Small-Format Chain Stores and the Neighborhood Grocery Market

Transcription

1 Small-Format Chain Stores and the Neighborhood Grocery Market Jinhwa Chung Keimyung University Janghee Cho Jeju University Hailey Hayeon Joo Sogang University April 2016 Abstract Big-box stores are located in downtowns of cities in many Asian countries. This initial condition may create a disparate competitive environment in the retail industry, as small-format chain stores enter into local food markets. By exploiting Korea s Consumer Panel Data, we examine how the location of a small-format influences consumers store choices. Results show that if the nearest small-format is located within 500 meters distance from household residence, the shopping expenditure at big-boxes decreases by about 6% point for all food categories. If either within 500 or between 500 and 1000 meters, the spending at local incumbents increases by 3% point, which is mainly driven by whole foods consumption. And, these location effects get even stronger in a more congested area like Seoul. Therefore, small-formats and incumbents can be complementary and such complementarity is induced by incumbents product differentiation. Key words: grocery market, product differentiation, big-box store, small-format chain store

2 1 Introduction In last two decades, the big-box retailers rapidly penetrated into the downtown areas of many Asian cities and, more recently, began to enter again as a small-format. As a consequence, local incumbents have encountered these two similar but different types of competitors for a relatively short time period. Particularly, these retail environments in Asian countries are distinguished from those in the U.S. and European countries, because big-boxes are located not on city borders but inside of the central cities. For this reason, the entry effect of small-formats in Korea would not be the same as that of the U.K. case found in Sadun (2015). Therefore, the primary goals of our paper are to examine (i) whether the small-formats and incumbent stores are substitutable or complementary in Korea and (ii) if complementary, what causes such effect. According to the literature, product differentiation can be a potential explanation for store complementarity (Zhu et al., 2011; Igami, 2011). Zhu et al. (2011) provided both theoretical and empirical evidences that an incumbent store could gain profits by selling a differentiated product, even competing with large discount stores. And, Igami (2011) found that the entry of big-box stores expelled similar-sized (i.e., large or medium-sized) incumbent supermarkets, but the survival rates of small-sized stores increased. Thus, in this paper, we extend the discussion in Zhu et al. (2011) and Igami (2011) by exploring the relationship among three types of stores: big-box, small-format chain, and incumbent stores. For theoretical model, we assume that the levels of product differentiation as well as congestion and transportation costs vary over store types. Our first assumption is that big-boxes carry all types of foods, while small-formats or incumbents carry some (but their product assortments are complementary to each other). This implies that the one-stop shopping at a big-box store can save a consumer s time and efforts, when big-boxes and incumbents are competing in the initial point. The second assumption is that the congestion cost at the big-box stores is higher than those at the other stores and that it gets larger in more urban areas. For example, if a consumer lives in a more congested area, he/she would hesitate to go to the big-box store. Then, if a small-format chain store enters in his/her neighborhood market near an incumbent store, his/her total shopping costs would decrease, as he/she goes to the neighborhood market and saves a huge congestion cost of going to the big-box store. As a result, if a small format enters nearby, those who previously go to the big-box are likely to return to the neighborhood market. For empirical analysis, we exploit (i) the Consumer Panel Data collected by the Rural Devel- 1

3 opment Administration (RDA) and (ii) Retail Industry Almanac in Korea. The Consumer Panel Data provides the real-time detailed information about consumers purchasing behaviors including product brand, price, amount, purchasing venue, and so on. Our final sample covers 636 households who lived in Seoul/Incheon metropolitan areas or Gyeongi Province but did not move between 2010 and In particular, by using both datasets, we compute the distance between a household s home address and the nearest small-format store, based on which we create two location indicators: (i) the indicator of whether the nearest small-format is located within 500 meters distance from the household residence and (ii) the indicator of whether it is located between 500 and 1000 meters. The FE estimation results suggest that if a small format is located within 500 meters, the shopping expenditure at the big-box stores decreases by about 6% point, but otherwise, it does not. In particular, this reduction effect at the big-boxes is almost constant regardless of food categories. On the other hand, if the small format is located within 1000 meters, the shopping expenditure at incumbent stores increases by 3% point. However, this increasing effect disappears in case of processed food. Moreover, these opposite effects get even stronger in a more congested area like Seoul. Our empirical findings support that incumbents and small-formats are complementary and that this complementarity is caused by the product differentiation among store types. This intriguing finding suggests that small-formats can generate a positive spillover in local grocery market in case of Asian countries. The remainder of the paper is organized as follows: Section 2 explains the retail grocery industry in Korea. Section 3 develops the theoretical model, describes the data, and provides the estimation model. Section 4 presents the empirical results and Section 5 concludes. 2 The Retail Grocery Industry in Korea Big-box stores in Korea, as defined by the Retail Industry Development Act, are the shopping sites with gross floor areas of 3,000 square meters or more to sell not only foods but also home appliances and household goods. Similar to other Asian countries, they are located inside of the central cities. During the period of , there existed at least one big-box in 58 out of 66 counties in the Seoul Capital Area (hereafter SCA), i.e., the metropolitan area containing Seoul, Incheon, and Gyeonggi and referred as Sudogwon in Korean. Thus, most consumers living in the SCA could easily drive to a big-box within 5 kilometers distance from their residences (see Appendix 2

