“Industrial Concentration, Economic Growth, and Inequality dynamics throughout the stages of industrialisation: a comparative and transnational analysis”
Bibliographic record
Abstract
The analysis of the trends for industrial concentration, GDP growth per capita, and income inequality – based on data from IPUMS International, Maddison Project, Clio-Infra, and World Bank Open Data – in the context of the US, Canada and other selected countries from Europe, South America, and East Asia, has led to the following results:\na) after a comparison between the Krugman Index values and the GINI coefficients for the historical series of US, UK, and Spain, I argue in the first place, that the 1970-2000 series for the group of East-Asian countries subject to the research is coherent with the presence of “displaced” Kuznets’ waves – where the latter is a theoretical tool (introduced by Milanovic) that revises the original Kuznets’ hypothesis by shifting the focus from the long-run to more limited period of times.\nSecondly, for the group of South American countries analysed, I confirm the results of Deinenger and Squire on the unidirectionality of the trends for economic growth and income inequality between the 1960s and the 2000s. Namely, that both trends are raising, instead of diverging at a certain point, as it would have been expected, according to the original Kuznets’ hypothesis. Nevertheless, the inversion of the income inequality levels for Brazil, and the extreme oscillatory nature of the trends for Argentina, seem to prospect a potential displacement of a Kuznets’s wave for the two countries in a subsequent period. Limitations in the available datasets for the years after 2000s hindered, though, a consistent verification of this hypothesis.\nb) I argue on the one hand, that, for the Western countries analysed, the series for industrial concentration and income inequality between 1860 and 1970 are fully compatible with a Kuznets’ wave. On the other, that the series after the 1970s are instead in contrast with Milanovic’s thesis of a second Kuznets’ wave starting during these years. Nevertheless, the value for industrial concentration that I found for the US in 2015 can have some relationship with the rising income inequality levels analysed by Milanovic. Further research should be\n75\ndevoted to the analysis of this issue when the census datasets for the 2020s decade will be made available.\nc) I argue that a further theoretical insight, derived from my analysis, can be considered as a corollary of Krugman’s theory on industrial specialisation dynamics. Namely, that being equal the transportation costs and the level of technology/productivity, lighter economic shocks trigger increasing levels of industrial concentration, whereas highly disruptive shocks for the industrial tissue, such as wars and structural economic crises, produce instead decreasing levels of industrial concentration. Further research is necessary, though, in order to corroborate this theory.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".