compositional and temporal dynamics of international migration in the eu/efta: a new metric for...

Compositional and TemporalDynamics of International Migrationin the EU/EFTA: A New Metric forAssessing Countries’ Immigration andIntegration Policies1

Jack DeWaardCenter for Demography and Ecology, University of Wisconsin-Madison

In this article, I derive estimates of migrants’ expected years of resi-dence in each of 31 countries in the European Union and EuropeanFree Trade Association each year from 2002 to 2007. A country-levelmeasure summarizing the temporal dynamics of international migra-tion, I compare my results against the often used compositional measureof the percent foreign born, and show that these two measures reflectdifferent population processes. I likewise demonstrate the utility of themeasure derived here as a tool to assess countries’ integration policieson long-term residence per their scores in the Migrant Integration Pol-icy Index. Key theoretical and policy implications are discussed.

INTRODUCTION

Country-level patterns of international migration can be described inmany ways. Often, these descriptions attend to the size of migration flows,

1The author is supported by NICHD Training Grant T32-HD07014 and center grant

R24-HD047873 to the Center for Demography and Ecology at the University of Wiscon-sin-Madison, and is grateful for suggestions provided by Theodore P. Gerber, KatherineJ. Curtis, Jenna Nobles, Mara Loveman, Timothy Smeeding, Alberto Palloni, James

Montgomery, Mariano Sana, Kyle Crowder, Lincoln Quillian, Marek Kupiszewski, fouranonymous reviewers and the editor of the International Migration Review, Ellen PercyKraly. The author is likewise grateful for the support of James Raymer, Mary M. Kritz,

and Douglas T. Gurak. An early version of this article was presented at the annual meet-ing of the American Sociological Association on August 20, 2012, the Demography andEcology Seminar at the University of Wisconsin-Madison on July 3, 2012, and the annual

meeting of the Population Association of America on May 3, 2012.

© 2013 by the Center for Migration Studies of New York. All rights reserved.DOI: 10.1111/imre.12023

IMR Volume 47 Number 2 (Summer 2013):249–295 249

what I term the compositional dynamics of migration. Yet, internationalmigration patterns can likewise be envisioned as “the spacing of moves intime” (Roseman, 1971:596). To date, few efforts have sought to modeland measure the temporal dynamics of international migration at the levelof place. Drawing on theories of social disorganization and intergroupcontact, I suggest that the temporal stability of migrants in receivingcountries is a key benchmark for successful immigration and integrationpolicies. To date, however, analysts and policy-makers lack a tool withwhich to assess whether countries’ immigration and integration – forexample, around long-term residence – translate into aggregate migrationflows which are more (or perhaps less) temporally stable – that is, longterm.

The model and measure developed in the current article representsuch a tool. Using what I refer to as a multiregional “bridge” model, whichis an extension of standard multiregional (Rogers, 1975, 1995) or multi-state (Schoen, 1988) models, I estimate migrants’ expected years ofresidence in each of 31 countries in the European Union and EuropeanFree Trade Association (EU/EFTA) each year from 2002 to 2007. A con-ditional period life expectancy at birth (Palloni, 2001), this measure sum-marizes the average number of years lived by migrants in each receivingcountry given period migration and mortality schedules at each age. Afterdetailing these estimates and their substantive interpretation, I comparethem to the often used compositional measure of the percent foreign bornto examine the degree of empirical support for distinguishing the tempo-ral and compositional dynamics of migration. I conclude by consideringthe utility of the measure derived as a tool to assess countries’ integrationpolicies on long-term residence per their scores in the Migrant IntegrationPolicy Index (MIPEX) (Geddes et al., 2005; Huddleston et al., 2011;Niessen et al., 2007).

THEORETICAL AND POLICY FRAMEWORKS

Compositional and Temporal Dynamics of Place-Based Migration

Place-based research on country-level patterns of international migrationoften focuses on the “volume, origin, and internal composition of immi-gration” (Czaika and de Haas, 2011:5), what I call here the compositionaldynamics of migration. In empirical research, this often takes the form ofdescribing the size of flows over the short- and long run using migration

250 INTERNATIONAL MIGRATION REVIEW

rates and measures of foreign-born stocks, respectively (DeWaard andRaymer, 2012). The compositional dynamics of migration are routinelyinvoked in research on both the causes and consequences of migration.For example, Quillian (1995:586) suggested that “the numerical size” ofthe foreign-born population in receiving countries emboldens perceptionsof ethnic threat among native-born persons. Labeled the “visibility-dis-crimination” thesis (Beggs, Villemez, and Arnold, 1997:65; Blalock, 1957,1967), the size of the foreign-born population is envisioned as a “stimulusobject” for threat perceptions (Allport, 1954:165), which, in turn, shapeexclusionary attitudes and behaviors (Pettigrew, Wagner, and Christ,2010; Scheepers, Gijsberts, and Coenders, 2002; Schlueter and Scheepers,2010; Schlueter, Schmidt, and Wagner, 2008; Schneider, 2008; Semyo-nov, Raijman, and Gorodzeisky, 2006; Wagner et al., 2008).

Of course, as McGinnis (1968:712) noted some time ago, sincemigration, “in its most skeletal form, is just motion through…space, itsanalysis clearly must include a temporal component.” To date, place-basedresearch on the temporal dynamics of international migration and theimplications for migrants’ social exclusion in receiving countries is sorelylacking.2 This is not for a lack of theoretical and policy motivations, but,rather, due to the methodological challenges of operationalizing thesedynamics at the level of place. Research on the temporal character ofmigration flows has a rich history, originating in McGinnis and Pilger’s(1963) research on the axiom of cumulative inertia. This is the idea thatan individual’s time of residence in a locale lowers the probability of emi-gration, thus contributing to a reduction in population turnover at thelevel of place (Herting, Grusky, and Van Rompaey, 1997; Land, 1969).While research on the axiom of cumulative inertia conceptualizes the tem-poral dynamics of migration as an individual-level process (Land, 1969;McGinnis and Pilger, 1963; McGinnis, 1968; Morrison, 1967; Myers,McGinnis, and Masnick, 1967; Roseman, 1971; Toney, 1976), there areimportant theoretical and policy reasons for understanding these dynamicsas a property of place.

Concerning the former, social disorganization theory posits thatgroups’ “residential stability” in a place, envisioned as one of three

2I mean social exclusion in the broadest sense to include both macro- and micro-leveldynamics. For a discussion of the former, see Huddleston et al. (2011). For discussion ofkey micro-dynamics, see recent papers on anti-foreigner sentiment (Pettigrew, Wagner,

and Christ, 2010) and immigrants’ labor market outcomes (Adsera and Chiswick, 2007).

TEMPORAL DYNAMICS OF INTERNATIONAL MIGRATION: A NEW METRIC 251

structural factors along with socioeconomic conditions and group size, is a“temporal process” which shapes the “social fabric” of communities andplaces more generally (Sampson and Groves, 1989:780; see also Alexan-der, 2005; Bursik, 1988; Crutchfield, Geerken, and Gove, 1982; Samp-son, 1984; Shaw and McKay, 1942; Sutherland, 1939). While group sizemarks its visibility in a place, the temporal stability of a group in a locale– what Quillian (2003:243) refers to as a group’s “persistent exposure”3 –influences the potential for recognition and familiarity between residents.This potential for interpersonal contact is what ultimately informs keymeso- and micro-level processes whereby residents supervise their mem-berships, build and maintain friendship networks, and promote participa-tion in local activities and associations (Bursik, 1988; Crutchfield,Geerken, and Gove, 1982; Sampson and Morenoff, 2006; Sampson andSharkey, 2008; Simcha-Fagan and Schwartz, 1986; Wilson, 1987, 1991).

A similar logic is also found in intergroup contact theory (Cook,1962; Pettigrew, 1998), although the role of structural conditions is muchless explicit. While intergroup contact is viewed as a property of actors, itis equally a function of place. Cook (1962:74–75), for example, coinedthe term “acquaintance potential,” by which he means the “opportunityprovided by the situation for the participants to get to know and under-stand one another.” Although the link to place is less apparent than insocial disorganization theory, it remains that Cook (Ibid.) is invoking adynamic more contextual than the mere presence or absence of interper-sonal ties. The object of his analysis is something beyond the individual,specifically the “contact situation.” This distinction, though subtle, is the-oretically warranted and highly informative. There is a qualitative differ-ence between interpersonal contacts – that is, ties which exist or fail tobetween individuals representing different groups – and “the potential forextensive and repeated contacts” (Pettigrew, 1998:76). The former is aproperty of actors; the latter is a property of situations and, arguably,places. In the current article, depending on the confluence of internationalmigration patterns (in addition to internal migration, fertility, and mortal-ity), group membership in a place is continuously changing; thus, so isthe potential for intergroup contact. It is this potential for intergroup

3This notion of exposure is from Massey and Denton’s (1985:287, emphasis mine) workon residential segregation, where exposure is not defined merely with respect to individu-als, but in terms of “the degree of potential contact” between groups in places. Ultimately,

the micro-dynamics of residential segregation flow from this potentiality.


contact which ultimately shapes whether and how interpersonal tiesdevelop between individuals, a key micro-process involved in the issue ofimmigrant integration in receiving countries.4

The primary theoretical argument of this article is that the insightsof social disorganization theory and intergroup contact theory can bescaled-up in an effort to study the temporal dynamics of internationalmigration in receiving countries. While the local community lies at thecenter of social disorganization theory, I understand receiving countries ina similar way, as national communities with a real interest in the “residen-tial stability” of their populations (Sampson and Groves, 1989:780).Social disorganization theory suggests that groups’ temporal stability in alocale promotes community supervision, friendship networks, and localparticipation. Analogously, I suggest that there are strong reasons forviewing the temporal dynamics of international migration as central toissues of “[immigrant] incorporation” at the level of receiving countries(Hollifield, 2000:138).

