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Wine Retail Price Dispersion in the United States: Searching for Expensive Wines?



Similar to other markets in which deviations from Jevons' "law of one price" is the norm rather than the exception, the retail wine market in the United States is characterized by large price dispersions. Drawing on a large sample of retail prices from we find an average per-wine coefficient of variation of 23 percent. Some of this is due to differential market conditions, especially state regulations. Our evidence suggests that dispersion also depends positively on price levels, after controlling for consumer, market, and state heterogeneity.
American Economic Review: Papers & Proceedings 2011, 101:3, 136–141
Like other markets in which deviation from
Jevons’s “law of one price” is the norm rather
than the exception, the retail wine market in
the United States is characterized by enormous
price dispersions. For instance, in our data retail
prices for 2005 Chateau Latour range from
$695 in a Petaluma, California, wine store to
$2,000 in a wine store in Champaign, Illinois.
Similarly, at the lower end of the price distribu-
tion, the observed retail price of 2007 Yellowtail
Merlot ranges from $4.99 in Buffalo, New York,
to $9.99 in Jersey City, New Jersey. Price disper-
sion in the wine market can be caused by various
factors, such as differences in production and
distribution cost, differences in price elasticities
of demand, or different market regulations and
Since the ratication of the 21st Amendment
repealing Prohibition, the US wine market has
been primarily regulated at the state level, more
or less impairing or effectively abolishing com-
petition between wine retail outlets. In addition
to the federal wine tax, wine is levied by state-
specic wine and sales taxes. Eighteen states
maintain a monopoly over the wholesale and
retail sales of wine; others restrict the sales of
wine to certain outlets and/or certain times, or
do not allow the payment for wine purchases
Wine Retail Price Dispersion in the United States:
Searching for Expensive Wines?
By David A. Jaeger and Karl Storchmann*
Discussants: Joyce Jacobsen, Wesleyan University;
Victor Ginsburgh, University of Brussels; Bronwyn
Hall, University of California-Berkeley; Charles Mason,
University of Wyoming.
* Jaeger: Center for Macroeconomic Research, University
of Cologne, Albertus-Magnus-Platz, 50923 Cologne,
Germany, and CUNY Graduate Center (e-mail: jaegerd@; Storchmann: Economics Department, New
York University, 19 W. 4th St., 6FL, New York, NY 10012
(e-mail: We are grateful to our
discussant, Joyce Jacobsen, and the participants of the Wine
Economics Session at the AEA Annual Conference for help-
ful comments. We also thank Etan Schwartz and Vadim
Zhitomirsky for excellent research assistance.
with credit cards. Many states prohibit direct
wine shipments from out-of-state producers and
retailers, while others even prohibit in-state pro-
ducers and retailers to ship wine to consumers.
Price differences between states or counties are
thus not surprising. In this paper we examine
whether state- or county-specic effects fully
explain the observed price dispersion or if price
variations remain, even after controlling for
location differences. If so, is the degree of price
dispersion identical across all price brackets, or
does the dispersion for expensive wines reect
greater returns to search?
A large body of information-theoretic litera-
ture suggests that markets, even for standard-
ized products, may exhibit considerable price
dispersion. Following George J. Stigler’s (1961)
paper, several authors model how equilibrium
price dispersion can arise as a result of heteroge-
nous information (e.g., Steven Salop and Joseph
E. Stiglitz 1977, 1982; Jennifer F. Reinganum
1979; Hal R. Varian 1980; Kenneth Burdett and
Kenneth L. Judd 1983; John A. Carlson and
R. Preston McAfee 1983; and Dale O. Stahl II
1989). In general, price dispersion can persist
in equilibrium if obtaining information is costly
(through, for example, search costs) and some
fraction of consumers chooses to be uninformed.
A variety of empirical studies have explicitly
examined the association between consumer
search and price dispersion for homogenous
goods. John W. Pratt, David A. Wise, and
Richard Zeckhauser (1979) examine price dis-
persions for 39 consumer goods in the Boston
area and report coefcients of variation (CV)
for the product prices between 4 and 71 percent.
They also nd that the price dispersion substan-
tially increases with the average price of the
good, suggesting that the search cost for expen-
sive items is higher. This may be explained by
the fact that expensive products are purchased
less frequently, reducing the incentive of a
VOL. 101 NO. 3 137
buyer to search. Bev Dahlby and Douglas S.
