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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".