An Asymmetrical Portrait

Abstract

While a large body of work has focused on gendered income inequalities in other fields, virtually no literature has explored this phenomenon within artistic careers. We use the Strategic National Arts Alumni Project (SNAAP)—a nationwide survey of 33,801 individuals who have received degrees in the arts—to assess the gendered earnings gap for artists and for nonartists. We find that the gendered earnings gap is comparable for artists and nonartists, and that artistic careers are subject to some of the same social forces that drive disparity in other occupational realms. Yet in the arts, we do not find the wage penalty to motherhood that has been documented in virtually every other field. Broader implications for scholarship on gender and work, as well as suggestions for further research and policy, are discussed.

Keywords

gender income wage gap artists

Introduction

Previous research has demonstrated the persistence of a gender wage gap within almost every occupation. One analysis from the Institute for Women’s Policy Research (2015), for example, looking across 116 occupations for which data were publicly available, found that in only two categories—“health practitioner support technologists and technicians” and “stock clerk and order fillers”—men’s median earnings were not higher than women’s. A wealth of prior work has highlighted a variety of elements that are associated with these differential labor market outcomes for men and women (for a summary, see Kunze 2008). These include outright discrimination, differences in hours worked, the impacts of occupational sex segregation, disparities in network structures, and family-level dynamics such as the female wage loss and male wage gain to parenthood.

Despite this extensive literature, little scholarship has examined how the wage gap manifests within artistic fields. In fact, no studies of which we are aware specifically examine labor market factors that are differentially associated with male and female incomes in the arts. Some prior research has analyzed men’s and women’s roles in the arts—for instance, the arts/science gender divide in higher education (Thomas 1988), and women’s greater attendance at high-culture arts events (Dumais 2002) and higher rates of fiction reading (Tepper 2000). Other scholars have touched upon gendered elements of the arts, though gender has not been their primary object of study. For example, Lang and Lang (1988) indicated a connection between gender and the survival of artistic reputation among painter-etchers, and Craig and Dubois (2010) mentioned gender in their exploration of the importance of readings to poets’ careers. Few scholars have focused on the connection between gender and income within artistic careers.

Yet the arts are a compelling lens through which to examine gender dynamics in particular, in that they potentially represent a counterfactual. Although we might expect the same sources of gender inequality that exist within other fields to persist within the arts, it is also plausible that some of the uniquely flexible features of artistic occupations might mediate the effects of these sources of difference. Scholars such as Pierre-Michel Menger (1999) have identified flexible employment as a key feature of artistic careers (see also Kalleberg 2000). Careers in the arts are marked by project-based labor markets (Bielby and Bielby 1999; Faulkner and Anderson 1987; Jones 1996; Lingo and Tepper 2013). Artists are more likely to participate in contract-based work and much more likely to go through periods of self-employment than the average professional (Lindemann et al. 2012). Based on prior research indicating that flexibility benefits women’s careers, we might expect to see the wage gap narrowed in the arts. In fact, among the self-employed, women more than men tend to emphasize the importance of flexibility for balancing their work and family lives (Loscocco 1997; Pew Research Center 2000). Previous work also suggests that providing flexibility in the workplace is one of the keys to keeping career women on the professional track (Hewlett 2007).

Yet other facets of arts careers, such as their heavy reliance on professional network structures (Faulkner and Anderson 1987; Giuffre 1999; Menger 1999:549; Zafirau 2008), arguably make them less hospitable to women than some other fields. Prior research has demonstrated that men are more likely to self-promote and negotiate on their own behalf. There are disparities in the amount of pay that men and women negotiate for and feel that they deserve (Babcock and Laschever 2003; Jackson, Gardner, and Sullivan 1992; Kaman and Hartel 1994; Major and Konar 1984). Within artistic careers, assertiveness can translate into the ability to be entrepreneurial and successfully market oneself. Scholars have pointed to the importance of symbolic demonstrations of one’s own expertise (Jones 2002) and the marshal-ing of entrepreneurial abilities (Neff, Wissinger, and Zukin 2005) within arts markets. In sum, the informal skills and resources that predict economic success in artistic careers link up with the same skills and resources that scholars have found create inequalities between men and women more generally in relationship to self-employment and project-based work.

In fact, despite the flexibility of arts careers, the scant research that has analyzed gender dynamics in this realm suggests that men still retain an income advantage. Looking at the arts as a whole, despite the fact that women major in the arts in much larger numbers (National Center for Education Statistics 2013; Zafar 2009), men are slightly more likely to work as artists, and they slightly outnumber women (National Endowment for the Arts 2008:24). Furthermore, one study that assesses gendered income inequalities in the arts finds a persistent gender income gap among artists but also concludes that in terms of gender and race, artists’ occupations are more unequal than the labor force generally (Stern 2005:19). Indeed, our own prior work (Strategic National Arts Alumni Project [SNAAP] 2013:17) has identified sharp disparities in artists’ earnings by gender, though we have not previously explored the factors associated with that gap.