Temporal Dynamics of Migration in Immigration and IntegrationPolicy

In addition to the compositional dynamics of migration, receiving coun-tries have strong interests in the temporal dynamics of flows — for exam-ple, in managing immigration and integration (Corneilus, Martin, andHollifield, 1994; Geddes, 2000; Parsons and Smeeding, 2006), preservingthe solvency core social welfare institutions (Bale, 2009; Ireland, 2004;Faist and Ette, 2007), and, more recently, addressing the potential after-shocks of the “global economic crisis” (Papademetriou, Sumption, andTerrazas, 2011:2). These interests are most directly seen in the immigra-tion policies of nation-states and in the policy recommendations of supra-national entities, such as the EU. For example, around the issue of familyreunification, a fairly recent directive by the European Council (EC,2003b:16) notes that member states might “require the sponsor to havestayed lawfully in their territory for a period not exceeding 2 years.”Duration of residence is also used as a criterion for defining seasonalmigrants as one who “resides temporarily” in EU countries for the pur-pose of employment (European Commission, 2010:17).

4Indeed, one of the European Commission’s (2005:9) Common Basic Principles (CBPs)

of integration is “[f]requent interaction between immigrants and Member State citizens.”


Of course, nowhere are the appeals to the temporal dynamics ofinternational migration more apparent than countries’ integration policies.For instance, the European Council (EC 2003a:44) suggests that the“main criterion for acquiring the status of long-term resident should bethe duration of residence” in the receiving country. A recent report byHuddleston et al. (2011:20) suggests that long-term residence is animportant aspect of countries’ integration policies, and, in many ways, isthe very “key to successful immigration” policies (European Commission,2008:7; see also Czaika and de Haas, 2011). The MIPEX certainly atteststo this. With support from the European Fund for the Integration ofThird-Country Nationals, the MIPEX is a project by scholars at the Brit-ish Council and the Migration Policy Group to score countries’ integra-tion policies. The first iteration, called the “European Civic Citizenshipand Inclusion Index” (Geddes et al., 2005), combined nearly 100 policyindicators to assess five core integration constructs. These included labormarket inclusion, family reunification, nationality, anti-discrimination andlong-term residence. Regarding long-term residence, countries were scoredon 18 indicators tapping the eligibility, acquisition, security and rightsassociated with long-term resident status.

There are clear reasons why long-term residence, and, by extension,the temporal dynamics of migration, figures so centrally into integrationmetrics like the MIPEX. Long-term residence serves as a guarantee of “asequal treatment as possible with EU citizens,” which encourages immi-grants to “contribute to society whilst maintaining their links with theircountry of origin” (Geddes et al., 2005:15). Although the form andmechanisms vary across countries and individuals, a number of importantmicro-processes are involved. For example, long-term residence is a pre-condition for spatial assimilation (see Alba and Logan, 1993; Massey andDenton, 1985; Quillian, 1996; Alba et al., 1999; Sampson and Morenoff,2006), which affords opportunities for local residents to “physically con-front one another” (Massey and Denton, 1987:806). As “frequent interac-tion between immigrants and Member State citizens is a fundamentalmechanism for integration” (European Commission, 2005:9), long-termresidence can therefore be viewed as a necessary, but insufficient, condi-tion for this important policy objective.

Long-term residence also provides a key point of entry for the acqui-sition of nationality and citizenship (Kastoryano, 2002; Niessen et al.,2007). Brochmann (2002:179) rightly suggests that nationality andcitizenship denote “a classification and a process.” Bracketing the


transnational mechanisms that are involved (Baub€ock, 1998; Jordan andD€uvell, 2003; Smith, 1996; Teitelbaum and Winter, 1998), in the con-text of receiving countries, “merely temporary” migrations tend to pre-clude being “legally admitted and permitted to naturalize” (Castles andMiller, 2003:255; Medrano and Koenig, 2005). As a result, where immi-grants’ political participation is concerned, the often-cited adage thatmigration signifies “voting with the feet” fails to gain any substantialpolitical leverage in the receiving country if these migrations do not resultin temporally “stable groups with trusted representatives and clear-cutinterests” (Jordan and D€uvell, 2003:52).

In the current article, the above sorts of micro-processes are envi-sioned as rooted in national contexts, which are an important source ofinformation, resources, and exchange required for integration (Bauer andGang, 2002; Hollifield, 2000; Ireland, 2004; Morissens, 2006). Integra-tion metrics like the MIPEX are thus viewed as barometers of the policyenvironments of receiving countries. Of course, at the level of receivingcountries, scholars and policy-makers currently lack a systematic metricwith which to assess whether countries’ integration policies on long-termresidence correspond to migration flows which are more temporally stable— that is, long-term or permanent. Importantly, commonly employedcompositional measures of migration, like the percent foreign born, aresilent on this point.

In the next section, I develop a platform to model and measure thetemporal dynamics of international migration in receiving countries. Asintegration policies do not always achieve their stated objectives, one of thecontributions of this research is to provide a tool that can be used to assesscountries’ long-term residence policies. In more general terms, the contribu-tion of this research lies in answering calls in the literature on immigrants’social exclusion (broadly conceived) to enhance the “notion of contextthrough the creation of new measures…[which] allow better tapping of acountry’s institutional environment” (Ceobanu and Escandell, 2010:323;see also Bail, 2008; Pachucki, Pendergrass, and Lamont, 2007:338).

DATA AND METHODS

Below, I detail the data and model used to generate estimates of the tem-poral dynamics of international migration, defined as migrants’ expectedyears of residence, in each of 31 countries in the EU/EFTA each year from2002 to 2007.


Harmonized Estimates of Country-to-Country Migration Flows

Data requirements for the model developed in this article include esti-mates of age-specific, country-to-country migration flows. Despite the factthat publicly available data of this sort are provided by statistical agenciessuch as Eurostat, with “high standards of data collection and stan-dardization of definitions to make comparability across countries reliable”(Semyonov, Raijman, and Gorodzeisky, 2006:434), it remains that thesedata are not cross-nationally comparable due to a range of problems withhow countries differentially collect, process, and report migration statistics(Bilsborrow et al., 1997; DeWaard, Kim, and Raymer, 2012; Kupiszewskaand Nowok, 2008; Poulain, Perrin, and Singleton, 2006). For example,countries may use one or more types of data collection systems, includingpopulation registers, censuses, border surveys, and other administrative sta-tistics. Countries likewise differ on how long migrants must reside in thereceiving country for the transition to be officially counted. Timing crite-ria can range from the absence of any criterion to one of permanence, withthree-, six-, and 12-month variants in between. In theory, migration reportsshould reflect a single timing criterion across countries – for example, the12-month long-term criterion suggested by the United Nations (1998) –however, this is rarely the case in practice (Kupiszewska and Nowok,2008).

As a result of these issues, publicly available data “on internationalmigration flows do not provide a clear idea of the relative scale of move-ments across countries” (Lemaitre, 2005:1). Since the early 1990s, theseissues have catalyzed efforts to harmonized – that is, make consistent indefinition – international migration data (Abel, 2010; de Beer et al.,2010; DeWaard, Kim, and Raymer, 2012; Poulain, 1993, 1999; Raymerand Abel, 2008; Raymer et al., 2010; Raymer, de Beer, and van der Erf,2011; van der Erf and van der Gaag, 2007). Among these efforts, cur-rently the largest and most complete set of harmonized estimates is thatdeveloped by the MIgration MOdeling for Statistical Analysis (MIMOSA)Project (de Beer et al., 2010; Raymer, de Beer, and van der Erf, 2011).5

Benchmarked to a 1-year timing criterion for long-term migration

5The methodology and estimates are available at: http://www.nidi.nl/Pages/NID/24/928.bGFuZz1VSw.html. Harmonized estimates of country-to-country migration flows areavailable by previous/next country of residence. Unfortunately, harmonized estimates

cross-classified by country of birth and/or country of citizenship do not exist.


(United Nations, 1998), the MIMOSA Project produced harmonizedestimates of country-to-country migration flows, disaggregated by age andsex, following an extension of Poulain’s (1993, 1999) optimization proce-dure, and subsequently imputing missing flows using a hierarchical,regression-based approach (de Beer et al., 2010; Raymer, 2008; Raymer,de Beer, and van der Erf, 2011).

In the current article, I use the MIMOSA estimates of age-specificmigration between each and every pair of EU/EFTA countries, as wellas from and to each EU/EFTA country and a residual “rest of theworld” region, each year from 2002 to 2007. The MIMOSA estimatesare provided for each of 18 five-year age groups. Accordingly, the totalnumber of migration transitions analyzed in this article is 110,592.6 Asthe inputs for the model described below are age-specific, country-to-country migration rates, the MIMOSA Project also provides harmonizedage-specific estimates of the total population in EU/EFTA countries.Comparable estimates for the “rest of the world” are taken from theU.S. Census’ International Database. Finally, the model also requiresdata on age-specific mortality rates. These data are taken from Eurostat’sNew Cronos database for EU/EFTA countries, and from global modellife tables provided by the World Health Organization for “the rest ofthe world.”

Modeling and Measuring the Temporal Dynamics of InternationalMigration

The model and measure developed below can be exported to any situationwhere the interest is with the expected duration of residence of groups inplaces by exploiting the age pattern of occurrence–exposure migration andmortality rates. Countries exhibit clear regularities with respect to theirage profiles of international migration. Although four variants are distin-guished in the literature (Raymer and Rogers, 2008), migration often hasthree characteristic peaks at early, working, and retirement ages (Rogersand Castro, 1981). Countries’ age profiles of migration are useful forexamining both the form and prevalence of important life course transi-tions (Elder, Johnson, and Crosnoe, 2003; Mayer, 2009) – for example,retirement and subsequent return migrations (Klinth€all, 2006; Reagan andOlsen, 2000; Rogers and Raymer, 2005). Countries’ age patterns of

6This total includes all instances where i = j.


migration figure centrally in models and measures of the temporaldynamics of migration at the level of place because migrants will begin toaccrue time in receiving countries depending on the age(s) at whichmigrations occur. These age profiles of migration can ultimately be trans-lated into expectations of remaining life to be lived in receiving countries(DeWaard and Raymer, 2012; Palloni, 2001; Rogers, 1975, 1995;Schoen, 1988).

