Building a business case for workplace menstrual hygiene management programs: a social cost–benefit analysis
Bibliographic record
Abstract
Abstract Inadequate menstrual health and hygiene (MHH) pose a great challenge for working women, affecting their productivity, job satisfaction, attendance, and advancement, and also have implications for their employers. Yet there has been little research to quantify the impacts of poor MHH conditions or to consider the value add of workplace MHH programs. As part of USAID's Water Sanitation and Hygiene Partnerships for Learning and Sustainability project, we conducted a social cost–benefit analysis (CBA) of the Menstrual Hygiene Management (MHM) in the Workplace Action Research, a 10-month intervention in private sector enterprises in Nepal and Kenya. The intervention aimed to determine if providing adequate MHM in the workplace contributes to women's economic empowerment, including improved business and social outcomes. This CBA of a workplace MHM intervention – the first of its kind – found a positive return for investing in workplace MHM programs. The average benefit–cost ratio in the base-case across factories in a 10-month intervention was 1.4, which increased to 2.3 when projected over 24 months. These early results of a pilot CBA on MHM in the workplace should serve as a call for greater attention by governments and businesses to the needs of menstruating women.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".