MLA Annotated Bibliography

Abstract: 

The gender wage gap is among the most persistent and durable characteristics of labor markets and women's lives. Despite differences in focus, almost all studies of the gender wage gap focus on the adult labor market; however, almost every teenager in the United States works before adulthood. Therefore, an overwhelming majority of the population experiences the labor market, and possibly the gender wage gap, well beforehand. This article focuses on the early labor market experiences of youth and analyzes the gender differentials in earning in the youth labor market. The findings show there are no gender differences in wages for twelve- to thirteen-year-old youths. However, we see the emergence of the gender wage gap around fourteen, which widens with age. The wage differential in the early labor market is explained mostly by occupational factors such as types of jobs in which boys and girls are employed. In this way, the "cost of being a girl'" still remains.

Keywords" gender wage gap / youth employment / inequality

Full Text: 

The gender wage gap is among the most persistent and durable facts about labor markets and women's lives in the United States. Due to widespread social and academic interest in the topic, much progress has been made in explaining the gender wage gap, though a substantial portion remains unexplained. Many social scientists have approached the problem of pay discrepancy between men and women from different perspectives ranging from economics to psychology to sociology. However, despite the differences in approach almost every study on the gender wage gap focuses on adult employment. Whether it is part-time or full-time work, as Paula England summarizes (1992; see also Blau, Ferber and Winkler 2006), most studies on the topic focus on the adult labor market. However, in the United States almost every teenager works while still in school (Entwisle, Alexander and Olson 2000; Manning 1990). Therefore, employment--and hence, possibly, the gender wage gap--actually begins well before adulthood.

The gender wage gap is typically measured by annual earnings of either full-time workers (England 1997) or part-time employees (Corcoran and Duncan 1979; see also Polacheck 1981). Despite the variance in measurement, this sort of analysis can only fully explain the gender wage gap if we agree that the experience of work begins with adult employment. On the contrary, men and women in the United States rarely enter the labor market for the first time after the completion of their education. According to the United States Department of Labor's Report on the Youth Labor Force, almost every American high-school student works at sometime (Herman 2000; see also Finch, et al. 1991; Greenberger and Steinberg 1986; Mortimer and Finch 1986; Paternoster, Bushway, Brame and Apel 2003; Schoenhals, Tienda and Schneider 1998; Steinberg and Dornbusch 1991). Thus, in the United States, the labor market experience of both men and women begins well before adult employment. Explanations of the gender wage gap cannot be complete if only adult employment is considered; rather, an understanding of the gap requires that we also examine the pay patterns of teenagers in the labor market. Furthermore, as Barbara Reskin and Irene Padavic demonstrate, "the younger the workers are, the more equal women's and men's pay" (1994, 107). Therefore, a study focusing on early employment patterns not only offers a more comprehensive understanding of the gender based wage differentials by including a previously excluded--yet substantial--portion of the labor force, but also provides the opportunity to trace the origins of the gender wage gap while allowing us to control for individual characteristics such as domestic and maternal duties.

Prior Research

A substantial body of research attempts to explain the difference in pay for men and women. Prior inquiries operate under two distinct strands: studies that focus on individual differences between men and women, and studies that focus on occupational differences between men and women.

The human capital/productivity approach focuses predominantly on the individual differences between men and women and attempts to explain the gender wage gap through individual differences which might result in lower productivity (Becker 1985, 1993; Bielby and Bielby 1988; Mincer 1962; Schultz 1960). In such explanations, the lower earnings of women are argued to be the result of lower productivity among women and are often associated with their domestic duties and childcare responsibilities, or by interruptions in employment due to these duties (Berk and Berk 1979; Hersch and Stratton 1997; Hochschild 1989; Mincer and Ofek 1982; Ross 1987; Waldfogel 1998).

