The United States has long been perceived as a land of opportunity for immigrants. Yet, both in the past and today, US natives have expressed concern that immigrants fail to integrate into US society and lower wages for existing workers. This paper reviews the literatures on historical and contemporary migrant flows, yielding new insights on migrant selection, assimilation of immigrants into US economy and society, and the effect of immigration on the labor market.

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).

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.

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 percent) false positive rates. The automated methods trace out a frontier illustrating the trade-off 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.