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.
HOME TAX PRICE and TAXRATIO are calculated on the basis of
sales-tax rates. However, the effects of the sales tax in a given
jurisdiction will depend on the tax base, as well as on the tax rate.
(If an item is excluded from the sales-tax base, its effective sales-tax
rate is zero, regardless of the statutory sales-tax rate.) There is
considerable variation among the states in the base of the sales tax.
(Although counties have some latitude to set the sales-tax rate in 33 of
the 46 states with a retail sales tax, the tax base is set at the state
level.) (13) There may also be cross-state differences in the intensity
of sales-tax enforcement. As a result of these differences, there is
variation among the states in sales-tax revenue, even when we hold
constant the level of the sales-tax rates. In an attempt to control for
these differences, we have created a variable called TAXBASE. In
creating TAXBASE, we begin by calculating the sales tax as a percentage
of personal income in each state. (14) We then divide by the weighted
average of the sales-tax rates in the state. For TAXBASE, as for HOME
TAX PRICE, there is considerable variation by region, but relatively
little variation across years.
LOGINCOME is the log of the resident's household income. We
expect a positive coefficient for LOGINCOME, since use of the Internet
for purchases is almost certain to be a normal activity. (15)
FEMALE, WHITE, and MARRIED are binary variables for the
individual's demographic characteristics. HIGHGRAD, COLLGRAD, and
PROGRAD are a set of dummy variables for the individual's education
level, indicating whether his or her highest level of educational
attainment is a high-school diploma (HIGHGRAD), a Bachelor's degree
(COLLGRAD), or an M.A., Ph.D., or professional degree (PROGRAD). (The
omitted category is the group who have less than a high-school
education.) NUMCOMP is the number of computers in the household. AGE15,
AGE20, AGE30, AGE50, and AGE60 are a set of age-group dummy variables.
For example, AGE15 is equal to one if the consumer's age is from 15
to 19, AGE20 equals one for consumers aged from 20 to 29, and AGE60
equals one for those whose age is greater than or equal to 60. (The
omitted category is the group of consumers aged 40-49.) D2001 is a dummy
variable, equal to one when an observation is taken in 2001, and zero
when it is taken in 1997.
We include the percentage change in the Consumer Price Index (which
we call CPI-CHANGE) as an explanatory variable. (16) The inflation rate
in the local area may play a role in driving consumer awareness of the
desirability of out-of-area shopping methods, such as mail-order
shopping and Internet shopping. Therefore, we expect inflation rates to
have a positive effect on Internet shopping.
Table 1 presents summary statistics for several of the most
important variables in our data set. Table 1 includes separate columns
for those without Internet access, those with Internet access who do
shop online, and those with Internet access who do not shop online, as
well as for the entire sample. Not surprisingly, the sample is
predominantly white. More than half of the individuals in our sample are
married.
For the sample as a whole, the average sales-tax rate at the county
level is about 6.29 percent. The means reported in Table 1 suggest that
those with Internet access tend to be somewhat younger, more affluent,
and more highly educated than those without Internet access. Those with
Internet access are also more likely to be unmarried, white, and male,
although the differences among the sub-groups tend to be relatively
modest. Of course, caution must be used when interpreting simple
univariate means. In particular, the simple means in Table 1 indicate
that sales-tax rates are actually lower for Internet shoppers than for
Internet users who do not shop, on average. This simple univariate
relationship would appear to contradict our hypotheses. However,
multivariate analysis suggests that Internet shopping is more likely for
those who face higher sales-tax rates, all else equal.
The mean value of TAXRATIO is about 1.0066. This indicates that the
average individual in our sample lives adjacent to a county in which the
sales-tax rate is about two-thirds of one percentage point lower than in
his or her home county.
The mean value of TAXBASE is about 0.41. This means that, on
average, each percentage point of sales-tax rate generates sales-tax
revenue equal to about four-tenths of one percent of personal income.
One important reason why the mean value of TAXBASE is less than one is
that, in most states, the sales tax applies to relatively few services.
EMPIRICAL SPECIFICATION
Counties may differ in a variety of ways, even when they are in the
same state, and even when they have the same sales-tax rate. (For
example, some counties have better Internet infrastructure and/or better
transportation networks than others.) Therefore, we control for county
effects in all of our econometric specifications.
Our dependent variable, SHOP INTERNET, is equal to one if the
consumer shops over the Internet, and zero otherwise. It is possible to
use SHOP INTERNET as the dependent variable in a standard probit model,
using the sample of Internet users. However, this procedure will not
necessarily generate consistent estimates. The coefficients may be
subject to sample-selection bias, because a consumer can only shop
online if he or she has access to the Internet in the first place.
