New water purification technologies led to large mortality declines by helping eliminate typhoid fever and other waterborne diseases. We examine how this affected human capital formation using early-life typhoid fatality rates to proxy for water quality. We merge city-level data to individuals linked between the 1900 and 1940 Censuses. Eliminating early-life exposure to typhoid fever increased later-life earnings by one percent and educational attainment by one month. Instrumenting for typhoid fever using typhoid rates from cities that lie upstream produces results nine times larger. The increase in earnings from eliminating typhoid fever more than offset the cost of elimination.

The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

We are also thank participants at the 2017 NBER Summer Institute Development of the American Economy meeting and the 2018 World Economic History Congress.The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

This article reviews the global health and economic consequences of the 1918 influenza pandemic, with a particular focus on topics that have seen a renewed interest because of COVID-19. We begin by providing an overview of key contextual and epidemiological details as well as the data that are available to researchers. We then examine the effects on mortality, fertility, and the economy in the short and medium run. The role of non-pharmaceutical interventions in shaping those outcomes is discussed throughout. We then examine longer-lasting health consequences and their impact on human capital accumulation and socioeconomic status. Throughout the paper we highlight important areas for future work. (JEL E24, E32, I12, I15, J13, J24, N30)

Researchers make hundreds of decisions about data collection, preparation, and analysis in their research. We use a many-analysts approach to measure the extent and impact of these decisions. Two published causal empirical results are replicated by seven replicators each. We find large differences in data preparation and analysis decisions, many of which would not likely be reported in a publication. No two replicators reported the same sample size.Statistical significance varied across replications, and for one of the studies the effect's sign varied as well. The standard deviation of estimates across replications was 3-4 times the mean reported standard error.

Historical studies of labor markets frequently suffer from a lack of data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes. Using modern Census data, we find that the use of OCCSCORE biases results towards zero and can frequently result in statistically significant coefficients of the wrong sign. We use a machine learning approach to construct a new adjusted score based on industry, occupation, and individual demographics. Our alternative score substantially outperforms OCCSCORE in both modern and historical contexts. We illustrate our approach by estimating racial and gender earnings gaps in the 1915 Iowa State Census and intergenerational mobility elasticities using linked data from the 1850-1910 Censuses.JEL codes: C21, J71, N32

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