What Do We Really Know About Wages: The Importance of Nonreporting and Census Imputation
Bibliographic record
Abstract
In the most frequently used microdata sets, over a quarter of all respondents now refuse to answer some questions about their incomes. The Census Bureau has dealt with this problem, which has been increasing in severity over time, by imputing incomes of non-respondents. Their imputation procedure, called the hot deck, essentially matches nonrespondents with demographically similar donors. In this paper we evaluate the census imputation methodology and raise some questions. First, the census procedure is tied to commonality of events in the population rather than the more appropriate informational content of regressors. Clearly, the census procedure severely understates income in certain occupations. Because it is based on the apparently invalid assumption that income does not affect reporting propensities, it most likely understates average incomes as well.
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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 | 0.163 | 0.618 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.022 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".