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Measuring the Eject of Occupational Sex and Race Composition on Earnings ELAINE SORENSEN During the past 20 years, the federal government has instituted laws and regu- lations to combat economic discrimination against women and minorities. Despite these efforts, women and minorities still earn consiclerably less than white men. Because progress toward greater equality has been slow, many policymakers are directing their interest toward a strategy of equal pay for comparable worth, or pay equity. Proponents of comparable worth strate- gies believe that such an approach addresses a substantial portion of sex- and race-basec! pay disparities (National Committee on Pay Equity, 1987~. They argue that a principal factor contributing to these pay differentials is the persistence of occupational segrega- tion in the U.S. labor market. According to this view, occupational segregation allows firms to pay lower wages to workers in jobs with an overrepresentation of women or minorities. The purpose of a comparable worth policy is to eliminate the effect of occupational segregation on earnings within a firm once legitimate factors that influence earnings have been taken into account. Opponents of comparable worth argue 49 that occupational segregation within a firm is not a major factor contributing to earnings disparities. Instead, they argue, differences in productivity-relatec! characteristics (e.g., men have, on average, more work experi- ence than women) are the major factors contributing to the persistence of the earn- ings differentials (Polachek, 1987). Others claim that a significant portion of the earn- ings differentials is explained by industrial (differences (e.g., men are more likely to work in manufacturing, and women are more likely to work in services, Johnson and Solon, 1986~. Comparable worth policies thus cannot effectively reduce the national sex- or race-based earnings disparities. The purpose ofthis research is to measure the extent to which occupational segregation by sex anti race contributes to the national earnings disparities between different sex/ race groups. Other factors contributing to the national earnings disparities are also examined, such as industrial and produc- tivity-related characteristics. These esti- mates in(licate the magnitude of the phe- nomenon that a national comparable worth policy would attempt to reme(ly.
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50 RESEARCH METHOD This section develops an appropriate method for analyzing the effect of occupa- tional segregation on earnings. It builds on the basic design applied in comparable worth studies in the public sector and extends that approach to the national economy. Comparable worth studies in the public sector have measured the extent to which occupational segregation affects earnings in two steps. First, they conduct a job eval- uation of all occupations, typically employ- ing an a priori factor-point job evaluation plan. These plans consist of a set of factors and weights that are expected to reflect the requirements of a job. The factors usually fall into four broad categories: skill, effort, responsibility, and working conditions. The weights are applied to each factor and in- dicate its relative importance. An evaluation team evaluates jobs in terms of each factor and assigns points commensurate with the amount of the factor required on the job. These factor scores are summed for each job to produce a total point score, or job evaluation score. Employers often conduct job evaluations without reference to the issue of comparable worth. In such cases, the results from the preexisting job eval- uation plan are typically used in the com- parable worth study. Once the job evaluation plan is com- pleted, an earnings equation is estimated with the occupational salary as the depen- dent variable and an occupation's job eval- uation score and a variable indicating the sex composition of the occupation as the independent variables. The latter variable can be measured by the proportion of wom- en in an occupation. It varies from 0 to 1, with 0 indicating that none of the employees in the occupation is a woman and 1 indi- cating that all are women. The dependent variable, the occupational salary, is defined as one of the salary levels within an occupation to which workers may be assigned. Thus, an occupational salary PAY EQUITY: EMPIRICAL INQUIRIES is not the same as an individual salary; rather, it represents one of the salaries that an individual could receive if employed in that job. Comparable worth studies have tended to examine occupational salaries and job requirements rather than individual sal- aries and individual productivity-related characteristics because in the public sector, where these studies have been conducted, occupational salaries and job requirements are well specified. Moreover, it is the con- tention of most civil service systems that occupational salaries reflect the require- ments of the job and not the characteristics of the individuals who hold the job. The functional form of the occupational earnings equation has varied among com- parable worth studies in the public sector, but the following equation typifies the ap- proach: W0 = aO + alIO + a2 PFo + uO (1) where, the subscript 0 indicates the set of occupations; w is the occupational salary; l is the occupational job evaluation score; PF is the proportion of women in an occupation; and u is the random error term. It is expected that the coefficient on the variable measuring the proportion of women in an occupation will be negative. In other words, occupational earnings are expected to be lower in female-dominated jobs even after controlling for differences in the re- quirements of the job. The goal of com- parable worth initiatives is to eliminate this negative effect on occupational salaries. One approach for eliminating it is to subtract id PFo from each occupational salary. Once a comparable worth policy is implemented in this manner, occupational salaries would reflect the requirements of the job and not the predominant sex of the workers em- ployed in the occupation. Occupational segregation by race has been examined in some comparable worth studies in the public sector, most notably in the state of New York. These studies typically add another independent variable to the
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EFFECT OF SEX AND RACE ON EARNINGS earnings equation to reflect the race com- position of the occupation (National Com- mittee on Pay Equity, 1987~. In this analysis, an independent variable that measures the proportion of minority workers in an oc- cupation is added to the earnings equation. Thus, the race composition of an occupation is treated in the same manner as the sex composition of an occupation. It is expected that the coefficient on this variable will be negative, indicating that earnings decline as the proportion of minority workers in an occupation increases. A comparable worth policy might then eliminate the negative effect of both variables from the earnings equation. This analysis can be extended to individ- ual earnings if we make an assumption re- garding the relationship between individual and occupational earnings. Let us assume that individual earnings within a firm are a function of the entry-level occupational wage, an individuaT's tenure on the job, the sex and race of the employee, and any education or previous experience that the employee may have that is greater than the amount required on the job. ~ Then, individual earn- ings can be written in the following manner: w; = wO + be ti + bisect; - ed,`) 7 / _ ~ \ _ . Cl/ + D2(Xi - To) + b3 Si ~ b4 Rt + b5 SRi + Hi where, wi is the inclividual's wage; tZ is the individual's tenure on the job; Eli is the iThe job evaluation score is separated into its con- stituent factor scores in this model. This allows a comparison between actual and required levels of education and experience. Using factor scores rather than the overall job evaluation score is a basic difference between policy-capturing and a priori job evaluation plans. Policy-capturing plans use regression analysis to determine the appropriate weights for each factor score. In contrast, a priori plans apply previously determined weights to each factor and sum those scores to produce a total factor score. An earnings regression is then estimated using the total factor score as an independent variable. Thus, we are assuming that a policy-capturing approach is used to estimate the oc- cupational earnings equation. 51 indiviclual's educational attainment; edO is the level of education required for the job; xi is the incliviclual's experience level; xO is the level of experience required for the job; Si is 1 if the worker is a woman and O if not; Fi is 1 if the worker is a minority and O if not; SRi is 1 if the worker is a minority woman and O if not. Substituting Equation (1) into Equation (2), adcling the race composition of the oc- cupation as an independent variable into Equation (1), separating the job evaluation score into its factor scores, and rearranging terms yield the following equation: Wi = aO + car Jo + a2 PFo+ a3 PMo + C41i + bits. + bier + b5 SRi + vi (3) where, .! equals a vector of job characteris- tics (i. e., educational and experience requirements as well as other job charac- teristics, such as working con(litions); PF equals the proportion of women in an oc- cupation; PM equals the proportion of mi- norities in an occupation; I equals a vector of indiviclual characteristics (i.e., educa- tional attainment, work experience, and ten- ure); S. R. and SR are defined as above; and v is the ran(lom error term. Thus, individual earnings within a firm are assumed to be a function of the char- (2) acteristics of the job, the productivity char- acteristics of the incliviclual, the sex and race composition of the occupation, and the sex and race of the incliviclual. It is expected that the estimated coefficients on the job characteristics that have individual coun- terparts (i. e., education ancT experience) will be (lifferent in the indiviclual earnings equa- tion than in the occupational earnings equa- tion. But the estimated coefficients on the variables measuring the proportion of wom- en and minorities are expected to be un- changed. Similarly, it is anticipated that the estimated coefficients on the sex and race of the individual will be unchanged between Equations (2) ant! (31. It is expected that as and as are negative.
