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Journal of Retailing and Consumer Services
journal homepage: www.elsevier.com/locate/jretconser
Retail channel price discrimination
Steven S. Cuellar a,n, Marco Brunamonti b
a
b
Department of Economics, Sonoma State University, Rohnert Park, CA, USA
Sonoma Research Associates, Glen Ellen, CA 95442, USA
art ic l e i nf o
Keywords:
Price discrimination
Market segmentation
Fixed effects models
a b s t r a c t
This paper examines price differentials of identical items across retail channels. Many consumer
packaged goods are sold through both grocery and drug stores. Liquor is unique in that in much of
the country there is a third retail channel of distribution, liquor stores. If consumers in each retail
channel differ in their willingness to pay for certain items, then sellers can exploit those differences and
charge different prices for the same items in each channel. We examine a unique data set of pooled cross
sectional retail scanner data on wine to test whether sellers use retail channel to identify heterogeneous
consumer market segments and engage in price discrimination. We begin by presenting a model of price
discrimination by retail channel along with behavioural assumptions regarding shoppers in each
channel. Next we examine sales by retail channel and find persistent price differentials for the same
item across retail channel after controlling for sample selection bias and seasonality. Lastly, we estimate
the price elasticity of demand correcting for endogeneity and find differences across channel consistent
with the price differentials. The extent of price differential, however, differs significantly with respect to
price point.
& 2013 Elsevier Ltd. All rights reserved.
1. Literature review
This paper investigates the difference in price of identical items
across retail channel. We argue that these retail channel price
differentials are a form of first degree or market segmented price
discrimination in which consumers, who differ in their price
elasticities of demand, self-select themselves into each retail
channel. Modern concepts of price discrimination in noncompetitive markets go back at least to Pigou (1920), whose
categorization of price discrimination into first, second and third
degree is still used today. Robinson (1933) elaborated on the
conditions required for firms to engage in effective third degree
price discrimination, namely that there exist identifiable market
segments that differ in their price elasticities of demand. Using
this background, Blattberg and Sen (1974, 1976) and Blattberg et al.
(1978) show how market segmentation based on identifiable
demographic characteristics can be effectively exploited. More
recently, Hoch et al. (1995) use scanner data to show how
demographic characteristics can be used to price discriminate by
store location. Where differences in price elasticity are not easily
identifiable, Moorthy (1984) provides a model where firms exploit
differences in consumer preferences across market segments by
offering product variants at different prices, allowing consumers to
self-select among those products. More generalized models of
price discrimination in contestable markets with differentiated
products have been developed by Salop and Stiglitz (1977),
Narasimhan (1984), Borenstein (1985) and Holmes (1989). The
type of consumer behavior closest to that examined in this paper
is that of Narasimhan (1984), who presents a model of coupon use
as a form of price discrimination for identical goods. Specifically,
Narasimhan presents a model in which consumers, who differ in
their price elasticity of demand, self-select themselves into coupon
use based on comparing the savings associated with using coupons with the opportunity cost of using coupons. We extend this
model by allowing consumers to compare the savings associated
with one retail outlet with the associated opportunity cost as
defined in Kahn and Schmittlein (1989) and Bell et al. (1998).
Finally, with respect to retail channel, Gerstner et al. (1994)
examine price discrimination by retail channel, however, their
paper concentrates on the effect of retailer mark-up on the size of
discount offered, while Park and Keh (2003) look at the effect of
manufacturers utilizing both the traditional retail channel as well
as selling direct to consumers. Our paper, on the other hand,
provides a unique perspective on the use of retail channel itself as
a means of price discrimination.
2. A model of price discrimination
n
Corresponding author. Tel.: +1 707 664 2305.
E-mail addresses: [email protected] (S.S. Cuellar),
[email protected] (M. Brunamonti).
We model retail channel as a form of market segmentation.
Just as coupons serve as a means of consumers self-identifying
0969-6989/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jretconser.2013.06.004
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
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themselves into market segments according to their price elasticities of demand (Narasimhan, 1984), retail channel can serve a
similar function. From the consumer's perspective, purchasing
some goods at a lower cost retail channel provides an alternative
as long as the savings associated with shopping at that channel are
greater than the costs. In this context, choice of retail channel is
consistent with Narasimhan's (1984) model of coupon usage on
several dimensions. First, both coupon usage and retail channel are
decisions of self-selection by rational utility maximizing consumers. Second, consumers will decide to purchase a specific
product at a lower priced retail channel as long as the savings is
greater than the opportunity cost required to search, travel to, and
shop at that channel for the specific good in question. This is
analogous to the model of coupons where usage depends on the
savings being greater than the opportunity cost in time required to
search, clip (print etc.), store, organize, retrieve and use coupons.
