Number of Sellers, Average Prices, and Price Dispersion John M. Barron † Department of Economics Purdue University W. Lafayette, IN 47907-1310 [email protected]Beck A. Taylor Department of Economics Baylor University Waco, TX 76798-8003 [email protected]John R. Umbeck Department of Economics Purdue University W. Lafayette, IN 47907-1310 [email protected]Forthcoming, International Journal of Industrial Organization Abstract: A variety of models provide differing predictions regarding the effect of an increase in the number of competitors in a market (seller density) on prices and price dispersion. We review different approaches to generating equilibrium price dispersion and then empirically estimate the relationship between seller density, average product price, and price dispersion in the retail gasoline industry using four unique gasoline price data sets. Controlling for station-level characteristics, we find that an increase in station density consistently decreases both price levels and price dispersion across four geographical areas. JEL Codes: L13, D43, D83 Key Words: search costs; price dispersion; retail gasoline † Corresponding author is John M. Barron. Ph. 765-494-4451; Fax 765-494-9658.
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Number of Sellers, Average Prices, and Price Dispersion
Forthcoming, International Journal of Industrial Organization Abstract: A variety of models provide differing predictions regarding the effect of an increase in the number of competitors in a market (seller density) on prices and price dispersion. We review different approaches to generating equilibrium price dispersion and then empirically estimate the relationship between seller density, average product price, and price dispersion in the retail gasoline industry using four unique gasoline price data sets. Controlling for station-level characteristics, we find that an increase in station density consistently decreases both price levels and price dispersion across four geographical areas. JEL Codes: L13, D43, D83 Key Words: search costs; price dispersion; retail gasoline
† Corresponding author is John M. Barron. Ph. 765-494-4451; Fax 765-494-9658.
1
Number of Sellers, Average Prices, and Price Dispersion
I. Introduction
It has been shown theoretically that price dispersion can result under a variety of different
circumstances. Some suggest that dispersion arises as a simple extension of standard
monopolistic competition. Others adopt a search-theoretic framework that suggests price
dispersion is generated when some consumers do not know the location of a low price. Both
approaches are widely accepted, yet their predictions sometimes diverge concerning the
correlation between the number of sellers in a market and the moments of the resulting
equilibrium distribution of prices.
Intuition might suggest that in markets more densely populated with buyers, the resulting
higher number of sellers would be associated with a “more competitive” market, characterized
by lower prices and less price dispersion. These associations do appear in modifications of the
standard models of monopolistic competition that allow for price variation across sellers.
However, this is not necessarily the case for models that adopt the search-theoretic approach to
price dispersion. For instance, Rosenthal (1980) finds conditions under which “increasing the
number of sellers … induces [an increase] in the sellers’ … equilibrium [price] distribution”
(p.1579). If markets with fewer sellers have higher search costs, then Samuelson and Zhang
(1992) develop a model in which “as search costs increase, prices and price dispersion may
decrease” (p.55). Lest one thinks that such models are isolated and special cases, consider the
well-known paper by Stiglitz (1987) whose very title “Are Duopolies More Competitive than
Atomistic Markets?” highlights the potential for “counter-intuitive” results, namely that markets
2
with a larger number of competitors may have higher prices, with the result depending on
assumptions regarding search costs.1
Given this theoretical ambiguity, the purpose of this paper is two-fold. First, we highlight
the sources of the conflicting theoretical predictions. Section II reviews the approaches to
generating an equilibrium price distribution based on monopolistic competition and on search-
theoretic models. For the monopolistic competition approach, we develop two alternative
modifications of the classic model presented by Perloff and Salop (1985). For the search-
theoretic approach, we consider a variant of Carlson and McAfee (1983), a model based on
optimal sequential search by consumers with heterogeneous search costs, and Varian’s (1980)
model of sales that generates a mixed-strategy pricing equilibrium. In each model, the number
of sellers in the market is determined by a zero-profit condition, and a change in the number of
sellers occurs if there is a change in market size (in terms of the number of consumers) or a
change in the fixed costs of production. For each of the four models considered, we derive the
predicted correlation between a change in the number of sellers (seller density) and the first two
moments of the equilibrium price distribution, namely the average price in the market and the
level of price dispersion.
