EARHART concluding meeting at NHH in Bergen

Evidence concerning inequality in ability to realize aspirations is prevalent: overall, in specialized segments of the labor market, in self-employment and high-aspirations environments. Empirical literature and public debate are full of case studies and comprehensive empirical studies documenting the paramount gap between successful individuals (typically ethnic majority men) and those who are less likely to “make it” (typically ethnic minority and women). So far the drivers of these disparities and their consequences have been studied much less intensively, due to methodological constraints and shortage of appropriate data. This project proposes significant innovations to overcome both types of barriers and push the frontier of the research agenda on equality in reaching aspirations.

Overall, project is interdisciplinary, combining four fields: management, economics, quantitative methods and psychology. An important feature of this project is that it offers a diversified methodological perspective, combining applied microeconometrics, as well as experimental methods.

The project funded through Norway Grants.

1. EARHART: EquAl in ReacHing AspiRaTions
UW & FAMEjGRAPE & NHH
April 2024, NHH, Closing event

2. Funded by Norway and EEA Grants 2014{2021
Basic Research Programme operated by the National Science Centre
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 1

3. Implemented by a partnership of University of Warsaw, NHH and FAMEjGRAPE
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 2

4. Work packages
1.
Siri & Magda & JT
Bram & Lucas & JT
2.3.
Hubert & Sebastian & JT
Hubert & Sebastian
Kasia & Sebastian & JT
4.
Hubert & Bram & JT
Hubert & JT
5.
Magda & Lucas & JT
the role of information avoidance in evaluating performance of minority managers ?
[experiment ongoing]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 3

5. Work packages
1.
Siri & Magda & JT
Bram & Lucas & JT
2.3.
Hubert & Sebastian & JT
Hubert & Sebastian
Kasia & Sebastian & JT
4.
Hubert & Bram & JT
Hubert & JT
5.
Magda & Lucas & JT
the role of information avoidance in evaluating performance of minority managers ?
[experiment ongoing]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 3

6. Work packages
1.
Siri & Magda & JT
Bram & Lucas & JT
2.3.
Hubert & Sebastian & JT
Hubert & Sebastian
Kasia & Sebastian & JT
4.
Hubert & Bram & JT
Hubert & JT
5.
Magda & Lucas & JT
the role of information avoidance in evaluating performance of minority managers ?
[experiment ongoing]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 3

7. Work packages
1.
Siri & Magda & JT
Bram & Lucas & JT
2.3.
Hubert & Sebastian & JT
Hubert & Sebastian
Kasia & Sebastian & JT
4.
Hubert & Bram & JT
Hubert & JT
5.
Magda & Lucas & JT
the role of information avoidance in evaluating performance of minority managers ?
[experiment ongoing]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 3

8. Work packages
1.
Siri & Magda & JT
Bram & Lucas & JT
2.3.
Hubert & Sebastian & JT
Hubert & Sebastian
Kasia & Sebastian & JT
4.
Hubert & Bram & JT
Hubert & JT
5.
Magda & Lucas & JT
the role of information avoidance in evaluating performance of minority managers ?
[experiment ongoing]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 3

9. New data
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 3

10. Gender board diversity across Europe throughout four decades
NEW DATA
Orbis: 2+ mln rms
ˆ(Quasi-)Administrativedata: 1986 - 2020 for 29 European countries
ˆMost of these rms are private (0.4% are listed)
ˆBoard members: one-tier and two-tier systems
Sample properties
ˆAverage # of years in the sample per rm: 7 (mean), 6 (median), 35 (max)
ˆAverage # of people per rm: 6+
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 4

11. Gender board diversity across Europe throughout four decades
NEW DATA
Orbis: 2+ mln rms
ˆ(Quasi-)Administrativedata: 1986 - 2020 for 29 European countries
ˆMost of these rms are private (0.4% are listed)
ˆBoard members: one-tier and two-tier systems
Sample properties
ˆAverage # of years in the sample per rm: 7 (mean), 6 (median), 35 (max)
ˆAverage # of people per rm: 6+
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 4

12. Gender board diversity across Europe throughout four decades
NEW DATA
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 5

