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Three Essays on Taxation and E-Commerce

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THREE ESSAYS ON TAXATION AND E-COMMERCE


By

MARK A. SCANLAN













A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2005

































Copyright 2005

by

MARK A. SCANLAN















ACKNOWLEDGMENTS

I would like to thank my dissertation committee Jonathan Hamilton, Lawrence

Kenny, Steven Slutsky, and Sandra Kramer. I am especially grateful to Jonathan

Hamilton for his continued guidance and encouragement through my academic career.

Special thanks go to Lawrence Kenny, Steven Slutsky, and Sandra Kramer for their

helpful comments and many suggestions during my research.

I would also like to give a heartfelt acknowledgment to my wife, Mandy Scanlan,

for her endless loving support, and to my family who were always there for me when I

needed them.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ......... ................ ............................................................... iii

A B STR A C T ................... ............................................................................ ......ix

CHAPTER

1 IN T R O D U C T IO N ....................................................................... 1

2 TAX SENSITIVITY OF ELECTRONIC COMMERCE: IS IT ERODING OVER
T IM E ................................................................................ 6

2.1 Introduction ....................................................................... .............. 6
2 .2 D ata ......................................................................................... 1 0
2 .3 M o d e l .................................................................. .....................................1 2
2.4 B baseline R results ...................................................................... 15
2.5 A advanced R results .................................................................................. .......17
2.5.1 Computer and Internet Experience .............. ..................................... 17
2.5.2 Exem pt goods .................................................................................19
2.5.3 Household versus Individual ....................... ....... .......................... 21
2.6 Confidentiality and Broadband............................................. 22
2.7 Result Interpretations and Trend Analysis .............. .................................... 23
2.8 Conclusion................................................ 24

3 E-COMMERCE, RACE, AND THE DIGITAL DIVIDE ..................................35

3 .1 In tro d u ctio n ................................................................................................. 3 5
3 .2 L literature ............................... .......................... .............................38
3.3 Data and Model ....... ......... ....... ....... .... ........40
3.4 Results ..................... .................................. 43
3 .5 A dv an ced R esu lts ........................................................................................ 4 5
3.6 Broadband ............... ......... ................. 46
3.7 Product Information ................. ...... ...................... .............. 48
3.8 C onclusion................................................ 51

4 USE TAX EVASION WITH DECREASING TRANSPORT COSTS IN
ASSYMETRIC JURISDICTIONS .................................................. ...................66

4.1 Introduction .............. ..... ...................................... 66










4 .2 O v e rv iew ............................................................... ...................................6 8
4 .3 L literature .......................................................................................... ........70
4 .4 M o d e l .................................................................. .....................................7 3
4 .5 B en c h m ark ................................................................................................... 7 9
4.6 Sim ulation B background ................................................................... 80
4.7 Results: U se Tax Evasion ............................................................. ...... .... 81
4.8 Results: Lower Transport Costs ...................... ........................................ 83
4.9 R obustness C heck .................................................................................... ........85
4.10 One Exogenous Tax Rate ........................................................... ... ....... 87
4.11 Both Tax Rates Exogenous ................................. ..................... ............ 89
4.12 C onclu sion ........................................................................................ ........90

5 CONCLUSION ............................................. ................. 100

APPENDIX

A MISSING DATA AND SPLINE WEIGHTS ........ ........ ...... .............. 103

A 1 M missing D ata.................................................. 103
A.2 Spline Tax Rate and Weighted Averages ........ ........ ..... ............... 104

B VARIABLE DEFINITIONS .................................. ......................... .....105

B. 1 Variables ... .......................................................... 105
B 1.1 D dependent V ariable ........................................................... ..... 105
B.1.2 Household Income......................................... 105
B 1.3 E education .................................................................................... ........ 105
B .1.4 Age ..................................................................... ........ 106
B 1.5 Race ................................................................. .... ......... 106
B 1.6 M arrived ......................................... ................... ... ....... 106
B. 1.7 Own a Business ................................. .............................. 106
B 1.8 Com puter U sed at W ork or School ....................................................... 106
B.2 Computer and Internet Experience................ ......... .............. 106
B .2.1 Com puter Experience ..................................................................... 106
B.2.2 Internet Experience...................... .............. 106

L IST O F R E FE R EN C E S ................................................................................... 108

BIOGRAPHICAL SKETCH ........................................................... .............. 114












v
















LIST OF TABLES


Table page

2-1 Sum m ary Statistics ....................................................................................... ......26

2-2 Results for the Benchmark, Splined Tax Rate, and Splined Tax Rate with Zero
Percent Tax Rates Omitted Specifications'................... ..............27

2-3 Correlation M atrix .......................... .. ............. ... .............. .............. 30

2-4 Computer and Internet Experience Variables are added to the Benchmark and
Splined Tax Rate Specifications. A Higher Spline Knot is also Tested................30

2-5 Individual Level Results that Include Clothing Tax Rates and Splined Clothing
Tax Rates'..................................... .................................. .......... 31

2-6 Household Level Results for the Benchmark, Splined Tax Rate, and Splined Tax
Rate with Zero Percent Tax Rates Omitted Specifications ....................................32

2-7 Confidentiality Concern and Broadband Access Variables are added to the
Benchmark and Splined Tax Rate Specifications ..............................................34

3-1 Sum m ary Statistics ....................................................................... .......... 53

3-2 Probability of Owning a Computer and of Accessing the Internet Given
C om puter O w nership ................................................ ............................... 55

3-3 Probability of Shopping Online Given Internet Access from Home With and
W without the Proxied Tax Variable'............... .................... .............. 57

3-4 Probability of Shopping Online Including Confidentiality Concern Interaction'....58

3-5 Probability of Shopping Online Conditional on Specific Income Levels"..............59

3-6 Household Income Summary Statistics..... ..................... ...........60

3-7 Probability of Shopping Online for Daily Internet Users along with the
Probabilities of Having Broadband Access or Searching for Product Information
O n lin e" ....... .............. .............................. ......................................6 1

3-8 Probability of Shopping Online with Broadband Interactions Included'...............62









3-9 Probability of not Buying Online Given Searched for Product Information, and
Probability of Buying Online Given no Search for Product Information ..............64

4-1 Benchmark Parameter Values Used in Simulations................... ............. 93

4-2 Benchmark Cases and 20% Lower Transport Costs Cases ................ ..............93

4-3 Tax-Inclusive Prices from Benchmark Results............. ...... ..................95

4-4 The Benchmark Case, Increasing the Share of the Population Located in
Jurisdiction 1, and Adding One Firm to Each Jurisdiction................. ..............96

4-5 The Benchmark Case and Decreasing the Number of People Who Opt Not to
Buy ......................... ........ ......... .................. .......... 97

4-6 Fixed Tax Rates for the Larger Jurisdiction and Decreasing Transport Costs........98

4-7 Both Tax Rates Fixed, Decreasing Transport Costs, and Changing One Tax Rate.99
















LIST OF FIGURES

Figure page

2-1 Splined Tax Rate and Online Shopping .......................................................29

2-2 D distribution of T ax R ates ........................................................... ....................29

3-1 Online Activity by Race ..................................................................... 54

3-2 Frequency of Internet Use by Race .......................... ............................54

3-3 Reason for no Broadband Access ................................... .. ..............63















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

THREE ESSAYS ON TAXATION AND E-COMMERCE

By

Mark A. Scanlan

August 2005

Chair: Jonathan Hamilton
Major Department: Economics

My dissertation studies the relationship between electronic commerce (E-

Commerce), sales taxation, and race. Previous research into the impact of taxation on E-

Commerce has concluded that there is a significant positive relationship between local

sales tax rates and the likelihood that a person will shop online. Chapter 2 builds on this

research by examining whether sensitivity on Internet sales has changed in the years

since the prior studies. I find that tax sensitivity for online purchases has declined and is

no longer significant under the previous general models. However, by using a splined tax

rate function, I find that users living in counties with high sales tax rates are still

significantly sensitive to tax rates when deciding whether to shop online, while those in

counties with low tax rates exhibit no sensitivity.

Chapter 3 finds that Blacks and Hispanics lag behind Whites in computer

ownership, Internet access, and online shopping. This contradicts recent studies that have

found no difference in online shopping behavior across races after controlling for

education and income. Interestingly, my results also indicate that Whites appear more









likely to free-ride off the services offered by local outlets only to eventually shop online

while Blacks and Hispanics appear more likely to research pricing information and

product details online only to later shop at a local outlet.

Finally, Chapter 4 studies the impact of proposed legislation that would require all

firms to collect taxes on out of state purchases regardless of whether the firm has any

storefronts in the destination state. I use simulation analysis to predict what impact this

legislation would have on the taxing behavior of local governments and the pricing

behavior of firms. I find that firms and consumers in both the small and large

jurisdictions are negatively impacted with consumers located in the less populated areas

being especially harmed by the collection of these taxes. My results also indicate that, as

transport costs decline over time, governments in both jurisdictions miss out on potential

tax revenue at an increasing rate due to their inability to effectively collect taxes on cross

border purchases.














CHAPTER 1
INTRODUCTION

The history of sales taxation in the United States reveals a pattern of continual

opposition and legal hazards. Under the Articles of Confederation, the Continental

Congress was unable to resolve the issue of states imposing unduly large duties on goods

shipped in from neighboring states. By imposing these hefty tax rates on out of state

goods, state governments were able to protect local businesses while shifting a portion of

state revenue financing to out of state citizens. These abuses threatened to halt the flow

of free trade and commerce within the fledgling country. Not until the U.S. Constitution

was ratified and the Commerce and Due Process Clauses were implemented was

Congress able to control this issue. The two clauses are similar, with Due Process

requiring a firm to have "minimum contacts" and the Commerce Clause requiring

"substantial nexus" in a state before a company is required to collect and remit taxes to

that state. The U.S. Supreme Court, wary of the abuse of taxes in the past, has

continually erred on the side of caution in the area of tax collection going so far as to say

in Leloup vs. Port Mobile (1888) that "no State has the right to lay a tax on interstate

commerce in any form" (Linder 2005). However, two developments have forced the

courts to refine their stance on interstate commerce and taxation: the establishment of the

current sales tax structure and improved transportation methods which led to a boom in

catalog sales and subsequently online sales.

During the 1930's at the height of the depression when personal incomes were at

their lowest, 24 states opted to enact state level sales taxes in an effort to bolster state









revenues (Fox 2003). Since each state had different sales tax rates it became a concern

that those states with low rates or no tax at all would become tax havens, leading people

to cross border shop in an attempt to reduce or avoid sales taxes. Fearing that cross

border shopping would cause a decrease in the sales tax base and that it would harm local

businesses, states adopted a complement to the sales tax called the "use tax." Any

tangible product purchased across state borders but used, consumed, or stored in an

individual's home state is subject to the use tax which is to be paid to the home state. In

theory this halted the benefit of shopping out of state based on sales tax rates alone;

however, in reality, use tax collection has become a thorn in the states' sides.

Originally states had hoped to make the company who made the cross border sale

collect and remit any use tax that may be applicable. However, this proved more difficult

to implement than first hoped, and a growing mail order industry further complicated the

issue by dramatically increasing the number of cross border sales. In the landmark case

of National Bellas Hess vs. the Department of Revenue of the State of Illinois (1967) the

U.S. Supreme Court found that states could not compel firms to collect use taxes from a

customer if the firm did not have "nexus," defined as physical presence, in the state. The

majority opinion noted that because of the Commerce and Due Process Clauses of the

Constitution, states did not have the right to burden firms with collecting use taxes in a

state if their only contacts within the state are "via the United States mail or common

carrier" (FindLaw 2005). Following the Bellas Hess ruling, states were still able to force

businesses that had branches, offices, or even a contingent sales force in a state to collect

use taxes on purchases shipped in from across the border, but they were unable to compel

firms to collect the tax if their only presence in a state was through mailed advertisements









or a delivery truck's presence during shipping. The use tax would still be due on these

purchases but the responsibility fell to the consumer to voluntarily submit the taxes to the

home state. With little threat from non-compliance, the voluntary payment of these use

taxes by consumers was, and continues to be, very low.

The U.S. Supreme Court's interpretation of the Due Process and Commerce

Clauses of the Constitution left little recourse for states since the Due Process Clause is

located in the fourteenth amendment and thus immune from conventional congressional

interference. With little power to intervene, states watched as revenues that were lost

from untaxed mail-order sales grew to over 3.4 billion dollars by 1990 (Advisory Council

on Intergovernmental Relations [ACIR] 1991). Fortunately for state governments, the

U.S. Supreme Court, taking note of the exponential growth of mail-order sales in the late

1980's and early 1990's, decided to revisit the topic by agreeing to hear the case of Quill

Corporation vs. North Dakota (1992). Similar to the Bellas Hess case, Quill Corporation

was a vendor located outside of the reference state whose only presence in the state was

through catalogs, flyers, electronic communication, and telephone calls. The North

Dakota Supreme Court had found that Quill Corporation was responsible for collecting

use taxes in North Dakota despite their lack of "warm body" presence.

We conclude that Quill's significant economic presence within
the State and its retained ownership of property within the State
generate a constitutionally sufficient nexus to justify
imposition of the purely administrative duty of collecting and
remitting the use tax. We also conclude that the State provides
benefits, services, and opportunities which significantly aid
Quill's business and which are related to the use tax.
Accordingly, the use tax ... does not violate the Due Process
Clause or the Commerce Clause. (North Dakota 1991)









As in the Bellas Hess case the U.S. Supreme Court reversed the lower court's

ruling and freed Quill Corporation of any obligation for collecting use taxes in North

Dakota. However, in doing so the court made an explicit distinction between the Due

Process Clause and the Commerce Clause that it had not done in the past. The U.S.

Supreme Court found that Quill Corporation had in fact fulfilled the requirements of the

Due Process Clause through their extensive economic presence noting:

In "modern commercial life" it matters little that such
solicitation is accomplished by a deluge of catalogs rather than
a phalanx of drummers: the requirements of due process are
met irrespective of a corporation's lack of physical presence in
the taxing State. Thus, to the extent that our decisions have
indicated that the Due Process Clause requires physical
presence in a State for the imposition of duty to collect a use
tax, we overrule those holdings as superseded by developments
in the law of due process. (Legal Information Institute 1992)

The U.S. Supreme Court, however, disagreed with the North Dakota Supreme

Court who felt that fulfillment of the Due Process Clause was sufficient to fulfill the

Commerce Clause. The court explained that they were independent clauses with distinct

applications. Therefore, even though Quill Corporation did fulfill the Due Process

Clause's need of "minimum contacts," it did not fulfill the Commerce Clause's more

stringent requirement of "substantial nexus."

The Quill case is of particular significance since by broadening the interpretation of

the Due Process Clause it has opened the door for the U.S. Congress, who has the right to

regulate commerce under the Commerce Clause, to step in and compel out of state

vendors to collect and remit use taxes. This has become increasingly important as the

growth of electronic commerce (E-Commerce) has dramatically accelerated state revenue

losses from uncollected use taxes. To address this issue the recent Streamlined Sales Tax









Project (SSTP)1 has set out to normalize taxing systems between and within states in a

prelude to asking Congress to authorize SSTP participating states to compel out of state

firms to collect and remit use taxes. The states belonging to the SSTP feel that advances

in technology and a more uniform taxing environment would greatly decrease the burden

on remote vendors in collecting and remitting use taxes. This reduced burden, they feel,

allows Congress to pass the legislation needed without violating the spirit of the

Commerce Clause.

If the SSTP succeeds then purchases made online will become subject to the same

taxes as purchases made at brick and mortar stores. This development could have two

significant negative impacts on E-Commerce based firms. First, it would lay the burden

of tax collection, remittance, and compliance on individual companies that must find a

way to effectively calculate and collect the tax rates of consumers living in thousands of

distinct local taxing jurisdictions. Second, it would decrease online purchases by those

consumers who are the most tax sensitive. While the former has important legal

ramifications in terms of the Commerce Clause, this body of work will focus on the latter

and to what degree online firms may see decreases in sales from collection of use taxes. I

further examine the potential reaction of local governments and firms if states were

indeed able to compel companies to collect use taxes. Finally, I analyze the shopping

behavior of individuals online and attempt to determine whether minorities are less likely

to shop online after controlling for things such as income and education.