Finally, both coupon usage and choice of retail channel are
decisions consumers make based on the potential savings on
individual products and not overall savings on all products.
Specifically, we do not assume that some retail channels are more
or less expensive for all or even most products, but rather that
some retail channels are more or less expensive for one particular
product.
From the firms’ perspective, it will engage in price discrimination as long as the marginal revenue from price discrimination is
greater than the marginal cost. While price discrimination is
generally associated with monopolistic or oligopolistic industries,
models of price discrimination in differentiated product markets
have been developed by Borenstein (1985) and others.
In its simplest form, given “i” different market segments, which
differ in their price elasticities of demand, and for whom arbitrage
among the market segments is costly, we can investigate discriminatory behavior by examining the firms’ profit function,
n
Π ¼ ∑ TRi ðQ i Þ−TCðQ Þ;
i¼1
where:
i denotes the different market segments identified by the firm.
TRi(Qi) represents total revenue in market i from unit sales Qi.
TC(Q) represents total cost of production across all three
channels so that Q ¼ ∑ni¼ 1 Q i .
Profit maximization results in the usual first order conditions:
∂TRi
∂TC i ∂Q i
;
¼
∂Q i
∂Q i ∂Q
∀i
If we assume costs are common to all markets
can rewrite the optimizing condition as,
∂TRi
∂TC
;
¼
∂Q
∂Q i
∂Q i
∂Q
¼1
∀i
which produces the familiar condition that MRi ¼ MC.
Since
TRi ¼ P i Q i
∂TRi
∂P
¼ MRi ¼ P i þ Q i i ;
∂Q i
∂Q i
∀i
which can be rewritten to express in elasticity form,
MRi ¼ P i ð1 þ
1
Þ;
εi
∀i
For our three-market (channel) scenario, this results in
1
1
1
¼ P2 1 þ
¼ P3 1 þ
:
P1 1 þ
ε1
ε2
ε3
In this form we see that the price in each market is inversely
proportionate to the absolute value of the price elasticity of
demand in that market. That is, P1 oP2 oP3, if |ε1| 4|ε2| 4|ε3|.
3. A model of consumer behavior
We view channel price discrimination similar to that of coupon
usage in which consumers self-identify themselves for coupon
usage by comparing the marginal cost with the marginal benefit.
We propose that consumers self-select themselves into each retail
channel (drug, food and liquor store) based on demographic
characteristics and shopping intent. As with coupons, shoppers
then compare the cost and benefit of searching out a lower priced
good, in this case, a bottle of wine.
Consider first, drug store shoppers, who can be characterized as
one of two groups. Drug store shoppers can be considered category
specific shoppers, ostensibly shopping for goods other than wine.
In this case, wine may be considered an unplanned purchase,
which Bucklin and Lattin (1991) show have a relatively high price
elasticity of demand. Drug store patrons may also consist of quick
or fill-in shoppers, who as Kahn and Schmittlein (1989) show, tend
to have “smaller-sized families, lower incomes … and more
retired”, a group that we would expect to purchase relatively lower
priced wines and to have a relatively high price elasticity of
demand. For drug store shoppers, both shopping intent and
demographic characteristics would lead to greater price sensitivity.
In contrast to drug store shoppers, for whom wine may be an
impulse good, wine morel likely to be part of a larger shopping list
or basket of goods for grocery store shoppers who make more
infrequent regular shopping trips. Kahn and Schmittlein (1989) and
Bucklin and Lattin (1991) show that these types of shoppers tend to
be from a larger family, which would increase search costs, and
from families with a higher incomes, which would decrease price
sensitivity.
Liquor store shoppers on the other hand have a specific
shopping intent. Category specific shopping of this type tends to
reduce search cost and decrease price sensitivity (Bell et al., 1998).
