Cigarette Taxes and Producer Surplus Kyle Rozema∗ Abstract NO JEL codes: H22, H71, D22, L16, L11, L22. T Q UO TE O R CI RC U Empirical research on tax incidence usually investigates the burden of taxes on various groups of consumers and the producer of goods. In this article, I estimate the causal effect of cigarette taxes on the profits of retail outlets. To study this question, I exploit a dataset that allows me to directly observe weekly profits generated from cigarette sales for over 27,000 retail outlets in 48 states from 2008 to 2012. My data includes Nielsen Retail scanner data matched with weekly state cigarette prices and brand-specific input prices from administrative data obtained from state treasuries for this project. Using this novel balanced-panel dataset, I employ a retailer fixed effects specification and find that increases in cigarette taxes trigger modest increases in retailer profits of around $40 per week. LA TE . April 22, 2015 PR EL IM IN AR Y DR AF T. DO Keywords: Tax Incidence; Pass Through Rate; Producer Surplus Allocation; Supply Side Market Dynamics; Double Marginalization. ∗ Ph.D. Candidate, Cornell University, Department of Economics, 459 Uris Hall, Ithaca, NY 14850, e-mail: [email protected]. Many thanks to James Brodowicz of the State of New Jersey Department of the Treasury for providing me with cigarette cost data. I would like to thank Aaron Bodoh-Creed, John Cawley, Josh Chafetz, Gary Cohen, Mike Frakes, Teevrat Garg, Jacob Goldin, Tatiana Homonoff, Alan Mathios, Don Kenkel, Mike Lovenheim, Alex Rees-Jones, Nicolas Ziebarth, and participants in the seminar at the Institute on Health Economics, Health Behavior and Disparities at Cornell University. I take responsibility for all remaining errors. The research reported in this paper is not the result of a for-pay consulting relationship. My employer does not have a financial interest in the topic of the paper that might constitute a conflict of interest. 1 Introduction Tax incidence is the study of the effects of tax policies on the distribution of economic RC U LA TE Sullivan, 2009; Keeler et al., 1996), search behavior (DeCicca et al., 2013), and over consumers’ life-cycle (Lyon and Schwab, 1995). This research typically treats the producer of goods as a hybrid of firms in the applicable industry.1 For the cigarette market, this assumption would amount to studying the effect of taxes on the single entity farming tobacco leaves, producing . welfare (Fullerton and Metcalf, 2002). The focus of empirical research on the incidence of excise taxes has almost exclusively been on (i) the tax pass through rate and thus the division of the tax burden between consumers and producers, and (ii) the burden of taxes among different consumer groups in terms of income (Harding et al., 2012), geographic location (Hanson and R CI cigarette paper and filters, processing tobacco leaves into a smokable product, packaging the smokable product into cigarettes, shipping cigarettes from the packaging location to wholesale distributors, adhering cigarette tax stamps to cigarette packages, distributing the final product UO TE O to retail stores, and then finally selling the cigarettes to consumers. Even though most talk of the cigarette industry is directed at big tobacco companies, tobacco policies also affect retail outlets and wholesale distributors, among other firms. To fully translate the effects of taxes on prices to tax incidence, the econometrician must trace back to the factors of production the T Q change in producer surplus in the aggregate and the change in allocation of that surplus among heterogeneous owners and workers of firms. To my knowledge, no empirical research has investigated the impact of cigarette taxes DO NO on realized producer surplus generated from cigarette sales of actual firms. Probably due to data limitations, the empirical reduced form literature has also been silent regarding firms’ behavioral responses to the imposition of taxes in terms of pricing strategy. Yet, the impact of cigarette taxes on equilibrium retail prices could be driven by a number of supply-side dynamics, DR AF T. all of which likely induce different producer surplus allocation and thus have different welfare implications. For instance, taxes that increase profits for the firms manufacturing goods (e.g., tobacco companies) at the expense of the firms selling those goods (e.g., gas stations) have the potential to be regressive in terms of non-consumers of those goods. In sum, the lack of empirical IN AR Y attention the literature has directed to producers is unfulfilling in the tax incidence sense for the simple reason that tax incidence deals with economic welfare (utility) and producers do not have utility functions. In this article, I estimate the causal effect of cigarette taxes on the profits of retail PR EL IM outlets. My main panel dataset merges administrative data on daily manufacturer prices of individual cigarette brands with weekly retail prices and sales volume of individual retail outlets from Nielsen Retail Scanner Data from 2008 to 2011. With this information, I am able to track retail outlets’ actual producer surplus generated from cigarette sales on a weekly basis. With the goal of studying actual firm-level producer surplus, I first examine cigarette factor prices that serve as the input in calculating producer surplus. In the U.S., cigarette manufacturers sell cigarettes in bulk to wholesalers, who then distribute the cigarettes to retailers in several states. While some studies assume U.S. cigarette manufacturers set a single national 1 Most exceptions are from the Industrial Organization literature (such as Delipalla and ODonnell (2001), Reny et al. (2012), and Anderson et al. (2001)), but other notable exceptions include Barnett et al. (1995) and Poterba (1996). 2 LA TE dence that cigarette manufacturers price-discriminate by state (Keeler et al., 1996). However, to my knowledge no research has investigated cigarette manufacturers price-discrimination using data on actual factor prices. Using actual manufacturing prices, I find strong evidence that that cigarette manufacturers price-discriminate by state by charging a higher price in high tax states than in low tax states, which pushes the tax burden onto consumers. My main analysis investigates the impact of state cigarette taxes on firm pricing strategy and producer surplus. Extending reduced form methods used in the current empirical tax incidence literature, I first study the impact of taxes on the retail outlet profits via . price for each cigarette brand (Tennant, 1950; Barnett et al., 1995), there is at least some evi- RC U standard difference-in-difference research designs. I also study the behavior of retail firms by investigating markup of cigarettes (retail price minus cost) as well as the other channel driving changes in profits, namely, demand. To obtain a deeper understanding of the mechanisms TE O R CI driving the results and to study the impact of taxes on producer surplus in the long run, I also take a semiparametric approach by presenting visual assessments of the relationship between cigarette taxes and mean retailer markup, mean cigarette demand, and mean producer surplus for retailers by state. The results show that state cigarette taxes increase retailer mark-up Q UO and producer surplus in the short term following a tax increase. The semiparametric evidence clearly shows the associations between state cigarette taxes and markup, sales, and producer surplus in the long run: mark-up and producer surplus adjusts downward and can even dip DO NO T below their respective pre-tax-increase levels. Even though increases in state taxes appear to lead to sharp decreases in cigarette sales at in-state retailers in at least some states in the short run (but not in other states), there is no discernible trend in all states in the long run. The rest of this paper is organized as follows. Section 2 describes the institutional AF 2.1 Institutional Setting The Market Players DR 2 T. setting and Section 3 the data. Section 4 is the empirical analysis. Section 5 concludes. AR Y “Big tobacco” usually refers to the large, publicly traded cigarette producing corporations that are thought to make up a tight oligopolistic market. Even though the major PR EL IM IN players in big tobacco have changed over time, most economists agree the market structure has not. In 1998, for instance, the market consisted in descending order of market-share of Philip Morris Companies Inc. (now named Altria Group, Inc.), R.J. Reynolds Tobacco Company (a subsidy of Reynolds American Inc., which is itself partially owned by British American Tobacco), Brown & Williamson (a subsidy of British American Tobacco), and Lorillard Tobacco Company (then a unit of Loews - until 2008). In various forms and under various owners, these entities or subsidies of these entities are more or less considered big tobacco in the United States. Until recently, these entities along with Imperial Tobacco of the United Kingdom have also traditionally been the worldwide suppliers of cigarettes. International players now include China National Tobacco Corporation (owned by the Chinese government) and ITC Limited (India). In the US, these cigarette manufacturers sell cigarettes in bulk to wholesalers, who then distribute the cigarettes to retailers in several states. 