Center for 
Studies in Demography 
and Ecology

The Determinants of Earnings for Women in Same-Sex and Different-Sex Couples

by

Marieka M. Klawitter
University of Washington

UNIVERSITY OF WASHINGTON			                       CSDE Working Paper No. 98-07

The Determinants of Earnings for Women in Same-Sex and Different-Sex Couples

Marieka M. Klawitter
Assistant Professor
Graduate School of Public Affairs
University of Washington
Box 353055
Seattle Washington 98l95-3055
206/616-1673    	marieka@u.washington.edu

Paper prepared for presentation at Allied Social Science Associations meetings, Chicago, January 1998.  

Introduction
The 1980s and 1990s have seen more change than stability in attitudes, beliefs, and public policies about sexual orientation.  Attitudes of Americans have moved markedly toward condemnation labor market discrimination based on sexual orientation (Moore 1993).  Beliefs about the “causes” of homosexuality as originating in nature or nurture, brains or bad behavior, have been debated in the scholarly literature and popular press.  Public policies have, for the most part, increasingly protected workers against sexual orientation discrimination in labor markets (Klawitter and Flatt, 1997), though different treatment under social policies like marriage and adoption still predominates (Harvard Law Review Editors, 1989).  
A burgeoning social science literature examining issues of sexual orientation has accompanied these changes and debates in the public sphere.  However, there have been only a few studies of earnings or employment differences by sexual orientation.  This paper will add to the nascent literature on employment and sexual orientation by exploring the sources of earnings differences among women living with partners.  The 1990 U.S. Census provides a large sample of women in same-sex and different-sex couples allowing comparison of earnings and the factors that contribute to earnings.  

Sexual Orientation and Earnings

Two recent multivariate studies have used national data from random samples to explore the effects of sexual orientation on earnings.  Badgett (1995) used data from the General Social Survey to estimate earnings for individual men and women.  She found that, compared to heterosexual men, gay men had earnings lower by 11 to 27 percent after controlling for education, marital status, race, potential experience, urban residence, region, and occupation.  Among women, lesbians earned less than heterosexual women, but the difference was not statistically significant, especially after accounting for selection into the sample of full-time workers.  In the same models, married men earned significantly more than unmarried men.  For women, marriage decreased earnings, though the difference was not statistically significant.  
Klawitter and Flatt (1997) found similar results using the 1990 Census data in earnings regressions that controlled for education, age, race, urban residence, region, English proficiency, and work disability.  They found that men in same-sex couples earned about 26 percent less than married men, but their earnings were similar to those for men in unmarried different-sex couples.  Earnings for women in same-sex couples were higher than those for married women, but this difference disappeared for a sample limited to women who were full-time, full-year workers.  The effect of sexual orientation on average earnings did not seem to be affected by the presence of laws prohibiting employment discrimination based on sexual orientation.  
These papers, in addition to estimating the effects of sexual orientation on earnings, point to several reasons why sexual orientation could have an impact on employment and earnings.  Discrimination is one route of influence.  Employers may treat gay men or lesbians differently because of their own prejudice or that of customers or employees.  Differential treatment could involve hiring, firing, promotions, or pay increases, all of which could result in lower average earnings for gays and lesbians.  

Human capital formation decisions could also affect earnings for gays and lesbians.  Education levels or past labor market experience could differ by sexual orientation.  Some, but not all, studies have found higher education levels among gays and lesbians or, similarly, larger numbers of homosexuals among those with more education (Blumstein and Schwartz, 1983; Laumann et al, 1994; Klawitter and Flatt, 1997; in contrast, Badgett found no differences).  Gays and lesbians could choose to get more education in anticipation of discrimination, or because educational institutions are relatively hospitable.  Alternatively, individuals with more education may be more comfortable revealing their homosexuality in a survey.  

