Imputing rent in consumption measures, with an application to consumption poverty in Canada, 1997–2009
Bibliographic record
Abstract
Abstract We consider two econometric problems in the measurement of poverty, both relating to rent imputation. First, we account for quality differences correlated with selection into owner‐occupied versus rental tenure. This correction increases estimated household consumption by 5% over uncorrected estimates and decreases estimated poverty rates quite dramatically. Second, we propose that measurement error induced by the imputation be corrected by imputing a consumption distribution, rather than a consumption level, for each household. This correction increases estimated poverty rates slightly. We use our methods to measure consumption poverty in Canada, and find that the imputation strategy used influences the patterns observed. For example, measured poverty among the elderly barely declines when one uses our methods, in contrast to the almost 6 percentage point reduction we find using traditional methods. In our assessment of the over‐time evolution of consumption poverty, we find that substantial progress has been made on overall poverty and on child poverty, but that poverty among the elderly hardly changed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".