Thus, a superior estimation strategy is to use maximum-likelihood
techniques to estimate simultaneously a probit equation for Internet
access and a probit equation for Internet shopping. (For discussion of
models of this type, see Alm and Skidmore (1999) and Van De Ven and Van
Praag (1981).) The Internet-access equation strengthens the
interpretation of the Internet-shopping equation, but the coefficients
from the Internet-access equation are also of interest in their own
right. (17) The dependent variable in the selection equation for
Internet access, ACCESS INTERNET, is equal to one if the consumer has
Internet access, and zero otherwise.
Thus, our model consists of equations [1] and [2]:
[1] [SHOP INTERNET.sub.i]
= 1 if [SHOP INTERNET.sup.*.sub.i]
= [x.sub.i][beta] + [u.sub.i] > 0;
= 0 otherwise;
[2] ACCESS INTERNET
= 1 if [ACCESS INTERNET.sub.i]
= [z.sub.i] [gamma]+ [e.sub.i] > 0;
= 0 otherwise.
In equation [1], [SHOP INTERNET.sup.*] is a latent variable, x is a
vector of explanatory variables, [beta] is a vector of coefficients, and
u is an error term. In equation [2], [ACCESS INTERNET.sup.*] is a latent
variable, z is a vector of explanatory variables, [gamma] is a vector of
coefficients, and e is an error term.
We specify equation [1] using the explanatory variables already
described, along with a full set of interactions among the variables for
race, sex, and marital status. Thus,
[3] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
We specify the selection equation for Internet access with the same
variables that were shown in equation [3]. Thus,
[4] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
It is possible for the Internet-shopping equation to include all of
the variables in [4]. However, if the selection equation and the
equation of primary interest include the same set of explanatory
variables, identification is achieved exclusively through the
non-linearity of the probit. Thus, it is preferable for at least one of
the explanatory variables in the sample-selection equation to be absent
from the equation of primary interest. (For discussion, see Wooldridge
(2002) and Van Ham and Buchel (2004).) We will report on five
specifications of the maximum-likelihood model, in which different
combinations of variables are excluded from the Internet-shopping
equation. For purposes of comparison, one of these specifications
includes the same set of explanatory variables in both the selection
equation and the equation of interest.
We assume that the error terms in the two equations are distributed
according to a bivariate standard normal distribution. Following the
notation of Alm and Skidmore (1999), we denote the correlation between u
and e as [[rho].sub.ue]. If [[rho].sub.ue] = 0, probit estimates based
on the selected sample of those with Internet access would be
consistent. However, in the more general case in which [[rho].sub.ue] is
nonzero, probit estimates based on the selected sample will be biased,
since
[5] E([SHOP INTERNET.sup.*.sub.i] | [X.sub.i], [Z.sub.i], &
[ACCESS INTERNET.sub.i] = 1) = [X.sub.i][beta] + E(u | [X.sub.i],
[Z.sub.i], & [ACCESS INTERNET.sub.i] = 1) = [X.sub.i][beta] +
[[rho].sub.ue](e | [Z.sub.i] & [ACCESS INTERNET.sub.i] = 1).
The likelihood function for this model is
[6] [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [N.sub.1] refers to those who do not have access to the
Internet, [N.sub.2] refers to those who have access to the Internet but
do not shop over the Internet, [N.sub.3] refers to those who have access
to the Internet and do shop over the Internet, [[PHI].sub.1] is the
univariate standard normal density function, and [[PHI].sub.2] is the
bivariate standard normal density function.
Results for Internet Access
Our results are shown in Table 2. (18) The first part of Table 2
shows the estimates for the Internet-shopping equation; the second part
shows the estimates for the Internet-access equation. We begin our
discussion of the results from the system of two equations by looking
briefly at the results for Internet access.
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.
We estimate a system of equations, with a probit selection equation
for Internet access, as well as a probit equation for Internet shopping.
In all of our estimates, we find that the probability of Internet
shopping is higher for those with higher incomes, all else equal. The
probability of Internet shopping increases rapidly with age until
consumers are in their thirties, and then decreases. Those with less
than a high-school education are less likely to use the Internet for
shopping than those with more education, all else equal. And, not
surprisingly, we find a sharp increase in the propensity to use the
Internet for shopping between 1997 and 2001.
