Integrated ComputableSimulation Approach
Bibliographic record
Abstract
Conventionally, the analysis of macro-economic shocks and the analysis of income distribution and poverty require very different methodological techniques and sources of data. Over the last decade however, the natural divide between both approaches has diminished, as evaluating the impact of macro-economic shocks on poverty and income distribution within a CGE framework complemented by household survey data has flourished. This paper focuses on explicitly integrating into a CGE model each household from a nationally representative household survey. The aim of this paper is threefold. First, we show that explicitly modelling each household in the CGE model addresses Kirman's critique (1992) and overcomes the strong micro-economic assumption of representative agent. Second, we respond, albeit in a simple way, to the recommendation of Bourguignon and Perreira (2003) to integrate ―real‖ households within a CGE framework rather than using representative households. Third, by providing applications to Nepal and the Philippines, we demonstrate that this technique is straightforward to implement and requires only a standard CGE model and a nationally representative household survey with information on household income and consumption.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".