In a widely cited studies Matsa and Miller (2011, 2013) argue that the presence of women on supervisory boards is conducive to greater gender diversity among top management. This result is consistent with a range of policy recommendations to legally mandate women’s presence of supervisory boards (currently 16 countries have such policies). Yet such studies focus on stock-listed companies, i.e. companies under public scrutiny. Meanwhile, majority of companies remain private both in a sense that supervisory boards positions are not subject to regulations on board diversity and in a sense that they remain beyond public scrutiny. Typically, studies rely on stock-listed companies and focus on identifying firm-level correlates of women share on management boards. A rich body of literature analyzes the stock-listed companies US, UK, France, Finland,  Japan, Italy, and BRIC countries.

1. Gender diversity spillovers and token women on boards
Hubert Drażkowski (FAME|GRAPE & Warsaw University of Technology)
Siri Terjesen (Florida Atlantic University & NHH)
Joanna Tyrowicz (FAME|GRAPE, University of Regensburg, and IZA)
ASSA, New Orleans, 2023
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2. Do women help women? Mechanisms of diversity spillovers
• Gender diversity: non-executive roles ⇒ executive roles
Matsa and Miller (2011, 2013)
• Public firms = public scrutiny ⇒ reputation cost of no diversity
• In public firms: helpless if token
• In non-public firms: no public eye at all
What we do?
1. Go beyond stock-listed firms (novel data)
2. Explore data from 1985 onwards (novel gender identification)
3. Do women help women in corporate Europe? (novel specification)
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3. What we know
• Women in supervisory roles ⇒ women in executive roles in firms under public scrutiny
Matsa and Miller (2011, 2013)
• We know that women on boards are frequently token ... in Denmark
Smith and Parrotta (2018)
• Managers more likely hire same gender people ... in Germany
Bossler et al. (2020)
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4. Our contribution: board diversity spillovers
Isolate two overlapping mechanisms
1. Tokenism – is one woman enough?
2. Public eye scrutiny – stock-listed companies vs private firms
Matsa & Miller:womanMB
t = β0 + βi + δwomanSB
t−1 + γcontrolsi,t + ϵi,t
We v.1:womanMB
t = β0 + βi + δ̃womanOB
t−1#no public eyei + γcontrolsi,t + ϵi,t
We v.2:womanMB
t = β0 + βi + δ̃womanOB
t−1#tokeni + γcontrolsi,t + ϵi,t
We v.3:womanMB
t = β0 + βi + δ̃womanOB
t−1#tokeni #no public eyei + γcontrolsi,t + ϵi,t
+ replicate M&M with SB (for robustness) + refocus on firms with the first woman in MB
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5. Our contribution: data
Orbis: 2+ mln firms
• (Quasi-)Administrative data: 1986 - 2020 for 29 European countries
• Both kinds of firms: subject to public eye scrutiny + under veil of silence
• Board members: one-tier and two-tier systems
Sample properties No of observations over time
• Average no of years in the sample per firm: 7 (mean), 6 (median), 36 (max)
• Average no of people per firm: 5+
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6. Our contribution: data
Assignment
• Gender assignment based on linguistic rules across languages
• Board assignment extracted from word parsing
Manipulation checks Assignment details
• Gender assignment accurate in 99%
• Board assignment: Orbis less reliable
• One-tier and two-tier systems confused
• Some position names are not definite (e.g. “board member” or “president of the board”)
• very restrictive: management board (MB) + supervisory board (SB)
• intermediate step: top management (BD)
• less restrictive: non-executive group as residual (OB = assigned to BD but not assigned to MB)
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7. One interesting empirical observation
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8. The broad majority of firms have NO WOMEN on boards
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9. The broad majority of firms have NO WOMEN on boards
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10. Results
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11. Replicating Matsa & Miller
Dummies in our specification
womanMB
t OB SB M&M share ∼ share (4) M&M Any female ∼ share (1)
womanOB
t−1 0.02** 0.01 0.04** 0.09***
(0.009) (0.012) (0.018) (0.03)
Firms with women 0.29 0.35 0.24 0.24
Observations 45 054 25 929 13 491 13 491
Firms 9 460 5 852 1500 1500
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Industry
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12. Replicating Matsa & Miller
We also do shares for comparison
womanMB
t OB SB M&M share ∼ share (4) M&M Any female ∼ share (1)
share womanOB
t−1 0.09*** 0.04 0.04** 0.09***
(0.02) (0.03) (0.018) (0.03)
Firms with women 0.29 0.35 0.24 0.24
Observations 45 054 25 929 13 491 13 491
Firms 9 460 5 852 1500 1500
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Industry