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52 A comparable worth policy could eliminate this negative effect by subtracting a2 PFo and d3 PMo from each occupational salary. If this is clone, a comparable worth policy eliminates the effect of the variables mea- surin~ the proportion of women and mi- nor~t~es in an occupation from individual earnings ant] controls for differences in pro- cluctivity-related characteristics and the sex and race of the individual workers involved. Scholars have noted that different sex/ race groups have significantly different es- timated earnings equations. Thus, it would be more appropriate to estimate separate earnings equations for each sex/race group. In addition, I would like to estimate the effect of occupational segregation on earn- ings within firms, since comparable worth policies ad~lress intrafirm effects of occu- pational segregation. Unfortunately, nation- al data samples do not identify an individ- uaT's firm. Thus, the analysis includes variables that approximate the firm, such as region, Standard Metropolitan Statistical Area (SMSA), union status, employer size, and major industrial categories. Taking these issues into account, Equation (3) can be rewritten for each sex/race group. The fol- lowing equations are for white males and white females: Wwm = do + d'~.Jwm + (~2 PFwm + 43 PMwm + d:4 Iwm + d'5 Zwm + Vwm an(l WWf = eO + en |wf + e2 PFWf + e3 PMWf + e4 lwf + eS Zwf + Vwf (5) where, Z equals a vector of industrial/re- gional characteristics. Consequently, with a national (lata set, it is assumed that the indiviclual earnings of each sex/race group are a function of job- related productivity characteristics, the sex and race composition of the occupation, individual-related productivity characteris- tics, ant] industrial/regional characteristics PAY EQUITY: EMPIRICAL INQUIRIES (i.e., region, SMSA, major industrial cat- egories, union status, and employer size). These earnings equations are different trom those in most previous economic re- search on sex- and race-based earnings (lif- ferentials. First, they include the sex and race composition of an occupation as in- dependent variables. These variables are included to measure the extent to which occupational segregation affects earnings. Although most research has not addressed this issue, a few studies have inclucled these variables to measure the effect of occupa- tional crowding on earnings (see Treiman ant] Hartmann, 1981~. The second atypical characteristic of these earnings equations is that they include both human capital variables and job character- istics. Most previous models of wage de- termination have focused on either human capital or job characteristics, not both. This analysis includes both types of variables because of the assumptions made above about the relationship between occupational earnings and individual earnings. Those as- sumptions imply that an adequate assess- ment of the effect of a comparable worth policy on individual earnings cannot be made without both types of variables in the equa- tion. Finally, these equations include detailed variables representing industrial categories and employer size. These variables are in- (4) eluded to control for the type of firm as closely as possible given that the data are national in scope. Since comparable worth initiatives are intrafirm policies, such con- tro} is important. DISCUSSION OF THE DATA Earnings equations are estimated using the Current Population Survey (CPS) from May and June 1983 (data tapes available from Bureau of the Census). The sample refers to all nonagricultural civilian wage and salary workers who are at least 16 years old. Four sex/race groups are distinguished:
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EFFECT OF SEX AND RACE ON EARNINGS white males, white females, minority males, and minority females. White males and fe- males consist of all whites who are not Hispanic. Minority males and females in- clude all blacks and Hispanics. Other mi- nority groups are excluded from the anal- ys~s. The CPS is particularly well suited for this analysis because it has cletailec] occu- pational information on a large national sam- ple (approximately 17,000 individuals). Oth- er surveys have a much smaller sample and/ or no detailed occupational information. The May and June 1983 matched sample has particular advantages over other CPS data because the survey included questions re- garding job tenure and employer size, in addition to the usual questions concerning employment status. Thus, the data provicle better estimates of a worker's human capital and more detailed information on the em- ployer than most CPS data. In addition, the survey was conducted while the econ- omy was experiencing relatively normal un- employment. The CPS provides the dependent variable in the analysis, which is the logarithm of the ratio of usual weekly earnings to usual weekly hours, referre(l to as "earnings." The data user! to construct the proportion of women in an occupation and the pro- portion of blacks and Hispanics in an oc- cupation are from the 1980 census. The three-digit census occupational categories are used to define occupations in this anal- ysis. There are 503 such occupations. lob characteristics are proxied by the following variables from the Dictionary of Occupa- tional Titles, as reported by Miller et al. (1980~: general educational development, specific vocational preparation, strength, physical demands, and undesirable envi- ronmental conditions. The CPS provides the variables for inclividual characteristics and industrial setting. Complete definitions of the variables are given in Appendix A. Appendix B reports the means of the vari- ables for each sex/race group. 53 EMPIRICAL RESULTS First, simple earnings regressions are es- timated for each sex/race group that include the proportion of women and the proportion of minorities in an occupation as the only inclependent variables. These estimates cle- scribe the gross relationship between earn- ings and the sex ant] race composition of occupations. The first line of Table 2-1 re- ports the estimated coefficients from these regressions. For white men, the estimates! coefficients for the proportion of women and the proportion of minorities in an occupation are - .332 and - 2. 498, respectively. This implies that white male earnings decline by 3.3 percent for each 10 percentage point increase in the percentage of women in an occupation. In a(l(lition, white male earn- ings clecline by 25 percent for each 10 percentage point increase in the percentage of minorities in an occupation. The esti- matec] coefficients for the other sex/race groups are smaller than for white men, but the magnitudes are similar. These coefficients (lo not take into account differences in worker or in(lustrial charac- teristics. The second row of Table 2-1 re- ports the estimated coefficients for the sex an(l race composition variables from the full regression model. The most striking result is the dramatic decline in the estimated coefficients for the proportion of minorities in an occupation after other independent variables are accounted for in the model. In(leed, these estimated coefficients (recline by 80 to 113 percent after controlling for other factors. The estimate(l coefficients for this variable in the full mode} are -.490 for white men; .012 for white women; - .077 for minority men; and .166 for minority women. In other words, white male earn- ings decline by 4.9 percent for each 10 percentage point increase in the percentage of minorities in an occupation. For the other sex/race groups, however, earnings do not significantly (decline as the Percentage of . ~ minorities in an occupation increases in the
