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Record W1520423001 · doi:10.3386/w18630

The Effects of Information, Social and Economic Incentives on Voluntary Undirected Blood Donations: Evidence from a Randomized Controlled Trial in Argentina

2012· report· en· W1520423001 on OpenAlexafffund
Victor Iajya, Nicola Lacetera, Mario Macis, Robert Slonim

Bibliographic record

VenueNational Bureau of Economic Research · 2012
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsUniversity of Toronto
FundersCarey Business School, Johns Hopkins UniversitySocial Sciences and Humanities Research Council of CanadaUniversity of British ColumbiaSimon Fraser UniversityJohns Hopkins University
KeywordsIncentiveTurnoverRandomized controlled trialBusinessEconomicsPublic economicsMicroeconomicsMedicineInternal medicineManagement

Abstract

fetched live from OpenAlex

In many low-and middle-income countries blood donations per capita are substantially lower than in advanced economies. In these countries blood supply is mostly collected through donations by relatives and friends of individuals needing transfusions or to replace blood used in emergencies. The World Health Organization considers this method of blood supply inefficient compared to undirected voluntary donations. To examine methods to motivate undirected voluntary donations, we ran a large-scale, natural field experiment in Argentina testing the effectiveness of information, social and economic incentives. We find that only higher-valued economic incentives generated more donations, increasing in the value of the incentive. These incentives did not create adverse selection in the safety and usability of the donated blood. We discuss the implications of our findings for researchers interested in understanding motivations for pro-social behavior and for health agencies and policymakers concerned with the current and growing shortages in blood supply in low-and middle-income countries.

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)0.013
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.271
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.146
GPT teacher head0.421
Teacher spread0.275 · 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 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

Citations7
Published2012
Admission routes2
Has abstractyes

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