international migration and its political sources: a network analysis

International Migration and its Political Sources:

A Network Analysis

Christian BreunigAssistant Professor

Department of Political ScienceUniversity of Toronto

Xun CaoLecturer

Department of GovernmentUniversity of Essex

Adam LuedtkeAssistant Professor

Department of Political ScienceUniversity of Utah

November 7, 2008

Abstract: The quantitative study of human migration lags far behind itspolitical significance in recent years. We now have sophisticated models forexplaining transnational flows of goods, services, and capital. But despite ever-growing numbers of humans crossing national borders, political science analysisof these migration flows has suffered from the following shortcomings: (1) anarrow focus on migration from emerging economies to the developed world,which captures only a small share of world migration; (2) a focus on unilateralmigration flows (receiving countries), without analyzing bilateral relationships.We propose to remedy these shortcomings by performing network analysis ofbilateral migration covering all regions of the world. We explicitly model bi-lateral migration using a generalized-bilinear-mixed-effects model. Making theanalysis even more relevant for political science, we propose that the politicaldynamics of a country’s regime type determine human migration. based ona micro- and a macro-level logic, we argue that democratic regimes attractless migrants than non-democratic regimes and that democracies send moreimmigrants to authoritarian states. In addition to this political hypothesis,the analysis also controls for several basic alternative explanations prevalent inimmigration literature, including the effects of economic factors, civil conflictand natural disasters in the sending and the receiving country, geographicaldistance, and historical factors (such as colonial ties and common languages).Our analysis seeks to advance the study of human migration into new terrainby developing a political theory of global migration flows, by employing a sta-tistical method that captures the higher-order dependencies of network flows,and by introducing a more comprehensive data set.

Migration and its Sources November 7, 2008

Introduction

The main theme in The Economist ’s (2008a) recent survey of world migration is thathuman migration flows into Europe and the United States, which had grown steadily through-out the 1990s, are beginning to slow. The article also notes that “many places, includingAustralia, the Persian Gulf, parts of Asia and much of Africa will no doubt see migration con-tinue apace for some time yet” (Economist 2008a, 31). This surprising finding highlights thefact that political science has taken a myopic, overly narrow view of world migration. First,many of the states receiving the “new” migration flows do not have democratic regimes, incontrast to the oft-heard argument that liberal democracies attract immigrants by virtue oftheir political freedoms. Secondly, liberal democratic regimes have in recent years done abetter job of responding to anti-immigrant public opinion by controlling immigration andrestricting inflows, often through international coordination. The European Union’s commonimmigration policy, for example, is largely focused on policing external borders and coordi-nating exclusionary tactics between ostensibly liberal democratic regimes, with such tacticsas fingerprint databases and increased border patrol assistance to countries in Southern andEastern Europe (Guiraudon 2000). Finally, past studies of the determinants of migration havetended to focus on migration only to Europe or the US, and often have looked only at “pull”factors (conditions in the rich world that attract migrants) or “push” factors (conditions in thedeveloping world that cause migrants to “target” the rich world) in isolation from each other.In short, the literature has suffered from three shortcomings: 1) an untested assumption thatliberal democracy, in and of itself, attracts migration; 2) a focus on developing-to-developedcountry migration; and 3) a lack of analysis that combines push and pull factors in order tomeasure their relative causal weight in terms of bilateral immigration flows.

According to a study by Deutsche Bank (2003), “most international migrants staywithin the same geographic region and migrate to neighboring countries” (18). Furthermore,the analysis finds that “the ratio of foreigners to nationals is in many cases much lower [inthe developed world] than in many countries of the second and third world” (19). In fact,Deutsche Bank found that the five countries with the highest percentage of migrants in thetotal population were the United Arab Emirates, Kuwait, Jordan, Israel, and Singapore.None of these countries fits the definition of a traditional liberal democracy, in terms ofgranting robust political rights and freedoms to all legal residents. Yet political scienceanalysis of immigration rarely focuses on regions or countries such as these, despite thefact that (apparently) increasing numbers of migrants are choosing non-OECD countries asdestinations.

It is important to study these flows not only because they are quantitatively increasing,but also because they have strong implications for the politics of immigration. First, thesenew migration flows give us a picture of changes in the structure of the world economy.Leblang (2007) finds a strong correlation between human migration flows and cross-nationalinvestment. Thus, the implications for international political economy need to be analyzed,since investment, migration and wealth creation will be inexorably linked with political power.

Further, the fact that a growing share of world migration is “targeting” non-OECDcountries has implications for the politics of migration in receiving countries. Political scienceanalysis of immigration politics has tended to focus on the “rights” of immigrants, which areoften grounded in international norms and legal instruments, but may also rely on the domes-tic institutions of liberal democracy for enforcement (Jacobson 1996, Baubock 1994, Soysal1994). But what happens when immigrants arrive in non-liberal democracies? What are the

1


primary motives for migrants to move in the first place, and does the regime type of a send-ing or receiving state play a role above and beyond other factors like economics, geographicdistance, or linguistic ties? Political scientists should begin to investigate this question, andin doing so, begin to explore the links between political institutions and migration flows, asour paper attempts to do.

The second looming task for scholars of migration politics is to move beyond one-wayanalysis of “push” or “pull” factors, and to capture the bilateral dynamics of immigrationflows. In doing so, we aim to build on the work of Leblang (2007) and Leblang, Fitzgeraldand Teets (2007), who perform network analysis on bilateral matrices of sets of countries, inorder to gain a more complete picture of the causal factors driving migration flows. The mostcomprehensive bilateral data set, containing 226 receiving and sending countries, has beenconstructed by Parsons et al. (2005), working in conjunction with the Development ResearchCenter on Migration, Globalization and Poverty at the University of Sussex. We describe andanalyze these data below. But first, we present some hypothesis regarding the driving forcesof international bilateral migration flows. We begin by explaining the casual mechanisms thatlead us to expect that democracies will not attract more immigrants than non-democracies,and that non-democracies will not send more immigrants to other countries than democracies.We then present several theoretical arguments showing why the flows may actually go in theopposite direction, with a higher rate of democracy-to-authoritarian migration. We then offertheoretical justification for a series of alternative explanations.

The Effects of Political Regime Type on Bilateral Migration Flows

In this section we develop our argument that democratic regimes are not likely toattract more migrants from other countries, and to send less migrants to other countries, thanauthoritarian regimes. In fact, we have reason to believe that the majority of migration flowsmay take the inverse direction: democracies sending more emigrants, and non-democraciesreceiving more immigrants, ceteris paribus. We first outline several theoretical argumentsrefuting the conventional wisdom that immigrants leave dictatorships and seek democracies.Since these arguments just argue that regime type will not affect migration flows, we thenmove on to outlining several causal mechanisms that enable migrants to leave democraciesbut not non-democracies; and that non-democracies receive more immigrants from othercountries than democracies.

To begin, we suggest looking at the micro-foundations of a migrant’s motivation. Agreat deal of both theoretical work and empirical evidence suggests that the majority ofmigrants are not motivated by political factors in their choice of departure and destination.

Secondly, new or unconsolidated democracies might face unstable living conditionsand a lack of rule of law, which will prompt citizens to leave in search of a more stablesocial and economic climate (Huntington 1968). While we are not suggesting that all newdemocracies suffer from the syndromes of a “weak state” (corruption, policy unpredictability),we only suggest that until a new democracy is consolidated, it may not be able to providethe socioeconomic stability that the majority of immigrants seek (Massey, Arango, Hugo,Kouaouci, Pellegrino & Taylor 1993). This instability makes such democracies no moreattractive as migration destinations than dictatorships, especially if those dictatorships canprovide the socioeconomic benefits of a strong state, such as economic stability, rule of law,or policy predictability.

Finally, many analysts might grant that the majority of refugees are motivated bysocioeconomic factors, but could argue that there is a special class of migrants – political

2


refugees or asylum-seekers – who tend to leave dictatorships and seek political freedoms.However, the empirical evidence shows that refugees tend to be vastly clustered in countriesclose to their country of origin, most of which are non-democratic. While it is true that atrickle of refugees do reach the democratic world, the vast majority of the world’s refugees donot have the resources to seek asylum in a democratic country. Instead, they tend to end upin camps or in other precarious situations, in neighboring countries that might be no moredemocratic than the country they are fleeing.

The above arguments merely refute the common story that immigrants flee dictator-ship and seek democracy. However, we wish to go beyond this refutation, by positing signifi-cant flows in the other direction (see also (Mirilovic 2007)). Why would this be? The dawningof the 21st century has seen a tremendous surge of economic growth and diversification in“developing” countries, many of which are non-democracies. The Economist magazine’s re-cent “Special Report on Globalization” (Economist 2008b)) found that dynamic economicgrowth in non-democratic regimes has created an incredible inflow of not only capital invest-ment, but also human inflows, both among high-skilled managers from developed countries,and low-skilled workers from other developing countries. While these workers may be leavingdemocratic regimes, in many cases they are arriving in high-growth non-democracies like theUnited Arab Emirates or the People’s Republic of China.

