La transparence salariale réduit-elle les écarts de salaire femmes-hommes ?
Emma Duchini1, Stefania Simion2, Arthur Turrell3
1Department of Economics, University of Warwick 2School of Economics, University of Bristol 3King’s College, ONS
PSE et IPP: Inégalités femmes-hommes : le rôle des entreprises
Les opinions exprimées ne reflètent pas celles de l’ONS.
1 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 I Débat croissant: l’inégalité des salaires persiste en partie parce qu’elle est cachée
I Transparence salariale: obligation de publier des informations sur les salaires des employés I Canada (’96), Danemark & Italie (’06), Autriche (’11), Royaume-Uni & Allemagne (’17)
I Théoriquement: I Choc d’information qui affecte la réputation des entreprises I Modifie le pouvoir de négociation vis-à-vis de l’entreprise de manière opposée selon le sexe I Peut améliorer les salaires relatives des femmes I Effet ambigu sur la rétention et la productivité des employés I Ampleur des effets depend de la diffusion de l’information
I Notre papier considère la loi de transparence introduite au Royame-Uni en 2017: I Imposée à toutes les entreprises avec au moins 250 employés I Obligation de publier sur un site dedié du gouvernement: I % Écarts salariaux moyens/médians entre hommes-femmes cumulés (GPG) I Proportion des femmes dans chaque quartile de la distribution des salaires
I Aujourd’hui, je discute l’impact de ce mandat sur les salaires des hommes et des femmes I Double Difference: comparer les salaires de 2 groupes d’entreprises (200-249 vs 250-300), avant / après mandat
2 / 8 Contexte institutionnel
1. Dirigée aux entreprises avec au moins 250 employés en avril Comptage 250? 2. Timing: publication des indicateurs des entreprises attendue à la fin de l’exercice suivant (avril) 3. Conformité I 100% conformité avec quelque entreprise publiant après la date limite Conformité I Sanctions potentielles sur les non-conformistes (pas sur mauvaises performances) 4. Choc d’information I Seulement 1/3 des entreprises calculaient les salaires par sexe avant ce mandat I Seulement 3% rendaient ces chiffres public (Government Equalities Office 2015) 5. Notorieté (Salience) de l’information I Indicateurs publiés sur un site web gouvernemental dédié I Grande attention médiatique à la date limite (FT 2020, 2019, BBC, Guardian 2018) I Pic de recherche sur Google pour “écart de rémunération entre les sexes” à la date limite Recherches sur Google Statistiques descriptives 3 / 8 Contexte institutionnel
1. Dirigée aux entreprises avec au moins 250 employés en avril Comptage 250? 2. Timing: publication des indicateurs des entreprises attendue à la fin de l’exercice suivant (avril) 3. Conformité I 100% conformité avec quelque entreprise publiant après la date limite Conformité I Sanctions potentielles sur les non-conformistes (pas sur mauvaises performances) 4. Choc d’information I Seulement 1/3 des entreprises calculaient les salaires par sexe avant ce mandat I Seulement 3% rendaient ces chiffres public (Government Equalities Office 2015) 5. Notorieté (Salience) de l’information I Indicateurs publiés sur un site web gouvernemental dédié I Grande attention médiatique à la date limite (FT 2020, 2019, BBC, Guardian 2018) I Pic de recherche sur Google pour “écart de rémunération entre les sexes” à la date limite Recherches sur Google Statistiques descriptives 3 / 8 Contexte institutionnel
1. Dirigée aux entreprises avec au moins 250 employés en avril Comptage 250? 2. Timing: publication des indicateurs des entreprises attendue à la fin de l’exercice suivant (avril) 3. Conformité I 100% conformité avec quelque entreprise publiant après la date limite Conformité I Sanctions potentielles sur les non-conformistes (pas sur mauvaises performances) 4. Choc d’information I Seulement 1/3 des entreprises calculaient les salaires par sexe avant ce mandat I Seulement 3% rendaient ces chiffres public (Government Equalities Office 2015) 5. Notorieté (Salience) de l’information I Indicateurs publiés sur un site web gouvernemental dédié I Grande attention médiatique à la date limite (FT 2020, 2019, BBC, Guardian 2018) I Pic de recherche sur Google pour “écart de rémunération entre les sexes” à la date limite Recherches sur Google Statistiques descriptives 3 / 8 Contexte institutionnel
1. Dirigée aux entreprises avec au moins 250 employés en avril Comptage 250? 2. Timing: publication des indicateurs des entreprises attendue à la fin de l’exercice suivant (avril) 3. Conformité I 100% conformité avec quelque entreprise publiant après la date limite Conformité I Sanctions potentielles sur les non-conformistes (pas sur mauvaises performances) 4. Choc d’information I Seulement 1/3 des entreprises calculaient les salaires par sexe avant ce mandat I Seulement 3% rendaient ces chiffres public (Government Equalities Office 2015) 5. Notorieté (Salience) de l’information I Indicateurs publiés sur un site web gouvernemental dédié I Grande attention médiatique à la date limite (FT 2020, 2019, BBC, Guardian 2018) I Pic de recherche sur Google pour “écart de rémunération entre les sexes” à la date limite Recherches sur Google Statistiques descriptives 3 / 8 Double Difference: effets sur les salaires
Log salaire Log salaire Heures travaillées Log salaire Allocations Primes horaire hebdomadaire par semaine contractuel (par heure) (par heure) (1) (2) (3) (4) (5) (6) Panel A: Hommes Effet de la politique -0.0277∗∗ -0.0217∗ 0.128 -0.0257∗∗ -0.0329 -0.00306 (0.0111) (0.0127) (0.226) (0.0113) (0.0249) (0.0196) Observations 24658 24658 24658 24658 24658 24658 Moyen pre-politique 16.92 618.00 36.51 16.6 0.49 0.23
Panel B: Femmes Effet de la politique 0.00243 -0.00571 -0.311 0.00709 -0.0140 -0.0286 (0.0143) (0.0188) (0.398) (0.0137) (0.0256) (0.0196) Observations 21484 21484 21484 21484 21484 21484 Moyen pre-politique 13.89 432.55 30.86 13.29 0.23 0.12
EF Année, Entreprise, Individue XXXXXX EF Année*Région XXXXXX P-value Hommes Vs Femmes 0.078 0.480 0.321 0.052 0.588 0.359
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
I 2.8% ↓ des salaires des hommes −→ 15% ↓ GPG Effet dynamique Regression I Marges d’ajustement: les salaires plutôt que les heures I Marges d’ajustement: partie contractuelle plutôt que les allocations / primes Robustness checks I Combinaison de réductions nominales et de gel des promotions Salaires nominales
4 / 8 Mécanismes: 1. Réponse comportementale
% Δ in GPG compared to baseline - 2019 vs 2018
80
60
40
20
% Δ median gender pay gap 0
-20
-20 0 20 40 60
2018 % median gender hourly pay gap
Source: UK Government Equality Office (GEO). Note: Bottom and top 1% excluded. N. = 8,454.
I Les entreprises avec un écart plus grand au départ le baissent le plus au cours des 2 années (Allcott 2011)
5 / 8 2. Le role de la réputation des entreprises auprès des femmes
I Nous mergeons les indicateurs d’égalité avec GPG & réputation auprès des femmes les données de YouGov Women’s Rankings: Note de réputation des femmes (1) (2) I Notes de réputation obtenues en sondant un Panel A: 2 années ensemble échantillon représentatif des femmes entre GPG median -0.0393** février 201x et janvier 201x+1 (0.0190) % femmes dans top quartile 0.0351*** (0.0129) I “Dans l’ensemble, de laquelle des marques Observations 2014 2014 suivantes avez-vous une impression Panel B: 2017-2018 positive/négative ?” GPG median -0.0320 (0.0250) P ositiveRep−NegativeRep I Note = × 100 % femmes dans top quartile 0.0320* T outesRep (0.0181) Observations 996 996 I Résultats: pires indicateurs d’égalité des Panel C: 2018-2019 GPG median -0.0489* sexes associés à une note inférieure des (0.0291) femmes % femmes dans top quartile 0.0378** (0.0183) Observations 1018 1018 I Le souci de leur réputation peut aider à expliquer la réponse des entreprises Source: GEO, YouGov 2018-19. *** p<0.01, ** p<0.05, * p<0.1.