How does the gendered earnings gap in the arts stack up to other fields? What social forces account for the gender disparity in this realm? And what can we learn from artists about gendered disparities in the labor market more generally? We use the 2011 administration of SNAAP—a nationwide survey of 33,801 individuals who have received degrees in the arts—to explore whether arts careers are subject to the same forces that affect gendered income inequality in other fields.

We assess the magnitude of the gender earnings gap in the arts vis-à-vis other fields. We then perform a stepwise analysis examining the impact on the gap of a variety of factors—including hours worked, field and sectoral differences, network connections, and family-level dynamics—that have been associated with the gendered earnings gap in other occupational realms. Finally, we examine whether any of the explanatory factors of gender inequality work differently for men and women.

We find that despite their ostensible uniqueness, careers in the arts are much like careers in other fields when it comes to gendered labor market processes. However, some elements “work” differently in the arts than they do in other fields and represent important sites for future exploration. We conclude by discussing the broader implications of these findings for scholarship on gender and careers, as well as offering suggestions for research and policy.

Factors Associated with Gendered Income Inequality: Literature Review and Hypotheses

Much prior literature has examined the social forces influencing men’s and women’s asymmetrical earnings. In this section, we explore the elements pinpointed by this literature and hypothesize their potential impact within artistic careers.

First, previous work has demonstrated that time spent on work is one factor driving differentials in men’s and women’s earnings. In all industrialized countries, employed women are more likely than employed men to be working part-time (Blossfeld and Hakim 1997; Fagan and O’Reilly 1998). In the United States, women’s share of part-time employment is about 65 percent (Kalleberg 2000). As a result, most sociological work on differential labor market outcomes controls for part-time work. We expect to find that controlling for women’s part-time employment will mediate some of the gendered earnings gap in the artistic sphere as well.

A long line of sociological scholarship has also pointed to occupational sex segregation as a driving force behind gendered earnings differentials (Jacobs 1999; Petersen and Morgan 1995; Reskin 1984). Based on their socialization into and preference for different occupational roles (Daymont and Andrisani 1984), men and women assort into different types of careers and roles that yield differential returns. Although our data do not allow us to look at the particular roles played by male and female artistic workers within their organizations, we can assess the impact on earnings of gendered assortment into different subdisciplines.

Prior work on the arts has demonstrated that the gender composition of these careers varies widely by field. For instance, 2003–2005 Census data indicate that 76 percent of dancers were women whereas almost two thirds of musicians were men (National Endowment for the Arts 2008:24). Our own research (SNAAP 2013) has found that among artistic workers, dancers are at the low end of the income spectrum. Based on this evidence that women inhabit some lower-paying artistic professions, we expect to find that controlling for artistic field will mediate some of the effects of the gendered earnings gap in the arts.

Along similar lines, research on other types of careers has pinpointed sectoral differences as a source of gendered income inequality. Women are overrepresented in the nonprofit sector (Goulet and Frank 2002), and workers in this often lower-paying sector may be more likely than those in the for-profit world to privilege other facets of job satisfaction over financial gain (Benz 2005). We expect that the arts will follow this pattern, with men more likely to work in for-profit industries, and that controlling for sector type will mediate some of the pay differences between men and women artists.

Prior work has also demonstrated that differences in men’s and women’s networks also have a role in producing income inequality. Structural inequalities are perpetuated when men tend to form homophilous networks with other men and women with other women, leading men to reap greater network returns (Ibarra 1992). There are also important qualitative differences in the types of career networks men and women form (Fox 2001:660). Furthermore, as discussed, multiple studies have identified network ties as the crucial building blocks of artists’ career trajectories (Faulkner and Anderson 1987; Giuffre 1999; Menger 1999:549; Zafirau 2008). Following this literature, we expect that controlling for use of networks will lessen some of the observable gap between men’s and women’s compensation in the arts.

Last but not least, an important factor that comes into play in shaping the careers of women and men is the effect of divergent family roles. Gender-specific cultural expectations concerning women’s responsibilities for child care and the domestic sphere have been linked to negative labor market outcomes such as lower hourly pay (Budig and England 2001). Numerous studies have produced evidence of the so-called “motherhood penalty” for heterosexual women, suggesting that career interruptions for childbirth and child care impede female career advancement (Anderson, Binder, and Krause 2002; Avellar and Smock 2003; Budig and England 2001; Gangl and Ziefle 2009; Waldfogel 1997). ¹ Yet other work has indicated that men do not experience these same drawbacks to fatherhood, and in fact, men with children earn a wage premium (Lundberg and Rose 2000) and are socially viewed as more deserving of promotions (Correll, Benard, and Paik 2007). ² Even in the absence of children, husbands experience a “marriage premium” (Korenman and Neumark 1991; Loh 1996); estimates of this earnings gain range from 10 to 40 percent (Ginther and Zavodny 2001).

