The labour market gender gap has a long history in economics research; its persistency is one of the great puzzles in labour economics and one of the most important issues in labour market policy. Over the years, the gap has evolved considerably as women have increased their labour force participation and have overcome men in terms of educational attainment. Despite the convergence between men and women in many labour market indicators, women are still vastly underrepresented in the higher levels of ﬁrms’ hierarchies (See for example, Bertrand and Hallock 2001, Wolfers 2006, Gayle et al 2012, Dezsö and Ross 2012 for the US. For other countries, see Cardoso and Winter-Ebmer 2010 (Portugal), Ahern and Dittmar 2012 and Matsa and Miller 2013 (Norway), Smith et al 2006 (Denmark)). Speciﬁcally, recent US data from the 2012 Current Population Survey and ExecuComp show that even though women are a little more than 50% of white collar workers, they represent only 4.6% of executives. Our own Italian data show that about 26% of workers in the manufacturing sector are women compared with only 3% of executives and 2% of CEOs.
In a recent paper, we exploit a detailed matched employer-employee longitudinal data set for Italy to uncover two original pieces of empirical evidence that may help our understanding of the causes of the underrepresentation. We ﬁrst investigate what is the eﬀect of the CEO’s gender on ﬁrms’ wage policies. Our data allows us to analyse the impact on the entire wage distribution within the ﬁrm while accounting for various observed and unobserved ﬁrm and workforce diﬀerences.
Our regressions by wage quantiles show that ﬁrm and workforce heterogeneity are relevant both by gender and by wage levels. The impact of female CEOs is positive on the wages of women at the top of the wage distribution but negative on wages of women at the bottom of the wage distribution. The impact on men is the opposite: female CEOs lower wages at the top and increase them at the bottom of the male wage distribution. As a result, female CEOs reduce the gender wage gap at the top and widen it at the bottom of the wage distribution, with essentially no eﬀect on the average.
Figure 1. Coeﬃcients of female CEO dummy on average wages by quantile of the female and male wage distributions
The estimated eﬀects are reported in Figure 1. The ﬁgure reports the percentage diﬀerences between wages of workers employed in ﬁrms with a female and a male CEO, by quartiles of the female (continuous line) and male (dashed line) wage distribution. For example, females in a woman-led ﬁrm in the top quartile of the wage distribution earn approximately 10 percent more than females working for a male in the same quartile. Men, on the other hand, earn about 4 percent less if they work in ﬁrms with a female CEO. At the bottom of each distribution the eﬀects are smaller, but of the opposite sign.
It is important to notice that these eﬀects are estimated with ﬁrm ﬁxed-eﬀects, i.e. they are identiﬁed by ﬁrms switching from a male to a female CEO or vice versa. They are also robust across various speciﬁcations. In particular, they hold after taking into account industry eﬀects, cohort eﬀects, time trends, ﬁrm size, region, and individual workforce and executive’s observable characteristics and unobservable skills. They are also robust to specifying a diﬀerent measure of female leadership – the proportion of female executives in the ﬁrm.
The second piece of empirical evidence we uncover refers to the impact of a female CEO on ﬁrm performance, an issue that has occupied the literature for some time. Prior evidence is mixed and depends on the estimation methods but overall seems to indicate that female CEOs neither improve nor worsen ﬁrm performance (Wolfers 2006 and Albanesi and Olivetti 2009). We ﬁnd, however, that ﬁrms with female leadership perform better the higher the fraction of women in the workforce. This eﬀect is large and highly statistically signiﬁcant. We measure performance according to three diﬀerent dimensions: sales per worker, value added per worker, and total factor productivity (TFP). In our preferred speciﬁcation, sales per worker are 6 percent higher for every 10 percent increase in the share of women in the workforce, and value added per worker increases by approximately 8%.
What are the possible drivers of these wages and productivity diﬀerences? Our paper discusses three possible sources.
The ’glass ceiling’ literature emphasises the possibility that prejudice against women in male-dominated ﬁrms aﬀects women’s wages and promotion prospects. We do indeed ﬁnd that women at the top of the wage distribution earn less when employed by males, a result that is consistent with this hypothesis. However, this explanation is not consistent with the negative eﬀect of female leadership on female wages at the bottom of the distribution, or the positive impact of female leadership at the bottom of the male wage distribution. Only ad-hoc preferences for or against males and females with diﬀerent skill levels could reconcile a prejudice-based explanation with our evidence. Moreover, if female CEOs favoured female workers against male workers, especially when these workers have high skills, we would not observe the positive eﬀect on productivity we ﬁnd when female CEOs employ a larger share of females, as favouritism should be negatively associated with performance.
A second explanation focuses on peer-group, role model eﬀects, or other complementarities between female CEOs and their highly skilled female employees. For example, if females at the higher end of the skill distribution ﬁnd it easier to interact with female ﬁrm leaders, or if the transmission of knowledge is easier between people of the same gender, we should ﬁnd both the eﬀects on productivity shown in our results, and positive impacts at the top of the female wage distribution. However, this alternative explanation is unable to generate the negative eﬀect of female leadership on female wages at the bottom of the distribution, and any eﬀect of female leadership on the male wage distribution.
While peer and role-model eﬀects may play a positive role in breaking the glass ceiling and improving women’s condition in the workplace, they are consistent only with some of the evidence we uncover. We therefore propose a third explanation based on a model with statistical discrimination that reconciles all of our evidence. In our model, we assume that CEOs are better (i.e., more accurate) at assessing skills of workers of their own gender. This assumption may be motivated by gender differences in language, verbal and non-verbal communication styles and perceptions that may aﬀect the assessment of personal skills and attitudes, improve conﬂict resolutions, and favour a correct job-task assignment. A large socio-linguistic literature has found support for this assumption. For example, Dindia and Canary (2006) and Scollon et al (2011) ﬁnd diﬀerences in verbal and non-verbal communication styles between groups deﬁned by race or gender that may aﬀect economic and social outcomes. Recent employee surveys also indicate that signiﬁcant communication barriers between men and women exist in the workplace (Angier and Axelrod 2014, Ellison and Mullin 2014). We also assume that complex tasks require more skills to be completed successfully, and that there is a comparative advantage to employ higher human capital workers in complex tasks.
Two main empirical implications result from this model. First, thanks to the more precise signal they receive from female workers, female CEOs can reduce the mismatch between female workers’ productivity and job requirements. As a result, ﬁrm performance will increase more the higher the fraction of females employed. This is what we ﬁnd in our data.
Second, and again because of the more accurate assessment of female workers’ skills, female CEOs will make more eﬃcient task assignments of female workers, who will receive compensation closer to their actual productivity. Wages at diﬀerent ends of the wage distribution are aﬀected diﬀerently by information of diﬀerent quality. When information is noisy (as we assume happens when female workers are employed by male CEOs), worker-task mismatches occur more frequently. Therefore, female workers with high skill will have on average lower wages when employed by male CEOs – some of them are mismatched to lower productivity jobs. The opposite happens at the bottom of the distribution – male CEOs assign some low productivity workers to higher paid tasks, increasing their pay, on average, relative to similarly skilled female workers employed by female CEOs. More precise information therefore results in higher wages at the top of the female skill distribution and lower wages at the bottom when they are employed by female CEOs, relative to the case when information is less precise, when female workers are employed by male CEOs. The opposite occurs to male workers, in line with our results summarised in the ﬁgure above.
This theory has implications on the eﬀects of policies that have been recently implemented in the EU and other countries to reduce gender inequality at the top of ﬁrms’ hierarchy. These policies are justiﬁed on three grounds: justice for women who deserve the same opportunity to reach the top of the corporate ladder as men; improved company performance, which can be driven by several sources as we noted above; and a wider public interest in equality of opportunities for all (see Walby 2013). We can assess the second motivation by simulating how ﬁrm performance would change if a larger fraction of companies in our sample had a female CEO. While this is not exactly the policy that most countries have implemented, it is suggestive of the eﬀect of policies aimed at increasing female representation at the top of the corporate ladder.
We performed two counterfactual experiments. In one experiment, we assign a female CEO randomly to one half of the ﬁrms in our sample. In the second experiment, we allocate female CEOs to the same number of ﬁrms, but targeting the assignment only to the ﬁrms that have the largest fraction of female employees. We do this to generate the largest productivity eﬀects. Results show that when female CEOs are allocated randomly, the average percent change in ﬁrm performance is generally small. In contrast, our ‘targeted’ exercise delivers large positive eﬀects in the ﬁrms that are assigned a female CEO, and also positive eﬀects overall. For example, in this scenario sales per worker would increase by 14.2% in the ﬁrms whose CEO’s gender has changed, and by 6.7% in the overall sample of ﬁrms. Although our exercises ignore general equilibrium eﬀects (including endogenous re-employment of workers of diﬀerent gender to ﬁrms with diﬀerent leadership), these results conﬁrm that, based on our estimates, the order of magnitude of the eﬃciency gains from having a larger female representation in ﬁrm leadership can be quite large.
Ahern, K R and A K Dittmar (2012) “The changing of the boards: The Impact on ﬁrm valuation of mandated female board representation,” The Quarterly Journal of Economics, 127 (1), 137–197.
Albanesi, S and C Olivetti (2009) “Home production, market produc tion and the gender wage gap: Incentives and expectations,” Review of Economic Dynamics, 12 (1), 80–107.
Angier, M and B Axelrod (2014) “Realizing the power of talented women,” McKinsey Quarterly, 3, 107-115.
Bertrand, M and K F Hallock (2001) “The gender gap in top corporate jobs”, Industrial and Labor Relations Review, 55 (1), 3–21.
Cardoso, A R and R Winter-Ebmer (2010) “Female-led firms and gender wage policies”, Industrial and Labor Relations Review, 61 (1), 143–63.
Dezsö, C L and D G Ross (2012) “Does female representation in top management improve ﬁrm performance? A panel data investigation”, Strategic Management Journal, 33 (9), 1072–1089.
Dindia, K and D J Canary (2006) Sex diﬀerences and similarities in communication, Psychology Press.
Ellison, S F and W P Mullin (2014) “Diversity, social goods provision, and performance in the firm”, Journal of Economics & Management Strategy, 23 (2), 465–481.
Flabbi L, M Macis, A Moro and F Schivardi (2014), "Do Female Executives Make a Difference? The Impact of Female Leadership on Gender Gaps and Firm Performance", CEPR Discussion Paper 10228, November.
Gayle, G L, L Golan and R A Miller (2012) “Gender diﬀerences in executive compensation and job mobility”, Journal of Labor Economics, 30 (4), 829–872.
Matsa, D A and A R Miller (2013) “A female style in corporate leadership? Evidence from quotas”, American Economic Journal: Applied Economics, 5 (3), 136–69.
Scollon, R, S W Scollon, and R H Jones (2011) Intercultural communication: A discourse approach, John Wiley & Sons.
Smith, N, V Smith and M Verner (2006) “Do women in top management aﬀect ﬁrm performance? A panel study of 2,500 Danish ﬁrms”, International Journal of Productivity and Performance Management, 55 (7), 569–593.
Walby, S (2013) “Legal instruments for gender quotas in management boards”, European Union, Directorate-General for Internal Policy.
Wolfers, J (2006) “Diagnosing discrimination: Stock returns and CEO gender”, Journal of the European Economic Association, 4 (2-3), 531–541.