Academic journal article Journal of Agricultural and Applied Economics

Business Establishment Growth in the Appalachian Region, 2000-2007: An Application of Smooth Transition Spatial Process Models

Academic journal article Journal of Agricultural and Applied Economics

Business Establishment Growth in the Appalachian Region, 2000-2007: An Application of Smooth Transition Spatial Process Models

Article excerpt

Business establishment growth in the Appalachian region (2000-2007) was regressed on industry sector composition controlling for demographic, physical, and economic determinants. We test the hypothesis that local response to growth determinants is geographically heterogeneous using Smooth Transition spatial process models. This class of models exhibiting endogenous regime switching behavior provides another tool for exploring the spatially heterogeneous effects of local determinants on economic growth.

Key Words: Appalachia, business establishment growth, smooth transition models, spatial processes

JEL Classifications: C21, C51, 047, RIl

Models explaining geographic heterogeneity of economic growth are ubiquitous. For example, Partridge et al. (2008) found that the effects of fiscal policies and other local characteristics on growth varied considerably across rural areas in the United States using Geographically Weighted Regression (GWR). Lambert and McNamara (2009) explained food manufacturer location decisions using discrete spatial regimes, finding that the importance of local determinants varied depending on how counties were classified as metropolitan, micropolitan, or noncore. Wojan, McGranahan, and Lambert (2010) allowed parameters to vary across metropolitanmicropolitan-noncore counties and three resource amenity categories, finding that the interaction effects between individuals in creative class occupations and entrepreneurs on economic indicators were heterogeneous across regimes. Arbia, Basile, and Piras (2005) applied a nonparametric regression estimator to examine geographic nonlinearities of factors explaining the regional heterogeneity of growth in Italy. Other examples of spatial econometric models admitting individual or group-specific responses are numerous, including spatial adaptive filters (SAF) (Foster and Gorr, 1986), quantile regression (Lambert et al., 2007), Quant's (1958) regime switching regression, Casetti's (1972) spatial expansion model, multilevel hierarchical modeling (Voss, White, and Hammer, 2004), and random coefficient models (Anselin, Wendy, and Cho, 2002). Regional studies using these approaches typically regress some economic indicator on local factors hypothesized to explain growth according to parameters or functional forms unique to spatial units. The underlying tenets of these methods are generally consistent with the conventional idea that the constraints, opportunities, and politics guiding growth are ultimately context-dependent and that development strategies are more likely to succeed when tailored to local conditions (Irwin et al., 2010). Global solutions implied by models that restrict responses to local determinants to be the same everywhere may also understate important sources of heterogeneity that could provide insight about connections to wider economies and specific solutions to regionwide resource allocation problems.

This research applies a relatively new spatial econometric model to explain business establishment growth in the Appalachian region from 2000-2007, the Smooth Transition Regression (STAR) model of Pede (2010) and Pede, Florax, and Holt (2009). Like the GWR and SAF methods, parameter estimates of the STAR model assume different values at different locations. However, GWR and SAF models are extreme examples of the incidental parameter problem because each spatial unit has its own vector of coefficients; probabilistic statements about the coefficients are impossible, and interpretation is restricted to the idiosyncrasies of the sample (Anselin, 1988). In addition, the calibration of the GWR model is sensitive to outliers, heteroskedasticity, and possibly spatial error dependence (Cho, Lambert, and Chen, 2010). Poor calibration of GWR models may also lead to data oversmoothing. An advantage of the STAR model is that the incidental parameter problem is circumvented and the usual robust covariance estimators can be applied to make inferential statements. …

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