Gender-specific expectations for parenthood and household responsibilities are only one mechanism that drives these differences. In addition, social attitudes about these family roles create disparities at the level of hiring and promotion (Correll et al. 2007). On one hand, we might expect these same dynamics to work in the arts, as they do in other fields. On the other hand, based on what we know about the arts as a site for flexible employment, it is plausible that these forces will not be as significantin shaping gendered income inequalities in this realm.

Prior research has left us with little information about the relationship between gender and income for artists. In using this unique data set to seek the answers to this question, we not only provide, for the first time, a sustained, multivariate analysis of the gendered dimensions of arts careers but we also extend the literature on gendered career disparities more generally.

Data and Method

SNAAP

Data are drawn from SNAAP—a nationwide survey of individuals who have received degrees in the arts. SNAAP uses a broad definition of “the arts,” which includes performance art, design, art history, writing, film, graphic arts, music, fine arts, and others. The SNAAP survey is uniquely suited for our analyses in that it provides information about artistic careers—such as data about networks—that goes beyond the information collected by other instruments such as the U.S. Census.

Although SNAAP is administered annually, our results come from the 2011 iteration of the survey. SNAAP is an institution-based survey, and all arts alumni from all graduation cohorts at participating institutions were eligible to take the survey in 2011. In total, 33,801 arts alumni from 66 educational institutions (eight arts high schools and 58 postsecondary institutions) in the United States responded to the survey.

Our analysis is divided into three phases: (1) a comparison of gendered labor market outcomes between artists and nonartists, (2) an in-depth exploratory analysis of gendered labor market outcomes in the arts, and (3) a focused analysis of the differential effects of gender on each explanatory variable in artistic labor markets.

Parameters for Inclusion: Artistic Employment

In the first phase of analysis, we include respondents who worked either full time or part-time for pay in 2010 (N = 23,784). This sample is comprised of 7,680 artists—operationalized as respondents who spend the majority of their work time in the arts—and 16,104 working nonartists, all of whom have a degree from an arts institution. Although scholars, and artistic workers themselves, have difficulty reaching a consensus on the definition of “artist” (Lena and Lindemann 2014), for the purposes of this article, “artists” include craft artists; fine artists; photographers; film, TV, or video artists; actors; dancers or choreographers; musicians (including instrumental, vocal, conductors, composers, and arrangers); graphic designers, illustrators, or art directors; Web designers; multimedia artists or animators; writers, authors, or editors; interior designers; other designers; and individuals who indicated that they worked in an “other” occupation within the arts.³ Respondents were given the opportunity to write in their occupations when they selected the “other” category. These write-in responses were diverse and included occupations such as “lighting designer,” “3D visualization,” “job at knit shop,” and “arts advocate.”

In the second and third phases of analysis, we focus on artistic workers by removing the 16,104 working nonartists from our analytic sample. In these analyses, we draw on several additional explanatory variables to understand gendered labor market outcomes in the arts. We use a total of 4,768 complete cases for these analyses.

Table 1 includes descriptive statistics for the sample of working artists.

Table 1.

Descriptive Statistics for Artistic Workers Analytic Sample.

Variables	Sample	Women	Men
Dependent variable
Income (mean)	$52,331.17	$43,177.23	$63,061.50
Work factors
Employment sector
For-profit sector	56.38%	55.15%	57.81%
Nonprofit sector	18.20%	18.19%	18.22%
Government sector	2.50%	2.37%	2.64%
Mix sector	18.37%	18.54%	18.18%
Sector not relevant	4.55%	5.75%	3.14%
Artistic field
Design	12.21%	13.60%	10.57%
Visual arts	35.47%	41.78%	28.06%
Performing arts	22.80%	18.58%	27.74%
New media arts	13.30%	8.90%	18.45%
Writing and editing	4.49%	5.44%	3.37%
Other arts	11.74%	11.70%	11.80%
Hours of paid labor
Less than 20 hours/week	19.99%	22.39%	17.18%
20 to less than 35 hours/week	20.47%	21.14%	19.68%
35 to less than 60 hours/week	51.85%	50.02%	53.99%
60 hours or more/week	7.70%	6.45%	9.16%
Used alumni networks	34.25%	33.50%	35.13%
Marriage and family
Single	32.86%	34.05%	31.48%
Married	58.12%	56.04%	60.55%
Divorced	7.84%	8.36%	7.24%
Widowed	0.94%	1.20%	0.64%
Children
No dependents	69.36%	71.32%	67.06%
1 dependent	13.53%	13.33%	13.76%
2 dependents	13.05%	12.20%	14.03%
3 or more dependents	4.07%	3.15%	5.15%
Controls
Highest level of education
High school diploma	0.29%	0.19%	0.41%
Two- or four-year degree	60.32%	62.22%	58.09%
Graduate degree	39.39%	37.58%	41.50%
Years of experience
Less than one year	2.96%	3.42%	2.41%
one to less than three years	9.04%	11.08%	6.65%
three to less than five years	9.69%	10.42%	8.84%
five to less than 10 years	18.94%	20.44%	17.18%
10 to less than 15 years	15.48%	14.89%	16.17%
15 or more years	43.90%	39.76%	48.75%
Race
White	88.99%	89.82%	88.02%
Black	2.43%	2.37%	2.51%
Hispanic	4.26%	3.46%	5.19%
American Indian	1.41%	1.44%	1.37%
Hawaiian	0.48%	0.39%	0.59%
Asian	4.74%	5.13%	4.28%
Other	3.27%	3.26%	3.28%
Age
24 or younger	5.20%	5.91%	4.37%
25–29	16.59%	18.93%	13.85%
30–39	29.36%	29.62%	29.07%
40–49	19.99%	19.01%	21.14%
50–59	17.62%	16.79%	18.59%
60 or older	11.24%	9.76%	12.98%
	N = 4,768	n = 2,573	n = 2,195

