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Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized Experiment Preliminary Manuel F. Bagüés & Berta Esteve-Volart (Universidad Carlos.

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Presentation on theme: "Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized Experiment Preliminary Manuel F. Bagüés & Berta Esteve-Volart (Universidad Carlos."— Presentation transcript:

1 Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized Experiment Preliminary Manuel F. Bagüés & Berta Esteve-Volart (Universidad Carlos III) (York University)

2 Motivation Gender parity or gender quotas imposed or considered in many countries –France: electoral party lists –Norway: public enterprises’ boards –Spain: cabinet, considering all public sector recruitment committees (legislation project approved by Government in March 8) No previous evidence of gender quota effectiveness We use data on public exams in Spain

3 Why? Few women in top positions –Politics: women occupy at least 30% parliamentary seats in 12 out of 179 countries –Boards of large private companies: women are 2% in Spain, 3% in Italy, 4% in France Policy: from equal opportunities to gender parity –The failure of the pipeline theory

4 How? Directly: women hire more women Indirectly: -Role model transmission -Women in top positions can choose policy more adequate for women, -Private sector: flexible working hours -Politics: public expenditure more useful to women (Duflo and Chattopadhyay 2004)

5 Will it work?

6 Empirical evidence Data on individual productivity –General: evidence of wage gap (Blau & Kahn 1994) –Top management: Bertrand & Hallock (1999) –Researchers: CSIC (2003), Veugelers (2006), Long (1993), Mairesse & Turner (2002) Data on firm productivity (Wolfers 2006) Experimental data –Blind Evaluation vs Non-Blind Evaluation Blank (1990), Goldin & Rouse (2000), Lavy (2005) –Randomization Lab Experiments (Gneezy et al 2003)

7 Background Information We use data from public examinations in Spain They determine the access to public positions (judiciary, diplomacy, notaries, economists, tax inspectors, and many others) Every year 175,000 young university graduates take public exams Only a small number of candidates pass exams Elite formation: many political figures had to pass public exam (e.g. Aznar)

8 Characteristics of public exams Each committee examines 500 candidates Random allocation of candidates to evaluating committees Evaluation –Oral –Two or three stages, all qualifying –Voting by majority basis –Multiple choice test introduced in 2003 for some exams

9 Data All results are published in the state official bulletin (BOE) We examine public exams to the judiciary, years (new data: ) Type of exams: judge, prosecutor, court secretary 150 committees 75,000 candidates involved About 1,700 judges, prosecutors and court secretaries recruited

10 Data: what do we know? Characteristics of evaluators –Gender, age, age of entry, rank Characteristics of successful candidates for all years –Gender, age, age of entry, rank Characteristics of all candidates for 2003 and 2004 We do NOT know the individual vote of each committee member

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15 Empirical strategy 1) Committee-level information: where y is an outcome variable, s is female share in committee, X are committee characteristics

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18 Interpretation 1.Female evaluators are tougher with female candidates 2.Male evaluators are more generous with female candidates Possible non-linearities?

19 2) Candidate-level information for years 2003 and 2004 (multiple choice test):

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21 Quantitatively A female candidate’s chances to pass the public exam are 5.5% greater if evaluated by a committee with fewer women than the median committee, than if evaluated by a committee with more women that the median committee

22 Caveats What is the motivation of the evaluators? 1)Evaluators have ‘irrational taste’ 2)Evaluators behave according to rational choice but: -Women think women are worse (lack of confidence) -Since the men in committees discriminated in the past, men in committees now are more generous with female candidates (past discrimination) -Women want to increase their group’s average quality (statistical discrimination)

23 Next step Evolution over time of the observed gender bias –What happened since the first committee with a female member? Data before 1995


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