West (1986) nd a CV of 18 percent for auto
insurance policies in Alberta. After ruling out
quality or cost differences, they conclude that
this price dispersion is almost exclusively due
to costly consumer search. Alan T. Sorenson
(2000) examines the retail prices of pharma-
cies in two geographically distinct markets and
nds a CV of 22 percent. While at most one-
third of the observed price dispersion is due to
pharmacy heterogeneity, most is due to costly
search. Sorenson also nds that frequently
purchased prescriptions exhibit lower price
Most relevant for our study is the hypothesis
that the Internet and the emergence of online
markets substantially lower search cost result-
ing in lower price dispersion (e.g., J. Yannis
Bakos 1997). Xing Pan, Brian T. Ratchford,
and Venkatesh Shankar (2002) analyze the price
dispersion of 581 goods in 8 product categories
in online markets. After controlling for sellers’
heterogeneity and especially service quality,
however, they nd online price dispersion to
be substantial and persistent. Erik K. Clemons,
Il-Horn Hann, and Lorin M. Hitt (2002) report
similar results for the market of airline tickets
sold by online travel agents. Kathy Baylis and
Jeffrey M. Perloff (2002) analyze Internet prices
of a specic type of digital camera and a atbed
scanner over a 14-week period, and also nd sig-
nicant price dispersion, which even increases
when controlling for service quality. In contrast
to Varian’s (1980) model of mixed strategies,
they nd a pure-strategies equilibrium, with
high-price rms and low-price rms remaining
xed in the overall ranking over time. They con-
clude that information costs (the time taken to
negotiate the website to discover stock and, to
some extent, price information) are an important
determinant of online price dispersion, and that
rms may discriminate among consumers based
on their knowledge, search costs, or patience.
Because a high degree of price dispersion
indicates large potential gains to search by
consumers, such dispersion may also suggest
that the market in question is inefcient with
regard to information. Empirical research has
shown that consumer search in most cases stops
before full information is obtained; sometimes
no search takes place at all (Ratchford 2009).
Given that search is costly, however, the opti-
mum search point is reached when marginal
search cost equals its marginal benet. Ratchford
and Narasimhan Srinivasan (1993), Edward J.
Fox and Stephen J. Hoch (2005), and Dinesh
K. Gauri, K. Sudhir, and Debabrata Talukdar
(2008) provide empirical evidence that is con-
sistent with this normative rule.
Given that the search cost is essentially xed
per wine and independent of its price (e.g.,
searching a website), it is possible that search
is more protable for expensive wines, result-
ing in smaller price dispersion with increasing
average prices. On the other hand, less expen-
sive wines face a stiffer competition from close
substitutes than expensive wines do. In contrast
to a $200 wine, when a consumer shops for a
$5 wine the brand and vintage are likely to be
of less importance. Monopoly pricing power
may therefore increase with price, potentially
leading to a price dispersion that increases with
a wine’s average price. Alternatively, learning
through experience may play a role and lead to
the same dispersion-price relationship. Low-
price wines sell at much higher quantities than
high-end wines. Information about quality and
prices of lower-tier wines may thus more easily
penetrate the market (for “learning-by-buying”
and “word-of-mouth,” see Ratchford 2009).
In this analysis, we draw on a large database
of wine retail prices to examine the relationship
between price level and price dispersion. We rst
examine the role of local characteristics such as
the number of retail wine establishments, per
capita income, and local demographics on wine
prices. We control for the regulatory environ-
ment by using state xed effects. After also
controlling for wine-vintage xed effects, we
then examine whether the residual variation in
prices is related to the wine’s average price. In
general, we nd a signicant and positive rela-
tionship between residual variation in prices and
(adjusted) price levels.
I. Data and Descriptive Statistics
We use wine retail prices from 2006 to 2008
provided by, an Internet
wine price search site on which wine retail out-
lets worldwide can post prices of their wines. For
sellers in the United States,
currently lists approximately 2.5 million prices
posted by about 6,300 wine stores. Since
many wines are available only in a few stores,
we restrict our analysis to 186 wine brands of
various vintages. For all but one of these wines
we observe well over 200 prices, and for many
we observe more than 1,000 prices.