Lower productivity of women has been associated with proposed differences in education and experience between men and women. Earlier studies have argued that different levels of formal education between men and women account for the gender wage gap, but this explanation has failed to explain fully the gender-based differences in earnings (England 1997; Tomaskovic-Devey 1993; Treiman and Hartmann 1981). However, many similar studies have shown that years of experience on the job accounts for a substantial portion of the gender wage gap. Due to women's traditional domestic duties, such as child rearing and housekeeping, the average woman on the labor market has fewer years of experience than the average man, which contributes to the difference in pay between men and women (Light and Ureta 1995; Mincer and Polachek 1974; Reskin and Padavic 1994; Sandell and Shapiro 1978).

Some scholars have also focused on differential preferences of men and women. Filer (1983) argues that different tastes and personality characteristics predict differential earnings for men and women. However, the hypothesized effect is an indirect one, where values and preferences predict different types of jobs, which result in unequal pay; the mechanisms through which these values lead to a wage disparity remain unaddressed. A more direct association between values and the wage gap can be observed in a number of studies that examine the relative importance men and women place on earnings and other occupational characteristics. These findings indicate that men place more importance on earnings than women do, and, therefore, men end up in higher paying jobs (Brenner and Tomkiewicz 1979; Herzog 1982; Lueptow 1980; Major and Konar 1984; Peng, Fetters, and Kolstad 1981), though some studies fail to find such a difference in attitudes towards earnings (Walker, Tausky, and Oliver 1984). More recently, however, the field has accepted as conclusive the Jacobs and Steinberg (1990) argument and rejected this line of reasoning based on different preferences and values. Jacobs and Steinberg show that men and women's attitudes towards earnings do not explain the gender wage gap.

A second set of explanations, instead of focusing on the characteristics of the employees, focuses on structural and occupational differences between men and women. This view argues that the pay differential between men and women is predominantly due to occupational characteristics. First, this strand of research argues that the difference in pay between men and women is due to employment in different industries (Bayard, Hellerstein, Neumark, and Troske 2003; Blau, 1977; Daymont and Andrisani 1984; Groshen 1991). Earlier studies show that women are more likely than men to be employed in traditionally feminine occupations which require nurturing social skills and which are generally associated with lower pay (Jacobs and Steinberg 1990; Kanter 1977; Kilbourne, et al. 1990; Steinberg 1990). In addition to sex segregation by occupation, there is also sex segregation by firm. The disproportional employment of women in lower-wage firms only adds to the gap created by occupational sex segregation (Aldrich and Buchele 1989; Beck, Horan, and Tolbert 1980; Blau 1977; Coverdill 1988; Ferber and Spaeth 1984; Hodson and England 1986). More importantly, for men and women, there is a difference in the types of jobs they hold: Where men tend to pursue managerial positions, women typically occupy nonmanagerial jobs (Blau, Ferber, and Winkler 2006).

In addition to these explanations, the gender wage gap has been explored based on characteristics of women--such as race, ethnicity, and age--which exacerbate the gender wage gap, showing that women of different racial and ethnic backgrounds experienced the gender wage gap differently (Goldin 1990; Jones 1985; Kessler-Harris 1986). Finally, instead of explaining pay discrepancy in terms of demand and supply, many feminist scholars have explored the organizational setting (Acker 1990; Feldberg and Glenn 1979; Ferguson 1984; Kanter 1977; MacKinnon 1979; Smith 1979). These scholars argue that the gendered nature of the workplace contribute to the gender wage gap.

All these explanations focus on different aspects of employment that contribute to the gender wage gap, but even when all of these explanations are considered, a substantial part of the gender wage gap remains unexplained. Given that a substantial portion of the labor force experienced the labor market before adulthood, it is possible that gender wage gap originates from early employment. Hence, studying early employment patterns not only complements the current literature on the gender wage gap by offering an analysis of a substantial portion of the youth labor force, but also provides an opportunity to trace the origins of the gender wage gap. For the first time, we will be able to see how early labor market experiences contribute to the gender wage gap.

