State Tax Differentials, Cross-Border Commuting, and Commuting Times in Multi-State Metropolitan Areas David Agrawal and William Hoyt Discussant: Byron Lutz, Federal Reserve Board

Disclaimer The opinions expressed are those of the author and do not necessarily express the opinion of the Board of Governors of the Federal Reserve.

Many strong points Important and policy relevant topic – Commuting is a major “cost” for many households – Provides insight on numerous additional issues Behavioral response to income taxation Urban form – density and sprawl Very original – Reciprocity of the state income tax appears to be severely understudied – Relatively little empirical work on how taxes influence urban form

Many strong points (cont.) Theoretical model is concise and tractable, yet yields numerous interesting and testable predictions – Would be very interesting to test some of the predictions other than commuting Theory and empirics are tightly linked Empirical results are convincing

Elasticity of Demand for Land Multi-state metro with some workers from state 2 commuting to state 1 for work Mobile households achieve equal utility at all locations First part of model under reciprocity – Income taxes paid on basis of residence – Income tax increase in state 1 causes out migration to state 2 – Size and density of state 2 increase – Pushes up land values in state 2

Elasticity of Demand for Land (cont.) Second part of model under reciprocity: commute times – State 2 is geographically larger = longer commutes – Effect of increase in density depends on where density changes If price elasticity of demand for land (ε) ‹ 1 then density increases distant from CBD and commute times increase Not much intuition provided for role of ε Value of ε is very important – Potentially reverse sign of commute effect – Density in center versus density at edge

Elasticity of Demand for Land (cont.) More fully develop intuition about role of elasticity of demand for land – Intuition for where density increases Empirical evidence – Small and somewhat dated empirical literature suggests elasticity is less than 1 (Rothenberg, et. al. 1991)

Variation in Reciprocity Reciprocity status is a key source of identifying variation What causes this variation? Is it likely correlated with unobserved determinants of commute times? Need some discussion

Source: Rork and Wagner 2012

Other Taxes and Spending Taxes are used to fund spending – If the spending is valued by the individual being taxed then tax differentials will not capitalize => no change in location of residence or jobs = > commute times are unchanged A state with relatively low income taxes may have relatively high burdens for other taxes (e.g. sales and property tax) – If a income tax differential is offset by an opposite signed differential from a different tax => no capitalization Authors prefer not to include these “other” state policies because spending is measured at the state level and contain measurement error

Other Taxes and Spending Including these other policies causes the tax rate differential coefficient to fall to near zero for reciprocity states Suggestions 1.Show more specifications with these controls 2.Control for MSA-specific elements of local government spending and taxation (property tax) Per-pupil K-12 school spending is available by district annually Census of governments provides taxes and other local spending in 2007 – roughly mid-point of sample – Assume differential is fixed over time OR – Impute missing years using state level aggregation of local taxes/spending

Wildsain (85) MTR Effect Higher marginal tax rate reduces the opportunity cost of time => longer commutes Results interpreted as confirming the empirical prediction Statistically significant, but extremely small in magnitude Upper bound on MTR coefficient (95% confidence level) is about 3 ½ seconds Example: a state increases the mean marginal tax rate by 3 percentage points => ~ 10 second increase in average commute “Precisely estimated zero”

Rivers Model suggests major mechanism through which commute times are altered by tax differentials is through sprawl Requires that there be room for metro expansion – Saiz (2010) shows many MSAs are constrained by geography Requires that expansion not be blocked by land regulation Fortunately a majority of the sample appears to be on major rivers (Ohio, Mississippi, Missouri, etc.) which generally have ample room for expansion and are not known for heavy land use regulation Robustness check limiting to “rivers” portion of the sample