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13. Go beyond Matsa & Miller: no spillovers when no public eye
womanMB
t All firms The first woman in MB
womanOB
t−1 -0.01** 0.138*** -0.03*** 0.055***
(0.001) (0.009) (0.003) (0.012)
no public eyet 0.027 0.004
(0.009) (0.016)
womanOB
t−1 # no public eyet -0.149*** -0.088***
(0.009) (0.014)
Firms with women 0.25 0.25 0.37 0.37
Observations 13 903 488 13 903 488 1 232 595 1 232 595
Firms 2 214 249 2 214 249 133 585 133 585
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Firm
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14. Go beyond Matsa & Miller: no spillovers when no public eye
subsample with bigger OB in t − 1
womanMB
t All firms The first woman in MB
womanOB
t−1 0.001 0.121*** -0.023*** 0.09***
(0.001) (0.009) (0.003) (0.013)
no public eyet 0.02 0.001
(0.01) (0.02)
womanOB
t−1 # no public eyet -0.123*** -0.119***
(0.009) (0.013)
Firms with women 0.22 0.22 0.59 0.59
Observations 5 023 962 5 023 962 671 745 671 745
Firms 954 670 954 670 114 818 114 818
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Firm
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15. Go beyond Matsa & Miller: no spillovers when token
subsample with bigger OB in t − 1
womanMB
t All firms The first woman in MB
womanOB
t−1 0.001 0.006*** -0.023*** -0.019***
(0.001) (0.001) (0.003) (0.004)
token womanOB
t−1 -0.006*** -0.004
(0.001) (0.003)
Firms with women 0.22 0.22 0.59 0.59
Observations 5 023 962 5 023 962 671 745 671 745
Firms 954 670 954 670 114 818 114 818
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Firm
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16. Robustness
• Weighting sample by population size (counteract large samples from small countries)
• SB instead of all OB
• Focus on two-tier system countries
• Alternative estimators (HDFE)
• We even run a placebo...
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17. More John’s than all women together...
nameMB
t All All The first name in MB The first name in MB
nameOB
t−1 -0.027*** 0.06*** 0.007* 0.07***
(0.002) (0.015) (0.005) (0.02)
no public eyet -0.02 -0.04
(0.016) (0.03)
nameOB
t−1 # no public eyet -0.093*** -0.07***
(0.015) (0.02)
Firms with names 0.15 0.15 0.52 0.52
Observations 2 997 867 2 997 867 383 816 383 816
Firms 611 965 611 965 68 085 68 085
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Firm
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18. Combine token and public eye hypotheses
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19. Combine token and public eye
subsample with bigger OB in t − 1
womanMB
t All All The first woman in MB The first woman in MB
womanOB
t−1 -0.007*** 0.199*** -0.019*** 0.129***
(0.002) (0.01) (0.004) (0.012)
token womanOB
t−1 -0.008*** -0.136*** -0.005 -0.076***
(0.001) (0.01) (0.004) (0.015)
no public eyet 0.02* 0.004
(0.01) (0.02)
no public eyet # womanOB
t−1 -0.131*** -0.157***
(0.013) (0.016)
no public eyet # token womanOB
t−1 -0.197*** 0.077***
(0.012) (0.015)
Firms with women 0.22 0.22 0.59 0.59
Observations 5 023 962 5 023 962 671 745 671 745
Firms 954 670 954 670 114 818 114 818
Clustered SE Yes Yes Yes Yes
Fixed effects Firm Firm Firm Firm
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20. Conclusions
Women help women in corporate Europe?
1. No public scrutiny ⇒ no help
2. One woman ⇒ no help
3. Spillovers under the public scrutiny ⇒ not if token
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21. Thank you!
Questions or suggestions?
w: grape.org.pl
t: grape org
f: grape.org
e: j.tyrowicz@grape.org.pl
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22. Bibliography
References
Bossler, M., Mosthaf, A. and Schank, T.: 2020, Are female managers more likely to hire more
female managers? evidence from germany, ILR Review 73(3), 676–704.
Matsa, D. A. and Miller, A. R.: 2011, Chipping away at the glass ceiling: Gender spillovers in
corporate leadership, American Economic Review 101(3), 635–39.
Matsa, D. A. and Miller, A. R.: 2013, A female style in corporate leadership? evidence from quotas,
American Economic Journal: Applied Economics 5(3), 136–69.
Smith, N. and Parrotta, P.: 2018, Why so few women on boards of directors? empirical evidence
from danish companies in 1998–2010, Journal of Business Ethics 147(2), 445–467.
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23. Data – details Go back
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24. Assignment – details Go back
People related to firms at each point in time
• Assign gender
• In some languages: name or surname is unequivocal (parse names to first names & surnames)
• In other languages: book of names (from first names)
• In case of conflict (e.g. expats), check on a case-by-case basis
• Overall: < 1% of cases was not resolved
• Assign boards: management (MB), supervisory (SB), top management (BD), other (OB)
• MB ∪ SB ⊂ BD
• OB = BD − MB
⇒ lower bound on absolute value of coefficients
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