when they differ, the tax rates will almost always be lower inthe suburbs, this error in measurement will tend to bias the estimated tax elasticities downward.For practical purposes, I assume, next, that anyone living outside the primary state residesin the physically closest county to the main city (measured using Rand McNally, 1997). If thatstate is one that applies both city and county rates, such as Oklahoma, I assume that they live inthe largest city in that closest county.In total, this yields 351 different city-state locations. The tax rates for most of these 351locations were then compiled individually either from direct conversation with the department ofrevenue in the state or from documents on the department’s website. For states withoutcentralized information, I contacted a local chamber of commerce in the city or county. Specificlocal information was not necessary for some of the states since several have uniform statewidetax rates and four have no sales tax at all (Montana, New Hampshire, Delaware, and Oregon).The average sales tax rate in a state (weighted by population) is shown in figure 2.Table 1 gives summary statistics of the sample of people with online access and dividedaccording to whether or not they have ever purchased something online. The two are not verydifferent in most measures, although online buyers appear to be slightly better educated than otheronline users, are somewhat more likely to be female, and so on.B. Model and SpecificationThe idea of the paper is simple. An individual choosing whether to buy a good at a storeversus online will compare the relative prices. Assuming that he avoids paying use tax on theonline transaction and that local sales taxes do not affect local retail prices (i.e., elastic localsupply), the individual will be more likely to buy online the greater is the relative price ratioPPSIt) (1 + ,where the t is the sales tax, P is price and the subscript S indicates in a retail ...
