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.
Moore, Dylan T. and Slemrod, Joel. (2021) "Optimal Tax Systems with Endogenous Behavioral Biases". Journal of Public Economics, Volume 197, May 2021, https://doi.org/10.1016/j.jpubeco.2021.104384. (Link to working paper).
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.
When should tax policy be used to influence political donation behavior? In a model of electoral politics where campaign spending is financed by citizen donations, inequality of political influence favoring the "donor class" can arise. Adopting the normative stance that such inequality is undesirable, characterizations of optimal linear and nonlinear taxation of political donations are presented. Sufficient statistics for optimal policy include not only donation demand elasticities, but also the marginal efficacy of campaign spending, and the effect of taxes on the sensitivity of donations to candidate policy platforms. The results help to rationalize some observed policies. For example, taxing donations reduces campaign spending, driving up the marginal return to campaign spending and potentially increasing political inequality. Nonlinear subsidies targeting small donors - such as those seen in Canada - can decrease the relative influence of large donors without decreasing campaign spending.
A Political Matthew Effect: Democratic Redistribution with Plutocratic Feedback Loops (draft coming soon)
Is the coexistence of political equality and economic inequality stable? I consider this question using a simple dynamic model of democratic redistribution which captures the idea that economic and political inequality may be mutually reinforcing. Two candidates iteratively compete in elections fought over income tax policy. They engage in campaign spending financed by citizen donations. This campaign finance mechanism creates a feedback loop. In each period the citizens with higher levels of after-tax income higher relative political influence, which in turn results in more favorable tax treatment of these citizens in the next period. Long run convergence to plutocracy can occur for arbitrarily small levels of initial economic inequality. However, the opposite is also possible: a society which is initially extremely unequal may be destined for egalitarianism. The long run outcome can also exhibit extreme sensitivity to initial conditions.
Kinking at Kink Points: Estimating the Elasticities of Ironing Agents
Standard theory predicts that agents facing a convex budget constraint will "bunch" at kink points, but empirical evidence often seems at odds with this prediction. Data on choices about taxable income as well as water and electricity demand frequently provides little if any evidence of bunching at convex kink points. One possible explanation for this discrepancy is that agents may be employing the "ironing" heuristic: making decisions as if they believed their marginal tax rate were equal to their average tax rate. Although the distribution of choices generated by a population of ironing agents does not feature bunching, it does exhibit generate an alternative regularity which can - in principle - be leveraged for identifying the local average price elasticity of agents at a convex kink. I explore the possibility of applying this identification strategy in practice. Key challenges include optimization errors and other frictions, as well as the case of a mixed population of agents containing both ironing and non-ironing agents.
Mir, Asfandyar and Moore, Dylan T. (2019) "Drones, Surveillance, and Violence: Theory and Evidence from a US Drone Program." International Studies Quarterly, Volume 63, Issue 4, Pages 846–862, https://doi.org/10.1093/isq/sqz040
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.