Complexity, Efficiency, and Fairness
of Multi-Product Monopoly Pricing?
Eugenio J. Miravete†
Katja Seim‡
Jeff Thurk§
PRELIMINARY - DO NOT CITE
June 2016
Abstract
We investigate the implications of public monopoly in the heavily regulated alcoholic
beverage industry, an industry that generates significant tax revenue but also poses
health concerns. Using detailed data from the Pennsylvania Liquor Control Board,
a public monopoly that purchases alcoholic beverages from upstream distillers for
sale in state-run stores, we estimate a discrete choice demand model allowing for
flexible substitution patterns among consumers while not imposing an objective for the
regulator. We use the estimated demand model to show that current policy over-prices
spirits, our object of interest, in order to decrease aggregate consumption. Moreover,
both downstream monopolization and the uniform pricing policy implicitly discourage
alcohol consumption among consumers who are low income, poorly educated, and/ or
white. Privatization leads to lower retail prices and greater consumption, particularly
for consumers which current policy protects, making it politically infeasible.
Keywords: Multi-Product Price Discrimination, Complex Pricing,
Taxation by Regulation.
JEL Codes: L12, L21, L32
?
†
‡
§
We thank Thomas Krantz at the Pennsylvania Liquor Control Board as well as Jerome Janicki and Mike
Ehtesham at the National Alcohol Beverage Control Association for helping us to get access to the data.
We are also grateful for comments and suggestions received at several seminar and conference presentations,
and in particular to Jeff Campbell, Kenneth Hendricks, and Joel Waldfogel. We are solely responsible for
any remaining errors.
The University of Texas at Austin, Department of Economics, 2225 Speedway Stop 3100, Austin, Texas
78712-0301; Centre for Competition Policy/UEA; and CEPR, London, UK. Phone: 512-232-1718. Fax:
512-471-3510. E–mail: [email protected]; http://www.eugeniomiravete.com
Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6372; and NBER. E–mail:
[email protected]; https://bepp.wharton.upenn.edu/profile/712/
University of Notre Dame, Department of Economics, Notre Dame, IN 46556. E–mail: [email protected];
http://www.nd.edu/ jthurk/
1 Introduction
Washington state’s privatization of alcohol in June 2012 reinvigorated debate about how best
to regulate the sales of alcoholic beverages – a debate Americans have been having since the
early 20th Century. Further, there is no uniform approach across states as many utilize ad
valorem “sin taxes” to increase retail alcohol prices, while others regulate wholesale or retail
sales of some or all alcoholic beverages via public monopoly. These alcohol “control states”
face an interesting dilemma as alcohol sales generate significant tax revenue while consumption potentially leads to a host of negative health effects.1 Despite the large number of
alcohol control states (17), there exists little evidence of the implications of public monopoly
in this industry, either in aggregate or across consumer demographics.2 Our objective is to
fill this void.
We do so by estimating a structural model of consumer demand using detailed price
and quantity data for 2005 across all retail liquor stores in the alcohol control state of
Pennsylvania – a state which monopolizes both the wholesale and retail markets via the
Pennsylvania Liquor Control Board (PLCB ). Established in 1933 to, in the words of then
Governor Gifford Pinchot, “discourage the purchase of alcoholic beverages by making it as
inconvenient and expensive as possible,” the PLCB today generates significant tax revenue
for the state’s general fund ($584 million in 2014-2015). The relative weights the Board
places on these potentially conflicting objectives is unclear, however.
There are two ways in which our public monopolist influences consumption. First, it
employs a simple and observable pricing rule which transforms the wholesale prices chosen
by upstream suppliers to retail prices. This enables us to estimate demand without having
to specify the Board’s objective function, which might – in addition to profitability – reflect
distributional value judgments or attempts to control the important safety and health-related
externalities associated with excessive alcohol consumption. Consequently, our estimates
allow us to evaluate how far its stated or implicit goals are from those of a multi-product
1
2
Carpenter and Dobkin (2010) indicate that most studies find evidence that higher alcohol taxes prices deter
at least some crimes (see, e.g., Markowitz, 2005). Similarly, availability of alcohol due to extended opening
hours and proximity to retail outlets is linked to minor, property and violent crimes (Gyimah-Brempong,
2001; Heaton, 2012), with evidence of significantly more pronounced effects in low socioeconomic status
neighborhoods (Teh, 2008).
As of 2016, the list of control states includes: Alabama, Idaho, Iowa, Maine, Maryland (Montgomery,
Somerset, Wicomico counties), Michigan, Mississippi, Montana, New Hampshire, North Carolina, Ohio,
Oregon, Pennsylvania, Utah, Vermont, Virginia, West Virginia, and Wyoming.
–1–
profit maximizing monopolist, as we can compare the current profits with the ones that
would have been obtained under profit maximization.
We find that PLCB policy does indeed discourage aggregate alcohol consumption
by over-pricing spirits, our object if interest, by $1.65 per bottle, on average, in order to
decrease aggregate consumption.3 The net result is that the Board left $21 million (12.1%
of our sample) of potential tax revenues on the table in 2005. This is equivalent to “paying”
$2.60 in foregone tax revenue for each liter of ethanol not consumed – a significant trade-off.
While these aggregate results are important, perhaps our most novel contribution
pertains to the political economy of public monopoly. We show the PLCB ’s current policy
places particular emphasis on reducing alcohol consumption of residents who are low income,
poorly educated, and/ or white by distorting retail prices across all consumers. The key
feature here involves the simple uniform pricing rule employed by the PLCB in which retail
price for a particular good (e.g., 1.75 liter Captain Morgan Spice Rum) is mandated to
not vary across geographic markets at a point in time. As demand is correlated with
demographics and varies significantly across the state – an aspect which our rich dataset
allows us to show and account for – this one-size-fits-all policy generates important price
distortions which vary systematically across consumer types, leading the PLCB to effectively
tax one group of consumers to reduce consumption of another. Such redistribution via
taxation is not exclusive to alcohol distribution, but common to many regulated industries.
In the words of Posner (1971):
“. . . one the functions of regulation is to perform distributive and allocative chores
usually associated with the taxing or financial branch of government. (. . . )
Uniform rates, based on averaging together the costs of services whose cost
characteristics are in fact very different, are conspicuous features of regulated
rate structures.”
In oder to protect these consumer groups the uniform pricing policy acts like a
progressive tax, increasing the retail price of products preferred by high income consumers
(12.7%) more than for low income consumers (11.0%). We conclude that current policy
implicitly targets particular consumers but its inability to tax these groups directly leads to
price distortions that impact other consumers as well.
3
We focus on spirits due to its significance both in terms of tax revenue and ethanol volume therefore likely
posing a greater health concern than beer or wine.
–2–
Second, our public monopolist aggregates downstream demand across local stores
thereby preventing upstream distillers from engaging in third-degree price discrimination
(3DPD) by choosing wholesale prices which are conditional on local demand. We show that
allowing upstream firms to 3DPD has little aggregate effect to either consumption or tax
revenue but generates significant variation across different consumer types. In particular,
downstream monopolization generates higher retail prices for low income, poorly educated,
and minority residents reducing ethanol consumption among these groups 2.5 to 3.5 percent.
Conversely, it suppresses retail prices for hgh income and well educated residents leading
to greater consumption among these citizens. Downstream monopoly, therefore, appears to
help the regulator discourage alcohol consumption among these consumers but at a cost of
encouraging consumption for other demographic groups.
We conclude our analysis by assessing the implications of privatization where the
ad valorem alcohol tax chosen by the state keeps aggregate tax revenues fixed, i.e., the
approach attempted by Washington. Under such a policy, average retail price falls about one
percent and aggregate ethanol consumption increases 8.99 percent. Further, privatization
leads to greater consumption among low income, poorly educated consumers as well as
non-minorities – all groups which the PLCB appears interested in protecting. We therefore
conclude that public monopoly of alcohol enables the state to discourage alcohol consumption
both in aggregate and for particular consumer groups which the PLCB appears to target for
protection.
The paper is organized as follows. Section 2 presents our data, documents heterogeneous consumption patterns across demographic goods and discusses the rules currently governing PLCB ’s pricing and its interaction with the upstream market firms, the
distillers. Section 3 presents an equilibrium discrete choice model of demand of horizontally
differentiated spirits that incorporates the features of the current pricing regulations while
accounting for competition in the upstream distillery market. In Section 4 we discuss the
estimation, paying particular attention to the estimation procedure and how it encompasses
the institutional features of the regulated market for spirits in Pennsylvania. We also explain
in detail the sources of identification, how we deal with the potential endogeneity of prices
using retail price information from other control states, and how we use the estimated model
to infer upstream market power among the distillers. In Sections 5 and 6 we address the
research objective of this paper by using the estimated model to quantify and decompose
–3–
the implications of public monopoly both in aggregate and across different consumer types.
Section 7 concludes.
2 PLCB Pricing and Sales Data
In this section we describe our data and institutional details that inform our theoretical
modeling and econometric specification. We first describe the data we obtained from the
PLCB and other sources on the sales, prices, and characteristics of product sold by the
PLCB . We then summarize Pennsylvania’s current pricing regulations of alcoholic beverages.
They determine the link between wholesale and retail prices of spirits, where the PLCB acts
as both the single wholesale buyer and the single retail distributor. We also document
how upstream firms’ pricing is constrained by rules regarding the frequency and duration
of temporary wholesale price adjustments. The fact that distillers need to decide far in
advance when to put their products on sale temporarily significantly reduces the endogeneity
concerns common in the estimation of models of demand for differentiated products. We
then document the broad set of firms competing in the upstream distillery market. Finally,
we document the heterogeneity of consumer preferences for different types of spirits, a key
source of identification in our empirical strategy and ultimately the driving force behind the
redistribution implied by PLCB policy.
2.1 Data: Quantities Sold, Prices, and Characteristics of Spirits
We obtained store-level panel data from the PLCB under the Pennsylvania Right-to-Know
Law. The data contain daily information on quantities sold and gross receipts at the UPC and
store level for all spirits and wines carried by the PLCB during 2005. In addition, we received
information on the wholesale cost of each product. These wholesale prices are constant across
stores, but vary over time according to well defined pricing periods. We geocode the stores’
street addresses to assign them to a geographic location, which we link to data on population
and demographic characteristics for nearby consumers based on information from the 2000
Census. Because stores open and close during the year, the characteristics of stores’ ambient
consumers also change over time.4
4
Appendix A describes how we assign different Census block groups to each store and how we deal with
store openings and closings. We also document that the large majority of spirits are sold at every store;
this alleviates concerns about assortment differences between stores in estimation.
–4–
The PLCB acts as a monopolist in the wholesale and retail distribution of wine
and spirits, directly operating a system of 624 state-run retail stores (as of January 2005)
spread across the state.5
Each store carries a multitude of products among which we
chose to focus on sales of popular 375 ml, 750 ml, and 1.75 L bottles of products in the
spirits category, representing 67.6% of total sales by volume and 74.5% of total sales by
revenue.6 The advantage of focusing on spirits is two-fold. First, alcohol in general has a
long history of regulation in the United States with the intent of limiting adverse health
and safety externalities. Spirits, being much higher in alcohol content than beer or wine,
are consequently of first-order concern from a public health perspective. Second, spirits
constitute a well-defined and mature product category that can be described by few, easily
measurable product characteristics, including the type of spirit, the alcohol content, whether
or not a fruit or other flavor is added, and whether or not the product is imported.7 Table 1
reports the product characteristics included in our sample.
We consider seven types of spirits: brandy, cordials, gin, rum, tequila, vodka, and
whiskey. For products within these categories, the average proof is 75.46; 46.52% of product
sales are for imported products; and 16.8% of products contain flavor add-ins.8 Table 1
summarizes these product characteristics further by type of spirit for the 377 products
included in our sample. Within spirits, vodkas and whiskeys have significantly larger market
shares (32.78% and 22.44%, respectively), than rum (16.99%), cordials (13.31%), brandy
(6.94%), or tequila (1.64%). The differences in product variety within each category mirror
the differences in market shares, with only approximately one half as many brandy and
gin varieties as vodka and whiskey varieties. Flavored products are primarily cordials and
brandies as well as some rums and vodkas. We also see variation in domestic versus imported
5
6
7
8
See Seim and Waldfogel (2013, §2) for a detailed account of the welfare losses induced by the very limited
entry allowed in the wine and spirit segment of the Pennsylvania market. Pennsylvania also has a private
system for the sale of beer, allowing the controlled entry of private retailers.
Our sample exhibits a long right tail common to consumer goods with many products being available to
consumers but being purchased rarely. We further drop rare product sizes (e.g., 50 ml, 100 ml, 1 L), which
account for only 18.1% of bottles sold and 7.6% of revenue. In total, these two restrictions allow us to
drop a total of 1,313 products from our sample.
This contrasts favorably with wines whose quality determinants are mostly unobserved, with a large number
of products with limited life cycles. This leads to tiny, highly volatile market shares of wines with frequent
entry and exit of products of different vintages.
