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

TL;DR

This paper develops a novel spatial econometrics framework to analyze U.S. state population growth, revealing heterogeneous convergence, stable core drivers, and significant spillover effects extending beyond neighboring states.

Contribution

It introduces the first unified estimation approach combining data-inferred networks, heterogeneous slopes, and fixed effects in spatial panel models.

Findings

Approximately 75% of states show convergence in growth.

Core drivers like amenities and income have stable effects.

Spatial spillovers account for about one-third of total impacts.

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.