The Gender Wage Gap: Taking Into Consideration Occupational Characteristics and Gender Segregation

Transcription

1 The Gender Wage Gap: Taking Into Consideration Occupational Characteristics and Gender Segregation Roumiana Zlateva Prof. Robert Turner Prof. Jyoti Khanna* Colgate University This study investigates the effect of working in gender-dominated occupations on wages. It controls for a set of key skill requirements and working conditions that define an occupation in order to isolate the impact of working in an environment with a higher percentage of women. The main goal is to take advantage of the detailed information about occupational characteristics provided by the O*NET database by combining it with data from the Census 5% sample of the US population in 2000 to analyze earnings and the wage differential between men and women. This approach builds on previous studies that have drawn occupational information from the Dictionary of Occupational Titles which is meant to be replaced by the O*NET. Using this new source of rich occupational data, this paper aims to provide better estimates of both the cost of working in highly feminized occupations and the contribution of occupational segregation to the measured gender wage gap. JEL Codes: J16, J24, J31, J71 * Special thanks also to Prof. Chad Sparber and Prof. Ulla Grapard

2 Introduction The existence of a gap between the wages of men and women is a well-established though very controversial fact in the economics of labor. It attracts the attention of academics, politicians, and the general public because it raises an important question about how human effort and achievement is valued. It asks whether the valuation is biased by prejudice and social stereotypes which lead to discrimination against women in the labor market or it is founded on inherent differences in the productivity of the two genders. To assess empirically the validity of these two explanations, researchers have used the residual approach by measuring how much of the wage gap can be attributed to the difference in the endowments of productivity-enhancing characteristics of the two genders and claiming the remaining unexplained portion of the wage gap as evidence of discrimination. While very practical in the analysis of real-world data, this approach is very vulnerable to the use of misleading measures of objective differences that might seem to help explain wage inequality but in reality are capturing the more subtle ways in which discrimination operates. One such factor widely used in the literature on the subject is occupational gender segregation. It is a stylized fact that the majority of women work in occupations which are female-dominated and offer lower wages. Throughout much of the twentieth century, the wage gap has been accompanied by unequal occupational distribution of women, which measured by the Duncan index of segregation has stood well above 60% (Blau et al., 2010). Therefore, it is not surprising that occupational choice and the percentage of women in different occupations could help explain the variation of wages between the two genders. In so far as people decide on a particular type of work solely on the basis of their own abilities and preferences and the job requirements, such an explanation could be objective and unbiased. However, that would not be the case if women are guided by social stereotypes into specific occupations that are perceived as more appropriate for a woman or if women choose fields where they expect to face less discrimination. In these situations, wage differences that result from variation of occupational choices and segregation would be further evidence of discrimination against women. This is why the interpretation of results obtained using the residual approach is very sensitive to the assumptions made about the underlying causes of occupational segregation. In this study, I will attempt to disentangle these confounding effects by estimating the impact of occupational segregation on the wages of men and women while controlling for the heterogeneity of skill demands and working conditions across occupations. Replacing the typical controls for occupational categories with these more specific characteristics, I will take into consideration the particular factors 1

3 that make some occupations more attractive for women and others more attractive for men. Thus, the measure of occupational segregation will capture the residual effects of gender barriers in different occupations. This will allow me to obtain a more insightful estimate of the portion of the wage gap that can be explained by discrimination and the portion that can be explained by the different abilities of men and women to meet various job requirements and conditions. To begin this analysis, I will first examine the theory on the wage gap and gender segregation. Theoretical Background and Literature One of the most obvious ways for gender wage differentials and occupational segregation to come about through labor market discrimination is, according to Blau et al. (2010), when employers treat men and women differently, even when there are no productivity differences between them. Such behavior might seem irrational for profit-maximizing employers who want to hire the most productive workers at the lowest wage possible. However, since hiring decisions are made on an individual basis, selecting the best candidate among equally qualified men and women might become problematic if there are differences between the two genders on average. As Anker (1997) points out, there are significant search and information costs to hiring and promotion. Moreover, obtaining perfect information about all candidates is not only costly, but also almost impossible. Hence, employers have to make choices under imperfect information and uncertainty (Blau et al., 2010). In such cases, it might be less costly for them to rely on information about averages rather than individuals, which gives rise to statistical discrimination. If employers know that on average, women are less experienced, less productive, more likely to quit, etc. they might prefer to hire a man, even if there is an equally qualified woman. Even though this theory might appear less useful in explaining biases against promoting women to better occupations with the same employer, Blinder (1973) finds evidence that one-third of the wage differential between men and women can be attributed to discrimination in attaining occupational status and job seniority, while about another two-thirds owes to direct labor market discrimination. Another explanation for labor market discrimination is the taste for discrimination model, proposed by Gary Becker (Blau et al., 2010). If employers, employees or customers perceive working with a man as preferable to working with a woman, they might be willing to pay a premium to work with a man. If employers view hiring a woman as a cost, they would be willing to hire her only at a lower wage equal to the wage of an equally qualified man minus the cost. Similarly, if male employees see working with women as a cost, they would be willing to do it only if they are compensated by the amount of that cost. In all cases, the differential between the male and female wage would be equal to 2