The model used to estimate migrants’ expected years of residence inreceiving countries is an extension of Rogers’ (1975, 1995) multiregional,or Schoen’s (1988) multistate, model. As these models ultimately generateequivalent results (DeWaard and Raymer, 2012), hereafter, I simply usethe term multiregional to refer to these. The intuition of standard multire-gional models is to provide an accounting framework for tracking the age-specific migration and mortality transitions of a hypothetical, or synthetic,birth cohort from a sending country of interest, for example, country i.At each age, the model tracks the number of person-years lived by thiscohort in each receiving country, which, when divided through by the sizeof the hypothetical birth cohort, produces a conditional life expectancy atbirth. This “waiting time” of migration (Palloni, 2001:265), e

ij0 , can be

viewed as a measure of the temporal dynamics of international migrationat the country level and summarizes the “average number of years lived in[country] j beyond age [zero] by an individual residing in [country] i ” atexact age zero – that is, at birth in a hypothetical cohort (Willekens andRogers, 1978:40).

eij0 ¼ T

ij0

l i0ð1Þ

Where Tij0 is the number of person-years lived in receiving country j

above age zero by those residing in country i at exact age zero, and l i0 isthe size of the hypothetical birth cohort in country i. To connect themeasure in (1) to the commonly used measure of a life expectancy, sum-ming over all receiving countries (j = 1,2,…,k), including for i = j, resultsin total life expectancy at birth for country i, ei�0 . In contrast to the condi-tional life expectancy in (1), the measure in (2) is termed an unconditionallife expectancy at birth (Palloni, 2001).


e i�0 ¼X

j

Tij0

l i0ð2Þ

Depending on the set of countries used to condition life expectancyat birth, two key summary measures can be derived. The first taps coun-tries’ “holding power” (Herting, Grusky, and Van Rompaey, 1997:268)and is a “retention expectancy” which summarizes the average number ofyears that a person can be expected to live in their country of [hypotheti-cal] birth, e ii0 (DeWaard and Raymer, 2012:572). The second measureexpresses the average number of years that one can expect to live outsideof their country of [hypothetical] birth, e i � i

0 – that is, in any and allreceiving countries (j = 1,2,…,k for i 6¼ j).7

e ii0 ¼ T ii0

l i0ð3Þ

e i � i0 ¼

Xj

Tij0

l i0; i 6¼ j ð4Þ

To put the above four measures into context, consider the exampleof persons born in Slovakia in 2002. According to DeWaard and Raymer(2012:583–585), persons born in Slovakia in 2002 could expect to live atotal of e i�0 = 73.3 years, on average. Of this total, e ii0 = 49.4 years couldbe expected to be lived in Slovakia, while e i � i

0 = 23.9 years could beexpected to be lived outside of Slovakia. Years lived outside of Slovakiacan be further conditioned on one (or more) receiving countries of inter-est, e

ij0 . For example, of the 23.9 years lived outside of Slovakia, 7.8 years

could be expected to be lived outside of the EU/EFTA system in the “restof the world,” 5.1 years in the Czech Republic, 4.1 years in Germany,etc.

The measures in (1) and (4) are useful for summarizing the tempo-ral dynamics of international migration from the perspective of sendingcountries. Often times, however, scholars and policy-makers requiresnapshots of international migration from the vantage points of receiving

7As I am describing the experiences of a hypothetical birth cohort, I use [hypothetical] birthsynonymously with at exact age zero.


countries – for example, to gauge the potential labor market pressuresassociated with immigration (Bauer and Zimmermann, 1999). Recently,DeWaard and Raymer (2012) showed that there is nothing inherent inthe mechanics of multiregional models prohibiting one from adopting theperspective of receiving countries. For each receiving country j, theyshowed that migrants’ expected years of residence is simply an average ofthe quantities in (1) above, for i 6¼ j. As such, e

� jj0 expresses the “average

number of years that a person is expected to live in receiving country jabove age zero given prior residence outside of j ” – that is, in any countrybut j, at exact age zero (DeWaard and Raymer, 2012:570).

e� jj0 ¼ T

� jj0

l� j0

¼Pk

i¼1 Tij0Pk

i¼1 li0

; i 6¼ j

ð5Þ

Assuming l i0 is constant across all sending countries (i = 1,2,…,k fori 6¼ j), the expression in (5) simplifies to:

e� jj0 ¼ 1

k

Xk

i¼1

Tij0

l i0; i 6¼ j ð6Þ

The main take-away message from (6) is that multiregional modelsare easily modified to summarize the temporal dynamics of internationalmigration from the vantage point of receiving countries. However, there isa substantial problem with this measure. The measure in (6) is the ratioof actual person-years lived by migrants in receiving country j to the setof all persons who potentially could have migrated to j. This is problem-atic because most persons do not migrate (Castles and Miller, 2003).What is needed, then, is a more refined accounting system which is capa-ble of tracking the number of persons who actually migrate to receivingcountry j above age zero. Standard multiregional models are not equippedto handle this. To understand why, consider the mechanics of multire-gional models, a schematic of which is provided in Figure I.

The state-space in Figure I is comprised of four states – countries i,j, k, and death d. At each age (note, age subscripts are omitted), persons


may migrate to another country, remain in their country of residence, ordie. For persons in country i, l i, these transition probabilities are denotedin Figure I as qij and qik, qii, and qid, respectively. These transition prob-abilities are calculated from occurrence–exposure migration and mortalityrates, derived from the MIMOSA and other data sources detailed above.While there exist well-known procedures for converting transition ratesinto transition probabilities (Schoen, 1988:70–76), I use the conversionbelow, which incorporates the assumption of linearity (Palloni, 2001:269),but allows for transitions to occur at each single year of age within theage interval x to x+n.8 At each single year of age a, the transition proba-bilities governing country-to-country migrations and death are expressedin the matrix, Q(a).

Country i

Death Country j

Country k

Figure I. Multiregional Model of Transitions Between Three Countries and to Death

Source: Rogers (1975, 1995) and Schoen (1988).

8Given my data, n = 5.


QðaÞ ¼

nM iix

P ix

nMijx

P ix

. . . nM ikx

P ix

nDidx

P ix

nMjix

Pjx

nMjjx

Pjx

. . . nMjkx

Pjx

nDjdx

Pjx

..

. ... . .

. ... ..

.

nM kix

Pkx

nMkjx

Pkx

. . . nM kkx

Pkx

nDkdx

Pkx

0 0 . . . 0 1

26666666664

37777777775

xþn�a

; x � a < x þ n ð7Þ

For country i, nM ijx is the number of persons migrating from coun-

try i to j in the age interval x to x+n, while nDidx is the number of persons

in i who died. The denominator, Pix , represents (by assumption) those at

risk of migration and death at exact age x, the start of the age interval.

Pix ¼ nN

ix þ 0:5 nD

idx þ

Xk

j¼1 nMijx

h i; i 6¼ j ð8Þ

Where nNix is the observed population in country i in the age inter-

val x to x+n.The rationale for raising the matrix in (7) to power x+n-a is to

account for heterogeneity within the age interval x to x+n, because personsage x are exposed to the risk of migration and death governing the ageinterval for longer than persons age a (for x<a<x+n). I illustrate a way tothink about this in Figure II using a probability tree.

Due to reasons of space, Figure II maps the transition probabilitiesfrom country i to j between age x and the ages of x + 1, x + 2, and x + 3,using the hypothetical example of three countries, i, j, and k. Between theages of x and x + 1, the probability of transition is qij. Between ages x andx + 2, we must also consider the indirect pathways by which persons reachcountry j; thus, the transition probability is qii qij + qij q jj + qik qkj. Thefurther away we go (in years) from age x, the more involved these probabili-ties become. This complexity becomes even more pronounced as morecountries are added to the state-space. In an effort to have transition proba-bilities governing the age interval x to x+n which express the direct and indi-rect pathways by which migrants reach their destinations, the Q(a) matrixaccomplishes this for each single year of age. Taking an element-by-elementweighted average of these transition probabilities within each age interval,with weights proportional to the number of single years between age a and


Figure II. Probability Tree of Age-Specific Transitions Over Three Ages

Probability of transition from country i to country j between ages x and:x + 1: q ij

x + 2: q ii q ij + q ij q jj + q ik q kj

x + 3: qii q ii q ij + q ii q ij q jj + qii q ik q kj + q ij q ji q ij + qij q jj q jj + qij q jk q kj + q ik qki q ij + qik qkj q jj + q ik qkk qkj


age x+n,9 the matrix, Q(x), thus expresses these probabilities for the ageinterval as a whole.

QðxÞ ¼Xxþn�1

a¼xQðaÞwðaÞ

¼

nqiix nqijx . . . nqikx nqidx

nqjix nqjjx . . . nqjkx nqidx

..

. ... . .

. ... ..

.

nqkix nqkjx . . . nqkkx nqkdx0 0 . . . 0 1

266666664

377777775

ð9Þ

The k + 1 by k + 1 matrix, Q(x), contains the transition probabili-ties via migration and to death in the age interval x to x+n. Note that thelast row specifies that transitions from death are not possible – that is,death is an absorbing state.

To understand the population dynamics of standard multiregionalmodels, two further elements are required.

lðxÞ ¼ ½l ix l jx . . . l kx l dx � ð10Þ

lðx þ nÞ ¼ lðxÞQðxÞ ð11ÞThe 1 by k + 1 row vector, l(x), contains the number of persons in

each country at exact age x, that is, at the start of the age interval x tox+n. At exact age zero, these elements represent the size of the hypotheti-cal birth cohort in each country, with the last element fixed at zerobecause persons have yet to die.10 Equation (11) gives the populationdynamics. The resulting 1 by k + 1 row vector, l(x + n), is just the l(x)vector updated to reflect transitions as governed by the probabilities in thecorresponding Q(x) matrix. These vectors are used to generate the countof person-years, that is, the numerator in equation (6), required to esti-mate migrants’ expected years of residence.

