do female executives make a difference? the impact of ... · i firm-level heterogeneity i firms...
TRANSCRIPT
-
Do Female Executives Make a Difference? TheImpact of Female Leadership on Gender Gaps
and Firm Performance
presentation: Andrea Morocoauthors: Luca Flabbi, Mario Macis, and Fabiano Schivardi
OECD, February 20, 2015
-
Motivation / Literature
I Executives’ characteristics matter for firm performanceBertrand and Schoar (2003); Malmendier and Tate (2005); Kaplan, Klebanov,Sorensen (2012); Bennedsen, Perez-Gonzales, Wolfenzon (2012); Lazear,Shaw, Stanton (2012).
I Gender potentially relevant - underrepresentation of females atthe top might impact efficiencyUS: ExecuComp data [Bertrand and Hallock (2001); Wolfers (2006); Gayle,Golan, Miller (2011)] Europe: Ahern and Dittmar (2012); Matsa and Miller(2013).
I Recent literature on the impact of female leadershipAlbanesi and Olivetti (2009), Bertrand et al. (2014), Cardoso and Winter-Ebner(2010), Dezso and Ross (2012), Gagliarducci and Paserman (2014), Matsaand Miller (2012); Kunze and Miller (2014).
Introduction
-
Motivation / Literature
I Executives’ characteristics matter for firm performanceBertrand and Schoar (2003); Malmendier and Tate (2005); Kaplan, Klebanov,Sorensen (2012); Bennedsen, Perez-Gonzales, Wolfenzon (2012); Lazear,Shaw, Stanton (2012).
I Gender potentially relevant - underrepresentation of females atthe top might impact efficiencyUS: ExecuComp data [Bertrand and Hallock (2001); Wolfers (2006); Gayle,Golan, Miller (2011)] Europe: Ahern and Dittmar (2012); Matsa and Miller(2013).
I Recent literature on the impact of female leadershipAlbanesi and Olivetti (2009), Bertrand et al. (2014), Cardoso and Winter-Ebner(2010), Dezso and Ross (2012), Gagliarducci and Paserman (2014), Matsaand Miller (2012); Kunze and Miller (2014).
Introduction
-
Motivation / Literature
I Executives’ characteristics matter for firm performanceBertrand and Schoar (2003); Malmendier and Tate (2005); Kaplan, Klebanov,Sorensen (2012); Bennedsen, Perez-Gonzales, Wolfenzon (2012); Lazear,Shaw, Stanton (2012).
I Gender potentially relevant - underrepresentation of females atthe top might impact efficiencyUS: ExecuComp data [Bertrand and Hallock (2001); Wolfers (2006); Gayle,Golan, Miller (2011)] Europe: Ahern and Dittmar (2012); Matsa and Miller(2013).
I Recent literature on the impact of female leadershipAlbanesi and Olivetti (2009), Bertrand et al. (2014), Cardoso and Winter-Ebner(2010), Dezso and Ross (2012), Gagliarducci and Paserman (2014), Matsaand Miller (2012); Kunze and Miller (2014).
