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The impact of host language proficiency on employment outcomes among immigrants in Spain

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Abstract

This paper presents estimates of the impact of host language proficiency on the probability of immigrants having an employment. The article uses data from the Spanish National Immigrant Survey and relies on two complementary econometric approaches, a 2SLS instrumental variable model and a bivariate probit model. Host language proficiency is instrumented using Bleakley and Chin (Rev Econ Stat 86:481–496, 2004) strategy, which exploits the fact that younger children learn languages more easily than older ones. Average treatment effects indicate that host language proficiency raises the probability of having a job by about 15 and 22 percentage points among men and women respectively. However, local effects can be more than twice as large.

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Notes

  1. We thank an anonymous referee for drawing our attention to this point.

  2. One of the main concerns of the National Statistics Institute was to design an effective strategy to locate immigrants, as they typically are a notoriously difficult population to pinpoint. The strategy adopted was to resort to the Spanish municipal population census. By 2007 immigrants in the census list had automatic right to basic medical care for themselves and their families, and to educational privileges for their children. This remarkably liberal migration policy in Spain provided a powerful incentive for immigrants to be present on this population register regardless of their legal status. In this respect, the potential problem of immigrants attempting to be ‘invisible’ was minimized (see Reher and Requena 2009). An effective sample of 17,700 households, in which there was at least one foreign-born person 16 years or older, was generated. The response rate (i.e., households with at least one individual completing the full questionnaire) was 87.5%.

  3. The results of the paper do not change when inactive individuals are dropped from the analysis.

  4. Individuals whose age at arrival—a crucial variable in our analysis—was reported to be higher than their current age were dropped (6.9% of the selected sample). Similarly, individuals with missing data in variables that were relevant in the regression stage (Spanish proficiency, age at arrival, schooling level and labour status) were dropped from the analysis (6.8% of the sample). In Table 8 in the “Appendix” section we show summary statistics for the filtered and unfiltered data. We find that item non-response does not alter substantially the composition of the sample.

  5. The paper follows a stringent criterion by considering only individuals who claim to be able to speak Spanish ‘very well’. Results under the alternative classification 1–2 against 3–4 displayed slightly lower returns and are available upon request.

  6. Alternative parameterizations yielded similar results, as shown in Table 9 of the “Appendix” section.

  7. This is done by using STATA command biprobittreat developed by Chiburis et al. (2011). See Habibov and Zainiddinov (2015) for further applications.

  8. The GDP is from the World Bank, https://data.worldbank.org/indicator/NY.GDP.PCAP.CD. Accessed: July 17, 2018.

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Correspondence to Carlos Martínez de Ibarreta.

Appendix

Appendix

See Tables 8, 9 and 10.

Table 8 Data with and without item-non-response
Table 9 2SLS effects of Spanish proficiency using other parametrizations of the instrument
Table 10 Immigrants by country of birth

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Budría, S., Colino, A. & Martínez de Ibarreta, C. The impact of host language proficiency on employment outcomes among immigrants in Spain. Empirica 46, 625–652 (2019). https://doi.org/10.1007/s10663-018-9414-x

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