The Internet as a Tax Haven?: The Eect of the Internet on Tax Competition

Transcription

1 The Internet as a Tax Haven?: The Eect of the Internet on Tax Competition David R. Agrawal University of Georgia

2 Research Focus This paper will study the tax setting behavior of jurisdictions in the new economy. Does the Internet cause cities and towns to raise or lower their sales tax rates? If online transactions are untaxed, the Internet may act as a tax haven and put downward pressure on local tax rates. On the other hand, if e-commerce is taxed, the Internet may act as an anti-haven, allowing cities and towns to collect taxes on remote transactions that previously went untaxed. Tax havens and tax competition: Slemrod and Wilson (2009) vs. Johannesen (2010)

3 Motivation

4 Online Institutions Are online rms required to remit sales taxes from consumers? It depends. Based o the court ruling Quill Corp. v. North Dakota, online rms are only required to remit sales taxes from consumers living in a state where the rm has nexus. Nexus means the company must have a physical presence in the state from which it prots. For rms without nexus, the consumer is required to remit the tax to the government, but this is easily evaded.

5 Nexus and Local Governments Local governments and sales taxes: Extremely decentralized Over 30 states allow local governments to levy sales taxes That these local taxes range between 0 and 7% And contribute between 0 and 52% of municipal revenue. Nexus in local governments Once a rm has nexus in a state, it has nexus in every locality The rm must collect local sales taxes from online purchases from everyone in the state even if its physical presence is only in one city.

6 Online Sales Source: Bill Fox and CBER analysis of e-commerce

7 Is this Big Enough for a Town to Care?

8 Geography of the Model

9 Before Introducing the Internet to a Standard Model Governments: Agrawal (forthcoming, AEJ: Policy) model The focus is on two towns that are in dierent states where the state tax rates dier at the border: T H T L = b > 0. The towns can set tax rates t H and t L on top of the state tax rates Town tax rates are set in a Nash game taking the state rates as parameters Consumers: Standard Nielsen (2001, SJE) model, variant of Kanbur and Keen (1993, AER) 1 Choice is where to buy the good, not how much of the good to buy Purchase the good abroad if V 1 t L T L ds > V 1 t H T H = b+t H t L d Solution to consumer problem allows me to construct the revenue functions 1 Devereux, Lockwood, and Redoano (2007) with perfectly inelastic demand.

10 Solution with No Internet R H = t H (1 b+t H t L d ), R L = t L (1 + b+t H t L d ) Solution: t N H = d b 3, tn L = d + b 3 t N L tn H = 2b 3 = tn L > tn H In a competitive game, the local tax rate in the high-tax state is lower than the local tax rate in the low-tax state.

11 Solution with No Internet

12 Solution with Tax Free Internet R H = t H (1 b+t H t L d ) θt H (t H + T H ) and R L = t L (1 + b+t H t L d ) θt L (t L + T L ) t I H = d(3 4T Hθ+T L θ)+2d 2 θ(1 T H θ) b (1+2dθ)(3+2dθ) t I L ti H = (2+dθ)b 3+2dθ & ti H θ Solution:, t I L = d(3+t Hθ 4T L θ)+2d 2 θ(1 T L θ)+b (1+2dθ)(3+2dθ) < 0, ti L θ < 0 and th I θ < ti L θ. Tax rates fall in the presence of the tax haven; moreso in low-state tax state.

13 Solution with Tax Free Internet

14 Introducing Taxable e-commerce Taxable online sales allows the jurisdiction to capture some additional revenue because some cross-border shoppers now buy online (preferences for online, added variety online, low costs of buying online, etc.) R H = t H (1 (1 θ) b+t H t L d ) and R L = t L (1 + (1 θ) b+t H t L d ) Solution: t A H = d 1 θ b 3, ta L = d 1 θ + b 3 t A L ta H = 2b 3 & ta H θ = ta L θ > 0 Tax rates are now higher in the equilibrium than they were in the baseline case. New idea of the Internet as an anti-haven.

15 Introducing Taxable e-commerce

16 Data [Maps] Data on Internet penetration comes from the national broadband map and provides me with data at the Census Place, county, and state level (earliest wave: 2011). Any Internet provider; Any wired provider; Any wireless provider A particular type of Internet provider (cable, DSL, etc) Access to higher speed providers More than one (2, 3, etc) provider Local sales tax data on every town, county, state, and sub-municipal district in the country. I will use a single cross-section of this data (2011) Geographic proximity data (driving time to borders).