The second purpose of this paper, presented in Section III, is to use four unique and
comprehensive firm-level data sets from the retail gasoline industry to estimate the relationships
between seller density and the level and dispersion of gasoline prices. The empirical literature
that addresses associations between seller density, average prices, and price dispersion is
relatively small when compared to the vast theoretical work on the subject, in part because of
problems related to market definition, access to firm-level data that could be used to distinguish
1 The intuition behind Stiglitz’s seemingly odd result is that, given the common search-model assumption that
searchers know the distribution of prices but not the location of specific prices, an increase in the number of
competitors makes it more costly for searchers to find a low-cost seller.
3
differences in product characteristics, and a researcher’s ability to survey all of the relevant
prices and market conditions at a single point in time. Fortunately, our empirical work relies on
four data sets that contain information on the location and characteristics of the over 3,000
gasoline stations in the San Diego, San Francisco, Phoenix, and Tucson areas. Further, the
prices contained in each of the data sets were collected on a single day, so that we have four
complete “census” price surveys. Our data provide us with a rare opportunity to examine the
relationships between the number of competitors, average product prices, and price dispersion
for a frequently-purchased, homogeneous product.
Controlling for station-level differences, we find convincing evidence across all four
geographic areas that in markets with a higher number of sellers, there is a statistically
significant, albeit modest, decrease in both the mean price and price dispersion for regular
unleaded gasoline. This evidence is consistent with variants of the standard models of
monopolistic competition, but is at odds with some of the predictions of widely cited search-
based price dispersion models. In the conclusion, we suggest some features that could be added
to often-used search-theoretic approaches to improve their ability to explain what we observe.
II. Models of Equilibrium Price Dispersion
In this section, we contrast the predicted relationship between the number of sellers (seller
density) and moments of the equilibrium price distribution, namely the average price and the
extent of price dispersion, for a variety of models that generate an equilibrium price distribution.
Our goal is to identify and provide insight regarding the potentially conflicting predictions of
these models, and thus set the stage for our subsequent empirical analysis. Although there are
substantive differences in the key assumptions of the various models considered, there are
common elements as well, so we begin by introducing these common features.
4
A. Common Elements of the Models
Let ≥N be the total number of sellers in the market. For seller i, production of units
of output involves a common fixed cost component k, and a constant marginal cost component
2 iq
iα . That is,
( )i i iC q k qiα= + , (1)
where and 0>k 0>iα , i = 1, …, N. Let there be L buyers in the market, each purchasing one
unit of the good. Let consumer j’s value of the good offered by seller i, jiθ , equal a common
reservation value, r, minus a “visiting” or “search” cost, , such that jiv ji r v= − jiθ .2 For
consumer j, the gain to purchasing the product from seller i at price is then ip
ji ji iu r v p= − − . (2)
If prices are known and consumers consider the good to be differentiated across sellers, then
consumer j’s visiting cost for seller i can be viewed as being drawn from the non-degenerate
distribution . This is the setting of monopolistic competition models. Note that we follow
Anderson and de Palma’s (2001) generalization of Perloff and Salop (1985) in allowing
consumer j’s realized value of the good offered by seller i,
( )iF v
ji ijr vθ = − , to be drawn from a
2 This specific form is chosen so that we can use common notation to represent both the monopolistic competition
and the search-theoretic models. Although subsequent discussions attribute variations in value to variations in
“visiting” costs, such variations in value could easily be more broadly interpreted to reflect a variety of factors that
might influence individuals’ preferences. For simplicity, we assume the upper bound of the distribution of such
visiting costs is sufficiently low that all consumers in the market will purchase one unit of the good from one of the
sellers in the market at equilibrium prices.
