Crowding Out in the Labor Market: A Prosocial Setting Is Necessary
Why this work is in the frame
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Bibliographic record
Abstract
Recent studies, mostly from prosocial settings, suggest that monetary rewards may crowd out effort exertion by economic agents. We design a field experiment with data entry workers to investigate the extent of such crowding-out effects in a labor market. Using simple variations in the job description of a task, we induce a natural work setting under the work frame and emphasize social preference under the social frame. We find that crowding out of labor participation critically depends on framing—whereas small monetary rewards reduce the participation rate under the social frame, the participation rate is nondecreasing in the wage rate under the work frame. Moreover, among the workers who participate in the task, those who receive a positive wage perform a considerably higher amount of work than those who are paid zero wage under either frame. Thus, there is weak evidence of crowding out only when the task is explicitly given a prosocial flavor and not under a regular work setting. Furthermore, emphasizing social preference in the labor market in such a way reduces the overall labor supply and seems to have an adverse effect on the quality of work. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1807 . This paper was accepted by John List, behavioral economics.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it