KEO Discussion Paper No. 118

Measurement of consumer benefit of competition
in retail outlets ∗Toshiyuki Matsuura †Keio University
andMitsuru Sunada
Japan Fair Trade Commission

Abstract
In this paper, we estimated the consumer benefits from the competition in retailindustry. In our analysis, we incorporated the service quality of retail outlets as outputs.
In Japan, in the process of the deregulation of entry restriction on large-scale retailstores, specialty supermarkets have increased its market share with low price strategy.
At the same time, despite its high price, the convenience store has increased its market
share through 1990s. We demonstrate changes in market share of each retail format are
explained by the changes in the service quality of each retail format.

∗

We thank Masahiro Kuroda, Takanobu Nakajima, Hitoshi Hayami, Sadao
Nagaoka, Hiroyuki Odagiri, Daiji Kawaguchi, Ayako Suzuki, Kyoji Fukao, Yosuke
Okada and other seminar participants at Keio University, Hitotsubashi University, Semi
Annual Meeting of Japan Economic Association at Sendai and Research Institute of
Economy Trade and Industry (RIETI), Competition Policy Research Center, Japan Faire
Trade Commission (CPRC-JFTC) for their helpful comments and suggestion. The views
expressed in this paper are ours, and do not reflect the views of RIETI and CPRC-JFTC.
†
Correspondence author: E-mail: matsuura@sanken.keio.ac.jp1

1.

Introduction

Entry regulation has anti-competitive effects, because it prevents innovative new firmfrom entering a market and it brings about high price and consequently hinders welfare
improvement. Besides, the entry regulation might reduce incumbents’ incentive forinnovation due to the lack of threat of a competition hence it might have a harmful
effect on productivity growth. Therefore, to stimulate the economic growth, policy
makers as well as researchers have strong interest in the competition effects ofderegulations of entry.
In Japan, it is well known that the Large-Scale Retail Store Law (LSRS Law,
hereinafter) restricted the entry and floor expansion of large scale retailers. Due to theregulations, incumbent large stores enjoyed monopolistic rent and lead to higher prices.
And because of lack of competitive pressure, so-called Pop and Mum shops, traditional
small stores were allowed to continue their business even if unprofitable. The LSRS
Law has been severely criticized through “The Japan-U.S. Structural Impediments
Initiative,” in late 1980s and it was gradually relaxed and finally repealed in 2000.
However, contrary to all expectations, the share of large Generalized MerchandiseStore (GMS) did not increase so much. Moreover, Carrefour and Wal-Mart, French andU.S., multinational retail chains, struggled in the Japanese retailing market: because of
low profitability, Carrefour left Japan in 2005 and, Wal-Mart has not yet succeeded in
expanding the market share. Judging from these facts, the deregulation seemed to havenot affected Japanese retail market. Nevertheless, so-called new retail formats, such as
specialty supermarket (middle-sized discount store) and convenience stores havesubstantially increased their market shares, which feature low prices or distinctive
services. Therefore, to assess the consumer benefits from severe competition due to the
deregulations, we should take into account the differentiation in retail services.
The purpose of this study is two fold. First, to measure the welfare gain fromincreased competition, by shedding light on low price and distinctive service offered byretailing new formats; specialty supermarket and convenience stores; Second, to explore
why gigantic GMSs, such as Carrefour and Wal-Mart, failed to dominate retailingmarket despite abolishment of LSRS Law.
The organization of the paper is as follows. The next section introduces the historicaland institutional background of Japanese retail industry and discuses measurement
issues. Third section surveys related literature and then, we will present the model at the
4th section. The 5th section explains our data set, while the 6th section will provide
estimation results. A summary and conclusion are presented in the final section.

2

2.

BackgroundIn this section, we first review the historical overview of the entry restrictions.
Second, we present some indicators about Japanese retail market. Third, the definitionof retail output will be discussed.
2.1. Historical Overview of Large Scale Retail Store Law (LSRS Law)
In Japan, the business of large-scale retailers has been highly restricted by law toprotect the businesses of smaller-sized retailers. 1 The protection for small retailbusinesses was originated from “Department Store Law” established in 1937. Although
the law was once repealed after the WWII by GHQ, it came to life again in 1956 inalmost the same manner as before. In 1974, the law was enforced as “Large Scale RetailStore Law (LSRS law)" targeting the stores with floor space with more than 500 metersquares, which includes not only department stores but also large superstores. At thesame time the new law had another purpose to restrain new entrants with large capitalfrom abroad. The law has not only been that of protecting smaller businesses but also
restricting the competition among large retailers through controlling the entry of new
businesses.
In 1978 the law was reinforced. When a large-scale retailer starts a new business in acertain area, it first has to notify the Minster of International Trade and Industry. The
minister investigates the effect of the new entry on smaller retailers in that area. If asignificant negative effect is expected after the investigation, the minister urges theentrant to modify his business plan regarding their service characteristics such as floor
space, business days, closing times, or the number of holidays.
The role of the minister is just to illustrate guidelines. Representatives in regional
business districts carry out substantial adjustments. Furthermore, local governments
have been allowed to impose additional regulations on an entry of large stores, theirfloor space and operating hours. Figure 1 (a) and (b) depict the relationship between thefood price index by GMS and the entry regulation indices, and between operating hours
by GMS and the entry regulations at prefecture level in 1991. The entry regulation
indices are based on the information about additional regulations imposed by city or
prefecture government. It is an indicator of strictness of entry restrictions. For foodprice by GMS and the regulations in panel (a), there seems positive correlation between
them, suggesting the price level is higher in prefectures with strict entry regulations.
The weak negative correlation between operating hours by GMS and the indices in
1

