Gender, Parental Status, and the Wage Premium in Finance

Abstract

Previous research documents a growing wage premium for elite financial workers since the 1980s. A second line of research finds substantial gender disparities in earnings and career mobility among elite financial workers. Yet little is known about whether women in finance still receive a wage premium compared with their nonfinance counterparts. In addition, few studies examine whether similar gender disparities exist among nonelite financial workers. This article examines how the wage premium for working in the financial sector varies by gender and parental status across the wage distribution. We report that women earn a greater wage premium than men in low-wage financial jobs, while almost all of the increase in wages in high finance is captured by elite men, particularly fathers. Consequently, the financial sector simultaneously exacerbates and mitigates gender inequalities at different locations of the labor market. Our findings highlight the significance of institutional context in amplifying and attenuating the reward and penalty associated with gender and parental status.

Introduction

The dramatic expansion of the financial sector is one of the most significant transformations of the U.S. economy in the past three decades. The financial sector’s share of all corporate profits tripled from a stable postwar average of 15 percent to a peak in 2002 of 45 percent (Krippner 2011; Tomaskovic-Devey and Lin 2011). As a whole, financial workers greatly benefitted from this boom, as compensations in this sector have grown rapidly since the 1980s (Lin 2015; Philippon and Reshef 2012), solidifying many financial professionals’ position in the top 1 percent of earners. However, not all financial workers share these record compensations equally. Previous research documents stark gender disparities in wages and mobility among professionals in finance, and finds that even women with comparable qualifications generally receive lower compensations than their male counterparts (Blair-Loy and Wharton 2004; Madden 2012; Roth 2006; Turco 2010). Parental status exacerbates these gender disparities: fathers earn significantly higher wages than childless men, while mothers earn less than women without children (Roth 2006).

Yet the previous literature does not address whether women in finance fare better than their counterparts outside the financial sector. In theory, higher compensation might provide additional resources for women to ameliorate some of the negative impacts of the gender wage gap. However, hegemonic masculinity associated with elite workers may exacerbate gender inequalities by conferring additional benefits to men (Connell 2005). In addition, most women in the financial sector work as low-level bookkeepers or customer-facing positions. Little is known about gender dynamics in these nonelite segments of the financial sector, such as among bank tellers and receptionists (e.g., Acker 1994; Skuratowicz and Hunter 2004). Given that the financial sector is strikingly stratified by gender, this omission limits our understanding of how a sizable portion of women financial workers fare amid rising compensations and persistent gender disparities.

This article analyzes how the financial sector premium varies according to gender, parental status, and wage level. By financial sector premium, we refer to the logged wage difference between workers in the banking/securities industries and workers in other sectors who are otherwise similar in observed characteristics. In other words, we investigate the wage differences between workers within and outside of the financial sector for mothers, fathers, childless women, and childless men at different locations in the wage distribution. By comparing how this financial sector premium differs among women and men, we examine how the financialization of the U.S. economy may exacerbate gender inequality.

The financial sector provides a significant case of inquiry for three key reasons. First, it has the highest gender wage gap (Catalyst 2015) and remains a bastion of male domination. Thus, examining gender dynamics in finance provides a deeper understanding of workplace inequality in the United States. Second, finance constitutes a high proportion of top earners relative to other industries (Bakija, Cole, and Heim 2012; Kaplan and Rauh 2010). Overall, finance has been a pivotal driver of widening income inequality. Like other elites, top earners in finance wield considerable power that shapes how institutions distribute resources (Khan 2011; Zald and Lounsbury 2010). Our analyses provide insight into who benefits from the distribution of resources within these institutions. Third, finance’s prominence in the U.S. economy (Krippner 2011) allows it to establish employment and compensation norms throughout the labor force (Cobb 2015; Fligstein and Shin 2007; Lin and Tomaskovic-Devey 2013; Shin 2014). Financial investors pressure corporations to downsize, outsource, and computerize to maximize shareholders’ value (Lazonick and O’Sullivan 2000). As a result, the expansion of finance has transformed corporate governance, household debt, and state fiscal policy around the globe (Davis 2009; Lin 2016). As such, the workplace practices and rewards in this industry have significant implications for understanding inequality in the labor force as a whole.

Unlike existing studies that focus solely on gender inequality in finance, we take a comparative perspective to understand the relative rewards and penalties for working within and outside the financial sector. We address the following questions:

1. Do women and men receive comparable wage premiums for working in finance?

2. How do these financial sector premiums compare among mothers, fathers, childless women, and childless men?

3. How do these financial sector premiums change among these workers across the wage distribution?

Our findings show that since the 1980s elite, high-earning fathers—and to a lesser extent childless men—reaped the majority of the growth in the financial sector premium. Women, regardless of their parental status, receive little or no premium at the upper end, despite having similar characteristics and earnings potential. In contrast, the structure reverses at the bottom of the wage distribution: Women receive a 20 to 40 percent premium over their nonfinance counterparts, while men receive no benefit for working in finance. As a result, the financial sector simultaneously exacerbates and mitigates gender inequalities at different locations of the U.S. labor market.

This study advances the existing literature on gender, finance, and rising inequality in three ways. First, this article contributes to the literature examining variation in the motherhood penalty and fatherhood bonus in the U.S. labor market. Previous studies have focused on the interaction between parental status and other attributes like race (Glauber 2007, 2008), education (Hodges and Budig 2010), class position (Budig and Hodges 2010), and occupational prestige (Magnusson 2010). We argue that because workers are embedded in workplaces, the local institutional context plays a crucial role in activating or attenuating categorical distinctions (Avent-Holt and Tomaskovic-Devey 2010; Ridgeway and Correll 2004; Tomaskovic-Devey 2014). Our findings suggest that gender distinctions are further accentuated in the context of high finance, as masculinity facilitates access to considerable rewards among elite earners. Moreover, although fatherhood provides higher status and rewards to men in high finance, motherhood does not confer an additional wage penalty or premium relative to women without children.

