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 United States 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
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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 | 0.033 | 0.064 |
| 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 | 0.029 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".