This paper examines the comprehensive IRS data set of state-state migration flows for evidence that differences in state income tax rates are associated with migration patterns. Using annual data on moves between every pair of states, pooled time-series cross-section regressions indicate that in the 1992–2010 period states with higher top marginal income tax rates experienced relatively greater outmigration of taxpayers and gross income. To illustrate the magnitude of the tax effect, we estimate that by 2010 cumulative losses since the enactment of New Jersey’s 2004 “millionaires’ tax” were as much as 42,000 taxpayers and $6.9 billion in annual adjusted gross income. These results suggest that sustained, relatively high income tax rates could gradually erode a state’s population and revenue base.
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The Tiebout  model suggests that individuals will migrate to the community that offers the optimum “bundle” of amenities and taxes, under the assumptions that individuals have perfect information and zero migration costs.
See Molloy, Smith, and Wozniak  for an excellent review of recent migration studies.
Under a reciprocity agreement, income taxes are paid only to the state of residency, even if income is earned in another state. For example, New Jersey has a reciprocity agreement with Pennsylvania, but not with New York. Thus, New Jersey commuters to Pennsylvania are not subject to Pennsylvania income taxes, but New Jersey commuters to New York are subject to New York state income tax.
For a thorough discussion of these data, see Gross .
Intraregional flows count moves that cross state lines, but remain in the same region. Intrastate moves are omitted.
The West, except for the period between 1990 and 1995, also experienced positive net gains from migration.
Florida and Texas do not have any income tax. Tennessee levies a 6 percent tax on income from interest and dividends.
It is not clear why this has been referred to as a “millionaires’ tax,” but that has been common usage in New Jersey, which means in discussions of the levy people earning as little as $500,000 a year have been referred to as “millionaires.”
Many commuters to New York City experience see little or no change in their total state income tax payments when New Jersey taxes change, as there are offsetting changes in the amount of their New York state liability that can be credited against their New Jersey tax bill.
In principle, the benefits of spending funded by an income tax increase—or the benefits of cuts in other taxes—could offset the negative impact of the income tax increase, possibly leading to a net gain in migration. However, it is reasonable to have an idea of the potential amount of outmigration resulting from an income tax increase, taken by itself.
The logit model assumes Independence of Irrelevant Alternatives, which implies that error terms are independent and identically distributed. As a result, the model assumes that given a choice set of two states A and B, the addition of an alternative state C has no impact on the ratio of migration probabilities p i /p j . For a more detailed discussion of the multinomial logit model and its weaknesses, see Lattin, Carroll, and Green .
Results for log linear models were broadly similar to those of the linear probability models reported. While the log linear specification is more strictly in accord with the theoretical framework, the results of the linear regressions are easier to interpret, since the estimated effects of each right hand side variable on migration counts are independent of the values of the other variables.
Of course, as homes are assets, if a high housing price signals anticipated rapid gains it could actually attract more migrants to a state.
Population centers for each state are determined using the Census Bureau’s “center of population” method. A state’s center of population is calculated as the point on which a state would perfectly balance, if the state surface were rigid and flat and each person had identical weight [U.S. Census Bureau, 2011].
Although counts of exemptions are included in the IRS migration data, we do not report the results when they are used to measure outmigration. Individuals can claim additional exemptions when they meet certain criteria (such as being blind, elderly), meaning that the number of exemptions on a given tax return may exceed the number of family members. Admittedly, our use of tax returns to represent “families” may also lead to over-counting; for example, if a couple filed separate tax returns, they would be counted as two families. In any case, using exemptions as the dependent variable did not significantly alter the results.
We do not take into account local income taxes. For example, New York City’s resident income tax would not be reflected in the New York state tax rate.
The actual number of fixed effects is lower, as the Census omits migration pairs if the migrant count is extremely low (for privacy reasons). Cohen, Lai, and Seindel  present results of a similar model without controlling for pairwise state fixed effects.
The weighted average marginal tax rate is the amount that a state’s revenues increases from a one dollar increase in aggregate income that leaves the distribution of income unchanged.
Another study using the IRS data claims to find no statistically significant income tax rate effect on migration [Hanauer and Krueger 2013]. However, the article provides only scanty details about the estimation.
It seems likely that an increase in a state’s median home price relative to all other states is associated with developments that make the state an attractive destination for domestic migrants.
Tannenwald , in commenting on earlier work on this topic, criticized estimates made from regressions estimated using the weighted average marginal rate, as that rate is affected both by explicit policy changes as well as changes in the distribution of income. This study highlights results using the top marginal tax rate, which is an explicit policy rate
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Thanks to Dan Feenberg and Emily Gross for supplying data, and Ranjana Madhusudhan, Joe Mengedoth, and seminar participants at the Federal Reserve Bank of New York, the Federal Reserve Bank of Philadelphia, and Rutgers University for their feedback. Special thanks to Jennifer Hunt, David Rosen, and Alicia Sasser for their comments on an earlier draft of this paper, and Garrett Lau for excellent research assistance. All remaining errors are our own.
The views expressed in this paper are those of the authors, and do not necessarily reflect those of the New Jersey state government in general or the New Jersey Treasury Department in particular.
*Roger S. Cohen is an assistant treasurer and head of the Office of Revenue and Economic Analysis at the New Jersey Department of the Treasury. He received his doctorate from the University of North Carolina at Chapel Hill. His work involves the analysis of the revenue and economic impact of proposed changes to New Jersey taxes.Andrew E. Lai was formerly a policy analyst at the Office of the Chief Economist of the New Jersey Department of the Treasury. He received his Bachelor’s degree from Harvard University and his Master’s from Boston University. He is currently enrolled at the Cornell University School of Law.Charles Steindel is Chief Economist for the New Jersey Department of the Treasury. He received his doctorate from MIT. He works on economic and revenue forecasts for the state of New Jersey, and previous employers include the Federal Reserve Bank of New York, the First National Bank of Chicago, and the Federal Reserve Board. Much of his research has involved consumer behavior, especially in response to tax changes, as well as interpretation of economic statistics. He is a former member of the NABE Board of Governors and currently serves on the editorial board of Business Economics.