Crime, Punishment and the Measurement of Poverty in the United States, 1979-1997
Bibliographic record
Abstract
The rate of incarceration has increased dramatically in the U.S. since 1980. We explore the implications of this increased incarceration on national poverty measurement using micro data for the period 19791997. We make use of an as-yet unexplored data set on prisoner earnings, in conjunction with the Current Population Survey to compute earnings of the whole population. It is found that the traditional measurement of poverty, which omits this increased share of the population that has become institutionalized, understates the true degree of poverty in the nineteen nineties to a significant degree. This underestimation has increased during the time period of study. Furthermore, it is the depth of poverty associated with the higher incarceration rate, rather than the higher rate of incarceration alone that has had the greatest impact upon poverty. These results stand in marked contrast to western European economies and Canada.
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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 | 0.022 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".