Household debt and the Great Recession: Fisher's debt and deflation theory and how we should view the household debt burden
Bibliographic record
Abstract
This paper compares household debt conditions in the United States before and after the Great Recession of 2007 - 2009. I use a combination of data from the World Bank Development Indicators and the Bank for International Settlements to estimate a model using the Synthetic Control Method for Stata, which creates a depiction of what household debt in the U.S. would have looked like without the impacts of the Great Recession. As a reference point for countries that could be used to compile the weighted synthetic control group, I study the economic characteristics of Australia, Canada, France and Germany because these countries performed well under recessionary pressure. However, upon running the model, I find that the best fit for the data set uses Belgium, Netherlands, Canada, Korea, Portugal, Germany and Denmark. The estimated model is best interpreted through a graph of the household debt service ratio in actual U.S. data and for the synthetic control group changing over time. It shows a clear rapid deleveraging trend for households over the course of the Great Recession, contrasting with a household debt service ratio that remains relatively high for the control group through the recovery period. I then discuss possible implications of my findings, including how the model's similarities to Fisher's household debt theory suggest its relevance in explaining household debt behavior and how it suggests we should view the household debt burden.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".