Introduction
-
This paper
I Study the impact of female leadership on gender gaps and firmperformance using a large, employer-employee dataset from Italy
I Motivate analysis within the context of a theoretical frameworkwith statistical discrimination and job assignment
I Relevant for gender quotas in corporate boards
I France: companies with 500+ employees or e50m+ assets:20% by 2013, 40% by 2016
Introduction
-
Theoretical framework
I Based on a statistical discrimination model with job assignment
I Highlights a mechanism through which women executives havean impact on wage gaps and firm performance
I Generates testable predictions
I Enables us to evaluate cost of underrepresentation of women intop positions
Theory
-
A statistical discrimination model
Basic model: Phelps 1972
I Ability q ∼ N(µ, σ)I Managers observe a signal s = q + �, � ∼ N(0, σ�g), g = m or fI Competitive market: wage = expected productivity
I Wages increasing in signal:
E(q|s) = (1 − αg)µ+ αgs
where αg = σ2
σ2�g+σ2
Theory
-
A statistical discrimination model
Basic model: Phelps 1972
I Ability q ∼ N(µ, σ)I Managers observe a signal s = q + �, � ∼ N(0, σ�g), g = m or fI Competitive market: wage = expected productivity
I Wages increasing in signal:
E(q|s) = (1 − αg)µ+ αgs
where αg = σ2
σ2�g+σ2
Theory
-
Wages in the basic model
wage = (1 − α) ·mean prod. + α · signal
signal s
��
���������
��
precise signal
noisy signal
����
����
����
�
µ
I No average inequality
I No productivity effects
Theory
-
Wages in the basic model
wage = (1 − α) ·mean prod. + α · signal
signal s
��
���������
��
precise signal
noisy signal
����
����
����
�
µ
I No average inequality
I No productivity effects
Theory
-
Wages in the basic model
wage = (1 − α) ·mean prod. + α · signal
signal s
��
���������
��
precise signal
noisy signal
����
����
����
�
µ
I No average inequality
I No productivity effects
Theory
-
Wages in the basic model
wage = (1 − α) ·mean prod. + α · signal
signal s
��
���������
��
precise signal
noisy signal
����
����
����
�
µ
I No average inequality
I No productivity effects
Theory
-
Our Model: Environment
I Two types of Executives: able to extract signal differentlyI WomenI Men
I Two types of Jobs: requiring different skillsI SimpleI Complex
I Executives decideI WagesI Job assignments
I ASSUMPTION: Executives better at extracting signals fromworkers of their own gender.Lang (1986); Cornell and Welch (1996); Morgan and Vardy (2009); Baguesand Perez-Villadoniga (2013).
Theory
-
A simple extension: 2 jobs, employer’s gender
I Productivity:q ≤ q q > q
Simple job −l l l = lowSkilled job −h h h = high
I Female CEOs receive more precise signals from females
Female wages
Theory
-
A simple extension: 2 jobs, employer’s gender
I Productivity:q ≤ q q > q
Simple job −l l l = lowSkilled job −h h h = high
I Female CEOs receive more precise signals from females
Female wages
Signal
Wag
es
−4.0 2.0 3.5 10.0
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0 σε = 1
Theory
-
A simple extension: 2 jobs, employer’s gender
I Productivity:q ≤ q q > q
Simple job −l l l = lowSkilled job −h h h = high
I Female CEOs receive more precise signals from females
Female wages
Theory
-
A simple extension: 2 jobs, employer’s gender
I Productivity:q ≤ q q > q
Simple job −l l l = lowSkilled job −h h h = high
I Female CEOs receive more precise signals from females
Female wages
Signal
Wag
es
−4.0 2.0 3.5 10.0
−1.0
−0.5
0.0
0.5
1.0
1.5
2.0 σε = 1σε = 2
Female CEO
Male CEO
Theory
-
Theoretical female wage distributions
Female wages
Den
sity
Male CEOsFemale CEOs
−1 0 1 2
0.0
0.5
1.0
1.5
Theory
-
Empirical implications
Empirical implication 1I Female workers at the top of the distribution earn more if
