Women Helping Women? Gender Spillovers in Career Progression

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1 Women Helping Women? Gender Spillovers in Career Progression Astrid Kunze and Amalia R. Miller * December 2013 Abstract: This paper studies gender spillovers in career advancement using 10 years of employer-employee matched data on the population of white-collar workers at over 4,000 private-sector establishments in Norway. Our data contain unusually detailed job information for each worker, which enables us to define 7 hierarchical ranks that are consistent across firms and time and to measure promotions (defined as year-to-year rank increases) even for individuals who change employers. We first find that women have significantly lower promotion rates than men across all ranks of the corporate hierarchy, even after controlling for a range of individual characteristics (age, education, tenure, experience) and including fixed effects for current rank, year, industry, and even work establishment. In measuring the effects of female coworkers, we find positive gender spillovers across ranks (flowing from higher-ranking to lower-ranking women) but negative spillovers within ranks. The finding that greater female representation at higher ranks narrows the gender gap in promotion rates at lower ranks suggests that policies that promote greater female representation in corporate leadership will have spillover benefits to women in lowers ranks. Keywords: Women in corporate leadership, workplace gender spillovers, employer-employee matched data JEL Codes: J6, J7, M5 * Kunze: NHH Norwegian School of Economics and IZA, Astrid.Kunze@nhh.no. Miller: University of Virginia, armiller@virginia.edu. We are grateful to David Matsa, Øivind Anti Nilsen and Carmit Segal for helpful comments and discussions.

2 1. Introduction Although women now comprise about half of the labor force, they remain significantly underrepresented among corporate leaders. 1 Many theories proposed to explain persistent male dominance at the highest corporate levels focus on supply-side factors related to sex differences in preferences or abilities (Bertrand, 2010). Nevertheless, the persistence of the gender gap in leadership, even following decades of women s increasing pre-market human capital investments and labor force attachment, suggests that discrimination and demand-side barriers block advancement of even highly-skilled and ambitious women. 2 Concerned about these potential barriers, in recent years, policymakers around the world have taken dramatic steps to promote greater female representation among corporate leadership and especially on corporate boards. 3 Several countries in Europe have adopted gender quotas for boards and EU-wide quotas have been proposed. The U.S. and Australia now impose disclosure requirements for board diversity. Policies aimed at boards increase demand for female board members, which directly benefits a relatively small and elite group of women. However, if female leaders help other women in their organizations advance professionally, by serving as mentors or role models or by changing discriminatory practices, these policies aimed at leaders can have spillover benefits to large groups of women employed at all ranks. 1 In the US, women comprise nearly half of the labor force, but are only 6 percent of corporate CEOs and top executives (Matsa and Miller 2011; Bertrand and Hallock 2001). In Norway, where female employment is at 81 percent and women are 49 percent of the labor force, women hold only 5 percent of CEO positions and 10 percent of all management jobs. 2 For example, a 2010 report by McKinsey & Company on Women at the Top of Corporations argues that changing the promotion system is critical as the increasing number of women graduates will not be sufficient to close the gender gap in top management (Desvaux, Devillard and Sancier-Sultan 2010) 3 In explaining his motivation for proposing the path-breaking Norwegian quota, Ansgar Gabrielsen, the Minister of Trade and Industry, described the perceived problem of boys clubs excluding women from corporate boards (Reiersen and Sjåfjell 2008). 1

3 This paper uses comprehensive data on a sample of over half a million worker-year observations across over 4,000 work establishments in Norway to provide new evidence on gender differences in career progress. We explore the reasons for women s low representation in higher ranks of corporate hierarchies and measure the spillover effects from increasing female representation in workplaces. Unlike most recent work on gender spillovers that focuses exclusively on how high-ranking women (managers or top executives) affect lower ranking workers, our study considers the entire organizational hierarchy (white collar workers) and changes in the female share of two groups of coworkers: those at the same rank and those at a higher rank. 4 We measure effects of the sex composition of the workplace on overall promotion rates and on the gender gap in those rates. The first question we address in our empirical analysis is whether promotion rates differ between men and women, and if so, to what degree the differences in promotion rates can be explained by differences in observable characteristics of workers or by differences in their workplaces. This question has been studied extensively, but the results have not been consistent. Though many papers have found lower promotion rates for women, after controlling for different sets of observable factors, a few have found higher rates. 5 4 Because these variables are likely to be correlated with one another, estimated effects of female leadership using observational data with no controls for the female shares among peer are likely to suffer from omitted variable bias. 5 Some examples with higher promotion rates are from the public sector, such as Barnett, Baron, and Stuart (2000) on public sector workers in California, Hersch and Viscusi (1996) on workers at a public utility, and Powell and Butterfield (1994) on promotions to senior executive positions in the federal government. Gerhart and Milkovich (1989) finds higher promotion rates for women between 1980 and 1986 in a sample of lower-level workers (who remained at the company for both years) at a single private company. The company was operating under an affirmative action plan at the time, though the plan did not directly apply to the sample. Booth, Francesconi, and Frank (2003) find similar promotion rates between men and women after controlling for observable factors, but find that women receive lower wages from promotions (which they term the sticky floor that keeps female wages stuck at the lower part of the wage scale within rank). 2

