The Impact of Macroeconomic Conditions on the Health Insurance Coverage of Americans
Bibliographic record
Abstract
In March 2001, the longest economic expansion in U.S. history ended, and an economic recession began. This paper seeks to provide a better understanding of the historical relationship between macroeconomic variables and health insurance coverage.We use data from two nationally representative samples: the Survey of Income and Program Participation (SIPP) and the National Longitudinal Survey of Youth (NLSY). The longitudinal nature of our data allows us to remove individual-specific, time-invariant heterogeneity and to focus on changes in health insurance status in response to changes in macroeconomic variables.The results confirm our prediction that the probability of any health insurance coverage is negatively associated with unemployment rate. We find that a one percentage point increase in the state unemployment rate is associated with a decrease in the probability of health insurance coverage, through any source, of 0.62 percent for men, 0.54 percent for women, and 1.1 percent for children. However, our prediction that an indicator variable for national recession would be negatively correlated with the probability of health insurance coverage is not supported by the data. We find that changes in employment status explain roughly one-quarter of the correlation between health insurance coverage and unemployment rates. Our estimates imply that 440,000 men, 436,000 women, and 494,000 children have lost health insurance coverage during the current recession.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".