For historical survey on this issue, see Nishimura and Tachibana (1996), Lark (1994)
and Mayer-Ohle(2003).
3

panel (b) infers the regulation is effective for not only price level, but also retailers’service quality, such as operating hours.
2.2. Market Transition
In the 1990s, the trend changed from protectionism to deregulation as a result of``The Japan-US Structural Impediments Initiative,” which was aimed at creating a
Japanese open market and promoting competition. In 1994, LSRS law was eased to give
more freedom to new entrants to the retail industry with less than 1000 square meters offloor space. And, finally, in 1998 the law was completely repealed.
Along with the process of deregulation, the entry and exit rate has been increased inlate 1990s. Figure 2 presents annual average entry and exit rate of retail outlets from1979 to 2004. After 1997, both entry and exit rate has been increasing substantially. At
the same time, while the share of small retailers has been decreasing, non-traditional,
new retail formats has emerged through 1990s. Figure 3 indicates the transition of thesales share among retail formats. While we saw the share of traditional stores has been
shrinking from 70% to 58%, specialty supermarkets and convenience stores havesubstantially increased their market shares 2 .
On the other hand, from the viewpoint of international comparison, the share of small
retailers, such as convenience stores and traditional stores still remain large despite ofincreased competitive pressure. Compared with US, UK and France, the share ofJapanese traditional stores in food retailing is the highest among them (Figure 4). At thesame time, although there are substantial industry dynamics, the productivity of retailindustry has remained at the same level through 1990s. Figure 5 presents the transitionof labor productivity and it indicates upward trend has disappeared after 1990.
These trends are puzzling, since the increased competitive pressure should replacesmaller unproductive outlets with large retail outlets along with the increased
competitive pressure. A possible explanation is that the conventional labor productivity
index could not measure ‘true productivity” accurately, and, especially, it fails toaccount for changes in both the input and the output. As for the input, Census ofCommerce, a comprehensive data source for Japanese retail industry, does not survey
input information except for the number of employees. Besides, while the number ofpart-time worker has drastically increased through 1990s 3 , the data of each employment
status is available only after 1999. In case of the output for retail sector, the situation is2

The definition of retail formats are summarized in Table 2.
According to Matsuura (2007), the ratio of the number of part-time worker to total
employee in retail industry has increased from 33.4% to 54.5 between 1991 and 2004.
3

4

more complicated, because it is related to the question; what is the appropriate
definition of retail output?
2.3 How should we define retail output?
The definition of retail output is the much debated topic among economist.
Conceptually, as augured by Oi (1992) and Betancourt and Gautschi (1988), the retail
outputs are considered as mix of distribution services, such as assortment, assurance andetc. Betancourt and Gautschi (1988) discuss the economic function of retail organizationand propose concepts of distribution service in the empirical perspective. According totheir definition, distribution service is classified as the following five categories: (1)accessibility of location, (2) assortment, (3) assurance of product delivery in the desired
form and at the desired time, (4) information and ambiance. And then, they step forward
to the empirical analysis in Betancourt and Gautschi (1993) and Betancourt and Gautchi(1996).
The emergence of non-traditional outlets might be explained by their distinct
distribution services. While specialty supermarkets have increased its share bydiscounting prices, convenience stores have grown its share because of its innovative
distribution services 4 . In Table 1, there is substantial price gap among retail formats.
While specialty supermarkets attract consumers with low price products, convenience
stores offer relatively high prices among retail formats. The reason why convenience
stores have grown its share despite relatively high prices might lie in its distinctive
services. For example, advanced information system, such as Pos-System, enables
convenience stores to implement advanced merchandising. 7-elven Japan, the largest
convenience store operator, deals with their own brand merchandise and it amount to
50% by sales 5 .
Besides, long business hours and additional services are also fundamental toconvenience stores operation. Basically, in Japan, most convenience stores operate
24-hours and offer additional services, such as making photocopies, photo development,
postal package handling, and payment of utility charges.
Finally and most importantly, Japanese convenience store chains have well-organized
distribution and supply systems. These days, major chains operate deliveries to eachretail outlets from 3 to 5 times a day, since Japanese consumers prefers fresh, sometimes
4

Some specialty supermarket chain has increased the market share because of itsdistinctive services. See chapter 9 to 11 in Larke and Causton (2005).
5
For details of Japanese convenience stores, see chapter 8 in Larke and Causton(2005).
5

highly perishable food products. While the sales share of convenience stores are still
low compared with specialty stores, their profitability is competing with that ofUS-level. According to MGI (2001), the profitability of stores operated by 7-eleven
Japan is 50% higher than that of US average level.
In this study, we focus the consumer benefit from distribution servicese. Consumer
benefits can be measured by estimating demand function, and in case of US, Hausman
and Leibtag (2006) quantified the benefits of price competition by the entry of Wal-Mart.
As for the quality of services in retail sector, Sunada (2004) estimated the changes inservice quality and consumer welfare for shopping. In this paper, we extend Hausman or
Sunada’s specification to incorporate the regional differences of prices and service
quality, and estimate consumer benefits from increased market dynamics.

3.

Related literature

There are many empirical frameworks, which enable us to estimate a demand
structure in differentiated product settings by utilizing the market-level or aggregateddata. The most well-known type of such frameworks is the family of logit model. Berry(1994) introduces the useful empirical framework with the extreme-value distribution of
consumers preference, and Berry, et al. (1995) apply the random coefficient model to
the U.S. automobile market. Other applications of this type of framework are, forexample, Nevo (2001) to the U.S. cereal market, Ohashi (2003) to the U.S. VTR market,
and Werden and Froeb (1996) to the merger simulation. The logit family may be themost standardized framework in the recent empirical IO literatures.
Feenstra (1995) summarizes the exact hedonic price indexes, which are derived fromvarious random utility models, and among them, he introduce the CES model with theextreme-value distribution of consumers preference, and a different type of functional
forms of individual indirect-utility from logit family. Sunada (2004) uses the CES
model in order to construct the cost-of living index of the Japanese Retail industry. The
most different feature of the CES model from the logit family and (vertical models) isthat, while consumers are assumed to purchase only one unit of products in the logit
model, there is not such a restriction in the CES model.
On the other hand, Bresnahan (1981 and 1987) analyze the U.S. automobile market
utilizing the vertical differentiation model in which differentiated products are orderedin product quality or price, and the marginal utility of product quality is assumed to vary
among consumers, and follow a certain type of distribution. Such an ordering structure
may be somewhat strong assumption. Sunada (2005) applies the vertical model in orderto access the quality change and new products in the Japanese mobile

