یادگیری از سرمایه گذاری خارجی توسط شرکت های رقیب: نظریه و شواهد

We offer an alternative explanation for follow-the-leader behavior in foreign investment decisions based on Bayesian learning by rival firms. We test the implications of the model through a panel count data sample of MNEs that have invested in Central and Eastern Europe over the period 1990–1997. Interacting the measure of rivals' investment in country-industry pairs with uncertainty, we are able to identify the channel of Bayesian learning about revenue postulated by the model as the only one consistently generating the detected follow-the-leader behavior of foreign investments. The empirical findings are robust with respect to different model specifications.

In the literature on foreign direct investment (FDI) it is well established the idea that foreign entry by a firm may trigger a rival reaction, leading to a follow-the-leader (FTL) behavior in foreign investment decisions: firms (the followers) invest abroad as a reaction to the set up of a foreign affiliate by a first-mover competitor (the leader). A possible rationale of such a behavior has been originally discussed in the business literature by Knickerbocker (1973) and Flowers (1976), and it is known as, ‘oligopolistic reaction’1: the intuition is that firms, uncertain of production costs in the country to which they currently export, run the risk of being underpriced by a rival that switches from exporting to establishing a manufacturing subsidiary in the host country. By imitating the behavior of the lead investor, the follower firms can instead match the production cost of the rival firm abroad and thus avoid being underpriced. This paper tests for the presence of learning to rationalize the follow-the-leader behavior observed in the patterns of foreign direct investment in the market. To the best of our knowledge, this is the first paper proposing a learning mechanism to rationalize the observed FTL behavior of multinational enterprises (MNEs) and testing for it in the data.2 In our model, learning can be either about costs (as in Vettas, 2000) or about revenues. Moreover, in line with a recent literature on firm heterogeneity, the assumption we make on the prior distribution of the variables of concern implies a Pareto distribution for the observations through which learning takes place. In general, follow-the-leader behavior in FDI decisions is supported by broad empirical evidence. Controlling for variables relevant for the decision to undertake FDI (e.g. the market size of the host country and the distance from the investor's home to its host country), Yu and Ito (1988) consider FTL behavior in two industries, the US tire and textiles. By finding follow-the-leader FDI only in the tire industry, they conclude that firms only react oligopolistically in moderately concentrated industries such as the tire one and not in more competitively structured industries as textiles. More in general, by examining all Japanese investment into the U.S., Hennart and Park (1994) find evidence that FDI by a Japanese enterprise group in the U.S. is more likely if other Japanese rivals have already invested in the U.S.; Ito and Rose (2002) show that, in the same tire industry, firms like Continental and Bridgestone imitate FDI decisions by leading firms like Goodyear and Michelin, with follow-the-leader behavior measured as the impact of the total number of foreign firms (regardless of when they entered) on the probability of investment by another foreign firm in a given year. Though these results provide compelling evidence for the phenomenon and have a straight-forward economic interpretation, they however fail to identify the theoretical channels through which the reaction of rival multinational enterprises can arise. Moreover, all the previously quoted studies are based on the study of only one or two industries, while a broader analysis encompassing the relation between industry-specific characteristics and rivals' reaction is lacking. In our paper, we link the finding of our theoretical model with the recent flow of FDI to Central and Eastern European Countries (CEECs). The sample is chosen since it provides an interesting ‘natural experiment’: first, the existence of a learning effect seems plausible after the fall of the iron curtain, as many firms considered investing in Eastern Europe because of the expected lower marginal cost and/or possible new market opportunities in the region. Second, the fall of the Berlin wall in 1989 enables us to monitor over time the number of foreign investments taking place in CEECs and the follow-up behavior by rivals, thus controlling for initial conditions. In particular, it is possible to exclude the effect of learning from domestic firms, since these companies were either non-existing or subject to a heavy restructuring process in the early years of transition. Our sample therefore consists of the yearly number of European Union's foreign investors over the period 1990–1997, over a large set of industries and the most important CEECs.3 By identifying the order of entry from the very first investor to late investors, and using a panel negative binomial regression model relating foreign investment in a given year, industry and country to changes in the total number of investors operating in the same industry and country in the previous year,4 we are able to explicitly test for a foreign firm's reaction to other firms'entry. In addition we test for information spillovers from foreign investors in the previous year in the same industry but in other countries. An additional advantage of our approach is related to the comparison of alternative channels put forward by the literature for explaining the FTL behavior. Head, Mayer and Ries (2002) formalize the original Knickerbocker (1973) rationale within a Cournot-type model where there is cost uncertainty and a certain minimal degree of risk aversion by firms. Though their model is elegant, the derivation of their main result pays however tribute to strong restrictions on the parameters'space and on the underlying assumptions of the model (e.g. equal slopes of the demand curves in both the home and the host country). In addition, if there is not enough uncertainty and/or firms are not sufficiently risk averse, FDI decisions turn out to be, in their framework, strategic substitutes rather than strategic complements.5 Leahy and Pavelin (2003) provide a simpler theoretical explanation for follow-the-leader behavior in FDI decisions. In their model domestic rivals may be motivated to imitate the leader's FDI when this facilitates collusive behavior in the foreign market. However, since FTL foreign investment only hinges on the possibility to collude, neither uncertainty nor risk aversion play a role in driving their main result; moreover, their framework is also exclusively based on the existence of an oligopolistic market structure. An alternative explanation for follow-the-leader FDI can be inferred within the theories of economic geography. FTL foreign investment is in fact consistent with a pattern of FDI that is spatially agglomerated: if the trade-off between competition effects and agglomeration forces is solved in favor of the latter, it becomes profitable to follow abroad the leader investor.6 Thus, the agglomeration channel for FTL foreign investment predicts that the latter is more likely the higher the number of early investors, in contrast with the findings of the previously quoted papers, where an oligopolistic market structure is crucial for the generation of a follow-the-leader behavior. In particular, Crozet et al. (2004) perform an analysis of FDI in French regions, showing that the location of new entrants is positively and significantly infuenced by the proximity of other MNEs of the same nationality.7 Analogously, Buch et al. (2005) in their study of German FDI determinants find positive agglomeration effects working through the number of other German firms that are active in a given host country. Finally, a follow-the-leader pattern in the undertaking of foreign direct investment might be exogenously generated by firm or industry-specific characteristics: firms (thus with no leader-follower relation) would commonly observe a signal, unobserved by the econometrician, that reduces their uncertainty, but some of them might be more efficient in reacting to this signal and exploit first-mover advantages, with the ‘losers’ bunching behind them in terms of investment timing. In a similar way, time-to build heterogeneity might generate an exogenous sequential pattern in FDI inflows. Our framework is able to encompass the main implications of all these alternative FTL models. Therefore, the exercise allows us to precisely identify the channel which, among the possible alternative explanations, is more consistent with the detected pattern of sequential investment by MNEs. To this extent, our results show that, alongside more traditional determinants of FDI, follow-the-leader behavior driven by our proposed channel of Bayesian learning by rival firms plays a significant role in driving MNEs' decisions to invest abroad. More specifically, the results indicate that firms learn about revenue rather than cost. This result is robust with respect to different model specifications which control for both industry and country heterogeneity. The paper is structured as follows. Section 2 presents a simple model of rival MNEs' reaction through Bayesian learning, whose implications are tested through the econometric approach presented in Section 3 against possible alternative channels driving FTL foreign investment. The results are discussed in Section 4, while Section 5 extends the empirical approach by considering learning from investments in other CEECs than the host country. Finally, Section 6 concludes.