Second, our findings contradict previous research that attributes the increasing compensation in the financial sector to an increasingly skilled workforce (Kaplan and Rauh 2010; Philippon and Reshef 2012). Men, particularly those who uphold the masculine ideal of fatherhood, have captured almost all the increase in earnings in finance. Women with similar education and experience do not confer the same abundance of monetary rewards. Thus, a skill-based account is insufficient in explaining the compensation growth in the financial sector.

Lastly, although there has been considerable research documenting the persistence of a gender wage gap (Cech 2013; Cha and Weeden 2014; Charles and Grusky 2005; England 2010), this literature has been largely separate from the discussion on the rise of top incomes (Kaplan and Rauh 2010; Philippon and Reshef 2012; Piketty 2014; Piketty and Saez 2006). Our results suggest that the hyperconcentration of income among top earners is potentially a gendered phenomenon: The financial sector produces more top earners than other industries, and these top earners are more often men than women. We suspect that similar gendered dynamics may take place in other industries that have a high number of top earners and emphasize masculine ideals such as a tendency to take on risk and devotion to work. This has significant implications for the study of elites, which does little to explore the role of gender (Keister 2014; Khan 2011). Cornwell and Dokshin (2014) found that the most influential elite networks are less segregated by gender, race, and class; however, our findings suggest that gender continues to play a major role in distributing resources among elite workers.

The following sections review the previous literature that motivates this study on the rise of the financial sector premium and workplace inequality in finance. From this review, we develop our hypotheses. Next, we present our data and methods. Then, we describe our empirical findings, specifically how the financial sector premium is distributed across the wage distribution according to gender and parental status. Finally, we provide potential explanations for our results and conclude with this study’s broader implications.

Background

The recent financial crisis renewed both public and scholarly attention to the excessive earnings in the financial sector. Although many find the high bonuses on Wall Street unfathomable amid economic turmoil, some studies proclaim that these bonuses reflect the increased productivity in the financial services industry. For example, Kaplan and Rauh (2010) argued that financial workers’ increased earnings result from advancements in information technology, augmenting the relative output of highly skilled workers. Operating under the same assumption that greater demand drives the financial sector premium, Philippon and Reshef (2012) introduced alternative explanations, including financial deregulation, corporate finance’s increasing complexity, and, to some extent, financial innovation.

A growing body of literature challenges the view that high compensation in finance is the product of market competition for skilled workers. Instead, these scholars emphasize the role of political and institutional factors. Tomaskovic-Devey and Lin (2011), for example, examined the processes through which economic and political dynamics mutually constitute one another: the preeminence of a neoliberal policy model, the emergence of a shareholder-value model of corporate governance, the concentration in the financial sector, and the growth of institutional investors have jointly created durable rent-producing positions for financial workers. Productivity alone does not account for the rising earnings in finance.

Ideally, a rising tide might lift all boats. Despite an enduring gender wage gap in the financial sector (Catalyst 2015), women in finance may still benefit from working in a sector with unparalleled growth in both profits and compensation over the past three decades. Based on the previous literature, we anticipate that women who work in finance will earn more relative to their nonfinancial counterparts at the same level of the wage distribution. This leads to our first hypothesis:

Hypothesis 1a: Women financial workers will incur a wage premium relative to their nonfinancial counterparts.

Gender Inequality among Financial Professionals

Gender Wage Gaps in the Financial Sector

Although the previous literature on the rapid growth in financial sector compensation examines dynamics across the wage distribution, it pays little attention to how gender impacts this phenomenon. In the demand account, the labor market and financial firms only discriminate based on labor output; therefore, gender is irrelevant unless it affects one’s productivity either by human capital or other supply-side factors. Although some institutional accounts (e.g., Tomaskovic-Devey and Lin 2011) do mention that men are likely to benefit more from the rise of finance than other demographic groups, limited discussion and evidence have been provided in this regard.

In contrast to this omission, studies of workplace inequality show stark gender disparities in pay in high finance. Even among highly successful men and women in finance, Blair-Loy and Wharton (2004) found a sizable gender earnings gap in a sample of 500 managers and professionals at a large finance firm. Similarly, in a study of a cohort of elite MBA graduates who entered the industry during the bull market of the 1990s, Roth (2006) found that women with prestigious credentials often receive lower compensation than their male counterparts with the same tenure and credentials. Furthermore, Madden (2012) reported that women stockbrokers at firms with performance-based pay earn only 64 percent of what men take home because they tend to receive less sales support and fewer sales assignments.

Roth (2006) argued that performance-based compensation systems, homophily preferences, and the failure to fully implement family-friendly, sexual harassment, and diversity policies all contribute to gender disparities in high finance. Similar to Madden’s finding, Roth documents how supposedly meritocratic performance-based reward systems often leave ample space for arbitrariness, misinformation, and discrimination. Homophily preferences lead managers to assign men to high-level client-facing positions, facilitating the accumulation of their social capital. It also deters some women from pursuing jobs involving client relationships.

Moreover, the industry’s strong devotion to work has been well-documented to penalize women with families (Blair-Loy 1999; Cha 2013; Cha and Weeden 2014). Even though many Wall Street firms adopted more family-friendly policies in the 1990s, the expectation to work around the clock remains a significant barrier for mothers who balance family needs and career advancement (Blair-Loy 2005; Turco 2010). Blair-Loy and Wharton (2004) further found that these expectations for overwork make it difficult for women to raise children while working in elite jobs in finance, where the high earnings are associated with long work hours.