6telecommunication market, in which the marginal utility of quality is assumed toconform of the uniform distribution.
Another type of empirical frameworks is the almost ideal demand system (AIDS).
Hausman (1996) and Hausman and Leonard (2002) apply the AIDS to the U.S. cereal
market and the U.S. bath tissue market, respectively, and access the welfare effects of
new products. Hausman (1997), and Hausman, et al. (1994) utilize the AIDS in order toaccess the competitive effects of mergers in the U.S. bath tissue and the beer industries.
On the other hand, Okada and Hatta (1999) study the Japanese telecommunications
market applying the AIDS. However, there is an important problem in the AIDS: theparameters to be estimated increase with the number of differentiated products in themarket.

4.

Model

Consumer Choice
We suppose that consumers are choosing over type of outlets j=1… N, and have theindirect utility function[]

V jh = ln y − ln φ p j , z j + ε hjh ,j = 1, L N(1)where y is consumers’ income, pj is the price of type j service, and zj is a vector of thecharacteristics of type j retail outlets. Each consumer chooses their preferred product jwith probability[

Pj = Pr ob V j > Vk ,∀k = 1,K, N](2)

If type j retail outlet is chosen, then the quantity consumed by a consumer is determinedfrom the indirect utility function in (1), using Roy’s Identity;
xj = −∂V jh ∂p j
∂V jh ∂y= y (∂ ln φ ∂p j )(3)

It follows that expected demand for each product is

X j = x j Pr j .

(4)

In this setting, we allow the consumer to make a continuous choice of the quantity
purchased. This falls into the category of so-called continuous/discrete models.
According to McFadden (1978, 1981), if the random term in (2) follows type 1 extreme
value distribution, it turn out that the following aggregated indirect utility function is
obtained;7G ( p1 , z 1 , K , p N , z n , y ) = ln y + ln H [φ ( p1 , z 1 ) −1 , K , φ ( p1 N , z N ) −1 ] (5)CES Demand System
Here, we specify the individual utility function as CES form, such as,[]

V jh = ln y − α ln p j f ( z j ) + ε hjh

α >0,

(6)

where we are measuring prices relative to consumers’ perceived “quality” of productsf(zj).
We will choose the function H as linear such as,
H (e − ε , K , e − ε ) =
1

N

∑e
N

j =1

−ε j

,

(7)

We obtain the aggregated utility function as[

U = G( p1 , z1 ,L, p N , z N , y ) = y ∑ j =1 p j f ( z j )N] , (8)−αso that expected aggregate demand isXj =−⎛ α ⋅ p −α −1 f ( z ) ⎞j⎟= y⎜ N j−α ⎟ ,⎜∂G / ∂y⎝ k =1 [ pk f ( z k )] ⎠∂G / ∂p j∑(9)

The elasticity of substitution for CES indirect utility function or associated CES demand
function is α + 1 .
Given this demand function, market share function for type j service is reduced asfollows;( p f ( z ))=S =∑ p X ∑ [ p f ( z )]−α

pj X j

j

j

j

−α

N

k

k

k

k =1

k

(10)

k

Specify service quality function as f ( z j ) = exp(γz j + ζ j ) , we obtain the following

regression equation to be estimated.

⎛ pX ⎞⎛S ⎞⎛p ⎞ln⎜ i i ⎟ = ln⎜ i ⎟ = −α ln⎜ i ⎟ + α ⋅ γ (zi − z j ) + α (ξ i − ξ j )⎜p X ⎟⎜S ⎟⎜p ⎟⎝ j j⎠
⎝ j⎠
⎝ j⎠

(11)

ξ j is the unobservable service characteristics of type j retail outlet, which is a randomvariable with the mean equal zero.
Nested CES system

8Now, suppose that the consumers have a choice between two levels of thedifferentiated retailing service. The structure of consumer choice is presented in Figure
6. First, an individual decides whether to purchase a product from each of g=1,…, Ggroups (for example, GMS, specialty supermarket store, or convenience store), andsecond, the individual decides which of outlets in that group to purchase. Suppose thatthe outlets available in each group g are denoted by J g ⊂ {1,K, N } . Utility for
consumer h is given by the following equation.

[]

V jh = ln y − α ln p j f ( z j ) + ε hjh

α >0.

(12)

Following from Berry(1994), the random error term ε hj are defined as follows.

ε hj = ζ gh + (1 − ρ )e hj , for j ∈ J g(13)where the errors e hj are iid extreme value.
We will choose the function H as linear such as,[

H (e −ε1 , K, e −ε N ) = ∑g =0 ∑ j∈J e
G]−ε j /(1− ρ g ) (1− ρ g )g

We obtain the aggregated utility function as[[

U = G( p1 , z1 ,L, p N , z N , y ) = y ∑g =0 ∑ j =1 p j f ( z j )GN]−α /(1− ρ g )],(1− ρ g )(14)

, (15)

so that expected aggregate demand is(1− ρ )
⎛ αy ⎞ pk f ( z k )Dg g= y⎜ ⎟Xj =−× G (1− ρ ) , , for j ∈ J (16)g⎜p ⎟ D∂G / ∂yg⎝ j⎠∑g =1 Dg g

∂G / ∂p j

where the term Dg =

∑ [p
j∈J

f ( z k )]−α /(1− ρ g )kis the “inclusive value”