3 2.2 Cigarette Taxes 2.3 RC U LA TE 2008 to 2011. For instance, state taxes vary from 7 cents for North Carolina in 2008 to $4.35 for New York after August 1, 2010, which yields nice identifying variation across and within states and over time. Federal taxes were raised from $0.39 per pack to $1.01 per pack on April 1, 2009. . Cigarette taxes are levied by the federal government, each state government, and some local governments (e.g., Cook County and New York City). The legal incidence (who remits the tax) of federal taxes is on manufactures, while the legal incidence of state and local taxes is usually on wholesalers. Cigarette taxes at each level has varied considerably even from Market Structure and Producer Surplus CI A general economic principle is that taxes tend to be borne by inelastic producers TE O R or consumers. This principle implies that an industry’s market structure partly determines how taxes on consumption goods affect the price of the goods at different levels of the supply chain (the factor prices). The principle also implies that the market structure partly determines the relationship between taxes on consumption goods and changes in the size and allocation of Q UO producer surplus (Weyl and Fabinger, 2013; Besley and Rosen, 1999). At least in regards to the cigarette market as a whole, the evidence on how the elasticity of supply of cigarettes compares to the elasticity of demand is unclear. On the one hand, the studies finding that cigarette DO NO T taxes are over-shifted to consumers are consistent with the notion that the “tobacco industry” is dominated by a tight oligopoly (DeCicca et al., 2013). On the other hand, the evidence that suggests consumers and producers share the tax burden is consistent with a competitive “tobacco industry” (Harding et al., 2012). AF T. In this section, I briefly present how the market structure can change the impact of cigarette taxes on prices, producer surplus, and the allocation of producer surplus. In the interest of clarity, it is worth emphasizing that I present the extreme example where the market PR EL IM IN AR Y DR structure consists of monopoly at each level of the supply chain, but the intuition holds more generally under other market structures with the existence of some market power, such as when the downstream industry is monopolistically competitive rather than a monopoly (West, 2000). Note that if any level of the supply chain is more (less) competitive than other levels, an increase in taxes likely increases (decreases) the portion of producer surplus at that level. For instance, if retailers hold little market power while cigarette manufacturers exhibit more market power, an increase in taxes would likely shift some producer surplus from retailers to manufacturers. I also show the stylized two stage example of manufacturers and retail outlets acting as monopolists (rather than a triple monopoly structure with wholesalers), but again the intuition will hold for three levels of the vertical market rather than two. Monopoly and Double Marginalization A field of theoretical and empirical research has built up around Spengler (1950)’s socalled double marginalization problem.2 Standard double marginalization occurs in a vertical 2 For recent surveys, see Sudhir and Datta (2009) and Lafontaine and Slade (2014). For recent empirical research, see Brenkers and Verboven (2006), Villas-Boas and Hellerstein (2006), Villas-Boas (2007), Gil (2009), 4 LA TE and retailing. Figure 1 shows the equilibrium prices p∗ , quantities q ∗ , allocation of profits (π), and deadweight loss (DWL) under monopoly (1a) and double marginalization (1b) under the respective market structures. The consumer demand curves under monopoly and double marginalization are the same (Dm = Dr ). p∗r is the retail price of cigarettes under double marginalization whereas p∗w is the retail price of cigarettes under monopoly. Referring to panel (b), p∗w is the wholesale price, M Rr is the retailer marginal revenue curve, M Rw is the wholesaler marginal revenue curve, D is the retailer demand curve, Dw is the wholesaler demand curve, M Cw is . relationship between successive monopolies at two stages of production, e.g., manufacturing RC U the exogenous manufacturing cost of cigarettes, and t is the cigarette tax. Note that the retailer demand curve is equal to the wholesaler marginal revenue curve because the wholesaler is selling to the retailer. TE O R CI The market dynamics in standard double marginalization problems is as follows. The retail monopolist takes as given the wholesale price pw , and optimally chooses its quantity qr and price pr (i.e., the quantity and price for the end consumers). The wholesale price pw is determined by the wholesaler, meaning that two successive monopolies determine the final Q UO price. Compared to a single monoplist, the market outcome for monopolists in vertically related ∗ markets is higher retail prices (p∗r > p∗m ), lower output (qr∗ > qm ), lower total profits to be split ∗ ∗ ∗ (πm > πm + πm ), and the market is less efficient (DW Ldm > DW Lm ). Note that in most NO T scenarios, double marginalization decreases social welfare (DWLdm >DWLm ). For cigarettes where there is presumed to be too much demand, however, lower sales that are normally thought to be deadweight loss might actually be positive externalities because people are smoking less. DO [Insert Figure 1 about here] AF T. Given the three levels of the supply chain in the cigarette market, the market structure at each level will determine which firms are harmed or benefit from cigarette taxes. Whatever Data & Descriptive Statistics IM IN 3 AR Y DR the particular market dynamics in the cigarette industry, the empirical question I set out to investigate in this paper is whether and how producer surplus (here, profits) for cigarette retail outlets is affected by cigarette taxes. I built a panel dataset to investigate the impact of taxes on the (sale generated) PR EL producer surplus from merging the Nielsen Retail Scanner Data (RMS) data for cigarette retail prices and sales volume from years 2008 to 2011 with (i) daily manufacturing sale prices for various brands of cigarettes from years 2008 to 2011 from administrative data obtained by the state treasury of both New Jersey and Indiana (TD), and (ii) weekly cigarette taxes from the Tax Burden on Tobacco (TBT). The RMS data consists of weekly cigarette pricing and sales volume of participating retail stores. The RMS includes approximately 35,000 participating grocery, drug, mass Bonnet and Dubois (2010), and Cohen (2013). Recent theoretical contributions include Adachi and Ebina (2014), Abito and Wright (2008), Lantz (2009), Rey and Verg (2010), Shy and Wang (2011), and Rey and Salant (2012). 