employed by female CEOs. Female workers at the bottom of thedistribution earn less if employed by female CEO
I Opposite for wages of male workers
Empirical implication 2I The productivity of firms with female CEOs is higher, the higher
the share of female workers
Theory
-
Empirical implications
Empirical implication 1I Female workers at the top of the distribution earn more if
employed by female CEOs. Female workers at the bottom of thedistribution earn less if employed by female CEO
I Opposite for wages of male workers
Empirical implication 2I The productivity of firms with female CEOs is higher, the higher
the share of female workers
Theory
-
Empirical implications
Empirical implication 1I Female workers at the top of the distribution earn more if
employed by female CEOs. Female workers at the bottom of thedistribution earn less if employed by female CEO
I Opposite for wages of male workers
Empirical implication 2I The productivity of firms with female CEOs is higher, the higher
the share of female workers
Theory
-
Data Sources
I INVIND: Representative sample of ~1,000 Italian manufacturingfirms (50+ employees) collected by the Bank of Italy over1980-1997
I INPS: Social Security records of all workers ever employed atany INVIND firm (follows also workers leaving INVIND firms)
I CADS: Balance Sheet information INVIND firms 1982-1997
I Our core sample: balanced panel 1988-1997
I Observations:Firm-year Firms Years
Unbalanced 5,590 795 10Balanced 2,340 234 10
18.9 million worker-year observations
Empirical Analysis
-
Features of the data
I Longitudinal matched employer-employee data over 15 yearsI Nice Properties:
I We observe the entire labor force at each INVIND firmI We observe all the workers’ transitions through INVIND and
non-INVIND firmsI We observe INVIND firms’ balance sheet informationI No measurement error in definition of executiveI Administrative Data vetted by the Bank of Italy
I Limitations:I We do not observe ranks within executive categoryI Very basic individual-level controlsI INVIND sample limited to manufacturing sector
Empirical Analysis
-
Descriptive statistics
Female under-representation by rank level
INPS-INVIND ExecuComp*Italy 1997 US 1992/2006
Women Obs. Women Obs.% # % #
CEOs 1.86 590 1.52 30,942Executives 4.04 7,723 4.62 120,069
* From Gayle, Golan and Miller (2009)
Empirical Analysis
-
Female executives, Italy 1980-1997
Shares: Female execs Firms at least one female execFirms with female CEO
0
0.05
0.1
0.15
0.2
0.25
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Share Execs that are Female Share firms with female CEO Share firms with at least one female exec (right scale)
Empirical Analysis
-
Descriptive statistics: female under-representation
INVIND-INPS INVIND-INPS-CADS
Unbalanced panel Balanced panelMean Std.Dev. Mean Std.Dev. Mean Std.Dev.
% Non-prod. wrk 31.3 29.8 (17.7) 30.0 (17.3)% Executives 2.2 2.5 (1.7) 2.6 (1.8)
% Females 21.1 26.2 (20.9) 25.8 (20.1)% Fem. execs. 2.5 3.3 (10.3) 3.4 (10.1)% Female CEO 2.1 1.8
Firm size (empl.) 675.0 (2,628.6) 704.2 (1,306.9)N. Observations 18,664,304 5,029 2,340
N. Firms 448,284 795 234N. Workers 1,724,609
Empirical Analysis
-
Firms with male and female CEOs
Male CEO Female CEOMean St.Dev. Mean St.Dev.
CEO’s age 49.5 (7.1) 46.6 (7.1)CEO’s tenure 4.4 (3.7) 4.0 (2.8)
CEO’s pay 165,238 (130,560) 115,936 (54,030)
% Non-prod. workers 30.0 (17.8) 22.2 (13.1)% Executives 2.5 91.7) 2.4 (1.4)
% Females 25.9 (20.7) 37.2 (27.0)% Female executives 2.4 (6.9) 46.8 (29.5)
N. Observations 4,923 106N. Firms 788 33
Empirical Analysis
-
Firms with male and female CEOs - continued
Male CEO Female CEOMean St.Dev. Mean St.Dev.