In 16th century England, if a pellet of gunpowder soaked in a spirit could still burn determined whether
the spirit was “proof” and thus taxed at a higher rate. Only if the alcohol by volume in rum exceeds
57.15% will gunpowder ignite. To simplify, since 1848 in the U.S., a 100 proof corresponds to a spirit with
50% alcohol by volume content. See Jensen (2004).
–5–
Table 1: Product Characteristics by Spirit Type
Products Price
Share
% Flavored
% Imported
Proof
30
79
29
51
6
84
98
16.13
15.37
15.47
13.72
19.83
16.62
17.34
6.94
13.31
5.91
16.99
1.64
32.78
22.44
30.00
31.65
3.45
21.57
0.00
30.95
0.00
30.00
49.37
27.59
17.65
100.00
42.86
58.16
75.87
57.28
83.65
69.94
80.00
80.14
81.32
By Price and Size:
expensive
cheap
375 ml
750 ml
1.75 L
182
195
58
211
108
21.16
11.33
9.47
14.76
22.21
46.56
53.44
15.22
50.38
34.40
14.84
23.08
13.79
26.07
8.33
64.84
23.59
50.00
44.08
38.89
77.05
71.55
74.41
72.27
77.88
all products
377 16.35
100.00
16.80
46.52
75.46
By Spirit Type:
brandy
cordials
gin
rum
tequila
vodka
whiskey
Notes: “Price” is the simple average price in 2005. “Share” is based on number of
bottles.
varieties across spirit types as 100% of tequilas in our sample are imported; imported products comprise less than than half of the products in the other spirit types with the exception
of the whiskey segment where 58.16% of products are imported. We complement these
product characteristics obtained from the PLCB with data on spirit product quality from
Proof66.com, a spirits ratings aggregator. The reported product score is largely informative
within, but not across, spirit types.
We denote spirits as expensive when their simple averaged price in 2005 exceeds the
mean price of other spirits of the same type and bottle size. Table 1 shows that expensive
spirits are purchased nearly as often as cheaper varieties, but are less likely to be flavored or
domestically produced and have higher proof. The 750 ml bottle is the most popular size of
product in terms of unit sales and product variety, accounting for 50.38% of unit sales and
56% of available spirits products, closely followed by the 1.75 L bottle with a share of 34.40%
of unit sales and 28.7% of available spirit products. The smallest bottles we consider, those
in the 375 ml format, account for 15.22% of units sold and 15.38% of spirit varieties.9
McManus (2007) addresses the case of second-degree price discrimination along the
product size dimension. Given the large number of products in our sample, rather than
characterizing hundreds of distributions of consumer preferences across bottle sizes (one for
9
Appendix B explores further the availability of products, pricing, and share of revenues by bottle size and
type of spirit.
–6–
each spirit), in the absence of individual purchase information we opted for treating bottles of
different sizes of the same spirit as different products with identical observable characteristics
other than size and focus on horizontal differentiation between products.
2.2 The Mechanics of the Current Pricing Regulation
The Pennsylvania State Legislature exerts regulatory control over several aspects of the daily
operations of the stores. Most notably, as per the Pennsylvania Liquor Code (47 P.S. §1-101
et seq.) and the Pennsylvania Code Title 40, the legislature imposes a uniform markup rule
upon the prices that the PLCB charges both across products and across stores. Prices of
wines and spirits are thus identical across the state and are determined based on a common
pricing rule known to all consumers and upstream manufacturers.
This pricing rule has been modified only infrequently over the years. From 1937 until
1980, the retail price for all products was based on a 55% markup over wholesale cost for
all gins and whiskeys and 60% markup for other spirits. In 1980, the markup was reduced
to 25% for all products, and a per-unit handling fee, the Logistics, Transportation, and
Merchandise Factor (LTMF ), of $0.81 was introduced. As a result, the average retail price
on expensive products fell, while the price of inexpensive wines and spirits increased. After
a short legal battle with bars and restaurants, the PLCB raised the unit fee to $0.85 in 1982.
The agency instituted the current 30% markup in 1993 when it also modified the unit
fee to vary by bottle size to better reflect transportation costs from the PLCBś centralized
warehouses to the retail stores. The LTMF unit fee for the 375 ml, 750 ml, and 1.75 L
bottles in our sample amounts to $1.05, $1.20, and $1.55, respectively. In the Appendix
(Table B.3) we document that the unit fees, though minor (≈ 25% of the total markup),
play a greater role in influencing the retail price of low price products (i.e., cheap and 375
ml products). Finally, consumers also pay an 18% ad valorem tax, the “Johnstown Flood
Tax,” on all liquor purchases.10
10
The original 10% tax was instituted in 1936 to provide $41 million for the rebuilding of the flood-ravaged
town of Johnstown. Despite reaching the funding goal after the initial six years, the tax was never repealed,
but instead rose twice to 15% in 1963 and to 18% in 1968.
–7–
Putting all of these elements together reveals the simple uniform pricing rule employed
by the PLCB where the retail price p of a given product with wholesale price, pw , is:11
p = [pw × 1.30 + LTMF ] × 1.18 .
(1)
The PLCB has a limited ability to depart from this uniform percent markup rule. It
operates seven outlet stores close to the state’s borders, in an effort to address the border bleed
of consumers who illegally import lower-priced products into Pennsylvania from neighboring
states. While these stores offer wines and spirits at discounted prices, the PLCB remains
within the uniform markup policy by selling products in the outlet stores not found in regular
stores, for example multi-packs or unusual bottle sizes for a particular product. Controlling
for these stores has little qualitative or quantitative effects on our results (related robustness
checks are reported in Appendix C).
Because of the legislated pricing formula, any retail price changes must originate in
wholesale pricing decisions of the PLCB ’s suppliers – the distillers. The PLCB purchases
directly from distillers, who set wholesale prices. A new product’s wholesale price remains
fixed for one year after its introduction. For the mature products we consider, distillers can
modify the wholesale price they charge the PLCB at set intervals and any change in the
wholesale price results in a change to the retail price passed on to consumers. This is a useful
aspect of our data set that allows us to easily aggregate sales data across days and stores.
The PLCB places some limitations on how often distillers can change the wholesale
price for mature products. Temporary wholesale price changes, typically price reductions or
sales, amount to 90% of price changes in 2005 and last for four or five weeks, beginning on
the last Monday of each month. Distillers can temporarily adjust their wholesale prices up
to four times a year, or once per quarter, but need to submit such proposed price changes
to the PLCB at least five months before the start of the promotion. A product can thus go
on sale for one month, but not for two in a row.
Permanent price increases take place at the beginning of one of the PLCB ’s four-week
long reporting periods for accounting purposes, a slightly different periodicity from the sale
periods. Price increases are instituted at the beginning of the quarter’s first full reporting
period, with some discretion on the part of the PLCB as to the choice of actual reporting
11
An additional 6% Pennsylvania sales tax is then applied to the posted price to generate the final price
paid by the consumer.
–8–
period. There is a time lag, however: distillers have to submit the request for a price
increase by the start of the previous quarter. Permanent wholesale price decreases may be
submitted at any time and take effect immediately. We discuss the periodicity of the price
series further in Appendix A. The pricing periods we use in our analysis below follows the
periodicity of sales, resulting in 15 periods in 2005. Note that the delay between the request
and effectiveness of either permanent or temporary price adjustments limit the ability of the
distillers to respond to temporary demand shocks – an issue we revisit when discussing price
endogeneity concerns in Section 4.
Table 2: Percent of Products Placed on Sale Over the Year
Spring
Summer
Fall
Winter
Holiday
Year
Times
43.33
34.18
34.48
39.22
50.00
51.19
42.86
40.00
30.38
41.38
47.06
50.00
48.81
44.90
40.00
36.71
41.38
45.10
33.33
50.00
53.06
36.67
32.91
31.03
31.37
66.67
40.48
52.04
40.00
36.71
41.38
41.18
33.33
45.24
48.98
60.00
55.70
62.07
60.78
83.33
72.62
72.45
3.29
2.66
3.06
3.21
2.60
2.98
3.09
cheap
expensive
375 ml
750 ml
1.75 L
37.95
46.15
17.24
45.50
48.15
34.87
50.55
17.24
44.55
51.85
40.51
51.10
17.24
47.87
56.48
36.41
43.96
8.62
44.08
49.07
34.36
52.20
5.17
46.92
55.56
57.95
74.18
29.31
72.51
72.22
2.25
2.87
3.41
2.97
3.04
All Products
41.91
42.44
45.62
40.05
42.97
65.78
3.00
By Spirit Type:
brandy
cordials
gin
rum
tequila
vodka
whiskey
By Price and Size:
Notes: “Cheap” (“Expensive”) products are those products whose mean price is below (above) the
mean price of other spirits in the same spirit type and bottle size. The “Holiday” season is defined
as the two pricing periods that encompass Thanksgiving through the end of the year. Statistics
reflect the percent of products with a temporary price reduction during the corresponding season
except for “Times,” which denotes the average number of times that spirits in each category are
on sale during the year.
From Table 2 we see that distillers temporarily change a product’s price three times
a year on average. Sales are not rare events as 65.78% of spirits are on sale at least once
in 2005. This is true across spirit types, with distillers placing tequilas, vodkas, expensive
varieties, and larger bottles on sale more frequently than the rest. In Table 2 we further
document the seasonal pattern of sales across spirit types by tabulating the average share
–9–
of sale products in a given spirit category and quarter. Spirits are more likely to go on sale
during the Fall and less likely during the Winter. Over the holiday period (defined as the
two pricing periods that overlap with Thanksgiving through the end of the year), 42.97% of
spirits, ranging from 33.33% of rum to 55.56% of 1.75 L bottles, are placed on sale at least
once. The monthly sales activity, as well as variation in the amount of the price reduction,
are our primary source of price variation that we exploit below in the estimation of our
demand model.
2.3 The Upstream Distillers
In Table 3, we present a snapshot of the upstream distillery market which is composed of 35
firms exhibiting a broad array of product portfolios. The single market leader, Diageo,
accounts for 20.94% of total ml unit sales and 24.89% of all revenue while a large set
small producers account for 45.22% and 44.49% of total industry quantity (ml) and revenue,
respectively. Eighteen of these firms operate product portfolios of less than 5 products and
8 are single product firms.
Table 3: The Upstream Market
Share of Spirit Market
Firm
Diageo
Bacardi
Beam
Jacquin
Sazerac
Other Firms (30)
Top Selling Product
Products
By Revenue
By Quantity
Name
Type
83
28
37
32
18
179
23.02
9.14
9.09
9.05
7.63
42.07
25.43
10.94
8.23
5.67
4.47
45.26
Captain Morgan
Bacardi Light Dry
Windsor Canadian
Jacquin’s Vodka
Rum
Rum
Whiskey
Vodka
SKYY (Campari)
Vodka
Notes: Upstream distillers sorted in descending order according to revenue share. Quantity share
based on volume (ml).
The large firms such as Diageo and Bacardi operate extensive product portfolios that
extend into all of the spirit types and bottle sizes. However, even among this group there
is substantial heterogeneity across firms in their product offerings. For example, Diageo has
a relatively balanced portfolio where rums, vodkas, and whiskeys generate 21.7.6%, 31.7%,
and 22.5% of its total revenue, respectively. In contrast, 59.1% of Bacardi’s revenue comes
from rums compared to just 4.3% of revenue for Beam. As we are interested in exploring
the consequences of alternative PLCB policies and upstream firms are likely to adjust their
pricing in response to such changes, our objective then is to allow for rich substitution
– 10 –
patterns in the demand estimation that can accurately capture the extent of market power
in our model of supplier behavior.
2.4 Evidence of Heterogeneity of Preferences for Liquor
An advantage of our setting is the rich demographic heterogeneity across Pennsylvania that
allows us to observe consumer preferences across a wide range of demographic profiles.
The legislated single price across the state means furthermore that observed differences in
consumption are not due to endogenous price differences across localities to exploit preference
heterogeneity, which would otherwise need to be accounted for in assessing the degree to
which different types of consumer favor different spirits.
Figure 1 illustrates some interesting systematic patterns between consumption and
demographics. First, markets with a greater percentage of minorities (i.e., residents who
identify themselves as “non-white”) tend to consume vodka, and brandy but not whiskey.
Areas where the population has more residents with college experience, however, tend to
consume more vodka and less rum and brandy. As we look at areas with a larger share of high
income residents (i.e., a greater percentage of households with income greater than $50,000
in annual income), purchases of expensive spirits increase suggesting, not surprisingly, that
high income consumers are more willing to buy expensive, high-quality spirits. We also
see high income consumers more likely to purchase vodkas and whiskeys but less likely to
consume rums and brandies. Finally, young consumers (i.e., those in their 20s) are more
likely to purchase flavored spirits though the overall share remains small.
