Author(s)

Sinan Jin ¹

Affiliation(s)

¹ School of Labor Economics, China University of Labor Relations, Beijing, China

Corresponding Author

Sinan Jin

ABSTRACT

With the rapid development of the digital economy and platform economy, flexible employment has become an important form of employment in the labor market. However, problems such as widening income disparities, insufficient income stability, and inadequate social security coverage are gradually becoming prominent. Addressing the shortcomings of traditional statistical analysis methods in characterizing the nonlinear relationships and interactions of income-influencing factors, this paper proposes a method for measuring income inequality and identifying influencing factors among flexible workers based on the Extreme Gradient Boosting (XGBoost) algorithm and Shapley Additive Explanations (SHAP). First, the Gini coefficient, Theil index, and Lorenz curve are used to measure and visualize the income distribution of flexible workers to characterize the degree of income inequality within the sample. Second, XGBoost is constructed to predict the income level of flexible workers and compared with other models. Finally, the Shapley Additive Explanation method is used to interpret the prediction results of the Extreme Gradient Boosting model, identifying the contribution and direction of different variables to income disparities. This study provides a computational analysis method that combines predictive and explanatory power for measuring income inequality among flexible workers, identifying low-income risks, and analyzing factors influencing income.

KEYWORDS

Flexible employment; Income inequality; Identification of influencing factors; Income forecasting; Interpretable analysis

CITE THIS PAPER

Sinan Jin. Research on Income Inequality Measurement and Influencing Factor Identification among Flexible Workers Based on Interpretable Machine Learning. Population, Resources & Environmental Economics (2026). Vol. 7, No. 1, 1-12. DOI: http://dx.doi.org/10.23977/pree.2026.070101.

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