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This paper derives empirically implementable formulas for the incidence and efficiency costs of taxation that account for tax salience effects as well as other optimization errors. Contrary to conventional wisdom, the formulas imply that the economic incidence of a tax depends on its statutory incidence and that a tax can create deadweight loss even if it induces no change in demand. The results are derived using simple supply and demand diagrams and familiar notions of consumer and producer surplus. The approach to welfare analysis proposed here yields robust formulas because it does not require specification of a positive theory for why agents fail to optimize with respect to tax policies.
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I derive bounds on price elasticities in a dynamic model that is mis-specified due to optimization frictions such as adjustment costs or inattention. The bounds are a function of the observed effect of a price change on demand, the size of the price change, and the degree of frictions. I measure the degree of frictions by the utility losses agents tolerate to make choices that deviate from the frictionless optimum. I apply these bounds to the literature on taxation and labor supply, allowing for frictions of 1% of consumption in choosing labor supply. Such small frictions reconcile the difference between micro and macro elasticities, extensive and intensive margin elasticities, and several other disparate findings. Pooling estimates from twenty existing studies yields bounds on the intensive margin labor supply elasticity of (0.47,0.54).
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The debate about behavioral economics - the incorporation of insights from psychology into economics - is often framed as a question about the foundational assumptions of economic models. This paper presents a more pragmatic perspective on behavioral economics that focuses on its value for improving empirical predictions and policy decisions. I discuss three ways in which behavioral economics can contribute to public policy: by offering new policy tools, improving predictions about the effects of existing policies, and generating new welfare implications. I illustrate these contributions using applications to retirement savings, labor supply, and neighborhood choice. Behavioral models provide new tools to change behaviors such as savings rates and new counterfactuals to estimate the effects of policies such as income taxation. Behavioral models also provide new prescriptions for optimal policy that can be characterized in a non-paternalistic manner using methods analogous to those in neoclassical models. Model uncertainty does not justify using the neoclassical model; instead, it can provide a new rationale for using behavioral nudges. I conclude that incorporating behavioral features to the extent they help answer core economic questions may be more productive than viewing behavioral economics as a separate subfield that challenges the assumptions of neoclassical models.
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