Silviase/jgov_v1.4_full_ann huggingface dataset:
Figure 2: The sales share of food and beverage by retail formats

Note: See Appendix B for the definition of retail formats.

Table 1: Price gap by products and retail formats

Rice (Brand A, 5 kg, Yen)	Hen eggs (M size, 10 pieces, Yen)	Chocolate (National brand A, Yen)	Instant noodle (National brand A, Yen)	Plain yogurt, (National brand A, Yen)
GMS	2675.0	191.0	97.81	82.06	216.3
Specialty supermarket stores	2573.0	188.7	97.85	81.14	208.4
Department stores	2918.0	206.9	101.09	83.59	230.1
Traditional Stores	2752.0	206.1	100.63	82.30	226.0
Convenience stores	2939.0	208.2	101.33	83.74	242.8

Source: National Survey of 1997. Ministry of Internal Affairs and Communication.

Note: For the details of the definition of retail formats, see Appendix B.

The emergence of specialty supermarket stores may be attributed to their price policy as well as the deregulation. Table 1 shows a substantial price gap between retail formats. While specialty supermarket stores attract consumers by offering products with the lowest prices, convenience stores offer relatively high prices.

Convenience stores in Japan are quite different from what would be expected in Europe and the U.S. For example, while the majority of small stores cannot adopt advanced information technology in Japan, major convenience store operators actively make use of these technological advances such as the POS System, which enables them to organize an efficient distribution system. Currently, major chains organize deliveries to each retail outlet 3 to 5 times a day and provide consumers with fresh and sometimes highly perishable food products. Some convenience store operators collect records of each customer's gender and age for each purchase, and make active use of
this information for their original brand product development and merchandising. According to Larke and Causton (2005), Seven-Eleven Japan, the largest convenience store operator, sells its own branded merchandise, which amounts to 50% in total sales. Moreover, long business hours and additional services are also fundamental to convenience stores' operation. In Japan, most convenience stores generally operate 24 hours a day, and offer additional services such as making photocopies, photo developing, postal package handling, and payment of utility charges.

These dynamic market transitions affect consumer welfare. However, previous studies have not empirically examined their impact during periods of regulatory transition. Since previous studies such as Flath (1990), Nariu and Flath (1996), and Nishimura and Tachibana (1996) are all cross-sectional studies, it does not distinguish the cause from the effect. Our welfare assessment based on the structural estimation of demand function for shopping will reveal how the dynamics of entry and exit has impacted the retail market and to what extent entry restrictions have been influential.

### 3. Model

## Consumer Choice

We suppose that consumers have a choice between two levels of differentiated retailing services. The structure of consumer choice is presented in Figure 3. First, an individual consumer decides whether to purchase a product from each of $ g=1,\cdots,G $ groups (for example, GMS, specialty supermarket store, or convenience store). Second, the consumer decides from which outlet $ j=1,\cdots,N $ in that group to purchase. Suppose that the outlets available in each group g are denoted by $ J_{g}\subset\{1,\ldots,N\} $ . Utility for consumer h is given by the following equation:

$$ u_{j}{=}\beta z_{j}{-}\alpha p_{j}{+}\xi_{j}{+}\varepsilon_{j h}^{h}\quad\alpha{>}0. $$

where $ p_{j} $ is the price of retail outlet j. And While $ z_{j} $ is a vector of the observable characteristics of service quality for retail outlet j, $ \xi_{j} $ is unobservable service quality for outlet j.

Following from Berry (1994), the random error term $ \varepsilon_{j}^{h} $ is defined as follows:

$$ \varepsilon_{j}^{h}=\zeta_{g}^{h}+(1-\rho)e_{j}^{h},\mathrm{f o r}j\in J_{g}, $$
where the errors $ e_{j}^{h} $ are iid extreme values. $ \zeta_{g}^{h} $ is common random variable to all the products h in group g. And $ \rho $ is the parameter which induces a correlation between the products in group g.

Following Berry (1994), we obtain the share of retail outlet j as

$$ s_{j}{=}\frac{e^{u_{j}/(1-\rho)}}{D_{g}}\times\frac{D_{g}^{(1-\rho)}}{\sum_{g=0}^{G}D_{g}^{(1-\rho)}},\quad\mathrm{f o r}\quad j{\in}J_{g} $$

where the term $ D_{g}=\sum_{j\in J}e^{u_{j}/(1-\rho)} $ is the “inclusive value.” The first term on the right-hand side of equation (3) is the share that an individual will choose the outlet $ j\in J_{g} $ in retail group g. The second term is the share of choosing any outlet from group g.