5
distribution that can vary across sellers.3 Section B below considers the emergence of an
equilibrium price distribution in this setting.
If prices are not known until the consumer visits a seller, and consumers consider the good
to be homogenous across sellers, then consumer j’s common cost of visiting any seller is a single
draw from the distribution . This is the setting of many search-theoretic models. Section C
below considers the emergence of an equilibrium price distribution in this alternative framework.
The models described in Sections B and C have in common a zero-profit condition to determine
the equilibrium number of sellers in the market.
( )F v
B. Monopolistic Competition and Equilibrium Price Dispersion
Monopolistic competition arises when consumers perceive differentiated products across
sellers. The standard monopolistic competition model assumes all sellers have the same realized
marginal cost ( ) and that there is a common distribution across sellers from which each
consumer draws the visiting cost for the good offered by each seller ( ). As Perloff
and Salop (1985) have shown, such symmetry assumptions can result in a single equilibrium
price with expected sales by each seller equal to L/N. The zero-profit condition then determines
the number of sellers, with the resulting equilibrium characterized by an identical price at all
αα =i
( ) ( )iF v F v=
3 There is, however, a difference in how we characterize seller heterogeneity from Anderson and de Palma (2001).
Anderson and de Palma define the value of seller i’s good to consumer j by ji iq jiθ ε= + , where is a measure of
product quality and
iq
jiε is a random variable i.i.d. across sellers. Thus, in terms of our notation, Anderson and de
Palma focus on differences across sellers in the mean of the distribution of visiting costs, ( )iF v . Our more general
notation allows us to also consider differences across sellers in terms of the variance of the visiting cost distribution,
and in fact we focus on this latter source of seller heterogeneity. In other words, in Anderson and de Palma, seller
heterogeneity reflects quality differences, while our seller heterogeneity reflects differences in consumer value
heterogeneity across different types of sellers.
6
sellers equal to the common marginal cost, α , plus average fixed cost, k/(L/N). Thus,
equilibrium price dispersion in the standard monopolistic competition model will require the
introduction of asymmetry across firms.
Two types of asymmetry are suggested for generating equilibrium price dispersion in a
monopolistic competition model. One method of introducing asymmetry is to assume
heterogeneity in the distribution of visiting costs across sellers that can result in differences in
sellers’ price elasticities of demand if all sellers were to charge the same price. Walsh and
Whelan (1999), among others, have adopted the assumption of heterogeneous demand elasticity
as a key source of price dispersion. A second method of introducing asymmetry is to assume
heterogeneity across sellers in their realized marginal production cost. We consider below the
implications of these two types of asymmetry for the relationship between seller density and
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______, 1996, Oligopolistic pricing with heterogeneous consumer search, International Journal
of Industrial Organization 14, 243-268.
Stiglitz, J.E., 1987, Competition and the number of firms in a market: Are duopolies more
competitive than atomistic markets, Journal of Political Economy 95, 1041-1061.
Van Hoomissen, T., 1988, Price dispersion and inflation: Evidence from Israel, Journal of
Political Economy 96, 1303-1314.
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32
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33
Number of sellers and
average price
Number of sellers and price
dispersion
Range of visiting or search costs
and average price
Range of visiting or search costs
and price dispersion
Product-differentiation model with heterogeneity in sellers' demands or heterogeneity in sellers' costs
negative negative positive positive
Search-theoretic model with heterogeneity in consumers' search costs and sellers' costs (Carlson and McAfee, 1983)
negative positive positive uncorrelated
Search-theoretic model with heterogeneity in consumers' search costs (Varian, 1980)
positive positive positive ambiguous
Predicted Correlations Between:
TABLE 1Summary of Predicted Correlations*
Model of equilibrium price distribution:
*Recall that for the Varian-type model, a reduced range of search costs is interpreted as an increase in the proportion of consumers that are informed. The results reported above reflect the correlations suggested by changes in market size or entry costs that alter both the number of sellers in a market and the equilibrium prices in the context of different search models. The results for the product differentition models are reasonable extensions of results for the homogeneous case. The results for Carlson and McAfee are reported in their paper, and reflect, among other factors, a specific distributional assumption on search costs. The Varian results are generated using a specific example provided by Varian.