4 Therefore, before turning to gender spillovers, we first provide evidence on this more basic question using our longitudinal employer-employee matched data on white-collar privatesector workers in Norway for the time period Our data contain unusually detailed job information that enables us to assign workers to one of seven hierarchical levels that are defined consistently across firms and over time. Hence, unlike many studies of gender gaps in promotions that focus on a single firm (e.g., Yap and Konrad, 2009; Ransom and Oaxaca, 2005; Giuliano et al., 2005; Petersen and Saporta, 2004; Jones and Makepeace, 1996; Hersch and Viscusi, 1996; Cannings and Montmarquette, 1991; Gerhart and Milkovich, 1989), our sample includes a wide range of employers. Also, the panel feature of our data and presence of multiple coworker observations at each establishment (not typically available in samples based on household or firm surveys that include multiple firms), 6 allow us to control for unobservable differences in promotion rates between workplaces and initial ranks using a wide range of fixed effects. Another advantage of our data is that it includes individuals who change workplaces; we can track those changes and define promotion outcomes for those workers as well. This allows us to separately study internal promotions that depend upon continued employment at the same firm and promotions that may involve changing employers. This distinction may be important, for example, if men are more likely to change jobs if they are not promoted quickly, and women are more likely to wait for internal promotions (possibly because they are less geographically mobile). The one weakness of our data relative to sources used in some previous studies (such as 6 Olson and Becker s (1983) sample consists of two waves of a survey of workers; Blau and DeVaro (2007) s sample has information on the most recent hires at a cross-section of firms; and Winter-Ebmer and Zweimuller (1997) study white-collar workers from a 1% cross-sectional sample of the Austrian population, defining past promotions implicitly using current rank and education. 3

5 Blau and DeVaro, 2007, and several of the single-firm analyses) is that we do not observe performance reviews or evaluations for workers. 7 Our first finding is that women in our sample have a lower annual likelihood of advancing a rank than do men. We observe lower promotion rates for women in the raw data and in models with increasing sets of controls for individual characteristics and workplace fixed effects. We also observe the promotion gap from each of the 6 lower ranks in our data. The promotion gaps are present both when promotion is defined exclusively for changes within the same work establishment and when it includes advancing to a higher rank at a new work establishment. Although our individual controls and workplace fixed effects should rule out many supply-side explanations, it is still possible that unmeasured supply factors, such as productivity or preferences, are part of the explanation for women s slower promotion rates. In the second part of our empirical analysis, we further isolate the effects of certain demand-side explanations for sex differences in promotion rates by studying how the sex composition of higher-ranked coworkers affects the gender gap in promotion rates. The qualitative effects of altering the gender composition of a workplace on promotion rates for male and female workers are theoretically ambiguous. Several theories predict positive gender spillovers in promotions, particularly from higher-ranking to lower ranking women. These include theories of mentoring, role modeling and advocacy and related theories of tastebased discrimination (against people from the opposite sex) or statistical discrimination (for example, if people are better at evaluating the work performance of others who are more similar to them). It is also possible that greater female representation in higher ranks changes the 7 This means that we are not able to distinguish between gender gaps in promotion rates that come from women needing to meet higher performance requirements to obtain promotions and those that come from women being assigned lower evaluation scores for equal performance. 4

6 attitudes of men in those ranks, by weakening the masculine gendered associations of leadership (Koenig et al. 2011). But there are also theories that would predict negative gender spillovers, from bosses to lower ranking women, such as the queen bee phenomenon in which a woman who achieves career success in a male-dominated field blocks other women from advancing (Staines, Tavris and Jayarante 1974). Although some studies find that women are more favorable judges of other women s work, there is evidence of the opposite pattern in some male-dominated fields (possibly because of a self-enhancement motive to identify with the male majority; Bagues and Esteve-Volart 2010). Gender spillovers among peers can also be positive or negative. For example, women may be more willing to compete with other women (as in Gneezy et al. 2003), which could lead to higher overall female performance and higher promotion rates. They may also collaborate more with other women. 8 However, unlike the laboratory setting with individual tasks, the workplace often involves both individual and cooperative tasks. If women feel that their closest competitors for promotion are other women (possibly as an artifact of tokenism at workplaces in which only one woman is likely to be promoted to higher ranks; Kanter 1977), they may be less cooperative with one another and possibly sabotage each other s chances of promotion, which could lower women s promotion rates. This theoretical ambiguity motivates our empirical analysis of the gender spillovers on promotions from two distinct types of coworkers: bosses and peers. We find evidence of significant spillover effects from the female share of coworkers of either type, but these spillovers have opposite effects on the gender gap in promotion rates. More women in the next 8 There is also experimental evidence from cognitive psychology that unconscious biases against women in performance ratings are less severe when women comprise a larger share of the team (summarized in Valian, 1998, pp ). 5