1 The SSTP was organized by state governments and works closely with the business community to find a
resolution to the cross border tax issue. Their website is: http://www.streamlinedsalestax.org/.














CHAPTER 2
TAX SENSITIVITY OF ELECTRONIC COMMERCE:
IS IT ERODING OVER TIME

2.1 Introduction

The need for empirical work on the tax sensitivity of online shoppers increases

each year as sales from E-commerce continue to expand and state and federal legislation

takes form under the Streamlined Sales Tax Project (SSTP). The point of contention is a

little known, and widely evaded, tax called the use-tax. This tax, which mimics a

jurisdiction's sales tax, allows states to collect tax revenue on remote purchases of

tangible goods if they are intended to be used in the consumer's home jurisdiction. The

problem is that, due to Supreme Court cases such as National Bellas Hess vs. the

Department of Revenue of the State of Illinois (1967) and Quill Corporation vs. North

Dakota (1992), companies without a "substantial presence" in a state cannot be required

by that state to collect and remit sales taxes. This leaves the burden of remitting these

taxes on the buyer; however, limited knowledge of the tax, along with its ease of evasion,

has led to a high level of non-compliance as documented in a study by the Washington

State Department of Revenue (2003).

The SSTP is in the process of addressing the non-compliance issue by both

working to simplify each state's tax code and lobbying Congress to change the

requirement for compelling a firm to collect taxes within a state from the current

requirement of "physical nexus" to the weaker standard of "economic presence." This

new wording would force businesses that do substantial business within a state to collect









and remit sales taxes to that state even if they do not have a physical presence there. If

successful, previously untaxed E-commerce and mail-order sales will become subject to

the full sales tax liability.1 A movement towards a system in which Internet and mail-

order sales are taxed would have two main effects: first, there would be a potentially

large increase in previously uncollected sales tax revenues for each state, and second,

there would be a shift of marginal shoppers away from online purchases back towards

brick and mortar stores.

The first effect regarding current and future losses in state sales tax revenue from

use-tax evasion has been well documented in studies by Goolsbee and Zittrain (1999),

Cline and Neubig (1999), McQuivey and Demoulin (2000), and the U.S. GAO (2000).

Bruce and Fox (2001) use the concepts from these studies and estimate the annual

revenue loss from business to consumer (B2C) commerce alone to be between 1.7 and

2.6 billion dollars. Furthermore, each study predicts continued growth in state revenue

losses in the future as the number of Internet users expands.

There has been considerably less work done on the second effect regarding what

the potential impact of collecting these taxes would be on Internet shopping. Though

literature on the topic of E-commerce tax sensitivity is growing, empirical work is still

hampered by a lack of reliable data. The breakthrough papers on the subject are

Goolsbee (2000, 2001), with his latter paper updating and expanding on his initial study.

His research is based on the concept that online shopping allows all consumers to act as if

they are living on a tax border. This draws from research done by authors such as Fox

(1986) and Walsh and Jones (1988), who found that individuals living in border towns

1 There are de minimis provisions included in the proposals to protect small businesses. They require a
firm to meet a preset level of sales into a state before they are required to collect taxes.









looked at tax differentials across borders when deciding where to make their purchases.

Goolsbee's assertion is that E-commerce now allows individuals living away from

borders also to make purchase decisions based on tax differentials thanks to the

increasing number of products sold over the Internet and the widespread evasion of use-

taxes. His samples are from 1997 and 1998 surveys,2 which had 110,000 and 85,0003

household responses respectively. In the first study, he finds that imposing taxes on all

Internet sales would reduce the number of online buyers by up to 24%. His later work

still finds significant tax sensitivity and separates the effects on the relatively non-

sensitive new Internet users from the very sensitive experienced users. He does this by

breaking Internet users into two groups: those who have been online for more than two

years and those who have been online for fewer than two years. He finds that users who

have been online longer are much more sensitive to tax rates when deciding whether to

shop online.

Since Goolsbee's papers, two other studies have confirmed his findings that sales

taxes play a major role in online shopping. Brynjolfsson and Smith (2001), examining

price dispersions of online book sellers through shopbots,4 find that buyers in 1999 were

twice as sensitive to changes in taxes and shipping costs as they were to changes in

prices, even if the end price was the same in both cases. They also find that shoppers

prefer to buy from brand name outlets online and are even willing to pay a premium to do

so. Contrary to this, Ellison and Ellison (2003), analyzing shopbot data on the sale of

2 Details of the survey and methodology are not publicly provided by Forrester.

3 His regression analysis includes only those with Internet access which limits the sample to 24, 617 in
1997 and 35,959 in 1998.

4 Shopbots are websites that collect price data from multiple E-Commerce websites and allows the
consumer to choose where to buy from based on price and previous customers' experiences.









memory modules from 2000 to 2001, find a more intuitive result that online shoppers are

actually more sensitive to price differences than to tax differences. They are also able to

confirm Goolsbee's results by finding that there are indeed more sales to states with high

sales tax rates than to those with lower rates.5 Since this study focuses on a specialized

product, usually with a high price and bought through a shopbot, the shoppers are thought

to be more sophisticated than the average online shopper. This leads to large tax

sensitivities, where a 1% increase in a state's tax could lead to an 8% increase in online

sales to that state. The fact that both studies analyze data from Internet shopbots may

indicate that they are picking up only the most advanced Internet shoppers, thus those

most sensitive to tax rate changes. This means that the general policy implications from

their studies are harder to apply to the population as a whole. It is also important to note

that both studies focused on price dispersion online and only looked at taxation as a

secondary issue.

My study makes three significant contributions to this literature. First, I quantify

the extent of any changes in tax sensitivity that may have occurred since the Goolsbee

studies by using a new, and very detailed, tax data set that I assembled. This is of

particular importance since his results are currently cited by state legislators when

debating whether to accept the SSTP's recommendations. Second, I employ a splined tax

rate function to allow me to clearly separate the differing tax sensitivities of consumers in

low versus high tax areas. Last, I update Goolsbee's model to include issues such as

broadband use and online security which have not been fully considered in the past.


5 Average statewide tax rates are used in their study.









I find that under a model similar to Goolsbee's, the general tax sensitivity of online

shoppers in 2001 is extremely low and not significantly different from zero. Further

analysis based on a splined tax rate indicates that only those facing the highest tax rates

are still sensitive to rate changes. Finally, including broadband and security variables in

the model does not improve the significance of the tax coefficient even though both new

variables are highly significant.

2.2 Data

The primary data used in this paper are from the "Computer and Internet Use"

supplement in the 2001 Current Population Survey (CPS), which is jointly sponsored by

the U.S. Census Bureau and the Bureau of Labor Statistics. The supplement on computer

and Internet usage has been given periodically since 1994. Each year it is given, the

questions get more detailed and in-depth. The sample is drawn from 72,199 households

with a reference person6 answering for each person in the household (see Appendix A).

A complete description of the survey's design and methodology is available in U.S.

Census Bureau (2002).

State and local sales tax rates are matched to the CPS data using FIPS7 state and

county codes. State, county, and city tax rates were collected from each state's

department of revenue website or through contact with a state tax official. Since the CPS

does not record the city of residence, I focus on the county level when assigning







6 The reference person is usually the homeowner.

7 FIPS is an acronym for "Federal Information Processing Standards".










individuals their tax rates. If a state allows city level sales taxes, I use city populations

from 20018 to calculate the appropriate county tax rate as follows:


Total County Tax9 = t + wtc (1)
1=1
where
Citypop,
w = i= 1,2,...,n (2)
Countypop
and

-w, =1 (3)
i=1
where "Citypopi" is the population of city i. "Countypop" is the county population where

city i is located, and ti"it and county are the city and county level sales tax rates

respectively. Notice that if there are no city level taxes in equation (1) ticit would equal

0, making

Total County Tax = tconty (4)

One concern with the CPS residence data is that for some observations the county

identifier is not collected. In this case I use the MSA code instead.10 For these

individuals I use the Total County Tax (TCT) shown in equation (1) to calculate a

population weighted MSA tax rate.


Total MSA Tax11= 'w, *TCT (5)
i=1

where





8 Population data was collected from the census website.

9 Total County Tax is added to the statewide tax rate to get the effective rate owed.

10 This is done for 10,452 individuals.

1 Total MSA Tax is added to the statewide tax rate to get the effective rate owed.









Countypop,1,2,.. (6)
MSApop

and


w = 1 (7)
i=1

Where "Countypopi" is county i's population, "MSApop" is the MSA population where

county i is located, and TCTi is the Total County Tax calculated in equation (1).12 This

weighting process allows me to more accurately assign tax rates to consumers than in any

previous study. Goolsbee's data have only state and television market identifiers so he

assumes that everyone living in the television market is located in its largest city, and he

drops any observations outside of the television markets.

2.3 Model

In order to isolate the effect of tax rates on an individual's decision to shop online,

one must first understand the factors that motivate this decision. I expect the probability

that an individual will shop online to rise as the cost savings from shopping online

increases:

Cost savings from shopping online = tPQ FC Shipping + Z (8)

where P is the price of the good,13 t is the tax rate, Q is quantity purchased, and FC are

fixed costs such as buying a computer, getting Internet access, and acquiring the skills

needed to use the Internet. The first term can be interpreted as the tax savings from

shopping online. The last term picks up other non-pecuniary influences on a person's


12 For MSAs that span 2 or more states all counties outside of the state of interest are dropped from the
MSA's population and are ignored in calculating the MSA tax.

13 The price is assumed to be similar for the same good offline and online. Goolsbee (2000) controlled for
local prices and found it did not affect the results.









decision to shop online and could be thought of as the benefits minus the costs of online

shopping. Benefits would include things such as convenience, increased selection,

avoiding hassle from salespersons, less time needed to make a purchase, and less driving.

Costs would include security and confidentiality concerns, shipping time, inability to feel

or try on a product, difficulty returning goods,14 and no immediate customer service for

questions or concerns about a product. Equation (8) is used to develop a regression

specification and to help predict the signs on the variable coefficients.

The dependent variable in the model is whether the individual has "used the

Internet to purchase products or services" in 2001. The responses are conditional on

having access to the Internet from home, work, or school. The dependent variable is set

to 1 if the person has purchased a product online and to 0 otherwise. To deal with the

discrete nature of the dependent variable I use a Logit regression model, though I check

my results using a probit and receive similar results. The main variable of interest is tax

rate, which is presented as (1+t) to conform with previous studies. It is expected to have

a positive impact on Internet purchasing, as can be determined from equation (8). This

would imply that an increase in sales tax would lead to a higher probability that local

residents will shop online. The explanatory variables and their summary statistics are

shown in Table 2-1 and include household income and individual education level. Both

the household income and education questions ask the respondent to select a range in

which household income or education falls. The income and education variables utilize

the midpoints of the reported ranges in order to make the variables continuous. (see



14 Many multi-channel companies have alleviated this cost by allowing returns to physical storefronts. This
however generates nexus for the company which necessitates collecting the relevant sales taxes.









Appendix B).15 Other explanatory variables include whether the respondent lives in a

metro area, age,16 race, number of children in the household, marital status, gender,

whether he owns his own business, whether he uses a computer at work or school, and

regional dummies. Table 2-1 shows that online buyers on average have higher household

incomes, more education, and are predominantly White compared to those who do not

shop online. Online buyers are unexpectedly older than non-buyers, but this could be due

to the large percentage of teens in the sample that do not have access to credit cards.

The expected results of the regression are determined by referring to equation (8)

and are as follows: household income is expected to have a positive coefficient, since

higher incomes should lead to larger quantities purchased (Q). Education is also

expected to have a positive coefficient, since those who are educated tend to have more

exposure to computers and a better understanding of technology. The coefficients on

owning one's own business and using a computer at work or school should be positive

due to increased technical knowledge and comfort with computers. Age is predicted to

have a negative coefficient since younger people have more exposure to advanced

technology and are often less concerned with security. Black, Hispanic, and Native

American should all have negative coefficients, reflecting larger confidentiality concerns,

lack of minority related content online, and possible language barriers. A possible gender

divide should make the coefficient for female be negative, though this divide is less

documented. The number of children in the household and marital status are both

expected to have positive coefficients, reflecting a household network externality as

15 Income is topcoded at $75,000. I use $125,000, which the census bureau has as the average income for
those earning over $75,000.
16 Age is top-coded at 90 years old.









family members learn from one another. The dummy for metro area status could have

either sign. A negative sign may reflect possible agglomeration of businesses nearby or

increased selection from adjacent suburbs. And a positive sign could reflect a more

entrenched Internet infrastructure, a large amount of local content online, more users

nearby to learn from, or a broader acceptance of technology. For a full analysis of

whether online activities are a substitute or complement for big cities and the impact of

the level of local content, see Sinai and Waldfogel (2003).

2.4 Baseline Results

The initial results use a similar specification as Goolsbee (2001) to give a baseline

regarding how the results from the CPS data set in the updated year compare to previous

findings. The baseline results are presented in column (1) of Table 2-2 and include

dummies for three of four regions. Almost all of the results have the expected sign and

are statistically significant at the 5% level. These include the positive and significant

coefficients on education and income, and the negative and significant coefficient on

Black, indicating the continued racial digital divide. The notable exception to the

expected results is that the coefficient on tax rate is negative, though not statistically

different from zero. Thus, an increase in the tax rate does not appear to have a significant

effect on an individual's decision to shop online. This result is in stark contrast to what

has been found in previous literature and implies that there has been a dramatic shift in

the tax sensitivity of online shoppers.

Even though taxes may no longer affect the decision to shop online in general,

some tax sensitivity must still exist in some circumstances. Equation (8) shows that tax

sensitivity affects online shopping in three distinct ways: through the tax itself, as a

multiplier on prices, and as a multiplier on quantity purchased. I would expect that large









or very expensive purchases are still affected by the tax rate faced by consumers:

however, the data I use do not specify the type of goods purchased or their prices. I can

instead use my detailed tax data set to determine if those living in high tax areas who are

most likely to be affected by changes in tax rates actually are. To do this I employ a

spline tax function that will include both a low and high tax component. Many different

kink, or "knot", locations were tried and a rate of 6.6% was determined to be the best

fit.17 The spline function is constructed as:

1+t if 1+t <1.066
Low Tax = if+ 1.0(9)
1.066 if 1+t > 1.066

0o ifl+t <1.066
High Tax = (10)
(1+t)-1.066 ifl+t >1.066

where ti is the total effective sales tax faced by the individual. At low rates, tax savings

are low and therefore not a large consideration in online shopping as rates change. At

high rates, tax savings are more substantial causing consumers to be more aware of

variations in the tax rate when shopping online. Thus, in low tax locations the slope

should be relatively flat, while in the high tax areas a much steeper slope is expected.

Column (2) of Table 2-2 gives the results from the regression with the splined function at

a 6.6% knot. The log likelihood estimate for this new specification indicates it has a

slightly better fit to the data than the baseline regression. Also, as predicted we observe a

positive and significant coefficient on high tax rate. Figure 2-1 gives the graph from

doing this piecewise linear regression. 18



1 6% is the median and mean rate for the sample

18 Note that the maximum tax rate in the sample is 9.23%.









The low tax coefficient was expected to be near zero and not significantly different

from zero: column (2), however, indicates a negative and significant coefficient. This

result causes a little concern because it suggests that in low sales tax locations an increase

in the tax rate decreases a person's probability of shopping online. One potential cause of

this counterintuitive result is the distribution of taxes for low taxing jurisdictions. Figure

2-2 displays the distribution of tax rates and displays the gap between those facing no tax

rate and those facing a positive rate, with the smallest positive rate equal to 4%. To see if

this idiosyncrasy in the distribution of tax rates is causing the unexpected solution, I rerun

the regression with a 6.6% spline tax rate but drop all observations with a tax rate equal

to 0. Column (3) of Table 2-2 presents the results from doing this and shows that under

this specification the coefficient on low tax rate is still negative but is no longer

significantly different from zero. Taken together the base results presented in Table 2-2

indicate that the sensitivity to local sales tax rates documented by Goolsbee just three

years earlier has mostly disappeared and can now only be seen in the highest tax rate

jurisdictions. In the next sections I will outline several checks on the robustness of these

results and will discuss the likely cause of these new findings.

