Fiscal Policy and Income Distribution: Measurement for Argentina 1995 ¨C 2010
Bibliographic record
Abstract
This paper studies the effect of consolidated ¨Cnational and provincial¨C fiscal policy on personal income distribution in Argentina, building a novel panel data for 1995-2010. We find that fiscal policy reduces income inequality, summarized with the Gini coefficient, by 0.06 in 1995- 2001 (out of an ex ante average value of 0.490), and 0.08 in 2003-2010 (out of 0.497). Expenditures (mainly social services) are the tool for redistribution because taxes are regressive. Provincial expenditures account for two-thirds of the reduction in the Gini coefficient, indicating that there is no incompatibility between decentralization and redistribution. The contribution in-kind expenditures to redistribution is more important than that of cash transfers; although the latter gain relevance in 2003-2010. The impact of social expenditures and income taxes on the Gini coefficient is similar to effects found in other Latin American countries, but significantly lower than results found for OECD and European Union countries. The case of Argentina may provide useful lessons for other federal countries, in particular, considering all the expenditures and taxes and the responsibility of the different levels of governments and their effect on income distribution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".