In addition, the old boys’ network that dominates high finance remains an obstacle for women to achieve equal status in an industry that emphasizes trust and social connections. Men tend to monopolize the most valuable connections, while women struggle to access these informal networks (Ho 2009; McGuire 2002; Roth 2006). Position in the labor market accounts in part for this obstacle, as women are less likely to have the resources and positions that provide access to high-status networks (McGuire 2000). Even women in more well-connected positions with greater resources receive fewer benefits than their male counterparts because women are treated differently by people in their networks (McGuire 2002).

Although these studies suggest that gender inequality persists among financial professionals, they shed limited light on whether women in finance are advantaged or disadvantaged compared with their nonfinancial counterparts. In Hypothesis 1a, we anticipated that women who work in the financial sector would earn more than their counterparts in other sectors. The literature on gender inequality within finance leads us to expect that women will incur a lower financial sector premium than their male colleagues, as the result of working in a sector that rewards unconditional devotion and male-dominated networks. We therefore expect the following hypothesis:

Hypothesis 1b: Women will incur a lower financial sector premium than their male colleagues.

Motherhood Penalties and Fatherhood Premiums

Research on employer discrimination against mothers demonstrates that potential employers perceive mothers as less competent and worth lower salaries, yet evaluate fathers as more appealing employees (Benard and Correll 2010; Correll, Benard, and Paik 2007). These penalties in job opportunities and starting salaries for women translate to significant economic disparities over time (Hodges and Budig 2010; Killewald 2013; Killewald and Gough 2013). The motherhood penalty and fatherhood bonus are observed among high finance workers. For example, Roth (2006) found that mothers make two-thirds of what childless women earn and half of what fathers earn. In contrast, fathers earn 25 percent more than childless men even though fathers generally work fewer hours.

We anticipate that the cultural norms and expectations for work in the financial services industry—specifically the expectation for risk-taking, networking, and working long hours—will differentiate the financial sector premium among mothers and fathers. First, the financial services industry values calculative risk-taking and compensates it with high rewards. Gendered beliefs about risk-taking create status hierarchies that contribute to the industry’s high gender wage gap (de Goede 2004; Fisher 2012; van Staveren 2014). Elite financial workers must balance a high risk-tolerance with caution, which is associated with elite masculinity (de Goede 2000). Wealthy fathers may be perceived as more responsible, discerning, and capable when taking investment risks. Second, the trust-based networks based on social similarity in finance may confer additional benefits to fathers. Last, the industry’s competitive environment requires workers to provide their undivided attention and commitment (Ho 2009; Levin 2001; Zaloom 2006). Fathers may transfer housework and childcare to their spouses or to paid care workers (Williams 2001), whereas mothers are expected to be fully dedicated to family responsibilities and are perceived as less capable of committing to work (Blair-Loy 2005). We therefore anticipate that the expectations for risk-taking, social capital, and overwork may result in women, particularly mothers, being omitted for consideration for the highest paying jobs.

Below, we list two hypotheses for the effects of parental status that are informed by previous findings on the motherhood penalty and fatherhood bonus as well as the research on workplace inequality in the financial services industry. We anticipate that fathers will receive a greater premium for working in the financial sector than mothers and childless men, while mothers will incur a lesser premium than childless men and women. These premiums arise from the specific context of the financial sector.

Hypothesis 2a: Fathers will incur a higher financial sector premium than women and childless men.

Hypothesis 2b: Mothers will incur a lower financial sector premium than men and childless women.

Variation across the Earnings Distribution

Last, an exclusive focus on professional workers overlooks other segments of the financial sector, where women constitute the majority of the workforce. In many ways, middle- or low-level jobs in finance are the mirror opposites of those in high finance. Instead of the around-the-clock demand, retail jobs such as bank tellers have restricted schedules. Rather than performance-based pay, these jobs generally earn hourly wages. For many low-wage client-facing positions, building long-term networks is superfluous because interactions tend to be arm’s length transactions. And finally, women rather than men tend to dominate these jobs. Taking these factors into account, it is unclear whether low-wage women face similar disadvantages to their counterparts in high finance, even though both groups are nominally in the same sector or may even work for the same firm.

The previous research on motherhood penalties and fatherhood bonuses leads us to expect that location in the earnings distribution will significantly effect the degree of wage premium that mothers and fathers receive for working in the financial sector. Throughout the labor force, fatherhood bonuses are greater for men with high educational attainment and skill-intensive jobs (Hodges and Budig 2010; Magnusson 2010) and increase along the earnings distribution (Cooke 2014). Similarly, Budig and Hodges (2014) found that women in the lower half of the earnings distribution incur larger motherhood penalties than higher earning women. The literature on the interaction between class and parental status leads us to anticipate that high-earning fathers will incur the greatest premium and low-earning mothers will receive no premium, or even a penalty, for working in finance.

Hypothesis 3a: Fathers will receive the greatest premium for working in finance at the upper end of the wage distribution.

Hypothesis 3b: Mothers will receive no premium for working in finance at the lower end of the wage distribution.

Data, Variables, and Methods

Data

We use the Current Population Survey (CPS) March files (King et al. 2010) from 1975 to 2009 to examine how the financial sector premium varies according to gender and parental status. The CPS is a monthly, population-representative survey conducted by the U.S. Census and the Bureau of Labor Statistics, and it serves as the primary source of labor force statistics such as unemployment rate for the Federal government. Every March, the survey asks respondents about their employment characteristics and earnings in the previous year. We use the March data in our analysis, because it includes earnings from bonuses, a crucial form of compensation for financial and other professional workers, which may be omitted in monthly data. The sample includes full-time (35+ hours) full-year (50+ weeks) employees aged 25 to 65 years with annual earnings of at least 100 dollars.¹ The financial sector is defined as the combination of the banking and securities industries, which include about 55 thousands observations or around 4 percent of our total sample.

