Targeted Foreign Aid and International Migration: Is Development-Promotion an Effective Immigration Policy?

Targeted Foreign Aid and International Migration: Is Development-Promotion an Effective... Abstract Faced with the failure of traditional immigration controls, policymakers in the United States and Western Europe increasingly look to foreign aid to reduce migrant inflows. Some analysts expect assistance to improve living standards in source countries, thereby deterring residents from moving abroad. While this idea makes intuitive sense, research on aid and migration shows mixed results: some scholarly work supports aid-based migration policies, but other analyses suggest that aid actually enables migration by providing individuals with resources that facilitate movement across borders. We suggest that this tension in the literature reflects a failure to distinguish between different types of foreign aid. Drawing on recent work demonstrating the heterogeneous effects of various aid projects, we posit that governance aid should deter emigration by enhancing government capacity and alleviating political push factors; in contrast, economic and social aid should enable migration by increasing individuals’ means and capabilities to move. We test our hypotheses on a panel of 101 developing countries spanning twenty-five years (1985–2010). We find that governance aid does reduce emigration rates from developing countries, while other types of aid appear not to affect migration. Introduction Concerns over immigration from poor and often conflict-ridden countries dominate electoral politics in much of the advanced industrialized world.1 These concerns reflect perceptions that immigrants drive down wages, drain social insurance and social welfare funds, increase crime rates, and threaten social and cultural cohesion.2 Typical responses from policymakers include enhancing security patrols along migration routes, as well as erecting physical and bureaucratic barriers to entry. While these policies may excite nativist constituents, they do not necessarily reduce inflows of migrants. They also raise humanitarian concerns.3 Thus, some critics advocate a development-friendly alternative: promoting economic growth, job creation, and development in source countries to reduce the poverty and underdevelopment that push migrants to exit for more highly developed neighboring countries (Böhning 1994, 170–76; Olesen 2002, 143). In particular, advanced industrialized countries, they argue, may effectively manage migration inflows by providing foreign aid to source countries. Some policymakers are exploring and implementing aid-based migration policies. For example, in 2001 the Danish Ministry of Foreign Affairs commissioned a study to better align their aid and immigration policies (Nyberg-Sørensen, Van Hear, and Engberg-Pedersen 2002). More recently, the European Commission publicized its plans to manage migration from the Middle East and Africa through “financial allocations devoted to tackling the root causes” of migration (European Commission—Press Release 2016) and the US government announced that it will allocate one billion dollars in aid to Central America to lower migration from the region by addressing its root causes (US Department of State 2015). Some academics also support aid-based policy approaches (Neumayer 2005, 405; Katseli, Lucas, and Xenogiani 2006, 59), and academic research indicates that donor countries target foreign aid to prevent inflows of migrants (Bermeo and Leblang 2015, 639–51). Despite growing interest from policymakers, scholars disagree about the effectiveness of aid as a tool for managing migration. Many analysts are skeptical of this approach. Some even argue that aid enables migration by providing poor individuals who want to emigrate with material means to do so (de Haas 2007, 831–34). Others express more optimism about the deterring effects of aid, arguing that aid should raise the quality of individuals’ lives and make them less inclined to exit their countries of residence (Morrison 1982, 11–15). Few quantitative studies explore these questions and available research offers contradictory findings (Rotte and Volger 2000; Berthélemy, Beuran, and Maurel 2009). We think that some kinds of foreign aid are likely to increase migration, while other kinds are likely to reduce it. Studies that simply aggregate all foreign aid will produce misleading results. Thus, our study distinguishes among aid based on whether it aims to promote social, economic, or political development. We hypothesize that economic and social development assistance increases migration from recipient countries, while political development assistance reduces such migration.4 We expect aid targeting economic and social development to increase outward migration rates from recipient countries because it provides would-be migrants with newfound economic assets that they can use to exit (de Haas 2007, 831–34). In contrast, assistance targeted to governance improves political institutions (Jones and Tarp 2016, 272–73). More capable and representative government and inclusive political rights enhance individuals’ life satisfaction and reduce the likelihood that they will uproot themselves and emigrate. We test these hypotheses through analysis of cross-national time-series data covering 101 developing countries over a time series spanning twenty-five years (1985–2010). The results support the latter hypothesis: aid directed toward governance projects negatively affects the emigration rates of developing countries. These findings suggest that aid-related political development deters exit by would-be migrants through the accompanying improvements to rule of law, human rights, and governance quality. We do not, however, find evidence that other types of aid affect migration patterns. In the remainder of this article, we provide further background to our research problem and more extensively review the literature relevant to foreign aid and international migration. We then establish the bases of our hypotheses about the deterring and enabling effects of aid types (economic, political, and social) on migration rates. Next, we discuss our research design and data and our results. We conclude with policy implications. Migration and Foreign Aid Governments in the United States and Western Europe have strengthened immigration deterrence policies in response to growing concerns about irregular migration. Starting in 2000, the United States ramped up border security in an attempt to limit illegal immigration from Central American countries and Mexico.5 Similarly, countries in Europe seek to deter migration from the Middle East and North Africa through sea route patrols, border fences, and deportations (de Haas 2013, 1309–10). Policy platforms built on stemming the flow of immigrants dominated recent political campaigns in both the United States and Europe. Pledges to restrict movement between the US and Mexico border, and to ban refugee populations from countries of the Middle East, fueled the successful presidential campaign of Donald Trump in the United States (Milligan 2016). Likewise, the Brexit campaign in the United Kingdom likely succeeded in part because of anti-immigration sentiment (Goodwin and Heath 2016, 329). Despite the eagerness of politicians to display their commitments to border security and legal limits on migration, there is reason to believe that these policies are generally ineffective (Bhagwati 2003; Castles 2004; Black et al. 2006, 554). For example, more than 13 million people moved to Western Europe between 1992 and 2001 despite immigration restrictions enacted by various European governments during the 1990s (Hatzipanayotou and Michael 2012, 199). The ineffectiveness of these traditional migration policies likely reflects their failure to account for root causes of migration within source countries, such as poverty, inequality, and political conflict.6 Recognition that traditional immigration policies do not attend to “push” factors has inspired alternative strategies to relieve migration pressure within sending countries. Advanced by both academics (Morrison 1982, 24; Böhning 1994; Stalker 2002, 152) and politicians,7 these strategies focus on economic development through foreign assistance. Aid-based immigration policies hinge on the following two notions. First, poverty and underdevelopment compel migrants in countries of the Global South to leave their home countries. Second, aid-supported development in these countries can mitigate these push factors and thus reduce migrant outflows.8 Policymakers in countries of the Global North appear at times to implement aid policy in order to reduce immigration (Bermeo and Leblang 2015, 630–33). For example, Myers and Papageorgiou (2000, 184) argue that the US government explicitly rationalized aid disbursements to Haiti and Mexico on the basis that this support would lower migration pressure. Some criticize not only this approach, but also the logic underpinning it. For example, de Haas (2010, 1305) objects to the “conventional wisdom underlying such argumentations . . . that war and poverty are the root causes of mass migration.” He argues that development assistance may actually lead to greater emigration rates, either by providing would-be migrants with the capacity to emigrate (de Haas 2007, 2013) or by exacerbating push factors.9 Such criticisms tie into broader skepticism about the effectiveness of foreign aid itself in assuaging poverty and underdevelopment.10 Empirical evidence on the effects of aid on migration is limited and contradictory. While Rotte and Volger (2000, 495–507) do not identify significant relationships between aid and migration to Germany, Berthélemy et al. (2009: 1592–94) find that aid increases migration. Faini and Venturini (2010) show that aid to low-income European countries generated short-term upticks in emigration from recipients. Similarly, Stalker (1994, 26–28) and Cornelius (2002) conclude that economic development (whether supported by aid or not) increases migratory pressure in the short run. Hatton and Williamson (2005) argue that the effect of income growth on international migration depends on economic and security dynamics in the geographic region. Thus, income growth in a typical country of Western Europe, East Asia, or South America is accompanied by reduced migration to the United States, whereas income growth for a typical African country leads to greater migration to the United States. While these studies offer important insights, they do not consider that different types of aid may have different effects on international migration. We suspect that some aid types may deter emigration while other types lead to greater migrant outflows. Drawing on the work of de Haas (2007) and Clemens (2014), we expect economic and social aid to lead to greater emigration rates from aid recipient countries. Such aid offers would-be migrants greater material means that they can use to leave their countries of residence. In contrast, we expect aid targeting political development to lower emigration rates from recipients. Such governance aid improves political institutions (Jones and Tarp 2016, 272–73), thereby strengthening governmental capacity and attenuating political push factors, such as corruption, repression, and discrimination, that compel citizens to leave their home countries. Theory and Hypotheses Scholars conceptualize the effect of foreign aid on migration in two ways. In the first approach, foreign assistance addresses the root causes of migration, thereby deterring migration from source countries. Aid addresses the root causes by generating employment and higher wages (Arndt, Jones, and Tarp 2010, 16–22; Lof, Mekasha, and Tarp 2015), enhancing political rights (Finkel, Pérez-Liñán, and Seligson 2007, 422), strengthening border protection by providing aid-recipient governments with greater control over population movements (de Haas 2007, 820; Bhagwati 2003, 98), lowering the risk and duration of conflict (Collier et al. 2003, 175–84; de Ree and Nillessen 2009, 309), and improving political institutions (Jones and Tarp 2016). In the second approach, scholars consider aid as an enabling factor for migration. In this tradition, foreign aid enhances individuals’ material resources, allowing them to afford the costs of migration (Faini and Venturini 2010). Aid projects also transfer skills and information about opportunities abroad, which may make individuals more likely to migrate. Additionally, aid increases emigration through its exacerbation of push factors (de Haas 2007, 828), for example, by worsened civil conflict (Nunn and Qian 2014; Narang 2015, 1653–55; Tahir 2017, 122–26). While scholars have contrasted the effects of bilateral and multilateral aid on migration (Berthélemy et al. 2009; Ontiveros and Verardi 2012) and the effects of aid targeting urban and rural areas on migration (Gamso and Yuldashev 2018), no previous work explores the possibility that aid projects targeting different sectors might have varying impacts on international migration. This is striking given evidence that aid projects have different objectives (Clemens et al. 2012, 594) and heterogeneous outcomes (Mavrotas and Nunnenkamp 2007; Savun and Tirone 2018) and operate through varying mechanisms (Jones and Tarp 2016, 267–68). In light of this evidence, we theorize that some types of aid projects enable migration and others deter it. First, we expect that aid projects targeting economic growth and development enable migrants, leading to higher emigration rates from aid recipient countries. Earlier research demonstrating positive impacts of aid and economic development on migration outflows informs this expectation (Berthélemy et al. 2009, 1592–94; Faini and Venturini 2010). We suspect that the effects identified in these studies are generated by a subset of aid projects that contribute to growth in income and other material resources. Such improvement in resources, in turn, afford individuals in aid-recipient countries the otherwise out-of-reach costs of emigration. H1:Economic aid increases emigration rates. Hypothesis 1 assumes that economic aid boosts per capita income in aid recipient countries. However, scholars disagree about the relationship between aid and income: some studies find a positive and statistically significant relationship between aid and per-capita income (for example, Lof et al. 2015), whereas others do not (for example, Nowak-Lehmann et al. 2012; Dreher and Langlotz 2017). Even if economic aid is positively associated