In this section we try to identify the link between aid and migration and suggest some mechanisms through which foreign aid might exert an effect on the size and skill composition of emigrants.
The mechanisms driving the composition of migration with respect to skill level have been studied for a long time. Borjas (1987), stated that different self-selection patterns with respect to education levels may be observed depending on whether the wage skill profile is steeper at origin or destination. By assuming constant migration costs in the skill level of individuals, Borjas (1987) concludes that in countries having relatively high returns to education and earnings inequality, immigrants are drawn primarily from the lower half of the skill distribution of their home country. In addition to income differentials, recent work by Chiquiar and Hanson (2005), Fernández-Huertas (2011), highlight that different self-selection patterns with respect to education levels may also be observed depending on migration costs. These authors show that depending on the size and distribution of migration costs with respect to skill, emigrants might come from the lower, intermediate or upper half of the education distribution even if earnings inequality is high in sending countries.6
In this framework, it is unlikely that international cooperation might influence the selection of emigrants by affecting the distribution of rewards to skill in LDCs. However, aid might help to ease migration costs in several ways: i) Aid might reduce transaction costs by providing opportunities for the highly skilled to migrate thanks to the attribution of scholarship grants, and ii) reduce informational costs by providing information on donor countries, as aid creates bridges between the receiving and the donor countries. iii) Aid might also create networks and screen high level professionals by providing direct contacts and opportunities for top workers to get a job abroad. iv) Finally, although international cooperation might not influence the wage-skill profile, aid might allow migration costs to be affordable by supporting growth, contributing to finance national incomes and thus increase wages (via income). There is evidence showing that if aid does foster development, it induces migration, see Rotte and Vogler (2000). However, the aggregate effect on self-selection is unknown, an increase in LDCs’ wages may help willing unskilled or skilled emigrants to bear the costs of migration (overcome budget constraints), but it might also incentivate them to stay at home. Whether the skilled or the unskilled are more sensitive to changes in income is unclear. Orrenius and Zavodny (2005), for example, find that better economic conditions in Mexico provide a higher disincentive to migrate among undocumented skilled Mexicans than among the undocumented unskilled. Moreover, if the migration costs are decreasing in skill level as in Chiquiar and Hanson (2005), an improvement of the economic conditions would alleviate mainly the liquidity constraints faced by the skilled.
In this section, hereafter we present some results supporting each of these possible mechanisms that might explain the effect of aid on migration selection. The approach adopted follows that of the aid effectiveness literature. Aid pursues multiple objectives when granted to developing countries, and different types of aid are likely to have different economic outcomes, some of them associated to migration. Therefore, we disaggregate aid into specific-purpose categories that are more likely affecting the mechanisms described previously and test the direct effect of aid categories on migration.7
We start by testing the effect of aid on migration seeing it as a tool to overcome liquidity constraints and reduce transaction costs, mainly for the highly skilled, through the attribution of scholarship grants, tuition fees, flight tickets, etc., by donor countries. This brings an undeniable opportunity and incentives for many students and professionals to go abroad. Recently, the IOM (2008) stated that international students represent around 20% of the skilled migration. To test this mechanism we check for the direct effect of technical cooperation from the six countries on the selection rate and on the skill composition of emigrants. During the period 1990-2006 technical cooperation represented annually in average 24% of ODA net disbursements, OECD (2007). We assume that overstaying is rather high.8
We also test the effect of aid on migration by considering the bilateral relation between donor and recipient countries either by projects or by diplomatic ties, which creates opportunities for contacts between both countries, easing access to information about requirements and labor market conditions in donor countries, and reducing procedure costs to the attainment of legal permissions. Hence, we expect that the better the relations between donor and recipient countries, the higher the reduction in these transaction and information costs (specially for educated workers) and the easier the ways to migrate. We use as an indicator of the closeness between donor and recipient countries, the proportion of bilateral aid from the six donors to total aid received from all DAC donors. Higher values of this ratio may be understood as better links for the recipients with these countries, compared to all remaining donors. Furthermore, we test the effect of aid considering it as mechanism to provide information, create networks and screen high level native professionals. We use for that project aid (understood as the funds used to implement specific projects in which allocation, financing and management are controlled by the donors). More project interventions might be related to better information for natives on donor countries, more contacts and networks, and more opportunities for the top educated workers to be employed abroad.
Finally, another specific mechanism through which aid might influence self-selection is by modifying incentives to migrate via income. It is however still not clear what is the efficiency of aggregate aid in sustaining growth (see Rajan and Subramanian, 2008). Some authors have presented evidence showing that categories of aid related to the support of development exert a positive effect on growth, see Clemens et al. (2004), Gomanee et al. (2005), Minoiu and Reddy (2010). As a consequence assuming that aid targeted exclusively to promote development is supporting growth, contributing to financing the gross national income in recipient countries and thus increasing wages, aid might (dis)incentive new emigrants. For testing the effect of developmental aid on migration selection we use many proxies.
