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on Economic Geography |
| By: | Fischer, Manfred M.; LeSage, James P. |
| Abstract: | Spatial econometrics deals with econometric modeling in the presence of spatial dependence and heterogeneity, where observations correspond to specific spatial units such as points or regions. Traditional estimation techniques assume independent observations and are inadequate when spatial dependence exists. This article provides an overview of spatial econometric models, highlighting the challenges posed by spatial dependence in cross-sectional data. It examines key models, including the Spatial Autoregressive (SAR), Spatial Error (SEM), and Spatial Durbin (SDM) models, while detailing maximum likelihood estimation (MLE) techniques and computational advancements for handling large datasets. Alternative estimation approaches, such as the generalized method of moments, Bayesian methods, non-parametric locally linear models, and matrix exponential spatial models, are also discussed. The article explores methods applicable to continuous, dichotomous, and censored variables. Interpreting spatial regression model estimates correctly is crucial for drawing valid inferences. Distinguishing between direct, indirect (spillover), and total effects and careful specification of the spatial weight matrix is essential. Misinterpretation can lead to flawed conclusions, undermining policy relevance – especially when assessing interventions with potential spillovers. By adhering to rigorous interpretation practices, researchers can fully leverage spatial regression models while mitigating analytical pitfalls. |
| Keywords: | Bayesian methods; censored dependent models; cross-sectional models; generalized method of moments; marginal effects; matrix exponential spatial models; maximum likelihood; non-parametric locally linear models; spatial dependence; spillover effects |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wus046:72854367 |
| By: | Alrababah, Ala; Delouis-Jost, Maelle (University of Zurich); Gauthier, Germain; Polak, Adam |
| Abstract: | Do place-based interventions that raise visibility and economic activity affect far-right voting? We study the Tour de France (TdF) as a case of brief but highly visible exposure that combines economic activity with symbolic recognition. Using variation in the annual TdF route between 2002 and 2022, we show that exposed municipalities experience declines in far-right support of 0.03–0.04 standard deviations. The effect exceeds 0.1 standard deviations in recent elections and is strongest in poorer areas and in towns with high prior far-right support. We find evidence consistent with the symbolic mechanism and mixed evidence for the economic one. TdF exposure increases local GDP per capita, effects on voting are larger when French riders win stages, and a two-wave survey around the 2025 TdF provides suggestive evidence that residents in exposed towns report greater pride and recognition. These results contribute to research on geographic inequalities, symbolic politics, and the electoral consequences of place-based interventions. |
| Date: | 2025–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:fj4vh_v1 |
| By: | Micael Castanheira De Moura; Giovanni Paolo Mariani; Clemence Tricaud |
| Abstract: | Public goods are meant to be universal, but they are inherently place-based. This paper systematically measures spatial access to public goods and quantifies the implications of distance to public facilities for income inequality. First, we map all schools and hospitals across Belgium. We compute the distance to facilities for each of the 20, 000 neighborhoods and document large spatial inequalities in access to public facilities. Second, we find that this unequal distribution favors high-income neighborhoods: allocating public goods spending proportionally to our access index increases income inequality compared to measures based solely on disposable income. Third, we show that the positive relationship between income and access can be rationalized by a simple model of public goods allocation with an inequality-neutral social planner. Finally, we provide evidence that access is strongly correlated with educational and health outcomes, emphasizing the need to consider the place-based nature of public goods when measuring inequality. |
| Keywords: | Public Goods, Inequality, Geography |
| URL: | https://d.repec.org/n?u=RePEc:eca:wpaper:2013/393488 |
| By: | Daniel J. Wilson |
| Abstract: | Understanding the effects of weather on macroeconomic data is critically important, but it is hampered by limited time series observations. Utilizing geographically granular panel data leverages greater observations but introduces a “missing intercept” problem: “global” (e.g., nationwide spillovers and GE) effects are absorbed by time fixed effects. Standard solutions are infeasible when the number of global regressors is large. To overcome these problems and estimate granular, global, and total weather effects, we implement a two-step approach utilizing machine learning techniques. We apply this approach to estimate weather effects on U.S. monthly employment growth, obtaining several novel findings: (1) weather, and especially its lags, has substantial explanatory power for local employment growth, (2) shocks to both granular and global weather have significant immediate impacts on a broad set of macroeconomic outcomes, (3) responses to granular shocks are short-lived while those to global shocks are more persistent, (4) favorable weather shocks are often more impactful than unfavorable shocks, and (5) responses of most macroeconomic outcomes to weather shocks have been stable over time but the consumption response has fallen. |
| Keywords: | weather; Macroeconomic fluctuations; employment growth; granular shocks |
| JEL: | Q52 Q54 R11 |
| Date: | 2025–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:101766 |
| By: | Edward L. Glaeser; Joseph Gyourko; Braydon Neiszner |
| Abstract: | Do more populous neighborhoods grow less quickly than less populous areas? Is local housing price growth associated with initial population density? The Longitudinal Tract Data Base’s (LTDB) panel of Census tracts is the standard tool for measuring neighborhood change. The LTDB is based on 2010 Census tract boundaries, and Census tracts are partially designed so that they have a similar level of population. In this paper, we show that defining neighborhoods to equalize ex post population levels can significantly impact estimated coefficients in regressions in which population changes are regressed on initial population levels or with variables that are correlated with initial population levels. Most obviously, if neighborhood populations are ex post equalized, then a regression of population change on initial population must yield a coefficient of -1. We address this challenge by offering five alternative panels of tracts using 1970, 1980 and 1990 boundaries, which can be thought of as ‘reverse LTDBs’. The significant mean reversion of both population and housing units that appear in the LTDB before 2000 either dramatically ameliorates or reverses using the reverse LTDB. Comparing the LTDB with the reverse LTDB also finds that using tracts based on ex post borders also can influence estimated growth relationships where other tract-level attributes such as house price are correlated with initial population levels. This does not imply that using ex ante borders always is superior; earlier borders almost always means fewer observations, especially in rapidly growing areas. |
| JEL: | R10 R12 R31 R38 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34238 |
| By: | Gabriel Agostini; Rachel Young; Maria D. Fitzpatrick; Nikhil Garg; Emma J. Pierson |
| Abstract: | Fine-grained migration data illuminate important demographic, environmental, and health phenomena. However, migration datasets within the United States remain lacking: publicly available Census data are neither spatially nor temporally granular, and proprietary data have higher resolution but demographic and other biases. To address these limitations, we develop a scalable iterative-proportional-fitting based method that reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data. We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010-2019 that captures flows between 47.4 billion pairs of Census Block Groups — about four thousand times more granular than publicly available data. These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data. We use MIGRATE to analyze both national and local migration patterns. Nationally, we document temporal and demographic variation in homophily, upward mobility, and moving distance: for example, we find that people are increasingly likely to move to top-income-quartile CBGs and identify racial disparities in upward mobility. We also show that MIGRATE can illuminate important local migration patterns, including out-migration in response to California wildfires, that are invisible in coarser previous datasets. We publicly release MIGRATE to provide a resource for migration research in the social, environmental, and health sciences. |
| JEL: | J19 R2 R23 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34263 |