Characterising Vulnerability to Poverty in Rural Haiti: A Multilevel Decomposition Approach
Bibliographic record
Abstract
Abstract This article characterises vulnerability to poverty in Haiti using a unique survey conducted in 2007 in rural areas. In a first step, using two‐level linear random coefficient models of both per capita consumption and per capita income, the article assesses the impact of self‐reported shocks on households' economic well‐being. In a second step, the prediction model is used to calculate various measures of vulnerability to poverty, considering various types of shocks. Empirical findings show that self‐reported (or observable) idiosyncratic shocks, in particular health‐related shocks, have larger impact on vulnerability to poverty than observable covariate shocks. These results are in line with the fact that many households reported idiosyncratic health shocks as being the worst shocks they experienced. On the other hand, unobservable idiosyncratic shocks appear to have generally more influence on households' vulnerability to poverty than unobservable covariate ones. We also show that omitting self‐reported shocks in the analysis leads to an underestimate of households' vulnerability to poverty.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".