6 / 8 Comparaison avec la littérature
À qui Quoi Stratégie Effet sur Horizon d’idéntification salaires temporel (1) (2) (3) (4) (5) Danemark Aux syndicats Salaires cumulés Double Difference ↓ salaires des hommes Court terme par sexe Bennedsen et al., JoF 2021 Canada Au public Salaires Double Difference ↓ salaires des hommes Long terme individuels des professeurs d’université Baker et al., DT 2019 Autriche Aux syndicats Salaires par sexe Double Difference Effet nul Court terme et profession Gulyas et al., DT 2020 Böheim & Gust, DT 2021 Royaume Uni Au public Ecarts des salaires Double Difference ↓ salaires des hommes Court terme cumulés Blundell, DT 2021 Duchini et al., DT 2020
7 / 8 Discussion et pistes de reflexions politiques 1. La transparence salariale diminue le GPG : I Par une réduction des salaires des hommes: Est-ce la voie souhaitable ? I Aucun effet significatif sur la rétention et la productivité des employés 2. La transparence salariale ne fonctionne-t-elle que si les informations sont accessibles au public ? I Au Royaume-Uni, la réputation semble jouer un rôle I La transparence salariale a des effets mixtes lorsque les informations ne sont pas accessibles au public (Bennedsen et al. 2019, Gulyas et al. 2020) I Les informatios publiés peuvent être utilisés par les chercheurs Stratégies d’embauche 3. Quelle information est-il utile de publier ? I GPG cumulé: simple mais moins informatif I GPG par profession/inexpliqué: plus informatif mais plus compliqué à calculer 4. La transparence salariale, est-elle plus efficace pour lutter contre l’inegalité homme-femme que les quotas de genre ? (Bertrand et al. 2019)
5. Nous avons identifié des effets à court terme ! 8 / 8 Merci beaucoup!
1 / 79 Appendix
2 / 79 Public gender equality indicators
2017-18 2018-19 Change (%) (1) (2) (3) Mean gender hourly pay gap 14.34 14.19 -0.01
(14.91) (14.21)
Median gender hourly pay gap 11.79 11.88 0.01
(15.84) (15.51)
Mean gender bonus gap 7.67 15.44 1.01
(833.02) (200.70)
Median gender bonus gap -21.71 -0.86 -0.96
(1,398.97) (270.51)
Share men receiving bonus 35.39 35.72 0.01
(36.33) (36.68)
Share women receiving bonus 33.93 34.40 0.01
(36.02) (36.38)
% women lower quartile 53.67 53.88 0.00
(24.13) (24.11)
% women lower-middle quartile 49.49 49.82 0.01
(26.09) (26.19)
% women upper-middle quartile 45.14 45.62 0.01
(26.22) (26.32)
% women top quartile 39.20 39.75 0.01
(24.41) (24.48)
Observations 10,557 10,812
Source: UK Government Equality Office (GEO).
Back
3 / 79 ASHE Summary statistics - pre-mandate period
Treated men Control men Treated women Control women (1) (2) (3) (4) Above-median-wage occupation 0.69 0.70 0.65 0.64 (0.46) (0.46) (0.48 (0.48 Bottom tercile 0.19 0.18 0.32 0.33 (0.39) (0.39) (0.47) (0.47) Middle tercile 0.32 0.32 0.21 0.24 (0.47) (0.47) (0.41) (0.42) Top tercile 0.49 0.50 0.46 0.43 (0.50) (0.50) (0.50) (0.50) Changed job since last year 0.18 0.20 0.21 0.22 (0.39) (0.40) (0.41) (0.41) Tenure in months 86.83 88.01 72.62 72.10 (96.70) (98.11) (78.86) (80.72) Leaving the firm in t+1 0.11 0.11 0.09 0.10 (0.31) (0.31) (0.29) (0.30) Hourly pay 16.92 16.71 13.89 13.88 (14.83) (12.38) (9.15) (10.64) Weekly pay 618.00 610.86 432.55 430.39 (551.95) (456.36) (318.64) (329.67) Receiving allowances 0.23 0.22 0.15 0.14 (0.42) (0.42) (0.36) (0.34) Allowance amount 18.26 17.06 6.96 7.29 (70.23) (57.28) (26.55) (37.01) Allowance amount (per hour) 0.49 0.46 0.23 0.23 (1.88) (1.59) (0.88) (1.12) Receiving bonus pay 0.08 0.10 0.05 0.05 (0.28) (0.30) (0.23) (0.23) Bonus amount 9.10 11.27 3.48 3.16 (78.46) (104.69) (29.55) (27.42) Bonus amount (per hour) 0.23 0.30 0.12 0.09 (1.91) (2.87) (1.81) (0.77) Weekly hours 36.51 36.71 30.86 30.76 (8.14) (7.95) (10.35) (10.52) Overtime hours 1.51 1.50 0.55 0.50 (4.23) (4.03) (2.24) (2.09) Full-time 0.90 0.91 0.67 0.67 (0.29) (0.29) (0.47) (0.47) Private sector 0.90 0.91 0.79 0.77 (0.30) (0.29) (0.41) (0.42) Covered by collective agreement 0.28 0.28 0.32 0.34 (0.45) (0.45) (0.47) (0.47) Observations 8,350 9,859 6,988 8,706
Source: ASHE, 2012-2017.
Back
4 / 79 Impact on log hourly wages by tenure
Entire Tenure ≤ 2 years Tenure > 2 years (1) (2) (3) Panel A: Men Treated Firm*Post -0.0277∗∗ -0.0182 -0.0215∗ (0.0111) (0.0327) (0.0113) Observations 24658 7973 16685 Pre-Treatment Mean 16.92 13.51 19.20 P-value Low vs High 0.928
Panel B: Women Treated Firm*Post 0.00243 0.0449 0.000524 (0.0143) (0.0480) (0.0160) Observations 21484 13867 7617 Pre-Treatment Mean 13.89 11.65 14.71 P-value Low vs High 0.306
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
I Point estimates suggest that newly hired women have experienced wage increases Back
5 / 79 Raw trends: log real hourly pay
Men Women
2.8 2.6
2.7 2.5
2.6
Log hourly pay Log hourly pay 2.4 2.5
2.4 2.3 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year
Firm size: 250-300 employees Firm size: 200-249 employees Firm size: 250-300 employees Firm size: 200-249 employees
I Similar trends between treatment and control group even after 2018 for women
Back
6 / 79 Anecdotal evidence
I Firms have claimed that they have been trying to attract women: I “Last year, [Easy Jet] recruited 49 new female co-pilots, a nearly 50 per cent increase on the previous year, taking the proportion of women among its new pilots to 13 per cent.” (Financial Times 2018) I “Ashfords, a law firm whose pay gap went up from 15.8 per cent to 39.4 per cent, said the figures were driven by a large number of junior women joining the company, which offset the effect of women being promoted to senior positions.” (Financial Times 2019)
I Some male CEOs cut their salary following the introduction of the policy: I “Johan Lundgren, EasyJet’s chief executive, is taking a 4.6 percent pay cut to match the salary of his female predecessor” (New York Times 2018) I BBC: “Six high-profile male presenters have already agreed to pay cuts, including John Humphrys, Jeremy Vine and Nick Robinson” (The Guardian 2018)
Back 7 / 79 Impact on log hourly wages by occupation
Entire Below-median Above-median sample wage wage (1) (2) (3) Panel A: Men Treated Firm*Post -0.0277** -0.0142 -0.0222 (0.0111) (0.0150) (0.0144) Observations 24658 9625 15033 Pre-Treatment Mean 16.92 10.12 21.47 P-value Low vs High 0.70
Panel B: Women Treated Firm*Post 0.00243 0.00587 -00853 (0.0143) (0.0211) (0.0188) Observations 21484 8547 12917 Pre-Treatment Mean 13.89 8.83 17.09 P-value Low vs High 0.61
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
I Potentially larger negative effects on better paid men Back
8 / 79 Raw trends: log nominal hourly basic pay
Men Women
2.8 2.6
2.7 2.5
2.6
2.4 2.5
Log hourly basic pay Log hourly basic pay 2.3 2.4
2.3 2.2 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year
Firm size: 250-300 employees Firm size: 200-249 employees Firm size: 250-300 employees Firm size: 200-249 employees
I Effect on men visible on nominal wages
Back
9 / 79 Hiring practices and gender equality indicators Conditional Correlations
.15
.1
.05
Estimate 0
-.05
-.1 Female-oriented wording Offer flexible work Wage posted
% women at the top of firm wage distribution 95% C. I. % median gender hourly pay gap 95% C. I.
