2017
DOI: 10.1007/s00148-017-0677-5
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Social networks and the labour market mismatch

Abstract: Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in… Show more

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Cited by 24 publications
(17 citation statements)
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References 46 publications
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“…My results contradict Kalfa and Piracha (2018), who found that ethnic concentration increases the probability of overqualification, particularly for females. While there are many differences in their study, such as the different juridical migration systems in Australia and Germany and the different compositions of the foreign‐born populations, three main transmission channels explain both a positive effect from ethnic networks and the different results by gender.…”
Section: Main Empirical Resultscontrasting
confidence: 99%
See 3 more Smart Citations
“…My results contradict Kalfa and Piracha (2018), who found that ethnic concentration increases the probability of overqualification, particularly for females. While there are many differences in their study, such as the different juridical migration systems in Australia and Germany and the different compositions of the foreign‐born populations, three main transmission channels explain both a positive effect from ethnic networks and the different results by gender.…”
Section: Main Empirical Resultscontrasting
confidence: 99%
“…The significant effect of the foreign share in Model 1 becomes insignificant after controlling for the ethnic share in Models 3 and 4 18 . Instead of a continuous measurement of occupational mismatch, I adopt the approach of Kalfa and Piracha (2018) and model a binary variable to capture the probability of overqualification. This tackles the disadvantage that undereducation and overeducation are conflated into one variable in equation [1].…”
Section: Main Empirical Resultsmentioning
confidence: 99%
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“…With respect to immigrant–native difference, immigrants in each destination country tend to be more vulnerable to overeducation than are native workers because of their more circumscribed social networks (Kalfa and Piracha 2018) and because of human capital’s limited transferability (Chiswick and Miller 2009). Social networks provide information about opportunities to job seekers and make referrals about prospective hires to employers (Fernandez, Castilla, and Moore 2000).…”
Section: Labor-market Structures Immigration System and Overeducationmentioning
confidence: 99%