13. Gender board diversity across Europe throughout four decades
NEW DATA
People in boardrooms
ˆGender attributionbased on linguistic rules across languages
ˆBoard assignmentextracted from word parsing
Manipulation checks
ˆGender assignment accurate in 99%
ˆBoard assignment: Orbis less reliable
ˆOne-tier and two-tier systems confused
ˆSome position names are not denite (e.g. oard member" or \president of the board")
ˆWe identify the roles: management + supervisory + other board members
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 6

14. Gender board diversity across Europe throughout four decades
NEW DATA
People in boardrooms
ˆGender attributionbased on linguistic rules across languages
ˆBoard assignmentextracted from word parsing
Manipulation checks
ˆGender assignment accurate in 99%
ˆBoard assignment: Orbis less reliable
ˆOne-tier and two-tier systems confused
ˆSome position names are not denite (e.g. oard member" or \president of the board")
ˆWe identify the roles: management + supervisory + other board members
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 6

15. Gender board diversity across Europe throughout four decades
NEW DATA
People in boardrooms
ˆGender attributionbased on linguistic rules across languages
ˆBoard assignmentextracted from word parsing
Manipulation checks
ˆGender assignment accurate in 99%
ˆBoard assignment: Orbis less reliable
ˆOne-tier and two-tier systems confused
ˆSome position names are not denite (e.g. oard member" or \president of the board")
ˆWe identify the roles: management + supervisory + other board members
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 6

16. Gender board diversity across Europe throughout four decades
NEW DATA
One interesting empirical observation: broad majority of rms has NO WOMEN in boardrooms
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 7

17. Gender board diversity across Europe throughout four decades
NEW DATA
Two published papers
ˆProduced database of measurers of GBD across countries & industries + over time
!Nature Scientic Data + Harvard Dataverse + GitHub
ˆEvolution of labor share in Poland
!a major Polish journal
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 8

18. Gender board diversity across Europe throughout four decades
NEW DATA
Two published papers
ˆProduced database of measurers of GBD across countries & industries + over time
!Nature Scientic Data + Harvard Dataverse + GitHub
ˆEvolution of labor share in Poland
!a major Polish journal
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 8

19. Work package: Corporate boards diversity
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 8

20. Paper: Gender board diversity spillovers
[submitted]
Do women help women? Mechanisms of diversity spillovers
ˆGender diversity: non-executive roles)executive roles
Matsa and Miller (2011, 2013)
ˆPublic rms = public scrutiny)reputation cost of no diversityˆIn public rms: helpless iftokenˆIn non-public rms: no public eye at all
What we do?
1.novel data)
2.novel gender identication
3.novel specication)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 9

21. Paper: Gender board diversity spillovers
[submitted]
Do women help women? Mechanisms of diversity spillovers
ˆGender diversity: non-executive roles)executive roles
Matsa and Miller (2011, 2013)
ˆPublic rms = public scrutiny)reputation cost of no diversityˆIn public rms: helpless iftokenˆIn non-public rms: no public eye at all
What we do?
1.novel data)
2.novel gender identication
3.novel specication)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 9

22. Paper: Gender board diversity spillovers
[submitted]
Do women help women? Mechanisms of diversity spillovers
ˆGender diversity: non-executive roles)executive roles
Matsa and Miller (2011, 2013)
ˆPublic rms = public scrutiny)reputation cost of no diversityˆIn public rms: helpless iftokenˆIn non-public rms: no public eye at all
What we do?
1.novel data)
2.novel gender identication
3.novel specication)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 9

23. Paper: Gender board diversity spillovers
[submitted]
Do women help women? Mechanisms of diversity spillovers
ˆGender diversity: non-executive roles)executive roles
Matsa and Miller (2011, 2013)
ˆPublic rms = public scrutiny)reputation cost of no diversityˆIn public rms: helpless iftokenˆIn non-public rms: no public eye at all
What we do?
1.novel data)
2.novel gender identication
3.novel specication)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 9

24. Paper: Gender board diversity spillovers
[submitted]
Do women help women? Mechanisms of diversity spillovers
ˆGender diversity: non-executive roles)executive roles
Matsa and Miller (2011, 2013)
ˆPublic rms = public scrutiny)reputation cost of no diversityˆIn public rms: helpless iftokenˆIn non-public rms: no public eye at all
What we do?
1.novel data)
2.novel gender identication
3.novel specication)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 9