Here, we assume the equality of price $ p_{j} $ , and service quality $ z_{j} $ and $ \xi_{j} $ within group g; in other words, we assume $ p_{j}=p_{k}=p_{g} $ , $ z_{j}=z_{k}=z_{g} $ and $ \xi_{j}=\xi_{k}=\xi_{g} $ for $ j, k \in J_{g} $ . Then, we obtain

$$ D_{g}{=}\sum_{j\in J}e^{u_{j}/(1-\rho)}{=}e^{u_{g}/(1-\rho)}N_{g} $$

where Nq is the number of outlet in retail group g. The aggregated market share for type g retail group is derived as follows:

$$ s_{g}=s_{j}N_{g}=\frac{e^{u_{g}}N_{g}^{(1-\rho)}}{\sum_{g=0}^{G}e^{u_{g}}N_{g}^{(1-\rho)}}. $$

We introduce outside goods that have $ u_{0}=0 $ and inclusive value $ D_{0}=1 $ . From equation (3), it's share can be written as $ s_{0}=[\sum_{g=0}^{G}D_{g}^{(1-\rho)}]^{-1} $ . Taking log of the ratio of $ s_{g} $ to $ s_{0} $ , we obtain

$$ \ln s_{g}-\ln s_{0}=\beta z_{g}-\alpha p_{g}+(1-\rho)\ln N_{g}+\xi_{g}. $$

where $ \xi_{g} $ represents the unobservable service characteristics of type g retail format, which is a random variable with the mean value of zero. In our regression analysis, we treat this term as an error term. The dependent variable is the difference of logged share of retail format g and logged share of outside goods. Independent variables include the price, quality of the service and the number of outlet for retail format g. Note that if $ \rho=1 $ , the model is degenerated into logit model and increase in $ N_{g} $ does not affect the share of group g. $ ^{5} $ On the other hand, when $ 0<\rho<1 $ , the sales share of group g increases as entry of new outlets in group g.
Figure 3 : Structure of consumers' choice

## Extension: Welfare

We now demonstrate how we can estimate the changes in consumer welfare that arise from the expansion of various services. The change in consumer surplus that results from a change in the vector of price and quality, namely from $ u_{g} $ to $ u_{g}' $ , is given by

$$ \begin{array}{r}{\varDelta C S=\frac{1}{\alpha}[\ln\left(1+\sum_{g=1}^{G}e^{u_{g}^{\prime}}N_{g}^{(1-\rho)}\right)-\ln\left(1+\sum_{g=1}^{G}e^{u_{g}}N_{g}^{(1-\rho)}\right)],}\end{array} $$

where $ u_{g}=\beta z_{g}-\alpha p_{g}+\xi_{g}. $ ^{6} Using estimated coefficients, $ \widehat{\beta} $ , $ \widehat{\alpha} $ and residual $ \widehat{\xi}_{g} $ , this framework enables us to decompose the source of the changes in consumer surplus into three factors; changes in price, quality and the number of outlets. As we mentioned, the deregulation promotes the entry of large retail stores. We can access the impact of the deregulation by comparing the consumer surplus with and without increases in the number of GMS and Specialty supermarket stores.

### 4. Data and Estimation Methodology

Our dataset is the regional data set for food retailing outlets covering 1997, 2002, and 2007. 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 the establishments belonging to the wholesale and retail industry. From the Census, we obtained the data for the sales at six-digit product level and establishment characteristics. One limitation of the Census is the lack of price information. Therefore, we referred to the National Survey of Prices (Ministry of Internal Affairs and Communications), which provides the commodity-level price information. Both data sets are available at establishment level. However, Since National Survey of Price is sampling survey, it is difficult to match these two statistics at establishment-level. Thus, we aggregate both data sets at regional level. Besides, the Census of Commerce and the National Survey of Prices use the different definition of retail formats. When aggregating establishment-level data, we adjust the retail format type for National Survey of Prices to match the one in the Census of Commerce. $ ^{7} $ As for price data, we first calculate commodity-level average price by region, retail format and year. Then, Price indices by region and retail formats are constructed by aggregating the commodity-level price with the weight of the Consumer Price index (as compiled by the Ministry of Internal Affairs and Communications). Our regional unit is Metropolitan Area which is defined in National Survey of Price by dividing each prefecture into three or four areas and includes 189 areas.

