View in EDS

Explaining regional wage disparities with machine learning: A SHAP-based interpretation approach.

Saved in:
Bibliographic Details
Title: Explaining regional wage disparities with machine learning: A SHAP-based interpretation approach.
Authors: Dudek, Andrzej, Pełka, Marcin, Skibac, Artur
Source: Statistical Review / Przegląd Statystyczny; 2025, Vol. 72 Issue 3, p1-27, 27p
Subject Terms: MACHINE learning, ARTIFICIAL neural networks, ECONOMIC impact, REGIONAL economic disparities, DEMOGRAPHIC characteristics
Geographic Terms: POLAND
Abstract: The aim of the study is to provide an explanation for the factors that most influence the differences in wage levels between Polish powiats (equivalent to counties). This study investigates regional wage disparities in Poland by applying machine learning models enhanced by Explanatory Model Analysis techniques. Using powiat-level data from the Local Data Bank (Pol. Bank Danych Lokalnych – BDL) for 2010 and 2023, a neural network framework was developed to predict wage levels based on economic, demographic, infrastructural and environmental variables. To interpret the model, we employed the Variable Importance over Permutation (VIP) and SHapley Additive exPlanations (SHAP) approaches, which provide insights into both the global feature importance and the local contributions of individual variables. The results indicate that the share of the productive population, unemployment rates and social vulnerability remain key determinants of wage differences, although their relative influence shifts significantly over time. The SHAP analysis demonstrates how regional contexts such as the Jelenia Góra and Wrocław powiats exhibit distinct factor dynamics, with demographic and infrastructural variables playing varying roles across the studied years. The findings highlight the potential of combining machine learning with explainability methods to uncover complex, nonlinear determinants of wages, offering a more transparent analytical basis for understanding evolving regional disparities. [ABSTRACT FROM AUTHOR]
Copyright of Statistical Review / Przegląd Statystyczny is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:The aim of the study is to provide an explanation for the factors that most influence the differences in wage levels between Polish powiats (equivalent to counties). This study investigates regional wage disparities in Poland by applying machine learning models enhanced by Explanatory Model Analysis techniques. Using powiat-level data from the Local Data Bank (Pol. Bank Danych Lokalnych – BDL) for 2010 and 2023, a neural network framework was developed to predict wage levels based on economic, demographic, infrastructural and environmental variables. To interpret the model, we employed the Variable Importance over Permutation (VIP) and SHapley Additive exPlanations (SHAP) approaches, which provide insights into both the global feature importance and the local contributions of individual variables. The results indicate that the share of the productive population, unemployment rates and social vulnerability remain key determinants of wage differences, although their relative influence shifts significantly over time. The SHAP analysis demonstrates how regional contexts such as the Jelenia Góra and Wrocław powiats exhibit distinct factor dynamics, with demographic and infrastructural variables playing varying roles across the studied years. The findings highlight the potential of combining machine learning with explainability methods to uncover complex, nonlinear determinants of wages, offering a more transparent analytical basis for understanding evolving regional disparities. [ABSTRACT FROM AUTHOR]
ISSN:00332372
DOI:10.59139/ps.2025.03.1