Familiarity Breeds Investment August 2010 TABLE 2.Determinants of Cross-border Foreign Direct Investment (1) (2) (3) (4) (5) Log(migrant stock from din s) 0.174* 0.163* 0.157* 0.151# (0.0638) (0.0637) (0.0738) (0.0639) Log(product of GDPs) 0.163* 0.156* 0.152* 0.149* 0.162 (0.0484) (0.0479) (0.0486) (0.0454) (0.0488) Log(distance) -1.358* -1.173* -1.115* -1.122* -1.026* (0.243) (0.240) (0.238) (0.273) (0.242) Common border 0.907 0.700 0.693 0.835 0.676 (0.645) (0.603) (0.594) (0.612) (0.595) Official common language 2.096* 1.774* 1.667* 1.193* 1.619# (0.624) (0.560) (0.568) (0.645) (0.566) Correlation of growth rates 0.177 0.172 0.162 0.258 0.155 (0.207) (0.205) (0.205) (0.273) (0.203) Common exchange rate peg 0.820 0.774 0.739 0.320 0.738 (0.628) (0.603) (0.599) (0.822) (0.598) Dual taxation treaty 0.714 0.625* 0.629* 0.489 0.615* (0.311) (0.315) (0.316) (0.338) (0.316) Preferential trade agreement 0.155 0.147 0.127 0.362 0.0685 (0.410) (0.410) (0.411) (0.482) (0.414) Log(bilateral telephone volume) -0.0394 -0.0494 -0.0519 -0.119 -0.0509 (0.103) (0.103) (0.102) (0.152) (0.102) Common legal heritage 0.237 0.503* 0.213 (0.249) (0.259) (0.251) Common dominant religion 0.204 0.362 0.202 (0.277) (0.313) (0.278) Genetic distance -0.000504 -0.000161 -0.000528 (0.000366) (0.000486) (0.000365) Cultural difference -0.000885 (0.199) Log(bilateral trade) 0.0954 (0.0585) Constant -125.6* -122.7* -119.5* -116.8* -131.9# (42.89) (43.00) (43.60) (41.04) (43.80) Observations 3.573 3.573 3,573 2,366 3,573 Adjusted R2 0.639 0.641 0.642 0.624 0.643 GDP gross domestic product. Robust standard errors in parentheses. Dependent variable:log(FDI from origin to destination). Robust standard errors clustered by both origin and destination country All models include both origin and destination dummy variables. p<.10:*p<.05. Educated Migrants Real Estate(FIRE)sectors.24 We also include the log of The results thus far support the hypothesis that migrant the total number of migrants so that the former variable networks encourage cross-border investment because is not capturing the effect of the entire migrant stock. they provide investors with information about invest- The results in column 1 of Table 4 report results for ment opportunities across particular destinations.We portfolio investment using the share of migrants with expect that this effect will be more pronounced when higher education,column 2 replaces the higher edu- the migrant themselves are involved in the investment cation variable with the share of migrants employed process.Unfortunately,we cannot directly measure in FIRE.and column 3 includes both measures.The migrant-based investment,so we proxy for it using the results indicate that higher education per se does not percentage of migrants from country d living in coun- have a statistically significant influence on portfolio in- try s that have higher (tertiary)education and.more vestment,whereas the opposite is true for employment directly,those employed in the Finance,Insurance,or 24 We use education data from the OECD's Immigration and Expa- triate Database.This database only has information on immigrants countries into poor countries,the statistical significance of this result into OECD countries;consequently,the sample size is greatly re. goes away.Whether that is due to a difference in sample or because duced,but the most recent release (accessed January 15.2009)con- of different investment opportunities across these sets of countries tains migrants broken down by occupation based on the standard is difficult to sort out given our use of cross-sectional data with both classification of occupations(ISCO-88 codes).We use those codes to source and destination dummy variables. construct the measure of employment in FIRE. 592Familiarity Breeds Investment