Source: BGT 2015-2019, GE0 2018-2019. N. = 4,722. Regression Regression table LFS
I Firms employing these hiring practices have a higher % of women at the top of the wage distribution (blue bars) I The offer of FWA and wage posting are associated with a lower gender pay gap (red bars) Back 10 / 79 Event study: Pr(working in above-median-wage occupations) Men Women
.1 .1
.05 .05
0 0
Estimated coefficient -.05 Estimated coefficient -.05
-.1 -.1 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year
I Comparable evolution between treatment and control group before 2018 I Increasing and positive effect on women’s probability of working in occupations in top two quartiles of the wage distribution
Construction outcome Raw trends Wage terciles Managers Back 11 / 79 Event study: log hourly pay
Men Women
.09 .09
.06 .06
.03 .03
0 0
-.03 -.03 Estimated coefficient Estimated coefficient
-.06 -.06
-.09 -.09 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year
I No significant differential trend before 2018 I Negative effect on male wages significant from 2019
Back
12 / 79 Identification strategy
DiD strategy exploiting variation across firm size and over time in the mandate:
I TreatedFirmj = 1[Firm size ≥ 250 employees in 2015] Firm size density
I Focus on firms with +/- 50 employees from the 250 cutoff
I Postt = 1[Year ≥ 2018]
0 0 Yijt = αj + θt + β (T reatedF irmj ∗ P ostt) + Xitπ + Zjtδ + uijt i = employee; j = firm with 200-300 employees; t = year between 2012 to 2019
Yijt = Outcomes: occupation; job mobility; real wages; bonuses and allowances; hours
αj = Firm fixed effects, θt = Year fixed effects
Xit = (Age, age squared/Individual fixed effects); Zjt = Firm-level time varying controls
Heteroskedasticity-robust SE clustered at the firm level; LFS weights Back
13 / 79 Robustness checks 1. Parallel-trend assumption I Restricting pre-treatment period Wages 2. Contemporaneous shock I Triple-difference model Triple-diff I Diff-in-disc model Diff-in-disc I Placebo regressions: fake cutoff Wages 3. Specification I Sensitivity to bandwidth choice Wages I Change year to define treatment status Treatment status Actual firm size I Other sample restrictions (weights, age, full-time) Wages I Clustering standard errors (firm size, industry*firm size) Wages 4. External validity: I Composition estimation sample vs entire ASHE External validity
Back 14 / 79 Changes compared to baseline
% Δ in GPG compared to baseline - 2019 vs 2018 % Δ women in top quartile - 2019 vs 2018
80 30
60 20
40
10 20
0 % Δ women in top quartile
% Δ median gender pay gap 0
-20 -10 -20 0 20 40 60 0 20 40 60 80 100
2018 % median gender hourly pay gap 2018 % women in top quartile
Source: UK Government Equality Office (GEO). Note: Bottom and top 1% excluded. N. = 8,454.
I Greater improvement for worse-performing firms at baseline (Allcott 2011) Back I Firms with larger gpg at baseline decrease it more over the 2 years I Firms with lower % women in top quartile at baseline increase it more over the 2 years
15 / 79 Impact on job mobility
Pr(above-median-wage Pr(changed job Tenure Pr(leaving the firm occupation) since last year) in months in t+1) (1) (2) (3) (4) Panel A: Men Treated Firm*Post -0.00340 0.0181 -0.990 0.0216 (0.0118) (0.0149) (2.698) (0.0240) Observations 24658 24658 23986 21539 Pre-Treatment Mean 0.69 0.18 86.83 0.11
Panel B: Women Treated Firm*Post 0.0309∗∗ 0.0438∗∗∗ -7.786∗∗∗ 0.0162 (0.0138) (0.0157) (2.393) (0.0230) Observations 21484 21484 20847 18652 Pre-Treatment Mean 0.65 0.21 76.62 0.09
Year, Firm FE XXXX Age, age squared XXXX Year*Region FE XXXX P-value Men Vs Women 0.056 0.224 0.056 0.865
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
I Change in women’s occupational distribution due to newly hired women I No significant impact on employees’ retention
Back 16 / 79 Impact on labor productivity
(1) (2) (3) (4) Treated Firm*Post -0.0292 -0.0290 -0.0285 -0.0296 (0.0350) (0.0353) (0.0353) (0.0353) Observations 22800 22800 22800 22800 Pre-Treatment Mean 3.98 3.98 3.98 3.98
Year*Region FE XXX Product Market Concentration X Industry Trends X Source: BSD, 2012-2018. *** p<0.01, ** p<0.05, * p<0.1.
I For this analysis, we use the Business Structure Database (BSD), covering 99% of UK firms
0 Yjt = αj + θt + β (T reatedF irmj ∗ P ostt) + Zjtδ + jt
I Negative but insignificant effect on labor productivity (log output per employee)
Back
17 / 79 Outcome construction
1. Restrict the sample to pre-treatment period (2012-2017)
2. Compute the median wage by 1-digit occupation
3. Rank occupations by the median wage w
4. Above-median-wage occupations are those with w ≥ wmed across occupations
5. These are managerial, professional, technical, administrative, skilled-trades occupations
Back
18 / 79 Raw trends: Pr(working in above-median-wage occupations) Men Women
.8 .8
.7 .7
.6 .6 Share of employees Share of employees
.5 .5 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year
Firm size: 250-300 employees Firm size: 200-249 employees Firm size: 250-300 employees Firm size: 200-249 employees
I Comparable evolution between treatment and control group before 2018 I Potential increase in women’s probability of working in above-median-wage occupations
Back 19 / 79 Event study: Pr(working in wage terciles)
Top Tercile Middle Tercile Bottom Tercile
.1 .1 .1
.05 .05 .05
0 0 0
Estimated coefficient -.05 Estimated coefficient -.05 Estimated coefficient -.05
-.1 -.1 -.1 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year Year
Women Men Women Men Women Men
I Movements from the bottom to the middle of the distribution seem to drive the effect on women
Back
20 / 79 Raw trends: Pr(working in managerial occupations) Managerial occupation - Men Managerial occupation - Women
.2 .2
.15 .15
.1 .1
Share of employees .05 Share of employees .05
0 0 2012 2013 2014 2015 2016 2017 2018 2019 2012 2013 2014 2015 2016 2017 2018 2019
Year Year
Firm size: 250-300 employees Firm size: 200-249 employees Firm size: 250-300 employees Firm size: 200-249 employees
I Comparable evolution between treatment and control group before 2018 I Potential increase (decrease) in women’s (men’s) probability of working in managerial occupations
Back 21 / 79 Impact on above-median-wage occupations - restricting pre-period
Main From From From From specification 2013 2014 2015 2016 (1) (2) (3) (4) (5) Panel A: Men Treated firm*post -0.00340 -0.00634 -0.00770 -0.0125 -0.00273 (0.0118) (0.0116) (0.0113) (0.0107) (0.0103) Observations 24658 21954 19018 15811 12693 Pre-Treatment Mean 0.69 0.69 0.70 0.70 0.71
Panel B: Women Treated firm*post 0.0309∗∗ 0.0339∗∗ 0.0362∗∗∗ 0.0371∗∗ 0.0315∗∗∗ (0.0138) (0.0137) (0.0137) (0.0132) (0.0131) Observations 21484 19248 16896 14103 11321 Pre-Treatment Mean 0.65 0.65 0.65 0.64 0.66
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
22 / 79 Impact on log hourly wage - restricting pre-period
Main From From From From specification 2013 2014 2015 2016 (1) (2) (3) (4) (5) Panel A: Men Treated firm*post -0.0277∗∗ -0.0277∗∗ -0.0265∗∗ -0.0165 -0.0158 (0.0111) (0.0109) (0.0107) (0.0104) (0.0104) Observations 24658 21954 19018 15811 12693 Pre-Treatment Mean 16.92 17.06 17.12 17.36 17.61
Panel B: Women Treated firm*post 0.00243 0.00382 0.00452 0.00794 0.00412 (0.0143) (0.0143) (0.0145) (0.0147) (0.0154) Observations 21484 19248 16896 14103 11131 Pre-Treatment Mean 13.89 13.91 14.03 14.13 14.49
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
23 / 79 Diff-in-Diff vs Triple Diff