More importantly, democracies allow freedom of exit for migrants, while dictatorshipsoften block their citizens from leaving the country. And if immigrants do make it to democ-racies, they will find strongly limited political rights (especially a lack of voting franchise).Finally, democracies are also be better at responding to xenophobic public opinion and keep-ing potential migrants out, because immigration tends to be politically unpopular, even inthe most liberal of democracies. Dictators, however, are spared these political costs and donot have to be responsive to public xenophobia.

Because the argument that democracies will not attract or retain migrants runs counterto the conventional wisdom, let us take each of these arguments in turn, in order to furtherflesh out the causal logic at work here. First, we will detail the arguments pushing towardsindeterminacy. Then, we will offer a more detailed treatment of the causal mechanismspushing migrants away from democracies and towards authoritarian states.

First, we emphasize that one must look at the individual-level factors and motiva-tions that are driving an migrant’s choice of exit and destination. (Massey et al. 1993), intheir exhaustive review of the theoretical literature explaining “the initiation of internationalmigration”, highlight four bodies of scholarship that explain individual and household-levelmigration causes: “neoclassical economics: macro theory”, “neoclassical economics: microtheory”, “the new economics of migration”, and “dual labor market theory” (35-41). In theirwrite-up of these four bodies of theoretical and empirical work on why people migrate, notonce do (Massey et al. 1993) mention political freedoms/democracy (or the lack thereof) aseither a cause of choice of exit, or choice of destination. Instead, these four theories focusupon socioeconomic factors such as wage differentials, capital investment, labor markets, ed-ucation and skills, costs of migration versus expected returns, and economic structures andlabor recruitment in a receiving society.

To lend further evidence to the point that immigrants are individually motivated bysocioeconomic factors more than political ones, we performed a basic correlation analysis ondata from the World Values Survey (2006) comparing the effects of belief that democracy isthe best form of government on life satisfaction, versus the effect of personal finances on lifesatisfaction. The results showed that finance, not democracy, matters for life satisfaction,lending credence to the point that if democracies attract immigrants, it may be simply because

3


they are rich; hence the importance of controlling for a country’s wealth, as our analysis does(see Alternative Explanations section below).

Our next causal mechanism draws upon classic modernization theory (Huntington1968) to not only explain why dictatorships can provide prosperity (through socioeconomicstability and rule of law) to foreign workers, but also why new or emerging democraciesmight actually experience outflows of citizens. The work of Huntington and related scholarson new democracies and the process of state-building shows us that citizens (and business)often prefer order over freedom. While many dictatorships feel a “growing pressure . . . todeliver high-quality, value-for-money infrastructure” in the new global economy, many newor unconsolidated democracies are beset with problems common to “weak states”: factionalstruggles, political turbulence, and a range of other factors that could lead to corruption,socioeconomic instability, and a lack of both rule of law and of certainty and predictabilityabout the future. In such conditions, both firms and workers may feel that their futures (atleast in the short term) are safer in a prospering authoritarian state, especially given theemphasis that migration theory places upon social stability and the mitigation of risk.

Our next causal mechanism addresses the question of refugees, who are often assumedto be fleeing authoritarian systems and seeking political liberties. This argument is especiallyimportant to tackle, considering the magnitude of global refugee flows in the post-Cold Warera. “Humanitarian measures as a result of civil strife . . . led to a renewed increase in thenumber of migrants . . . the early 1990s were marked by a strong increase in internationalmigration flows—not least due to the geopolitical turmoil accompanying the fall of the SovietUnion. The collapse of many multiethnic states triggered conflicts and thus migration flows”(Deutsche Bank 2003, 17-18). We grant that refugees will often flee undemocratic countries(though new or unconsolidated democracies might give rise to refugee flows as well, if ethnicor other tensions persist after democratization). However, the conventional wisdom thatrefugees migrate to democratic countries is belied by the empirical evidence. According tothe United Nations High Commissioner for Refugees (United Nations 2006), Syria, Pakistanand Iran are three of the world’s top four refugee-receiving countries, with Pakistan leadingthe table at over one million refugees. This situation makes sense when one considers thatasylum-seekers arriving in the rich world must first have the resources to make the journey,often paying tens of thousands of dollars to human smugglers. The fact that most refugeescannot afford this journey means that they end up in camps or other precarious situationsin neighboring (and often non-democratic) countries. Only a tiny fraction make it to thedemocratic world. Thus, we propose that authoritarian states receive as numbers of refugeesas democracies, or at least that regime type has little if any effect upon a refugee’s destination.

Turning now towards the argument that dictatorships receive migrants from democ-racies, it is clear that we must take into account the global economic rise of “rich dic-tatorships” and their attraction of foreign labor. This focus builds on the work of Mir-ilovic (2007) regarding flows of unskilled labor away from democracies and into dictator-ships. In debunking the conventional wisdom that democracies are inherently liberal intheir immigration policy because of their tendency to grant political rights to non-citizens(Jacobson 1996, Baubock 1994, Soysal 1994), Mirilovic argues that the two central goals ofimmigration policy are finding low-cost labor and ensuring that immigrants pay more in taxesthan they receive in entitlements. The most common way of meeting both goals at the sametime is a guest worker program where foreigners are ineligible for entitlements. Mirilovic’swork shows that dictatorships are the most likely to implement such programs, because theyhave less entitlement programs and more flexible labor markets. Thus, we expect that dicta-torships, compared to democracies, have permissive immigrant admissions policies especially

4


for unskilled workers than democracies. This permissiveness leads to immigration into richdictatorships.

This theory justifies why authoritarian states might attract more unskilled labor,but what about the flow of skilled labor away from democracies and into dictatorships?The Economist’s “Special Report on Globalization” (2008) highlights the trend of rich-worldmultinationals setting up high-end operations in fast-growing authoritarian states. IBM isgiven as an example of a multinational that, while nominally headquartered in a democraticstate, relies increasingly on “the growing pressure on emerging-country governments–eventhose that are not strictly democratic–to deliver high-quality, value-for-money infrastructure”(Economist 2008, 13). In other words, non-democratic states are becoming increasinglytalented at providing stable investment climates with high-profit potential, which stimulatesthe flow of high-skilled labor into these countries (and encourages native high-skilled laborto stay home as well). In the words of (Massey et al. 1993, 36), “the movement of capital[into developing countries] also includes human capital, with highly skilled workers movingfrom capital-rich to capital-poor countries in order to reap high returns on their skills in ahuman capital-scarce environment, leading to a parallel movement of managers, techniciansand other skilled workers.”

So fa, we have established that there are strong individual level motives for migrationthat do not consider political over economic rationales. Our second set of causal mechanismsfocuses explicitly on the macro-institutional differences between democracies and authoritar-ian states in regard to migration. We show why it is easier to immigrate into non-democraciesand easier to emigrate from democracies. Let us take the latter point first. The UniversalDeclaration of Human Rights states in Article 13 that “everyone has the right to leave anycountry, including his own” (United Nations 2002, 24). However, no corresponding right ofentrance is given. Thus, democracies, which tend to take international law more seriously, areobliged to allow all persons on their soil to leave the country, but they are not obliged to grantentrance to immigrants who do not fit into narrow categories (refugees, family migrants). Inmost democracies, there are no formal requirements at all for leaving and, at worst, individ-ual citizens may be asked to deregister their residency. In contrast, many non-democracieshave policies in place that bar exit (United Nations 2002) and thereby are cited for violatingthe Universal Declaration of Human Rights. Among other things, this divergence makes iteasier from skilled workers to leave poor democracies and seek work elsewhere than they doleaving poor dictatorships. The fact that Cuba, Belarus and Turkmenistan have some ofthe world’s highest rates of doctors per capita is a telling sign here. In short, the increasedfreedom of exit found in democracies leads to a higher-than-expected outflow of immigrants,and the restrictions placed on exit by dictatorships leads to a lower-than-expected outflow ofimmigrants.

What about the politics of inflows? Instead, we argue that democratic leaders areforced to respond to public xenophobia by tightening immigration policy, while dictators arefree to ignore these costs. The authors of a 2006 work on immigration politics and policy,The Migration Reader, set the stage for the debate well: “Given that the primary respon-sibility of the state is to tend to the welfare of its citizens . . . nation-states need torestrict who belongs within their societies and to protect their ’insiders’ from ’outsiders”’(Messina & Lahav 2005, 32-33). Whether or not one agrees with this political imperativeon ethical or utilitarian grounds, the main puzzle of empirical research on this topic hasbeen the relatively consistent xenophobia of publics in democratic states, despite norma-tive justifications or economic benefits that may accrue from increased migration. Indeed,most scholarship confirms that public opinion regarding immigration tends to be relatively

5


restrictionist (Fetzer 2000, Hansen 2000, Guiraudon 2000, Kessler & Freeman 2005, Simon& Alexander 1993). Although opinion can be driven by differential demographic, economic,identity-based and ideological factors, a general trend of majoritarian anti-immigration sen-timent is common in the developed world (Kessler & Freeman 2005). In a recent reviewarticle, (Cornelius & Rosenblum 2005, 104) summarize the existing evidence by stating that“a substantial body of political science literature examines general public responses to immi-gration, which are characterized throughout the industrialized world by opposition to existingimmigration levels and negative feelings about the most recent cohort of migrants.”