4 the cost of working with women. Thus, employers, employees, and customers tastes for discrimination would lead to a decrease in the demand for women, thereby lowering female wages. According to Vella (1993), demand discrimination is largely responsible for the wage differential. Moreover, employers and employees tastes for discrimination would have even stronger effects in male-dominated occupations where they would also severely restrict the access of women to such jobs and reinforce gender segregation. Discrimination affects women s position in the labor market also through social stereotypes that influence both employers hiring decisions as well as women s career choices. It is important to recognize that the foundation for statistical discrimination, the idea that on average men and women differ in their abilities, is based on some deeply rooted stereotypes about what women can, should, and should not do. For example, Anker (1997) provides a detailed list of qualities and skills that are considered stereotypically female. Kilbourne et al. (1994) take this idea further to suggest that society values typically female abilities and effort less. They conclude that, in accordance with the cultural feminist theory of gendered valuation, the processes determining which occupations pay well are gendered, such that occupations lose pay if they have a higher percentage of female workers or require nurturant skill. Thus, social perceptions and pressures funnel women into typically female occupations where women s work is undervalued. What is worse, the expectation of such discrimination shapes women s educational and career decisions. As Chevalier (2007) explains, women follow social expectations, and choose careers that reduce the likelihood of discrimination or allow them to fulfill other commitments, such as childcare. Societal discrimination affects women s choices even at earlier stages of their lives. Fuchs (1971) underscores this point, "In my opinion, most of the 40 percentage points [differential] can be explained by the different roles assigned to men and women. Role differentiation, which begins in the cradle, affects the choice of occupation, labor force attachment, location of work, and other variables that influence earnings. Role differentiation can, of course, result from discrimination. In this situation wage inequality and gender segregation are both products of the direct and indirect effects of social biases. Hence, it is deceptive to treat occupational choice and gender distribution as explanations for the existence of gender wage differentials. Further insight into the gender wage gap can be obtained from the neoclassical human capital investment model which ties wage inequality to differences in the human capital endowments of men and women. It identifies education, on-the-job experience and training as the main drivers of worker productivity and therefore, of wages, although the argument could be extended to all productivity- 3

5 4 Zlateva, Roumiana enhancing skills that require investment (Kilbourne et al., 1994). Consequently, the model suggests that if there are systematic differences between the average amount of education and experience of women and those of men, there would be a wage differential between the two groups. In accordance with this prediction, most researchers who employ this model find that a significant portion of the wage gap can be explained by the different decisions the two genders make about human capital investment (Oaxaca, 2005; Blau and Kahn, 2000; Mincer and Polachek, 1974; Chevalier, 2007). For example, Blau and Kahn (2000) explain that because women anticipate shorter and more discontinuous work lives *due to childbearing and family obligations], they have lower incentives to invest in market-oriented formal education and on-the-job training. Moreover, women especially avoid jobs requiring large investments in skills which are unique to a particular enterprise, because the returns to such investments are reaped only as long as one remains with that employer (Blau and Kahn, 2000). Hence, women tend to pick occupations with lower educational and training requirements as well as lower penalties for time out of the labor force and for job changes. In contrast, men who on average expect longer and more continuous careers choose the occupations with the highest returns to education and experience. As a result, the different patterns of human capital investment of men and women result in different wages and different gender distribution across occupations. Another widely explored explanation for the variation in wages of equally skilled workers is the theory of compensating differentials. According to this theory, workers should be rewarded not only for their skills and abilities, but also for accepting to work in hazardous, stressful, and undesirable environments (Hwang and Polachek, 2004). Filer (1985) explains that each worker has a utility function in which wages and pleasant working conditions enter positively. This implies that workers face a tradeoff between wages and good working environments. If men and women make systematically different choices regarding this trade-off, i.e. if on average women have a higher affinity for good working conditions, as suggested in the literature, then women would be more likely to end up in occupations with lower pay and more desirable environments. This pattern is consistent with the observed wage gap and gender segregation. It is interesting and important to note that compensating differentials would exist only as long as the supply of workers ready to put up with undesirable conditions is limited. Filer (1985), Filer (1990) and England et al. (1988) emphasize the idea that given the heterogeneity of personal preferences across individuals, there will be no need for additional compensation if there are enough workers who prefer or are indifferent to a job s nonpecuniary characteristics. Filer (1985) concludes that no preconceived notions of whether these characteristics are good or bad are required. The data will tell us how the marginal worker evaluates them. Therefore, compensating