7 higher rank tend to increase promotion rates for women relative to men, but more women in the same rank tend to reduce them. Although several previous studies have measured gender spillovers in hierarchical organizations, this paper is the first to measure effects of both types of coworkers on promotion rates using private sector data. The only other study we are aware of that also measures effects of demographic interactions from both peers and superiors on promotion rates is Karaca-Mandic et al. s (2013) study of racial and gender spillovers among the lowest-ranking enlisted members of the U.S. Army. Although that setting is quite different from the white-collar workforce in Norway, the pattern of gender spillovers in that study is the same: positive effects of female superiors but negative effects of female peers. Papers that have focused exclusively on downward flowing gender spillovers on promotion rates at lower ranks tend not to find significant effects. Blau and DeVaro (2007) use cross-sectional survey about recent hires in U.S. firms: they find female workers have lower promotion rates and the sex of the worker s immediate supervisor does not interact significantly with the worker s own sex in affecting promotion probability. Giuliano et al. (2006) also find no differential effects of supervisor sex on promotion rates for female employees (though they do find a slight reduction in quit rates); they are this may be because their sample is relatively youthful and predominantly female (p. 3). Using data on Danish companies, Smith et al. (2013) find lower rates of promotion from VP to CEO positions for women (including both internal and external hires); female leadership at the hiring company does not mediate this effect. In contrast with these results focused on promotions for individual workers, studies that estimate effects of female leaders on the representation of women at lower ranks in their organizations do tend to find positive spillovers. These include Cohen et al. s (1998) analysis of 6

8 333 savings and loan banks in California and Kurtulus and Tomaskovic-Devey s (2012) study of the more comprehensive data on the demographic composition of their workforces reported by private sector employers to the U.S. Equal Employment Opportunity Commission (EEOC). Matsa and Miller (2011) and Bell (2005) find evidence of women helping women at the highest levels of major US corporations: increases in female board representation are followed in later years by greater female representation at the CEO and top executive level and smaller gender pay gaps among top executives. However, because the spillover effects found in these papers are not based on longitudinal data on individual workers, the estimates are not able to separately identify increases in promotion rates from changes in hiring patterns (the models also cannot include controls for worker characteristics). Indeed, Cohen et al. (1998) argue that a possible source of the spillovers they find is that female leaders are better able to identify and recruit talented female workers to work at their companies. A related group of recent papers finds evidence of positive downward spillovers from female managers to women at lower ranks in their organizations in the form of higher (absolute or relative to men) compensation (Flabbi et al., 2013; Tate and Yang, 2013, Cardoso and Winter- Ebmer, 2010; Hensvik, 2011). These papers often use high-quality matched employer-employee data from administrative sources and hence are able to control for individual worker characteristics and/or fixed effects. Including worker fixed effects (or controlling for the workers lagged compensation) allows researchers to disentangle the true spillover effects (through which high-ranking women improve outcomes for other women) from effects that run solely through hiring better quality female workers. Two studies that investigate this issue arrive at opposite conclusions. Hensvik (2011) finds the apparent effects of female leaders on reducing gender pay gaps at Swedish firms are eliminated once worker fixed effects are included in the 7

9 model. Tate and Yang (2013) focus on US workers displaced by plant closures and find a substantial reduction in the gender gap in wage losses for workers hired at female led firms, even after controlling for worker fixed effects and fixed effects for original and new employers. Because promotions are often associated with wage increases, the analysis of promotions in this paper also has implications for gender pay gaps within companies. Although changes narrowing the gender gap in promotions is likely to also narrow the gender pay gap (which we explore in Section 4.2 of this paper), it is important to note that wage gaps can also change for other reasons. 2. Conceptual Framework and Estimation Estimation proceeds in two stages. In the first, we study sex differences in promotion rates by estimating a series of models that predict year-to-year promotion probabilities for all individuals in our sample at ranks below the highest. In the second, we study if and how the sex composition of the organization affects promotion rates for men and women. Adding coworker effects in the second stage allows us to go beyond measuring the overall gender gap in promotions to explore heterogeneity by workplace characteristics. Finding significant positive gender spillovers from bosses to lower-ranking workers will also indicate a role for certain demand-side explanations for women s lower promotion rates, and imply that gender gaps at higher level of corporate hierarchies are self-sustaining. 2.1 Gender Differences in Promotion Rates Men and women can experience unequal promotion rates for various reasons. They may differ, on average, in their productivity, their goals, and their opportunities. While various theories of gender differences in labor market outcomes emphasize the importance of supply-side factors 8

