Linking internal and international migration in 13 European countries: complementarity or substitution?

ABSTRACT

Internal and international migration form part of the same continuum of population movement but are typically conceptualised, measured and studied separately. Despite early theoretical attempts at conceptualising internal and international migration jointly, existing evidence remains partial and fragmented, reflecting a diversity of traditions in migration research. To address this gap in knowledge, this paper takes a step towards integration by comparing the triggers, constraints and resources that shape internal and international migration decisions at the micro-level. To accomplish this, we analyse retrospective migration histories from a 13-country cross-national dataset (the Survey of Health, Ageing and Retirement in Europe, n = 201,061 person-years from 6,112 individuals) using multinomial random-effect logistic regression models that account for duration dependence. The results show that internal and international migration are linked to the same life-course events, although economic-related transitions are more strongly associated with international than internal migration. We also find that the same resources (e.g. education) and constraints (e.g. homeownership) shape internal and international migration decisions. Altogether, our findings suggest that there is an opportunity for greater theoretical cross-fertilisation between internal and international migration.

KEYWORDS:

• Complementarity and substitution
• Europe
• internal migration
• international migration
• Survey of Healt Ageing and Retirement in Europe (SHARE)

Acknowledgements

This paper uses data from SHARE Wave 3 (DOI: 10.6103/SHARE.w3.700) see Börsch-Supan et al. (2013) for methodological details. The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909, SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982) and Horizon 2020 (SHARE-DEV3: GA N°676536, SERISS: GA N°654221) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org). This paper also uses data from the generated Job Episodes Panel based on SHARE Waves 3 (DOI: 10.6103/SHARE.w3.700) see Brugiavini et al. (2019) for methodological details.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 For example, the initial sample of the HILDA Survey at baseline comprised individuals who resided in private households in Australia in 2001. If individuals subsequently move within Australia (say, from Melbourne to Sydney), then they are followed and re-interviewed. However, if individuals move outside of Australia (say, from Australia to New Zealand), the survey stops tracking them. Furthermore, there is no reliable variable within the study indicating that individuals relocated overseas, compared to simply being lost to follow up through other forms of non-contact that do not involve international relocation. Hence, it is not possible to use these data to compare the profiles (or indeed the outcomes) of those individuals who moved internally and those who moved overseas. Further, the subsequent residential careers of the individuals who moved overseas would remain completely untraceable. This situation applies, to a large degree, to all of the major household panel surveys in the developed world.

2 For example, Dutch linked administrative data contain detailed information on the residential addresses of all individuals who reside within the Netherlands, sometimes precise enough to determine the specific day in which an internal relocation took place and the specific street where the person started and ended the move. However, the situation would be very different if a person relocates internationally (say, crossing the border to Belgium). First, the Dutch administrative data would be unable to identify that the person is now living in Belgium—all that could be inferred is that the person no longer resides in the Netherlands. Naturally, the data would not track any subsequent domestic or international moves undertaken by this hypothetical person—except, of course, if the person ever returned to the Netherlands. Similarly, these administrative data would contain no information on the migration careers prior to entering the country of individuals who were not born in the Netherlands, but who migrated there at some point in their lives.

3 This is a three-step process that consists of (i) creating a base person-year dataset containing all individuals interviewed, (ii) assembling an event dataset containing information drawn from the life history files, and (iii) merging the two datasets based on individuals’ age and unique identifier (for details, see Brugiavini et al. 2019)