In the youth employment literature, research on youth wages is extensive in two major areas: structural and individual factors affecting wages. The first, and more dominant, wave of studies--structural factors that determine youth wages--considers governmental policies and restrictions, parental cash transfers (Pabilonia 2001), training programs (Andrews, Bradley, and Upward 1999; Grossberg and Sicilian 1999; Lynch 1989, 1992; Schiller 1994; Sweet 1995; Umana 1992), minimum hour requirements (Chen 1991), labor unions (Oklan 1987), neighborhood characteristics (Vartanian 1999), and, most importantly, changes in the minimum wage (Abowd, Kramarz, Lemieux, and Margolis 1999; Currie and Fallick 1993; Mangan and Johnston 1999; Meyer and Wise 1983; Neumark 1995; Neumark and Wascher 1999; Welch and Cunningham 1978).

While the literature on youth wages is dominated by such structural explanations and emphasizes the role of external factors on wages, the personal differences amongst young workers have received relatively scant attention. Partially because of data limitations and partially because all youths were assumed to make approximately the same amount of money, i.e., minimum wage, wage disparity in the youth labor market was rarely noted until recent years. Now, however, there is burgeoning literature on the role of individual or personal factors in determining youth wages. Because all youths were assumed to make similar wages, the most dominant wave of research in this new literature focused on "deviant" youth and attempted to identify factors that resulted in lower wages for some teenagers. Extensive inquiries explored the effects of smoking (Levine, Gustafson and Velenchik 1997), alcohol consumption (Kenkel and Ribar 1994), drug use (Gill and Michaels 1992; Kaestner 1991), arrests (Grogger 1995), teenage pregnancy (Klepinger, Lundberg, and Plotnick 1999), obesity (Register and Williams 1990), and interrupted schooling (Light 1995) on wages. In addition to deviant behavior, more recent studies have also explored the effects of nondeviant activities, such as extracurricular activities (Ewing 1995) and academic majors (Weinberger 1999) on wages. Finally, demographic factors have been included in the study of youth wages, with the most important demographic factor being race (Shapiro 1984; Weinberger 1998).

In the youth employment literature, almost no work exists on the effects of gender on youth earnings. Typical studies of the gender wage gap often portray the youth labor market as a "gender utopia," where there is no marked difference between what boys and girls earn. A limited number of studies do, however, point to different earnings of boys and girls, the most definitive one being by Greenberger and Steinberg (1983). But even these analyses have various shortcomings: First, the gender wage differential is noted only on the descriptive level--the discrepancies in pay are not explained; second, these youth employment patterns are not linked to the adult labor market; finally, due to data restrictions, an accurate difference from a nationally representative sample has not been offered. Because there have been so few studies of gender inequality in the youth labor market, this paper focuses on that understudied part of youth employment--youth wages and different pay by gender--and reveals that the youth labor market does not, in fact, offer a gender utopia.

The Gender Utopia of Youth Work

The main source of data in this line of inquiry is the National Longitudinal Study of Youth 199, NLSY97 (Bureau of Labor Statistics, 1997). NLSY97 provides ample data on income and employment variables along with demographic information on youths. To enable analysis and to explore changes over time, the data have been clustered into three age groups: 12- to 13-year-olds, 14- to 15-year-olds, and 16- to 19-year-olds. These data are particularly valuable not only in terms of the detailed, nationally representative information provided on the employment characteristics of youths, but also because of the inclusion of 12- to 15-year-olds, most of whom are traditionally omitted from analyses of youth labor. Typical accounts portray youth labor force participation rates among boys and girls as nearly equal, creating what seems to be a type of "gender utopia."

If we focus solely on labor force participation rates, in fact, we observe no significant differences in any of the three age groups. Among the 12to 13-year-olds, 36 percent of girls and 37 percent of boys work; among the 14- to 15-year-olds, 47 percent of girls and 51 percent of boys work; and among the 16- to 19-year-olds, 66 percent of girls and 65 percent of boys are employed. Such marginal differences mean that we can treat the labor participation rates, at least, as being approximately equal. This well-known finding, perhaps, leads to the perception of the youth labor market as a place of gender equality.