25. Paper: Gender board diversity spillovers
[submitted]
Do women help women? Mechanisms of diversity spillovers
ˆGender diversity: non-executive roles)executive roles
Matsa and Miller (2011, 2013)
ˆPublic rms = public scrutiny)reputation cost of no diversityˆIn public rms: helpless iftokenˆIn non-public rms: no public eye at all
What we do?
1.novel data)
2.novel gender identication
3.novel specication)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 9

26. Paper: Gender board diversity spillovers
[submitted]
Samples used in gender board diversity spillover literature
Study # Firms / # obs. Public / PrivateCountries Sample
Harrigan (1981) 112 / 221 Public USA Fortune 500
Farrell and Hersch (2005)300 / 2972 Public USA Fortune 500 & Service 500
Elkinawy and Stater (2011)n.a. / 31,594 Public USA ExecuComp
Matsa and Miller (2011) 1,500 / 13,491 Public USA ExecuComp
Matsa and Miller (2013) 1,207/1,664 Both Nordic Orbis & Norwegian Register
? 3,053 / 57,632 Both Denmark Statistics Denmark
Kunze and Miller (2017) 4,000 / 744,531 Private Norway Statistics Norway & NHO survey
Smith and Parrotta (2018)3,625 / 47,125 Both Denmark Statistics Denmark & Experian
Guldiken et al. (2019) 184 / 747 Public USA BoardEx
Knippen et al. (2019) 1,699 / 14,325 Public USA BoardEx, COMPUSTAT
Bossler et al. (2020) 33,237 / 148,131 Both Germany IEB of IAB
Kirsch and Wrohlich (2020)200 / 1,400 Public Germany DIW Berlin
Bozhinov et al. (2021) 149 / 673 Public Germany German annual reports
Schoonjans et al. (2023) 3,353 / 27,486 Public 17 European Orbis
Our paper 1,940,368 / 11,370,748Both 29 European Orbis (GBDD)
Notes:the number of observations refers to rm-years, except for Elkinawy and Stater (2011),?and Kunze and Miller (2017), where the observations refer
to person-years.
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 10

27. Paper: Gender board diversity spillovers
[submitted]
Our contribution: board diversity spillovers
Disentangle two overlapping mechanisms
Public eye scrutiny { stock-listed companies vs private rms
SEPARATE it from tokenism
Matsa & Miller:woman
M
t= 0+i+woman
S
t1 +controlsi;t+i;t
Our approach:woman
M
t= 0+i+~woman
S
t1#no public eye
i
+controlsi;t+i;t
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 11

28. Paper: Gender board diversity spillovers
[submitted]
Our contribution: board diversity spillovers
Disentangle two overlapping mechanisms
Public eye scrutiny { stock-listed companies vs private rms
SEPARATE it from tokenism
Matsa & Miller:woman
M
t= 0+i+woman
S
t1 +controlsi;t+i;t
Our approach:woman
M
t= 0+i+~woman
S
t1#no public eye
i
+controlsi;t+i;t
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 11

29. Paper: Gender board diversity spillovers
[submitted]
Replication of Matsa & Miller (2011)
(1) (2) (3) (4)
Table 2 of Matsa and Miller (2011) Column (1) Column (3) Column (4) Column (5)
Panel A: BoD comprises of all board members not assigned to management (executive) board, broad BoD
Share of women in BoD in t-1 0.050*** 0.14*** 0.015 0.034***
(0.008) (0.014) (0.010) (0.004)
Share of women in management board in t-1 0.83***
(0.006)
Number of observations 49,423 49,172 49,423 45,115
Panel B: BoD consistsonlyof supervisory (non-executive) board members, strict BoD
Share of women in BoD in t-1 0.042*** 0.12*** 0.015 0.025***
(0.009) (0.012) (0.012) (0.004)
Share of women in management board in t-1 0.78***
(0.007)
Number of observations 29,026 28,840 29,026 27,607
Time FE Year Year Year Year
Firm FE - Industry Firm Industry
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 12