Variables Used

Income

The dependent variable used in these analyses is the respondents’ individual income earned in 2010. Individual income is an ordinal variable with 12 response categories: ranging from $10,000 or less to more than $150,000, in brackets of $10,000 (with the exception of the final bracketed response category, which is a $50,000 range: $100,000 to $150,000), and a category for more than $150,000. For the purposes of our analyses, we have replaced these categorical values with the midpoint value of each income range. This variable is treated as a continuous numeric variable ranging between $5,000 and $200,000. As seen in Table 1 above, the mean annual income for all artists in our sample was $52,331.17; men and women earned on average $63,061.50 and $43,177.23, respectively, which represents a total wage gap of $19,884.27 before controls.⁴

The key independent variable is gender. Although SNAAP survey data provide three response categories—male, female, and transgender—the 40 transgender cases have been dropped due to the limited statistical power of this relatively small group.

Explanatory variables

We have broken the analysis of several explanatory variables into two blocks: work-level factors and family-level influences.

For the analysis of work-level factors that may influence annual earnings, we test the effects of the number of hours of paid work performed in an average week. The response categories for this ordinal variable include less than 20 hours; 20 to less than 35 hours; 35 to less than 60 hours; and 60 hours or more.

Sector of employment is measured using a series of five binary survey items to assess the sector(s) in which each respondent is employed: for-profit, nonprofit, government, mixed sector employment, or sector not relevant. We use for-profit employment as the reference category in each of our regression analyses.

Artistic discipline was created by aggregating respondents’ current primary occupations into six mutually exclusive categories: (1) visual arts—which includes graphic designers, illustrators, art directors, craft artists, fine artists, and photographers; (2) performing arts—which includes actors, dancers and choreographers, musicians (including instrumental, vocal, conductors, composers, and arrangers), and theater and stage directors or producers; (3) new media arts—which includes Web designers, multimedia artists and animators, and film, TV, and video artists; (4) writing and editing fields—which includes writers, authors, and editors; (5) design fields—which includes interior designers and other designers; and (6) other artists—which includes artists in occupations not listed but associated with the arts. We use performing artists as the reference category in the analyses.

Finally, within this block of variables, we include a measure to assess whether or not respondents had used alumni networks. This dichotomous variable is created from a survey question asking “Since leaving your institution, which of the following support services that may be offered by your institution have you used?” with “networking opportunities” as one of the check-all responses. A 0 indicates that the respondent has not used networking opportunities provided by their institution whereas a 1 indicates that the respondent has used these services.

The second block of our analysis includes measures of family-level factors. Marital status is measured through a series of four dichotomous variables: single/never married, married, divorced, or widowed. Single/never married is the reference category used in each of the regression analyses. Data were also collected on the number of dependent children under the age of 18 for whom each respondent was responsible. This ordinal variable was recorded as no dependents, one dependent, two dependents, or three or more dependents.

Control variables

We control for race using several binary categorical variables: white, black, Hispanic, American Indian, Hawaiian, Asian, and other race. Whites are used as the reference category in the regression analyses.

We control for age using an ordinal variable that has been recoded into six response categories: 24 or younger, 25 to 29, 30 to 39, 40 to 49, 50 to 59, and 60 or older. The mean age bracket for our sample is 30 to 49.

Years of experience is measured using a survey item asking respondents “How many years have you worked in an occupation as an artist (where you create or perform your art)?” Ordinal response categories include less than one year, one to less than three years, three to less than five years, five to less than 10 years, 10 to less than 15 years, and 15 or more years.

Finally, we control for highest level of education completed: high school diploma, two- or four-year degree,⁵ or graduate degree. Because data were collected from alumni of arts schools, information on incomplete degrees was not available.

Analytic Approach

We use ordinary least squares (OLS) regression to assess the gendered income gap in both arts and nonarts fields using the larger sample of employed respondents. Subsequent stepwise regression analyses use OLS regressions to test the effects of each of the blocks of explanatory variables on expected annual earnings. In evaluating the significance of each of the parameter estimates, we used p < .05 to indicate statistical significance. Marginally significant results (p < .10) and nonsignificant findings are indicated in the tables but not discussed in our written analyses. For all models, the Blinder-Oaxaca test was conducted to assess the proportion of the gender earnings gap explained by each block of explanatory variables.

