Consumers and the Economy, Part I: Household Credit and Personal Saving
Bibliographic record
Abstract
In the years since the bursting of the housing bubble, the personal saving rate has trended up from around 1 % to around 6%, while the ratio of household debt to disposable income has dropped from 130 % to 118%. Changes over time in the availability of credit to households can explain 90 % of the variance of the saving rate since the mid-1960s, including the recent uptrend, according to a simple empirical model. Following a 20-year decline, the U.S. personal saving rate bottomed out at around 1 % in the third quarter of 2005. Since then, the rate has been trending upward, reaching around 6 % in the third quarter of 2010. The era of declining saving rates coincided with a period of expanding credit availability for households that contributed to a dramatic increase in leverage as measured by the ratio of household debt to personal disposable income. During the boom years of the mid-2000s, the combination of declining saving rates and rapidly rising household debt allowed consumer spending to grow much faster than disposable income, providing a significant boost to the economy. Recently however, the rebound in the saving rate has coincided with a reduction in household debt—a deleveraging—that has acted as a drag on consumer spending and the economy. In this Economic Letter, we show that movements in the availability of credit are very important for
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".