5 LA TE dividual store for every UPC code that the store had any sales volume during the week. After grouping UPCs together for each subbrand of cigarette (e.g., Marlboro lights, Marlboro reds) using provided UPC descriptions, I merged onto these weekly retail prices the TD manufacturing sales prices, New Jersey wholesale prices, and Indiana wholesale prices. I then merged by the week ending day state and federal cigarette taxes from the Tax Burden on Tobacco (2012). Finally, from this retailer-week-subbrand data, I generated a dataset where the unit of observation is a week-retailer with average markup, sales volume, and the actual producer surplus (markup of a brand times quantity of sales of that brand) as the main outcome variables of . merchandiser, and other stores in 52 U.S. markets. The RMS reports weekly data for each in- RC U interest. The producer surplus for each retailer-week-subbrand was first calculated, and then the data were collapsed around retailer-week while summing together producer surplus from cigarette sales and sales volume of the various subbrands and averaging the prices and markup. TE O R CI To reiterate, the combined retailer-wholesaler producer surplus is the actual producer surplus from cigarette sales that the retailers and wholesalers actually witnessed. My final sample follows 27,092 retailers in 48 states plus Washington DC each week for up to 4 years (208 weeks). In this time period, there were 20 states that increased state Cigarette Manufacturers Price-Discriminate by State T 3.1 Q UO cigarette taxes and 25 changes in state cigarette taxes (some states had more than one increase), which serve as my identifying variation. DO NO The TD data include manufacturing prices as reported by the New Jersey treasury. Using this data merged with the raw RMS data on brand cigarette prices, I briefly confront the issue of whether manufacturers’ set nationwide prices or if they price discriminate by state. AF T. Dating back to Tennant (1950), claims have been made that wholesalers in different states face the same price from manufacturers, that is, cigarette manufacturing prices are independent of location. Sumner (1981) argued that state laws prevent arbitrage across state lines, giving AR Y DR rise to the inability of cigarette manufacturers to price discrimination by state. To investigate precisely this question, Keeler et al. (1996) used panel data for the 50 U.S. states between 1960 and 1990 to analyze the interactive effects of oligopoly pricing and state taxation on retail cigarette prices by state. They found some evidence that cigarette manufacturers price- PR EL IM IN discriminate by state. The main limitation of Keeler et al. (1996) is that they did not actually observe manufacturer prices in one or more states; rather, they relied on retail prices and cost shifters to estimate a structural model that backs out differences in manufacturing prices. To date, no empirical research has relied on actual manufacturer prices to investigate whether cigarette manufacturers price-discriminate by state. Under the assumption that wholesale distributors and retail outlets do not set prices below cost, my data allow for a direct test of whether manufacturers price-discriminate by state. One need only compare manufacturer prices from one state (here, New Jersey) to the retail prices in other states after netting out state specific cigarette taxes. The intuition is that even if wholesalers and retailers set prices at cost, if manufacturing prices are nationwide then retail prices in states other than New Jersey will not be less that manufacture prices as reported by New Jersey plus the applicable state tax. To that end, Table A1 in the Appendix provides a breakdown of summary statistics of 6 LA TE cigarette brands in Kentucky. I choose to focus attention on one state rather than the all states so as to minimize my digression but at the same time providing a concrete example. I chose Kentucky because it was one of seven states that consistently reported retail prices below costs plus. The nature of my state selection process arises because I tackle the price-discrimination question by framing the question backwards, along the lines of a proof by contradiction. The statistics in Table A1 for the other states that consistently report negative markup are very similar to those for Kentucky (as discussed below). For each brand, Table A1 reports the sales weighted average manufacturing price, . the raw data used to generate the average markup and producer surplus for some of the major RC U retail price, markup, and sales volume broken down into pack sales and carton sales. As expected, per pack retail prices are higher for cigarette sold as a pack than for cigarettes sold by the carton (a discount for purchasing in bulk). The “markup” varies considerably between TE O R CI brands, where again markup here is not the actual markup but the difference between New Jersey manufacturing prices and Kentucky retail prices. In terms of pack pack sales, “markup” ranges from as low as $-1.29 for Pall Mall and $-0.66 for L&M to as high as $0.62 for Virginia Slims, with many brands around a noisy zero markup (e.g., Marlboro has an average markup of Q UO $0.04). It is worth emphasizing that the standard deviation of retail prices cigarettes vary due to an increase in cigarette taxes during the sample period, but the variation in markup does not suffer from the state tax variation. Relative to the mean “markup”, one observes highly DO NO T variable markup even within brands. The deviations in markup are typically on the order of magnitude of the mean markup, which suggests that many retailers are selling below cost for cigarette brands that have a nonnegative mean markup. Considering that the “markup” in Table A1 includes the markups of both wholesalers and retailers, Table A1 provides strong AF T. evidence that manufacturer prices are higher in New Jersey than in Kentucky. To demonstrate how these markups pan out in terms of mean producer surplus in Kentucky as well as in the six other states that consistently have negative “markup”, Table A2 in the Appendix provides descriptive statistics for seven states that have negative mean weekly AR Y DR producer surplus from cigarette sales in my sample. Note that three states witness negative mean “markup” (GA, KY, and MO), but the mean producer surplus is negative in all the seven states listed. Taken together, Tables A2 and A1 suggests that cigarette manufacturers IM IN price-discriminate by state. That GA, KY, and MO have some of the lowest cigarette taxes of all states in the US, cigarette manufacturers appear to be charging higher prices to wholesalers in states with higher state taxes. PR EL 3.2 Assumptions & Sample Selection I proceed to my main analysis under the assumption that state price discrimination follows linear expansion paths for cigarette brands. That is, manufacturers price discriminate by state homogeneously between products. For instance, I assume that the manufacturing price differences between Marlboros’ and Virgina Slims’ is constant between states, but that the prices are scaled up or down depending on the state. Under this assumption, I am able to can rely on the fact that tobacco companies set statewide manufacturing prices and thus still analyze the effect of state cigarette taxes on changes in producer surplus. I excluded retailers that (i) were located in one of the seven states where mean 7 LA TE year. At the UPC-retailer level, I restrict the RMS data to cigarette products that contain 20 cigarettes per pack (i.e., drop packs with 25 cigarettes),3 and UPCs where the retail price is greater than cost. Cost here is the sum of the manufacturing price, which already includes federal taxes, and state taxes. Restricting to nonnegative markup drops observations for retailers not subject to state taxes under most conditions. This was necessary for two reasons. First, because of my inability to distinguish between retailers subject to taxes and retailers not subject to state taxes (e.g., retailers on Indian reserves), keeping these observations would bias the results. Second, even though RMS allows one to identify cigarettes, which are subject to . producer surplus was negative (Table A2), and (ii) were present in the sample less than one R Descriptive Statistics O 3.3 CI RC U an excise tax, and distinguish them from other tobacco products, which are not subject to an excise tax, dropping observations with negative markups drop products that are misclassified as cigarettes. Table A3 in the Appendix shows the shows the span of the observations across dif- Q UO TE ferent year-weeks between 2008 and 2011, where each observation is the number of