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Record W4410826592 · doi:10.1080/00220388.2025.2504425

The Impact of Shock-Responsive Social Cash Transfers: Evidence from an Aggregate Shock in Kenya

2025· article· en· W4410826592 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

VenueThe Journal of Development Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsInstitute of Health Economics
FundersBundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung
KeywordsShock (circulatory)Cash transfersAggregate (composite)CashEconomicsMonetary economicsBusinessMacroeconomicsMedicineMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

This study examines the effects of a nationwide shock-responsive social cash transfer scheme during the COVID-19 pandemic, with a focus on highly risk susceptible informal economy households in Kenya. Leveraging primary in-person survey data in a doubly robust difference-in-differences framework, we find that households receiving shock-responsive cash transfers were less likely to encounter income loss, poverty, and food scarcity compared to households not receiving them. The scheme also reduced the likelihood of engaging in costly risk coping such as selling productive assets. When comparing different pillars of the scheme with varying degrees of shock-responsiveness, we observe that the impacts were statistically significant only when payment cycles were pooled and the transfers were vertically scaled. The study adds to the global policy discussion on developing effective shock-responsive interventions, underscoring the merits of shock-responsive social cash transfers during crises.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.382
Teacher spread0.338 · 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