Firm size (employment) 683.7 92,655.4) 270.3 (409.9)Age (about the same)
Tenure (about the same)Wage (earnings/week) 401.6 (86.0) 341.3 (61.7)
Wage (males) 430.6 (92.8) 369.4 (64.2)Wage (females) 343.3 (66.2) 345.4 (97.1)
Sales per worker (ln) 4.9 (0.6) 4.7 (0.6)Value added per worker (ln) 3.8 (0.4) 3.6 (0.4)
TFP 2.5 (0.5 ) 2.4 (0.5)
N. Observations 4,923 106N. Firms 788 33
Empirical Analysis
-
Effect of female leadership: Identification challenges
I Firm-level heterogeneityI firms with male and female CEOs may be different
I Workforce-level heterogeneityI the labor force composition might be different at male- and
female-led firms
I Executive/CEO abilityI CEO and executives skills may differ by gender
We can control for:
I firm fixed effects (data is a panel)
I time-varying firm characteristics and unobserved workers and CEOability (matched employer-employee data)
Empirical Analysis
-
Effect of female leadership: Identification challenges
I Firm-level heterogeneityI firms with male and female CEOs may be different
I Workforce-level heterogeneityI the labor force composition might be different at male- and
female-led firms
I Executive/CEO abilityI CEO and executives skills may differ by gender
We can control for:
I firm fixed effects (data is a panel)
I time-varying firm characteristics and unobserved workers and CEOability (matched employer-employee data)
Empirical Analysis
-
Effect of female leadership: Identification challenges
I Firm-level heterogeneityI firms with male and female CEOs may be different
I Workforce-level heterogeneityI the labor force composition might be different at male- and
female-led firms
I Executive/CEO abilityI CEO and executives skills may differ by gender
We can control for:
I firm fixed effects (data is a panel)
I time-varying firm characteristics and unobserved workers and CEOability (matched employer-employee data)
Empirical Analysis
-
Effect of female leadership: Identification challenges
I Firm-level heterogeneityI firms with male and female CEOs may be different
I Workforce-level heterogeneityI the labor force composition might be different at male- and
female-led firms
I Executive/CEO abilityI CEO and executives skills may differ by gender
We can control for:
I firm fixed effects (data is a panel)
I time-varying firm characteristics and unobserved workers and CEOability (matched employer-employee data)
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
I yjt = Firm-level dependent var of interest:I Moments of workers’ wage distributionI Measures of firm performance
I FLEADjt = Female Leadership Measures:I Female CEO dummyI Proportion of female executives
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
I yjt = Firm-level dependent var of interest:I Moments of workers’ wage distributionI Measures of firm performance
I FLEADjt = Female Leadership Measures:I Female CEO dummyI Proportion of female executives
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
I yjt = Firm-level dependent var of interest:I Moments of workers’ wage distributionI Measures of firm performance
I FLEADjt = Female Leadership Measures:I Female CEO dummyI Proportion of female executives
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
Controls for firm heterogeneity:I FIRMjt = observed: size, industry, regionI λj = unobserved: firm fixed effects
I WORKjt = Workforce characteristics aggregated at firm-yearlevel:I Observed: age, tenure, occupation distribution, fraction femaleI Unobserved: average of workers’ fixed-effect from 2-way F.E.
regression
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
Controls for firm heterogeneity:I FIRMjt = observed: size, industry, regionI λj = unobserved: firm fixed effectsI WORKjt = Workforce characteristics aggregated at firm-year
level:I Observed: age, tenure, occupation distribution, fraction femaleI Unobserved: average of workers’ fixed-effect from 2-way F.E.
regression
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
Controls for Executives heterogeneity: EXECjt =I Observable: age, tenure as CEO or executiveI Unobservable: individual fixed-effect from 2-way F.E. regression
Controls for Time effects:I ηt = year dummiesI τt(j) = industry-specific time trends
Empirical Analysis
-
Empirical Specification
Unit of observation: firm j in year t :
Main equation
yjt = FLEADjtβ+ FIRM′jtγ+WORK′jtδ+EXEC
′jtχ+ λj + ηt + τt(j)t + εjt
Controls for Executives heterogeneity: EXECjt =I Observable: age, tenure as CEO or executiveI Unobservable: individual fixed-effect from 2-way F.E. regressionControls for Time effects:I ηt = year dummiesI τt(j) = industry-specific time trends
Empirical Analysis
-
2-way fixed effects regression
We get unobserved workers and CEO ability αi from:
wit = X′itβ+ ηt + αi + ψj(i,t) + ζit .