retailers in each week. Table A3 also shows the number of retailers who observed a tax change in that week (with the amount of the tax change conditional on the tax change), which serves as the identifying variation in the empirical section. In total, there are 2,471,370 retailer-week obser- DO NO T vations, 5,034 retailers who witnessed state tax changes with an average of $0.56 increase in state cigarette taxes conditional on a change (not shown). The sample spans extremely evenly across calendar weeks and years, with state tax changes occurring through out the years and of varying magnitudes. AF T. Table 1 provides descriptive statistics for retail prices, markup, weekly quantity of sales, and weekly profits for retailers in my sample. With a mean manufacturing price for a pack of cigarettes of $3.404 and a mean state tax of $1.30, the mean retail price in the sample AR Y DR for a pack of cigarettes is $5.37. The mean retailer that is observed for a little under four year (202 weeks) marks up on average a pack of cigarettes by $0.72 and earns weekly profits of $233 from mean weekly pack sales of 428 packs. However, the standard deviations suggest that the distribution of weekly markup, sales, and profits varies considerably. To get an idea for PR EL IM IN the shape of these distributions, Figure 3 plots the distribution of mean pack markup, weekly retailer pack sales, and weekly producer surplus. These measures all tend to be skewed heavily to the right. One notable feature of the data (as seen in Figure 3) is the lack of smoothness in the markup distribution. If markup is heterogeneous in taxes (i.e, markup varies by the amount of state taxes), one explanation for the unregular markup distribution is the clustering of state observations for markup. In addition, the 2009 federal tax increase could have shifted retailer markup in all states. To shed some light on how markup might be clustered by state and over federal taxes, Figure 2 shows three scatterplots of mean state markups for a given state tax level, where the left hand side plots markup on the y-axis against state taxes on the x-axis and the right hand side plots markup against the combined federal and state taxes. From Figure 2, 3 I do this because packs of 25 cigarettes are often taxed at a different rate than packs of 20, such as in New Jersey (NJ, 2009) 4 Note that the manufacturing price includes federal taxes because manufacturers pay federal, but not state, cigarette taxes and then set prices accordingly. 8 LA TE with the notion that the clsuters of markups are in part due to differences in state taxes. Next, to understand systematic differences between sales and profits between states, Table A4 in the Appendix shows the means of the measures in Table 1 broken down by state and provides a breakdown of observations by state. The sample, after the sample selection previously discussed, consists of 3,980,588 retailer-week observations, with the number of observations per state varying considerably from 5,545 in North Dakota to 347,040 in Florida. One observes relatively uniform mean manufacturing prices among states, which indicates that the populations in the states consume about the same quality of cigarettes. Mean weekly- . one observes a clear positive relationship between state taxes and markup, which is consistent RC U retailer profits vary considerably between states, ranging from as low as $78.89 and $85.14 in Connecticut and Michigan to as high as $569.69 and $546.61 in Massachusetts and North Dakota, respectively. There is also considerable heterogeneity in the sales quantity for the mean TE O R CI retailer-week observation between states. States with relatively more rural areas such as Iowa, North Dakota, and Wyoming tend to have higher weekly-retailer sales (837, 1107, and 771, respectively) compared with more urban areas such as Washington DC (164). Somewhat surprisingly, apart from North Dakota one does not observe a clear positive relationship between UO sales and producer surplus, which is telling about the endogeneity of markup and sales. Q [Insert Table 1 about here] 3.4 NO T [Insert Figures 2 and 3 about here] Event Study DO The panel nature of my retailer data naturally gives rise to a non-parametric visual assessment of cigarette factor prices, markup, and retail prices as well as for aggregate sales DR AF T. and producer surplus before and after cigarette tax increases through an event study. An event study is a power research design here because “treatments” (here, cigarette tax increases) are staggered in time across firms (over 209 different calendar weeks (Table A3)). Let’s define the event time as calendar week minus the week cigarette taxes were increased for each retailer so AR Y that the year-week of the increase in cigarette taxes becomes event time 0. In other words, we stacked the retailer week-year data around cigarette tax changes. For the event study, I achieved a balanced panel by restricting observations to those retailers observed in every period +/- 10 PR EL IM IN weeks of the tax change. For the purposes of the event study, I also excluded observations in the week of the tax change because of differences in retailers reporting of prices in these weeks. Using event time under this definition, I collapsed the data and computed statistics of interest including mean manufacturing prices, state taxes, markup, and retail prices as well as aggregate sales and producer surplus. Figure 4 shows the event study graph for cigarette factor prices, markup, and retail prices before and after cigarette tax increases, broken down into three categories of cigarette tax increases. Specifically, state tax changes of less than $0.50 are labeled “Low”, changes of $0.50 to $1.00 are labeled “Medium”, and changes of $1.00 or higher are labeled “High”. The x-axis indicates up to ten weeks prior and post the tax increases, and the y-axis plots the deviation from the mean prices during the 20 weeks of the event study. The intuition for reporting deviation from the means is to hone in on the small differences in changes in 9 LA TE price deviations in the right most column of Figure 4 is simply the sum of the deviations of state taxes, manufacturing prices, and markup. Referring to Figure 4, one observes no jump in manufacturing prices when state taxes change, but observes a very slight increasing trend more generally. Markup does not appear to change for small price increases (less than $0.50). However, changes in markup for price increases over $0.50 appear to be increasing in the magnitude of the price change: one observes a few cent increase in markup for medium sized tax changes and about a 30 cent increase in markup for tax increases $1.00 and over. These short term increases in markup following a . manufacturing prices and markup relative to the size of state taxes and retail prices. Retail RC U state cigarette tax hike are consistent with evidence that cigarette taxes are over-shifted to retail prices in the short run (DeCicca et al., 2013). That increases in markup appear to be increasing in the size of the tax change is far from surprising, but to my knowledge has not TE O R CI been specifically documented in the empirical tax incidence literature. Next, Figure 5 shows the event study for aggregate cigarette sales. It is worth emphasizing that aggregate cigarette sales is the summation of all sales from the balanced panel. It is a raw measure of the data. From Figure 5, one observes a slight increase in sales in the Q UO weeks prior to a tax change (consistent with the notion of of stockpiling) and a slight dip one week after a tax change relative to sales one week before the tax change (from about 5.7 million sales to 5.4 million sales). However, the decrease in sales in the week following the tax change DO NO T is hardly distinguishable from variations in weekly sales in the 10 periods before and after the tax change more generally. Finally, Figure 5 shows the event study for aggregate producer surplus, which is again a raw measure of the data. Note that any changes in producer surplus will result from AF T. the counteracting forces of markup increasing post a tax hike on the one hand and a slight dip in cigarette sales on the other. Given the evidence just presented for the endogeneity of markup and sales (that one did not observe a clear positive relationship between sales and producer surplus in Table A4), the short run impact of cigarette taxes on producer surplus is nontrivial. AR Y DR Figure 5 shows a slight increase in producer surplus in weeks 2 and beyond the tax change relative to producer surplus before the tax change. The fact that producer surplus is higher one week before the tax change than one week after the tax change is likely explained by the PR EL IM IN observed stockpiling behavior seen in Figure 5. The main empirical question I seek to investigate in the next section is whether the short term relationship between cigarette taxes and aggregate producer surplus shown in Figure 5 is robust at the firm level, and, if so, the size of the relationship. 