our sample contains approximately 106,000
prices on red and white wines. In Table 1 we
report some basic descriptive statistics on price
levels and price dispersion. Most of the wines
in our sample are produced in the United States
and two-thirds of them are red. We observe sub-
stantial differences in price dispersion, mea-
sured by the coefcient of variation. Compared
to the results of other empirical analyses, the
overall price dispersion of 23.4 percent is rather
The data contain observations for sizes in addition to
the standard 750ml bottle. We have dropped all observations
for nonstandard sizes. In addition, for each wine we have
dropped the 5 percent lowest and 5 percent highest observed
prices, to be sure that we were not capturing (mislabeled)
case prices or other measurement issues. We have also
dropped any observations in which the description indicated
that the bottle was damaged or irregular in any way. For
wines with both vintage and nonvintage prices reported, we
dropped any nonvintage prices when these constituted less
than 25 percent of the total number of observations for that
wine. We also eliminated from the data rosé, sparkling, and
fortied wines.
high. It is higher for red than for white wines and
higher for French wines compared to domestic
wines and other imports (mainly from Australia
and Italy). Also, expensive wines exhibit higher
price dispersion than do wines in lower price
brackets, suggesting the dominance of the sub-
stitution effect or learning from buying over
search cost hypotheses.
II. Determinants of Wine Prices
To examine how local market characteristics
affect wine prices, we estimate the equation
(1) log(pivcsy) = β
0 + β
1 E
cy + β
+ β
3 W
c + β
4 A
c + β
5 O
+ β
6 N V
i + θ
y + δ
+ λ
iv + ε
ivcsy ,
where i indicates wine, v indicates vintage, c
indicates county, s indicates state, and y indi-
cates year of price posting. The variable E is the
number of retail wine establishments in county
Table 1—Descriptive Statistic s on Retail Wine Prices
Avg. Avg. coefcient Avg. N per Number of
Sample mean price of variation wine wines
Full sample
Red $80.25 0.2495 984.45 136
White $27.84 0.1925 746.32 50
United States
Red $45.61 0.2002 914.41 66
White $15.17 0.1814 746.84 31
Red $148.64 0.3492 1,173.49 47
White $130.53 0.3732 689.33 6
Red $39.87 0.1874 799.13 23
White $10.66 0.1358 771.38 13
Average price < $15
Red $8.50 0.1668 661.92 36
White $8.71 0.1668 683.97 29
Average price < $50
Red $28.34 0.2576 842.47 32
White $22.83 0.1665 883.69 16
Average price $50
Red $142.65 0.2895 1,222.02 68
White $154.83 0.4099 668.4 5
Source: Authors’ calculations using data from Observations are from 2006–
2008, measuring prices of nonvintage and vintage wines from the 1998–2007 vintages.
VOL. 101 NO. 3 139
c in year y divided by the county population in
2000, taken from the county business patterns
data of the United States Census Bureau. I is
per capita income in the county in 2000, W is
the white share of the population in the county
in 2000, A is the share of the county population
in 2000 that is 25 or over (the population most
likely to drink wine), O is how old the wine is in
year y (nonvintage wines are coded to zero), NV
is an indicator for nonvintage wines, θ is a year
xed effect, δ is a state xed effect (capturing
differences in state regulations), λ is a wine ×
vintage xed effect, and ε is the idiosyncratic
term. In some specications, we use only simple
wine xed effects without letting the coefcient
vary across vintages.
The results of estimating variants of equation
(1) are presented in Table 2. The rst three col-
umns contain results for red wine and the last
three contain results for white wine. In columns
1 and 4 we constrain the λs as well as the coef-
cient wine age and nonvintage to be equal to
zero. It is clear from both columns that prices
vary with local market conditions, even with our
sample drawn from sellers who list their prices
on the Internet. Local market conditions explain
only between 7 (red) and 13 (white) percent of
the variation in prices, however. This is not sur-
prising—in this regression we are treating all
wines the same, regardless of where or by whom
they were produced.
In columns 2 and 5 we add xed effects for
each wine to the analysis, but constrain these to be
equal across vintages. The model now accounts
for 95 percent of the variation in log prices—
clearly the majority of variation in wine prices
comes from differences in origin and quality. The
coefcients on income, the share of whites in the
population, and age change somewhat, suggest-
ing that different wines are sold in different loca-
tions. In columns 3 and 6, we allow for a full set
of wine × vintage interactions. The results are
qualitatively similar to those in column 2.
