The Rise and Fall of Consumption in the 2000s: A Tangled Tale
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.
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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".