1A Theil decomposition of Latin American income distribution in the 20th Century: Inverting the Kuznets Curve? E.H.P.Frankema Groningen Growth and Development Centre
Bibliographic record
Abstract
This paper applies a Theil decomposition method to investigate long run changes in the functional income distribution of 20th century Latin America. Kuznets argued that the economic transition from a traditional rural into a modern urban economy eventually results, after an upswing in the early phase of industrialisation, in sustained lower levels of personal income inequality (Kuznets’ inverted U-curve hypothesis). In spite of various phases of strong economic growth and profound structural change a sustained decline in inequality has not taken place in Latin America. This paper argues that the apparent persistency of its inequality levels is the consequence of a trade-off between declining rural-urban income differences and increasing urban sector income differences. Urban sector inequality in a sample of major Latin American economies (Argentina, Brazil, Chile) was, from the start of the 20th century until the 1970’s, comparable to other advanced New World economies (USA, Canada, Australia). Yet, since the 1970’s initial levels of rural and rural-urban inequality were overtaken by a rapid increase of urban inequality in virtually all Latin American countries. In some cases this increase has been so pronounced that the Kuznets ’ curve should be “re-inverted ” to accurately picture the secular inequality trend.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".