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EFFECT OF SEX AND RACE ON EARNINGS full model. Thus, the negative relationship between earnings and the concentration of minorities in an occupation is eliminated for all sex/race groups except white men when other factors are taken into account. In contrast, controlling for other inde- pendent factors does not dramatically re- duce the estimated coefficients for the pro- portion of women in an occupation, except in the earnings equation for white women. This estimated coefficient declines by 55 percent for white women, but it declines by only 18 percent for white men and by 19 percent for minority women, and it ac- tually increases by 31 percent for minority men. Thus, the negative relationship be- tween earnings and the concentration of women in an occupation remains signifi- cantly negative for all sex/race groups even after accounting for other explanatory fac- tors. The actual estimated coefficients for this variable are the following: -.272 for white men; - .151 for white women; - .269 for minority men; and .209 for minority women. According to these results, white and minority men earn an average 2.7 per- cent less per 10 percentage point increase in the percentage of women in an occu- pation. For white women, wages decrease by 1.5 percent per 10 percentage point increase in the percentage of women in an occupation. Minority women's wages de- crease by 2.1 percent for a similar increase in the percentage of women in an occu- pation. 2 2Variables indicating the marital status and the pres- ence of children in the home were also included as explanatory factors in preliminary earnings regressions. They were not included, however, in the final versions of the equations because they are difficult to classify as legitimate factors for differentiating salaries. None- theless, the estimated coefficients for the proportion of women in an occupation in the white and minority female earnings equations were not affected by the inclusion of these variables. White (minority) women still earned an average 1.5 percent (2.1 percent) less per 10 percentage point increase in the percentage of women in their occupation. In the white female earn- ings equation, the estimated coefficients (and their 55 The estimated coefficients for the other variables in the analysis are reported in Appendix C. These estimated coefficients are similar to those found in other research on wage determination. For example, earn- ings increase as education and job tenure increase for all sex/race groups. On the other hand, only white male earnings significantly increase with potential experience. In acI- dition, the earnings of white and minority men increase as the number of undesirable working conditions increases (such as noise and cold temperature), but the earnings of white an(l minority women are not affected by the existence of such conditions. Oddly enough, increasing requirements of strength on the job decrease male earnings and have no significant effect on female earnings. Living in a SMSA increases earnings for all sex/race groups, except minority males. Earnings tend to be higher for indivicluals working in such industries as mining, con- struction, certain clurable goods industries, transportation, communications, public util- ities, and the federal government than in personal services. Finally, union member- ship significantly increases earnings for all sex/race groups. Using the full regression moclel, the sex- an(l race-based earnings differentials can be divided into four components. The first com- ponent measures the effect of sex- ant] race- based occupational segregation. The second component measures the effects of industrial and regional differences (i. e., two-digit standard errors) for the dummy variables married, divorced, and the presence of children in the home- were .062 (. 016), . 113 (. 019), and - .020 (. 014), re- spectively. In other words, married and divorced white women earned 6 and 11 percent more, respectively, than single white women. White women with children earned 2 percent less than white women without children. Similarly, the estimated coefficients (and their standard errors) for minority women were .061 (. 033), . 135 (. 036), - .052 (. 028~. Thus, married and divorced minority women earned 6 and 14 percent more, respectively, than single minority women. Fur- ther, minority women with children earned 5 percent less than minority women without children.
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56 Standard Industrial Classification PSIC] cat- egories, SMSA, region, union status, and employer size). The third component con- sists of differences in job characteristics and individual productivity-related characteris- tics (i.e., education, work experience, ten- ure, and part-time status). The final com- ponent measures the amount of the earnings gaps that is unexplained by these factors. There are two methods for estimating the extent to which each component contributes to the national earnings differentials. For instance, when decomposing the earnings differential between white women and white men, the coefficients from either the white male earnings equation or the white female earnings equation can be used as weights in the difference equation. These difference equations are reported below, first using white male coefficients as the weights and then using white female coefficients as the weights. Since there is no clear reason for selecting one set of weights over another, both are used to estimate the decomposition of the earnings gaps. The average of these two estimates is then calculated and re- ported in Table 2-2. In Clan In Waif = D(Xw~n Xwf) + XWf(DE) and In Ww7n In Wwf = E(xwmXwf ~ + XWm(D E) where, Xw~n (Xwf) is a vector of mean values for white males (white females) for the in- dependent variables in the earnings equa- tion and D (E) is a vector of coefficients in the white male (female) earnings equation. Allocating the national earnings differ- entials for different sex/race groups into four components is particularly useful because it separates the effect of occupational seg- regation from other factors that are fre- quently cited as more important explana- tions for these earnings differences. As stated before, the goal of a comparable worth policy is to eliminate the effect of occupational segregation, which is measured by the first PAY EQUITY: EMPIRICAL INQUIRIES component described above. The second and third components measure the extent to which differences in industrial segrega- tion and productivity contribute to the earn- ings differentials. These are the variables that are frequently cited as more important than occupational segregation. Table 2-2 reports the extent to which each component contributes to the national earnings differentials. A substantial amount of the sex- and race-based earnings dispar- ities is explained by the sex and race com- position of an occupation. In particular, the proportion of women and the proportion of minorities in an occupation account for 21 percent of the earnings disparity between white women and white men; 20 percent is due to the sex composition, and 1 percent is clue to the race composition of occupa- tions. These two variables explain 23 per- cent of the earnings differential between minority women and white men (21 percent is due to the sex composition; 2 percent is due to the race composition). For minority men, however, both the sex and race com- position of an occupation play a less im- portant role in maintaining low relative earnings. They explain 9 percent of the 6 earnings gap between minority and white men (3 percent is due to the sex composition; 6 percent is due to the race composition). In contrast, Table 2-2 shows that differ- ences in the variables measuring industrial (7) and regional characteristics (i.e., union sta- tus, size of employer, two-digit SIC code, region, and SMSA) account for much less of the earnings disparities between different sex/race groups. These variables explain 16 percent of the earnings disparity between white women and white men and 6 percent of the earnings disparity between minority women and white men. They actually in- crease the earnings of minority men, re- ducing their earnings gap by 4 percent. Table 2-2 also shows that variables mea- suring productivity characteristics (i. e., schooling, potential experience, tenure, job characteristics, and part-time status) explain a large portion of the national earnings dis-