Above-median-wage Log hourly pay occupation Men Women Triple Diff Men Women Triple Diff (1) (2) (3) (4) (5) (6) Treated Firm*Post -0.00340 0.0309∗∗ -0.00814 -0.0277∗∗ 0.00243 -0.0255∗∗ (0.0118) (0.0138) (0.0125) (0.0111) (0.0143) (0.0114) Treated Firm*Post*Female 0.0399∗∗ 0.0264 (0.0194) (0.0174) Post*Female -0.0285∗∗ -0.0191 (0.0120) (0.0117) Treated Firm*Female -0.0951 -0.131 (0.0136) (0.119) Observations 24658 21484 46142 24658 21484 46142 P-value Women 0.031 0.953
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
0 0 Yijt = αj + θt + β(T reatedF irmj ∗ P ostt) + F emi[γ0 + γ1T reatedF irmj + γ2P ostt + γ3(T reatedF irmj ∗ P ostt)] + Xitπ + Zjtδ + uijt
Back
24 / 79 Diff-in-Diff vs Diff-in-Disc
Above-median-wage Log hourly pay occupation Diff-in-Diff Diff-in-Disc Diff-in-Diff Diff-in-Disc (1) (2) (3) (4) Panel A: Men Treated Firm*Post -0.00340 -0.00299 -0.0277∗∗ -0.0278∗ (0.0118) (0.0147) (0.0111) (0.0148) Observations 24658 24658 24658 24658 Pre-Treatment Mean 0.69 0.69 16.92 16.92
Panel B: Women Treated Firm*Post 0.0309∗∗ 0.0303∗ 0.00243 -0.00915 (0.0138) (0.0167) (0.0143) (0.0176) Observations 21484 21484 21484 21484 Pre-Treatment Mean 0.65 0.65 13.89 13.89
Year FE XX Year*Region FE XX Post XX Post*Region FE XX Norm. Firm Size*Post XX Norm. Firm Size*Treated Firm*Post XX Individual controls XXXX P-value Men Vs Women 0.056 0.127 0.0823 0.399
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
0 Yijt = αj + P ostt[δ0 + δreg + δ1F irmSizej2015 + T reatedF irmj(β0 + β1F irmSizej2015)] + Xitπ + uijt
Back 25 / 79 Placebo cutoffs - above-median-wage occupations
Men Women
450 450
350 350
Cutoff Cutoff 250 250
150 150
-.08 -.04 0 .04 .08 -.08 -.04 0 .04 .08
Estimated Beta Estimated Beta
Back
26 / 79 Placebo cutoffs - log hourly pay
Men Women
450 450
350 350
Cutoff Cutoff 250 250
150 150
-.08 -.04 0 .04 .08 -.08 -.04 0 .04 .08
Estimated Beta Estimated Beta
Back
27 / 79 Varying bandwidth - above-median-wage occupations
Men Women
80 80
70 70
60 60
50 50 Bandwidth Bandwidth
40 40
30 30
-.1 -.05 0 .05 .1 -.1 -.05 0 .05 .1
Estimated Beta Estimated Beta
Back
28 / 79 Varying bandwidth - log hourly pays
Men Women
80 80
70 70
60 60
50 50 Bandwidth Bandwidth
40 40
30 30
-.1 -.05 0 .05 .1 -.1 -.05 0 .05 .1
Estimated Beta Estimated Beta
Back
29 / 79 Changing year to define treatment status
Above-median-wage Log hourly pay occupation 2015 2014 2013 2012 2015 2014 2013 2012 (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Men Treated Firm*Post -0.00340 -0.00973 -0.00273 -0.00273 -0.0277∗∗ -0.0163 -0.0199∗ -0.0369∗∗∗ (0.0118) (0.0128) (0.0128) (0.0135) (0.0111) (0.0112) (0.0114) (0.0120) Observations 24658 24586 24476 24239 24658 24586 24476 24239 Pre-Treatment Mean 0.69 0.70 0.69 0.69 16.92 16.80 17.01 17.09
Panel B: Women Treated Firm*Post 0.0309∗∗ 0.0501∗∗∗ 0.0394∗∗∗ 0.0235 0.00243 -0.00770 -0.00518 -0.00777 (0.0138) (0.0149) (0.0148) (0.0152) (0.0143) (0.0154) (0.0150) (0.0149) Observations 21484 21310 21097 20746 21484 21310 21097 20746 Pre-Treatment Mean 0.65 0.65 0.65 0.65 13.89 13.90 13.92 13.77
Individual controls XXXXXXXX Year*Region FE XXXXXXXX P-value Men Vs Women 0.06 0.19 0.03 0.00 0.08 0.10 0.16 0.63
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
30 / 79 Treatment status based on past vs actual firm-size
Above-median-wage Log hourly pay occupation Main specification Actual Main specification Actual (1) (2) (3) (4) Panel A: Men Treated Firm*Post -0.00340 -0.0159 -0.0277** -0.00235 (0.0118) (0.0118) (0.0111) (0.0114) Observations 24658 25256 24658 25256 Pre-Treatment Mean 0.69 0.68 16.92 16.84
Panel B: Women Treated Firm*Post 0.0309** 0.0149 0.0126 -0.00617 (0.0138) (0.0139) (0.0143) (0.0151) Observations 21484 22111 21484 22111 Pre-Treatment Mean 0.65 0.64 13.89 13.93
Individual controls XXXX Year*Region FE XXXX Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
31 / 79 Impact on above-median-wage occupations - changing estimation sample
Main specification Age 25+ Age 16-65 Full-time With Without With Without With Without With Without LFS weights LFS weights LFS weights LFS weights (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Men Treated Firm*Post -0.00340 -0.00202 -0.00393 -0.00271 -0.00170 0.00161 -0.00669 -0.00455 (0.0118) (0.0129) (0.0121) (0.0133) (0.0121) (0.0132) (0.0123) (0.0136) Observations 24658 24658 21895 21895 24146 24146 22088 22088 Pre-Treatment Mean 0.69 0.60 0.71 0.63 0.69 0.61 0.72 0.64
Panel B: Women Treated Firm*Post 0.0309** 0.0313** 0.0347** 0.0337** 0.0266* 0.0273* 0.0322** 0.0340** (0.0138) (0.0143) (0.0138) (0.0143) (0.0139) (0.0144) (0.0156) (0.0166) Observations 21484 21484 18922 18922 21116 21116 14161 14161 Pre-Treatment Mean 0.65 0.61 0.69 0.65 0.65 0.61 0.73 0.69
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
32 / 79 Impact on log hourly wages - changing estimation sample
Main specification Age 25+ Age 16-65 Full-time With Without With Without With Without With Without LFS weights LFS weights LFS weights LFS weights (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Men Treated Firm*Post -0.0277** -0.0268** -0.0202* -0.0193** -0.0267** -0.0258** -0.0221** -0.0204** (0.0111) (0.0105) (0.0104) (0.00984) (0.0112) (0.0105) (0.0104) (0.00980) Observations 24658 24658 21895 21895 24146 24146 22088 22088 Pre-Treatment Mean 16.92 15.82 17.96 16.74 16.95 15.88 17.44 16.37
Panel B: Women Treated Firm*Post 0.00243 0.00243 0.00569 0.00537 -0.000606 -0.000985 0.0109 0.0107 (0.0143) (0.0140) (0.0147) (0.0143) (0.0145) (0.0142) (0.0167) (0.0160) Observations 21484 21484 18922 18922 21116 21116 14161 14161 Pre-Treatment Mean 13.89 13.40 14.70 14.10 13.91 13.43 14.56 14.09
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
33 / 79 Impact on above-median-wage occupations - different clustering
S.E. clustered at the level of: firm firm-size firm-size* industry (1) (2) (3) Panel A: Men Treated Firm*Post -0.00340 -0.00340 -0.00340 (0.0118) (0.0106) (0.0105) Observations 24658 24658 24658 Pre-Treatment Mean 0.69 0.69 0.69
Panel B: Women Treated Firm*Post 0.0309∗∗ 0.0309∗∗∗ 0.0309∗∗ (0.0138) (0.0115) (0.0124) Observations 21484 21484 21484 Pre-Treatment Mean 0.65 0.65 0.65 Number of clusters 4639 101 655 P-value Men Vs Women 0.0578 0.026 0.035
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back 34 / 79 Impact on log hourly wages - different clustering
S.E. clustered at the level of: firm firm-size firm-size* industry (1) (2) (3) Panel A: Men Treated Firm*Post -0.0277∗∗ -0.0277∗∗∗ -0.0277∗∗∗ (0.0111) (0.00891) (0.00870) Observations 24658 24658 24658 Pre-Treatment Mean 17.630 17.630 17.630
Panel B: Women Treated Firm*Post 0.00243 0.00243 0.00243 (0.0143) (0.0114) (0.0107) Observations 21484 21484 21484 Pre-Treatment Mean 13.850 13.850 13.850 Number of clusters 4639 101 655 P-value Men Vs Women 0.082 0.041 0.028
Source: ASHE, 2012-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back 35 / 79 Occupational and industry distribution
Men Men Entire sample vs estimation sample Entire sample vs estimation sample
.2 .25
.2 .15
.15
.1 .1
.05 Industry distribution Occupational distribution .05
0 0 Sales Technical Personal Managerial Elementary Professional Agriculture Skilled-trades Construction Administrative Other services Energy & water Manufacturing Transport & Comm.Finance & Banking Distribution, Hospitality Plant and machine operative Public Admin., Edu., Heal.
Women Women Entire sample vs estimation sample Entire sample vs estimation sample
.2 .5
.4 .15 .3
.1 .2 .05 Industry distribution .1 Occupational distribution
0 0 Sales Technical Personal Managerial Elementary Professional Agriculture Skilled-trades Construction Administrative Other services Energy & water Manufacturing Transport & Comm.Finance & Banking Distribution, Hospitality Plant and machine operative Public Admin., Edu., Heal.
