Craig AC, “Optimal Income Taxation with Spillovers from Employer Learning”. [Working Paper] Abstract
I study optimal income taxation when human capital investment is imperfectly observable by employers. In my model, Bayesian employer inference about worker productivity compresses the wage distribution. This lowers the private return to investment in human capital, and workers invest too little. The model implies an externality: given the same information, employers form more favorable beliefs about an individual when workers are generally more productive. This externality lowers optimal marginal tax rates. Its quantitative significance hinges on the accuracy of employers’ beliefs and the responsiveness of human capital investments to taxation. I calibrate the model to match empirical moments from the United States, including evidence on the speed of employer learning about labor market entrants. Taking into account the spillover from human capital investment introduced by employer inference reduces optimal marginal tax rates by 13 percentage points at 100,000 dollars of income, with little change in the tails of the income distribution.
Tax policy can play important roles in limiting the spread of communicable disease, and in managing the economic fallout of a pandemic. Taxes on business activities that bring workers or customers into close contact with each other offer efficient alternatives to broad regulatory measures such as shutdowns, which have been effective but enormously costly. Corrective taxation also helps raise the revenue required to cover elevated government expenditure during the pandemic. Moreover, the restricted consumer choice that accompanies a pandemic reduces the welfare cost of raising tax revenue from higher-income taxpayers, making it a good time for deficit closure. Current U.S. tax measures serve some of these functions, but additional measures could further limit the spread of disease while also addressing government budget deficits.
Does relaxing strict school discipline policies improve student achievement, or lead to classroom disorder? We study a 2012 reform in New York City public middle schools that eliminated suspensions for non-violent, disorderly behavior, replacing them with less disruptive interventions. Using a difference-in-differences framework, we exploit the sharp timing of the reform and natural variation in its impact to measure the effect of reducing suspensions on student achievement. Math scores of students in more-affected schools rose by 0.05 standard deviations relative to other schools over the three years after the policy change. Reading scores rose by 0.03 standard deviations. Only a small portion of these aggregate benefits can be explained by the direct impact of eliminating suspensions on students who would have been suspended under the old policy. Instead, test score gains are associated with improvements in school culture, as measured by the quality of student-teacher relationships and perceptions of safety at school. These improvements benefited students even if they were unlikely to be suspended themselves.
We introduce a model of two-sided statistical discrimination in which worker and firm beliefs are complementary. Firms try to infer whether workers have made investments required for them to be productive, and simultaneously, workers try to deduce whether firms have made investments necessary for them to thrive. When multiple equilibria exist, group differences are sustained by both sides of the interaction – workers and firms. Strategic complementarity between the two sides complicates both empirical analysis designed to detect discrimination and policy meant to alleviate it. Affirmative action is much less effective than in traditional statistical discrimination models. More generally, we demonstrate the futility of policies that are designed to correct gender and racial disparities but do not address both sides of the coordination problem. We propose a two-sided version of “investment insurance” – a highly effective and potentially cheap policy in which the government (after observing a noisy version of the employer’s signal) offers to hire any worker who it believes to be qualified and whom the employers do not offer a job. The paper concludes by proposing a way to identify statistical discrimination by employers when beliefs are complements.
We estimate and compare the effect of increased time costs on consumer satisfaction and behavior. We are able to move beyond the existing literature, which focuses on satisfaction and intention, and estimate the effect of waiting time on return behavior. Further, we do so in a prosocial context and our measure of cost is the length of time a blood donor spends waiting. We find that relying on satisfaction data masks important time cost sensitivities; namely, it is not how the donor feels about the wait time that matters for return behavior, but rather the actual duration of the wait. Consistent with theory we develop, our results indicate that waiting has a significant longer-term social cost: we estimate that a 38% increase (equivalent to one standard deviation) in the average wait would result in a 10% decrease in donations per year.