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EFFECT OF SEX AND RACE ON EARNINGS TABLE 2-2 Percentage of Earnings Disparities Accounted for by Different Factors 57 White Female/ Minority Male/ Minority Female/ White Male White Male White Male Factors Earnings Gap Earnings Gap Earnings Gap Total $3. 32 $2. 11 $3. 53 Occupational segregation by sex (race) 20% (1%) JO (6%) 21% (2%) Industrial and regional differences 16% -4% 6% Differences in job and productivity characteristics 26% 49% 23% Unexplained residual 39% 46% 48% NOTES: Variables included as productivity characteristics are schooling, potential work experience, job tenure, job characteristics, and part-time status. Variables included as industrial and regional characteristics are 42 two- digit SIC industrial categories, union status, employer size, region, and SMSA. Percentages may not add to 100 because of rounding. SOURCES: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983); Dictionary of Occupational Titles as reported in Miller et al. (1980). parities. These variables explain 26 percent of the white female/white male earnings gap and 23 percent of the minority female/white male earnings gap. Forty-nine percent of the earnings disparity between minority ant] white men is clue to these variables. Although these earnings equations in- clude an extensive array of explanatory vari- ables, Table 2-2 shows that differences in these variables explain only 52 to 61 percent of the earnings disparities between cli~erent sex/race groups, which leaves large unex- plained resicluals. In summary, these findings suggest that even though differences between women and men in productivity and industrial charac- teristics explain about 40 percent of the na- tional earnings disparity between women and men, another 20 percent is due to occupa- tional segregation by sex, the portion of the earnings disparity that a comparable worth policy seeks to eliminate. Thus, this study finds that a national comparable worth policy would address a sizable component of the national sex-based earnings cli~erential. jobs with a Disproportionate Number of Women and Minorities In this section of the paper, alternative measures of the independent variables mea- suring the sex and race composition of an occupation are examined. Many comparable worth studies use dummy variables that inclicate the predominant sex or race in an occupation as explanatory factors in their earnings equations, rather than the pro- portion of women or minorities in an oc- cupation. These dummy variables are clis- continuous; they equal 1 if there is a disproportionate number of women or mi- norities in an occupation and O otherwise. Most of these studies use the 70 percent rule to define female (male)-clominated jobs, which states that any occupation in which 70 percent or more of the employees are female (male) is a female (male)-dominatecl job. As others have pointed out, this rule is arbitrary. Moreover, there is no custom- ary rule for defining jobs with an overre- presentation of minorities, or minority-(lom- inated jobs. Despite these limitations, I estimate earnings equations for each sex/ race group using the customary 70 percent rule for defining female-dominatec! jobs and a (lefinition clevelope(1 for minority-domi- nated jobs (described below). Histograms of the variables measuring the percentage of women and the percent- age of minorities in an occupation were examined (see Figures 2-1 and 2-2~. They are based on the CPS sample, which iclen- tifies the three-digit census occupational code for each individual. This code is then matche(l to data from the 1980 census indi- cating the percentage of women and minor-
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58 FIGURE 2-l Percentage of work force employed, by percentage female in an occupation. Source: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983). FIGURE 2-2 Percentage of work force employed, by percentage of minorities in an occupation. Note: "*" indicates values less than .05 percent. Source: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983~. PAY EQUITY: EMPIRICAL INQUIRIES 35 30 IIJ o ~ 25 CC O 20 30 C) LO 2 25 LL o 'V 1 1 15 10 Q 15 10 5 o 5 35 _ _ o 7 qHq~ 5 10 15 20 25 30 35 40 45 50 55 6065 70 75 80 85 90 95 100 PERCENTAGE FEMALE IN AN OCCUPATION 1 5 10152025 035404550 556065707580859095100 PERCENTAGE MINORITY IN AN OCCUPATION
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EFFECT OF SEX AND RACE ON EARNINGS ities in that occupational category. Figures 2- 1 and 2-2 show the percentage of the work force that is employed in each 5 percent interval of the variables measuring the per- centage of women or minorities in an occu- pation. For example, Figure 2-1 reports that 13.5 percent of the work force is employed in occupations in which, at most, 5 percent of the employees are women. At the same time, Figure 2-2 reports that 33 percent of the work force is employed in occupations in which between 5 and 10 percent of the em- ployees are minorities. As Figures 2-1 and 2-2 show, the histograms of the percentage of women and the percentage of minorities in an occupation are quite different. The histogram in Figure 2-1 is almost U-shaped, with two peaks, one at 5 percent and one at 100 percent female. The histogram in Figure 2-2 has a skewed distribution with one peak at 10 percent minority. Since the histograms of these two vari- ables are so different, the definitions de- veloped for minority- and white-dominated jobs are not patterned after the 70 percent rule used in clefining female- ant] male- dominatec] jobs. In the sample, minorities make up 15 percent of the work force. Thus, a minority (white)-dominated job is defined as one in which the percentage of minorities in that occupation is greater (less) than 20 percent (10 percent). Any job that employs 10 to 20 percent minority workers is con- sidered a minority-integrated job. Table 2-3 reports the hourly wage that the average member of each sex/race group can expect to earn given employment in different types of occupations. It shows that hourly earnings for all sex/race groups are substan- tially affected by employment in jobs that have a disproportionate number of women or minorities. For instance, Table 2-3 shows that an average white man can expect to earn $9.52 if he is employed in a white male dominated job. In contrast, if he is employed in a job that has a disproportionate number of minority female workers he can expect to earn $7.57, 20 percent less than the salary 59 received in a white male dominated job. Similarly, an average minority woman can expect to earn $5.02 if she is employed in a job that has a disproportionate number of minority female workers, but she can expect to earn $6.31 if she is employecl in a job held predominantly by white men. (The full regres- sion results for these earnings equations are available from the author.) Empirical Results by Industrial Sector Finally, separate earnings equations are estimated for white men and white women within each of the following major inclustrial sectors: manufacturing, public sector, and nonmanufacturing industries. These equa- tions use the proportion of women and minorities in the worker's occupation as the measure of occupational segregation. The results are reported in Appendix D. From these results, comparisons can be macle across different sectors. Other researchers have argued, for example, that the variable measuring the proportion of women in an occupation affects earnings in the public sector more than in the private sector and that this explains, in part, why the public sector has taken the initiative in this area (see Johnson and Solon, 1986~. The results show that both the sex and race composition of an occupation strongly affect white mate and white female earnings in the nonmanufacturing private sector. White male (female) earnings decline 2.7 (2.1) per- cent for each 10 percentage point increase in the percentage of women in an occupation. Moreover, if the percentage of minorities in an occupation increases 10 percentage points, white male (female) earnings (recline 9.7 (3.5) percent. In this sector, the ratio of white female to white male earnings expressed in percentage terms is 63 percent, which leaves an earnings disparity between white women and white men of 37 percentage points. Ap- proximately 26 percent of this earnings dis- parity is attributable to the sex and race composition of the occupation.