The expected demand on the right-hand side of (16) is composed of three terms: the frist⎛ αy ⎞⎟ , is the conventional Cobb-Douglas demand function, the second term,
term y⎜⎜⎟p⎝ j⎠
pk f ( z k ) Dg , is the share or the probability of choice of outlets j in the demand for(1− ρ g )group of retailing format g, the third term, Dg

9

∑ D
G

g =1

(1− ρ g ), is the share or thegprobability of choice of group of retail format g.
Here, we assume the symmetry of price pj and zj within group g, in other words, we
assume pj=pg, f(zj)=f(zg) and for j ∈ J g 6 .
Since Dg =

∑ [p
j∈J

f ( z k )]−α /(1− ρ g )k[= pg f ( z g )]−α /(1− ρ g )

N g , the aggregateddemand function is derived as follows;( p f ( z )) NX =−=p N ⎜ ∑ [ p f ( z )] N
∂G / ∂y⎝⎞αy ⎛⎜ ( p f ( z )) N⎟
X =X N =αy 1 ⎛⎜∂G / ∂p j

j

g

−α

L

g

g

l =1

l

l

(1− ρ g )
g

−α

g

g

j

⎞
⎟
(1− ρ g ) ⎟l⎠(1− ρ g )
g

−α

g

g

p g ⎜ ∑L [ pl f ( zl )]−α N l(1−ρ g ) ⎟⎝ l =1
⎠

g

(17)

(18)

Given this demand function, market share function for type j service is reduced asfollows;( p f ( z )) N=S =∑ p X ∑ [ p f ( z )] N
g

g

g

−α

L

l

l

(1− ρ g )g−α

pg X g

l

l =1

l

l

(1− ρ g )l

(19)

Specify service quality function as f ( z g ) = exp(γz g + ξ g ) , we obtain the following
equation to be estimated. ξ g is the unobservable service characteristics of type g retail
format, which is a random variable with the mean equal zero.

⎛ pg X g ⎞
⎛S ⎞
⎛p ⎞
⎛N ⎞
⎟ = ln⎜ g ⎟ = −α ln⎜ g ⎟ + α ⋅ γ (z i − z j ) + (1 − ρ g ) ln⎜ g ⎟ + α (ζ g − ζ g ' ) (20)ln⎜⎜p X ⎟
⎜S ⎟
⎜p ⎟
⎜N ⎟
⎝ g' g' ⎠⎝ g' ⎠⎝ g' ⎠⎝ g' ⎠
Notice that if ρ g =1, the increase in Ng does not affect the affect the share of group g 7 .
On the other hand, when 0< ρ g <1, the sales share of group g increases as entry ofoutlets in group g .

6This specification is proposed and used by Berry and Waldfogel (1999), whichquantifies the social inefficiency of free entry in radio broadcasting.
7
When ρ g =1, increases Ng does affect only the sales share of outlet j in each group ofretail format g. For details, see Appendix A.
10Extension: Welfare
We now demonstrate how we can estimate the changes in consumer welfare that arise

form the expansion of variety of service.
The correspondence expenditure function to the social welfare function or indirect
utility function (21),

[

[

]

]

−1

(1− ρ g )GN−α /(1− ρ g )⎞ G
E (U , p, z : α ) = ⎛⎜ ∑g =0 ∑ j =1 p j f ( z j )
⎟
⎠
⎝

(

[

= ∑g =0 p g f ( z g )
G

] N
−α

) G.

(1− ρ g ) −1
g

.

(21)

We define compensating variation as the log-difference in the consumers’ expenditure
function, holding utility constant at the beginning of the periods;

CV = ln E (U , p 0 , z 0 , N 0 : α ) − ln E (U , p1 , z1 , N1 : α ) .

(22)

where p 0 , z 0 , N 0 is the price and quality index and number of outlets in the beginningof sample periods and p1 , z 1 , N 1 are those of the end of sample periods.
Following from Sunada (2005), the changes in compensation variation can be
decomposed into two parts as follows;

CV = ln E (U , p 0 , z 0 , N 0 : α ) − ln E (U , p 0 , z1 , N 0 : α )+ ln E (U , p 0 , z1 , N 0 : α ) − ln E (U , p 0 , z1 , N1 : α )+ ln E (U , p 0 , z1 , N1 : α ) − ln E (U , p1 , z1 , N1 : α )(23)= CVq + CVN + CV p
The first term CVq represents the increase in welfare due to the average quality change,holding the prices and number of outlets in group g at their pre-change level. The
second term CVN and third term CVp are the welfare changes due to the changes in
number of outlets and prices respectively, holding other factors fixed. The increase in
CVN is considered as the effect of changes in share of retail format with high service
quality.

5.

Data and Estimation Methodology
11Our dataset is food retailing outlets prefecture-level panel dataset covering 1991, 1997
and 2002. The primary data source is the Census of Commerce, which is compiled by
the Ministry of Economy, Trade and Industry. The Census of Commerce covers all theestablishments which belong to wholesale and retail industry. From the Census, we canobtain the sales by commodity, and characteristics of establishments. One limitation of
the Census is the lack of cost and price information. Therefore, as for the priceinformation, we use National Survey of Prices (Ministry of Internal Affaire andCommunication), which provides us the commodity-level price information by region
and the type of retail outlets (retail formats). Since the Census of Commerce and theSurvey of Price have different definition of the retail formats, we aggregatedestablishment-level data of the Census of Commerce in order to match the definition oftype of retail formats in Survey of Price. Table 2 indicates our definition of retail
formats. Note that not all specialty superstores have been affected by deregulation,
because they contain stores with floor space less than 500 meter squires, which are notcovered by LSRS raw. However, the sales share of the stores with floor space between
250 and 500 meter squires in total specialty superstores is 1.2% in 2002. Therefore, we
consider the entry of specialty superstores has been strongly affected by deregulations.
And we assume the retailing markets are segmented by region, that is, 47 prefectures.
The food retailing sales shares by retail format are presented in Figure 7 8 . Price indicesby region, and retail formats are estimated by aggregating commodity-level price withthe weight of Consumer Price index (Ministry of Internal Affaire and Communication).
As for the service quality indicator, we use “Log of operating hours”, “Goods in stock
per sales”, “Single store ratio”, “Log of floor space per employee”, “Method of
payment” and “Breadth assortment.” “Log of operating hours” is the proxy for time
accessibility or assurance of product delivery. We expect a positive significant effect of“Log of operating hours” on the market share. “Goods in stock per sales” is the inverseof merchandise turnover ratio, and it is defined as the ratio of value of stocks to sales of
products. Low “Goods in stock-sales ratio” imply adoption of just-in time delivery
system, which enables retailers to provide fresh food products with customers. We
expect positive and significant coefficient. “Log of floor space per employee” is proxyfor broad assortment services or information supply for customer. Retailing outlets withlarger floor space offer broader variety of products. On the other hand, to provide
sufficient information of prices or characteristics of products for customers, managers
have to increase the number of employees. The expected sign of the coefficient will be
8Although GMS deals with various products, sales data here are restricted to food andbeverage products.
12

