Trade Liberalisation, Growth and Poverty in Senegal: a Dynamic Microsimulation CGE Model Analysis
Bibliographic record
Abstract
Much current debate focuses on the role of growth in alleviating poverty. However, the majority of computable general equilibrium (CGE) models used in poverty and inequality analysis are static in nature. The inability of this kind of model to account for growth (accumulation) effects makes them inadequate for long run analysis of the poverty and inequality impacts of economic policies. They exclude accumulation effects and do not allow the study of the transition path of the economy where short run policy impacts are likely to be different from those of the long run. To overcome this limitation we use a sequential dynamic CGE microsimulation model that takes into account accumulation effects and makes it possible to study poverty and inequality through time. Changes in poverty are then decomposed into growth and distribution components in order to examine whether de-protection and factor accumulation are pro-poor or not.\n\nThe model is applied to Senegalese data using a 1996 social accounting matrix and a 1995 survey of 3278 households. The main findings of this study are that trade liberalisation induces small increases in poverty and inequality in the short run as well as contractions in the initially protected agriculture and industrial sectors. In the long run, it enhances capital accumulation, particularly in the service and industrial sectors, and brings substantial decreases in poverty. However, a decomposition of poverty changes shows that income distribution worsens, with greater gains among urban dwellers and the non-poor.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".