Emerging-Market Multinational Enterprises in Developed Markets:

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estimate is significantly different from zero, there is endogeneity. Table 1A reports the first step results which suggest that the effective corporate tax rate of the host market is a good IV because it is significantly correlated with all three measures for host country technological resources. Table 1B reports the second step results with the T-test for the significance of the residual’s parameter estimate. Table 1B shows that the estimates for the residuals collected in Step One are not significantly different from zero, which suggests that endogeneity is not a problem, and allows us to use the original econometric specification. We also replicated the tests by using the summation measures for the host market’s technological resources and by using different year lags, and found similar results. [Insert Table 1A here] [Insert Table 1B here] RESULTS Table 2 presents summary statistics and the correlation matrix for all the variables, which suggest that multicollinearity is not a concern for the study. [Insert Table 2 here] Table 3 reports regression results using three different measures for the independent variable. The results in Table 3 show supportive evidence for our hypothesis when technological resources in the host market are measured by R&D. Both R&D measures 1 (host-market number of R&D employment) and 2 (host-market dollar value of R&D investments) have significant, positive effects on the R&D expenditures of EM MNEs’ parent companies in the home markets. For instance, Model 1 shows that when the number of industry-level R&D employment in a DM host market increased by 1% four years ago, the current R&D expenditures of the EM parent increase by 0.20%; Model 3 shows that when the dollar value of industry-level R&D investments in a DM host market increased by 1% four years ago, the current R&D expenditures of the EM parent increase by 0.10%. In addition, the overall explanatory power of the econometric specification is strong; Chi square values (goodness of fit tests) for all models are high and P-values (probability to reject the significance of all variables) are consistently close to zeros for all models. [Insert Table 3 here] 13
However, results using measure 3 (host-market number of patents applied) suggest that an EM parent with affiliates in a patent-rich DM is not significantly more active in R&D investments. We speculate that as patents represent codified, quantified knowledge and are tradable in the market, EM MNEs can access such knowledge through market transactions (e.g., direct acquisitions of patents, licensing) (David, 1992; Grabowski, 1968; Griliches, 1984). Therefore, even if EM MNEs have no direct presence in a patent-rich host market, they can still tap into patent resources in that market. Other R&D resources such as R&D skills and know-how embedded in technological networks and R&D personnel, however, are more tacit, non-codified, and are mainly accessible when EM MNEs locate subsidiaries in the market (Polanyi, 1958; Keller, 2004). Some results for the control variables are worth noting. First, the positive and significant effects of home R&D environment suggest that an R&D-active environment at home encourages the parent companies to be more technologically active. Second, both home and host market sizes have positive effects on parent companies’ R&D, which suggests that the fixed cost nature of R&D expenditures requires access to large markets. We also found that the geographic distance between markets has a negative effect, confirming the idea that any international knowledge diffusion is likely to decay over distance (Conley and Ligon, 2002; Maurseth and Verspagen, 2002; Sjöholm, 1996; Keller, 2004). Lastly, the results that firm size has a positive effect and firm age has a negative effect on parent companies’ R&D expenditure confirm prior findings that large firms are more capable of investing in R&D activities but old firms are less motivated to be innovative (e.g., Balasubramanian and Lee, 2008; Hansen, 1992; Shefer and Frenkel, 2005). Robustness Checks First, we relaxed the assumption based on Mansfield (1985) and Mansfield and Romeo (1980) that a four-year time lag is needed for technology diffusion. Instead, we used a wider range of possible numbers of year lags (between 0 and 5). Table 4 shows the results using our first measure of technological resources in the host market. Specifically, we found that host country number of R&D employment in the current year or one to five years ago generates significant, positive spillover benefits that improve EM MNEs’ technological capabilities. These results are highly consistent with our main regression results in Table 3. We further replicated these robustness tests using measures 2 14
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Page 27 and 28: Table 2 Summary Statistics and Corr
Page 29 and 30: Independent Variable Measure 1 Tabl

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