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60 PAY EQ UIf"Y: EMPIRICAL INQ UIRIES TABLE 2-3 Predicted Hourly Earnings for Each Sex/Race Group by Type of Occupation White Male White Female Minority Male Minority Female Group Dominated Job Dominated Job Dominated Job Dominated Job White males $9.52 $8.19 $~.80 $7.57 White females 6.11 5.51 6.03 5.45 Minority males 7.60 6.44 6.92 5.86 Minority females 6.31 5.52 5.74 5.02 NOTES: Definitions for the different types of occupations are the following: a white male dominated job is any job in which 90 percent or more of the employees are white and not Hispanic and 70 percent or more are male; a white female dominated job is any job in which 90 percent or more of the employees are white and not Hispanic and 70 percent or more are female; a minority male dominated job is any job in which 20 percent or more of the employees are Hispanic or black and 70 percent or more are male; a minority female dominated job is any job in which 20 percent or more of the employees are Hispanic or black and 70 percent or more are female. Predicted hourly earnings are calculated from earnings regressions that are available from the author on request. SOURCES: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983~; Dictionary of Occupational Titles as reported in Miller et al. (1980~. In the public sector, white male and white female earnings are significantly influenced by the proportion of women in an occupation, but the proportion of minorities in an oc- cupation has no significant effect. White male (female) earnings decline 4.2 (1.8) percent for each 10 percentage point increase in the percentage of women in an occupation. The female/male earnings ratio in this sector is 71 percent, a much higher figure than in the nonmanufacturing and manufacturing sectors. Although the earnings disparity is smaller in the public sector, one-third of it is explained by the sex composition of an occupation. As a result, this variable has its largest impact in the public sector. In the manufacturing sector, only white male earnings are significantly affected by the sex composition of an occupation, declining 1.8 percent for each 10 percentage point increase in the percentage of women in an occupation. The race composition of an oc- cupation does not significantly influence white male or white female earnings. The white female/male earnings ratio in this sector is 63 percent, the same as in the nonmanufac- turing sector, which leaves an earnings dis- parity of 37 percentage points. Only 6 per- cent ofthis earnings disparity can be explained by the sex composition of an occupation. In brief, these results show that the sex composition of an occupation is an important factor in determining wages in the public and nonmanufacturing sectorsit explains one-third of the earnings gap between white women and white men in the government sector and one-quarter in the nonmanufac- turing sector. It does not appear to be as influential in the manufacturing sector it explains only 6 percent of this earnings disparity. For white women and white men, the race composition of an occupation was significant only in the nonmanufacturing sector, reducing earnings by 9.7 and 3.5 percent, respectively, for each 10 percent- age point increase in the proportion of mi- ... . . norltles in an occupation. SUMMARY AND CONCLUSION This study argues that the purpose of a comparable worth policy is to eliminate the effect of occupational segregation by sex and race from earnings within firms. It then es- timates this effect as closely as possible using a national data set, the Current Population Survey. It finds that occupational segregation by sex significantly affects the earnings of all sex/race groups, even after controlling for differences in productivity and industrial char- acteristics. It accounts for 20 percent of the earnings disparity between white women and
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EFFECT OF SEX AND RACE ON EARNINGS white men, 21 percent of the earnings dis- parity between minority women and white men, and 3 percent of the earnings disparity between minority and white men. At the same time, occupational segregation by race is not a significant factor influencing earnings once other factors are accounted for, except for white men. It does, however, explain 6 percent of the earnings disparity between minority and white men ant] 2 percent of the earnings disparity between minority women and white men. Contrary to earlier studies Johnson and Solon, 1986), this research finds that in- dustrial and regional differences explain considerably less of the national earnings disparities between different sex/race groups than do both the sex and race composition of an occupation. These factors accounted] for only 16 percent of the white female/ male earnings gap and 6 percent of the minority female/white male earnings gap, and they increased the earnings of minority mates, reducing their earnings gap by 4 percent. In contrast, productivity-related differences explained considerably more of the earnings disparities than industrial and regional differences; the former explained 26 percent of the earnings gap between white women and white men, 23 percent of the gap between minority women and white men, and 49 percent of the gap be- tween minority and white men. Finally, the study fincis that the effect of occupational segregation on earnings de- pends on the industrial sector. In particular, occupational segregation by sex explains one- third of the earnings disparity between white women and white men in the public sector ancT one-fourth of this earnings gap in the nonmanufacturing private sector. But, in the manufacturing sector, it explains only 6 percent of this earnings differential. This suggests that a national comparable worth policy would address a larger phenomenon in the public and nonmanufacturing private sectors than in the manufacturing sector. In conclusion, the study suggests that a 61 national comparable worth policy would seek to eliminate approximately 20 percent of the white female/male earnings disparity. Moreover, such a policy would address more of the earnings disparity between minority women and white men than between any other sex/race groups. On the other hand, this policy approach would affect only about 10 percent of the earnings gap between minority and white men. If a comparable worth policy is implemental on a smaller scale, of course, its ability to remedy these national earnings disparities would! be re- cluced. Further, the initial gains of a national comparable worth policy could be atten- uated by secondary effects caused by the policy. REFERENCES Aldrich, Mark, and Robert Buchele 1985 The Economics of Comparable Worth. Cam- bridge, Mass.: Ballinger. Bureau of the Census 1983 Detailed Occupation and Years of School Completed by Age, for the Civilian Labor Force by Sex, Race, and Spanish Origin: 1980. PC80-S1-8. Washington, D.C.: U. S. Government Printing Office. Johnson, George, and Gary Solon 1986 Estimates of the direct effects of comparable worth policies. American Economic Review 7645~:1117-1125. Miller, Ann R., Donald J. Treiman, Pamela S. Cain, and Patricia A. Roos, eds. 1980 Work, Jobs, and Occupations: A Critical Re- view of the Dictionary of Occupational Titles. National Research Council, Committee on Occupational Classification and Analysis. Washington, D.C.: National Academy Press. National Committee on Pay Equity 1987 Pay Equity: An Issue of Race, Ethnicity, and Sex. Washington, D.C.: National Committee on Pay Equity. Polachek, Solomon William 1987 Occupational segregation and the gender wage gap. Population Research and Policy Review 6:47-67. 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.
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62 APPENDIX A Variable Definitions PAY EQUITY: EMPIRICAL INQUIRIES Variable Names Definitions Log wage Logarithm of usual weekly earnings divided by usual weekly hours Occupational variables Proportion female Proportion of women in a three-digit census occupational category Proportion minority Proportion of blacks and Hispanics in a three-digit census occupational cat- egory Human capital variables Schooling Number of years of schooling completed Experience (potential) Potential years of experience, computed as age minus education minus 6 Experience squared Potential experience squared Tenure Number of years worked for current employer Tenure squared Tenure squared Voluntary part time 1 if voluntarily employed part time, 0 otherwise Involuntary part time l if involuntarily employed part time, 0 otherwise Job characteristics G.E. D. Formal and informal education required to perform the job (ranges from 1 to 6) S.V.P. Specific vocational training required to perform the job (ranges from 1 to 9) Strength Strength involved in performing the job (ranges from l [sedentary] to 5 Every heavy work]) Physical demands Number of physical demands required on the job (e. g., climbing, stooping, kneeling, and reaching; ranges from 0 to 4) Environment Number of environmental conditions existing on the job (e. g., extreme cold or heat, noise, or hazards; ranges from 0 to 6) Industrial/regional variables Northest 1 if lives in Northeast region, 0 otherwise North Central 1 if lives in North Central region, 0 otherwise West 1 if lives in West region, 0 otherwise Large SMSA 1 if lives in a Standard Metropolitan Statistical Area (SMSA) with a popula- tion of at least 3 million people, 0 otherwise Medium SMSA 1 if lives in an SMSA with a population of at least 1 million people, but less than 3 million people, 0 otherwise Small SMSA 1 if lives in an SMSA with a population of less than 1 million people, 0 otherwise Mining through forestry Dummy variables that equal 1 if employed in the relevant two-digit SIC industrial cate~orv. 0 otherwise ~ ~ . Local government 1 if employed in local government, 0 otherwise State government l if employed in state government, 0 otherwise Federal government 1 if employed in federal government, 0 otherwise Small firm 1 if employed in a location with 25 to 99 employees, 0 otherwise Medium firm 1 if employed in a location with 100 to 499 employees, 0 otherwise Large firm 1 if employed in a location with 500 to 999 employees, 0 otherwise Very large firm 1 if employed in a location with 1,000 or more employees, 0 otherwise Union member 1 if a union member, 0 otherwise NOTES: G.E.D. = General Educational Development; S.V.P. = Specific Vocational Preparation. SOURCES: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983); Dictionary of Occupational Titles as reported in Miller et al. (1980).