with emigration, as hypothesized, it does not necessarily follow that economic growth and development enable migration, as Clemens (2014) and de Haas (2007) argue. Instead, other mediating factors may explain the relationship.11 Our analysis of a control variable measuring economic development, in tandem with the economic aid variable, will help to clarify the effects of economic aid and economic development on migration. We discuss this in greater depth below. In contrast to economic aid, we expect migration-deterring effects from governance aid. Improving the effectiveness and responsiveness of political institutions and civil society via governance-oriented foreign aid makes populations in low-income countries feel more included, better represented, and safer at home. Notwithstanding scholarship that argues broadly that foreign aid worsens governance by creating dependency (Knack 2001, 312–14; Bräutigam and Knack 2004, 260–66) or acting as nontax revenue (Morrison 2009, 108–13), more nuanced work finds significant and positive impacts of governance aid on political institutions (Jones and Tarp 2016, 272–73). Goldsmith (2001, 138–44) and Dunning (2004, 417–21) find governance aid effective in improving state capacity in Africa, and Savun and Tirone (2011, 237–42) demonstrate that governance aid enhances stability in fragile democracies. Likewise, USAID democracy-assistance projects appear to advance democratization (Finkel et al. 2007, 421–33). Further evidence suggests that emigration rates are lower where sound political institutions exist. For example, individuals exit their countries of residence at lower rates where government stability and democratic accountability are more robust (Dutta and Roy 2011, 446–53; Hiskey, Montalvo, and Orcés 2014, 101–5), whereas corruption appears to encourage emigration (Dimant, Krieger, and Meierricks 2013, 1271–74; Cooray and Schneider 2016; Schneider 2016, 299–305). Therefore, aid that improves political institutions should lead to lower rates of emigration. Additionally, unlike economic aid, governance aid does not increase the short-term material resources of individuals. Thus, governance aid contributes to better political leaders and governments that provide greater quality of life to their citizens, without providing those citizens with direct monetary support (Goldsmith 2001, 136). We therefore expect that governance aid reduces emigration from developing nations. H2:Governance aid decreases emigration rates. Like economic aid, evidence suggests that social aid does not improve political institutions (Jones and Tarp 2016, 272–73) and therefore will not reduce migration. We posit that social aid mirrors economic aid by enabling migration and increasing emigration rates. Social aid projects provide access to social services such as education, water and sanitation, and healthcare, freeing up funds that individuals would otherwise use to support their own care. Individuals may use these newly available resources to emigrate. Additionally, social aid should increase individuals’ knowledge and skills by bolstering both local education and cross-national networks. New information about opportunities abroad and enhanced perceived ability to thrive beyond national borders should, in turn, encourage emigration. H3:Social aid increases emigration rates. Research Design To test our hypotheses, we conduct cross-national time-series analyses on a dataset consisting of 101 low- and middle-income countries. Our independent variables measure different types of foreign aid: economic aid, governance aid, and social or other aid (each as a share of the recipient country's GDP [gross domestic product]), and our dependent variable captures aid-recipient countries’ emigration rates. Emigration rate is only recorded in five-year intervals (1985, 1990, 1995, 2000, 2005, and 2010). Therefore, we analyze a panel with six periods of observations spanning twenty-five years (1985–2010). Dependent Variable The dependent variable in this analysis is emigration rate—the total number of emigrants from Country A living in Organization for Economic Co-operation and Development (OECD) countries, divided by the population of Country A. Brücker, Capuano, and Marfouk (2013) created this measure through analysis of twenty OECD-receiving countries’ census and population registrar statistics.12 This follows earlier efforts to measure emigration. Defoort (2008), for example, used the same methodology to calculate emigration rates to the six largest migrant destination countries (the United States, Canada, Germany, the United Kingdom, France, and Australia). The measure of emigration from Brücker et al. (2013) constitutes a critical advancement in cross-national time-series studies of international migration, as the lack of emigration data has been a persistent hindrance to conducting such work. The measure is imperfect, as it only covers a subset of emigration (migrant flows to twenty OECD countries) over a limited series of years. However, it is the best data available, as comprehensive migration data recorded on an annual basis does not yet exist.13 Independent Variables Our independent variables are economic aid, governance aid, and other aid, each measured as a percentage of recipient country GDP. We use aid data from Jones and Tarp (2016), who obtained their data from AidData (Tierney et al. 2011) and classified it into categories (economic, governance, and other) on the basis of project type. Governance aid includes assistance directed to government and civil society and assistance for nongovernmental organizations (NGOs).14 Economic aid includes assistance for transport and storage, communications, energy generation and supply, banking and other services, agriculture, forestry and fishing, industry, mining and construction, and trade policy, regulations, and tourism. Other aid includes money allocated for education, health care and services, water and sanitation, women and development, and food aid, meaning this category largely encompasses social development aid.15 Table 1 provides aid categories and descriptions. Table 1. Descriptions of aid projects included in each aid category Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women View Large Table 1. Descriptions of aid projects included in each aid category Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women View Large Control Variables In addition to foreign aid, several other variables may affect migration trends. These include push and pull factors and other types of international transfers. We include several controls in our models to isolate the relationship between the independent and dependent variables, including measures of per-capita GDP, population density, regime type, civil war, foreign direct investment (FDI), oil rents, and international trade, as well as GDP and terrorist deaths in OECD countries. We provide description of each control variable in the paragraphs that follow.16 Several push factors within developing countries may encourage citizens to emigrate, including economic malaise, conflict, political repression, and overpopulation. We include controls corresponding to each of these push factors in our models. First, we control for GDP per capita (log).17 While low per-capita GDP is an intuitive push factor and higher GDP per capita therefore seems likely to reduce emigration rates, much of the literature on aid and migration argues that income growth encourages emigration (de Haas 2007; Clemens 2014). Therefore, consistent with Hypothesis 1, we expect higher incomes to generate larger migrant outflows. However, if per-capita GDP correlates negatively with emigration rate and economic aid correlates positively with emigration rate, it will suggest that greater economic aid does not create emigration through its effects on economic growth. Next, we control for civil war (conflict), as individuals may flee countries characterized by extreme violence. We utilized the three-point measure of civil war intensity from the UCDP/PRIO Armed Conflict Dataset v.4–201 (Gleditsch et al. 2002; Pettersson and Wallensteen 2015). A score of “0” indicates no conflict, “1” indicates between 25 and 999 battle-related deaths, and “2” signifies one thousand or more battle-related deaths. Political repression may also encourage emigration. To account for political repression we control for regime type using a variation of the Quality of Governance (QoG) measure of democracy from Jones and Tarp (2016).18 We expect that fewer migrants will emigrate from democratic countries, as satisfaction with democratic governance should deter exit (Hiskey et al. 2014). Finally, we control for population density on the basis that greater competition for scarce resources occurs in overpopulated countries, prompting individuals to seek opportunities elsewhere. We draw population density data from the Basic QoG dataset (Dahlberg et al. 2017).19 We include additional controls for international trade and FDI (each as a percentage of GDP). Scholars highlight these types of transfers as development-friendly migration controls in the same way as foreign aid (Böhning 1994, 171–76) because migration is often associated with economic and trade relations between source and destination countries (Sassen 1996, 82). Therefore, we control for them to isolate the effects of foreign aid. We also control for oil rents (as a percentage of GDP) as oil and foreign aid can have similar effects in developing countries (Morrison 2009, 108–9) and because of the relationship between oil production and migrant labor (Halliday 1977; Arnold and Shah 1984, 294–95). We draw data for these three variables from the World Bank's World Development Indicators. We control for additional variables in a series of secondary models. First, we include squared GDP per capita, following the argument that the relationship between income and migration follows an inverted-U shape (Martin and Taylor 1996). Next, we consider a model with variables corresponding to pull factors within rich migrant destination countries. Specifically, we control for labor market dynamics and border security in OECD countries. To account for the labor market, we include a measure of (logged) annual GDP for all OECD countries (at purchasing power parity). This variable captures wealth in migrant destination countries and accompanying potential labor market opportunities for migrants.20 We use the annual number of fatalities from terrorism in the twenty OECD countries included in the dependent variable as a proxy for border security.21 Enhanced border security often follows major terrorist incidents (Andreas 2003; Alden 2008, 91–95). For example, the United States emphasized border enforcement and immigration policy reform following terrorist incidents in 1993, 1995, and 2001 (Coleman 2007, 54). Likewise, migration became a heightened security concern across Europe in the 1990s following attacks by Algeria's Armed Islamic Group and by the Kurdistan Workers Party (Adamson 2006, 166). The European Union tightened borders further in response to the 2001 terrorist attack on the United States and subsequent attacks in Spain and the United Kingdom (Baldaccini 2008, 31–2; von Houtum 2010, 957–58).22 Model Specifications Our primary statistical model includes country fixed effects to account for the impacts of unobserved country-specific characteristics on the dependent variable.23 Likewise, we include year fixed effects to control for unexpected variation and specific events that affect the dependent variable over the course of the time series. We lag independent and control variables by one year to account for the potential delayed reaction of migration outflows to aid inflows. Finally, we include robust standard errors to correct for heteroscedasticity and cluster standard errors by country to account for autocorrelation.24 The regression equation for our main model is: \begin{eqnarray*} &&{{{\rm{Y}}_{{\rm{it}}}}{\rm{\, = Economic\, Ai}}{{\rm{d}}_{{\rm{it - 1}}}}{\rm{\, + \,Governance\, Ai}}{{\rm{d}}_{{\rm{it - 1}}}}}\nonumber\\ &&{\rm{ \quad \, +\, Other\, Ai}}{{\rm{d}}_{{\rm{it - 1}}}}{\rm{\, +\, }}{{\rm{X}}_{{\rm{it - 1}}}}{\rm{\, +\, }}{{\rm{U}}_{\rm{i}}}{\rm{\, +\, }}{{\rm{T}}_{\rm{t}}}{\rm{\, +\, }}{{\rm{E}}_{{\rm{it}}}}\end{eqnarray*} where Yit is emigration rate, Xit–1 corresponds to control variables, Ui is country effects, and Tt is time effects. In addition, we estimate a mixed effects model to combine the potential random effects of relatively slower-moving variables with our fixed effects models. We also consider timeline dynamics using independent variable lags, as well as models with additional variables and alternative measures of contestable variables. Finally, we account for endogeneity using instrumental variable models. We discuss these robustness tests in greater detail after presenting our main results. Results The results show a negative relationship between governance aid and emigration, as countries that receive larger amounts of aid for governance-oriented projects have lower emigration rates. This supports Hypothesis 2 and suggests that the aid-supported improvement of political institutions deters outward migration by alleviating key push factors and enhancing government performance. In contrast, economic aid and other aid are not significantly related with emigration rate. Table 2 shows our main set of results. Model 1 includes aid variables as well as trade and FDI inflows, as the literature presents these three types of economic flows as important and complementary determinants of migration (Hiemenz and Schatz 1979; Böhning 1994, 171–76; Böhning and Schloeter-Paredes 1994, 5–6). Model 2 features results with the key independent and control variables. Model 3 includes the quadratic GDP term. Model 4 features mixed effects.25 Model 5 includes variables for labor market dynamics and border security in migrant destination countries. Table 2. The negative and significant relationship between governance aid and emigration rate holds across model specifications Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Notes: (1) Robust standard errors in parentheses. (2) Statistical significance: ***p < 0.01, **p < 0.05, *p < 0.1. (3) The leftmost column provides the most basic model; Model 2 includes major variables of interest; Model 3 includes GDP per capita squared; Model 4 features mixed effects; and Model 5 includes pull factor variables. The sample covers 101 developing countries, over a time series spanning twenty-five years (1985–2010) in five-year intervals (1985, 1990, 1995, 2000, 