For market share, we use sales for food products by each retail format because we focus on the choice behavior of food retailing stores. Value of sales is deflated by price index constructed by National Survey of Price. As for the share of outside goods, we use the ratio of the expenditure for eating-out in total food related expenditure by prefecture and year, which is obtained from Family Income and Expenditure Survey (Ministry of Internal Affairs and Communications).

For service quality indicators, we referred to previous studies that address the definition of retail output, e.g., Oi (1992) and Betancourt and Gautschi (1988). In these previous studies, retail outputs are considered as a mix of distribution services, such as assortment and assurance. Betancourt and Gautschi (1988) discussed the economic function of retail organization and proposed the concepts of distribution service from an empirically-based perspective. According to their definition, distribution service is classified into the following four categories: (1) accessibility of location, (2) assortment, (3) assurance of product delivery in the desired form and at the desired time, (4)
information and ambience. We refer to these concepts and construct the following service quality indicators based on different retail format characteristics: operating hours, goods in stock per sales, broad assortment index, single store ratio, floor space per employee, and ratio of cash payment to total sales.

As proxy for time accessibility or assurance of product delivery, we use “operating hours” which is normalized by dividing 24 hours. We expect “operating hours” to have a positive effect on market share. “Goods in stock per sales” is the inverse of the merchandise turnover ratio, and is defined as the ratio of value of stocks to sales of products. Low “goods in stock-sales” ratio implies adoption of just-in-time delivery system, which enables retailers to provide fresh food products to customers. For assortment, we prepare a herfindahl index for products sales which is defined as the following;

$$ \begin{array}{r}{H I_{j}=\sum_{k=1}^{K}\omega_{j k}^{2},}\end{array} $$

where $ \omega_{ik} $ indicates the sale share of products k for retail outlet $ j^{8} $ . The more retail outlets deal with the product lines, the index HI will approach 0. On the other hand, those retail outlets dealing with only 1 product line have HI=1. Product information is available at six-digit level. This index is calculated by each establishment, and aggregated by region and retail formats with the sales share weight. This index covers not only food and beverage products but also all the commodity line, such as apparel products, drags and toiletries and other miscellaneous products.

“Single store ratio” is defined as the share of single store to total number of outlets by retail format, region and year. It is expected to have negative effects on market share, because single stores are inferior to multiple stores in terms of providing price information, availability, and other characteristics. “Log of floor space per employee” is proxy for broad assortment services or information supply to customers. Retail outlets with larger floor space offer a broader variety of products. On the other hand, in order to provide consumers with sufficient price and product information, managers have to increase the number of employees. The expected sign of the coefficient will be positive or negative depending on consumer preferences. “Ratio of cash payment to total sales” is defined as the ratio of cash settlement to total sales, which suggests an index for a variety of payment. While the majority of non-traditional or chain stores usually offer a
Welfare Assessment of Entry of New Retail Formats and Deregulation for Restriction on Large Retail Scale Stores in Japan

(小売業における新業態参入と大型店舗の規制緩和の厚生評価)

Table 2 : Average service characteristics for food retailing outlets in 1997

Traditional Store	GMS	Specialty Supermarket	Convenience store	Department store
Price Index	1.0100	0.9700	0.9400	1.0100	1.0800
Operating hours	0.4840	0.4230	0.4540	0.8650	0.3840
Floor space per employee	11.8	47.2	22.7	9.5	46.3
Goods in stock per sales	0.0002	0.0230	0.0011	0.0011	0.0556
Method of Payments	0.7890	0.9090	0.9780	0.9490	0.6940
Single stores ratio	0.7060	0.0318	0.1170	0.7800	0.1750
Herfindahl index	0.8670	0.2870	0.8340	0.6820	0.2060

Source: Authors' calculation based on Census of Commerce.

Note: For normalization, operating hours are divided by 24.

variety of payment methods, such as debit card, credit card, or prepaid electric money card, traditional stores do not. Thus, a negative sign is expected for the coefficient. Finally, to capture unobservable format-specific service quality, retail format dummy variables are included in the equation. Finally, to capture the service characteristics common across sales formats that adopt self-service system, such as GMS, Specialty supermarket stores, and convenience stores, we include the dummy variable, "Self-service format."