6 differentials can be helpful in better understanding the wage gap and occupational segregation, but their explanatory power depends on the diversity of tastes for working conditions. The matching model is an extended neoclassical model that joins together the human capital investment model and compensating differentials. It postulates that each occupation can be described and uniquely identified by the combination of skills it requires and the working conditions it offers (Hwang and Polachek, 2004). When choosing an occupation, workers can match these characteristics to their own abilities and expectations. According to Anker (1997), workers seek out the best-paying jobs after taking into consideration their own personal endowment (eg. education and experience), constraints (eg. young child to take care of), and preferences (eg. a pleasant work environment). Thus, workers can make rational decisions, based on their comparative advantage in the skills required and their preferences for the conditions offered in each occupation (Hwang and Polachek, 2004). Following the logic of this model, occupational gender segregation and the gender wage gap could be a rational consequence of the systematic differences in the human capital endowments and preferences of men and women described in the two models above. As Filer (1985) puts it, if two groups of workers differ in their relative preferences for working conditions and wages, economic efficiency will produce different average wages between the groups and different concentrations of group members across firms and occupations. In such a case, no redistribution of workers into other occupations or firms could make either group better off. In light of this model, gender segregation and the wage gap are not the unequivocal results of discrimination. On the contrary, they could be the consequences of efficient labor markets that allocate human resources to their best use where returns are highest. Therefore, it is particularly significant to take into consideration the matching model and evaluate whether occupational segregation remains a significant predictor of earnings after controlling for occupational characteristics. A common factor among all of the above theories which attempt to explain what gives rise to gender segregation and the wage gap is that they all point to a crowding of workers into typically female occupations, i.e. a relative increase in the supply of workers into these occupations. Regardless of whether these workers are attracted by the lower human capital investment requirements, the more appealing working conditions, or the lower likelihood of discrimination, more women and perhaps even more men than there would be in the absence of such systematic differences end up competing for the same jobs (Zellner, 1972; Macpherson and Hirsch, 1995; Anker, 1997; Kilbourne et al., 1994; Blau et al., 2010). The higher supply of workers into predominantly female occupations thereby leads to lower 5

7 wages in these occupations. An interesting observation is that this theoretical prediction is also consistent with some findings which indicate that men also receive lower wages in female-dominated occupations (Blau et al., 2010). The crowding model provides another significant link between the common causes of gender wage difference and segregation. As suggested by the theory of social discrimination and by the crowding model there is significant feedback among the effects of discrimination, segregation, the wage gap, and the labor market decisions workers make. While discrimination leads to both gender segregation and wage inequality, the latter adversely affect the human capital investment decisions and occupational choices of women (Oaxaca, 1973; Brown et al., 1980; Blau and Kahn, 2000; Blau et al., 2010). In turn, the smaller human capital endowments of women and their crowding into occupations with lower skill demands further perpetuate the stereotypes and the statistical evidence that fuel discrimination. The result is a vicious spiral that motivates women to keep making choices that put them at a disadvantage in the labor market. While some researchers suggest that women s labor market s attitudes are determined in their childhood (Vella, 1994) and assume that they are exogenous (Chevalier, 2007), it is more reasonable to believe that these attitudes are influenced by women s experience and expectations about the labor market. The tendency among women to invest less in their own skills and abilities and to gravitate towards typically female occupations could reflect the adaptation of women to the biases in the labor market (Oaxaca, 1973). As Blinder (1973) points out, educational and occupational aspirations are often formed simultaneously in one s life, which further complicates the question which personal characteristics and choices can be considered objective and exogenous. Hence, it is essential to understand the interdependence between the factors that determine women s position in the labor market and to recognize the endogeneity of human capital investment and occupational choices as the main limitation of all the models. Unfortunately, there are some connections that cannot be disentangled because of the ex-post nature of the way wage and occupational data is collected and analyzed. However, the relationships between wages, segregation, and occupational characteristics can be elucidated by a different empirical approach and this is the main goal of this research project. Empirical Approaches and Evidence in the Literature In a significant portion of the studies on the wage gap, including Bayard et al. (2003), Kidd and Shannon (1996), and Jacobs and Steinberg (1990), researchers control for a worker s occupation to estimate the effect of occupational choice on wages. While they claim that these measures capture productivity differences across occupations, this approach has some significant drawbacks. First, it is 6