9In using single years of age, I permit persons to migrate no more than once within each

single-year age interval. While migration is clearly a continuous process, using single yearsof age is appropriate given the MIMOSA data, which, despite only being available for5-year age intervals, are nonetheless provided for single calendar years.10Cohorts are assumed the same size to minimize any effects of differential fertility (Herting,

Grusky, and Van Rompaey, 1997).


The problem with this model is that there is no mechanism forpartitioning individuals who actually transition to the receiving country ofinterest. For example, suppose we are interested in persons who transition toreceiving country j at some age during their lifetime. Provided that these per-sons remain in country j, tracking them is no issue. However, if some or allof these persons emigrate from country j, they are lost to the analyst. Onceoutside of country j, standard multiregional models have no way to separateformer migrants to j from non-migrants to j. To track and, importantly, notdouble-count persons who have migrated to country j at one or more priorages, we need a way to siphon off and separately track these individuals. Todo this, I display what I call a multiregional “bridge” model in Figure III.

In the multiregional “bridge” model, upon transitioning to receivingcountry j,11 persons enter a parallel system of states and flows, comprisedexclusively of persons who have transitioned to j at one or more prior ages.As such, receiving country j can be considered a pseudo-absorbing state, link-ing two parallel state-spaces. To write these dynamics mathematically, onemust respecify (and reorganize) theQ(x) matrices and l(x) vectors as follows.

QðxÞ ¼

1 0 . . . 0 0 0 . . . 0 0qkdx qkkx . . . qkix qkjx 0 . . . 0 0

..

. ... . .

. ... ..

.0 q ..

. ...

qidx qikx . . . qiix qijx 0 . . . 0 00 0 . . . 0 qjjx qjix . . . qjkx qjdx0 0 . . . 0 qijx qiix . . . qikx qidx... ..

.q 0 ..

. ... . .

. ... ..

.

0 0 . . . 0 qkjx qkix . . . qkkx qkdx0 0 . . . 0 0 0 . . . 0 1

2666666666666664

3777777777777775

ð12Þ

lðxÞ ¼ l dx l kx . . . l ix l j0

x l i0

x . . . l k0

x l d0

x

h ið13Þ

The dimensions of the l(x) vectors are now 1 by 2k+2�1. Excludingcountry j, the receiving country of interest, all remaining states are

11Like any multiregional model used to estimate conditional life expectancies, the modelmust be constructed and run separately for each receiving country, one at a time (Palloni,2001:265–266). To be consistent with my previous examples, I continue to treat country jas the receiving country of interest.


included twice in the model, where l ix denotes the number of persons incountry i at the start of the age interval x to x+n who have never transi-tioned to j at any prior age, and l i

0x represents those in i who have transi-

tioned to j at one or more prior ages. The Q(x) matrices contain theassociated transition probabilities. Note how receiving country j serves to“bridge” the upper-left and lower-right quadrants of the matrix. As I donot have data at the level of detail that is required (indeed, such data donot exist), I assume that corresponding pairs of transition probabilities inthe upper-left and lower-right quadrants in (12) are equal. The populationdynamics are then written the same way as before in (11).

This model permits tracking the number of persons who actually,versus potentially, migrate to receiving country j above age zero, j l

� j0 .12

Figure III. Multiregional “Bridge” Model of Transitions Between Three Countries and

to Death

Source: DeWaard (in press) Notes: Probabilities of not migrating are omitted to save space, but are nonethelessimplied.

12Compare this quantity to the denominator in equation (5).


This is achieved by adding up any and all persons who transitioned to jby the close of the transition process, age ∞ in equation (14).

j l� j0 ¼ l d

01 þ

Xk

i¼1l i

01 ð14Þ

This quantity is the missing link required to estimate migrants’ expectedyears of residence in receiving country j, e

� jj0 , the quantity of interest in

this article.

e� jj0 ¼ T

� jj0

j l� j0

ð15Þ

RESULTS

Migrants’ Expected Time of Residence in EU/EFTA Countries

In standard multiregional models, migrants’ expected years of residence inreceiving countries is purely a function of countries’ age profiles of migra-tion. As a result, countries with pronounced migration peaks, especially atrelatively young ages, will have higher times of residence than countrieslacking these peaks. To see this correspondence between countries’ ageprofiles of migration and migrants’ expected years of residence derivedfrom standard multiregional models, I display in Figure IV the age pro-files of emigration and immigration for EU/EFTA countries in 2007.13

Figure IV confirms what is already well-known about migrationtrends from and to EU/EFTA countries. For instance, Germany, Spain,France, Italy, and the United Kingdom have relatively pronounced agepatterns of immigration. With the exception of Spain, these countries alsohave relatively low emigration propensities at each age. As such, from astandard multiregional model, one should expect that these five countrieswill have the highest times of residence among all EU/EFTA countriesbecause they accumulate more migrants than they lose to emigration. Like

13As model inputs are country-to-country transition probabilities, the emigration andimmigration probabilities in Figure IV are averaged over all receiving and sending coun-

tries, respectively.


0.01

2n

Pro

babi

lity

AT

0.01

2n

Pro

babi

lity

BE

0.01

2n

Pro

babi

lity

CH

0.01

2n

Pro

babi

lity

CY

0.01

2n

Pro

babi

lity

CZ

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80AgeAge Age

0.01

2n

Pro

babi

lity

BG

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80AgeAge Age Age Age

0.01

2n

Pro

babi

lity

DE

0.01

2on

Pro

babi

lity

DK

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80AgeAge Age

0.01

2 EE

0.01

2 ES0.

000

0.01

2M

ean

Tran

sitio

n P

roba

bilit

y

0 20 40 60 80Age

EE

0.00

00.

012

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n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

ES0.

000

0 20 40 60 80Age

0.00

0

0 20 40 60 80Age

0.01

2 FI

0.01

2 FR

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

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lity

0 20 40 60 80Age

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0.00

00.

012

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n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

FR

0.00

0

0 20 40 60 80Age

0.00

0

0 20 40 60 80Age

0.01

2 GR

0.01

2 HU

0.01

2 IE

0.01

2 IS

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

GR

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

HU

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

IE

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

IS

0.00

0

0 20 40 60 80Age

0.00

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0 20 40 60 80Age

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0.01

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tion

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LU

0.01

2Tr

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tion

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LV

0.01

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tion

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MT

0.01

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NL0.

012

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0 20 40 60 80Age

0.00

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0 20 40 60 80Age

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0 20 40 60 80Age

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tion

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0 20 40 60 80Age

noitargimmInoitargimE

0.01

2 PL

0.01

2 PT

0.01

2 RO

0.01

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0.01

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babi

lity

SI

0.01

2ro

babi

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tion

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0 20 40 60 80Age

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0.00

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012

Mea

n Tr

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tion

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0 20 40 60 80Age

PT

0.00

00.

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n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

RO

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

SE

0.00

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tion

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0 20 40 60 80Age

SI

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

SK

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.01

2 UK

0.00

00.

012

Mea

n Tr

ansi

tion

Pro

babi

lity

0 20 40 60 80Age

UK

0.0

0 20 40 60 80Age

Figure IV. Mean Age-Specific Probabilities of Emigration and Immigration for EU/

EFTA Countries, 2007

Note: Transition probabilities denote average risk of migration in age interval x to x+5.


any life expectancy, these accumulations at young ages have more bearingon life expectancy than at older ages because younger persons have moreyears of life yet to be lived. Consequently, we should also expect thatcountries with relatively low immigration and high emigration propensi-ties – for example, Cyprus and Luxembourg – will experience very lowtimes of residence.

The age profiles in Figure IV reflect only the direct risks of migra-tion from and to each EU/EFTA country. To glimpse both the direct(i-to-j) and indirect (i-to-k-to-j) risks of migration, I display in Figure Vthe long-run probabilities of emigration and immigration between agezero and age x. These probabilities provide a more stylized view of coun-tries’ age profiles of migration over the [hypothetical] life course.

As standard multiregional models capture the direct and indirectrisks of migration, the age profiles in Figure V dictate that Germany willhave the highest time of residence in 2007, followed by (in this order) theUnited Kingdom, France, Spain, and Italy. After these countries come ahandful of others, including Austria, Belgium, Switzerland, the CzechRepublic, Denmark, the Netherlands, Poland, and Sweden. We also seethat countries such as Cyprus, Estonia, Iceland, Lichtenstein, Luxem-bourg, and Malta will have the lowest times of residence due to lowimmigration and high emigration propensities. Several other comparisonsare noteworthy, as well. Sweden can be expected to have a higher time ofresidence than Poland. While, at their peak, the long-run propensities ofimmigration to Sweden and Poland are each about 0.70 percent, Swedenhas a younger age profile of immigration than Poland, which translatesinto a longer time of residence.

To examine if the intuitions detailed above are ultimately born out,I display in Table 114 two sets of estimates of migrants’ expected years ofresidence in each EU/EFTA country each year from 2002 to 2007. Thefirst set, in Panel A, is derived from a standard multiregional model anduses the set of all persons who potentially could have migrated to eachreceiving country j, l

� j0 , in the denominator. The second set, in Panel B,

is derived from the multiregional “bridge” model and considers only thosewho actually migrated to each receiving country j, j l

� j0 :

From Panel A, taking Austria in 2007 as an example, 0.36 canbe interpreted as the average number of years that persons who are

14A similar set of estimates is provided in Appendix 1 for non-EU27 migrants, rather than

any/all migrants.