(Abowd - Kramarz - Margolis 1999)I X: age, tenure, non-production workers’ dummy, executives
dummy, full set of interactions with gender, year f.e.
I About 70% have more than one employer in 1980-1997I 8 -15 % of workers change employer from one year to the nextI 99% connected groupI We check for exogenous mobility (Card-Heining-Kline 2013)
I Sizable (and symmetric) wage changes for movers betweenquartiles of the firm fixed effects distribution; small or no changesfor movers within the same quartile
I Match fixed effects: not much improvement in model fitI Past residuals do not predict quality of subsequent firm
Empirical Analysis
-
2-way fixed effects regression
We get unobserved workers and CEO ability αi from:
wit = X′itβ+ ηt + αi + ψj(i,t) + ζit .
(Abowd - Kramarz - Margolis 1999)I X: age, tenure, non-production workers’ dummy, executives
dummy, full set of interactions with gender, year f.e.I About 70% have more than one employer in 1980-1997I 8 -15 % of workers change employer from one year to the nextI 99% connected group
I We check for exogenous mobility (Card-Heining-Kline 2013)I Sizable (and symmetric) wage changes for movers between
quartiles of the firm fixed effects distribution; small or no changesfor movers within the same quartile
I Match fixed effects: not much improvement in model fitI Past residuals do not predict quality of subsequent firm
Empirical Analysis
-
2-way fixed effects regression
We get unobserved workers and CEO ability αi from:
wit = X′itβ+ ηt + αi + ψj(i,t) + ζit .
(Abowd - Kramarz - Margolis 1999)I X: age, tenure, non-production workers’ dummy, executives
dummy, full set of interactions with gender, year f.e.I About 70% have more than one employer in 1980-1997I 8 -15 % of workers change employer from one year to the nextI 99% connected groupI We check for exogenous mobility (Card-Heining-Kline 2013)
I Sizable (and symmetric) wage changes for movers betweenquartiles of the firm fixed effects distribution; small or no changesfor movers within the same quartile
I Match fixed effects: not much improvement in model fitI Past residuals do not predict quality of subsequent firm
Empirical Analysis
-
Specifications we run
Benchmark:I Firms: balanced panelI Workers: only those hired before CEO appointmentI Measure of female leadership: CEO
RobustnessI Firms: full (unbalanced) panelI Workers: “stayers” and “movers”I Leadership: fraction of female workersI “New CEO” controlI Without controls for unobserved workers and CEO heterogeneity
Empirical Analysis
-
Specifications we run
Benchmark:I Firms: balanced panelI Workers: only those hired before CEO appointmentI Measure of female leadership: CEO
RobustnessI Firms: full (unbalanced) panelI Workers: “stayers” and “movers”I Leadership: fraction of female workersI “New CEO” controlI Without controls for unobserved workers and CEO heterogeneity
Empirical Analysis
-
Results: Wages - point estimates
Coefficients of female CEO dummy on average wages, by quantile
Quantiles
Coe
ffic
ien
t
1 2 3 4
−0.0
30.
000.
030.
10
Female wages
Results
-
Results: Wages - point estimates
Coefficients of female CEO dummy on average wages, by quantile
Quantiles
Coe
ffic
ien
t
1 2 3 4
−0.0
30.
000.
030.