4 [Insert Figures 4, 5, and 6 about here] Empirical Analysis I estimate the impact of cigarette taxes on sales volume, markup, and producer surplus by regressing them on cigarette taxes and other covariates: yijt = α + βτjt + ηi + δj + ψt + εijt 10 (1) where, depending on the specification, yijt is either the sales volume, markup, or the producer LA TE systematic differences in unobserved taxes faced by retailers (e.g., county and city taxes). More importantly, ηi also controls for latent differences in retailers in terms of the socio-demographics of the usual patrons of the store as well as in terms of persistent differences in retailer producer surplus from cigarettes, pricing behavior (markup), and sales. This allows me to abstract from . surplus for retailer i in state j and in week t, τjt is the per-pack state cigarette excise tax in state j in week t, and ηi are firm fixed effects. My empirical approach follows the standard identification convention in the cigarette tax literature. The use of panel data and the inclusion of firm fixed effects ηi has a number of advantages over cross sectional data. First, ηi controls for RC U the differences between each and every store (e.g., Shell gas station in Chicago located at a busy intersection versus a Shell gas station right around the corner in a less busy location), thereby netting out differences in responses of the consumers at each store location. As a result, TE O R CI ηi accounts for a huge source of noise that aggregate data or, more importantly, just using the average tax pass through rate to make statements about the entire market, fails to capture. For instance, if store X charges relatively high prices for the area or state because it sells to high income, price inelastic smokers, my specification accounts for such differences. Q UO As in any firm or individual fixed effects specification, time-invariant firm characteristics (or, in the case of individual panel data, demographics like race and gender for individuals) drop out of the estimation because they are perfectly colinear with the firm fixed effects. Here, DO NO T firm fixed effects are perfectly colinear with state fixed effects because firms do not move states in the data. At least in terms of consumer responses to cigarette taxes, there is, at least in principle, a consensus in the economics literature that (changes in) state-level taxes are exogenous. I AF T. am able to find little to no discussion that has focused on whether cigarette taxes are exogenous to retail firms. It may be that retail firms go out of business due to higher cigarette taxes, which would bias my results. My approach conditions the findings on the behavior of firms in specific high or low-tax states. It is not obvious that firms in low-tax state A would react in AR Y DR their pricing strategies in the same manner in a high-tax state B to changes in taxes. Moreover, patrons of firms in low-tax state A might react by changing consumption patterns in a manner different from patrons of firms in high-tax state B. Even though I cannot entirely preclude that IM IN sorting of firms (from dropping out of the marked) based on tax changes bias my results, one would need to assume that the decrease in profits on cigarettes alone due to higher cigarette taxes causes firms to go out of business, which is unlikely to be the case. Empirically, retailer sorting based on cigarette tax increases should be negligible. PR EL Consequently, all estimates ought to be interpreted as intent-to-treat (ITT) estimates. In my opinion, ITT estimates are the policy-relevant estimates and provide evidence on how retail outlets are actually affected by cigarette taxes in the real-world settings. This means that I deliberately allow for compensatory behavior of the firm in terms of pricing strategy. I also allow for compensatory behavior of patrons of the firms (e.g., reducing consumption or quitting altogether, cross-border shopping, tax evasion, switching to more expensive or higher nicotine content cigarettes, or becoming a more efficient smoker). Thus, the main identification assumption is that there are no other unobserved factors that are correlated with both cigarette tax increases and an above trend changes in producer surplus at the firm-week level (adjusting 11 LA TE cigarette tax changes and firm profits from selling cigarettes. Table 2 provides the results. The dependent variable in the Columns are specified in the table heading and are on the weekly-retailer level. For each dependent variable as described above, the columns differ only by the inclusion retailer fixed effects. The dependent variable in Columns (1) and (2) is the retail price of cigarettes from the Nielsen data. These specifications represent the standard tax pass through estimates, and show that taxes are over shifted to prices by a factor of about 1.15. The dependent variable in Columns (3) and (4) is the input price of cigarettes from the treasury data, which represent the tax pass through estimates . for cyclical patterns). It is hard to imagine a concrete factor that strongly correlates with both RC U at the manufacture level. The results suggest that state cigarette taxes are not statistically significantly related to manufacturing prices. The dependent variable in Columns (5) and (6) is cigarette markup. The estimates TE O R CI on state cigarette taxes can be interpreted as strategic pricing responses to cigarette taxes: each $1 increase in cigarette taxes is associated with an average 15 cent increase in the markup of cigarettes. The dependent variable in Columns (7) and (8) is the weekly cigarette sales volume. The estimated 44 less packs of cigarettes sold per week for each $1 increase in cigarette taxes Semi-Parametric Methods and Results NO 4.1 T Q UO becomes imprecise when retailer fixed effects are included. The main results are shown in Columns (9) and (10): under both specifications, a $1 increase in cigarette taxes increases weekly retailer profits by about $40. DO In this section, I present graphical semiparametric assessments of the impact of cigarette taxes on producer surplus, markup, and sales. These are semiparametric rather than AF T. nonparametric because I report means generated from the data rather than the raw data. In presenting these assessments, I seek to gain a deeper understanding of the short and long term impacts of cigarette taxes on the outcome measures in the previous section. AR Y DR Figure 7 plots the weekly mean producer surplus by state. The thick vertical line in each subplot of Figure 7 (in 2009) represents the federal increase in cigarette taxes, and the thin vertical lines each represent changes in state cigarette taxes in their respective states. One can interpret the changes in producer surplus around state tax changes as further evidence for the PR EL IM IN point estimates in Columns (1) and (2) of Table 2. In 13 of the 20 states with state tax changes, one observes clear short term increases in producer surplus around some state tax changes: AL, DE, FL, MA, MS, NH, NJ, NM, NC, SC, UT, VT, and WI. These short term jumps in the producer surplus drive the statistically significant point estimates of state taxes on producer surplus in Table 2. There appears to be no direct impact of increases in state taxes on producer surplus in AR, CT, DC, MN, PA, and WA. In New York, the 2008 state tax increase appears to be unrelated to the mean producer surplus, but the 2010 cigarette tax increase appeared to lead to a sharp