How Do Countries Adapt to Immigration? * Simonetta Longhi (slonghi@essex.ac.uk) Yvonni Markaki (ymarka@essex.ac.uk) Institute for Social and Economic Research, University of Essex JEL Classification: F22; J31; J64 Keywords: Immigration; Europe; Immigration History This paper combines individual panel data from the European Survey on Income and Living Conditions (EU SILC) and annual population estimates from the European Labour Force Survey (EU LFS) to analyse the impact of diversity, generated by international migration, on labour market opportunities of people living in European countries. Our aim is to analyse whether differences in the impact of immigration across countries are related to the diversity of the immigrant population, to differences in immigration histories and in institutions across countries. Extended Abstract The impact that immigration can exert on host countries has been largely analysed by the literature. One strand, which measures the impact of immigration by the share of immigrants over the population, focuses on labour market opportunities of natives (e.g. Dustmann et al. 2005; Carrasco et al. 2008; Longhi et al. 2010). Theoretical models that assume substitutability between natives and immigrants expect more immigrants to have a negative impact on the labour market opportunities of natives. Empirical studies however find that, on average, the impact of immigration is rather small or inexistent, with large heterogeneity across countries (Longhi et al. 2005). Many of these studies compare regional shares of immigrants to regional average wages or unemployment rates. * We would like to thank Tina Rampino for research assistance. This work is part of the project Migrant Diversity and Regional Disparity in Europe (NORFACE-496, MIDI- REDIE) funded by NORFACE; financial support from NORFACE research programme on Migration in Europe - Social, Economic, Cultural and Policy Dynamics is acknowledged. This work also forms part of a programme of research funded by the Economic and Social Research Council (ESRC) through the Research Centre on Micro-Social Change (MiSoC) (award no. RES-518-28-001). The support provided by ESRC and the This work is part of the project Migrant Diversity and Regional Disparity in Europe (NORFACE-496, MIDI- REDIE) funded by NORFACE; financial support from NORFACE research programme on Migration in Europe - Social, Economic, Cultural and Policy Dynamics is acknowledged. This work also forms part of a programme of research funded by the Economic and Social Research Council (ESRC) through the Research Centre on Micro-Social Change (MiSoC) (award no. RES-518-28-001). The support provided by ESRC and the University of Essex is gratefully acknowledged.
Another strand of literature focuses on the impact of diversity on firm productivity, growth, wages, etc., most often measured by the number or proportion of immigrants of different nationalities (Ottaviano and Peri 2005; 2006). In most cases diversity seems to have a positive effect on the host country. However, countries are very heterogeneous in terms of their labour market institutions, immigration histories and the types of immigrants they attract. The extent of diversity within the immigrant population also varies depending on the country. However, as immigrants assimilate to the host country s culture and labour market and host countries adapt to immigration, the impact of immigration and of diversity might decrease with time. Are countries with a longer history of immigration more able to gain from immigration itself, compared to countries where immigration is a more recent phenomenon? Are countries more able to adapt to immigration if the countries of origin of immigrants are culturally similar to the host country? Are labour market regulations and other institutional settings (e.g. how citizenship is transmitted) helping the labour market to profit from migration? We combine data from the European Survey on Income and Living Conditions (EU SILC) and the European Labour Force Survey (EU LFS) to analyse the impact of diversity, generated by international migration, on labour market opportunities of people living in European countries. Our aim is to analyse whether differences in the impact of immigration across countries are related to the diversity of the immigrant population, to differences in immigration histories and in institutions across countries. The use of individual panel data, rather than regional averages or repeated crosssections, allow us to better control for individual observed characteristics and individual unobserved heterogeneity, which may act as sources of endogeneity bias, when analysing the impact of immigration on the host country. Furthermore, the EU LFS allows us to compute relevant aggregate measures that vary over time, such as the overall proportion of immigrants, the diversity of the immigrant population and the proportion of immigrants who arrived in the host country more than 10 years