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

INTRODUCTION

Consumers can purchase goods in any of several ways. They can buy in their own jurisdiction, paying sales taxes if the jurisdiction levies a sales tax. Alternatively, consumers can travel across a border and make their purchases in a different jurisdiction, paying the sales taxes that apply in that jurisdiction. A third possibility is that they can buy over the Internet. (1) Currently, state and local governments cannot require an online firm to collect sales taxes or use taxes, unless the firm has a physical presence (nexus) in the taxing jurisdiction. (See Fox and Murray (1997) for a discussion of the relevant Supreme Court rulings.) In 2003, several large retailers announced that they would begin to collect state sales tax on their online sales. Also, several states are attempting to negotiate an agreement for mutual enforcement of these taxes. Nevertheless, the data analyzed in this paper are for 1997 and 2001, which are years in which it is believed that evasion of sales taxes on Internet purchases was very substantial. (2)

In this paper, we combine the analysis of the effects of taxes on Internet shopping with the analysis of cross-border shopping. We analyze Internet shopping behavior, using a data set for the United States that includes the general retail sales-tax rates in the consumer's home county and in adjacent counties. (3) Our results are consistent with the interpretation that Internet shopping, cross-border shopping, and home-county shopping are substitutes.

The literature on cross-border shopping includes both theoretical and empirical contributions. (4) For example, in a theoretical paper, Ohsawa (1999) uses a Hotelling-type model of spatial competition to examine the effects of the size and spatial arrangement of countries on tax rates and government revenues, in a Nash-equilibrium setting. There is also a substantial empirical literature. Gordon and Nielsen (1997) estimate that 3.9 billion Danish Kroner of value added escaped taxation by Denmark in 1992 because of cross-border shopping. FitzGerald (1992) finds substantial evidence of tax-induced cross-border shopping between the Republic of Ireland and the United Kingdom. Ferris (2000) analyzes border crossings from Canada to the United States from 1972 to 1997, and finds that taxes play a significant role, as do exchange rates and other variables. Garrett and Marsh (2002) use 1998 county-level data from Kansas to investigate cross-border lottery shopping between Kansas and its neighboring states. Their results suggest that the Kansas lottery gains revenue from Oklahomans who cross into Kansas, but suffers a net loss of revenue because of Kansans who cross into Missouri and Nebraska.

There is also a recent literature on the effects of taxation on Internet sales. Goolsbee (2000) tests the relationship between sales-tax rates and Internet purchases, using data for 1997 from Forrester Research (a market research company). Goolsbee knows the metropolitan area in which a consumer lives, but not the county of residence. Thus, Goolsbee assumes that the tax rate is uniform throughout each metropolitan area. (5)

Alm and Melnik (2005) also investigate the effects of sales-tax rates on Internet purchases. They use data from the Current Population Survey for 2001. They define the tax rate at the state level, using the lowest sales-tax rate available to the consumer in his/her state of residence.

In this paper, we also use the Current Population Survey. However, we exploit the fact that the county of residence is known for some of the respondents in the sample. Thus, we define the sales-tax rate at the county level. Performing the analysis at the county level plays an important role in our study of cross-border shopping because the extent of cross-border shopping is likely to diminish with distance. (6) Virtually all residents of the United States are fairly close to a county boundary, but many are much farther from the nearest state line. (7)

Goolsbee (2000) and Alto and Melnik (2005) address only the tax-evasion aspect of Internet shopping (i.e., they do not deal with cross-border shopping). Goolsbee finds that Internet purchases are highly sensitive to sales taxes, with tax-price elasticities in the range of two to four. Alm and Melnik also find a significant effect of sales taxes on Internet purchases, although their estimated tax-price elasticities are much smaller, around 0.5.

We also find that Internet purchases are significantly more likely for consumers who face higher sales-tax rates, all else equal. Our estimated tax-price elasticities are closer to those of Alm and Melnik than to those of Goolsbee. Even so, we consider the quantitative magnitude of the effects to be fairly substantial.

We also find that consumers whose home county is adjacent to a county with a lower sales-tax rate are significantly less likely to use the Internet for shopping, all else equal. This does not provide any direct evidence regarding cross-border shopping, but it is consistent with the interpretation that consumers are engaging in cross-border shopping.

The focus of most of this literature is on the effect of tax rates. Clearly, however, the definition of the tax base also has the potential to affect shopping behavior. There is wide variation among the states in the definition of which goods and services are subject to the retail sales tax. We include a variable that is designed to capture this effect. We find that Internet shopping is significantly more likely for consumers who reside in states in which the base of the sales tax is broader, all else equal. Thus, our results suggest that consumers may use the Internet to avoid sales taxation, because of a high sales-tax rate in their jurisdiction, and/or because the sales tax in their jurisdiction applies to a wide range of purchases.

DATA AND VARIABLES

We use data from the Current Population Survey (CPS) for 1997 and 2001. (8) Our key dependent variable, SHOP INTERNET, measures whether a consumer engages in online shopping. SHOP INTERNET is a binary variable. (9)

The CPS data include some individuals who have access to the Internet, but also some who do not. If we were to restrict the sample to include only those who have access to the Internet, there is the possibility of sample-selection bias. We deal with the issue of sample selection by estimating a system of two probit equations, including a selection equation for Internet access, as well as the equation for Internet shopping. The variable that measures whether the consumer has access to the Internet, ACCESS INTERNET, is also a binary variable.

One key explanatory variable is HOME TAX PRICE, which is the tax price associated with the sales tax of the consumer's home county. HOME TAX PRICE = (1 + [t.sub.h]), where [t.sub.h], is the sales-tax rate in the home county (measured as a proportion). The states can be divided into four categories, with respect to their general retail sales taxes. First, there are no general retail sales taxes in Delaware, Montana, New Hampshire, and Oregon, either at the state level or at the county level. (10) Second, 13 states impose a uniform sales-tax rate throughout the state, (11) and the District of Columbia has a uniform sales-tax rate within its borders. Third, eight states have variation in sales-tax rates across counties, but no variation within counties. (12)

For the remaining 25 states, there are variations in sales-tax rates, both across counties and within counties (i.e., cities within the same county can have different sales-tax rates). However, although we know the sales-tax rates in each city, we only have data on the county of residence. Thus, we calculate a weighted average sales-tax rate for each county, using the populations of cities within the county as weights. All consumers in a given county are then assumed to face the same sales-tax rate.

Our data on sales-tax rates were collected by a variety of methods, including telephone and mail contacts with state and county revenue officials, as well as visits to the websites maintained by some state and county revenue offices. In many counties, the sales-tax rate was the same in 1997 as it had been in 2001. In jurisdictions where the tax rates did change during this period, the changes were usually small. Thus, most of the variation in HOME TAX PRICE in our pooled cross-section data set is across counties, rather than across years.

We also include a variable that measures the relationship between the sales-tax rate in the consumer's home county and the sales-tax rates in the immediately adjoining counties. This variable, TAXRATIO, is (1 + t.sub.h])/(1 + [t.sub.f]), where [t.sub.h] is the sales-tax rate of the home county and [t.sub.f] is the minimum of the sales-tax rates among all of the adjacent counties.

This specification for TAXRATIO does not necessarily capture all aspects of cross-border shopping. For instance, a consumer may engage in cross-border shopping by going two or more counties away, rather than to an adjoining county. Also, we treat everyone in a given county the same, regardless of whether he or she lives close to the county line or miles away. Nevertheless, we argue that this variable provides valuable information regarding the incentives for cross-border shopping.