Sebastian Kripfganz, Vasilis Sarafidis
arXiv 15 Jan 2026 · Econometrics · publishedSpatial Economic Analysis (2026) · 1 citations (OpenAlex)
arXiv:2601.10444 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | J. Chen and G. Cui and V. Sarafidis and T. Yamagata (2025) IV Estimation of Heterogeneous Spatial Dynamic Panel Models with Interactive Effects self | 1.000 | 6 | 3 | 100% |
| 2 | A. Juodis and G. Kapetanios and V. Sarafidis (2025) Identification and Estimation of Panel Network Models Using High-Dimensional Regression self | 0.956 | 8 | 4 | 88% |
| 3 | Melanie Krause and Sebastian Kripfganz (2025) Regional Dependencies and Local Spillovers: Insights From Commuter Flows self | 0.843 | 3 | 3 | 100% |
| 4 | S. Kripfganz and V. Sarafidis (2025) Estimating Spatial Dynamic Panel Data Models with Unobserved Common Factors in Stata self | 0.811 | 4 | 2 | 100% |
| 5 | Benny Kleinman and Ernest Liu and Stephen J. Redding (2023) Dynamic Spatial General Equilibrium | 0.693 | 6 | 1 | 100% |
| 6 | J Chen and Y Shin and C. Zheng (2022) Estimation and inference in heterogeneous spatial panels with a multifactor error structure | 0.644 | 2 | 2 | 100% |
| 7 | J. Paul Elhorst (2014) Spatial Econometrics: From Cross-Sectional Data to Spatial Panels | 0.644 | 2 | 2 | 100% |
| 8 | S. Kripfganz and V. Sarafidis (2021) Instrumental Variable Estimation of Large-T Panel Data Models with Common Factors self | 0.644 | 2 | 2 | 100% |
| 9 | G. Cui and V. Sarafidis and T. Yamagata (2023) IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude Toward… self | 0.585 | 3 | 1 | 100% |
| 10 | G. Cui and M. Norkute and V. Sarafidis and T. Yamagata (2022) Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects self | 0.511 | 2 | 2 | 50% |
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