The effects of the tax-rate variables on Internet shopping are
always of the predicted sign, and they are statistically significant in
all cases. In addition, the coefficients for the tax-rate variables are
fairly robust across our different model specifications. The sales-tax
rate in the consumer's own county has a positive effect on online
shopping, all else equal. Our interpretation is that those who live in
areas with high sales-tax rates are more likely to use the Internet for
shopping, because sales-tax evasion on Internet sales is not difficult,
and the benefit from evasion is greater when the sales-tax rate is
higher. In addition, we find that Internet purchases are less likely for
those who reside in a county that is adjacent to another county with a
lower sales-tax rate, all else equal. We interpret this as evidence of
cross-border shopping. When we account for the direct marginal effect of
the home-county tax price, as well as the indirect effect on the
relationship between the home-county tax rate and the lowest tax rate in
an adjacent county, the tax-price elasticity is about 0.20. These
elasticity estimates are closer to those of Alm and Melnik (2005) than
to the higher estimates of Goolsbee (2000). However, this does not
suggest to us that the behavioral responses are terribly small. We hope
we have contributed to an emerging consensus that the effects are fairly
substantial, although there remains controversy in the literature about
the precise quantitative magnitude of the effects.
Goolsbee (2000) and Alm and Melnik (2005) focus exclusively on tax
rates. However, holding constant the statutory sales-tax rate, a state
could have high sales-tax revenues because the sales tax applies to more
items, or because the sales tax is enforced more rigorously, or both. In
either case, a more extensive sales tax would provide a larger incentive
for Internet shopping, all else equal. In this paper, we introduce a new
variable that is designed to capture the effect of differences in the
sales-tax base. This new variable is sales-tax revenue as a proportion
of personal income in a state, normalized by the weighted average of the
sales-tax rates in the state. We find that, even after controlling for
the tax rates themselves, the relative amount of tax revenue has an
important effect on Internet shopping behavior: All else equal, Internet
purchases are more likely for those who reside in a state in which
sales-tax revenues are a larger fraction of personal income, after
normalizing by sales-tax rates. We interpret this as additional evidence
that consumers respond to the tax-related incentive to use the Internet
for shopping.
Goolsbee (2000) and Alm and Melnik (2005) make very substantial
contributions. We believe that our paper advances the profession's
understanding beyond these earlier papers in three ways. First, we
investigate cross-border shopping and sales-tax evasion through Internet
shopping in a single framework. We consider both the effect of the tax
rate in the consumer's local jurisdiction, and the effects of tax
rates in adjacent jurisdictions.
Second, we define some of the tax-rate variables at the county
level. We believe that this allows us to measure the tax-rate variables
with greater precision. We also believe that this is crucial for our
study of cross-border shopping. It would be very difficult to study
cross-border shopping in a meaningful way, unless tax rates can be
defined over reasonably small geographic areas.
Third, we introduce a new variable that is designed to capture
differences in the sales-tax base. Previous papers in this literature
control for tax rates, but not for the tax base. Our results indicate
that tax rates and the tax base have similar effects: High sales-tax
rates encourage Internet shopping, as do sales taxes that are widely
applied.
The dependent variable in our study indicates whether the consumer
uses the Internet for shopping. Unfortunately, we do not have
information on the dollar value of the Internet shopping that is
undertaken by the consumers in our sample. Consequently, any attempt to
use our results to estimate the effects of the Internet on sales-tax
revenue would have to be fairly speculative.
Our data are taken from a time period when the Internet was still
in its infancy. As this new medium of commerce becomes more and more a
regular feature of the economic landscape, we anticipate that the
determinants of Internet shopping may change, possibly by a substantial
amount. Thus, we do not consider our estimates to be the last word on
the subject. Instead, we look forward to further research.
Acknowledgments
We are grateful to Jeff Biddle, Steven Haider, Yoon-Sang Kim, Larry
Martin, Therese McGuire, Paul Menchik, Ed Outslay, Leslie Papke,
Jean-Francois Wen, Jeff Wooldridge, and two anonymous referees for
helpful comments and suggestions. We are also grateful to Adam Cogswell
and Michael Gallagher for excellent research assistance. Any errors are
our responsibility.
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(1) It is also possible for consumers to buy from smugglers in
order to reduce or eliminate their sales-tax payments. However, the data
analyzed in this paper do not shed any light on smuggling.
(2) Typically, "evasion" refers to illegal methods of
reducing tax liability, whereas "avoidance" refers to legal
methods. When consumers reduce their taxes by Internet shopping, it is
not immediately clear whether they should be considered to be engaging
in evasion or avoidance: The consumers are violating laws, but the
Supreme Court has ruled that those laws cannot be enforced. In any
event, in this paper, we will usually refer to "evasion."
(3) Throughout this paper, we use "county" to refer to
counties as well as to county equivalents, such as the parishes of
Louisiana.
(4) For a concise summary of the literature on cross-border
shopping, see Bruce and Fox (2005).
(5) Goolsbee deals with this problem in a variety of ways. For
example, in one specification, he restricts his sample to observations
from states with uniform sales-tax rates within the state.