34
Phoenix Areaa Tuscon Areaa San Diego Areaa San Francisco Areaa
Price at station (regular unleaded self-serve) 131.9 128.2 137.7 130.4(4.39) (3.56) (6.22) (7.61)
Number of fueling positions at the station 7.88 6.8 7.41 7.56(3.96) (3.44) (2.98) (2.64)
Station brand is ARCO b 0.1 0.06 0.15 0.09(0.3) (0.23) (0.36) (0.28)
Station brand is Chevron 0.07 0.13 0.12 0.18(0.26) (0.34) (0.32) (0.39)
Station brand is Citgo 0.05 0.03 0.1 0.01(0.21) (0.17) (0.3) (0.1)
Station brand is Exxon 0.09 0.08 0.01 0.07(0.28) (0.26) (0.04) (0.26)
Station brand is Mobil / BP 0.15 0.1 0.04(0.36) (0.3) (0.19)
Station brand is Shell 0.03 0.01 0.14 0.2(0.15) (0.07) (0.35) (0.4)
Station brand is Texaco 0.13 0.16 0.11 0.02(0.34) (0.36) (0.31) (0.11)
Station brand is Unocal 0.31 0.39 0.1 0.17(0.47) (0.49) (0.3) (0.38)
Number of observations 767 239 670 1521
TABLE 2Descriptive Statistics for Four Areas
Descriptive Statistics: Mean (standard error)
b The excluded brands are independents. The brand BP replaces Mobil in San Francisco.
Variables
a Price data are from a one-day census of each area performed by Lundberg, Inc. For Phoenix, the price census occurred on 10/23/97. For Tucson, the price censusoccurred on 10/23/97. For San Diego, the census on prices was taken 6/18/97. For San Francisco, the census on prices was taken 6/19/97. The analysis is restrictedto stations that had complete information on self-serve prices, hours, and location.
35
A A A A B B B B
Phoenix Areaa Tuscon Areaa San Diego Areaa
San Francisco Areaa Phoenix Areaa Tuscon Areaa San Diego
AreaaSan Francisco
Areaa
Log of number of stations within a 1.5-mile radius -0.0086 -0.0151 -0.0147 -0.0182 -0.0071 -0.0145 -0.0127 -0.0184(4.46) (4.03) (5.48) (9.54) (3.37) (3.57) (3.90) (9.22)
Distance to closest station 0.0017 0.0032 0.0037 -0.0003(1.57) (1.02) (0.80) (0.18)
Station also sells full-service gasoline 0.0084 -0.0140 0.0039 0.0069 0.0081 -0.0143 0.0038 0.0069(2.00) (1.70) (0.91) (3.11) (1.94) (1.70) (0.88) (3.09)
Log of number of fueling positions at the station -0.0138 -0.0040 -0.0228 -0.0155 -0.0134 -0.0036 -0.0220 -0.0156(4.98) (1.03) (3.59) (3.53) (4.87) (0.93) (3.47) (3.54)
Station brand is ARCO b -0.0347 -0.0275 0.0081 -0.0035 -0.0342 -0.0275 0.0078 -0.0035(6.56) (4.78) (1.69) (1.03) (6.50) (4.73) (1.65) (1.03)
Station brand is Chevron 0.0238 0.0162 0.0550 0.0828 0.0243 0.0145 0.0559 0.0829(3.72) (2.06) (9.33) (30.36) (3.82) (1.95) (9.50) (30.31)
Station brand is Citgo -0.0155 -0.0028 -0.0009 -0.0019 -0.0147 -0.0025 0.0004 -0.0020(2.35) (0.36) (0.11) (0.19) (2.24) (0.31) (0.05) (0.20)
Station brand is Exxon 0.0145 0.0038 -0.0108 0.0688 0.0149 0.0028 -0.0077 0.0688(2.45) (0.46) (2.22) (17.70) (2.52) (0.34) (1.40) (17.69)
Station brand is Mobil / BP 0.0187 0.0555 0.0765 0.0193 0.0566 0.0765(3.50) (9.82) (14.82) (3.64) (9.96) (14.81)
Station brand is Shell 0.0040 0.0356 0.0507 0.0917 0.0045 0.0366 0.0517 0.0917(0.42) (4.52) (10.32) (33.70) (0.47) (4.40) (10.44) (33.69)
