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Article Excerpt
INTRODUCTION
Cigarette taxes have garnered increasing interest in the United States by both government and public health officials over the past 30 years. The former are interested in using state--level excise taxes to increase government revenues, while the latter believe increased taxes could be used to reduce smoking behavior. The degree to which each of these goals can be met is a function of the demand elasticity of cigarettes. If cigarette demand is price elastic, then increasing taxes will reduce the amount of smoking but will be less effective in raising revenues. Conversely, if cigarette demand is price inelastic, then tax increases will succeed in raising revenues but not in reducing smoking behavior.
Due to the potential gains from cigarette taxation, many states have increased their cigarette taxes markedly since the 1970s (Orzechowski and Walker, 2006). The differential increase across states in the United States has caused large interstate price differences in many areas of the country. For example, as of November 2001, there was a seventy--three cents per pack tax difference between Washington, D.C. and Virginia, despite the fact that the average consumer in Washington, D.C. lives less than four miles from the Virginia border. Of the five states that had cigarette taxes over one dollar per pack in 2001, there was an average tax difference of eighty--three cents between them and the closest lower--price border. The median consumer in these states was less than 38 miles from the nearest lower--priced jurisdiction.
This cross--state price variation can confound many of the potential gains from cigarette taxation as increased taxes may cause individuals to purchase cigarettes in a nearby lower--price locality. Such "casual smuggling" behavior can limit the effectiveness of state--level cigarette excise taxes in reducing smoking and in increasing state tax revenues. (1) This study seeks to estimate the extent of casual smuggling as well as its effect on cigarette demand elasticities in order to assess how this type of tax avoidance impacts the revenue--generating potential and the smoking reduction benefits of cigarette taxes.
There is much evidence from previous literature regarding the existence of casual cigarette smuggling, though few studies have been able to estimate the extent of such behavior or its effect on demand elasticities. Because smuggling causes a bias in sales as a measure of consumption, the majority of cigarette demand studies using taxed sales data control for smuggling incentives. Many studies have found a negative relationship between the average border state tax or price differentials weighted by border populations and taxed sales (Chaloupka and Saffer, 1992; Keeler, Hu, Manning, and Sung, 2001; Coates, 1995; Yurekli and Zhang, 2000). Coates (1995) uses this specification to estimate sales elasticities with respect to both the home state price and all cigarette prices. He finds 80 percent of the sales elasticity is due to cross--border sales. Alternatively, Baltagi and Levin (1986, 1992) control for the minimum border state price and conclude an increase in this minimum price increases home state sales.
There are a small number of studies that utilize individual consumption data paired with sales data in order to identify the existence of cigarette smuggling. In their detailed study of smoking in Canada, Gruber, Sen, and Stabile (2003) compare taxed sales elasticities from provinces in which smuggling is low to consumption elasticities from household expenditure data. Since prices do not vary appreciably across provinces, the authors argue these methods are effective in controlling for the biases associated with demand estimation when there is smuggling. They find ignoring smuggling causes them to overstate the price elasticity of cigarettes in absolute value (2) and estimate smuggling--corrected elasticities between -0.45 and -0.47.
Stehr (2005) uses a similar methodology in the United States to explain the per-capita differences in reported consumption and taxed sales as a function of the difference between home and the border state taxes from states in which the tax is higher than in the home state (i.e., the "export" states). He finds between 59 and 85 percent of the taxable sales elasticity is due to changes in the locality of purchase and almost 13 percent of cigarettes in 2001 were purchased without payment of the home state tax. While he attributes only 0.7 percent of the smuggling behavior to casual smuggling, (3) his casual smuggling estimates are based on variation in the average difference between home and export states' taxes over time, which is likely to cause a downward bias in his estimates. (4) Further, he is unable to account for where consumers live in each state with respect to the lower--price borders, which limits his ability to identify casual smuggling behavior. Individuals may also be traveling to nearby lower--price jurisdictions that are not border states.
This paper uses micro--data on cigarette consumption from the 1992-1993, 1995-1996, 1998-1999, and 2001-2002 Current Population Survey (CPS) Tobacco Supplements combined with geographic information on the location of consumers with respect to lower-price jurisdictions to estimate cigarette demand models that incorporate the decision of whether to smuggle cigarettes across a state or Native American Reservation border. This is, therefore, the first study to estimate the extent and impact of casual smuggling using only micro data on consumption. I also address a central empirical problem inherent in using such data: the state of cigarette purchase for each consumer is not identified. In the presence of casual smuggling, using the home state cigarette price as a proxy for the true cigarette price can bias the estimate of the effect of price changes on cigarette demand. (5) The bias stems from the fact the home state price is a biased estimator of the "true" price at which consumers purchase cigarettes, and this bias is systematically correlated with smuggling incentives. I present regression residuals from traditional cigarette demand regressions by quartile of distance to a lower-price border that argue strongly for the existence of this type of bias.
To correct for the home state price bias, I explicitly model the decision to smuggle and then incorporate the parameters of this decision into the demand model. The distance to a lower--price locality is then used to proxy for unobserved heterogeneity in the response of demand to changes in the home state price that has been ignored by previous studies.
