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Cash on delivery: Results of a randomized experiment to promote maternal health care in Kenya

2019· article· en· W2757153932 on OpenAlexaff
Karen A. Grépin, James Habyarimana, William Jack

Bibliographic record

VenueJournal of Health Economics · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsVoucherRandomized experimentCash transfersCopaymentCashConditional cash transferPsychological interventionBusinessRandomized controlled trialHealth careMedicineFinanceEconomicsNursingPovertyEconomic growthHealth insurance

Abstract

fetched live from OpenAlex

• Full vouchers and conditional cash transfers were highly effective at increasing institutional deliveries among poor, rural women in Kenya. • Co-payments reduced the effectiveness of vouchers and unconditional transport subsidies were not effective. • A free care policy instituted by the government also demonstrated no effect on institutional deliveries. • SMS text reminders did not have any effect on institutional deliveries. We conducted a randomized controlled experiment to test whether vouchers, cash transfers, and SMS messages were effective in boosting facility delivery rates among poor, pregnant women in rural Kenya. We find a strong effect of the full vouchers and the conditional cash transfers: 48% of women with access to both interventions delivered in a health facility, while only 36% of those with neither did. Amongst women who did not receive a cash transfer, we find that a small copayment dramatically reduced voucher effectiveness, suggesting a discontinuous impact of cost-sharing on the demand for health services. Both the unconditional cash transfer and the text messages had limited effect on the use of health services. Finally, we also find no evidence that a government policy to eliminate user fees increased demand for maternal health services.

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)0.002
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0000.000

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 head0.013
GPT teacher head0.304
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations42
Published2019
Admission routes1
Has abstractyes

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