Closing the Gender Gap in Corporate Advancement: Insights and Solutions from Behavioural Economics
Bibliographic record
Abstract
English Abstract: Despite evidence that both gender and ethnically diverse leadership is good for businesses’ bottom line, just one in five senior North American business leaders is female, one in thirty a woman of colour. Little literature exists applying behavioural economics [BE] concepts to explain gender gaps. Yet, as demonstrated by the 2010 UK Conservative-Liberal Democrats coalition government, the Obama government in the US and Trudeau government in Canada, lawmakers, policymakers and business leaders are interested in BE’s persuasive power to influence behaviour. My contribution exploits this interest, builds on the excellent existing scholarship analyzing gender gap concepts from a BE perspective, and fills this gap. Applying concepts of bounded rationality, bounded willpower, bounded self-interest, and the endowment effect to 2017’s North American-focused Women in the Workplace report (Report) published by LeanIn and McKinsey, a vast study examining HR practices and pipeline data of 222 companies employing 12 million+ people and surveying 70,000+ employees’ experiences, I find that hiring and promotion decisions are affected by the three bounds and endowment effect, undercutting businesses’ compelling economic interest in diverse leadership. BE offers solutions to tackle biased behaviour and shows how gender gap scholars’ and the Report’s recommendations can be taken further to close the gender gap in advancement. I argue that normative best practice adoption by business and nudges and tax incentives from governments, ideally in combination, can spur businesses to adopt debiasing behaviours and practices that will contribute to closing the gender gap in advancement. Enabling women to achieve their full leadership and economic potential will enhance women’s wellbeing, improve businesses’ performance, and lead to greater social equity. French Abstract: Malgre les donnees qui indiquent que la diversite sexuelle et ethnique aux postes de direction ameliore les resultats financiers des entreprises, la haute direction des entreprises en Amerique du Nord compte a peine une femme sur cinq et une femme de couleur sur trente. La litterature qui applique les concepts de l’economie comportementale (EC) pour expliquer l’ecart entre les genres est peu abondante. Pourtant, comme l’ont demontre le gouvernement de coalition britannique entre les Conservateurs et les Liberaux-democrates en 2010, le gouvernement Obama aux Etats-Unis et le gouvernement Trudeau au Canada, les legislateurs, les decideurs politiques et les dirigeants d’entreprise s’interessent au pouvoir de persuasion de l’economie comportementale pour influencer le comportement. Mon article exploite cet interet, s’appuie sur l’excellente litterature savante qui analyse les concepts de l’ecart entre les genres du point de vue de l’economie comportementale et comble cette lacune. En appliquant les concepts de la rationalite limitee, de la volonte limitee et de l’interet personnel limite ainsi que l’effet de dotation au rapport Women in the Workplace (le rapport), publie par LeanIn et McKinsey, vaste etude de la situation en Amerique du Nord qui a examine les procedures des ressources humaines et les donnees en serie de 222 compagnies employant plus de 12 millions de personnes, et qui a fait une enquete sur l’experience de plus de 70 000 employes, je conclus que les decisions relatives a l’embauche et a la promotion sont influencees par les trois limitations et l’effet de dotation, ce qui est contraire a l’interet economique pressant des entreprises de pouvoir compter sur un leadership diversifie. L’economie comportementale offre des solutions pour lutter contre les comportements partiaux et montre comment les recommandations figurant dans les etudes sur l’ecart entre les genres et dans le Rapport peuvent etre poussees plus loin pour reduire l’ecart entre les genres dans l’avancement au travail. Je soutiens que l’adoption de pratiques exemplaires normatives par les entreprises ainsi que des incitations et des encouragements fiscaux provenant des gouvernements, qui iraient idealement de pair, peuvent pousser les entreprises a adopter des pratiques et des comportements impartiaux qui contribueront a reduire l’ecart entre les genres dans l’avancement au travail. Si on permet aux femmes de realiser pleinement leur potentiel economique et leur potentiel de leadership, cela ameliorera leur bien-etre et le rendement des entreprises et amenera une plus grande equite sociale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".