(6) For example, in a study of Swedes who cross into Denmark to
purchase alcohol, Asplund, Friberg, and Wilander (2005) find that
distance from the border has a substantial negative effect on
cross-border shopping. For further evidence on the importance of
distance, see Engel and Rogers (1996).
(7) By defining the sales-tax rate at the county level, we also
reduce the extent of measurement error in this variable. In some states
and metropolitan areas, there is substantial variation in sales-tax
rates. By defining the sales-tax rate at the level of the county, we
capture most of this variation. The details of our tax-rate calculations
are described below. One shortcoming of our approach is that information
on county of residence is not available for a substantial fraction of
the individuals in the CPS data set.
(8) The CPS data are available at
http://www.census.gov/cps/cpsmain.htm. The CPS data for 1998 and 2000
also contain information on certain Internet activities, but the survey
questions in those years are not useful for our purposes. Specifically,
the Internet-shopping variable in 1998 and 2000 asks whether the
consumer uses the Internet for paying bills or other commercial
activities, as well as for shopping.
(9) We would like to know not just whether consumers use the
Internet for shopping, but how much they spend when they shop online.
Unfortunately, in this data set, a person who spends $10 per year via
the Internet is coded the same as one who spends $1,000 per year.
(10) In Alaska, the state government does not have a general sales
tax, but local governments impose their own sales taxes.
(11) These are Connecticut, Hawaii, Indiana, Kentucky, Maine,
Maryland, Massachusetts, Michigan, Mississippi, Rhode Island, Vermont,
Virginia, and West Virginia.
(12) These are Florida, Georgia, Nevada, North Carolina,
Pennsylvania, South Carolina, Wisconsin, and Wyoming.
(13) It should be noted that an important part of the differences
among the sales-tax bases in different states is due to differential
taxation of business-to-business transactions.
(14) We use data for sales--tax revenue from the Census of
Governments, at http: // www.census.gov/govs/www/estimate.html, and data
for personal income from the Bureau of Economic Analysis, at
http://www.bea.gov/bea/regional/spi/.
(15) LOGINCOME may also act as a proxy for the opportunity cost of
time. It would be valuable to have data on wage rates in addition to the
income data. Unfortunately, wage-rate data are unavailable for a
substantial portion of the sample.
(16) The Bureau of Labor Statistics reports the Consumer Price
Index (CPI) for 26 metropolitan areas annually (see
http://www.bls.gov/cpi/home.htm). However, the data are normalized in
such a way that comparisons of price levels cannot be made across the
metropolitan areas. Thus, we use the percentage changes in the price
level in the 26 metropolitan areas.
(17) See Bruce, Deskins, and Fox (2004) for an empirical study of
the effects of tax variables and other influences on Internet access.
(18) The z-statistics reported in Table 2 are robust z-statistics,
adjusted for cluster sampling within counties.
(19) It is not clear that any of our variables is an ideal
instrument. As can be seen from Table 2, most of the variables have
strongly significant effects on both Internet access and Internet
shopping. We have excluded a wide variety of different combinations of
variables from the Internet-shopping equations. The results are fairly
insensitive to these exclusions.
(20) Our estimate of the effect of the sales-tax rate on Internet
access is of the same sign as the estimate of Bruce, Deskins, and Fox
(2004). By contrast, however, their estimate (based on data at the state
level) is strongly statistically significant.
(21) In Specifications (1), (2), (3), and (5), the p-value for rho
is 0.0000. The p-value is 0.0677 for Specification (4).
(22) However, for purposes of comparsion, we also ran a simple
probit model using the selected sample of consumers with Internet
access. (The results are available upon request.) For many of the
variables in the Internet-shopping equation, the coefficient estimates
and levels of significance for the selected sample are very similar to
those reported in Table 2. This suggests that, even though the simple
probit model suffers from a selection bias that is statistically
significant, the quantitative magnitude of the bias is not necessarily
very large. We also estimated the linear probability model. The results,
which are available on request, are very similar to the probit results,
in terms of the magnitude of the marginal effects, as well as the
significance levels.
(23) We also estimated the model using HOME TAX PRICE as the only
tax-related explanatory variable. (In other words, in this case, we
eliminated TAXRATIO and TAXBASE from both the selection equation and the
Internet-shopping equation.) The results from this specification are
probably most directly comparable with the results of Goolsbee and Aim
and Melnik. In this case, the tax-price elasticity is about 0.399.
Charles L. Ballard
Department of Economics, Michigan State University, East Lansing,
Michigan 48824-1038
Jaimin Lee
Korea Transport Institute, Goyang-city, Gyeonggi-do 411-701, Korea
TABLE 1 MEANS AND STANDARD DEVIATIONS FOR SELECTED VARIABLES (a)
Those with Internet Access
All Internet Non-Internet
Individuals Shoppers Shoppers
No. of observat