If democratic public opinion towards migration is so broadly and consistently xenopho-bic, then this gives rise to an important puzzle: why do states continue to allow migration?A prominent strain in the literature on immigration politics sees government policies as beingunable to control immigration flows (Bhagwati 2003, Jacobson 1996, Massey, Alarcon, Durand& Gonzalez 1990, Portes 1997, Sassen 1996, Soysal 1994). Scholars like Jacobson (1996) andSoysal (1994) all focus on evolving global norms that constrict liberal governments’ ability tocurtail the rights of foreigners. Bhagwati (2003) and Sassen (1996) focus on the increasingirrelevance of national governments in the face of economic globalization. And finally, Masseyet al. (1990) and Portes (1997) focus on global social networks of migrants and their ethnickin in sending countries, who take advantage of normative and economic factors to perpet-uate migration even in the face of harsh objective conditions. Finally, (Hollifield 1992) and(Freeman 2002) are two prominent scholars who offer institutional explanations for immigra-tion policy outputs that are more liberal than public opinion would prefer. Hollifield (1992)argues that institutions of “embedded liberalism” protect the rights of immigrants againstmajoritarian xenophobia, through institutions such as judicial review. Freeman (2002) and(Joppke 1999) point to the importance of the relative power of interest groups, such as busi-ness and human rights lobbies, in capturing the state and pushing policy away from majoritypreferences.

However, the work of(Lahav 2004) makes a strong case that public opinion matters agreat deal more than the above scholars assume, and that democratic states are politicallyeffective in blocking immigration. Lahav argues three key points. First, to refute the con-ventional wisdom that public opinion on migration is based on ethnocentrism alone, Lahavargues that “publics are more informed about immigration-related issues than traditionallypresumed” (1152). Secondly, to refute the identity-based arguments that publics are “irra-tional”, Lahav argues that the public’s “policy preferences respond to broad societal interestsor diffuse costs, if one revises the self-interested motivations imputed to immigration policypreferences” (1152). And finally, to refute the claim that immigration policy is more lib-eral than public opinion would prefer, Lahav argues that policy “reflects norms that broadlyreflect public opinion” (1152).

Regarding the first point, that publics are more informed than traditionally presumed,Lahav finds that Europeans from across the continent were fairly well-informed about num-bers of non-EU foreigners residing in various locales. In other words, the pejorative portraitof citizens feeling “swamped” by small numbers of immigrants and exaggerating their effectsfar beyond their quantitative numbers may not always hold true. In Lahav’s words, “atti-tudes towards immigrants or immigration cooperation may not be so random or volatile butmay instead be founded on some objective circumstances and informed evaluations of them. . . knowing the number of immigrants in a country tells us a great deal about peoples’attitudes and orientations toward immigration, because perceptions clearly have some realitybase” (1162–63).

6


Regarding the second point, that public preferences are more rational than presumedby many scholars, Lahav posits that one must distinguish between personal and societal”interest”. Of course, citizens may ”calculate” that their personal economic interests arethreatened by migration. However, Lahav argues that even if native citizens’ economic in-terests are not personally threatened by immigration, one must still take “sociotropic” con-cerns into account. These concerns are about the nation at large (Citrin, Green, Muste &Wong 1997, Sears & Citrin 1982), and may have little to do with a citizen’s personal situation.”Personal economic circumstances figure less prominently in public opposition to immigrantsthan beliefs about the national economy” (Lahav 2004: 1170).

Lahav’s third point, that immigration policy reflects public preferences much morethan assumed, is validated by her study of European policy cooperation on immigration.Despite the fact that the European Union is a liberalizing project that privileges the ”freemovement” of goods, services, capital and (most importantly) labor, EU-level migrationpolicy has thus far been quite restrictive, in line with public preferences (Guiraudon 2000).Additionally, Breunig and Luedtke (2008) conduct an empirical study showing that the moreclosely a given political system is forced to cater to ”raw” public preferences through a lackof checks and balances, the more anti-immigration that country’s governing party is (evenwhen controlling for partisanship, economics and levels of immigration). Thus, we theorizethat democracy not only facilitates the exit of migrants from its territory, but it also blocksthe entry of migrants. Authoritarian leaders, on the other hand, have a freer hand in ignoringpublic opinion and tailoring immigration policy to the needs of the labor market.

These causal mechanisms for individual level motives and macro-level barriers, partic-ularly the “pull” factors in authoritarian states and the “push” factors in democratic states,allow us to propose the paper’s main hypothesis.

H1: As a sending country becomes more democratic, or a receiving countrybecomes less democratic, we expect that the volume of bilateral migration flowswill increase.

Alternative Explanations

Obviously, geographical factors play a role in human migration. All things being equal,it is normally easier to send goods, services or capital across borders than it is for humansto cross international borders. Despite the fact that technology and the diminished cost oftravel have lowered barriers to migration, the physical and political distance traveled is stilla significant obstacle. Thus, we propose that geographic proximity should play a causal rolein diminishing or augmenting bilateral migration flows. Geographical distance can also bea proxy measure for more socially-related variables, such as culture. According to DeutscheBank (2003), “geographic distance is also an indicator of cultural proximity. The greater thedistance between the country of destination and the country of origin, generally the higherthe required investment in society—specific know-how—which makes integration into the newenvironment more difficult” (17).

H2: As the geographic distance between two countries decreases, we expectthe volume of bilateral migration flows to increase.

Theories based on economics must also be taken into account, given our emphasisabove on the economic nature of migration motivation. Despite the fact that many migrantsare “forced” to move by war, natural disaster, or political or other types of persecution, manywould argue that economic opportunity (calculation) plays a role in a large majority of human

7


migration, even when other, non-economic factors might be causally relevant as well. In otherwords, even when poverty is not the reason for leaving a country, economic opportunity mightinfluence the choice of destination (Massey et al. 1993, Todaro & Maruszko 1987, Todaro 1976,Harris & Todaro 1970, Sjaastad 1962, Ranis & Fei 1961, Lewis 1954, Borjas 1990). By thesame token, poverty might be the actual or primary reason for leaving in many cases. Masseyet al. (1993) argue that “the volume of international migration is directly and significantlyrelated, over time and across countries, to the size of the international gap in wage rates” (50).However, Deutsche Bank (2003) reminds us that “due to the substantial costs of migration,marginal differences in wages between regions do not necessarily lead to migratory flows.Migration will only set in from a certain wage gap. One-third is often mentioned in relevantliterature as [a] critical threshold, i.e. wages have to differ by over one-third for sizeablemigration to another region to occur” (17). Thus, we do not expect economic gaps betweencountries to be the most important causal variable. However, it is clear that when significantwealth differences occur, migration can be triggered.

H3: As GDP per capita increases in a receiving country, or decreases in asending country, we expect the volume of bilateral migration flows to increase.

Massey et al. (1993), in their review of migration theory, also bring our attentionto social networks. Leblang, Fitzgerald and Teets (2007) test the causal impact of socialnetworks and find that “the existence and volume of migrant social networks, which provideinformation and support to would-be migrants, are key to understanding the perpetuation ofmigrant flows” (1), though their analysis is limited to only 26 destination countries. Further,Massey et al. (1993) argue that scholars should go beyond solely economics-based models.“This basic migratory process should be augmented by the existence of ideological and ma-terial ties created by prior colonization as well as ongoing processes of market penetration. Ifone were to specify a model of international migration flows . . . one would want to includeindicators of prior colonial relationships, the prevalence of common languages” (55). Giventhat migration is a costly endeavor, the existence of a common language should significantlylower the barriers to entry, and thus be a key causal variable in its own right.

Additionally, the existence of prior or ongoing colonial or neo-colonial relationshipscan be a source of social networks independent of language. Randall Hansen (2002) demon-strates that the colonial “subject” status held by large numbers of North Africans (vis-a-visFrance) and residents of the Caribbean and Indian subcontinent (vis-a-vis Britain) was inand of itself responsible for large postwar migration flows between the ex-colonies and themetropole, given that economic slowdowns and political hostility could not block migrationflows. For world systems theorists (Wallerstein 1974), this is only one chapter in a long storyof colonially-induced migration flows based on social networks. According to Massey et al.(1993, 42),(1993) “capitalist investment foments changes that create an uprooted, mobilepopulation in peripheral countries while simultaneously foreign strong material and culturallinks with core countries, leading to transnational movement”. The same authors explain thesocial effects of colonization on increasing the likelihood of migration as follows: “interna-tional migration is especially likely between past colonial powers and their former colonies,because cultural, linguistic, administrative, investment, transportation and communicationlinks were established early and were allowed to develop free from outside competition dur-ing the colonial era, leading to the formation of specific transnational markets and culturalsystems” (42).