30. Paper: Gender board diversity spillovers
[submitted]
Gender board diversity spillovers: public (listed) dier from private (non-listed) rms
Woman in management board int (1) (2) (3) (4) (5) (6)
Panel A: BoD comprises of all board members not assigned to management (executive) board, broad BoD
Woman in BoD int1 () -0.015*** 0.16*** 0.16*** 0.14*** 0.12*** 0.093***
(-22.81) (19.99) (17.07) (15.06) (7.44) (5.95)
Private (non-listed) int() 0.041*** 0.050*** 0.024** 0.0044 0.028
(6.02) (5.49) (2.31) (0.20) (1.35)
Woman in BoD int1 () -0.18*** -0.19*** -0.18*** -0.17*** -0.15***
(-22.08) (-20.34) (-19.22) (-9.96) (-9.37)
% of rms w/ 1+ woman (private) 0.21 0.21 0.21 0.21 0.21 0.21
% of rms w/ 1+ woman (public) 0.29 0.29 0.29 0.29 0.29 0.29
# of observations 10,958,286 10,958,286 10,958,286 9,687,341 9,687,341 9,687,341
# of rms 1,822,687 1,822,687 1,822,687 1,710,905 1,710,905 1,710,905
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 13

31. Paper: Gender board diversity spillovers
[submitted]
Gender board diversity spillovers: public (listed) dier from private (non-listed) rms
ˆWe get very robust positive eect of women in public rms, and ...
ˆ... even more robustnegativeeect in private (non-listed) rms
ˆWe test for
ˆFirm xed eects, sector xed eects, time xed eects, country weights
ˆDegree of competitiveness in an industry (HHI)
ˆEliminating self-promotion
ˆAdjusting for the size of the boards
ˆThese results replicate for strict denition of supervisory roles and alternative operationalizations of
public eye.
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 14

32. Paper: Gender tokenism
[(almost) submitted]
Conict
ˆTokenism hypothesis
ˆDiversity spillover hypothesis
We provide
1.
bariers to more women in the boardroom:token
B
the rst woman entering boardrooms at all:token
F
2.
3.
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 15

33. Paper: Gender tokenism
[(almost) submitted]
Conict
ˆTokenism hypothesis
ˆDiversity spillover hypothesis
We provide
1.
bariers to more women in the boardroom:token
B
the rst woman entering boardrooms at all:token
F
2.
3.
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 15

34. Paper: Gender tokenism
[(almost) submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 16

35. Paper: Gender tokenism
[(almost) submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 17

36. Paper: Gender tokenism
[(almost) submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 18

37. Paper: Gender tokenism
[(almost) submitted]
Probability oftoken
X
= 1 in timet (1) (2) (3) (4) (5)
Panel A: no more than one woman int(relative to more women)
Women in boards int1 -0.050*** -0.12*** -0.13*** -0.13*** -0.15***
(-76.32) (-176.96) (-100.61) (-100.61) (-71.64)
% of rms withtoken
B
t
= 1 0.76 0.76 0.76 0.76 0.73
# of observations 21,576,163 17,866,597 17,866,597 17,866,597 2,112,415
# of rms 3,306,271 2,988,771 2,988,771 2,988,771 336,414
Panel B: one woman (relative to no women)
Women in board in t-1 0.64*** 0.61*** 0.60*** 0.62*** 0.64***
(1245.94) (1108.68) (561.09) (452.20) (452.20)
% of rms withtoken
F
t
= 1 0.46 0.46 0.46 0.7 0.53
# of observations 36,017,902 29,748,286 29,748,286 15,528,035 2,907,858
# of rms 5,368,752 4,863,848 4,863,848 2,566,093 338,583
Firm xed eects YES YES YES YES YES
Sector xed eects - YES YES YES YES
Time xed eects - YES YES YES YES
Log of number of people in C-suite - YES YES YES YES
HHI - YES YES YES YES
Country weights - - YES YES YES
Without never-takers - - - YES YES
Without always-takers - - - - YES
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 19