Based on the search costs, shopping intent and demographic
characteristics we expect drug store patrons to be the most price
sensitive, followed by grocery store shoppers and liquor store
shoppers: |εDrug| 4|εGrocery| 4|εLiquor|. If this is the case, then we
should observe prices for the identical wines to cheapest at drug
stores, more expensive at grocery stores and most expensive at
liquor stores: PDrug oPGrocery oPLiquor.
we
4. Data
We use scanner data of retail purchases of wine in the US to
investigate price differentials across three retail channels: Drug
stores, food or grocery stores and liquor stores. Retail scanner data,
provided by proprietors such as Information Resources Incorporate (IRI) and the Nielsen Company, is increasingly becoming the
primary source of data for analytics in the consumer packaged
goods industry due to the ready availability of data at the item
level on factors such as price, quantity, promotional activity and
sales channel. In this paper, we use Nielsen Scantrack data to
construct a pooled cross section of data on point of sale purchases
of wines from major U.S. retail chains, for the years 2007–2010.
The data consist of national sales of all wines, foreign and
domestic, purchased from major retail chain stores, defined as
those with sales of over 2 million dollars per year. The data are
aggregated for all markets and include the price paid, quantity
sold, store keeping unit (SKU) and retail channel of each item. For
uniformity, we concentrate on wine purchases of standard 750 ml
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
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glass bottles (approximately 84% of all purchases) and exclude
boxed wine, as well as smaller or larger bottles. The benefit of scan
data is that it represents actual purchases of wine by consumers
and is thus more reflective of the consumer demand than
manufacturers’ suggested retail price. The drawback of scan data
is that it only records purchases in major U.S. retail chains and
does not represent wine sold on premise at wineries, purchases
through wine clubs or purchases at restaurants. Despite these
limitations, the scan data works well for our analysis of pricing
behavior across major retail channels.
Furthermore, wine is unique in that it is sold through three retail
channels, food, drug, and liquor stores. Finally, the wine industry
provides an ideal example of a differentiated product market
characterized by a high degree of price and non-price competition.
5. Summary statistics
The data used to examine retail channel price differentials consist
of 44 four-week periods from 2007 to 2010. Each period contains
approximately 14,000 unique items sold. However, not all items are
sold through all three channels. Cursory examination of mean prices
can lead to spurious price differentials based on sample selection bias
and not discriminatory pricing behavior. For instance, if drug stores
sell wines with lower average prices than grocery stores, and liquor
stores sell wines with higher average prices than grocery stores, then
differences in mean prices across channels would be due to the
differences in wines carried across the channels and not differences
in the price of the same wines across channel. This is illustrated in
Table 1 and Fig. 1, which shows summary statistics on prices for the
full sample across channel.
Fig. 1 illustrates several points. To begin with, the distribution of
high priced wines increases from drug to liquor stores with food
stores selling a significantly greater range of higher priced wines than
drug stores, and liquor stores selling a greater range of high priced
wines than food stores. Fig. 1 also shows a systematic increase in the
inter-quartile range of prices in the full sample of all wines moving
from drug to liquor stores. Furthermore, Table 1 demonstrates that
the maximum price at drug stores is approximately $43 compared
with a maximum price at grocery and liquor stores of $178 and over
$200, respectively. Obviously differences of this magnitude are the
result of differences in the composition of items sold in each channel.
A closer examination of the data shows that the highest priced wine
sold through drug stores channel was a Conn Creek Napa Valley
Cabernet Sauvignon blend which sold for $42.68, while the highest
priced wine sold through the food channel was an Opus One Red
blend that sold for $177.97 and the highest priced wine sold through
Table 1
Prices by channel and sub sample.
Channel
Full sample
Mean
Minimum
Maximum
Observations
Drug
$8.11
$0.57
$52.51
29,949
Food
$11.77
$0.15
$177.97
259,067
Liquor
$13.22
$0.48
$223.72
226,454
Matched Sample 1
Mean
Minimum
Maximum
Observations
$8.41
$1.18
$52.51
26,479
$9.07
$1.98
$58.14
26,664
$9.34
$2.69
$65.44
26,692
Matched Sample 2
Mean
Minimum
Maximum
Observations
$7.66
$2.89
$24.02
9,396
$8.15
$3.11
$27.03
9,502
$8.47
$3.35
$27.35
9,537
Fig. 1. Mean price by channel and sample.
the liquor channel was a Penfolds Shiraz that sold for $223.72.
Differences in the composition of items sold across channels are
further illustrated by the difference in the number of observations
across channels shown in Table 1.