– 11 –
Figure 1: Consumption and Demographics
Income
100
18.8
Income
23.0
100
90
30.0
80
37.8
70
60
50
18.1
14.2
40
7.6
30
20
11.9
6.1
4.0
14.9
13.6
70
60
58.8
50
46.7
40
30
20
10
0
10
Low
Whiskey
Gin
0
High
Vodka
Brandy
Low
Expensive
Cheap
(b) Income and Price
Minority
28.3
Income
16.7
90
100
Market Share (%)
Market Share (%)
17.3
50
17.6
40
20
8.1
4.1
4.6
16.8
12.3
70
50.3
50.2
60
50
40
30
20
21.6
13.1
10
0
12.1
10
Low
Whiskey
Gin
0
High
Vodka
Brandy
Low
Rum
Other
Young
20.1
22.2
27.0
37.6
100
90
Market Share (%)
Market Share (%)
89.9
8.9
10.1
80
60
19.4
50
14.5
6.9
70
60
50
40
30
11.3
6.6
30
20
15.3
5.0
14.1
20
10
0
91.1
90
70
40
750 ml
(d) Income and Bottle Size
College
80
High
375 ml
1.75 ltr
(c) Minority and Spirit Type
100
37.6
80
28.5
60
30
28.1
90
32.4
80
70
High
Rum
Other
(a) Income and Spirit Type
100
53.3
90
Market Share (%)
Market Share (%)
80
41.2
10
Low
Whiskey
Gin
0
High
Vodka
Brandy
Rum
Other
Low
High
Unflavored
Flavored
(e) Level of Education and Spirit Type
(f) Age and Flavored
Notes: Markets are split into quintiles according to the demographic considered. “Minority” is based on
the percent of the market which identifies as “non-white.” “‘Education” is defined as the percent of the
population with at least some college experience. “Income” is the percent of the population with household
income greater than $50,000. “Age” is defined as the percent of the population in the 21 to 29 years old age
group. “High” refers to markets in the top 20% while “Low” refers to markets in the bottom 20% for the
corresponding demographic trait. Stores located in the Philadelphia or Pittsburgh MSAs are referred to as
“Urban” and remaining stores are “Rural.”
– 12 –
In Figure 2 we break-down total ethanol consumption and tax revenue (i.e., the
PLCB ’s dual mandate) across consumers groups. The most striking feature is that while
stores located in the Philadelphia and Pittsburgh MSAs (“Urban”) contribute the majority
of tax revenues (i.e., PLCB profit) and consumption but stores outside of these areas are
also significant indicating that markets outside of these metropolitan areas are quantitatively important. We also observe higher consumption and PLCB profits in markets with
high concentrations of wealthy, well-educated, young, and non-white consumers, though no
particular segment of the population or state appears to dominate.12
Figure 2: Demographics and the PLCB ’s Objectives
High / Urban
Low / Rural
High / Urban
60
Low / Rural
70
50
Ethanol Share (%)
44.6
40
30
27.2
25.0
24.0
22.1
19.3
20
18.0
16.3
13.1
Share of Total Tax Revenue (%)
55.4
10
0
60
58.8
50
40
41.2
30
28.0
20
17.8
Income
Minority
College
0
Young
(a) Ethanol Consumption
17.6
14.5
10.6
10
Location
26.0
25.4
23.3
Location
Income
Minority
College
Young
(b) Tax Revenue
Notes: Markets are split into quintiles according to the demographic considered. “Minority” is based on
the percent of the market which identifies as “non-white.” “‘Education” is defined as the percent of the
population with at least some college experience. “Income” is the percent of the population with household
income greater than $50,000. “Age” is defined as the percent of the population in the 21 to 29 years old age
group. “High” refers to markets in the top 20% while “Low” refers to markets in the bottom 20% for the
corresponding demographic trait. Stores located in the Philadelphia or Pittsburgh MSAs are referred to as
“Urban” and remaining stores are “Rural.”
From Figures 1 and 2 we conclude that the PLCB faces a difficult problem as it
regulates a large number of differentiated products where success requires understanding
the heterogenous responses of not only different consumers types but also a large number
of upstream firms with potentially different degrees of market power. Our objective in the
remaining sections is to blend theory and data to identify the impacts of PLCB policy on
both consumers and upstream firms.
12
As “urban” areas tend to have higher concentrations of both minorities as well as wealthy, well-educated,
and young consumers; the similarity between demographics and location is not surprising.
– 13 –
3 Model
In this section we describe a static model of oligopoly price competition with differentiated
goods. We assume that each period upstream spirit manufacturers simultaneously choose
wholesale prices {pw } to maximize profits. The downstream firm, the PLCB , takes these
prices as given and generates the final retail price facing consumers by applying a 30%
markup, a per-unit handling fee that varies by bottle size, and an 18% liquor tax. Finally,
consumers in each market choose the product that maximizes their utility. As is common in
this class of models, we solve the model backwards – first presenting downstream consumer
demand and then progressing to the profit-maximization problem of the upstream spirit
manufacturers.
3.1 Downstream Market - A Discrete Choice Model of Demand for Spirits
In modeling demand for spirits as a function of product characteristics and prices, we follow
the large literature on discrete-choice demand system estimation using aggregate market
share data, e.g., Berry (1994), Berry, Levinsohn and Pakes (1995) (BLP), and Nevo (2001).
This facilitates the estimation of own and cross-price elasticities for a large-dimensional set
of products, in our case a maximum of 369. Importantly, by mapping the distribution of consumer demographics into preferences, the model enables us to estimate realistic substitution
patterns between products.
In pricing period t, consumer i in market l obtains the following indirect utility from
consuming a bottle of spirit j ∈ Jlt :
uijlt = xj βi∗ + αi∗ pjt + [ht q3t ]γ + ξjlt + ijlt ,
where i = 1, . . . , Mlt ;
j = 1, . . . , Jlt ;
l = 1, . . . , L;
(2)
t = 1, . . . , T .
The n × 1 vector of observed product characteristics xj is identical in all markets l
where product j is available and fixed over time, though the availability of different spirits
changes over time due to product introductions or removals or due to the seasonal availability
of particular products. We also include a holiday dummy variable ht that indicates whether
period t coincides with the end-of-year holiday season from Thanksgiving to the New Year
and a summer dummy variable q3t for the July, August, and September periods. The price
– 14 –
of product j at time t is denoted pjt where, in accordance with the PLCB ’s pricing mandate,
the retail price does not vary across markets l within period t. We further allow utility to vary
across products, markets, and time via the time and location-specific product valuations ξjlt ,
which are common knowledge to consumers, upstream firms, and the PLCB but unobserved
by the econometrician. Lastly, ijlt denotes the unobserved preferences by consumer i for
product j in market l and period t , which we assume to be distributed Type-I extreme value
across all available products Jlt .
We assume that consumer i in market l is characterized by a d-vector of observed
demographic attributes (e.g., age, education, income, race), Dil , and one unobserved demographic attribute, νil . To allow for individual heterogeneity in purchase behavior, we
model the distribution of consumer preferences over characteristics and prices as multivariate
normal with a mean that shifts with these consumer attributes:
αi∗
βi∗
!
=
α
β
!
+ ΠDil + Σνil ,
νil ∼ N (0, In+1 ) .
(3)
Π is a (n+1)×d matrix of coefficients that measures the effect of observable individual
attributes on the consumer valuation of spirit characteristics, while Σ measures the covariance
in unobserved preferences across characteristics. We restrict Σjk = 0 ∀k 6= j, and estimate
only the variance in unobserved preferences for characteristics. Introducing unobserved
preferences for a given characteristic j allows cross-price elasticities among products with
similar characteristics (e.g., flavored) to be higher than in a simple Logit model, thereby
relaxing the restrictive substitution patterns generated by the Independence of Irrelevant
Alternatives (IIA) property of the multinomial logit model. Similarly, the contribution of
demographic and product characteristic interactions (Π) allows cross-price elasticities to
vary differentially in markets with observed differences in demographics (e.g., expensive
vodkas can have higher cross-price elasticities in markets where there are more high-income
consumers). For example, if we estimated that consumers have economically significant
unobserved preferences for flavored beverages, introducing a flavored vodka into a store
would impact the market shares of other flavored spirits more than unflavored spirits. If
young consumers had a positive valuation for flavored spirits (ΠF lav > 0), this effect would
be larger in markets with more young people.
– 15 –
We define the potential market, Mlt , as all off-premise (i.e., not consumed in a restaurant or bar) consumption of alcoholic beverage, including spirits (our object of interest),
beer, and wine. Because of the aggregate, store-level nature of our data, we convert all spirit
quantities into 750-ml equivalent bottles and make the common assumption that during a
particular time period, each consumer selects either one bottle of the Jlt spirits available in
her market, or opts to purchase beer or wine, again denominated in 750 ml bottle-equivalents.
We denote this outside option by j = 0 with zero mean utility.13
The average drinking-age Pennsylvania resident consumed 120.3 liters of alcoholic
beverages in 2005 (Haughwout, Lavallee and Castle, 2015), 75.3% of which was consumed
off-premise, resulting in per-capita off-premise volume consumed of 90.3 liters. The annual
potential market for location l is then the number of drinking-age residents scaled by offpremise consumption converted into 750 ml bottle-equivalents, which we allocate across
pricing periods according to the period’s length to give us Mlt . Note that this definition
accounts for the total volume of alcoholic beverages (in 750 ml bottle equivalents) but not
for the different ethanol contents of beer (4.5% on average), wine (12.9% on average), and
spirits (37.7% on average in our sample).
The set of individual-specific characteristics leading to the optimal choice of spirit j
is:
Ajt (x· , p·t , ξ·t ; θ) = {(Dil , νil , ·lt ) |uijlt ≥ uiklt
∀k = 0, 1, . . . , Jlt } ,
(4)
where we summarize all model parameters by θ. We follow the literature in decomposing the
deterministic portion of the consumer’s indirect utility into a common part shared across
consumers, δjlt , and an idiosyncratic component, µijlt . These mean utilities of choosing
product j and the idiosyncratic deviations around them are given by:
δjlt = xj β + αpjt + [ht q3t ]γ + ξjlt ,
13
(5a)
Nevo (2000, p.401) discusses limitations of the present discrete choice approach when instead, individuals
purchase several products or multiple bottles of the same product at the same time. If such consumer
behavior were important, Hendel (1999) and Hendel and Nevo (2006) show that assuming single-unit
purchases could understate price elasticities in the case of assortment decisions, but overstate own-price
elasticities in the case of stockpiling. In our data, we find no evidence of stockpiling on aggregate or
within markets of similar income. For example, regressing market shares on lag or lead product prices
yields economically and statistically insignificant coefficients. Similarly, Seim and Waldfogel (2013) present
suggestive evidence that the PLCB ’s demand does not respond to price declines more strongly than average
in areas with higher travel costs to the store where customers have a higher incentive to buy larger quantities
or assortments.
– 16 –
µijlt =
xj pjt (ΠDil + Σνil ) .
(5b)
In estimating the model, we take advantage of the additive specification of normallydistributed deviations from mean utility and extreme-value random shocks to integrate
over the distribution of it giving rise to Ajt analytically. The probability that consumer
i purchases product j in market l in period t is then given by:
sijlt =
exp (δjlt + µijlt )
P
.
exp(δklt + µiklt )
1+
(6)
k∈Jlt
Deriving product j’s aggregate market share in each location requires integrating over the
distributions of observable and unobservable consumer attributes Dil and νil , which we
denote by PD (Di ) and Pν (νi ), respectively. Thus, the model predicts a market share for
product j in market l at time t of:
Z Z
sijlt dPD (Di )dPν (νi ) .
sjlt =
νl
(7)
Dl
We evaluate the integral in Equation (7) using simulation techniques. For each market
l we simulated the consumption choices of 100 randomly drawn heterogeneous consumers
who vary in their demographics and are endowed with a level of income. We constructed the
sample for each market using census data on its composition based on race, age, educational
attainment, and income, incorporating correlations between demographic attributes where
possible. We used categorical data on income by minority status to fit generalized beta distributions that convert income into a continuous variable. We similarly obtained information
on educational attainment by minority status to derive the share of the minority and white
population with at least some college education. Lastly, we obtained the unconditional share
of each market l’s population between the ages of 21 and 29. Conditional on a realization
of a consumer’s minority status, we take random draws from the corresponding income and
educational attainment distributions and assign the consumer’s age status based on the
market area’s unconditional mean. See Appendix A. Since the ambient population of stores
changes with store openings and closings over the course of the year, the simulated set of
agents changes in each pricing period.
– 17 –
3.2 An Oligopoly Model for Upstream distillers
Although our data includes wholesale prices, they are the outcome of an equilibrium game
in the upstream market under the current PLCB pricing rule. Should the PLCB alter
this rule, these upstream distillers are free to choose different wholesale prices ultimately
leading to different retail prices and different consumer purchase decisions. Since answering
our research question requires evaluating distiller and consumer responses under alternative
policies, it is therefore important that incorporate upstream distiller behavior in the model.