8


H4: Countries that have common languages or a prior colonial relationshipshould have a higher volume of bilateral migration flows than countries withoutcolonial or linguistic ties.

So far, our alternative explanations have focused on rational models of migration in-volving cost, whether this cost be geographical (travel), economic (wealth gaps) or social(linguistic). But none of these variables takes into account the phenomenon of migrationstemming from civil war or natural disasters, which accounts for a growing share of worldmigration (2003). Thus, our last two explanatory variables focus on forced migration forthese reasons. Many refugees flee from disasters, whether natural or man-made. To capturethis effect, our analysis will test for the impact of natural disasters as well as civil conflict insending countries. Although victims of natural disasters are not “refugees” as recognized bythe Geneva Convention, this certainly does not mean that they do not fall into the “forcedmigrant” category. According to the Forced Migration Learning Module at Columbia Uni-versity’s Mailman School of Public Health, “forced migration” is “a general term that refersto the movements of refugees and internally displaced people (those displaced by conflictswithin their country of origin) as well as people displaced by natural or environmental disas-ters, chemical or nuclear disasters, famine, or development projects” (Popfam 2008). Manyvictims of civil conflict, on the other hand, might fall into the definition of a legally-recognized“refugee”, though not all do. To qualify as a refugee under international law, one must resideoutside of one’s country of nationality, and be unable or unwilling to return because of a“well-founded fear of persecution on account of race, religion, nationality, membership in apolitical social group, or political opinion.” While most civil conflicts obviously produce suchrefugees, there is a potentially larger group of forced migrants resulting from civil conflictswho may not be able to meet any one of these strict standards. However, such people do mi-grate nonetheless, and our analysis will attempt to model these flows by including a variablefor civil conflict.

H5: If a sending country experiences a natural disaster or a civil conflict,we expect that bilateral migration flows between this country and other countrieswill increase.

Empirical Analysis

In this section, we model bilateral stock of migration as a function of dyadic ex-planatory variables, that is, common language, colonial ties, and distance in geography, andcharacteristics of sender and receiver countries of the migration stock. We have five variablesto describe sender and receiver countries: polity, total population, GDP per capita, naturaldisaster, and civil conflict. In the following, we first describe the bilateral migration dataand dyadic as well as sender and receiver country variables in detail. We then present alatent space model for modeling network data that take into account not only the effects ofcovariates but also random country effects (for both sender and receiver country) and higherorder dependencies often seen in network data. We discuss the empirical findings at the end.

Bilateral Migration Data. The most comprehensive bilateral data set on bilateral migra-tion flows has been constructed by Parsons, Skeldon, Walmsley and Winters (2005), work-ing in conjunction with the Development Research Center on Migration, Globalization andPoverty at the University of Sussex. The data contains information on immigrants and em-igrants for 226 countries for the 2000 round of censuses. Starting with all available census

9


Table 1. Basic Characteristics of the Bilateral Migration Data, 2000.

Ranking Senders Receivers Receivers/SendersTop Ten RUS 12098614 USA 34634797 REU 619.40

MEX 10140846 RUS 11976818 GUF 45.26IND 9059424 DEU 9143243 SPM 44.37BGD 6832522 UKR 6947118 OMN 38.11UKR 5877810 FRA 6277188 GLP 31.03CHN 5820295 IND 6270665 QAT 25.65GBR 4201866 CAN 5717007 NCL 22.59DEU 4078251 SAU 5254812 SAU 21.60KAZ 3598107 GBR 4865539 MTQ 15.51PAK 3426337 PAK 4242682 ARE 15.51

Bottom Ten NCL 1808 NFK 1428 SUR 0.02TUV 1758 GNQ 1413 NIU 0.02GUF 1634 VUT 1326 ALB 0.01MDV 1067 SPM 1198 GNQ 0.01NRU 1026 SHN 951 AFG 0.01FRO 559 TUV 315 JAM 0.01NFK 369 TKL 169 VNM 0.01MYT 304 MSR 167 MSR 0.01REU 171 NIU 139 GUY 0.00SPM 27 MYT 0 MYT 0.00

data, the authors employ several steps in order to estimate missing data and identify previ-ously unclassified cases (e.g. foreigners classified as “Others” or countries that you not reportan immigrant’s origin) in order to describe the total world population. Their final matrix,entailing only foreign-born people, combines all available information into one matrix of in-ternational bilateral migrant stocks. This stock data is the fullest available data but clearlymight be sensitive to the estimation techniques employed by Parsons et al..

The final version of the database contains 175.7 million international migrants. Table 1displays some basic characteristics. Russia, Mexico, and India are the top three sendingcountries. Each of them has more than 9 million people emigrating. The top three receivingcountries are the United States, Russia, and Germany with more than 9 million immigrants.Interestingly, among the top 10 receivers we also find Saudi Arabia, Ukraine, India, andPakistan. Clearly, small island states are among the least sending and receiving countries.The final two columns of the table provide a first glimpse at the flow between countries.The columns present the ration between immigrants and emigrants for each country. Atthe extreme, for each emigration from Reunion will be about 619 immigrations; or for 100Jamaican emigrants will be one immigrant.

Figure 1 displays some of the information of the 226 by 226 country matrix of bilateralmigrant stocks. The plot displays all migration flows of more than 500,000 people for the2000 census. The plot employs the Fruchterman-Reingold algorithm for placing the vertices.The width of the edges corresponds to the size of each flow. While some of the centralcountries—such as the United States, Germany, Russia, and India are on different continents,the “spokes” of these centers clearly are in close geographic proximity. Russia is sending and

10


Figure 1. Fruchterman-Reingold layout of migration flows of more than500,000 people, 2000.

AUS NZL

CHNHKG

JPN

KOR

TWN

PRKIDN

MYS

PHL

SGP

THA

VNM

KHM

LAO

MMR

BGDIND

LKA

AFG

NPL

PAK

CANUSA

MEX

COL

PER

VEN

BOL

ECU

ARG

BRA

CHL

URY

GUY

PRY

SUR

CRI

SLV

GTM

HND

NIC

PAN

DOM

HTI

JAM

PRI

TTO

CUB

AUTBEL

FIN

FRA

DEU

GBR

GRC

IRL

ITA

NLDPRT

ESP

SWE

CHEBIH SCG

ALB

BGR

HRV

CZE

HUN

POL

ROM

SVK

EST

LVA

RUS

ARM

AZE

BLR

GEOKAZ

KGZ

MDA

TJK

TKM

UKR

UZBTUR

BHR

IRN

IRQ

ISR

JOR

KWT

LBN

PSE

OMN

QAT

SAU SYR

AREYEM

MAR

TUN

DZA

EGY ZAF MWIMOZ

TZA

ZMB

ZWE

AGO

COD

UGA

BEN

BFA

BDI

TCD

COG

CIV

ERI

ETH

GHA

GIN

KEN

MLI

NER

NGA

RWA

SEN

SOM

SDN

receiving migrants from its former provinces of the Soviet Union. By far, the largest flowof migrants is from Mexico to the United States. The Arabian Peninsula is drawing a largenumber of its immigrations from the surrounding parts of Asia. Finally, a considerable partof the “large” African migration flows occur within the continent.

11


Dyadic Covariates. Common language and colonial tie are both binary variables with 1denoting the existence of a common language/previous colonial relationship and 0 otherwise.Data on countries’ primary language(s) are obtained from the CIA Factbook (CIA 2004).Data on colonial relationship are obtained from the online data archive from Rose (2004).1 Wecontrol for the effects of proximity in geography by calculating distance using the Haversineformula with data on latitude and longitude of capital cities taken from the world.citiesdatabase maintained as part of the maps package in the R statistical programming package.These are available from http://cran.r-project.org. Distance is calculated in 1000s ofKilometers (logged).

Sender and Receiver Country Characteristics. We first control for the size of popula-tion for both sender and receiver countries of migration. We also include GDP per capita,measured in purchasing power parity (PPP), for both sender and receiver countries to testwhether the common wisdom holds: people leave poor countries and move to rich countries.Data on total population and GDP per capita are both from World Development Indicators.2

We use the annualized polity score which ranges from −10 (for highly authoritarian states) to+10 (for highly democratic regimes) to gauge the effects of domestic institutions in pushingand pulling migration flows.3

Natural disasters refer to “nature-induced cataclysmic events or situations which over-whelm local capacity, often (although not necessarily) resulting in a request for externalassistance.” (Nel & Righarts 2008) The OFDA/CRED International Disaster Database(http://www.em-dat.net) carries comprehensive records of natural disasters in the worldand defines an event as a natural disaster if one or more of the following criteria are met:1) ten or more people reported killed, (2) one hundred people reported affected, (3) it leadsto the declaration of a state of emergency, and/or (4) it leads to calls for international as-sistance.4 We operationalize the natural disaster variable for our empirical analysis as thenumber of years that one country experienced natural disaster(s) in the previous 10 years be-fore 2000. For the civil conflict variable, we use data from the UCDP/PRIO Armed ConflictDataset (Version 4-2007) which defines conflict as “a contested incompatibility that concernsgovernment and/or territory where the use of armed force between two parties, of which atleast one is the government of a state, results in at least 25 battle-related deaths”. (Gleditsch,Wallensteen, , Eriksson, Sollenberg & Strand 2002) We operationalize the civil conflict vari-able as the number of years that one country experienced civil conflict(s) in the previous 10years before 2000.

