Spatial Wage Inequality: Evidence from Italian Provinces
Bibliographic record
Abstract
This research has the aim to analyse the inequality of wages and productivity across the Italian provinces. This analysis is implemented on the ISTAT's "Rilevazione sulle Forze di Lavoro" quarterly cross-sectional public data, in a temporal space that goes from I quarter of 2014 to IV quarter of 2016, which has been divided into four periods. The research follows a two step procedure already known and used in literature, in which at the first stage the focus of the analysis is on the relationship between the individual wage and the individual observed characteristics, the province of workplace, the industrial sector in which the individual works and a dummy variable that controls whether the individual moved her domicile because of the current job. In the second step, we try to describe the relationship between the Estimated Provincial Total Factor Productivity from the first step and provincial characteristics such as population, employment density, land area, administrative fragmentation, market potential, human capital and province's industrial composition. The main results from the first step concern, as expected, the positive and concave impact of the experience, the positive return on education and a positive impact on wage due to a change in the domicile. The Estimated Provincial Total Factor Productivity shows persistence in its differentials during the four periods, with an increasing gap between the top and the bottom of the productivity distribution. The analysis on the determinants of Estimated Provincial Total Factor Productivity indicates positive impacts of population, land area, market potential, administrative fragmentation. While we found a negative impact of human capital inequality measured by Gini Index, meaning that greater inequality within the provincial human capital decreases the provincial productivity. Finally the Herfindahl Index on industrial composition shows a negative coefficient, which means a negative impact on productivity due to the higher sectoral concentration. These results are slightly different from those founded in literature for Italy. The possible causes might be the use of different databases and different periods of analysis and the different methodology followed. According to our results, we are in presence of a spatial inequality among Italian provinces' Total Factor Productivity and we believe that it is due to differences in population, administrative fragmentation and market potential. Human Capital is another important factor that plays a very important role in provincial productivity differentials.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".