10 that are rooted in sex differences in preferences or abilities (in the market or in home production) that lead to lower levels of career investments or promotion attempts from women, 9 other theories focus on demand-side factors causing employers to promote women at lower rates than men. Male owners or supervisors may incur psychic costs from promoting women to positions of authority and display taste-based discrimination in favor of male candidates (Arrow 1973). Another possibility is that performance evaluations used to determine promotions are imperfect and partly subjective. Evaluators may hold (possibly unconscious) gender biases or stereotypes that affect their decisions. 10 It is also possible that evaluations are unbiased on average but that signals of potential are less perceived as less precise when interpreted across gender lines: risk averse male supervisors and firms may then prefer to promote men over equally qualified women (Aigner and Cain, 1977). 11 Although the sharp distinction between supply-side and demand-side explanations is appealing, and in fact essential for discussing discrimination, it can also be misleading if it is seen as precluding the possibility that differential promotion rates stem from interactions between the two sides. An important example of this type of interaction relates to childcare and home production. In Lazear and Rosen s (1990) model, profit maximizing employers expect that women of childbearing ages are more likely to go on extended leaves or quit their jobs at some 9 Examples of preference differences include tastes for risk (Eckel and Grossman 2008), competition (Niederle, Segal and Vesterlund 2013) or valuing money and career (Fortin 2008). For recent surveys, see Bertrand (2010) and Croson and Gneezy (2009). 10 Ibarra, Carter and Silva (2010) discuss gender bias in evaluations the corporate setting and Goldin and Rouse (2002) find evidence of bias against female candidates for orchestral positions. Bagues and Esteve- Volart s (2010) study of public examinations for judicial positions in Spain also finds evidence of gender bias; but in their setting, evaluators are harsher on candidates of their same sex. For promotions into management or leadership, evaluations may additionally be tainted by unconscious associations between masculinity and leadership or competence (Koenig et al. 2011). 11 These concerns, for example, are reflected in a recent McKinsey & Company report that lists institutional feeling that promoting a woman would be too risky as an institutional mindset that is a barrier to women s advancement (Barsh and Yee 2011). Bjerk (2008) also considers the possibility that women have fewer chances to the signal their skill level before entering the labor market or early in their careers. 9

11 point in the future and therefore invest less in their training and development. The fact that women tend to bear larger biological (and time) burden of childbearing and childrearing does imply a real difference in labor supply for many women (and for women as group). However, the extent to which this difference affects women s productivity and chances of promotions is also the result from how employers treat mothers and assess workers (Miller 2011). 12 In the first part of the empirical analysis, we measure sex differences in annual promotion rates and explore the extent to which the differences are explained by different observable differences across workers. Our basic estimation equation takes the following form: (1) Promotion ijt = β F Female i + β X X ijt + ε ijt The unit of observation is an individual i observed in year t working at establishment j. Where Promotion ijt is an indicator for that person being observed at higher rank in year t+1 than in year t, Female i is an indicator for the worker being female, X ijt is the set of covariates, and ε ijt is a random error term. We estimate all models using OLS and account for potential correlations in promotion rates among peers by clustering standard errors at the level of the plant-rank-year group. In the first phase of estimation, the X ijt vectors in different models include different sets of individual characteristics and fixed effects. In the basic version of the model, we control only for year and industry fixed effects. As we vary the elements of X ijt, we can explore how individual observable characteristics affect promotion rates and contribute to or diminish the overall sex differences in β F. Although productivity and preferences are not observed in our data, we can control for differences in several key productivity-related variables, such as age, 12 Another example is Coate and Loury s (1993) model, in which skill differences between groups arise endogenously in response to unequal treatment by employers: members of the group with lower expected skill levels invest less in these skills because of the higher hiring requirements imposed on them (and therefore lower returns to skill investments). 10

12 premarket human capital investments in years of schooling as well as years of work experience, job tenure and current rank. We also estimate a model with an indicator for earning a high relative wage for one s rank and establishment to proxy for high productivity in some models. Our preferred specification omits this variable, however, because it combines true productivity with evaluation and compensation decisions by employers that may contain gender biases. Other factors that predict future productivity and commitment to the labor market, such as motivation and childbearing plans, are not observable. To the extent that these future plans affect women s current labor market decisions, and particularly their choice of workplace, they can be a source of gender differences in promotion. For example, women who anticipate future career interruptions may seek to protect themselves against career penalties from childbearingrelated work interruptions by taking jobs in the public sector or at family friendly employers (Nielsen et al. 2004) and these employers may offer fewer chances for career advancement. Hence, we address the possibility that promotion differences between men and women derive from differences in promotion rates across employers (rather than men and women have different promotion rates at the same employers) by controlling for employer fixed effects, either at the firm or plant level. 13 Identification of the β F coefficient is possible as long as male and female workers have overlapping presence across plants, years, and ranks. 14 In addition to estimating average gender differences in promotion across all ranks, we also estimate heterogeneous effects for each of the six starting ranks. This allows us to examine whether sex differences in promotion rates are mainly at the lower, middle, or higher ranks. The well-known glass ceiling theory posits that women are able to progress at rates similar to men 13 Firm fixed effect should be sufficient if promotion rates are similar across establishments at multiestablishment firms. 14 Because we do not interact the plant and year or plant and rank fixed effects, our model does not require that male and female workers are observed in the same plants at the same or time or in the same ranks, although we do observe such overlaps in our data. 11

