Many studies have examined the determinants of the total amount of foreign direct investment (FDI) into countries (e.g., Loree and Guisinger, 1995; Grosse and Trevino, 1996; Habib and Zurawicki, 2002; Sethi et al., 2003; Cuervo-Cazurra, 2006; Buckley et al., 2007), thus defining FDI locations as entire countries. Yet other work has shown that foreign direct investors not only choose between countries, but also between subnational regions within a country chosen for investment (e.g., Belderbos et al., 2020; Chung and Alcacer, 2002; Mataloni, 2011). Accordingly, several studies have explored how the characteristics of a country’s subnational regions influence the total amount of FDI that these regions receive (Chidlow et al., 2009; Deichman et al., 2003; Hill and Munday, 1992; Meyer and Nguyen, 2005; Villaverde and Maza, 2012, 2015). Perhaps somewhat surprisingly however, none of these studies explored the determinants of FDI flows into US states, even though the US has long been the world’s largest and most advanced economy and therefore the main recipient of FDI. A few studies did analyze aggregated data on the gross plant, property, and equipment of foreign-owned affiliates in US states (Bobonis and Shatz, 2012; Halvorsen, 2012), but these data do not accurately reflect inward FDI flows, i.e. new foreign investments. This project therefore aims to extend the literature on FDI in subnational regions by shedding light on the determinants of FDI flows into US states.
The dependent variable is the total amount of new FDI in a given US state in a given year. The data on this variable are available from the US Bureau of Economic Analysis (BEA) for the period 2014-2019 (see column 1 of the Excel table under the bullet point "State by Type of Investment" at or the interactive tool at).
The independent variables cover various characteristics of US states, notably their economic size and level of economic development (measured by their annual GDP and GDP per capita, respectively, as reported by the BEA at), their imports as a percentage of their GDP (with the former being reported by the US Census Bureau at), the average level of education of their population (measured by the percentage of inhabitants with a bachelor’s degree or higher, as reported by the US Census Bureau at ), their corporate income tax rate (as reported by the Tax Foundation at ), whether they host one or more so-called global cities (as identified by the Globalization and World Cities (GaWC) Research Network at, and their cultural tightness (as reported in Harington and Gelfand, 2014).
Since the variables are observed for the same US states over multiple years, the regression models will be estimated with panel data methods, either random effects analysis or feasible generalized least squares analysis (Slangen and Beugelsdijk, 2010; Slangen et al., 2011). To control for time-varying effects on US states’ FDI inflows, a set of year dummies will be included, with the earliest observation year serving as the baseline year.
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