2025 Update: Introducing the THIAs: Total Haig-Simons U.S. Household Income, Consumption, and Wealth Accounts, 1960–2023
Bibliographic record
Abstract
This paper presents the Total Haig-Simons U.S. Household Income Accounts (THIAs), an open-access data set providing balance-sheet-complete measures of income, saving, and wealth accumulation for U.S. households from 1960 to 2023, with prototype distributional estimates for all measures by income quintile since 2000. By integrating NIPA income and saving measures with accrued Holding Gains and Other Volume Changes from the Integrated Macroeconomic Accounts (IMAs), the THIAs construct integrated Haig-Simons income series for use by researchers. Distributional analysis reveals that 86% of Total saving over the past quarter century accrued to the top 20% of households, driven by disproportionate exposure to asset-price appreciation and significantly lower propensities to consume. The data set enables researchers to examine inequality dynamics through a fully integrated income-consumption-wealth lens, addressing researchers’ methodological calls for integrated “3D” national accounts.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.055 |
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".