13 in lower and middle ranks but they are blocked from reaching higher ranks. This could happen if the basis for promotion becomes more subjective for higher ranks, if the associations between leadership and masculinity become stronger for higher levels, or if the conflict between career and family is most pronounced at the highest ranks. Alternatively, the source of gender differences in leadership may come from a sticky floor or mid-level bottleneck (e.g., Bjerk 2008; Yap and Konrad 2009) 15 that prevents women from accessing (or choosing) career-track positions that lead to future promotions from the outset. Even women who start on a career-track may find their progress stalled if they have children and interrupt their careers before establishing themselves sufficiently. 16 Women who overcome these early career barriers may have distinguished themselves as especially talented and committed and may not face the same difficulties in obtaining promotions. Although different papers have found evidence of gender gaps in promotion at different points in organizational hierarchies (Smith et al find gaps at the highest level for promotions from VP to CEO but not for promotions to VP, while singlefirm studies such as Yap and Konrad 2009, Jones and Makepeace 1996, and Petersen and Saporta 2004 only find gender gaps at the lower and middle ranks), we are not aware of previous multi-firm studies that estimate promotion gaps separately at different hierarchical levels. 2.2 Gender Spillovers in Promotion Rates The second phase of estimation asks if and how the gender mix of coworkers in an organization affects promotion rates overall and differentially for female workers. We test for separate effects of peers within the same organizational rank and of bosses defined as individuals in higher ranks 15 This concept of a sticky floor differs somewhat from that Booth, Francesconi, and Frank (2003) which relates to wages within ranks. 16 Miller (2011) and Ejrnæs and Kunze (2013) find that motherhood delay improves women s career outcomes and that wage-age profiles flatten dramatically after the birth of a first child, suggestive of a mommy track. 12

14 by adding these additional variables and interaction terms to our empirical model. Our estimation equations in this phase take the following: (2) Promotion ijt = β F Female i + β X X ijt + β P FemaleSharePeers ijt + β PF FemaleSharePeers* ijt Female i + β P FemaleShareBosses ijt + β PF FemaleShareBosses* ijt Female i + ε ijt These models include both level effects for the shares of female coworkers (in the same plant, year and rank) and bosses (in the same plant and year but one ranks higher). Two features of our measure of female leaders and supervisors are worth noting. First, our measure of the female presence at higher ranks is based on establishment level rates. We are not able to include an indicator for immediate supervisor sex (as used in Karaca-Mandic et al and Blau and DeVaro 2007). If the effect of female leaders runs exclusively within the chain of authority, then our estimates will understate the importance of female bosses. To the extent that promotion decisions involve multiple decision-makers and that mentoring and role model effects occur outside of the immediate chain of command, our approach will capture meaningful spillover effects. Second, our measure of female leadership is rank-specific, and focuses on female representation at the next higher rank. This approach differs from studies that focus exclusively on female shares in top leadership (or indicators for female-led firms or plants). It has the advantage of measuring effects of female leadership deeper within organizational hierarchies and also of exploring a channel for the effects of top leaders to influence outcomes at lower levels (as opposed to setting firm-wide policies that favor women, for example). A primary reason for studying the effects of female leaders on promotion rates is to isolate the effects of a certain subset of the demand-side theories for gender differences in 13

15 promotion rates discussed above. In particular, if the reasons for women s lower promotion rates are from taste-based or statistical discrimination by male bosses, then more female bosses could shift promotion rates to be more equal. Although men and women are both susceptible to gender biases in evaluating candidates, the problem may be worse for male evaluators. Furthermore, the presence of women performing well at higher levels, even if they are not direct decision-makers in promotions, can provide assurances that women are capable of success at the higher rank and reduce negative stereotypes against female candidates for leadership positions (Koenig et al. 2011). Finally, female bosses may help other women increase their productivity at work, through mentoring (information sharing, role modeling and emotional support), and they may help women s contributions be recognized by advocating for them in job assignments and promotion decisions (Athey et al. 2000). However, as discussed in Section 1 above, gender spillovers in promotions can also be negative. Female leaders, having achieved success in male-dominated sphere, may act as queen bees and actively prevent other women from advancing (Staines, Tavris and Jayarante 1974). In the context of public examinations for Spanish judicial positions, Bagues and Esteve-Volart (2010) find that female evaluators are harsher on female candidates. Gender spillovers among peers can also be positive or negative. If women are more willing to compete in promotion tournaments with other women, their performance may improve with more female peers (Gneezy et al. 2003). Equivalently, there would be positive spillovers among female peers if male coworkers resist collaboration with them. However, if women mainly compete with one another for promotion (either because of their own perceptions or preference or because of informal promotion policies), then more having female peers can lower relative promotion rates for women. In particular, because women are under-represented at 14

