Rural electrification and women's empowerment in Côte d’Ivoire
Bibliographic record
Abstract
Purpose This paper assesses the impact of this program on the rural women's employment opportunities using data from the 2015 round of the household's living standard survey (HLSS) of Côte d'Ivoire. Design/methodology/approach In 2013, in order to improve the living conditions of the rural population, the Ivorian government launched the National Program for rural electrification (PRONER) to electrify all localities with more than 500 inhabitants. Findings The results show that PRONER, while reducing the time allocated to performing household chores, increases women's employment through the reallocation of time to full-time paid work in the agricultural and non-agricultural sectors. The authors also find that the allocation of men's time is not affected by this programme. A possible mechanism that would explain such a pro-women effect is the labour-saving technology introduced to home production as an effect of the reform. Research limitations/implications As a limitation, it is important to note that these results were obtained in the specific context of PRONER in Côte d’Ivoire and are not necessarily applicable to rural electrification programmes in other contexts. Furthermore, the choice of other indicators to measure women's empowerment is limited by the quality of the data available. It would be interesting for future research to extend this analysis to include other aspects of women's empowerment and household welfare. Originality/value This paper is the first to the author’s knowledge to apply a robust econometric method by combining an inverse probability weighted regression adjustment model with Heckman sample selection method to access a robust causal effect of the PRONER in Côte d'Ivoire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".