During the Age of Mass Migration , the US maintained an open border, absorbing 30 million European immigrants. Prior cross-sectional work on this era finds that immigrants initially held lower-paid occupations than natives but experienced rapid convergence over time. In newly-assembled panel data, we show that, in fact, the average immigrant did not face a substantial occupation-based earnings penalty upon first arrival and experienced occupational advancement at the same rate as natives. Cross-sectional patterns are driven by biases from declining arrival cohort quality and departures of negatively-selected return migrants. We show that assimilation patterns vary substantially across sending countries and persist in the second generation.Ran Abramitzky
During the age of mass migration (1850–1913), one of the largest migration episodes in history, the United States maintained a nearly open border, allowing the study of migrant decisions unhindered by entry restrictions. We estimate the return to migration while accounting for migrant selection by comparing Norway-to-US migrants with their brothers who stayed in Norway in the late nineteenth century. We also compare fathers of migrants and nonmigrants by wealth and occupation. We find that the return to migration was relatively low (70 percent) and that migrants from urban areas were negatively selected from the sending population.
“Keep, ancient lands, your storied pomp!” cries she With silent lips. “Give me your tired, your poor, Your huddled masses yearning to breathe free, The wretched refuse of your teeming shore. Send these, the homeless, tempest-tost to me, I lift my lamp beside the golden door!”——Emma Lazarus (1883)1
The recent digitization of complete count census data is an extraordinary opportunity for social scientists to create large longitudinal datasets by linking individuals from one census to another or from other sources to the census. We evaluate different automated methods for record linkage, performing a series of comparisons across methods and against hand linking. We have three main findings that lead us to conclude that automated methods perform well. First, a number of automated methods generate very low (less than 5%) false positive rates. The automated methods trace out a frontier illustrating the tradeoff between the false positive rate and the (true) match rate. Relative to more conservative automated algorithms, humans tend to link more observations but at a cost of higher rates of false positives. Second, when human linkers and algorithms use the same linking variables, there is relatively little disagreement between them. Third, across a number of plausible analyses, coefficient estimates and parameters of interest are very similar when using linked samples based on each of the different automated methods. We provide code and Stata commands to implement the various automated methods.
During the Age of Mass Migration (1850–1913), the United States maintained an open border, absorbing 30 million European immigrants. Prior cross-sectional work finds that immigrants initially held lower-paid occupations than natives but converged over time. In newly assembled panel data, we show that, in fact, the average immigrant did not face a substantial occupation-based earnings penalty upon first arrival and experienced occupational advancement at the same rate as natives. Cross-sectional patterns are driven by biases from declining arrival cohort skill level and departures of negatively selected return migrants. We show that assimilation patterns vary substantially across sending countries and persist in the second generation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.