17 Internet Usage What is observable to the researcher is the percent of people with potential access to the Internet. I wish to see which of the Internet penetration rates is the best proxy for Internet usage. Alternative view: this data is available to policy makers, so it is the actual policy reaction variable. Internet usage is available from the CPS 2010 at STATE level. At home, do you or any member of this household access the Internet? Also use per capita ebay sales.

18 Proxy Variable Selected: 4 or More Providers

19 Specication I will present results of the baseline specication using a fractional response model (Papke and Wooldridge 1996, Papke and Wooldridge 2008, Wooldridge 2014): E(τ i x i ) = G(x i β) = G(α 0 + β 0 I i + ζ + m X im γ 0m ) In the fractional response model, 0 τ i 1 where the extreme values can occur with positive probability. To mechanically implement this, I apply quasi-maximum likelihood estimation (QMLE) using a Bernoulli log likelihood function where I use the probit functional form for the mean response in order to bound the predicted tax rates.

20 Baseline Results (2) (2') (4) (4') OLS Fractional Response Multiple Providers (I ) -.050** (.022) (.017) -.058** (.022) (.023) N 14,459 14,459 14,459 14,459 R Dependent Town + Town Town + Town Variable County County Controls Y Y Y Y State Fixed Eects Y Y Y Y [Size Details]

21 Specication: Borders E(τ i ) = G(α 0 + β 0 I i + β 1 H i + β 2 I i H i + 3 k=1 ζ + m δ k (d i ) k + X im γ m ) 3 k=1 ρ k I i (d i ) k + 3 k=1 λ k I i H i (d i ) k + An initial test is to study local taxes in high- vs. low-tax states, but a more theoretically grounded exercise looks in the limit as distance 0. Polynomial order selected using leave-one-out cross validation. In the paper I show that there is no signicant dierence in Internet penetration at borders. Under the border design, the threat to identication is an omitted variable that is spatially correlated with I i but that also has an asymmetric aect on the high- and low-tax sides of borders.

22 Results: Towns by Type of State Border (2) (4) (5) Marginal Eect: Low-Tax State -.069** (.029) -.204** (.046) (.035) Marginal Eect: High-Tax State (.020) (.037).006 (.020) N Tax Rate Town Town Town Jurisdiction Size All Large Small Restriction / Addition None None None Controls Y Y Y State Fixed Eects Y Y Y

23 Eects Not Conditional on Distance

24 Results as d 0 and By Dierentials Panel A: Distance Large Towns Low Side High Side At the border (0) -.458*** (.116) -.178** (.083) 10 minutes -.247*** (.067) (.054) 20 minutes -.141** (.065).058 (.051) 50 minutes -.225** (.058).024 (.043) 90 minutes -.193** (.075) (.052) Panel B: Tax Dierentials Large Towns Low Side High Side Nearly Same (0).155 (.083).073 (.046) 1 percentage point (.054).031 (.039) 3 percentage points -.318*** (.046) (.037) 5 percentage points -.483*** (.051) -.105** (.041)

25 Eects at the Border

26 The Role of Nexus Expect that the anti-haven eect will be largest in states that have a large number of rms with nexus = more taxable online sales. Data on nexus status is not readily available. Bruce, Fox, and Luna (2014) collect nexus data by visiting approximately 200 e-tail websites and make purchases using a zip code from every state. The authors record if taxes are due.

27 The Role of Nexus (1) (2) (2') Marginal Eect at d 0: Low-Tax State & Low Nexus State -.957*** (.178) -.804*** (.149) (.192) Marginal Eect d 0: Low-Tax State & High Nexus State.019 (.200).858** (.432).367* (.207) Marginal Eect d 0: High-Tax State & Low Nexus State -.263** (.114) -.227*** (.074) -.149* (.090) Marginal Eect d 0: High-Tax State & High Nexus State (.143).327 (.317).793 (.511) Marginal Eect: Low-Tax State & Low Nexus State -.366*** (.0751) -.328*** (.059) (.043) Marginal Eect: Low-Tax State & High Nexus State (.060).244 (.157).157*** (.038) Marginal Eect: High-Tax State & Low Nexus State -.125** (.054) -.084** (.039) (.024) Marginal Eect: High-Tax State & High Nexus State.122** (.050).259*** (.0787).062** (.031) N Tax Rate Town Town Town Jurisdiction Size Large Large Small Nexus Denition Above Mean Total Number Top Quartile Total Number Top Quartile Total Number

28 Measurement Error and Multiple Proxies [LW Details] Using a proxy variable induces attenuation bias in the estimated coecient. Furthermore, by using a single proxy variable when I have multiple possible variables available I am throwing away a great deal of information. Lubotsky and Wittenberg (2006) show how attenuation bias can be maximally reduced and develop a procedure that allows the researcher to use multiple proxies in the estimating equation. Results are larger in absolute value.