positive or negative depending on consumers’ preferences. “Method of payment” isdefined as the ratio of cash settlement to total sales, which suggest an index for varietyof payment. While most of non-traditional or chain stores usually offers variety ofmethods of payments, such as davit card, credit cards or prepaid electric money card,
traditional stores does not. Thus, the negative sign is expected for the coefficient.
“Single store ratio” will have negative effect on the market share, since single stores areinferior to multiple stores in terms of providing information of prices, availability andother characteristics. “Breadth assortment” means dealing with broad variety of productlines. The following index is used as the proxy for “Breadth assortment”,
Di = 1 − ∑k =1 ω ik2 ,K

where ω ik indicates sale share of products k for outlet i 9 . The more retail outlets deals
with the product lines, the index D will approach 1. On the other hand, Retail outlets
dealing with only 1 product line have D=0. This index is calculated by eachestablishment, and aggregated by prefecture and retail formats with the sales shareweight. We constructed two indexes according to commodity basket. One is the index
calculated over all commodities, and the another one is calculated only about food andbeverage products. While former indicates the depth of assortment regarding allcommodities, the latter represents width of variety within food and beverage products.
Finally, to capture unobservable format specific service quality, sales format dummy
variables are included in the equation.
Table 3 indicates summary of variables we used as service characteristics for foodretailing outlets. While Convenience stores have the second highest price level, their
distinctive services are reflected by longer operating hours and low goods in stock per
sales.
Since the relative price and number of outlets are considered as endogenous variables,
the OLS estimates are not consistent. Therefore, the instrument variables are needed. In
our study, as discussed in Barry (1994), Berry, et al. (1995), Hausman (1996), and Nevo
(2001), we use standard instrumental variables, such as (1) the observed own service
characteristics, (2) the mean of service characteristics of other type of retail formats, (3)the mean of price of same retail formats in other market (prefecture), and (4) cost side
variables; average wage by prefecture and retail formats. We obtain wage informationfrom Census of Wage Structure (Ministry of Health and Labor).

6.
9

Estimation Results

Product line is defined according to 4 digit commodity code.
13

The estimation results are presented in Table 4. We estimated the model with both
OLS (Model 1 and Model 3) and GMM (Model 2, Model 4) including retail format
dummies, prefecture dummies and year dummies. While OLS estimates for thecoefficients of relative price are all positive, those for GMM are negative andsignificant. The specification of Model 1 and Model 2 does not include log of number ofoutlets, which is equivalent to CES demand function expressed as in the equation (11).
Nested CES specification is estimated in Model 3 and Model 4, and the coefficients forlog of number of outlets, which are correspondence with 1- ρ g , are estimated between 0
and 1. Moreover, while the Hansen’s J test is rejected for Model 2, Model 4 does not;therefore we conclude the nested CES demand specification is more appropriate than
CES demand specification 10 .
Focusing on other variables in Model 4, log of operating hours, method of payments,
single stores ratio and two breadth assortment have also significant coefficients andexpected sign. Negative coefficients for “Goods in stock per sales” suggest that since
low “Goods in stock per sales” implies adoption of just-in time delivery systems,
consumers highly evaluate those outlets which provide fresh food products. The
coefficients for log of floor space per employee are negative but insignificant for nested
CES model with the GMM estimation.
Furthermore, using estimated parameters, we calculated the quality indices by retail
formats. Since dummy variable for retail format captures service quality each retail
format offers, we regard the coefficient for retail format dummy as part of service
quality. The indices are normalized so as the quality index for traditional store in 1991
to be 1.0. The estimated indices are presented in Table 5. We can find substantial
variation in quality indices among retail formats. The services quality indices for large
retailers, such as GMS, Specialty superstores and Department stores are almost 2 or 2.5
times larger than that of Traditional stores. Convenience stores offer the highest services
quality. In addition, services quality for Convenience stores has been increasing during
sample periods and it has amounted to 3.56 in 2002. Therefore, the substantial share
increase for convenience stores can be attributed to the improvement of its service
quality.
Table 6 presents the annual average welfare change and its decomposition. In thiscalculation, changes in price are measured as relative price changes against general CPI.
10

Wald test statistics for significance of log of number of outlets in Model 4 is 58.55
and the null hypothesis is rejected at the 1% level of significance. Test statistics for the
hypothesis ρ g =0 is 9.17 and rejected at 1% level of significance, as well.
14

The annual average welfare changes between 1991 and 2002 are around 3.7% to 1.9%,
more than half of which are explained by CVq, the quality changes. It amounts to 2.1%
or 1.2% between 1991 and 1997 and between 1997 and 2002, respectively. We also
found significant contribution of CVN (1.4%) during 1990s. Since CVN is considered as
the effects of changes in share of number of outlets by retail format, positive CVN
reflects the decreases in stores with low service quality, such as traditional stores andthe increases in non-traditional stores. Considering the fact that there is substantial
increase in consumers’ perceived quality for convenience store in sample periods andthe increases in the share, emergence of convenience store have played an importantrole of changes in welfare for food retailing.