A total of F distillers compete in the upstream market where each firm f ∈ F
produces a subset Jtf of the j = 1, . . . , Jt products. In each period t the upstream firms
simultaneously choose the vector of wholesale prices {pw
jt }j∈J f to maximize period t profit:
t






L
X
X

w
w
(pjt − cjt ) ×
.
max
M
s
(p(p
),
x,
ξ;
θ)
l
jlt


{pw
jt }


l=1
j∈J f

{z
}
|
(8)
statewide demand for
product j in period t
where Ml is the potential market (i.e., total purchases of off-premise spirits, beers, or wines)
in market l and cjt denotes the marginal cost of producing product j in period t. The static
nature of the firms’ pricing decisions allows us to omit the period t subscripts for the sake
P
of clarity, which we do going forward.14 Define as sj (p, x, ξ; θ) = Ll=1 Ml sjl (p, x, ξ; θ) the
state-wide demand for product j. Profit maximization in the upstream market implies that
f
upstream firm f chooses prices pw
j ∀j ∈ J to solve the following set of first-order conditions:
sj (p(pw ), x, ξ; θ) +
X
w
(pw
m − cm )sm (p(p ), x, ξ; θ) ×
m∈J f
The final term
∂sm
∂pw
j
∂sm
= 0.
∂pw
j
(9)
is the change in quantity sold for product m in response to a change in
the wholesale price and, through the pricing rule, the retail price, of product j.
14
Note that the PLCB limits the number of times distillers can temporarily reduce a product’s price. In the
data, the average product goes on sale only 3.0 times (recall Table 2) and 73.5% of products go on sale
three times or less, indicating that this regulation does not constrain upstream pricing for the majority
of products. As a result, we decided not to address any dynamic considerations to the timing of pricing
decisions over the course of the year and chose to employ a computationally simpler static pricing model.
– 18 –
Assuming a pure-strategy Bertrand-Nash equilibrium in wholesale prices, the vector
of profit-maximizing wholesale prices solves:
pw = c + [Ow ∗ ∆w ]−1 × s(p(pw ), x, ξ; θ) ,
|
{z
}
(10)
vector of markups
where Otw denotes the ownership matrix for the upstream firms with element (j, m) equal to
one if goods j and m are in Jf and upstream firm f chooses their prices jointly. We define
∆w as a matrix that captures changes in demand due to changes in wholesale price:

∂s1
∂p1
 .
.
∆w = 
 .
∂sJ
∂p1
...
..
.
∂s1
∂pJ
...
∂sJ
∂pJ

 ∂p
1
dpw
1
 .
.. 
 .
. 
× .
∂pJ
dpw
1
...
..
.
dp1
dpw
J
...
dpJ
dpw
J
0
.. 
d p0
. 
 =∆ ∆ ,
(11)
where ∆d is the matrix of changes in quantity sold in period t due to changes in retail price
with element (r, m) equal to
∂sr
∂pm
and ∆p is the matrix of changes in retail price due to
changes in wholesale price with element (m, j) equal to
dpm
.
dpw
j
The vertical structure is simple in our case. Central to the focus of this paper is
the fact that ∆p is fixed: regulation commits the PLCB to the uniform markup rule from
Equation (1) and limits its ability to respond to changes in wholesale prices chosen by the
upstream firms. Accordingly,
dpj
dpw
j
is simply 1.30 × 1.18, reflecting the 30% uniform markup
and the 18% liquor tax that translate a change in the wholesale price for product j to the
product’s retail price. Under the pricing rule, the retail price for product m does not respond
to a change in the wholesale price for product j:
dpm
dpw
j
= 0 ∀m 6= j. This simplifies the price
p
response matrix ∆ to:


1.534 . . .
0
 .
.. 
..
..
∆p = 
.
. 

,
0
. . . 1.534
(12)
and the demand response matrix ∆w of Equation (11) to:

∂s1
∂p1
 .
.
∆w = 
 .
∂sJ
∂p1


1.534 . . .
0
 .
.. 
.. 
...
 ..
×
. 
. 
 
.
∂sJ
. . . ∂p
0
. . . 1.534
J
...
...
∂s1
∂pJ

– 19 –
(13)
Intuitively, by employing a rigid price regulation the PLCB has committed itself to not
differentially responding to changes in the vector of wholesale prices – effectively transferring
its market power in the downstream market to the upstream manufacturers. At the same
time, it has also eliminated the ability of upstream manufacturers to price discriminate across
different regions. To assess whether these effects have significant implications for equilibrium
wholesale and retail prices, upstream conduct and profits, as well as the effectiveness of
downstream regulation is the goal of the remaining sections.
4 Identification, Estimation, and Demand Model Results
In this section we outline an estimation approach similar to Nevo (2000) but adapted to the
institutional features surrounding the price regulation of spirits in Pennsylvania. We present
a three-step estimation procedure that takes advantage of the fact that distillers set a single
wholesale price that is translated into a single retail price across all local markets. This
allows us to separately identify the contribution to demand of unobserved (Σ) and observed
(Π) demographic taste heterogeneity across the state at a point-in-time and the contribution
of time varying shifters of demand that are common across demographic groups and pin
down the mean utility parameters (α, β). The latter include the mean price response, α,
and we discuss potential sources of price endogeneity, which in our case stem from responses
by distillers to demand shocks rather than from the PLCB ’s pricing behavior.
4.1 Estimating the Random Coefficients and Demographic Interactions
We begin with a description of the first of the three stages of our estimation procedure where
we estimate the contributions of product characteristics Σ and demographic interactions Π
to the deviations from mean utility, µijlt , controlling for location and product by time fixed
effects. We follow the earlier literature in using a generalized method of moments estimator,
GMM , that interacts a structural demand side error ω(Σ, Π) with instruments Z. To define
ω, we decompose the unobserved product valuations, ξjlt , as follows:
ξjlt = ζl1 + ξjt + ∆ξjlt .
– 20 –
(14)
In Equation (14), ζl1 is a location fixed effect that captures systematic variation
across localities in the preference for spirits consumption, relative to beer and wine. This
also accounts for the fact that while per-capita consumption of alcoholic beverages likely
varies across Pennsylvania, such disaggregated data is not available and the potential market
is defined based on the average Pennsylvanian’s consumption. We control for systematic
variation in preferences for a given product over time via ξjt , to reflect the fact that across
the state, a product’s mean demand varies over the course of the year. With this specification,
the mean utility of product j, δjlt in Equation (5a), simplifies to:
2
+ ∆ξjlt ,
δjlt = ζl1 + ζjt
(15)
2
subsumes the effect of product characwhere the product and time specific fixed effect ζjt
teristics, seasonal buying, and price on a product’s mean utility.
Equation (15) highlights an advantage to our setting: since price does not vary across
locations l, we are able to control for its mean contribution to utility via product by time
fixed effects, which we then use in a second stage estimation to isolate α. The remaining
structural error ω represents deviations in unobserved product valuations within a store,
∆ξjlt , from these mean product-time valuations, controlling for the average taste for spirits
in market l.
Given θA = {Σ, Π}, we solve for the structural error ω(θA ) = ∆ξjlt using the following
algorithm.
2
, Sjlt ; θA )
For a given guess at θA , we find the mean-utility levels δjlt (ζl1 , ζjt
that set the predicted market share of each product, sjlt in Equation (7), equal to the
market share observed in the data, Sjlt .15 We follow Somaini and Wolak (2015) and use a
2
within transformation of δ to remove the store and product-period fixed effects ζl1 and ζjt
,
leaving only ω. Define Z + as the within transformation of the instruments matrix; e.g., for
+,k
k
k
instrument k, Zjlt
= Zjlt
− Zjt
− Zlk .
The GMM estimator exploits the fact that at the true value of parameters (Σ? , Π? ),
0
the instruments Z + are orthogonal to the structural errors ω(Σ? , Π? ), i.e., E Z + ω(θ? ) = 0,
so that the GMM estimates solve:
15
We rely on the contraction mapping outlined in Appendix I of BLP . In order to ensure convergence to
consistent stable estimates we follow the advice of Dubé, Fox and Su (2012, §4.2) and set the norm for
mean value contraction equal to 1e-14.
– 21 –
n
o
0 +
+ +0
θ̂A = argmin ω(θA ) Z W Z ω(θA ) ,
(16)
θA
0
where W + is the weighting matrix, representing a consistent estimate of E[Z + ωω 0 Z + ].16
Finding a global solution to a highly nonlinear problem such as this is difficult and any
line, gradient, or simplex search will likely only result in a local solution. To increase the
likelihood of achieving a global minimum, we employed the Knitro Interior/ Direct algorithm
suggested by Dubé et al. (2012) starting from several different initial conditions to ensure
robustness of our results.
Identification of Σ comes from correlation between a product’s market share and its
characteristics relative to other more or less similar products – see Berry and Haile (2014).
We construct two instruments similar to those used in Bresnahan, Stern and Trajtenberg
(1997). First, we employ the number of products in the market that share product j’s
characteristic. For example, to identify a random coefficient on brandies, we use the total
number of competing brandies of the same bottle size in location l as the instrument for
a given brandy. Second, we use spirit product scores from Proof66.com as a measure
of product quality and compute the average distance, measured in squared deviations, of
product j to other products that share its characteristic. Thus, for the above brandy,
this would be the average distance in product score space from other brandies in l. This
instrument provides additional identifying power since it allows for differential effects of
introducing a high-quality brandy into a market with other high quality brandies versus a
market populated by largely low quality brandies.
Similarly, identification of Π is based on correlation between a product’s market share
in a given store market and the demographics of the population served by each store. We
thus interact the above two instruments with the prevalence of a given demographic attribute
in each market. For example, we would identify the differential taste of young households
for the above brandy by interacting our earlier two instruments with the share of young
consumers in each market. In the case of the interaction of income with price, we construct
the first set of instruments based on the product’s price category (cheap vs. expensive) and
interact them with the share of households in the market with income about $50,000. Berry
16
In constructing our optimal weighting matrix, we first assume homoscedastic errors and use W + =
0
[Z + Z + ]−1 to derive initial parameter estimates. Given these estimates, we solve for the structural error
0
ω and construct E[Z + ωω 0 Z + ]−1 as a consistent estimate for W + .
– 22 –
and Haile (2010) point out that these “Waldfogel” instruments (Waldfogel, 2003) are valid
provided there exist no demand spillovers from consumers in other similar markets.
4.2 Estimating Mean Utility Coefficients
In the second of the three stages of the estimation procedure, we decompose the mean utility
implied by the estimated first-stage coefficients θ̂A , δjlt (θ̂A ), into the implied location and
2
2
onto price and the
(θ̂A ). We then project ζjt
product by type fixed effects, ζl1 (θ̂A ) and ζjt
holiday indicator, controlling for product fixed effects ζj :
2
= [ht q3t ]γ + αpjt + ζj + ξjt .
ζjt
(17)
Equation (17) highlights the potential for price endogeneity, to the extent that price
responds to time varying preference variation for a given product that is common across
locations, in the form of, for example, category-specific seasonal variation in consumption.
Since the PLCB ’s pricing is a fixed markup rule over wholesale price irrespective of
local or seasonal demand responses, however, its pricing cannot respond to such unobserved
demand shocks. But the predictable link between wholesale and retail prices opens the
possibility to spirit prices being endogenous not because of the pricing practices of the PLCB ,
but because of the pricing behavior of upstream distillers whose chosen wholesale prices
reflect, through market shares, the unobserved common tastes for product characteristics of
spirits, ξjt . Recall the pricing optimality conditions in Equation (10).
In principle, such endogeneity concerns are mitigated by the fact that, as discussed
above, distillers need to request both temporary and permanent changes to their wholesale
price a number of months before the new price takes effect. Prices thus only respond to
predictable variation in a product’s demand over time. At the same time, none of our product
characteristics vary across time, limiting our ability to flexibly represent such time varying
preference heterogeneity at the level of the product. We therefore use instrumental variables
techniques to estimate the parameters in Equation (17) using the contemporaneous average
price of a given product from liquor control states outside of the Northeast and Mid-Atlantic
regions (Idaho, North Carolina, Oregon, and Wyoming) as an instrument for price denoted
as ZB . Our identifying assumption is that cost shocks are national (since products are
often produced in a single facility) but demand shocks are at most regional, perhaps due to
– 23 –
differences in demographics or climate. For example, whiskey consumption, more so than the
consumption of other spirits, peaks when it is cold outside, typically during the colder fall and
winter months. Whiskey consumption also varies significantly across demographic groups;
for example, African American households consume larger amounts of whiskey than other
racial groups relative to their baseline levels of spirit consumption. Tequila consumption,
on the other hand, is higher during the summer and is more prevalent among Hispanic
households.