Sexual orientation could also influence individual choice of occupations, employment sectors, or industries, which could in turn affect earnings.  Badgett and King (1997) found fairly wide variation in levels of worker tolerance of homosexuality across broad occupational categories, though the differences were not statistically significant after controlling for age, race, education, marital status, region, urban residence, and sexual orientation.  The ranking of occupations were similar before and after the multivariate controls: people in Professional/Technical and Managerial occupations exhibited the most tolerance and those in Craft/Operative occupations exhibited the least tolerance.  The same study showed that gays and lesbians were distributed differently across occupations than heterosexuals, though again the results were not statistically significant, perhaps because of the very small numbers of homosexuals.  Occupational tolerance does not account for these findings, however, because Badgett and King found that lesbians were more likely than heterosexual women to be in Service or Craft/Operative jobs (the low tolerance occupations), and less likely to be in Professional/Technical or Clerical/Sales jobs (high and middle tolerance occupations, respectively). 
Beyond these obvious drivers of earnings, factors like urban residence and regional location could affect earnings across local labor markets.  Klawitter and Flatt (1997) found that members of same-sex couples were more likely to live in urban areas and that average earnings were higher in those areas.  Race continues to affect human capital, labor market opportunities, and earnings in the U.S.  Race could also interact with sexual orientation to affect earnings differently for women in same-sex and different-sex couples.   The earnings impact of age, another standard explanatory factor in earnings equations, could differ by sexual orientation because of historical changes in acceptability of homosexuality and work norms for heterosexual women.  Younger women in same-sex couples are more likely to have been open about their relationships.   Among women in different-sex couples, changing attitudes toward market work for women could create cohort differences in earnings.  

Child-rearing cannot be ignored by a study of employment or earnings for women.  Women in same-sex couples are less likely to be rearing children than women in different-sex couples.  This could increase labor supply, labor market experience, and subsequently, earnings for lesbians over those for heterosexual women.  Waldfogel (1997) found that women faced a “wage penalty” for having children even after controlling for individual heterogeneity and the choice of part-time work.  However, child-rearing is also the outcome of a decision-making process--one that could be closely tied to outcomes in the labor market.  In fact, Neumark and McLennan (1995) find that women who reported labor market discrimination were later more likely to have children than are those reporting no discrimination.   It’s also possible that indicators of child-rearing might partly serve as a proxy for differences in past labor market experience when measures of experience are absent.  And, the effects of children on experience might related to occupation of employment (Filer, 1993).  Because of the possible endogeneity of child-bearing, I present models with and without controls for the presence of children.  
Women in the U.S. continue to earn significantly less than men, though the differences have diminished over time (Ashraf, 1996).  This means that women in same-sex couples can anticipate lower earnings for their partners (or future partners) then can women in different-sex couples.   This could affect job choices and earnings and would be entangled with the earnings effects of sexual orientation, per se. 
Together, these past studies point out that sexual orientation could affect earnings through direct discrimination and by influencing factors that affect earnings.  This paper advances this literature by more closely examining the determinants of earnings by sexual orientation for women in couples.  Specifically, I estimate models which allow factors to have different effects on earnings for heterosexuals and homosexuals.  Also, I detail the particular sources of women’s earnings differences across various characteristics and the labor market pay-offs to those characteristics.  

Decomposition of Earnings

Human capital theory, developed by economists and sociologists, suggests that individual earnings are determined by the levels of human capital (e.g., education, job experience, and other productive characteristics) and by the pay-off to each kind of human capital.  A large literature explores the differences in the determinants of earnings by race and gender (e.g., Margo, 1995; Gunderson, 1989; Kilbourne, England, and Beron, 1994; Oaxaca and Ransom, 1994; Ashraf, 1996).  Consistent with theory, these studies differentiate between the earnings effects of different levels of human capital  (e.g., higher education levels for Whites than for Blacks would increase Whites’ earnings) and different pay-off to the same levels of human capital (e.g., men could be paid more than women for each year of education).  These distinctions are crucial for understanding the sources of earnings differences and for targeting public policies designed to eliminate earnings inequality.  For example, if a group was found to have low educational attainment, policies could target discrimination within educational institutions or the funding process for higher education.  Alternatively, if the pay-off to education was lower for a group, policies should be aimed at changing employer hiring, promotion, and firing policies--the direct discrimination as discussed above.   	Unfortunately, the distinction between levels and pay-offs is not quite this simple because of feed-back effects on workers’ and employers’ decisions.  Gays and lesbians could decide to get more education because they anticipate a lower pay-off to education.  Similarly, employers could resent higher education among gays and lesbians and choose lower pay-offs for their accumulated education.   However, estimation of the earnings contribution of pay-offs and characteristics can serve as a starting point for discussions about the source of earnings differences and policies.  
Multivariate regression can decompose earnings differences into the effects of characteristics and pay-offs.  First, regressions estimate the effects of individual characteristics on average earnings levels separately for groups (here, for women in same-sex, married, and unmarried different-sex couples).  Then, regression results divide the difference in average earnings into the amounts attributable to differences in the average values of the characteristics and the amount attributable to the pay-offs. 
The outcome variable in these regressions is the natural logarithm of earnings (or wage rates), and the explanatory characteristics usually include education, experience (or age), geographic location, occupation, and industry.  
Following Oaxaca and Ransom (1994), the difference in wages can be decomposed as:

This shows that the natural logarithm of the overall wage ratio comparing wages for married women and women in same-sex couple divides into three components.  The first term on the right side is the difference between the current pay-offs to women in same-sex couples (       ) and those expected under a “nondiscriminatory” wage system (       ), weighted by the average characteristics for women in same-sex couples (        ).  This estimates how much women in same-sex couples are over or under paid for their current characteristics.   The second term, similarly defined, estimates how much women in married couples are over or under paid relative to the “nondiscriminatory” pay-offs.  Together these first two terms comprise the “discrimination” component of earnings--the contribution of differences in pay-offs to two groups.  The final term measures the contribution of average characteristics to the overall wage differences; it weights the differences in average characteristics for the two groups by the “nondiscriminatory” pay-offs.  Most refer to the last term as the “productivity” component of wage differences, though it also captures the effects of discrimination on choices or opportunities in accumulating human capital (e.g., discrimination in education).   
Oaxaca and Ransom propose using estimates from pooled samples of the groups as the “nondiscriminatory” baseline of comparison for wages.   This assumes that wages for all under a nondiscriminatory system would be a weighted average of estimates from the separate groups.  In this paper, I follow their procedure of using the pooled estimates of pay-offs.  
I also add to my model an initial step to correct for selection bias into the sample of full-time full-year workers.  Ashraf (1996) shows that estimates of wage decompositions can differ significantly after adding controls for possible selection bias.  

I use a standard two-step adjustment for selection bias, employing a first stage logit model that estimates the probability of being a full-time worker (Heckman, 1979).  These estimates allow construction of “inverse mills ratios” which serve as explanatory variables in the second stage earnings equations.  This adjusts the conditional mean of earnings for the selection bias and yields consistent estimates of the coefficients in the earnings equation. 
Thus, my strategy was to estimate selection-corrected earnings equations for women in each separate group (same-sex, married, unmarried different-sex couple members) and for the pooled sample of all women.  The coefficient estimates from these regressions are then used to calculate the components of earnings differences associated with different pay-offs (coefficients) and with different levels of characteristics.  

Data and Sample:
The data for this study come from the 1990 U.S. Census 5% Public Use Microdata sample.  This data set uses information from the long form of the census that asks detailed questions about household members in 5 percent of U.S. households.  These data provide the opportunity to study earnings and employment for a very large sample of same-sex couples obtained from a national random sample.  

For the first time, the 1990 census permitted identification of unmarried couples by adding the category “unmarried partners” to the list of possible relationships between household members.  In order to use this category, partners must have been living together and one partner would have to be the designated householder (home-owner or leaser-holder of residence).  The addition of this information to the census allows identification of different-sex and same-sex unmarried couples, though the number of couples in these categories were lower than other sources would predict.   I used all same-sex couples from the census, but only subsets of married couples and unmarried different-sex couples to maintain a workable sample size.  
Members of different-sex couples serve as a comparison group to people in same-sex couples because the census does not allow identification of individual lesbians or gays.  Presumably, same-sex couples include some combination of couples who would be married, if legally possible, and those who would not.  An ideal data set would contain a reasonable size sample of individuals who identified as gay or lesbian and thus permit analysis of earnings for those with and without partners.  However, there is no data set based on a national random sample which contains more than about 100 gays and lesbians combined--far too small a sample to permit reasonable comparisons within any sub-group.  The census provides an alternative which limits findings to members of co-habitating couples, but permits more detailed comparisons using a broad array of variables.  

