Do Female Executives Make a Difference? The Impact of ... · I Firm-level heterogeneity I ﬁrms with male and female CEOs may be different I Workforce-level heterogeneity I the labor

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

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

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





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





Female wages

Theory





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

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

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



Main equation



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



Main equation



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



Main equation



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



Main equation



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





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


Empirical Analysis





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


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





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





(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

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: 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

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

Documents

Do Female Executives Make a Difference? The Impact of ... · I Firm-level heterogeneity I ﬁrms with male and female CEOs may be different I Workforce-level heterogeneity I the labor