Who is poor and who is not? Toward an empirical basis for identifying the poor
Bibliographic record
Abstract
In "Poverty: An Ordinal Approach to Measurement" (1976), Amartya Sen defines the problem of identifying the poor as one of two that have to be addressed when we operationalize poverty.In this dissertation, I propose an innovative empirical approach to responding to this problem.My dissertation unfolds in four stages.First, I argue that an empirical approach to the identification problem is both desirable and possible.Second, I propose my own approach.Drawing on work in the basic needs tradition, I argue that experiencing poverty should result in the life trajectories of the poor looking different from those of the non-poor.Specifically, they should be uniquely vulnerable to suffering a cumulative negative dynamic in functioning over time, which I elect to call "deprivation."Third, I model this dynamic and propose a method for using it to evaluate poverty indicators.Fourth, I estimate the dynamic in the United States and Canada using panel data on individual earnings and household income and dynamic panel data estimators.Then, I use these results to assess the relative poverty indicator most widely used in rich countries, known in Canada as the Low-Income Measure (LIM).I find that a household in the UnitedStates needs to command about 61% of median adjusted after-tax income and in Canada, 43%, to be able to protect themselves against deprivation.Broadly speaking, if we accept the theoretical arguments offered in this dissertation, then the LIM-which is defined at 50%-strikes a rough balance between these two empirical standards.Dans «La pauvreté: une approche ordinale de la mesure» (1976), Amartya Sen définit le problème de l'identification des pauvres comme l'un des deux problèmes à résoudre lorsque nous opérationnalisons la pauvreté.Dans cette thèse, je propose une approche empirique innovante pour répondre à ce problème.Ma thèse se déroule en quatre étapes.Premièrement, je soutiens qu'une approche empirique du problème d'identification est à la fois souhaitable et possible.Deuxièmement, je propose ma propre approche.Basé sur le travail de la tradition des besoins essentiels, je soutiens que faire l'expérience de la pauvreté devrait donner aux trajectoires de vie
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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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".