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EFFECT OF SEX AND RACE ON EARNINGS APPENDIX B Means of Variables in Log Wage Equations 63 White White Minority Minority Variable Names Males Females Males Females Log wage 2.1955 1.7349 1.9284 1.6970 Constant 1.0000 1.0000 1.0000 1.0000 Proportion female 0.2415 0.6782 0.2681 0.6762 Proportion minority 0.1354 0.1486 0.1886 0.1944 Female-dominated job 0.0564 0.5696 0.0881 0.5626 Female-integrated job 0.2342 0.2834 0.2576 0.3078 Minority-dom. job 0.1990 0.2300 0.4320 0.4478 Min.-integ. job 0.4005 0.3757 0.3960 0.3452 Schooling 14.2052 14.0560 12.5319 13.1374 Experience (potential) 16.8770 17.7282 18.0763 17.6478 Experience squared 459.0151 500.2187 502.8012 478.4948 Tenure 8.1440 5.7788 6.7563 6.0026 Tenure squared 141.3462 77.2591 99.9474 73.7422 G.E.D. 3.8316 3.7799 3.2781 3.4656 S.V.P. 5.6754 5.1133 4.7283 4.6418 Strength 2.3372 1.8947 2.6721 2.0817 Physical demands 1.7245 1.4806 2.0371 1.6112 Environment 0.6049 0.1973 0.7700 0.3435 Northeast 0.2328 0.2336 0.1628 0.1365 North Central 0.2944 0.2935 0.1208 0.1365 West 0.1679 0.1753 0.2424 0.2070 Large SMSA 0.1251 0.1245 0.2609 0.2104 Medium SMSA 0.2456 0.2503 0.2710 0.2826 Small SMSA 0.3022 0.2839 0.2517 0.2896 Mining 0.0188 0.0043 0.0109 0.0026 Construction 0.0741 0.0097 0.0839 0.0043 Lumber 0.0072 0.0018 0.0193 0.0017 Furniture 0.0070 0.0042 0.0050 0.0026 Stone 0.0085 0.0039 0.0109 0.0078 Primary metals 0.0148 0.0028 0.0117 0.0035 Fabricated metals 0.0214 0.0091 0.0252 0.0070 Machinery, exe. elect. equip. 0.0438 0.0145 0.0294 0.0087 Electrical equipment 0.0254 0.0204 0.0134 0.0235 Automobiles 0.0171 0.0027 0.0185 0.0078 Aircraft 0.0091 0.0042 0.0059 0.0026 Other transport equip. 0.0093 0.0015 0.0042 0.0043 Professional equip. 0.0102 0.0078 0.0042 0.0043 Toys 0.0009 0.0012 0.0008 0.0035 Miscellaneous 0.0033 0.0036 0.0025 0.0035 Food 0.0209 0.0124 0.0268 0.0209 Tobacco 0.0013 0.0004 0.0008 0.0009 Textiles 0.0055 0.0070 0.0076 0.0165 Apparel 0.0039 0.0162 0.0067 0.0443 Paper 0.0097 0.0054 0.0059 0.0052 Printing 0.0242 0.0168 0.0151 0.0070 Chemicals 0.0182 0.0090 0.0117 0.0087 Petroleum 0.0040 0.0007 0.0034 0.0026 Rubber and plastics 0.0102 0.0057 0.0185 0.0052 Leather 0.0012 0.0036 0.0050 0.0070 Transportation 0.0379 0.0141 0.0436 0.0070 Communications 0.0153 0.0159 0.0134 0.0322 Utilities 0.0169 0.0048 0.0159 0.0017 Wholesale trade 0.0640 0.0300 0.0411 0.0157 Continued
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64 APPENDIX B Continued PAY EQUITY: EMPIRICAL INQUIRIES White White Minority Minority Variable Names Males Females Males Females Retail trade 0.1418 0.2019 0.1409 0.1565 Banking 0.0191 0.0464 0.0159 0.0357 Insurance 0.0256 0.0387 0.0336 0.0348 Business 0.0289 0.0262 0.0210 0.0235 Repair 0.0179 0.0054 0.0227 0.0043 Entertainment 0.0111 0.0087 0.0101 0.0035 Hospitals 0.0130 0.0631 0.0243 0.0617 Medical, exe. hosp. 0.0057 0.0584 0.0042 0.0504 Educational 0.0119 0.0246 0.0126 0.0174 Social 0.0030 0.0145 0.0067 0.0296 Professional services 0.0338 0.0428 0.0151 0.0183 Forestry 0.0008 0.0003 0.0000 0.0000 Local government 0.0876 0.1260 0.1049 0.1565 State government 0.0411 0.0499 0.0302 0.0496 Federal government 0.0428 0.0306 0.0721 0.0530 Small firm 0.2376 0.2517 0.2584 0.2687 Medium firm 0.2059 0.2090 0.2030 0.2443 Large firm 0.0598 0.0640 - 0.0663 0.0800 Very large firm 0.1318 0.0969 0.1158 0.1209 Voluntary part time 0.0688 0.1996 0.0587 0.1078 Involuntary part time 0.0348 0.0604 0.0705 0.0843 Union member 0.2796 0.1894 0.3532 0.2765 NOTES: G.E.D. = General Educational Development; S.V.P. = Specific Vocational Preparation. SOURCES: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983); Dictionary of Occupational Titles as reported in Miller et al. (1980~. APPENDIX C Estimated Coefficients (and Standard Errors) in Log Wage Equations White White Minority Minority Variable Names Males Females Males Females Constant 1.3010 * 0.3426 * 1.0356 * 0.8256* (0.0960) (0.0828) (0.2066) (0.1703) Proportion female 0.2716 * - 0.1511 * - 0.2695 * - 0.2089* (0.0342) (0.0291) (0.0751) (0.695) Proportion minority 0.4902 * 0.0125 - 0.0771 0.1659 (0.1326) (0.1233) (0.2801) (0.2357) Schooling 0.0415 * 0.0457 * 0.0266 * 0.0264* (0.0029) (0.0034) (0.0054) (0.0059) Experience (potential) 0.0076 * - 0.0030 * 0.0040 - 0.0086* (0.0015) (0.0014) (0.0035) (0.0032) Experience squared 0.0002 * 0.0001 * - 0.0001 0.0001 (0.0000) (0.0000) (0.0001) (0.0001) Tenure 0.0292 * 0.0324 * 0.0396 * 0.0369* (0.0021) (0.0023) (0.0052) (0.0054) Tenure squared - 0.0006 * - 0.0007 * - 0.0009 * - 0.0009* (0.0001) (0.0001) (0.0002) (0.0002)