4 A for the further details). 1 Small-format chain stores are operated by the big-box s headquarter or affiliated firm, the sizes of which vary from 165 to 3,000 square meters (i.e., smaller than big-box stores but larger than mom-and-pop or convenience stores). Since 2008, they have accelerated to enter into local grocery markets (see Figure 1 for their expansion in the SCA between 2009 and 2011). The small-formats are located near or even inside of apartment complexes so that the consumers nearby can walk to go shopping without a car. Their locational convenience should be attractive to those living in a more congested area like Seoul (rather than Incheon or Gyeonggi). [ Insert Figure 1 Here ] However, compared to big-boxes, the small-formats carry small assortments, due to limited space. According to the Retail Industry Almanac (2011), the average number of products sold at a small-format is about one tenth of that of treated items at big-boxes. Particularly, the small-format does not provide raw agricultural products but fresh-cut produces and processed foods, while bigboxes deal with the three of them and also organic foods. That is, a consumer purchasing fresh-cut produces and/or processed foods at the small-format may have to visit other retailers to get raw agricultural products or organic foods. Therefore, even though small-formats are the big-box s spin-offs, their distinctive features induce a different effect on the neighborhood grocery market. Specifically, the big-box s business stealing effect is on all local incumbents, while those of small-formats depend on store types. For example, if incumbent stores carry raw agricultural products and/or organic foods (i.e., the items not traded in a small-format), such product differentiation would make them complementary to the smallformat. In this regard, as seen in Figure 2(c), the number of Seoul s local grocery stores has sharply decreased in Phase 1 (i.e., the expansion period of big-boxes before 2008), whereas the decrease has stopped in Phase 2 (i.e., that of small-formats after 2008). Moreover, a local cooperative market developed by the small-format and incumbent stores can emerge as the one viable challenger to big-boxes. Korea Fair Trade Commission (2011) found that the small-formats are more competitive with big-boxes than local incumbents such as traditional markets, by evaluating merger proposal of E-Mart and Kim s Club. These tendencies are also found in Nielson Shopper Trends (2005; 2008; 2010). The proportion to choose big-boxes as a main grocery shopping place increased from 1 Jeon and Whang (2010) assumed that the customer base of Korea s big box store is those who can approach it within 5 up to 10 kilometers distance from their residences (i.e., about 30 minutes drive). Haltiwanger et al. (2010) used 1 to 5 miles as the U.S. customers shopping distance to big box stores. 3

5 2004 to 2007, but then deceased in 2010 and returned back to the level of In particular, the decrease between 2007 and 2010 was converted to shopping at small-format chain stores as well as local incumbents. [ Insert Figure 2 Here ] However, the entries of small-formats can still threaten some local incumbents. Figure 2(b) shows the persistent decrease in the number of Seoul s local supermarkets in not only Phase 1 but also Phase 2 and Figure 2(d) presents the proportion changes in small and medium-sized supermarkets throughout both phases. First, in Phase 1, the competition with big-boxes had forced medium-sized supermarkets to exit the market. Then, in Phase 2, the market competition has become more severe resulting from the entries of small-formats, which have made the mediumsized supermarkets more unprofitable and finally some of them replaced by small-formats. Similarly, small-sized supermarkets have lost their customers continuously. 3 Methodology 3.1 Theoretical Model We assume that there exist an incumbent store (call store A) and a big-box store (call store B), before a small-format chain store (call store C) enters into the market. Also, to concentrate our discussion on the local grocery market, we assume that all retailers sell grocery products only. We denote goods 1, 2, and 3 by the raw agricultural product, fresh-cut produce, and processed food, respectively. Then, store A carries goods 1 and 2; store B sells all goods; and store C provides goods 2 and 3. If consumers purchase good i at store j, they pay p ij, for i = 1, 2, 3 and j = A, B, C. We assume that there are no price changes of stores A and B after store C s entry (hereafter entry). 2 [ Insert Figure 3 Here ] Consumers are uniformly distributed on the line with the length of 1. Each consumer has the same reservation price of good i which is denoted by v i for i = 1, 2, 3. We assume for v 1, v 2, and v 3 that satisfy the following two conditions: (i) All consumers purchase goods 1 and 2 regardless of entry. (ii) All consumers buy good 3 after entry, but only some of those do before entry. 2 Because small-formats are operated by big-boxes, they don t have a strong incentive for severe price competition. Also, we cannot find the evidence of price competition among firms after entry from our data. 4

