Heterogeneity of Rural Poverty in Indonesia: An Analysis Using the Moment Panel Quantile Approach

Abstract

Background: Rural poverty in Indonesia remains consistently higher than urban poverty; mean-based regression methods are heavily used but conceal strong spatial and distributional heterogeneity across provinces. A methodological gap exists in understanding how provinces experience the effects of policy variables across different points in the poverty distribution.

Objective: This study employs a quantile-sensitive approach to investigate the heterogeneous determinants of rural poverty growth in 33 Indonesian provinces from 2015 to 2023.

Methods: Data from 33 provinces (2015–2023, n = 297 observations) are analyzed using the Method of Moments Quantile Regression (MMQR) with fixed effects, following cross-sectional dependence (CSD) and CIPS panel unit root tests.

Results: The effects of development variables on rural poverty vary considerably across quantiles. The capitalization of village funds appears to correlate negatively with rural poverty prevalence in provinces experiencing extreme deprivation and positively elsewhere, suggesting there is inefficiency and/or misallocation at play. Land use change can alleviate poverty in semi-urbanizing regions but harms deeply agrarian, poor communities. Migration tends to reduce poverty only in wealthier areas. Agricultural growth reduces rural poverty mainly in middle-quantile regions, while unemployment consistently exacerbates poverty across all quantiles.

Conclusion: The study contributes novel heterogeneity evidence to the rural poverty literature and recommends quantile-differentiated policy targeting for Indonesian rural development.