In addition to the repeated cross-sectional data set, we take advantage of the CPS’s rotating panel design: Households are interviewed for four consecutive months, are not in the sample for the next eight months, and then are interviewed for four more consecutive months. We match individuals in the consecutive March files and create a two-year, panel data set. The panel data set includes repeated observations for the same individual, which allows us to evaluate whether unobserved skill differences account for the financial sector premium. We match respondents using the combination of state, household identifier, household number (which changes when a new family moves into the sampled housing unit), individual line number, gender, and racial identification. The matched observations are then verified by the age difference across time. When the change in age is less than −1 or larger than 3, we drop both observations from the panel, following Madrian and Lefgren’s (1999) recommendation.

The primary sampling unit in the CPS is the household, so we omit respondents who moved away from their original household. We also exclude respondents who changed their gender or racial identification. The matching rates vary from around 43 percent to 70 percent across different years (see Online Appendix A). To address potential selection bias, we estimate a series of year-specific logistic regressions that include race, gender, age, employment, education, marital status, children, and state to predict the likelihood that the respondent would be identified in the subsequent survey. We then divide the sampling weights with the predicted probabilities of entering the panel to moderate potential selection bias. This procedure weights observations that are less likely to be matched to make the matched sample representative of the population. Some consecutive years cannot be matched due to occasional redesigns of the CPS (see Madrian and Lefgren 1999 for technical discussion; Ziliak, Hardy, and Bollinger 2011 for similar matching results).

Cross-sectional and panel data sets have their respective strengths. The panel data set has repeated observations for the same respondent. This provides an opportunity to examine whether the financial sector premium can be attributed solely to unobserved skill differences. Yet the survey design of the CPS precludes linking individuals who moved away from their original housing unit. The cross-sectional data, in contrast, are population-representative and provide more complete coverage of the U.S. labor market. We present the results from both data sets in this article.

Although the CPS provides detailed information on workers’ earnings and other characteristics, a well-known limitation is that the survey imputes the earnings of top earners to ensure anonymity. The proportion of earners who were top-coded slowly increased over time. For women, the number increased from 0.02 percent in 1975 to 0.86 percent in 2007; for men, the number increased from 1.18 percent to 2.59 percent (see Burkhauser and Larrimore 2009 for more discussion on this issue). As a result, this study is unable to examine the wage dynamics at the very top (e.g., 1 percent) of the distribution. However, because the scope of analysis is restricted to those at or below the 95th wage percentile—in contrast to previous studies that focus on the average—our findings are robust against top-coding.

Variables

The outcome of interest is logged hourly wage, calculated as the inflation adjusted annual wage and salary earnings divided by annual work hours. Since 1980, the survey specifically prompts the respondents to include over-time pay, tips, bonuses, and commissions.² We conduct additional analysis using logged annual earnings and unimputed earnings data to gauge the potential impacts of work hours polarization (Jacobs and Gerson 2005) and hot-deck imputation (Mouw and Kalleberg 2010). The results are reported in Online Appendixes B and C, which are substantively identical to the results presented below.

Due to a limitation in the survey data, we measure parental status by the presence of children under age 18 years in the household, which omits parents of older children who have already moved out. Because empty nesters are included in the nonparent category, we anticipate that using this variable will provide a more conservative estimate of the premiums. Unless otherwise specified, the control variables in the cross-sectional analysis include year; age and its squared term; race; the interaction between region and metropolitan area status; level of education; marital status; whether the respondent have a 50-hour or longer workweek (Cha and Weeden 2014); and year fixed effects. The region and metropolitan area interaction accounts for the concentration of financial firms in high-wage areas. Table 1 presents the weighted means and standard deviations of our variables.

Table 1.

Weighted Descriptive Statistics, Cross-sectional Data Set, 1970–2009.

Variables	1970–1979	1980–1989	1990–1999	2000–2009
Inflation-adjusted logged wages	3.004	2.930	2.893	2.960
SD	0.580	0.585	0.629	0.658
Finance sector	0.033	0.045	0.043	0.045
Childless men	0.249	0.269	0.283	0.294
Fathers	0.446	0.351	0.308	0.287
Childless women	0.151	0.183	0.196	0.206
Mothers	0.154	0.197	0.213	0.213
White	0.853	0.812	0.762	0.688
Black	0.088	0.095	0.105	0.108
Hispanic	0.045	0.065	0.093	0.141
Asian	NA	0.009	0.035	0.050
Other	0.014	0.019	0.006	0.014
Age	41.922	39.831	40.308	42.119
SD	11.208	10.739	9.980	10.409
Work 50+ hours	0.168	0.162	0.208	0.199
Primary	0.273	0.155	0.110	0.100
High school	0.416	0.417	0.356	0.314
Some college	0.155	0.198	0.272	0.280
College	0.100	0.147	0.187	0.213
Advanced	0.056	0.083	0.076	0.093
Married	0.765	0.681	0.638	0.614
Single	0.101	0.152	0.183	0.202
Other	0.134	0.167	0.179	0.184
Metropolitan	0.737	0.795	0.827	0.856
New England	0.062	0.062	0.055	0.051
Middle Atlantic	0.186	0.165	0.145	0.139
East North	0.212	0.181	0.178	0.163
West North	0.070	0.070	0.071	0.070
South Atlantic	0.154	0.172	0.183	0.192
East South Central	0.060	0.056	0.059	0.056
West South Central	0.092	0.102	0.106	0.110
Mountain	0.039	0.047	0.054	0.066
Pacific	0.126	0.144	0.149	0.153
N	232,484	286,359	302,231	477,343

We do not include occupation in our main analysis for three reasons. First, occupational sorting should be considered as a mechanism that generates differential earnings, not exogenous variation. Second, occupational classification may be endogenous to earnings and education. Indeed, many former sales positions in finance are recoded as professionals as their earnings and education increase. Third, the estimates with the controls for major occupational groups (Online Appendix D) are largely similar to the main results.