2.5 Advanced Results

2.5.1 Computer and Internet Experience

A concern is that unlike data used by Goolsbee (2000, 2001), the CPS does not

have a straightforward variable that accounts for computer and Internet experience. The

Forrester Research data used by Goolsbee includes a question asking how long a person

has had access to a computer. Without a variable of this type I could be missing an

important part of the costs described in equation (8) relating to comfort with computers

and technology. The impact of this omission may be muted, since it is partially picked up









by the computer use at work and school variable, but the issue should be explored. While

the CPS data does not ask any historical questions on computer and Internet activity, I

can construct two variables from the available questions that demonstrate a person's

overall comfort with technology. The variables equal the sum of the number of activities

that are done 1) via the computer or 2) via the Internet. If an individual responded that he

only uses the computer for one activity other than shopping online, the value of the

variable would equal one, while if he uses it for ten other activities the variable would

equal ten.19 Therefore, the variables are set up to be larger the more technical experience

a person has accumulated. Since increased technical experience lowers the fix costs

associated with shopping online, the coefficients are expected to be positive.

One concern in building these new variables is their potential to be correlated with

each other or the dependent variable. The correlation matrix for the three variables is

given in Table 2-3, and multi-collinearity does not appear to be a problem. Column (1)

of Table 2-4 gives the results from the inclusion of the two new variables in the original

model. The coefficients on both computer experience and Internet experience are

positive and significant as predicted, but the coefficient on the tax rate variable remains

insignificant. It is important to note that the log likelihood for this new specification

shows it provides a much better fit for the data than the baseline regression. The

importance of adding the two new variables is most pronounced in column (2) of Table

2-4 where, like before, I rerun the specification from column (1) but include a splined tax

rate of 6.6%. While the coefficient for those living in high tax rate locations is shown to

be higher than in Table 2-2 and still significant at the 5% level, it is for the low tax


19 A list of these other activities is available in Appendix B. The Internet variable is constructed similarly.









locations that there is the biggest change. Column (2) of Table 2-2 showed the

counterintuitive result of a negative and significant coefficient on the low tax spline

variable. Column (2) of Table 2-4, however, indicates that the inclusion of the technical

experience variables makes the coefficient on the low tax variable smaller and no longer

significantly different from zero. Also, column (3) of Table 2-4 shows that the overall fit

of the model is best with a spline knot at 7%.

2.5.2 Exempt goods

Since there is no distinction in the CPS data about what types of goods are being

purchased, I cannot determine if they are products that could have been purchased tax-

free even if they were bought at a local brick and mortar store. If a good could have been

bought offline tax-free, then its inclusion in the model could bias the results. The level of

the bias should be proportional to the number of these goods included in the regression.

As a check for this potential bias I will focus on the practice of some states to give sales

tax exemptions on the purchase of clothing and apparel.20 The focus on clothing is based

on the dramatic increase in online apparel sales in the years leading up to the survey.

Ernst and Young (2001) note that this growth of clothing sales online has put it behind

only books, CD's, and computer equipment in total sales.21

I deal with this in the model by including a variable that reflects the tax rate owed

on purchases of clothing in each jurisdiction. The new clothing tax variable is

constructed as:

Clothing tax = (1 + td) (11)

20 States exempting clothing with no limit include MN, NJ, PA, and RI. Those with a limit include CT
($75), MA ($175), NY ($110), and VT ($110). Sales tax holidays are not considered.
21 Food, drugs, and clothing are the main exemption categories across states. Clothing is used since it is the
one with the largest online demand.









where, dlI if no clothing exemption (12)
w0 if clothing is exempt

By attempting to separate the impact of clothing taxes from all other taxes, I expect to see

similar results to Table 2-2 for the coefficients on clothing tax rate. Specifically, I expect

the coefficient on clothing tax to be near zero and not statistically significant, the

coefficient on the high clothing tax spline to be positive and larger and more significant

than Table 2-2, and the coefficient on low tax to be small and not significant. Column (1)

of Table 2-5 shows the results from including the new tax variable that reflects clothing

exemptions. It shows that both the general tax rate coefficient and the coefficient on

clothing taxes are small and not significantly different from zero. The interesting impact

of this new specification occurs in column (2) of Table 2-5 which includes the splined

version of both tax variables. As expected, the coefficient on the high tax rate clothing

coefficient is positive and significant; while the low tax rate clothing coefficient is

negative and not significant. Further, the high tax coefficient is larger and more

significant than its counterpart in Table 2-2. This indicates that people living in locations

with high tax rates on clothing are sensitive to changes in the tax rate when deciding

whether to purchase clothing online.

The coefficients on the general tax rate coefficients are both negative and not

significantly different from zero. These coefficients are picking up the noise left over

after taking out clothing taxes. Their lack of significance may reflect the high number of

low cost goods that are sold on the Internet, such as books and music, which do not

generate large tax savings.









2.5.3 Household versus Individual

So far all the results have come from looking at online buying decisions at the

individual level; one concern with doing this is that since the data include observations

for each person in a household, the results may be sensitive to the number of people

living in the household.22 Network externalities in computer knowledge may make each

individual within a household more likely to shop online regardless of the tax rate.

Therefore, as a check of the results, I aggregate the data to the household level and rerun

the model. The new dependent variable is whether anyone in the household has bought a

product online over the past year. Education is now the average education of all adults in

the family (highest education within the household was also checked), age is the average

age of the adults, and married is whether there is a married couple living at the

residence.23 Table 2-6 gives the results for household level data which consists of 24,806

observations. Column (1) gives the results from using the baseline explanatory variables

and tax rate similar to those in Table 2-2. The signs on the coefficients are all similar to

those in the baseline specification. Column (2) of Table 2-6 shows that, similar to Table

2-2, using a splined tax rate results in a positive and significant coefficient on the high tax

variable. For the household specification however, the kink in the tax rate giving the best

fit is slightly higher at 6.8%. Also, the counterintuitive result on the coefficient for the

low tax variable persists in the household data, and again disappears when tax rates equal

to zero are excluded as presented in column (3). These results indicate that the use of



22 Households include families and non related residents.

23 Hispanic is assigned a value of 1 if every member of the household is Hispanic, is assigned a value of 0.5
if at least one, but not all, members of the household are Hispanic, and assigned a value of 0 otherwise.
Other race variables are generated similarly.









individual level data is reasonable for this data set since the household level regression

yield similar results.

2.6 Confidentiality and Broadband

The final specification includes two new variables that have tremendous

importance when it comes to online activity. The first of these variables is

broadband/high-speed access to the Internet which allows Internet users to access

information and complete transactions more quickly than with dial-up. Along with

eliminating the need to "sign on"24 in order to access the Internet, this increased speed

streamlines the process of getting information on products and making purchases online.

To pick up this growing vehicle of E-commerce, I include a dummy variable that equals

one if the individual has high-speed access to the Internet and equals zero otherwise. The

second variable I add deals with confidentiality concerns, which remains a major

roadblock for many people when it comes to shopping online. Apprehension over giving

credit card numbers or personal information over the Internet has slowed the growth of E-

commerce and has become a central concern of companies doing business online.

To account for this important aspect of Internet activity, I use a question in the CPS

that asks: "Compared to providing personal information over the telephone, how

concerned are you about providing personal information over the Internet?" This

question is used to build a dummy variable equal to one if the individual is more

concerned about giving information over the Internet than they are over the phone, and

equal to zero if they are equally or less concerned. The coefficient on this variable is

expected to be negative, since it reflects a substantial cost for Internet shopping. Table 2-


24 Broadband connections are continually signed on to the Internet and do not require a dialing-in process.









7 presents the results from the inclusion of these variables into the regression model.

Column (1) shows both variables have the expected signs on their coefficients and are

strongly significant. There is not a dramatic effect on the coefficient for tax rate with the

inclusion of these variables, but the overall fit of the specification is better than in Table

2-3. Column (2) presents the results using a splined tax rate and exhibits a similar overall

impact as Table 2-3. Thus, while both confidentiality concerns and broadband are

important in a consumer's decision to shop online, they do not vary in importance

between high and low tax rate areas.

2.7 Result Interpretations and Trend Analysis

Goolsbee (2001) found that new users were less sensitive to tax rates when

shopping online than more experienced users. He also concluded that new users become

more sensitive to tax rates over time as they become better educated on the tax code and

its applications to E-commerce. Therefore, he predicts that during times when many new

users are going online the overall tax sensitivity will be down, and during slower periods

of Internet adoption tax sensitivities will rise. This conclusion may partially explain why

I find weaker tax sensitivities than he found for individuals in 1998 because of the large

influx of new users, but even after several attempts to control for computer and Internet

experience my results persist. Therefore, while changes in consumer tax sensitivity over

time may be occurring, an outside force is likely driving my results.

Ernst and Young (2001) in their Global Online Retailing report look at the

transformation of E-commerce through 2001. They note that starting in 2000 there was a

substantial shift away from "pure play" (online-only) firms towards firms with both a

physical and online component. This was largely due to the technology shakeup which

saw many "pure play" companies go out of business. Since "pure play" firms that were









so popular in the 1990's were less likely to have nexus in multiple states, they were able

to offer more tax-free sales. Ernst and Young not only found that the number of online

only firms had decreased significantly, but they also found that the number of brick and

mortar stores establishing an online presence had grown and would continue to grow.

This meant there had not been a dramatic change in the number of goods sold online, but

that there had been a drop in the number of sites that could sell products tax-free. This

translates to a lower overall tax sensitivity for online sales.

A similar study done by McKinsey & Company and Salomon Smith Barney (2000)

found that "pure play" firms were losing money on most sales because the revenues were

not covering the overhead costs of warehousing, maintaining a website, and brand

advertising. These findings led Joanna Barsh, a director at McKinsey & Company, to

state: "The notion of a 'pure play' is turning out to be the wrong play." The study found

that a multi-channel approach that combined an online presence with physical storefronts

was more appropriate. The changing online environment documented in these reports is

the likely cause of my findings and predicts that future results will show similar or lower

tax sensitivity for online consumers regardless of the pace of Internet adoption.

2.8 Conclusion

Previous work has found that, in the late 1990's when the number of "online-only"

stores was at its height, a small change in local sales tax rates could lead to a large jump

in the percentage of online shoppers in that location. The implication was that if every

business were suddenly required to collect sales taxes from online sales that the total

level of online sales would drop significantly. This study finds that this result no longer

holds true, as there has been a fall in the tax sensitivity of online shoppers. In fact, I find

that the decision to shop online is no longer significantly impacted by changes in local









tax rates except in areas where the tax rate is very high. Therefore, if policy makers are

able to induce firms to collect use-taxes, either through legislation or incentive programs,

the negative effects would be limited to high tax jurisdictions. Thus online firms would

see an overall decrease in sales, but it would be far below what was previously

concluded. Pure-play firms, as previous studies have stated, would be the most harmed,

especially if they are currently only able to stay in business due to demand for their tax-

free goods.

Future research in the area could study the impact that the tax amnesty agreement

signed by companies like Wal-Mart and Toys R Us has had on their online shopping

demand. Also, quantification of the number of sales that go untaxed on auction sites such

as Ebay would be of interest. Another extension of my work would be to examine how

states and businesses would react if use-tax collection were mandatory either in a subset

of states or across the U.S. In each case, given tax sensitivities like the ones found in this

study, it is of interest to consider firm location decisions, along with local governments'

decisions on what the new appropriate sales tax rates should be.











Table 2-1
Summary Statistics"


Variable


(1+t)

In Metro Area

Age

Number of Kids

HH Income

Own Business

Computer at Work/School

Married

Education

Female

White

Hispanic

Black

Native American

Asian


a Standard errors in parentheses


Entire
Sample

40,989

1.0587
(.021)
.871
(.335)
36.828
(13.417)
.956
(1.154)
74547.3
(42054.1)
.193
(.395)
.790
(.408)
.546
(.498)
13.799
(2.401)
.496
(.500)
.813
(.390)
.060
(.237)
.074
(.261)
.008
(.087)
.046
(.211)


Online
Buyers

19,017


1.0587
(.021)
.886
(.317)
38.051
(12.242)
.851
(1.084)
81240.1
(41731.2)
.218
(.413)
.837
(.370)
.599
(.490)
14.452
(2.273)
.482
(.500)
.855
(.352)
.042
(.201)
.050
(.217)
.006
(.078)
.047
(.212)


Non-
Buyers

21,972


1.0586
(.021)
.858
(.349)
35.769
(14.273)
1.046
(1.205)
68754.7
(41469.4)
.172
(.377)
.749
(.434)
.501
(.500)
13.233
(2.364)
.508
(.500)
.776
(.417)
.075
(.263)
.094
(.292)
.009
(.095)
.046
(.209)









Table 2-2
Results for the Benchmark, Splined Tax Rate, and Splined Tax Rate with Zero
Percent Tax Rates Omitted Specificationsb,c


Basic


Spline: 6.6% knot


Spline: Drop 0%d


(1+t) -.3227
(.5304)


(1+t) up to knot --


(1+t) beyond knot


In Metro Area

HH Income

Education

Age

Asian

Hispanic

Black

Native American

Number of Kids

Married

Female

Own Business

Comp. at Work/School

Constant term


.1963**
(.0334)
3.16e-06**
(2.67e-07)
.1943**
(.0051)
-.0089**
(.0010)
-.2666**
(.0504)
-.4666**
(.0474)
-.6084**
(.0432)
-.1689
(.1225)
-.1150**
(.0100)
.2007**
(.0254)
-.0724**
(.0209)
.2089**
(.0267)
.3656**
(.0265)
-2.7225**
(.5560)


-1.3526*
(.6778)
4.6851*
(2.1203)
.1935**
(.0335)
3.14e-06**
(2.67e-07)
.1943**
(.0051)
-.0089**
(.0010)
-.2680**
(.0504)
-.4702**
(.0474)
-.6115**
(.0432)
-.1722
(.1225)
-.1149**
(.0100)
.2020**
(.0254)
-.0725**
(.0209)
.2089**
(.0267)
.3663**
(.0265)
-1.6493*
(.7087)


b Standard errors are in parentheses.

c indicates significance at the 5% level and ** indicates significance at the 1% level or smaller.


d Drops tax rates of 0%.


-2.3809
(2.0093)
4.7369*
(2.4014)
.2162**
(.0386)
3.08e-6**
(2.78e-7)
.1948**
(.0053)
-.0089**
(.0010)
-.2772**
(.0520)
-.4813**
(.0484)
-.6164**
(.0444)
-.1515
(.1328)
-.1123**
(.0104)
.2014**
(.0266)
-.0813**
(.0219)
.1921**
(.0280)
.3802**
(.0277)
-.5996
(2.1150)






28


Table 2-2 Continued


(2)
Spline: 6.6% knot


(3)
Spline: Drop 0%e


Dummies
N
Log Likelihood
LR chi2(18)

e Drops tax rates of 0%.


(1)
Basic


Regional
40,989
-26,372
3865.31


Regional
40,989
-26,369
3871.26


Regional
37,608
-24,166
3593.87
















Shopping
online







1019

1 1.066


.0213 -- .. ........................ 1.071 1.0923

Tax Rate

Figure 2-1f
Splined Tax Rate and Online Shopping



.25



.2-



.15
0

L.
.5
S-
.1 -









1 1.02 1.04 1.06 1.08 1.1
Tax Rate

Figure 2-2
Distribution of Tax Rates



fWhere 1.09231 is the maximum weighted tax rate in the sample.