A Generalized Latent Space Network Model. We use a generalized version of the latentspace model (Generalized-Bilinear-Mixed-Effects Model) from Hoff (2005). The model canbe written as follows:

yi,j = β′dxi,j + β

′sxi + β

′rxj + ai + bj + γi,j + z

′izj,(1)

1The variable name in Rose’s data set is Colony — a binary variable that is unity ifone country ever colonized the other or vice versa.See Rose 2004. Data are available athttp://faculty.haas.berkeley.edu/arose/RecRes.htm.2See World Bank’s World Development Indicator online database. Data accessed on February, 2008.3These data are from http://www.systemicpeace.org/polity/polity4.htm.4We gratefully received a replication data set from Philip Nel and Marjolein Righarts of the Uni-versity of Otago that provides data on natural disasters (Nel & Righarts 2008).

12


where

β′dxi,j = dyadic effects: common language, colonial ties, distance

β′sxi = sender effects: population, GDP per capita, polity, natural disaster, civil conflict

β′rxj = receiver effects: population, GDP per capita, polity, natural disaster, civil conflict

ai = random effect of sender

bj = random effect of receiver

γi,j = reciprocity

z′izj = separate latent positions for sender and receiver

yi,j is the response variable, that is, the stock of immigrants from sender country i to receivercountry j in 2000. In addition to the effects of the dyadic covariates (β

′dxi,j: common language,

colonial ties, and geographic distance), this model setup adds covariates that are specific tosender (β

′sxi) and receiver (β

′rxj) countries of migration, that is, population, GDP per capita,

polity, natural disaster, and civil conflict of both sender and receiver countries.The model includes random effects of sender (ai) and receiver (bj) countries. The

rationale is that in addition to the sender and receiver specific covariates already includedin the model, other variables characterizing sender and receiver countries might also affectmigration. One can easily come up with a list of variables that are potentially important butnot included in our model due to data unavailability, for example, labor market regulations ofboth sender and receiver countries and the influence of far right parties in receiving countries,to name but a few. Moreover, random effects of sender (ai) and receiver (bj) countries canaccount for the heterogeneity among countries as senders and receivers of migration. Somecountries are special even after controlling for all the possible variables that we can name. Forexample, Mexico is often considered an unusual case — a country sending large amount ofmigration targeting the US that might not only be explained by variables such as geographicproximity, population, GDP per capita, polity, natural disaster, and civil conflict.5

Considering γi,j as the residual error term, yi,j = β′dxi,j + β

′sxi + β

′rxj + ai + bj + γi,j

is a typical regression model setup with random effects. The assumption for such modelis observational independence. However, this assumption is often violated in network databecause of the existence of higher-order dependencies in such data. It is well-know in the socialnetwork literature that second- and third-order dependencies are prevalent in most networkenvironments. Second order dependence refers to what is often described as reciprocity inthe context of directed relationship. This means that we expect yi,j and yj,i to be positivelyco-related. In the study of international relations, strong reciprocity often exists among alarge number of dyads. Previous research on trade shows that imports from country i toj are more likely to go up as the flow of commodities in the opposite direction within thesame dyad increases (Ward & Hoff 2007). Ward, Siverson, and Cao (2007) also reveal strongreciprocity in the context of interstate conflict: if country i initiates a conflict with j, oneexpects j to reciprocate.

With regard to the migration network, we lack empirical evidence from previous re-search on the reciprocal nature of migration flows. We can only speculate that at least for

5We model the random effects as being multivariate normal. In this way, we can estimate theircovariance structure: σ2

a is the variance of the sender random effects and σ2b the variance of the

receiver random effects. Additionally, the covariance between these two components is given by σab.

13


some areas in the world, such as in Europe, we might expect high level of reciprocity inmigration. For example, Germans immigrate to neighboring countries and create connec-tions between the German labor market and those of neighboring countries; this in itselfmight trigger migration in the opposite direction: more French, Austrians, and Italians go toGermany to find jobs. However, whether this is empirically the case and whether this canbe generalized to other parts of the world is a question that our empirical analysis seeks toanswer. The generalized-bilinear-mixed-effects model, therefore, further parameterizes thecovariance of the errors across dyads, that is, the covariance of the errors between γi,j andγj,i, as ρσ2

γ, allowing a specific measure of reciprocity to be estimated by ρ.Similar to second-order dependence, third-order dependence is commonly recognized

in the literature of social network analysis. Aspects of this higher order dependence includetransitivity, balance, and clusterability (Wasserman & Faust 1994).6 A often used examplein the literature that illustrates third-order dependence is that, consider a triad {i, j, k}, ifwe know that i considers j as a friend and j is a friend with k, then the probability that kwill also be a friend with i is likely to be higher than for a random person outside of thistriad, since i and k are at least indirectly connected in the friendship network by virtue oftheir separate linkages to j. In other words, knowing something about the relationships inthe first two dyads in a triad often tells us something about the relations in the third dyad.As we think about the nature of the third-order dependence in some network data sets, aconceptualization of a somehow “unobserved” or latent “social space” where every networkactor is embedded in is very helpful. Thus, for example, the observation of two links, i → jand j → k, suggests that i and k are not too far away from each other in this social space(which is often unobservable), therefore are also likely to have a link between them. Thethird-order dependence is an expression of the underlying probability of a link between twoactors. We don’t observe the complete set of all of these characteristics therefore their relativepositions to each other, but we can infer them from the pattern of dyadic linkages. In otherwords, the “social space” summarizing these unobserved characteristics is another “image” ofthe third-order dependence in network data. If we can somehow map out the latent positionsof each actor in the “social space”, we can then assume that the ties in the network areconditionally independent given these positions. Series of latent models have been recentlydeveloped by Hoff, Raftery, and Handcock (2002) and Hoff (2005) where latent vectors, sayzi and zj, for any two actors i and j are used to locate them in the social space and calculatetheir distance.

The generalized-bilinear-mixed-effects model includes an estimate of the unmeasuredlatent positions of each country in the migration network (zi and zj). These latent positions(zi and zj) index the propensities for country pairs to have similar interaction patterns to-ward other countries. Put in a simple way, if two countries share similar positions in thelatent space, they have a higher probability to interact with each other. In the context oftransnational migration, this means high level of bilateral migration.

Empirical Findings. We model bilateral migration stock in 2000 as a function of connec-tions between countries including common language and colonial tie, geographical distance,

6 Transitivity follows the logic of “a friend of a friend is a friend.” A triad i, j, k is said to be balancedif all pairs of actors relate to one another in an identical fashion, specifically: yi,j × yj,k × yk,i > 0.The idea is that if the relationship between i and j is “positive” then both will relate to anotherunit k identically. Clusterability is a relaxation of the concept of balance. A triad is clusterable if itis balanced or the relations are all negative. The idea is that a clusterable triad can be divided intogroups where the measurements are positive within groups and negative between groups.

14


Table 2. Bayesian estimates for 2000, using equation (1), are the posteriormeans for the estimated quantities. Quantile-based, empirical credible intervalsof 95% are presented.

Posterior Distributions of Estimated Coefficients for Year 2000.

2.5% Mean 97.5%Constant −20.06 −17.18 −14.34

Dyadic Effects: Common Language 0.52 0.59 0.65Colonial Tie 1.89 2.07 2.24Distance −1.11 −1.08 −1.05

Sender Effects: Population 0.59 0.67 0.74GDP per capita −0.01 0.01 0.02Polity 0.02 0.03 0.05Natural Disaster −0.06 −0.01 0.03Civil Conflict −0.03 0.00 0.04

Receiver Effects: Population 0.56 0.73 0.91GDP per capita 0.09 0.12 0.16Polity −0.09 −0.05 −0.01Natural Disaster −0.05 0.05 0.15Civil Conflict −0.16 −0.08 −0.01

Random Effects: Common Sender (σ2a) 0.26 0.32 0.41

Sender-Receiver (σa,b) −0.02 0.11 0.24Common Receiver (σ2

b ) 1.43 1.81 2.25

Dependencies: Reciprocity (ρ) 0.06 0.07 0.09Error Variance (σ2

ε ) 1.28 1.31 1.33Latent Dimensions 1 (σ2

z1) 0.46 0.58 0.73Latent Dimensions 2 (σ2

z2) 0.24 0.32 0.42Latent Dimensions 3 (σ2

z3) 0.24 0.31 0.41

and sender and receiver country characteristics such as polity, population, GDP per capita,natural disaster, and civil conflict. The generalized latent space model we applied also takesinto account sender and receiver country random effects as well as second and third orderdependencies in network data. Table 2 reports our empirical findings. The estimates of thedyadic effects on migration prove the common wisdoms. Sharing a common language andhistorical colonial tie both increase bilateral migration. Also notice these two dyadic vari-ables are binary. Therefore, the predicted logged difference between a pair of countries thatshare a common language and a country pair that does not, all else equal, is about 0.6. Andthe effect of previous colonial relationship is almost three times larger than that of commonlanguage: the predicted logged difference between a pair of countries with a colonial tie anda country pair without it, all else equal, is about 2.1. Finally, a negative estimate at −1.08illustrates the importance of proximity in geography for migration as the distance betweencountries increases, their bilateral migration decreases.

