Details to other migrant networks (Figure 2)

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

MIG 72.71* 140.0*** 157.4*** 26.60 279.3*** 16.66

(1.65) (8.44) (2.92) (0.19) (2.60) (0.59)

MIG*(1‐DIR) ‐20272.3 36489.4** 28891.1* ‐116595.0* 188388.8 ‐3927.4

(‐0.61) (2.48) (1.84) (‐1.84) (1.48) (‐0.06)

MIG*DIR 67.09 153.2*** 177.9*** ‐58.88 304.6*** 14.19

(1.52) (8.19) (3.17) (‐0.40) (2.81) (0.30)

Trade creation (%)

MIG 0.178 0.183 0.150 0.0201 0.156 0.0419

MIG*(1‐DIR) ‐39.07 60.96 31.66 ‐58.54 185.9 ‐9.411

MIG*DIR 0.164 0.200 0.170 ‐0.0444 0.170 0.0357

Tariff equivalent (%)

MIG 0.0254 0.0261 0.0214 0.00287 0.0222 0.00599

MIG*(1‐DIR) ‐7.079 6.799 3.929 ‐12.58 15.01 ‐1.412

MIG*DIR 0.0234 0.0286 0.0242 ‐0.00635 0.0243 0.00510

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

MIG ‐537.6*** 72.60 191.7** 53.49*** ‐3.474 362.8***

(‐6.49) (0.30) (1.99) (3.29) (‐0.22) (13.23)

MIG*(1‐DIR) 1497948.4 334190.4** 35113.3 7731.8 ‐277.4 61897.8***

(0.58) (2.38) (0.82) (0.93) (‐0.31) (5.67)

MIG*DIR ‐382.7 231.0 201.2** 61.01*** ‐4.590 393.4***

(‐0.06) (0.90) (2.07) (5.48) (‐0.30) (14.36)

Trade creation (%)

MIG ‐0.614 0.0488 0.230 0.150 ‐0.0304 0.486

MIG*(1‐DIR) 2.80344e+09 845.1 52.35 24.15 ‐2.403 128.5

MIG*DIR ‐0.437 0.155 0.242 0.171 ‐0.0402 0.527

Tariff equivalent (%)

MIG ‐0.0879 0.00697 0.0328 0.0214 ‐0.00435 0.0692

MIG*(1‐DIR) 245.0 32.09 6.015 3.090 ‐0.347 11.81

MIG*DIR ‐0.0626 0.0222 0.0345 0.0244 ‐0.00575 0.0750

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

MIG 38.49*** 92.07 ‐10.16* 113.3*** 21.78 ‐2.021

(3.05) (0.96) (‐1.70) (3.72) (0.55) (‐0.18)

MIG*(1‐DIR) ‐5395.6** ‐173799.2*** ‐4600.0 ‐13007.8** 63118.3 39988.9***

(‐2.24) (‐3.23) (‐1.45) (‐2.10) (1.57) (4.95)

MIG*DIR 36.08*** 51.97 ‐10.80* 92.82*** 23.73 10.15

(2.86) (0.55) (‐1.78) (2.86) (0.59) (0.87)

Trade creation (%)

DZA ECU EGY ESP ETH FIN

BRA

CAN CHE CHL COL DEU DNK

ARG AUS AUT BEL BOL

MIG 0.118 0.0640 ‐0.0285 0.334 0.0197 ‐0.00348

MIG*(1‐DIR) ‐15.24 ‐70.11 ‐12.11 ‐31.84 76.83 99.16

MIG*DIR 0.111 0.0361 ‐0.0303 0.274 0.0214 0.0175

Tariff equivalent (%)

MIG 0.0169 0.00914 ‐0.00407 0.0477 0.00281 ‐0.000497

MIG*(1‐DIR) ‐2.362 ‐17.25 ‐1.844 ‐5.475 8.143 9.842

MIG*DIR 0.0158 0.00516 ‐0.00433 0.0391 0.00306 0.00250

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

MIG ‐7.372 9.719** 1614.1*** 86.08 7.188 253.7***

(‐0.53) (2.35) (3.09) (0.71) (0.04) (3.82)

MIG*(1‐DIR) 1071.8 233.5* ‐284870.3 ‐75510.0** ‐46917.5 97605.1***

(0.63) (1.68) (‐0.89) (‐2.06) (‐0.94) (2.60)

MIG*DIR ‐5.109 10.41** 1565.6*** 53.67 ‐29.45 296.3***

(‐0.36) (2.50) (2.99) (0.45) (‐0.17) (4.71)

Trade creation (%)

MIG ‐0.0299 0.153 0.544 0.121 0.00557 0.257

MIG*(1‐DIR) 4.441 3.737 ‐61.63 ‐65.32 ‐30.46 168.4

MIG*DIR ‐0.0207 0.164 0.528 0.0753 ‐0.0228 0.300

Tariff equivalent (%)

MIG ‐0.00427 0.0218 0.0775 0.0172 0.000795 0.0367

MIG*(1‐DIR) 0.621 0.524 ‐13.68 ‐15.13 ‐5.190 14.10

MIG*DIR ‐0.00296 0.0234 0.0752 0.0108 ‐0.00326 0.0428

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

MIG ‐20.52* 9.146* 51.68 ‐558.8 2452.4*** 1949.3***

(‐1.69) (1.80) (1.62) (‐1.44) (6.69) (3.08)

MIG*(1‐DIR) 196.8 ‐353.1 ‐3490.5 14744.0 2182238.5*** ‐2102390.7**

(0.20) (‐1.63) (‐0.27) (1.24) (5.90) (‐2.17)

