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The results show that, controlling for many observable characteristics, people who live in locations with high sales taxes are significantly more likely to buy things over the Internet. The estimated tax responsiveness of both participation and spending are large and resemble the tax effects found in previous research on retail sales in geographic border areas. The results are quite robust; the tax sensitivity is clear nationally, within regions, within states, and even within metropolitan areas. Further results suggest that the tax effect cannot be explained by unobserved heterogeneity across cities. The magnitudes in the paper suggest that to apply existing sales taxes to Internet commerce would reduce the number of online buyers by 25% and spending by more than 30% with some specifications suggesting even larger effects. I wish to thank David Gross, James Hines, Pete Klenow, Steve Levitt, Casey Mulligan, Joel Slemrod, Michelle White, Alwyn Young, Jonathan Zittrain and seminar participants at the University of Chicago and the University of Michigan for helpful discussions.ഊ2 The extraordinary growth of the Internet in the last few years, from fewer than 5 million users in 1993 to 62 million in 1997 (Department of Commerce, 1998), has led some to speak of the birth of a “world without borders,” where free communication, competitive markets, and extensive comparison shopping are a matter of course (see The Economist, 1997a; Hof, 1998; Pouliot, 1998). This apparent lack of geography in cyberspace, however, has raised some difficult issues regarding government policy toward the “new” economy, particularly regarding tax policy. The sales of products and services over the Internet have grown even faster than the overall use of the Internet; data from Jupiter Communications suggests online sales have been rising about 300% per year (Krantz, 1998) and are expected to continue growing at tremendous rates. Forrester Research predicts that, by the year 2000, total online sales will exceed $200 billion. At the extreme, Nicholas Negroponte, founder of the MIT media laboratory has predicted that such commerce may exceed $1 trillion by 2000. Although online transactions currently make up only a very small fraction of total retail sales, predictions such as these have caused state policy makers to become highly concerned with the fact that most online transactions pay no sales or use If these growth predictions are anywhere close to accurate, tax policy toward Internet commerce promises to have serious consequences for future state tax policy since the sales tax makes up the largest single component of state revenues. Economists have long argued that consumer sensitivity to tax rates will be larger for people living along geographic borders or in an open economy, more generally, where the costs of arbitraging tax rates across locations are low, and that this can have important implications for the 1 In general, Internet sales are treated the same as mail-order sales. No sales tax is collected from companies that have no presence (known as nexus) in the state. Although sales tax is not collected, technically states apply a use tax which requires consumers to pay the equivalent of the state sales tax on their mail-order and Internet purchases. The supreme court has ruled, however, that under existing law, an out-of-state vendor without nexus in a state cannot be required to collect the use tax for that state even if the customer lives there (National Bellas Hess, 386 U.S. 753, 1967; Quill, 504 U.S. 298, 1992). This has meant that use taxes have rather extreme compliance problems and are little known by most consumers (see Trandel, 1992). Enforcement of the use tax on consumers has only been effective for goods like automobiles that must be registered in the state of use.ഊ3 Empirical work on the responsiveness in border communities to taxes and other such policies has tended to bear out these predictions by finding large elasticities in such Against this backdrop, then, perhaps the key issue that the Internet poses for tax policy is not so much its potential to create a world without borders but rather to create a world of only borders-a world in which everyone is as responsive to local taxation as people who live on geographic borders. With that perspective, it is clear why state lawmakers and some in the popular press have decried “the disappearing taxpayer” and have called for reform (see The Economist, 1997a; 1997b). State Tax Notes has declared the issue of taxes and electronic commerce “the hottest topic in multistate taxation” (Sheppard, 1998) and the National Governors Association itself has called for a uniform tax on all Internet and mail-order sales. They argue that this would protect their revenue base and protect “Main Street” businesses. On the other hand, there is also serious opposition to taxes on the Internet. The opponents have argued that introducing taxes now might seriously impede the Internet’s growth at a critical stage of early development (see, for example, Stephenson and Zeisser, 1998). The recent passage of the Internet Tax Freedom Act is certainly one manifestation of this view although in its final form it does not prohibit uniform sales taxes. that compliance with the thousands of local tax rates, or even monitoring those rates, would be unduly burdensome on out-of-state companies, especially for small businesses or for sellers of information goods that are delivered online and often do not even have a customer’s mailing 2 Such theoretical discussions can be found in Gordon (1983), Mintz and Tulkens (1986), Braid (1987), Kanbur and Keen (1993), Trandel (1992; 1994) and Gordon and Neilsen (1997). 3 Empirical work on sales taxes and excise taxes in border areas can be found in Mikesell (1970), Fox (1986), Walsh and Jones (1988), or Rappaport (1994). Holmes (1998) gives similar evidence on the effects of right-to-work 4 To actually impose such a tax, however, would likely require a specific federal law permitting states to require out-of-state companies without nexus in a state, nevertheless, to collect use taxes for that state (see footnote 1). Fox and Murray (1997) give a good discussion of the issues. The role of digital content is not as clear (see McLure, 5 The ITFA establishes a moratorium on new taxes but, applying a sales tax to physical goods sold over the Internet would not be considered a new tax so long as it applies to retail and out-of-state mail-order sales.