7.

Discussion

Combined with the estimation results and service characteristics by sales format in
table 3, the reason why GMS failed to expand market share despite deregulation ismainly due to insufficient price competitiveness and failure to differentiate its service
characteristics against specialty superstores. For example, in table 3, while the priceindex for GMS is 0.963, that for specialty superstores is 0.907. Index for operating
hours and goods in stock per sales for GMS are also same with those for specialty
superstores. And insignificant coefficient on floor space per employment reflects thefact that Japanese consumers dose not place an importance on huge floor space for daily
food shopping. According to Aoyama (2007), retailing MNEs, such as Wal-Mart andCarrefour, has succeeded in oversea market by collaborating with manufacturers andadopting factory-direct model, which enables them to undercut competitors’ prices.
However, in Japan, their offers are refused by Japanese manufacturers and failed toadopt the model, thus, both had no cost advantages. Besides, taking advantages of huge
floor space, Carrefour and Wal-Mart stick to their strategies for low-cost operations,such as stack-them-up-and-sell-them-cheap strategy, where products are displayed
without being taken out of corrugated boxes. However, bulk purchase is not familiar
with Japanese consumers and that strategy failed to lure consumers.

8.

Concluding remarks

In this paper, we propose the framework which enables us to evaluate consumer
benefits from the competition in retail industry. In our framework, we incorporated theservice quality of retail outlets as outputs. In Japan, in the process of the deregulation ofentry restriction on large-scale retail stores, specialty supermarkets have increased its
market share with low price strategy. At the same time, despite its high price, the15

convenience store has increased its market share through 1990s. We demonstratechanges in compensation variation are explained by the changes in prices, the number ofoutlets and the service quality of each retail format.
However, this research offers various agenda for future research. First, since our data isbased on prefecture panel data set, we can compare the changes in welfare among
regions. Market structure for food retailing sector varies significantly between major
metropolitan areas and other areas. According to Matsuura and Motohashi (2006), thegrowth rate of sales for specialty supermarket stores for rural area has been significantly
larger than for major metropolitan areas between 1997 and 2002, reflecting the fact that
there is substantial increase in car ownership rate in rural areas and it encourages
consumers to go shopping at large scale specialty supermarket in outside city.
Second, our framework enables us to make an international comparison on the
differences in services quality and consumer gains from retail outlets. The difficulties ininternational comparison on the productivity in services sector lie in incorporating thedifferences in consumer testes. In our framework, we can expect differences in
consumer testes are reflected by parameters for price and service characteristics.
Third, this research framework can be applied not only to retail industry, but also toother service sector with service differentiation, such as hotel, amusement park andrestaurant. There are difficulties in estimating productivity in those sectors sinceconventional output index are not adjusted quality change. However, our framework
enables us to estimate quality adjusted cost of living index based on compensation
variation and construct quality adjusted output index. Productivity in services sector has
recently been paid much attention to by economists, thus it must be promising research
agenda.

16

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19

Appendix A: Historical overview of Large Scale Retail Law
In Japan, the business of large-scale retailers has been highly restricted by law toprotect the businesses of smaller-sized retailers. 11 The protection for small retailbusinesses was originated from “Department Store Law” established in 1937. Although
the law was once repealed after the WWII by GHQ, it came to life again in 1956 inalmost the same manner as before. In 1974, the law was enforced as “Large Scale Retail
Law (LSR law)" targeting not only department stores but also large superstores. At thesame time the new law had another purpose to restrain new entrants with large capitalfrom abroad. The law has not only been that of protecting smaller businesses but also
restricting the competition among large retailers through controlling the entry of new
businesses.
In 1978 the law was reinforced. When a large-scale retailer starts a new business in acertain area, it first has to notify the Minster of International Trade and Industry. The
minister investigates the effect of the new ently on smaller retailers in that area. If asignificant negative effect is expected after the investigation, the minister urges theentrant to modify his business plan regarding such items as floor space, business days,
closing times, or the number of holidays.
The role of the minister is just to illustrate guidelines. Representatives in regional
business districts carry out substantial adjustments. Furthermore, local governments
have been allowed to impose additional entry regulations on large stores.
In the 1990s, the trend changed from protectionism to deregulation as a result of ``The
Japan-US Structural Impediments Initiative," which was aimed at creating a Japanese
open market and promoting competition. In 1994, LSRS law was eased to give more
freedom to new entrants to the retail industry with less than 1000 square meters of floor
space. And, finally, in 1998 the law was completely repealed.

11

For historical survey on this issue, see Lark (1994) and Mayer-Ohle(2003).
20

Appendix A: The effects of the increase in Ng on market share.
As we saw in the equation (10), the share or choice probability of group g is1− ρ gPrg =( p g / f ( z g )) −1 / α N g.

∑l ( pl / f ( z l )) −1 / α N l

1− ρ gTherefore, the elasticity of choice probability of group g to Ng , η g ,is−ρ⎡( p g / f ( z g )) −1/ α N g g ⎤= (1 − ρ g ) ⎢1 −ηg =⎥ = (1 − ρ g )(1 − Pr j )1− ρ−1 / α∂N g Prg⎢⎣ ∑l ( pl / f ( zl )) N l g ⎥⎦∂ Prg N g

One the other hand, the choice probability of outlet j in group g is1− ρ

−1 / αg
1 ( p g / f ( z g )) N g,
Pr j = Pr j| g Prg =N g ∑ ( pl / f ( zl )) −1/ α N l1− ρ glwhere 0 ≤ ρg ≤ 1.
If ρg = 1,

η g = 0 and η j = −1 . Therefore, while the share of outlets j in group g isdecreasing as increase in Ng, the share of group g dose not.
If ρg = 0, Pr j =( p g / f ( z g )) −1/ α∑ ( p / f ( z )) α N

ηg = 1−ll( p g / f ( z g )) −1/ α∑ ( p / f ( z )) α N= 1 − Pr j < 0 ,

−1 /

l

l

ηj = −

,

−1 /

l

l

l

( p g / f ( z g )) −1/ α∑ ( p / f ( z )) α N= − Pr j .