Results

Data from SNAAP suggest that gender-based income inequalities persist within the arts just as they do in other fields. We find significant income differences between male and female arts graduates who work both inside and outside of the arts. As detailed in Table 2, for instance, after we include controls for age, race, and highest level of education, the gendered earnings gap for those working outside the arts is estimated to be $20,250.39. Using the Blinder-Oaxaca twofold decomposition, we estimate that only $2,145.85 of the $20,250.39 gendered income gap, an estimated 10.6 percent, is explained by the respondents’ age, race, and level of education.

Table 2.

Regression Results of Income on Control Variables for Artists and Nonartists.

Variables	Artists	Nonartists
Intercept	23,727.68***	5,118.40*
Gender	17,079.44***	18,104.54***
Controls
Race
Black	−7,181.69*	−539.85
Hispanic	−4,084.50	−80.39
American Indian	−9,389.17*	−3,294.28
Hawaiian	−3,279.66	−3,363.29
Asian	1,029.46	2,592.90
Other	−5,470.93	−8,489.72***
Age	1,987.58***	8,859.66***
Years of experience	5,584.515***	655.14*
Highest level of education	−5,331.58***	2,268.82**
	F = 65.41***	F = 149.71***
	Adjusted R² = .1097	Adjusted R² = .1548
	n = 5,229	n = 8,121

Note. Reference groups: whites, for-profit sector, performing arts, single/never married.

†

p < .1. *p < .05. **p < .01. ***p < .001.

Within the arts, a comparable earnings gap persists. Using the Blinder-Oaxaca decomposition, we find that demographic controls of the working artists sample explain only $2,148.80 of $19,228.24 gendered earnings gap in the arts. This is only 11.18 percent of the gap. A post hoc test for equality of coefficients between samples of artists and nonartists indicates that the effect of gender on income is not significantly different for artists and nonartists.

In the analyses below, we dig into this gendered earnings gap in the arts by exploring the effects of the explanatory factors that previous research has shown to differentially influence male and female earnings outcomes in other fields.

Work-level Factors

Table 3 includes results from the stepwise OLS regressions for each block of explanatory variables for working artists.

Table 3.

Stepwise Regression Results of Income on Explanatory and Control Variables.

Variables	Model 1	Model 2	Model 3	Model 4
Intercept	23,334.54***	−50,392.00***	21,869.77***	−51,442.99***
Gender	17,787.48***	13,633.64c***	17,483.43***	13,268.25***
Controls
Race
Black	−5,263.48	−5,105.64	−4,825.67	−4,699.06
Hispanic	−3,398.91	−2,370.93	−3,172.21	−2,185.04
American Indian	−13,519.10**	−6,847.32	−12,688.83**	−6,029.46
Hawaiian	−3,817.60	−691.023	−3,241.42	−177.019
Asian	1,801.79	730.50	2,582.45	1,548.97
Other	−4,807.41	−3,562.27	−4,730.91	−3,381.85
Age	7,300.71***	4,387.50***	2,177.57***	4,026.73***
Years of experience	5,495.162***	5,320.45***	4,676.13***	4,460.22***
Highest level of education	5,551.267***	400.70	−4,842.956***	888.53
Work factors
Employment sector	—		—
Nonprofit sector	—	−11,996.2***	—	−12,418.60***
Government sector	—	−1,475.06	—	−2,391.65
Mixed sector	—	−10,236.58***	—	−9,831.29***
Sector not relevant	—	−10,848.68***	—	−10,131.44***
Artistic field
Visual arts	—	−724.20	—	−1,815.98
Design	—	8,216.33***	—	7,273.61***
New media arts	—	11,418.37***	—	10,537.45***
Writing and editing	—	5,748.078*	—	5,280.62^†
Other arts	—	6,569.39**	—	5,510.571*
Hours paid	—	16,284.99***	—	16,496.76***
Alumni social networks	—	909.46	—	75.67
Marriage and family
Martial status
Married	—	—	3,368.90*	4,346.51**
Divorced	—	—	2,848.94	2,763.98
Widowed	—	—	535.09	6,376.59
Dependents	—	—	4,982.55***	4,685.55***
	F = 63.35***	F = 88.72***	F = 51.02***	F = 92.36***
	Adjusted R² = .1157	Adjusted R² = .2787	Adjusted R² = .1281	Adjusted R² = .2933

Note. Reference groups: whites, for-profit sector, performing arts, single/never married.

†

p < .1. *p < .05. **p < .01. ***p < .001.

Using the subsample of 4,768 artists, the estimated gender wage gap after controlling for race, age, experience, and education is $19,884.28—of which controls explain only $2,096.80. An estimated $6,250.64 is explained by the combination of work-level factors and controls. Specifically, we find that working in the nonprofit arts significantly decreases predicted earnings for all artists by $11,996.20 relative to artists working in for-profit sectors. Mixed sector and “sector not relevant” also significantly decrease earnings by $10,236.58 and $10,848.68, respectively. For each bracket increase in hours of paid work, earnings significantly increase by $16,284.99.

