Evolution of Consumption Volatility for the Liquidity Constrained Households over 1983 to 2004
Bibliographic record
Abstract
We study whether the increased income uncertainty in the US over the last quarter-century had a negative impact on household welfare by looking at variability of household consumption growth. We are particularly interested in understanding the effect of greater uncertainty on the liquidity constrained households. We study the evolution of liquidity constraints in the US in the Panel Study of Income Dynamics, extending Jappelli et al. [1998] methodology using information from the Survey of Consumer Finances. We find that although household indebtedness increased substantially, reflecting greater availability of credit, there was no decline in the proportion of liquidity constrained households between 1983 and 2007. Applying methodology developed in Gorbachev [2009], we find that the evolution of consumption volatility for the liquidity constrained households increased by economically and statistically more than for the unconstrained households. This increase was lower than that of family income volatility for these groups. Nevertheless, the welfare cost to society is substantial: we estimate that an average household would be willing to sacrifice 4.7 percent of nondurable consumption per year to lower consumption risk to its 1984 levels.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".