III. The Relationship between Price and Variance
Our fundamental research question is whether
there is a relationship between residual price
variation and price level. In Table 3, we report
the slope coefcients from a regression of the
Table 2—Determinants of Wine Prices
Red White
Variable (1) (2) (3) (4) (5) (6)
Number of wine retailers per 0.0031 0.0002 0.0002 0.0023 0.0002 0.0001
2000 county population (0.0002) (0.0001) (<0.0001) (0.0004) (0.0001) (0.0001)
Log per capita income (2000)0.1568 0.0300 0.0083 0.1423 0.0208 0.0107
in county (0.0011) (0.0068) (0.0007) (0.0386) (0.0099) (0.0079)
White share of county population 0.0243 0.0017 0.0189 0.2334 0.0214 0.0066
(2000) (0.0073) (0.0085) (0.0060) (0.0515) (0.0129) (0.0101)
Share of county population that is 0.0247 0.1714 0.1987 1.4587 0.2767 0.2412
25 or older (2000) (0.0249) (0.0393) (0.0282) (0.2307) (0.0608) (0.0479)
Wine age (nonvintage = 0)0.0075 0.1411 0.0231 0.0533
(0.0008) (0.0034) (0.0023) (0.0042)
Nonvintage 0.0573 0.0152
(0.0042) (0.0067)
Year xed effects X X X X X X
State xed effects X X X X X X
Wine xed effects X X
Wine × vintage xed effects X X
R20.07 0.95 0.98 0.13 0.94 0.97
Observations 82,698 23,919
Note: Dependent variable is log price. Estimated via OLS. Heteroskedasticity-consistent standard errors are in parentheses.
Source: Authors’ calculations using data from Observations are from 2006–2008, measuring prices of non-
vintage and vintage wines from the 1998–2007 vintages.
average squared residual on the xed effect
for each wine × vintage combination, both
taken from columns 3 and 6 of Table 2 for red
and white wines, respectively. That is, we are
e iv
= ϕ + φ ˆ λ
iv + ξ
iv ,
where e are the residuals from the estimation of
equation (1). Here we nd that, overall, there is a
positive relationship between residual variation
in prices and their level. To put the magnitude of
the coefcient in context, the average value of
the dependent variable for red wines (that is, the
average mean squared residual) is 0.0287. Thus,
the estimated coefcient on the full sample of
1,117 wine × vintage combinations is about
one-tenth of this average. For white wines, the
average mean squared residual is 0.0209 and the
estimated coefcient for the full sample is about
three-tenths of this (0.0064). For both red and
white wines, we nd a stronger statistical rela-
tionship between dispersion and average price
for vintage wines than for nonvintage wines.
IV. Conclusion
In this paper we show that there is a fair
amount of price dispersion for red and white
wines in the United States, with an average per-
wine coefcient of variation of 23 percent. Some
of this is due to differential market conditions.
But our evidence suggests that dispersion also
depends (weakly) on price levels, after control-
ling for consumer, market, and state heterogene-
ity. These results are consistent with the theory
of “learning-by-buying” in which goods that are
purchased more often are predicted to have less
price heterogeneity. The results are less consis-
tent with a search costs story. To be consistent
with our results, search costs would have to
be higher for expensive wines relative to less-
expensive wines. This seems less plausible to
us because the search mechanisms are likely to
be the same for both inexpensive and expensive
wines. It may be, however, that buyers of more
expensive wines have a higher opportunity cost
of time and are less willing to spend time search-
ing for the lowest price.
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All 0.0024 1,117 0.0064 398
(0.0010) (0.0020)
Vintage 0.0025 1,056 0.0062 352
(0.0010) (0.0020)
Nonvintage 0.0291 61 0.0171 46
(0.0151) (0.0133)
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prices of nonvintage and vintage wines from the 1998–2007
VOL. 101 NO. 3 141
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... A large and growing body of information-theory literature has analyzed and found considerable price dispersion even for standardized products (for a survey, see Jaeger and Storchmann, 2011). Various empirical studies have explicitly examined the association between consumer search and price dispersion for homogenous goods (e.g., Jaeger and Storchmann, 2011;Pratt, Wise, and Zeckhauser, 1979;Sorensen, 2000). In general, when search costs are high, consumers may be less inclined to look for the retail outlets with the lowest prices, allowing for higher price dispersion. ...
