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Record W1509868282 · doi:10.3386/w17636

Rewarding Altruism? A Natural Field Experiment

2011· report· en· W1509868282 on OpenAlexaff
Nicola Lacetera, Mario Macis, Robert Slonim

Bibliographic record

VenueNational Bureau of Economic Research · 2011
Typereport
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsSurpriseIncentiveAltruism (biology)DonationBlood donationsSocial psychologyPsychologyNatural experimentProsocial behaviorIntervention (counseling)EconomicsMicroeconomicsBlood donorMedicine

Abstract

fetched live from OpenAlex

We present evidence from a natural field experiment involving nearly 100,000 individuals on the effects of offering economic incentives for blood donations. Subjects who were offered economic rewards to donate blood were more likely to donate, and more so the higher the value of the rewards. They were also more likely to attract others to donate, spatially alter the location of their donations towards the drives offering rewards, and modify their temporal donation schedule leading to a short-term reduction in donations immediately after the reward offer was removed. Although offering economic incentives, combining all of these effects, positively and significantly increased donations, ignoring individuals who took additional actions beyond donating to get others to donate would have led to an under-estimate of the total effect, whereas ignoring the spatial effect would have led to an over-estimate of the total effect. We also find that individuals who received a reward by surprise were less likely to donate after the intervention than subjects who received no reward, suggesting that for some individuals a surprise reward adversely affected their intrinsic motivations. We discuss the implications of these findings for understanding pro-social behavior.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.553
GPT teacher head0.602
Teacher spread0.049 · 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
GenreOther

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

Citations17
Published2011
Admission routes1
Has abstractyes

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