before. Finally, we add external data on labour market institutions and include many European countries in order to test whether differences in the impact of immigration depend on the host country s immigration history and institutions. Data We estimate the impact of diversity on wages and the probability of being employed (both as employee and self-employed) using the European Survey on Income and Living Conditions (EU SILC). The EU SILC is an annual large-scale survey of private households on income and living conditions in the European Union member states. Areas covered include basic demographic characteristics, education and qualifications, gross income as total and components on an individual and household level. We use the 2009 release of the longitudinal component, which is comprised of a 4-year panel of individuals within households (2006-2009). The analysis is based on individuals between the ages of 23 and 64. For each household, and thereby individual, the EU SILC provides information on the region of residence (NUTS1), which we match with aggregate population estimates
computed from the EU LFS. Diversity generated by international immigration is measured by the composition of the resident population in terms of country of birth (the EU LFS does not supply information on ethnicity). Dependent variables: Individuals are identified as being in employment based on the EU SILC supplied variable on current employment status and include those who are in paid work as employees, as selfemployed and as paid family workers. The reference category in this dependent variable corresponds to individuals of working age who are either unemployed or inactive. Wages are measured as the gross monthly earnings for employees over the income reference period (calendar year or 12 months prior to the interview, depending on the country). The EU SILC does not provide information on basic hourly wage rates but rather supplies the gross cash income for employees over the income reference period, along with information on the individual s activity status for each month of the year (full time versus part time). In addition to lack of information on hours worked per week over the income reference period, it should also be noted that cash income in this case also includes secondary and casual jobs, allowances for transport to and from work, bonuses and profits-sharing paid in cash, holiday and additional payments and others, thereby not allowing for the calculation of hourly wages. Models We estimate OLS regression models with fixed effects in which the dependent variables are log monthly earnings (W irt ) and the probability of being in employment (E irt ). We include the usual individual level controls along with aggregate measures of diversity in the previous year (share of immigrants; number of nationalities with share >= 25%; number of nationalities with share 10-25%; number of nationalities with share < 10%). Furthermore, we add regional/country dummies and their interactions with the share of immigrants in addition to information on institutions and the country s immigration history. Preliminary Results
Variables Ln monthly earnings In employment Years in paid work 0.004** 0.018** (0.002) (0.001) Years in work squared -0.000-0.000** (0.000) (0.000) 2007 0.049** -0.009** (0.004) (0.002) 2008 0.100** -0.018** (0.004) (0.002) 2009 0.148** -0.036** (0.006) (0.002) Densely populated area -0.016 0.034** (0.022) (0.009) Thinly populated area -0.003-0.004 (0.022) (0.010) Diversity index 0.970** 0.485** (0.166) (0.074) Diversity index squared -1.861** -0.741** (0.353) (0.156) Constant 7.401** 0.495** (0.027) (0.011) Statistics Observations 118,872 243,732 Sigma_u 0.644 0.422 Sigma_e 0.343 0.216 Rho 0.779 0.792 R2_o 0.013 0.008 R2 0.023 0.009 Standard errors in parentheses; *p<0.05 **p<0.01; Results of fixed effects OLS regressions; Diversity index here is computed from the EU LFS based on country of birth (immigrants born in other EU countries versus those born in Non-EU countries) at NUTS1 regional level, in the previous year (t-1). References
Carrasco, R., Jimeno, J.F. and Ortega, A.C. (2008) The Effect of Immigration on the Labor Market Performance of Native-Born Workers: Some Evidence for Spain. Journal of Population Economics 21(3): 627-648. Dustmann, C., Hatton, T. and Preston, I. (2005) The Labour Market Effects of Immigration. The Economic Journal 115(507): F297-F299. Longhi, S., Nijkamp, P. and Poot, J. (2005) A Meta-Analytic Assessment of the Effect of Immigration on Wages. Journal of Economic Surveys 19(3): 451-477. Longhi, S., Nijkamp, P. and Poot, J. (2010) Joint Impacts of Immigration on Wages and Employment: Review and Meta-Analysis. Journal of Geographical Systems 12: 355-387. Ottaviano, G.I. and Peri, G. (2005) Cities and Cultures. Journal of Urban Economics 58: 304-337. Ottaviano, G.I. and Peri, G. (2006) The Economic Value of Cultural Diversity: Evidence from Us Cities. Journal of Economic Geography 6(2): 9-44.