Energy Poverty and Education: Empirical Evidence from Indonesia
Bibliographic record
Abstract
Energy poverty in Indonesia has brought negative impacts on various sectors, including education which is the fourth target in the Sustainable Development Goals. This study explores how energy poverty, which is proxied by the percentage of households consuming <32.4 kwh per month in district or cities in Indonesia in 2015 and 2017, affects education, which is proxied by average years of schooling in district or cities in Indonesia in 2019. By applying the 2SLS method, the instrument variable approach used is the geographical characteristics of an area which is the mean elevation value approach in districts or cities to accurately predict the impact of energy poverty on average years of schooling. The results show a negatively significant impact on education for both energy-poor condition. The results for the first condition (2015) shows that 0.993 year of average years of schooling will be lost due to energy poverty. Whereas in the second condition (2017), 0.164 year of average years of schooling will be lost. This research also serves as an empirical evidence that energy poverty does not directly affect the average years of schooling in districts and cities in Indonesia.
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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 | 0.000 | 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 | 0.001 | 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) | 0.012 | 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".