Paying tribute to the original intuition by Knickerbocker (1973), we have been able to derive a general theoretical model of rival MNEs' reaction based on Bayesian learning from first mover investors, encompassing the main implications of alternative models developed by the literature, and testing the resulting propositions on the actual behavior of rival MNEs. We find evidence for our theoretical propositions, showing that, alongside more traditional determinants of FDI, follow-the-leader behavior driven by Bayesian learning by rival firms plays a significant role in driving MNEs' decisions to invest abroad. More specifically, the results indicate that firms learn about revenue rather than cost. This result is robust with respect to different model specifications which control for both industry and country heterogeneity. Two future lines of research are evident to us. First of all, the long studied issue of FDI seen as strategic substitutes or complements might be worth another closer look. In our paper, FDI are strategic substitutes only after a certain threshold in the number of rivals is reached, and only with respect to FDI undertaken in countries different than the one in which the considered investment is taking place. When previous investments in the same country are considered, instead, our study suggests that agglomeration effects and Bayesian learning make FDI decisions strategic complements. As a result, the effects of FDI substitution or complementarity seem to be a function of the geographical space in which rivals are considered. Second, it is obvious that the use of categorical dummies for modelling previous investments suffers from some potential shortcomings, threshold effects being the most evident ones. Therefore, the results of this paper should be validated as soon as the new econometric techniques on dynamic discrete panel data models move from the frontier of theoretical research to more routinely methodological tools.