−1 /

l

l

l

l

l

Hence,
0 ≤ η g ≤ 1 − Pr j and − 1 ≤ η j ≤ − Pr j . So, when 0< ρg < 1, increases in Ng
accompanies the increases in the share of group g and the decreases in the share ofoutlet j in group g.

21

Table 1. Price gap by products and retail formats
013 Pillow type instantnoodle, Chicken Ramen(per package, Yen)

078 Plain yogurt, MEIJI
Bulgaria LB51 (500ml,
Yen)

Average price in all over Japan
81.60
228.17
Average price in Tokyo
82.30
231.81
Chain Supermarket
82.06
230.21Other Supermarket
81.14
227.60Convenience stores
83.74
235.70Department stores
83.59
239.61
General retail outlets
82.30
236.02
Source: National Price Survey in 1992, Management and coordination Agency

22

108 Imported dress shirt,
130 Toothpaste, Lion
023 Microwave, Toshiba made in South East Asia,
Dentor T Lion (170g,
ER-VS6 (Yen)
35-percent-cotton, 65Yen)
percent polyester (Yen)81,1692,298
212.0
81,2302,316
213.1
79,442
2,295
216.0
71,500
1,682
210.7
223.0
86,0372,279
224.3
80,8552,313
207.2

Table 2. Definition and Concordance of Retail Formats(1) The definition of retail formats
Self-service system
1
2
3

GMS
Specialty supermarket
Department stores

Yes
Yes
No

4

Traditional stores

No

Sales floor space

Operating hours

2

3000m or over
250m2 or over
3000m2 or over

2
3
2

5
14 hour or more
Convenience storesYes
between 30m2 and 250m2
Note1) Self-service stores are defined as those establishments who adopt the self-service system in 50%
or more of the sales floor.
The "Self-service system" means that the establishment meets the following conditions;i) Merchandise are put on the shelf unwrapped or prepackaged with price labeled on them.
ii) Shopping baskets or shopping carts are provided for customers.
iii) Customers pay for all of the purchases at the check-out counters.
2) Department and General supermarkets are stores that retail clothing, food and housing products,
in which retail sales for each of these categories is over 10% but under 70%.
3) Specialty supermarkets are those stores which retail clothing, food or housing products,
one of which retail sales is 70% or over(2) Concordance of Retail formats

GMS
1
2 Specialty supermarket
3 Department stores

National Survey of Price
1997, 2002
1992
Supermarket
Chain Supermarket
Specialty supermarket Other Supermarket
Department stores
Department stores

4

General retail outlets

Traditional stores

5 Convenience stores

Convenience stores

General retail outlets
Convenience stores

23

Note

Census of Commerce
General Supermarkets
Specialty supermarket
Department stores
Other Supermarket
Specialty stores
Semi-specialty stores
Convenience stores

Table 3. Average service characteristics for food retailing outlets(i) Price level
1991
1997
1 GMS
0.908
0.991
2 Specialty Supermarkets
0.922
0.966
3 Department store
1.024
1.121
4 Traditional store
0.96
0.974
5 Convenience store
0.969
1.021

2002
0.963
0.907
1.139
0.953
0.982

(ii) Operating hours
1 GMS
2 Specialty Supermarkets
3 Department store
4 Traditional store
5 Convenience store
Note: Operating hours are divided by 24.

1991
0.385
0.419
0.364
0.423
0.71

1997
0.425
0.448
0.384
0.419
0.878

2002
0.452
0.45
0.388
0.405
0.919

(iii) Single stores ratio
1 GMS
2 Specialty Supermarkets
3 Department store
4 Traditional store
5 Convenience store

1991
0.047
0.192
0.181
0.806
0.753

1997
0.029
0.172
0.152
0.781
0.799

2002
0.02
0.138
0.21
0.777
0.774

(iv) Goods in stock per sales
1991
1997
1 GMS
0.07
0.08
2 Specialty Supermarkets
0.06
0.08
3 Department store
0.10
0.09
4 Traditional store
0.14
0.14
5 Convenience store
0.05
0.04
Note: Goods in stock per sales = Value of goods in stock/Total Sales

2002
0.09
0.09
0.08
0.14
0.04

(v) Floor space per employee
1 GMS
2 Specialty Supermarkets
3 Department store
4 Traditional store
5 Convenience store

2002
42.82
34.31
55.02
17.35
8.39

1991
37.89
24.98
34.55
15.07
11.59

1997
48.60
33.76
45.52
17.26
10.57

(vi) Method of Payments
1991
1997
2002
1 GMS
0.266
0.208
0.118
2 Specialty Supermarkets
0.650
0.650
0.527
3 Department store
0.022
0.039
0.051
4 Traditional store
0.431
0.435
0.442
5 Convenience store
0.655
0.696
0.704
Note: Method of payments defined as ratio of cash settlement to total sales(vii) Index of breadth assortment
1 GMS
2 Specialty Supermarkets
3 Department store
4 Traditional store
5 Convenience store

1991
0.74
0.25
0.81
0.15
0.26

Source: Authors' calculation based on Census of Commerce.

