Closing the Gender Gap in Corporate Advancement: Insights and Solutions from Behavioural Economics
Bibliographic record
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.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".