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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