Chasing Opportunity: Spillovers and Drivers of U.S. State Population Growth

2026-01-15

AI Summary

This study investigates the drivers of population growth across U.S. states and their spatial spillover effects, addressing key limitations of conventional spatial models that rely on predefined geographic contiguity and neglect endogeneity and inter-state dependencies. To this end, the authors develop a dynamic spatial panel model that endogenously identifies interstate network structures and employ an instrumental variable approach accommodating heterogeneous slopes and interactive fixed effects, analyzing data from 49 states over 1965–2017. This work is the first to simultaneously integrate data-driven spatial weights, endogenous regressors, and pervasive cross-state dependence within a spatial econometric framework, thereby achieving consistent estimation. The findings reveal conditional convergence in approximately 75% of states, with productivity effects significant only under data-inferred networks. Spatial spillovers are pronounced, with indirect effects accounting for roughly one-third of the total impact and extending well beyond geographically adjacent states.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We study the drivers and spatial diffusion of U.S. state population growth using a dynamic spatial model for 49 states, 1965-2017. Methodologically, we recover the spatial network structure from the data, rather than imposing it a priori via contiguity or distance, and combine this with an IV estimator that permits heterogeneous slopes and interactive fixed effects. This unified design delivers consistent estimation and inference in a flexible spatial panel model with endogenous regressors, a data-inferred network structure, and pervasive cross-state dependence. To our knowledge, it is the first estimation framework in spatial econometrics to combine all three elements within a single setting. Empirically, population growth exhibits broad yet heterogeneous conditional convergence: about three-quarters of states converge, while a small high-growth group mildly diverges. Effects of the core drivers, amenities, labour income, migration frictions, are stable across various network specifications. On the other hand, the productivity effect emerges only when the network is estimated from the data. Spatial spillovers are sizable, with indirect effects roughly one-third of total impacts, and diffusion extending beyond contiguous neighbours.

Problem

• population growth
• spatial spillovers
• drivers
• conditional convergence
• cross-state dependence

Innovation

• spatial econometrics
• data-driven network
• endogenous regressors
• interactive fixed effects
• heterogeneous slopes