A Simulation-Based Approach to Measuring Income Responses to Taxation with Several Behavioral Margins
The design of the tax system is one of the key aspects of economic policy. To reach an efficient allocation, taxes and transfer programs should achieve the desired redistribution, and the tax system should raise net tax revenue at the lowest possible social cost. Consequently, it is crucial to assess the effects of tax policy on economic behavior.
The elasticity of the taxable income with respect to the net-of-tax-rate (ETI) has gained much attention during recent years, as it provides a general approach to assess the how the taxable income changes in response to taxation by summarizing effects along all relevant behavioral margins. Despite the clear practical relevance of the ETI, it is difficult to establish its size based on earlier research. The two conventional methodological approaches, the IV regression approach and the bunching approach, fail to produce similar point estimates. Estimates also differ considerably across studies within the approaches. We have recently suggested a new approach, based on Indirect Inference principles, which can produce much more reliable estimates than the conventional methods of measuring the ETI. In this project, we aim at (i) applying our new approach empirically to produce more reliable estimates of the ETI using Swedish and German data, and (ii) extending our new approach, both in simulated environments and empirically, by using more realistic behavioral models, such as allowing for life-cycle choices and behavioral heterogeneity.