dorsal/arxiv
View SchemaChasing Opportunity: Spillovers and Drivers of U.S. State Population Growth
| Authors | Sebastian Kripfganz, Vasilis Sarafidis |
|---|---|
| Categories | |
| ArXiv ID | 2601.10444vv1 |
| URL | https://arxiv.org/abs/2601.10444 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
{
"annotation_id": "5d74519d-f908-43f6-9508-19948908f806",
"date_created": "2026-02-17T05:53:24.338000Z",
"date_modified": "2026-02-17T05:53:24.338000Z",
"file_hash": "2eca0e59d52258b40cddc9a30bb47b3e248b0b4414c02da53cb8ae10a8bafd1f",
"private": false,
"record": {
"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.",
"arxiv_id": "2601.10444",
"authors": [
"Sebastian Kripfganz",
"Vasilis Sarafidis"
],
"categories": [
"econ.EM"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Chasing Opportunity: Spillovers and Drivers of U.S. State Population Growth",
"url": "https://arxiv.org/abs/2601.10444",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "3564a1d6-3756-4201-9c03-a031e78d2f16",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}