0.03

0on

Pro

babi

lity

AT

0.03

0on

Pro

babi

lity

BE

0.03

0on

Pro

babi

lity

CH

0.03

0on

Pro

babi

lity

CY

0.03

0on

Pro

babi

lity

CZ

0.00

0Mea

n Tr

ansi

tion

Pro

b

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

b

0 20 40 60 80Age

0.00

0Mea

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b

0 20 40 60 80Age

0.00

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0 20 40 60 80Age

0.00

0Mea

n Tr

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tion

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b

0 20 40 60 80AgeAge Age

0.03

0on

Pro

babi

lity

BG

0.00

0Mea

n Tr

ansi

tion

Pro

b

0 20 40 60 80AgeAge Age Age Age

0.03

0on

Pro

babi

lity

DE

0.03

0on

Pro

babi

lity

DK

0.00

0Mea

n Tr

ansi

tion

Pro

b

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

b

0 20 40 60 80AgeAge Age

y y y0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

EE

0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

ES

0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

FR

0.00

0Mea

nT

0 20 40 60 80Age

0.00

0Mea

nT

0 20 40 60 80Age

y 0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

FI

0.00

0Mea

nT

0 20 40 60 80Age

0.00

0Mea

nT

0 20 40 60 80Age

y y y y0.03

0M

ean

Tran

sitio

n P

roba

bilit

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Age

GR

0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

HU

0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

IE

0.03

0M

ean

Tran

sitio

n P

roba

bilit

y

Age

IS

0.00

0Mea

nT

0 20 40 60 80Age

0.00

0Mea

nT

0 20 40 60 80Age

0.00

0Mea

nT

0 20 40 60 80Age

0.00

0Mea

nT

0 20 40 60 80Age

0.03

0Tr

ansi

tion

Pro

babi

lity

IT

0.03

0Tr

ansi

tion

Pro

babi

lity

LI

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.00

0Mea

n Tr

ansi

tion

Pro

0 20 40 60 80Age

0.03

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ansi

tion

Pro

babi

lity

LT

0.03

0Tr

ansi

tion

Pro

babi

lity

LU

0.03

0Tr

ansi

tion

Pro

babi

lity

LV

0.03

0Tr

ansi

tion

Pro

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lity

MT

0.03

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ansi

tion

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ansi

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0 20 40 60 80Age

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0 20 40 60 80Age

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0 20 40 60 80Age

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0 20 40 60 80Age

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roba

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PT

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RO

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SE

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bilit

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tion

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tion

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lity

RO

0.00

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ansi

tion

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00.

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ansi

tion

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lity

SI

0.00

00.

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ansi

tion

Pro

babi

lity

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0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

0.0

0 20 40 60 80Age

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0 20 40 60 80Age

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y

UK

0.00

00.

030

Mea

n Tr

ansi

tion

Pro

babi

lity

UK

0.0

0 20 40 60 80Age

Figure V. Mean Long-Run Probabilities of Emigration and Immigration from Age

Zero to Age x for EU/EFTA Countries, 2007

Note: Transition probabilities denote risk of migration between age zero and age x.


TABLE1

MIG

RANTS’EXPECTEDTIM

EOFRESID

ENCE(INYEARS)IN

EU/EFTARECEIV

INGCOUNTRIES,2002–2

007

ReceivingCountry

PanelA:from

multiregionalmodel

PanelB:from

multiregional“bridge”model

2002

2003

2004

2005

2006

2007

2002

2003

2004

2005

2006

2007

AT-Austria

0.24

0.24

0.40

0.36

0.32

0.36

47.84

46.66

46.36

45.11

43.90

43.78

BE-Belgium

0.37

0.38

0.41

0.39

0.42

0.42

49.33

49.12

49.13

48.52

47.72

47.64

BG-Bulgaria

0.11

0.10

0.11

0.12

0.12

0.12

44.09

41.63

42.53

43.82

42.44

41.14

CH-Switzerland

0.33

0.30

0.30

0.32

0.32

0.42

44.89

45.01

44.64

44.67

44.04

43.99

CY-C

yprus

0.02

0.03

0.03

0.03

0.02

0.02

25.43

31.85

28.42

23.01

28.49

22.00

CZ-C

zech

Republic

0.18

0.22

0.19

0.23

0.24

0.42

35.71

33.72

32.84

37.28

34.61

39.08

DE-G

ermany

1.78

1.62

1.71

1.68

1.65

1.80

46.23

46.24

46.11

46.52

46.52

46.07

DK-D

enmark

0.45

0.41

0.43

0.41

0.43

0.45

52.21

52.27

51.96

51.86

51.35

51.59

EE-Eston

ia0.03

0.02

0.03

0.03

0.03

0.03

40.86

41.49

40.91

41.90

41.45

41.64

ES-Spain

0.56

0.75

0.75

0.79

0.86

0.91

50.20

49.48

49.09

48.77

46.60

43.72

FI-Finland

0.21

0.21

0.25

0.26

0.31

0.33

52.09

52.83

52.59

52.43

53.24

53.08

FR-France

0.79

0.77

0.86

0.94

0.97

1.02

50.93

50.43

50.81

50.64

50.10

50.20

GR-G

reece

0.22

0.19

0.21

0.24

0.22

0.26

52.05

50.21

51.70

53.70

52.38

54.12

HU-H

ungary

0.07

0.07

0.14

0.09

0.10

0.11

44.33

43.82

56.55

40.51

42.96

43.06

IE-Ireland

0.09

0.09

0.14

0.19

0.20

0.20

45.54

45.06

45.30

45.79

44.67

44.22

IS-Iceland

0.02

0.02

0.03

0.03

0.04

0.03

48.56

46.36

45.77

45.84

45.43

44.55

IT-Italy

0.63

0.98

0.77

0.70

0.73

0.73

51.77

50.82

50.42

50.33

50.52

50.55

LI-Liechtenstein

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01

47.79

46.62

47.00

47.36

46.02

44.99

LT-Lithuania

0.02

0.02

0.03

0.05

0.05

0.06

37.00

37.72

37.82

39.20

39.98

39.76

LU-Luxembourg

0.05

0.05

0.05

0.05

0.04

0.05

36.24

35.20

35.41

34.30

35.16

33.73

LV-Latvia

0.02

0.02

0.03

0.05

0.04

0.07

39.78

45.21

43.25

45.87

39.33

40.60

MT-M

alta

<0.01

<0.01

<0.01

<0.01

0.01

0.01

45.74

43.03

45.30

45.55

44.03

44.74

NL-N

etherlands

0.27

0.24

0.27

0.25

0.28

0.36

50.87

50.76

50.23

49.38

48.97

49.22

NO-N

orway

0.26

0.20

0.19

0.21

0.24

0.31

54.19

54.25

54.27

54.20

53.93

53.73

PL-Poland

0.21

0.23

0.30

0.30

0.31

0.45

43.19

43.19

42.87

42.50

38.68

39.38

PT-Portugal

0.16

0.17

0.18

0.18

0.17

0.19

49.62

49.35

50.01

49.52

48.74

48.74

RO-Rom

ania

0.13

0.12

0.14

0.16

0.16

0.18

41.01

36.67

37.23

38.24

37.06

36.70

SE-Sweden

0.48

0.46

0.45

0.46

0.51

0.55

53.66

53.91

53.91

53.37

52.83

52.67

SI-Slovenia

0.02

0.01

0.01

0.02

0.02

0.05

44.62

45.42

45.15

46.59

47.34

48.39

SK-Slovakia

0.04

0.04

0.06

0.14

0.19

0.13

37.94

40.16

35.17

35.39

35.50

35.16

UK-U

nited

Kingdom

1.07

1.25

1.47

1.36

1.44

1.40

49.49

50.12

50.70

50.71

50.11

50.81

Mean

0.30

0.32

0.34

0.35

0.35

0.38

45.59

45.44

45.59

45.25

44.65

44.49

St.Deviation

0.38

0.40

0.42

0.40

0.41

0.42

6.48

5.95

6.79

6.76

6.29

6.91

Source:Author’scalculation

s.See

also

DeW

aard

andRaymer

(2012)forcomparablefiguresto

thosein

PanelA.


[hypothetically] born outside of Austria can be expected live in Austriaover their lifetime given the age-specific migration and mortality condi-tions observed in 2007. This estimate is comparable to that by DeWaardand Raymer (2012:569) of 0.72 years; however, it is somewhat smallergiven that these authors incorporated different assumptions in derivingthe transition probabilities from the MIMOSA data. As expected, Ger-many, the United Kingdom, France, Spain, and Italy exhibit the highesttimes of residence each year, ranging from 0.56 years in Spain to1.80 years in Germany in 2002 and 2007, respectively. At the other endof the spectrum are the countries of Cyprus, Estonia, Iceland, Lichten-stein, Luxembourg, and Malta. In each of these six countries migrants’expected years of residence ranged from <0.01 years in Lichtenstein to0.05 years in Luxembourg. Similar results were likewise obtained byDeWaard and Raymer (Ibid.). Finally, considering my earlier observationwith respect to Sweden and Poland in 2007, migrants’ expected years ofresidence in the former was higher than in the latter, albeit by one-tenthof a year. This is due to the fact that Sweden’s age pattern of immigrationin 2007 was younger than Poland’s.

The estimates in Panel A clearly reflect countries’ age profiles ofmigration; however, they make very little intuitive sense. For instance,what does it mean to say that migrants’ expected years of residence inLichtenstein is <0.01 years, or just under four days? The problem withthe estimates in Panel A is that they ultimately reflect the experiences ofall persons at risk of migration. While the age-specific transition probabili-ties used in multiregional models should always reflect the risk of migra-tion, the accounting framework in standard multiregional models isinsufficient for separating out the experiences of actual migrants. Torectify this issue – which, importantly, is one of accounting and not ofexposure – I display in Panel B of Table 1 estimates of migrants’ expectedyears of residence from the multiregional “bridge” model developed inthis article.

Again considering Austria in 2007, 43.78 can be interpreted as theaverage number of years that persons who are [hypothetically] born out-side of Austria can expect to live in Austria over their lifetime assumingthat they migrate to Austria at least once. This expectation is thus duallyconditioned on both starting the transition process at exact age zero out-side of Austria, and subsequently migrating to Austria at one or moreages. Standard multiregional models only account for the former consider-ation. By this metric, the top receiving countries in 2007 were Greece,


Norway, Finland, Sweden, and Denmark. While the United Kingdom,France, and Italy were not far behind, Germany and Spain ranked 13thand 20th among all EU/EFTA countries, respectively. At the other end ofthe spectrum, the ten countries with the lowest times of residence in 2007were new accession countries to the EU. Although two expansions of theEU in 2004 and 2007 generally produced greater mobility during thistime (DeWaard and Raymer, 2012), this had the effect of loweringmigrants’ expected time of residence across all EU/EFTA countries byabout 1 year, from 45.59 years in 2002 to 44.49 years in 2007.