August 2010 TABLE 2. Determinants of Cross-border Foreign Direct Investment (1) (2) (3) (4) (5) Log(migrant stock from d in s) 0.174∗∗ 0.163∗∗ 0.157∗∗ 0.151∗∗ (0.0638) (0.0637) (0.0738) (0.0639) Log(product of GDPs) 0.163∗∗ 0.156∗∗ 0.152∗∗ 0.149∗∗ 0.162∗∗ (0.0484) (0.0479) (0.0486) (0.0454) (0.0488) Log(distance) −1.358∗∗ −1.173∗∗ −1.115∗∗ −1.122∗∗ −1.026∗∗ (0.243) (0.240) (0.238) (0.273) (0.242) Common border 0.907 0.700 0.693 0.835 0.676 (0.645) (0.603) (0.594) (0.612) (0.595) Official common language 2.096∗∗ 1.774∗∗ 1.667∗∗ 1.193∗ 1.619∗∗ (0.624) (0.560) (0.568) (0.645) (0.566) Correlation of growth rates 0.177 0.172 0.162 0.258 0.155 (0.207) (0.205) (0.205) (0.273) (0.203) Common exchange rate peg 0.820 0.774 0.739 0.320 0.738 (0.628) (0.603) (0.599) (0.822) (0.598) Dual taxation treaty 0.714∗∗ 0.625∗∗ 0.629∗∗ 0.489 0.615∗ (0.311) (0.315) (0.316) (0.338) (0.316) Preferential trade agreement 0.155 0.147 0.127 0.362 0.0685 (0.410) (0.410) (0.411) (0.482) (0.414) Log(bilateral telephone volume) −0.0394 −0.0494 −0.0519 −0.119 −0.0509 (0.103) (0.103) (0.102) (0.152) (0.102) Common legal heritage 0.237 0.503∗ 0.213 (0.249) (0.259) (0.251) Common dominant religion 0.204 0.362 0.202 (0.277) (0.313) (0.278) Genetic distance −0.000504 −0.000161 −0.000528 (0.000366) (0.000486) (0.000365) Cultural difference −0.000885 (0.199) Log(bilateral trade) 0.0954 (0.0585) Constant −125.6∗∗ −122.7∗∗ −119.5∗∗ −116.8∗∗ −131.9∗∗ (42.89) (43.00) (43.60) (41.04) (43.80) Observations 3,573 3,573 3,573 2,366 3,573 Adjusted R2 0.639 0.641 0.642 0.624 0.643 GDP, gross domestic product. Robust standard errors in parentheses. Dependent variable: log(FDI from origin to destination). Robust standard errors clustered by both origin and destination country. All models include both origin and destination dummy variables. ∗ p < .10; ∗∗ p < .05. Educated Migrants The results thus far support the hypothesis that migrant networks encourage cross-border investment because they provide investors with information about invest￾ment opportunities across particular destinations. We expect that this effect will be more pronounced when the migrant themselves are involved in the investment process. Unfortunately, we cannot directly measure migrant-based investment, so we proxy for it using the percentage of migrants from country d living in coun￾try s that have higher (tertiary) education and, more directly, those employed in the Finance, Insurance, or countries into poor countries, the statistical significance of this result goes away. Whether that is due to a difference in sample or because of different investment opportunities across these sets of countries is difficult to sort out given our use of cross-sectional data with both source and destination dummy variables. Real Estate (FIRE) sectors.24 We also include the log of the total number of migrants so that the former variable is not capturing the effect of the entire migrant stock. The results in column 1 of Table 4 report results for portfolio investment using the share of migrants with higher education, column 2 replaces the higher edu￾cation variable with the share of migrants employed in FIRE, and column 3 includes both measures. The results indicate that higher education per se does not have a statistically significant influence on portfolio in￾vestment, whereas the opposite is true for employment 24 We use education data from the OECD’s Immigration and Expa￾triate Database. This database only has information on immigrants into OECD countries; consequently, the sample size is greatly re￾duced, but the most recent release (accessed January 15, 2009) con￾tains migrants broken down by occupation based on the standard classification of occupations (ISCO-88 codes). We use those codes to construct the measure of employment in FIRE. 592