Abstract In the early 1900s, telephone operation was among the most common jobs for American women, and telephone operators were ubiquitous. Between 1920 and 1940, AT&T undertook one of the largest automation investments in modern history, replacing operators with mechanical switching technology in over half of the U.S. telephone network. Using variation across U.S. cities in the timing of adoption, we study how this wave of automation affected the labor market for young women. Although automation eliminated most of these jobs, it did not reduce future cohorts’ overall employment: the decline in operators was counteracted by employment growth in middle-skill clerical jobs and lower-skill service jobs, including new categories of work. Using a new genealogy-based census-linking method, we show that incumbent telephone operators were most affected, and a decade later more likely to be in lower-paying occupations or no longer working.

Abstract Does family history matter for policy making in democracies? Linking members of Congress (MCs) to the census, we observe countries of birth for members, their parents, and their grandparents, allowing us to measure ancestry for the politicians in office when U.S. immigration policy changed dramatically, from closing the border in the 1920s to reshaping admittance criteria in the 1960s. We find that legislators descended from immigrant parents or grandparents support more permissive immigration legislation. They are also less likely to speak negatively about immigration in speeches before Congress. A regression discontinuity design analyzing close elections, which addresses district-level selection and holds district composition constant, confirms our results on roll call voting and speech. Efforts to account for selection into immigration—such as comparing international immigrants to domestic migrants and exploiting variation in restrictive legislation targeting specific regions of origin—further confirm the relationship between family immigration experience and more permissive stances on immigration policy. We then explore mechanisms, finding support for in-group identity in connecting family history with policy making. MCs name their children in ways that express immigrant identity, and immigrant-descended MCs discuss immigration using more personal frames, emphasizing family over economic considerations. Our findings illustrate the important role of personal background in legislative behavior in democratic societies, even on major and controversial topics like immigration, and suggest how experiences transmitted from previous generations can inform lawmakers’ views.

Abstract Americans with more education vote more, but we know little about whether this effect on civic participation arises from educational quality or quantity. Using admissions lotteries at Boston charter schools, we find that charter attendance boosts voter participation, substantially increasing voting in the first presidential election after a student turns 18 by six percentage points from a baseline of 35 percent. This effect operates through increased turnout, as there is no increase in registration. Rich data enable us to explore multiple potential channels of this voting impact. Our evidence suggests that charters increase voting by increasing noncognitive skills.

Abstract Fears of immigrants as a threat to public health have a long and sordid history. At the turn of the 20th century, when immigrants made up one-third of the population in crowded American cities, contemporaries blamed high urban mortality rates on the newest arrivals. We evaluate how the implementation of country-specific immigration quotas in the 1920s affected urban health. Cities with larger quota-induced reductions in immigration experienced a persistent decline in mortality rates, driven by a reduction in deaths from infectious diseases. The unfavourable living conditions immigrants endured explains the majority of the effect as quotas reduced residential crowding and mortality declines were largest in cities where immigrants resided in more crowded conditions and where public health resources were stretched thinnest.

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. (JEL C81, C83, N01, N31, N32)