8 7 Zlateva, Roumiana debatable what degree of disaggregation of occupational classifications should be used. Both Bayard et al. (2003) and Kidd and Shannon (1996) find that increasing the level of disaggregation, increases the explained portion of the wage gap. For example, Kidd and Shannon (1996) conclude that using 36 occupational categories instead of 17 increases the fraction of the wage gap attributed to occupational choice from 17.8% to 27.3%. The sensitivity of these estimates raises major concerns about the actual implications and meaning of the occupational control variables. Fuchs (1971) criticizes this approach, arguing that the more detailed the occupational classification the smaller would be the observed sex differential in earnings. Indeed, I am convinced that if one pushes occupational classification far enough one could explain nearly all of the differential. In doing so, however, one merely changes the form of the problem. We would then have to explain why occupational distributions differ so much. If indeed the pattern of occupational distribution and the significance of the occupational controls owe entirely to productivity differences across occupations, then it would be appropriate to capture these differences using a full set of occupational characteristics (Filer, 1990). The advantage of this approach would be that the results would lend themselves to more clear and easy interpretation and would provide insight into the specific job attributes that lead to wage differentials. Second, as suggested in the previous section, in so far as women s occupational choices are determined by the anticipation of discrimination and by the barriers to entry they face in some fields, it is unacceptable to consider the fraction of the wage gap attributed to differences in occupational attainment explained (Brown et al., 1980). In this case, the use of occupational characteristics would be even more instrumental. These characteristics would reflect the effects of both genders occupational choices based on a rational matching process. At the same time, the indirect effects of discrimination on career paths would be captured by the measures of gender and segregation. Thus, it is preferable to replace the occupational category controls with measures of specific skill demands and working conditions to separate the simultaneous effects of discrimination and matching according to comparative advantage. A very similar story could be told about the percentage of women in an occupation which is widely used in the literature to measure occupational segregation and to help explain the wage differential between men and women. Hwang and Polachek (2004) and Macpherson and Hirsch (1995) emphasize the idea that this variable could serve as a proxy for unmeasured skills, preferences, and job attributes. This lends further support to the argument that it is essential to incorporate controls for occupational characteristics to isolate the effects of discrimination from those of rational worker choices. Following such controls, the percentage of women in an occupation would provide more unambiguous evidence about the unexplained share of the wage gap brought about through the social

9 pressures that guide women into some career paths and the discriminatory barriers that prevent them from entering others. Therefore, it is worthwhile to examine the results of previous studies about gender wage inequality and segregation and compare the conclusions of studies that use controls for job attributes and those that do not. The majority of the researchers who estimate the effects of occupational segregation on wages without controls for occupational characteristics find a significant negative coefficient on the percentage female, indicating a penalty for both men and women working in female-dominated fields. Some of the most prominent papers include Groshen (1991), Bayard et al. (2003), and Fuchs (1971). However, in many of the cases the penalty for working in female-dominated occupations is higher for women, which intensifies wage inequality. Measuring occupational segregation at the industry, occupation, establishment, and job cell level Bayard et al. (2003) find that it accounts for 51.4% of the gender wage gap in the US. In a similar study, Groshen (1991) estimates that the variation in the percentage of women across occupations explains 1/2 to 2/3 of the gap. Using data for six European countries, Gannon et al. (2007) add to the evidence that a significant portion of the differential owes to gender segregation. Brown et al. (1980) takes this idea a step further to suggest that if women could attain men s distribution there would be more women in better-paying occupations and fewer in lower-paying ones. Thus, there is some raw evidence that indicates that regardless of its underlying causes, occupational segregation is a significant predictor of lower earnings. In contrast to these findings, other studies find contrary evidence about the relationship between gender occupational distribution and wages. For example, using 1992 cross-sectional data for workers in East and West Germany, Jurajda and Harmgart (2007) find that occupational segregation fails to explain a significant portion of the gender wage gap in West Germany. Moreover, they estimate that in East Germany female-dominated occupations actually pay higher wages to both genders. Incorporating occupational characteristics into their analysis, Hwang and Polachek (2004) obtain even stronger evidence that gender segregation does not necessarily put workers in female occupations at a disadvantage. On the contrary, using a McFadden logit model to take into consideration the probability that a worker chooses a particular occupation based on its skill demands and working conditions, Hwang and Polachek (2004) get results perfectly in line with the predictions of the matching model. They find that women earn higher wages in female-dominated jobs, while men earn higher wages in maledominated jobs. This implies that occupational gender segregation is the consequence of a rational process of matching which ensures that workers end up in the occupations where they are most 8

