Representative versus Real Households in the Macroeconomic Modeling of Inequality
Bibliographic record
Abstract
: To analyze issues of income distribution, most disaggregated macroeconomic models of the computable general equilibrium (CGE) type specify a few representative household groups (RHG) differentiated by their endowments of factors of production. To capture “within-group” inequality, it is often assumed, in addition, that each RHG represents an aggregation of households in which the distribution of relative income within each group follows an exogenously fixed statistical law. Analysis of changes in economic inequality in these models focuses on changes in inequality between RHGs. Empirically, however, analysis of household surveys indicates that changes in overall inequality are usually due at least as much to changes in within-group inequality as to changes in the between-group component. One way to overcome this weakness in the RHG specification is to use real households, as they are observed in standard household surveys, in CGE models designed to analyze distributional issues. In this integrated approach, the full heterogeneity of households, reflecting differences in factor endowments, labor supply, and consumption behavior, can be taken into account. With such a model, one could explore how household heterogeneity combines with market equilibrium mechanisms to produce more or less inequality in economic welfare as a consequence of shocks or policy changes. An integrated microsimulation–CGE model must be quite large and raises many issues of model specification and data reconciliation. This paper presents an alternative, top-down method for integrating microeconomic data on real households into modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".