Non-linear impacts of local fiscal expenditure on farmers’ income: A SHAP-informed machine-learning analysis

Abstract

Understanding how local fiscal spending shapes rural income is central to China’s rural revitalisation strategy. Using panel data from 17 prefecture-level cities in Henan Province for 2010–2023, this study investigates the non-linear and heterogeneous effects of fiscal expenditure on farmers’ per-capita income. A hybrid econometric–machine learning framework is developed, combining city–year fixed effects with a residual XGBoost learner and SHapley Additive exPlanations (SHAP) to capture both the linear baseline relationships and complex non-linear interactions among fiscal items while retaining interpretability. Model evaluation based on repeated nested cross-validation and 500 permutation tests demonstrates high predictive reliability (out-of-sample R 2  = 0.924, p = 0.002). SHAP-based analysis reveals that healthcare and education spending are the dominant determinants of rural income, jointly accounting for over 40% of model influence. Partial-dependence plots uncover clear threshold effects: education and health expenditures exhibit inverted-U shapes with turning points at approximately ¥1,800 and ¥1,050 per rural resident (2015 prices), respectively. Infrastructure investment shows consistently positive but diminishing returns, while social-security transfers produce concave yet non-negative effects. Heterogeneity analysis further indicates that low-capacity cities derive greater benefits from technology and transport spending, whereas high-capacity cities gain more from health and urbanisation budgets. Robustness tests across alternative learners (Random Forest, LightGBM) and variable definitions confirm the stability of these non-linear thresholds. The results highlight the importance of optimising fiscal composition rather than merely increasing total spending, suggesting that city-specific expenditure ceilings—particularly for education and health—could raise rural incomes by 2–3% while enhancing fiscal efficiency.