Abstract
The empirical literature on tax-induced migration (TIM) primarily focused on estimating the average elasticity of migration to interregional tax differentials but ignores the potential effect of the variations around this average. This paper extends the work of Moretti and Wilson (Am Econ Rev 107:1858–1903, 2017) and finds salient nonlinearity in the TIM of star scientists between 1977 and 2010. The results suggest that differences in personal income tax and research and development (R & D) tax credits between two states generate nonlinear impacts on migration; there is evidence of an important inertia range in which the differences generate little impact on migration. In contrast, the corporate income tax has approximately linear effects and investment tax credit has consistent effects only when the destination state initially has higher credits than the origin state. As different taxes or tax credits have distinctive nonlinear effects on migration, decision makers are cautioned of using average elasticities of TIM in policy making.
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Notes
Star scientists are defined as exceptional inventors that, in a given year, are at or above the 95th percentile in number of patents over the past 10 years (Moretti and Wilson 2017).
Employees who live in one state but work in another only need to pay state and local taxes of his/her home state if the two states have tax reciprocity agreement.
For example, Sasser (2010) modified the logistics model to investigate the relative importance of three migration drivers—labor market conditions, per capita incomes, and housing affordability over time. The logistic model in Gabriel et al. (1993) allows for testing a hypothesis of asymmetric information flow between the origin and destination states.
Specification (6) of Table 2a) in Moretti and Wilson (2017) has provided the baseline regression results. It was the preferred specification among a variety of models controlling for state fixed effects, state-year fixed effects (state-specific time trends) or region-year fixed effects (region-specific time trends).
Figure 4 in Moretti and Wilson (2017) show a series of bin-scatterplots of the log odds ratio against the log net-of-tax rate after demeaning the log odds ratio and the log net-of-tax rates by their within-pair and within-year sample means. They used 40 bins sorted along the x-axis, here in this paper we use 80 bins.
The B-splines only illustrate the one-to-one partial relationship between outmigration and each tax, leaving the effects of other taxes out of consideration. Additionally, the choice of B-spline with three degrees of freedom is ad hoc. In contrast, generalized additive model (GAM) with smoothing spline terms does not require manual choices of degrees of freedom and simultaneously incorporates all taxes.
The smoothing splines display changes of marginal effects at ± 0.04 for ATR, zero for ITC, ± 0.1 for R and D credits, and no change for CIT. Therefore, for the bin regression, we interact ATR with the indicator functions \( 1({\text{ATR}} \ge 0.04) \) and \( 1({\text{ATR}} \le -\,0.04) \), as well as CIT/ITC with \( 1({\text{CIT}}/{\text{ITC}} \ge 0) \). ITC is further interacted with \( 1({\text{ITC}} \le -\,0.02) \). R and D credit is interacted with \( 1({\text{Cred}}) \ge\,0.1 \) and \( 1({\text{Cred}} \le -\,0.1) \).
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Acknowledgements
The authors would like to thank Sandy Dall’erba and Benjamin Crost for their suggestions and comments on earlier drafts. We would also like to thank Enrico Moretti and Daniel Wilson for sharing their data and analytical files. We have no relevant or material financial interests that relate to the research described in this paper.
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