Employment in various artistic fields also significantly affects expected earnings for artists. Artists employed in design fields have a significantly increased expected annual earnings of $8,216.33 compared with performing artists. Those in new media see an expected earnings increase of $11,418.37 compared with performing artists. Writers also see higher annual earnings than performing artists—an expected increase of $5,748.08. Finally, those artists employed in other arts fields have an increased expected earnings of $6,569.39 compared with those employed in performing arts. Although work-level and control variables account for 31.44 percent of the income gap between men and women, $13,633.64 is still unexplained.

Family-level Factors

Table 3 also reveals a relationship between family-level factors and income across the sample. Marriage and children are significant predictors of income; however, as demonstrated in the third phase of our analyses, these effects are gendered. The cumulative effect of marriage and family factors across our sample explains $2,400.85 of the gender wage gap, leaving $17,483.43 unexplained.

All Explanatory Variables

With all variables included in the model, nonprofit, mixed sector, and “not relevant” sector employment significantly decrease expected income relevant to employment in the for-profit sector. Hours of paid work significantly increase expected income. Artists employed in design and new media, and “other” artistic fields earn more than performing artists. Marriage and the presence of dependents significantly increase predicted earnings across the full sample of artists. With all of the variables included in the model, 32.52 percent, or $6,461.79, is explained. This leaves $13,406.77 of the gender wage gap in the arts unexplained.

Assessing Men’s and Women’s Differential Earnings

Our final phase of analysis focuses on determining if any of the explanatory factors work differently for male and female artists. We run split-level analyses by gender and compare the coefficients using a postestimation test for the equality of coefficients using the suest Stata function. The results of the split-level analyses are reproduced in Table 4. The symbol ^‡ indicates that that coefficients for men and women are statistically different. We find that the coefficients for age, nonprofit sector, government sector, mixed sector, marriage, and children significantly affect men and women differently.

Table 4.

Split Model Regression Results of Income on Explanatory and Control Variables by Gender.

Variables	Men	Women
Intercept	−51,818.65***	−35,010.19***
Controls
Race
Black	−9,748.98^†	−495.66
Hispanic	−3,709.29	923.55
American Indian	−7,516.55	−4,738.56
Hawaiian	2,932.16	−5,481.32
Asian	−673.33	3,398.68
Other	−6,539.77	−1,406.92
Age	5,763.58***^‡	2,173.37***^‡
Years of experience	5,209.71***	4,277.53***
Highest level of education	2,458.70	−800.44
Work factors
Employment sector
Nonprofit sector	−16,810.55***^‡	−7,243.35***^‡
Government sector	−10,362.03^‡	5,462.55^‡
Mixed sector	−13,112.26***^‡	−6,848.42***^‡
Sector not relevant	−11,388.90*	−8,352.293***
Artistic field
Visual arts	−3,038.02	−711.37
Design	9,230.36**	6,433.43**
New media arts	12,317.04***	8,974.20***
Writing and editing	4,414.65	6,358.36*
Other arts	5,904.61^†	4,638.56*
Hours paid	16,003.59***	15,233.68***
Alumni social networks	−817.29	648.40
Marriage and family
Martial status
Married	7,210.89**^‡	1,619.26^‡
Divorced	2,670.16	3,363.67
Widowed	−480.70	8,647.45^†
Dependents	8,084.09***^‡	779.73^‡
	F = 36.59***	F = 36.02***
	Adjusted R² = .2802	Adjusted R² = .2463

Note. Reference groups: whites, for-profit sector, performing arts, single/never married.

†

p < .1. *p < .05. **p < .01. ***p < .001.

‡

Coefficients are significantly different at the .05 level.

Both men and women employed in nonprofit artistic fields see a decrease in their expected annual earnings. However, according to the postestimation tests for the equality of coefficients across gendered samples, men’s earnings are more affected by employment in the nonprofit arts compared with their female counterparts. Men working in the nonprofit arts are expected to earn $16,819.55 less than males working in the for-profit sector. For women, this decrease in expected earnings is only $7,243.35 compared with their female peers working in the for-profit arts. This suggests that employment in the nonprofit sector has a stronger negative impact on earnings for men than it does for women. Mixed sector employment (compared with for-profit arts) has a significant negative impact on earnings for both men and women. But the impact is significantly stronger for men ($13,112.26) compared with women ($6,848.42).⁶

Finally, marriage and children significantly increase men’s earnings. Married men earn $7,210.80 more than their single male peers. For men, each additional dependent—up to three dependents—increases their expected annual earnings by $8,084.09. Marriage and motherhood do not significantly affect female artists’ annual wages.