In more substantive terms, it is remarkable that four of the top fivereceiving countries each year during this period were Nordic countries,each with fairly substantial social protection expenditures (Warin and Sav-ton 2008), not to mention a regional migration agreement, the Inter-Nor-dic Migration Agreement, in place during this time (Poulain, Perrin, andSingleton, 2006). In contrast to the estimates shown in Panel A ofTable 1, the estimates in Panel B tell a different story about the relativeattractiveness of receiving countries, most especially Nordic countries(Kaczmarczyk and Ok�olski, 2005, 2008).15 Countries like Italy and Spain,while important migrant destinations themselves, have also been describedin the literature as a “‘back door’ to the rest of Europe” (Calavita,2003:347; see also Castles and Miller, 2003). The estimates in Panel Bseem to support this idea. Also, note the changing position of several newaccession countries to the EU. Slovenia experienced the largest increase(+3.76 years) in migrants’ expected time of residence over the period.This observation is consistent with Andreev’s claim (2007):314) thatSlovenia has increasingly emerged as a “transit country,” and thus animportant destination in patterns of step-migration, typically “towardricher countries” (Trimikliniotis and Demetriou, 2007:46; see also Trian-dafyllidou and Gropas, 2007).

To obtain a visual sense of what is driving the differences betweenthe estimates in Panels A and B, I display in Figure VI estimates from themultiregional “bridge” model of the percentage of persons at risk ofmigrating to each receiving country j who actually migrate to j.

Standard multiregional models do not track and provide the sort ofinformation displayed in Figure VI. This is problematic, as there clearly

15This observation was corroborated in a recent discussion with the Director of theCentral European Forum for Migration and Population Research (CEFMR) on May 4,

2012.


3.9

cent

AT

3.9

cent

BE

3.9

cent

CH

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cent

CY

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CZ

0.0

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2002 2007Year

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2002 2007YearYear Year Year Year

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0

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0

2002 2007Year

UK

0.0

3.9

Per

cent

UK

0

2002 2007Year

Figure VI. Percentage of Persons at Risk of Migrating to Each EU/EFTA Country

Who Actually Migrate, 2002–2007

Notes: Calculated from multiregional “bridge” model, with size of hypothetical birth cohort set to 100,000 in eachsending country, including for “rest of world” and excluding the receiving country of interest.


exists considerable heterogeneity across receiving countries with respect tothose who actually (versus potentially) migrate to these destinations. InFigure VI, less than four percent of persons at risk of migrating to eachEU/EFTA country actually do so. Across all EU/EFTA countries, thisfigure ranges from <0.01 percent in Lichtenstein each year during theperiod to 3.90 percent in Germany in 2007. As the estimates in Table 1clearly show, ensuring that the numerator, T

� jj0 , and the denominator,

j l� j0 , reflect actual migration is consequential for deriving estimates of

migrants’ expected time of residence in receiving countries. This is mainthe contribution of the multiregional “bridge” model developed in thisarticle.

Comparisons with Compositional and Policy Measures

One of the aims of this article is to show that the compositional andtemporal dynamics of international migration capture different aspects ofmigration patterns. Accordingly, each measure should be standard fare indescriptive accounts of international migration (DeWaard and Raymer,2012). To compare one commonly employed compositional measure ofinternational migration against that of migrants’ expected years of resi-dence derived in this article, I provide in Table 216 harmonized esti-mates of the percent foreign born in EU/EFTA countries, estimated aspart of the MIMOSA Project (Kupiszewska, Wi�sniowski, and Bijak,2009).

In examining the figures in Table 2, the largest foreign-born concen-trations were typically in small countries, for example, Lichtenstein, Lux-embourg, and Switzerland. In contrast, those countries with the smallestforeign-born concentrations tended to be new accession countries to theEU, for example, Bulgaria, Romania, and Poland. Over the 2002–2007period, the largest gain (+5.36 percent points) was recorded by Spain. Incontrast, the largest decline (�1.93 percent points) was in Latvia. Alsonoteworthy, the percent foreign born increased in 24 countries over theperiod, while migrants’ expected years of residence (see Table 1, Panel B)declined in 23 of 31 EU/EFTA countries. In any given year, the bivariatecorrelation between the percent foreign born and migrants’ expected timeof residence is negative, and ranges from �0.073 in 2006 to �0.132 in

16A similar set of estimates is provided in Appendix 2 for non-EU27 migrants, rather than

any/all migrants.


2002. The correlation between these two measures, each differenced overthe period, is �0.416, as shown in Figure VII.

Although scholars are well-aware of the limitations of compositionalmeasures of country-level migration patterns, the information displayedin Figure VII is noteworthy because it shows that the compositional andtemporal dynamics of migration are inversely related. Thus, measures ofthe former cannot necessarily be considered as proxies for the latter.Instead, these measures look to capture different population processes.Compositional measures like migration rates and the percent foreign

TABLE 2FOREIGN-BORN POPULATION STOCKS IN EU/EFTA RECEIVING COUNTRIES, 2002–2007

Receiving Country

Percent Foreign-Born

2002 2003 2004 2005 2006 2007

AT-Austria 12.75 13.17 13.57 13.98 14.38 14.80BE-Belgium 10.79 11.12 11.40 11.68 12.07 12.51BG-Bulgaria 0.55 0.55 0.54 0.54 0.53 0.53CH-Switzerland 21.42 21.59 21.75 21.90 22.04 22.19CY-Cyprus 12.81 13.06 13.34 13.63 13.90 14.12CZ-Czech Republic 4.47 4.45 4.43 4.42 4.40 4.38DE-Germany 12.81 12.89 12.71 12.84 12.85 12.84DK-Denmark 7.29 7.47 7.59 7.69 7.84 8.04EE-Estonia 17.98 17.65 17.31 16.97 16.62 16.25ES-Spain 6.20 7.73 8.55 9.90 11.26 11.56FI-Finland 2.79 2.92 3.04 3.18 3.36 3.56FR-France 10.36 10.53 10.70 10.85 10.85 10.85GR-Greece 10.36 10.47 10.58 10.68 10.79 10.89HU-Hungary 2.82 2.77 2.72 2.68 2.63 2.58IE-Ireland 10.37 9.10 8.91 9.88 14.67 13.93IS-Iceland 6.40 6.61 6.72 7.04 8.23 9.88IT-Italy 4.02 4.46 4.91 5.35 5.78 6.21LI-Liechtenstein 38.81 38.43 38.03 37.68 37.31 36.93LT-Lithuania 6.10 6.36 6.89 6.61 6.61 6.56LU-Luxembourg 32.72 33.03 33.35 33.64 33.96 34.27LV-Latvia 18.04 17.24 16.96 16.65 16.34 16.11MT-Malta 5.55 5.69 5.83 5.96 6.06 6.04NL-Netherlands 10.40 10.59 10.65 10.65 10.62 10.59NO-Norway 6.96 7.33 7.59 7.84 8.20 8.65PL-Poland 2.08 2.01 1.94 1.88 1.81 1.75PT-Portugal 6.40 6.53 6.66 6.78 6.89 7.00RO-Romania 0.61 0.62 0.62 0.62 0.63 0.64SE-Sweden 11.54 11.78 12.01 12.21 12.44 12.90SI-Slovenia 10.89 10.92 11.00 10.87 11.10 11.31SK-Slovakia 2.83 3.49 4.14 4.05 4.15 4.31UK-United Kingdom 8.40 8.50 8.83 9.26 9.96 9.97Mean 10.09 10.20 10.32 10.46 10.80 10.91St. Deviation 8.67 8.57 8.50 8.46 8.44 8.38

Source: MIMOSA Project; DeWaard and Raymer (2012).


born summarize the relative size of flows over the short- and long run,respectively. Migrants’ expected years of residence, by contrast, usesinformation on the size of flows to normalize migrants’ accumulation oftime – that is, person-years – in each receiving country, as was illustratedin Figure VI.

If the measure of migrants’ expected years of residence can be arguedto represent a departure from commonly used compositional measures ofmigration, then it is an open question whether descriptive accounts of thetemporal dynamics of flows have any substantive and policy import. Toillustrate that the measure developed here can be profitably applied to pol-icy debates and assessments, in Figure VIII, I compare migrants’ expectedyears of residence and countries’ long-term residence scores from theMIPEX in 2007 (Niessen et al., 2007).17

3

2 CZSI

LT

1

Resi

denc

e0,

2.37

)

LV EE

GR

FIUK

0

ts'A

vera

ge Y

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of R

esid

dard

ized

: μ=

-1.1

0, σ

= 2.

3

BECH

DEDK

PTFRMT

HUNL

NO

SE

IT IE

-1

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igra

nts'

Ave

r(S

tand

ardi

zed

AT

BG

CY

LI

PL

RO

LUSK

IS

-2ES

-3-2 - 32101

Δ Percent Foreign-Born(Standardized: μ = 0.86, σ = 1.52)r = -0.416*

Figure VII. Change in Migrants’ Expected Time of Residence and Foreign-Born

Population Stocks in EU/EFTA Countries, 2002–2007

Source: Author’s calculations; MIMOSA Project.

17The MIPEX does not provide long-term residence scores for Bulgaria, Iceland, Lichten-stein and Romania in 2007. In 2004, the other period available for comparison, long-term

residence scores are only available for 15 countries.