Entire sample 200-300 employees Entire sample 200-300 employees
I Comparable occupational and industry distribution between estimation sample and entire data set for both men and women Back 36 / 79 Gendered wording The psychology literature (Gaucher et al. 2011) has introduced the concept of
I Gendered words: traits/behaviors commonly associated to a specific gender in lab experiments
I Ex: women: cooperative, committed; men: competitive, ambitious
I Firms make increasing use of job-ad de-biasing services (Textio, Unitive)
I List of gendered words from Implicit Association Tests List
I This list could be used to assign a gender score to job ads as follows:
Score = (| F emW | − | MaleW |)/ | W |
Back 37 / 79 Gendered wording The psychology literature (Gaucher et al. 2011) has introduced the concept of
I Gendered words: traits/behaviors commonly associated to a specific gender in lab experiments
I Ex: women: cooperative, committed; men: competitive, ambitious
I Firms make increasing use of job-ad de-biasing services (Textio, Unitive)
I List of gendered words from Implicit Association Tests List
I This list could be used to assign a gender score to job ads as follows:
Score = (| F emW | − | MaleW |)/ | W |
Back 37 / 79 Gendered wording The psychology literature (Gaucher et al. 2011) has introduced the concept of
I Gendered words: traits/behaviors commonly associated to a specific gender in lab experiments
I Ex: women: cooperative, committed; men: competitive, ambitious
I Firms make increasing use of job-ad de-biasing services (Textio, Unitive)
I List of gendered words from Implicit Association Tests List
I This list could be used to assign a gender score to job ads as follows:
Score = (| F emW | − | MaleW |)/ | W |
Back 37 / 79 Gendered wording The psychology literature (Gaucher et al. 2011) has introduced the concept of
I Gendered words: traits/behaviors commonly associated to a specific gender in lab experiments
I Ex: women: cooperative, committed; men: competitive, ambitious
I Firms make increasing use of job-ad de-biasing services (Textio, Unitive)
I List of gendered words from Implicit Association Tests List
I This list could be used to assign a gender score to job ads as follows:
Score = (| F emW | − | MaleW |)/ | W |
Back 37 / 79 Gendered wording The psychology literature (Gaucher et al. 2011) has introduced the concept of
I Gendered words: traits/behaviors commonly associated to a specific gender in lab experiments
I Ex: women: cooperative, committed; men: competitive, ambitious (a) Male-oriented (b) Female-oriented
I Firms make increasing use of job-ad .5 de-biasing services (Textio, Unitive) .4
.3
I List of gendered words from Implicit Density .2 Association Tests List
.1
0 I This list could be used to assign a -4 -2 0 2 4
gender score to job ads as follows: Gender Score (per 100 words) (c) Sample distribution
Score = (| F emW | − | MaleW |)/ | W | Source: BGT 2015-2019. N. = 13,357,248. I Large variation in gender orientation of job ads Back 37 / 79 Flexible Work Arrangements (FWA) I Importance of gender differences in preferences for temporal flexibility to explain persistence in GPG
(Goldin 2014, Wiswall and Zafar 2018, Cortes and Pan 2019)
I Does the mandate push firms to offer more FWA?
I We construct a vocabulary of flexible work using:
10 I Timewise: job ads for flexible work I LFS definition of flexible work
ACAS guide to flexible working I 5 % Vacancies I Focus on full-time FWA that help the employee maintain work-life balance Flex words
0 At least 1 FWA Flexible work Remote work I We disregard FWA that give the employer
discretion over the employee schedule Source: BGT 2015-2019. N. = 13,357,248. I shift work, on call work, etc. (Adams et al. 2020) I Around 5% of job ads offer FWA I Expressions related to “flexitime” more Back prevalent than remote work 38 / 79 Wage posting
Annual real wages (2015-19) - BGT vs LFS I Established evidence on gender differences in bargaining skills .00008 .00008 (Bowles et al. 2007, Leibbrandt and List 2015)
.00006 .00006 I Upfront wage information may help firms attract women
.00004 .00004 Density Density I We extract offered wages/frequency using natural language processing: .00002 .00002
I We use targeted ‘regular expressions’ 0 0 such as: 0 20000 40000 60000 80000 100000 0 20000 40000 60000 80000 100000
BGT wage posted (£) LFS wage of newly hired (£)
I “30-35k per annum”, “20,000/year” I All values are converted into annual wages I Good/not great match with LFS entry wages
Back I Less than 30% of vacancies post wage information
Occupation and industry distributions
39 / 79 Gendered words
Male-oriented Female-oriented active dominant affectionate nag adventurous dominate cheered nurture aggression domination cheerful nurtured aggressive domineering cheers nurtures aggressiveness forced cheery nurturing aggressor forceful commitment pleasantly ambitious greedy committed polite ambitiousness headstrong committing quietly asserting hierarchical communal respond assertive hierarchy compassionate responsibility asserts hostile connected responsible athlete hostility connecting responsive athletic impulsive connections responsively athleticism individualistic considerate sensitive autonomous intellectual cooperating sensitivity autonomy leader cooperative submissive boasted leading dependable supported boaster logic depending supporting boasting masculine emotional sympathetic challenged objective empathetic sympathy challenger opinion empathic tenderly challenging outspoken empathy togetherness compete persist feminine trusted competence principled flatterable trusting competent reckless gentle trusts competing self-reliance honest understanding competitive self-reliant interdependence understands confident self-sufficiency interdependent warming courage self-sufficient interpersonal warmly courageous stubborn interpersonal warms decide superior interpersonally whine decision kind whining decisions kinship yielded decisive loyally yielding determination loyalty yields determined modesty
Source: Based on Gaucher et al. [2011]. Back
40 / 79 Gendered wording
Male wording Female wording
force empathy tender determination compassion loyalty objective polite understands determined honest competence responsive emotional competent connect connected decision sensitive decisions trusted compassionate active warm kind ambitious respond trusts challenging depending confident supported interpersonal leader together understand individual responsibility lead commitment supporting competitive understanding committed leading responsible
0 10 20 30 0 10 20 30
Avg # occurences every 100 vacancies Avg # occurences every 100 vacancies
Back
41 / 79 Anecdotal evidence
I According to a survey conducted by the Investment Association, the trade body of investment managers:
I 50% of interviewed firms want to de-bias the wording of their job advertisements
Back
42 / 79 Anecdotal evidence
I Some firms have started offering flexible work arrangements in senior positions:
I “The British bank Barclays, for example, has sought to hire, and retain, more senior female executives by offering a new 12-week “internship” targeted at experienced women who are coming off a career break and introducing greater flexibility in existing jobs” (New York Times 2018)
Back
43 / 79 Vocabulary for flexible work arrangements
(1) (2) annualised hours mobile working compressed hours nine day fortnight flextime remote working flexible working telework four and a half day week teleworking home work returner home working mobile work
Source: ACAS, LFS and Timewise. Back
44 / 79 Hiring practices by industry and occupation
Industry distribution Occupation distribution
Agriculture Plant & machine operatives
Construction Skilled-trades
Distribution, Hospitality Sales & customer service
Manufacturing Elementary
Other services Managerial
Finance & Banking Ass. prof. & tech.
Transport & Comm. Administrative
Energy & water Professional
Public Admin., Edu., Heal. Caring & leisure
0 20 40 60 0 20 40 60
% %
Offer flexible work Wage posting Female-oriented wording
Source: BGT 2015-2019. N. = 13,357,248.
I Large variation in hiring practices across occupations and industries Some positive correlation across 3 hiring practices Back I 45 / 79 Hiring practices and gender equality: conditional correlations
Yj = β1F emW ordingj + β2F lexibleW orkj + β3W ageP ostingj
+ αi + θOccSharesj + uj
j = firm publishing GPG indicators both in 2018 and 2019 [N = 4722]
Yj = Outcomes: % Women in top quartile of wage distribution; Median gender pay gap (both averaged across 2018 and 2019)
F emW ordingj = Avg % of female-oriented vacancies over 2015-2019
F lexibleW orkj = Avg % of vacancies offering FWA over 2015-2019
W ageP ostingj = Avg % of vacancies posting wages over 2015-2019
αi = Industry fixed effects
OccSharesj = Occupational composition of a firm’s vacancies over 2015-2019 Back
46 / 79 Hiring practices and gender equality indicators
% women at the top Median Gender Pay Gap
(1) (2) (3) (4) (5) (6) (7) (8) Female-oriented wording 0.0217∗∗ 0.0206∗∗ 0.0582∗∗∗ 0.0591∗∗∗ (0.00966) (0.00963) (0.00796) (0.00789) Offer flexible work 0.0870∗∗∗ 0.0827∗∗∗ -0.0461∗∗∗ -0.0451∗∗∗ (0.0231) (0.0230) (0.0175) (0.0173) Wage posted 0.0452∗∗∗ 0.0440∗∗∗ -0.0580∗∗∗ -0.0580∗∗∗ (0.00998) (0.00997) (0.00801) (0.00794) Observations 4722 4722 4722 4722 4722 4722 4722 4722
Occupation shares XXXXXXXX Industry FE XXXXXXXX Source: BGT 2015-2019, GEO 2018-2019. *** p<0.01, ** p<0.05, * p<0.1.
Back
47 / 79 Impact on hiring practices - ASHE sample
Entire sample Low GPG High GPG (1) (2) (3) Panel A: Gender Score Treated Firm*Post 0.0342 0.0205 0.120 (0.0575) (0.0617) (0.0806) Pre-Treatment Mean 0.12 0.15 0.05 P-value difference 0.29
Panel B: Flexible Work Treated Firm*Post 0.0110 0.00643 0.0250 (0.0130) (0.00984) (0.0249) Pre-Treatment Mean 0.03 0.03 0.03 P-value difference 0.42
Panel C: Wage Posting Treated Firm*Post -0.0193 -0.0181 0.0191 (0.0239) (0.0236) (0.0247) Pre-Treatment Mean 0.29 0.28 0.32 P-value difference 0.29
Observations 129877 94980 34897
Source: BGT, GEO, FAME 2013-19. *** p<0.01, ** p<0.05, * p<0.1.
