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Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized Experiment Preliminary

Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized Experiment Preliminary. Manuel F. Bagüés & Berta Esteve-Volart (Universidad Carlos III) (York University). Motivation. Gender parity or gender quotas imposed or considered in many countries

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Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized Experiment Preliminary

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  1. Will Gender Parity Break the Glass Ceiling? Evidence from a Randomized ExperimentPreliminary 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 1995-2004 (new data: 1985-2005) • 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

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

  12. Interpretation • Female evaluators are tougher with female candidates • Male evaluators are more generous with female candidates • Possible non-linearities?

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

  14. 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

  15. Caveats • What is the motivation of the evaluators? • Evaluators have ‘irrational taste’ • 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)

  16. 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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