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

Sebastian Kripfganz, Vasilis Sarafidis

TL;DR

This paper tackles the drivers and diffusion of U.S. state population growth using a dynamic spatial panel model that endogenously estimates the inter-state network, allows state‑specific slopes, and incorporates latent common factors. The authors develop a unified estimation framework combining data‑driven network recovery (BOLMT), mean‑group IV, and interactive fixed effects, enabling consistent inference under endogeneity and pervasive cross‑sectional dependence. Empirically, population growth shows broad yet heterogeneous convergence, with indirect spillovers accounting for about one‑third of total effects and diffusion extending beyond geographic contiguity; notably, productivity effects appear only when the network is data‑driven. The findings highlight the importance of endogenously modelling networks and cross‑state shocks for policy design, offering a diagnostic toolkit for spillovers and regionally targeted interventions, and revealing substantial latent factor influence on residual variance. The work advances spatial econometrics by integrating endogenous networks, slope heterogeneity, and interactive effects within a single robust framework.

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.

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

TL;DR

This paper tackles the drivers and diffusion of U.S. state population growth using a dynamic spatial panel model that endogenously estimates the inter-state network, allows state‑specific slopes, and incorporates latent common factors. The authors develop a unified estimation framework combining data‑driven network recovery (BOLMT), mean‑group IV, and interactive fixed effects, enabling consistent inference under endogeneity and pervasive cross‑sectional dependence. Empirically, population growth shows broad yet heterogeneous convergence, with indirect spillovers accounting for about one‑third of total effects and diffusion extending beyond geographic contiguity; notably, productivity effects appear only when the network is data‑driven. The findings highlight the importance of endogenously modelling networks and cross‑state shocks for policy design, offering a diagnostic toolkit for spillovers and regionally targeted interventions, and revealing substantial latent factor influence on residual variance. The work advances spatial econometrics by integrating endogenous networks, slope heterogeneity, and interactive effects within a single robust framework.

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.
Paper Structure (13 sections, 1 theorem, 24 equations, 3 figures, 5 tables)

This paper contains 13 sections, 1 theorem, 24 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Model Specification and Motivation
  3. Dynamic Spatial Panel Data Model with Common Factors
  4. Spatial Weighting Matrices
  5. Estimation Methodology
  6. Results
  7. Homogeneous vs Heterogeneous Models
  8. Marginal Effects
  9. Network Mapping
  10. Network Formation and Homophily Sources
  11. Regional Homophily
  12. Homophily in Bilateral Trade and Migration Inflows
  13. Concluding Remarks

Key Result

Proposition 1

Under certain regularity conditions listed in ChenCuiSarafidisYamagata2025, as $N,T\rightarrow\infty$ with $N/T^2\rightarrow0$, the MGIV estimator, $\widehat{\boldsymbol\theta}_{MGIV}$, is consistent for the population mean $\boldsymbol{\theta}$. If, it further holds that $N/T^{6/5}\rightarrow0$, th where $\boldsymbol{\Sigma}_{\boldsymbol\theta}$ denotes the variance-covariance matrix of $\widehat

Figures (3)

  • Figure 1: Heat Map of Estimated Coefficients of Lagged Log-Population
  • Figure 2: Population Spillovers Originating from States in the Midwest Region
  • Figure 3: U.S. States Population Growth Network

Theorems & Definitions (1)

  • Proposition 1