These examples suggest that the trends in consumption over the course of the year
depend both on temperature and on the prevalence of different demographic groups. The
idea behind the price instrument we employ – the average price of a product in control states
outside of the Mid-Atlantic and North-East states – is that demand shocks in these states,
by being distant from Pennsylvania, reflect significantly different climates and demographic
profiles that would support the assumption that they are uncorrelated with Pennsylvania’s.
For example, the states whose prices we use as instruments (Michigan, Mississippi, North
Carolina, Oregon, and Utah) all have at least a 50% higher share of Hispanics than Pennsylvania’s low six percent. North Carolina (22%) and Mississippi (37%) both greater share
of African Americans than Pennsylvania (11%) but also have very different climates.
Figure 3: Identifying the Price Coefficient
Shelf inPrice
(Changes
PriceAcross
Across Time
Time)
(Select Products)
28
26
24
22
20
18
16
14
12
10
Jan
Mar
Bacardi Limon - 750 ml
($12.79)
May
Aug
Bacardi Limon - 1.75 ltr
($25.70)
– 24 –
Sep
Dec
Jose Cuervo - 750 ml
($18.33)
Collapsing the second stage parameters into vector θB , this implies the following
parameter estimates:
0
0
θ̂B = (XˆB XˆB )−1 XˆB ζ 2
(18)
where X̂B = ZB (ZB0 ZB )−1 ZB0 XB , with XB = [ht q3t pjt ζj ].
The price coefficient is identified by variation in prices over time, benefiting from the
fact that distillers do not change the wholesale prices pw for all products at the same time.
This introduces variation in relative prices depicted in Figure 3. It presents the price series
for three popular products, illustrating that even similar products such as Bacardi Limon in
two different bottle sizes are on temporary sale at different times of the year, while some,
such as José Cuervo, experience permanent price increases.
In the third and final estimation step, we recover product fixed effects ζj from
Equation (18) and project them onto observable product characteristics xj , resulting in:
θ̂C = (x0 x)−1 x0 ζ
(19)
where mean preferences for these product characteristics are identified by variation in market
shares of spirits of differing characteristics, e.g., proof or spirit type.
4.3 Estimation Results
Table 4 presents the demand estimates of our preferred specification of the mixed logit model.
Given the size of our sample, parameters are very precisely estimated.
The demographic interactions and random coefficients were attained via GMM in
our first estimation step. We allow for rich variation across demographics by interacting
the young, minority, and college educated indicators with spirit type, bottle size, and proof.
The estimates of Π reveal significant differences in tastes for spirits across demographic
groups. We find that demand becomes steeper as consumers become wealthier consistent
with the increased consumption of expensive spirits by “high income” consumers presented in
Figure 1. Young people, minorities, and those with some college education all have positive
valuation for rums and vodkas over brandies (the reference category). These groups do
not always favor the same spirits. Young and college educated people dislike brandy and
favor whiskeys. Minorities have opposite preferences: they favor brandy and like whiskey
– 25 –
Table 4: Mixed-Logit Demand
price
Mean Utility
Random Coeff.
(β)
(σ)
-0.2957B
0.2683B
(0.0071)
summer
0.039B
(0.0071)
375 ml
-1.1187C
(0.3322)
1.75 L
2.3711C
(0.3351)
Income
4.8276
(0.3989)
brandy
Young
Minority
College
-1.4204
(0.4109)
3.5458
(0.1101)
-1.1730
(0.1800)
-4.8757
(1.1563)
2.4980
(0.0681)
-0.5406
(0.0646)
1.6538
(0.1072)
0.1238
(0.0237)
0.3643
(0.0323)
0.0841
(0.0024)
(0.0456)
holiday
Demographic Interactions (Π)
cordials
4.9554C
(0.6348)
gin
5.2618C
(0.7408)
rum
4.6072C
(0.6290)
tequila
6.0564C
(0.8079)
vodka
4.4343C
(0.6047)
0.6942
(0.0585)
0.1984
(0.0258)
1.2914
(0.0320)
whiskey
5.2625C
(0.6349)
0.5176
(0.0722)
-0.3808
(0.0206)
0.2926
(0.0267)
flavored
-11.3964C
(0.3202)
7.4084
(0.7174)
2.6699
(0.2741)
-0.9673
(0.0728)
0.3840
(0.0891)
imported
0.8725C
(0.2890)
proof
0.9494C
(0.9918)
2.3620
(0.1145)
0.7312
(0.0965)
0.2637
(0.0399)
0.9850
(0.0573)
quality
-7.4017C
(2.5029)
constant
-11.3879C
(1.2635)
Notes: Robust standard errors are reported in parentheses. Estimates for random coefficients (Σ)
and demographic interactions (Π) based on GMM estimation based on 1, 407, 340 observations in
4, 679 markets (store-period pairs) and 100 simulated agents in each market (J-statistic of 1,132).
Estimates marked with B are based on the projection of estimated product-period fixed from the
GMM estimation onto corresponding characteristics plus product fixed effects. Estimates marked
with C are based on the projection of estimated product fixed effects onto the remaining observable
product characteristics. Estimates for category-product score interactions not reported.
less than gin. Two additional notable differences among the demographic groups is that
young people are less likely to purchase a 750 ml bottle, relative to a 1.75 L, our reference
category, and strongly prefer flavored beverages. All groups, and in particular the college
educated, favor spirits with higher proof. This characterization of demand for spirits mirrors
well-documented patterns of spirit consumption across demographic groups. In particular,
– 26 –
budget constrained young drinkers favor less expensive products (per ml – see Figure 1), and
“sweeter” (i.e., flavored) spirits that are explicitly marketed to this age group.
As in many mixed-logit models, estimating a large dimensional set of random coefficients Σ is difficult since identification is based on exit and entry of products in the
product set for a market and our product set is mature so we see little exit and entry of
products. Therefore, after some experimentation, we chose to focus on a set of economically
meaningful random coefficients for brandy, whiskey, and flavored spirits.17 The estimated
random coefficients are large and statistically significant, indicating that after controlling for
the large degree of demographic differences in tastes via Π, there still exist commonalities
among products within these categories that influence their substitution patterns.
We can also use the estimated model to assess the validity of simpler model specifications. A natural alternative to the full mixed logit model is the nested logit model with nests
defined based on spirit type and/or bottle size. While simpler to to estimate, the nested logit
places ex ante restrictions on the substitution patterns and across nests (e.g., substitution
from rums to vodkas is the same as vodkas to whiskeys). The difference in the magnitudes
of the random coefficient we estimate and display in Table 4 suggests that these restrictions
do not represent patterns in the data well.
Beyond the model estimates, we also provide descriptive evidence that the simplest
model alternative, the multinomial logit model with demographic interactions, places unnecessarily restrictive assumptions on the cross-price elasticities. We follow Gandhi and
Houde (2016) to test the validity of this model in our context by regressing the ratio of
the log of a product’s market share and the log of the outside share on product-period and
store fixed effects as well as product-demographic interactions and our GMM instruments.
If the multinomial logit were sufficient, the GMM instruments should have no explanatory
power, which we reject using a simple F-test (F-statistic of F = 2, 572 for all instruments;
F = 1, 539 and F = 222 for the subsets related to the random coefficients and demographics,
respectively. These results provide model-independent evidence in support of using a more
flexible model specification.
17
It is not surprising that these types of spirits show substantial heterogeneity in preferences. By their
nature, flavored spirits are heterogeneous as they include a wide variety of flavor add-ins across products
of different spirit types. Similarly, the brandy and whiskey categories both include a wide array of products;
in the case of brandies, this ranges from cognacs to grappa and other fruit-based brandies, while whiskeys
include Bourbons and Scottish whiskey, but also single-malt whiskeys that occupy a different quality tier
valued by connoisseurs, similar to highly aged whiskeys.
– 27 –
We obtain mean utility estimates in the second estimation step by projecting the
product-period fixed effects from step one onto price, seasonal dummies, and brand fixed
effects, which we then in step three project onto product characteristics. Demand increases
during the summer and the holiday season. On average, consumer valuations of gins, cordials,
rums, and brandy are similar, even though the valuation of the latter differs significantly
across demographic groups. Flavored spirits are far less valued on average, continuing to
occupy a niche segment favored by young people. In addition to alcohol content, consumers
are to be willing to pay a premium for imported goods.
As discussed above, we use the contemporaneous average price in distant control states
as an instrument for price in the second step. In Table 5, we consider the sensitivity of our
results to the particular instrumentation strategy. We compare the estimated price coefficient
using alternative two-stage least squares results of the estimated first stage product-period
fixed effects projected onto price, seasonal dummies, and product fixed effects. Relative to
IV1, our preferred specification, the estimated price coefficients are stable across alternative
instruments, and, as expected, entail larger price responses than an uninstrumented OLS
specification.
Table 5: Price Endogeneity
price
OLS
IV1
IV2
IV3
-0.2448
(0.0039)
-0.2957
(0.0456)
-0.3429
(0.0559)
-0.2951
(0.0469)
X
X
X
X
X
X
X
X
X
X
X
X
37.5535
5,648
27.0660
5,648
35.5100
5,648
States Included:
Michigan
Mississippi
North Carolina
Oregon
Utah
F-Statistic
N
5,648
Notes: Specifications include the same covariates as in Table 4. Price instruments
based on the average contemporaneous price among alternative sets of control states
outside the Northeast and Mid-Atlantic regions.
Each estimated price coefficient is significant at the 95% level and the set of IVs
generates significant F-statistics for all specifications. The price coefficient is stable across
specifications and decreases as we employ the instrumental variables to remove endogeneity
in price. This suggests that, as expected, our observable characteristics are unable to fully
– 28 –
capture the seasonal variation in a product’s preferences that distillers base their pricing
decisions on.
Table 6: Price Elasticities by Spirit Type, Price, and Size
Price
Average
SD
By Spirit Type:
- brandy
- cordials
- gin
- rum
- tequila
- vodka
- whiskey
16.13
15.37
15.47
13.72
19.83
16.62
17.34
-4.08
-3.92
-4.02
-3.57
-4.86
-4.17
-4.22
2.35
1.75
1.92
1.21
1.83
1.83
1.83
By Price and Size:
- expensive
- cheap
- 375 ml
- 750 ml
- 1.75 L
21.16
11.33
9.47
14.76
22.21
-5.12
-3.02
-2.51
-3.85
-5.18
1.82
1.04
0.94
1.47
2.02
all products
16.35
-4.04
1.81
“Price” is measured in dollars; “Average” refers to the simple average of product
price elasticities within the relevant category. Appendix D presents the full elasticity
distributions.
Table 6 presents descriptive statistics for product elasticities using the IV1 price
coefficient. At the product-level, we estimate an average own-price elasticity of −4.04, which
is consistent with findings by Conlon and Rao (2015).18 The large set of random coefficients
and demographic interactions also generate sensible cross-price elasticities. For example, an
increase in price for an average whiskey (e.g., Jack Daniels) results in consumers 3.8x more
likely to substitute to another whiskey.19
Our results also exhibit heterogeneity in estimated demand elasticities within spirit
types and bottle sizes. The empirical distribution of estimated price elasticities is most spread
for whiskeys and brandy (two spirit types with significant random coefficients) and most
concentrated for rums. Spirit types are thus further segmented into higher and lower quality
products with different responses to price changes. The bottom of Table 6 corroborates
18
Comparable product-level elasticities for spirits are rarely available in the literature, in part because spirits
are not commonly carried in grocery stores, the most reliable source of scanner data. In analyzing a distilled
spirits merger, Ashenfelter and Hosken (2010), for example, have to rely on a ten-city sample of California
drug stores and do not find consistently negative quantity adjustments in response to post-merger price
increases.
19
We detail similar substitution patterns for different spirit types, bottle sizes, and product characteristics
in the appendix (Table D.1).
– 29 –
this hypothesis as the demand for 375 ml bottles is more inelastic than for the 1.75 L
bottles, with the medium-sized 750 ml bottles in-between. As expected, we also find that
expensive products are estimated to have more elastic demands than the approximately half
as expensive cheap spirits, even though high-income consumers have significantly lower price
elasticities for expensive products than the average consumer – a point illustrated in Table 7.
Markets with high concentrations of educated consumers exhibit product demands which are
similarly more inelastic, while markets with concentrations of minorities and young people
tend to have more elastic product demand.
Table 7: Estimated Product Elasticities Across Demographics
Income
Young
Minority
College
Low
High
Low
High
Low
High
Low
High
brandy
cordials
gin
rum
tequila
vodka
whiskey
-4.41
-4.23
-4.25
-3.87
-5.24
-4.43
-4.47
-3.63
-3.55
-3.75
-3.24
-4.43
-3.83
-3.92
-3.77
-3.74
-3.94
-3.41
-4.64
-3.99
-4.07
-4.39
-4.07
-4.05
-3.70
-5.04
-4.31
-4.36
-3.80
-4.01
-4.25
-3.68
-4.96
-4.22
-4.29
-4.49
-4.07
-3.99
-3.70
-5.09
-4.33
-4.38
-4.11
-4.14
-4.27
-3.80
-5.14
-4.33
-4.42
-3.77
-3.61
-3.75
-3.29
-4.50
-3.88
-3.96
all products
-4.32
-3.71
-3.88
-4.18
-4.11
-4.20
-4.23
-3.76
Notes: Statistics are average product elasticity in the relevant category-demographic pair. “High” refers to
markets in the top 20% while “Low” refers to markets in the bottom 20% for the corresponding demographic
trait.
