Measuring Poverty and Inequality in a Computable General Equilibrium Model
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims to evaluate the relevance of different types of macroeconomic general equilibrium modelling for measuring the impact of economic policy shocks on the incidence of poverty and on the distribution of income. In the literature three approaches are identified. The first is based on a traditional form of the CGEM which specifies a large number of households. In this case, we can only observe inter group income inequalities. The next uses survey data to estimate the distribution function and average variations by group, which allows one to estimate the evolution of poverty. The third approach, which we present in detail, includes individual data directly in the general equilibrium model according to the principles of micro simulations. This treatment provides a more reliable picture of income distribution but is also more complex. Given this, we develop, within a co-ordinated statistical framework representing an archetypal economy, the three types of model described above. More precisely, this exercise allows us to break down the contribution of average income variations, of the poverty line, and of income distribution in the evolution of the main poverty indicators. The results obtained show the importance of intra group information and therefore the relevance of micro simulation exercises.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it