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Abstract
Debates have persisted about the character of the East–West population flows that followed the EU expansions. Some of the discussions surround the extent to which the mobility has been temporal and hence how likely these migrants are to settle permanently or to stay for long periods in host countries. This paper enhances the understanding of such issues through examining survey data on 700 Polish recent migrants in seven English and Welsh towns and supplemented by an analysis of qualitative data. Three categories of migrants are initially identified on the basis of their intentions of stay in the UK. Multinomial logit models are then estimated to examine the characteristics of individuals in each category to establish the factors that influence migration strategies and changes in plans. Results indicate that although standard socio-economic characteristics tend to be insignificant, migration strategies and changes in intentions are affected by migrant's view of whether their job matches their expectations, the time of entry into the UK and remittances. Analysis of the qualitative information reinforces some of the key findings and also indicates the importance of migrants' ability to reference their experiences in two settings.
Keywords:
Acknowledgements
Financial support from the NORFACE research programme on Migration in Europe—Social, Economic, Cultural and Policy Dynamics (as part of the TEMPO project), is gratefully acknowledged. Helpful comments on the paper have been received at the Migration: Economic Change, Social Challenge Conference held in London and the TEMPO Conference on International Migration held in Vienna. We are also grateful for the comments received from three anonymous referees.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
[1] The accession countries are the eight Central and Eastern European Countries that joined the EU in May 2004, along with Cyprus and Malta.
[2] Post-enlargement migration from Poland has been spread widely across the UK. The main reason for the focus on England and Wales in this paper is because of the statistical connection between these two countries given that they are covered by the same statistical authority (the Office for National Statistics).
[3] In order to clarify the final wording and sequencing of questions, a pilot survey was carried out in two locations (London and Southampton).
[4] The NINo database should provide the most accurate estimate of Polish migrant workers entering the UK for the first time. Although the database is likely to contain the majority of Polish migrant workers to the UK over this period, it will also underestimate total migration flows from the Poland to the UK since it does not capture some of the dependents and spouses who have accompanied the migrant workers. See Harris, Moran, and Bryson (Citation2012) for further discussion of NINo data in relation to migration from Poland.
[5] Drinkwater and Garapich (Citation2011) provide more background information to the survey and discuss the data-set in more detail.
[6] The influence of the family variables is similar when different measures are used. These include having a partner in the UK or Poland and the number of children.
[7] The language variables were derived from a single question asking respondents to state their English language skills on a 1–10 scale, where 1 was no ability and 10 was fluent. Three dummy variables were then constructed from this information: poor English (1–3 on the self-reporting scale), moderate English (4–6 on the scale) and good English (7–10 on the scale).
[8] There are no important differences to note in the estimates for the socio-economic characteristics in comparison to those reported in and , although the employed dummy has been removed because of the inclusion of the job-related factors in .
[9] These variables are also insignificant when the remittances controls are excluded.
[10] The male dummy is also significant at the 10% level, which is the only significant gender effect in any of the models that have been estimated.
[11] The information in parentheses after each quote relates to the respondent’s gender, age category and location.
[12] Variables capturing the importance of social benefits and pensions were not significant factors when added to the multinomial logit regressions.