6 The transportation costs are presumed to vary before and after entry. First, as seen in Figure 3(a), stores A and B are located at the end points of the line before entry. The transportation cost to store B can be slightly lower than that to store A, because consumers can enjoy a one-stop shopping at store B. Thus, we assume that the unit transportation cost to store A, denote by (t + τ), is greater than that to store B, denoted by t, where t > 0 and 0 < τ < t. However, because big-box stores are located in congested areas, an additional cost is incurred by c > 0. Thus, the total cost to store A is (t + τ)x, while that to store B is t(1 x) + c. On the other hand, after entry, consumers visit either store B only or stores A and C together, both of which make a one-stop shopping possible. Thus, we assume that the total cost to stores A and C gets lower to tx, while the total cost to store B is still t(1 x) + c. The notations used in the theoretical model are listed in Table 1. [ Insert Table 1 Here ] We denote x by the indifferent consumer between stores A and B, before entry. Then, x is computed by: v 1 p 1A + v 2 p 2A (t + τ)x = v 1 p 1B + v 2 p 2B + v 3 p 3B t(1 x) c x = (p 1B p 1A ) + (p 2B p 2A ) (v 3 p 3B ) + t + c 2t + τ After entry, we can find x who is indifferent to visiting stores A and C together or to visit store B only. By denoting p 2 by the minimum of p 2A and p 2C, we have: v 1 p 1A + v 2 p 2 + v 3 p 3C tx = v 1 p 1B + v 2 p 2B + v 3 p 3B t(1 x ) + c x = (p 1B p 1A ) + (p 2B p 2 ) + (p 3B p 3C ) + t + c 2t Statement 1. Regardless of τ, store B loses its customers after entry. Proof. Denote B by the change in store B s consumers due to store C s entry, i.e., B (1 x ) (1 x ) = x x. Take the partial differentiation of B with respect to τ, we have: 5

7 B τ = (p 1B p 1A ) + (p 2B p 2A ) (v 3 p 3B ) + t + c (2t + τ) 2 < 0, i.e, as τ increases, B decreases. Also, if τ decreases down to 0, we have: lim B = (p 2A p 2 ) (v 3 p 3C ). τ 0 2t Because (i) p 2A p 2 min(p 2A, p 2C ) and (ii) v 3 p 3C (by assumption for v 3 ), lim τ 0 B < 0. Therefore, regardless of τ, store B loses the consumers after entry. Q.E.D. Statement 2. As c increases, store B loses more customers. Proof. Take the partial differentiation of B with respect to c, we have: B c = 1 2t + τ 1 2t = τ 2t(2t + τ) < 0, Q.E.D. 3.2 Data We exploited the Consumer Panel Data from January 1, 2010 to December 31, 2012 collected by RDA, which consists of 1047 households who lived in the SCA. We excluded those that reported a change in home address or stopped sending shopping information, and thus our final sample size is 636 households. In our sample, 230 households lived in Seoul and 406 in Incheon or Gyeonggi. Our Consumer Panel Data contains each household s detailed shopping information about all grocery products such as (i) the payment, amount/volume, brand, and food category per item, (ii) shopping date and time, and (iii) store type and name (if available), along with the household s home address and socio-demographic information. 3 Because our main goal is to analyze the impact of product differentiation taken by local incumbents, we focused on traditional markets, organic 3 The store types provided in the consumer panel data are as follows: big-box stores, small-format chain stores, small and medium-sized supermarkets, traditional markets, organic stores, meat shops, fruit shops, department stores, online stores, and so on. 6