Table 2 provides a summary of the variables we use as service characteristics by retail formats. Three items are noteworthy. First, 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. Second, looking at floor space per employee, there are clear differences between large and small retail formats. While floor space per employee for GMS and Department store exceeds 40, those for Traditional stores and Convenience stores are 11.8 and 9.5, respectively. Third, Traditional stores offer higher price and they are inferior to other retail formats in terms of service quality, such as operating hours.

Since an error term, which is the unobserved service characteristics, may correlates with price and the number of outlets, the price and number of outlets are considered as endogenous variables and OLS estimates are not consistent. Therefore, instrumental variables are needed. As discussed in Berry, et al. (1995), Hausman (1997), and Nevo (2001), we use standard instrumental variables, such as (1) the mean of service characteristics of other types of retail formats and (2) degree of competition in their local market; the number of outlets in other sales format locating in same area. As for the first instruments, since we assume that retailing firms determine their service characteristics before their location decisions and subsequent price competition, we
consider their competitors' service characteristics are exogenous. The latter variable is an instrumental variable for the logged number of outlet, lnN. This measure will be correlated with the logged number of outlet, but uncorrelated with the unobservable service characteristics, $ \xi_{g} $ .

### 5. Estimation Results

The estimation results are presented in Table 3. We estimate the model using both OLS (Column 1) and GMM (Column 2) methods including retail format dummies, and area-year dummies. Three things are noteworthy. First, OLS estimates for the

Table 3 : Estimation result of market share equation

VARIABLES	(1)	(2)
OLS	IV
Price	-0.355***	-2.368**
(0.124)	(0.954)
lnNg	0.836***	0.869***
(0.0183)	(0.0520)
Operating hours	0.842***	0.641***
(0.132)	(0.182)
Goods in stock per sales	-5.644***	-5.876***
(0.350)	(0.478)
Floor space per employee	-0.187***	-0.222***
(0.0238)	(0.0304)
Single store ratio	-0.321***	-0.354***
(0.0492)	(0.0547)
Ratio of cash payment	-0.927***	-1.263***
(0.104)	(0.195)
Self-service store ratio	1.058***	1.075***
(0.191)	(0.205)
Herfindahl index	0.0878	-0.316
(0.148)	(0.246)
Observations	2,480	2,477
R-squared	0.935	0.926
Number of year-market	566	563
Area-Year FE	Yes	Yes
First stage F test
Price	9.93
lnNg	83.36
Hansen J test p-value	0.195

Note: 1) Figures in brackets are standard errors.

2) "***," "***," and "**" represent level of significance at 1%, 5%, and 10%, respectively.
Table 4 : Decomposition of Annual Average Changes in Consumer Surplus

1997-2002	2002-2007
$ \Delta $ CS	2.36%	1.92%
Changes in Price	1.50%	1.54%
Changes in Quality	0.82%	0.45%
Changes in Ng	0.04%	-0.07%

coefficients of price are smaller than those by GMM in absolute value, suggesting that OLS estimates may suffer from endogenous bias. Second, the coefficients for log of number of outlets, which correspond to $ 1-\rho $ , are all estimated between 0 and 1, which is consistent with our theoretical framework. Third, first stage F test statistics are sufficiently high and the test of over identification is not rejected for column 2.

Focusing on other variables in column 2, we can observe that the operating hours has significantly positive impact on market share. Coefficients for Goods in stock per sales, Floor space per employee, Single store ratio and Ratio of cash payment are all negative and significant. A negative coefficient for goods in stock per sales suggest that since low goods in stock per sales implies the adoption of just-in-time delivery systems, consumers highly value retailing outlets that provide fresh food products. The coefficient for floor space per employee, which is also negative and significant, may implies that Japanese consumers prefer to smaller stores.

Table 4 presents the changes in consumer surplus and its decomposition. The changes in the consumer surplus from 1997 to 2002 and from 2002 to 2007 are 2.36% and 1.92%. We decompose the changes in consumer surplus into changes in price, quality and the number of outlets by holding two out of three attributes constant. For the period between 1997 and 2002, more than half of which are explained by changes in price, which amounts to 1.50 percent point. As we mentioned in Figure1, we have seen the surge of increase in the number of retail store whose floor space is more than 500 $ m^{2} $ and less than 1000 $ m^{2} $ between 1997 and 2002. Price may decrease due to increases in competitive pressure.