Table 2-3
Correlation Matrix


Dependent
Variable


Computer
Experience


Internet
Experience


Dependent Variable 1.000 --

Computer Experience 0.212 1.000 --

Internet Experience 0.408 0.361 1.000




Table 2-4
Computer and Internet Experience Variables are added to the Benchmark and
Splined Tax Rate Specifications. A Higher Spline Knot is also Testedg'h
(1) (2) (3)
Basic Spline: 6.6% knot Spline: 7% knot

(1+t) .6889
(.5767)
(1+t) up to knot --- -.3300 -.5078
(.7364) (.6849)
(1+t) beyond knot --- 5.6630* 10.3372**
(2.3099) (3.0366)
Computer Experience .0900** .0899** .0899**
(.0067) (.0067) (.0067)
Internet Experience .3299** .3299** .3299**


Dummies
N
Log Likelihood
LR chi2(18)


g The same explanatory variables from Table 2 are also included in these regressions.
omitted to save space and since their values are nearly identical to those in Table 2.


Their coefficients are


h indicates significance at the 5% level and ** indicates significance at the 1% level or smaller.


(.0051)


Regional
40,989
-23,078
10,454.19


(.0051)


Regional
40,989
-23,075
10,459.13


(.0051)

Regional
40,989
-23,072
10,464.66











Table 2-5
Individual Level Results that Include Clothing Tax Rates and Splined Clothing Tax
Rates"'


(1+t)


(1+t) up to knot

(1+t) beyond knot


Clothing (1+t)

Clothing (1+t) up to knot

Clothing (1+t) beyond knot


Dummies
N
Log Likelihood
LR chi2(18)


Spline: 6.6% knot


'The same explanatory variables from Table 2 are also included in these regressions.
omitted to save space and since their values are nearly identical to those in Table 2.


Their coefficients are


J indicates significance at the 5% level and ** indicates significance at the 1% level or smaller.


Basic


-.4276
(.7488)


.1419
(.7146)


-.5727
(.8807)
-5.6443
(4.2829)


-1.1599
(.8233)
13.5462**
(4.8396)


Regional
40,989
-26,365
3879.29


Regional
40,989
-26,372
3865.35









Table 2-6
Household Level Results for the Benchmark, Splined Tax Rate, and Splined Tax
Rate with Zero Percent Tax Rates Omitted Specificationsk'


Basic


(1+t)

(l+t) up to knot

(1+t) beyond knot


In Metro Area

HH Income

Education

Age

Asian

Hispanic

Black

Native American

Number of Kids

Married

Own Business

Comp. at Work/School

Constant term


Dummies


(2) (3)
Spline: 6.8% knot Spline: Drop 0%"


-1.0648
(.7040)


.2460**
(.0431)
8.02e-6**
(3.93e-7)
.1896**
(.0077)
-.0202**
(.0013)
-.3147**
(.0710)
-.6024**
(.0634)
-.7656**
(.0529)
-.3212
(.1734)
-.0971**
(.0133)
.1166**
(.0322)
.2695**
(.0372)
.4355**
(.0460)
-1.4498
(.7424)

Regional


-2.3015**
(.8734)
6.4424*
(3.2046)
.2431**
(.0431)
8.00e-06**
(3.93e-07)
.1896**
(.0077)
-.0202**
(.0013)
-.3182**
(.0710)
-.6081**
(.0635)
-.7696**
(.0529)
-.3292
(.1735)
-.0970**
(.0133)
.1182**
(.0322)
.2694**
(.0372)
.4369**
(.0460)
-.1616
(.9170)

Regional


-1.6588
(2.3736)
4.8607
(3.5895)




























-.8909
(2.5012)

Regional


k The omitted explanatory variables in column (3) are similar to those in column (2).

S* indicates significance at the 5% level and ** indicates significance at the 1% level or smaller.


m Drops tax rates of 0%










Table 2-6 Continued
(1) (2) (3)
Basic Spline: 6.8% knot Spline: Drop 0%n


N 24,806 24,806 22,806
Log Likelihood -15,487 -15,484 -14,244
LR chi2(18) 3090.77 3096.55 2857.17

n Drops tax rates of 0%











Table 2-7
Confidentiality Concern and Broadband Access Variables are added to the
Benchmark and Splined Tax Rate Specifications op


Basic


(1+t)

(1+t) up to knot

(1+t) beyond knot


Broadband Connection

Confidentiality Concern

Computer Experience

Internet Experience


Dummies
N
Log Likelihood
LR chi2(18)


Spline: 6.6% knot


.6214
(.5782)


.3008**
(.0306)
-.2362**
(.0051)
.0869**
(.0067)
.3260**
(.0051)

Regional
40,989
-23,007
10595.74


-.3110
(.7381)
5.1771*
(2.3159)
.3000**
(.0306)
-.2355**
(.0368)
.0869**
(.0067)
.3260**
(.0051)

Regional
40,989
-23,005
10599.87


o The same explanatory variables from Table 2 are also included in these regressions. Their coefficients are
omitted to save space and since their values are nearly identical to those in Table 2.

P indicates significance at the 5% level and ** indicates significance at the 1% level or smaller.














CHAPTER 3
E-COMMERCE, RACE, AND THE DIGITAL DIVIDE

3.1 Introduction

As computer and Internet use continues to expand, there is a concern that not all

groups are benefiting equally from these technologies. The fear is that minorities, the

less educated, and poorer individuals may continue to fall further behind in an

increasingly digital age. This would mean not only missing out on conveniences such as

email and games but also uses such as researching health information, doing job searches,

learning about the government, or even finding lower priced goods. 1 In an attempt to

eliminate this gap between the "haves" and "have nots," policy makers implemented a

variety of governmental plans including grants to get computers in schools located in

disadvantaged areas, subsidies to connect schools to the Internet (E-Rate), and the

Presidential initiative to achieve universal affordable broadband access by 2007.

Therefore, it is of interest to policy makers whether the gap is due to a lack of

access to computers and the Internet or from more basic reasons such as inadequate

technical knowledge or lack of interest in online content. If the latter dominates, then

even universal access to broadband services will not fully eliminate the digital divide. In

this paper I will explore the disparity in technical usage between races and determine

whether the divide between White and minority computer use, Internet access, and online

shopping is based on more than simple income and education differences. I find that


1 This is especially relevant since online shopping has been cited as the fastest growing segment of online
activity in the U.S. (NTIA, 2000).









there are significant differences in how minorities and Whites view the relevance of the

computers, the Internet, and broadband access and in the perceived benefits of purchasing

goods online that cannot be explained by simple income and educational differences.

For many years the US Department of Commerce's National Telecommunications

and Information Administration (NTIA) has monitored the progress of the U.S. at

achieving universal access for telephone services. With the spread of computers and the

Internet, they now also monitor who is being left behind in the digital age. In their first

report they noted significant gaps in computer ownership and Internet penetration that

existed based on race, income, rural locations, and age (NTIA1998). More recent reports

by the NTIA still note that these gaps exist but paint a more optimistic picture of the

future of the digital divide.

The NTIA in 2002 found that Blacks and Hispanics still lag behind Whites in both

computer usage and Internet access, with 70% of Whites using a computer and 60%

having access to the Internet, compared to 56% of Blacks who use a computer and 40%

with Internet access.2 Hispanics were found to have a computer usage rate of 49% and

Internet access rate of 32%. The NTIA also found that while all racial groups have

increased their Internet usage, the growth in new Black and Hispanic users has outpaced

the growth of new White users by as much as 12%. The study indicates a growth rate of

31% and 26% for Blacks and Hispanics respectively since 1998, while White usage only

grew at 19% (NTIA 2002). These numbers along with those of a recent Pew survey

(Lenhart et al. 2003) indicate that many aspects of the digital divide may be narrowing,

but have yet to be fully eliminated.


2 The NTIA refers to White here as White non Hispanic, Black as Black non Hispanic, while Hispanics
could be of any race









Recent debate has surrounded the Bush administration's plan to cut the Technology

Opportunities Program (TOP) and Community Technology Centers (CTC) subsidies

from the federal budget. Administration officials cite the NTIA's findings of widespread

connectivity at libraries and schools as proof that the programs have served their purpose

and resources should be shifted to improving overall education and increasing literacy as

ways to finish bridging the divide (Benner 2002). Commerce Secretary Donald Evans

stated: "With the expansion of the Internet and related technologies into all sectors of

society, the administration believes federal subsidies are no longer justified to prove the

usefulness of such technologies." This shows a shift in priorities of the White House

from Clinton Gore, who fought for general access to computers and the Internet, to

Bush Cheney, who are interested in improving general skills that will make the Internet

more accessible and relevant for disadvantaged individuals. I find that the decision to

completely end federal subsidies that promote public access may be premature, since a

significant divide still exists in computer ownership and Internet access. However, the

President's plan to improve literacy and technical education may be very beneficial in

eliminating the portion of the racial divide not explained by income and education

differences, and is a worthwhile aim for governmental policy.

This study looks at whether there are social differences in the desire to access the

Internet and shop online across races given similar educations, incomes, and online

status. In particular, I will focus on the perceived costs and benefits of shopping over the

Internet and how this differs among Whites, Blacks, and Hispanics.3 The results are


3 Figure 1 illustrates online activities by race for all computer owners and indicates Whites are more likely
to do activities such as shop online, look for product information online, or use E-mail while Blacks and
Hispanics are more likely to browse product information without buying, play games, or look for job
information.









done conditionally so only individuals with Internet access at home are included in the

sample of potential online shoppers. This means I am not analyzing the "have-nots" of

the digital divide but potentially the "want-nots" (Arrison 2002).

Using 2003 Current Population Survey (CPS) data, my results confirm a

statistically significant gap in both computer ownership and Internet access (conditional

on computer ownership) between Whites and Blacks and Hispanics. I then find that the

gap persists to browsing for product information and shopping online, where both are

conditional on having Internet access. The results indicate that Blacks and Hispanics are

less willing to shop online than Whites even after controlling for income, education,

security concerns, broadband access, and a variety of demographic variables. This

implies that Blacks and Hispanics still do not see the same benefits of shopping online as

Whites. It also means that minorities are not receiving equal "value" as Whites from

being better able to match preferences to available goods (Borenstein and Saloner 2001).

I find that Blacks are still able to reap some of the benefits of price comparison and

access to detailed product information found online even if they are not willing or able to

purchase the goods over the Internet. Finally, I find broadband access plays an important

role in whether any individual is willing to shop online, with Blacks and Hispanics being

especially sensitive. My findings suggest that there is still a need for technical education

and online experience for low income minorities along with a need for more targeted

content to draw minority users online.

3.2 Literature

There has been a substantial effort to analyze and explain the differences in

computer ownership and Internet use across a variety of groups such as men compared to

women, rich compared to poor, and White compared to minorities. Articles such as









Hoffman and Novak (1998) (2000), Hoffman, Novak, and Yung (2000), and Lenhart et al

(2003) have all done statistical comparisons regarding the racial digital divide. Fairlie

(2004) does a good job of detailing the current literature on the topic; using regression

analysis he finds that while income and education play an important role in explaining the

racial digital divide there remains a significant portion of the divide that remains

unexplained. My results are consistent with these findings and provide novel evidence of

a White/minority digital divide in E-Commerce.

There have been mixed results in the literature on how racial preferences affect an

individual's decision to shop online. Spooner and Rainie (2000) as part of the Pew

Internet & American Life Project find that Whites outpace Blacks in E-Commerce

adoption with 48% of Whites and 35% of Blacks having bought a product online.

However, this report was done using only a simple comparison of means rather than the

multivariate regression analysis I employ in this study. Both Goolsbee (2000) and

Chapter 2 of this work include race variables in their analysis of online shoppers' tax

sensitivities with Goolsbee finding no significant gap between Whites and non-Whites in

the probit regression. Chapter 2 of this work, using logit regressions on 2001 CPS data,

finds Blacks and Hispanics are less likely to have shopped online than Whites. Since

both papers main focus was on tax sensitivity, neither spent time checking the robustness

of the race variables or analyzing policy implications of the race based results.

A recent paper by Ono and Zavodny (2003) is the most similar to my current study,

as they analyze the Internet shopping behavior of minorities. They use a data set

generated by Nomura Research Institute to analyze online shopping decisions along with

differences across races in the frequency and amount spent shopping online.









Unfortunately, the data set they use only surveyed 1,009 individuals ages 15 to 59, of

which only 868 are used in their regression analysis. My data set, however, is drawn

from 140,057 individuals ages 15 to 80, of which 56,750 are available for my regression

analysis regarding online shopping. Ono and Zavodny find that Whites are not

significantly more likely to start shopping online than Blacks or Hispanics but that

"blacks appear to shop online more frequently and, among people who use the Internet, to

spend more than non-Hispanic Whites." If true their results would have significant

implications for the digital divide, especially given Novak, Hoffman, and Yung's (2000)

finding that shopping online is a very skill intensive Internet activity. My results differ

from theirs in that I find there still exists a significant divide in online shopping between

White and minority Internet users. Ono and Zavodny also present seemingly

contradictory results in their paper from using an August 2000 CPS dataset, but they

minimize the importance of the results because the data had too few and too vague

questions regarding E-Commerce. As discussed in the next section, the more current

CPS data I use has resolved these issues by expanding the number of questions that

correspond to online activity and by increasing the specificity of the questions. My

findings also differ from their results in that I examine in detail the impact of security

concerns and broadband use, as well as addressing the issue of "free-riding" product

information.

3.3 Data and Model

The data used for this study comes from the "Computer and Internet Use"

supplement in the 2003 Current Population Survey (CPS), which is jointly sponsored by

the U.S. Census Bureau and the Bureau of Labor Statistics. The supplement on computer

and Internet usage has been given periodically since 1994. Each year it is given, the









questions get more detailed and in-depth. For example, in the August 2000 CPS

supplement respondents were asked whether they "use the Internet to shop, pay bills, or

(for) other commercial activities", while the 2003 supplement addresses these issues in

three separate questions, one regarding online shopping behavior, one asking about

online banking, and a question about commercial activities. The 2003 sample is drawn

from 55,135 households with a reference person4 answering for each person in the

household. A complete description of the survey's design and methodology is available

in United States Census Bureau (2002). State and local sales tax rates are matched to the

CPS data using FIPS5 state and county codes. State, county, and city tax rates were

collected from each state's department of revenue website or through contact with a state

tax official (Chapter 2 of this work). Note that tax rates from 2001 are used here as

proxies for 2003 tax rates. However, I find that removing the tax variable does not

significantly change my results.

The model to be studied is similar to Chapter 2 of this work, where the probability

that an individual will shop online increases as the cost savings from shopping online

increases:

Cost savings from shopping online = tPQ FC Shipping + Z (1)

where P is the price of the good,6 t is the tax rate, Q is quantity purchased, and FC are

fixed costs such as buying a computer, getting Internet access, and acquiring the skills

needed to use the Internet. The first term can be interpreted as the tax savings from


4 The reference person is usually the homeowner.

5 FIPS is an acronym for "Federal Information Processing Standards".

6 The price is assumed to be similar for the same good offline and online. Goolsbee (2000) controlled for
local prices and found it did not affect the results.









shopping online. The last term picks up other non-pecuniary influences on a person's

decision to shop online and could be thought of as the benefits minus the costs of online

shopping. Benefits would include things such as convenience, increased selection,

avoiding hassle from salespersons, less time needed to make a purchase, and less driving.

Costs would include security and confidentiality concerns, shipping time, inability to feel

or try on a product, difficulty returning goods,7 and no immediate customer service for

questions or concerns about a product. Equation (1) is used to develop a regression

specification and to help predict the signs on the variable coefficients.

