THE GREAT OVERESTIMATION: TAX DATA AND INEQUALITY MEASUREMENTS IN THE UNITED STATES, 1913–1943
Bibliographic record
Abstract
Historical measures of income inequality in the United States must grapple with the challenge of data quality. We examine one such problem affecting the well‐known estimates of income inequality produced by Piketty and Saez (2003) using the records of the Internal Revenue Service (IRS). Prior to 1943, incomes were self‐reported. Combined with lax enforcement on the part of the IRS, self‐reporting of incomes could provide a misleading portrait of the income distribution. To test the accuracy of IRS records, we compare them to independently tabulated state income tax returns between 1919 and 1945 from states with more comprehensive and rigorously enforced tax collection procedures. State income tax records show lower overall levels of income inequality than IRS records. However, we still find that top income concentrations declined across the period between 1929 and World War II. These findings attest to the sensitivity of distributional estimation to the reporting selectivity and economic quality of underlying tax data, suggesting that the existing IRS‐derived series systematically overstates top‐income concentration in the interwar period.(JELH2, N32, D31, E01)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".