Familiarity Breeds Investment August 2010 countries;a phenomena that would call for clustering portfolio investment is positively influenced by reli- on d. gious similarity,but not by a common legal heritage We deal with this potential bias by estimating stan- or by genetic distance.Both latter variables are statis- dard errors that are robust to multiway clustering as tically insignificant.Column 4 includes a more direct developed by Cameron.Gelbach.and Miller (2006). indicator of cultural similarity by including the World Their approach allows for arbitrary correlations be- Values Survey-based measure of cultural difference tween errors that belong to "the same group (along As expected,increasing cultural difference decreases either dimension)"(p.7).As they point out,this esti- cross-border portfolio investment.Adding these mea- mator is applicable in situations when the errors exhibit sures of cultural affinity or institutional familiarity do spatial correlation.Consequently,we report standard not,however,significantly affect the parameter esti- errors that are clustered by both source and desti- mate for migrant stock. nation countries.21 It should be noted that Cameron. It is also possible that patterns of bilateral investment Gelbach,and Miller mention that multiway clustering reflect other economic relationships between countries. increases-by an order of magnitude-the size of stan- Rauch and Trindade (2002)were the first to report a dard errors.In the results reported here,the standard positive relationship between diaspora networks and errors are between 60%and 100%larger than tradi- bilateral trade.If investment follows trade and not mi- tional robust standard errors.Hence,our results are gration,then inclusion of this variable should render very conservative. migrant stock statistically insignificant-or at least de- crease its substantive impact.Consequently,in column EMPIRICAL FINDINGS 5,we include a measure of bilateral trade.Trade has a negative effect on bilateral investment,indicating that these flows are substitutes rather than complements Central Results and its inclusion does not decrease the statistical or Table 1 reports models of dyadic portfolio investment. substantive importance of migrant networks. The specification in column 1 is our benchmark model, Table 2 repeats this exercise,substituting FDI as the where we just control for the variables used in prior dependent variable.Note that due to data limitations, studies of portfolio investment.Consistent with a stan- the FDI models refer to a much smaller number of dard gravity model,portfolio investment is a positive source countries.For the sake of space,we summarize function of country size (as measured by the product rather than walk through the findings from Table 2 of GDPs)and a negative function of distance.Surpris- We find that the gravity specification is a reasonable ingly,common language and common border are sta- benchmark because economic size and distance are tistically insignificant,as is the proxy for diversification statistically significant and consistently signed.The log (correlation of growth rates).Shared policies-a com- of migrant stock has a positive and statistically signif- mon exchange rate peg,a shared dual taxation treaty, icant effect on bilateral FDI that does not go away and membership in a preferential trade agreement- once we use other variables to measure cultural and have a positive and statistically significant effect on institutional familiarity. portfolio investment.We fail to find evidence that bilat- eral telephone traffic-a measure of information costs in previous studies(Portes and Rey 2005)-influences Migrant Networks and cross-border portfolio investment. Heterogeneous Investments In column 2.we add our measure of diaspora networks-the size of the migrant stock from the desti- The findings thus far support the argument that migrant nation residing in the source country.Consistent with networks serve as a conduit for capital flows,and they our hypotheses,we find that migrant networks have a point to the importance of migrant networks in the positive and statistically significant effect on portfolio provision of information.In this section,we test the in- investment.Because both the portfolio investment and formational hypothesis more directly.Following Rauch migrant stock have been transformed into logs,we can and Trindade(2002),we argue that the informational interpret the coefficient as an elasticity.This means role of migrant networks should be more important that increasing the migrant stock from a destination for trade in heterogeneous commodities,where private in a source country by 1%results in 0.2%increase information has greater value.We view FDI opportuni- in portfolio investment.Evaluated at their means,this ties as more heterogeneous than portfolio investment translates to a contribution of $450 per migrant to his opportunities.Not only are there an infinite number or her home country. of FDI opportunities-ranging from joint ownership to The migrant stock,of course,could simply be cap- greenfield investments-they also differ in that their turing cultural affinity or institutional familiarity.In risk of expropriation is greater.Portfolio investment, column 3,we include additional variables to control in contrast,can only be made in assets that are publicly for this possibility.These results are surprising because issued by either governmental or corporate interests entities that provide relatively more information to markets.Because portfolio investment is more liquid 21 We use Cameron,Gelbach,and Miller's (2006)cgmreg ado file. it can more easily be moved from market to market version 3.0,downloaded on August 2,2009,from http://gelbach. and from asset to asset,something that requires rela- eller.arizona.edu/~gelbach/ado/cgmreg.ado. tively less information than FDI.We therefore expect 590Familiarity Breeds Investment