In terms of sender and receiver country characteristics, we found that the total popu-lation size of both sending and receiving countries has the most important effect on bilateralmigration stock: the higher the total population of a country, the more migration it sent

15


and received. This finding is consistent with Lewer and Van den Berg’s recent research of agravity model of immigration based on data from 16 OECD countries for 1991-2000 (Lewer& Van den Berg 2008). The gravity model of trade specifies bilateral trade as a positive func-tion of the attractive “mass” of two economies (and a negative function of distance betweenthem). Our finding here indicates that in the world of immigration, the attractive masses arethe population size of the sender and receiver countries of migration.

GDP per capita of the migration-sending country does not have a significant effect onmigration. This does not support the common wisdom that poverty is often the reason whypeople leave their home country in search of a better life. GDP per capita of the receivingcountry (a “pull” factor of migration), on the other hand, has a significant and positive effecton migration. This means that high living standards (proxied by GDP per capita) attractimmigrants.

Turning to our main theory, the polity of the sending country has a a significant andpositive effect on migration: the more democratic a country is, the more migration it sends,all else equal. Additionally, there is a significant but negative effect of receiving country’spolity on migration. These findings provide strong validation for our central argument.

We find no effect of natural disaster of both sender and receiver countries on migration.Finally, countries with civil conflicts do not send out more immigrants than countries withno or fewer incidents of civil conflicts. However, a significant and negative effect of receivercountry’s civil conflict record indicates that people are less likely to move to countries withcivil conflicts.

The generalized latent space model includes random sender and receiver country effectsto account for heterogeneity among countries. Here we find that both the variance of thereceiver random effects (σ2

b ) and the variance of the sender random effects (σ2a) are important

and non-negligible. However, the variance of the receiver random effects, σ2b = 1.81, is much

important than the variance of the sender random effects, σ2a = 0.32. This means that after

taking into account of the effects of the covariates, the 148 countries included in our analysisdisplay much higher level of heterogeneity as the receiving countries of migration than as thesending countries of migration. Table 3 lists the top 10 and bottom 10 countries ranked bymean random effects for sender and receiver countries respectively. The mean random receivercountry effects are much more widely distributed than the mean random sender countryeffects (they are both centered around zero). The maximum mean random receiver countryeffect (Jordan, 3.56) is much higher than the maximum mean random sender country effect(Lebanon, 1.40). The minimum mean random receiver country effect (Azerbaijan, −3.54) ismuch lower than the minimum mean random sender country effect (Mongolia, −1.57 ).

Also notice that none of the top 10 countries ranked by mean random receiver coun-try effects, that is, Jordan (3.56), Nepal (2.88), Philippines (2.62), Pakistan (2.56), Kuwait(2.44), Zaire (2.41), Burkina Faso (2.38), Guinea (2.33), Uzbekistan (2.31), and Ghana (2.05),are OECD countries that previous analysis has been focusing on as migration destinations(Leblang, Fitzgerald & Teets 2007, Lewer & Van den Berg 2008). This suggests that outsidethe usual (often narrow) focus on developed countries as migration destination countries,there are other dynamics that drive international migration that cannot be easily explainedby current immigration theorizing, at least not by the variables we included in the model sofar. In other words, even after controlling for the effects of geographical proximity (distance),cultural similarity and historical legacies (common language and colonial tie), and a battery ofvariables describing the political institutions, level of economic development, demographies,natural disasters, and civil conflicts of sender and receiver countries of migration, there are

16


Table 3. Top 10 and Bottom 10 countries ranked by mean random senderand mean random receiver country effects.

Sender Mean Random Receiver Mean RandomCountry Sender Effect Country Receiver EffectLebanon 1.40 Jordan 3.56Guyana 1.20 Nepal 2.88

Cape Verde 1.05 Philippines 2.62Morocco 1.05 Pakistan 2.56

Russia 1.04 Kuwait 2.44Mexico 0.96 Zaire 2.41Congo 0.92 Burkina Faso 2.38

New Zealand 0.87 Guinea 2.33Eritrea 0.85 Uzbekistan 2.31

Kazakhstan 0.78 Ghana 2.05...

......

...Brazil −0.83 Saudi Arabia −2.04

Cote d’Ivoire −0.89 China −2.06Slovenia −0.94 Iran −2.13

Saudi Arabia −0.95 Bangladesh −2.29Namibia −0.99 Cen. Afri. Rep. −2.33Uganda −1.04 Laos −2.59

Pap. New Guinea −1.33 Japan −2.70Botswana −1.38 Rep. of Korea −3.26

Oman −1.53 Sudan −3.34Mongolia −1.57 Azerbaijan −3.54

still factors that we don’t know (yet) that make some developing countries attractive as thedestinations of migration.7

Table 2 also reveals a significant, positive, but weak reciprocity parameter, ρ = 0.07.This finding proves the existence of the second-order dependencies in the migration data.Moreover, we choose a three-dimensional latent space (k = 3) to capture the third-order de-pendencies in the data. The variances of these three dimensions (σ2

z1, σ2z2, σ2

z3) are importantand we collapse these three dimensions to two (for better graphical presentation) to displaythe latent space in Figure 2. Recall that in the latent space, the closer the two countries, thehigher the chance of interactions between them. In the context of transnational migration,this means higher level of migration. A closer look at Figure 2 reveals some clustering amongcountries in the latent space. In the top-left corner of the space we find most of the Europeancountries such as Iceland (ICE), Britain (UKG), France (FRN), Denmark (DEN), Luxem-bourg (LUX), and Portugal (POR). Notice that the model has already controlled for theeffect of geography. This means that there are factors that beyond proximity in geography

7A similar story can be told for the top 10 countries ranked by mean random sender country effects:Lebanon (1.40), Guyana (1.20), Cape Verde (1.05), Morocco (1.05), Russia (1.04), Mexico (0.96),Congo (0.92), New Zealand (0.87), Eritrea (0.85), and Kazakhstan (0.78). After controlling for theeffects of dyadic and sender and receiver specific covariates, there are still factors that we don’tknow that make these countries stand out as the main senders of migration.

17


Figure 2. Latent positions for migration, 2000.

AUL

NEW

FJI

PNG

SOLCHN

JPN

ROK

MON

INS MAL

PHISIN

THI

CAM

LAO

BNG

IND

SRIBHU

NEP

PAK

CAN

USA

MEX

COLPERVEN

BOL

ECU

ARG

BRACHLURU

GUY

PAR

SUR

BLZ

COS

SAL

GUAHON

NIC

PANBHM

DOM

HAI

JAMTRI

AUS

BEL

DEN

FIN

FRN

GMYUKG

GRC

IREITA

LUX

NTH

POR

SPNSWDSWZ

ICE

NOR

MAC

ALB

BUL

CRO

CYP

CZRHUN

POL

SLV

EST

LIT

RUS

ARM AZEBLRGRG

KZK

KYRMLDTAJ

TKM

UKR

UZB

TUR

BAH

IRN

ISR

JORKUWLEB OMASAUSYR

YEM

MORTUNALG

EGY

BOT

SAF

LES

NAM

SWA

MAWMZM

TAZZAM

ZIMANG

DRC

MAS

UGA

BENBFO

BUI

CAOCAP

CENCHA

COM

CONCDIDJI

EQGERI

ETH

GAB

GAMGHAGUI

GNB

KEN

MLI

MAA

NIR

NIG

RWA

SENSIE

SUD

TOG

(and other variables included in the model) to make these countries mutually “attractive”as migration destinations. Similar clustering can be found on the right-hand side of thespace where we find most of the African countries such as Kenya (KEN), Lesotho (LES),and Ethiopia (ETH). Unlike random country effects (ai and bj) that reveal heterogeneityamong countries as sender and receiver of migration at the monadic level, positions in thelatent space give us more information for dyadic interactions among countries. Different fromGreen, Kim, and Yoon’s approach of adding dyadic fixed effects to control for dyadic levelheterogeneity (Green, Kim & Yoon 2001), the latent space model solves the same problem(more efficiently) by locating countries in a latent space and use the cross-product to capturethe same country-pair heterogeneity (z

′izj).

