Testing the Theory of Multitasking: Evidence from a Natural Field Experiment in Chinese Factories
Bibliographic record
Abstract
A well-recognized problem in the multitasking literature is that workers might substantially reduce their effort on tasks that produce unobservable outputs as they seek the salient rewards to observable outputs. Since the theory related to multitasking is decades ahead of the empirical evidence, the economic costs of standard incentive schemes under multitasking contexts remain largely unknown. This study provides empirical insights quantifying such effects using a field experiment in Chinese factories. Using more than 2200 data points across 126 workers, we find sharp evidence that workers do trade off the incented output (quantity) at the expense of the non-incented one (quality) as a result of a piece rate bonus scheme. Consistent with our theoretical model, treatment effects are much stronger for workers whose base salary structure is a flat wage compared to those under a piece rate base salary. While the incentives result in a large increase in quantity and a sharp decrease in quality for workers under a flat base salary, they result only in a small increase in quantity without affecting quality for workers under a piece rate base salary.
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How this classification was reachedexpand
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".