The Effects of Gendered Occupational Roles on Workplace Authority: Evidence from Microfinance
Bibliographic record
Abstract
Research in sociology and social psychology has documented how the gendering of occupational roles can affect a variety of outcomes for workers and organizations. Although laboratory experiments offer insights into the processes by which occupational roles become gendered and lead to systematically gendered outcomes, there is a relative dearth of evidence from field settings. Such field-based evidence is scarce because existing occupations are rarely gender balanced and workers’ tasks often change with new occupants. In the present paper, we fill this gap by utilizing unique data from a commercial microfinance bank in Central America. We examine how the occupational role of a loan manager becomes gendered, and how such initial gendering affects the authority of subsequent role occupants. Our findings both confirm and extend existing research. On average, male loan managers are more likely to obtain borrower compliance (i.e. on-time loan payments) than female managers. However, the gender of the initial manager continues to shape clients’ compliance even when clients are transferred to a second manager. Overall, this paper offers a unique empirical test of existing theories and demonstrates how a single individual can inscribe gendered expectations onto an occupational role, thereby generating divergent outcomes for male and female managers.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".