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I develop a method for decomposing the elasticity of taxable income (ETI) into its constituent parts (wage income, deductions, etc.), which is not possible using the existing ‘bunching’ methods for estimating the ETI. My method exploits a ‘conditional notch’, a common feature of tax systems, whereby a discontinuity in tax liability applies only to a subset of taxpayers (the treated). The treated income density is reweighted successively on the basis of past tax return data so that, one-by-one, the marginal densities of the tax return items match those among the non-treated. The relative effect of each reweighting on the treated income density indicates the relative contribution of that item to the ETI. I apply my method to a contingent notch in the Australian income tax schedule, relying on the universe of Australian administrative tax data.
Steven Hamilton is a PhD candidate in Economics and Public Policy at the University of Michigan, and his research focuses on behavioral responses to taxes. Before moving to Michigan, Steven was an analyst at the Australian Treasury, and received an Honours degree in economics from The University of Queensland.
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