To begin with, i) we consider Net aid from the six donors which, following Gomanee et al. (2005), is defined as ODA disbursements minus food aid and humanitarian non food aid.9 We also consider ii) Developmental aid which, following Minoiu and Reddy (2010), is defined as the part of ODA disbursements highly associated with development enhancement. This variable is not readily available but is generally proxied by the aid donated by Scandinavian countries, see Minoiu and Reddy (2010). And, iii) Early impact aid which, according to Clemens et al. (2004), is defined as the part of aid that has short-term effects. Broadly speaking, early-impact aid is budgetary support while long-run aid is related to infrastructure investments and social aid. iv) We also focus on Chang et al. (1999) and their criticism on the potential overstating in the level of assistance by ODA. We hence examine the effect of aid measured by the Effective Development Assistance (EDA) from DAC donors, i.e. the sum of grants and the grant equivalents of official loans. v) Finally, we also consider inflows of aid (ODA) from overall DAC donors. A description of all these proxies is presented in Additional file 1: Table S1A in Appendix.
The instrumental setup is the same as last section, i.e. we use the external debt to GDP ratio and inflation as proxies for good policy. Two exceptions are the variables EDA, where inflation is replaced by the annual growth of M2 to avoid weak instruments. And, the proportion of bilateral aid from the six donors to total aid from all DAC donors, where we use as instrument population 65 years old on as a proxy for development. All instruments for the equations considered pass the statistical tests of under, weak and overidentification. However the mix of instruments used for technical cooperation, the proportion of bilateral to total aid, and project aid are rather weak (first-stage F-statistic slightly larger than 10).10 Thus, following Stock and Yogo (2005), by using IV, these estimates might exhibit severe finite-sample biases, and their finite-sample distribution may be very different from their asymptotic distribution, misrepresenting the size of tests and the range of confidence intervals. To address this we opt to use in addition to IV, the Fuller’s modified limited information maximum-likelihood estimator (Fliml), Fuller (1977). As shown by Hahn et al. (2004), Flores-Lagunes (2007), these estimators perform better overall with weak instruments. The Fliml estimator belongs to the so called k-class estimators and sets , where λ is the liml eigenvalue, L = number of instruments, and corresponds to the Fuller parameter constant. The Fuller estimator with =1 yields the best unbiased estimator and is recommended when one wants to test hypotheses; the Fuller with =4 estimator minimizes the mean squared error of the estimator. We report estimations based on both Fuller constants 1 and 4. Panel A of Table 3 shows that using either of these Fuller estimators produces estimates that are quite similar to the IV estimates.
The structure of Table 3 is as follows: there are three blocks corresponding to our three dependent variables of interest. In each block the coefficients of aid associated with each of the estimators considered are reported in separate columns. There are two sections, panel A contains the variables estimated by the Fuller’s limited information maximum-likelihood and IV estimators. Whereas inside panel B we find estimates based on FE, IV and GMM, respectively. Below the estimated parameters, between brackets, robust standard errors are presented, followed by either the Kleibergen-Paap rk Wald F statistic for weak instruments in case IV is implemented, or the Hansen C statistic in case GMM is implemented.
As can be seen in the first row of Table 3, technical cooperation tends to increase the education gap between emigrants and non emigrants in LDCs since it presents a significant and positive effect on the selection rate and it is positively associated to skilled migration, but not to unskilled migration. In the second row, the proxy for bilateral relations does not have any effect on the selection rate, and its effect on skilled migration is significantly positive (though small). In the first row of Panel B, the “network and screening” mechanism does not influence either skilled or unskilled migration as project aid is non-significant in all cases. Indeed highly skilled workers could be hired to work for projects in their home country and therefore do not necessarily migrate as a consequence of “project” aid. To summarize, we conclude that foreign aid through technical cooperation, likewise major formal links between donors and recipient countries, may help in reducing transaction and information costs for highly educated workers and in this way ease skilled migration.
Finally, the coefficients associated to aid targeted to development (rows 2 to 5 of Panel B) show that skilled workers are more responsive to better economic conditions enhanced by aid than unskilled workers, i.e. the effect of an increase in developmental aid contributes to the overcoming of the budget constraints of skilled emigrants. This might suggest that migration costs are decreasing in skill level as in Chiquiar and Hanson (2005). These results are somehow different to the findings in Berthélemy et al. (2009), who suggest that unskilled people are more sensitive to changes in income (the effect of total aid on unskilled migration is larger compared to the effect on skilled migration). In row 6 of Panel B we use aid (ODA) coming from all DAC donors as explanatory variable, and the results are similar to that obtained using bilateral proxies, that is a larger and more significant effect of aid on skilled migration than on unskilled migration. Based in our results, we believe that the effect of foreign aid is to increase the flow of skilled emigrants and to widen the education gap between emigrants and natives (brain drain).