16 higher ranks, female peers might compete for the scare resource of attention (mentoring and advocacy) from higher-ranking women, diluting their overall effectiveness. 3. Data and Empirical Setting The analysis in this paper uses unique longitudinal, administrative, employer-employee linked data on workers in Norway for the time period 1987 to We use annual data on workers born between 1936 and What is unique about our dataset compared to employeremployee linked administrative datasets from other countries (or from Norway for other time periods) is that we are able to supplement the usual employee information about demographic and work characteristics with information about the employee s role within the organization, which we use to create a measure of rank for each worker-year observation. Notably, our rank variable is based on job description categories that are defined to be consistent across firms. This differs from definitions used elsewhere in the literature based only on pay rates for employees (to define relative rank positions), such as Cardoso s (2012) study of Spanish administrative data. Specifically, we construct a measure of employee rank using data from an establishment level survey conducted by the Confederation of Norwegian Enterprise (NHO, Næringslivets Hovedorganisasjon). Hence, our estimation sample is limited to workers at establishments that are members of NHO. Member establishments are part of private-sector firms in industries including manufacturing, construction and machinery, oil, transport, and hotels and restaurants. All employees at these establishments are included in the data. These workers comprise approximately 40 percent of all private sector employment in Norway. As a result, our sample 17 Our analysis includes the longest time period in which all variables are available. Before 1987, only limited register information is available. After 1997, the NHO ceased collecting the data. Statistics Norway now runs a more restricted version of the survey; those data are not used in this study. 15

17 has broad coverage, and includes a substantial share of the labor market. However, it is not representative of the entire labor market, as NHO establishments tend to be larger and older than the average private sector establishment in Norway and their employees tend to be more educated and to earn higher wages. 18 Table 1 shows the distribution across sectors in 1990 in our sample and in the population of Norway, including workers born between The industrial mix in our sample is biased towards manufacturing and excludes the public sector: almost 50 percent of workers are in manufacturing and 16 per cent in wholesale and retail sales. The data also exclude certain private sector industrial groups, such as banking. 19 Finally, we limit our analysis to white-collar workers to ensure a relatively comparable set of jobs with substantial presence of both male and female workers. An important restriction is that the sample only includes workers, which means that people who are unemployed or out of the labor market are not in the sample. Workers who are on temporary leave from a job, such as disability or parental leave, are included in the data. Using information on the occupational groups and hierarchical ranks in the NHO data, 20 we distinguish seven ranks in hierarchies of establishments. Workers are assigned one of six occupational groups: technical white-collar, supervisor, administrative, task in shop, in storage and others. Within each occupational group, seven task levels (ranks) are distinguished. The highest rank includes technical directors and leading positions. The lowest rank is defined to include unskilled workers conducting more routine tasks. 18 See Appendix A for more details on data construction and the occupations included in each of the ranks. 19 Statistics Norway and the NHO as the primary employer association in Norway, compiled the data. The primary purpose for the data collection was to obtain an overview of earning levels and annual earnings growth among white-collar workers. See Lønnsstatistikk for funksjonærer (Income statistics for white collar workers), various years. 20 For more details, see Lønnsstatistikk for funksjonærer - Norsk arbeidsgiverforening,

18 Constructing hierarchical ranks within organizations is inherently challenging even for a single firm (e.g., Baker, Gibbs, and Holmstrom 1994). Hence, before proceeding to estimation, we first confirm the validity of our ranking variable. We find that the constructed ranks are meaningfully related to other employment outcomes such as earnings levels, which are increasing across ranks. We also observe greater wage growth around the time of rank increases. Finally, the pattern of rank mobility is consistent with prior studies: upward moves of one or two ranks are most common; increases of more than two ranks and downward moves are rare, yet also observed. The focus of this paper is on gender differences and changes in ranks, which measures vertical segregation and career progress. Some progress across ranks occurs within occupations but other changes also involve occupational transitions. Since men and women typically segregate in different types of occupations potentially our results partly reflect segregation and that occupations vary in the potential for career progression. For example, in our data we observe men are more likely to work in technical occupations (group A occupations in our data) while women are more likely in clerical occupations (group C). It has been documented with Swedish data constructed in a manner similar to the NHO data that workers in technical occupations were historically the most likely to be promoted to leadership positions within their firms, but this tendency has decreased over time (see Meyersson-Milgrom and Pedersen, 2006). The major advantage of the NHO data is that the seven ranks are consistently defined across establishments and over time. This consistency is necessary to make meaningful comparisons across establishments and over time. We define promotion as a year-to-year increase in an individual s rank. Because not all establishments have jobs in all seven ranks, a promotion across multiple ranks may have different meaning in different establishments. For that 17