I examine several dependent variables in this model including whether the

individual owns a home computer, whether he/she has Internet access conditional on

owning a home computer, and whether he/she has "used the Internet to purchase products

or services" conditional on having access to the Internet from home. The relevance of

conditioning on home access to measure the digital divide is explained in Fairlie (2004)

and NTIA (2000). To deal with the discrete nature of the dependent variable I use a

Logit regression model.8 The explanatory variables and their summary statistics are

shown in Table 3-1 and include household income and individual education level. Both

the household income and education questions ask the respondent to select a range in

which household income or education falls. The income and education variables utilize

the midpoints of the reported ranges in order to make the variables continuous.9 Other



7 Many multi-channel companies have alleviated this cost by allowing returns to physical storefronts. This
however generates nexus for the company and they are required to collect relevant sales taxes.

8 I get similar results using a Probit regression analysis.

9 Income is topcoded at $150,000 but I use $175,000 which the census bureau has as the average income
for those earning over $150,000.









explanatory variables include whether the respondent lives in a metro area, age,10 race,

number of children in the household, marital status, gender, whether he/she owns his/her

own business, whether he/she uses a computer at work or school, and regional dummies.

3.4 Results

Table 3-1 shows that compared to the entire sample: computer owners, individuals

with online access at home, and those that shop over the Internet are progressively

wealthier, younger, better educated, and less likely to be Black or Hispanic.11

Table 3-2 presents results from a logit regression where the dependent variables for

columns (1) and (2) are whether the individual owns a home computer and whether the

individual has home access to the Internet, respectively. As expected, the coefficients on

Black and Hispanic are negative and significant. This confirms that my data reflect the

documented digital divide in home computer ownership and online access. The results

also indicate that wealthier, younger, married, better educated individuals, as well as

those who own their own business or live in a metro area are more likely to own a

computer and to go online conditional on owning a computer. The lack of significance

on the coefficient for females in column (1) and for confidentiality concerns in column

(2) is of interest. The former is consistent with the NTIA (2002) finding that there is no

longer a gender gap in computer ownership; however I find that one may still exist in

terms of Internet access. The positive and not significant coefficient on confidentiality

concern is surprising since greater concern was expected to result in a lower probability

of having Internet access. However, I do find in later results that confidentiality concerns



10 Age is top-coded at 80 years old, I use an age of 85 for these individuals.

11 See Chapter 2 of this work for a full description of independent variables and expected signs.









have significantly negative impacts on higher risk situations such as having "always

connected" broadband access and purchasing items over the Internet.

The incidence of online shopping in this study is done conditional on individuals

having home online access to ensure that differences in desire and perceived benefits of

buying over the Internet across races is being studied rather than differences in computer

ownership and Internet access. Table 3-3 presents the results of Logit regressions where

having shopped online is the dependant variable. In the baseline regression in column (1)

I find that, similar to Chapter 2 of this work, household income, education, broadband

access, computer and Internet experience, and owning a business have positive and

significant coefficients while Black, Hispanic, and confidentiality concern have negative

and significant coefficients. These results indicate that even after controlling for Internet

access, education, income, computer and Internet experience, and frequency of Internet

use, Blacks and Hispanics are significantly less likely to shop online than Whites. This

outcome is in contrast with the conclusions of Ono and Zavodny (2003), Goolsbee

(2000), and Hoffman and Novak (2000), who find no significant difference. However, it

confirms the means comparison done by Spooner and Rainie (2000) and the regression

results found by Chapter 2 of this work based on 2001 data. The results indicate that

Blacks and Hispanics are less likely to find shopping online to be beneficial than Whites.

One concern with the results in column (1) is that the tax rate variable, used to pick

up tax sensitivity, is actually a proxy variable using 2001 rates instead of 2003 rates. To

check whether this has any impact on the variables of interest I repeat the regression

without including any tax rate data. The results in column (2) indicate that using the









proxy tax rates does not affect any of the other results and has very little impact on the

overall fit of the model.

3.5 Advanced Results

A likely contributor to the racial gap in online shopping is that Blacks and

Hispanics are more concerned about Internet security than Whites. Spooner and Rainie

(2000) find that Blacks are the most concerned racial group when it comes to online

privacy and are the most distrusting of others in general. Table 3-3 indicates that

confidentiality concerns are important in an individual's decision to shop online; thus it

may be of interest to see if Blacks and Hispanics have larger marginal effects of concern

than Whites. To check this, two interaction variables are added to the basic model by

interacting both Black and Hispanic with confidentiality concern. Table 3-4 presents the

results from doing the interaction and indicates that neither Blacks nor Hispanics have a

significantly larger marginal effect of confidentiality concerns than Whites. This result

contradicts what is found by Spooner and Rainie.

Another potential source of unreported differentials is due to minorities being more

likely to earn lower incomes than their White counterparts and therefore may find it more

difficult to obtain the credit cards that are often necessary to shop over the Internet.

Without available data on credit card ownership, I decide to analyze only individuals in

higher income brackets who are more likely to be eligible for credit regardless of race.

Columns (1) and (2) of Table 3-5 restrict the sample to individuals living in households

with incomes over $40,000 and $70,000 respectively. These cutoff points represent

households in the top 50% of household income and those in the top 25%.12 Table 3-5


12 Table 6 illustrates describes household income for my sample.









indicates that restricting the sample to those households earning over $70,000 still results

in a negative and significant coefficients on both the Black and Hispanic variables. Since

these are measures of household income, there may be a bias from larger families with

more sources of income. Therefore, column (3) of Table 3-5 presents the results if

average per person income in a household is over $27,000.13 The table indicates that

even when income is restricted on a per person basis the results still hold.

The last issue addressed in this section regards the frequency of online use. A CPS

question asks whether an individual accesses the Internet daily, weekly, monthly, or less

than monthly. Figure 3-3 illustrates the frequency of online use by race in the sample.

This variable allows me to focus on only the most avid Internet users and avoid any

concerns over the frequency of Internet use across racial groups. Therefore, column (1)

of Table 3-7 illustrates the results from restricting the regression to only those who access

the Internet every day. The regression shows that even among the most frequent users

Blacks and Hispanics are less likely to shop online.

3.6 Broadband

It is important to carefully study the impact of broadband access on Internet

shopping since the increased speed and reliability may have a large impact on shopping

decisions. Table 3-3 indicates that broadband access has a positive and significant

association with the probability that an individual will shop online. These results show

that having broadband access increases the probability of online shopping. It is therefore

of interest to determine whether all racial groups' decisions to shop online are impacted



13 Average per person income is calculated as "total household income divided by the number of people in
the household". The mean Average per person income for the sample is $26,835. Higher values lead to
lower point estimates and smaller significance for both race variables.









similarly by having high-speed access. I therefore create interaction variables with

Black, Hispanic, and broadband access. The results are presented in Table 3-8 and

indicate that Blacks have a negative and significant interaction coefficient while the

coefficient on Hispanic's interaction term is negative and not significant. These results,

along with the positive and significant coefficient on having broadband access, indicates

that Blacks do not realize as much marginal benefit from using broadband services to

shop online as other racial groups receive.

This decreased marginal benefit can also be seen in Black's overall lower demand

for broadband access. Column (2) of Table 3-7 employs a logit regression with

broadband access as the dependent variable and indicates that after controlling for a

variety of variables Blacks and Hispanics are still less likely to have high-speed Internet

access in their homes than are Whites. For my purposes it is important to distinguish

whether these results are a consequence of lower demand for high-speed Internet or of

insufficient supply of broadband access in predominantly Black and Hispanic areas.

There is very little literature detailing the demand side of broadband access using

rigorous regression analysis. The NTIA (2000), using means comparisons, finds that

Blacks and Hispanics lag behind Whites in broadband subscriptions. Both Prieger (2003)

and Prieger and Hu (2005) use probit regression analysis and find that Blacks and

Hispanics simultaneously have lower demand than Whites for broadband access and

equal supply of broadband services in a given area. While I lack the required data to

attempt to duplicate their results, Figure 3-3 shows that my sample likely follows the

same pattern. It displays the reasons given by individuals with Internet access as to why

they do not upgrade to broadband connections. The figure indicates that Whites are more









likely to cite a lack of broadband availability in their area while Blacks are more likely to

cite a lack of interest in having access to broadband or concerns over the price. The

results in this previous section help give a better understanding of why the digital divide

in online shopping exists but it does not fully explain the negative and significant

coefficient on Black found in the baseline regression. In the following sections of this

study I will outline several possibilities that explain the lingering gap in E-commerce

between races that I find.

3.7 Product Information

To this point I have shown that even after controlling for variables such as income

and education, Blacks and Hispanics are still less likely to own a computer, have Internet

access, and shop online. This does not mean that they are not finding other ways to

benefit from this medium. In this section I will outline one such instance where minority

users find a way to benefit from product information found on the Internet even if they

are not willing to shop online.

Not surprisingly, column (3) of Table 3-7 indicates that Blacks and Hispanics are

less likely to use the Internet to look up product information in general. However, it is

important to note that the dependent variable in this regression equals one if an individual

looked for product information online regardless of whether they subsequently made a

purchase online or not, and equals zero if the individual has Internet access and has not

searched for product information online. By manipulating this, and other variables

provided by the CPS, I will be able to make some inferences about the shopping

behaviors of different racial groups. In particular I find that Whites are more likely to

"free-ride" from local stores then purchase online than are minorities, while Blacks and









Hispanics are more likely to "free-ride" from information found online and then shop

offline.

Searching for product information online can potentially give users a variety of

benefits including detailed product information, price comparisons between online stores,

price comparisons between online stores and local outlets, and price information at local

outlets. The empirical literature estimating the price differences between online and

conventional sellers of tangible goods is conflicting. Brynjolfsson and Smith (2000a)

find prices online to be lower than their "brick and mortar" counterparts, Bailey (1998)

finds that the opposite is true, while Scholten and Smith (2003) find little difference in

prices. There has been very little work done, however, specifically on which groups of

individuals are more likely to gain price and product information online and then shop

offline, as I attempt to do here.

Articles discussing the Internet as a tool to search for product information

regardless of where or if the product is purchased include Alba, Lynch, Weitz, et al.

(1997), Bakos (Dec 1997), and Ratchford, Talukdar, and Lee (2001). Ratchford et al.

(2001) believe the Internet competes with printed ads more than with television or direct

interaction with products since getting factual information off a webpage is easier than

getting any "sensory gratification". They also find that the easier online searches become

and the more product information found online that complements goods that can be found

locally, individuals become increasingly likely to buy at a storefront. Bakos (2001) and

Dykema (2000) quantify this proposition by estimating that purchases at brick and mortar

stores that are "influenced" by the Internet will grow from $13 billion in 2000 up to $647

billion in 2005. While none of the above studies break consumers down by race, the









findings are provocative and may help explain my results that Black consumers, at a

significantly different rate than other groups, are finding product information online but

are opting not to purchase the goods electronically.

Column (1) of Table 3-9 presents the results of a logit regression where the

dependent variable equals one if the individual has browsed for product information

online but decided not to make a purchase online, and equals 0 if the individual browsed

for product information online and did decide to make a purchase online. Notice that

these results are done conditional on having Internet access and having searched the

Internet for product information. The results indicate that Blacks and Hispanics are

significantly more likely to browse online and opt not to purchase over the Internet than

are Whites. This could illustrate the minority use of the Internet as a tool to improve their

information on prices and product details on certain goods which could be subsequently

purchased at local outlets. Another possible explanation for these findings is that Blacks

and Hispanics are more likely to look for product information online only to discover the

price is too high or that the product searched for does not fit their needs. Without more

detailed information on shopping behavior I cannot conclusively determine which of the

above scenarios is occurring. However, given the substantial growth in the number of

individuals browsing online and then shopping at local outlets described in Bakos (2001)

and Dykema (2000), along with the persistent unexplained divide I have outlined above,

the latter explanation seems likely.

Column (2) of Table 3-9 presents results from a logit regression where the

dependent variable equals one if the individual has purchased a product online without

having searched for product information online and equals zero if an individual has









Internet access and has not searched for product information online. The results are

therefore conditional on having Internet access and on not having searched online. These

types of purchases could either be classified as impulse buying or purchases made after

product information has been collected offline. With the latter referring to the frequently

cited "free-riding" issue where shoppers get the sensory benefits of feeling or trying on a

good along with individual customer service at a local store only decide to spend their

money purchasing the good online. Column (2) indicates that Blacks are significantly

less likely than any other racial group to participate in spur of the moment online

shopping or in "free-riding" off local stores.

The results in this section not only help explain the gap in online shopping found in

Table 3-3 it also represents the first known attempt to use regression analysis to study

"free-riding" by different racial groups. I find that Whites are more likely to "free-ride"

off of information gleamed from local stores and then purchase online while Blacks are

more likely to "free-ride" on the detailed pricing information and product specifications

found online only to shop offline. If these inferences are true, they imply that Blacks

may value having the item immediately as opposed to waiting for shipping while Whites

may value the lower prices of some goods found online regardless of shipping times.

3.8 Conclusion

Unlike any previous study using regression analysis I conclude that Blacks and

Hispanics with Internet access are significantly less likely to shop online than are Whites.

This is done while controlling for variables such as household income, education, sex,

living in a metro area, and computer and Internet proficiency. Furthermore, I find that

this digital divide across racial groups regarding E-commerce is not the result of differing

marginal rates of confidentiality concern, differing frequencies of online use, or lack of









access to credit cards. The results indicate that there may be two areas that help explain

the gap and warrant consideration for future research. The first is demand for broadband

access and its effect on a person's decision to shop online. Broadband access is found to

have a smaller impact on shopping online for Blacks than for Whites. The second area

involves the issue of "free-riding". Whites seem to be more likely to "free-ride" off

information gained at storefronts and then shop online, while Blacks and Hispanics seem

to be more likely to gain information online to assist in their offline purchases. This

study finds that the long standing divides in computer and Internet access across races

still exists and therefore requires the continued effort of policy makers to bridge this gap

of access. However I find a second divide relating to broadband access and online

shopping that indicates that even with equal access a divide exists. Whether this is a

result of differences in culture, lack of relevant content online for minorities, or some

engrained distrust for technology is unknown.









Table 3-1
Summary Statistics"
Variable Entire Computer Internet Shop
Sample Owners Access Online

n 89,160 62,516 55,824 30,374


White


Black


Hispanic


Native Am.


Asian


Education


Age


HH Income


Comp. at Work/School


In Metro Area


Married


# of Children


Female


Own a Business


a Standard errors in parentheses


0.758
(0.425)
0.092
(0.288)
0.096
(0.294)
0.015
(0.111)
0.037
(0.186)
12.924
(2.765)
43.928
(18.122)
61169
(47417)
0.457
(0.498)
0.753
(0.431)
0.547
(0.498)
1.178
(1.789)
0.520
(0.500)
0.153
(0.360)


0.809
(0.389)
0.068
(0.251)
0.068
(0.251)
0.012
(0.096)
0.041
(0.196)
13.497
(2.518)
41.511
(16.206)
72923
(48266)
0.559
(0.496)
0.770
(0.421)
0.602
(0.489)
1.262
(1.768)
0.513
(0.500)
0.187
(0.390)


0.822
(0.378)
0.063
(0.242)
0.061
(0.238)
0.010
(0.089)
0.042
(0.198)
13.620
(2.478)
41.464
(16.039)
76286
(48375)
0.576
(0.494)
0.780
(0.414)
0.612
(0.487)
1.252
(1.749)
0.511
(0.500)
0.194
(0.395)


0.860
(0.343)
0.047
(0.211)
0.045
(0.206)
0.009
(0.081)
0.037
(0.188)
14.319
(2.238)
40.499
(13.913)
82753
(49569)
0.706
(0.455)
0.791
(0.407)
0.626
(0.484)
1.175
(1.709)
0.524
(0.499)
0.208
(0.406)














100 0%


90 0%


80 0%


70 0%


60 0%


50 0%


40 0%


300%


200%


100%

0 000 <
00%








Figure 3-1

Online Activity by Race


70 0%




60 0%




50 0%




40 0%




30 0%




20 0%




10 0%




0 0%


Less than once a Month


At Least Once a Month At Least Once a Week At Least Once a Day


Figure 3-2

Frequency of Internet Use by Race


SWhite
SBlack
O Hispanic


iWhite
*Black
O Hispanic










Table 3-2b',
Probability of Owning a Computer and of Accessing the Internet Given Computer
Ownership
(1) (2)
Own a Comp. Online Accessd

Sample Everyone Comp. Owners
Black -0.700** -0.516**
(0.029) (0.047)
Hispanic -0.827** -0.505**
(0.029) (0.046)
Native Am. -0.617** -0.639**
(0.070) (0.108)
Asian -0.134** -0.009
(0.051) (0.076)
Education 0.154** 0.070**
(0.004) (0.006)
Age -0.023** -0.002*
(0.001) (0.001)
HH Income 1.65e-5** 1.40e-5**
(2.82e-7) (4.17e-7)
Comp at work/school 0.728** 0.059*
(0.021) (0.031)
In Metro Area 0.126** 0.297**
(0.020) (0.031)
Married 0.615** 0.196**
(0.019) (0.030)
# of Children in HH 0.058** -0.023**
(0.005) (0.008)
Female 0.012 -0.069**
(0.017) (0.027)
Own a Business 0.436** 0.138**
(0.029) (0.040)
Comp Experience 0.242**
(0.010)


b Results continued to the next page.

