Revisiting the Relationship between Financial Wealth, Housing Wealth, and Consumption: A Panel Analysis for the U.S.
Bibliographic record
Abstract
Based on the seminal paper of Case, Quigley, and Shiller (2013), we investigated the effects of financial and housing wealth on consumption. Using quarterly data from 1975 to 2016 for all states of the U.S. economy, and a different methodology in measuring wealth, we report relatively greater financial effects than housing effects on consumption. Specifically, in our basic utilized model, the calculated elasticity for financial wealth was 0.060, while for housing it was 0.045. The results were not in agreement with the ones obtained by Case, Quigley, and Shiller. In an attempt to investigate this disparity, we proceeded by incorporating the introduction of the Tax Reform Act in 1986, which increased incentives for owner-occupied housing investments. Finally, due to distributional factors at work, and taking into account the pronounced uneven distribution of wealth, we investigated the effects of wealth for eight states that included the metropolitan areas comprising the well-known Case–Shiller 10 City Composite Index. Now the housing effect on consumption was much stronger and larger than the financial effect. Additionally, we forecasted the consumption changes at the time of high rise and large drops in house prices for these states. Forecasts showed a recession from the fall of Lehman Brothers until the fourth quarter of 2011. These forecasts were not verified. Probably, the new techniques used by policies played an important role. We also found that extreme behaviors cannot be predicted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".