The Effects of Model Specification on Foreign Direct Investment Models: An Application of Count Data Models.

• Author: Tomlin, KaSaundra M.

KaSaundra M. Tomlin [*]

Previous studies have drawn a theoretical and empirical connection between foreign direct investment (FDI) and exchange rates using continuous measures of FDI. However, FDI data are often in discrete count form. I take a representative study of the FDI/exchange rate relationship by Jose M. Campa (1993), and I analyze the sensitivity of the results to specification of the dependent variable. Whereas Campa uses a Tobit specification, I use a count data specification to model counts of FDI occurrences. Using data on FDI in the United States from 1982 to 1993, controlling for the traditional determinants of FDI, I find that the results are sensitive across specifications. Significance levels and the magnitude of the coefficients change when going from a continuous Tobit specification to a zero inflated Poisson (ZIP) model designed for count data. Formal statistical testing finds that the ZIP specification likely models the data most properly. Thus, I indicate that misspecification bias from modeling discrete data with continuous distributions is important.

1. Introduction

   The rapid rise in foreign direct investment (FDI) over the past few decades has heightened interest in the relationship between FDI flows and exchange rates. For example, several studies have established a connection between the exchange rate level and FDI decisions; the exchange rate level determines multinational firms' expected return, and hence the decision to invest abroad (Froot and Stein 1991; Goldberg and Kolstad 1995; Blonigen 1997; Tomlin 1998).

   In these studies, the exchange rate level is a vital determinant of FDI; however, volatile exchange rates are also a determinant, discovered empirically to have a deterrent effect on FDI. Campa (1993) investigates the influence of the exchange rate level and exchange volatility on FDI. Firms invest abroad when expected profits exceed the sunk cost necessary to enter. The entry decision depends on sunk costs, the critical exchange rate level, exchange rate volatility, and other vital determinants. Using a count measure of FDI entries into four-digit standard industrial classification wholesaling industries, Tobit estimates suggest that sunk costs and exchange rate volatility depress FDI and that high exchange rate levels induce FDI during the 1980s. An abundance of research exists in this area; however, one common problem encountered by researchers relates to FDI data difficulties. Some data are aggregated, industry specific, or even discrete in nature.

   FDI data are regularly provided in discrete count form, whereas typically studies FDI as a continuous variable. In past research, economists seeking data on complete transactions of FDI at the industry level obtained data from the International Trade Administration (ITA). ITA data have myriad missing values for EDI transactions, leaving only count data to analyze. Although in some cases continuous measures are applicable, count measures provide the actual number of FDI occurrences, thereby differentiating between no entry occurrences and positive events of entry. Moreover, total reliance on continuous measures may create the possibility of significant specification bias.

   I take a representative study of FDI by Campa (1993), conduct a systematic examination of the empirical FDI/exchange rate relationship, and analyze the sensitivity of the results to specification of the dependent variable. Whereas Campa (1993) uses a Tobit model, I employ maximum-likelihood techniques to obtain estimates from the zero inflated Poisson model (ZIP). The ZIP model accounts for the discrete nature of the data and excess zeros in the dependent variable. To my knowledge, no one has verified that estimates will improve if more technical distributions are used which model the data better. Hence, I obtain Tobit estimates and use them as a benchmark to compare to ZIP estimates to ascertain the extent to which specification matters. I address two issues in the literature: misspecification bias from modeling discrete data with continuous variable models; and controlling for excess zeros in the dependent variable when using discrete data. Ultimately, the ZIP model presents an opportunity to ameliorate es timates used for policy discussions on the FDI-exchange rate relationship.

   I find that the estimates are sensitive to specification. There are salient differences between the Tobit and ZIP estimates. Statistical tests indicate that the ZIP model, which specifically models the discrete nature of the dependent variable, is an appropriate specification for this sample. Statistical significance changes across the two models, and importantly, there are economic differences in the marginal effects as well. Whereas a Tobit specification finds a statistically and economically significant relationship between real exchange rate changes and EDI entry, this is not true for the ZIP specification. The results also suggest that the Tobit model underestimates the negative impact of labor costs and advertising expenses on FDI entry into U.S. wholesaling industries.

2. Specification Issues

   Following the Dixit (1989b) model, I present a formal model based on option pricing theory. In recent studies using option pricing analogies, the FDI decision...