Discussion

Almost no previous research has focused on the gender wage gap in the arts. Although we might expect this realm to represent a counterfactual due to its potential for flexible work arrangements, artistic fields are subject to many of the same social forces that drive gender inequality in other occupations. Female artistic workers earn significantly less than males. This gap is not solely a function of differences in hours worked, assortment into different artistic disciplines or occupational sectors, differences in alumni networking, or family-level dynamics. Yet although the arts function like other fields in these respects, our analyses also returned some surprising results. As we discuss below, two of our most compelling findings involve sectoral differences and the absence of an income penalty for motherhood.

Our analyses uncover interesting dynamics between occupational sector, gender, and earnings. First, men and women employed in the for-profit sector have the highest expected earnings of all artists in our sample. As seen in Table 3, those working outside the for-profit arts, with the exception of government employees, experience a significant reduction in expected annual earnings. This result is in accordance with prior research suggesting that there is less wage discrimination in federal than in private employment (Moulton 1990:289). We suspect that part of the reason employment in government does not translate to a loss of wages, compared with employment in the for-profit sector, may be due to the presumed muted influence of self-promotion and career networks—factors that, as discussed, have been shown to contribute to wage inequality in other fields.

Employment in sectors other than government (nonprofit, mixed, and sector not relevant) negatively affects earnings for both men and women.

However, as seen in Table 4, impacts on wages are greater for men than they are for women. We suspect that because women earn significantly less than men overall (average salary for female artists = $43,177.23; male artists = $63,061.50), there is a wage floor that women are closer to reaching than their male counterparts when employed outside for-profit and government sectors. This hypothesis is in line with previous research suggesting that beyond legal requirements for minimum wages, some firms also have incentives to engage in sector-level wage deals establishing wage floors (Petrakis and Vlassis 2004). Furthermore, this finding underscores how little women are earning relative to men across sectors.

Second, and perhaps our most surprising finding is that although, like men in other occupations, male artistic workers experience an earnings gain to marriage and having children, female artistic workers fail to incur the “motherhood penalty” found in previous work on other occupations (Anderson et al. 2002; Avellar and Smock 2003; Budig and England 2001; Gangl and Ziefle 2009; Waldfogel 1997). One potential theoretical explanation for this finding lies in the particular forms of flexibility related to some arts careers. As discussed in the introduction, although some artists have nine-to-five jobs, the arts, more than other occupations, are characterized by project-based labor markets and periods of self-employment. Artists’ ability to work from home and maintain flexible hours may partially explain why we do not find evidence of the motherhood penalty in this realm.

Another potential explanation for this finding lies in the attitudes of artistically oriented individuals. Prior work has indicated that artists tend to possess more liberal ideologies than other professionals (Lipset 1960). If individuals employed within the arts have relatively liberal attitudes about femininity, domestic labor, and the gendered separation of spheres and if their employers within the arts share these attitudes, these facts could partially explain why we do not see a significant income penalty to motherhood here. In fact, literature on so-called “egalitarian” and “traditional” families suggests a strong connection between gender ideologies and child-rearing practices, with parents who embrace more progressive gender labels and sex role ideologies being more likely to share parenting equally (Bulanda 2004; Fagot and Leinbach 1995).

However, the fact that men still receive larger income gains to both marriage and fatherhood suggests that either gender attitudes are not, in fact, the primary force driving these differences or that employers and employees in the arts may view women’s contributions from a relatively liberal standpoint but continue to hold traditional cultural expectations regarding masculinity. Prior research on masculinity lends support to the latter explanation by suggesting that although men reap a “patriarchal dividend” (Connell 1995:79) for existing in contrast to women, in some senses, cultural attitudes concerning masculine gender roles are more rigid than those concerning feminine roles (Katz and Ksansnak 1994). Moreover, qualitative scholarship has emphasized the durability of the cultural construct of men as expected breadwinners (Ashwin and Isupova 2014; Thébaud 2010).

Another cultural explanation for the lack of a motherhood premium in the arts may lie in gendered expectations regarding the separation of public and private spheres. Previous research has indicated that women are less able than men to separate their work and home spheres, with family demands increasing “spillover” from work more for women than for men (Mennino, Rubin, and Brayfield 2005). Moreover, artists are a unique occupational group in the sense that they often simultaneously pursue both personal and professional artistic work (Lena and Lindemann 2014). This aspect of the arts may lead to work and home spheres being particularly blurred in this realm, exacerbating gender-related difficulties in separating spheres. Along similar lines, prior work has demonstrated that traditional gender and family role expectations are reflected in the “gender typing” of work-based travel as a male activity and that having young children consistently decreases the travel activity of women but not men (Gustafson 2006). Some artistic careers, particularly in the performing arts, include demands for travel to performances and shows, which may be another reason why we do not find a motherhood premium in this realm. In sum, it is plausible that although some structural features of arts careers may allow women to better juggle family and work, cultural expectations regarding masculinity and femininity continue to perpetuate the fatherhood premium—but not a motherhood premium—in this realm.

