ne by about .005.Since the mean probability of purchase is approximately .20, the estimated elasticity of online9 The impact of local taxes on local prices has been examined by Poterba (1996) who finds no effect on pre-taxprices and by Besley and Case (1998) who find that higher taxes actually raise pre-tax prices.ഊ9buying with respect to the tax price (one plus the tax rate) is 2.3. The other coefficients aresignificant and have predictable signs. Higher income, more education, and using a computer atwork make a person more likely to have bought online. Being older, male, or married make aperson less likely.Column 2, presents the Tobit version of the same specification where the dependentvariable is the amount the individual has spent online. Again, there is evidence of a significanteffect of tax rates on Internet commerce. At the mean, the elasticity of spending with respect toone plus the tax rate is 3.6.Both of these estimates may suffer from an obvious potential source of bias. They showthat, controlling for observables, people who live in high sales tax locations tend to buy more overthe Internet. It is clearly possible that places with high levels of “technological sophistication”where people are disproportionately likely to have bought online may primarily be urban areas likeNew York and San Francisco which also happen to have high sales tax rates, leading to a spuriouscorrelation. In theory, the relationship could even be created by city level policies. If a city raisedits sales tax in order to pay for Internet infrastructure to make online use easier, this wouldcertainly make high sales taxes look influential for online buying but for a different reason. I willattempt to deal with potential unobserved heterogeneity bias in a number of ways but it isimportant to note that the bias need not go in this direction. Other factors may bias the results inthe opposite way. The results in table 1, for example, conditio...