19 reason, we focus on binary measures of progress instead of comparing the size of the rank change. The earnings per hour variable is constructed from information on monthly earnings and on normal hours of work from the survey. The normal hours of work exclude overtime hours, and earnings are from work and benefit claims, but exclude overtime payments. Additional lump sum pay and performance pay are in a separate bonus pay variable. Note that overtime in these sectors is usually not paid, which is why this information was not collected. Education is defined as years of formal schooling. We have two main variables for coworker demographics. The first is the female share among peers at the same rank, work establishment and year. This variable has a mean of 28% in the full sample. Consistent with sex segregation across workplaces and ranks, the mean is 61% for female workers, but only 15% for male workers. Over 10% of observations are from maleonly peer groups and over 5% are female-only peer groups. The second coworker variable is the female share among bosses working at the next highest rank at the same work establishment and year. This variable has a lower mean of 16.8% (and a median of only 6.7%), consistent with women s under-representation at higher ranks. The gap between male and female workers in this variable is smaller than for the peer variable; the mean values are 24% for women and 14% for men. Across all workers in our sample, over 25% have no female bosses but only 1% have all female bosses. Summary statistics on the main outcomes and control variables are reported in Table 2. Promotion rates are about 6.5 percent higher for men than for women (6.5 percent of men versus 6.1 percent of women are promoted each year), but because of men s higher turnover rates (33.8 versus 31.5 percent), their rates of internal promotion are slightly lower (3.97 versus

20 percent). Conditional on staying at the same establishment, men s promotion rates are slightly higher (3.97/0.662 = 6.00 versus 4.04/0.685 = 5.90). Conditional on changing establishments, men s promotion rates are substantially higher (6.81 versus 6.25 percent). These gender differences in promotion and turnover rates may be related to other gender differences in the characteristics of workers or of their employers. Indeed, the men in our sample are older, more educated, and experienced than the women. They are more likely to be working in manufacturing and less likely to be in transport and communication or public administration in our sample. Women are also significantly more likely to be working part-time than men (26.2 versus 6.9 percent), likely because of their greater time commitment to household responsibilities and childcare. The empirical analysis that follows controls uses a series of controls and fixed effects to account for observable differences between workers and unobservable differences across industries, establishments, hierarchical ranks that could affect promotion rates. Our goals in this paper are, first, to determine how much of the difference in promotion rates is explained by these factors and how much can be attributed to sex differences in outcomes for otherwise similar workers, and second, to assess if and how the sex mix of coworkers affects sex differences in promotion rates. 4. Results 4.1 Gender Differences in Promotion Rates Table 3 presents our main results showing statistically and economically significant gender differences in promotion rates. Each column of the table reports the coefficient on the Female indicator from a regression taking the form of equation (1), in which the promotion outcome is regressed on different sets of covariates. 19

21 In the top panel, the promotion outcome is any promotion, either at the same establishment or at a different establishment (or firm) in the sample. We are able to study this outcome because the longitudinal data track workers over time and because of the extensive coverage of the NHH establishment survey. This means that we can compute promotion information about individuals, even after they transition across workplaces. Workers who leave the sample, for example, because they exit the labor force or move to a non-covered firm, are missing observations. In the first column, the model only includes fixed effects for industry, current rank and calendar year. The estimate of (standard error of ) indicates that, conditional on these variables, women are 3.1 percentage points, or about 50 percent, less likely than men to be promoted in a given year. This is larger than the raw sex difference in promotion rates in Table 1, because women are far more likely to be working in the lowest rank where promotion rates are highest. The importance of controlling for current level may explain some of the discrepancies across other studies of sex differences in promotion rates: studies without such controls that compare men at higher starting ranks than women may not find significant gender differences even if they are present conditional on the same starting rank. Unlike studies that collect survey information on promotions alone, our consistent rank estimator allows us to define promotion in an objective and consistent way across firms and ranks and to control for initial rank, which is a major predictor of promotion rates (the different mean promotion rates across ranks in our sample are shown in the final column of Table 4). The estimated gender gap is remarkably stable across the next 4 columns of Table 3. Adding fixed effects for age and schooling (in Column 2) and then quadratic controls for experience and tenure (in Column 3) leave the Female coefficient unchanged at The next 20

