Doctoral Dissertation Research: The Price Elasticity of R&D: Evidence From State Tax Policies

University Of California-Irvine, Irvine CA

Investigators

Abstract

In tax year 2008 the federal research and development (R&D) tax credit paid out over $8 billion to businesses, which was 7% of total federal expenditures on research. The intent of the tax credit is to provide an incentive for firms to raise their private level of R&D funding. This project will investigate how effective tax incentives are at increasing R&D. This evaluation is difficult because, while we can observe R&D spending before and after a tax policy change, we can only speculate on what R&D would have been without the tax policy change. Because policymakers implement tax incentives in response to current and/or expected economic conditions, a simple comparison of R&D before and after a tax incentive is implemented will lead to inaccurate inferences about the effects of the tax incentive. For example, if R&D in a given year is low, then policymakers may respond with a tax incentive. While a rebound in the following year could be due to the tax incentive, it might also reflect R&D simply returning to its mean value. Alternatively, policymakers might foresee a decline in R&D and implement a tax incentive to prevent the decline. Subsequently observing no change in R&D after the tax incentive takes place would be evidence supporting the efficacy of the tax incentive. To correct for the endogeneity of tax incentives, we will use state-level tax variation driven by changes in the U.S. federal R&D tax credit. While state governments are attentive to state-level economic conditions when forming their idiosyncratic state-level tax policies, the federal government sets a uniform national tax policy and is less attentive to individual state economic conditions. In addition, changes in the federal R&D tax credit have differential impacts on state-level tax incentives across states due to the interaction of federal and state taxes. These two features imply a regression mode that can generate an unbiased estimate of the effect of tax incentives on R&D. Broader Impacts: This study will make several important contributions, in addition to supporting the training of a doctoral candidate. First, the project will create a dataset on state corporate tax laws that will be more detailed than any existing dataset on state R&D tax incentives. These data will allow a descriptive analysis of how the overall tax burden for R&D has changed over time and across states/regions. Second, the project will generate an unbiased estimate of how tax incentives affect R&D. The final contribution will be an estimate of the endogeneity bias driven by self-selection of tax policies, which will help future economic research on tax incentives and uncover evidence on mechanisms behind the implementation of tax policies.