Recentered Influence Function Regression

We use recentered influence function (RIF) regressions (see Firpo, Fortin, and Lemieux 2009; Fortin, Lemieux, and Firpo 2011; Killewald and Bearak 2014; Lin 2015 for more discussion) to detect how the financial sector premium may vary along the lines of gender and parental status. We define financial sector premium as the logged wage difference between workers in the banking/securities industries and in other sectors who are otherwise similar in observed characteristics at a given unconditional wage percentile. The logic of this method is based on the statistical concept of influence function: the relation between a data point and the statistics of interest, such as quantile or variance. By recentering the influence function with statistics of interest and regressing each observation’s recentered influence on the explanatory variables, one can estimate how the explanatory variables influence the unconditional statistics of interest. In this light, the standard ordinary least squares (OLS) model could be viewed as an RIF regression, where the statistics of interest is the mean and the influence function is $Y-\text{μ}$, and the RIF is simply $Y-\text{μ}+\text{μ}$. In the case of quantiles, the RIF for the $\text{τth}$ percentile is

$$\text{RIF}\left(y\text{;}{q}_{\text{τ}}\right)=\frac{\text{τ}-1\left\{y\le q_{\text{τ}}\right\}}{f_Y\left(q_{\text{τ}}\right)}+q_{\text{τ}}\text{,}$$ (1)

where $y$ denotes the observed outcome; $1\text{{}y\le q_{\text{τ}}\text{}}$ is an indicator function, which equals 1 when $y$ is equal or smaller than $q_{\text{τ}}$ and equals 0 otherwise; and $f_Y\text{(}{q}_{\text{τ}}\text{)}$ is the probability density of $Y$ at $q_{\text{τ}}$, which is approximated through a kernel function in the analysis. Put differently, we first estimate a series of linear probability models to obtain the influences of the explanatory variables on one’s likelihood to be above or below a given quantile. We then divide the coefficients with the local probability density to obtain their effects on wages.

For cross-sectional estimates, we estimate the models as

$$\begin{array}{l}\text{RIF}\left(y_{t\text{,}i}\text{;}{q}_{\text{τ,}t}\right)={\text{α}}_{\text{τ,}t}+{\text{β}}_{1\text{,τ,}t}{F}_{t\text{,}i}+\\ \sum _r^R{\text{β}}_{r\text{,τ,}t}{P}_{r\text{,}t\text{,}i}+\sum _q^Q{\text{β}}_{q\text{,τ,}t}{F}_{t\text{,}i}{P}_{q\text{,}t\text{,}i}+\\ \sum _k^K{\text{β}}_{k\text{,τ,}t}{X}_{k\text{,}t\text{,}i}+{\text{ε}}_{t\text{,}i}\text{,}\end{array}$$ (2)

where $t$ denotes time period; $\text{τ}$ denotes quantile; $i$ denotes individual; $F$ indicates whether worker $i$ is in the financial sector; $P$ is a series of dichotomous variables indicating the gender and parental status of worker $i$; $F_{t\text{,}i}{P}_{q\text{,}t\text{,}i}$ represent the interaction terms between $F$ and $P$; while $X$ denotes other controls in the models. The interpretation of these coefficients is similar to that of the OLS regression. For the OLS regression, the coefficient represents how much the expected value (i.e., mean) changes per one-unit change in the covariate. For the RIF regression, it represents how much the value at the $\text{τth}$ percentile changes per one-unit change in the covariate. In other words, these coefficients represent the marginal difference in logged wage between groups across the unconditional wage distribution.

For the panel estimates, we specify the models with an additional lagged term for the hourly wage from the year before:

$$\begin{array}{l}\text{RIF}\left(y_{t\text{,}i}\text{;}{q}_{\text{τ,}t}\right)={\text{α}}_{\text{τ,}t}+{\text{β}}_{0\text{,τ,}t}{y}_{t-1\text{,}i}+\\ {\text{β}}_{1\text{,τ,}t}{F}_{t\text{,}i}+\sum _r^R{\text{β}}_{r\text{,τ,}t}{P}_{r\text{,}t\text{,}i}+\\ \sum _q^Q{\text{β}}_{q\text{,τ,}t}{F}_{t\text{,}i}{P}_{q\text{,}t\text{,}i}+\sum _k^K{\text{β}}_{k\text{,τ,}t}{X}_{k\text{,}t\text{,}i}+{\text{ε}}_{t\text{,}i}\text{,}\end{array}$$ (3)

where $y_{t-1\text{,}i}$ represents the logged wage from the year before, which absorbs unobserved individual differences associated with wages and reduces the omitted variable bias. A common approach is to specify a fixed-effect model, which absorbs time-invariant individual differences and identifies the effects of the explanatory variables with the average wage differences of respondents who switched between categories. Because we have only two observations per individual, it is not a feasible approach for the linear probability models (see Equation 1). Other longitudinal data sets have more observations on the same individual, yet have too few workers in finance (<5 percent) for us to examine the joint effects of working in finance and parental status.