2005, and 2010). (4) Country and year fixed effects; robust standard errors clustered by country; independent variables lagged one year. View Large Table 2. The negative and significant relationship between governance aid and emigration rate holds across model specifications Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Notes: (1) Robust standard errors in parentheses. (2) Statistical significance: ***p < 0.01, **p < 0.05, *p < 0.1. (3) The leftmost column provides the most basic model; Model 2 includes major variables of interest; Model 3 includes GDP per capita squared; Model 4 features mixed effects; and Model 5 includes pull factor variables. The sample covers 101 developing countries, over a time series spanning twenty-five years (1985–2010) in five-year intervals (1985, 1990, 1995, 2000, 2005, and 2010). (4) Country and year fixed effects; robust standard errors clustered by country; independent variables lagged one year. View Large These results show the significant and negative relationship between governance aid and emigration rate across model specifications. On average, a 0.138 percent decline in emigration rate accompanies a percentage point increase in governance aid as a share of recipient country GDP. For an average country, such as Algeria, which is near the sample average in terms of population (35.5 million in 2010) and GDP ($161.2 billion in 2010), increasing governance aid as a share of GDP by one standard deviation (approximately 1.7 percent) would—all else equal—amount to an additional $2.8 billion in governance aid ($78.9 per capita); this would then reduce emigration by about 40,900 migrants over a decade. This increase in governance aid would not constitute a significant expenditure for a country like the United States, which disbursed approximately $41 billion in aid in 2015,26 or for France, Germany, the United Kingdom, or Japan, each of which gave more than $10 billion in assistance in 2015 (OECD 2016). The coefficients of economic aid and other aid are negative but not significant. The insignificant coefficients for nongovernance aid types could indicate either that aid does not affect economic development27 or that the economic development supported by economic and social aid has no bearing on migration patterns. The insignificant coefficient for GDP per capita supports the latter explanation, as neither economic aid nor the level of economic development appears to influence emigration rates.28 However, the current state of the aid-growth literature supports the former explanation: most studies find a negative or insignificant relationship between aid and growth, while a few studies report a small but positive association in the long run.29 Squared GDP per capita is not significant either, meaning that we do not find support for the income-based migration hump.30 The positive and significant coefficient of trade (% GDP) across models could further indicate that nongovernance types of aid do not affect economic development. Economists widely believe that trade supports economic development (Krugman 1990; Dollar 1992; Frankel and Romer 1999; Bhagwati and Srinivasan 2001; Dollar and Kraay 2004; Abbot, Bentzen, and Tarp 2009). Thus, the significant and positive coefficient of the trade variable could mean that economic development generated through trade (but not through economic or social aid) has an enabling effect on migration. This would support the notion that political and economic development produce contrasting impacts on migration patterns. However, we hesitate to speculate about the role of trade within this framework, as many other possible mechanisms could explain the positive relationship between trade and emigration.31 At any rate, the positive coefficient for trade is consistent with prior work on trade and migration (Martin 1993; Massey, Durand, and Malone 2002, 81). Democracy is negative but not significant. This may reflect nonuniformity among both democracies and nondemocracies. That is, unstable and weak democratic regimes exert greater pressures on individuals to leave, whereas democratic regimes with strong and stable state institutions do not generate such pressures. Likewise, some nondemocracies restrict emigration (Breuing, Cao, and Luedke 2012, 843–51) but others do not, and only highly rigid dictatorships, such as North Korea, forbid emigration outright. These policy variations are consistent with the work of Fukuyama (2013, 350) and others (for example, Holt and Manning 2014) that parse out factors such as state quality and public-sector capacity from democracy. This perspective would suggest that donors focus on making public agencies more capable and responsive rather than prioritizing elections and promoting democracy. Conflict is significant and negative in Model 4, suggesting that civil war generates reductions in outward migration. This finding seems counterintuitive, as we would expect violence to act as a push factor.32 However, conflict may not affect migration unless accompanied by destruction of the economic and social infrastructure (Adhikari 2012, 593–94). Bohra-Mishra and Massey (2011, 417–20) argue that conflict can even reduce outward migration. Additionally, countries often limit immigration from nations in conflict. For example, during the Arab Spring of 2011, Italy and France launched sea and air patrols to prevent inflows of refugees from Middle Eastern countries (Paoletti 2014, 144). In 2013, Greece erected fencing at the Greek-Turkish border to reduce inflows of Syrian refugees (Park 2015). Neumayer (2006, 78) reports that countries often use visa restrictions to deny entry to passport holders from countries characterized by armed political conflict.33 As expected, GDP in OECD (PPP) is significant and positive and terror deaths in OECD is significant and negative. These results suggest that positive economic performance in OECD countries leads individuals to emigrate at higher rates. At the same time, heightened border security following the occurrence of terrorist activity makes emigration more difficult. Timeline Considerations The effects of aid may differ in terms of timing. Clemens et al. (2012) argues that that aid sometimes works slowly, depending on the types of projects being implemented. Delays may reflect the time it takes for aid to affect the intermediate mechanisms through which it impacts the dependent variable of interest and how long it then takes those intermediate mechanisms to impact the dependent variable. Given that we propose separate mechanisms to explain how each type of aid affects migration, it is possible that these mechanisms operate on different timelines. Clemens et al. posit further (in their supporting information) that aid for projects such as road construction and budget support (which are economic aid projects, according to our classification) produce an “early-impact” on economic growth—the intermediate mechanism in our hypothesized relationship between economic aid and migration. As such, we expect economic aid to impact incomes and economic growth relatively quickly; in turn, higher incomes should facilitate international movement quickly as well. On the other hand, Clemens et al. suggest that projects for public health, basic education, and humanitarianism affect economic growth slowly, if at all. Our theoretical model predicts that other aid (the category that encompasses these sorts of social aid projects) affects migration through similar channels as economic aid (producing higher discretionary incomes) as well as through increases in knowledge and skills. But given the slow timeline associated with these social aid projects and the length of time needed for individuals to acquire knowledge and skills, social aid almost certainly has a slow impact on migration. The timeline for governance aid probably falls somewhere in the middle. As Jones and Tarp (2016, 268) note, political institutions (both formal and informal) can change rapidly,34 and the nature of governance aid provision suggests that donors envision rapid institutional changes to result. However, weak institutions can also change slowly, often reflecting deeply rooted and highly persistent corruption (Herzfeld and Weiss 2007, 1570). Additionally, the impact of improved political institutions on migration may be subject to moderate delay (Méon, Sekkat, and Weill 2009). We expect economic aid to affect emigration more rapidly than governance aid, and still more quickly than other aid. We capture these timeline dynamics through two empirical strategies. First, we analyze models with our independent variables lagged by two years and more.35 Second, we consider models with cumulative aid variables, such that governance aid for 2004 consists of aid from 2004 added to aid from all proceeding years. The latter approach36 allows us to observe the long-term impact of each type of aid on emigration rates. Results of each modeling approach (presented in Table 6 in the online appendix) provide support for Hypothesis 2: governance aid is associated with a decrease in the emigration rates of aid recipient, whereas other types of aid generate little or no impact. Interestingly, we see some evidence in the cumulative model that economic aid leads to decreases in emigration, although we hesitate to infer too much from this result given the nonsignificant coefficients for economic aid across our other models. Other aid, which carries the longest expected timeline, is not significant in any of our models, including models with lagged independent variables going back ten years.37 Additional and Alternative Variables While we include a large number of controls across the models presented in Table 2, additional variables merit consideration, as do alternative measures of some variables. We begin by considering several additional variables corresponding to political institutions and migration policies in sample countries. The inclusion of these variables reflects our concern that results presented so far may be spurious, as key policies and institutional factors of sample countries simultaneously drive variation in aid allocations and migration. To mitigate this concern, we consider controls for several facets of governance and policy: checks, executive constraints, political terror, judicial independence, and political corruption. We also measure freedom of foreign movement (freedom of movement) on the basis that states’ restrictions on migrant flows may drive variations in emigration rates.38 Models 11–16 in Table 7 of the online appendix show that the governance aid variable remains significant and negative when these controls are included. This suggests that the apparent relationship between governance aid and emigration is not a spurious one.39 Next, we consider alternative measures of immigration policies in migrant destination countries. While we believe that terrorism is a solid indicator that captures states’ security concerns and corresponding policies, there are countries with relatively secure borders despite not having experienced terrorist attacks (for example, Australia). With this in mind we analyze supplemental models with two alternative border security variables: annual averages of the migration policy and UN policy variables from Bermeo and Leblang (2015). These authors code migration policy based on whether countries’ entry policies moved into a more liberal or restrictive direction relative to the preceding year (a higher number reflects greater restrictiveness). The coding of UN policy reflects responses from a survey of immigration offices carried out by the United Nations’ Department of Economic and Social Affairs (again, a higher number reflects greater restrictiveness). As Table 8 in the online appendix shows, the coefficient for our governance aid variable remains significant and negative when these variables substitute for terrorism deaths in OECD countries. Finally, we consider an alternative measure of emigration: emigrant stock. We draw this measure from the World Bank's Global Bilateral Migration dataset, which records emigrant stock in ten-year intervals (1960, 1970, 1980, 1990, and 2000). Two of these years, 1990 and 2000, fit into our time series. Emigrant stock broadens our measurement by capturing migration to destinations outside of the twenty OECD countries used to create our primary dependent variable (emigration rate). As Table 9 in the online appendix shows, our results regarding governance aid hold when we analyze emigrant stock using variables featured in Model 1 of Table 2.40 Tests for Endogeneity It is possible that the apparent relationship between governance aid and emigration rate is spurious, such that variation in both governance aid and emigration rate owes to some unaccounted variable(s). Alternatively, our causal explanation could be backward, meaning that lower emigration rates cause some countries to receive higher levels of governance aid. To account for endogeneity, we turn to instrumental variable analysis. The literature highlights several instruments for foreign aid41; however, finding a suitable instrument becomes considerably more difficult when analyzing disaggregated aid variables, as the commonly suggested instruments intend to capture aid in the aggregate. In the absence of a suitable instrument, we use the second and third moments of governance aid as instruments for this potentially endogenous independent variable. Lewbel (1997) introduced this instrumental variable approach,42 and it has appeared in subsequent scholarship (for example, Rudra 2005). As Table 10 in the online appendix shows, the relationship between governance aid and emigration rate holds in instrumental variable models. Conclusions Our results indicate that some foreign aid does reduce outward migration from developing countries. In particular, we find a negative relationship between aid directed toward governance-oriented projects and emigration. This supports our theoretical expectation that governance aid lowers emigration rates by promoting better political institutions in recipient countries. Better political institutions generated through governance aid foster less corruption and more political stability, democratic accountability, and governing capacity. These improvements reduce push factors and remove incentives for emigration, thereby making individuals less likely to uproot their lives to move abroad. In contrast, we do not find evidence that other types of aid affect emigration rates, either positively or negatively. These results show that aid can serve as a tool of immigration policy