Combining Microsimulation with CGE and Macro Modelling forDistributional Analysis in Developing and Transition Countries
Bibliographic record
Abstract
This paper overviews recent work that has attempted to bring together microsimulation, Computable General Equilibrium (CGE) and macro models to perform distributional analysis in developing and transition countries. Particular attention is paid to applications relating to aspects of economic growth and political economy. Applications in which macro, CGE and microsimulation models are either layered or integrated are considered. It is demonstrated that different combinations of such models, including those where only a single model-type is used, are appropriate for different problems. For short-run impact analysis, microsimulation on its own may be appropriate. For longer-run analyses, where interest is in the interrelationship between changes in disposable income, consumption and labour supply, these models need to be supplemented a combination of microsimulation on the one hand, and general equilibrium price changes or changes in macro variables on the other hand. In the case of national subregions, or countries embedded in free-trade areas, it is argued that microsimulation may adequately be combined with pure macro models. That is, CGE modelling may not be necessary. For distinct national economies, however, the first step beyond microsimulation should likely be integration with CGE modelling. Whilst much promising work has been undertaken on dynamic integrated CGE microsimulation work in developing countries, CGE work is most advanced for Less Developed Countries. At present several groups of development researchers are found to be putting these two approaches together, and in some cases are adding macroeconomic and financial modelling as well. In contrast, with a few conspicuous exceptions, little such work is being done for the transition economies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".