Cigarette Taxes and Producer Surplus
Kyle Rozema∗
Abstract
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JEL codes: H22, H71, D22, L16, L11, L22.
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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.
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April 22, 2015
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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
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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
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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
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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
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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
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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
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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,
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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
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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
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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).
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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
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price for each cigarette brand (Tennant, 1950; Barnett et al., 1995), there is at least some evi-
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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
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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
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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
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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
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2.1
Institutional Setting
The Market Players
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setting and Section 3 the data. Section 4 is the empirical analysis. Section 5 concludes.
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“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
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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.
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2.2
Cigarette Taxes
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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.
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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
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A general economic principle is that taxes tend to be borne by inelastic producers
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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
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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
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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).
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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
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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),
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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
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relationship between successive monopolies at two stages of production, e.g., manufacturing
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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.
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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
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price. Compared to a single monoplist, the market outcome for monopolists in vertically related
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markets is higher retail prices (p∗r > p∗m ), lower output (qr∗ > qm
), lower total profits to be split
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(πm > πm + πm ), and the market is less efficient (DW Ldm > DW Lm ). Note that in most
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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.
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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
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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)
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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).
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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
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merchandiser, and other stores in 52 U.S. markets. The RMS reports weekly data for each in-
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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.
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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
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cigarette taxes and 25 changes in state cigarette taxes (some states had more than one increase),
which serve as my identifying variation.
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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.
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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
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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-
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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
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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,
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the raw data used to generate the average markup and producer surplus for some of the major
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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
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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
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$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
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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
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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
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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
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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.
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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
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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
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producer surplus was negative (Table A2), and (ii) were present in the sample less than one
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Descriptive Statistics
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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-
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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-
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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.
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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
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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
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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.
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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-
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one observes a clear positive relationship between state taxes and markup, which is consistent
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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
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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
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sales and producer surplus, which is telling about the endogeneity of markup and sales.
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[Insert Figures 2 and 3 about here]
Event Study
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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
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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
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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
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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
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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
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manufacturing prices and markup relative to the size of state taxes and retail prices. Retail
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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
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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
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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
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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
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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.
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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
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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
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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
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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,
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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.
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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5
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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.
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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
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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
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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
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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
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Barnett, P., T. Keeler, and T. Hu (1995). Oligopoly Structure and the Incidence of Cigarette
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.
Anderson, S., A. de Palma, and B. Kreider (2001). Tax Incidence in Differentiated Product
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Besley, T. J. and H. S. Rosen (1999). Sales Taxes and Prices: An Empirical Analysis. National
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Bonnet, C. and P. Dubois (2010). Inference on Vertical Contracts Between Manufacturers and
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Brenkers, R. and F. Verboven (2006). Liberalizing a Distribution System: The European Car
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UO
Cohen, M. (2013). A Study of Vertical Integration and Vertical Divestiture: The Case of Store
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DeCicca, P., D. Kenkel, and F. Liu (2013). Who Pays Cigarette Taxes? The Impact of
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DO
Delipalla, S. and O. ODonnell (2001). Estimating Tax Incidence, Market Power and Market
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Fullerton, D. and G. Metcalf (2002). Tax incidence. in Alan Auerbach and Martin Feldstein,
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DR
Gil, R. (2009). Revenue Sharing Distortions and Vertical Integration in the Movie Industry.
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Hanson, A. and R. Sullivan (2009). The Incidence of Tobacco Taxation: Evidence from Geographic Micro-Level Data. National Tax Journal 62 (4), 677–98.
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Harding, M., E. Leibtag, and M. Lovenheim (2012). The Heterogeneous Geographic and Socioeconomic Incidence of Cigarette Taxes: Evidence from Nielsen Homescan Data. American
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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.
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Lafontaine, F. and M. Slade (2014). Incentive and strategic contracting: Implications for the
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Lyon, A. and R. Schwab (1995). Consumption Taxes in a Life-Cycle Framework: Are Sin Taxes
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NJ
(2009).
P.L.
2009,
Chapter
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www.njleg.state.nj.us/20082009/PL09/70 .HTM, last retrieved 22 January 2015.
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report.
Poterba, J. (1996). Retail Price Reactions to Changes in State and Local Taxes. National Tax
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Sudhir, K. and S. Datta (2009). Pricing in Marketing Channels. Edward Elgar Publishing, Inc.
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Spengler, J. (1950). Vertical Integration and Antitrust Policy. Journal of Political Economy 58 (4), 347–352.
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Sumner, D. A. (1981). Measurement of Monopoly Behavior: An Application to the Cigarette
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Tax Burden on Tobacco (2012). Historical Compilation.
CI
Tennant, R. B. (1950). The American Cigarette Industry: A Study in Economic Analysis and
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Villas-Boas, S. (2007). Vertical Relationships between Manufacturers and Retailers: Inference
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West, D. (2000, June). Double Marginalization and Privatization in Liquor Retailing. Review
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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
.
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R
Price
p∗r
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πr
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Price
∗
πm
∗
M Cr (t, πw
) = p∗w
DWLdm
πw
(set by wholesaler)
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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
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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
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209
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AF
T.
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Source: Nielsen Retail Scanner Data merged with administrative data
on manufacturing prices, 2008 to 2011.
18
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StDev
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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
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.
Figure 2: Mean Statewide MarkUp (Retail Price - Manufacturing Price) of Cigarettes versus
State Taxes (left) and Combined State and Federal Taxes (right)
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Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
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T.
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Figure 3: Mean Retailer-Week Per Pack MarkUp (Retail Price - Manufacturing Price) of
Cigarettes (left), Sales (middle), and Producer Surplus (right)
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Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
19
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T.
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.
Figure 4: Event Study: Deviation from Mean of Retailer-Week Cigarette (a) State Taxes, (b)
MFG Prices, (c) MarkUp, and (d) Retail Prices
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Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
20
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.
Figure 5: Event Study: Aggregrate Weekly Cigarette Sales
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Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
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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
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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
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.
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 ($)
(4)
(3)
DR
(1)
(2)
MFG Price
Retail Price
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Table 2: Effect of Cigarette Taxes on Retailer Producer Surplus (Columns 1 and 2), Weekly Sales (Columns 3 and 4), Percentage of Sales from Packs
(Columns 5 and 6), Markup (Columns 7 through 12)
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Figure 7: Mean Producer Surplus from Cigarettes by State
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Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
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.
24
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T.
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Figure 8: Mean MarkUp from Cigarettes by State
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Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
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.
25
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Figure 9: Weekly Cigarette Sales for Mean-Retailer by State (in Packs of Cigarettes Sold)
Source: Nielsen Retail Scanner Data merged with administrative data on manufacturing prices, 2008 to 2011.
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T.
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.
6
Appendix
26
Table A1: Evidence of Manufacture State Price Discrimination: New Jersey Manufacturing
Prices, Kentucky Retail Prices, and Markup by Brand (mean, standard deviation, minimum,
maximum)
MFG
Price
Pack Statistics
Price ($)
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