Public expenditure policy in Bolivia: Growth and welfare
Bibliographic record
Abstract
It has been widely documented that fiscal policy can promote economic growth, when it is based on an efficient provision of pubic capital. But little work has been done, in Bolivia, in relation to the macroeconomic and sectoral impacts of increasing public investment in infrastructure. This paper develops a Dynamic Stochastic General Equilibrium (DSGE) model for a small open economy with five sectors: Non-tradable or services, importable or manufacturing, hydrocarbons, mining and agriculture. The model is parameterized and solved for the Bolivian economy and several interesting scenarios are simulated by changing government expenditures, taxes, country risk, Total Factor Productivity, effectiveness of public capital and terms of trade. This analysis is relevant for the Bolivian economy, because the government is using fiscal policy as one of its main tool to attack poverty and aims to put public investment as the foremost instruments to promote growth and welfare.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".