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Record W4392145531 · doi:10.3386/w32110

Lives vs. Livelihoods: The Impact of the Great Recession on Mortality and Welfare

2024· report· en· W4392145531 on OpenAlexaff
Amy Finkelstein, Matthew Notowidigdo, Frank Schilbach, Jonathan Zhang

Bibliographic record

VenueNational Bureau of Economic Research · 2024
Typereport
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsMcMaster University
FundersBooth School of Business, University of Chicago
KeywordsLivelihoodWelfareRecessionGreat recessionEconomicsDemographic economicsDevelopment economicsGeographyLabour economicsKeynesian economicsMarket economyAgriculture

Abstract

fetched live from OpenAlex

We leverage spatial variation in the severity of the Great Recession across the United States to examine its impact on mortality and to explore implications for the welfare consequences of recessions.We estimate that an increase in the unemployment rate of the magnitude of the Great Recession reduces the average, annual age-adjusted mortality rate by 2.3 percent, with effects persisting for at least 10 years.Mortality reductions appear across causes of death and are concentrated in the half of the population with a high school degree or less.We estimate similar percentage reductions in mortality at all ages, with declines in elderly mortality thus responsible for about three-quarters of the total mortality reduction.Recession-induced mortality declines are driven primarily by external effects of reduced aggregate economic activity on mortality, and recession-induced reductions in air pollution appear to be a quantitatively important mechanism.Incorporating our estimates of pro-cyclical mortality into a standard macroeconomics framework substantially reduces the welfare costs of recessions, particularly for people with less education, and at older ages where they may even be welfare-improving.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.542
GPT teacher head0.669
Teacher spread0.127 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2024
Admission routes1
Has abstractyes

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