5.1. Matched Sample 1
To control for the sample selection bias caused by the difference in the composition of items sold across channels, we examine
two subsets of the data. The first subset contains only those items,
matched by store keeping unit (sku), sold across all three channels
in any specific period. Thus, in each period, only items of the same
sku sold in all three channels in that period are analysed. Matched
Sample 1 is illustrated in Fig. 1, and again shows a monotonically
increasing relationship across the three channels, albeit, with
significantly less variation in prices across each channel. Note also,
that the highest prices are significantly lower than in the full
sample and are more similar across channels. Table 1 confirms the
graphical display, showing that the highest prices differ by less
than twenty dollars across channels compared to a nearly two
hundred dollar differential in the full sample. Table 1 also shows
that the number of items sold across channels is nearly identical.
Fig. 2 shows the price difference for identical bottles of wine
sold at grocery stores and drug stores in Sample 1. Differences
greater than zero indicate wines at drug stores priced higher than
the same wines sold at a grocery store, and differences less than
zero indicate drug store prices less than the same wine sold at
grocery stores. As can be seen from Fig. 2, not all wines sold at
drug stores are priced lower than the same wine at grocery stores.
However, for the majority of wines in the sample, drug store prices
are lower than grocery store prices for the same wine.
Fig. 3 shows the price difference for identical bottles of wine sold
at liquor stores and grocery stores in Matched Sample 1. Once again,
Fig. 3 makes clear that not all wines sold at liquor stores are more
expensive than the same wine sold at a grocery store. However, for
the majority of wines in the sample, prices paid at liquor stores are
greater than those paid at a grocery store for the same wine.
5.2. Regression specification
While the summary statistics in Table 1 and Figs. 2 and 3
indicate a consistent pattern of price differentials, we now take a
closer look at retail channel price differentials. We estimate the
following semi-log fixed effects hedonic model of price
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
doi.org/10.1016/j.jretconser.2013.06.004i
S.S. Cuellar, M. Brunamonti / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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Drug is an indicator variable equal to one if the bottle is sold in
a drug store.
Liquor is an indicator variable equal to one if the bottle is sold in
a liquor store.
Month is a vector of monthly indicator variables to account for
seasonality.
Year is a vector of year indicators.
Brand is a vector of indicators for 478 different brands.
Varietal is a vector of indicators for 30 different varietals.
Country is a vector of indicators for 37 different countries of
origin.
differentials:
ln Pricijt ¼ β0 þ β1 Drug it þ β2 Liquor it þ λMonth þ ΩYear
þ χBrand þ ϕVarietal þ θCountry þ uijt
ð1Þ
The model specifies the price of each bottle of wine (i) as a
function of channel (j) at time (t).where:
lnPriceijt is the natural logarithm of price for each unique bottle
of wine “i” sold in market “j” at time “t”.
We estimate the price differentials in Eq. (1) using a fixedeffects model for year, brand, varietal and country of origin. The
initial regression results for each sample are shown in column
(2) of Table 2. The results from Table 2 indicate that, on average,
the price of a bottle of wine sold at a drug store is approximately
7.8% lower than the same bottle of wine sold at a grocery store
while for wines sold at liquor stores, prices are approximately 4.1%
more, on average, than the same bottles of wine sold at a liquor
store. Both results are statistically significant at a 1% level of
significance.
5.3. Econometric issue #1-sample composition
-10
-5
0
5
Drug Store-Grocery Store Price Differential
Fig. 2. Drug store-grocery store price differentials.
-4
-2
0
2
4
Liquor Store Price-Grocery Store Price Differential
Fig. 3. Liquor store-grocery store price differential.
6
While we control for the sample selection across channel, it
should be noted that the wine market is highly segmented by
price. To control for the difference in the composition of wines
sold across channel, we construct three wine price segments:
Under $10 per bottle, $10 to under $20 per bottle and over $20 per
bottle. Fig. 4 shows the distribution of mean monthly cases sold
through each channel by sample. As Fig. 4 clearly shows, food or
grocery stores have the highest volume of sales across all price
segments in Matched Sample 1. However, moving up price segments, drug and food stores decline in sales volume while liquor
stores increase sales volume.
Regression results by price segment are shown in columns 3–5
in Table 2. For the lowest price segment, under $10 per bottle, drug
store prices are on average 8% lower than grocery store prices
while liquor store prices are 5.3% greater than grocery store prices
for the same bottles of wine. Both results are statistically significant. For the $10 to under $20 per bottle price segment, prices
at drug stores are 6.3% lower than grocery stores, while liquor
store prices are 6% greater than grocery stores. Both results are
statistically significant at the 1% level of significance. For the
highest price segment, $20 and over, drug store prices are 2%
lower, while liquor store prices are 1% higher than grocery store
prices. Both results are statistically significant.
