Distortions, Social Infrastructures and Tax Evasion
Bibliographic record
Abstract
In this paper, we examine the hypothesis that poor social infrastructures in developing countries influence firms' tax evasion. Using firm-level data from the World Bank Enterprises Survey (WBES) across 38 African and Latin American countries, we provide evidence which is consistent with this hypothesis. In particular, we show empirically that the level of distortions generated by poor social infrastructures is positively correlated with firms' tax evasion. We develop a general equilibrium model where entrepreneurs use tax evasion to counterbalance additional costs and losses generated by poor social infrastructures. In our model, the economic environment consists of heterogeneous firms which maximize their profits and make tax evasion according to the level of idiosyncratic distortions generated by poor social infrastructures. We calibrate the model to the United States economy and treat this countries as an economy with no distortions as is standard in the literature. The model is simulated for a sample of African and Latin American countries using each country specific joint distribution of productivity and distortions. We validate the model by showing that simulated aggregate output and tax evasion are strongly correlated with the data. Moreover, the model explains the observed income variation and 61% of variation in tax evasion across these countries.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".