Clearly, if the measure of migrants’ expected years of residence hasany import, then it should exhibit a positive association with countries’long-term residence scores because, from a policy perspective, the eligibil-ity, acquisition, security and rights associated with long-term resident sta-tus should have at least some bearing on the actual amount of time thatmigrants ultimately reside in receiving countries. Indeed, this is exactlywhat we find in Figure VIII. Migrants’ expected time of residence andcountries’ long-term residence scores are positively associated. Above themean MIPEX long-term residence score,18 migrants’ expected years ofresidence averages 48.07 years. In contrast, below the mean long-termresidence score, migrants’ expected years of residence averages 40.23 years.This correspondence is remarkable because it lends empirical support tothe idea that countries’ long-term residence policies have at least somebearing on the temporal dynamics of migration flows. The reverse is also

2

1

ce

FR

DKUKIT

SENOGR

FI

0

Year

sof

Res

iden

ce44

.88,

σ=

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)

ESMT

DE

ATCHHU

IE

BE

PTSI

NL

-1

Mig

rant

s' A

vera

ge Y

ears

of

(Sta

ndar

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= 44

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EE

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LT LV

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-2Mig

ra(S

ta

-3

CY

-4-2 - 2101

MIPEX Long-Term Residence Score (Standardized: μ = 59.63, σ = 9.86)r = 0.556*

Figure VIII. Migrants’ Expected Time of Residence and MIPEX Long-Term Residence

Scores in EU/EFTA Countries, 2007

Sources: Author’s calculations; MIPEX (Niessen et al. 2007). Notes: Long-term residence scores are not provided bythe MIPEX for Bulgaria, Iceland, Lichtenstein and Romania.

18See Niessen et al. (2007:4–6) for a discussion of the unit of measurement. Additional

information is available online at: http://www.mipex.eu/


possible, as long-term residence policies may come to reflect thedemographic realities of migration patterns – that is, policy drift.

While there are a number individual cases which stand out in Fig-ure VIII, the focus of this article is with the overall pattern of this associa-tion, and the utility of the measure of migrants’ expected time ofresidence to assess countries’ long-term residence policies. That said, it isworth noting the presence of several key outliers. For example, despiteabove average long-term residence scores, migrants’ expected years of resi-dence in the Czech Republic, Estonia, Spain, Malta and Poland is belowaverage. Both Germany and France, by contrast, have below average scoreson long-term residence and above average scores on migrants’ expectedyears of residence. These outliers are informative because they demonstratethat integration policies do not always align with the demographic realitiesof international migration flows. Germany, for example, was one ofseveral countries to maintain restrictions on labor migration during thisperiod (Brenke, Yuksel, and Zimmermann, 2009). However, for a varietyof reasons, primarily geographic and economic (Hess et al., 2011;Kaczmarczyk and Ok�olski, 2005, 2008), migration to Germany was muchhigher than what one might expect on the basis of policy considerationsalone (Kahanec, Zaiceva, and Zimmermann, 2009).

Summary measures of the sort developed in this article are usefulpolicy tools. Unfortunately, they also conceal much in the way of hetero-geneity. One of the benefits of the measure developed here is that it canbe disaggregated by country or countries of origin. Exploring these “chan-nelized” migration pathways is a key point of entry for future case studiesaddressing the temporal dynamics of flows (McHugh 1987:187). Considerthe case of migration from Poland to Germany and the UK before andafter Poland’s accession to the EU in 2004. What makes this case trulyunique is the fact that Germany maintained migration restrictions duringand after the 2002–2007 period, including on new EU-accessioncountries. The UK, by contrast, opened its borders immediately in 2004(Kahanec, Zaiceva, and Zimmermann, 2009). As noted by Kaczmarczykand Ok�olski (2008:601), the “increase in the spatial mobility of Poles wasanticipated but the actual scale and dynamic of Polish migration…[was]spectacular and largely unexpected.” In the span of only 1 year, from2003 to 2004, Polish migration to Germany and the UK increased by 33percent and 20 percent, respectively. The actual volume of migrationflows from Poland to both Germany and the UK peaked at 132,292 in2006 and 43,877 in 2007, respectively. Today, migration from Poland to


each of these countries remains pronounced to Germany and to a lesserextent to the UK. While the UK has become a magnet for highly edu-cated and skilled migrants, the distribution is relatively bimodal for Polishmigrants to Germany, a contingent of which are agricultural workers(Hess et al., 2011).

The story of emigration from Poland during the 2002–2007 periodbegan in the mid-1980s.19 With the restructuring of state-owned industryand agriculture after the collapse of the communist regime in 1989,rural-to-urban internal migration to central cities such as Warsaw experi-enced large declines. Historically, transportation subsidies were providedto workers from rural areas to commute to central cities for work. Withthese subsidies eliminated, two things happened. First, while the popula-tions of central cities in Poland declined, these cities experienced masssuburbanization where housing was more affordable. Second, for those inrural areas unable or unwilling to relocate to the suburbs, many of theseindividuals emigrated from Poland. Thus, the first thing to realize is thatthe story of emigration from Poland is largely one of the exodus ofmigrants from rural areas.

Prior to the collapse of the communist regime in Poland, emigrationfrom Poland tended to be permanent. This was due to the fact that emi-grations were highly regulated. As a result, there tended to be high levelsof undocumented emigration. After Poland’s transition, however, itbecame much easier to emigrate, especially short term. As one mightexpect, this gave rise to short-term employment in receiving countries likeGermany. To avoid overstaying a commonly allocated 3-month visa and,at the same, not forfeit employment in receiving countries, jobs wereincreasingly passed down within families. For instance, a mother mightemigrate to Germany for 3 months and find work as an au pair duringthis time. When her visa expired, she would then return to Poland andher daughter would then emigrate to Germany to fill her mother’s posi-tion. At the end of 3 months this position might be passed along to hersister, cousin, or aunt. Thus, even stays of fairly short duration resulted inentrenched migration pathways that continue to persist today.

This exodus of rural emigrants from Poland was out of economicnecessity. Prior to Poland’s accession to the EU, unemployment in Poland

19I am grateful to Marek Kupiszewski, Director of the Central European Forum forMigration and Population Research (CEFMR), for an illuminating conversation on this

issue on May 4, 2012.


reached upwards of 20 percent, and was higher in rural areas. With verylittle in the way of employment prospects, especially for younger workers,an entire industry began to develop around ferrying people to the borderwith Germany and onto key destination cities like Hamburg and Brussels.The primary goal of many Polish migrants was to eventually reach andwork in the UK, en route to finally settling in countries such as Norway

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Figure IX. Polish Migrants’ Expected Time of Residence in EU/EFTA Countries and

Rest of World, 2002–2007


and Sweden. Importantly, these sorts of processes of chain migration areevident in my results. By disaggregating migrant’s expected years ofresidence in receiving countries by country of origin, a picture emergesof the relative attractiveness of receiving countries over the [hypothetical]life course. I provide such a snapshot for migrants from Poland inFigure IX.

As Figure IX shows, migrants from Poland have increasingly spentmore time, on average, in Nordic countries. Norway and Sweden are, byfar, more highly desirable receiving countries, demographically speaking.In 2002, migrants from Poland could be expected to live a total of50.5 years in Norway and 49.7 years in Sweden. By 2007, the corre-sponding figures were 52.5 years and 50.9 years. As the history of Polishmigration above suggests, we also see in Figure IX that Polish migrants’expected years of residence was higher in the UK than in both Belgiumand Germany. Substantively, this makes sense given that the latter twocountries are, in many ways, intermediary destinations en route to theUK and Nordic countries.

Additionally, we know from gravity models of migration that theeconomic and social costs of migration tend to be higher when the send-ing and receiving countries are not geographically contiguous (DeWaard,Kim, and Raymer, 2012; Greenwood 1997; Kim and Cohen 2010).Figure IX suggests that one way that Polish migrants have mitigated thesecosts has been to stay for longer in receiving countries with greater migra-tion risks, perhaps to recoup their investment. While Germany is animportant destination for many Polish migrants, the costs and risks associ-ated with migrating to Germany are far less than those associated withmigration to other receiving countries like the UK. After all, whether dueto “failure” or “innovation” (Klinth€all, 2006:154), if migrations toGermany do not go as planned, one need merely drive to and cross theborder home into Poland. By contrast, receiving countries such as the UKpresent a greater risk of “entrapment” (Quillian, 2003:221). Indeed, asmigration (and return migration) takes at least some minimal level offinancial and social resources to obtain (Andrienko and Guriev 2004), oneof the dynamics potentially captured by the measure of migrants’ expectedyears of residence is that of the inability of migrants’ to return home.

The point of Figure IX is not to offer a definitive approach to exam-ine heterogeneity. Instead, my aims are more modest, insofar as I simplywish to demonstrate that the measure derived in this article is somewhatflexible in addressing heterogeneity issues. Although, in the final section of


this article, I highlight several issues that this measure remains vulnerableto, the measure derived in this article provides a much needed metric forevaluating countries’ integration policies on long-term residence in explic-itly temporal (versus compositional) terms. To date, scholars and policy-makers lack such a tool that is both systematic and cross-nationallycomparable in its construction and scope.

DISCUSSION

In this article, I provided a theoretical and empirical rationale for concep-tualizing and measuring the temporal dynamics of international migrationat the level of receiving countries. Presently, few efforts have modeled andmeasured the temporal dynamics of international migration at the level ofplace due to the methodological challenges involved. However, there areimportant theoretical and policy reasons for doing so (Geddes et al.,2005; Huddleston et al., 2011; McGinnis, 1968; McGinnis and Pilger,1963; Niessen et al., 2007; Sampson and Groves, 1989). Among these,issues of immigrants’ incorporation in receiving countries are bound upwith the temporal stability of migrants in these places (Hollifield, 2000).Long-term residence, while no guarantee, is an important precondition formicro-processes which include the socioeconomic advancement of immi-grants, their political participation and incorporation, and the socioculturalimplications of changing population dynamics, such as anti-foreigner sen-timent (Quillian, 1995; Semyonov, Raijman, and Gorodzeisky, 2006).