10 productive and where they are most satisfied with the combination of wage and nonpecuniary attributes of their jobs. Even among studies that use occupational characteristics to control for the heterogeneity of skill requirements and working conditions across occupations, for the most part the effect of the percentage of women on wages remains negative and significant (Jacobs and Steinberg, 1990; England et al., 1988; Kilbourne et al., 1994). These conclusions provide more unambiguous evidence that discrimination contributes significantly to the wage gap through its effects on the unequal distribution of men and women across occupations. While the results about the relationships between particular occupational characteristics and wages are mixed, it is important that all of these studies take job attributes into consideration in order to make a stronger and clearer statement about the connection between discrimination, gender segregation, and wage inequality. It is also noteworthy to observe that according to some estimates, as for example those in Macpherson and Hirsch (1995), the effect of occupational feminization on wages becomes less negative when controls for occupational characteristics are included. This finding provides support for the hypothesis that on its own the percentage of women is both a measure of the discriminatory pressures that funnel women into certain occupations and a proxy for the ability requirements and working conditions in an occupation. Therefore, it is crucial to take into consideration measures of gender segregation and occupational characteristics in order to be able to tell a more complete and clear story about the sources of gender wage inequality. The evidence about the effects of particular job attributes on wages is indeed mixed. There is a striking conflict among the results about working conditions. According to Filer (1985), the compensating differentials for the hazards that workers in different occupations are exposed to explain between 37% and 44% of the gender wage gap. In contrast, Jacobs and Steinberg (1990) determine that jobs that involve working in hot, cold, or noisy conditions, cleaning other s dirt, engaging in strenuous physical activity, and even risking injury the most direct measure of on-the-job hazard are each associated with lower wages than are other jobs. Moreover, they estimate that only four out of the fourteen job attributes they include are significant and lead to compensating differentials. These variables are stress, fumes, handling sick patients, and unexpected problems. In the analysis done by Kilbourne et al. (1994), the compensating differentials associated with hot and cold working conditions explain less than 1% of the wage gap each. What is more surprising is that hazards lead to a premium on female wages and a penalty on male wages, thereby contributing 6% of the wage gap. Given the 9

11 10 Zlateva, Roumiana heterogeneity of these results, it is difficult to form an expectation about the possible results of my own analysis. The theory of compensating differentials does not provide a definite prediction either. It only claims that the valuation of a particular occupational characteristic will depend on the tastes of the marginal worker because a compensating differential will not be necessary as long as the supply of workers who prefer or are indifferent to this attribute is high enough to fill all such occupations. Furthermore, if a particular unpleasant job attribute is correlated with another desirable unmeasured characteristic, the supply of workers into that occupation could be so high as to produce negative compensating differentials, as seen in the literature. Therefore, in the following section, which describes my choice of data and variables, I will refrain from making predictions about the expected signs of the relationships between occupational attributes and wages. Instead, I will only highlight the main variables of interest and will provide further discussion after obtaining the regression estimates. Data The data for this study is collected from two major data sources, the IPUMS-USA (Ruggles et al., 2008) which provides Census 2000 statistics and the O*NET database (O*NET 14.0) which provides survey data on occupational characteristics. The Census data consists of the 5% population sample which includes more than 15 million individuals. The sample has been narrowed down to people who are of working age (16 to 64 years old), are in the labor force, are working for wages, and are not in the armed forces. Furthermore, the sample excludes self-employed people because they would not be subject to labor market discrimination and their occupational choices could obscure the evidence about the effects of discrimination on segregation and wages. Finally, following the example of Krueger and Summers (1988) and Macpherson and Hirsch (1995), individuals who earn wages less than $1/ hour have also been eliminated from the sample. The Census data provides information about three main groups of variables: personal, educational, and work-related characteristics which could have significant effects on wages. The set of personal variables includes: age, gender, marital status, number of children (and number of children of age 4 and under), family size, education, race, citizenship, English speaking ability, and disabilities. The group of work-related characteristics includes employment status, class of the worker, occupation, industry, number of weeks worked, usual number of hours worked per week, pre-tax wage and salary income, place of work (state), place of work type (metropolitan status), and transit time to work. Of these, employment status and class of worker are used only in sample limiting. The number of weeks worked, usual number of hours worked per week, and pre-tax wage and salary income are used to