The model estimates imply an estimated price elasticity of off-premise spirit demand
of −3.32 overall. That is, a one percent increase in the price of all spirits leads to a 3.32%
decrease in the aggregate quantity of off-premise spirits demanded, with some variation in
the aggregate price elasticity by income and educational attainment. A large literature
documents price elasticities for the consumption of alcoholic beverages. In a review, Leung
and Phelps (1993) conclude that the price elasticity of demand for distilled spirits is −1.5,
more inelastic than our demand estimates imply. A challenge in relating our estimates to the
earlier literature is that we exclude on-premise consumption in bars and restaurants, which is
likely less price sensitive than off-premise consumption. Second and perhaps more important,
most earlier studies use aggregate (e.g., state or national) consumption data whereas we have
detailed local data on consumption choices. In Appendix C we show that aggregation in our
data set drives price coefficient (and consequently the estimated elasticity for spirits) towards
zero. Intuitively, most structural models of demand (e.g., multinomial logit, mixed logit)
are inherently non-linear so aggregating the consumption responses of different agents across
– 30 –
markets reduces variation with respect to price, leading to both steeper demand curves and
imprecise estimates.
With robust demand estimates in-hand, we use the upstream firm first-order conditions (Equation 10) to back out reasonable estimates of marginal costs. We find the marginal
cost of expensive products is on average 2.5 times that of inexpensive products; for the subset
of brandies and whiskeys with information on the number of years they were aged, we find
that aged (i.e., it is aged more than four years) brandies and whiskeys have costs of 1.3 and
1.4 times, respectively, that of non-aged products. Imported products are 1.8 times more
costly than non-imported products on average. Further, we find that upstream firms have
significant market power; on average enjoying price-cost margins of 34.3 percent. That is,
for every one dollar of product sold to the PLCB distillers take home 34 cents in income.
5 Does the PLCB Maximize Tax Revenue?
In this section we use our estimated model to test whether PLCB policy is consistent with
profit-maximization comparing our benchmark results to one in which the Board chooses
both single markup and a vector of size-based unit fees to maximize profits (i.e., tax revenue).
Specifically, we model a two-stage Stackelberg game in which the regulator chooses its policy
in the first stage given its knowledge of the distillers’ price response in the second stage,
assuming the product ownership structure observed in the data and consistent with the
estimated marginal costs from Section 4.3.20 In order to account for potentially empirically
important seasonal effects, we solve for the optimal policy for each pricing period and report
yearly average effects. Finally, throughout the analysis we hold the value of the outside
option fixed, implicitly assuming that retail beer and wine prices are unchanged. This is
a reasonable simplification in our context since the beer industry is competitive (see Miller
and Weinberg 2015) and retail wine prices are controlled by the PLCB .21
20
Alternatively, one could assume the regulator is naı̈ve and fails to account for the upstream response. In
Miravete, Seim and Thurk (2016) we show such naivete has significant implications for the effectiveness of
policy.
21
Since profits/ tax revenue in our model are based on spirits and exclude wine, there is a risk that our
results overstate the returns to alternative policies due to cannibalization of wine sales and changes in the
value of beer licenses. This is likely minor, however, as wine only amounted to 40% of PLCB profit in 2005
(versus 60% for spirits) and beer licenses are both fixed in number and private property – bought and sold
in secondary markets. Consequently, changes in the value of these licenses has no effect on PLCB revenue.
– 31 –
Table 8: Does the PLCB Maximize Profits?
Data
Π-Max
Markup (%)
53.40
37.64
Unit Fees ($)
- 375 ml
- 750 ml
- 1.75 L
1.24
1.42
1.83
1.82
1.16
0.27
Consumption (M)
- Liters
- Bottles
- Ethanol
28.00
27.00
10.78
48.50
40.72
18.78
Price ($)
- Distillers
- Retail
9.51
16.09
9.76
14.44
69.48
171.13
116.91
191.89
Profits ($M)
- Distillers
- PLCB
Notes: Markup and unit fees include the 18% Johnstown
Flood tax. Distiller wholesale price responses assume product ownership observed in 2005. Ethanol measured in
liters. See Table E.1 for detailed results.
So does current PLCB policy maximize profits? The simple answer is “no” as profits
increase significantly (12.1%) in our counterfactual exercise (“Π-max”). The large change is
due to a significant reduction in the markup (from 53.4% to 37.6%) and a shifting of unit
fees to take advantage of the difference in estimated price elasticities (Table 6). Since small
bottles tend to be inexpensive, the regulator increases the fee for these bottles 47.2% (58
cents). In comparison, large bottles both have a wider range in retail prices and are more
expensive on average leading the PLCB to lower the unit fees on these products to 27 cents
(an 85% reduction), relying instead on the single markup to extract surplus. Therefore, the
regulator maximizes profits by employing unit fees to target 375 ml and cheap products
and the single markup to target more expensive products.
This change in policy elicits a 2.63% increase in average wholesale price by the
upstream distillers, ultimately decreasing retail prices 10.28%, or $1.65 on average. The
reduction in retail prices leads to significant increase in consumption in terms of bottles,
liters, and ethanol; increasing the state’s per capita ethanol consumption from the 44th
lowest in the country to the 10th highest (source: Haughwout et al. 2015).
– 32 –
We conclude that current policy over-prices spirits in order to discourage aggregate
spirit consumption and, presumably, lower adverse health effects. Further, the implicit
value the Board places on these externalities is significant as the regulator is foregoing
$20.77 million in profits per year to prevent Pennsylvania residents from consuming 13.7
million bottles and 8.0 million liters of ethanol, or $1.51 and $2.60 per bottle and liter,
respectively. Allowing the regulator more sophisticated tools to price discriminate magnifies
these results.22
6 Measuring Consumer Protection
Thus far we have shown that there exists a great deal of demand heterogeneity among
consumers and that PLCB policy discourages the consumption of spirits by over-pricing these
goods. In this section we investigate the degree to which PLCB policy implicitly targets
certain consumer types, thereby putting numbers to Posner’s “Taxation by Regulation”
argument. We do so by decomposing the PLCB ’s role as public monopolist. First, we
assess the implications of restricting consumption on different consumer groups using the
profit-maximizing policy from Section 5, thereby taking advantage of the richness of our data
to ask whether the simple uniform policy implicitly protects particular consumer groups by
distorting retail prices faced by all consumers. Second, we investigate the degree to which
downstream monopoly implicitly targets different consumer groups by preventing upstream
firms setting wholesale prices which account for local demand. Finally, we ask who benefits
from privatization when the state is committed to remaining revenue neutral, as in the case
of Washington.
6.1 Does Current Policy Protect Certain Consumers?
In Section 5 we showed that current policy was not consistent with tax revenue maximization
and that current policy discourages aggregate consumption. Here, we investigate whether
current PLCB policy not only discourages aggregate consumption but also implicitly targets
22
We find the regulator could increase profits even further by offering more complex taxes with variable
markups and fees based on spirit type as well as store location, though the marginal returns to such
complex policies likely outweigh the administrative costs. In contrast, restricting the state to just taxing
spirits leads to a smaller increase in profits ($16.58 million, 9.7%) mostly due to the fact that the inclusion
of the unit fees enables the PLCB to price discriminate as the estimated price elasticities vary significantly
along these margins.
– 33 –
particular customer groups. The key mechanism is the uniform pricing rule employed by the
regulator in which all consumers face the same retail price for a given product at a point in
time. Consequently, the regulator’s apparent decision to reduce total consumption can lead
to differential effects across consumers with heterogenous demand.
In Figure 4 we compare the current equilibrium to the “Π-max” equilibrium from
Section 5 which we use as reference. We illustrate the differential effects of PLCB policy
across consumer types by assigning each market to different quintiles based on its demographic profile. We then identify which consumers are implicitly protected by PLCB policy
by looking at the change in ethanol consumption for each demographic bin, focusing on the
top and bottom for simplicity (Figure 4, right column).
Figure 4: Does PLCB Policy Target Specific Consumers?
TwoPart
Low
TwoPart
High
Low
20
High
0
12.7
11.0
13.2
12.4
10.9
11.6
13.0
11.2
10
Percent Change (%)
Percent Change (%)
-10
15
5
-20
-30
-40
-41.0
-40.5
-41.6
-43.0
-44.9
-41.8
-41.7
-44.1
-50
0
Income
Minority
College
Young
Income
(a) Price
Minority
College
Young
(b) Ethanol Consumption
Notes: Statistics represent percent change from tax revenue-maximizing equilibrium to the current equilibrium. Markets are split into quintiles according to the demographic considered. “Minority” is based on
the percent of the market which identifies as “non-white.” “‘Education” is defined as the percent of the
population with at least some college experience. “Income” is the percent of the population with household
income greater than $50,000. “Age” is defined as the percent of the population in the 21 to 29 years old age
group. “High” refers to markets in the top 20% while “Low” refers to markets in the bottom 20% for the
corresponding demographic trait.”
While current policy does reduce ethanol consumption for all demographic groups,
heterogeneity in consumer preferences leads to non-uniform responses. In particular, consumers who are low income, poorly educated, and/ or white reduce their consumption at
greater rates than their peers while differences in age are immaterial. We view this as
evidence that the policy currently employed by the PLCB does indeed implicitly target
these consumer groups.
– 34 –
In a simple world the PLCB could target these consumers directly by setting retail prices based on consumer type (i.e., by third-degree price discrimination), however,
its policy lacks the legal mandate to target these consumers directly presumably due to
either difficulties in implementing such a complicated scheme or the fact that such a policy
may be politically infeasible.23 The current policy therefore has the potential to introduce
important price distortions where non-targeted consumer groups end up paying for reduced
consumption of targeted consumers. Our goal then is to evaluate whether this is the case.
As the PLCB pricing rule amounts to a taxation rule, we treat changes in retail price
as a proxy for the implicit tax imposed upon different consumers. Intuitively, the idea is to
look at the interaction of retail prices and demand, asking whether a high income consumer
pays a relatively higher price for the products she prefers than a low income consumer. In
the left column of Figure 4 we present the average percentage change in price for products
facing consumers in each market where we weight retail prices by number of bottles sold
under the current policy to account for differences in demand along consumer types.
Indeed, Figure 4 demonstrates that current policy acts like a progressive tax as the
increase in retail price for high income consumers is 1.7% higher for the products they prefer
than the increase in retail price for products favored by low income consumers (12.7% versus
11.0%). We also see a greater increase in retail prices for products preferred by consumers
who are young and well-educated as well as white consumers. We conclude that current
policy is indeed redistributive as it “taxes” some consumers (e.g., high income) to protect
others (e.g., low income).
6.2 Downstream Monopoly and Consumer Protection
In this section we investigate the implications of the PLCB ’s monopolization of the downstream market thereby negating the ability of upstream firms from setting market-specific
wholesale prices (i.e., it prevents them from third-degree price discrimination). In particular
we ask whether this monopolization materially impacts consumers, both on aggregate and
across types.
23
In an earlier version we show that the returns (profits) to sophisticated 3DPD are actually quite low even
before accounting for increased administrative costs and consumer arbitrage.
– 35 –
Figure 5: Who Benefits from Downstream Monopoly?
PD
PD
Quantity
Tax Rev.
Price
4
4
3
3
2
2
1
1.2
0.6
0.5
0.8
0.5
0
-0.6
-1
-2
-3
-1.9
Percent Change (%)
Percent Change (%)
Price
Quantity
Tax Rev.
2.8
2.0
1.8
1.2
1
0.3
0
-1
0.2
-0.1
-0.2
-0.5
-0.8
-0.3 -0.3
-2
-2.3
-2.5
-2.7
-3.3
-3
-3.0
-4
-4
Income
Minority
College
Young
Income
(a) Low
Minority
College
Young
(b) High
Notes: Statistics represent percent change from the equilibrium in which upstream firms can 3DPD to the
current equilibrium. Markets are split into quintiles according to the demographic considered. “Minority” is
based on the percent of the market which identifies as “non-white.” “‘Education” is defined as the percent
of the population with at least some college experience. “Income” is the percent of the population with
household income greater than $50,000. “Age” is defined as the percent of the population in the 21 to 29
years old age group. “High” refers to markets in the top 20% while “Low” refers to markets in the bottom
20% for the corresponding demographic trait.
