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Familiarity Breeds Investment: Migrant Networks and Cross-Border Capital
Unformatted Document Text:  20 The results in columns 3 and 4 of table 1 support the role of highly educated immigrants in the investment process. Highly skilled immigrants increase both portfolio and foreign direct investment to their home country; a conclusion consistent with the anecdotal evidence from Saxenian (2002, 2003), Kleman (1996), and Kapur (2001). We should point out that inclusion of a variable measuring the human capital of migrants does not render our measure of migrant stock statistically significant; a result that would occur if only entrepreneurial migrants were the ones investing. In columns 5 and 6 of table 1 we attempt to account for the fact that migrants can often provide “bad” information; information that would lead investors away from their country of origin. Again, we cannot measure this concept directly so we proxy for it using the number of refugees (asylum seekers) in country j that originate in country i. Refugees, by definition, are designated as such, because of the adverse conditions that exist within their country of origin; conditions that likely are not conducive to investment. Inclusion of the size of the refugee stock (logged) along with the size of the migrant population provides substantively and statistically compelling results. Countries that are the source of large refugee populations received smaller amounts of investment (both portfolio and foreign direct). And inclusion of this variable does little to change the effect of the migrant stock—our primary measure of information networks. We check the robustness of our central findings—those in columns 1 and 2 of table 1—in a number of ways. In table 2 we include fixed effects for source countries, for destination countries and for both source and destination countries. 17 While the parameter estimate on migrant stock changes across specifications it remains positive and statistically significant, 17 As we are working with a cross-section we cannot add dyad specific dummy variables.

Authors: Leblang, David.
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20
The results in columns 3 and 4 of table 1 support the role of highly educated immigrants in
the investment process. Highly skilled immigrants increase both portfolio and foreign direct
investment to their home country; a conclusion consistent with the anecdotal evidence from
Saxenian (2002, 2003), Kleman (1996), and Kapur (2001). We should point out that inclusion of
a variable measuring the human capital of migrants does not render our measure of migrant
stock statistically significant; a result that would occur if only entrepreneurial migrants were
the ones investing.
In columns 5 and 6 of table 1 we attempt to account for the fact that migrants can often
provide “bad” information; information that would lead investors away from their country of
origin. Again, we cannot measure this concept directly so we proxy for it using the number of
refugees (asylum seekers) in country j that originate in country i. Refugees, by definition, are
designated as such, because of the adverse conditions that exist within their country of origin;
conditions that likely are not conducive to investment.
Inclusion of the size of the refugee stock (logged) along with the size of the migrant
population provides substantively and statistically compelling results. Countries that are the
source of large refugee populations received smaller amounts of investment (both portfolio and
foreign direct). And inclusion of this variable does little to change the effect of the migrant
stock—our primary measure of information networks.
We check the robustness of our central findings—those in columns 1 and 2 of table 1—in a
number of ways. In table 2 we include fixed effects for source countries, for destination
countries and for both source and destination countries.
17
While the parameter estimate on
migrant stock changes across specifications it remains positive and statistically significant,
17
As we are working with a cross-section we cannot add dyad specific dummy variables.


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