24

1997
0.72
0.24
0.80
0.16
0.31

2002
0.68
0.24
0.78
0.15
0.35

Table 4. Estimation result of market share equation
Model 1Model 2Model 3Model 4
Number of obs
1410
1410
1410
1410
Adj R-sq:
0.92290.92290.97390.9739Relative price
0.82
-4.94
1.23
-1.45
[3.53]*** [-4.68]*** [9.11]***[-1.84]*Log(Number of outlets)
0.82
0.65
[51.29]*** [6.92]***Log(operating hours)
1.95
2.39
0.59
1.01
[13.32]*** [11.53]*** [6.60]*** [4.42]***
method of payments
-0.25
-0.18
-0.29
-0.24
[-2.71]***[-1.73]*[-5.38]*** [-3.67]***
Good in stock per sales
-0.19
-3.36
-3.74
-4.27
[-0.35][-3.68]*** [-11.30]*** [-7.28]***Single store ratio
-0.63
-0.39
-0.3
-0.35
[-6.86]*** [-2.35]** [-5.65]*** [-3.43]***Log(Floor space per employee
-0.07
0.13
-0.09
-0.01
[-1.27][1.57][-2.87]***[-0.30]
Breadth assortment
0.57
0.47
0.39
0.39
(all products)[6.45]*** [4.35]*** [7.51]*** [6.71]***
Breadth assortment
1.34
1.47
0.83
1.00
(food and beverage)[6.13]*** [5.63]*** [6.50]*** [6.52]***
Sales format dummy(base= GMS)
Speciality Supermarket
-0.08
0.03
2.14
1.64
[-0.39][0.10][16.27]*** [5.79]***Department
2.26
2.13
1.75
1.73
[16.71]*** [9.34]*** [22.07]*** [12.70]***
Traditional store
-0.91
0.07
2.33
2.01
[-4.07]***[0.22][16.12]*** [6.18]***
Convenience store
3.56
3.8
-0.21
0.64
[29.18]*** [22.34]*** [-2.10]**[1.35]
const
-0.1
-0.28
-0.02
-0.17
[-1.06][-3.20]***[-0.36][-4.26]***Prefecture dummy
Yes
Yes
Yes
Yes
Year dummy
Yes
Yes
Yes
Yes
Estimation Method
OLS
GMM
OLS
GMM
Hansen J Statistics
16.6955.445
Chi-sq p-value
0.011
0.364
Note:
1) Figures in brackets are t-value.
2) "***","**", and "*" represents level of significance at 1%, 5% and 10%, respecti

25

Table 5. Estimated Quality Indices by sales formats
1991
1997
2002
1 General Supermarkets
2.701
2.799
2.913
2 Specialty supermarket
2.038
1.913
1.912
3 Department stores
2.207
2.439
2.455
4 Traditional stores
1.000
1.009
0.990
5 Convenience stores
2.348
3.194
3.560
Note; The quality indices are estimated by using estimated regression
coefficients and normalized so that Traditional stores' in 1991 equals 1.

Table 6. The decomposition of annual average welfare changes
Table 6. The decomposition of annual average welfare chan
1991-1997 1997-2002
CV
3.7%
1.9%
CV q
2.1%
1.2%
CV N
1.4%
0.1%
CV p
0.2%
0.5%

26

Figure 1
panel (a)Food Price (GMS) v.s. Regulation Index

1
0.98

Food Price (GMS)

0.96
0.94
0.92
0.9
0.88
0.86
0.84
0.82
0

2

4

6

8

Regulation index

panel (b)Operating Hours (GMS) v.s. Regulation Index

10.2

Operating Hours

10
9.8
9.6
9.4
9.2
9
8.8
0

2

4

Regulation Index

27

6

8

Figure 2.Annual Average Entry and Exit rate

10.0%

entry rate

8.0%

exit rate

net growth rate
5.9%
4.3%

6.0%
3.5%

4.0%
2.0%

1.1%

0.0%
-2.0%
-4.0%

-2.4%

2.7%

2.6%

3.2%

0.9%

2.6%

2.3%

-0.2%

-0.3%

-1.6%

-1.7%

-4.4%

-6.0%

-2.0%

-1.6%

-2.0%
-3.7%

-2.6%

-3.3%

7.9%

-4.7%
-5.9%

-8.0%

-9.6%

-9.9%

19992002

20022004-10.0%
19791982

19821985

19851988

19881991

19911994

19941997

19971999

Source: Census of Commerce

Figure 3. The tranition of the sales sahre by retail

100%
90%

13.9%
←9.9%

80%

→
70% 3.3%

14.0%

12.5%

12.3%

GMS

13.8%

17.5%

18.1%

Specility
supermarkets

5.2%
6%

Convenient stores
Other supermarkets

5%

←2.2%
7%

←3.5%
7%

60%
50%
40%
69%

Traditionals
62%

58%

58%

1991
1997
Source: Census of Commerce

2002

2004

30%
20%
10%
0%

Note: See footnote at Table 2 for the definition of retail formats.
28

Figure 4. International Comparison on the share of food retailing by formats
100%

6%

9%

7%

6%

59%

64%

GMS

90%
80%
30%
70%
60%

5%

50%

11%

Department
Stores65%

Supermarket

40%
30%
45%

20%

Small
outlets

10%

10%

3%

Convenience

24%

27%

Traditional

UK 1995

France 1995

13%

16%

0%
Japan 1997
US 1995
Source: Mckinsey Global Institute (2000)

25(milion yen)

Figure 5. Retail sales per employee

20

15

10

5

0
1972 1974 1976 1979 1982 1985 1988 1991 1994 1997 1999 2002 2004
Source: Census of Commerce29

Figure 6. Structure of consumers’ choice

1st stage: Decision to choose
the group of retail format g

The elasticity of substitution
1+ α
between groups:
2nd stage: Decision to choose
the outlet j in retail format g

Convenience
StoresGMS

Traditional Stores

Assumption:
Symmetry of quality and prices within group

Figure 7. The sales share of retail formats by commodity group and retail formats
Food and Beverage

3.8%
1991 7.2%

25.5%

58.8%
4.7%

1997

9.5%

3.8%

31.5%

47.3%
7.8%

2002

9.3%

3.5%

35.3%

41.6%
10.4%

0%

20%
40%
60%
General Supermarkets
Speciality supermarket
Traditional outlets
Department stores

30

80%
100%
Convenience stores