The Rise and Fall of Consumption in the 2000s: A Tangled Tale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
US consumption has gone through steep ups and downs since 2000. We quantify the statistical impact of income, unemployment, house prices, credit scores, debt, financial assets, expectations, foreclosures and inequality on county‐level consumption growth for four subperiods: the ‘dot‐com recession’ (2001–3), the ‘subprime boom’ (2004–6), the Great Recession (2007–9) and the ‘tepid recovery’ (2010–12). Consumption growth cannot be explained by a few factors; rather, it depends on a large number of variables whose explanatory power varies by subperiod. Growth of income, growth of housing wealth and fluctuations in unemployment are the most important determinants of consumption, significantly so in all subperiods, while fluctuations in financial assets and expectations are important during only some subperiods. Lagged variables, such as the share of subprime borrowers, are significant but less important.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it