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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".