In the presence of smuggling, there are three elasticities of interest: the home state price elasticity, the home state sales elasticity, and the full price elasticity. The home state price elasticity is the percent change in consumption of state residents when the home state price changes by one percent, the home state sales elasticity is the percent change in home state sales when the home state price changes by one percent, and the full price elasticity is the percent change in consumption or sales when all prices change by one percent such that smuggling incentives are unaffected. The home state elasticities yield insight into how home state prices actually affect consumption and sales, holding constant the price of cigarettes in border localities, while the full price elasticity reveals the potential for cigarette prices to impact consumption or sales in the absence of smuggling. (6)
From either a state tax or a public health policy perspective, all three elasticities are of interest. Most studies that attempt to correct for smuggling biases are implicitly attempting to estimate the full price elasticity as this is the elasticity in the absence of smuggling. Coates (1995) is the only previous study to distinguish between the home state sales and full price elasticities using taxed sales data. (7) This analysis presents the first estimates of the home state price elasticity in the literature, which is arguably of more value to state policy makers than the full price elasticity as they cannot control prices in border localities.
I find home state price elasticities vary significantly with the geographic distribution of each state and are indistinguishable from zero on average, due primarily to the close proximity of most individuals to the closest lower-price border. The full price elasticities tell a much different story, however, and are universally negative and non-negligible in magnitude.
The final contribution of this analysis is to estimate the impact of smuggling on cigarette consumption and the percentage of consumers who casually smuggle. (8) I find cross-border sales cause a modest increase in consumption, and between 13 and 25 percent of consumers purchase cigarettes in border localities in the CPS sample. While these estimates are large relative to previous studies (Stehr, 2005), they are consistent with the significant savings potential from purchasing cross-borders and with the close proximity of many individuals to these borders. Though I cannot estimate the home state sales elasticity, my estimates indicate large differences across states in the effects of casual smuggling on taxed cigarette sales, with states such as New Hampshire, Kentucky, and Virginia gaining sales and states such as New York, Kansas, and Maryland losing significant sales due to cross-border purchases.
The remainder of this paper is organized as follows. The second section provides a description of the data used throughout the analysis. The third section presents evidence on the home state price bias, and the fourth section derives the demand model used throughout this study and discusses its implications. The estimation strategy is described in the fifth section, and all results are presented in the sixth section. The seventh section concludes.
DATA
The individual-level data in this analysis come from the CPS Tobacco Supplements: September 1992, 1995, and 1998; January 1993, 1996, and 1999; March 1993, 1996, and 1999; June and November 2001; and February 2002. These surveys span nine years in four waves given approximately every two years. Because I am interested in combining these data with a measure of smuggling distance, I restrict the sample to those living in an identified metropolitan statisical area (MSA); this is the most specific level of geographic identification available in the CPS. As there are MSAs that split state lines, each identifiable state--MSA combination is taken as a separate MSA. (9) I will use state--MSA and MSA interchangeably.
I combine these data with state average price and tax data from The Tax Burden on Tobacco compilation (Orzechowski and Walker, 2006). All prices are inflated to real 2004 dollars using the gross domestic product (GDP) implicit price deflator. Prices listed in this compilation are spot prices as of November of that year. To construct a more accurate price series, I subtract the November excise tax in each state from the listed price and smooth the pre-tax price changes evenly over the entire year. I then add in the appropriate excise and sales taxes for each state and month in the Tobacco Supplement. (10)
The central variable in the analysis is the distance to a lower-price locality. I use 2000 Census geographic data to estimate a population--weighted average distance from each state--MSA combination to the closest lower-price border. (11) This calculation is done by finding the "crow-flies" distance from each census block point in a state--MSA to each intersection between a state border and "major road." (12) Once I calculate the distance from each block point to each road crossing, I take the closest crossing from each block point to a given border state and calculate a population--weighted average across block points for each border state. By measuring distance from the population center rather than the geographic center of a given MSA, I am able to more accurately characterize the distance an average individual must travel to smuggle cigarettes. In the tables that follow, the distance measure is the distance to the closest lower-price border, which is often, but not always, a border state. (13)
In addition to neighboring states, many individuals can obtain lower-price cigarettes from Native American Reservations. Native American Reservations are considered separate legal entities from the United States and are thus not subject to sales and excise taxes. In 1976, the U.S. Supreme Court ruled in Moe v. Confederated Salish and Kootenai that states have the right to impose sales and excise taxes on cigarette sales occurring on reservations to non-tribal members. Although evidence suggests a substantial amount of sales occur on reservations to non-tribal members (ACIR, 1985; FACT Alliance, 2005), only 12 states have passed legislation that allows taxation of these sales. Table 1 contains information on which states tax non-tribal reservation sales and the case law or regulation that legitimates these taxes. I collected these data using Cigarette Tax Evasion: A Second Look (ACIR, 1985), which documents much of the case law and state legislation through 1985 on Native American cigarette sales. I augmented and updated this information using state taxation statutes found through LexisNexis. Reservations in the states listed in Table 1 are excluded from the analysis. (14)
Table 2 presents means of distance, price differences, and tax differences for all identified MSAs by state. The table also lists the number of tax changes observed in the data as well as all of the closest lower-price localities for each state. Table 2 illustrates the heterogeneity across states in smuggling incentives. For example, consumers in Massachusetts, New York, Illinois, and Wisconsin live close to areas in which cigarettes are substantially less expensive. However, in states such as Delaware, Nevada, and Oregon, consumers likely live too far away from the lower-priced jurisdictions to realize the savings from purchasing cigarettes there.
Because my empirical models all include MSA fixed effects (see the fifth section on estimation strategy), I will be restricted to using within--MSA variation in distance over time. Cross-time variation in distance within a state--MSA is driven by price changes; when a home or border state changes its cigarette price, the closest lower-price border can change, thereby generating variation in distance. Table 3 contains the number of distance changes, the average change in distance, and the...
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