store and I indicatesഊ8an online merchant.9I will follow the common assumption in the literature on sales taxes acrossregions and assume the relative price, P P S I / , is constant across regions. Two comparisons ofInternet and retail prices (Goldman Sachs, 1997; Bailey, 1998) suggest that the ratio is close to 1for many common products so I will assume that for the elasticity calculations.I will use two related specifications for the individual’s choices about Internet commerce,each of which will include the tax rate and a number of economic and demographic controls suchas income, age, and education. For the probability of having bought something online, I will use aprobit model. For the amount that an individual reports having spent online, I will use a Tobitmodel (censored because a large number of people report no spending).3. ResultsThe initial results from estimating the probit regression of the {0,1} response of havingever bought online (conditional on having Internet access) are presented in column 1 of table 2.The coefficients listed give the estimated marginal effects of the covariates on the probability ofbuying online. The mean probability of buying conditional on having online access is estimated tobe 20.3%. The explanatory variables other than the sales tax term include income, education,age, race, gender, marital status, as well as dummies for the presence of children under 18 in therespondent’s household, and whether the respondent operates their own business, uses acomputer at work, or owned a computer in the previous year. The standard errors are correctedfor the fact that tax rates do not vary at the individual level but only by city and state.The results show that the sales tax does appear to have a significant impact on the decisionto buy online and has the predicted sign. The magnitude suggests that at the mean of thecovariates, raising the sales tax by .01 increases the probability of buying onli...
