This document summarizes a study on gender diversity on corporate boards using a large dataset. It begins by reviewing previous literature on gender diversity which has found mixed results. It then describes the novel approach taken in the study, which uses a large dataset of over 100 million firms over 20 years (Amadeus data) to assign gender to board members based on names. The study will analyze patterns in gender diversity across industries, countries and time periods. It will also compare diversity on supervisory boards versus management boards and test existing hypotheses about the impact of gender diversity.

1. Gender assignment Data Results Conclusions
All on board?
New evidence on board gender diversity from a large panel of firms
Joanna Tyrowicz & Jakub Mazurek
FAME|GRAPE & University of Warsaw
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

2. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

3. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

4. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

5. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

6. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

7. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

8. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

9. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

10. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

11. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence mixed, mostly from “natural experiments” (e.g. Norway, Israel)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

12. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence mixed, mostly from “natural experiments” (e.g. Norway, Israel)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

13. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence mixed, mostly from “natural experiments” (e.g. Norway, Israel)
Problems in this literature
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

14. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence mixed, mostly from “natural experiments” (e.g. Norway, Israel)
Problems in this literature
Typically data on stock listed companies
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

15. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence mixed, mostly from “natural experiments” (e.g. Norway, Israel)
Problems in this literature
Typically data on stock listed companies
Choice of board(s) members is not random
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

16. Gender assignment Data Results Conclusions
Established in the literature
Fun fact: More men by the name John than women in NYSE boards
Glass ceilings:
Stock listed companies loose market value subsequent announcing female
CEOs (Chapple & Humphrey, 2014) even if no evidence of weaker
performance (Wolfers, 2006)
Different management strategies: e.g. risk-taking (Nakano and Nguyen,
2012; Berger et al, 2014; Facio et al, 2016) M&A (Levi et al, 2014), etc.
Matsa & Miller (2011, AER): women help women in corporate America
→ promote women to supervisory boards
Evidence mixed, mostly from “natural experiments” (e.g. Norway, Israel)
Problems in this literature
Typically data on stock listed companies
Choice of board(s) members is not random
Disentangling decision-makers own resentiment from perception of
customers/shareholders tastes
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

17. Gender assignment Data Results Conclusions
What we do
Provide a novel method for gender assignment
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

18. Gender assignment Data Results Conclusions
What we do
Provide a novel method for gender assignment
Using (8 waves of) Amadeus data ⇒ 100 mio firms over nearly 20 years
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

19. Gender assignment Data Results Conclusions
What we do
Provide a novel method for gender assignment
Using (8 waves of) Amadeus data ⇒ 100 mio firms over nearly 20 years
Show patterns for industries, countries and over time
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

20. Gender assignment Data Results Conclusions
What we do
Provide a novel method for gender assignment
Using (8 waves of) Amadeus data ⇒ 100 mio firms over nearly 20 years
Show patterns for industries, countries and over time
Compare supervisory boards to management boards
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

21. Gender assignment Data Results Conclusions
What we do
Provide a novel method for gender assignment
Using (8 waves of) Amadeus data ⇒ 100 mio firms over nearly 20 years
Show patterns for industries, countries and over time
Compare supervisory boards to management boards
Test Matsa & Miller (2011) hypothesis in corporate Europe
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

22. Gender assignment Data Results Conclusions
What we do
Provide a novel method for gender assignment
Using (8 waves of) Amadeus data ⇒ 100 mio firms over nearly 20 years
Show patterns for industries, countries and over time
Compare supervisory boards to management boards
Test Matsa & Miller (2011) hypothesis in corporate Europe
Test Adams & Kirchmaier (2016) intuition
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

23. Gender assignment Data Results Conclusions
Table of contents
Gender assignment
Data
Results
Conclusions
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

24. Gender assignment Data Results Conclusions
Contents
Gender assignment
Data
Results
Conclusions
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

25. Gender assignment Data Results Conclusions
Exact names of board(s) members
Heuristics to identify gender
1. H1: in some languages gender directly identifiable
e.g. vowel ending names in some Slavic languages, -ova in Czech, etc.
2. H2: the books of names
e.g. dedicated lists for each of the Scandinavian languages
Resolving conflicts & dropping “impossible” countries
e.g. the Netherlands
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

26. Gender assignment Data Results Conclusions
Exact names of board(s) members
Heuristics to identify gender
1. H1: in some languages gender directly identifiable
e.g. vowel ending names in some Slavic languages, -ova in Czech, etc.
2. H2: the books of names
e.g. dedicated lists for each of the Scandinavian languages
Resolving conflicts & dropping “impossible” countries
e.g. the Netherlands
Manipulation check: 2010 & 2014 waves of Amadeus have salutations →
compare our gender assignment to salutations
total name-type-observations assigned: 16,254,928;
total with Amadeus confirmed gender: 15,371,479;
total men attributed as men: 10,074,034;
total women assigned as women: 4,048,932;
total men assigned as women: 10,963;
total women assigned as men: 10,626 so I think we are ok
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