I Coefficients change little compared to main estimates Back
48 / 79 Impact on hiring practices - outcomes
Entire sample Low GPG High GPG (1) (2) (3) Panel A: Gender Score Treated Firm*Post 0.0463 0.0644 0.115 (0.0703) (0.0716) (0.0876) Pre-Treatment Mean 0.62 0 .73 0.33 P-value difference 0.60
Panel B: Flexible Work Treated Firm*Post 0.00645 0.00166 0.0228 (0.0147) (0.0119) (0.0278) Pre-Treatment Mean 0.06 0.06 0.06 P-value difference 0.43
Panel C: Wage Posting Treated Firm*Post -0.0233 -0.0214 0.0199 (0.0234) (0.0232) (0.0253) Pre-Treatment Mean 0.25 0.24 0.28 P-value difference 0.14
Observations 97831 71354 26477
Source: BGT, GEO, FAME 2015-19. *** p<0.01, ** p<0.05, * p<0.1.
I Coefficients change little compared to main estimates Back
49 / 79 Compensating differentials?
(1) (2) Wage posted Log real wage Offer flexible work 0.0457 -0.0249 (0.0309) (0.0345) Gender score 0.00676*** -0.0139* (0.00234) (0.00794) Observations 76196 19268 Occ, Month, Firm FE XX Source: BGT, GEO, FAME 2015-2017. *** p<0.01, ** p<0.05, * p<0.1.
I FWA potentially come with a wage penalty
Back
50 / 79 Gender equality indicators and firms’ reputation in the workforce
Workforce’s reputation score (1) (2) Panel A: two years pooled Median GPG -0.00612 (0.0137) % women at the top 0.0106 (0.00923) Observations 2018 2018 Panel B: 2017-2018 Median GPG -0.00393 (0.0183) % women at the top 0.0107 (0.0131) Observations 998 998 Panel C: 2018-2019 Median GPG -0.00902 (0.0208) % women at the top 0.0105 (0.0130) Observations 1020 1020 Source: GEO, YouGov 2018-19. *** p<0.01, ** p<0.05, * p<0.1.
I Insignificant but similar patterns of correlation Back
51 / 79 Abnormal returns
Abnormal returns
1.5
1
.5
0
Abnormal returns -.5
-1
-1.5 -5 -4 -3 -2 -1 0 1 2 3 4 5
Days relative to publication
Back
52 / 79 5-day CARS
5-day cumulative abnormal returns
1.5
1
.5
0 5-day CARs -.5
-1
-1.5 -5 -4 -3 -2 -1 0 1 2 3 4 5
Days relative to publication
Back
53 / 79 3-day CARs around publication explained
(1) (2) (3) (4)
Group-avg GPG + 0.618 0.669 0.651 0.392 (0.678) (0.709) (0.670) (0.748) Group-avg GPG -0.0265 -0.0266 -0.0277 -0.0426 (0.0509) (0.0503) (0.0516) (0.0547) Group-avg GPG.*group-avg GPG + 0.0365 0.0359 0.0363 0.0477 (0.0524) (0.0513) (0.0526) (0.0582) Constant -1.057 2.163∗∗∗ 2.823∗∗∗ 0.560 (0.640) (0.723) (0.909) (1.702) Observations 405 405 405 383
Ownership structure XXX Industry FE XX Other controls X Source: Datastream, FAME, GEO 2017-18. *** p<0.01, ** p<0.05, * p<0.1.
I No differential effect on firms with GPG in favor of men Back 54 / 79 Institutional setting: UK 2017 GPG reporting
1. Targeted at firms with 250+ employees in April Headcount Why 250/ other policies
2. Timing: Firms’ report due by the end of next financial year (April) Timeline
3. Info disclosed: % GPG, % women by wage quartile Summary stats 4. Compliance I 100% compliance with some reporting after deadline Compliance I Potential sanctions on non-compliers (but not on bad performance) I Only 3% below 250; little correlation between timing and GPG GPG by publication 5. Information shock I Only 1/3 firms used to keep track of pay by gender before this mandate I Only 3% made these figures publicly available (Government Equalities Office 2015) 6. Salience I Indicators published on a dedicated government website I Large media attention at deadline (FT 2020, 2019, 2018, BBC 2018, Guardian 2018) I Spike of Google searches for "gender pay gap" at deadline Google searches Back 55 / 79 Institutional setting: UK 2017 GPG reporting
1. Targeted at firms with 250+ employees in April Headcount Why 250/ other policies
2. Timing: Firms’ report due by the end of next financial year (April) Timeline
3. Info disclosed: % GPG, % women by wage quartile Summary stats 4. Compliance I 100% compliance with some reporting after deadline Compliance I Potential sanctions on non-compliers (but not on bad performance) I Only 3% below 250; little correlation between timing and GPG GPG by publication 5. Information shock I Only 1/3 firms used to keep track of pay by gender before this mandate I Only 3% made these figures publicly available (Government Equalities Office 2015) 6. Salience I Indicators published on a dedicated government website I Large media attention at deadline (FT 2020, 2019, 2018, BBC 2018, Guardian 2018) I Spike of Google searches for "gender pay gap" at deadline Google searches Back 55 / 79 Institutional setting: UK 2017 GPG reporting
1. Targeted at firms with 250+ employees in April Headcount Why 250/ other policies
2. Timing: Firms’ report due by the end of next financial year (April) Timeline
3. Info disclosed: % GPG, % women by wage quartile Summary stats 4. Compliance I 100% compliance with some reporting after deadline Compliance I Potential sanctions on non-compliers (but not on bad performance) I Only 3% below 250; little correlation between timing and GPG GPG by publication 5. Information shock I Only 1/3 firms used to keep track of pay by gender before this mandate I Only 3% made these figures publicly available (Government Equalities Office 2015) 6. Salience I Indicators published on a dedicated government website I Large media attention at deadline (FT 2020, 2019, 2018, BBC 2018, Guardian 2018) I Spike of Google searches for "gender pay gap" at deadline Google searches Back 55 / 79 Institutional setting: UK 2017 GPG reporting
1. Targeted at firms with 250+ employees in April Headcount Why 250/ other policies
2. Timing: Firms’ report due by the end of next financial year (April) Timeline
3. Info disclosed: % GPG, % women by wage quartile Summary stats 4. Compliance I 100% compliance with some reporting after deadline Compliance I Potential sanctions on non-compliers (but not on bad performance) I Only 3% below 250; little correlation between timing and GPG GPG by publication 5. Information shock I Only 1/3 firms used to keep track of pay by gender before this mandate I Only 3% made these figures publicly available (Government Equalities Office 2015) 6. Salience I Indicators published on a dedicated government website I Large media attention at deadline (FT 2020, 2019, 2018, BBC 2018, Guardian 2018) I Spike of Google searches for "gender pay gap" at deadline Google searches Back 55 / 79 1. Information shock I Only 1/3 firms used to keep track of pay by gender before this mandate I Only 3% made these figures publicly available (Government Equalities Office 2015) 2. Salience I Indicators published on a dedicated government website I Large media attention at deadline (Financial Times 2020, Financial Times 2019, BBC 2018, Financial Times 2018, The Guardian 2018) I Spike of GPG searches on google at deadline:
100
80
60
40
Frequency (normalised to 100) 20
0 01/04/2015 01/04/2016 01/04/2017 01/04/2018 01/04/2019
Date
Back
56 / 79 Who counts as an employee
I Extended definition of employee:
I Employees: those with a contract of employment
I Workers and agency workers (those with a contract to do work or provide services)
I Some self-employed workers (where they have to personally perform the work)
I Each part time worker counts as one employee
I Partners enter in employee headcount but not in GPG calculations
Back
57 / 79 Other policies
I Firms with 250+ employees are considered large firms
I Accounting for 0.1% of businesses, 40% of employment and 48% of turnover
I Other policies affecting only firms with 250+ employees
I Since 2010: employees’ right to request time off for training
I Starting in 2020: Publication of pay gaps between CEO and median employee
I only applies to publicly listed companies
Back
58 / 79 The Timeline of the Mandate
Back
59 / 79 Compliance 2017-2018
.08
.06
.04
.02
0
01-2018 02-2018 03-2018 05-2018 06-2018
05-04-2018-Deadline
Source: UK Government Equality Office (GEO).
Note: The sample is restricted to firms publishing from January 2018. N. = 9,985.
Back
60 / 79 Submission date and GPG
2017-2018
100
50
0
-50 Median gender pay gap
-100
04-2017 10-2017 12-2017 02-2018 06-2018 08-2018
05-04-2018-Deadline
Submission date
Beta=-0.002; p-value=0.00.
Source: UK Government Equality Office (GEO).
Note: N. = 10,557.