38. Paper: Gender tokenism
[(almost) submitted]
Probability oftoken
X
= 1 in timet (1) (2) (3) (4) (5)
Panel C: no more than one woman (relative to more women)
token
B
in t-1 0.51*** 0.40*** 0.39*** 0.39*** 0.45***
(786.66) (599.70) (311.40) (311.40) (182.63)
% of rms withtoken
B
t
= 1 0.76 0.76 0.76 0.76 0.72
# of observations 21,089,177 17,449,241 17,449,241 17,449,241 1,918,100
# of rms 3,155,973 2,859,083 2,859,083 2,859,083 272,323
Panel D: one woman (relative to no women), specication (??)
token
F
in t-1 0.66*** 0.63*** 0.61*** 0.63*** 0.65***
(1305.92) (1147.57) (567.08) (454.76) (448.12)
% of rms withtoken
F
t
= 1 0.45 0.45 0.53 0.69 0.52
# of observations 35,561,600 29,383,412 29,383,412 15,242,353 2,831,960
# of rms 5,328,305 4,835,640 4,835,640 2,543,792 335,514
Firm xed eects YES YES YES YES YES
Sector xed eects - YES YES YES YES
Time xed eects - YES YES YES YES
Log of number of people in C-suite - YES YES YES YES
HHI - YES YES YES YES
Country weights - - YES YES YES
Without never-takers - - - YES YES
Without always-takers - - - - YES
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 20

39. Paper: Firm performance and gender board diversity
[submitted]
Motivation
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 21

40. Paper: Firm performance and gender board diversity
[in progress]
Methods
log(yi;t) =0+GBDi;t+klog(ki;t) + +llog(li;t) +i+t+"i;t
zi;t=share
i;t
0gk;t=
#women
i;t
0
#managers
i;t
0

#women
k;t
#managers
k;t
#women
k;t
0
#managers
k;t
0
GBDi;t=0+1zi;t+klog(total assetsi;t) +llog(employmenti;t) +i+t+i;t
log(yi;t) =0+\GBDi;t+klog(total assetsi;t) +llog(employmenti;t) +i+t+"i;t
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 22

41. Paper: Firm performance and gender board diversity
[in progress]
Methods
log(yi;t) =0+GBDi;t+klog(ki;t) + +llog(li;t) +i+t+"i;t
zi;t=share
i;t
0gk;t=
#women
i;t
0
#managers
i;t
0

#women
k;t
#managers
k;t
#women
k;t
0
#managers
k;t
0
GBDi;t=0+1zi;t+klog(total assetsi;t) +llog(employmenti;t) +i+t+i;t
log(yi;t) =0+\GBDi;t+klog(total assetsi;t) +llog(employmenti;t) +i+t+"i;t
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 22

42. Paper: Firm performance and gender board diversity
[in progress]
Methods
log(yi;t) =0+GBDi;t+klog(ki;t) + +llog(li;t) +i+t+"i;t
zi;t=share
i;t
0gk;t=
#women
i;t
0
#managers
i;t
0

#women
k;t
#managers
k;t
#women
k;t
0
#managers
k;t
0
GBDi;t=0+1zi;t+klog(total assetsi;t) +llog(employmenti;t) +i+t+i;t
log(yi;t) =0+\GBDi;t+klog(total assetsi;t) +llog(employmenti;t) +i+t+"i;t
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 22

43. Paper: Firm performance and gender board diversity
[in progress]
(1) (2) (3) (4) (5) (6) (7) (8) (9)
OLS IV IV OLS IV IV OLS IV IV
Dependent variable: log(operating revenue)
Gender Board Diversity 0.005 0.283*** 0.154** 0.005 0.120 -0.077 -0.008 0.253** 0.312**
(0.005) (0.051) (0.075) (0.005) (0.089) (0.135) (0.012) (0.111) (0.139)
log(total assets) 0.527*** 0.527*** 0.560*** 0.523*** 0.523*** 0.556*** 0.464*** 0.465*** 0.509***
(0.003) (0.003) (0.005) (0.003) (0.003) (0.005) (0.009) (0.008) (0.012)
log(employment) 0.447*** 0.446*** 0.406*** 0.446*** 0.446*** 0.401*** 0.476*** 0.475*** 0.413***
(0.003) (0.003) (0.005) (0.003) (0.003) (0.005) (0.009) (0.008) (0.012)
Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year trend Yes Yes Yes Yes Yes Yes Yes Yes Yes
Country weights Yes Yes Yes Yes Yes Yes Yes Yes Yes
Board denition all all all all all all MB MB MB
NACE level 2 digits 2 digits 2 digits 4 digits 4 digits 4 digits 2 digits 2 digits 2 digits
Observations 2,198,743 2,198,743 2,198,743 2,125,301 2,125,301 2,125,301 369,921 369,921 369,921
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 23