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... This is especially important because price dispersion can be highly heterogeneous across product categories and purchase environments. For instance, wine prices tend to be more variable for reds than for whites (Jaeger and Storchmann 2011), flight prices tend to be more variable for unpopular destinations than for popular destinations (Borenstein and Rose 1994), prices tend to be more variable in online stores than in brickand-mortar stores (Zhuang, Leszczyc, and Lin 2018), and some stores offer a much wider range of prices than others (Ancarani and Shankar 2004). ...
Price knowledge is a key antecedent of many consumer judgments and decisions. This paper examines consumers’ ability to form accurate beliefs about the minimum, the maximum, and the overall variability of prices for multiple product categories. Eight experiments provide evidence for a novel phenomenon we call dispersion spillover: Consumers tend to overestimate price dispersion in a category after encountering another category in which prices are more dispersed (versus equally or less dispersed). Our experiments show that this dispersion spillover is consequential: It influences the likelihood that consumers will search for (and find) better prices and offers, and how much consumers bid in auctions. Finally, we disentangle two cognitive processes that might underlie dispersion spillover. Our results suggest that judgments of dispersion are not only based on specific prices stored in memory, and that dispersion spillover does not simply reflect the inappropriate activation of prices from other categories. Instead, it appears that consumers also form “intuitive statistics” of dispersion: Summary representations that encode the dispersion of prices in the environment, but that are insufficiently category-specific.
... wine guides, wine reports) (Landon and Smith, 1998;Schamel, 2006;Brentari et al., 2011;Cacchiarelli et al., 2016;Galati et al., 2017). Hedonic analysis is also applied to MP such as retail online prices IJWBR 31,3 (Panzone and Simoes, 2009;Jaeger andStorchmann, 2011), scanner data (Mueller Loose andSzolnoki, 2012;Cuellar and Claps, 2013) and survey data . Mostly, hedonic works are aimed at determining to what extent the bottle price is affected by wine quality while focusing on regional wines or appellations (Combris et al., 1997;Landon and Smith, 1997;Combris et al., 2000;Schamel and Anderson, 2003;Lecocq and Visser, 2006;Benfratello et al., 2009). ...
Purpose The purpose of this paper is to analyze the market of rosé wines in Italy, to outline retail strategies and to investigate to what extent the price is affected by branding these wines. Design/methodology/approach A survey has been carried out on retailers by collecting data about wines as intrinsic attributes (grape variety, blending, origin, alcohol content, etc.) and extrinsic attributes (brand, price, packaging, etc.) and about outlet and retail environment. The hedonic analysis required a rearrangement of data survey, while a Box-Cox transformation allowed to control the strong heteroskedasticity detected of the data. Findings Results provide strategies for still, semi-sparkling and sparkling rosé market segments. Still rosé wines are strongly differentiated, while the price is affected by the appellation, grape variety, blending, brand and outlet features. Two main strategies are suggested: the first focuses on appellations endorsing consumer’s brand loyalty; the second is driven by retailers while involving weaker brands. Different pictures emerged for semi-sparkling and sparkling wines, as producers and retailers tend to follow consumer’s preferences for fresh and easy drinking wines as well as to extend the product assortment. Research limitations/implications Results for sparkling rosé wines cannot be generalized. The high fragmentation hinders the hedonic model performance in capturing the price effects of brands, appellations, grape variety and wine blend. Practical implications The hedonic analysis provides suggestions for rosé wine producers that should reinforce their brand through associations among intrinsic attributes, such as appellation, and extrinsic ones, such as price, while satisfying retailer requirements. Originality/value The paper contributes to the knowledge base about the Italian rosé wine market, which is mostly export-oriented. Model results help to understand why the domestic consumption is stagnant with respect to other countries such as France or the USA.
... A few studies have looked into price dispersion and deviations from the law of one price, and as an extension, at the segmentation of the wine market. For instance, Jaeger and Storchmann (2011) analyse wine price dispersion across retail outlets in the US and find that deviations from the law of one price are common. They indicate even stronger deviations for the high-priced wines due, amongst others, to diverging state regulations. ...
We introduce a heterogeneous agent model to explain the dynamics of fine wine investments. Our results show evidence of the existence of both fundamentalists – those who trade on mean-reversion towards a fair value – and chartists – those who extrapolate recently observed price trends – in the wine market. Moreover, we document that market participants switch between the two trading strategies, allocating more weight to the strategy that has been the most accurate in forecasting wine index values in the recent past. This switching behaviour can explain the large variations in index values (bubbles and crashes) that are observed in the fine wine market.