The latent model is much more difficult to estimate compared to a simple OLS re-gression. Our rationale to use the latent model, despite the extra time and computing powerrequired for a Bayesian Monte Carlo Markov Chain (MCMC), is that it controls for higherorder dependencies in the data and heterogeneity at sender and receiver and dyadic levels.One way to test whether the latent model outperforms an OLS regression is to look at thetheir predictive power. Figure 3 plots the predicted values of bilateral migration on the actualvalues, separately for an OLS and a latent space model. A 45 degree line is drawn for eachplot on which perfect predictions shall fall. The latent model seems to provide better predic-tion as the points on the plot are closer to the 45 degree line. We also calculate the mean root

squared error (RMSE) for both OLS and latent models (RMSE =√

1n

∑(yi − yi)2, where yi

is the predicted value, yi the actual value, and n is the number of observations). The latentmodel has a much smaller mean root squared errors (1.35) than the OLS model (1.96).

18


Figure 3. A comparison of in-sample prediction between simple OLS andlatent space model.

0 5 10 15

−5

05

1015

OLS prediction

actual value of migration

pred

icte

d va

lue

of m

igra

tion

0 5 10 15

−5

05

1015

Latent model prediction

actual value of migration

pred

icte

d va

lue

of m

igra

tion

Finally, as robustness checks, we also run the same latent space model for differentsamples of countries. First, we exclude all the former Soviet republics, the rationale is that theSoviet Union had considerable internal migration (some forced) and when it split up, manyof the people involved were recorded as migrants as the result of changing country definitionrather than of any movement that they chose to undertake (Parsons, Skeldon, Walmsley& Winters 2005). We report the findings based on all countries excluding 15 former Sovietrepublics in Table 4 under the header “Exclude Soviet Rep.”. Moreover, we also run the modelseparately for non-OECD countries and OECD countries. What we find in the analysis basedon non-former-Soviet-republic countries and on all non-OECD countries almost exactly matchwhat we find in the previous section when we include all the countries with available data:all else equal, migrants tend to move to countries that are rich, less democratic, and withoutcivil conflict. However, the findings from the analysis based on the OECD countries suggestthat receiver country’s polity and civil conflict do not have significant effect on migration.This is largely due to the lack of variation in polity and civil conflict as receiver countryvariables: 25 out of 30 current OECD countries have a polity score of 10 and the averageamong these 30 countries are 9.31; meanwhile, 26 out 30 current OECD countries have noincident of civil conflict and the only outlier is Turkey that experienced 10 incidents of civilconflicts in the previous 10 years before 2000. Finally, we observe high level of reciprocityfor within OECD migration as indicated by a reciprocity parameter ρ estimated around 0.7.This detection of much higher level of second dependence in the migration data of the OECDcountries suggests that within the sphere of developed countries, the flow of immigrants fromone country to another in itself induces the increase in migration in the opposition direction.

19


Conclusion

This study is motivated by the pressing need for social scientists to accurately modeland explain migration flows. As a central political topic in many countries, as well as a topicthat strongly intersects with other crucial issues (security, nationalism, trade), migration isdeserving of all the attention that social scientists give it. However, the literature thus far hasbeen hampered by a narrow focus on either receiving conditions in the advanced industrialeconomies, or sending conditions in the developing world. Given that this dichotomy hardlygives a complete picture of world migration (witness the recent riots in South Africa againstmigrants from Zimbabwe and Mozambique, among other fellow developing countries), ouranalysis attempted to explain bilateral flows among all categories of countries.

Our findings both confirm and cast doubt on the conventional wisdom, dependingon the particular variable in question. Regarding our confirmation of existing scholarshipon bilateral variables, we found that linguistic/colonial ties and a lower geographic distanceboth play a strong causal role in increasing the volume of migration between two givencountries. The linguistic/colonial variable seems to validate theories based on social networksas independent drivers of migration, and if geographic distance can be taken as a proxy forcultural distance, as suggested by the Deutsche Bank (2003) study, then social networks doindeed appear to be an important independent variable in several respects.

Regarding receiving country variables, the findings were fairly predictable: migrantstend to be attracted to countries that are larger, have more wealth, and have less civilconflict. However, we also found evidence for our counter-intuitive theory that democraciessend more migrants and receive less (though this was indeterminate for a sample of OECDcountries only): the impact of polity (political institutions) is thus an important dimensionof migration. Although many would predict that democracies attract migrants because of thepolitical refuge and freedoms that they can offer, our model actually showed that democraciesreceive less migrants, all other things being equal. This would seem to validate at least someof the five causal mechanisms detailed above, which should be disaggregated and subjectedto further analysis in future studies. To our knowledge, no researchers other than Mirilovic(2007) have offered either a theoretical explanation or an empirical test for why democraciesmight attract significantly less immigrants than non-democracies. This would seem to be afruitful area for political science research.

The most surprising result of our study came from the variables affecting the vol-ume of migrants leaving sending countries. First, three seemingly important variables hadno significant effect on pushing migrants to leave their countries of origin. Wealth, natu-ral disasters and civil conflict were apparently not significant here. We could explain thelack of significance for the latter two variables, natural disasters and civil conflict, by point-ing out that a large portion (if not a majority) of the victims of these phenomena do notleave their country of origin, but instead become “internally displaced persons” (Messina &Lahav 2005). The fact that poverty does not push migrants to leave, however, begs moredetailed explanation. Upon first glance, it might appear improbable that poverty does notcause migration. However, the work of de Haas (2005) shows us that the poverty-emigrationrelationship is much more complex than some might suspect. In fact, migrants do not tend tocome from the poorest of poor countries. Instead, migrants tend to come from middle-incomecountries who are experiencing mid-to-late stages of development. The reason for this is thatdevelopment causes a rise in economic expectations, which is often not matched in the shortterm by the domestic labor market. Development increases such trends as education andurbanization, which give potential migrants the skills and resources to leave their countries

20


of origin, at least until such time as their native country can satisfy their expectations. Thislittle-studied phenomenon is particularly fascinating considering that it casts doubt upon theattempts of many rich-world governments to reduce migration flows through development as-sistance to sending countries (e.g. the European Union’s plan to “combat” illegal migrationthrough development aid). In fact, the implication of our finding is that such efforts may becounter-productive (if the goal is reducing migration), at least in the short term.

21

Migration and its Sources November 7, 2008Table

4.

Usi

ng

differ

entsa

mpl

esof

countr

ies.

Excl

ude

Sovie

tR

ep.

Non-O

EC

DC

ountr

ies

OEC

DC

ountr

ies

2.5%

Mea

n97

.5%

2.5%

Mea

n97

.5%

2.5%

Mea

n97

.5%

Con

stan

t−1

9.53

−16.

50−1

3.56

−19.

02−1

5.90

−12.

73−2

8.89

−19.

55−8

.70

Dya

dic

Effec

ts:

Com

mon

Lan

guag

e0.

370.

450.

520.

390.

460.

54−0

.28

0.15

0.58

Col

onia

lT

ie1.

771.

982.

171.

211.

832.

510.

901.

542.

15D

ista

nce

−1.1

2−1

.09

−1.0

5−1

.20

−1.1

6−1

.13

−1.1

2−0

.94

−0.7

4

Sen

der

Effec

ts:

Pop

ula

tion

0.58

0.66

0.74

0.52

0.61

0.71

0.54

0.69

0.84

GD

Pper

capit

a−0

.01

0.01

0.03

−0.0

6−0

.03

0.01

−0.0

4−0

.01

0.02

Pol

ity

0.02

0.03

0.05

0.02

0.03

0.05

0.07

0.17

0.28

Nat

ura

lD

isas

ter

−0.0

6−0

.01

0.03

−0.1

1−0

.05

0.00

0.00

0.07

0.15

Civ

ilC

onflic

t−0

.03

0.00

0.04

−0.0

20.

020.

05−0

.13

−0.0

30.

07

Rec

eive

rE

ffec

ts:

Pop

ula

tion

0.49

0.68

0.87

0.55

0.75

0.95

0.07

0.58

1.02

GD

Pper

capit

a0.

100.

140.

170.

040.

120.

190.

050.

140.

22Pol

ity

−0.1

0−0

.06

−0.0

1−0

.09

−0.0

5−0

.01

−0.0

80.

190.

49N

atura

lD

isas

ter

−0.0

40.

070.

17−0

.12

0.01

0.13

−0.0

20.

170.

36C

ivil

Con

flic

t−0

.16

−0.0

8−0

.01

−0.1

7−0

.08

−0.0

1−0

.16

0.12

0.41

Ran

dom

Effec

ts:

Com

mon

Sen

der

(σ2 a)

0.24

0.31

0.39

0.30

0.39

0.50

0.07

0.16

0.31

Sen

der

-Rec

eive

r(σ

a,b)

−0.0

10.

110.

25−0

.07

0.09

0.25

0.15

0.36

0.73

Com

mon

Rec

eive

r(σ

2 b)

1.36

1.74

2.19

1.44

1.87

2.42

0.91

1.64

2.78

Dep

enden

cies

:R

ecip

roci

ty(ρ

)0.

050.

070.

090.

000.

030.

050.

600.

670.