Station brand is Texaco 0.0201 0.0223 0.0442 0.0486 0.0203 0.0214 0.0452 0.0486(3.49) (2.65) (8.18) (5.16) (3.52) (2.50) (8.31) (5.16)
Station brand is Unocal 0.0055 0.0097 0.0372 0.0640 0.0063 0.0095 0.0382 0.0640(1.08) (2.27) (6.47) (21.04) (1.24) (2.23) (6.64) (21.03)
Number of observations 767 239 670 1521 767 239 670 1521R-Square 0.31 0.33 0.35 0.68 0.31 0.33 0.35 0.68Dependent variable mean 4.8808 4.8529 4.9235 4.8689 4.8808 4.8529 4.9235 4.8689Dependent variable standard dev. 0.0332 0.0270 0.0441 0.0583 0.0332 0.0270 0.0441 0.0583
c Huber/White/sandwich (robust) estimator of variance is used to compute t-statistics (absolute values in parentheses).
a Price data are from a one-day census of each area performed by Lundberg, Inc. For Phoenix, the price census occurred on 10/23/97. For Tucson, the price census occurred on 10/23/97. For San Diego, thecensus on prices was taken 6/18/97. For San Francisco, the census on prices was taken 6/19/97. The analysis is restricted to stations that had complete information on self-serve prices and location. Notreported for the San Francisco area are dummy variables identifying the different counties. b The excluded brands are independents. The brand BP replaces Mobil in San Francisco.
TABLE 3AStation Density and Prices: Specifications A and B
Independent Variables
Location Location
Specification (dependent variable log of price)c
36
C C C C D D D D
Phoenix Areaa Tuscon Areaa San Diego Areaa
San Francisco Areaa Phoenix Areaa Tuscon Areaa San Diego
AreaaSan Francisco
Areaa
Number of stations within a 1.5-mile radius -0.0006 -0.0015 -0.0014 -0.0018 -0.0775 -0.1979 -0.2003 -0.2281(2.49) (2.92) (4.86) (9.71) (2.60) (2.93) (4.75) (9.36)
Distance to closest station 0.0024 0.0055 0.0058 0.0038 0.3350 0.7094 0.8462 0.5073(2.84) (1.75) (1.24) (2.22) (2.80) (1.70) (1.24) (2.19)
Station also sells full-service gasoline 0.0074 -0.0151 0.0037 0.0071 0.9970 -2.0069 0.5301 0.9516(1.75) (1.61) (0.85) (3.17) (1.77) (1.62) (0.88) (3.18)
Log of number of fueling positions at the station -0.0131 -0.0044 -0.0235 -0.0166 -1.7594 -0.5997 -3.4664 -2.2554(4.64) (1.09) (3.56) (3.57) (4.61) (1.11) (3.54) (3.41)
Station brand is ARCO b -0.0358 -0.0289 0.0076 -0.0039 -4.6529 -3.5873 1.0407 -0.4663(6.71) (4.73) (1.64) (1.13) (6.69) (4.47) (1.63) (1.08)
Station brand is Chevron 0.0235 0.0155 0.0557 0.0829 3.0364 2.0084 7.5877 10.6720(3.63) (1.96) (9.57) (30.23) (3.51) (1.93) (9.30) (29.97)
Station brand is Citgo -0.0164 -0.0025 -0.0010 -0.0028 -2.2415 -0.3564 -0.3753 -0.4510(2.44) (0.37) (0.13) (0.27) (2.52) (0.40) (0.34) (0.35)
Station brand is Exxon 0.0137 0.0037 -0.0083 0.0684 1.7219 0.4980 -1.2922 8.7234(2.26) (0.43) (1.56) (17.38) (2.15) (0.46) (1.69) (17.03)
Station brand is Mobil / BP 0.0187 0.0564 0.0773 2.3826 7.7050 9.9373(3.46) (9.90) (15.30) (3.34) (9.58) (14.75)