10
Female wagesMale wages
Results
-
Women’s wages - Coeffs. on Female CEO dummy
DependentVariable→
Wage standarddeviation
Average wagesBelow median Above median
Coefficient 0.475
-0.030 0.078
St. Error (0.122)
(0.022) (0.028)
1-tail P-value 0.000
0.090 0.003
(standard errors “clustered” at the firm level)
Results
-
Women’s wages - Coeffs. on Female CEO dummy
DependentVariable→
Wage standarddeviation
Average wagesBelow median Above median
Coefficient 0.475 -0.030 0.078St. Error (0.122) (0.022) (0.028)1-tail P-value 0.000 0.090 0.003
(standard errors “clustered” at the firm level)
Results
-
Women’s wages - Coeffs. on Female CEO dummy
DependentVariable→
Wage standarddeviation
Average wagesBelow median Above median
(a) BenchmarkCoefficient 0.475 -0.030 0.078
1-tail P-value 0.000 0.090 0.003
(b) All workersCoefficient 0.418 -0.032 0.049
1-tail P-value 0.000 0.041 0.062
(d) Unbalanced panelCoefficient 0.403 -0.016 0.073
1-tail P-value 0.000 0.206 0.000
(e) Female leadership: prop. female executivesCoefficient 2.108 -0.036 0.310
1-tail P-value 0.000 0.188 0.000
Results
-
Women’s wage distribution: more disaggregation
Dependentvariable:→
Wage deciles Wage quantiles1 10 1 2 3 4
(a) BenchmarkCoefficient -0.043 0.167 -0.031 -0.026 0.006 0.104
1-tail P-value 0.158 0.007 0.175 0.131 0.432 0.004
(b) All workersCoefficient -0.038 0.121 -0.036 -0.027 -0.020 0.072
1-tail P-value 0.177 0.016 0.134 0.062 0.801 0.039
(d) Unbalanced panelCoefficient -0.004 0.170 -0.007 -0.022 -0.006 0.096
1-tail P-value 0.448 0.000 0.390 0.121 0.370 0.000
(e) Female leadership: fraction of female managersCoefficient -0.114 0.789 -0.053 -0.022 -0.007 0.421
1-tail P-value 0.141 0.000 0.202 0.293 0.574 0.000
Results
-
Women’s wage distribution: more disaggregation
Dependentvariable:→
Wage deciles Wage quantiles1 10 1 2 3 4
(a) BenchmarkCoefficient -0.043 0.167 -0.031 -0.026 0.006 0.104
1-tail P-value 0.158 0.007 0.175 0.131 0.432 0.004
(b) All workersCoefficient -0.038 0.121 -0.036 -0.027 -0.020 0.072
1-tail P-value 0.177 0.016 0.134 0.062 0.801 0.039
(d) Unbalanced panelCoefficient -0.004 0.170 -0.007 -0.022 -0.006 0.096
1-tail P-value 0.448 0.000 0.390 0.121 0.370 0.000
(e) Female leadership: fraction of female managersCoefficient -0.114 0.789 -0.053 -0.022 -0.007 0.421
1-tail P-value 0.141 0.000 0.202 0.293 0.574 0.000
Results
-
Men’s wages - Coeffs. on Female CEO dummy
Dependentvariable:
→ Standard Wage decile Wage quantilesDeviation 1 10 1 2 3 4
(a) BenchmarkCoefficient -0.107 0.029 -0.069 0.031 0.016 0.010 -0.039
1-tail P-value 0.130 0.091 0.116 0.047 0.193 0.667 0.148
(b) All workersCoefficient -0.113 -0.023 -0.071 -0.016 -0.015 -0.014 -0.047
1-tail P-value 0.095 0.919 0.078 0.901 0.870 0.183 0.076
(d) Unbalanced PanelCoefficient -0.152 0.058 -0.092 0.049 0.019 0.005 -0.054
1-tail P-value 0.021 0.000 0.035 0.000 0.038 0.630 0.049
Results
-
Men’s wages - Coeffs. on Female CEO dummy
Dependentvariable:
→ Standard Wage decile Wage quantilesDeviation 1 10 1 2 3 4
(a) BenchmarkCoefficient -0.107 0.029 -0.069 0.031 0.016 0.010 -0.039
1-tail P-value 0.130 0.091 0.116 0.047 0.193 0.667 0.148
(b) All workersCoefficient -0.113 -0.023 -0.071 -0.016 -0.015 -0.014 -0.047