Standard errors are in parentheses. indicates significance at the 5% level and ** indicates significance
at the 1% level or smaller.


d Online access is done conditional on owning a home computer.






56



Table 3-2 Continuede
(1) (2)
Own a Comp. Online Accessf

Conf. Concern -0.035
(0.043)
Constant -1.427** 0.215**
(0.058) (0.090)
Log Likelihood -40,955 -18,925
Observations 89,160 62,516


e Standard errors are in parentheses. indicates significance at the 5% level and ** indicates significance
at the 1% level or smaller.


f Online access is done conditional on owning a home computer.











Table 3-3
Probability of Shopping Online Given Internet Access from Home With and
Without the Proxied Tax Variableg'h


Sample
Black

Hispanic

Native Am.

Asian

Education

Age

HH Income

In Metro Area

Female

Conf. Concern

Broadband

Comp Experience

Int. Experience


(1)
Shop Online'
Benchmark

Internet Users
-0.571**
(0.041)
-0.311**
(0.043)
-0.483**
(0.104)
-0.475**
(0.052)
0.152**
(0.005)
0.002**
(0.001)
2.48e-6**
(2.26e-7)
-0.062**
(0.025)
0.035*
(0.020)
-0.219**
(0.032)
0.289**
(0.022)
0.102**
(0.006)
0.320**
(0.005)


(2)
Shop Online
No Tax Variable

Internet Users
-0.573**
(0.041)
-0.315**
(0.043)
-0.480**
(0.104)
-0.476**
(0.052)
0.152**
(0.005)
0.002**
(0.001)
2.49e-6**
(2.26e-7)
-0.066**
(0.024)
0.035*
(0.020)
-0.219**
(0.032)
0.289**
(0.022)
0.102**
(0.006)
0.320**
(0.005)


g Standard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

h Variables included but not shown include: Married (+) significant at 1%, # of children (-) significant at
1%, Own a Business (+) and significant at 1%, In Metro Area (-) significant at 1%, Computer at
work/school (+) not significant. Each has similar results for both specifications.


1 Online shopping is done conditional on having Internet Access at home.









Table 3-3 Continued
(1) (2)
Shop Online Shop Online
Benchmark No Tax Variable

Frequency Online 0.309** 0.309**
(0.014) (0.014)
Tax Rate -0.479
(0.497)
Constant -4.486** -4.987
(0.526) (0.081)**
Log Likelihood -31,484 -31,484
Observations 56,750 56,750


Table 3-4
Probability of Shopping Online Including Confidentiality Concern Interactionk'
(1)
Shop Onlinem
Interactions Included

Sample Internet Users
Black -0.565**
(0.044)
Hispanic -0.304**
(0.046)
Conf. Concern -0.212**
(0.034)
Black*Conf Concern -0.055
(0.128)
Hispanic*Conf Concern -0.064
(0.142)
Log Likelihood -31,484
Observations 56,750





J Online shopping is done conditional on having Internet Access at home.
k Standard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

1 All variables from column (1) of Table 3 are included in the regression but are not shown.


m Online shopping is done conditional on having Internet Access at home.









Table 3-5
Probability of Shopping Online Conditional on Specific Income Levelsn'0'p
(1) (2) (3)
Shop Onlineq Shop Online Shop Online

Avg. Per Person
Sample HHI > $40,000 HHI > $70,000 Avg. Per Person
Income > $27,000
Black -0.630** -0.622** -0.576**
(0.050) (0.066) (0.070)
Hispanic -0.279** -0.345** -0.271**
(0.055) (0.074) (0.087)
Conf. Concern -0.258** -0.317** -0.299**
(0.036) (0.046) (0.048)
Log Likelihood -23,656 -14,824 -13,015
Observations 42,690 27,371 24,465


n Standard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

o All variables from column (1) of Table 3 are included in the regression but are not shown.

P Column (1) restricts the sample to HH incomes over $40k, column (2) over $70k, and column (3) restricts
average per person income to over $27k


q Online shopping is done conditional on having Internet Access at home.











Table 3-6
Household Income Summary Statistics
HH Income Frequency Percent of Sample Percent of Total
2500 2,162 2.4 2.4
6250 2,114 2.4 4.8
8750 2,113 2.4 7.2
11250 2,930 3.3 10.5
13750 2,747 3.1 13.5
17500 4,238 4.8 18.3
22500 5,733 6.4 24.7
27500 6,022 6.8 31.5
32500 6,128 6.9 38.3
37500 5,525 6.2 44.5
45000 8,653 9.7 54.2
55000 8,411 9.4 63.7
67500 9,657 10.8 74.5
125000 18,204 20.4 94.9
175000 4,523 5.1 100.0
89,160 100










Table 3-7
Probability of Shopping Online for Daily Internet Users along with the Probabilities
of Having Broadband Access or Searching for Product Information Onlinerst
(1) (2) (3)
Shop Onlineu Broadband Access Product Information

Sample Daily Internet Users Internet Users Internet Users
Black -0.674** -0.187** -0.387**
(0.060) (0.040) (0.043)
Hispanic -0.242** -0.204** -0.377**
(0.062) (0.043) (0.046)
Native Am. -0.459** -0.023 -0.374**
(0.150) (0.106) (0.108)
Asian -0.469** 0.341** -0.487**
(0.068) (0.048) (0.059)
Education 0.169** 0.008* 0.114**
(0.006) (0.005) (0.006)
Age 0.004** -0.007** 0.004**
(0.001) (0.001) (0.001)
HH Income 2.17e-6** 7.30e-6** 8.97e-7**
(3.06e-7) (2.11e-7) (2.74e-7)
In Metro Area -0.079* 0.632** -0.060*
(0.035) (0.025) (0.029)
Female 0.032 -0.146** -0.084**
(0.027) (0.019) (0.023)
Conf Concern -0.228** -0.096** 0.004
(0.044) (0.032) (0.037)
Broadband 0.358** 0.108**
(0.028) (0.027)
Comp Experience 0.095** 0.087** 0.128**
(0.008) (0.006) (0.008)
Int. Experience 0.354** 0.119** 0.381**
(0.007) (0.005) (0.006)



SStandard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

s All variables from column (1) of Table 3 are included in the regression but now shown.

t Column (2) looks at Broadband access. Variables not displayed in column (2) include: Computer at
Work/School (-), Married (+), Number of Children (+), Own a Business (+), and Frequency Online (+) all
of which are significant at 1%.

" Online shopping and Broadband access is done conditional on having Internet Access at home.











Table 3-7 Continuedv

Tax Rate 0.069 -1.310*
(0.698) (0.590)
Constant -4.328** -3.019** -1.277*
(0.737) (0.079) (0.623)
Log Likelihood -16,418 -32,986 -24,305
Observations 30,818 56,750 56,750


Table 3-8
Probability of Shopping Online with Broadband Interactions Includedw"x


Sample
Black

Hispanic

Broadband

Black*Broadband

Hispanic*Broadband


Log Likelihood
Observations


Shop Onliney

Internet Users
-0.493**
(0.049)
-0.280**
(0.051)
0.311**
(0.023)
-0.260**
(0.087)
-0.102
(0.093)
-31,479
56,750


v Standard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

w Standard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

x All variables from column (2) of Table 6 are included in the regression but are not shown.

Y Online shopping and broadband access is done conditional on having Internet Access at home.









63








500% -


450%


400%


350%


300%


250%
*Black

200%


150%


100%


50%


00%
Don't need it, not Too expensive Can use it Not available in Computer Privacy and Other reasons
interested somewhere else my area inadequate security concern
Reason Given

Figure 3-3

Reason for no Broadband Access











Table 3-9
Probability of not Buying Online Given Searched for Product Information, and
Probability of Buying Online Given no Search for Product Informationz


Browse, don't buy""


Buy, don't browse'


Sample

Black

Hispanic

Native Am.

Asian

Education

Age

HH Income

In Metro Area

Female

Conf Concern

Broadband

Comp Experience

Int. Experience


Internet Users Who Have
Searched for Product
Information Online
0.501**
(0.046)
0.202**
(0.050)
0.413**
(0.119)
0.352**
(0.060)
-0.142**
(0.005)
-0.001
(0.001)
-2.74e-6**
(2.57e-7)
0.043
(0.028)
-0.044*
(0.022)
0.234**
(0.035)
-0.298**
(0.024)
-0.086**
(0.006)
-0.224**
(0.006)


Internet Users Who Have
Not Searched for Product
Information Online
-0.497**
(0.123)
-0.217*
(0.117)
-0.383
(0.310)
-0.603**
(0.166)
0.115**
(0.014)
0.0001
(0.002)
8.32e-8
(6.96e-7)
-0.016
(0.076)
0.053
(0.059)
-0.310**
(0.103)
0.331**
(0.066)
0.071**
(0.020)
0.244**
(0.017)


z Standard errors are in parentheses, indicates significance at the 5% level and ** indicates significance at
the 1% level or smaller.

aa Variables included but not shown include: Married (-) significant at 1%, # of children (+) significant at
1%, Own a Business (-) and significant at 1%, In Metro Area (+) not significant, Computer at work/school
(-) significant at 1%.
bb Variables included but not shown include: Married (+) significant at 1%, # of children (+) NS, Own a
Business (-) NS, In Metro Area (+) NS, Computer at work/school (-) significant at 1%.









Table 3-9 Continued
(1)
Browse, don't buy"c


(2)
Buy, don't browsedd


Frequency Online -0.296** 0.219**
(0.017) (0.039)
Tax Rate 0.126 -0.535
(0.560) (1.521)
Constant 3.816** -4.535**
(0.592) (1.609)
Log Likelihood -24,720 -3,987
Observations 44,196 12,554


cc Variables included but not shown include: Married (-) significant at 1%, # of children (+) significant at
1%, Own a Business (-) and significant at 1%, In Metro Area (+) not significant, Computer at work/school
(-) significant at 1%.
dd Variables included but not shown include: Married (+) significant at 1%, # of children (+) NS, Own a
Business (-) NS, In Metro Area (+) NS, Computer at work/school (-) significant at 1%.














CHAPTER 4
USE TAX EVASION WITH DECREASING TRANSPORT COSTS IN ASSYMETRIC
JURISDICTIONS

4.1 Introduction

Amazon.com, FTD.com, and Dell.com have done it. So have Priceline.com and

Canadian companies like Quixtar.com and Officesupplies.com. These companies have

established e-commerce Web sites and are selling books, CD's, videos, flowers,

computers, and a myriad of other products directly to consumers. This is direct

competition with more traditional "bricks and mortar" establishments. The number of

consumers choosing to purchase tangible retail goods over the Internet versus purchasing

at conventional retail stores has continued to grow since e-commerce began. Predictions

from survey companies such as Forrester and Jupiter Metrix show that this growth should

continue at an increasing rate well into the future (McQuivey, 1998). This increase in

online purchases can be attributed to several causes, such as the additional convenience

of shopping from home, lower prices, increased selection, and the current absence of

sales taxes on most purchases.

Currently state and local governments are unable to collect taxes on out of state

purchases by local residents and find it too difficult to collect taxes on in-state purchases

by non-residents, when the shopping is done by mail order or over the Internet. The

problem stems from two Supreme Court decisions; the first is National Bellas Hess vs.

the Department of Revenue of the State ofIllinois (1967), which asserted that firms are

only required to collect sales taxes from consumers in states where the firm has a









physical presence referred to as a "nexus". Later the Supreme Court in Quill

Corporation vs. North Dakota (1992) affirmed the earlier ruling and left it up to Congress

to pass legislation legitimizing cross-border taxation. Thus, Amazon.com in Washington

state is only required to collect sales taxes from Washington customers and not customers

purchasing from out of state.1 Consumers that buy from out of state are legally supposed

to pay a "use tax" on the item to their local government. This tax is widely evaded due to

ignorance of the tax in general and because of the difficulty of enforcing payment, since

"use taxes" require the consumer to voluntarily send in a tax payment2 to their state

government for all applicable untaxed remote purchases. As it stands, collecting the use

tax is only successful on goods such as cars and boats that need to be registered in the

consumer's state. This widespread tax evasion has upset many conventional retailers

who feel that Internet retailers have an unfair advantage from not being effectively taxed.

Though collection of use taxes has been problematic since the original Supreme

Court ruling in 1967, the recent growth of E-commerce has magnified the problem and

led many lawmakers to attempt to find a solution. The main concern of state officials is

the erosion of state revenues that results from citizens' ability to avoid paying taxes on an

item simply by shopping remotely.3 Goolsbee (2001) finds that while many estimates of

current losses in state revenue are inflated, the losses are very large and will become

substantial within five to ten years. To curb these losses, several states (19 at last count)

have attempted to join together in passing legislation that would call for a harmonized tax


1 If warehouses or significant offices are established in another state, nexus will then apply there as well.
2 Several states have tried putting "use taxes" owed on income tax forms with varied success.

3 Many of the arguments for e-commerce taxation are similar to those put forward when mail order
purchases were first subjected to a similar tax rule (ACIR, 1986).









code across jurisdictions. This would allow congress to pass legislation giving states the

right to compel firms to collect and remit use taxes on all remote sales. Therefore, in this

paper I use simulation analysis to predict the impact on consumers, producers, and

governments of changing tax regimes from the current situation where use taxes are

widely evaded to a situation where these taxes are fully collected. I also simulate the

impact that shrinking transport costs have on the urgency to find a way to collect use

taxes. My results indicate that a shift to full use tax collection leads to a decrease in total

sales for all firms, lower profits for individual firms, higher tax inclusive prices, and

increases tax revenue collected by state governments. I also find that businesses located

in less populated jurisdictions4 are the hardest hit, with as much as a 31.44% increase in

prices paid for their products by out of state shoppers. Finally, I find that decreasing

transport costs along with a continued inability to collect use taxes would lead to ever

growing losses in tax revenue and to a decrease in demand for firms located in heavily

populated jurisdictions.

4.2 Overview

My paper differs from previous literature in three main ways 1) I employ a logit

demand setup with market overlapping that allows for product differentiation and market

power, 2) I use a weighted government objective function, and 3) I employ numerical

simulations to endogenize tax rates along with prices. The previous literature has only

made the prices endogenous, with the taxes left as exogenous.

I use a two-jurisdiction spatial model with an asymmetric distribution of customers

and firms across jurisdictions. I model a two-stage game where the governments


4 An example of this would be Amazon.com located in Washington state.









simultaneously choose tax rates that affect the subsequent pricing decision of firms. In

choosing the tax rates, governments are concerned with tax revenue used to fund a public

good, consumer surplus, producer surplus, and local employment. Firms have market

power derived from both their spatial separation and from selling differentiated products.