Station brand is Shell 0.0043 0.0470 0.0509 0.0920 0.5001 6.0842 6.8791 11.8912(0.43) (6.44) (10.16) (33.42) (0.38) (6.40) (9.80) (33.08)
Station brand is Texaco 0.0197 0.0217 0.0446 0.0493 2.5364 2.8519 6.0158 6.3080(3.35) (2.48) (8.15) (5.11) (3.23) (2.45) (7.85) (4.99)
Station brand is Unocal 0.0056 0.0089 0.0383 0.0642 0.6296 1.1344 5.1423 8.1912(1.08) (1.98) (6.68) (20.63) (0.91) (1.92) (6.42) (19.69)
Number of observations 767 239 670 1521 767 239 670 1521R-Square 0.30 0.28 0.35 0.67 0.30 0.27 0.34 0.66Dependent variable mean 4.88 4.85 4.92 4.87 131.81 128.16 137.61 130.40Dependent variable standard dev. 0.03 0.03 0.04 0.06 4.39 3.55 6.22 7.60
Independent Variables
Specification (dependent variable price)cSpecification (dependent variable log of price)c
TABLE 3BStation Density and Prices: Specifications C and D
Location Location
c Huber/White/sandwich (robust) estimator of variance is used to compute t-statistics (absolute values in parentheses).
a Price data are from a one-day census of each area performed by Lundberg, Inc. For Phoenix, the price census occurred on 10/23/97. For Tucson, the price census occurred on 10/23/97. For San Diego, thecensus on prices was taken 6/18/97. For San Francisco, the census on prices was taken 6/19/97. The analysis is restricted to stations that had complete information on self-serve prices and location. Notreported for the San Francisco area are dummy variables identifying the different counties. b The excluded brands are independents. The brand BP replaces Mobil in San Francisco.
37
Phoenix Areaa Tuscon Areaa San Diego Areaa
San Francisco Areaa Phoenix Areaa Tuscon Areaa San Diego
AreaaSan Francisco
Areaa
Log of number of stations within a 1.5-mile radius -0.0006 -0.0006 -0.0013 -0.0009 -0.0005 -0.0008 -0.0008 -0.0007(4.47) (4.21) (3.41) (2.14) (3.92) (4.10) (2.00) (2.34)
Distance to closest station -0.0000 -0.0003 0.0005 -0.0000(0.88) (2.88) (1.23) (0.04)
Station also sells full-service gasoline 0.0001 -0.0006 -0.0000 -0.0001(0.56) (1.79) (0.03) (0.54)
Number of observations 767 239 670 1521 767 239 670 1521R-Square 0.07 0.11 0.08 0.03 0.11 0.23 0.19 0.09Dependent variable mean 0.0008 0.0005 0.0013 0.0011 0.0008 0.0005 0.0013 0.0011Dependent variable standard dev. 0.0015 0.0013 0.0034 0.0038 0.0015 0.0013 0.0034 0.0038
Location
TABLE 4Station Density and Dispersion
Dependent variable square of error term for specification B of the price regressionc
Independent Variables
Location
a The residuals are from the log-log specification of the price equation with distance to closest station (specification B) reported in Table 3A. Note that similar results obtain for the other specifications (C andD). These results are reported in a supplement to this paper that is available from the authors.b The excluded brands are independents. The brand BP replaces Mobil in San Francisco.c Huber/White/sandwich (robust) estimator of variance used to compute t-statistics (absolute values in parentheses).