MIG*DIR ‐20.22 9.516* 50.07 ‐556.6 2604.1*** 1464.7**

(‐1.63) (1.88) (1.59) (‐1.42) (7.19) (2.20)

Trade creation (%)

MIG ‐0.103 0.0773 0.0623 ‐0.986 0.384 0.455

MIG*(1‐DIR) 0.992 ‐2.940 ‐4.116 29.89 2917.1 ‐99.25

MIG*DIR ‐0.101 0.0804 0.0603 ‐0.982 0.407 0.341

Tariff equivalent (%)

MIG ‐0.0147 0.0110 0.00889 ‐0.142 0.0547 0.0648

MIG*(1‐DIR) 0.141 ‐0.426 ‐0.601 3.736 48.67 ‐69.87

MIG*DIR ‐0.0145 0.0115 0.00861 ‐0.141 0.0581 0.0487

IDN IND IRL IRN

Chapter 4

The Pro-Trade Effect of the Brain-Drain: Sorting Out Confounding Factors ¹

4.1 Introduction

In the perfect-competition aggregate production function framework emigration triggers a static welfare loss for remaining individuals as the marginal productivity of immobile complementary factors declines. Skill-biased emigration (a brain drain) may add a dynamic loss if the source country’s average human capital falls.

Docquier and Marfouk (2006) show that thetotal stock of migrants from poor Southern countries in the rich OECD has grown from about 19 million people in 1990 to 31 million in 2000. Moreover, the average rate ofhigh-skilled emigration has been 6.6 percent in 1990 and 7.2 in 2000, with higher numbers for least-developed countries.

Theory papers discuss channels which may mitigate this brain drain. Besides remit-tances, migration prospects may increase the incentives for higher education, so that average human capital in the non-migrant population may actually rise. Moreover, a diaspora may improve access to foreign markets, thereby encouraging international trade or investment.

However, Lucas (2006) concludes that “the empirical evidence on each of these ... channels remains highly controversial. The most systematic portion of this evidence looks at the links between migration and trade, though difficulties eliminating spurious associations remain”

1This Chapter is based on an article forthcoming in theEconomics Letters. For the working paper version, see Felbermayr and Jung (2008b). The concept for the paper was developed jointly, empirical analysis and writing were shared equally.

(p. 373).

Spurious association arises due to confounding factors that determine both, the vol-ume of bilateral trade and the bilateral stock of migrants. For example, cultural proximity matters for bilateral trade volumes, but may also affect emigration rates. Similar consid-erations apply for the ease of geographical mobility. If unobserved components of cultural and geographical proximity positively affect migration, OLS estimates would suffer from endogeneity bias and overestimate the true effect of migration on trade.

We include the bilateral stock of migrants into a theory-grounded gravity equation. Re-cent data on the stock of emigrants from poor sending countries comes from Docquier and Marfouk. The data has a time dimension and distinguishes between three different educa-tional classes. The panel nature of the data allows to account for unobserved heterogeneity by differencing out unobserved country-pair specific characteristics. Baier and Bergstrand (2007) have recently shown the advantages of this approach in a comparable gravity con-text. Moreover, we can perform a regression-based test for strict exogeneity (Wooldridge, 2002).

We report three major results. First, failing to control for unobserved heterogeneity indeed leads to overestimation. Second, there is, nevertheless, a statistically and economi-cally significant causal effect of migration on trade. Third, low- and high-skilled migrants strongly boost bilateral trade by comparable quantities while medium-skilled migration does not seem to matter.

So far, empirical gravity studies have typically focused on a single anchor country, see the survey of Wagner et al. (2002). Dunlevy (2006) and Bandyopadhyay et al. (2008) document a pro-trade effect of migration on the exports of US states. Kugler and Rapoport (2007) analyze how emigration into the US fosters capital formation; Docquier and Lodigiani (2009) extend this exercise to a cross-section of host countries. The two latter papers use the same data than ours; however, we seem to be the first to exploit the temporal and bilateral dimensions of the data in a theory-grounded South-North gravity model.

4.2 Econometric specification

We augment the theory-based gravity framework described in Feenstra (2004) with the bilateral stocks of migrants. We strive to explain the volume of trade T_snt between a (poor) Southern sending country, s, and a (rich) Northern receiving country, n, at time t∈ {1990,2000}. We investigate the effect of M IG^k_snt,the stock of foreign-born residents from s inn by education k (k∈ {l, m, h}, l: low-skilled, m: medium-skilled, and h: high-skilled).

Our gravity equation is lnT_snt= X

k∈{l,m,h}

β^klnM IG^k_snt+γPROX⁰_sn+δPOL⁰_snt+ν_st+ν_nt+ε_snt, (4.1) where the vector PROXsn collects indicators of cultural and geographical proximity, and POLsnt measures time-variant bilateral trade policy. We include a comprehensive set of country-and-time effectsνst andνnt to control for all source and destination specific deter-minants, in particular for multilateral resistance terms.²

We impose the error structure ε_snt = c_sn+u_snt, where c_sn is a dyad-effect and u_snt the usual idiosyncratic error term. In the presence of unobserved confounding factors ex-planatory variables will be correlated with the error term u_snt so that OLS is invalid.

Following Baier and Bergstrand, we difference equation (4.1) to eliminatecsn. As suggested by Wooldridge (p. 285), in a two-period framework we can test whether the differenced version of (4.1) satisfies the assumption of strict exogeneity E(∆usn|∆Xsn) = 0, where

∆Xsnis the vector of first differences of all explanatory variables. We include thestocks of foreign-born residents in the differenced version of equation (4.1) and perform an F-test for joint significance. Failing to reject the null would signal that differencing has indeed solved the endogeneity concern.

Im Dokument Unpacking Trade Costs: Theory and Evidence (Seite 80-84)