Considering the number of models (4 periods and 19 quantiles yields 76 separate regression models) and the complexity of the estimates, in the next section we only present estimates of interest or the predicted outcomes. Full estimates for other wage percentiles and the control variables are available upon request.

Findings

Motherhood Penalties and Fatherhood Premiums

First, Figure 1 presents the main effects of gender and parental status on workers’ wages throughout the labor market. The cross-sectional logged wage differences of fathers, childless women, and mothers from childless men (i.e., ${\beta }_{r\text{,τ,}t}$ in Equation 2) reveal several longitudinal trends that are worth noting. First, the results echo previous findings (Cotter, Hermsen, and Vanneman 2004; England 2005) that the gender difference in wage declined between 1975 and 1990 but the progress stalled since then. Second, it shows that the fatherhood bonus has been quite consistent over time in the periods of interest, ranging from 7 to 15 percent. Third, Figure 1 shows that the motherhood penalty seems to decline over time in tandem with the gender wage gap (Harkness and Waldfogel 2003) except at the very bottom of the wage distribution, which may reflect the constant lack of resources for childcare among low-wage women workers.

Figure 1.

Wage differences from childless men (cross-sectional estimates).

Figure 1 further shows that fathers at the upper end of the wage distribution receive higher fatherhood bonuses than their lower-end counterparts, a pattern that is consistent with research findings that bonuses are greater for men with higher educational attainment, more skill-intensive jobs, and greater earnings (Budig and Hodges 2010; Cooke 2014; Magnusson 2010). In contrast to the time-invariant, monotonic increase in fatherhood bonuses along the wage distribution, greater variation is observed in the wage gaps between mothers and nonmothers. Across the wage distribution, we see a larger motherhood penalty at the bottom half of the wage distribution, while mothers at the upper end receive similar (1975–1989) or even higher (1990–2009) wages. This result is similar to Budig and Hodges’s (2014) findings using the National Longitudinal Study of Youth and alternative model specifications.

Gender, Parental Status, and the Financial Sector Premium

Figure 2 presents the comparative wages of fathers, mothers, and men and women without children in finance to those of their nonfinance counterparts (i.e., the sum of ${\beta }_{1\text{,τ,}t}$ and ${\beta }_{q\text{,τ,}t}$ in Equation 2). Figure 2 presents the wage premiums for the four gender and parental status groups at the 5th, 25th, 75th, and 95th percentiles (see Online Appendix E for estimates at other percentiles). It shows that the financial sector premium identified in the previous literature has stark variation across groups. The findings confirm Hypothesis 1a, which stated that women financial workers incur a premium relative to their nonfinancial counterparts. Hypothesis 1b, however, is only partially confirmed: Men receive a greater premium than women only in the upper half of the earnings distribution. This trend reverses in the bottom half, where women incur higher premiums for working in the financial sector.

Figure 2.

Logged wage premium for working in finance by gender and parental status (cross-sectional estimates).

Women have received the wage premium at the bottom since the 1970s (see Figure 2). Both mothers and childless women earned close to 40 percent more than their nonfinance counterparts in the 1970s and the 1980s, while men received much less or no premium. The premium for low-wage women financial workers declined to about 25 percent in the subsequent years but nevertheless remained substantial. Even after taking the main effect of parental status into account (see Figure 2), low-wage women in finance still earn a similar if not higher wages than their male counterparts, which counters Hypothesis 2a that predicted that fathers would incur a higher financial sector premium than women and childless men. This does not occur at the bottom half of the earnings distribution. Similarly, it counters Hypothesis 2b, which anticipated that mothers would incur a lower financial sector premium than men and childless women.

These hypotheses do, however, generally hold true in the upper half of the earnings distribution where men—particularly fathers—capture the rising premium. Between 1975 and 1979, childless men and fathers received moderate premiums, 21 percent and 37 percent respectively, at the 95th percentile. The premiums started to grow significantly in the subsequent years, when childless men gained 50 percent higher (a logged difference of 0.40) wages and fathers earned 180 percent (a logged difference of 1.04) more than their nonfinance counterparts between 2000 and 2009. We expect that these estimates are conservative, because the CPS data do not allow us to examine the dynamics among top earners above the 95th percentile of the earnings distribution.

Women financial workers at the upper end, however, received no significant premium for the first three periods and only a modest premium at the end. As predicted in Hypothesis 2b, mothers incur a lower financial sector premium than fathers and childless men, yet only in the upper half of the distribution. Contrary to Hypothesis 2b, however, high-earning mothers do not receive a significantly lower premium than childless women. Furthermore, it should be noted that, although Roth (2006) found that mothers make two-thirds of what childless women earn, we do not observe a motherhood penalty among these women professionals, which suggests parental status is a meaningful status signifier for men but not for women in this institutional context.

These findings reveal a more complicated story than our initial hypotheses. The results confirm Hypothesis 3a that fathers will receive the greatest premium for working in finance at the upper end of the wage distribution. In fact, fathers almost monopolize all the benefits of working in finance at the upper end of the distribution. Yet the finding contradicts our Hypothesis 3b that mothers will receive no premium for working in finance at the lower end of the wage distribution. Instead, we find that mothers incur the greatest relative rewards for working in the financial sector.

To examine whether these gendered patterns vary by race and ethnicity, we reestimated the cross-sectional model to investigate how the premium varied by gender, parental, and racial/ethnic status in 1990–2009, when the financial sector premium increased. The findings are consistent with respect to gender: Across all racial/ethnic groups, men receive greater financial sector premiums at the top, while women gain larger premiums at the bottom. However, white fathers clearly benefit more from working in high finance than minority fathers (see Online Appendix F).