ഊ4 Despite ongoing policy interest and despite the potential relevance to the broader field of open economy tax policy, there has been little empirical work on the impact of tax policy on Internet commerce. Most economic work on the Internet has focused on more basic issues such Existing discussions of taxes and Internet commerce have either centered on legal and practical issues (see, for example, Horner and Owens, 1996; Bourgeois and Blanchette, 1997) or else on the conceptual basis for Internet tax policy (Fox and Murray, 1997; McLure, 1997). Nor can we take lessons from previous research on mail-order sales which face exactly the same issues. There is virtually no empirical work on that subject, either. Indeed past work that has advocated administering taxes on out-of-state mail-order sales, has explicitly assumed that there are no behavioral impacts (ACIR, 1986). In this paper, I attempt to provide a rigorous empirical foundation for discussions of Internet commerce by estimating how such commerce responds to local tax rates. To do so, I turn to a major recent survey of the online purchase patterns of approximately 25,000 people with access to the Internet in more than 350 cities and metropolitan areas and match these data to the The results show that Internet sales are highly sensitive to local taxation. This is true nationally, within regions, within states, and even within metropolitan areas. Controlling for individual characteristics, people who live in high sales tax locations are significantly more likely to buy over the Internet and I can show that this is unlikely to result from unobserved heterogeneity across locations. The paper also uses data on purchases of computers to compare the apparent tax responsiveness of both Internet and mail-order sales and finds similar effects for both of the “non-taxed” channels. The estimated tax elasticities of Internet commerce with respect to local taxation are quite large and thus resemble the elasticities found in previous sales tax studies of geographical border 6 Examples of such work include Mackie-Mason and Varian (1995), Downes and Greenstein (1998), and the papers in McKnight and Bailey (1995). 7 In accordance with the data, the results concern sales to consumers not to businesses.ഊ5 areas. In policy terms, the results suggest that to apply existing sales taxes to Internet purchases would reduce the number of online buyers by 20-25% and spending by 25-30%. The large elasticities estimated here raise important questions about long-run tax policy toward Internet The paper proceeds as follows. Section II describes the data used in the paper and the general approach. Section III presents the basic evidence that taxes impact online buying decisions and shows it to be highly robust. Section IV examines the mail-order/Internet distinction for the case of computer purchases. Section V gives policy simulations of proposed taxes on Internet commerce as well as some discussion of the longer-run role of sales taxes. A major problem with doing empirical work on Internet commerce has been data. Aggregate data is not very useful. Observing that Internet sales are high in places with high taxes may just indicate that places with high taxes have higher incomes, higher computer ownership, higher education, or any number of factors that are correlated with online buying. Individual data are extremely important but few consumer surveys even ask about the Internet and if they do, it is hard to get sufficient sample sizes. In any random sample of the population, less than half the respondents are likely to have computers. Of those, only a fraction have Internet access and only a small share of those have ever bought something online. To examine the response of such purchases to local taxation then requires further dividing up the Internet shoppers by geographic location. It is easy to see why this will tend to yield small samples. In this paper, I turn to an extensive private survey conducted in late 1997 for Forrester Inc., a market research company in Cambridge, Massachusetts. The survey was conducted by the NPD group and is meant to provide a nationally representative portrait of the computer andഊ6 technology decisions of more than 110,000 consumers. The survey includes detailed information about various demographic characteristics such as income, age, gender, education, as well as the state and metropolitan area of residence. The information covers the ownership of computers, as well. About 45% of the respondents had computers at the time of the survey. Those owners were then asked whether they had online access and if so, how often they used it and were asked for their online buying history. In total this yields a sample of about 25,000 online users, sixteen percent of whom report having purchased something online. For each individual that has bought something online, the survey asks how much they have spent (in four groupings: $0-25, $25-100, $100-500, and $500+) for different types of goods such as books, software, or computers. I use the Forrester imputed values for each spending category and sum across the types of goods to approximate the total amount spent online. Using this measure, the average amount spent, conditional on buying, is $322. This is similar to existing estimates of average online spending such as Krantz (1998). Although the analysis will be done at the individual level, for expositional purposes, figure 1 shows the share of Internet users that have purchased something online in each state. There is decided variation across locations. Matching the purchase data to local sales tax rates requires making some assumptions because the data do not give the actual town name of the respondent. Even if it did, there are as many as 6400 different sales tax rates across the United States (see Rappaport, 1994) and information on them is not kept by the federal government and must be collected at the state and I assume, first, that any one living in the main state of the metropolitan area actually lives in the primary city itself. In other words, I assume that a person that reports living in the Chicago metropolitan area and the state of Illinois actually lives in the city of Chicago proper. This prevents me from distinguishing between city and suburb within the same state, but is necessary 8 The metropolitan areas are actually defined as the television market the respondent lives in. These are generally larger than the corresponding SMSA. San Francisco, for example, includes the entire bay area.