Remittances, Household Investment and Poverty in Indonesia
Bibliographic record
Abstract
This paper analyzes the impact of international remittances on household investment and poverty using panel data (2000 and 2007) from the Indonesian Family Life Survey (IFLS). Using a three-stage conditional logit model with instrumental variables to control for selection and endogeneity, it finds that households receiving remittances in 2007 spend more at the margin on one key consumption good (food) and more at the margin on one important investment good (education) compared to what they would have spent on these goods without the receipt of remittances. Using a bivariate probit model with random effects to control for selection and simultaneity, the paper also finds that households receiving remittances are less likely to be poor compared to a situation in which they did not receive remittances. These findings are important because they show that households can use remittances to help build human capital and to reduce poverty in remittance-receiving countries.
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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".