so long as it targets governance-oriented projects. In our sample, aid allocation for governance projects is three times smaller, on average, than that for economic projects and seven times smaller than that for other (including social) projects. Greater distribution of governance-related assistance can produce more effective management of migration, independent of the multiplier effects associated with investments in political institutions and public-sector capacity building. Governance aid cannot constitute the whole of immigration policy for migrant destination countries, but it can serve as an additional tool for national governments to manage migrant inflows. It could effectively complement better donor coordination, policy coherence, cost-sharing measures among sending and destination countries, and hotspot-based implementation—targeting sizeable governance aid projects to specific areas inhabited by populations with high risks of migration.43 Our findings also suggest that governments may unintentionally generate more irregular migration when they cut their foreign aid budgets or transfer aid funds to defense agencies.44 For example, the recent budget proposal by the Trump administration includes plans to slash the foreign aid budget (Krieg and Mullery 2017). If these cuts include reductions in governance aid to Central American countries, then it seems likely that higher flows of immigrants from these countries to the United States will follow. Such an outcome would prove counterproductive for an administration that considers reducing migrant inflows to the United States a key policy goal. The same principle holds for other nativist governments proposing cuts in foreign aid budgets, such as Theresa May's Conservative government in the United Kingdom (Elgot and Walker 2017) and Malcolm Turnbull's Liberal-National Coalition government in Australia (Hitchick 2017). Finally, our results show that economic and social forms of aid have no discernable impact on migration. However, they could serve other donor goals, such as increasing education or reducing poverty in recipient countries. Indeed, aid projects have multiple, and sometimes contradictory, objectives (Brainard 2007). Powerful states use foreign aid to secure alliances (Alesina and Dollar 2000; Bearce and Tirone 2010), to elicit support in intergovernmental organizations (Dreher, Nunnenkamp, and Theile 2008), to support regime transitions (Bermeo 2011), and to achieve security and humanitarian objectives (Lai 2003), among other things.45 We find that foreign aid can be used to manage migration as well, provided that it is directed toward governance-oriented projects. However, allocating scarce budgetary resources to governance aid in an attempt to quell immigration may undermine other donor priorities. Therefore, the use of governance aid to lower immigration must be contextualized within a comprehensive set of tools at policymakers’ disposal that affect multiple policy outcomes. Notes Authors’ note: We thank Daniel Tirone, the editors of International Studies Quarterly, and two anonymous reviewers for thoughtful comments on earlier drafts of this article. We also thank participants at the 2016 American Political Science Association meeting, where an earlier version of this article was presented. Jonas Gamso is an assistant professor of International Trade and Global Studies at Arizona State University's Thunderbird School of Global Management. Farhod Yuldashev is a PhD candidate in Public and International Affairs at the University of Pittsburgh. His peer-reviewed research is published in Risk, Hazards & Crisis in Public Policy, Journal of Public Affairs Education, and Resources Policy. Footnotes 1 Data show that the immigrant stock of developed countries increased steadily from approximately 80 million in 1990 to 140 million in 2015, suggesting that basis exists for this political emphasis on immigration. Figure 1 in the online appendix charts this trend. 2 See Azam and Berlinschi (2010) for a survey of academic and policy articles on the consequences of migration. 3 See Léonard (2010, 232); Pécoud and de Guchteneire (2006, 72–74), for example. 4 This might explain the nonsignificant coefficients for aggregate measures of aid in previous studies (for example, Neumayer 2005). 5 The United States passed several laws toughening immigration enforcement during the Bush administration, including the Enhanced Border Security and Visa Entry Reform Act of 2002 (EBSVERA), the REAL ID Act of 2005, and the Secure Fence Act of 2006. Likewise, the Obama administration ramped up deportations and oversaw heightened spending on border protection. For Bush-era policies, see Rosenblum and Brick (2011, 6, 10–11); for Obama-era policies, see Meissner et al. (2013, 18) and Martínez and Rosen (2016, 127–28). 6 Critics also highlight negative impacts of conventional migration policies on human rights (Léonard 2010, 232; Pécoud and de Guchteneire 2006, 72–74). 7 See de Haas (2007, 820–27) for relevant quotes and paraphrasing from European Commission then President José Manuel Barrosom, then African Union head Alpha Oumar Konare, and then Prime Minister Rasmussen of Denmark. 8 Development promotion efforts may also deter emigration by compelling leaders in source countries to tighten up migration controls. As noted by Bhagwati (2003, 98), the prime ministers of the United Kingdom and Spain proposed in a 2002 European Council meeting that the European Union should reduce aid to those countries that do not make sufficient efforts to curtail migration to Europe. The proposal failed, but other policymakers may accept the logic underlying it. 9 Citing Castles and Miller (2003), de Haas (2007, 828) notes that “development assistance has often been used as a political instrument leading to ‘aid’ in the form of weapons and other types of support for autocratic regimes . . . This has increased insecurity, provoked armed conflict, created refugee problems, and exacerbated rather than decreased problems of underdevelopment.” 10 The vast scholarship analyzing effects of aid on economic growth and development offers little conclusive evidence for either a positive or negative relationship. See Doucouliagos and Paldam (2008, 2009) for meta-analyses of the literature on aid and growth. 11 For example, if economic aid flows to economic elites, then aid may increase inequality and push poorer people to migrate. In this scenario, the hypothesized relationship between aid and migration stays the same, but operates through a different mechanism. 12 The twenty countries are the following: Australia, Austria, Canada, Chile, Denmark, Finland, France, Germany, Greece, Ireland, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States. Figure 2 (in the online appendix) shows an increasing trend in migration to these twenty OECD countries. 13 This variable also encompasses refugees to the extent that refugees flow to the twenty OECD countries used to calculate the variable. We cannot separate out refugees from other types of migrants using the Brücker et al. (2013) dataset. However, researchers may wish to analyze the relationship between aid and refugees in future work. 14 Government and civil society and support for NGOs each include several types of aid projects. We provide a complete list of governance aid project types, and the funding for projects corresponding to each in Table 3 in the online appendix. 15 Unlike Bermeo and Leblang (2015) we do not treat emergency response and disaster reconstruction efforts. While collected in a relatively shorter span of time, donors generally disburse this assistance over a longer time and elites may divert it to projects unrelated to disaster relief (based on our observations in the field). 16 See Table 4 (in the online appendix) for descriptive statistics of all variables included in this study. 17 We draw data for this variable from Jones and Tarp (2016). 18 The QoG measure, from Teorell et al. (2016), is calculated by averaging the Freedom House and Polity scores. The variation of the measure used here includes imputed values for country-years in which Polity data is missing. Jones and Tarp (2016) standardized the QoG measure to center the mean at zero and the standard deviation at 100. See Hadenius and Teorell (2005) for discussion of this measure and its favorable performance relative to the individual measures that contribute to it. 19 We acknowledge that population density offers an imperfect measure of overpopulation and resource scarcity. In fact, it may be the case that many countries with high emigration rates have relatively low rates of population density due to their low levels of urbanization. Our results hold when we control for population (logged) in lieu of population density (results of models with logged population are not included in the article but are available on request). 20 We draw data for this variable from Euromonitor International. 21 Data for terrorism deaths comes from the RAND Database of World Terrorism Incidents. 22 We acknowledge that terrorist attacks constitute an imperfect measure for border controls. Critics may argue that states use terrorist incidents to justify border controls that governments already intended to implement and migration controls do not always follow incidents of terrorism (Boswell 2007). Moreover, the nature of our data source makes it impossible to disentangle domestic and international terror incidents. We consider alternative measures of border security in the robustness tests described below in light of these concerns. 23 Hausman testing supports our utilization of country fixed effects. 24 All of our findings also hold when we do not use robust standard errors. 25 We also estimated the mixed effects model with a quadratic GDP term, but we do not report results because the quadratic term is not significant and the results for other variables match those in Model 2. 26 Of that $41 billion, the US Government directed less than $4 billion toward government and civil society programs, suggesting that the US Government does not prioritize governance aid. See the USAID data explorer for additional details (USAID 2017). 27 This would be consistent Nowak-Lehmann et al. (2012) and Dreher and Langlotz (2017). 28 We also analyzed models controlling for GDP growth, lagged one, two, three, and four years, to account for possible short- and long-term impacts of nationwide growth. Our findings hold when these variables are included in the models. We do not report results for these models here, but they are available on request. 29 See the recent debate between Clemens et al. (2012) and Roodman (2015). See also Arndt, Jones, and Tarp (2015) for a discussion of long-term effects. 30 Following Brambor, Clark, and Golder (2006), we conducted joint significance test of GDP per capita and the quadratic term. Testing for various levels of GDP did not produce significant results in adjusted conditional coefficients and standard errors, indicating that the quadratic specification does not improve the model. The joint significance test results are not shown but are available on request. 31 Trade openness may disrupt small- and medium-sized industries thereby leading to displacement and exit (Massey et al. 2002, 50). Alternatively, high trade levels may reflect strong demand from rich countries, which in turn corresponds to economic growth in those countries and accompanying opportunities for migrants. At the same time the benefits of trade to workers may be limited or those countries with more liberal trade policies may also have more liberal migration policies. 32 For example, Hyndman (2003, 266) argues that many in Sri Lanka migrated to Canada due to conflict. 33 We control for annual deaths by terrorism in aid-recipient countries in a supplemental model, on the basis that terrorist activities may lead to restrictions on foreign movement. We again use data from the RAND Database of World Terrorism Incidents. This variable is significant and negative but its inclusion does not affect the sign or significance of the independent variables. See Table 5 in the online appendix. 34 These authors highlight instances of rapid democratic transition. 35 As a point of reference, Savun and Tirone (2018) lag all aid variables two years in a study similar to this one, in which terrorism serves as the dependent variable. 36 Informed by Arndt, Jones, and Tarp (2016). 37 For the sake of space we only show results for two-, three-, and four-year independent variable lags. Governance aid remains significant through a five-year lag. Again, these results are presented in Table 6 in the online appendix. 38 We draw data for these variables from Jones and Tarp (2016), with the exception of political corruption, which we draw from the QoG dataset (Dahlberg et al. 2017), and freedom of movement, which we draw from the CIRI Human Rights Data Project (Cingranelli, Richards, and Clay 2014). 39 Fluctuations in governance aid provisions may affect emigration rates to a greater extent in aid-dependent countries. With this in mind we interact our key independent variable (gov. aid [% GDP]) with a dummy variable coded “0” where countries receive less than the mean level of governance aid for the sample; and “1” where countries receive more than the mean level of governance aid. The interaction term is not significant, suggesting dependence on governance aid does not moderate the effect of governance aid on emigration rate. We do not include results in the article, but they are available on request. 40 The results do not hold when we add the full array of control variables featured in Model 2. However, we suspect that the parsimonious model offers a better fit for analysis of emigrant stock given the small sample size. 41 See Bazzi and Clemens (2013) for discussion of the instrumental variables used for foreign aid. 42 When instrumental variables are unavailable or insufficient, Lewbel suggests the use of second moments (Xi – mean(X))2 and third moments (Xi – mean(X))3 as instruments. 43 Czaika (2009, 163) offers similar recommendations with respect to managing refugee inflows. 44 The UK defense minister recently argued that the government should transfer foreign aid funds to the defense ministry to manage migration flows (Dominiczak 2015). 45 Stone, Randall W. 2010. “Buying Influence: Development Aid Between the Cold War and the War on Terror.” Unpublished Manuscript. 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Targeted Foreign Aid and International Migration: Is Development-Promotion an Effective Immigration Policy?

