Tax Evasion in Africa and Latin America : the role of distortionary infrastructures and policies
Bibliographic record
Abstract
This paper examines the impact of the quality of the business environment as well as the monitoring capacity of the tax agency on firms'tax evasion and production decisions. First, the paper uses firm-level data for 30 African and Latin American countries to show that tax evasion and distortions stemming from the business environment are positively and significantly correlated, while sales not reported for tax purposes and institutional quality are negatively and significantly correlated. Second, the paper develops a general equilibrium model where heterogeneous firms make tax evasion decisions based on their assessment of the quality of their business environment as well as the monitoring capacity of the tax agency. The model simulations for each country in the African and Latin American sample show that the model can explain 35 percent of the variation in tax evasion and more than 49 percent of the dispersion in output per worker across the sample countries. Finally, a series of counterfactual experiments shows that, at the current level of deterrence, governments could decrease sales not reported for tax purposes by 21 percent, by reducing distortions stemming from the business environment by half. The paper presents empirical supporting evidence consistent with testable predictions of the model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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 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".