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

Summary/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.

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