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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".