Consumers are assumed to have random utility functions; thus, with differentiated goods,

consumers may prefer a product sold in an outside jurisdiction from which he lives,

which results in market overlapping.5 A model that is well suited to this setup is the logit

demand model, which is extensively discussed in Anderson, De Palma, Thisse (1992).

The demand structure is a multinomial logistic, where a consumer can choose to buy

from any of N1+N2 stores or an outside option that captures the possibility of not

purchasing anything. The tax structure I study is ad valorem since it is the most

prevalent form of retail sales tax. The transport cost variable will be of importance since

these costs are declining as technology improves, making out of state shopping more

attractive.

Using firm profits and my weighted objective function, I examine both the

derivative of profits with respect to prices and the derivative of the objective function

with respect to the tax rate. The resulting equations are intractable, and thus my results

are achieved by running simulations to get tax and price pairs for different possible tax

regimes. I analyze the current real world cases of full use tax evasion where a single firm

is located in a jurisdiction with very few of its customers or its competitors (e.g.

Amazon.com). I then run simulations to see how the tax and price pairs change if there

was no use tax evasion and these taxes were fully collected. Under each tax regime I am


5 The market overlapping in this paper is not just one-way as it is in Trandel (1992).









able to get an estimate of intra- and inter-jurisdictional demands, each jurisdiction's

elasticity of demand, their price cost margins, and total per capital sales in the industry

(see Appendix B). The benefit of using simulation analysis is that it enables me to solve

an otherwise intractable first order condition; also it gives comparative statics of key

variables; it requires less restrictive assumptions, and most importantly, it helps examine

the magnitudes of the comparative statics (Froeb and Werden 2000).

4.3 Literature

Many studies of tax competition use spatial models similar to Hotelling's

framework where firms are located on a line or circle with customers being uniformly

distributed. The customers then decide if the benefit of buying from one firm is worth

the cost of traveling there, as measured by the distance to the firm (Braid 1987, Kanbur

and Keen 1993). Braid's (1993) analysis is similar to these types of studies, except that

he deals with jurisdictions as separated segments along a line where firms choose their

location within these jurisdictions to maximize their number of customers. My model is

not explicitly set up as a Hotelling line or circle. Instead, I use a transport cost variable 6,

which reflects the difference between the cost of travelling within one's jurisdiction and

of having a product sent in from the opposing jurisdiction. Thus, 6 represents the

difference in search costs, transport costs, and hassle between shopping at home and

away. It is therefore assumed that e-commerce will lower 6 in my model. With this in

mind, it will be interesting to see the effect of lowering 6 on a state's tax revenues.

Much of the spatial tax competition literature also deals with firms that are

perfectly competitive, setting prices equal to marginal cost (Braid 1993, Kanbur and

Keen 1993). My model has product differentiation, which allows firms to set prices









above marginal cost and to earn profits (Anderson and De Palma, 1992). This approach

is supported by Bailey (1998), who finds that there is indeed variance in online retailer

pricing. He is also finds that since online shoppers currently "tend to be affluent", firms

are able to employ some degree of price discrimination on these buyers.

The asymmetry in the number of consumers within the two jurisdictions in my

paper is similar to studies by Wilson (1991), Kanbur and Keen (1993), and Trandel

(1994), each of which has one large and one small area. My paper goes one step further

however and assumes that not only are the number of customers across each jurisdiction

skewed, but the number of firms is similarly nonuniform. This will allow me analyze the

current state of affairs within the U.S. Hamilton and Thisse (2002) examine a more

symmetric model that captures the tax rate harmonization proposals being discussed by

groups such as the National Tax Association (http://www.ntanet.org/) and the National

Governors Association (http://www.nga.org/).

Previous studies have looked at the taxation of capital as the main source of tax

revenue for funding a public good, with differential tax rates causing capital movement

across jurisdictions (Wilson 1986, Zodrow and Mieszkowski 1986, Wildasin 1988).

More recently, Wilson expanded this idea to taxes on both capital and labor within a

jurisdiction (Wilson 1991). My analysis will draw from Goolsbee's recognition that the

sales tax makes up the largest single component of the state tax revenue (Goolsbee 2000)

and from Trandel (1994) and Kanbur and Keen (1993) where governments are concerned

with consumers' welfare, not just tax revenue. Therefore, I construct the government's

objective function as a weighted average of tax revenue, firm profits, consumer surplus,

and local employment.









Trandel (1992) is most similar to my study. He deals with use tax evasion

implications in mail order purchases with potential welfare losses resulting from

enforcing use tax collection. He focuses on an experiment by New York and New Jersey

to collect use taxes on cross border purchases and finds that, while tax revenues may

increase, firms' market power increases as well since people become less likely to shop

across borders. This new market power leads to higher prices, which he finds could wipe

out any welfare gains from increased tax revenue for the state. His paper assumes equal

sized jurisdictions each with two firms, a standard demand setup, and did not account for

market overlap. As in Hotelling, every consumer in his model shops where they obtain

the lowest full price. Governments maximize the weighted sum of profits, tax revenue,

and utility, which is more restrictive than my model. Later Trandel (1994) extends his

earlier work to deal with different sized jurisdictions. Again he does not deal with

differing numbers of firms across jurisdictions and only looks at the tax revenue and

welfare effects as separate cases instead of doing any weighted averages. Braid (1993)

maximizes the straight sum of consumer surplus and tax revenues, but assumes perfect

competition among firms, and has other differences with my paper as mentioned above.

Empirical work in the area has been limited due to a lack of reliable data and

because of rapid changes in this market. Goolsbee (2000) uses survey data from

Forrester Research to test online shopper's sensitivity to sales taxes. I will use his results

as a rough guideline to see if my simulated results are reasonable. Of most interest to my

research is that he estimates demand elasticity magnitudes and overall demand shifts

resulting from tax changes.









The simulation literature is still mostly based in industrial organization where it is

often used to gauge the effects of mergers on market competitiveness. For a good

explanation of the advantages of simulation analysis (including simulations of Logit

demands) and for a list of several authors who have used this method see Froeb and

Werden (2000).

4.4 Model

The following general model is similar to Hamilton and Thisse (2002). As

described before, my model is a multinomial logit demand under ad valorem taxation.

Following Trandel (1992), Anderson and De Palma (1992), and Anderson, De Palma and

Thisse (1992), I include an outside option in the model so the customer can choose a

numeraire good or choose not to purchase at all.

Jurisdiction 1 will be the "Amazon.com" type location having few customers

located there and having fewer firms then competing areas. The subscript i will denote a

representative firm in jurisdiction 1, and j will denote a representative firm in jurisdiction

2. The quantity demanded from firm i is given as Di(Pi, P1, P2) and j's demand as

Dj(Pj,P1,P2) where:

Pi = price charged by firm i

P1 = price charged by other firms in jurisdiction 1

Pj = price charged by firm j

and P2 = price charged by other firms in jurisdiction 2.6





6 Since P, = Pi and P, = P2, I only distinguish the representative firm from others in the same jurisdiction
when analyzing price choices within that jurisdiction.









Since Pi and Pj are producer prices,7 the profit functions for the representative

firms are: Hi = (Pi ci)Di(Pi, Pi, P2)

and 1Ij = (Pj c2)Dj(Pj, P1, P2),

Where ck's represent marginal costs, and fixed costs are ignored.

The demand functions are weighted sums of home (H) and foreign (F) demands,

with weights being the share of population in each the home and foreign jurisdictions,

denoted by y and (1-y) respectively. Thus, demand for a representative firm in

jurisdiction 1 is:

D, = yH + (1- y)F (1)

When use taxes can be effectively avoided, which is currently true within the U.S.,

a consumer has four choices: 1) buy from firm i in the home jurisdiction and pay

]' (1+ rz), 2) buy from a firm other than firm i in the home jurisdiction and pay P1 (1+ rz),

3) buy from a firm in jurisdiction 2 and pay P2 + ,, 8 or 4) the consumer can choose the

outside option, by not buying anything, and obtain Vo.


exp -P( +r)
H, = (2)


A, = exp (I + )+(N 1) exp -+l G+ N2 exp + exp -


Hi is the probability that the best alternative for a consumer in jurisdiction 1 is firm i; it is

aggregated over all consumers to get an expected demand. Ni is the number of firms in


7It is assumed there is no price discrimination across jurisdictions.
8 Note that there is no tax on P2 here due to full use tax evasion.









each jurisdiction; y is the share of the population in jurisdiction 1 (total population is

normalized to one), and [t is a measure of consumers' desire for differentiated products.9

Vo represents the outside option (as Vo approaches negative infinity, no one opts for the

outside option), 6 represents transport costs, and zI is the tax rate on purchases. The

Logit model is derived from the assumption that the unobservable (random) parts of

individuals' utilities are i.i.d. according to the double exponential distribution.10

"Foreign" demand for a representative firm in jurisdiction 1 is similar in structure.

A foreign consumer could: 1) buy from firm i at a cost of 17 + 2) buy from a firm other

than i in jurisdiction 1 at a cost of P1 + 3, 3) buy from a firm in jurisdiction 2 at a cost of

P (1+ r2), or 4) they could choose the outside option. Thus, foreign demand for a

representative firm in jurisdiction 1 is:


exp -
eF= P ( (3)
A,2


A, = exp 2+ (Nl -1) exp + N2 exp (- 2)+ exp K T


The demand for a representative firm in jurisdiction 2 can be derived similarly.

Dj = yFj + (1 y)Hj (4)

Home demand for the representative firm is:






9 As p approaches zero the consumer does not value variety, and as gp approaches infinity price differences
do not matter.

10 An explanation of the logit demand setup can be found in Anderson, De Palma, and Thisse (1992).










exp P, (1 + r2)
H = (5)
j2

-Pe -x P (1 P(1+ ) 2 Vy
=+ expG- +
A j2 exp + (N2 1)exp + exp + exp


And foreign demand for the firm is:


exp /
F = (6)



A,, P~ (1 + z, ) P2 e P-V
A, = N exp + P+ (N2 1) exp P2 exp +exp)+


After observing the tax rates set by each government, retailers simultaneously

choose their prices by maximizing profits subject to own price. The first order conditions

(FOC) for the best replies are:

n, OD
= D, + (0P c,) =0 (7)


and

= D +(P -c )2 = 0 (8)
J J

As described previously, the tax rates are set by governments maximizing a

weighted sum of tax revenue (R), consumer surplus (CS), firm profits (rT), and

employment (E) where employment is proxied for by output (see Appendix A for

derivations). The weight on tax revenue is set larger than the weight on consumer surplus

because the government's desire to fund the public good. Also note that the weight on

profits could be set lower than the weight on consumer surplus if the firms are partly









owned by nonresidents. Employment is added to the objective function since many have

argued that use tax evasion is taking jobs from higher taxing jurisdictions. I use the same

weights in both jurisdictions for convenience, but the simulation analysis could just as

easily handle asymmetric weights. This weighted sum "welfare function" is similar to

the approach used in regulation problems by Laffont and Tirole who also used consumer

and producer surplus in their model (1993). My objective functions (Wi) are denoted:

W, = a, R1 + a2 CS + a3 + (1-, a2 a)E (9)

and

W2 = a1R2 +a2CS2 +a3 r2 +(1 a a3)E 11 (10)

Governments choose Ti and T2 to maximize Wi and W2 respectively

(e.g. R,(T1,T2)= R,(P(T, T2 ), P2 (1,, 2 ), 12 1 2


= +a2 + 3 +(1-aa2 a,) = 0 (11)


8W, aR, -CS, a7-, 8E,
-=a -- +a22 +3 1 +(1 -a, 2 a3) a = 0 (12)
a 22 2 2 2 2

Running the two-stage game with both prices and tax rates as endogenous will give

equilibrium price and tax rate pairs. Two other games that are analyzed in this paper

using this model are 1) making one of the tax rates exogenous and seeing the effect of a

small change of the exogenous tax rate on the endogenous rate and prices, and 2) making

both tax rates exogenous. When both are exogenous, one can be held fixed while the

other is changed to see what effect this change has on prices. The simulations will be


11 See Appendix A for derivation of terms. The bar over each variable denotes the fact that these are
equilibrium levels.









done under both the use tax evasion environment and the proposed complete use tax

enforcement model.

The use tax enforcement model, where all purchases are subject to sales taxes

regardless of where they are made, results in different tax-inclusive prices for cross

border purchases than would result when use taxes are not collected. Demand for a

representative firm in jurisdiction 1 when there is full use tax collection is:

D, = y,+ (1 y)F (13)

Home demand is similar to equation (2) except now the cost from buying in jurisdiction 2

is P, (1 + r, )+ 3, where the new (1+'c) term represents enforcement of use tax collection.

Therefore, home demand for a representative firm in jurisdiction 1 under full

enforcement is:


exp P- (1+)
H, = (14)
A,1


Al = exp +-(+ + (N1 -1)exp P-(1+r) +


-P (1+ Z)- ep (,
+ N exp (1 + ) + exp -


And by a similar logic, foreign demand for a representative firm in jurisdiction 1 under

full enforcement is:



F, -=P (15)
A,2


A,2 = exp ,(-2)-- +(Ni -1)exp -v-( r +
I u ^ I ^









N2 e P (1+ 2) + expF
+ exp -- + ep


A similar specification is done for jurisdiction 2.

4.5 Benchmark

Determining the magnitudes of the variables of interest depends on the equilibrium

levels of the strategic variables, taxes and prices. In order to run proper simulations that

lead to usable variable interpretations, it is important to use reasonable parameter values.

Parameter values will be judged by their ability to induce equilibrium prices and tax rates

that result in sensible values for price-cost margins, demand elasticities, and total sales.

These targets will come from previous empirical literature and anecdotal evidence. First,

Goolsbee (2000) estimates the tax-price elasticity of online shopping to be from -2.3 to -

4.3, depending on the specification of the model. He does his analyses using mail-in

survey data, which can be unreliable, so I use these numbers only as a rough guideline for

my target elasticity. Second, evidence from a local bookstore owner indicates that $6 is

paid to the publisher on a book with a list price of $10. This implies a price-cost margin

(PCM) of 40%, where the price-cost margin is computed as follows:


PCM P MC- -6 .40
P 10

Since the wholesale price paid to the publisher is just one of the variable costs in getting a

book to the customer, I would expect the marginal cost to be larger than $6. A larger MC

implies a lower PCM. Thus I will use 20%-30% as my target range for PCMs. The level

of total sales in my model can be varied by adjusting Vo. There is no empirical work in

this area for guidance, so I simply aim to avoid the extreme of having almost no one buy

(less negative Vo). I do check extreme values to make sure the model works in this case









(not reported) and will confine my effort to total sales that result in greater than 40% of

people buying.

My benchmark simulation will use parameter values whose sign and relative

magnitudes are consistent with my assumptions. The benchmark values are listed in

Table 4-1. Jurisdiction 1 has 10% of the population (y) and one firm (N1) while

jurisdiction 2 has 90% of the population (l-y) and three firms (N2). For the benchmark

case I set ci=c2=1. I also check the impact of cost differences across all firms but find it

has little effect on my results. Since governments are mostly concerned with raising tax

revenue to fund the public good, I set the weight on tax revenue (brev) in the objective

function equal to 0.86, while the weights on consumer surplus and profits are each set to

0.07. For now the weight on employment is set to 0. The magnitudes of the transport

cost (6) and variety preference ([t) parameters have little meaning by themselves and are

adjusted until the target outputs are attained. The magnitude of the variety preference (Lt)

parameter has little meaning by itself and is adjusted until the target outputs are attained.

The magnitude of the transport cost (6) does have some restriction, since its magnitude

should be some fraction of the equilibrium tax-inclusive price. With this in mind, I find

6=0.50 and lt=0.30 work well.