decrease in mean producer surplus. As I will further discuss in the next section, the likely reason for the anomaly decrease in NY can be explained by the short term increases in sales and producer surplus in bordering states when NY taxes were increased (mainly NJ). The July 2010 increase in New York state taxes resulted from nearly the same state taxes in New York and bordering New Jersey to over a $1 differential in state taxes. Even though my regression analysis does not investigate the impact of federal taxes 12 LA TE again that federal taxes are paid by manufacturers whereas state taxes are paid by wholesalers. Even though the legal incidence of taxes is usually assumed to be irrelevant for the economic incidence of taxation, there is at least some evidence in Figure 7 that the legal incidence might matter. If legal incidence did not play a role in determining retail prices, one would expect retailers to increase markup following federal cigarette tax increases in the same way as state increases. But—at least in some states—this does not appear to be the case. For instance, notice that in SC, NC, UT, VT, WA, and WI federal taxes did not seem to impact markup whereas state taxes at different points in time in these states did. Moreover, for many of the . on producer surplus, Figure 7 shows that federal taxes can also impact producer surplus. Note RC U states where markup increased around the time of the federal tax change, high markups do not seem to persist for more than a short time period (IL, KS, KY, LA, ME, MS, MO, and WY). Following the conventions in Figure 7, Figures 8 and 9 plot the mean markup and TE O R CI sales by state, respectively. In 14 of the 20 states with state tax changes, one observes from Figure 8 clear short term increases in markup around some state tax changes: AR, CT, DE, FL, MA, MS, NH, NJ, NC, SC, UT, VT, WA, and WI. As with producer surplus, these short term jumps in the markup drive the statistically significant point estimates of state taxes on markup Q UO in Columns (7) to (12) in Table 2. In other states, however, one observes no clear relationship (DC, the 2009 MN tax change, NM, NY) while in even one instance a decrease (the 2011 MN increase). Referring now to mean-weekly-sales in Figure 9, cigarette sales appears to be cyclical T. Conclusion AF 5 DO NO T with varying degrees of state-specific magnitudes. Only in about half of the changes in state taxes does one observe a clear drop in sales following a state tax increase (CT, FL, MA, MS, NH, NM, NY, SC, WI). With some state tax changes, one observes a spike in sales before the tax increase (most notably, MA and NH), which is consistent with other evidence of stockpiling. AR Y DR The tax incidence literature unrealistically assumes that the diverse range of firms in the cigarette supply chain can be treated as a single hybrid firm spanning across the production of cigarettes. I am the first in the literature to identify the tax burden on some of these unique firms. Just as cigarette taxes can have differential impacts on heterogeneous smokers who differ IM IN in terms of income, cigarette taxes can impact the profits of some firms differently than others that can have real welfare implications. For instance, taxes that increase or decrease profits for tobacco companies should not be viewed in the same light as taxes that increase or decrease profits on gas stations. I estimate the causal effect of cigarette taxes on the profits of retail PR EL outlets. In doing so, I begin to explore the possibility that cigarette taxes could be regressive or progressive in terms of non-consumers of cigarettes (the owners and workers of firms). To study this question, I exploit a unique dataset I constructed that allows me to directly observe weekly profits generated from cigarette sales for over 27,000 retail outlets in 48 states from 2008 to 2012. Using this novel balanced-panel dataset, I employ a powerful retailer fixed effects specification and find that increases in cigarette taxes trigger modest increases in retailer profits of around $40 per week. Insomuch that owners and workers of retail outlets have lower income than the owners and workers of tobacco companies, the results partly alleviate the concern that big tobacco companies earn high profits at the expense of low income individuals at 13 least in the sense that local retail outlets share in these profits. An interesting avenue of future PR EL IM IN AR Y DR AF T. DO NO T Q UO TE O R CI RC U LA TE . research would be to investigate how cigarette taxes impact profits of tobacco companies and explore how cigarette taxes influence the allocation of total producer surplus in the cigarette industry. 14 References Adachi, T. and T. Ebina (2014). Double Marginalization and Cost Pass-Through: WeylFabinger and Cowan Meet Spengler and Bresnahan-Reiss. Economics Letters 122 (2), 170– 175. LA TE Barnett, P., T. Keeler, and T. Hu (1995). Oligopoly Structure and the Incidence of Cigarette Excise Taxes. Journal of Public Economics 57 (3), 457–470. . Anderson, S., A. de Palma, and B. Kreider (2001). Tax Incidence in Differentiated Product Oligopoly. Journal of Public Economics 81 (2), 173–192. RC U Besley, T. J. and H. S. Rosen (1999). Sales Taxes and Prices: An Empirical Analysis. National Tax Journal 52 (2), 157–178. R CI Bonnet, C. and P. Dubois (2010). Inference on Vertical Contracts Between Manufacturers and Retailers Allowing for Nonlinear Pricing and Resale Price Maintenance. RAND Journal of Economics 41 (1), 139–164. TE O Brenkers, R. and F. Verboven (2006). Liberalizing a Distribution System: The European Car Market. Journal of the European Economic Association 4 (1), 216–251. UO Cohen, M. (2013). A Study of Vertical Integration and Vertical Divestiture: The Case of Store Brand Milk Sourcing in Boston. Journal of Economics and Management Strategy 22 (1), 101–124. NO T Q DeCicca, P., D. Kenkel, and F. Liu (2013). Who Pays Cigarette Taxes? The Impact of Consumer Price Search. Review of Economics and Statistics 95 (2), 516–529. DO Delipalla, S. and O. ODonnell (2001). Estimating Tax Incidence, Market Power and Market Conduct: The European Cigarette Industry. International Journal of Industrial Organization 19 (6), 885–908. AF T. Fullerton, D. and G. Metcalf (2002). Tax incidence. in Alan Auerbach and Martin Feldstein, Handbook of Public Economics (4), 1787–1872. DR Gil, R. (2009). Revenue Sharing Distortions and Vertical Integration in the Movie Industry. Journal of Law, Economics, and Organization 25 (2), 579–610. AR Y Hanson, A. and R. Sullivan (2009). The Incidence of Tobacco Taxation: Evidence from Geographic Micro-Level Data. National Tax Journal 62 (4), 677–98. IM IN Harding, M., E. Leibtag, and M. Lovenheim (2012). The Heterogeneous Geographic and Socioeconomic Incidence of Cigarette Taxes: Evidence from Nielsen Homescan Data. American Economic Journal: Economic Policy 4 (4), 169–98. PR EL Keeler, T., T. Hu, P. Barnett, W. Manning, and H. Sung (1996). Do Cigarette Producers Price-Discriminate by State? An Empirical Analysis of Local Cigarette Pricing and Taxation. Journal of Health Economics 15 (4), 499–512. Lafontaine, F. and M. Slade (2014). Incentive and strategic contracting: Implications for the franchise decision. in Kalyan Chatterjee and William Samuelson, Game Theory and Business Applications (194), 137–188. Lyon, A. and R. Schwab (1995). Consumption Taxes in a Life-Cycle Framework: Are Sin Taxes Regressive? Review of Economics and Statistics 77 (3), 389–406. NJ (2009). P.L. 2009, Chapter 70. Technical www.njleg.state.nj.us/20082009/PL09/70 .HTM, last retrieved 22 January 2015. 