12 11 Zlateva, Roumiana calculate the average hourly wage of an individual as well as to construct two dummy variables for parttime workers (if usual weekly hours <= 20) and workaholics (if usual weekly hours >=60). The state of work variable was used to construct several dummies for region of work, Northeast, Midwest, West, South, and abroad. The metropolitan status was also used to construct a dummy variable indicating whether an individual works in an urban area or not. The occupational codes and the gender variable were used to estimate the share of women in each occupation (taking into consideration the sample weights provided by the Census to indicate how representative an individual in the sample is of the entire US population). Finally, the Census occupation codes were also used to match O*NET statistics on job attributes to individuals based on their occupations. Blau and Kahn (2000) point out a key warning about this type of data, employer s job categories are far more detailed than those used by the Census. Thus, some Census listings probably combine individual job categories that are predominantly male with some that are predominantly female, producing apparently integrated occupations. It is likely that this data could understate both gender segregation and the heterogeneity of occupational characteristics. However, all data has its limitations and the best that could be done in this case is to use the highest level of occupational disaggregation. Thus all analyses in this research project are based on the most detailed 3-digit occupational codes. The O*NET database is a collection of survey data on a variety of occupational measures including abilities, skills, knowledge, activities, work context, work styles, and work values. It claims to be the replacement of the Dictionary of Occupational Titles (DOT) and the nation's primary source of occupational information (O*NET 14.0). While many previous studies of gender segregation and wage inequality have used the DOT as their main source of data on job attributes, I am not aware of any recent projects that have tapped the new data provided by O*NET. Thus, the major contribution of this research will be to combine the most recent Census data from 2000 with a selection of occupational variables from the O*NET surveys in order to explore the relationship between wages, segregation, and occupational characteristics. The variables selected from O*NET were recoded and matched, following the procedure designed by Peri and Sparber (2008). First, the responses to the survey questions were used to obtain the level of each characteristic across all occupations recognized by O*NET (O*NET uses a more-detailed version of the Standard Occupational Classification codes, which can be matched to the occupational codes used in the Census). Second, the crosswalk provided by Peri and Sparber (2008), which maps the O*NET codes to Census codes, was updated and used to match the measures of occupational characteristics from O*NET to the occupations defined in the Census. In the process, a few occupations that O*NET does not collect information about were eliminated from the sample. Third, for

13 ease of comparison and interpretation, the occupational characteristics were recoded from levels to percentiles that measure the share of all employed people who use a particular characteristic to a lesser degree than a worker in the occupation to which the rating is assigned. Fourth, each individual in the Census sample was assigned the corresponding values for the characteristics of his or her occupation (workers in the few occupations for which O*NET does not collect data dropped out of the sample). In this way, I obtained a matched dataset containing personal, educational, work-related, and occupational information for 5,391,563 individuals. Due to computer memory and processing concerns, a random sampling method was used to narrow down the number of individuals to 1,000,000. When regressions are run, only observations for 859,736 individuals are used due to missing values. Results and further discussion are based on these 859,736 observations. A detailed list of all occupational characteristics of interest extracted from O*NET along with some sample statistics from the matched dataset are provided in Table 1. Some interesting preliminary observations can be made about the data. First, the average male wage is $19.85, while the average female wage is $ This indicates that on average, female wages are 22.51% lower than male wages or male wages are 29.06% higher than female wages. Second, there is obvious evidence of occupational segregation since women work in occupations, which on average are 66.24% female, while men end up in occupations that on average are only 30% female. Finally, there are conspicuous differences in the importance of occupational attributes in women s versus men s work. It appears that some characteristics are more pronounced in men s jobs while others are more pronounced in women s jobs. For example, the ability to work in very hot or cold temperatures is almost twice as important in the occupations men choose as in the occupations women end up in, while social perceptiveness is more than one and a quarter times as important in the occupations women are in as in the occupations men are in. This heterogeneous pattern of importance of different job characteristics across male and female occupations offers good promise that these characteristics will have a significant relationship with occupational segregation and the gender wage differential. An important side note to make here is that the occupational characteristics are divided into four groups for ease of analysis and interpretation. The first group includes work characteristics that are perceived as typically female. The selection of characteristics in this group is based on the discussion of stereotyped characteristics of women and their expected effect on occupational segregation by sex, in Anker (1997) as well as on additional discussions of what makes a job female found, for example, in Kilbourne et al. (1994). The second group comprises a broad list of productivity-related qualities that are 12

14 often considered valuable in a variety of occupations. The third group contains unpleasant or dangerous working conditions that could potentially give rise to compensating differentials. Finally, the fourth group consists of only one factor, good working conditions, which presumably draws workers who seek pleasant working environments (especially women) into occupations that place much importance on good working conditions. 13

15 Table 1. Sample Summary Statistics Men Women Variable Mean Std. Dev. Mean Std. Dev. Wage logwage % women in one's occupation Manual Dexterity Assisting and Caring for Others Performing or Working Directly with the Public Social Perceptiveness Service Orientation Repeating Same Tasks Integrity Strength Thinking Creatively Interacting With Computers Foreign Language Reading Comprehension Writing Speaking Persuasion Judgment and Decision Making Time Management Math and Science Installation and Programming Responsibility for Outcomes and Results Achievement Deal With Unpleasant or Angry People Deal With Physically Aggressive People Sounds, Noise Levels Very Hot or Cold Temperatures Time Pressure Outdoors Work Exposure to Hazards Stress Tolerance Working Conditions