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EFFECT OF SEX AND RACE ON EARNINGS APPENDIX C Continued 65 White White Minority Minority Variable Names Males Females Males Females G. E. D. - 0.0351 0.0729 * 0.0918 0.1086* (0.0190) (0.0203) (0.0482) (0.0462) S.V.P. 0.0452 * 0.0386 * - 0.0021 0.0011 (0.0084) (0.0098) (0.0201) (0.0228) Strength - 0.0727 * - 0.0021 - 0.0777 * - 0.0104 (0.0174) (0.0131) (0.0391) (0.0311) Physical demands - 0.0122 * 0.0497 * - 0.0027 - 0.0016 (0.0100) (0.0114) (0.0263) (0.0261) Environment 0.0350 0.0029 0.0539 * - 0.0343 (0.0127) (0.0194) (0.0245) (0.0309) Northeast 0.0148 - 0.0001 0.1023 * 0.0936* (0.0160) (0.0164) (0.0461) (0.0442) North Central 0.0208 - 0.0184 0.1244 * 0.1554* (0.0150) (0.0153) (0.0480) (0.0421) West 0.1366 * 0.1390 * 0.2293 * 0.1454* (0.0175) (0.0178) (0.0382) (0.0369) Large SMSA 0.0941* 0.1440* 0.0203 0.1343* (0.0196) (0.0200) (0.0464) (0~0457) Medium SMSA 0.0877* 0.1271* 0.0667 0.2004* (0.0154) (0.0155) (0.0414) (0 0377) Small SMSA 0.0374* 0.0479* - 0.0122 0.0899* (0.0143) (0.0147) (0.0414) (0.0367) Mining 0.3739 * 0.4655 * 0.5578 * 0.1428 (0.0658) (0.0928) (0.1580) (0.2507) Construction 0.3331 * 0.2928 * 0.2234 * 0.0866 (0.0560) (0.0673) (0.1037) (0.1991) Lumber 0.1193 0.0970 - 0.0561 - 0.1047 (0.0833) (0.1381) (0.1330) (0.3030) Furniture 0.0730 0.3622 * 0.3884 - 0.2860 (0.0842) (0.0943) (0.2040) (0.2498) Stone 0.0592 0.2203 * - 0.0719 0.1222 (0.0795) (0.0974) (0.1541) (0.1539) Primary metals 0.2229* 0.1984 0.2686 0.2654 (0.0696) (0.1120) (0.1528) (0.2225) Fabricated metals 0.1555* 0.3044* 0.1396 0.0984 (0.0642) (0.0691) (0.1208) (0.1650) Machinery, exe. elect. equip. 0.2482* 0.2902* 0.0915 0.1311 (0.0582) (0.0590) (0.1174) (0.1488) Electrical equipment 0.1877* 0.2268* 0.1971 0.1446 (0.0625) (0.0535) (0.1446) (0.1054) Automobiles 0.2692 * 0.3413 * 0.0916 0.3880* (0.0684) (0.1147) (0.1328) (0.1584) Aircraft 0.2006 * 0.3358 * 0.3849 * - 0.2570 (0~0785) (0.0954) (0.1955) (0.2503) Other transport equip. 0.1339 0.2262 0.2117 0.0048 (0.0782) (0.1509) (0.2242) (0.2008) Professional equip. 0.1510 * 0.1676 * 0.0846 0.1433 (0.0757) (0.0733) (0.2203) (0.1985) Toys 0.0544 0.3142 0.0232 - 0.0145 (0.1919) (0.1676) (0.4662) (0.2202) Miscellaneous - 0.0710 0.0811 0.0241 - 0.0649 (0.1086) (0.1007) (0.2767) (0.2203) Continued
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66 APPENDIX C Continued PAY EQUIP: EMPIRICAL INQUIRIES White White Minority Minority Variable Names Males Females Males Females Food 0.1295 * 0.2303 * 0.2673 * 0.1870 (0.0645) (0.0618) (0.1198) (0.1100) Tobacco 0.2635 0.2316 0.5054 - 0.1910 (0.1634) (0.2692) (0.4638) (0.4228) Textiles 0.0427 0.1562 * 0.1676 0.2023 (0.0910) (0.0768) (0.1760) (0.1179) Apparel 0.0709 0.0893 - 0.2527 - 0.0205 (0.1031) (0.0572) (0.1831) (0.0870) Paper 0.1849 * 0.2561 * 0.1338 0.2202 (0.0770) (0.0847) (0.1927) (0.1820) Printing 0.0592 0.1201 * 0.2550 - 0.1211 (0.0624) (0.0558) (0.1386) (0.1608) Chemicals 0.3101 * 0.2902 * 0.3076 * 0.2541 (0.0665) (0.0698) (0.1500) (0.1502) Petroleum 0.3341 * 0.4492 * 0.5412 * - 0.0892 (0.1020) (0.2103) (0.2443) (0.2507) Rubber and plastics 0.1751 * 0.1557 0.0840 0.1528 (0.0757) (0.0831) (0 1299) (0.1828) Leather 0.1311 0.1805 - 0.0913 0.1675 (0.1708) (0.1014) (0.2044) (0.1627) Transportation 0.2523 * 0.3919 * 0.2839 * 0.1652 (0.0591) (0.0592) (0.1095) (0.1610) Communications 0.2899 * 0.3522 * 0.3104 * 0.2742* (0.0691) (0.0577) (0.1458) (0.0977) Utilities 0.2641 * 0.2955 * 0.2608 0.2347 (0.0673) (0.0892) (0.1361) (0.3027) Wholesale trade 0.1581* 0.1785* 0.0257 -0.0176 (0.0562) (0.0476) (0.1096) (0.1183) Retail trade - 0.0336 - 0.0282 0.0251 - 0.0887 (0.0530) (0.0372) (0.0944) (0.0727) Banking 0.2366 * 0.1691 * 0.1799 0.0280 (0.0652) (0.0436) (0 1358) (0.0944) Insurance 0.2228 * 0.1761 * - 0.0491 0.0791 (0.0618) (0 0451) (0.1132) (0.0950) Business 0.1589 * 0.2774 * 0.0317 - 0.0228 (0.0606) (0.0490) (0.1272) (0.1033) Repair 0.0894 - 0.0403 0.2425 0.0126 (0.0663) (0.0844) (0.1257) (0.1978) Entertainment 0.0767 - 0.1511 * 0.2121 - 0.0514 (0.0733) (0.0696) (0.1582) (0.2171) Hospitals 0.0729 0.1567 * 0.1658 0.1041 (0.0718) (0.0435) (0.1233) (0.0850) Medical, exe. hosp. 0.1241 0.1587* 0.0194 0.0011 (0.0900) (0.0416) (0.2242) (0.0848) Educational - 0.1080 0.0120 0.1480 - 0.0569 (0.0730) (0.0504) (0.1471) (0.1162) Social - 0.2371 * - 0.0952 0.3112 - 0.0715 (0.1142) (0.0585) (0.1836) (0.0967) Professional services 0.0130 0.2067* 0.1365 0.1006 (0.0605 ~ (0.0442) (0.1397) (0.1129) Forestry 0.1783 0.4155 0.0000 0.0000 (0.2063) (0 3285) (0.0000) (o.