Back 61 / 79 GPG searches on Google
100
80
60
40
Frequency (normalised to 100) 20
0 01/04/2015 01/04/2016 01/04/2017 01/04/2018 01/04/2019
Date
Back
62 / 79 ReferencesI
W. Abel, S. Tenreyro, and G. Thwaites. Monopsony in the uk. 2018. J. M. Abowd, F. Kramarz, and D. N. Margolis. High wage workers and high wage firms. Econometrica, 67(2):251–333, 1999. ACAS. Flexible working and work-life balance. Technical report, Advisory, Conciliation, and Arbitration Service, 2015. A. Adams, M. Balgova, and Q. Matthias. Flexible work arrangements in low wage jobs: Evidence from job vacancy data. CEPR Discussion Paper No. 15263, Centre for Economic Policy Research, 2020. K. R. Ahern and A. K. Dittmar. The changing of the boards: the impact on firm valua- tion of mandated female board representation. The Quarterly Journal of Economics, 127(1):pp. 137–197, 2012. G. A. Akerlof and J. L. Yellen. The fair wage-effort hypothesis and unemployment. The Quarterly Journal of Economics, 105(2):pp. 255–283, 1990.
63 / 79 ReferencesII L. Alderman. Britain aims to close gender pay gap with transparency and shame. New York Times, 2018. URL https://www.nytimes.com/2018/04/04/business/ britain-gender-pay-gap.html. H. Allcott. Social norms and energy conservation. Journal of Public Economics, 95 (9-10):1082–1095, 2011. H. Allcott and J. B. Kessler. The welfare effects of nudges: A case study of energy use social comparisons. American Economic Journal: Applied Economics, 11(1):pp. 236–76, 2019. M. Anderson and J. Magruder. Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal, 122 (563):pp. 957–989, 2012. T. Andrabi, J. Das, and A. I. Khwaja. Report cards: The impact of providing school and child test scores on educational markets. American Economic Review, 107(6): 1535–63, June 2017. doi: 10.1257/aer.20140774. URL https://www.aeaweb.org/ articles?id=10.1257/aer.20140774.
64 / 79 ReferencesIII H. Antecol, K. Bedard, and J. Stearns. Equal but inequitable: Who benefits from gender-neutral tenure clock stopping policies? American Economic Review, 108(9): pp. 2420–41, 2018. C. Aumayr-Pintar. Pay transparency in europe: First experiences with gender pay reports and audits in four member states. 2018. D. Autor, D. Dorn, L. F. Katz, C. Patterson, and J. Van Reenen. The fall of the labor share and the rise of superstar firms. 2017. J. Azar, I. Marinescu, and M. Steinbaum. Measuring labor market power two ways. In AEA Papers and Proceedings, volume 109, pages pp. 317–21, 2019. J. Azar, I. Marinescu, and M. Steinbaum. Labor market concentration. Journal of Human Resources, pages 1218–9914R1, 2020a. J. Azar, I. Marinescu, M. Steinbaum, and B. Taska. Concentration in us labor markets: Evidence from online vacancy data. Labour Economics, 66:p. 101886, 2020b. G. Azmat, M. Bagues, A. Cabrales, and N. Iriberri. What you don’t know. . . can’t hurt you? a natural field experiment on relative performance feedback in higher education. Management Science, 65(8):pp. 3714–3736, 2019.
65 / 79 ReferencesIV L. Babcock, S. Laschever, M. Gelfand, and D. Small. Nice Girls Don’t Ask. Harvard Business Review, 81(10):pp. 14–16, 2003. J. Bagger, F. Fontaine, M. Galenianos, and I. Trapeznikova. Vacancies, employment outcomes and firm growth: Evidence from denmark. Employment Outcomes and Firm Growth: Evidence from Denmark (May 12, 2020), 2020. M. Baker, Y. Halberstam, K. Kroft, A. Mas, and D. Messacar. Pay transparency and the gender gap. NBER Working Paper No. 25834, National Bureau of Economic Research, 2019. D. BEIS. Ethnicity pay reporting. Technical report, 2018. B. Bell and S. Machin. Minimum wages and firm value. Journal of Labor Economics, 36(1):pp. 159–195, 2018. M. Bennedsen, E. Simintzi, M. Tsoutsoura, and D. Wolfenzon. Do firms respond to gender pay gap transparency? NBER Working Paper No. 25435, National Bureau of Economic Research, 2019.
66 / 79 ReferencesV A. Benson, A. Sojourner, and A. Umyarov. Can reputation discipline the gig economy?: Experimental evidence from an online labor market. Benson, Alan, Aaron Sojourner, and Akhmed Umyarov, 2019. M. Bertrand and E. Duflo. Field experiments on discrimination. In Handbook of Eco- nomic Field Experiments, volume 1, pages 309–393. Elsevier, 2017. M. Bertrand, C. Goldin, and L. F. Katz. Dynamics of the gender gap for young profes- sionals in the financial and corporate sectors. American Economic Journal: Applied Economics, 2(3):pp. 228–55, 2010. M. Bertrand, S. E. Black, S. Jensen, and A. Lleras-Muney. Breaking the glass ceiling? the effect of board quotas on female labor market outcomes in norway. The Review of Economic Studies, 86(1):pp. 191–239, 2019. J. Blundell. Wage responses to gender pay gap reporting requirements. Available at ssrn: https://ssrn.com/abstract=3584259, 2021. H. R. Bowles, L. Babcock, and L. Lai. Social Incentives for Gender Differences in the Propensity to Initiate Negotiations: Sometimes it Does Hurt to Ask. Organizational Behavior and human decision Processes, 103(1):pp. 84–103, 2007.
67 / 79 ReferencesVI E. Breza, S. Kaur, and Y. Shamdasani. The morale effects of pay inequality. The Quarterly Journal of Economics, 133(2):pp. 611–663, 2017. E. Breza, S. Kaur, and Y. Shamdasani. The Morale Effects of Pay Inequality. The Quarterly Journal of Economics, 133(2):pp. 611–663, 2018. I. Burn and K. Kettler. The more you know, the better you’re paid? evidence from pay secrecy bans for managers. Labour Economics, 59:pp. 92–109, 2019. I. Burn, P. Button, L. F. M. Corella, and D. Neumark. Older workers need not apply? ageist language in job ads and age discrimination in hiring. NBER Working Paper No. 26552, National Bureau of Economic Research, 2019. D. Card, A. Mas, E. Moretti, and E. Saez. Inequality at work: the effect of peer salaries on job satisfaction. American Economic Review, 102(6):pp. 2981–3003, 2012. D. Card, A. R. Cardoso, J. Heining, and P. Kline. Firms and labor market inequality: Evidence and some theory. Journal of Labor Economics, 36(S1):S13–S70, 2018. M. Carlana. Implicit stereotypes: Evidence from teachers’ gender bias. The Quarterly Journal of Economics, 134(3):pp. 1163–1224, 2019.
68 / 79 ReferencesVII S. Chaturvedi, K. Mahajan, and Z. Siddique. Words matter: Gender, jobs and applicant behavior in india. Working paper, 2020. P. Cortes and J. Pan. When time binds: Substitutes for household production, returns to working long hours, and the skilled gender wage gap. Journal of Labor Economics, 37(2):pp. 351–398, 2019. D. L. Costa and M. E. Kahn. Energy conservation “nudges” and environmentalist ideology: Evidence from a randomized residential electricity field experiment. Journal of the European Economic Association, 11(3):680–702, 2013. Z. Cullen and B. Pakzad-Hurson. Equilibrium effects of pay transparency in a simple labor market. In EC, page 193, 2019. Z. Cullen and R. Perez-Truglia. How Much Does Your Boss Make? The Effects of Salary Comparisons. NBER Working Paper No. 24841, National Bureau of Economic Research, 2018a. Z. Cullen and R. Perez-Truglia. The Salary Taboo: Privacy Norms and the Diffusion of Information. NBER Working Paper No. 25145, National Bureau of Economic Research, 2018b.
69 / 79 ReferencesVIII C. M. Dahl, D. Le Maire, and J. R. Munch. Wage dispersion and decentralization of wage bargaining. Journal of Labor Economics, 31(3):501–533, 2013. W. Dahlgreen, R. Mpini, D. Palumbo, and C. Guibourg. What is the gender pay gap at your company? BBC News, 2018. URL https://www.bbc.co.uk/news/ business-43632763. J. De Loecker and J. Eeckhout. The rise of market power. Technical report, mimeo, 2017. D. Deming and L. B. Kahn. Skill requirements across firms and labor markets: Evidence from job postings for professionals. Journal of Labor Economics, 36(S1):S337–S369, 2018. C. Downing, E. Garnett, K. Spreadbury, and M. Winterbotham. Company reporting: Gender pay data. Technical report, IFF Research, 2015. D. Dranove and G. Z. Jin. Quality disclosure and certification: Theory and practice. Journal of Economic Literature, 48(4):pp. 935–63, 2010. A. Dube, L. Giuliano, and J. Leonard. Fairness and Frictions: the Impact of Unequal Raises on Quit Behavior. American Economic Review, 109(2):pp. 620–63, 2019.