This study considers the functioning of the market for recreational marijuana during its transition to legal status in Colorado and Washington State. Contrary to the expectation that moving from covert to legal status generates a freer flow of information that improves market functioning, we find that the measures we consider, price dispersion and the incidence of low-quality deliveries (‘rip-offs’), both show statistically and economically significant increases, reflecting worsening market functioning. We attribute this adverse outcome to the partial nature of the initial decriminalization, which maintained sanctions against selling, even while legalizing purchase and possession of small quantities. Our results also confirm that purchases increased with decriminalization. An influx of unsophisticated new buyers would create a less-informed average customer; a less-informed customer pool is consistent with poorer market outcomes, especially when dealers must continue to keep transactions secret. This work contributes to the empirical price dispersion literature, providing results consistent with the hypothesis that price dispersion increases when participants are less informed, but inconsistent with the prediction that price dispersion decreases with lower cost of price information. It also provides further evidence consistent with the importance of ‘rip-off’ transactions that has been highlighted in the literature on covert markets.
The second-cheapest bottle on a restaurant wine list is widely thought to be priced to exploit naïve diners embarrassed to choose the cheapest option. This paper investigates whether this behavioral theory holds empirically. We find that the mark-up on the second-cheapest wine is significantly below that on the four next most expensive wines. It is therefore an urban myth that the second-cheapest wine is an especially bad buy.
Thorstein Veblen, author of The Theory of the Leisure Class (1899), is memorialized in economics by the “Veblen effect,” a term introduced by Harvey Leibenstein to represent a positive consumer demand response to an increase in price. However, Leibenstein’s 1950 QJE article introducing the world to the “Veblen effect” does not cite Veblen as one of its 17 references.I begin this dissertation by revisiting Veblen’s theory of consumer behavior and evaluating its subsequent treatment in the economics literature. I find that Leibenstein (1950) misunderstands Veblen’s concept of “conspicuous consumption” and contradicts Veblen’s empirical predictions.How might Veblen’s theory be interpreted more faithfully with relevance to present-day markets? I suggest that the notion of “Veblen goods” and “Veblen consumers” is misleading, because Veblen’s theories applied to nearly all goods and consumers. My alternative approach is to look for attributes and corresponding prices whose market behavior is in line with Veblen’s predictions. I divide the attributes of goods into three classes, and I suggest that each class has its own hedonic price component: “useful,” “decorative,” and “invisible” attributes.Invisible attributes cannot be detected by the consumer’s own unaided sensory apparatus. Examples of invisible attributes are a good’s market price, scarcity, reputation, ratings, place of origin, microbial content, organic certification, and age. I refer to decorative and invisible attributes, together, as “Veblen attributes”; and I refer to the premiums consumers are willing to pay for Veblen attributes as “Veblen premiums.”In this thesis, I attempt to observe some Veblen attributes and Veblen premiums in present-day consumer markets. I report experimental results from the U.S. retail markets for beer (part II), wine (part III), and food (part IV). I employ a variety of experimental techniques, including consumer surveys, blind tasting experiments, lab experiments, and undercover experiments. I include several first-person essays to round out the narrative.
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This paper examines the strong version of the law of one price (LOOP) on the auction market for fine wine. We draw on worldwide auction prices from eight auction houses, ¹ covering the time period from 2000 to 2012. Employing a hedonic approach, we find significant price premiums in particular in Hong Kong and between auction companies (independent of their locations). The price premiums by far exceed the expected transaction costs, casting doubt on the existence of the strong version of LOOP in the fine wine market. Our results suggest that heterogeneity in buyer preferences may crucially contribute to the observed price dispersion. In particular, although wines suspected of being counterfeits are sold at discounts in Western markets, they fetch price premiums in Hong Kong. (JEL Classifications: Q14, G11)
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The authors analyze cherry-picking in the context of grocery shopping, comparing the behavior of consumers who visit two grocery stores on the same day (8% of shopping trips) with single-store shoppers. The authors find cherry-picking to be consistent with rational economic behavior. Cherry pickers benefit by saving 5% more per item while buying systematically (67%) larger market baskets.
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The authors analyze cherry-picking in the context of grocery shopping, comparing the behavior of consumers who visit two grocery stores on the same day (8% of shopping trips) with single-store shoppers. The authors find cherry-picking to be consistent with rational economic behavior. Cherry pickers benefit by saving 5% more per item while buying systematically (67%) larger market baskets.