Panel Data and Predicted Wages

To ensure that the findings above are not solely driven by the differences in unobserved productivity, we reestimate the models with panel data and the lagged dependent variable (Equation 3), with the latter presumably capturing some unobserved differences in productivity. Figure 3 presents the estimates of financial sector premium between 1980 and 2009 (estimates for earlier years are unavailable due to sample size). It shows a more conservative but largely similar picture: Women in finance receive some degree of premium at the bottom of the wage distribution, while elite male workers capture most of the gain at the upper end.

Figure 3.

Logged wage premium for working in finance by gender and parental status (panel estimates).

Figure 4 summarizes the findings in Figures 2 and 3 and presents the predicted wages among finance and nonfinance workers by gender and parental status. It shows that, at the very bottom of the wage distribution, the financial sector premium for women reduces the gender wage gap. At this wage level, childless women in finance earn a significantly higher wage than fathers in and outside of finance. The within-sector gender wage gap emerges at the 25th percentile, with men in finance earning higher wages than their women counterparts. However, women still receive a greater premium for working in finance, leading childless women in finance to make similar wages to childless men in the nonfinance sector.

Figure 4.

Predicted wages among white workers by gender and parental status in 2000–2009 (cross-sectional estimates).

The pattern reverses at the upper half of the labor market. At the 75th percentile, both fathers and childless men obtain significantly greater premiums than women. Fathers in finance make 30 percent more than their nonfinance counterparts. Fathers are closely followed by childless men, who gain an advantage of 20 percent for working in finance. The differences exacerbate at the 95th percentile: Fathers in finance take home almost 400,000 annually for working a standard workweek, while mothers in similar positions receive only one-fourth of these earnings. Due to top-coding, our analysis is unable to say much about the gender disparities above the 95th percentile. However, if the patterns hold, we expect fathers reap even greater rewards than other groups at the very top of the wage distribution.

Discussion

What are the potential explanations for these group differences? The demand-side account would attribute them to unobserved productivity-related characteristics, yet the findings largely hold even when taking into account the previous year’s wages, a control for skill and productivity. The institutional account may better explain these findings, which are consistent with the prevailing explanations for workplace inequality in finance. A substantial body of literature documents how expectations for workers to take risks, build networks, and demonstrate dedication contribute to gender disparities in the financial sector.

The previous research finds that the financial services industry values risk-taking and believes that high risks lead to high rewards (Ho 2009). In this environment, gendered beliefs about risk become an avenue through which status hierarchies arise between women and men. A successful portfolio manager or financial analyst is expected to be risk-tolerant and daring, yet rational and discerning. The literature finds that these qualities are associated with elite masculinity (de Goede 2004), while women are perceived as either too imprudent (de Goede 2000) or too risk-averse (Fisher 2012). Based on this research, we expect that cultural expectations for fathers, which include a sense of responsibility and obligation (having to provide for his wife and children), make them more likely to be perceived as prudent risk-takers.

These perceptions of risk-taking matter, as studies find that riskier investments are associated with male-dominated jobs and higher financial rewards. For example, during the 2008 financial crisis, the jobs with the greatest impact on elevated market risks—trading, derivatives investing, and risk modeling—collected higher earnings, garnered more prestige, and were dominated by men (van Staveren 2014). Therefore, men, particularly fathers, are more likely to work in positions that take greater investment risks and generate higher returns.

The high uncertainty in financial markets also warrants an exceptional appreciation for trust and networks (see Gorman 2006 for the case of law firms). The literature finds that a successful financial worker is one who not only takes the “right” risks but also is trustworthy and has extensive social capital (Ho 2009; Roth 2004). However, access to networks, mentors, and investors are all shaped by gendered patterns of interaction (Ridgeway 2011; Ridgeway and Correll 2004). Earning respect and trust demands recognition of social similarity (Rivera 2012) gained through performances of masculinity as wise and ambitious—best captured by the icons of the stately, financial patriarch and the macho, plebian trader (McDowell 1997). Meanwhile, access to valuable networks is often closed to women and minority men (McGuire 2000, 2002). Women face a double bind: They must adhere to expectations set for men and masculinity to gain trust and respect from the peers, yet are penalized for not acting in accordance with the prevailing norms for femininity (Roth 2006). Based on this research, we anticipate that elite fathers can best embody these cultural ideals for workers in the financial services industry, which allows them to gain access to considerable financial rewards.

Last, although the lengthening workweek for professionals is not unique to finance (Cha and Weeden 2014; Jacobs and Gerson 2005), the industry perhaps holds one of the strongest beliefs in a “devotion to work” (Blair-Loy 2005; Turco 2010). Competitive workplaces like the trading floor and the investment bank demand undivided dedication (Levin 2001; Zaloom 2006) and provide little time for responsibilities outside the office. As a result, many investment bankers start off working as many as 120 hours a week (Ho 2009). Research finds that these demanding work hours penalize women with families (Blair-Loy 1999; Cha 2013), and being married to a man who works long hours compounds this effect (Cha 2010). Fathers are best equipped to outsource childcare and housework, either to their spouses or to paid care workers, both of who are likely to be women (Williams 2001).

Although we controlled for lengthy workweek in our measure for hourly wages, this “devotion to work” schema may penalize women, because colleagues may perceive them as less capable of committing to these workplaces. Therefore, women may not be considered for the highest paying jobs, which require long hours that conflict with existing or potential domestic obligations, whereas fathers may be favored because it is assumed that they have help at home. In theory, married men without children would have the most support at home and time to commit to work. Yet we find that high-wage fathers earn the highest premium. It is perhaps because fathers are perceived as having more at stake, since they have a responsibility to help provide for their children. As a result, fathers may be viewed as more responsible and committed workers.