44. Paper: Firm performance and gender board diversity
[in progress]
(1) (2) (3) (4) (5) (6) (7) (8) (9)
OLS IV IV OLS IV IV OLS IV IV
Dependent variable: GBD (rst stage)
iv -0.61 -0.59 -0.18 -0.16 -0.33 -0.32
[0.07] [0.08] [0.02] [0.02] [0.04] [0.04]
iv
2
0.056 0.052 0.009 0.007 0.005 0.004
[0.018] [0.017] [0.002] [0.002] [0.001] [0.001]
Instruments F test 143.54 80.19 135.70 65.61 28.85 30.98
[0.00] [0.00] [0.00] [0.00] [0.00] [0.00]
Overid. test 1.51 1.55 0.02 0.31 2.14 1.77
[0.22] [0.21] [0.89] [0.58] [0.14] [0.18]
Endogeneity test 28.55 3.44 1.70 0.74 5.05 4.16
[0.00] [0.06] [0.19] [0.39] [0.02] [0.04]
First Stage F stat. 1752.22 1345.50 1496.42 1146.70 354.37 298.28
Firm FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year trend Yes Yes Yes Yes Yes Yes Yes Yes Yes
Country weights Yes Yes Yes Yes Yes Yes Yes Yes Yes
Board denition all all all all all all MB MB MB
NACE level 2 digits 2 digits 2 digits 4 digits 4 digits 4 digits 2 digits 2 digits 2 digits
Observations 2,198,743 2,198,743 2,198,743 2,125,301 2,125,301 2,125,301 369,921 369,921 369,921
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 24

45. Paper: Firm performance and gender board diversity
[in progress]
Positive and moderate eect of GBD on rm performance
These results prove robust
ˆWe expand the sample to include nevertakers: IV still works and the results replicate
ˆWe study MB alone
ˆThe only thing that fails is too narrowly dened sector for IV
What next?
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 25

46. Paper: Firm performance and gender board diversity
[in progress]
Positive and moderate eect of GBD on rm performance
These results prove robust
ˆWe expand the sample to include nevertakers: IV still works and the results replicate
ˆWe study MB alone
ˆThe only thing that fails is too narrowly dened sector for IV
What next?
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 25

47. Paper: Firm performance and gender board diversity
[in progress]
Positive and moderate eect of GBD on rm performance
These results prove robust
ˆWe expand the sample to include nevertakers: IV still works and the results replicate
ˆWe study MB alone
ˆThe only thing that fails is too narrowly dened sector for IV
What next?
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 25

48. Work package: Inequality as a driver of enterpreneurship
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 25

49. Paper: Gender wage inequality and women's self-employment
[submitted]
Long standing debate: disadvantaged workers and self-employment
ˆmixed evidence, rarely direct
ˆaspirations vs. lack of alternatives (necessity)
Methodological constraints
ˆusually cannot observe that someone was \discriminated against"...
ˆ... prior to becoming self-employed
Story behind \discrimination" { can arguments work?
ˆno: clients' taste + co-workers' taste
ˆyes: statistical
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 26

50. Paper: Gender wage inequality and women's self-employment
[submitted]
Long standing debate: disadvantaged workers and self-employment
ˆmixed evidence, rarely direct
ˆaspirations vs. lack of alternatives (necessity)
Methodological constraints
ˆusually cannot observe that someone was \discriminated against"...
ˆ... prior to becoming self-employed
Story behind \discrimination" { can arguments work?
ˆno: clients' taste + co-workers' taste
ˆyes: statistical
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 26

51. Paper: Gender wage inequality and women's self-employment
[submitted]
Long standing debate: disadvantaged workers and self-employment
ˆmixed evidence, rarely direct
ˆaspirations vs. lack of alternatives (necessity)
Methodological constraints
ˆusually cannot observe that someone was \discriminated against"...
ˆ... prior to becoming self-employed
Story behind \discrimination" { can arguments work?
ˆno: clients' taste + co-workers' taste
ˆyes: statistical
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 26