Table 2
Price differential regression results.
Variables
Drug
Liquor
Constant
Observations
Adjusted R2
Matched Sample 1
Matched Sample 2
All wines
Under $10
$10 to o $20
$20 over
All wines
Under $10
$10 to o $20
$20 over
−0.078***
[0.000]
0.041***
[0.000]
2.936***
[0.000]
79,835
0.910
−0.080***
[0.000]
0.053***
[0.000]
2.147***
[0.000]
58,121
0.865
−0.063***
[0.000]
0.060***
[0.000]
1.793***
[0.000]
22,823
0.924
−0.025***
[0.000]
0.010***
[0.000]
2.762***
[0.000]
19,415
0.734
−0.056***
[0.000]
0.047***
[0.000]
1.781***
[0.000]
28,435
0.944
−0.016***
[0.000]
0.003
[0.291]
2.516***
[0.000]
5368
0.790
−0.043***
[0.000]
−0.020***
[0.000]
3.451***
[0.000]
2299
0.806
0.033***
[0.003]
−0.035***
[0.000]
3.167***
[0.000]
244
0.435
Coefficients for month, brand, country and varietal omitted.
Robust p-values in brackets.
*po 0.01. **p o 0.05. ***p o 0.01.
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
doi.org/10.1016/j.jretconser.2013.06.004i
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Under $10
5
$10 to under $20
3,000
Average Monthly Case Volume
2,000
1,000
0
DRUG
FOOD
LIQUOR
DRUG
FOOD
LIQUOR
Over $20
3,000
2,000
1,000
0
DRUG
FOOD
LIQUOR
Fig. 4. Matched Sample 1: case volume by price segment.
5.4. Econometric issue #2-sample consistency
In Matched Sample 1 we examine only those items sold across
all three channels. However, not all items are sold in all three
channels in all 44 periods. While we do control for seasonal
variations in price, we also examine a final matched sample of
only those items sold in all three channels in all 44 periods. This is
shown in Fig. 5 and Table 1 as Matched Sample 2. Fig. 5
demonstrates two relevant characteristics about Matched Sample
2. First, the average price of items sold in all three channels in all
periods is less than those in Matched Sample 1. Second, there is
less variation in prices across channels in Matched Sample 2 than
in Matched Sample 1.
Regression results for Matched Sample 2 are shown in Table 2
columns 6–9. For all wines, price differentials are consistent with
those of Matched Sample 1 with drug store prices are 5.6% lower,
and liquor store prices are 4.7% higher than grocery stores. Both
results are statistically significant. Similarly, for the under $10
price segment, drug store prices are 1.6% lower grocery stores
prices, with liquor store prices less than 1% higher. While the
direction of the price differentials is consistent with those of
Matched Sample 1, only the results from drug stores are statistically significant. For the $10 to under $20 price segment, our
results show that both drug store and liquor store prices are less
than grocery store prices. The results for the $20 and over price
segment are inconsistent with our previous results showing drug
store prices greater than grocery store prices and liquor store
prices less than grocery store prices. These results may be driven
by a relatively small sample size.
5.5. Econometric issue #3-endogeniety
Price discrimination occurs when differences in prices are
based on differences in consumers’ willingness to pay and not
on cost differences. One potential confounding factor is if the
observed price differentials are driven by cost differentials due to
quantity discounts on the part of wine producers or distributors.
That is, what we may be observing is not third degree price
discrimination based on market segmentation, but rather costs
differences at the retail level resulting from second degree price
discrimination by producers or distributors. While we do not have
Fig. 5. Comparison of Matched Sample 1 and Matched Sample 2.
data on costs, we can observe average volume sold through each
channel.
Fig. 6 shows average monthly sales in cases by retail channel
and price segment for Matched Sample 1. As is evident from Fig. 6,
food stores sell by far the most wine followed by liquor stores with
drug stores selling the least amount. This is true especially at the
lowest price segment where the price differential is greatest. If the
price differentials were based on quantity discounts, then grocery
stores should have the lowest prices followed by liquor stores and
drug stores. Since drug stores have the lowest observed prices,
followed by food and liquor stores, second degree price discrimination on the part of producers or distributors seems unlikely.
