1. 35 when all 225 variables are inclucled. All coefficients are statistically significant. The last number, $1.35, implies that the actual difference between men and women in the sex composition of their occupations would explain roughly 20 percent of the wage gap. We may all react clifferently to this. My own reaction is that this still con- stitutes an impact of significant magnitude. In the initial version of his paper, Filer argued that if this negative coefficient results from a crowding effect or because employers devalue occupations with lots of women in them, male and female members of an oc- cupation should be similarly affected. With this in mincI, the identical regression mode! was reestimated for men and women sep- arately. In these regressions, percent female becomes small in magnitude and statistically . r. ns~gn~cant. Filer argues that this result shows that comparable worth is a solution for a problem that floes not exist. Most of the female-maTe wage gap that remains after productivity and job characteristics are taken into account can be attributed to within-occupation sex differences. Before getting into more substantive mat- ters, let me ask why so much of the analysis on comparable worth is performed on the aggregate occupation level instead of the individual level. In both the Parcel and Filer papers, the dependent variable was mean wages in an occupation. Instead, the comparable worth variable (the fraction fe- male) could simply be added to a standard, gar :len-variety wage function (much as So- rensen, in this volume, does). The inter- pretation of the comparable worth variable would be pretty much the same in either case. The advantages run in favor of the microapproach: It allows for richer possi- bilities of statistical testing, avoids problems of what is lost in the aggregation, and pro- literature) for what to expect from other covariates. The absence of many variables in the micro-level data sets is no particular barrier either. The average characteristics (personality, working conditions) of occu- pations can simply be merged into a micro- leve! data set in much the same way Filer does. The use of richer variation in micro- leve} data would help us avoid some of the problems that Filer notices in his paper the lack of any effect of work experience on wages for women, a small effect of work experience for men, and the large effect of schooling on earnings. When results go against well-establishe(1 research results, I also become concerned about what else may be happening to other variables that we are taking quite seriously because they are sta- tistically significant. My first point about the method Filer used (as opposed to one that he dill not) is the large number of independent variables. Although that may pose no problem if the objective is to test how much firepower the coefficient on percent female can withstand, it (loes create preclictable problems with the believability of estimates for other things. Let me illustrate with some not-so-random selections. If everyone has clifficulty speak- ing English, wages in an occupation rise by $11. 52. Similarly, if we enter an occupation in which we all goof off, wages go up by $69 an hour, a whoppingly significant effect! (Perhaps the last two results go a long way toward explaining why economists are pair] so well.) These are not isolatecl examples. My intent is to raise an issue concerning the central purpose of the paper. If it is simply to put the coefficient of percent female to the test, then most of what hap- pens to other coefficients is not important. But our concern should increase if the coef- ficients of other variables are taken seri- ously. Then, the believability of the entire exercise is questionable. The former objec- tive is more important; in this revised ver- sion of the paper, Filer has appropriately

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COMMENTARY Reemphasized the structural wage function interpretations . Let me now argue the other side of the issue. Clearly, the approach taken by Parcel in her paper is a much cleaner one. Instead of presenting every subcomponent, she first aggregates the worth variables into mega- groups using factor analysis. Instead of all the variables in Filer's paper, Parcel uses five. But Filer's approach actually opens up important issues underlying the comparable worth debate, issues that also tend to sep- arate economists and sociologists. The ques- tion is how do we want to aggregate the subcomponents measuring worth into me- gabundIes. Parcel's approach looks first only at statistical information within the sets of variables, but never looks at what impact the subcomponents have on market earn- ings. The problem with that approach is that variables that may win an internal sta- tistical information battle may have little prominence in the marketplace. To an econ- omist's eye (this one included), the weights attached in aggregate should bear some relation to market earnings. In Filer's ap- proach, all the coefficients are related to market earnings because all variables enter the wage equation. 175 paper. Let me make a few additional points on some extensions Filer puts forth. Filer states, and correctly, that his finding that percent female in the mate salary anal- ysis is insignificant is provocative. It cer- tainly is. Perhaps it is most provocative in that it runs counter to all the other estimates in the literature, including many ofthe other papers in this volume. If true, given my reservations about interpreting the com- parable worth regression in any case, I would find this as stronger evidence of discrimination than the comparable worth result itself. In essence, this result suggests that men and women in the same occupation (i.e., the same percent female) are paid differently. The problem, of course, with this interpretation is that there is so much sex segregation that men and women may not really be in the same jobs, even when occupations with the same percent female are considered. Thus, Filer's interpretation that much ofthe problem is wage differences within occupations may be inaccurate, though the mechanism he describes in the final section of his paper (the movement of wom- en into occupations in which the level of pay is higher and the female-male pay gap is lower) would work well for discrimination My second major point about Filer's pa- for any cause. Filer's public policy rec- per relates to the "comparable worth vari- ommendations would thus be to encourage able," already discussed above for Parcel's the job mobility of women.

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