The Gender Gap at the Top: The Role of Networks

Despite progress towards gender equality, men and women still face different labor market conditions at all levels of employment, not least in top management positions. Even women managers who succeed in breaking the glass ceiling are rewarded less than their male counterparts. The economics literature has uncovered considerable evidence of gender bias. Geiler and Renneboog (2015) find that female top managers of listed UK firms earn about 23% less than their male counterparts, while Bell (2005) documents a bias of between 8% and 25% for female executives in US-listed firms, controlling for After doing so for differences in company size, professional title and industry. Bertrand and Hallock (2001) reveal a 45% gap among US firms, reduced to about 5% after accounting for observable differences, where gender segregation by firm size plays an important role.

Gender inequality comes at an economic cost, as shown by Lagarde and Ostry (2018) and Cavalcanti and Tavares (2007, 2015) from a macroeconomic perspective and in Criscuolo et al. (2021) from a firm productivity perspective. Further understanding the pay gap between male and female managers is thus critical to making substantial progress towards gender equality. It is also essential to facilitate proper legislation by reducing disparities analyzed in studies such as Djankov and Goldberg (2021) and Bagues and Esteve-Volart (2007). After all, it is likely that the productivity cost of gender inequality is, if anything, greater than that relative to managerial positions.

In a recent study (Sazedj and Tavares 2021b), we advance the existing literature on the gender pay gap among top managers with an as yet undocumented source of divergence: differences in professional networks. Using a combined employer-employee dataset with mandatory information on all private firms and wage earners operating in Portugal between 1986 and 2017, we track a worker’s complete professional history and thus calculate a measure of networks based on all past professional interactions. , within the same firm, namely with colleagues who later become top managers. While in Sazedj and Tavares (2021a), we show that networks do indeed play an important role in the wage determination process of top executives, in Sazedj and Tavares (2021b), we address the related and important question of how to assess these differences between professional gender at the top of the network. Contributes to the pay gap.

Figure 1 shows how, in 1995, the total pay of female top managers was only slightly more than two thirds of the pay of male managers. That is, for every euro earned by a male manager, a female manager earns 32 cents less. Although the gender pay gap for top managers narrowed by more than 10 percentage points over the 23-year period of our study, this did not necessarily account for the reduction in inequality. As of 2017, women’s wages represented about four-fifths of men’s wages. However, when we take into account observable characteristics including age, tenure and education and calculate an ‘adjusted’ gender pay gap (represented by the dashed line in Figure 1), we find that female top managers’ wages have increased due to skill gains only. , there is no reduction in the unexplained component of the wage gap, which is usually equated with gender inequality (Cardoso et al. 2016). Our results are consistent with the findings of Azamat and Petrongolo (2014), who document that, although the gender gap in education has closed or even reversed in many countries, gender bias in pay, employment levels or opportunities has not disappeared. Moreover, when we consider differences between managers’ networks (represented by the dotted line in Figure 1), we find that networks are crucial in explaining a large part of the gender pay gap.

Figure 1 The gender pay gap over time

Larger networks facilitate access to firms with more generous compensation policies

By estimating a wage equation with high-dimensional fixed effects and using the Gelbach decomposition method, we can unambiguously decompose the contribution of each source to the observed gender pay gap among top managers. Our results are presented in Figure 2. Taking into account the managers’ observable characteristics of age, tenure, and education, which explain about 4.1 percentage points of the pay gap, we show how a significant fraction of the remaining gender pay gap is explained by variation in firms’ compensation policies, as captured by firm fixed effects. In other words, the selection of managers into firms, where male top managers are distributed into firms with more generous pay policies, accounts for about 7.5 percentage points of the pay gap. Put differently, a random allocation of managers across firms, such that female managers are no longer disproportionately allocated to firms with lower compensation, would reduce the top gender pay gap by a third. Interestingly, we also estimate that more than 50% of this firm selection channel arises from network differences, as well-connected managers, typically male managers, have access to higher-paying firms.

Finally, we find that unobserved permanent characteristics of managers, captured by manager fixed effects, explain the remaining two thirds of the gender pay gap. These unobserved manager characteristics (not observed from the researcher’s perspective), can be equated both with unobserved skills but also with forms of gender discrimination that are unrelated to the selection of managers across firms.

Figure 2 Decomposing the gender pay gap at the top

The gender structure of networks is also important: women support women

Having established the key role of managers’ networks in explaining the gender pay gap among top managers, we further investigate how female managers can best use their networks to overcome the gender divide across firms. We examine the role of both network size as well as network gender composition. First, we find no evidence of a different role of network size for male and female managers. Nevertheless, and importantly, we uncover that network gender composition has important and different effects for female and male managers.

Figure 3 depicts the results of three different tests, run separately for male and female managers. We account for the following network characteristics: gender composition of networks, in terms of numbers (top panel); Gender composition of networks, with a greater weight given to stronger connections (middle panel); and gender composition of networks, giving a greater weight to close/deep ties (lower panel).

Figure 3 Sex-specific association values

Note:: Dots represent estimated coefficients from the propensity score matching method, while lines represent 90% confidence intervals. Red/blue figures refer to 3 different regressions on samples of female/male top managers. A negative value indicates that managers benefit more from female-dominated networks, a positive value is the opposite.

We focus on episodes of job transition and use a propensity score approach to compare managers in male-dominated networks with female-dominated networks with top managers, after controlling for manager characteristics. We find that, in terms of wage gains, both sexes benefit more than men from female linkages (upper panel). However, this result is biased by the fact that the most powerful top managers are male. Once we account for the strength of connections – in terms of the size of the firms they lead – we find that female top managers benefit equally from male or female dominated networks, while male managers benefit more from male connections (see middle panel). Finally , we account for the depth of connections by attributing a higher weight to connections with those who have worked for a longer period of time and/or at smaller firms, which we consider a proxy for the manager’s ‘inner circle’. In doing so, we find that both female and male managers benefit most from connections with managers of their own gender. In short, closer network connections appear to mostly benefit managers of the same gender. More specifically, female managers may benefit from close contact with other female managers.

Our results show how, in a male-dominated corporate world, gender bias can be perpetuated. In light of the existing over-representation of men in leadership roles and the bias towards favoring our peers, the role of women’s networks is an important, yet under-documented, role for women managers. Policies that favor increasing the presence of women in leadership positions can have measurable and important spillovers by facilitating more women’s access to senior management jobs. The exact form of these policies – quotas, mentoring programs or alternatives – is an important topic of further research.

reference

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Bagues, MF and B Esteve-Volart (2007), “Gender equality legislation: will it work?”, VoxEU.org, 27 July.

Bell, LA (2005), “Women-Led Firms and the Gender Gap in Top Executive Jobs”, Institute of Labor Economics (IZA) Discussion Paper 1689.

Bertrand, M and KF Hallock (2001), “The Gender Gap in Top Corporate Jobs”, Industrial and Labor Relations Review 55(1): 3–21.

Cardoso, AR, P Guimarães and P Portugal (2016), “What Drives the Gender Wage Gap? A Look at the Role of Firmness and Job Title Variation”, Oxford Economic Papers 68(2): 506–24.

Cavalcanti, T and J Tavares (2007), “Gender inequality reduces output per capita (a lot)”, VoxEU.org, 16 October.

Cavalcanti, T and J Tavares (2015), “Output costs of gender inequality: a model-based macroeconomic estimate”, The Economic Journal 126(590): 109-134.

Criscuolo, C, P Gal, T Leidecker and G Nicoletti (2021), “The human side of productivity: uncovering the role of skills and diversity for firm productivity”, VoxEU.org, 23 December.

Djankov, S and P Goldberg (2021), “Gendered laws do matter”, VoxEU.org, 24 May.

Geiler, P and L Renneboog (2015), “Are female top managers really underpaid?”, Journal of Corporate Finance 35(C): 345-369.

Lagarde, C and JD Ostry (2018), “The macroeconomic benefits of gender diversity”, VoxEU.org, 5 December.

Sazedj, S and J Tavares (2021a), “Networks and Manager Pay: Evidence from Time-varying Exogenous Metrics”, CEPR Discussion Paper 16475.

Sajedj, S and J Tavares (2021b), “The Gender Gap at the Top: How Network Size and Composition Impact CEO Pay”, CEPR Discussion Paper 16761.

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