Potential Limitations

One potential limitation of our analysis is that we aggregate different types of artistic occupations into one umbrella category of “the arts.” There is diversity among these suboccupations in terms of gendered composition, work structure, and size of the gender wage gap. In our analyses, we control for artistic field to account for these differences—which regrettably flattens the overall portrait of inequality that we present. Although we did run field-level analyses, space constraints prevent us from including that information, as it was not the main topic of this article.⁷

There are also several potential limitations to the particular data set we used. Uneven response rates are one source of potential bias for a survey of this kind. It is plausible that arts graduates who are more financially successful are more likely to respond to a survey of this kind. However, response bias tests conducted on the SNAAP survey have concluded that the sample is not meaningfully skewed in this respect.⁸

Another potential limitation of these data lies in the characteristics of artists as a group. Previous research has documented the existence of an artistic personality “type” (e.g., Zhang 2000). For example, undergraduate arts majors are less concerned about monetary rewards than other majors (Allport, Vernon, and Lindzey 1960; Getzels and Csikszentmihalyi 1968). Other scholarship has underscored the ways in which artists symbolically eschew economic rewards (Bourdieu 1993; Fine 2003; Taylor and Littleton 2008; Thornton 2008). Perhaps patterns in gendered wage inequality among artistic individuals are not solely about labor markets but also about some quality of artists themselves. However, this limitation of the SNAAP data set is also a strength: we have compared artistically trained individuals who work in the arts with artistically trained individuals who work outside of the arts to directly assess the differences in their labor market experiences.

A related potential drawback of these data is that they are based on a population of respondents who have received degrees in the arts, and they may not be generalizable to all artistic workers. Although we cannot extrapolate these results to all individuals currently employed as artists, this limitation of the data set is an asset as well. The data set allows for comparisons between male and female artistic workers with relatively homogeneous levels of education and cultural capital.

Another limitation of this large-scale quantitative data set is that it cannot be used to test some sources of inequality, such as discriminatory practices in hiring and promotion. The fact that, controlling for field and number of hours worked, female artistic workers still earn less than their male counterparts may point to discrimination as one explanation for the inequalities we observe. Furthermore, although we have controlled for field and sector of work, these disparities may also be partially due to differences in the specific types of positions in which men and women work—differences that are unobservable to us based on the data available.

Finally, although our findings did not provide significant evidence to support the hypothesis that networks have gendered effects on artists’ income, our networking variable only takes into account whether arts graduates have relied on alumni networks. It does not take into account the importance of other peer networks or family connections, and it does not assess network strength or reach. Further work on the potential role of network patterns in producing inequality would be advisable.

Conclusions

The lack of an observable motherhood penalty in the arts is a compelling finding that should be further explored. Scholars of gender and policymakers both may have much to gain by learning more about work/family balance in this unique domain. We might look to the arts as an example of how family roles and work may be more successfully integrated for women. For example, an in-depth interview study of artists providing context about their work schedules and domestic dynamics might help to shed light on our observations concerning gender, parenthood, and income in this realm. In fact, such research would dovetail well with a recent body of literature that has focused on the topic of female artists and mothering (Betterton 1996; Buller 2012; Heath 2013) but that has not, as of yet, tied this discussion to the wage gap.

Our findings suggest other avenues for future research as well. For example, qualitative scholarship might focus on a few key labor market processes—such as the role of informal skills and resources and outright discrimination in the arts—that we were not able to assess using these quantitative data. In addition, future research should call attention to potential interfield differences; as discussed, our analyses may have flattened some of these. Similarly, researchers should focus on sectoral differences in the wage gap—in particular, lending attention to the relative role of network ties in the for-profit, nonprofit, and government spheres.

Our results not only open up new possibilities for research on gender, labor, and the arts but they also offer clues as to what types of interventions might be useful in this occupational realm. Policy changes at the level of hiring and promotion could help to mediate these differences. Private employers in the arts would do well to look into the same affirmative action policies and income stabilization measures that appear to be effective in driving (relative) income parity in the governmental sector. Additional grants should be put in place to encourage the professional growth of female artists. Furthermore, if made better aware of these disparities, arts degree-granting institutions could place a heightened emphasis on building their students’ self-promotional skills and enhancing their portfolios of other abilities necessary to be able to navigate the unique, contract-based trajectories of arts careers. By highlighting some of the topography of these careers, we have taken a necessary first step toward investigating the unique operation of gender and income in the artistic realm—a step that has notable implications more broadly for scholars interested in occupational inequalities as well as gender and the family.

Footnotes

Acknowledgements

Danielle Lindemann was working as a postdoctoral scholar at the Curb Center for Art, Enterprise, and Public Policy at Vanderbilt University during data collection and analysis.

Authors’ Note

The authors Danielle J. Lindemann and Carly A. Rush contributed equally to this article and share first authorship.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Strategic National Arts Alumni Project (SNAAP) has received funding from the Surdna Foundation, the National Endowment for the Arts, the Houston Endowment, the Barr Foundation, the Educational Foundation of America, and the Cleveland Foundation.

Notes

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