We also found significant contribution of changes in quality (0.82 percent point) from 1997 to 2002. This result may indicate increases in new retail format such as convenient stores contribute to consumer welfare. On the other hand, changes in the number of outlets was 0.04% between 1997 and 2002 and -0.07% between 2002 and 2007.
### 6. Discussion

Combined with the estimation results and service characteristics by retail format in Table 2, the reason why GMS failed to expand market share despite deregulation can be explained by insufficient price competitiveness and failure to differentiate its service characteristics from those of specialty supermarket stores. For example, in Table 2, while the price index for GMS is 0.97, for specialty supermarket stores it is 0.94. The indices for operating hours and goods in stock per sales for GMS are also the same with those for specialty supermarket stores. Negative coefficient on floor space per employment reflects the fact that Japanese consumers do not place particular importance to huge floor space for daily food shopping. According to Aoyama (2007), retailing MNEs such as Wal-Mart and Carrefour, have succeeded in expanding their markets overseas by collaborating with manufacturers and adopting factory direct models, which enable them to undercut competitors' prices. However, in case of Japan, their offers were refused by Japanese manufacturers, and they failed to adopt the model. Thus, both Carrefour and Wal-Mart had no cost advantages in Japan. Moreover, taking advantages of huge floor space, Carrefour and Wal-Mart stuck to their low-cost operations, such as the stack-them-up-and-sell-them-cheap strategy, whereby products are displayed without being taken out of corrugated boxes. However, that strategy failed because Japanese consumers were not familiar with bulk purchase.

### 7. Concluding Remarks

In this study, we measured the welfare gain caused by the entry of new retailers. During the 1990s, the retail sector in Japan experienced significant reallocation dynamics. Two retail formats expanded their market share. The first, specialty supermarket stores, partially expanded their market shares due to the deregulation of large-scale stores. The second, convenience stores, took advantage of their distinctive service quality and efficient operation system. In our analysis, since retail service providers usually differentiate their service quality, we incorporated the differences in their service characteristics as well as in their price; consumer benefit was measured by estimating the nested logit demand function.
Our results revealed that changes in consumer surplus during the 1990s and 2000s are predominantly explained by changes in the price and quality of service. In particular, we observed the significant contribution of changes in the price during the regulatory transition period from 1997 to 2002. We found that the deregulation of entry restrictions forced retail stores to lower their price level. Moreover, the result suggested that deregulation influences consumer surplus.

Furthermore, estimated parameters for the demand function imply that Japanese consumers place more importance on long hours of operation rather than larger floor space for daily food shopping. This might be one of the reasons why retailing MNEs, such as Wal-Mart and Carrefour, faced difficulties in expanding market share in Japan.

Although this paper provides thoughtful insights, various avenues exist for future researches. First, following Berry et al. (1995), with some additional assumptions on the competition among firms, marginal cost or markup can be recovered from the estimated demand function. Accessing the impact of deregulation on markup might be an interesting exercise. Second, using the quality indicator calculated by our demand estimates, we will be able to construct the quality adjusted price index and real output of retail service. Furthermore, we will be able to estimate a quality adjusted productivity indicator for the retail industry. Third, exploring the regional differences in the regulation of large-scale stores might provide policy implications. While large-scale store law was a nationwide regulation, additional entry restrictions were imposed by the local government in some cities and prefectures when the law of large-scale store was effective. Using the information on additional entry restrictions, one may be able to access the impact of deregulation at a regional level.

## References

Aoyama, Y (2007). Oligopoly and the structural paradox of retail TNCs: An assessment of Carrefour and Wal-Mart in Japan, Journal of Economic Geography, 7 (4), 471-490.

Berry, S. T. (1994). Estimating discrete-choice model of product differentiation, RAND Journal of Economics, 25 (2), 242-262.

Berry, S. T., J. Levinsohn, and A. Pakes (1995). Automobile prices in market equilibrium, Econometrica, 63 (4), 841–890.

Berry, S. T., and J. Waldfogel (1999). Free entry and social inefficiency in radio broadcasting, RAND Journal of Economics, 30 (3), 397-420.

Betancourt, R. and D. A. Gautschi, (1988). The economics of retail firms, Managerial and Decision Economics, 9 (2), 133–144.

Flath, D. (1990). Why are there so many retail stores in Japan? Japan and the World