ഊ7 given the nature of the data. Since, when they differ, the tax rates will almost always be lower in the 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 resides in the physically closest county to the main city (measured using Rand McNally, 1997). If that state is one that applies both city and county rates, such as Oklahoma, I assume that they live in the largest city in that closest county. In total, this yields 351 different city-state locations. The tax rates for most of these 351 locations were then compiled individually either from direct conversation with the department of revenue in the state or from documents on the department’s website. For states without centralized information, I contacted a local chamber of commerce in the city or county. Specific local information was not necessary for some of the states since several have uniform statewide tax 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 divided according to whether or not they have ever purchased something online. The two are not very different in most measures, although online buyers appear to be slightly better educated than other online users, are somewhat more likely to be female, and so on. The idea of the paper is simple. An individual choosing whether to buy a good at a store versus online will compare the relative prices. Assuming that he avoids paying use tax on the online transaction and that local sales taxes do not affect local retail prices (i.e., elastic local supply), the individual will be more likely to buy online the greater is the relative price ratio where the t is the sales tax, P is price and the subscript S indicates in a retail store and I indicatesഊ8 I will follow the common assumption in the literature on sales taxes across regions and assume the relative price, P P S I / , is constant across regions. Two comparisons of Internet and retail prices (Goldman Sachs, 1997; Bailey, 1998) suggest that the ratio is close to 1 for 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 such as income, age, and education. For the probability of having bought something online, I will use a probit model. For the amount that an individual reports having spent online, I will use a Tobit model (censored because a large number of people report no spending). The initial results from estimating the probit regression of the {0,1} response of having ever 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 of buying online. The mean probability of buying conditional on having online access is estimated to be 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 the respondent’s household, and whether the respondent operates their own business, uses a computer at work, or owned a computer in the previous year. The standard errors are corrected for 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 decision to buy online and has the predicted sign. The magnitude suggests that at the mean of the covariates, raising the sales tax by .01 increases the probability of buying online by about .005. Since the mean probability of purchase is approximately .20, the estimated elasticity of online 9 The impact of local taxes on local prices has been examined by Poterba (1996) who finds no effect on pre-tax prices and by Besley and Case (1998) who find that higher taxes actually raise pre-tax prices.ഊ9 buying with respect to the tax price (one plus the tax rate) is 2.3. The other coefficients are significant and have predictable signs. Higher income, more education, and using a computer at work make a person more likely to have bought online. Being older, male, or married make a Column 2, presents the Tobit version of the same specification where the dependent variable is the amount the individual has spent online. Again, there is evidence of a significant effect of tax rates on Internet commerce. At the mean, the elasticity of spending with respect to Both of these estimates may suffer from an obvious potential source of bias. They show that, controlling for observables, people who live in high sales tax locations tend to buy more over the 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 like New York and San Francisco which also happen to have high sales tax rates, leading to a spurious correlation. In theory, the relationship could even be created by city level policies. If a city raised its sales tax in order to pay for Internet infrastructure to make online use easier, this would certainly make high sales taxes look influential for online buying but for a different reason. I will attempt to deal with potential unobserved heterogeneity bias in a number of ways but it is important to note that the bias need not go in this direction. Other factors may bias the results in the opposite way. The results in table 1, for example, condition on having online access. If the only people with online access in a technologically “backward” locations (that also tend to have low tax rates) are the diehard Internet users who frequently buy, this will bias the coefficient To deal with the unobserved city characteristics, I first present (columns 3 and 4) the same probit and Tobit regressions but restrict the sample to include only the top 30 metropolitan areas with the idea that these may be more comparable locations and will help eliminate any bias caused from comparison to rural and small town locations. This restriction reduces the sample by moreഊ10 than 40%. The coefficients on the sales tax, however, are not significantly different than in the full sample and the point estimates are larger. They still show that taxes appear to have a significant effect on Internet purchases. The elasticities here are 2.9 on buying and 3.4 on spending. Obviously this does not rule out the spurious correlation, but does suggest that whatever the correlation between technological sophistication and tax rates, it must be just as true among the top 30 cities as it is between large metropolitan areas and more rural areas. Next, in table 3, I use the variation in tax rates across geographic areas to further narrow the comparison groups. Column 1 and 2 include region dummies and asks whether individuals with the same observable characteristics and living in the same region are more likely to buy online in a city with a higher tax rate. In both the probit and Tobit regressions, taxes have a significant impact within regions. The elasticities at the mean are 2.4 and 3.5. Column 3 and 4 include state dummies and ask whether people with the same observables living in the same state are more likely to buy online where the sales tax is higher. Here the coefficients are particularly large and significant suggesting that taxes matter within a given state. The elasticities at the mean More importantly, however, columns 5 and 6 then inclu Bibliography: Word Count: 3590
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