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Abstract

Abstract Faced with the failure of traditional immigration controls, policymakers in the United States and Western Europe increasingly look to foreign aid to reduce migrant inflows. Some analysts expect assistance to improve living standards in source countries, thereby deterring residents from moving abroad. While this idea makes intuitive sense, research on aid and migration shows mixed results: some scholarly work supports aid-based migration policies, but other analyses suggest that aid actually enables migration by providing individuals with resources that facilitate movement across borders. We suggest that this tension in the literature reflects a failure to distinguish between different types of foreign aid. Drawing on recent work demonstrating the heterogeneous effects of various aid projects, we posit that governance aid should deter emigration by enhancing government capacity and alleviating political push factors; in contrast, economic and social aid should enable migration by increasing individuals’ means and capabilities to move. We test our hypotheses on a panel of 101 developing countries spanning twenty-five years (1985–2010). We find that governance aid does reduce emigration rates from developing countries, while other types of aid appear not to affect migration. Introduction Concerns over immigration from poor and often conflict-ridden countries dominate electoral politics in much of the advanced industrialized world.1 These concerns reflect perceptions that immigrants drive down wages, drain social insurance and social welfare funds, increase crime rates, and threaten social and cultural cohesion.2 Typical responses from policymakers include enhancing security patrols along migration routes, as well as erecting physical and bureaucratic barriers to entry. While these policies may excite nativist constituents, they do not necessarily reduce inflows of migrants. They also raise humanitarian concerns.3 Thus, some critics advocate a development-friendly alternative: promoting economic growth, job creation, and development in source countries to reduce the poverty and underdevelopment that push migrants to exit for more highly developed neighboring countries (Böhning 1994, 170–76; Olesen 2002, 143). In particular, advanced industrialized countries, they argue, may effectively manage migration inflows by providing foreign aid to source countries. Some policymakers are exploring and implementing aid-based migration policies. For example, in 2001 the Danish Ministry of Foreign Affairs commissioned a study to better align their aid and immigration policies (Nyberg-Sørensen, Van Hear, and Engberg-Pedersen 2002). More recently, the European Commission publicized its plans to manage migration from the Middle East and Africa through “financial allocations devoted to tackling the root causes” of migration (European Commission—Press Release 2016) and the US government announced that it will allocate one billion dollars in aid to Central America to lower migration from the region by addressing its root causes (US Department of State 2015). Some academics also support aid-based policy approaches (Neumayer 2005, 405; Katseli, Lucas, and Xenogiani 2006, 59), and academic research indicates that donor countries target foreign aid to prevent inflows of migrants (Bermeo and Leblang 2015, 639–51). Despite growing interest from policymakers, scholars disagree about the effectiveness of aid as a tool for managing migration. Many analysts are skeptical of this approach. Some even argue that aid enables migration by providing poor individuals who want to emigrate with material means to do so (de Haas 2007, 831–34). Others express more optimism about the deterring effects of aid, arguing that aid should raise the quality of individuals’ lives and make them less inclined to exit their countries of residence (Morrison 1982, 11–15). Few quantitative studies explore these questions and available research offers contradictory findings (Rotte and Volger 2000; Berthélemy, Beuran, and Maurel 2009). We think that some kinds of foreign aid are likely to increase migration, while other kinds are likely to reduce it. Studies that simply aggregate all foreign aid will produce misleading results. Thus, our study distinguishes among aid based on whether it aims to promote social, economic, or political development. We hypothesize that economic and social development assistance increases migration from recipient countries, while political development assistance reduces such migration.4 We expect aid targeting economic and social development to increase outward migration rates from recipient countries because it provides would-be migrants with newfound economic assets that they can use to exit (de Haas 2007, 831–34). In contrast, assistance targeted to governance improves political institutions (Jones and Tarp 2016, 272–73). More capable and representative government and inclusive political rights enhance individuals’ life satisfaction and reduce the likelihood that they will uproot themselves and emigrate. We test these hypotheses through analysis of cross-national time-series data covering 101 developing countries over a time series spanning twenty-five years (1985–2010). The results support the latter hypothesis: aid directed toward governance projects negatively affects the emigration rates of developing countries. These findings suggest that aid-related political development deters exit by would-be migrants through the accompanying improvements to rule of law, human rights, and governance quality. We do not, however, find evidence that other types of aid affect migration patterns. In the remainder of this article, we provide further background to our research problem and more extensively review the literature relevant to foreign aid and international migration. We then establish the bases of our hypotheses about the deterring and enabling effects of aid types (economic, political, and social) on migration rates. Next, we discuss our research design and data and our results. We conclude with policy implications. Migration and Foreign Aid Governments in the United States and Western Europe have strengthened immigration deterrence policies in response to growing concerns about irregular migration. Starting in 2000, the United States ramped up border security in an attempt to limit illegal immigration from Central American countries and Mexico.5 Similarly, countries in Europe seek to deter migration from the Middle East and North Africa through sea route patrols, border fences, and deportations (de Haas 2013, 1309–10). Policy platforms built on stemming the flow of immigrants dominated recent political campaigns in both the United States and Europe. Pledges to restrict movement between the US and Mexico border, and to ban refugee populations from countries of the Middle East, fueled the successful presidential campaign of Donald Trump in the United States (Milligan 2016). Likewise, the Brexit campaign in the United Kingdom likely succeeded in part because of anti-immigration sentiment (Goodwin and Heath 2016, 329). Despite the eagerness of politicians to display their commitments to border security and legal limits on migration, there is reason to believe that these policies are generally ineffective (Bhagwati 2003; Castles 2004; Black et al. 2006, 554). For example, more than 13 million people moved to Western Europe between 1992 and 2001 despite immigration restrictions enacted by various European governments during the 1990s (Hatzipanayotou and Michael 2012, 199). The ineffectiveness of these traditional migration policies likely reflects their failure to account for root causes of migration within source countries, such as poverty, inequality, and political conflict.6 Recognition that traditional immigration policies do not attend to “push” factors has inspired alternative strategies to relieve migration pressure within sending countries. Advanced by both academics (Morrison 1982, 24; Böhning 1994; Stalker 2002, 152) and politicians,7 these strategies focus on economic development through foreign assistance. Aid-based immigration policies hinge on the following two notions. First, poverty and underdevelopment compel migrants in countries of the Global South to leave their home countries. Second, aid-supported development in these countries can mitigate these push factors and thus reduce migrant outflows.8 Policymakers in countries of the Global North appear at times to implement aid policy in order to reduce immigration (Bermeo and Leblang 2015, 630–33). For example, Myers and Papageorgiou (2000, 184) argue that the US government explicitly rationalized aid disbursements to Haiti and Mexico on the basis that this support would lower migration pressure. Some criticize not only this approach, but also the logic underpinning it. For example, de Haas (2010, 1305) objects to the “conventional wisdom underlying such argumentations . . . that war and poverty are the root causes of mass migration.” He argues that development assistance may actually lead to greater emigration rates, either by providing would-be migrants with the capacity to emigrate (de Haas 2007, 2013) or by exacerbating push factors.9 Such criticisms tie into broader skepticism about the effectiveness of foreign aid itself in assuaging poverty and underdevelopment.10 Empirical evidence on the effects of aid on migration is limited and contradictory. While Rotte and Volger (2000, 495–507) do not identify significant relationships between aid and migration to Germany, Berthélemy et al. (2009: 1592–94) find that aid increases migration. Faini and Venturini (2010) show that aid to low-income European countries generated short-term upticks in emigration from recipients. Similarly, Stalker (1994, 26–28) and Cornelius (2002) conclude that economic development (whether supported by aid or not) increases migratory pressure in the short run. Hatton and Williamson (2005) argue that the effect of income growth on international migration depends on economic and security dynamics in the geographic region. Thus, income growth in a typical country of Western Europe, East Asia, or South America is accompanied by reduced migration to the United States, whereas income growth for a typical African country leads to greater migration to the United States. While these studies offer important insights, they do not consider that different types of aid may have different effects on international migration. We suspect that some aid types may deter emigration while other types lead to greater migrant outflows. Drawing on the work of de Haas (2007) and Clemens (2014), we expect economic and social aid to lead to greater emigration rates from aid recipient countries. Such aid offers would-be migrants greater material means that they can use to leave their countries of residence. In contrast, we expect aid targeting political development to lower emigration rates from recipients. Such governance aid improves political institutions (Jones and Tarp 2016, 272–73), thereby strengthening governmental capacity and attenuating political push factors, such as corruption, repression, and discrimination, that compel citizens to leave their home countries. Theory and Hypotheses Scholars conceptualize the effect of foreign aid on migration in two ways. In the first approach, foreign assistance addresses the root causes of migration, thereby deterring migration from source countries. Aid addresses the root causes by generating employment and higher wages (Arndt, Jones, and Tarp 2010, 16–22; Lof, Mekasha, and Tarp 2015), enhancing political rights (Finkel, Pérez-Liñán, and Seligson 2007, 422), strengthening border protection by providing aid-recipient governments with greater control over population movements (de Haas 2007, 820; Bhagwati 2003, 98), lowering the risk and duration of conflict (Collier et al. 2003, 175–84; de Ree and Nillessen 2009, 309), and improving political institutions (Jones and Tarp 2016). In the second approach, scholars consider aid as an enabling factor for migration. In this tradition, foreign aid enhances individuals’ material resources, allowing them to afford the costs of migration (Faini and Venturini 2010). Aid projects also transfer skills and information about opportunities abroad, which may make individuals more likely to migrate. Additionally, aid increases emigration through its exacerbation of push factors (de Haas 2007, 828), for example, by worsened civil conflict (Nunn and Qian 2014; Narang 2015, 1653–55; Tahir 2017, 122–26). While scholars have contrasted the effects of bilateral and multilateral aid on migration (Berthélemy et al. 2009; Ontiveros and Verardi 2012) and the effects of aid targeting urban and rural areas on migration (Gamso and Yuldashev 2018), no previous work explores the possibility that aid projects targeting different sectors might have varying impacts on international migration. This is striking given evidence that aid projects have different objectives (Clemens et al. 2012, 594) and heterogeneous outcomes (Mavrotas and Nunnenkamp 2007; Savun and Tirone 2018) and operate through varying mechanisms (Jones and Tarp 2016, 267–68). In light of this evidence, we theorize that some types of aid projects enable migration and others deter it. First, we expect that aid projects targeting economic growth and development enable migrants, leading to higher emigration rates from aid recipient countries. Earlier research demonstrating positive impacts of aid and economic development on migration outflows informs this expectation (Berthélemy et al. 2009, 1592–94; Faini and Venturini 2010). We suspect that the effects identified in these studies are generated by a subset of aid projects that contribute to growth in income and other material resources. Such improvement in resources, in turn, afford individuals in aid-recipient countries the otherwise out-of-reach costs of emigration. H1:Economic aid increases emigration rates. Hypothesis 1 assumes that economic aid boosts per capita income in aid recipient countries. However, scholars disagree about the relationship between aid and income: some studies find a positive and statistically significant relationship between aid and per-capita income (for example, Lof et al. 2015), whereas others do not (for example, Nowak-Lehmann et al. 2012; Dreher and Langlotz 2017). Even if economic aid is positively associated with emigration, as hypothesized, it does not necessarily follow that economic growth and development enable migration, as Clemens (2014) and de Haas (2007) argue. Instead, other mediating factors may explain the relationship.11 Our analysis of a control variable measuring economic development, in tandem with the economic aid variable, will help to clarify the effects of economic aid and economic development on migration. We discuss this in greater depth below. In contrast to economic aid, we expect migration-deterring effects from governance aid. Improving the effectiveness and responsiveness of political institutions and civil society