While we find that downstream monopoly has little effect on both aggregate consumption (liters, bottles, ethanol) and tax revenue (see Table E.1 for detailed results), we do find
that downstream monopolization has significant impacts across consumer types (Figure 5)
as it forces upstream firms to balance heterogenous demand responses of different consumer
types leading to lower prices for some consumers and higher prices for others. In particular,
downstream monopolization generates higher retail prices for low income, poorly educated,
and minority consumers face retail prices approximately 0.5% leading to significantly lower
ethanol consumption among these consumers ranging from 2.5 to 3.5 percent. Consequently,
we conclude that downstream monopolization plays a significant role in enabling the PLCB
to discourage ethanol consumption among these consumers.
On the other hand, high income and well-educated consumers face lower prices under
the current regulation as downstream monopoly prevents upstream distillers from setting
higher prices. The conclusion then is that downstream monopoly actually encourages consumption among these groups and to a lesser-extent among minorities. These results also
have implications for taxation as Figure 5 demonstrates that downstream monopoly shifts
the tax burden from low income, poorly educated consumers to high income, well-educated
consumers.
– 36 –
6.3 Consumption, Tax Revenue, and Privatization
We conclude our analysis by investigating the implications of privatization. To keep the
analysis tractable, we hold the number and location of the stores fixed and focus on the
PLCB ’s role as taxation authority while allowing upstream firms to 3DPD as above.24 We
also modify the pricing rule such that there are no unit fees (to mimic the taxation approach
followed in most non-control states) and solve for the ad valorem rate which replaces foregone
tax revenue (as was done theoretically in the case of Washington). Table 9 presents the
aggregate results.
Table 9: Aggregate Effects
of Privatization
Markup (%)
72.83
∆ Consumption (%)
- Liters
- Bottles
- Ethanol
9.07
11.47
8.99
∆ Price (%)
- Distillers
- Retail
−3.03
−0.98
∆ Profits (%)
- Distillers
- PLCB
−2.67
0.00
Notes: Markup includes the 18%
Johnstown Flood tax. Distiller wholesale price responses assume product
ownership observed in 2005. See Table E.1 for detailed results.
We find the tax revenue-equivalent single markup increases from 53.4% to 72.83% (a
19.4% increase) in order to replace revenue generated by the unit fees as well as counteract
upstream 3DPD. This leads to a one percent decrease in average retail price though this
statistic masks a great deal of heterogeneity across products as the average retail price for 375
ml decreases 7.3% while the average retail price for 1.75 L increases one percent. We also see
a similar pattern for cheap and expensive products (6.6% reduction versus 2.2% increase).
Distillers respond to the policy change by decreasing wholesale price 3.0% on average. While
24
Seo 2016 documents the increase in stores and the availability of alcohol for the case of Washington’s
privatization.
– 37 –
these price changes are profit-maximizing, total distiller profit decreases 2.7% nonetheless
though some firms (e.g., Beam) do benefit from the policy change.25
As we chose the single markup (i.e., tax rate) to keep total tax revenues constant, the
aggregate implications of privatization depend solely on changes in aggregate consumption
which we find to be significant. Total ethanol consumption increases 9.07%, 11.47%, and
8.99% for Liters, Bottles, and Ethanol, respectively; increasing the state’s per capita
ethanol consumption from the 44th to the 41st lowest in the country (source: Haughwout
et al. 2015).
Figure 6: Protecting Consumers Through Public Monopoly
MupMkt
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-10
-11
-12
-13
-14
MupMkt
Quantity
3.7
Tax Rev.
Price
3.4
2.9
1.8
0.6
-1.7
-2.6
-3.7
-4.1
Percent Change (%)
Percent Change (%)
Price
-12.4
-13.5
-13.7
Income
Minority
College
Young
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-10
-11
-12
-13
-14
Tax Rev.
3.4
2.7
2.5
1.0
2.4
0.5
0.3
-0.0
-1.1
-2.9
-9.4
Income
(a) Low
Quantity
Minority
-9.6
College
Young
(b) High
Notes: Statistics represent percent change from tax revenue-neutral, privatized equilibrium to the current
equilibrium. Markets are split into quintiles according to the demographic considered. “Minority” is based
on the percent of the market which identifies as “non-white.” “‘Education” is defined as the percent of the
population with at least some college experience. “Income” is the percent of the population with household
income greater than $50,000. “Age” is defined as the percent of the population in the 21 to 29 years old age
group. “High” refers to markets in the top 20% while “Low” refers to markets in the bottom 20% for the
corresponding demographic trait.
Further, Figure 6 demonstrates that privatization leads to lower retail prices, greater
consumption, and a larger share of the tax burden for residents with little income or education
– groups which the PLCB appears interested in protecting (Section 6). Thus, we conclude
that privatization would likely be undesirable even if the regulator could hold tax revenue
constant since it appears to value discouraging alcohol consumption both on aggregate across
consumer types.
25
See Table E.1 for detailed results.
– 38 –
7 Concluding Remarks
Limited systematic evidence exists to evaluate the performance of a public enterprise such as
the PLCB . The current paper focuses its analysis on a behavioral aspect that is commonly
overlooked: the implicit rent redistribution associated with price distortions induced by an
one-size-fits-all policy in the presence of heterogeneous consumers. As such, we study the
intensive margin of regulated alcohol distribution – pricing – and complement related work
by Seim and Waldfogel (2013) who analyze the size of the PLCB ’s store network, or the
extensive margin, as policy tools to affecting alcohol consumption in Pennsylvania.
Our approach requires the estimation of a parsimonious discrete choice demand
model where demand for liquors depends on both observable and unobservable product
characteristics and where our rich data set enables us to account for many of the important
dimensions along which customers differ: income, educational attainment, age, and minority
status. We then use the model as a laboratory to decompose the effects of public monopoly
both in aggregate and across different consumer types.
We show that current policy over-prices spirits in order to discourage alcohol consumption; leaving $21 million per year on the table, or equivalently paying $2.60 in foregone
tax revenue to prevent residents from consuming an additional liter of ethanol. Moreover,
current policy implicitly targets consumers who are low income, poorly educated, and/or
non-minorities. We further show that preventing 3DPD among upstream suppliers further
enables the PLCB to protect these consumers, and that tax-neutral privatization leads to
greater consumption both in aggregate and for these targeted consumer groups.
These latter points are particularly novel as they deliver, to the best of our knowledge, the first empirical evaluation of Posner’s “taxation by regulation” rent redistribution
argument whereby simple uniform policies can generate significant non-uniform effects to
heterogenous consumers – an aspect that is rarely discussed when evaluating the performance
of public monopolies like the PLCB .
– 39 –
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– 42 –
Appendix
A Data
Here, we provide detail on how we aggregate the initial daily, store-level PLCB data and
how we define market areas served by each store. To reduce the size of the estimation
sample, we consider the periodicity with which we observe price changes in the data. PLCB
regulation allows price to change only for two reasons: permanent and temporary wholesale
price changes. Both follow set timing requirements. Permanent price changes can take effect
on the first day of one of the PLCB ’s thirteen four-week long accounting reporting periods.
Temporary sales, on the other hand, begin on the last Monday of each month and last
for either four or five weeks until the day before the last Monday of the following month.
Reporting periods and temporary sales periods thus align largely, but not perfectly, with the
vast majority of days in a typical sales period overlapping with an initial reporting period,
and the remainder with the next. Since temporary price reductions are more prevalent than
permanent ones (90% of price changes in 2005 are temporary in nature) and to avoid having
multiple very short periods, we use sales periods as our time interval. In case of permanent
price changes that take effect at the beginning of a reporting period that bisects two sales
periods, we assume that the price change takes effect in the sales period that most overlaps
with the given reporting period. This results in 12 pricing periods during which prices remain
constant. In aggregating our daily sales data to the level of the sales pricing period, we treat
a product as being available in a store if it sold at least once during a given pricing period.
The length of the pricing period alleviates concern about distinguishing product availability
from lack of sales in the period.
Stores exhibit significant variation in the product composition of purchases. These differences reflect heterogeneity in consumer preferences more than differences in the availability
of products across stores: Of the 100 best selling products statewide in 2003, the median store
carried 98.0%, while a store at the fifth percentile carried 72.0% of the products. Similarly,
of the 1000 best selling products statewide in 2003, the median store carried 82.03%, while
a store at the fifth percentile carried 44.2% of the products. The product availability at
designated “premium” stores is somewhat better than the average, with the median premium
store carrying all of the top 100 products and 95.1% of the top 1000 products. In addition,
a customer can request to have any regular product in the PLCB ’s product catalog shipped
to his local store for free, should that store not carry the product.
The fact that most stores carry most popular products and can provide access to all
products in the catalog easily, together with the absence of price differences across stores,
supports an assumptions underlying our demand model: Differences in product availability
do not drive customers’ store choices to a significant degree and as a result, consumers
visit the store closest to them. In making this assumption, which allows us to focus on the
–i–
consumer’s choice between different liquor products available at the chosen store, we follow
previous studies using scanner data such as Chintagunta and Singh (2003).
Figure A.1: Pennsylvania Markets as of January 2003
In assigning consumers to stores, we calculate for each of Pennsylvania’s 10,351 regular
block groups the straight-line distance to each store and assign consumers to the closest open
store for each pricing period. In instances where the PLCB operates more than one store
within a ZIP code, we aggregate sales across stores to the ZIP code level; there are 114 such
ZIP codes out of a total of 1,775. Note that these instances include both store relocations,
where a store moved from one location in a ZIP code to another during 2003, but the data
contain separate records for the store in the two locations, and instances where the PLCB
operates two stores simultaneously within a ZIP code.26 We consider the resulting block
group zones as separate markets. Figure A.1 shows the 479 zones as of January 2003.
We derive consumer demographics for the store’s zone by calculating the total population and population-weighted average demographics, including the percent of the population
that is non-white, has at least some college experience, and is between the ages of 21 and
29 years, as well as the population-weighted income distribution. In the case of income, we
obtained detailed information on each block group’s discrete income distribution by racial
identity of the head of household, with household income divided into one of 16 categories.
We aggregate across racial groups and across block groups in a store’s market area to derive
the distribution for white households separately from non-white households. We construct
two income measures. First, we calculate the share of high-income households, defined as
26
We drop wholesale stores, administrative locations, and stores without valid address information, for a
total of 13 stores.
– ii –
households with incomes above $50,000. Second, we fit continuous market-specific distributions to the discrete distributions of income conditional on minority status. We employ
generalized beta distributions of the second kind to fit the empirical income distributions;
McDonald (1984) highlights that the beta distribution provides a good fit to empirical income
data relative to other parametric distributions.
We similarly obtained information on educational attainment by minority status and
aggregated across several categories of educational attainment to derive the share of the
population above the age of 25 with at least some college education, by minority status and
market area. Lastly, we obtained the share of the population between the ages of 21 and 29
by market area.
– iii –
B Additional Descriptive Statistics
Table B.1 presents the distribution of bottle prices contained in our sample of 369 products.
Average price is increasing across bottle sizes both within a category and for the whole
sample. Tequilas tend to be the most expensive products while Brandies, Rums, and Vodkas
as less expensive. These aggregate statistics mask a great deal of heterogeneity across the
products, however. For instance, Vodkas tend to be inexpensive on average ($13.82 per
bottle) but there also exist very expensive bottles (Grey Goose, 1.75 ml). The least and
most expensive products in the sample are Nikolai Vodka, 375 ml ($3.89) and Hennessey
Cognac (“Brandy” in our context), 1.75 ltr ($58.79).
Table B.1: Bottle Prices by Spirit Type and Bottle Size
Spirit Type
Average
Median
SD
Max
Min
brandy
375 ml
750 ml
1.75 L
14.20
9.13
14.15
21.09
11.22
5.99
9.89
19.29
8.21
4.36
7.39
9.77
58.79
15.06
36.38
58.79
5.39
5.39
9.29
16.69
cordials
375 ml
750 ml
1.75 L
15.21
10.55
15.12
25.85
14.99
10.40
15.20
25.14
6.40
3.26
5.49
8.39
39.99
18.96
30.99
39.99
5.99
5.99
5.99
12.64
gin
375 ml
750 ml
1.75 L
15.62
7.91
13.49
19.47
14.85
6.95
10.72
17.28
7.52
2.48
5.29
8.09
39.68
11.99
21.99
39.68
4.79
4.79
5.99
11.69
rum
375 ml
750 ml
1.75 L
14.11
6.58
12.53
19.83
13.58
6.41
12.82
21.02
5.30
0.76
2.31
4.88
26.58
7.49
19.52
26.58
5.09
5.09
7.79
12.99
tequila
375 ml
750 ml
1.75 L
18.92
11.71
17.72
35.26
18.29
11.71
18.29
35.26
6.79
0.00
2.81
0.00
35.26
11.71
24.26
35.26
10.91
11.71
10.91
35.26
vodka
375 ml
750 ml
1.75 L
13.82
5.34
15.18
16.89
12.29
4.09
14.49
12.90
7.43
2.65
5.00
7.46
48.53
14.99
26.62
48.53
3.89
3.89
6.19
10.81
whiskey
375 ml
750 ml
1.75 L
16.81
8.85
14.87
20.83
15.37
9.63
13.2
18.29
7.68
2.55
6.17
7.75
45.99
15.33
31.62
45.99
5.49
5.49
5.99
12.09
– iv –
Table B.2: Market Share by Type, Price, and Size
Share of Market
Products
By Quantity
By Revenue
By Tax Revenue
30
85
30
45
7
73
99
7.28
14.76
6.65
15.57
1.66
30.39
23.69
6.88
14.98
6.92
14.70
2.10
27.97
26.45
6.88
14.78
6.92
14.83
2.02
28.47
26.11
By Price and Size:
Expensive
Cheap
375 Ml
750 Ml
1.75 Ltr
190
179
56
206
107
51.74
48.26
15.08
51.21
33.71
36.51
63.49
7.41
49.5
43.09
38.92
61.08
8.19
49.17
42.64
All Products
369
100.00
100.00
100.00
By Spirit Type:
Brandy
Cordials
Gin
Rum
Tequila
Vodka
Whiskey
Notes: “Quantity” market share is based on bottles while “Revenue” and “Tax Revenue” are based on dollars. “Cheap” (“Expensive”) products are those products whose
mean price is below (above) the mean price of other spirits in the same spirit type
and bottle size.“Tax Revenue” is defined as retail price minus wholesale price times
quantity sold.