For this paper, I use information on individual women within each type of couple (same-sex, married, and unmarried different-sex).  The sample includes only women aged 18 to 65, the most common years spent in the labor market.  Earnings and labor market participation information describes individuals’ experiences in the year prior to the census, 1989.  My analysis here also draws on information about race, age, education, location, occupation and industry, and child-rearing.  Multivariate regressions estimate the determinants of earnings for full-time, full-year workers, though as discussed above, the sample of all women contributes to the first-stage estimates of the probability of being a full-time worker.  Full-time/full-year workers included those who worked more than 45 weeks in 1989 and more than 30 hours in a typical week. The full sample included 8,667 women in married couples, 10,510 women in unmarried different-sex couples, and 4,215 women in same-sex couples.  

Differences in Earnings: Descriptive Statistics
Table 1 shows descriptive statistics on earnings by couple type.   Participation in the labor market as full-time/full-year workers differed markedly across the groups.  Married women were the least likely to be full-time with only 40 percent in that category.  Sixty-four percent of women in same-sex couples and 53 percent of unmarried women in different-sex couples were full-time workers.  
Among the full-time workers, mean earnings followed a different pattern than the full-time participation rates: the average was highest for same-sex couple members and lowest for women in unmarried different-sex couples.  Figure 1 further illustrates this pattern with stem and leaf diagrams.  The diagrams show that median earnings (the 

center horizontal line of each figure) was highest for same-sex couple members.  The 
spread of the earnings distributions also followed this pattern with the greatest spread among women in same-sex couples and the least spread among those in unmarried different-sex couples (both interquartile range of the “box” and 1.5 times the interquartile range of the “stems”). 

These simple comparisons show that women in same-sex couples earned more, on average, than did women in different-sex couples.  The next section explores the underlying determinants of these patterns of earnings and more fully identifies the sources of the group differences.  

Table 2 shows the distribution of characteristics for women in each couple type.  The first panel of demographic characteristics shows that more women in same-sex couples were in the younger age categories than were women in married couples.  More than half of the women in same-sex couples were under 36 (12 + 45 =57 percent), but that age group included only 39 percent of married women.  Women in unmarried different-sex couples were younger still, with 66 percent under age 36.  There are also some statistically significant differences in the racial/ethnic distributions, but the size of those differences was generally very small--1 or 2 percentage points. The only exception to this was a larger percentage of Black women among unmarried different-sex couple members (13 percent) than for women in the other 2 groups (8 percent).
[Table 2 attached at back of paper.]
The panel of location variables shows the women in same-sex couples were much more likely to live in urban areas (54 percent) than were married women (37 percent) or unmarried women in different-sex couples (45 percent).  Urban wages are higher than those in other areas and this contributes to higher earnings for men and women in same-sex couples (Klawitter and Flatt, 1997).  More women in same-sex couples lived in the Pacific and New England regions than women in different-sex couples.  These areas, along with other northern regions, are also areas with more supportive public opinions about homosexuality (Moore, 1993) and more antidiscrimination policies for sexual orientation (Klawitter and Hammer, 1997).  

Higher education was much more common for women in same-sex couples--27 percent had a college degree compared to levels of about half of those for the women in different-sex couples.  Nineteen percent of women in same-sex couples had education beyond college, but only 7 percent of married women and 4 percent of unmarried women in different-sex couples.  As mentioned above, more education among women in same-sex couples could be a reflection of human capital choices, patterns of partnership for these women or of sample bias because of willingness or ability to identify as a same-sex couple on the census.  English proficiency and work disabilities were both slightly more common for the women in same-sex and unmarried different-sex couples than for married women.  
Table 2 shows some group differences in the distribution of women across occupations and industries.  Same-sex couple members were much more likely than the other women to be in a Managerial/ Professional job, the highest paid occupation.   They were much less likely to be in a Technical/Sales or a Operator/Fabricator job, and slightly more likely to be in a Production/Craft occupation.  Women in unmarried different-sex couples were more likely to be in Service occupations (one of the lowest paid occupations) and in the Operators/Fabricators than are women in the other groups.  These results differed from those of Badgett and King (1997).  They found that lesbians were less likely than heterosexual women to be in Professional/Technical or Clerical jobs, and more likely to be in Service or Craft/Operative occupations.  