16 Econometric Model I will estimate the following regression both using pooled data and separately for each gender and then use a Chow test to pick the appropriate specification: logwage i = β 0 + β 1 PF i + β 2 X i + β 3 Z i +β 4 W i + ε i where PF is the percentage of women in a person s occupation; X is a vector of the characteristics that describe the individual s occupation (the skills required and the working conditions offered); Z is a vector of productivity-related variables including education, part-time work, workaholism, geographical region of work, urban or nonurban location of work, travel time to work, and industry of work; W is a vector of personal characteristics including age, marital status, number of children, family size, race, citizenship, English speaking ability, and disabilities, which could also influence wages. This model will allow me to determine if gender segregation has an adverse effect on the wages of workers in predominantly female occupations and if this effect is different for women than for men. Moreover, estimating the regression with the vector of occupational characteristics, I will find out whether this effect is brought about by discrimination or by the matching of heterogeneous worker abilities and preferences to the variety of requirements and working conditions offered by different occupations. If the percentage of women remains a significant predictor of earnings, even with control for the job attributes contained in X, then the difference between the average wages of workers in male-dominated and female-dominated occupations would owe mostly to discrimination. If, however, the coefficient on PF is not significant that would be evidence that at least some of the variation in wages and in the distribution of women across occupation can be accounted for by the variation in worker occupational choices as predicted by the matching model. Finally, I will compare the results of the male and female regression to examine the sources of the gender wage gap by performing Oaxaca- Blinder decomposition as done in Hwang and Polachek (2004) and Macpherson and Hirsch (1995). This decomposition will enable me to divide the wage differential into an explained portion derived from the different endowments of Xs, Zs, and Ws of men and women and an unexplained portion produced by discrimination, which would manifest itself in different rewards to endowments for men and women (different β 2 s, β 3 s, and β 4 s, for men and women) plus an additional penalty not explained by the 15

17 personal, occupational, and productivity characteristics, but captured by the intercepts (higher β 0 s for men). There will also be a debatable portion of the wage differential that will measure the penalty for working among women (negative β 1 s for both genders). The method of performing the decomposition is based on the following equation: GD (gender log wage differential) = E (explained portion) + U (unexplained portion) + D (debatable portion) where: E = β 2 M (X M X F ) + β 3 M (Z M Z F ) + β 4 M (W M W F ) (endowment differential, excluding the differential in the endowments of the percent of women in one s occupation) U = (β 0 M β 0 F ) + (β 1 M β 1 F )PF F + (β 2 M β 2 F )X F + (β 3 M β 3 F )Z F + (β 4 M β 4 F )W F (coefficient differential, i.e. different rewards for possessing the same characteristics, owing to discrimination) D = β 1 M (PF M PF F ) (differential in the endowments of the percent of women in one s occupation) The central assumption in this analysis is that the male regression coefficients reflect the true rewards for possessing different characteristics and skills in the absence of discrimination. Therefore, the difference between the rewards men and women receive directly captures the impact of discrimination. Similarly, the difference in the regression intercepts for the two genders is also evidence of discrimination. These two combined measure the unexplained portion of the wage gap (U) that owes to discrimination. In addition, assuming that women face barriers to entry in male-dominated and/or gender-balanced occupations, the differential in the distribution of men and women across genderdominated occupation is also a consequence of discrimination. As far as there is some evidence that this differential is the result of women s own preferences and not discriminatory barriers it is debatable whether it should be considered explained or unexplained, which is why it is separated out from both in this analysis. Calculating the components of the wage gap in this way will provide two measures of the portion of the gender wage gap which results from discrimination the explained portion U GD and D+U GD as well as a measure of E that can be accounted for by the different decisions that men and women GD make about human capital investment, family, working conditions, choice of industry, etc. and by their different innate endowments of feminine qualities. 16

