Job Market Paper
I present a new method for evaluating proposed reforms of progressive, piecewise linear tax schedules. Typically, estimates of the elasticity of taxable income (ETI) are used to predict taxpayer responses to changes in tax rates and/or tax bracket thresholds. I show that elasticities are not always needed for this task: the “bunching mass” at a bracket threshold (the share of taxpayers locating there) is a sufficient statistic for the revenue effect of behavioral responses to small changes of the threshold. Building on this finding, revenue forecasting and welfare analysis of threshold changes can be conducted using the pre-reform distribution of taxable income alone. I apply these results in an analysis of the Earned Income Tax Credit, an exercise which motivates extensions addressing optimization error, tax rate heterogeneity, and large reforms. This new use case for bunching complements existing bunching methods: it is robust to key limitations of bunching-based ETI estimation, but addresses a relatively narrow set of policy questions.
We develop an optimal tax framework that combines two recent extensions of tax analysis: a tax-systems emphasis on non-rate policy instruments, and a recognition of the role of behavioral biases. Although the implications of taxpayers' biases for optimal tax rates have received considerable attention, a complete analysis of this aspect of optimal tax theory must account for the fact that such biases are often endogenous to the non-rate aspects of a tax system. We first generalize and extend the analysis of optimal tax systems to incorporate endogenous behavioral biases. We then develop a novel and important application of this issue, showing how misperception of the tax rate affects the optimal breadth of the tax base.
A Political Matthew Effect: Democratic Redistribution with Plutocratic Feedback Loops (draft coming soon)
Kinking at Kink Points: Estimating the Elasticities of Ironing Agents
We investigate the impact of the US drone program in Pakistan on insurgent violence. Using details about US-Pakistan counterterrorism cooperation and geocoded violence data, we show that the program was associated with monthly reductions of around nine to thirteen insurgent attacks and fifty-one to eighty-six casualties in the area affected by the program. This change was sizable, as in the year before the program, the affected area experienced around twenty-one attacks and one hundred casualties per month. Additional quantitative and qualitative evidence suggests that this drop is attributable to the drone program. However, the damage caused in strikes during the program cannot fully account for the reduction. Instead, anticipatory effects induced by the program played a prominent role in subduing violence. These effects stemmed from the insurgents’ perception of the risk of being targeted in drone strikes; their efforts to avoid targeting severely compromised their movement and communication abilities, in addition to eroding within-group trust. These findings contrast with prominent perspectives on air-power, counterinsurgency, and US counterterrorism, suggesting select drone deployments can be an effective tool of counterinsurgency and counterterrorism.