52. Paper: Gender wage inequality and women's self-employment
[submitted]
Our contribution
1. genderwage gaps (GWG)
2.
Intuitions / hypotheses: LM inequality amplies prevalence of (necessity) self-employment per seOur take
ˆPUSH: Discrimination makes self-employment potentially appealing
ˆPULL: Discrimination begets discrimination!access to nance and business-related services
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 27

53. Paper: Gender wage inequality and women's self-employment
[submitted]
Our contribution
1. genderwage gaps (GWG)
2.
Intuitions / hypotheses: LM inequality amplies prevalence of (necessity) self-employment per seOur take
ˆPUSH: Discrimination makes self-employment potentially appealing
ˆPULL: Discrimination begets discrimination!access to nance and business-related services
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 27

54. Paper: Gender wage inequality and women's self-employment
[submitted]
Our contribution
1. genderwage gaps (GWG)
2.
Intuitions / hypotheses: LM inequality amplies prevalence of (necessity) self-employment per seOur take
ˆPUSH: Discrimination makes self-employment potentially appealing
ˆPULL: Discrimination begets discrimination!access to nance and business-related services
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 27

55. Paper: Gender wage inequality and women's self-employment
[submitted]
Data
1.
2.
ˆ1600+ individual databases (HBS, LFS, census, LSMS & more)
ˆHarmonize for individual characteristics: age, education, family structure, industry, occupation, etc.
ˆObtain measures of gender wage gaps withincells: age, education (by country and time), adjusting for
occupation, industry, etc.
Methods
1.
2.
3. counterfactuallevels of self-employment for women
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 28

56. Paper: Gender wage inequality and women's self-employment
[submitted]
Data
1.
2.
ˆ1600+ individual databases (HBS, LFS, census, LSMS & more)
ˆHarmonize for individual characteristics: age, education, family structure, industry, occupation, etc.
ˆObtain measures of gender wage gaps withincells: age, education (by country and time), adjusting for
occupation, industry, etc.
Methods
1.
2.
3. counterfactuallevels of self-employment for women
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 28

57. Paper: Gender wage inequality and women's self-employment
[submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 29

58. Paper: Gender wage inequality and women's self-employment
[submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 29

59. Paper: Gender wage inequality and women's self-employment
[submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 30

60. Paper: Gender wage inequality and women's self-employment
[submitted]
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 30

61. Paper: The eects of gender board diversity on gender inequality
[submitted]
ˆA blossoming literature linking managers' gender and inequality within rms
ˆExisting results are mixed
ˆNo evidence of spill-overs at the top of the rm
Abendroth et al. (2017), Bertrand et al. (2019), Maida and Weber (2022)
ˆPositive eects from managers to employees, but negative within ranks
Hensvik (2014), Kunze and Miller (2017)
ˆMale and female wages react dierently
Cardoso and Winter-Ebmer (2010), Flabbi et al. (2019)
ˆCurrent results have limited external validity
ˆResults are country specic
ˆRestricted to stock listed rms
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 31

62. Paper: The eects of gender board diversity on gender inequality
[submitted]
ˆA blossoming literature linking managers' gender and inequality within rms
ˆExisting results are mixed
ˆNo evidence of spill-overs at the top of the rm
Abendroth et al. (2017), Bertrand et al. (2019), Maida and Weber (2022)
ˆPositive eects from managers to employees, but negative within ranks
Hensvik (2014), Kunze and Miller (2017)
ˆMale and female wages react dierently
Cardoso and Winter-Ebmer (2010), Flabbi et al. (2019)
ˆCurrent results have limited external validity
ˆResults are country specic
ˆRestricted to stock listed rms
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 31

63. Paper: The eects of gender board diversity on gender inequality
[submitted]
Contribution: We explore the link between gender board diversity and adjusted gender wage gaps
Proposed research questions
1.
2.
We link two databases at the industrycountryyear level (cells)
1. !GBDD
2. !EU-SES (linked employee-employer data on earnings)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 32

