Empirical methods in business location research

Abstract

A frequent theme in regional science is exploring the determinants of business establishment locations. We briefly review the theoretical perspectives motivating several frameworks underpinning locational determinant analyses. We summarize and review trade-offs involved in established and emerging econometric techniques that researchers use to analyse locational determinants. We conclude with opportunities for future research, including understudied frameworks, potential data sources and methodological developments.

Food and agricultural industry locational determinants research: Aggregation bias and size measurement in the agricultural support industry

“…Importantly, Woodward (2003, 2004) demonstrate not only that the Poisson count model presents a more tractable approach than conditional logit approaches but also that the coefficients of the Poisson model can be given an economic interpretation compatible with the framework of random utility (profit) maximization. Conveniently in the context of FAI, which include both services/retail and manufacturing, count data models are also compatible with the Central Place Theory (CPT) theoretical perspective, which researchers use in demand threshold analysis of retail and service sectors (Carpenter, Van Sandt, and Loveridge 2021). Furthermore, the count data regression is preferred when dealing with large sets of alternatives, since each spatial alternative becomes an observation, and thus, the excessive choice set problem faced by logit models becomes an advantage (Guimarães, Figueiredo, and Woodward 2003).…”

“…This section first outlines estimation methods of traditional locational choice and demand threshold literatures before weighing the trade-offs with several different estimators, largely following the recent review by Carpenter, Van Sandt, and Loveridge (2021) and applying their guidance to the FAI context. Carlton (1983) and Bartik (1985) are among the first to model the determinants of business location using multinomial logit (McFadden 1973).…”

“…Finally, previous research failed to account for overdispersion that remains after the zero inflation in ZIP (Carpenter, Van Sandt, and Loveridge 2021). This remaining overdispersion may result from unobserved heterogeneity between subjects and excessive concentration of firms, which itself increases in economies of scale and agglomeration.…”

“…The FSRDC data make several improvements over public or proprietary data, which have limitations that are especially constraining in locational outcome research. 3 Much of the literature revolving around business locational outcomes measures industry size by the number of establishments within an industry and region (Carpenter, Van Sandt, and Loveridge 2021).…”

“…Of course, a linear model would estimate the average partial effects consistently. But problems with estimating a linear model for threshold models include that fitted establishment count values can be negative, and the assumption of a mean linear in x because E yjx cannot truly be linear in x, with other approaches reviewed by Carpenter, Van Sandt, and Loveridge (2021) The Poisson model remains useful at least as a starting point for comparison to other estimation procedures. However, the Poisson model's assumption that the conditional variance of the dependent variable is equal to the conditional mean is often violated in practice due to multiple potential sources of overdispersion.…”