6. Price elasticities
The data examined shows a clear and consistent pattern of
price differentials across retail channel. Specifically, wines at drug
stores are consistently priced lower than the same wines at
grocery stores, while wines at liquor stores are consistently priced
above those same wines at grocery store prices. Moreover, we
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
doi.org/10.1016/j.jretconser.2013.06.004i
6
S.S. Cuellar, M. Brunamonti / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Fig. 6. Retail sales volume by channel and price segment.
have shown that these price differentials are greater for high
volume wines than for lower volume wines.
Opportunities for price discrimination exist when market
segments differ in their price elasticities of demand. Theoretically,
if PDrug oPGrocery oPLiquor, then |εDrug| 4|εGrocery| 4|εLiquor|. The previous section demonstrated that the price of a bottle of wine at a
drug store is less than the same bottle of wine at a grocery store,
which is less than the same bottle of wine at a liquor store. We
turn now to estimating the price elasticity of demand for wine
across all three channels. To estimate the price elasticity of
demand across channel, we use a standard double log demand
specification of the form:
lnCasesijt ¼ b0 þ b1 lnPriceijt þ b2 Drug it þ b3 Liquor it
þ b4 Incomet þ lMonth þ OYear þ wBrand þ ϕVarietal
þ θCountry þ uijt
ð2Þ
elastic than consumers at grocery store. These results are all
statistically significant and are generally consistent across price
segment and across sample, except for the last two price segments
of Matched Sample 2. The estimated price elasticities for liquor
stores are mixed. While the regression results indicate that liquor
store consumers are less price sensitive than those at grocery
stores in the highest price segment, the results for the lower price
segments indicate a greater price elasticity of demand at liquor
stores. While these results appear contrary to our hypothesized
relationship, these results are consistent with the conjecture that
liquor store sales are concentrated in the upper price segment.
These results are also consistent with the observed price differentials which were smaller than those observed for drug stores. In
this context, our results are consistent with the condition required
for price discrimination. That is, |εDrug| 4|εGrocery| 4|εLiquor|.
6.1. Econometric issue #4-identification
Drug is a vector including an indicator for drug store sales as
well as an interaction between the indicator and log price.
Liquor is a vector including an indicator for liquor store sales as
well as an interaction between the indicator and log price.
Income is the natural logarithm of income in period “t”.
Month, Year, Brand, Varietal and Country are defined as in Eq.
(1).
We estimate the price elasticities in Eq. (1) using a fixed-effects
model for year, brand, varietal and country of origin. The regression results are shown in Table 3 and illustrate a few noteworthy
points. To begin, across samples and price segments, the model
explains a significant portion of the variation in cases sold.
Additionally, the price elasticities across channel and price segment are all negative, indicating the “law of demand” remains
intact. Furthermore, our results for all wines, which ranges from
−1.26 for drug stores to −.89 for grocery stores are to similar to that
of Cuellar and Huffman (2008) who find a price elasticity of
demand of −1.232 and Fogarty (2006) who finds a mean price
elasticity of −.77. These values are, however, somewhat lower than
those found in Bijmolt et al. (2005) who find a mean price
elasticity of −2.62. Consistent with our hypothesized results,
Table 3 indicates that consumers at drug stores are more price
Estimating the price elasticity of demand inevitably raises
questions about endogeneity and identification (Andrews et al.,
2011; Cuellar and Huffman, 2008; Fogarty, 2006;Villas-Boas and
Winer, 1999). To correct for endogeneity we use lagged price
(Ebbes et al., 2009) as a readily available instrument for current
price. Our results are shown in Table 4. Note from Table 4 that the
estimated price elasticities are uniformly lower than the OLS
estimates in Table 3, although the magnitude of the difference is
not generally significant. For all wines, the price elasticity of
demand from the OLS estimate is −.738 while the estimated
elasticity from the IV regression is −.899. In addition, the differences in price elasticities across channels are generally similar to
those of the OLS estimates.
7. Discussion
We find that retail channel is used as an effective means of
price discrimination. Just as coupons and rebates offer discounts to
low willingness to pay consumers whose opportunity cost of time
is less than the associated savings or rebate, retail channel
provides a similar opportunity for discriminatory pricing based
on consumer self-selection. We show that drug stores offer a
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
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7
Table 3
Price elasticities of demand.