8 stores, meat shops, and fruit shops for our empirical analysis, while we did not consider small and medium-sized supermarkets, department stores, and online stores. 4 Moreover, we cannot precisely distinguish raw agricultural produces and fresh-cut products from the Consumer Panel Data. Thus, we defined both types of foods by whole foods (e.g., meats, fish/seafood, grains, fruits, and vegetables) and then use them for our analysis. In addition, to obtain the location information of big-box and small-format chain stores, we manually collected their addresses and opening and closing dates from the Retail Industry Almanac of By using a geographic information system program (Arc GIS 12.0), we measured the distance from a household to its nearest small-format chain store on an annual basis, from which we generated two location indicators for the household in a given year: (i) the indicator of whether the nearest small-format chain store is located within 500 meters from the household residence and (ii) the indicator of whether it is located between 500 and 1000 meters. As seen in Table 2, during the period from 2010 to 2012, about 42% of households could access to the nearest small-format chain store within 500 meters radius on average, while about 30% between 500 and 1000 meters. [ Insert Table 2 Here ] To combine the Consumer Panel Data with the location indicators, we converted the daily consumption information into the annual ones. For the dependent variable, we computed the ratios of (i) annual expenditures on all foods at big-boxes and incumbent stores to those on all foods at all store types, respectively, (ii) those on whole foods at big-boxes and incumbent stores to those on whole foods at all store types, respectively, and (iii) those on processed foods at big-boxes and incumbent stores to those on processed foods at all store types, respectively. As seen in Table 2, the expenditure ratio at big-box stores is greater than that at incumbent stores in each food category regardless of food categories, though it is much higher in case of processed foods. Also, for all food categories, the expenditure ratio at big-box stores in Seoul is less than that at big box stores in other regions, while that at incumbent stores in Seoul is greater than that at incumbent stores in other regions. These stylized facts may result from the complementarity between small-format chain and incumbent stores. 3.3 Estimation Model To empirically test the model explained in Section 3.1, we estimate the following equation: 4 Due to similar product assortments, there is no product differentiation between small-format chain stores and supermarkets. And, the customers using department and online stores are not confined to local consumers. 7

9 RAT IO i,j,t = β 0 + β 1 DIST 500 t + β 2 DIST 1000 t + γ X t + µ + δ t + ε i,j,t, (1) where RAT IO i,j,t is the ratio of annual expenditure on good i at store j in year t, i ={all foods, whole foods, processed foods}, j ={big-box stores, local incumbents}, and t ={2010, 2011, 2012}. DIST 500 t is an indicator of whether the nearest small-format chain store is located within 500 meters distance from the household residence and DIST 1000 t is an indicator of whether it is located between 500 and 1000 meters. Vector X t includes the control variables such as log of the county-level population and the expenditure ratio on whole foods at all stores. µ and δ t are the household and year fixed effects, respectively. ε i,j,t is the standard error term. 4 Empirical Results Table 3 presents the estimation results of equation (1). Columns (1)-(3) provide the results of the annual expenditure ratios at big-boxes by food category, and columns (4)-(6) show the results of those at incumbent stores by food category. [ Insert Table 3 Here ] In columns (1)-(3), we find that if the nearest small-format is located within 500 meters distance from the household residence, its location impact on big-box stores is negative and statistically significant at the 5% level, regardless of food categories. For example, it is found that the expenditure ratio is reduced by about %, though the reduction rate is slightly larger for processed foods. However, if it is located within 500 and 1000 meters, the location impact disappears. That is, when the small-format s location is close enough to the household residence, shopping venue is changed from big-box to small-format. The impact of expenditure ratio on whole foods is negative and statistically significant at the 1% level in columns (1) and (2), while it is positive but nonsignificant in column (3). Those spending more on whole foods are likely to reduce their expenditures especially on whole foods at the big-boxes. However, the log is population is not statistically significant. In columns (4)-(6), we find that when the small-format is located either within 500 or between 500 and 1000 meters distance, the location effect is positive for all columns and statistically significant at the conventional levels except processed foods. In particular, the magnitude of the effect on whole foods (about %) is greater than that of the effect on all foods (about %), 8