However, a lack of differences in the likelihood of youths working does not imply equality in all aspects of youth employment. Simply because boys and girls of a certain age are equally likely to work does not mean that they receive the same pay for their work or hold the same types of jobs.

The Emergence of the Wage Gap

Based on median annual earnings of dependent youths, we observe approximately equal annual median earnings for both genders during their early employment years. Among the 12- to 13-year-olds, boys, on average, make $120, while girls make only slightly more, $125. If for no other reason this is important because it is the first instance of gender equality in earnings in the American labor force. As a further analysis shows, by the time that the youths in the study are in the second age group, 14- to 15-year-olds, boys' earnings surpass girls' wages substantially, with boys earning an average of $400 a year, and girls earning only $266. Thus, we observe the beginnings of the gender wage gap, and it only widens with older groups. Within the third group, 16- to 19-year-old boys, on average, make $950, while their female counterparts earn only $750.

Although comparing yearly wages is the conventional and more reliable method of measurement, the low hourly pay and relatively fewer hours worked at earlier ages makes it important to consider the hourly pay rate. Interestingly, parallel with the above findings, we observe that, for the 14- to 15-year-olds, boys have higher hourly wages than girls. However, at these early ages, most working youths are engaged in freelance, non-hourly employment that is often paid on the basis of a completed task. Because of this, calculating hourly rates of pay lead to high standard deviations in the estimates of hourly earnings. Thus, annual earnings are a more reliable measure of earning power, but it is important to note that an analysis of hourly wages yields similar results as that of the median annual wages.

The mechanism behind the increasing gap in pay among genders is not immediately clear. Most of the traditional explanations of the gender wage gap fail when brought to bear on youth employment. Explanations of the gender wage gap based on individual differences in productivity or years of education would not be applicable in the case of youth employment. Girls, especially suburban girls as a group, in these early age groups would presumably not be less productive because they are having children.

Similarly, explanations based on differences in human capital between boys and girls are not applicable. For youth in the specified time bracket, difference in education, skills, credentials, or experience would not be relevant factors leading to different pay. All of them have the same education--less than high school--and the same experience--none.

Despite the fact that almost all individual explanations fail to account for the gender wage gap, the only applicable individual difference is the number of hours the youth works per week. When we look at the data, girls and boys, on average, work almost equal hours, with girls working slightly more than boys. Based on the NLSY97 data, we can see that girls, on average, work 7.9 hours per week, with a standard deviation on 9.9, while boys work 7.2 hours per week, with a standard deviation of 9.9. Comparing similar standard deviations, we can see that girls work slightly more hours than boys do; therefore, there is no reason to suspect that different hours of employment contribute to the gender wage gap. Since the gender wage gap in youth jobs cannot be explained by individual differences such as differences in productivity, education, and experience, especially due to domestic or maternal duties, we must look for alternative explanations.

The second set of explanations in the literature focuses on structural factors such as the type of job. One potential explanation is that boys and girls, while having equal labor force participation rates, may work in different types of jobs. The types of jobs in which youths are employed do seem to differ by gender. Among the 14- to 15-year-olds, girls are more likely to be employed in freelance jobs such as babysitting while boys tend to hold traditional employee-type positions such as those in retail stores or fast-food restaurants which offer more regular hours and set rates of pay (Herman, 2000).

We observe the gendering of jobs within both employee-type and freelance jobs (which will be discussed later at length) as early as age 12. However, it seems that teenagers also adopt traditional gender roles quickly upon entering the workforce, largely by concentrating either in employee-type or freelance jobs.