15 report. Poterba, J. (1996). Retail Price Reactions to Changes in State and Local Taxes. National Tax Journal 49 (2), 165–76. Reny, P. J., S. J. Wilkie, and M. A. Williams (2012). Tax Incidence under Imperfect Competition: Comment. International Journal of Industrial Organization 30 (5), 399–402. LA TE Sudhir, K. and S. Datta (2009). Pricing in Marketing Channels. Edward Elgar Publishing, Inc. . Spengler, J. (1950). Vertical Integration and Antitrust Policy. Journal of Political Economy 58 (4), 347–352. RC U Sumner, D. A. (1981). Measurement of Monopoly Behavior: An Application to the Cigarette Industry. Journal of Political Economy 89 (5), 1010–1019. Tax Burden on Tobacco (2012). Historical Compilation. CI Tennant, R. B. (1950). The American Cigarette Industry: A Study in Economic Analysis and Public Policy. Yale University Press. O R Villas-Boas, S. (2007). Vertical Relationships between Manufacturers and Retailers: Inference with Limited Data. Review of Economic Studies 74 (2), 625–652. UO TE West, D. (2000, June). Double Marginalization and Privatization in Liquor Retailing. Review of Industrial Organization 16 (4), 399–415. PR EL IM IN AR Y DR AF T. DO NO T Q Weyl, E. and M. Fabinger (2013). Pass-Through as an Economic Tool: Principles of Incidence under Imperfect Competition. Journal of Political Economy 121 (3), 528–583. 16 . LA TE RC U CI R Price p∗r UO πr TE O Price ∗ πm ∗ M Cr (t, πw ) = p∗w DWLdm πw (set by wholesaler) T Q p∗m NO DWLm DO M Cm M Rm qr∗ AF Quantity Dr Quantity (b) Double Marginalization DR (a) Monopoly M R w M Rr = D w Dm T. ∗ qm M Cw PR EL IM IN AR Y Figure 1: Equilibrium prices, quantity, allocation of profits, and deadweight loss under Monopoly (a) and Double Marginalization (b) 17 Min Max State Tax ($) 1.30 0.89 0.07 4.35 MFG Price ($) 3.40 0.44 1.18 4.97 Retail Price ($) 5.37 1.34 0.01 26.23 MarkUp ($) 0.72 0.55 0.00 19.86 Quantity (# Packs Sold) 428.39 453.04 1 27,770 Weekly Profits ($) 233.01 277.30 0.00 43,218.12 Store-Week Obs. 202.04 23.19 52 CI R 209 PR EL IM IN AR Y DR AF T. DO NO T Q UO TE O Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. 18 LA TE StDev RC U Mean . Table 1: Descriptive Statistics for State Cigarettes Taxes, MFG Prices, Retail Prices, MarkUp (Retail Price - MFG Price), Sales Quantity, Profits, and Retailer Panel Length O R CI RC U LA TE . Figure 2: Mean Statewide MarkUp (Retail Price - Manufacturing Price) of Cigarettes versus State Taxes (left) and Combined State and Federal Taxes (right) TE Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. AR Y DR AF T. DO NO T Q UO Figure 3: Mean Retailer-Week Per Pack MarkUp (Retail Price - Manufacturing Price) of Cigarettes (left), Sales (middle), and Producer Surplus (right) PR EL IM IN Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. 19 AF T. DO NO T Q UO TE O R CI RC U LA TE . Figure 4: Event Study: Deviation from Mean of Retailer-Week Cigarette (a) State Taxes, (b) MFG Prices, (c) MarkUp, and (d) Retail Prices PR EL IM IN AR Y DR Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. 20 UO TE O R CI RC U LA TE . Figure 5: Event Study: Aggregrate Weekly Cigarette Sales DO NO T Q Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. PR EL IM IN AR Y DR AF T. Figure 6: Event Study: Aggregrate Retailer-Week Producer Surplus from Cigarette Sales Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. 21 22 EL IN IM Yes Yes Year FE Month×Year FE 5075615 0.874 Yes Yes Yes Yes No 5.363 1.165*** (0.0133) [0.0054] T. 5042251 0.929 Yes Yes Yes No Yes 3.399 0.0024 (0.0059) [0.0008] AF 5042251 0.310 Yes Yes Yes Q No T Yes 0.721 (6) 5042251 0.260 Yes TE Yes Yes Yes No 0.721 O 0.1430*** (0.0100) [0.0100] UO 0.1560*** (0.0262) [0.0034] NO 5042251 0.961 Yes Yes Yes Yes No 3.399 DO -0.0022 (0.0080) [0.0080] (5) MarkUp R 5075615 0.050 Yes Yes Yes No Yes 429.802 -34.910* (13.25) [2.938] (7) 5075615 0.055 Yes Yes Yes Yes No 429.802 4.249 (16.91) [2.323] (8) Weekly Sales 5075615 0.093 Yes Yes Yes No Yes 233.280 34.28** (10.56) [1.94] (9) 5075615 0.090 Yes Yes Yes Yes No 43.34*** (5.45) [2.32] (10) Producer Surplus CI RC U LA TE . Source: Nielsen Retail Scanner Data merged with New Jersey wholesale prices, 2008 to 2011, merged with state-month level cigarette tax information (Tax Burden on Tobacco, 2012), own calculation and illustration. * p<0.05, ** p<0.01, *** p<0.001; standard errors are in parentheses and clustered at the store level (in parentheses) and also reported at state level (in brackets). Each column represents one regression as in Equation (1). The dependent variable in columns (1) and (2) is the average retail price cigarette for a retailer-week. The dependent variable in columns (3) and (4) measures the average MFG price of cigarettes for a retailer-week. The dependent variable in columns (5) and (6) measures the average markup (retail price minus manufacturing price minus the state tax). The dependent variable in columns (7) and (8) measures the packs of sales for the retailer-week. The dependent variable in columns (5) and (6) measures the actual producer surplus for the retailer-week (markup on a pack-by-pack basis times sales of those packs). The variable of interest indicates the state cigarette tax level in month. 5075615 0.829 Yes Month FE Observations R2 No Yes Covariates employed County FE Retailer FE 5.363 1.134*** (0.0239) [0.0039] Mean State Tax ($) (4) (3) DR (1) (2) MFG Price Retail Price AR Y Table 2: Effect of Cigarette Taxes on Retailer Producer Surplus (Columns 1 and 2), Weekly Sales (Columns 3 and 4), Percentage of Sales from Packs (Columns 5 and 6), Markup (Columns 7 through 12) PR 23 EL IN IM AR Y DR AF T. DO NO T Q UO TE O R Figure 7: Mean Producer Surplus from Cigarettes by State CI Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. PR RC U LA TE . 24 EL IN IM AR Y DR AF T. DO NO T Q UO TE O Figure 8: Mean MarkUp from Cigarettes by State R CI Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. PR RC U LA TE . 25 EL IN IM AR Y DR AF T. DO NO T Q UO TE O R CI RC U LA TE Figure 9: Weekly Cigarette Sales for Mean-Retailer by State (in Packs of Cigarettes Sold) Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. PR . PR EL IN IM AR Y DR AF T. DO NO T Q UO TE O R CI RC U LA TE . 6 Appendix 26 Table A1: Evidence of Manufacture State Price Discrimination: New Jersey Manufacturing Prices, Kentucky Retail Prices, and Markup by Brand (mean, standard deviation, minimum, maximum) MFG Price Pack Statistics Price ($) Carton Statistics MarkUp ($) Sales Price ($) MarkUp ($) Sales 3.04 3.83 0.31 30.40 3.37 -0.07 62.63 0.46 0.91 0.43 84.23 0.84 0.32 160.45 2.49 0.01 -4.02 0.00 0.00 -4.03 0.00 3.65 10.00 5.97 1,433.00 10.00 5.97 2,760.00 BENSON & HEDGES 4.05 4.98 0.45 12.14 4.70 0.16 12.77 0.60 0.94 0.48 34.86 0.90 0.34 24.94 3.21 0.01 -5.22 0.00 0.00 -5.09 0.00 4.77 10.00 5.01 394.00 10.00 5.01 190.00 CAMEL 3.29 4.20 0.40 35.10 3.83 0.06 31.79 0.29 0.81 0.61 135.63 0.78 0.54 82.48 2.76 0.01 -4.08 0.00 0.00 -4.09 0.00 3.71 7.35 3.22 2,387.00 10.00 5.91 1,774.00 DORAL 2.90 3.49 0.11 21.76 3.21 -0.17 41.35 0.28 0.70 0.36 49.01 0.68 0.35 90.02 2.49 0.01 -2.83 0.00 0.00 -3.61 0.00 3.31 5.62 1.85 746.00 6.94 3.17 1,180.00 GT ONE 1.49 2.26 0.39 19.95 2.12 0.30 48.49 0.34 0.47 0.21 98.97 0.42 0.15 254.09 1.26 0.75 -2.07 0.00 1.00 -1.82 0.00 2.21 3.66 1.13 1,224.00 5.00 2.18 4,350.00 L&M 3.42 3.35 -0.66 60.87 3.06 -0.99 48.38 0.16 0.38 0.37 132.71 0.30 0.29 102.31 2.49 0.01 -3.94 0.00 0.00 -3.95 0.00 3.65 10.00 6.05 1,846.00 5.75 1.86 1,170.00 MARLBORO 3.38 3.92 0.04 251.68 3.54 -0.31 343.19 0.45 0.63 0.30 998.42 0.65 0.25 1,185.89 2.76 0.01 -4.15 0.00 0.00 -4.22 0.00 3.92 8.49 5.43 20,631.00 8.49 5.43 59,692.00 MERIT 4.02 4.91 0.43 20.36 4.52 0.04 24.96 0.61 0.98 0.50 64.60 0.91 0.44 62.61 3.21 0.01 -4.32 0.00 0.00 -3.70 0.00 4.77 10.00 4.93 579.00 10.00 6.44 550.00 PALL MALL 3.91 3.15 -1.29 111.03 2.93 -1.47 155.89 0.82 0.46 0.69 334.48 0.48 0.71 352.47 3.21 0.01 -5.47 0.00 0.00 -3.51 0.00 5.02 4.71 1.14 4,641.00 4.69 0.72 4,130.00 PARLIAMENT 3.38 4.36 0.48 19.24 4.07 0.21 14.89 0.49 0.82 0.44 62.75 0.80 0.36 36.00 2.76 0.01 -4.28 0.00 0.00 -4.37 0.00 3.99 10.00 5.71 614.00 10.00 5.79 510.00 USA GOLD 2.39 3.45 0.55 18.93 3.00 0.17 13.61 0.44 0.65 0.30 37.03 0.59 0.31 32.27 1.62 0.01 -3.25 0.00 0.00 -3.26 0.00 2.74 7.99 4.78 422.00 10.00 6.66 530.00 VIRGINIA SLIMS 3.47 4.58 0.62 20.29 4.14 0.27 28.28 0.51 0.79 0.31 67.82 0.78 0.23 83.30 2.76 0.01 -4.37 0.00 0.00 -4.46 0.00 4.08 8.49 5.29 790.00 8.74 5.59 1,490.00 WAVE 2.20 2.84 0.13 70.83 2.66 0.01 42.25 0.45 0.56 0.23 362.78 0.62 0.21 148.59 1.52 1.80 -0.94 0.00 1.70 -0.68 0.00 2.73 3.75 0.63 5,913.00 3.58 0.56 3,340.00 WINSTON 3.25 3.83 0.08 13.93 3.48 -0.28 50.43 0.34 0.59 0.33 22.16 0.60 0.27 98.38 2.76 0.01 -3.08 0.00 1.77 -1.97 0.00 3.71 5.38 1.40 172.00 4.95 1.00 1,210.00 Source: Nielsen Retail Scanner Data merged with New Jersey wholesale prices, 2008 to 2011, merged with statemonth level cigarette tax information (Tax Burden on Tobacco, 2012), own calculation