Bibliographic record
Abstract
'This paper evaluates the use of microeconomic data, namely household income surveys from the Luxembourg Income Study (LIS), for researching interregional redistribution. Patters of regional growth and regional redistribution are the focus of a growing body of literature. Most of these studies use macroeconomic indicators like real GDP to estimate per capita income. LIS survey data offers researchers the opportunity to construct estimates of regional income distribution and interregional redistribution based on household income information for over 25 countries. The goal of this paper is to present a preliminary analysis of interregional inequality and redistribution in four federal states - the United States of America, Canada, Germany and Australia, using LIS data. Firstly, it estimates interregional inequality based on household income before and after redistribution. In the first method, interregional redistribution is defined as the percentage reduction in interregional inequality from before taxes and transfers to after and calculated by comparing the 'between-group' Theil Index before and after redistribution. In the second method, a simple regression model is used to estimate the effect of pre-redistribution mean income in a region on its post-redistribution mean income after controlling for population. The format of the paper is as follows. Section II offers a brief overview of the literature focusing on the key theoretical arguments that have framed the study of interregional redistribution. Section III provides a description of the LIS household income data and a discussion of research methodology. The empirical results from the data analysis are presented in Section IV. Finally, Section V sums up the main findings of this paper and explores ways to expand this research in the future.' (author's abstract)
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".