1-tail P-value 0.095 0.919 0.078 0.901 0.870 0.183 0.076
(d) Unbalanced PanelCoefficient -0.152 0.058 -0.092 0.049 0.019 0.005 -0.054
1-tail P-value 0.021 0.000 0.035 0.000 0.038 0.630 0.049
Results
-
Firm performance
Interactions of female leadership with proportion of females
Dep. Var.→ Sales per empl. VA p. empl. TFP
(a) BenchmarkFem. CEO 0.033 -0.120 -0.046 -0.245 0.059 -0.213
(0.039) (0.045) (0.038) (0.041) (0.029) (0.039)Interaction 0.610 0.795 0.616
1-tail p-val 0.000 0.000 0.000
Results
-
Firm performance
Interactions of female leadership with proportion of females
Dep. Var.→ Sales per empl. VA p. empl. TFP
(a) BenchmarkFem. CEO 0.033 -0.120 -0.046 -0.245 0.059 -0.213
(0.039) (0.045) (0.038) (0.041) (0.029) (0.039)Interaction 0.610 0.795 0.616
1-tail p-val 0.000 0.000 0.000
Results
-
Firm performance
Interactions of female leadership with proportion of females
Dep. Var.→ Sales per empl. VA p. empl. TFP
(a) BenchmarkFem. CEO 0.033 -0.120 -0.046 -0.245 0.059 -0.213
(0.039) (0.045) (0.038) (0.041) (0.029) (0.039)Interaction 0.610 0.795 0.616
1-tail p-val 0.000 0.000 0.000
Results
-
Firm performance: robustness
Dep. Var.→ Sales per empl. VA p. empl. TFP
(b) Unbalanced PanelInteraction 0.123 0.144 0.115
1-tail p-val 0.066 0.022 0.041
(c) Female leadership: fraction of female managersInteraction 0.610 0.795 0.616
1-tail p-val 0.000 0.000 0.000
(d) W/o controls for unobserved CEO and workers heterogeneityInteraction 0.523 0.677 0.513
1-tail p-val 0.001 0.000 0.002
Results
-
Summary of results
We find that female executives make a difference:
I They increase variance of female wages as a result ofpositive impact at the top, negative at the bottom
I They increase the firm’s performance as a result ofpositive interaction between female leadership and femaleworkers
This evidence is consistent with our model of statistical discriminationwith job assignment where CEOs are better at extracting informationfrom workers of their same gender
Conclusion
-
Alternative explanations
I Gender preferences?
I Complementarities between female leadership and skilledfemale workers?
Conclusion
-
Alternative explanations
I Gender preferences?
I Complementarities between female leadership and skilledfemale workers?
Conclusion
-
Policy Experiment: Underrepresentation and Quotas
I We increase the number of female CEOs to 50%1. With Targeting: all firms at least 40% female share2. At Random: any firm
I We look at the impact on firm performance
Counterfactual Avg. gain % Gain for treated %Sales VA TFP Sales VA TFP
(1) Targeting 6.7 4.2 2.1 14.2 8.7 4.3
(2) Random 1.9 -2.1 -2.7 3.7 -4.1 -5.4
I NB: All in partial equilibrium
Conclusion
-
Policy Experiment: Underrepresentation and Quotas
I We increase the number of female CEOs to 50%1. With Targeting: all firms at least 40% female share2. At Random: any firm
I We look at the impact on firm performance
Counterfactual Avg. gain % Gain for treated %Sales VA TFP Sales VA TFP
(1) Targeting 6.7 4.2 2.1 14.2 8.7 4.3
(2) Random 1.9 -2.1 -2.7 3.7 -4.1 -5.4
I NB: All in partial equilibrium
Conclusion
IntroductionTheoryEmpirical AnalysisResultsConclusion