This dissertation consists of three chapters that examine policy questions relevant to the low-income population in the United States. In the first chapter “Certification and Recertification in Welfare Programs: What Happens When Automation Goes Wrong?” (joint with Bruce Meyer), I examine how administrative burdens influence enrollment in different welfare programs and who is screened out at a given stage. I study the impacts of complications arising from the automation of welfare services, leveraging a unique natural experiment in Indiana in which the IBM Corporation remotely processed applications for two-thirds of all counties. Using linked administrative records covering nearly 3 million program recipients, I find that SNAP, TANF, and Medicaid enrollment fall by 15%, 24%, and 4% one year after automation, with these heterogeneous declines largely attributable to cross-program differences in recertification costs. Earlier-treated and higher-poverty counties experience larger declines in welfare receipt. More needy individuals are screened out at exit while less needy individuals are screened out at entry, a novel distinction that would be missed by typical measures of targeting which focus on average changes overall or at a single margin. The decline in Medicaid enrollment exhibits considerable permanence after IBM's automated system was disbanded, suggesting potential long-term consequences of increases in administrative burdens. In the second chapter “Does Geographically Adjusting Poverty Thresholds Improve Poverty Measurement and Program Targeting?” (joint with Brian Curran and Bruce Meyer), we assess the desirability of geographic cost-of-living adjustments to poverty measures by examining how well they achieve a central objective of a poverty measure: identifying the least advantaged population. We compare an exhaustive list of material well-being indicators – drawn from survey and administrative data and including material hardships, appliances owned, home quality issues, food security, public services, health, education, assets, permanent income, and mortality – of those classified as poor with and without a geographic adjustment. For nine of the ten domains of well-being indicators, we find that incorporating a geographic adjustment identifies a less deprived poor population. These results are broadly consistent across different poverty measures, various ways of implementing a geographic adjustment, and multiples of the poverty line. In the third chapter “The Use and Misuse of Income Data and Extreme Poverty in the United States” (joint with Carla Medalia, Bruce Meyer, and Victoria Mooers), we re-examine the rate of extreme poverty – defined as living on less than 2 dollars/person/day – in the U.S. by linking survey data to administrative tax and program for 2011. Of the 3.6 million non-homeless households with survey-reported cash incomes below 2 dollars/person/day, we find that more than 90% are not in extreme poverty once we include in-kind transfers, replace survey reports of earnings and transfer receipt with administrative records, and account for the ownership of substantial assets. Of the households remaining in extreme poverty, 90% consist of a single individual. These results caution against taking survey incomes in the far left tail at face value.