18 Results and Discussion The above model was estimated both using separate regressions by gender and using a pooled regression. When a Chow test was used to determine whether the coefficients are significantly different for the male and female subsamples, the null hypothesis that they are not was rejected at 1% confidence level. Thus, the results from the separate regressions are presented below. Table 2. Regressions of log(wage) for Men and Women log(wage) Regressions Male Regression Female Regression Intercept *** *** % Female *** *** Manual Dexterity 0.028*** 0.050*** Assisting and Caring for Others *** *** Performing or Working Directly with the Public *** Social Perceptiveness *** Service Orientation *** *** Repeating Same Tasks *** Integrity 0.117*** 0.063*** Strength *** 0.203*** Thinking Creatively 0.065*** *** Interacting With Computers *** 0.107*** Foreign Language 0.025*** *** Reading Comprehension 0.026*** Writing 0.045*** 0.135*** Speaking *** 0.016** Persuasion 0.054*** 0.069*** Judgment and Decision Making 0.033*** *** Time Management *** *** Math and Science 0.106*** 0.046*** Installation and Programming *** *** Responsibility for Outcomes and Results 0.030*** 0.024*** Achievement *** Deal With Unpleasant or Angry People 0.068*** Deal With Physically Aggressive People *** Sounds, Noise Levels ** 0.024*** Very Hot or Cold Temperatures *** *** Time Pressure 0.020*** 0.053*** Outdoors Work *** *** Exposure to Hazards Stress Tolerance 0.119*** 0.086*** Working Conditions 0.323*** 0.464*** R *** significant at 1% ** significant at 5% * significant at 10% 17

19 Percent of Women in a Person s Occupation For both genders working in an occupation with a larger percentage of women results in lower average wages ceteris paribus. However, the penalty for picking a highly feminized occupation is greater for men. For example, women in occupations where 90% of the workers are women earn 15.7% lower wages than women in occupations where 90% of the workers are men, on average. For men in the same occupations, the difference is 24%. This result is not surprising since both crowding and discrimination theories predict that workers should be penalized for working in female-dominated occupations. In the discrimination story, women s roles and effort are perceived as less valuable and therefore are rewarded less. This explains why both men and women doing typically female work are paid less on average. However, it fails to explain why men would be suffering a bigger penalty. Similarly, the crowding theory predicts lower wages in highly feminized occupations where there is an excess supply of workers. Still, the differential between women s and men s wages in such occupations remains a mystery even in the context of crowding. The fact that women are penalized for entering typically female occupations and that the penalty is higher for men fits even more poorly into the matching theory. According to this theory workers should enter occupations where they can reap the highest benefits for their characteristics and skills. The fact that both men and women are choosing occupations where they are penalized runs contrary to the idea that they are rational wage-maximizing individuals. While for women such behavior could be at least partially explained by the existence of barriers that prevent them from entering maledominated occupations, for men the choice of a typically female occupation seems glaringly bad since they are penalized even more severely than women. Another possible explanation for such decisions could be that women choose female occupations despite the penalty they face because they stand to gain much more from the typically female skills required in these occupations. Similarly, we can find an explanation for men s unusual choices of female occupations if we assume that female skills are widely predominant among women, but there are also some men who possess such skills. In this case, it would make logical sense for women and some men to pick female occupations if the rewards to their female skills exceed the penalty to entering the occupations. Therefore, it is crucial to examine the effects of typically female skills on wages. 18

20 Female Skills When examining the impacts of different skills and working conditions on earnings, it is key to keep in mind the way these variables were constructed and how they should be interpreted. While the O*NET database measures the importance of each of these characteristic in an occupation in relative levels, this study makes use of the recoding method defined in Peri and Sparber (2008) which converts the measure into percentiles. As a result, the value of each variable for a particular occupation indicates the percent of employed workers in whose occupations this characteristic has lower levels of importance. These values are matched to individuals from the Census sample based on their occupations. Hence, for each worker I obtain a measure of the percentage of other workers for whom each of the occupational characteristics is less important in their job. Thus, the coefficients on the skill and working condition variables are estimates of the reward (or penalty) an individual earns for each 1% increase in the share of employees who work in occupations where this characteristic is less important ceteris paribus. For example, the coefficients on assisting and caring for others suggest that moving to an occupation where it is more essential for workers to be able to care for others than for an additional 10% of all workers (for example, for a social worker as opposed to a secondary school teacher) results in a 1.05% decrease in wages for men and in a 0.43% decrease in wages for women ceteris paribus. Out of the seven female skills included in the regression three increase the wages of both genders. Manual dexterity and working with the public bring higher rewards to women (the coefficient on working with the public is zero for men), while integrity brings higher rewards to men. The remaining four skills carry a penalty for both genders. However, women are penalized less for doing work that is service oriented and that involves caring for others and repetition. The differences between the male and female coefficients are statistically significant for all female skills except for social perceptiveness. The theories of discrimination suggest that typically female skills are undervalued as well as that women are rewarded less for possessing such skills than are men. Only integrity seems to fit this story even though it is highly debatable whether women are indeed more honest and ethical. Hence, discrimination theories fail to explain the pattern of rewards and penalties related to female skills. The observation that women are paid more for working with the public and for having manual dexterity is inconsistent with this story. What is more, even in the presence of discrimination it is not logical that characteristics such as service orientation, caring for others, repetition, and social perceptiveness, which are crucial in many occupations in the service sector, have a negative impact on wages. 19