70 / 79 ReferencesIX J. Ekberg, R. Eriksson, and G. Friebel. Parental leave—a policy evaluation of the swedish “daddy-month” reform. Journal of Public Economics, 97:pp. 131–143, 2013. O. Galor, Ö. Özak, and A. Sarid. Linguistic traits and human capital formation. In AEA Papers and Proceedings, volume 110, pages 309–13, 2020. D. D. K. Gamage, G. Kavetsos, S. Mallick, and A. Sevilla. Pay transparency initiative and gender pay gap: Evidence from research-intensive universities in the uk. IZA Discussion Paper No. 13635, Institute of Labor Economics., 2020. D. Gaucher, J. Friesen, and A. C. Kay. Evidence that gendered wording in job adver- tisements exists and sustains gender inequality. Journal of Personality and Social Psychology, 101(1):p. 109, 2011. V. Gay, D. L. Hicks, E. Santacreu-Vasut, and A. Shoham. Decomposing culture: An analysis of gender, language, and labor supply in the household. Review of Economics of the Household, 16(4):pp. 879–909, 2018. M. Gelfand and H. Stayn. Gender differences in the propensity to initiate negotiations. Social Psychology and Economics, 239, 2013.
71 / 79 ReferencesX S. Gibbons, E. Neumayer, and R. Perkins. Student satisfaction, league tables and university applications: Evidence from britain. Economics of Education Review, 48: pp. 148–164, 2015. P. Giuliano. Gender and culture. NBER Working Paper No. 27725, National Bureau of Economic Research, 2020. U. Gneezy, K. L. Leonard, and J. A. List. Gender differences in competition: Evidence from a matrilineal and a patriarchal society. Econometrica, 77(5):pp. 1637–1664, 2009. C. Goldin. A grand gender convergence: Its last chapter. American Economic Review, 104(4):pp. 1091–1119, 2014. G. Government Equalities Office. Closing the gender pay gap. government consultations. Technical report, 2015. D. Greene, V. J. Intintoli, and K. M. Kahle. Do board gender quotas affect firm value? evidence from california senate bill no. 826. Journal of Corporate Finance, 60:p. 101526, 2020.
72 / 79 ReferencesXI I. Gregory-Smith, B. G. Main, and C. A. O’Reilly III. Appointments, pay and perfor- mance in uk boardrooms by gender. The Economic Journal, 124(574):F109–F128, 2014. V. Grembi, T. Nannicini, and U. Troiano. Do fiscal rules matter? American Economic Journal: Applied Economics, pages 1–30, 2016. A. Gulyas, S. Seitz, and S. Sinha. Does pay transparency affect the gender wage gap? evidence from austria. CRC TR 224 Discussion Paper Series No. 194, University of Bonn and University of Mannheim, Germany, 2020. M. Hallsworth, J. A. List, R. D. Metcalfe, and I. Vlaev. The behavioralist as tax collector: Using natural field experiments to enhance tax compliance. Journal of public economics, 148:14–31, 2017. N.-J. H. Hansen and H. H. Sievertsen. Measuring vacancies: Firm-level evidence from two measures. Working paper, 2016. M. S. Johnson. Regulation by shaming: Deterrence effects of publicizing violations of workplace safety and health laws. American Economic Review, 110(6):pp. 1866–1904, 2020.
73 / 79 ReferencesXII M. Kim. Pay secrecy and the gender wage gap in the united states. Industrial Relations: A Journal of Economy and Society, 54(4):pp. 648–667, 2015. H. Kleven, C. Landais, J. Posch, A. Steinhauer, and J. Zweimuller. Child penalties across countries: Evidence and explanations. In AEA Papers and Proceedings, volume 109, pages pp. 122–26, 2019. N. Kommenda, C. Barr, and J. Holder. Gender pay gap: What we learned and how to fix it. The Guardian, 2018. URL https://www.theguardian.com/news/ng-interactive/2018/apr/05/ women-are-paid-less-than-men-heres-how-to-fix-it. P. Kuhn and K. Shen. Gender discrimination in job ads: Evidence from china. The Quarterly Journal of Economics, 128(1):pp. 287–336, 2013. P. Kuhn, K. Shen, and S. Zhang. Gender-targeted job ads in the recruitment process: Evidence from china. NBER Working Paper No. 25365, National Bureau of Economic Research, 2018. D. S. Lee and A. Mas. Long-run impacts of unions on firms: New evidence from financial markets, 1961–1999. The Quarterly Journal of Economics, 127(1):pp. 333–378, 2012.
74 / 79 ReferencesXIII
A. Leibbrandt and J. A. List. Do Women Avoid Salary Negotiations? Evidence from a Large-Scale Natural Field Experiment. Management Science, 61(9):pp. 2016–2024, 2015. D. L. Luca. The digital scarlet letter: The effect of online criminal records on crime. Available at SSRN 1939589, 2018. M. Luca. Reviews, reputation, and revenue: the case of yelp. com. Com (March 15, 2016). Harvard Business School NOM Unit Working Paper, (12-016), 2016. I. Marinescu and R. Wolthoff. Opening the black box of the matching function: the power of words. Journal of Labor Economics, 38(2):535–568, 2020. A. Mas. Does transparency lead to pay compression? Journal of Political Economy, 125(5):pp. 1683–1721, 2017. R. C. McDevitt. Names and reputations: An empirical analysis. American Economic Journal: Microeconomics, 3(3):193–209, 2011.
75 / 79 References XIV T. Mikolov, W.-t. Yih, and G. Zweig. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 746–751, 2013. M. Niederle. Gender. NBER Working Paper No. 20788, National Bureau of Economic Research, 2014. OECD. The pursuit of gender equality. Technical report, Organisation for Economic Co-operation and Development, 2017. OECD. Gender wage gap indicators. 2019. A. J. Oswald, E. Proto, and D. Sgroi. Happiness and productivity. Journal of Labor Economics, 33(4):pp. 789–822, 2015. R. Perez-Truglia. The effects of income transparency on well-being: Evidence from a natural experiment. American Economic Review, 110(4):pp. 1019–54, April 2020. doi: 10.1257/aer.20160256. URL https://www.aeaweb.org/articles?id= 10.1257/aer.2016025.
76 / 79 ReferencesXV R. Perez-Truglia and U. Troiano. Shaming tax delinquents: Theory and evidence from a field experiment in the united states. Citeseer, 2015. G. Ruddick. Women at bbc criticise pay review over failure to identify gender bias. The Guardian, 2018. URL https://www.theguardian.com/media/2018/jan/30/ bbc-pay-review-claims-no-evidence-of-gender-biasl. L. A. Rudman and S. E. Kilianski. Implicit and explicit attitudes toward female au- thority. Personality and Social Psychology Bulletin, 26(11):pp. 1315–1328, 2000. G. R. Siniscalco, E. M. Connell, and C. Smith. State pay equity laws: Where a few go, many may follow. Technical report, Mimeo, 2017. D. A. Small, M. Gelfand, L. Babcock, and H. Gettman. Who goes to the bargaining table? the influence of gender and framing on the initiation of negotiation. Journal of Personality and Social Psychology, 93(4):600, 2007. G. Sran, F. Vetter, and M. Walsh. Employer responses to pay history inquiry bans. Available at SSRN 3587736, 2020. D. Strauss. Gender pay reporting: Does it make a difference? Financial Times, 2019. URL https://www.ft.com/content/a000155c-57b0-11e9-91f9-b6515a54c5b1.
77 / 79 References XVI S. Tang, X. Zhang, J. Cryan, M. J. Metzger, H. Zheng, and B. Y. Zhao. Gender bias in the job market: a longitudinal analysis. Proceedings of the ACM on Human-Computer Interaction, 1(CSCW):99, 2017. D. B. Turban and D. M. Cable. Firm reputation and applicant pool characteristics. Journal of Organizational Behavior: the International Journal of Industrial, Occu- pational and Organizational Psychology and Behavior, 24(6):733–751, 2003. M. Wasserman. Hours constraints, occupational choice, and gender: Evidence from medical residents. Available at ssrn: https://ssrn.com/abstract=3371100, 2019. J. Wei, Z. Ouyang, and H. Chen. Well known or well liked? the effects of corporate reputation on firm value at the onset of a corporate crisis. Strategic Management Journal, 38(10):2103–2120, 2017. A. Wisniewska and D. Thomas. Reporting of uk companies’ gender pay gaps tum- bles in pandemic. Financial Times, 2020. URL https://www.ft.com/content/ 7fac51bc-4334-4467-983d-dcc671845e14.
78 / 79 References XVII
A. Wisniewska, B. Ehrenberg-Shannon, and S. Gordon. Gender pay gap: How women are short-changed in the uk. Financial Times, 2018. URL https://ig.ft.com/ gender-pay-gap-UK/. M. Wiswall and B. Zafar. Preference for the workplace, investment in human capital, and gender. The Quarterly Journal of Economics, 133(1):pp. 457–507, 2018.
79 / 79