General Equilibrium Approach for Poverty Analysis: With an Application to Cameroon
Bibliographic record
Abstract
Abstract: In this paper we use a computable general equilibrium model to study the impact of a trade shock and a tariff reform on household poverty for an archetype developing country. Unlike other studies, we present the income distribution of each household group as a Beta statistical distribution. In contrast to other studies, this paper presents the poverty lines as being endogenous. With this specification, the poverty line will change following a variation in relative prices. With the new distributions and poverty line, the poverty levels of the base year are compared with the ex-post values. Foster, Greer and Thorbecke's (1984) poverty measures are used. We work with the Cameroon household survey data of 1995–96. We consider two scenarios. The first is a 30 percent fall in the world price of the country's export crop and the second is a reduction of 50 percent in the country's import tariffs. For the first simulation, results indicate a drop in all household incomes and a decrease in the poverty line. Unilateral trade liberalization also has negative consequences on all household incomes. As in the first simulation, the poverty line decreases with a unilateral trade liberalization. In the trade liberalization simulation, the poverty line effect counters the income effect in most cases analyzed. In the other simulation, the poverty line effect attenuates the decrease in the poverty measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".