Misallocation, Establishment Size, and Productivity
Why this work is in the frame
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Bibliographic record
Abstract
We consider a tractable model of heterogeneous production units that features endogenous entry and productivity investment to assess the quantitative impact of policy distortions on aggregate output and establishment size. Relative to the standard factor misallocation framework, policy distortions featuring a positive productivity elasticity of distortions imply larger reductions in output through smaller investments in establishment productivity. A calibrated version of the model implies that when the productivity elasticity of distortions increases from 0.09 in the U.S. to 0.5 in India, aggregate output and average establishment size fall by 53 and 86 percent, compared to 37 and 0 percent in the standard factor misallocation model. Entry productivity investment and factor misallocation contribute equally to the reduction in output, whereas the effect of lower life-cycle productivity growth is fully offset by increased entry and reduced productivity dispersion. Establishment size differences in the model are consistent with evidence from a comprehensive dataset we construct on average establishment size in manufacturing using census data for 134 countries.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it