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EFFECT OF SEX AND RACE ON EARNINGS APPENDIX C Continued White White Minority Minority Variable Names Males Females Males Females Local government 0.0378 0.1082* 0.0424 - 0.0157 (0.0553) (0.0397) (0.0974) (0.0731) State government 0.0287 0.1414* 0.1469 0.0259 (0.0590) (0.0438) (0.1177) (0.0855) Federal government 0.2646 * 0.2431 * 0.2402 * 0.1471 (0.0589) (0.0485) (0.1023) (0.0857) Small firm 0.0768* 0.0388* 0.0413 0.0876* (0.0153) (0.0154) (0 0363) (0.0353) Medium firm 0.1127* 0.0889* 0.1396* 0.0547 (0.0172) (0.0172) (0.0420) (0.0384) Large firm 0.1699 * 0.1559 * 0.2458 * 0.1213* (0.0262) (0.0268) (0.0624) (0 0557) Very large firm 0.1991* 0.1607* 0.1599* 0.1001* (0.0216) (0.0237) (0 0538) (0.0504) Voluntary part time 0.4939 * - 0.3224 * - 0.4461 * - 0.3139* (0.0247) (0.0155) (0.0604) (0.0432) Involuntary part time 0.2747 * - 0.2939 * - 0.2154 * - 0.3051 * (0.0314) (0.0250) (0.0540) (0.0485) Union member 0.1183* 0.0971* 0.1078* 0.1017* (0.0144) (0.0167) (0.0322) (0.0324) Adj. R2 0.4131 0.4168 0.4096 0.4124 N = 7,762 6,675 1,150 1,192 NOTES: G.E.D. = General Educational Development; S.V.P. = Specific Vocational Preparation. *p < .05. 67 SOURCES: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983), Dictionary of Occupational Titles as reported in Miller et al. (1980~. APPENDIX D Estimated Coefficients (and Standard Errors) in Log Wage Equations, by Industrial Sector Public Sector Nonmanufacturing Manufacturing White White White White White White Variable Names Males Females Males Females Males Females Constant 1.5040 * 0.3224 * 1.5926 * 0.5880 * 0.7828 * 0.4926* (0.1802 ~ (0.1537) (0.1137) (0.0982) (0.1979) (0.2406) Proportion female - 0.4241 * - 0.1826 * - 0.2725 * - 0.2166 * - 0.1837 * 0.0235 (0.0746) (0.0564) (0.0451) (0.0372) (0.0780) (0.0882) Proportion minority 0.0209 0.2009 - 0.3541 * - 0.3541 * 0.2095 - 0.2637 (0.2989) (0.2407) (0.1827) (0.1681) (0.2840) (0.2934) Schooling 0.0376 * 0.0546 * 0.0411 * 0.0418 * 0.0467 * 0.0454* (0.0068) (0.0071) (0.0041) (0.0045) (0.0050) (0.0074) Experience 0.0018 0.0004 0.0112 * - 0.0024 0.0017 - 0.0078* (potential) (0.0039) (0.0030) (0.0020) (0.0018) (0.0030) (0.0032) Experience squared - 0.0001 0.0000 - 0.0002 * 0.00005 -0.00004 0.0002* (0.0001) (0.0001) (0.00004) (0.00004) (0.0001) (0.0001) Continued
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68 APPENDIX D Continue~ PAY EQUITY: EMPIRICAL INQUIRIES Public Sector Nonmanufacturing Manufacturing . White White White White White White Variable Names Males Females Males Females Males Females Tenure 0.0325 * 0.0320 * 0.0320 * 0.0337 * 0.0265 * 0.0258* (0.0054) (0.0048) (0.0030) (0.0031) (0.0035) (0.0047) Tenure squared 0.0007 * - 0.0007 * - 0.0007 * - 0.0007 * - 0.0005 * - 0.0006* (0.0002) (0.0002) (0.0001) (0.0001) (0.0001) (0.0002) G.E.D. 0.0297 0.0923* -0.0863* 0.0615* 0.0796 0.1025 (0.0388) (0.0425) (0.0259) (0.0267) (0.0435) (0.0540) S.V. P. 0.0100 0.0321 0.0556 * 0.0433 * 0.0351 0.0089 (0.0204) (0.0228) (0.0110) (0.0123) (0.0181) (0.0248) Strength - 0.0661 0.0187 - 0.1143 * - 0.0422 * 0.0832 * 0.0845 (0.0366) (0.0254) (0.0238) (0.0162) (0.0409) (0.0446) Physical demands - 0.0719 * 0.0364 0.0131 0.1119 * - 0.0932 * - 0.0595 (0.0261) (0.0221) (0.0132) (0.0154) (0.0219) (0.0306) Environment 0.0605 * 0.0096 0.0506 * 0.0382 - 0.0005 - 0.0098 (0.0257) (0.0408) (0.0183) (0.0267) (0.0258) (0.0369) Northeast 0.0394 0.0404 0.0008 - 0.0249 0.0326 0.0768* (0.0396) (0.0356) (0.0224) (0.0217) (0.0282) (0.0339) North Central 0.0009 - 0.0563 0.0251 - 0.0220 0.0508 0.0577 (0.0347) (0.0315) (0.0211) (0.0202) (0.0264) (0.0322) West 0.1691 * 0.1464 * 0.1282 * 0.1401 * 0.1401 * 0.1213* (0.0388) (0.0354) (0.0236) (0.0233) (0.0365) (0.0420) Large SMSA 0.1300* 0.0493 0.0970* 0.1824* 0.0872* 0.1485* (0.0456) (0.0432) (0.0277) (0.0262) (0.0347) (0.0423) Medium SMSA 0.1369* 0.0913* 0.0938* 0.1481* 0.0412 0.1265* (0.0372) (0.0329) (0.0213) (0.0203) (0.0284) (0.0342) Small SMSA 0.0378 0.0542 0.0413 * 0.0509 * 0.0341 0.0365 (0.0325) (0.0296) (0.0204) (0.0197) (0 0249) (0.0307) Mining 0.2462 * 0.2972 * (0.0468) (0.0924) Construction 0.2056 * 0.1115 (0.0308) (0.0628) Transportation 0.1195 * 0.2293 * (0.0360) (0.0524) Communications 0.1346 * 0.1880 * (0.0514) (0.0516) Utilities 0.1286 * 0.1222 (0.0488) (0.0882) Wholesale trade 0.0642* 0.0256 (0.0293) (0.0369) Retail trade 0.1304 * - 0.1424 * (0.0232) (0.0210) State government 0.0079 0.0590 (0.0349) (0.0319) Federal 0.2514 * 0.1843 * government (0.0363) (0.0403) Durable 0.0260 0.0788* manufacturing (0.0211) (0.0249) Small f~rm 0.0202 - 0.0483 0.0876 * 0.0561 * 0.0484 0.0940* (0.0372) (0.0327) (0.0197) (0 0194) (0.0349) (0.0437) Medium firm 0.0326 - 0.0281 0.1440 * 0.1386 * 0.0695 * 0.1460* (0.0397) (0.0364) (0.0245) (0.0224) (0 0327) (0.0407) Large firm 0.0314 - 0.0209 0.2442* 0.2246* 0.1488* 0.2540* (0.0580) (0.0563) (0.0436) (0.0353) (0.0417) (0.0513)
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EFFECT OF SEX AND RACE ON EARNINGS APPENDIX D Continued Public Sector Nonmanufacturing Manufacturing White White White White White White Variable Names Males Females Males Females Males Females 69 Very large firm 0.0611 0.0051 0.2232 * 0.2149 * 0.2429 * 0.2907* (0.0452) (0.0427) (0.0375) (0.0334) (0.0353) (0.0474) Voluntary part time 0.5652 * - 0.3105 * - 0.4322 * - 0.3247 * - 0.8767 * - 0.3587* (0.0640) (0.0356) (0.0300) (0.0187) (0.0759) (0.0566) Involuntary part 0.4105 * - 0.3899 * - 0.2835 * - 0.2991 * - 0.1307 - 0.2565* time (0.1206 ~ (0.0752 ~ (0.0369 ~ (0.0306) (0.0795 ~ (0.0534) Union member 0.0366 0.0867* 0.1830* 0.0933* 0.0685* 0.0785* (0.0292) (0.0266) (0.0228) (0.0277) (0.0243) (0.0310) Adj. R2 0.3693 0.3723 0.3411 0.3711 0.4240 0.3896 N = 1,331 1,378 2,150 1,035 4,281 4,262 NOTES: G.E. D. = General Educational Development; S.V. P. = Specific Vocational Preparation. *p < .05. SOURCES: Current Population Survey data tapes, May and June 1983; Bureau of the Census (1983); Dictionary of Occupational Titles as reported in Miller et al. (1980).
Representative terms from entire chapter: