The research explores to what extent the presence of women on board affects gender inequality downstream. We find that increasing presence reduces gender inequality. To avoid reverse causality, we propose a new instrument: the share of household consumption in total output. We extend the analysis to recover the effect of a single woman on board (tokenism(

1. (Gender) Tone at the top
(Gender) Tone at the top
The effects of gender board diversity on gender inequality
Bram Timmermans [NHH]
Joanna Tyrowicz [ University of Warsaw & IZA ]
Lucas van der Velde [FAME|GRAPE & Warsaw School of Economics]
European Public Choice Society
April 2024

2. (Gender) Tone at the top
Motivation
Motivation
A blossoming literature linking managers’ gender and inequality within firms
Existing results are mixed
No evidence of spill-overs at the top of the firm
Abendroth et al. (2017); Bertrand et al. (2019); Maida and Weber (2022)
Positive effects from managers to employees, but negative within ranks
Hensvik (2014); Kunze and Miller (2017)
Male and female wages react differently
Cardoso and Winter-Ebmer (2010); Flabbi et al. (2019)

3. (Gender) Tone at the top
Motivation
Motivation
A blossoming literature linking managers’ gender and inequality within firms
Existing results are mixed
No evidence of spill-overs at the top of the firm
Abendroth et al. (2017); Bertrand et al. (2019); Maida and Weber (2022)
Positive effects from managers to employees, but negative within ranks
Hensvik (2014); Kunze and Miller (2017)
Male and female wages react differently
Cardoso and Winter-Ebmer (2010); Flabbi et al. (2019)
Current results have limited external validity
Results are country specific
Restricted to stock listed firms

4. (Gender) Tone at the top
Motivation
Our contribution
We explore the link between gender board diversity and adjusted gender wage gaps

5. (Gender) Tone at the top
Motivation
Our contribution
We explore the link between gender board diversity and adjusted gender wage gaps
Proposed research questions
1 Does the presence of women on board affect gender inequality?
2 Does the proportion of women act as a moderator?

6. (Gender) Tone at the top
Motivation
Our contribution
We explore the link between gender board diversity and adjusted gender wage gaps
Unique database: Gender Board Diversity Database Drazkowski et al. (2023)
Includes listed and non-listed firms
All European countries as of 2002
Comparable measures of AGWG (across c & t) using matched employee-employer data
Propose a candidate instrument: share of final consumption on total industry expenditure

7. (Gender) Tone at the top
Motivation
Our contribution
We explore the link between gender board diversity and adjusted gender wage gaps
Unique database: Gender Board Diversity Database Drazkowski et al. (2023)
Includes listed and non-listed firms
All European countries as of 2002
Comparable measures of AGWG (across c & t) using matched employee-employer data
Propose a candidate instrument: share of final consumption on total industry expenditure

8. (Gender) Tone at the top
Motivation
A bit on theory
Theory ambiguous on the link between female managers and gender inequality
1 Positive spillovers
Awareness of discriminatory practices (Hultin and Szulkin, 1999; Cohen and Huffman, 2007)
Role model (Linehan and Scullion, 2008; Zimmermann, 2022)
Better recognition of female talent (Tsui and O’Reilly III, 1989; Ridgeway, 1997)

9. (Gender) Tone at the top
Motivation
A bit on theory
Theory ambiguous on the link between female managers and gender inequality
1 Positive spillovers
Awareness of discriminatory practices (Hultin and Szulkin, 1999; Cohen and Huffman, 2007)
Role model (Linehan and Scullion, 2008; Zimmermann, 2022)
Better recognition of female talent (Tsui and O’Reilly III, 1989; Ridgeway, 1997)
2 No or negative spillovers
Queen-bee syndrome (Staines et al., 1974; Derks et al., 2016)
Another cog in the machine (Jia and Zhang, 2013; Torchia et al., 2011)

10. (Gender) Tone at the top
Data and methods
Databases
We link two databases at the industry × country × year level (cells)
1 Gender Board Diversity Database → GBDD
2 Employee-employer data on earnings → EU-SES

11. (Gender) Tone at the top
Data and methods
Gender Board Diversity Database
Based on Orbis data (Amadeus) → collects data from firm registries
Data available since early 00’s for most European countries
Two challenges
1 Identifying board members in each firm / year
2 Assigning gender to board members
We tackle these challenges following Drazkowski et al. (2023)

12. (Gender) Tone at the top
Data and methods
Two measures of gender board diversity
Mean SD P10 P50 P90
Share of firms with women on boardi,c,t 0.378 0.150 0.197 0.361 0.582
Avg. share of women in boardi,c,t 0.255 0.115 0.137 0.232 0.417
Sample used to obtain the measures (GBDD)
N. of firms with any board memberi,c,t 12065 24263 97 3463 28960
N. board membersi,c,t 20704 39009 179 6221 55468
N. female board membersi,c,t 4997 9187 49 1548 13868
Observations 1284
In an average cell, 60%+ of firms did not have a women on board!!
Around 1/4 of board members are women

13. (Gender) Tone at the top
Data and methods
Structure of Earnings Survey
A large and comprehensive database on earnings
Available for (almost) all EU countries every 4 years → focus on years 2010, 2014 and
2018.
A survey of firms
Detailed data on wages, hours, occupation, tenure, etc
Missing information on household (children, marital status).

14. (Gender) Tone at the top
Data and methods
Structure of Earnings Survey
A large and comprehensive database on earnings
Available for (almost) all EU countries every 4 years → focus on years 2010, 2014 and
2018.
A survey of firms
Detailed data on wages, hours, occupation, tenure, etc
Missing information on household (children, marital status).
Ñopo (2008) as a non-parametric decomposition method
Recovers the adjusted gap for workers in common support
No need to specify functional form
Adjust for: age, education, position (ft/pt), sector, occupation, size of firm.

15. (Gender) Tone at the top
Data and methods
Mean SD P10 P50 P90
Adj. Gender Wage Gapi,c,t 0.142 0.086 0.048 0.128 0.253
Matched meni,c,t (share) 0.896 0.117 0.741 0.936 0.988
Matched womeni,c,t (share) 0.941 0.061 0.864 0.958 0.994
Sample used to obtain measures(SES)
N. female workersi,c,t 13895 33864 625 3820 28542
N. male workersi,c,t 13345 22676 1211 5492 35705
Observations 1284
In an average industry, men earn 14% more than women

16. (Gender) Tone at the top
Data and methods
Method
We want to estimate the following
AGWGi,c,t = β0 + βOLS
1 GBDi,c,t + γc + γt + γs + ϵi,c,t (1)

17. (Gender) Tone at the top
Data and methods
Method
We want to estimate the following
AGWGi,c,t = β0 + βOLS
1 GBDi,c,t + γc + γt + γs + ϵi,c,t (1)
where
AGWGi,c,t is the adjusted gender wage gap within industry, country and period (t)
GBD is a measure of gender board diversity:
1 Share of firms with at least one woman on boards
2 Average proportion of women on boards
γc , γt, and γs are country, year, and sector f.e.

18. (Gender) Tone at the top
Data and methods
Method
We want to estimate the following
AGWGi,c,t = β0 + βOLS
1 GBDi,c,t + γc + γt + γs + ϵi,c,t (1)
However,
Unobserved time varying variables → e.g. use of flexible work arrangements
Reverse causality

19. (Gender) Tone at the top
Data and methods
Method
We want to estimate the following
AGWGi,c,t = β0 + βOLS
1 GBDi,c,t + γc + γt + γs + ϵi,c,t (1)
However,
Unobserved time varying variables → e.g. use of flexible work arrangements
Reverse causality

20. (Gender) Tone at the top
Data and methods
Main specification: IV
The IV specification is
AGWGi,c,t = β0 + βIV
1
[
GBDi,c,t + γc + γt + γs + ϵi,c,t
GBDi,c,t = α0 + α1HH. cons.i,c,t + δc + δt + δs + υi,c,t
Candidate instrument: HH.Cons. → Share of household consumption on industry i output.
Why? Direct contact with customers require “feminine” traits
Exclusion restriction: uncorrelated with adjusted gender wage inequality → once we account
for differences in characteristics

21. (Gender) Tone at the top
Results
Are women on board related to gender (in)equality?
.12
.13
.14
.15
.16
Adjusted
GWG
.1 .2 .3 .4 .5
Average share of women
on boards
.12
.13
.14
.15
.16
Adjusted
GWG
.2 .3 .4 .5 .6
Share of firms with
at least a woman on boards
Notes: GWG adjusted for age, education, position(ft, pt), ISCO 08 (1 digit), ownership, and size of firms.

22. (Gender) Tone at the top
Results
Are women on board related to gender equality? – Main specification
Average share of women Share of firms with 1+ women
OLS IV OLS IV
Gender board diversityi,c,t -0.00683 -0.302*** 0.00887 -0.285***
(0.0374) (0.110) (0.0279) (0.105)
FE: Sector, country, year Yes Yes Yes Yes
N 1284 1284 1284 1284
First stage F-statistic 68.01 52.97
One SD increase in share of firms with women on board (0.15) reduces AGWG by 0.0387
percentage points (−0.285 ∗ 0.15))
First stage regressions Go

23. (Gender) Tone at the top
Results
Gauging the effect: European gender quota directive
EU Directive → at least 33% of women on boards of listed firms
Simulate the average share of women if firms without women were to adopt these measures
Predict the AGWG with new values

24. (Gender) Tone at the top
Results
Gauging the effect: European gender quota directive
EU Directive → at least 33% of women on boards of listed firms
Simulate the average share of women if firms without women were to adopt these measures
Predict the AGWG with new values
Mean SD
Female share on boards
As observed 0.268 0.119
Predicted 0.461 0.078
Gender Inequality measures
As observed 0.140 0.082
Linear prediction 0.082 0.052
Observations 430

25. (Gender) Tone at the top
Results
Robustness checks
Focusing on senior managers instead of all boards
Controlling for the women to men ratio in the industry
Controlling for differences in workforce composition across industries
Focusing on the subsample of cells with enough people in common support

26. (Gender) Tone at the top
Results
How many women to make a difference?

27. (Gender) Tone at the top
Results
How many women to make a difference?
We estimate the following regression
AGWGi,c,t = β0 + βShare of firms with N = 0 womeni,c,t + γs + γc + γt + ei,c,t (2)
AGWGi,c,t = β0 + βShare of firms with N = 2+ womeni,c,t + γs + γc + γt + ei,c,t (3)
AGWGi,c,t = β0 + βShare of firms with N = 3+ womeni,c,t + γs + γc + γt + ei,c,t (4)

28. (Gender) Tone at the top
Results
How many women to make a difference?
-1
-.5
0
.5
1
β
IV
and
90%
CI
Main
specification
Share with
no women
Two or more
women
Three or more
women

29. (Gender) Tone at the top
Summary
Summary
We show that improving gender board diversity decreases gender inequality
Results from most EU countries and across periods
Reduction in gender inequality is meaningful
Leverage novel database (GBDD) & new candidate instrument
Effects increase in industries with the number of female board members

30. (Gender) Tone at the top
Summary
Bibliography I
Abendroth, A.-K., Melzer, S., Kalev, A., and Tomaskovic-Devey, D. (2017). Women at work: Women’s access
to power and the gender earnings gap. ILR Review, 70(1):190–222.
Bertrand, M., Black, S. E., Jensen, S., and Lleras-Muney, A. (2019). Breaking the glass ceiling? the effect of
board quotas on female labour market outcomes in norway. Review of Economic Studies, 86(1):191–239.
Cardoso, A. R. and Winter-Ebmer, R. (2010). Female-led firms and gender wage policies. ILR Review,
64(1):143–163.
Cohen, P. N. and Huffman, M. L. (2007). Working for the woman? female managers and the gender wage gap.
American Sociological Review, 72(5):681–704.
Derks, B., Van Laar, C., and Ellemers, N. (2016). The queen bee phenomenon: Why women leaders distance
themselves from junior women. Leadership Quarterly, 27(3):456–469.
Drazkowski, H., Tyrowicz, J., and Zalas, S. (2023). Gender board diversity across Europe throughout four
decades. GRAPE Working papers 87.
Flabbi, L., Macis, M., Moro, A., and Schivardi, F. (2019). Do female executives make a difference? the impact
of female leadership on gender gaps and firm performance. The Economic Journal, 129(622):2390–2423.
Hensvik, L. E. (2014). Manager impartiality: Worker-firm matching and the gender wage gap. ILR Review,
67(2):395–421.

31. (Gender) Tone at the top
Summary
Bibliography II
Hultin, M. and Szulkin, R. (1999). Wages and unequal access to organizational power: An empirical test of
gender discrimination. Administrative Science Quarterly, 44(3):453–472.
Jia, M. and Zhang, Z. (2013). Critical mass of women on bods, multiple identities, and corporate philanthropic
disaster response: Evidence from privately owned chinese firms. Journal of Business Ethics, 118:303–317.
Kunze, A. and Miller, A. R. (2017). Women helping women? evidence from private sector data on workplace
hierarchies. The Review of Economics and Statistics, 99(5):769–775.
Linehan, M. and Scullion, H. (2008). The development of female global managers: The role of mentoring and
networking. Journal of Business Ethics, 83:29–40.
Maida, A. and Weber, A. (2022). Female leadership and gender gap within firms: Evidence from an italian
board reform. ILR Review, 75(2):488–515.
Ñopo, H. (2008). Matching as a tool to decompose wage gaps. Review of Economics and Statistics,
90(2):290–299.
Ridgeway, C. L. (1997). Interaction and the conservation of gender inequality: Considering employment.
American Sociological Review, pages 218–235.
Staines, G., Tavris, C., and Jayaratne, T. E. (1974). The queen bee syndrome.
Torchia, M., Calabrò, A., and Huse, M. (2011). Women directors on corporate boards: From tokenism to
critical mass. Journal of Business Ethics, 102:299–317.

32. (Gender) Tone at the top
Summary
Bibliography III
Tsui, A. S. and O’Reilly III, C. A. (1989). Beyond simple demographic effects: The importance of relational
demography in superior-subordinate dyads. Academy of Management Journal, 32(2):402–423.
Zimmermann, F. (2022). Managing the gender wage gap - how female managers influence the gender wage gap
among workers. European Sociological Review, 38(3):355–370.

33. (Gender) Tone at the top
Additional tables and figures
First stage regressions
.32
.34
.36
.38
.4
.42
Share
of
firms
with
some
women
on
board
i,c,t
0 .1 .2 .3 .4 .5
Share of household expenditure
in total outputi,c,t
.2
.22
.24
.26
.28
.3
Avg.
share
of
female
in
boards
i,c,t
0 .1 .2 .3 .4 .5
Share of household expenditure
in total outputi,c,t
Notes: Bin scatter of the first stage regression. Variables are residualized on sector, country and year .
Back

34. (Gender) Tone at the top
Additional tables and figures
First stage regressions
Average share Share of firms
of women on boards with 1+ women
Household Consumption 0.145*** 0.154***
in final output (0.0176) (0.0212)
FE: Sector, country, year Yes Yes
N 1284 1284
Back