Imputing Rent in Consumption Measures, with an Application to Consumption Poverty in Canada 1997-2009
Bibliographic record
Abstract
Measures of household consumption-which may be used to investigate average consumption growth, mor consumption poverty and inequality-must account for the rental flow from owned accommodation. We consider two econometric problems relating to the imputation of rental flows for owned accommodation that have been thus far ignored in the literature on consumption poverty and inequality. First, using a Heckman-type correction, we account for quality differences correlated with selection into owner-occupied versus rental tenure. Using this correction increases the estimate of average household consumption by about 4 per cent in comparison with uncorrected estimates. Second, we propose a new way to measure poverty (or inequality) that accounts for the measurement error induced by the rent imputation. In particular, we argue that the researcher should impute a consumption distribution, rather than a single consumption level, for each household. We apply our methods to the measurement of consumption poverty in Canada. We estimate the rate of consumption poverty for all people, and for children and the elderly, over the period 1997-2009, allowing for interprovincial and intertemporal variation in commodity prices. Using a better rent imputation strategy matters a lot: the over-time pattern in poverty rates is quite different with the selection- and measurement-error corrected approach. We find that poverty declined dramatically over the study period, and that, although substantial progress was been made on overall poverty and child poverty, poverty among the elderly did not decrease very much.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".