29 Lightning and IT Andersen, Bentzen, Dalgaard and Selaya (2012, ReStat) show that the ash density of lightning strikes is a powerful predictor of IT growth over the 2000s and can be used as an instrument in an economic growth context. Intuition: lightning strikes cause power outages, which raise the cost of investing in Internet technologies, which reduces usage Lightning is not likely to be correlated with tax rates. Lightning may be correlated with weather related amenities; if these amenities are correlated with sales tax rates, then the instrument will not be valid. Include Census Region dummies.

30 Specics on the Instrument At the state level, I calculate the ash density of lightning.

31 Results (0) (1) (2) First Stage: F Stat OLS Second Stage Marginal Eect (.088) -.344** (.175) -.191*** (.070) Tax Rate State + State + Local Local Local Controls Y Y Y Region Dummies Y Y Y

32 Summary of Results Theoretically, access to e-commerce has osetting channels: haven eect vs. anti-haven eect. Empirically, access to the Internet puts downward pressure on tax rates for relatively large municipalities and the eect is as predicted by theory most pronounced in low-tax states. This provides evidence that the presence of tax-free shopping constrains a municipality's ability to raise revenue via rate increases, but only in places not previously constrained by cross-border shopping. The eect of the Internet in small jurisdictions is approximately zero. This may suggest the anti-haven eect is stronger in these jurisdictions or that these jurisdictions are less sophisticated.

33 The Consequences of the Marketplace Fairness Act This paper suggests that if the Marketplace Fairness Act is passed, jurisdictions may be able to raise their sales tax rates. Such an eect will come along two dimensions: 1 Tax rate increases because jurisdictions do not need to compete with the leaky bucket resulting from the tax-free Internet. 2 Tax rate increases driven by eective enforcement of the sales tax (this eect will be extremely important if individual's preferences shift to purchase goods online rather than from physical stores).

34 Taxes

35 Access to Multiple Providers

36 Summary Stats County Town County Tax (1.139) (1.162) Sub-County Tax.779 (1.064).718 (1.138) Any Tech.980 (.050).994 (.058) 3 providers.844 (.243).879 (.302) 4 providers.711 (.431).672 (.350) N ,459 [Back]

37 Discussion Worry the proxy variable is picking up other factors. Even after controlling for demographics, number of providers still explains some variation in usage at the state level. Reasons for validity: Faulhaber and Hogendorn (2000) shows that the sub-game equilibrium capacity... strategies depend only on the number of networks to which a household has access. Disataso et al. (2006) veries empirically that inter-platform competition such as DSL versus cable technologies (rather than intra-platform competition), increase Internet usage. Prieger and Hu (2008) also show empirically that competition in broadband markets is an important contributing factor of the Digital Divide that exists across races even though prices do not vary substantially across various markets.

38 The Role of Jurisdiction Size [Back] Jurisdiction size plays an important role in the tax setting behavior of jurisdictions (Kanbur and Keen 1993, AER). Many towns in the United States are small. Average town size: < 10,000. Median town size: <1500 Expect the eect of online shopping to be dierent for jurisdictions with many brick-and-mortar stores (large) vs. with no brick-and-mortar stores (small) Why? If you have no brick-and-mortar stores, you will be more likely to buy from taxable online stores than cities where residents can just get those goods from the store in their community.

39 The Role of Jurisdiction Size (1) (2) (1') (2') Total Local Rate Rate Town Tax Rate Multiple Providers (I ) -.091*** (.033) (.022) -.075** (.029) (.018) N R Jurisdiction Large Small Large Small Size Controls Y Y Y Y State Fixed Eects Y Y Y Y [Back]

40 Lubotsky and Wittenberg (2006) Estimate τ = α + n k=1 b k p k + ζ + X m γ m + ε, m [Back] and then can aggregate up to a single coecient of interest using: b ρ = n k=1 b k cov(τ, p k ) cov(τ, p 1 ) Which is equivalent to using the index p ρ = 1 n b ρ b k p k k=1

41 Results (1) (2) LW Low-Tax State -.366*** (.058) -.135*** (.038) Single Proxy Comp -.204** LW High-Tax State -.088** (.041) -.053*** (.018) Single Proxy Comp N R Dependent Variable Town Town Sample Large Small [Back]