Differences in the proportion of women in each industry are generally small in size (less than 3 percentage points), with the exception of Retail and Professional/Related Services.  About 19 percent of unmarried different-sex couple members were in the Retail industry, compared with only 14 percent of married women and 12 percent of same-sex couple members.  Same-sex couple members were more likely to be in Professional/Related than were women in the different-sex couples. 
Child-rearing was much more likely for married women than for women in the same-sex couples or unmarried different-sex couples.  Five percent of women in same-sex couples had children under 6 compared to 20 percent of married women.  Only 10 percent of same-sex couple members had children aged 6 to 17 as did 13 percent of unmarried women, but 38 of married women had older children.  
In summary, women in same-sex couples have more of many characteristics expected to pay off in the labor market: urban location, education, occupational status, and less frequent child-rearing.  The multivariate regressions allow us to assess the contributions of these characteristics and differential pay-offs to earnings differences.  

Decomposition of Earnings Differences: Multivariate Results

As outlined above, the process of decomposition of earnings differences entails the estimation of multivariate earnings equations for each group and, in the Oaxaca-Ransom model, for the groups pooled together.  Appendix A reports results of the selection-corrected earnings equations for the pooled samples and for the separate samples, for models with and without child-rearing variables.  This section first examines the overall decomposition of earnings differences into the components related to characteristics and pay-offs.  It then details the elements of the decomposition accounted for by variables measuring demographic characteristics, location, human capital, and occupation/industry.

Overall Decomposition
Table 3 shows the overall decomposition of logarithmic earnings comparing women in same-sex with those in married and unmarried different-sex couples.  The table shows how the selection-corrected regressions on logged annual earnings allocated the earnings differences to differences in coefficients and characteristics in three different models.  The first multivariate model restricts coefficients to be the same for all women and estimates the difference in average earnings after controlling for differences in characteristics (intercept difference).  This model is similar to estimations used in earlier studies of earnings and sexual orientation.  The second and third models allow the effects of characteristics to differ for women in the three types of couples by estimating separate coefficients.  The second model omits controls for having children and the third model adds indicators for having children under 6 and 6 to 17 years old).	 The first row of the table shows that, before accounting for differences in characteristics, average logged earnings were higher by .212 for women in same-sex couples than for women in married couples.  Women in unmarried different-sex couples had average logged earnings lower by .282.  

The multivariate model which does not allow coefficients to differ for the groups suggests that average earnings for women in same-sex couples are higher both because of more characteristics that pay-off (“difference due to characteristics”) and because of a higher base apart from the characteristics (intercept).  The second model, which allows different coefficients, allocates the earnings determinants more evenly between differences due to coefficients (including the intercept) and differences due to characteristics.   The difference in earnings between women in same-sex and married couples was evenly divided between coefficients and characteristics (.105 and .108, 

respectively).  A larger share of the same-sex and unmarried different-sex earnings gap was attributed to characteristics (.212) than to coefficients (.07).  
With the addition of controls for child-rearing, almost all the earnings gap between women in same-sex and the different-sex couples is attributed to characteristics rather than coefficient differences.  As I discuss below, the models do not predict a large direct negative effect of children on earnings, but the addition of the child controls changes the estimated effects of other characteristics.  Pay-offs for women in same-sex couples are slightly lower than those married women and nearly the same as those for unmarried women in different-sex couples.  
Together these models imply that women in same-sex couples have more of the characteristics that pay-off in the labor market.  The decompositions do not show discrimination through much lower pay-offs to market characteristics for women in same-sex couples.  However, the section below will show that although pay-offs for most characteristics are higher for women in same-sex couples, those women start from a much lower earnings base (intercept).   