The Making of the Gender Wage Gap: Type of Job

The overall analysis of the data shows a difference in the types of jobs girls and boys hold, and particularly exhibits a marked concentration of girls in freelance jobs and boys in employee-type jobs. However, we have yet to show that this is the cause of the gender wage gap. The data does show that there is a disproportionate concentration of girls in freelance jobs, as opposed to employee-type jobs, and points to the gendered nature of both broad categories. However, we cannot be immediately certain that the gender wage gap arises from these differences. First, there is no evidence to suggest different pay for different types of jobs among youths. The vast majority of youths employed in employee-type jobs receive the minimum wage, while there is no evidence to support that freelance jobs pay more to one gender than the other.

Second, the disproportionate employment of girls in freelance jobs cannot alone be the cause of the gender wage gap, as child labor restrictions prohibit youths under 16 from employment. Therefore, the 12- to 13-yearolds and 14- to 15-year-olds are predominantly employed in freelance jobs. If the disproportionate employment of girls in freelance jobs explained the gender wage gap, we would not observe the degree of difference in pay for 14- to 15-year-olds that we do, as relatively few of these teenagers work in employee-type jobs. In fact, the wage gap is significant even among this group. Moreover, while the concentration of girls and boys in traditionally gendered jobs increases, this does not map onto the earliest signs of the gender wage gap. Even among the 12- to 13-year-olds, where girls actually earn slightly more than their male counterparts, girls and boys tend to concentrate in traditionally gendered jobs. However, the extent to which job choice results in the discrepancy in pay needs to be tested.

In order to examine the effect of gender on income, I perform a multivariate regression analysis that will assess the effect of gender on income while controlling for other likely influences on income. Because structural explanations for the wage gap among youths suggest that the type of job can contribute to differences in wages, I include in this analysis a variable that measures whether the job is a freelance position in an employee type position. In addition to gender, I also include a number of demographic control variables traditionally included in similar wage analyses such as race (coded white or Asian versus nonwhite), age (older youths typically earn more than younger youths), and the socioeconomic status of the household (measured as household income, as a percentage of the local poverty level). I also control for the number of hours worked, as this will clearly influence overall pay across individuals. The results of this analysis will allow us to assess whether gender is significantly associated with pay, above and beyond the effects of race, age, socioeconomic status, hours worked, and type of jobs.

The results of the model show that this is the case. Accounting for all of the explanations applicable to the youths in our sample, including the number of hours worked and the nature of the job, girls can expect to earn about $93 less per year solely because of their gender. While this may not seem like a great deal, it is very large relative to the average earnings of a girl in the sample, which come out to only $606.76 per year. Thus, at these young ages, girls are making almost 13 percent less than boys.

Other factors in the model were used for control purposes only to identify the pure effect of gender, but also provide interesting comparisons to better comprehend the magnitude of the effects of gender on income. It is interesting to see that the effects of race result in an average of $63 per hour, while the pure effect of gender is $93.

In addition to the standard regression results, the heteroskedastic maximum likelihood regression also allows us to substantively interpret the causes of the variance in our model. As predicted, the variance in the model increases with age and income--the latter bolstering our assertion of depressed model fit due to reporting errors in the dependent variable-but decreases with the interaction of the two, a striking result, especially given the relative strengths of the coefficients. We can interpret this result to mean that as youths become more like adults, older and earning more money, the relationship of their demographic characteristics to their earnings becomes more predictable. It may be only this variance that allows for the equality of pay in the youngest groups, and it fades away rapidly with increases in age and earnings. A further analysis of the heteroskedasticity patterns shows an interesting clustering of higher age/higher income youths--most of whom are boys, with the marked omission of girls.

So far, I have identified the pure effect of gender on the earnings of youths by controlling for all possible explanations and demographic characteristics. However, it is important to show how these factors translate into the gender wage gap. While the maximum likelihood estimation identified the direct effect of gender, it also revealed unequal earnings through the high concentration of girls in freelance jobs. The total average gender wage gap in the youth sample is $130, of which 71 percent, or $93, is attributable purely to gender. In addition to this, 10 percent of this difference is accounted for by the higher concentration of girls in freelance jobs. Therefore, overall, the model accounts for 81 percent of the gender wage gap.

