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Record W3175684030 · doi:10.1257/pol.20200561

Employed in a SNAP? The Impact of Work Requirements on Program Participation and Labor Supply

2023· article· en· W3175684030 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Economic Journal Economic Policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsEarningsRegression discontinuity designWork (physics)Work hoursDemographic economicsSnapLabour economicsEconomicsBusinessWorking hoursMedicineAccountingEngineeringComputer science

Abstract

fetched live from OpenAlex

Work requirements are common in US safety net programs. Evidence remains limited, however, on the extent to which work requirements increase economic self-sufficiency or screen out vulnerable individuals. Using linked administrative data on food stamps (SNAP) and earnings with a regression discontinuity design, we find robust evidence that work requirements increase program exits by 23 percentage points (64 percent) among incumbent participants. Overall program participation among adults who are subject to work requirements is reduced by 53 percent. Homeless adults are disproportionately screened out. We find no effects on employment and suggestive evidence of increased earnings in some specifications. (JEL H75, I18, I32, I38, J22, J31)

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.154
GPT teacher head0.540
Teacher spread0.386 · 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