Empirical Issues in Lifetime Poverty Measurement
Bibliographic record
Abstract
<p>This paper demonstrates the implications of adopting an approach to measuring poverty that takes into account the lifetime experience of individuals rather than simply taking a static or cross-sectional perspective. Our approach follows the theoretical innovations in Hoy and Zheng (2008) which address various aspects of the specific pattern of any poverty spells experienced by an individual as well as a possible retrospective consideration that an individual might have concerning his life experience as a whole. For an individual, our perspective of lifetime poverty is influenced by both the snapshot poverty of each period and the poverty level of the permanent lifetime consumption; it is also influenced by how poverty spells are distributed over the lifetime. Using PSID data for the US, we demonstrate empirically the power of alternative axioms concerning how lifetime poverty should be measured when making pairwise comparisons of individual lifetime profiles of consumption (income) experiences. We also demonstrate the importance of taking a lifetime view of poverty in comparing poverty between groups by use of the classic FGT ‘snapshot’ poverty index in conjunction with period weighting functions that explicitly reflect concerns about the pattern of poverty spells over individuals’ lifetimes. </p>
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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 | 0.006 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".