22 two columns add fixed effects for the employer, first at the firm level (in Column 4) and then at the establishment level (in Column 5); the estimated gender difference increases in magnitude to In the next column, we also add an indicator for the worker earning a high wage (in the top 30% for that establishment-rank-year combination). 21 This variable is meant to proxy for the relative productivity of the worker and it is a strong predictor of promotion within the next year. The residual gender difference in promotion is somewhat smaller with this control (-0.025, standard error of ). This decline in the magnitude of the Female coefficient may indicate that some of the residual difference in promotion rates in the previous columns is coming from women being less productive than men, conditional on other observables. Alternatively, if the reason for women s lower promotion rates is related to differential treatment by superiors, such as taste-based discrimination, it is possible that the same process is also leading to higher compensation for men within the same rank. Because including the wage control runs a risk of over-controlling for sex differences in the firm, our preferred model omits this variable. The models in the final column of Table 3 include a control for the worker having parttime status in the current year, which is defined as having usual work hours of less than 37hours per week on average over the past year. This variable is defined based on contracted hours, which means that leave taking does not reduce workers average weekly hours. As shown in Table 2, women are significantly more likely to be working part-time than men, even in our sample of private-sector white-collar workers: 26% of women work part-time compared to 7% of men in our sample. Separate regressions shows that across all of the models in Table 2, working part-time is associated with a significantly lower promotion rate (by 2.5 percentage points). 21 This is somewhat analogous to the analysis in Table 7 in Flabbi et al. (2013) of promotion rates for workers in the top half or quartile of the wage distribution for their firm-year-occupation and sex, though our control for high-earner is not sex-specific. 21

23 Nevertheless, controlling for part-time status does not eliminate, or even substantially reduce, the sex gap in promotion rates in any of the models. The estimate from our preferred model is reported in Column 7, with the part-time control. The promotion gap is 3.1 percentage points. In the bottom panel of Table 2, we study promotion outcomes within the same establishment. Individuals who leave the establishment are assigned a zero value for this variable, so the sample size is unchanged between the two panels. The pattern in the top panel for any promotion is repeated in the bottom panel for internal promotions. The Female coefficient is again negative and significant across the different specifications, ranging from to This gender difference, conditional on covariates, reflects a 50 to 70 percent lower internal promotion rate for women than men the sample mean for men of about 4%. The similarity between the estimates for any promotion and for internal promotion suggests that promotion differences are not coming from men being more likely to change workplaces in order to obtain a promotion. We verified this with separate estimates of sex differences in turnover rates using the same models in Table 2 and found no significant effects. 22 A key question about these average differences in promotion rates is if the differences are present across ranks or if they are mainly concentrated at the lowest ranks (corresponding to a sticky floor story) or at the highest ranks (because of a glass ceiling beyond which promotions become more difficult or impossible for women). The results in Table 4 address this question by estimating an expanded version of Equation (1) with a full set of interaction terms between Female and the 6 rank indicators (for each of the levels from which promotion is possible). Column 1 reports estimates from a model with the main controls for industry, rank, 22 This does not rule out the possibility that some gender differences in wages or promotions are generating from men having a greater likelihood of receiving outside job offers, or conditional on receiving such an offer, having a greater chance of receiving an attractive retention offer at their current firm. If both of those events are more common for men, a gender gap could emerge for internal promotions without any significant differences in turnover rates across firms. 22

24 year, age and schooling, but with no firm or establishment fixed effects. Column 2 adds the controls for tenure and experience and firm fixed effects. Column 3 replaces the firm effects with establishment fixed effects and Column 4 adds the control for part-time work. Across all models and ranks, we find that women are less likely than men to be promoted to the next highest rank. This finding indicates the sex differences in promotion rates at the highest ranks found in Smith et al and for entry level workers in several single-firm studies are present across all hierarchical ranks. 4.2 The Importance of Promotion Rates in Explaining the Gender Pay Gap The results in the previous section show that women in our data experienced significantly lower promotion rates than similar men working at the same establishments. Over time, the accumulated effect of these lower (or slower) promotion rates for women is that women will tend to be over-represented at the lower ranks and under-represented at the higher ranks at their workplaces. In this brief section, we examine the empirical importance of women s concentration at lower ranks on the overall size of the gender pay gap in our data. We first quantify the overall importance of our hierarchical rank measure (with just seven categories) in the estimated size of the gender pay gap by estimating a log-wage equation with a Female indicator variable and different sets of controls. Column 1 of Table 5 shows the raw pay gap of 0.27 log-points from a model with no additional controls. This gap is reduced by 59.3 percent to 0.11 log-points when indicators for rank are included. The remaining pairs of columns in the table show how adding the rank controls reduces the size of the gender pay gap by 45.3 percent when the basic controls (for industry, year, age, and schooling) are also included in the model (Columns 3 and 4) and by 42.5 percent when the tenure and experience controls are 23

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