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Immigration, Attitudes of Natives and Immigrants Assimilation

Periodic Reporting for period 1 - Long-term migration (Immigration, Attitudes of Natives and Immigrants Assimilation)

Reporting period: 2019-09-01 to 2021-08-31

This project analyses two main research questions on the long-term consequences of immigration.

The first question is whether historical immigration affects attitudes of natives towards immigrants, measured as voting for parties supporting anti-immigration policies in recent elections.

The second question addresses whether historical immigration can affect the ability of immigrants to assimilate into the native’s society. Specifically, the aim is to analyze whether in locations with more historical migration recent immigrants tend to assimilate better, because of the existence of a more multicultural society. In this project, the focus is on economic assimilation.

Moreover, the mechanisms through which historical immigration affects the outcomes of interest are analyzed.

The setting is the United States. In this context, the historical migration wave considered corresponds to the age of mass migration in the United States, which happened between 1850 and 1914.

The methodology makes use of recently-developed machine learning methods for causal inference, which are particularly helpful in settings with a potentially large number of (possibly nonlinear) confounders, and to explore heterogeneous treatment effects.

The United States is a suitable setting to study the proposed research questions, as it experienced a large historical migration wave. However, the results of this project are informative for other countries that experience large inflows of immigrants, as many of the countries in the European Union. The analysis of mechanisms is especially important for external validity. By highlighting which variables drive the effect of historical immigration on the outcomes, this research is instructive on whether other countries can expect similar effects to those found in this setting.
The work was divided into 6 Work Packages.

WP 1) The most up-to-date literature on the economics of migration, particularly on the long-term effects of migration, and on the most recent and relevant causal machine learning methods, was reviewed. The contribution with respect to these strands of literature was clarified.

WP 2) The data was collected and harmonized.

WP 3) & 4) The long-term effects of immigration attitudes of immigration towards immigrants was analyzed. Specifically, the impact of historical immigration on the recent voting behavior of natives in the U.S. was explored. When using causal ML methods, the findings suggest no robust effect of historical immigration on voting for parties that propose anti-immigration policies.

WP 5) The impact of historical immigration on the ability of recent immigrants to assimilate in terms of economic outcomes is analyzed. Several outcomes have been explored, mainly the wage and unemployment gap between immigrants and natives. A positive and significant effect of historical immigration on immigrants' assimilation is found in some subgroups of the sample.

WP 6) The results have been summarized and described in detail, and a working paper is in preparation.

The dissemination activities mainly involved research presentations at universities across Europe, conferences attendance, participation in a panel discussion, and publication of a summary of research in the magazine of the host institution. The events that the fellow participated in were mostly held online due to the COVID-19 pandemic.
The results of the project contribute to the knowledge about how historical and recent immigration waves interact and are of interest to a broad academic audience concerned with the effects of migration, as well as international and governmental organizations. In particular, the results are very relevant for countries that experienced, or are experiencing, large inflows of immigrants.
Illustration of immigrants to the United States.