Variables
Matched Sample 1
All wines
lnprice
DrugnlnPrice
LiquornlnPrice
Drug
Liquor
lnIncome
Constant
Observations
Adjusted R2
nnn
Matched Sample 2
Under $10
nnn
$10 to o $20
nnn
$20 over
nnn
All wines
nnn
Under $10
nnn
$10 to o$20
nnn
$20 over
-0.899
[0.000]
−0.361nnn
[0.000]
−0.002
[0.864]
−1.342nnn
[0.000]
−1.648nnn
[0.000]
−0.790nnn
[0.002]
15.448nnn
[0.000]
−1.086
[0.000]
−0.283nnn
[0.000]
−0.075nnn
[0.007]
−1.483nnn
[0.000]
−1.493nnn
[0.000]
−0.897nnn
[0.003]
14.858nnn
[0.000]
−1.403
[0.000]
−0.272nnn
[0.000]
−0.065
[0.181]
−1.636nnn
[0.000]
−1.461nnn
[0.000]
−1.085nn
[0.017]
18.712nnn
[0.000]
−2.191
[0.000]
−0.428nn
[0.046]
1.306nnn
[0.000]
−1.732nn
[0.010]
−5.110nnn
[0.000]
1.560
[0.134]
−0.849
[0.929]
−1.527
[0.000]
−0.153nnn
[0.000]
0.037n
[0.083]
−1.122nnn
[0.000]
−1.603nnn
[0.000]
−1.037nnn
[0.000]
17.745nnn
[0.000]
−1.426
[0.000]
−0.194nnn
[0.000]
−0.048nn
[0.032]
−1.059nnn
[0.000]
−1.447nnn
[0.000]
−1.043nnn
[0.000]
17.697nnn
[0.000]
−2.832
[0.000]
0.633nnn
[0.000]
−0.220nnn
[0.005]
−3.290nnn
[0.000]
−0.964nnn
[0.000]
−1.969nnn
[0.003]
31.232nnn
[0.000]
−5.238nnn
[0.001]
3.984n
[0.061]
4.714nnn
[0.007]
−14.255nn
[0.027]
−15.043nnn
[0.007]
1.047
[0.822]
10.806
[0.800]
79,835
0.885
58,121
0.883
19,415
0.905
2299
0.940
28,435
0.936
22,823
0.946
5368
0.927
244
0.936
Coefficients for month, brand, country and varietal omitted.
Robust p-value in brackets.
n
po 0.1.
p o0.05.
nnn
p o 0.01.
nn
Table 4
Instrumental variable regressions.
Variables
Lnprice
DrugnlnPrice
LiquornlnPrice
Drug
Liquor
lnIncome
Constant
Observations
Adjusted R2
All wines
nnn
−0.738
[0.000]
−0.340nnn
[0.000]
−0.003
[0.848]
−1.332nnn
[0.000]
−1.654nnn
[0.000]
−0.919nnn
[0.000]
16.617nnn
[0.000]
78,562
0.886
Under $10
nnn
−0.962
[0.000]
−0.261nnn
[0.000]
−0.040
[0.166]
−1.485nnn
[0.000]
−1.569nnn
[0.000]
−0.972nnn
[0.001]
16.392nnn
[0.000]
57,216
0.884
$10 to o $20
nnn
−1.002
[0.000]
−0.188nn
[0.015]
−0.253nnn
[0.000]
−1.774nnn
[0.000]
−0.992nnn
[0.000]
−1.393nnn
[0.002]
20.415nnn
[0.000]
19,099
0.907
$20 over
All wines
−0.871
[0.460]
−0.776nn
[0.022]
−0.024
[0.949]
−0.567
[0.603]
−0.910
[0.432]
1.970n
[0.076]
−9.054
[0.456]
2247
0.935
nnn
−1.195
[0.000]
−0.198nnn
[0.000]
0.032
[0.109]
−1.012nnn
[0.000]
−1.609nnn
[0.000]
−1.043nnn
[0.000]
17.163nnn
[0.000]
28,222
0.936
Under $10
nnn
−1.315
[0.000]
−0.194nnn
[0.000]
−0.044n
[0.059]
−1.051nnn
[0.000]
−1.462nnn
[0.000]
−1.123nnn
[0.000]
18.231nnn
[0.000]
22,643
0.946
$10 to o $20
nnn
−2.315
[0.000]
0.471nnn
[0.000]
−0.561nnn
[0.000]
−2.860nnn
[0.000]
−0.111
[0.702]
−1.948nnn
[0.003]
28.816nnn
[0.000]
5336
0.926
$20 over
13.392
[0.682]
−17.723
[0.649]
−48.376
[0.486]
46.452
[0.694]
154.368
[0.486]
7.991
[0.643]
−98.169
[0.691]
243
0.544
Coefficients for month, brand, country and varietal omitted.
