Spatial dynamics of human development in Peru: Territorial effects of geography, investment, and resource endowments

Abstract

This study examines the relationship between territorial characteristics, public investment, and human development across Peruvian districts, with particular attention to spatial disparities and geographic heterogeneity. The analysis uses district-level data for 1,872 districts in 2019 obtained from the United Nations Development Programme (UNDP), the National Institute of Statistics and Informatics (INEI), and the Ministry of Energy and Mines. To account for spatial interactions among neighboring territories, a Spatial Autoregressive Model with Autoregressive Errors (SARAR) is estimated. The explanatory variables include accumulated public investment, altitude, urban agglomeration, geographic distance between district capitals, and mining specialization. The results reveal significant spatial autocorrelation in the Human Development Index (HDI), indicating that development outcomes are geographically clustered rather than randomly distributed. Higher altitude and greater spatial isolation are associated with lower HDI values, whereas greater urban concentration and higher levels of accumulated public investment are associated with better development outcomes. Mining districts also tend to exhibit comparatively higher levels of human development. Furthermore, the estimated spatial parameters indicate the presence of spatial spillover effects, suggesting that development conditions in one district are related to those observed in neighboring districts. These findings highlight the importance of incorporating territorial context and spatial interdependence into the analysis of regional development and the design of public policies aimed at reducing inequalities. However, the results should be interpreted as associative rather than causal because the analysis relies on cross-sectional data from a single year and may be influenced by unobserved factors not captured in the model. Future research could employ longitudinal data and alternative spatial specifications to further explore the mechanisms underlying territorial disparities and strengthen the evidence on spatial development dynamics in Peru.