38
Phoenix Areaa
Tuscon Areaa
San Diego Areaa
San Francisco
Areaa
Phoenix Areaa
Tuscon Areaa
San Diego Areaa
San Francisco
Areaa
Log of number of stations within a 1.5-mile radius 0.0223 0.0443 0.0328 0.0315 0.0168 0.0308 0.0252 0.0242(2.69) (2.52) (3.37) (3.70) (1.95) (1.78) (2.42) (2.71)
Distance to closest station -0.0060 -0.0660 -0.0144 -0.0268(2.36) (4.51) (1.33) (1.53)
Station also sells full-service gasoline -0.1079 -0.0272 -0.0799 -0.0524 -0.1069 -0.0203 -0.0794 -0.0524(3.00) (0.43) (3.00) (3.28) (2.96) (0.32) (2.96) (3.28)
Log of number of fueling positions at the station 0.0886 0.0242 0.1739 0.1428 0.0870 0.0170 0.1705 0.1420(6.29) (1.50) (7.75) (8.99) (6.09) (1.04) (7.67) (8.87)
Station brand is ARCO b 0.1474 0.0554 0.0923 0.0942 0.1456 0.0559 0.0934 0.0923(5.16) (1.67) (3.58) (4.38) (5.10) (1.66) (3.64) (4.30)
Station brand is Chevron 0.2582 0.0784 0.1422 0.2197 0.2563 0.1117 0.1387 0.2183(7.06) (1.30) (4.12) (11.04) (6.97) (2.14) (4.02) (10.98)
Station brand is Citgo 0.2196 0.0966 0.2581 0.2959 0.2165 0.0905 0.2530 0.2916(6.67) (2.69) (8.50) (14.34) (6.53) (2.34) (8.23) (13.74)
Station brand is Exxon 0.1222 -0.0061 -0.1623 0.1924 0.1206 0.0150 -0.1740 0.1913(2.77) (0.09) (7.41) (8.18) (2.73) (0.24) (7.89) (8.18)
Station brand is Mobil / BP 0.1987 0.0893 0.1684 0.1964 0.0851 0.1674(5.93) (2.82) (6.26) (5.83) (2.70) (6.24)
Station brand is Shell -0.0114 -0.5449 0.2125 0.2862 -0.0135 -0.5659 0.2085 0.2850(0.17) (9.37) (8.57) (18.37) (0.20) (9.64) (8.46) (18.35)
Station brand is Texaco 0.1563 0.0054 0.1387 -0.0534 0.1553 0.0236 0.1352 -0.0543(4.39) (0.12) (4.83) (1.15) (4.37) (0.51) (4.75) (1.14)
Station brand is Unocal 0.1881 0.0806 0.1667 0.1574 0.1849 0.0830 0.1631 0.1567(5.88) (2.54) (5.85) (8.31) (5.74) (2.63) (5.73) (8.26)
Number of observations 748 237 662 1278 748 237 662 1278R-Square 0.38 0.40 0.36 0.40 0.39 0.44 0.37 0.40Dependent variable mean 5.0546 5.0371 5.0054 4.9533 5.0546 5.0371 5.0054 4.9533Dependent variable standard dev. 0.1731 0.1820 0.2056 0.2154 0.1731 0.1820 0.2056 0.2154
Dependent variable log of weekly hours c
Independent Variables
TABLE 5Station Density and Hours of Operation
c Huber/White/sandwich (robust) estimator of variance used to compute t-statistics (absolute values in parentheses).
a The hours data are from the annual census of each area performed by Lundberg, Inc.The analysis is restricted to stations that had complete information on self-serve prices, hours, andlocation. Note that hours data for one county in San Francisco is not available.b The excluded brands are independents. The brand BP replaces Mobil in San Francisco.