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 rst 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 rst 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 rst 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 dierentials 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 Herndahl Index on industrial composition shows a negative coecient, which means a negative impact on productivity due to the higher sectoral concentration.These results are slightly dierent from those founded in literature for Italy.The possible causes might be the use of dierent databases and dierent periods of analysis and the dierent 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 dierences in population, administrative fragmentation and market potential.Human Capital is another important factor that plays a very important role in provincial productivity dierentials.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".