Detailed Decomposition:

The overall decomposition shows the allocation of differences to the coefficients and characteristics, but it doesn’t highlight the particular sources of differences in those components.  Here, I discuss the earnings contributions of estimated coefficients and the average characteristics for five broad groups of factors: demographic characteristics (age and race/ethnicity), location (urban and region), human capital measures (education, English proficiency, and work disability), occupation/industry, and child-rearing.  
Table 4 shows the more detailed decompositions for the models with and without the child controls (Models 2 and 3).  In Model 2 (with no child controls) the comparison to women in married couples shows the largest differences in earnings due to coefficients for the human capital variables for which pay-offs were lower for women in same-sex couples (-.075) and for occupations and industries for which pay-offs were higher (.163).  The largest contributions by differences in characteristics in Model 2 came from more lucrative locations (primarily more urban locations) for women in same-sex couples (.044) and higher levels of human capital (.083).  

After controlling for the presence of children (Model 3), each set of explanatory variables (demographic, location, human capital, and occupation and industry) showed much higher pay-offs for women in same-sex couples.   These were more than offset by the much lower constant term for women in same-sex couples--indicating a lower base salary.  For each kind of explanatory variable the contributions of differences in characteristics were similar to those in the without child controls: women in same-sex couples had lower earnings because of demographic influences (primarily age), and higher earnings because of location, higher levels of human capital (education), and the occupation and industry.   Contrary to expectations, women in same-sex couples earn less because of lower coefficients on the child variables and because they have fewer children than do married women.  These estimations suggested that women in same-sex couples faced a significant wage penalty for having children, but that married women did not.  Married women with children were much less likely to be full-time, full-year employees than were those without children.  Having children also decreased the chances of full-time market work for women in same-sex couples, and in addition decreased their average earnings.  
In comparison to unmarried women in different-sex couples, Model 2 estimated that women in same-sex couples had lower pay-offs to human capital (-.061) and higher earnings due to characteristics because of human capital (.104) and occupation and industry (.062).  After controlling for the child characteristics, the allocation of earnings differences is similar to the comparison to married women--most of the pay-offs to women in same-sex couples are higher than for unmarried women, but these are offset by the much lower intercept for women in same-sex couples.  As in the model without child variables, women in same-sex couples have higher earnings due to characteristics for each kind of explanatory variable.  The effects of children were similar for women in unmarried and married different-sex couples--a large impact on the chances of full-time employment and a smaller effect on earnings.  

In summary, the results of the decompositions and the estimated effects of various characteristics are very sensitive to the inclusion of the child variables.  Without controls for child-rearing, earnings for women in same-sex couples are most affected by higher levels of human capital and lower pay-offs to those levels.  After controlling for the effects of children, the earnings gap between women in same-sex couples and those in different-sex couples is allocated almost entirely to the influence of differing average characteristics.  However, the small contribution of coefficients to the earnings gap in those models is the result of adding the higher estimated pay-offs for most kinds of variables for women in same-sex couples with a much lower base level of salary (intercept).   
Conclusions:
Decompositions of earnings tell a much richer story of the differences across women in same-sex and different-sex couples than do simple mean differences or regression-adjusted means that do not allow for divergent pay-offs.  
Although women in same-sex couples earned more on average than married and unmarried women in different-sex couples, the decompositions show that much of the difference is attributable to differences in characteristics.  Women in same-sex couples lived in higher-paying labor markets, had more education, worked in higher-paying occupations and industries than do women in different-sex couples.  The models also suggest that women in same-sex couples had higher pay-offs to characteristics, but that these are offset by lower base salaries.   This pattern could reflect differing impacts of labor market discrimination on women in same-sex couples across education levels, locations, or occupations and industries.
Do employers discriminate against lesbians (or others in same-sex relationships)?  Surveys of gays and lesbians, the general public, employers, and employees enumerate perceptions of discrimination, lingering intolerance of homosexuality, and willingness to discriminate [Badgett, 1995; Klawitter and Flatt, 1997.]  This paper does not directly refute those findings, but does provide a more complex perspective on earnings patterns.  

Notes:

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