74E

rror

Var

iance

(σ2 ε)

1.29

1.32

1.35

1.15

1.18

1.21

0.84

0.99

1.19

Lat

ent

Dim

ensi

ons

1(σ

2 z1)

0.52

0.65

0.83

0.26

0.47

0.75

0.09

0.27

0.51

Lat

ent

Dim

ensi

ons

2(σ

2 z2)

0.21

0.29

0.40

0.23

0.31

0.42

0.07

0.26

0.53

Lat

ent

Dim

ensi

ons

3(σ

2 z3)

0.21

0.30

0.40

0.24

0.46

0.75

0.06

0.25

0.52

22


References

Baubock, Rainer. 1994. Transnational Citizenship: Membership Rights In International Mi-gration. Aldershot: Edward Elgar.

Bhagwati, Jagdish. 2003. “Borders Beyond Control.” Foreign Affairs 82(1):98–104.Borjas, George J. 1990. Friends or Strangers : The Impact of Immigrants on the U.S. Econ-

omy. Basic Books.Breunig, Christian & Adam Luedtke. 2008. “What Motivates the Gatekeepers? Explaining

Governing Party Preferences on Immigration.” Governance 21 (1):123–146.CIA. 2004. The World Factbook. Central Intelligence Agency, accessed: 03/22/2006:

http://www.cia.gov/cia/publications/factbook/fields/2122.html.Citrin, Jack, Donald Green, Christopher Muste & Cara Wong. 1997. “Public Opinion Toward

Immigration Reform: The Role of Economic Motivations.” Journal of Politics 59:858–881.

Cornelius, Wayne & Marc Rosenblum. 2005. “Immigration and Politics.” Annual Review ofPolitical Science pp. 99–119.

de Haas, Hein. 2005. International Migration, Remittances and Development: Myths andFact. Global Migration Perspectives 30 Global Commission on International Migrationhttp://www.gcim.org: .

Deutsche Bank Research. 2003. International Migration: Who, Where and Why? In TheMigration Reader: Exploring Politics and Policies, ed. Anthony Messina & Gallya Lahav.Boulder: Lynne Reinner Publishers pp. 15–23.

Economist. 2008a. “Briefing Migration: A Turning Tide?” Economist pp. 30–32.Economist. 2008b. “A Special Report on Globalization.” pp. 13–18.Fetzer, Joel. 2000. Public Attitudes Toward Immigration in the United States, France, and

Germany. Cambrdige: Cambridge University Press.Freeman, John R. 2002. “Competing Commitments: Technocracy and Democracy in the

Design of Monetary Institutions.” International Organization 56(4):889–910.Gleditsch, Nils Peter, Peter Wallensteen, , Mikael Eriksson, Margareta Sollenberg & Havard

Strand. 2002. “Armed Conflict 1946-2001: A New Dataset.” Journal of Peace Research39(5):615–637.

Green, Donald P., Soo Yeon Kim & David H. Yoon. 2001. “Dirty Pool.” International Orga-nization 55:441–468.

Guiraudon, Virginie. 2000. “European Integration and Migration Policy: Vertical Policy-making as Venue Shopping.” Journal of Common Market Studies 38 (2):251–271.

Hansen, Randall. 2000. Citizenship and immigration in Post-War Britain. Oxford: OxfordUniversity Press.

Hansen, Randall. 2002. “Globalization, Embedded Realism and Path Dependence: the OtherImmigrants to Europe.” Comparative Political Studies 35(3):259–283.

Harris, J. R. & Michael Todaro. 1970. “Migration, Unemployment and Development: A Two-Sector Analysis.” American Economic Review 60:126–142.

Hoff, Peter D. 2005. “Bilinear Mixed Effects Models for Dyadic Data.” Journal of the Amer-ican Statistical Association 100:286–295.

Hoff, Peter D., Adrian E. Raftery & Mark S. Handcock. 2002. “Latent Space Approaches toSocial Network Analysis.” Journal of the American Statistical Association 97(469):1090–1098.

Hollifield, James. 1992. Immigrants, Markets, and States: The Political Economy of PostwarEurope. Cambridge: Harvard University Press.

23


Huntington, Samuel P. 1968. Political Order in Changing Societies. New Haven: Yale Uni-versity Press.

Jacobson, David. 1996. Rights Across Borders: Immigration and the Decline of Citizenship.Baltimore: Johns Hopkins University Press.

Joppke, Christian. 1999. Immigration and the Nation-State: The United States, Germany,and Great Britain. Oxford: Oxford University Press.

Kessler, Alan & Gary Freeman. 2005. “Public Opinion in the EU on Immigration from Outsidethe Community.” Journal of Common Market Studies 43 (4)(4):825–850.

Lahav, Gallya. 2004. “Public Opinion Toward Immigration in the European Union: Does ItMatter?” Comparative Political Studies 37 (10)(10):1151–1183.

Leblang, David. 2007. Diaspora Bonds and Cross-Border Capital. Unpublished manuscriptDepartment of Political Science, University of Colorado.

Leblang, David, Jennifer Fitzgerald & Jessica Teets. 2007. Defying the Law of Gravity: ThePolitical Economy of International Migration. Working paper Department of PoliticalScience, University of Colorado.

Lewer, J.J. & H. Van den Berg. 2008. “A gravity model of immigration.” Economics Letters99(1):164–167.

Lewis, W. Arthur. 1954. “Economic Development with Unlimited Supplies of Labor.” Manch-ester School of Economic and Social Studies 22:139–191.

Massey, Douglas, Joaquin Arango, Graeme Hugo, Ali Kouaouci, Adela Pellegrino & J. Ed-ward Taylor. 1993. “Theories of International Migration: A Review and Appraisal.”Population and Development Review 19(3):431–466.

Massey, Douglas, Rafael Alarcon, Jorge Durand & Humberto Gonzalez. 1990. Return toAztlan: The Social Process of International Migration from Western Mexico. Berkeley:University of California Press.

Messina, Anthony M. & Gallya Lahav. 2005. The Migration Reader: Exploring Politics AndPolicies. Lynne Rienner Publishers.

Mirilovic, Nikola. 2007. Regime Type and Immigration. In 48th International Studies Asso-ciation Convention.

Nel, Philip & Marjolein Righarts. 2008. “Natural Disasters and the Risk of Violent CivilConflict.” International Studies Quarterly 52(1):159–185.

Parsons, Christopher R., Ronald Skeldon, Terrie L. Walmsley & L. Alan Winters. 2005.Quantifying the International Bilateral Movements of Migrants. Working Paper T13Development Research Centre on Migration, Globalisation and Poverty.

Popfam. 2008. Forced Migration Learning Module. Technical report TheHarriet and Robert Heilbrunn Department of Population and Fam-ily Health, Columbia University Mailman School of Public Healthhttp://www.columbia.edu/itc/hs/pubhealth/modules/forcedMigration/definitions.html:.

Portes, Alejandro. 1997. “Immigration Theory for a New Century: Some Problems and Op-portunities.” International Migration Review 31 (4):799–825.

Ranis, Gustav & J.C.H. Fei. 1961. “A Theory of Economic Development.” American Eco-nomic Review 51:533–565.

Rose, Andrew K. 2004. “Do We Really Know That the WTO Increases Trade?” The Amer-ican Economic Review 94(1):98–114.

Sassen, Saskia. 1996. Losing Control?: Sovereignty in an Age of Globalization. New York:Columbia University Press.

24


Sears, David & Jack Citrin. 1982. Tax Revolt: Something for Nothing in California. Cam-bridge: Harvard University Press.

Simon, Rita & Susan Alexander. 1993. The Ambivalent Welcome: Print Media, Public Opin-ion and Immigration. Praeger Publishers.

Sjaastad, Larry A. 1962. “The Costs and Returns of Human Migration.” Journal of PoliticalEconomy 70(5):80–93.

Soysal, Yasemin. 1994. The Limits of Citizenship: Migrants and Postnational Membership inEurope. Chicago: University of Chicago Press.

Todaro, Michael P. 1976. Internal Migration in Developing Countries. Geneva: InternationalLabor Office.

Todaro, Michael P. & Lydia Maruszko. 1987. “Illegal Migration and US Immigration Reform:A Conceptual Framework.” Population and Development Review 13:104–114.

United Nations. 2002. International Migration Report 2002. In The migration reader: explor-ing politics and policies, ed. Anthony M. Messina & Gallya Lahav. Croom Helm.

United Nations. 2006. UNHCR: 2006 Global Trends. United Nations Publications.United Nations. Department of Economic and Social Affairs. Population Division. 2003. In-

ternational Migration Report 2002. United Nations Publications.Wallerstein, Immanuel. 1974. The Modern World System. New York: Academic Press.Ward, Michael D. & Peter D. Hoff. 2007. “Persistent Patterns of International Commerce.”

Journal of Peace Research 44(2):157–175.Ward, Michael D., Randolph M. Siverson & Xun Cao. 2007. “Disputes, Democracies, and

Dependencies: A Re-examination of the Kantian Peace.” American Journal of PoliticalScience 51(3):583–601.

Wasserman, Stanley & Katherine Faust. 1994. Social Network Analysis: Methods and Appli-cations. Cambridge: Cambridge University Press.

25

international migration and its political sources: a network analysis

Documents