via governance-oriented foreign aid makes populations in low-income countries feel more included, better represented, and safer at home. Notwithstanding scholarship that argues broadly that foreign aid worsens governance by creating dependency (Knack 2001, 312–14; Bräutigam and Knack 2004, 260–66) or acting as nontax revenue (Morrison 2009, 108–13), more nuanced work finds significant and positive impacts of governance aid on political institutions (Jones and Tarp 2016, 272–73). Goldsmith (2001, 138–44) and Dunning (2004, 417–21) find governance aid effective in improving state capacity in Africa, and Savun and Tirone (2011, 237–42) demonstrate that governance aid enhances stability in fragile democracies. Likewise, USAID democracy-assistance projects appear to advance democratization (Finkel et al. 2007, 421–33). Further evidence suggests that emigration rates are lower where sound political institutions exist. For example, individuals exit their countries of residence at lower rates where government stability and democratic accountability are more robust (Dutta and Roy 2011, 446–53; Hiskey, Montalvo, and Orcés 2014, 101–5), whereas corruption appears to encourage emigration (Dimant, Krieger, and Meierricks 2013, 1271–74; Cooray and Schneider 2016; Schneider 2016, 299–305). Therefore, aid that improves political institutions should lead to lower rates of emigration. Additionally, unlike economic aid, governance aid does not increase the short-term material resources of individuals. Thus, governance aid contributes to better political leaders and governments that provide greater quality of life to their citizens, without providing those citizens with direct monetary support (Goldsmith 2001, 136). We therefore expect that governance aid reduces emigration from developing nations. H2:Governance aid decreases emigration rates. Like economic aid, evidence suggests that social aid does not improve political institutions (Jones and Tarp 2016, 272–73) and therefore will not reduce migration. We posit that social aid mirrors economic aid by enabling migration and increasing emigration rates. Social aid projects provide access to social services such as education, water and sanitation, and healthcare, freeing up funds that individuals would otherwise use to support their own care. Individuals may use these newly available resources to emigrate. Additionally, social aid should increase individuals’ knowledge and skills by bolstering both local education and cross-national networks. New information about opportunities abroad and enhanced perceived ability to thrive beyond national borders should, in turn, encourage emigration. H3:Social aid increases emigration rates. Research Design To test our hypotheses, we conduct cross-national time-series analyses on a dataset consisting of 101 low- and middle-income countries. Our independent variables measure different types of foreign aid: economic aid, governance aid, and social or other aid (each as a share of the recipient country's GDP [gross domestic product]), and our dependent variable captures aid-recipient countries’ emigration rates. Emigration rate is only recorded in five-year intervals (1985, 1990, 1995, 2000, 2005, and 2010). Therefore, we analyze a panel with six periods of observations spanning twenty-five years (1985–2010). Dependent Variable The dependent variable in this analysis is emigration rate—the total number of emigrants from Country A living in Organization for Economic Co-operation and Development (OECD) countries, divided by the population of Country A. Brücker, Capuano, and Marfouk (2013) created this measure through analysis of twenty OECD-receiving countries’ census and population registrar statistics.12 This follows earlier efforts to measure emigration. Defoort (2008), for example, used the same methodology to calculate emigration rates to the six largest migrant destination countries (the United States, Canada, Germany, the United Kingdom, France, and Australia). The measure of emigration from Brücker et al. (2013) constitutes a critical advancement in cross-national time-series studies of international migration, as the lack of emigration data has been a persistent hindrance to conducting such work. The measure is imperfect, as it only covers a subset of emigration (migrant flows to twenty OECD countries) over a limited series of years. However, it is the best data available, as comprehensive migration data recorded on an annual basis does not yet exist.13 Independent Variables Our independent variables are economic aid, governance aid, and other aid, each measured as a percentage of recipient country GDP. We use aid data from Jones and Tarp (2016), who obtained their data from AidData (Tierney et al. 2011) and classified it into categories (economic, governance, and other) on the basis of project type. Governance aid includes assistance directed to government and civil society and assistance for nongovernmental organizations (NGOs).14 Economic aid includes assistance for transport and storage, communications, energy generation and supply, banking and other services, agriculture, forestry and fishing, industry, mining and construction, and trade policy, regulations, and tourism. Other aid includes money allocated for education, health care and services, water and sanitation, women and development, and food aid, meaning this category largely encompasses social development aid.15 Table 1 provides aid categories and descriptions. Table 1. Descriptions of aid projects included in each aid category Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women View Large Table 1. Descriptions of aid projects included in each aid category Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women Aid categories (in italics) and descriptions Governance aid  Government and civil society  Support to NGOs Economic aid  Transport and storage  Communications  Energy generation and supply  Banking and financial services  Business and other services  Agriculture, forestry and fishing  Industry, mining, and construction  Trade policy, regulations and tourism Other aid  Education  General/basic health  Population policy and reproductive health  Water supply and sanitation  Other social infrastructure and services  General environmental protection  Women View Large Control Variables In addition to foreign aid, several other variables may affect migration trends. These include push and pull factors and other types of international transfers. We include several controls in our models to isolate the relationship between the independent and dependent variables, including measures of per-capita GDP, population density, regime type, civil war, foreign direct investment (FDI), oil rents, and international trade, as well as GDP and terrorist deaths in OECD countries. We provide description of each control variable in the paragraphs that follow.16 Several push factors within developing countries may encourage citizens to emigrate, including economic malaise, conflict, political repression, and overpopulation. We include controls corresponding to each of these push factors in our models. First, we control for GDP per capita (log).17 While low per-capita GDP is an intuitive push factor and higher GDP per capita therefore seems likely to reduce emigration rates, much of the literature on aid and migration argues that income growth encourages emigration (de Haas 2007; Clemens 2014). Therefore, consistent with Hypothesis 1, we expect higher incomes to generate larger migrant outflows. However, if per-capita GDP correlates negatively with emigration rate and economic aid correlates positively with emigration rate, it will suggest that greater economic aid does not create emigration through its effects on economic growth. Next, we control for civil war (conflict), as individuals may flee countries characterized by extreme violence. We utilized the three-point measure of civil war intensity from the UCDP/PRIO Armed Conflict Dataset v.4–201 (Gleditsch et al. 2002; Pettersson and Wallensteen 2015). A score of “0” indicates no conflict, “1” indicates between 25 and 999 battle-related deaths, and “2” signifies one thousand or more battle-related deaths. Political repression may also encourage emigration. To account for political repression we control for regime type using a variation of the Quality of Governance (QoG) measure of democracy from Jones and Tarp (2016).18 We expect that fewer migrants will emigrate from democratic countries, as satisfaction with democratic governance should deter exit (Hiskey et al. 2014). Finally, we control for population density on the basis that greater competition for scarce resources occurs in overpopulated countries, prompting individuals to seek opportunities elsewhere. We draw population density data from the Basic QoG dataset (Dahlberg et al. 2017).19 We include additional controls for international trade and FDI (each as a percentage of GDP). Scholars highlight these types of transfers as development-friendly migration controls in the same way as foreign aid (Böhning 1994, 171–76) because migration is often associated with economic and trade relations between source and destination countries (Sassen 1996, 82). Therefore, we control for them to isolate the effects of foreign aid. We also control for oil rents (as a percentage of GDP) as oil and foreign aid can have similar effects in developing countries (Morrison 2009, 108–9) and because of the relationship between oil production and migrant labor (Halliday 1977; Arnold and Shah 1984, 294–95). We draw data for these three variables from the World Bank's World Development Indicators. We control for additional variables in a series of secondary models. First, we include squared GDP per capita, following the argument that the relationship between income and migration follows an inverted-U shape (Martin and Taylor 1996). Next, we consider a model with variables corresponding to pull factors within rich migrant destination countries. Specifically, we control for labor market dynamics and border security in OECD countries. To account for the labor market, we include a measure of (logged) annual GDP for all OECD countries (at purchasing power parity). This variable captures wealth in migrant destination countries and accompanying potential labor market opportunities for migrants.20 We use the annual number of fatalities from terrorism in the twenty OECD countries included in the dependent variable as a proxy for border security.21 Enhanced border security often follows major terrorist incidents (Andreas 2003; Alden 2008, 91–95). For example, the United States emphasized border enforcement and immigration policy reform following terrorist incidents in 1993, 1995, and 2001 (Coleman 2007, 54). Likewise, migration became a heightened security concern across Europe in the 1990s following attacks by Algeria's Armed Islamic Group and by the Kurdistan Workers Party (Adamson 2006, 166). The European Union tightened borders further in response to the 2001 terrorist attack on the United States and subsequent attacks in Spain and the United Kingdom (Baldaccini 2008, 31–2; von Houtum 2010, 957–58).22 Model Specifications Our primary statistical model includes country fixed effects to account for the impacts of unobserved country-specific characteristics on the dependent variable.23 Likewise, we include year fixed effects to control for unexpected variation and specific events that affect the dependent variable over the course of the time series. We lag independent and control variables by one year to account for the potential delayed reaction of migration outflows to aid inflows. Finally, we include robust standard errors to correct for heteroscedasticity and cluster standard errors by country to account for autocorrelation.24 The regression equation for our main model is: \begin{eqnarray*} &&{{{\rm{Y}}_{{\rm{it}}}}{\rm{\, = Economic\, Ai}}{{\rm{d}}_{{\rm{it - 1}}}}{\rm{\, + \,Governance\, Ai}}{{\rm{d}}_{{\rm{it - 1}}}}}\nonumber\\ &&{\rm{ \quad \, +\, Other\, Ai}}{{\rm{d}}_{{\rm{it - 1}}}}{\rm{\, +\, }}{{\rm{X}}_{{\rm{it - 1}}}}{\rm{\, +\, }}{{\rm{U}}_{\rm{i}}}{\rm{\, +\, }}{{\rm{T}}_{\rm{t}}}{\rm{\, +\, }}{{\rm{E}}_{{\rm{it}}}}\end{eqnarray*} where Yit is emigration rate, Xit–1 corresponds to control variables, Ui is country effects, and Tt is time effects. In addition, we estimate a mixed effects model to combine the potential random effects of relatively slower-moving variables with our fixed effects models. We also consider timeline dynamics using independent variable lags, as well as models with additional variables and alternative measures of contestable variables. Finally, we account for endogeneity using instrumental variable models. We discuss these robustness tests in greater detail after presenting our main results. Results The results show a negative relationship between governance aid and emigration, as countries that receive larger amounts of aid for governance-oriented projects have lower emigration rates. This supports Hypothesis 2 and suggests that the aid-supported improvement of political institutions deters outward migration by alleviating key push factors and enhancing government performance. In contrast, economic aid and other aid are not significantly related with emigration rate. Table 2 shows our main set of results. Model 1 includes aid variables as well as trade and FDI inflows, as the literature presents these three types of economic flows as important and complementary determinants of migration (Hiemenz and Schatz 1979; Böhning 1994, 171–76; Böhning and Schloeter-Paredes 1994, 5–6). Model 2 features results with the key independent and control variables. Model 3 includes the quadratic GDP term. Model 4 features mixed effects.25 Model 5 includes variables for labor market dynamics and border security in migrant destination countries. Table 2. The negative and significant relationship between governance aid and emigration rate holds across model specifications Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Notes: (1) Robust standard errors in parentheses. (2) Statistical significance: ***p < 0.01, **p < 0.05, *p < 0.1. (3) The leftmost column provides the most basic model; Model 2 includes major variables of interest; Model 3 includes GDP per capita squared; Model 4 features mixed effects; and Model 5 includes pull factor variables. The sample covers 101 developing countries, over a time series spanning twenty-five years (1985–2010) in five-year intervals (1985, 1990, 1995, 2000, 2005, and 2010). (4) Country and year fixed effects; robust standard errors clustered by country; independent variables lagged one year. View Large Table 2. The negative and significant relationship between governance aid and emigration rate holds across model specifications Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Dependent variable (Model 1) (Model 2) (Model 3) (Model 4) (Model 5) Emigration rate Basic model Main model Income hump Mixed effects Pull factors Econ. aid (% GDP)t–1 –7.18e-05 –0.000242 –0.000258 –0.000232 –0.000242 (0.000289) (0.000315) (0.000311) (0.000367) (0.000315) Gov. aid (% GDP)t–1 –0.000892** –0.00138** –0.00138** –0.00125** –0.00138** (0.000353) (0.000556) (0.000546) (0.000591) (0.000556) Other aid (% GDP)t–1 –4.78e-05 –4.18e-05 –5.21e-05 –2.31e-05 –4.18e-05 (8.62e-05) (8.68e-05) (9.14e-05) (0.000174) (8.68e-05) FDI (% GDP)t–1 0.000143 6.64e-05 5.79e-05 0.000139 6.64e-05 (0.000114) (0.000141) (0.000133) (0.000189) (0.000141) Trade (% GDP)t–1 0.000126** 0.000161*** 0.000163*** 0.000168*** 0.000161*** (5.12e-05) (5.10e-05) (5.15e-05) (4.32e-05) (5.10e-05) GDP per capitat–1 –0.00376 –0.0353 0.000708 –0.00376 (0.00573) (0.0375) (0.00362) (0.00573) Population densityt–1 –4.58e-05 –3.71e-05 –1.60e-05 –4.58e-05 (3.93e-05) (4.31e-05) (2.70e-05) (3.93e-05) Democracyt–1 –1.55e-05 –1.39e-05 –8.66e-06 –1.55e-05 (2.29e-05) (2.26e-05) (1.39e-05) (2.29e-05) Conflictt–1 –0.00410 –0.00429 –0.00396** –0.00410 (0.00258) (0.00264) (0.00169) (0.00258) Oil rents (% GDP)t–1 1.71e-05 1.29e-05 –0.000130 1.71e-05 (0.000160) (0.000162) (0.000211) (0.000160) Terror deaths in OECDt–1 –1.01e-05* (5.53e-06) GDP in OECD (PPP)t–1 0.0177*** (0.00422) Squared GDP per capitat–1 0.00203 (0.00243) Constant 0.0207*** 0.0496 0.170 0.0135 –0.238*** (0.00472) (0.0433) (0.145) (0.0282) (0.0750) Observations 543 525 525 525 525 Number of countries 102 101 101 101 101 Country & year FE Yes Yes Yes Yes Yes Notes: (1) Robust standard errors in parentheses. (2) Statistical significance: ***p < 0.01, **p < 0.05, *p < 0.1. (3) The leftmost column provides the most basic model; Model 2 includes major variables of interest; Model 3 includes GDP per capita squared; Model 4 features mixed effects; and Model 5 includes pull factor variables. The sample covers 101 developing countries, over a time series spanning twenty-five years (1985–2010) in five-year intervals (1985, 1990, 1995, 2000, 2005, and 2010). (4) Country and year fixed effects; robust standard errors clustered by country; independent variables lagged one year. View Large These results show the significant and negative relationship between governance aid and emigration rate across model specifications. On average, a 0.138 percent decline in emigration rate accompanies a percentage point increase in governance aid as a share of recipient country GDP. For an average country, such as Algeria, which is near the sample average in terms of population (35.5 million in 2010) and GDP ($161.2 billion in 2010), increasing governance aid as a share of GDP by one standard deviation (approximately 1.7 percent) would—all else equal—amount to an additional $2.8 billion in governance aid ($78.9 per capita); this would then reduce emigration by about 40,900 migrants over a decade. This increase in governance aid would not constitute a significant expenditure for a country like the United States, which disbursed approximately $41 billion in aid in 2015,26 or for France, Germany, the United Kingdom, or Japan, each of which gave more than $10 billion in assistance in 2015 (OECD 2016). The coefficients of economic aid and other aid are negative but not significant. The insignificant coefficients for nongovernance aid types could indicate either that aid does not affect economic development27 or that the economic development supported by economic and social aid has no bearing on migration patterns. The insignificant coefficient for GDP per capita supports the latter explanation, as neither economic aid nor the level of economic development appears to influence emigration rates.28 However, the current state of the aid-growth literature supports the former explanation: most studies find a negative or insignificant relationship between aid and growth, while a few studies report a small but positive association in the long run.29 Squared GDP per capita is not significant either, meaning that we do not find support for the income-based migration hump.30 The positive and significant coefficient of trade (% GDP) across models could further indicate that nongovernance types of aid do not affect economic development. Economists widely believe that trade supports economic development (Krugman 1990; Dollar 1992; Frankel and Romer 1999; Bhagwati and Srinivasan 2001; Dollar and Kraay 2004; Abbot, Bentzen, and Tarp 2009). Thus, the significant and positive coefficient of the trade variable could mean that economic development generated through trade (but not through economic or social aid) has an enabling effect on migration. This would support the notion that political and economic development produce contrasting impacts on migration patterns. However, we hesitate to speculate about the role of trade within this framework, as many other possible mechanisms could explain the positive relationship between trade and emigration.31 At any rate, the positive coefficient for trade is consistent with prior work on trade and migration (Martin 1993; Massey, Durand, and Malone 2002, 81). Democracy is negative but not significant. This may reflect nonuniformity among both democracies and nondemocracies. That is, unstable and weak democratic regimes exert greater pressures on individuals to leave, whereas democratic regimes with strong and stable state institutions do not generate such pressures. Likewise, some nondemocracies restrict emigration (Breuing, Cao, and Luedke 2012, 843–51) but others do not, and only highly rigid dictatorships, such as North Korea, forbid emigration outright. These policy variations are consistent with the work of Fukuyama (2013, 350) and others (for example, Holt and Manning 2014) that parse out factors such as state quality and public-sector capacity from democracy. This perspective would suggest that donors focus on making public agencies more capable and responsive rather than prioritizing elections and promoting democracy. Conflict is significant and negative in Model 4, suggesting that civil war generates reductions in outward migration. This finding seems counterintuitive, as we would expect violence to act as a push factor.32 However, conflict may not affect migration unless accompanied by destruction of the economic and social infrastructure (Adhikari 2012, 593–94). Bohra-Mishra and Massey (2011, 417–20) argue that conflict can even reduce outward migration. Additionally, countries often limit immigration from nations in conflict. For example, during the Arab Spring of 2011, Italy and France launched sea and air patrols to prevent inflows of refugees from Middle Eastern countries (Paoletti 2014, 144). In 2013, Greece erected fencing at the Greek-Turkish border to reduce inflows of Syrian refugees (Park 2015). Neumayer (2006, 78) reports that countries often use visa restrictions to deny entry to passport holders from countries characterized by armed political conflict.33 As expected, GDP in OECD (PPP) is significant and positive and terror deaths in OECD is significant and negative. These results suggest that positive economic performance in OECD countries leads individuals to emigrate at higher rates. At the same time, heightened border security following the occurrence of terrorist activity makes emigration more difficult. Timeline Considerations The effects of aid may differ in terms of timing. Clemens et al. (2012) argues that that aid sometimes works slowly, depending on the types of projects being implemented. Delays may reflect the time it takes for aid to affect the intermediate mechanisms through which it impacts the dependent variable of interest and how long it then takes those intermediate mechanisms to impact the dependent variable. Given that we propose separate mechanisms to explain how each type of aid affects migration, it is possible that these mechanisms operate on different timelines. Clemens et al. posit further (in their supporting information) that aid for projects such as road construction and budget support (which are economic aid projects, according to our classification) produce an “early-impact” on economic growth—the intermediate mechanism in our hypothesized relationship between economic aid and migration. As such, we expect economic aid to impact incomes and economic growth relatively quickly; in turn, higher incomes should facilitate international movement quickly as well. On the other hand, Clemens et al. suggest that projects for public health, basic education, and humanitarianism affect economic growth slowly, if at all. Our theoretical model predicts that other aid (the category that encompasses these sorts of social aid projects) affects migration through similar channels as economic aid (producing higher discretionary incomes) as well as through increases in knowledge and skills. But given the slow timeline associated with these social aid projects and the length of time needed for individuals to acquire knowledge and skills, social aid almost certainly has a slow impact on migration. The timeline for governance aid probably falls somewhere in the middle. As Jones and Tarp (2016, 268) note, political institutions (both formal and informal) can change rapidly,34 and the nature of governance aid provision suggests that donors envision rapid institutional changes to result. However, weak institutions can also change slowly, often reflecting deeply rooted and highly persistent corruption (Herzfeld and Weiss 2007, 1570). Additionally, the impact of improved political institutions on migration may be subject to moderate delay (Méon, Sekkat, and Weill 2009). We expect economic aid to affect emigration more rapidly than governance aid, and still more quickly than other aid. We capture these timeline dynamics through two empirical strategies. First, we analyze models with our independent variables lagged by two years and more.35 Second, we consider models with cumulative aid variables, such that governance aid for 2004 consists of aid from 2004 added to aid from all proceeding years. The latter approach36 allows us to observe the long-term impact of each type of aid on emigration rates. Results of each modeling approach (presented in Table 6 in the online appendix) provide support for Hypothesis 2: governance aid is associated with a decrease in the emigration rates of aid recipient, whereas other types of aid generate little or no impact. Interestingly, we see some evidence in the cumulative model that economic aid leads to decreases in emigration, although we hesitate to infer too much from this result given the nonsignificant coefficients for economic aid across our other models. Other aid, which carries the longest expected timeline, is not significant in any of our models, including models with lagged independent variables going back ten years.37 Additional and Alternative Variables While we include a large number of controls across the models presented in Table 2, additional variables merit consideration, as do alternative measures of some variables. We begin by considering several additional variables corresponding to political institutions and migration policies in sample countries. The inclusion of these variables reflects our concern that results presented so far may be spurious, as key policies and institutional factors of sample countries simultaneously drive variation in aid allocations and migration. To mitigate this concern, we consider controls for several facets of governance and policy: checks, executive constraints, political terror, judicial independence, and political corruption. We also measure freedom of foreign movement (freedom of movement) on the basis that states’ restrictions on migrant flows may drive variations in emigration rates.38 Models 11–16 in Table 7 of the online appendix show that the governance aid variable remains significant and negative when these controls are included. This suggests that the apparent relationship between governance aid and emigration is not a spurious one.39 Next, we consider alternative measures of immigration policies in migrant destination countries. While we believe that terrorism is a solid indicator that captures states’ security concerns and corresponding policies, there are countries with relatively secure borders despite not having experienced terrorist attacks (for example, Australia). With this in mind we analyze supplemental models with two alternative border security variables: annual averages of the migration policy and UN policy variables from Bermeo and Leblang (2015). These authors code migration policy based on whether countries’ entry policies moved into a more liberal or restrictive direction relative to the preceding year (a higher number reflects greater restrictiveness). The coding of UN policy reflects responses from a survey of immigration offices carried out by the United Nations’ Department of Economic and Social Affairs (again, a higher number reflects greater restrictiveness). As Table 8 in the online appendix shows, the coefficient for our governance aid variable remains significant and negative when these variables substitute for terrorism deaths in OECD countries. Finally, we consider an alternative measure of emigration: emigrant stock. We draw this measure from the World Bank's Global Bilateral Migration dataset, which records emigrant stock in ten-year intervals (1960, 1970, 1980, 1990, and 2000). Two of these years, 1990 and 2000, fit into our time series. Emigrant stock broadens our measurement by capturing migration to destinations outside of the twenty OECD countries used to create our primary dependent variable (emigration rate). As Table 9 in the online appendix shows, our results regarding governance aid hold when we analyze emigrant stock using variables featured in Model 1 of Table 2.40 Tests for Endogeneity It is possible that the apparent relationship between governance aid and emigration rate is spurious, such that variation in both governance aid and emigration rate owes to some unaccounted variable(s). Alternatively, our causal explanation could be backward, meaning that lower emigration rates cause some countries to receive higher levels of governance aid. To account for endogeneity, we turn to instrumental variable analysis. The literature highlights several instruments for foreign aid41; however, finding a suitable instrument becomes considerably more difficult when analyzing disaggregated aid variables, as the commonly suggested instruments intend to capture aid in the aggregate. In the absence of a suitable instrument, we use the second and third moments of governance aid as instruments for this potentially endogenous independent variable. Lewbel (1997) introduced this instrumental variable approach,42 and it has appeared in subsequent scholarship (for example, Rudra 2005). As Table 10 in the online appendix shows, the relationship between governance aid and emigration rate holds in instrumental variable models. Conclusions Our results indicate that some foreign aid does reduce outward migration from developing countries. In particular, we find a negative relationship between aid directed toward governance-oriented projects and emigration. This supports our theoretical expectation that governance aid lowers emigration rates by promoting better political institutions in recipient countries. Better political