Internet purchases, cross-border shopping, and sales taxes.

For most variables, the coefficients and significance levels in the Internet-access equation are quite similar across the five specifications in Table 2. Thus, readers can get a good sense of the results by scanning down any of the five columns. We have a slight preference for Specification (2), because the interaction terms excluded from the Internet-shopping equation in that specification have insignificant effects in all of our other specifications. (19)

The results in the second part of Table 2 suggest that consumers in a high-tax county are more likely to have Internet access, and that those who live adjacent to a low-tax county are less likely to have Internet access. However, these effects are not statistically significant. (20) Table 2 also indicates that higher-income consumers are significantly more likely to have Internet access than those with lower incomes. Those with at least a high-school education are significantly more likely to have Internet access than those without a high-school education. Not surprisingly, the coefficient on the variable NUMCOMP (number of computers in the household) indicates that households with more computers are significantly more likely to have Internet access. The results indicate that Internet access (as opposed to Internet shopping) decreases monotonically with age.

The estimates of [[rho].sub.ue] (rho) are significantly different from zero in all five of our specifications. (21) Thus, the error term in the Internet-access equation is correlated with the error term in the Internet-shopping equation. This means that the simple probit model based on the selected sample of Internet users is indeed subject to sample--election bias, so that it is appropriate to use the model in which Internet access and Internet shopping are estimated as a system. (22)

Results for Internet Shopping

The results for the Internet-shopping equation are shown in the first part of Table 2. Below, we will consider the coefficients for the tax variables, HOME TAX PRICE, TAXRATIO, and TAXBASE. First, however, we consider some of the results for the non-tax variables.

(i) The coefficient estimate of LOGINCOME is positive and statistically significant. Thus, an increase in income is associated with an increase in the probability that a consumer would engage in online shopping, all else equal.

(ii) The dummy variables for educational attainment (HIGHGRAD, COLLGRAD, and PROGRAD) have positive signs, and most are highly statistically significant. These results suggest that those with a high-school diploma or a Bachelor's degree are more likely to use the Internet for shopping than those with less than a high-school education. Those with a graduate or professional degree are also more likely to shop via the Internet than those with less than a high-school education, although the effect is smaller than the effect of a high-school diploma or a Bachelor's degree.

(iii) The coefficients for the age-related dummy variables (AGE15, AGE20, etc.) indicate that the probability of Internet purchases has an inverse-U-shaped pattern by age, and that this pattern is statistically significant. The probability of Internet purchases rises until consumers are in their thirties, and then declines. Our conjecture is that teenagers are less likely to engage in online shopping because they are less likely to have access to credit, and that the elderly may be less familiar and less comfortable with online shopping, even after controlling for other variables.

(iv) The coefficient on the dummy variable for the year 2001, D2001, is large, positive, and highly significant. This is not surprising, since Internet usage increased very substantially between 1997 and 2001. Under the specification reported here, in which the year enters only as a dummy variable, we constrain the coefficient on HOME TAX PRICE to be the same in both years. However, it is possible that the behavioral response to taxes may have changed between 1997 and 2001. We tested this by including an interaction term between the tax-rate variable and the year. We found a significant effect in the equation for Internet access. However, the effect in the equation for Internet shopping was extremely small. (The z value on the coefficient for the interaction term was -0.01.) The results from the regression with the interaction term are available upon request.

Our assessment of these results is that most of the coefficients for the non-tax variables in the Internet-shopping equations (shown in the top portion of Table 2) can be interpreted in reasonable ways. Many of the coefficients are highly significant, and the magnitudes are economically meaningful.

The results in Table 2 also lend support to our hypotheses regarding the influence of sales taxes on Internet shopping. In each of the specifications in Table 2, the coefficient for the tax price in the local county (HOME TAX PRICE) has the expected positive sign, and it is statistically significant at the five-percent level. These results indicate that, all else equal, a resident of a county with a higher sales-tax rate is substantially more likely to use the Internet for shopping than a resident of a county with a lower sales-tax rate. These results are consistent with the notion that Internet shopping is used, in part, as a mechanism for sales-tax evasion.

In each of the specifications in Table 2, the coefficient for TAXRATIO in the Inter net-shopping equation has the expected negative sign, and is also significant at the five-percent level. The negative coefficient on TAXRATIO indicates that a consumer whose county is adjacent to a lower-tax county is less likely to use the Internet for shopping than he or she would otherwise be, all else equal. This result is consistent with an interpretation that involves cross-border shopping: All else equal, the tax benefits from Internet shopping are reduced if a lower-tax county is nearby. Because the sales-tax burden can be reduced, simply by driving across the county line to shop, those who live near a lower-tax county have less of an incentive to shop via the Internet, all else equal.

Recall that TAXBASE is defined as the sales-tax revenue as a proportion of personal income in a state, normalized by the weighted-average sales-tax rate in the state. In each of the specifications in Table 2, the coefficient for TAXBASE has the expected positive sign, and it is significant at the one-percent level. This suggests that, even after we control for the tax rates themselves, the relative amount of sales-tax revenue collected has an effect on Internet-shopping behavior. Some states may have a high value of TAXBASE because the sales tax applies to more goods and services, while others may have a high value of TAXBASE because of more stringent sales-tax enforcement. In either case, a high value of TAXBASE means that (holding constant the tax rates), the sales tax reaches further. Thus, shoppers have a stronger incentive to shop via the Internet. The results for TAXBASE point in the same direction as the results for HOME TAX PRICE: Sales taxes encourage Internet shopping, either because the sales-tax rate itself is high or because the sales tax is widely applied.

Our interpretation of the results for HOME TAX PRICE and TAXRATIO is that shopping in the home county, shopping in an adjacent county, and shopping on the Internet are all substitutes. Goolsbee (2000) and Alm and Melnik (2005) estimate the effect of the tax price on Internet shopping; they also find that a consumer who faces a high tax price would be more likely to engage in online shopping. Thus, broadly speaking, our results are consistent with those of Goolsbee and Alm and Melnik.

When we recover the marginal effects associated with the probit coefficients, we can calculate the elasticity of Internet shopping with respect to HOME TAX PRICE. Note that an increase in the tax price in the home county will also lead to an increase in the ratio of the tax price in the home county to the lowest of the tax rates in adjacent counties. Thus, a complete calculation of the elasticity of Internet shopping with respect to HOME TAX PRICE would also include the indirect effect through a change in TAXRATIO. A one-percent increase in HOME TAX PRICE would increase TAXRATIO by slightly more than 0.01. This, in turn, would decrease the probability of using the Internet for shopping, all else equal. For our preferred equation (Specification (2)), if we combine the direct effect through HOME TAX PRICE with the indirect effect through TAXRATIO, the resulting tax-price elasticity is about 0.198. (23)

CONCLUSION

A previous literature has provided estimates of the effect of sales-tax rates on Internet shopping, and another literature has considered the effect of taxes on cross-border shopping. Each of these is important to state and local governments, which have experienced a decline in their ability to raise revenues through sales taxes. We integrate these two avenues of research, by analyzing empirically the determinants of Internet shopping in the United States, using data from the Current Population Survey for 1997 and 2001.

The data indicate whether consumers used the Internet for shopping, and we use this binary variable as our dependent variable. Our data set also includes the sales-tax rate in the consumer's local county, a measure of the sales-tax rates in adjacent counties, a measure of the breadth of the sales tax in the consumer's state, and a wide range of economic and demographic variables.