Distortions, Efficiency and the Size Distribution of Firms
Bibliographic record
Abstract
Microdata information about firms' input choices and effective tax liabilities is used to quantify the extent of resource misallocation and efficiency losses due to large tax distortions and limited access to credit. We develop an equilibrium model of firms' behavior in which the tax and credit environments act as a selection mechanism restricting the growth of all but the most productive firms. We show that such a model, parameterized and validated using a variety of data restrictions, has the potential to rationalize several puzzling observations about firms' input choices, size and growth patterns. Counterfactual experiments are designed to gauge the losses associated to different deviations from first-best. We find that firms' optimal responses to the tax distortions are quite effective in reducing efficiency losses. As a consequence, tax distortions only account for 5% of the gap between an undistorted economy and the benchmark. On the other hand limited and expensive access to credit is associated to more significant misallocation of productive resources and leads to larger aggregate efficiency losses of the order of 95% of the gap between an undistorted economy and the benchmark. Our findings highlight the non-negligible quantitative importance of two relatively common distortions in developing economies, and identifies simple mechanisms which might contribute to their low measured TFP.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".