institutions generated through governance aid foster less corruption and more political stability, democratic accountability, and governing capacity. These improvements reduce push factors and remove incentives for emigration, thereby making individuals less likely to uproot their lives to move abroad. In contrast, we do not find evidence that other types of aid affect emigration rates, either positively or negatively. These results show that aid can serve as a tool of immigration policy so long as it targets governance-oriented projects. In our sample, aid allocation for governance projects is three times smaller, on average, than that for economic projects and seven times smaller than that for other (including social) projects. Greater distribution of governance-related assistance can produce more effective management of migration, independent of the multiplier effects associated with investments in political institutions and public-sector capacity building. Governance aid cannot constitute the whole of immigration policy for migrant destination countries, but it can serve as an additional tool for national governments to manage migrant inflows. It could effectively complement better donor coordination, policy coherence, cost-sharing measures among sending and destination countries, and hotspot-based implementation—targeting sizeable governance aid projects to specific areas inhabited by populations with high risks of migration.43 Our findings also suggest that governments may unintentionally generate more irregular migration when they cut their foreign aid budgets or transfer aid funds to defense agencies.44 For example, the recent budget proposal by the Trump administration includes plans to slash the foreign aid budget (Krieg and Mullery 2017). If these cuts include reductions in governance aid to Central American countries, then it seems likely that higher flows of immigrants from these countries to the United States will follow. Such an outcome would prove counterproductive for an administration that considers reducing migrant inflows to the United States a key policy goal. The same principle holds for other nativist governments proposing cuts in foreign aid budgets, such as Theresa May's Conservative government in the United Kingdom (Elgot and Walker 2017) and Malcolm Turnbull's Liberal-National Coalition government in Australia (Hitchick 2017). Finally, our results show that economic and social forms of aid have no discernable impact on migration. However, they could serve other donor goals, such as increasing education or reducing poverty in recipient countries. Indeed, aid projects have multiple, and sometimes contradictory, objectives (Brainard 2007). Powerful states use foreign aid to secure alliances (Alesina and Dollar 2000; Bearce and Tirone 2010), to elicit support in intergovernmental organizations (Dreher, Nunnenkamp, and Theile 2008), to support regime transitions (Bermeo 2011), and to achieve security and humanitarian objectives (Lai 2003), among other things.45 We find that foreign aid can be used to manage migration as well, provided that it is directed toward governance-oriented projects. However, allocating scarce budgetary resources to governance aid in an attempt to quell immigration may undermine other donor priorities. Therefore, the use of governance aid to lower immigration must be contextualized within a comprehensive set of tools at policymakers’ disposal that affect multiple policy outcomes. Notes Authors’ note: We thank Daniel Tirone, the editors of International Studies Quarterly, and two anonymous reviewers for thoughtful comments on earlier drafts of this article. We also thank participants at the 2016 American Political Science Association meeting, where an earlier version of this article was presented. Jonas Gamso is an assistant professor of International Trade and Global Studies at Arizona State University's Thunderbird School of Global Management. Farhod Yuldashev is a PhD candidate in Public and International Affairs at the University of Pittsburgh. His peer-reviewed research is published in Risk, Hazards & Crisis in Public Policy, Journal of Public Affairs Education, and Resources Policy. Footnotes 1 Data show that the immigrant stock of developed countries increased steadily from approximately 80 million in 1990 to 140 million in 2015, suggesting that basis exists for this political emphasis on immigration. Figure 1 in the online appendix charts this trend. 2 See Azam and Berlinschi (2010) for a survey of academic and policy articles on the consequences of migration. 3 See Léonard (2010, 232); Pécoud and de Guchteneire (2006, 72–74), for example. 4 This might explain the nonsignificant coefficients for aggregate measures of aid in previous studies (for example, Neumayer 2005). 5 The United States passed several laws toughening immigration enforcement during the Bush administration, including the Enhanced Border Security and Visa Entry Reform Act of 2002 (EBSVERA), the REAL ID Act of 2005, and the Secure Fence Act of 2006. Likewise, the Obama administration ramped up deportations and oversaw heightened spending on border protection. For Bush-era policies, see Rosenblum and Brick (2011, 6, 10–11); for Obama-era policies, see Meissner et al. (2013, 18) and Martínez and Rosen (2016, 127–28). 6 Critics also highlight negative impacts of conventional migration policies on human rights (Léonard 2010, 232; Pécoud and de Guchteneire 2006, 72–74). 7 See de Haas (2007, 820–27) for relevant quotes and paraphrasing from European Commission then President José Manuel Barrosom, then African Union head Alpha Oumar Konare, and then Prime Minister Rasmussen of Denmark. 8 Development promotion efforts may also deter emigration by compelling leaders in source countries to tighten up migration controls. As noted by Bhagwati (2003, 98), the prime ministers of the United Kingdom and Spain proposed in a 2002 European Council meeting that the European Union should reduce aid to those countries that do not make sufficient efforts to curtail migration to Europe. The proposal failed, but other policymakers may accept the logic underlying it. 9 Citing Castles and Miller (2003), de Haas (2007, 828) notes that “development assistance has often been used as a political instrument leading to ‘aid’ in the form of weapons and other types of support for autocratic regimes . . . This has increased insecurity, provoked armed conflict, created refugee problems, and exacerbated rather than decreased problems of underdevelopment.” 10 The vast scholarship analyzing effects of aid on economic growth and development offers little conclusive evidence for either a positive or negative relationship. See Doucouliagos and Paldam (2008, 2009) for meta-analyses of the literature on aid and growth. 11 For example, if economic aid flows to economic elites, then aid may increase inequality and push poorer people to migrate. In this scenario, the hypothesized relationship between aid and migration stays the same, but operates through a different mechanism. 12 The twenty countries are the following: Australia, Austria, Canada, Chile, Denmark, Finland, France, Germany, Greece, Ireland, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States. Figure 2 (in the online appendix) shows an increasing trend in migration to these twenty OECD countries. 13 This variable also encompasses refugees to the extent that refugees flow to the twenty OECD countries used to calculate the variable. We cannot separate out refugees from other types of migrants using the Brücker et al. (2013) dataset. However, researchers may wish to analyze the relationship between aid and refugees in future work. 14 Government and civil society and support for NGOs each include several types of aid projects. We provide a complete list of governance aid project types, and the funding for projects corresponding to each in Table 3 in the online appendix. 15 Unlike Bermeo and Leblang (2015) we do not treat emergency response and disaster reconstruction efforts. While collected in a relatively shorter span of time, donors generally disburse this assistance over a longer time and elites may divert it to projects unrelated to disaster relief (based on our observations in the field). 16 See Table 4 (in the online appendix) for descriptive statistics of all variables included in this study. 17 We draw data for this variable from Jones and Tarp (2016). 18 The QoG measure, from Teorell et al. (2016), is calculated by averaging the Freedom House and Polity scores. The variation of the measure used here includes imputed values for country-years in which Polity data is missing. Jones and Tarp (2016) standardized the QoG measure to center the mean at zero and the standard deviation at 100. See Hadenius and Teorell (2005) for discussion of this measure and its favorable performance relative to the individual measures that contribute to it. 19 We acknowledge that population density offers an imperfect measure of overpopulation and resource scarcity. In fact, it may be the case that many countries with high emigration rates have relatively low rates of population density due to their low levels of urbanization. Our results hold when we control for population (logged) in lieu of population density (results of models with logged population are not included in the article but are available on request). 20 We draw data for this variable from Euromonitor International. 21 Data for terrorism deaths comes from the RAND Database of World Terrorism Incidents. 22 We acknowledge that terrorist attacks constitute an imperfect measure for border controls. Critics may argue that states use terrorist incidents to justify border controls that governments already intended to implement and migration controls do not always follow incidents of terrorism (Boswell 2007). Moreover, the nature of our data source makes it impossible to disentangle domestic and international terror incidents. We consider alternative measures of border security in the robustness tests described below in light of these concerns. 23 Hausman testing supports our utilization of country fixed effects. 24 All of our findings also hold when we do not use robust standard errors. 25 We also estimated the mixed effects model with a quadratic GDP term, but we do not report results because the quadratic term is not significant and the results for other variables match those in Model 2. 26 Of that $41 billion, the US Government directed less than $4 billion toward government and civil society programs, suggesting that the US Government does not prioritize governance aid. See the USAID data explorer for additional details (USAID 2017). 27 This would be consistent Nowak-Lehmann et al. (2012) and Dreher and Langlotz (2017). 28 We also analyzed models controlling for GDP growth, lagged one, two, three, and four years, to account for possible short- and long-term impacts of nationwide growth. Our findings hold when these variables are included in the models. We do not report results for these models here, but they are available on request. 29 See the recent debate between Clemens et al. (2012) and Roodman (2015). See also Arndt, Jones, and Tarp (2015) for a discussion of long-term effects. 30 Following Brambor, Clark, and Golder (2006), we conducted joint significance test of GDP per capita and the quadratic term. Testing for various levels of GDP did not produce significant results in adjusted conditional coefficients and standard errors, indicating that the quadratic specification does not improve the model. The joint significance test results are not shown but are available on request. 31 Trade openness may disrupt small- and medium-sized industries thereby leading to displacement and exit (Massey et al. 2002, 50). Alternatively, high trade levels may reflect strong demand from rich countries, which in turn corresponds to economic growth in those countries and accompanying opportunities for migrants. At the same time the benefits of trade to workers may be limited or those countries with more liberal trade policies may also have more liberal migration policies. 32 For example, Hyndman (2003, 266) argues that many in Sri Lanka migrated to Canada due to conflict. 33 We control for annual deaths by terrorism in aid-recipient countries in a supplemental model, on the basis that terrorist activities may lead to restrictions on foreign movement. We again use data from the RAND Database of World Terrorism Incidents. This variable is significant and negative but its inclusion does not affect the sign or significance of the independent variables. See Table 5 in the online appendix. 34 These authors highlight instances of rapid democratic transition. 35 As a point of reference, Savun and Tirone (2018) lag all aid variables two years in a study similar to this one, in which terrorism serves as the dependent variable. 36 Informed by Arndt, Jones, and Tarp (2016). 37 For the sake of space we only show results for two-, three-, and four-year independent variable lags. Governance aid remains significant through a five-year lag. Again, these results are presented in Table 6 in the online appendix. 38 We draw data for these variables from Jones and Tarp (2016), with the exception of political corruption, which we draw from the QoG dataset (Dahlberg et al. 2017), and freedom of movement, which we draw from the CIRI Human Rights Data Project (Cingranelli, Richards, and Clay 2014). 39 Fluctuations in governance aid provisions may affect emigration rates to a greater extent in aid-dependent countries. With this in mind we interact our key independent variable (gov. aid [% GDP]) with a dummy variable coded “0” where countries receive less than the mean level of governance aid for the sample; and “1” where countries receive more than the mean level of governance aid. The interaction term is not significant, suggesting dependence on governance aid does not moderate the effect of governance aid on emigration rate. We do not include results in the article, but they are available on request. 40 The results do not hold when we add the full array of control variables featured in Model 2. However, we suspect that the parsimonious model offers a better fit for analysis of emigrant stock given the small sample size. 41 See Bazzi and Clemens (2013) for discussion of the instrumental variables used for foreign aid. 42 When instrumental variables are unavailable or insufficient, Lewbel suggests the use of second moments (Xi – mean(X))2 and third moments (Xi – mean(X))3 as instruments. 43 Czaika (2009, 163) offers similar recommendations with respect to managing refugee inflows. 44 The UK defense minister recently argued that the government should transfer foreign aid funds to the defense ministry to manage migration flows (Dominiczak 2015). 45 Stone, Randall W. 2010. “Buying Influence: Development Aid Between the Cold War and the War on Terror.” Unpublished Manuscript. 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

International Studies QuarterlyOxford University Press

Published: Dec 1, 2018

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