27. Gender assignment Data Results Conclusions
Exact names of board(s) members
Year % men in Amadeus attributed as % women in Amadeus attributed as
men women men women unassigned
2000 0.826 0.002 0.004 0.815 0.18
2001 0.824 0.002 0.005 0.808 0.187
2002 0.824 0.002 0.004 0.812 0.184
2003 0.823 0.002 0.004 0.809 0.187
2004 0.825 0.003 0.005 0.809 0.186
2005 0.825 0.002 0.005 0.810 0.185
2006 0.824 0.003 0.005 0.806 0.188
2007 0.835 0.003 0.005 0.815 0.179
2008 0.898 0.001 0.002 0.890 0.107
2009 0.990 0 0 0.985 0.015
2010 0.990 0 0 0.980 0.02
2011 0.989 0 0 0.981 0.019
2012 0.980 0 0 0.979 0.021
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

28. Gender assignment Data Results Conclusions
Contents
Gender assignment
Data
Results
Conclusions
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

29. Gender assignment Data Results Conclusions
Amadeus data
Comes from national information providers
Typically full registry data: ownership details, NACE and board(s)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

30. Gender assignment Data Results Conclusions
Amadeus data
Comes from national information providers
Typically full registry data: ownership details, NACE and board(s)
Often: employment
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

31. Gender assignment Data Results Conclusions
Amadeus data
Comes from national information providers
Typically full registry data: ownership details, NACE and board(s)
Often: employment
For many firms: balance sheet and profit/loss statement
Kalemli-Ozcan et al (2015): standard for cleaning the data
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

32. Gender assignment Data Results Conclusions
Amadeus data
Comes from national information providers
Typically full registry data: ownership details, NACE and board(s)
Often: employment
For many firms: balance sheet and profit/loss statement
Kalemli-Ozcan et al (2015): standard for cleaning the data
This study: no use of financial data → all available firms
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

33. Gender assignment Data Results Conclusions
Employment covered, relative to WIOD (LFS)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

34. Gender assignment Data Results Conclusions
Measurement
full data
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

35. Gender assignment Data Results Conclusions
Measurement
full data
trusted data – coverage too large or too small
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

36. Gender assignment Data Results Conclusions
Measurement
full data
trusted data – coverage too large or too small
reduced data – reduced - sector/years rapidly changing coverage
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

37. Gender assignment Data Results Conclusions
Measurement
full data
trusted data – coverage too large or too small
reduced data – reduced - sector/years rapidly changing coverage
How to measure gender board diversity?
a firm level share of women on board (unweighted average)
e.g. Matsa and Miller (2011); Ahern and Dittmar (2012); Adams and Kirchmaier
(2016)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

38. Gender assignment Data Results Conclusions
Measurement
full data
trusted data – coverage too large or too small
reduced data – reduced - sector/years rapidly changing coverage
How to measure gender board diversity?
a firm level share of women on board (unweighted average)
e.g. Matsa and Miller (2011); Ahern and Dittmar (2012); Adams and Kirchmaier
(2016)
sum of women on boards (relative to men = weighted average)
e.g. Wolfers (2006); Adams and Ferreira (2009)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

39. Gender assignment Data Results Conclusions
Measurement
full data
trusted data – coverage too large or too small
reduced data – reduced - sector/years rapidly changing coverage
How to measure gender board diversity?
a firm level share of women on board (unweighted average)
e.g. Matsa and Miller (2011); Ahern and Dittmar (2012); Adams and Kirchmaier
(2016)
sum of women on boards (relative to men = weighted average)
e.g. Wolfers (2006); Adams and Ferreira (2009)
fraction of firms that do not have women on board
novel indicator
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

40. Gender assignment Data Results Conclusions
Measurement
Full set Trusted set Reduced set
People Firms People Firms People Firms
Total # 141,364,816 112,010,296 116,440,950 92,505,280 27,805,441 8,203,535
Unique 19,488,701 18,610,968 18,233,902 16,900,260 7,609,661 1,338,729
In firms which should have a supervisory board
Total # 86,989,026 55,401,550 76,290,029 49,257,023 19,390,571 6,112,430
Total unique 10,774,244 8,360,777 10,333,102 7,983,919 3,035,300 1,001,916
Unweighted average
Management boards 18.8% 19.3% 16.9%
Supervisory boards 19.5% 19.7% 18.8%
Weighted average
Management boards 15.8% 15.8% 29.6%
Supervisory boards 28.2% 28.8% 29.8%
% of obs of firms with no women on boards
Management boards 80.4% 80.6% 68.2%
Supervisory boards 99.3% 99.2% 95.7%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

41. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

42. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
sector (broad) 9.20% 14.10% 8.20%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

43. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
sector (broad) 9.20% 14.10% 8.20%
sector (2 digits) 18.60% 30.60% 20.90%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

44. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
sector (broad) 9.20% 14.10% 8.20%
sector (2 digits) 18.60% 30.60% 20.90%
country and sector 36.50% 46.80% 47.60%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

45. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
sector (broad) 9.20% 14.10% 8.20%
sector (2 digits) 18.60% 30.60% 20.90%
country and sector 36.50% 46.80% 47.60%
year 7.70% 9.50% 10.00%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

46. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
sector (broad) 9.20% 14.10% 8.20%
sector (2 digits) 18.60% 30.60% 20.90%
country and sector 36.50% 46.80% 47.60%
year 7.70% 9.50% 10.00%
all 46.30% 64.60% 63.70%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

47. Gender assignment Data Results Conclusions
Sources of heterogeneity
Contribution to variance Full set Trusted set Reduced set
Uweighted average
country 20.90% 28.00% 35.70%
sector (broad) 9.20% 14.10% 8.20%
sector (2 digits) 18.60% 30.60% 20.90%
country and sector 36.50% 46.80% 47.60%
year 7.70% 9.50% 10.00%
all 46.30% 64.60% 63.70%
Fraction of firms with no women
country 43.90% 57.80% 55.20%
sector (broad) 2.10% 2.80% 2.30%
sector (2 digits) 5.40% 7.00% 6.80%
country and sector 49.90% 64.30% 59.90%
year 0.90% 1.30% 5.60%
all 50.80% 65.40% 65.40%
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

48. Gender assignment Data Results Conclusions
Sources of heterogeneity
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

49. Gender assignment Data Results Conclusions
Sources of heterogeneity
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

50. Gender assignment Data Results Conclusions
Sources of heterogeneity
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

51. Gender assignment Data Results Conclusions
Contents
Gender assignment
Data
Results
Conclusions
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

52. Gender assignment Data Results Conclusions
Two sets of results
Matsa & Miller (2011): test if probability of women on a management
board depends on presence of women on supervisory board.
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

53. Gender assignment Data Results Conclusions
Two sets of results
Matsa & Miller (2011): test if probability of women on a management
board depends on presence of women on supervisory board.
Add controls raised by Adams & Kirchmaier (2016): general openness to
women (in a given country & year) should make it easier for them to be
on supervisory boards ⇒ management?
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

54. Gender assignment Data Results Conclusions
Replicating M&M
Stock-listed firms from trusted data set (M&M)
(1) = (3MM) (2) = (4MM) (3) = (5MM) (4)
W in SB (t − 1) 0.226*** 0.226*** 0.010*** 0.020***
W in M (t − 1) 0.770*** 0.613***
Year FE Yes Yes Yes Yes
Sector FE Yes Yes Yes Yes
Firm FE No Yes No Yes
Constant 0.233*** 0.243*** 0.089*** 0.113***
# 111,214 111,214 111,214 111,214
# of firms 12,538 12,538 12,538 12,538
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

55. Gender assignment Data Results Conclusions
Extending M&M to nonlisted companies
Full data set (reduced sample is preferred)
(1a) (2a) (3a) (4a)
W in SB (t − 1) -0.043*** -0.047*** -0.080*** -0.091***
W in M (t − 1) 0.644*** 0.419***
Year FE Yes Yes Yes Yes
Sector FE Yes Yes Yes Yes
Firm FE No Yes No Yes
Constant 0.348*** 0.398*** 0.158*** 0.253***
# 6,038,840 6,038,840 6,038,840 6,038,840
# of firms 1,029,740 1,029,740 1,029,740 1,029,740
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

56. Gender assignment Data Results Conclusions
Is this big or small?
Placebo test Full data set (reduced sample is preferred)
(France, Germany & UK) (1a) (2a) (3a) (4a)
Name in S t − 1 -0.002*** -0.002*** -0.001 -0.001
Name in M t − 1 0.449*** 0.328***
Year FE Yes Yes Yes Yes
Sector FE Yes Yes Yes Yes
Firm FE No Yes No Yes
Constant 0.024*** 0.014*** 0.015*** 0.010***
# observations 2,612,525 2,612,525 2,612,525 2,612,525
# firms 433,724 433,724 433,724 433,724
French names: Philippe, Olivier, Laurent
German names: Thomas, Michael, Andreas
British names: David, Paul, John
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

57. Gender assignment Data Results Conclusions
Is this result robust?
Yes!
We also control for a number of other factors (e.g. size of firm, HHI,
innovativeness of the sector, etc.)
We analyze other samples (e.g. more reduced data, complete data)
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