4.6 Simulation Background

To solve the first order conditions of profit maximization, I use MATLAB's

internal 'fsolve' algorithm that is designed to solve systems of nonlinear equations.12



12 The 'fsolve' algorithm employs a method similar to Gauss Newton called the interior-reflective Newton
method. It works by accepting a starting guess from the user and seeing how close to zero the first order
condition (FOC) is with this guess. It then updates the guess so the FOC will be closer to zero, then
reiterates the process until it reaches a trust region. The trust region is a region around zero that the user
can specify is close enough to zero to stop the iteration. The default trust region is around 1.0*10 5. In









There are two main drawbacks to using this method 1) it requires the system of equations

to be continuous and 2) the solution depends on the accuracy of the starting guess for the

solution. My system of equations is continuous, which eliminates the first concern. To

deal with possible starting guess concerns, I first ran the simulation on the symmetric

case (y=50%, and Ni=N2), which converges easier. I then plug these results into the

simulation as starting guesses and check to make sure that doubling and halving these

guesses does not affect my solutions.

4.7 Results: Use Tax Evasion

In this section I describe the effects of a switch in tax policy from not collecting

any use taxes, as is the status quo, to vigorously collecting all use taxes, as is proposed in

current legislation. Thus, the model goes from an environment of complete use tax

evasion to one of complete use tax enforcement. The effect of this transition can be seen

in the first two columns of Table 4-2. Variables of interest include the equilibrium prices

(Pk) and tax rates (Tk).13 Other variables include: demands for a representative firm in

each jurisdiction (Dk), the population weighted components of the demand (Hk and Fk),

firm profits (Profk), the price-cost margins (PCMk), elasticity of demand (Elastk), total per

capital sales in the industry (Tsales), and tax revenue collected in each jurisdiction

(TaxRevk).







order to get more accurate results I set this region to 1.0*10-10. When iteration reaches this trust region, a
vector of solution values are given, which are price and tax pairs in my case.

13 The variables are bolded to denote that they are computed by simulation, and the subscript equals i, j, 1,
or 2 depending on jurisdiction and context.









The transition towards complete enforcement and collection of use taxes has the

consequence of raising "after-tax prices"14 faced by consumers in both jurisdictions.

Table 4-3 shows the after-tax price paid by consumers in each jurisdiction before and

after the change in tax collection. Row (1) shows the prices paid by consumers from

jurisdiction 1 who shop in jurisdiction 1; row (2) shows the prices for consumers in 1

who shop in jurisdiction 2. As expected, Table 4-3 indicates that those hardest hit by the

transition are those that cross-border shop. In particular, consumers in the larger

jurisdiction, jurisdiction 2, that shop in jurisdiction 1 experience a 31.44% increase in

after-tax price.

Jurisdiction 1, with only 10% of the population, relies heavily on demand from

jurisdiction 2, so even though demand within jurisdiction 1 rises (yHi rises) overall

demand (D1) decreases substantially when use taxes are collected. Interestingly, the same

is not true for demand in jurisdiction 2 where firms depend mostly on within jurisdiction

shoppers rather than on cross-border shoppers since the population in jurisdiction 2 is so

much larger. Thus, while demand from consumers in jurisdiction 1 (yFj) drops by

36.15% and demand from consumers within jurisdiction 2 ((1-y)Hj) only rises 5.74%, the

overall affect on demand is positive (D2 increases 2.03%).

Another consequence of the increase in the after-tax prices is that more customers

now choose the outside option, thus decreasing total sales (Tsales) by 14.88%. This

effect is driven by the significant increase in tax rates witnessed in both jurisdictions.

The larger tax rates induce firms to lower their prices in order to keep customers. The


14 This could also be called the "tax inclusive price".









positive side of higher taxes is that both governments are now able to collect more tax

revenue for funding the public good than was possible when use taxes were not collected.

In conclusion, I find that consumers in both jurisdictions oppose the collection of

use taxes since it leads to higher tax-inclusive prices charged by all firms. Consumers in

the larger jurisdiction are much less willing to shop in the smaller jurisdiction due to a

31.44% increase in prices. This decrease in demand from consumers in the larger

jurisdiction leads to drastically lower demand faced by smaller jurisdiction's firm and

consequently significantly lower profits. Firms in the larger jurisdiction witness a slight

increase in demand due to increased shopping at home since the tax advantage of

shopping abroad is eliminated. However, the increase in demand for firms in the more

heavily populated jurisdiction does not outweigh the impact of lower prices (P2);

therefore they too receive lower firm profits. These results indicate that regardless of

location neither consumers nor producers favor a system where use taxes are collected,15

with those in the smaller jurisdictions being especially opposed. As expected, however, I

find that governments in both jurisdictions do favor the new system since it results in

higher tax revenues collected in both locations.

4.8 Results: Lower Transport Costs

The transport cost variable 6, reflects the difference between the cost of travelling

within one's jurisdiction and of having a product sent in from the opposing jurisdiction.

Lower transport costs could result from 1) lowered search costs from increased Internet

efficiency or 2) increased efficiency and lower costs from package handlers, or the firms

themselves, employing more automation in getting the product to the customer. Columns

15 Notice, however, that the consumers' objections to use tax collection may be mitigated by increased
levels of the public good.









3 and 4 of Table 4-2 show the results from lowering the transport cost (6) from 0.5 to 0.4

(20% decrease) for both tax systems. Table 4-2 shows that, as expected, lower transport

costs lead to increased cross border shopping by all consumers as foreign demands, yFj

and (1-y)Fi, increase under both tax systems.

The firm located in the less populated jurisdiction attains the most benefit from

lower transport costs as the number of individuals cross border shopping from the more

populated area increases. The 20% decrease in transport costs generates 22.76% more

profit for the firm when consumers do not pay use taxes and generates 9.31% more

profits when use taxes are collected. Thus, while the firm in the smaller jurisdiction

gains profit regardless of the prevailing tax law, it clearly would prefer to delay

legislation that would require the collection of use taxes in order to benefit from falling

transport costs. Firms in the larger jurisdiction witness only marginal increases in firm

profits resulting from lower transport costs and therefore are unconcerned about the

timing of future tax legislation.

Table 4-2 also indicates that the longer it takes to find a way to collect use taxes the

more each jurisdiction loses out on tax revenues. In both jurisdictions lower transport

costs along with non-collected use taxes leads to lower profits. This indicates that over

time the inability to collect use taxes will further erode tax revenues as transport costs

decline, causing more people to shop online. This result is exacerbated by the finding

that effectively collecting use taxes would have actually lead to increased tax revenues

over time as transport costs declined, boosting total sales. These results indicate that if

the key issue is tax revenues there is a need to not only find a way to collect use taxes

lawfully but also to find a way as quickly as possible.









4.9 Robustness Check

Tables 4-4 and 4-5 present the comparative statics resulting from changes in certain

benchmark parameter values.16 First, columns three and four of Table 4-4 examine the

impact of making the populations across jurisdictions more even ceteris paribus. This is

done by increasing y from 0.10 to 0.20, which implies 20% of the population is located in

jurisdiction 1 and 80% is located in jurisdiction 2. Secondly, columns five and six of

Table 4-4 examine the impact of adding one firm into both jurisdictions, ceteris paribus.

Lastly, columns three and four of Table 4-5 illustrate the scenario where fewer consumers

decide on the outside option, leading to an increase in total sales.17 I find that in each

case the direction of change of each variable when use taxes go from being evaded to

being collected remains the same as they were in the benchmark case. However, the

change in parameter values does lead to interesting results.

Increasing the population in the smaller jurisdiction could be seen as 10% of the

population moving from jurisdiction 2 into jurisdiction 1. As the gap in populations

between the two jurisdictions narrows, the tax rates imposed by each also comes closer

together, which follow the findings of Trandel (1994). It is important to note that the

smaller jurisdiction's tax rate increases by more than the larger jurisdiction's tax rate

decreases. In terms of percentages, -l increased by 140% when use taxes are not

collected and by 68.4% when they are collected, while c2 decreased by 4.2% when use







16 The first two columns in both tables reflect the benchmark results found in Table 2.

1 This is done by making Vo larger in absolute value.









taxes are not collected and by 0.6% when they are collected.18 The effect on prices is

similar, with jurisdiction 1 seeing a more dramatic effect.

The number of firms in jurisdiction 1 (N1) was increased from one firm to two, and

in jurisdiction 2 the number of firms (N2) was increased from three to four. This

increases the total number of firms selling the good, and increases the relative number in

each jurisdiction as well. The effect is increased competition within, and across,

jurisdictions resulting in firms lowering prices (as compared to benchmark levels) to

attract customers. The governments take advantage of this competition and set taxes

above benchmark levels. The outcome is that the firms in both jurisdictions are hurt by

the increased competition resulting in lower profit under both tax regimes. At the same

time, the governments in both jurisdictions are benefiting from increased tax revenue.

The third and fourth columns of Table 4-5 analyze the effect of more people

deciding to buy the good hence, fewer people choosing the outside option. Table 4-5

shows that this change results in significantly higher total sales (Tsales) under both tax

regimes when compared to benchmark levels. Consequently, both firms' profits and

governments' tax revenues increase under both tax regimes.

As a final check I include a non-zero weight on employment in the governments'

objective functions. The weight on revenue is lowered to 0.79, making the weights on

consumer surplus, profits, and employment each equal to 0.07.19 I find that including a

non-zero weight on employment does not significantly affect my results (unreported).




18 These results occur where population has changed from 0.10 to 0.20, or 100% in jurisdiction 1

19 Again, the symmetry of the weights is done only for convenience, and the results hold under deviations
from this symmetry.









4.10 One Exogenous Tax Rate

Next I consider the case when one tax is assumed to be determined exogenously,

reflecting the difficulty of changing tax rates in large jurisdictions. Table 4-6 presents the

results when Ti is kept endogenous and T2 is made exogenous.20 In columns one and two,

jurisdiction 2's tax rate is fixed at 0.46466, which is the benchmark tax rate found in

Table 4-2 when use taxes are fully evaded. This value is used in order to examine the

effect of switching tax regimes from one of complete use tax evasion to one of complete

use tax collection. It is also of interest to analyze the impact of lowering transport costs,

when a jurisdiction holds their tax rate constant. The assumption is that the larger area,

jurisdiction 2, sets its tax rate under the current conditions of use tax evasion and

benchmark transport costs, and then is unable to change the tax rate when the tax regime

switches or when transport costs lower. Three scenarios are of interest in this section: 1)

what happens if there is a switch to complete use tax collection with transport costs held

the same? 2) What happens if there is still full use tax evasion but transport costs lower?

3) What happens if there is both a switch in tax regimes and a decrease in transport costs?

The first two columns of Table 4-6 illustrate the effect of holding one tax rate

constant when the tax regime changes to one of full use tax collection. In the benchmark

case I found that the transition from use tax evasion to use tax collection induced both

governments to increase their tax rates, and caused all firms to lower their prices. But

now, instead of raising its tax rate to 0.50440, jurisdiction 2 is forced to hold it at

0.46466. Jurisdiction 1 reacts by raising its tax rate by less than it did in the benchmark

case. Also, the firms in both jurisdictions raise their prices by more than they had in the


20 Both prices are still considered endogenous.









benchmark case. The impact of these changes is that firms in both jurisdictions are better

off than they were under the benchmark, while less tax revenue is collected by the states.

The most profound result of this analysis is on profits for firms in jurisdiction 2. I

find the direction that the variables change in this new model, as a result fully collecting

use taxes, is the same as in the benchmark case with the exception of profits for firms in

jurisdiction 2. I find that when the larger jurisdiction's tax rate is fixed the transition to

fully collecting use taxes leads to an increase in profits for firms located in that

jurisdiction. The implication of this is that when faced with a fixed tax rate, firms in the

larger jurisdiction support the transition to completely collecting use taxes, whereas they

had opposed their collection when taxes were determined endogenously. This is a result

of the government's inability to adjust the tax rate to the higher desired level.

The second question regarding the impact of lower transport costs when use taxes

are evaded and one tax rate is fixed can be seen by comparing the first and third columns

of Table 4-6. In this case it can be seen that the fixed tax rate of 0.46466 is higher than

the preferred rate set in the benchmark case when transport costs are low. The

consequence of the tax rate being fixed at a level higher than it would be if it were

determined endogenously is that firms' profits in the larger jurisdiction go from

increasing under the benchmark scenario to decreasing here.

The final question deals with both a change in tax regime and a decrease in

transport costs, and can be analyzed using the first and fourth columns of Table 4-6.

Column four indicates that the fixed tax rate is set lower than if the tax rate was

endogenous. The outcome of the shift from full use tax evasion and high transport costs

to no use tax evasion and low transport costs, when c2 is fixed, is that firms in both









jurisdictions generate higher profits, and tax revenues are lower than those found when

tax rates were endogenous.

4.11 Both Tax Rates Exogenous

This section assumes that taxes in both jurisdictions are set exogenously. In my

model, fixing both tax rates means only the second stage of the two-stage game is being

played. I analyze three concepts with this setup. 1) The effect of a transition from full

use tax evasion to complete use tax collection when both tax rates are fixed at 10%, 2)

what impact lowering transport costs has when both tax rates are fixed, and 3) the

consequence of an exogenous increase of one tax rate, ceteris paribus. Table 4-7

illustrates the simulation results when both tax rates are fixed. Tax rates of 10 and 15%

are used out of convenience and for their proximity to real life tax rates; however, the

model could be run using any level of fixed tax rates.

The fixed tax rates are set much lower in jurisdiction 2 than they would be if they

were determined endogenously. Consequently, tax revenue collected by jurisdiction 2 is

lower in all cases than in the benchmark simulations. Jurisdiction l's tax rate is fixed at a

higher level use taxes are evaded and to a lower level when use taxes are collected than it

would be if rates were set endogenously. Therefore, tax revenue for jurisdiction 1 is

higher when the tax rate is fixed above the benchmark level, and is lower when it is fixed

below the benchmark level. Total sales are greatly impacted by the fact that the tax rates

are fixed at a low level in the larger jurisdiction, as "Tsales" is much higher in Table 4-7

with fixed rates than was found in Table 4-2 with endogenous rates.

The most fascinating results of fixing the tax rates are on prices and profits in

jurisdiction 2. Columns one and two of Table 4-7 show that the transition from use tax

evasion to use tax collection causes prices and profits in jurisdiction 2 to increase; while









Table 4-2 shows that this same transition with endogenous taxes leads to lower prices and

profits in jurisdiction 2. There is a similar outcome from the lowering of transport costs

which is seen from comparing columns one and two to columns three and four of Table

4-7. When taxes are fixed, lowering transport costs leads to lower prices and profits

whether use taxes are collected or not in jurisdiction 2. In contrast, Table 4-2 shows that

lowering transport costs with endogenous tax rates leads to higher prices and profits in

jurisdiction 2 under both tax regimes.

Finally, Table 4-7 examines the impact of an exogenous increase in the tax rate for

the larger jurisdiction, jurisdiction 2. Columns five and six illustrate the impact of

increasing the tax rate under each tax regime. The increase in jurisdiction 2's tax rate

from 10% to 15% leads to more tax revenue and lower firm profits in that jurisdiction.

However, the impact on the smaller jurisdiction is mixed. While the increased tax rate

leads to higher profits in jurisdiction 1 when use taxes are evaded, as more marginal

customers in jurisdiction 2 now decide to cross-border shop, it does not raise profits when

use taxes are collected. Consequently, tax revenue collected by jurisdiction 1 decreases

under full use tax evasion, as home shoppers are crowded out, and only slightly increases

when use taxes are fully collected.

4.12 Conclusion

My paper lends insight into the debate regarding the outcomes of switching tax

regimes from the current system of use tax evasion to one of full use tax collection. It

also addresses how decreasing transport costs relate to the timing of this transition.

When both tax rates are endogenous in my model, I find that changing the tax

environment from full use tax evasion to having no use tax evasion leads to increased tax

revenues collected by both jurisdiction, higher tax-inclusive prices charged by all firms,