August 2010 countries; a phenomena that would call for clustering on d. We deal with this potential bias by estimating stan￾dard errors that are robust to multiway clustering as developed by Cameron, Gelbach, and Miller (2006). Their approach allows for arbitrary correlations be￾tween errors that belong to “the same group (along either dimension)” (p. 7). As they point out, this esti￾mator is applicable in situations when the errors exhibit spatial correlation. Consequently, we report standard errors that are clustered by both source and desti￾nation countries.21 It should be noted that Cameron, Gelbach, and Miller mention that multiway clustering increases—by an order of magnitude—the size of stan￾dard errors. In the results reported here, the standard errors are between 60% and 100% larger than tradi￾tional robust standard errors. Hence, our results are very conservative. EMPIRICAL FINDINGS Central Results Table 1 reports models of dyadic portfolio investment. The specification in column 1 is our benchmark model, where we just control for the variables used in prior studies of portfolio investment. Consistent with a stan￾dard gravity model, portfolio investment is a positive function of country size (as measured by the product of GDPs) and a negative function of distance. Surpris￾ingly, common language and common border are sta￾tistically insignificant, as is the proxy for diversification (correlation of growth rates). Shared policies—a com￾mon exchange rate peg, a shared dual taxation treaty, and membership in a preferential trade agreement— have a positive and statistically significant effect on portfolio investment.We fail to find evidence that bilat￾eral telephone traffic—a measure of information costs in previous studies (Portes and Rey 2005)—influences cross-border portfolio investment. In column 2, we add our measure of diaspora networks—the size of the migrant stock from the desti￾nation residing in the source country. Consistent with our hypotheses, we find that migrant networks have a positive and statistically significant effect on portfolio investment. Because both the portfolio investment and migrant stock have been transformed into logs, we can interpret the coefficient as an elasticity. This means that increasing the migrant stock from a destination in a source country by 1% results in 0.2% increase in portfolio investment. Evaluated at their means, this translates to a contribution of $450 per migrant to his or her home country. The migrant stock, of course, could simply be cap￾turing cultural affinity or institutional familiarity. In column 3, we include additional variables to control for this possibility. These results are surprising because 21 We use Cameron, Gelbach, and Miller’s (2006) cgmreg.ado file, version 3.0, downloaded on August 2, 2009, from http://gelbach. eller.arizona.edu/∼gelbach/ado/cgmreg.ado. portfolio investment is positively influenced by reli￾gious similarity, but not by a common legal heritage or by genetic distance. Both latter variables are statis￾tically insignificant. Column 4 includes a more direct indicator of cultural similarity by including the World Values Survey–based measure of cultural difference. As expected, increasing cultural difference decreases cross-border portfolio investment. Adding these mea￾sures of cultural affinity or institutional familiarity do not, however, significantly affect the parameter esti￾mate for migrant stock. It is also possible that patterns of bilateral investment reflect other economic relationships between countries. Rauch and Trindade (2002) were the first to report a positive relationship between diaspora networks and bilateral trade. If investment follows trade and not mi￾gration, then inclusion of this variable should render migrant stock statistically insignificant—or at least de￾crease its substantive impact. Consequently, in column 5, we include a measure of bilateral trade. Trade has a negative effect on bilateral investment, indicating that these flows are substitutes rather than complements, and its inclusion does not decrease the statistical or substantive importance of migrant networks. Table 2 repeats this exercise, substituting FDI as the dependent variable. Note that due to data limitations, the FDI models refer to a much smaller number of source countries. For the sake of space, we summarize rather than walk through the findings from Table 2. We find that the gravity specification is a reasonable benchmark because economic size and distance are statistically significant and consistently signed. The log of migrant stock has a positive and statistically signif￾icant effect on bilateral FDI that does not go away once we use other variables to measure cultural and institutional familiarity. Migrant Networks and Heterogeneous Investments The findings thus far support the argument that migrant networks serve as a conduit for capital flows, and they point to the importance of migrant networks in the provision of information. In this section, we test the in￾formational hypothesis more directly. Following Rauch and Trindade (2002), we argue that the informational role of migrant networks should be more important for trade in heterogeneous commodities, where private information has greater value. We view FDI opportuni￾ties as more heterogeneous than portfolio investment opportunities. Not only are there an infinite number of FDI opportunities—ranging from joint ownership to greenfield investments—they also differ in that their risk of expropriation is greater. Portfolio investment, in contrast, can only be made in assets that are publicly issued by either governmental or corporate interests, entities that provide relatively more information to markets. Because portfolio investment is more liquid, it can more easily be moved from market to market and from asset to asset, something that requires rela￾tively less information than FDI. We therefore expect 590