Although the institutional settings in high finance disadvantage women, the requirements for low-wage jobs in the financial sector may favor the employment of women. The research on gendered beliefs about risk lead us to expect that women may be more appealing for subordinate positions such as bank tellers and investment support staff. These low-wage positions often have access to large amounts of funds, making carefulness, compliance, risk-aversion, and rules-following even more desirable attributes for service and support staff. Thus, gendered beliefs that cast women as cautious may provide a cultural frame that privileges women’s access to low-wage positions in finance. Moreover, even though women’s presumed family responsibilities may make their commitment to work seem suspect (Benard and Correll 2010), the belief that women are innate caregivers may, in addition to their lack of alternative options, make them attractive candidates for positions that directly interact with retail customers and investors.

Although these cultural institutions are difficult to measure, previous studies indicate significant variation across subsets of workplaces in the financial sector. The culture of risk-taking, the emphasis on social capital, and the demand for unconditional devotion are most dominant in the securities and commodities industry (Ho 2009). By contrast, banks are subjected to higher levels of business and labor regulation and thus have relatively conservative investment strategies, comparably formal and long-term-oriented business relationships, and more standard labor practices. To assess whether these institutions drive the gendered divergence in financial sector premium, we reestimate Equation 2 with an industry interaction term. Figure 5 presents the male wage advantages in the two financial industries (i.e., the gender gaps in financial sector premium). Although deregulation of the sector has led financial firms to simultaneously operate in both industries, the comparison is valid because CPS respondents are coded based on the function of their workplace, not the main operation of the firm. Our finding shows that, for both parents and nonparents, the gender wage gap at the upper end is greater in the securities and commodities than in the banking industry, which provides some support for these explanations.

Figure 5.

Wage advantages for men in the banking and securities & commodities industries, 2000–2009.

Conclusion

While previous studies have documented an exponential growth in the wage premium for elite financial workers since the 1980s, how the premium varies along the lines of gender and parental status remains neglected. In the meantime, most studies on gender and finance focus on the disparities between men and women in high finance. These studies neither indicate whether women still receive a significant premium for working in finance, nor identify how the disparities vary across the wage distribution.

This article provides an empirical examination of how the financial sector premium varies along the lines of gender and parental status across the wage distribution. By broadening the scope of analysis, our research documents how gendered wage disparities in this sector compare to those throughout the labor market. Our findings indicate that much of the increase in the financial sector premium since the 1980s has been captured by elite white men, particularly fathers, who are compensated more than twice as much for their hourly labor as their nonfinance counterparts in the 2000s. This fatherhood premium translates into annual earnings of 400,000—4 times what women earn. It is likely that these findings are even starker for the top 1 percent of earners, whose earnings were top-coded and imputed to ensure anonymity.

Both mothers and childless women receive no or little premium at the upper end of the wage distribution. Therefore, women who have similar characteristics and earnings potential only make one-fourth of their male counterparts with children. In contrast, at the lower end of the labor market, women financial workers earn a 30 to 40 percent premium, while men do not receive such benefits. This result is largely driven by the fact that women receive much lower wages than men in the nonfinance sector but similar or slightly higher wages in finance. Consequently, the financial sector premium mitigates the gender wage gap in the low-wage labor market.

These findings have three main implications. First, while previous studies focus on how the effects of parental status on wages vary by other social categories, our results suggest that local institutional settings are critical in determining the status distinctions among similarly qualified workers. While men in general enjoy a certain degree of advantage over women in the labor market (Figure 1), men—particularly fathers—reap additional rewards for working in high finance, where risk-taking, social capital, and devotion to work are held with high regard. In contrast, women receive substantial benefits from working in low-wage jobs in finance, where risk-aversion and emotional labor are valued. As a result, these low-wage women make similar or slightly higher wages than their male counterparts within and outside of the financial sector (Figure 4). These findings highlight the importance of situating gender and parental status in a specific institutional context to better understand how gender influences access to rewards and penalties.

Second, the primary explanation of the high earnings in finance is that banks and investment firms must pay generous wages to retain talented workers in a highly competitive industry. Our findings suggest that not all high-skilled workers are rewarded equally. Even with extensive controls for human capital characteristics, including the wages from the previous year, we still find a large variation in financial sector premium along the lines of gender and parental status. This reveals how a skill-based, gender-neutral explanation is insufficient in explaining the compensation growth in the financial sector. Future studies should explore how masculinity is at play in generating the excessive compensation in finance.

Last, the findings about elite earners suggest that gender, a factor largely omitted from the existing studies on widening income inequality, is crucial for understanding how elites gain access to economic rewards and resources. We suspect that gendered institutions not only create a wage gap between men and women but may also contribute to widening income inequality by conferring additional rewards to high-status fatherhood. In the case of finance, most of its contribution to income inequality overall is driven by elite fathers’ extravagant earnings. This suggests that even as women gain access to elite networks and occupations (Cornwell and Dokshin 2014), women may not gain the same access to resources and financial rewards. Similar dynamics may be identified in other industries such as health care (Boulis and Jacobs 2003, 2008; Roth 2016), legal services (Gorman 2005, 2006), information technology (Glass et al. 2013; Shih 2006), academia (Damaske et al. 2014; Leahey 2007), and the oil and gas industry (Williams, Muller, and Kilanski 2012). All of these industries feature record-setting compensation and emphasize the importance of social capital, promote unfettered professional commitment, and demand round-the-clock availability. Future studies should further examine gender as a key element for understanding the contemporary rise in income inequality.