64. Paper: The eects of gender board diversity on gender inequality
[submitted]
Contribution: We explore the link between gender board diversity and adjusted gender wage gaps
Proposed research questions
1.
2.
We link two databases at the industrycountryyear level (cells)
1. !GBDD
2. !EU-SES (linked employee-employer data on earnings)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 32

65. Paper: The eects of gender board diversity on gender inequality
[submitted]
Contribution: We explore the link between gender board diversity and adjusted gender wage gaps
Proposed research questions
1.
2.
We link two databases at the industrycountryyear level (cells)
1. !GBDD
2. !EU-SES (linked employee-employer data on earnings)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 32

66. Paper: The eects of gender board diversity on gender inequality
[submitted]
Theory ambiguous on the link between female managers and gender inequality
1.
ˆAwareness of discriminatory practices(Hultin and Szulkin 1999, Cohen and Human 2007)
ˆRole model(Linehan and Scullion 2008, Zimmermann 2022)
ˆBetter recognition of female talent(Tsui and O'Reilly III 1989, Ridgeway 1997)
2.
ˆQueen-bee syndrome(Staines et al. 1974, Derks et al. 2016)
ˆAnother cog in the machine(Jia and Zhang 2013, Torchia et al. 2011)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 33

67. Paper: The eects of gender board diversity on gender inequality
[submitted]
Theory ambiguous on the link between female managers and gender inequality
1.
ˆAwareness of discriminatory practices(Hultin and Szulkin 1999, Cohen and Human 2007)
ˆRole model(Linehan and Scullion 2008, Zimmermann 2022)
ˆBetter recognition of female talent(Tsui and O'Reilly III 1989, Ridgeway 1997)
2.
ˆQueen-bee syndrome(Staines et al. 1974, Derks et al. 2016)
ˆAnother cog in the machine(Jia and Zhang 2013, Torchia et al. 2011)
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 33

68. Paper: The eects of gender board diversity on gender inequality
[submitted]
Mean SD P10 P50 P90
Share of rms 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 GBDD measures
N. of rms 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
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 GWG measures
N. female workersi;c;t 13895 33864 625 3820 28542
N. male workersi;c;t 13345 22676 1211 5492 35705
Observations 1284
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 34

69. Paper: The eects of gender board diversity on gender inequality
[submitted]
AGWGi;c;t=0+
OLS
1GBDi;c;t+c+t+s+i;c;t
AGWGi;c;tis the adjusted gender wage gap withinindustry,country and period (t)
GBD is a measure of gender board diversity:
c,t, andsare country, year, and sector f.e.
The IV specication is:
AGWGi;c;t=0+
IV
1
[GBDi;c;t+c+t+s+i;c;t
GBDi;c;t=0+1HH. cons.i;c;t+c+t+s+i;c;t
Instrument: proximity to nal consumer!Share of household consumption on industryioutput.
Exclusion restriction:uncorrelated with adjusted gender wage inequality!once we account for dierences
in characteristics
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 35

70. Paper: The eects of gender board diversity on gender inequality
[submitted]
AGWGi;c;t=0+
OLS
1GBDi;c;t+c+t+s+i;c;t
AGWGi;c;tis the adjusted gender wage gap withinindustry,country and period (t)
GBD is a measure of gender board diversity:
c,t, andsare country, year, and sector f.e.
The IV specication is:
AGWGi;c;t=0+
IV
1
[GBDi;c;t+c+t+s+i;c;t
GBDi;c;t=0+1HH. cons.i;c;t+c+t+s+i;c;t
Instrument: proximity to nal consumer!Share of household consumption on industryioutput.
Exclusion restriction:uncorrelated with adjusted gender wage inequality!once we account for dierences
in characteristics
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 35

71. Paper: The eects of gender board diversity on gender inequality
[submitted]
Average share of women Share of rms 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 rms with women on board (0.15) reduces AGWG by 0.0387 percentage points (0:2850:15))
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 36

72. Paper: The eects of gender board diversity on gender inequality
[submitted]-.1
-.05
0
.05
b
OLS
and 90% CI
At least
one woman
One woman Two women Three or more
women
EARHART - NCN GRIEG - UW & FAME jGRAPE & NHH 37

73. References
[
noframenumbering]
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