In our attempt to unravel the factors that contribute to the making of the gender wage gap, we have reviewed the two major approaches in explaining the pay differences between men and women. The first set of explanations, or the individualist approach, is not applicable to the youth labor market. Girls in these age groups do not show any differences from boys in education, skills, experience and the number of hours they work. Furthermore, they are neither married nor typically have maternal or domestic duties.

The final set of explanations, which explains the pay differences because of the types of jobs, however, provides a partial explanation for why boys make more than girls. When youths approach the ages of 14 and 15, girls tend to concentrate in freelance jobs, while boys seek employee-type jobs. This polarization explains an important portion of the gender wage gap but offers only a partial explanation. While only ten percent of the gender wage gap is explained by the difference in jobs, the most important portion of the gender wage gap is explained purely by gender. Over 80 percent of the gender pay discrepancy is "the cost of being a girl."

While our model explains a substantial portion of the gender wage gap, there is still a minor, unexplained, portion of this gap. This portion cannot be attributed to either individual or value differences, but it is possible to consider further inner differences, such as personal value placed on income, within the types of jobs boys and girls hold. It is important to note that, in our sample, the main and the most pronounced difference is between the kinds of jobs--freelance or employee type. Despite the small extent of such effects and the limitations of the data, it is important to acknowledge the inner differences within all categories. Such differences in the minor job assignments often are obscured through the data collection process. However, it is important to test for such effects for future research, especially after more detailed data collections enable us to observe the nuances in job selection.

Conclusion and Direction for Future Research

The analysis of the youth labor force, like a social laboratory, offers a time when most traditional explanations of the gender wage gap are not applicable and almost every individual characteristic like education and experience are equal. The absence of these potentially confounding variables makes youth labor the ideal laboratory for the study of the gender wage gap.

Previous explanations of the gender wage gap have attempted to account for the gap through differences in individual characteristics between men and women such as education, experience, skills, domestic obligations, or childcare practices. None of these explanations apply to youth labor, yet the gender wage gap remains. While a cursory examination of youth employment patterns shows almost no difference in labor force participation rates, and even seems to favor girls, a detailed analysis of the data shows the origins of the gender wage gap. In this paper, we have traced the creation of the gender gap through youth employment and the contribution of early market experiences of youths to the pay disparity. Our findings show that demographic variables such as race, age, and income significantly increase the gender wage gap, and the type of work explains the pay disparity between boys and girls. However, despite controlling for all possible explanations and testing for differences in values, the gender wage gap persists.

While our analysis of youth labor eliminated a number of the frequently used explanations of the wage gap, further research needs to unravel the mechanisms through which this gap is created, internalized, and translated into the gender wage gap in full-time employment. Many feminist scholars argue that organizations are gendered in full-time work. The next step would be to unravel the mechanisms, with detailed ethnographic work, through which the early labor market experiences translate and contribute to the adult gender wage gap and subsequently unravel the everyday experience of the gender wage gap for youths.

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Yasemin Besen-Cassino is an assistant professor of sociology at Montclair State University. Her work focuses on young people in the United States and corn bines qualitative and quantitative methods. Her research interests include work, labor, gender, and inequality. Her work has been published in Contexts, Berkley Journal of Sociology, Journal of Contemporary Ethnography, and Equal Opportunities International.

Besen-Cassino, Yasemin

Source Citation   (MLA 8th Edition)

Besen-Cassino, Yasemin. "The cost of being a girl: gender earning differentials in the early labor markets." NWSA Journal, vol. 20, no. 1, 2008, p. 146+. Literature Resource Center, go.galegroup.com/ps/i.do?p=GLS&sw=w&u=sant99200&v=2.1&id=GALE%7CA219075029&it=r&asid=a1e02835ae3da105948ac9b17bd7975c. Accessed 12 Nov. 2017.