Analysis of Trade Reforms, Income Inequality and Poverty Using Microsimulation Approach: The Case of the Philippines
Bibliographic record
Abstract
This paper uses a CGE mircosimulation approach to analyze the effects of tariff reduction on poverty and income inequality. The approach relaxes the representative household assumption in the traditional CGE modeling by replacing household groups with individual households. As such the approach allows one to model the link between trade reforms and individual households and their feedback to the general equilibrium of the economy. The present paper incorporates the whole 24,797 households of the 1994 Family Income and Expenditure Survey and simulates the tariff reduction from 1994 to 2000. Tariff reduction leads to higher imports and exports. Although domestic production for the local market declines, the overall production improves. These are due to substitution and scale effects of tariff reduction. Resource reallocation and factor movements favor the nonfood manufacturing sector. Agriculture wages, as well the rate of return to capital in agriculture, decline as a result of the drop in agriculture output and value added. Income of rural households in the different regions declines, while income of urban households in the various regions (including the NCR) improves. Tariff reduction results in poverty reduction in all areas not because of the improvement in household income, but because of the drop in consumer prices. Income inequality, however, worsens except in the NCR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".