58. Gender assignment Data Results Conclusions
Replicating and extending A&K
Supervisory board Management board
(all in 10y lags) Country level Person-level Country-level Person-level
FFEP 0.295*** 0.335** 0.086 -0.01 0.922*** 0.893*** 0.706*** -0.467***
Codeterm. 0.00 -0.021*** -0.017*** -0.032*** -0.002 -0.019* -0.014*** -0.056***
GNI / Capita -0.205*** -0.063 0.039 -0.194*** -0.448*** -0.329*** -0.112*** -0.133***
% of ff -0.076*** 0.007 0.058** -0.037*** 0.002 0.054 0.060*** 0.095***
% of w TE 0.373** 0.366 0.915*** 0.765*** -0.204 -0.091 0.013 0.243***
Birth Rate 0.016*** 0.014*** 0.010*** 0.009*** 0.001 -0.001 0.015*** 0.018***
Tax & SS 0.001 0.000 0.000 0.000 -0.001 -0.001 -0.001*** -0.000***
GWE -0.585*** 0.02 -0.103** -0.001 -0.482** -0.001 0.128*** -0.239***
Values Yes Yes Yes Yes Yes Yes Yes -0.033***
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Observations 83 83 105,671 1,843,719 103 103 267,864 36,659,171
R-squared 0.828 0.74 0.012 0.006 0.698 0.676 0.017 0.019
SE robust clustred robust clustred
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

59. Gender assignment Data Results Conclusions
Probability of a woman in a management board + country characteristics
Subsample: Managers Supervisors
# people on board 0.026*** 0.033*** 0.007 -0.001
Employment (in logs) 0.001** 0.001**
Innovative sector 0.004* 0.003* -0.002 -0.002
HHI -0.002 -0.002 -0.019 -0.019
FFEP 0.175 0.200* 0.577*** -0.114 -0.116 -0.199
% of we TE 0.077 0.071 0.051 0.047 0.04 0.195***
W social rights -0.003* -0.003* -0.001 -0.014*** -0.014*** -0.003*
Weconomic rights 0.001 -0.003** -0.004*** 0.021*** 0.020*** 0.004
Gender wage gap 0.02 0.02 0.098*** -0.156*** -0.148*** -0.157***
Gender employment gap -0.003 0.018
% parliament seats -0.105*** -0.089***
Women administrators 0.005 0.035**
Year fixed effects Yes Yes Yes Yes Yes Yes
C&S fixed effects Yes Yes Yes Yes Yes Yes
Firm fixed effects Yes Yes Yes Yes Yes Yes
Observations 47,597,469 32,199,906 2,687,032 2,231,247
R-squared 0.552 0.556 0.441 0.421
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

60. Gender assignment Data Results Conclusions
Contents
Gender assignment
Data
Results
Conclusions
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

61. Gender assignment Data Results Conclusions
Instead of conclusions
The data is unique and vast
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

62. Gender assignment Data Results Conclusions
Instead of conclusions
The data is unique and vast
The data beyond stoclisted firms tell story opposite to M&M and A&K
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

63. Gender assignment Data Results Conclusions
Instead of conclusions
The data is unique and vast
The data beyond stoclisted firms tell story opposite to M&M and A&K
Documented patterns: striking and inspiring more analyses
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

64. Gender assignment Data Results Conclusions
Instead of conclusions
The data is unique and vast
The data beyond stoclisted firms tell story opposite to M&M and A&K
Documented patterns: striking and inspiring more analyses
Women are becoming more numerous (and less “infrequent”) → changes
in selectivity patterns or changes in economy structure?
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

65. Gender assignment Data Results Conclusions
Instead of conclusions
The data is unique and vast
The data beyond stoclisted firms tell story opposite to M&M and A&K
Documented patterns: striking and inspiring more analyses
Women are becoming more numerous (and less “infrequent”) → changes
in selectivity patterns or changes in economy structure?
Perhaps changes in corporate Europe drive changes in institutional
Europe?
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

66. Gender assignment Data Results Conclusions
Instead of conclusions
The data is unique and vast
The data beyond stoclisted firms tell story opposite to M&M and A&K
Documented patterns: striking and inspiring more analyses
Women are becoming more numerous (and less “infrequent”) → changes
in selectivity patterns or changes in economy structure?
Perhaps changes in corporate Europe drive changes in institutional
Europe?
Account better for the changes in Amadeus
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw

67. Gender assignment Data Results Conclusions
Questions or suggestions?
Thank you!
w: grape.org.pl
t: grape org
f: grape.org
e: jtyrowicz@grape.org.pl
Evidence on board gender diversity from a large panel of firms FAME|GRAPE & University of Warsaw