Tax benefits tied to children form a central component of the social safety net in the United States. To participate in these programs, taxpayers must claim a child on their tax return. We study the claiming of children on tax returns by drawing on health insurance information returns to establish the presence of children in the United States. We estimate that the vast majority of insured children (approximately 95 percent) and a significant majority (between 88 and 97 percent) of all U.S. children are claimed on tax returns. Unclaimed children are disproportionately concentrated in lower income households.

The effect of a treatment may depend on the intensity with which it is administered. We study identification of ordered treatment effects with a binary instrument, focusing on the effect of moving from the treatment’s minimum to maximum intensity. With arbitrary heterogeneity across units, standard IV assumptions (Angrist and Imbens, 1995) do not constrain this parameter, even among compliers. We consider a range of additional assumptions and show how they can deliver sharp, informative bounds. We illustrate our approach with two applications, involving the effect of (1) health insurance on emergency department usage, and (2) attendance in an after-school program on student learning.

Economists often estimate causal effects of policies on multiple outcomes and summarize them into scalar measures of cost-effectiveness or welfare, such as the Marginal Value of Public Funds (MVPF). In many settings, microdata underlying these estimates are unavailable, leaving researchers with only published estimates and their standard errors. We develop tools for valid inference on functions of causal effects, such as the MVPF, when the correlation structure is unknown. Our approach is to construct worst-case confidence intervals, leveraging experimental designs to tighten them, and to assess robustness using breakdown analyses. We illustrate our method with MVPFs for eight policies.

We study the role of job transitions and firm pay policies in the Black-White earnings gap in the US. We use administrative data for the universe of employer-employee matches from 2005-2019 to analyze worker mobility in a general but tractable framework, which allows for firm effects that depend on workers’ job history. Using differences in average pay between origin and destination firms as the treatment intensity of a job move, we analyze transitions up and down the job ladder and estimate race-specific passthrough rates of average firm pay into a mover’s own earnings. First, we find race-specific asymmetry around the direction of the move, whereby losses experienced in downward transitions are meaningfully larger than gains from upward transitions with a similar treatment intensity. For a \$1 earnings increase in transitions up the job ladder, earnings passthroughs in transitions down the job ladder impose an earnings loss of \$1.25 among White workers and \$1.50 among Black workers. Second, we uncover career setbacks as a novel pathway in the evolution of racial earnings gaps. In transitions down the job ladder, Black workers lose an additional \$0.24 for every \$1 decrease in White workers’ earnings, a finding which prevails across sex and age. This “racial penalty” is not driven by differential pay, as it is completely absent when Black and White workers move between the same firm pairs. Instead, the penalty is due to differential sorting following career setbacks, so that Black workers regain employment in “worse” jobs, with strong evidence for racial differences in access to short-run liquidity as a mechanism. Overall, our findings offer a robust and computationally simple framework for modeling earnings determination processes and have implications for safety-net policies in the American labor market.

Can low-cost changes to existing digital tax administration improve the tax collection capacity of the state? India's Goods and Services Tax (GST), like many developing countries, has invested heavily in digitizing tax administration allowing it to develop novel enforcement tools targeting widespread evasion through fake input credits. Using administrative data and a difference-in-differences design, we find that India's use of existing e-filing portal to restrict firms from providing tax credits to their partners until taxes were paid reduces likely-fraudulent credits. Clients of targeted firms increase tax payments, implying a 5 percent decline in overall evasion at minimal cost.