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Price promotions are pervasive in grocery markets. A household can respond to price promotions by effective cherry picking through (1) spatial price search across stores and (2) temporal price search across time. But extant research has only analyzed these two dimensions of price search separately; thus they under-estimate both the consumer response to price promotions and the impact of promotions on retail profit. We therefore introduce the first integrated analysis of spatial and temporal price search. We seek answers to three questions: First, how effective are the temporal, spatial and spatio-temporal price search strategies in obtaining lower prices? Second, what is the impact of alternative price search strategies on retailer profit? Finally, what are the predictors of household decisions to perform either spatial or temporal price search, both or neither? We use a unique data collection approach that combines household surveys with purchase data to address these questions. Our key results are: Households that claim to search spatio-temporally avail about ¾ of the available savings on average; even those that claim not to systematically search on either dimension avail about ½ of the available savings. Households that search only temporally save about the same as ones that search only spatially. The negative effect of cherry picking on retailer profits is not as high as is generally believed. Geography (the spatial configuration of store and household locations) and opportunity costs are useful predictors of a household's price search pattern.
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Previous research has examined whether price dispersion exists in theoretically highly efficient Internet markets. However, much of the previous work has been focused on industries with low cost and undifferentiated products. In this paper, we examine the presence of price dispersion and product differentiation using data on the airline ticket offerings of online travel agents (OTAs). We find that different OTAs offer tickets with substantially different prices and characteristics when given the same customer request. Some of this variation appears to be due to product differentiation---different OTAs specialize by systematically offering different trade-offs between ticket price and ticket quality (minimizing the number of connections, matching requested departure and return time). However, even after accounting for differences in ticket quality, ticket prices vary by as much as 18% across OTAs. In addition, OTAs return tickets that are strictly inferior to the ticket offered by another OTA for the same request between 2.2% and 28% of the time. Overall, this suggests the presence of both price dispersion and product differentiation in the online travel market.
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Using data on search and choice behavior from a local automobile market, we estimate monetary returns to search in terms of lower prices resulting from additional time invested in price search. For our analytical framework, we adapt a model developed in the job search literature to the problem of consumer search; this framework is especially useful for illuminating the relationship between time spent searching, the outcome of search, and demand and supply side variables. Our results indicate that, for this particular sample of buyers, marginal returns to search are broadly consistent with what one might expect if consumers balance costs and benefits of search, and that potential gains from additional search for lower car prices do not appear to be large for most consumers. Our study highlights many of the methodological difficulties involved in estimating returns to search, including isolating returns to different outcomes of search, and sensitivity of results to model specification and sampling error. We deal with these problems by trying to isolate time spent searching for price from other uses of search time, by deriving our model used in estimation from a specific conceptual framework, and by extensive specification testing.
In most cases, consumers must search for information about prices and product attributes, and find it too costly to become perfectly informed. the consequent departure from perfect information affects the pricing behavior of sellers in a variety of ways. the purpose of this chapter is to review the literature on consumer search, and on the consequences of consumer search behavior for the behavior of markets. the review first focuses on summarizing theoretical models optimal search, and on how costly search may affect the behavior of markets. two of the key results in this literature are that price dispersion should exist in equilibrium, and that differences in search costs provide a motive for price discrimination. after summarizing the theoretical models, the review presents empirical results on consumer search, and on pricing by sellers given differences in consumer search costs. specific results for different information sources, including word of mouth, advertising, retailing and the internet are discussed.
From automobile insurance data for Alberta over the period 1974-81, we find thatpremiums are highly correlated across driver classes in a given year, but that premiums for a given driver class are not correlated over a period of more than 5 years. Firms' relative market shares among drivers over age 25 and married males under 25 are inversely related to their deviations from the mean premiums.In these driver classes, the variance of real premiums decreases with the numberof firms in the market and increases with the real loss cost per car insured and the number of cars insured. From these results we conclude that the price dispersion in automobile insurance in Alberta is based on costly consumer search.
An explicit solution of an equilibrium model with price-setting firms and searching customers makes possible a number of comparativestatics predictions about how cost differences among firms, search costs of customers, and taxes will affect the mean and variance of the distribution of market prices. Another implication of the model is that a firm's demand depends on the difference between its price and the average price in the market.
The identification of sellers and the discovery of their prices is given as an example of the role of the search for information in economic life.