From Table B.3 we see that the LTMF fee plays a minor role in determining final retail
price relative to the ad valorem markup (25.99% versus 74.01%, respectively) particularly
for products with high retail prices, e.g., expensive and 1.75 L products.
–v–
Table B.3: Impact of Ad Valorem and
Unit Fees on Retail Price
Products
Price
Markup
Fee
30
79
29
51
6
84
98
16.13
15.37
15.47
13.72
19.83
16.62
17.34
72.66
73.94
71.23
71.98
79.49
74.26
74.53
27.34
26.06
28.77
28.02
20.51
25.74
25.47
By Price and Size:
- expensive
- cheap
- 375 ml
- 750 ml
- 1.75 L
182
195
58
211
108
21.16
11.33
9.47
14.76
22.21
80.17
67.62
66.79
73.93
76.89
19.83
32.38
33.21
26.07
23.11
all products
377
16.35
74.01
25.99
By Spirit Type:
- brandy
- cordials
- gin
- rum
- tequila
- vodka
- whiskey
Notes: “Price” is average price in 2005. “Markup” is the share (in %) of the
difference between wholesale and retail price due to the single 30% markup
and the 18% liquor tax. “Fee” is the share (in %) of the difference between
wholesale and retail price due to the per unit LTMF fee adjusted by the
18% liquor tax.
– vi –
C Robustness
Table C.1: Demand Estimates Based on Different Samples
(Multinomial Logit Demand)
(i)
price
Product FEs
Premium Stores
Border Stores
Holiday Period
Statistics:
R2
N
Elasticities:
Average
% Inelastic
Off-Premise
−0.2644
(0.0042)
Y
Y
Y
Y
0.9584
4,422
−4.13
0.00
−3.73
(ii)
−0.2726
(0.0043)
(iii)
−0.2478
(0.0041)
(iv)
−0.2618
(0.0038)
Y
N
Y
Y
Y
Y
N
Y
Y
Y
Y
N
0.9589
4,422
0.9564
4,422
0.9736
3,684
−4.26
0.00
−3.89
−3.86
0.32
−3.44
−4.09
0.00
−3.69
Notes: The dependent variable for all models is the estimated product-period fixed
effects from a first-stage regression of log(Sjmt ) − log(S0mt ) onto product-period fixed
effects and demographic-product interactions. Robust standard errors in parentheses.
“% Inelastic” is the percentage of products with inelastic demand. “Off-Premise” is
the price elasticity of total PLCB spirit sales.
In Table C.1 we using a simple multinomial logit demand system to demonstrate the robustness of our demand estimation results to alternative samples. In Model (i) we present
a model most similar to the full model presented in the text. In particular, we follow a
similar estimation strategy where we first regress the logged ratio of product to outside
share on product-period and store fixed effects as well as interactions between demographics
on product characteristics (e.g., % minority-x-rum dummy). This model generates product
elasticities (both on average and for “off-premise” as a category) which are more elastic than
our preferred mixed-logit model. In models (ii)-(iv) we vary the number of markets to show
that including markets with premium and border stores as well as the holiday period has
little effect on our estimated price coefficient as well as the elasticities. This indicates that
restricting the sample has little effect on our results.
In Table C.2 we show our estimation approach based on disaggregated data provides
superior identification. In Model (i) we deviate from our multi-step approach and estimate the model in a single step; regressing the logged ratio of product share to outside
– vii –
share on price brand fixed effects, bottle size dummies, time dummies, store dummies,
and demographic interactions. Demand becomes much steeper relative to the baseline
model in Table C.1 when follow this alternative approach. Further, the product elasticities,
particularly the off-premise elasticity, is similar to the value found by Leung and Phelps
(1993). We view this model as inferior to the baseline specification, however, as the baseline
specification nests Model (i) and has superior predictive power (larger R2 ).
Table C.2: Demand Estimates Using Different Approaches
(Multinomial Logit Demand)
(i)
price
Brand FEs
Statistics:
R2
N
Elasticities:
Average
% Inelastic
Off-Premise
(ii)
(iii)
(iv)
−0.1190
(0.0004)
−0.0470
(0.0004)
−0.0891
(0.0025)
−0.0123
(0.0020)
Y
N
Y
N
0.8218
4,422
0.1441
4,422
0.5129
1,407,340
0.2420
1,407,340
−1.86
13.58
−1.70
−0.73
82.78
−0.67
−1.39
33.07
−1.27
−0.19
100.00
−0.17
Notes: The dependent variable for models (i)-(ii) is log(Sjmt ) − log(S0mt ) while it is
log(Sjt ) − log(S0t ) for models (iii)-(iv). Robust standard errors in parentheses. “%
Inelastic” is the percentage of products with inelastic demand. “Off-Premise” is the
price elasticity of total PLCB spirit sales.
In Model (ii) we replace the brand fixed effects with observable characteristics (e.g.,
dummies for spirit type, imported). Demand becomes even steeper and demand becomes
much more inelastic due the coarseness of our observable characteristics. For example, two
brands of imported rum could have different unobservable quality to consumers thereby
leading different product shares and firms choosing to charge different prices but in this
specification, the estimation wrongly correlates differences in price with the differences in
shares (quantity sold). In Models (iii)-(iv) we aggregate consumption to the state-level
requiring us to drop the demographic interactions but otherwise using the same controls
as Models (ii)-(iii). Again, we see the inclusion of brand FEs is important to absorbing
differences in unobservable (to the econometrician) characteristics across brands. We further
see that aggregation drives the elasticity of off-premise spirits to become more inelastic, well
within the set of estimates included in Leung and Phelps (1993).
– viii –
D Elasticities
25
25
20
20
15
15
Percent
Percent
Figure D.1: Distribution of Demand Elasticities by Spirit Type
10
10
5
5
0
0
-15
-10
-5
0
-15
25
25
20
20
15
15
10
10
5
5
0
-10
-5
0
-15
(c) Gin
0
-10
-5
0
-5
0
-5
0
(d) Rum
25
25
20
20
15
15
Percent
Percent
-5
0
-15
10
10
5
5
0
0
-15
-10
-5
0
-15
(e) Tequila
-10
(f) Vodka
25
25
20
20
15
15
Percent
Percent
-10
(b) Cordials
Percent
Percent
(a) Brandy
10
10
5
5
0
0
-15
-10
-5
0
-15
(g) Whiskey
-10
(h) All Products
– ix –
25
25
20
20
15
15
Percent
Percent
Figure D.2: Distribution of Demand Elasticities by Price and Bottle Size
10
10
5
5
0
0
-15
-10
-5
0
-15
25
25
20
20
15
15
10
10
5
5
0
-5
0
-5
0
-5
0
0
-15
-10
-5
0
-15
(c) 1.75 Ltr
-10
(d) Cheap
25
25
20
20
15
15
Percent
Percent
-10
(b) 750 ml
Percent
Percent
(a) 375 ml
10
10
5
5
0
0
-15
-10
-5
0
-15
(e) Expensive
-10
(f) All Products
–x–
Table D.1: Estimated Cross-Price Elasticities
Ratio
By Type:
brandy
cordials
gin
rum
tequila
vodka
whiskey
13.62
1.15
0.92
1.25
1.48
2.24
3.82
By Size:
375 ml
750 ml
1.75 L
3.02
2.33
3.19
By Characteristic:
Flavored
2.27
Notes: For each product we calculate the average
cross-price elasticity for (1) products within the
corresponding group and (2) products outside
the group. “Ratio” is the average ratio of (1)
to (2).
– xi –
E Detailed Results
Table E.1: Detailed Results for Alternative PLCB Policies
Data
Π-max
3DPD
Private
Markup (%)
53.40
37.64
53.40
72.83
Unit Fees ($)
- 375 ml
- 750 ml
- 1.75 L
1.24
1.42
1.83
1.82
1.16
0.27
1.24
1.42
1.83
0.00
0.00
0.00
Distiller Price ($)
- 375 ml
- 750 ml
- 1.75 L
- brandy
- cordials
- gin
- rum
- tequila
- vodka
- whiskey
- cheap
- expensive
9.51
5.36
8.70
13.30
9.57
9.10
9.07
7.97
11.99
9.84
10.31
6.41
12.83
9.76
5.66
8.95
13.53
9.82
9.35
9.32
8.22
12.23
10.09
10.55
6.13
13.08
9.51
5.37
8.71
13.30
9.60
9.11
9.08
7.99
11.98
9.84
10.31
6.43
12.83
9.22
5.08
8.43
12.99
9.33
8.82
8.78
7.69
11.69
9.54
10.01
6.13
12.53
Distiller Profits ($M)
- Diageo
- Bacardi
- Beam
69.48
16.66
6.51
6.30
116.91
28.09
11.39
10.69
69.75
16.72
6.53
6.32
67.63
15.33
5.75
6.40
Retail Price ($)
- 375 ml
- 750 ml
- 1.75 L
- brandy
- cordials
- gin
- rum
- tequila
- vodka
- whiskey
- cheap
- expensive
16.09
9.47
14.77
22.23
16.15
15.38
15.48
13.73
19.85
16.63
17.37
11.34
21.19
14.44
9.61
13.48
18.89
14.63
14.07
13.71
12.34
17.96
14.85
15.41
10.18
19.01
16.10
9.47
14.78
22.23
16.19
15.40
15.49
13.75
19.83
16.62
17.37
11.36
21.18
15.93
8.77
14.56
22.45
16.12
15.24
15.17
13.30
20.20
16.49
17.30
10.60
21.66
Consumption (M)
- Liters
- Bottles
- Ethanol
28.00
27.00
10.78
48.50
40.72
18.78
28.09
27.09
10.81
30.54
30.10
11.75
171.13
191.89
171.73
171.13
System Profits ($M)
Notes: Markup and unit fees include the 18% Johnstown Flood tax. Distiller
wholesale price responses assume product ownership observed in 2005. Ethanol
measured in liters.
– xii –
Web Appendix – Not for Publication
Table W.1: Most Popular Products by Income Decile
Spirit Product
Price
Share
12.32
19.32
18.97
10.78
6.07
6.85
13.67
11.79
7.99
26.64
1.77
1.18
1.16
1.15
1.12
1.09
1.02
0.82
0.79
0.77
18.97
11.84
19.32
26.59
21.04
11.79
12.32
19.11
15.39
21.99
1.76
1.64
1.54
1.47
1.14
1.08
1.01
0.99
0.92
0.73
Bottom Income Decile ($31,020):
1. BACARDI LIGHT-DRY P. R. RUM
2. JACK DANIEL’S OLD NO. 7 BLACK LABEL
3. ABSOLUT IMP. VODKA - 80 PROOF
4. SEAGRAM’S EXTRA DRY GIN
5. FIVE O’CLOCK EXTRA DRY GIN
6. NIKOLAI VODKA - 90 PROOF
7. BACARDI LIMON P. R. RUM
8. SMIRNOFF VODKA - 80 PROOF
9. JACQUIN’S WHITE RUM
10. GREY GOOSE IMP. FRENCH VODKA
Top Income Decile ($74,440):
1. ABSOLUT IMP. VODKA - 80 PROOF
2. SMIRNOFF VODKA - 80 PF. PORTABLE
3. JACK DANIEL’S OLD NO. 7 BLACK LABEL
4. GREY GOOSE IMP. FRENCH VODKA
5. KETEL ONE DUTCH VODKA
6. SMIRNOFF VODKA - 80 PROOF
7. BACARDI LIGHT-DRY P. R. RUM
8. JOSE CUERVO ESPECIAL REPOSADO TEQUILA
9. SKYY VODKA
10. TANQUERAY IMP. DRY GIN
Liquor products sorted by share of sales (bottles).
–I–