Migrant Networks and the Spread of Information
| DOI | http://doi.org/10.1111/obes.12216 |
| Published date | 01 June 2018 |
| Date | 01 June 2018 |
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©2017 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd.
OXFORD BULLETIN OF ECONOMICSAND STATISTICS, 80, 3 (2018) 0305–9049
doi: 10.1111/obes.12216
Migrant Networks and the Spread of
Information*
Benjamin Elsner†, Gaia Narciso‡ and Jacco Thijssen§
†Institute of Labor Economics (IZA), Bonn, Germany (e-mail: elsner@iza.org)
‡Department of Economics, Trinity College Dublin, Dublin, Ireland (e-mail: narcisog@tcd.ie)
§The York Management School, University of York, York, UK (e-mail: jacco.thijssen@york.
ac.uk)
Abstract
Diaspora networks provide information to future migrants, which affects their success
in the host country. While the existing literature explains the effect of networks on the
outcomes of migrants through the size of the migrant community, we show that the quality
of the network is an equally important determinant. We argue that networks that are more
integrated in the society of the host country can provide more accurate information to future
migrants about job prospects. In a decision model with imperfect signalling, we show that
migrants with access to a better network are more likely to make the right decision, that is,
they migrate only if they gain. We test these predictions empirically using data on recent
Mexican migrants to the United States. To instrument for the quality of networks, we
exploit the settlement of immigrants who came during the Bracero program in the 1950s.
The results are consistent with the model predictions, providing evidence that connections
to a better integrated network lead to better outcomes after migration.
I. Introduction
Prior to moving, migrants face significant uncertainty about their job prospects abroad,
which is why they often seek advice from existing diaspora networks. A large amount
of literature has shown that diaspora networks indeed influence the decision to migrate
and affect migrants’ success in the host country (Edin, Frederiksson and Aslund, 2003;
Pedersen, Pytlikova and Smith, 2008; Beaman, 2012). Throughout this literature, the size
of the network has been identified as the main determinant. In this paper, we provide a
different perspective on the role of diaspora networks by showing that the quality of these
JEL Classification numbers: F22, J15, J61.
*Wewould like to thank the Editor, two anonymous referees, as well as Simone Bertoli, Herbert Br¨ucker,Joanna
Clifton-Sprigg, Tommaso Colussi, Margherita Comola, Christian Danne, Rachel Griffith, Joachim Jarreau, Julia
Matz, Imran Rasul, Bas ter Weel, MathisWagner and seminar participants at IZA, University College Dublin, the
University of Southern Denmark, as well as the conferences ISNE, TEMPO, IEA, ESEM, OECD immigration
workshop, RES, NORFACE, ESPE, EALE, IZA/SOLE for helpful comments. Margaryta Klymak and Gaspare
Tortorici provided excellent research assistance. Elsner gratefully acknowledges funding from the Irish Research
Council for the Humanities & Social Sciences (IRCHSS).
660 Bulletin
networks – measured by their degree of integration in the host society – has an equally
important impact on the decisions and success of future migrants.
We argue that the integration of migrant networks in the host country determines both
the decision to migrate and the outcomes after migration. Because existing networks differ
in their degree of integration, some networks are able to providemore accurate information
than others concerning job prospects. Well-integrated networks that have a great deal of
interaction with the world surrounding them have better knowledgeof local labour markets
than enclaves, whose members typically have little social interaction outside the network.
Potentialmig rants with access to a better-integratednetwork can base their decision on more
accurate information, which in turn makes them more likely to make a correct decision:
they migrate if they can expect to secure a job that makes them better off, whereas they
stay if they can expect a job that makes them worse off.1
To illustrate the underlyingmechanism, we explore the link between information flows
and the success of migrants in a simple two-period decision model. Initially, the potential
migrant has some knowledge about her expected income abroad, albeit not enough to
convince her that migration will be beneficial. She then receives information from the
network and updates her beliefs about expected income from migration. To the extent that
a more integrated network providesa more tr uthful signal and spreads less misinformation,
a migrant who receives this information is more likely to make the right decision givenher
true income prospects in the receiving country.
We test this prediction using data on recent Mexican immigrants in the US. Mexican
communities are spread out all across the US, allowing us to exploit a significant degree
of variation in the characteristics of these communities. Communities in traditional desti-
nations such as Los Angeles and Houston are typically more enclaved than those in newer
destinations. Key to the empirical analysis is measuring both the quality of the network
and the success of immigrants. For the quality of the network, we compute an assimilation
index that measures the degree of similarity between Mexicans and Americans in an area
with respect to a wide range of characteristics. As the social networksliterature has shown,
people with similar characteristics have more interaction, which leads to a more efficient
aggregation of information (McPherson, Smith-Lovin and Cook, 2001; Acemoglu et al.,
2011), and ultimately to more accurate information on job prospects that can be passed on
to future migrants. Tomeasure the success of mig rants, we take the difference between the
wages of Mexicans in the US and Mexico.As the data do not allow us to observe Mexicans
in both countries at the same time, we predict counterfactual wages in Mexico based on
a large set of observable characteristics, and interpret a larger difference between income
in the US and Mexico as a lower likelihood that the migrant has made a mistake in her
decision to migrate.
Identification is threatened by the presence of unobserved factors that may induce
a spurious relationship between the characteristics of the established network and the
outcomes of newly arrived migrants. For example, a local industry may have attracted a
lot of low-skilled migrants in the past, and does so until today, resulting in a low degree
of integration of past immigrants, low wages of current immigrants and overall a positive
correlation between both variables. To address this endogeneity, we instrument for the
1Throughout the paper, we use the terms ‘integration’ and ‘assimilation’interchangeably.
©2017 The Department of Economics, University of Oxford and JohnWiley & Sons Ltd
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