and illustration. For a given brand and column, the table lists in the rows (1) mean, (2) standard deviation, (3) minimum value, and (4) maximum value. PR EL IM IN AR Y DR AF T. DO NO T Q UO TE O R CI RC U LA TE . BASIC 27 . LA TE RC U CI R O State Tax MFG Price Retail Price 0.37 0.51 0.17 0.41 0.26 0.62 1.41 3.47 3.43 3.54 3.41 3.43 3.43 3.49 3.96 3.82 3.66 4.06 3.87 4.10 4.99 Price MarkUp Sales Quantity Producer Surplus 703.45 1,640.05 1,136.78 799.15 829.36 791.85 800.19 -190.65 -527.14 -226.72 -23.41 -24.36 -48.63 -2.81 Obs. DO T. GA KY MO NC SC TN TX NO T State Q UO TE Table A2: Descriptive Statistics for State Tax, Mean Retail Price, MarkUp (Retail Price - MFG Price), Sales Quantity, and Producer Surplus by State -0.09 -0.31 -0.15 0.25 0.08 0.01 0.21 PR EL IM IN AR Y DR AF Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. Price MarkUp is the average of the (mean) markup of packs and cartons of the retailer-week. 28 170,258 75,728 68,929 259,741 113,633 143,361 294,644 29 1,235,685 23,036 23,336 23,341 23,356 23,382 23,413 23,429 23,461 23,476 23,483 23,498 23,525 23,553 23,578 23,562 23,585 23,609 23,617 23,623 23,642 23,665 23,685 23,694 23,715 23,721 23,744 23,757 23,770 23,792 23,806 23,822 23,845 23,864 23,878 23,906 23,956 23,958 23,956 23,983 24,013 24,018 24,035 24,053 24,082 24,113 24,143 24,162 24,194 24,213 24,214 24,217 24,206 Obs. PR 2,517 0.82 0.33 0.89 1.25 AR Y 1/3/2009 1/10/2009 1/17/2009 1/24/2009 1/31/2009 2/7/2009 2/14/2009 2/21/2009 2/28/2009 3/7/2009 3/14/2009 3/21/2009 3/28/2009 4/4/2009 4/11/2009 4/18/2009 4/25/2009 5/2/2009 5/9/2009 5/16/2009 5/23/2009 5/30/2009 6/6/2009 6/13/2009 6/20/2009 6/27/2009 7/4/2009 7/11/2009 7/18/2009 7/25/2009 8/1/2009 8/8/2009 8/15/2009 8/22/2009 8/29/2009 9/5/2009 9/12/2009 9/19/2009 9/26/2009 10/3/2009 10/10/2009 10/17/2009 10/24/2009 10/31/2009 11/7/2009 11/14/2009 11/21/2009 11/28/2009 12/5/2009 12/12/2009 12/19/2009 12/26/2009 Date IN IM ∆Tax ($) EL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1,452 0 0 0 795 0 0 0 0 0 0 0 0 0 0 0 0 270 0 0 0 0 0 0 0 0 0 0 0 0 Obs. w ∆Tax 0.46 0.25 0.95 0.27 0.45 0.7 T Q Date 1,283,283 TE 24,687 24,674 24,679 24,679 24,675 24,658 24,650 24,656 24,656 24,667 24,700 24,684 24,695 24,687 24,695 24,700 24,721 24,664 24,675 24,644 24,641 24,661 24,671 24,665 24,661 24,678 24,629 24,646 24,656 24,619 24,629 24,627 24,632 24,635 24,633 24,624 24,643 24,634 24,773 24,781 24,782 24,754 24,759 24,736 24,720 24,714 24,714 24,707 24,701 24,671 24,673 24,668 Obs. UO 1/2/2010 1/9/2010 1/16/2010 1/23/2010 1/30/2010 2/6/2010 2/13/2010 2/20/2010 2/27/2010 3/6/2010 3/13/2010 3/20/2010 3/27/2010 4/3/2010 4/10/2010 4/17/2010 4/24/2010 5/1/2010 5/8/2010 5/15/2010 5/22/2010 5/29/2010 6/5/2010 6/12/2010 6/19/2010 6/26/2010 7/3/2010 7/10/2010 7/17/2010 7/24/2010 7/31/2010 8/7/2010 8/14/2010 8/21/2010 8/28/2010 9/4/2010 9/11/2010 9/18/2010 9/25/2010 10/2/2010 10/9/2010 10/16/2010 10/23/2010 10/30/2010 11/6/2010 11/13/2010 11/20/2010 11/27/2010 12/4/2010 12/11/2010 12/18/2010 12/25/2010 NO 0.5 0.48 0.56 0.01 ∆Tax ($) DO 7,037 T. 234 0 0 0 0 0 0 0 0 175 0 0 0 485 0 0 0 0 0 154 0 0 0 0 0 0 2,732 0 0 0 159 0 0 0 0 1,678 0 0 0 342 0 0 0 0 1,078 0 0 0 0 0 0 0 Obs. w ∆Tax AF 1,269,631 DR 24,242 24,247 24,255 24,256 24,261 24,248 24,230 24,228 24,238 24,106 24,211 24,241 24,277 24,284 24,274 24,266 24,263 24,257 24,292 24,309 24,321 24,323 24,327 24,342 24,338 24,362 24,351 24,369 24,452 24,450 24,457 24,476 24,488 24,496 24,512 24,560 24,550 24,561 24,571 24,582 24,595 24,586 24,630 24,632 24,659 24,641 24,678 24,688 24,678 24,661 24,662 24,648 Obs. O 0.77 Date LA TE 1/1/2011 1/8/2011 1/15/2011 1/22/2011 1/29/2011 2/5/2011 2/12/2011 2/19/2011 2/26/2011 3/5/2011 3/12/2011 3/19/2011 3/26/2011 4/2/2011 4/9/2011 4/16/2011 4/23/2011 4/30/2011 5/7/2011 5/14/2011 5/21/2011 5/28/2011 6/4/2011 6/11/2011 6/18/2011 6/25/2011 7/2/2011 7/9/2011 7/16/2011 7/23/2011 7/30/2011 8/6/2011 8/13/2011 8/20/2011 8/27/2011 9/3/2011 9/10/2011 9/17/2011 9/24/2011 10/1/2011 10/8/2011 10/15/2011 10/22/2011 10/29/2011 11/5/2011 11/12/2011 11/19/2011 11/26/2011 12/3/2011 12/10/2011 12/17/2011 12/24/2011 12/31/2011 RC U 1.26 1 0.06 ∆Tax ($) CI 3,051 R 245 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 618 0 0 0 0 0 0 0 0 2,188 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Obs. w ∆Tax 1,287,016 . 24,379 24,392 24,373 24,374 24,380 24,368 24,366 24,378 24,366 24,434 24,430 24,426 24,417 24,407 24,398 24,375 24,396 24,394 24,382 24,360 24,359 24,344 24,345 24,334 24,327 24,306 24,306 24,288 24,280 24,284 24,254 24,260 24,255 24,238 24,238 24,244 24,230 24,221 24,215 24,225 24,217 24,198 24,205 24,195 24,183 24,136 24,131 24,132 24,136 24,083 24,019 24,028 24,005 Obs. Source: Nielsen Retail Scanner Data merged with New Jersey wholesale prices, 2008 to 2011, merged with state-month level cigarette tax information (Tax Burden on Tobacco, 2012), own calculation and illustration. Totals 1/5/2008 1/12/2008 1/19/2008 1/26/2008 2/2/2008 2/9/2008 2/16/2008 2/23/2008 3/1/2008 3/8/2008 3/15/2008 3/22/2008 3/29/2008 4/5/2008 4/12/2008 4/19/2008 4/26/2008 5/3/2008 5/10/2008 5/17/2008 5/24/2008 5/31/2008 6/7/2008 6/14/2008 6/21/2008 6/28/2008 7/5/2008 7/12/2008 7/19/2008 7/26/2008 8/2/2008 8/9/2008 8/16/2008 8/23/2008 8/30/2008 9/6/2008 9/13/2008 9/20/2008 9/27/2008 10/4/2008 10/11/2008 10/18/2008 10/25/2008 11/1/2008 11/8/2008 11/15/2008 11/22/2008 11/29/2008 12/6/2008 12/13/2008 12/20/2008 12/27/2008 Date Table A3: Nielsen Retailer Panel Observations Over Weeks 253 253 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Obs. w ∆Tax .02 0.02 ∆Tax ($) PR Total 1.30 3.40 170.03 328.32 254.94 207.44 326.85 409.77 78.89 142.97 196.65 142.64 419.19 213.72 481.47 186.73 197.80 376.17 150.01 569.69 85.14 122.17 128.39 206.23 454.69 258.26 363.95 272.04 278.62 272.14 546.61 189.24 331.03 201.65 165.65 296.83 314.35 206.23 327.17 264.72 284.12 95.55 163.86 412.58 T NO DO T. AF DR 5.37 354.02 519.64 542.90 295.07 619.51 568.09 253.56 164.06 478.10 339.24 398.94 623.65 837.41 629.55 496.65 593.79 268.07 552.94 255.78 405.40 321.43 362.94 765.91 514.29 940.77 346.93 672.62 275.95 1,107.57 461.68 536.79 328.35 350.19 627.10 588.00 476.16 493.11 465.55 334.52 273.33 384.45 771.25 0.72 429.73 233.75 N 203.71 205.20 199.49 204.56 203.93 198.81 201.70 194.03 198.75 198.26 201.39 201.33 203.90 195.91 204.59 203.76 200.98 200.28 204.50 201.79 199.96 205.11 202.95 203.83 204.28 200.13 198.94 199.35 197.98 203.14 202.26 206.94 202.97 203.90 201.99 198.15 206.62 202.63 205.82 204.73 190.14 202.55 57,593 129,142 39,187 523,873 97,799 63,378 34,288 6,227 347,040 21,629 195,089 78,855 82,087 33,062 75,401 44,706 102,751 138,971 144,188 51,115 31,444 12,061 28,335 42,921 51,167 143,186 20,416 312,237 5,545 182,021 31,869 70,950 227,929 27,628 9,140 24,731 22,945 197,609 130,975 40,422 86,630 14,046 202.04 3,980,588 RC U 0.63 0.86 0.53 0.92 0.70 0.86 0.45 1.30 0.60 0.57 1.52 0.40 0.75 0.45 0.50 0.78 0.75 1.30 0.46 0.37 0.51 0.75 0.67 0.67 0.57 0.90 0.56 1.23 0.56 0.51 0.54 0.77 0.58 0.56 0.63 0.54 0.74 0.80 0.99 0.46 0.53 0.77 CI 4.42 6.23 4.83 5.21 4.92 6.79 5.25 6.90 4.89 4.46 5.88 4.73 5.33 4.54 4.27 6.15 6.16 7.03 5.80 5.27 4.43 5.85 4.61 4.87 5.52 6.98 5.11 7.59 4.25 5.08 4.95 5.32 5.41 7.07 5.44 4.97 6.22 4.49 6.84 4.26 6.11 4.74 Store-Week Observations R 3.39 3.40 3.38 3.44 3.42 3.38 3.40 3.47 3.41 3.36 3.43 3.38 3.31 3.41 3.43 3.41 3.41 3.37 3.40 3.43 3.42 3.41 3.34 3.43 3.41 3.43 3.39 3.41 3.27 3.36 3.42 3.41 3.37 3.39 3.30 3.37 3.39 3.41 3.41 3.29 3.39 3.39 Producer Surplus O 0.43 2.00 1.00 0.87 0.84 2.58 1.43 2.13 0.98 0.57 0.98 1.00 1.36 0.79 0.36 2.00 2.00 2.39 2.00 1.54 0.52 1.70 0.64 0.80 1.58 2.66 1.21 3.26 0.44 1.25 1.03 1.18 1.49 3.16 1.53 1.11 2.13 0.30 2.46 0.55 2.26 0.60 Sales Quantity TE Price MarkUp UO Retail Price Q MFG Price IN EL IM AL AZ AR CA CO CT DE DC FL ID IL IN IA KS LA ME MD MA MI MN MS MT NE NV NH NJ NM NY ND OH OK OR PA RI SD UT VT VA WA WV WI WY State Tax AR Y State LA TE . Table A4: Descriptive Statistics for State Tax, Mean Retail Price, MarkUp (Retail Price - MFG Price), Sales Quantity, and Producer Surplus by State Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011. Price MarkUp is the average of the (mean) markup of packs and cartons of the retailer-week. 30
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