The multiregional “bridge” model developed in this article used toestimate migrants’ expected years of residence in EU/EFTA countries isan innovative extension of previous research (DeWaard and Raymer,2012; Rogers, 1975, 1995; Schoen, 1988). My results suggest little in theway of a one-to-one correlation with often used compositional measuresof migration – for example, the percent foreign born. They also suggestthe utility of the measure derived here a potential tool for use in assessingcountries integration policies, particularly those on long-term residence(Geddes et al., 2005; Huddleston et al., 2011; Niessen et al., 2007).While relatively more favorable long-term residence policies are positivelyassociated with higher times of residence among migrants in receivingcountries in the EU/EFTA, there are important outlying cases requiringexamination in future research.

Future research should also consider the following limitations withthe measure of migrants’ expected years of residence. First, the dynamics


of the multiregional “bridge” model follow a strictly first-order Markovprocess. This means that migration and mortality transitions at each ageeffectively lack memory. This is problematic because migration flows are,to some extent, memory dependent, as research on return migration toone’s place of birth suggests (Eldridge, 1965; Ledent, 1981; Long andHansen, 1977; Rogers, 1995; Rogers and Belanger, 1990; Rogers andPhilipov, 1979; Rogers and Raymer, 2005). Second, future research mightalso wish to consider the fact that migrants’ expected years of residencecarries different implications when calculated for specific age intervals –for example, for persons of working age. This issue is difficult to addressbecause, unlike single- or multiple-decrement life tables, multiregionalmodels allow populations to be renewed via immigration. Accordingly,migrants’ expected time of residence is not a decreasing function with age.Finally, assuming harmonized data become available, future efforts shouldconsider modeling and measuring the temporal dynamics of migration atdifferent scales – for example, regions within countries, municipalities,neighborhoods, etc.

Nonetheless, the current article provides a useful for starting pointfor connecting theories on the temporal stability of groups in places withmethodological approaches designed to explicitly test these ideas. There isalso an obvious policy dimension to the work in this article, which is toprovide a systematic and cross-nationally comparable metric to assesswhether countries’ long-term residence policies produce migration flowswhich are more temporally stable, and vice versa.


APPENDIX

1:NON-EU27

MIG

RANTS’

EXPECTED

TIM

EOFRESIDENCE(IN

YEARS)

INEU/EFTARECEIVIN

GCOUNTRIES,

2002

–2007

Receivingcountry

PanelA:from

multiregionalmodel

PanelB:from


2002

2003

2004

2005

2006

2007

2002

2003

2004

2005

2006

2007

AT-Austria

0.39

0.35

1.04

0.75

0.60

0.66

49.54

48.66

48.13

46.67

45.25

45.23

BE-Belgium

0.08

0.06

0.07

0.07

0.09

0.08

46.83

45.64

46.19

46.03

46.24

45.09

BG-Bulgaria

0.01

0.01

0.02

0.02

0.02

0.02

40.19

38.78

38.79

39.30

39.40

37.73

CH-Switzerland

0.49

0.42

0.46

0.47

0.41

0.57

46.13

46.46

46.15

45.54

44.76

45.09

CY-C

yprus

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01

26.11

31.54

28.92

23.59

28.76

22.61

CZ-C

zech

Republic

0.02

0.03

0.03

0.04

0.04

0.07

34.64

34.18

33.12

36.27

33.82

38.15

DE-G

ermany

1.96

1.68

1.79

1.51

1.68

1.68

47.43

47.26

47.24

47.23

47.61

46.93

DK-D

enmark

2.11

1.86

1.99

1.86

1.87

1.92

52.87

52.94

52.69

52.59

52.14

52.52

EE-Eston

ia0.01

0.01

0.01

0.02

0.02

0.01

42.62

43.07

42.61

44.63

43.97

42.20

ES-Spain

0.51

0.66

0.70

0.72

0.78

0.82

51.10

49.76

49.71

49.81

47.66

44.54

FI-Finland

0.26

0.26

0.26

0.21

0.25

0.22

52.41

53.55

52.70

52.61

54.58

54.00

FR-France

0.83

0.70

0.96

1.07

1.07

1.16

51.06

49.72

50.79

50.89

51.02

51.04

GR-G

reece

0.04

0.04

0.05

0.05

0.05

0.05

46.18

46.07

46.47

47.08

47.58

47.55

HU-H

ungary

0.02

0.02

0.06

0.03

0.03

0.03

41.57

42.16

55.98

37.00

39.24

39.95

IE-Ireland

0.04

0.04

0.04

0.05

0.05

0.06

43.98

43.87

43.67

43.76

43.25

42.74

IS-Iceland

0.04

0.03

0.03

0.04

0.05

0.02

48.05

45.44

44.70

44.79

44.52

43.98

IT-Italy

1.33

1.52

0.94

1.16

1.12

1.11

52.61

51.36

51.12

52.07

52.54

52.49

LI-Liechtenstein

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01

50.60

57.75

52.00

50.91

50.64

50.64

LT-Lithuania

0.01

0.01

0.03

0.06

0.05

0.07

41.52

39.65

40.00

42.23

42.27

42.44

LU-Luxembourg

0.02

0.02

0.02

0.02

0.01

0.01

36.03

35.04

35.26

34.13

34.94

33.63

LV-Latvia

0.02

0.02

0.01

0.02

0.02

0.05

48.27

49.31

43.26

49.25

42.95

45.95

MT-M

alta

<0.01

<0.01

<0.01

<0.01

<0.01

<0.01

46.90

43.33

46.29

43.70

44.57

44.50

NL-N

etherlands

0.34

0.25

0.27

0.21

0.24

0.30

52.41

52.34

51.75

50.43

50.18

50.75

NO-N

orway

0.95

0.61

0.49

0.48

0.50

0.53

55.84

56.02

56.36

56.29

56.22

56.33

PL-Poland

0.29

0.31

0.44

0.42

0.45

0.67

45.35

45.40

44.68

44.30

41.52

42.48

PT-Portugal

0.05

0.06

0.07

0.07

0.08

0.09

47.24

48.62

49.08

48.85

49.44

49.42


APPENDIX

1:(C

ONTIN

UED)

Receivingcountry

PanelA:from

multiregionalmodel

PanelB:from


2002

2003

2004

2005

2006

2007

2002

2003

2004

2005

2006

2007

RO-Rom

ania

0.03

0.03

0.04

0.04

0.05

0.05

38.14

35.00

34.87

35.31

34.96

34.16

SE-Sweden

1.54

1.43

1.28

1.24

1.28

1.26

54.34

54.58

54.71

54.15

53.65

53.55

SI-Slovenia

0.05

0.03

0.03

0.05

0.03

0.22

46.79

46.49

45.60

47.78

49.00

49.15

SK-Slovakia

0.02

0.03

0.04

0.10

0.15

0.08

37.86

41.52

35.32

35.82

36.26

35.64

UK-U

nited

Kingdom

1.06

1.18

1.44

1.36

1.38

1.36

50.21

51.22

51.37

51.28

50.73

51.40

Mean

0.45

0.42

0.45

0.43

0.44

0.47

45.96

46.02

45.79

45.30

45.15

44.90

St.Deviation

0.62

0.58

0.59

0.55

0.56

0.57

6.58

6.60

7.01

7.14

6.70

7.24

APPENDIX

2:NON-EU27

FOREIG

N-BORN

POPULATIO

NST

OCKSIN

EU/EFT

ARECEIVIN

GCOUNTRIES,

2002–2007

ReceivingCountry

PercentForeign-Born

2002

2003

2004

2005

2006

2007

AT-Austria

7.57

7.86

8.15

8.43

8.71

9.00

BE-Belgium

4.65

4.95

5.21

5.45

5.77

6.10

BG-Bulgaria

0.38

0.38

0.38

0.38

0.39

0.39

CH-Switzerland

8.87

9.02

9.16

9.30

9.43

9.56

CY-C

yprus

6.84

6.99

7.17

7.35

7.52

7.67

CZ-C

zech

Republic

0.88

0.89

0.90

0.91

0.92

0.93

DE-G

ermany

7.82

7.87

7.71

7.85

7.76

7.72

DK-D

enmark

5.34

5.50

5.61

5.68

5.76

5.84

EE-Estonia

17.26

16.93

16.60

16.26

15.91

15.56

ES-Spain

4.22

5.36

6.01

6.86

7.72

7.53


APPENDIX

2:(C

ONTIN

UED)

ReceivingCountry

PercentForeign-Born

2002

2003

2004

2005

2006

2007

FI-Finland

1.69

1.79

1.88

1.98

2.11

2.24

FR-France

7.05

7.23

7.41

7.58

7.62

7.65

GR-G

reece

7.97

8.02

8.07

8.12

8.17

8.22

HU-H

ungary

0.81

0.80

0.79

0.78

0.77

0.76

IE-Ireland

2.66

2.34

2.34

2.65

3.93

3.97

IS-Iceland

2.98

3.03

3.13

3.24

3.66

3.93

IT-Italy

2.82

3.16

3.51

3.85

4.18

4.51

LI-Liechtenstein

18.88

18.73

18.57

18.44

18.29

18.14

LT-Lithuania

5.61

5.84

6.30

5.98

5.98

5.84

LU-Luxembourg

5.94

6.04

6.14

6.23

6.33

6.42

LV-Latvia

16.49

15.73

15.44

15.13

14.81

14.58

MT-M

alta

2.97

3.04

3.10

3.15

3.18

3.18

NL-N

etherlands

8.19

8.37

8.43

8.45

8.40

8.34

NO-N

orway

4.42

4.40

4.97

5.17

5.41

5.61

PL-Poland

1.38

1.33

1.28

1.23

1.19

1.14

PT-Portugal

4.82

4.93

5.03

5.13

5.23

5.33

RO-Rom

ania

0.43

0.43

0.43

0.44

0.44

0.44

SE-Sweden

6.78

7.02

7.25

7.45

7.66

8.05

SI-Slovenia

9.51

9.55

9.64

9.56

9.73

9.95

SK-Slovakia

0.45

0.70

0.95

0.88

0.90

0.94

UK-U

nited

Kingdom

5.85

5.95

6.27

6.55

6.96

6.96

Mean

5.86

5.94

6.06

6.14

6.29

6.34

St.Deviation

4.74

4.64

4.56

4.50

4.42

4.36

Source:MIM

OSA

Project.


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