What Explains LPG Adoption? Role of Culture, Women's Empowerment, and Economics, Using the Indian Human Development Survey, 2005 and 2012
Bibliographic record
Abstract
INTRODUCTIONAccording to the Global Burden of Disease study, household air pollution from cooking with solid fuels killed over 700,000 Indians in 2016. Major national initiatives aim to combat the problem via increased distribution of connections to subsidized Liquefied Petroleum Gas (LPG). Here, we examine the relationship between LPG adoption / disadoption and household factors.METHODSWe used the 2005 and 2012 Indian Human Development Survey (IHDS), a nationally representative multi-topic panel dataset (re-contact rate: 84%). Using linear regressions, we estimated associations between LPG adoption/disadoption and (1) household economic status (income, assets, and consumption) and (2) cultural factors, including women’s empowerment. We developed a gender / women’s empowerment score based on questions on independence, family dynamics and decision making, and a binary variable for whether men eat first in the family.RESULTSOf the 12926 households (HH) who owned LPG in 2005, 1181 (9%) gave up LPG in 2012. Of the 23226 HH who did not have LPG in 2005, 5400 (23%) acquired LPG in 2012. We found significant association of economic and cultural factors with (1) LPG ownership in 2005, (2) LPG ownership in 2012, and (3) adoption/disadoption between 2005 and 2012. For example, for HH without LPG in 2005, holding all other variables constant, the odds of the HH acquiring LPG is 32% lower for HH where men eat first than in other HH (p < 0.001). For HH with LPG in 2005, holding all other variables constant, odds of the HH disadopting LPG is 39% higher for HH where men eat first than in other HH (p < 0.001).CONCLUSIONWomen empowerment can explain some of the dynamics of adoption and disadoption of clean-cooking options, and are potentially as important or more important than economic factors.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".