Robust p-value in brackets.
n
po 0.1.
p o0.05.
nnn
p o 0.01.
nn
selection and prices consistent with lower income and low
opportunity cost shoppers. This is illustrated by Figs. 4 and 6
which show that the sales of wine at drug stores decrease as you
move up each price segment. Because drug stores carry fewer
items in total than grocery stores, drug store shoppers may
represent more frequent “fill in” or quick shopping trips consisting
of smaller “shopping baskets” than grocery store shoppers. As
Kahn and Schmittlein (1989) note, “quick [shoppers] have lower
incomes, more older males and older females and more retired
people” than regular shoppers. In addition to demographic characteristics, shopper intent may play a role in the lower prices
observed at drug stores. If the primary purpose of shopping at a
drug store is to purchase items other than wine, then offering a
price lower than other retail outlets is an efficient means of
enticing marginal consumers. This is consistent with the behavior
observed by Bucklin and Lattin (1991) who show that consumers
are more price responsive when purchases are unplanned. The
greater price elasticity of demand estimated for drug store
shoppers supports both these explanations.
Grocery stores are the largest of the three retail channels for
wine but one whose market share falls as you move up the price
segments. In contrast to drug store shoppers, wine, on average, can
assumed to be part of a larger shopping list or basket of goods that
are purchased on more infrequent shopping trips. Such shoppers,
according to Kahn and Schmittlein (1989) tend to be from larger
families with higher incomes. We show that these larger, high
income families, who have a higher opportunity (or search) cost of
shopping, pay more for an identical bottle of wine than drug store
shoppers. While the typical grocery store shopper may not be
aware of the lower prices of wine at a drug store, it is not clear that
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
doi.org/10.1016/j.jretconser.2013.06.004i
S.S. Cuellar, M. Brunamonti / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎
8
this knowledge would result in an additional shopping trip. For
example, even if the typical grocery store shopper knew that an
identical wine can be purchased at a drug store for less, they must
weigh the costs of an additional shopping trip against the
associated savings. The costs associated with an additional shopping trip include the time needed to travel to a drug store
including parking, the in-store shopping time and the check-out
time. Travel time may or may not be a significant cost since many
shopping centers have grocery, drug and liquor stores located in
the same center or even adjacent to each other. Shoppers must
still, however, weigh whatever costs they incur with the potential
savings. Given that the potential savings would be approximately
4% to 8%, with the greatest savings being on wines less than $10
per bottle, the savings may not be enough to cover the costs of an
additional shopping trip. This relative price inelasticity is consistent with our estimated price elasticity as well as the shopping
behavior of those on planned trips observed by Bucklin and Lattin
(1991).
Finally, in contrast to drug and grocery stores, liquor store sales
increase as you move up the price segments indicating that
shoppers at liquor stores are more likely to purchase more
expensive wines. Additionally, liquor store patrons are ostensibly
category specific shoppers with a specific shopping intent, namely
to buy liquor. As Bell et al. (1998) note, while category specific
stores reduce the search costs of shopping for that category, this
specific shopping intent increases insensitivity to price. This is
confirmed by our estimated price elasticities of demand for liquor
store shoppers as well as our results that show liquor store prices
are on average greater than drug and grocery store prices for the
same wine.
8. Conclusion
Increased competition both domestically and internationally
has led firms to seek new sources of revenue. Price discrimination
across all dimensions, time, space, demographic characteristics
and offline versus online is one means of extracting surplus from
consumers. The proliferation of social network coupon sites is an
illustration of this. We show that use of retail channel is an
effective means of price discrimination based on demographic
self-selection and shopping intent. While the current research
shows a clear and consistent pattern of discriminatory price
differentials for a single category of goods, wine, there is no
reason to believe that other categories are not following similar
behavior. If not, we show that the opportunity may exist.
Acknowledgments
Economics Seminar Series for helpful comments. We would also
like to thank Sonoma State University’s Wine Business Institute for
funding this research.
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The authors would like to thank the anonymous referees as
well as participants at Sonoma State University’s Department of
Please cite this article as: Cuellar, S.S., Brunamonti, M.Please confirm that given names and surnames have been identified correctly and
are presented in the desired order.–>, Retail channel price discrimination. Journal of Retailing and Consumer Services (2013), http://dx.
doi.org/10.1016/j.jretconser.2013.06.004i