10 which implies that such location effect is mainly driven by whole foods consumption. Our findings confirm the hypothesis explained in Section 3, i.e., a small-format chain store are cooperative with incumbent stores, if they deal with different product assortments. Moreover, the impact of expenditure ratio on whole foods is positive and statistically significant at the 1% level across columns (4)-(6), which means that the household s shopping pattern to expend more on whole foods results in more spending at local incumbents. And, the log is population is not statistically significant. To evaluate the effect of the location convenience on store choice, we decompose the SCA into the more congested area (i.e., Seoul) and less congested area (i.e., Incheon and Gyeonggi) and re-estimate the small-format chain store s location effect for each area. As seen in Table 4, the location effects in Seoul are qualitatively almost similar to those in the SCA but get stronger, except the effect of 500-meters indicator in column (4). More specifically, in columns (1)-(3), when the nearest small-format chain store is located within 500 meters, the household expenditure ratio at big-box stores decrease by about %. And, in columns (5), when the nearest small-format chain store is located either within 500 or between 500 and 1000 meters, the household expenditure ratio on whole foods at incumbent stores decrease by about %. That is, in a more congested area, the combined market by the small-format chain and incumbent stores gets more attractive to consumers. [ Insert Table 4 Here ] On the other hand, as seen in Table 5, the location effects decline or even disappear in Incheon and Gyeonggi. In columns (1) and (2), if the nearest small-format chain store is located within 500 meters, the expenditure ratios at big box stores are reduced by about %, which are smaller by about 2% than those in the SCA. However, in column (3), such impact is no longer statistically significant. Moreover, in columns (4)-(5), the location impacts on incumbent stores totally disappear. That is, in a less congested arear, the small-format chain stores are substitutable to existing big-box stores, but they do not generate agglomeration effects with local incumbents. [ Insert Table 5 Here ] To check robustness, we consider the subsample excluding the households with a small-format chain store either within 500 meters or between 500 and 1000 meters in By using this subsample, we can eliminate any local distinctiveness of the counties with small-format chain stores in early period. As seen in Table 6, the significances and magnitudes of locational effects are a 9

11 little different, but the location effects themselves are qualitatively almost similar to those in our full samples. That is, the results in Table 5 also support that small-format chain stores compete with big-box stores and cooperate with local incumbents. [ Insert Table 6 Here ] 5 Concluding Remarks In this paper, we proposed a theoretical model and presented the empirical results of the production differentiation in the local grocery markets during the expansion period of small-format chain stores. If a small format is located within 500 meters distance from the consumer s residence, the shopping expenditure at big boxes decreased regardless of food categories. On the other hand, if it is located within 1000 meters, the expenditure at the incumbent store increased, especially for whole foods. Our empirical findings supported that incumbents and small-formats are complementary and that such complementarity is based on the product differentiation among store types. This paper contributes the literature by providing the evidence that the entry of a small format chain store can generate a positive spillover in the Asian countries. References Cho, J., Chun, H. and Lee, Y. (2015) How Does the Entry of Large Discount Stores Increase Retail Employment? Evidence from Korea, Journal of Comparative Economics 43: Choi, Y.J. and Jeong, J. (2015a), Effects of the Sunday Shopping Restriction in Korea, Contemporary Economic Policy 34(1): Choi, Y.J. and Jeong, J. (2015b), The Other Side of the Sunday Shopping Regulation: Congestion Costs and Loss of Efficiency, The Korean Journal of Economics 22(1): Haltiwanger, J., Jarmin, R. and Krizan, C.J. (2010) Mom-and-Pop meet Big-Box: Complements or Substitutes? Journal of Urban Economics 67(1): Igami, M. (2011) Does Big Drive Out Small? Entry, Exit, and Differentiation in the Supermarket Industry, Review of Industrial Organization 38(1):

12 Joo, H.H. and Choi, Y. (2015) Reviewing the Restrictions on Entry and Opening Hours of Large Retailers: Evidence from the OECD Retail Markets, Korean Industrial Organization Review 23(1): (in Korean) Korea Fair Trade Commission (2011) No restrictive trade practice caused by merger of E-Mart and Kim s Club, Korea Fair Trade Commission, Press Release of November 17, Jeon, S. and Whang, Y.-J. (2010) An Econometric Analysis of Competitive Effects of Eland- Carrefour Merger in 2006, Journal of Regulation Studies 19(2): (in Korean) Sadun, R. (2015) Does Planning Regulation Protect Independent Retailers? Economics and Statistics 97(5): The Review of Nielsen (2005) Nielsen Korea Shopper Trends 2005, Seoul: AC Nielsen. Nielsen (2008) Nielsen Korea Shopper Trends 2008, Seoul: AC Nielsen. Nielsen (2010) Nielsen Korea Shopper Trends 2010, Seoul: AC Nielsen. Zhu, T., Singh, V., and Dukes, A. (2011) Local competition, entry, and agglomeration, Quantitative Marketing and Economics 9(2):

13 Figure 1. The Expansion of Small-Format Chain Stores in the Seoul Capital City (a) The new entry in 2009 (b) The new entry between 2009 and 2011 Source: Retail Industry Almanac (2010; 2012) Note: The number is based on the small-format chain stores operated by E-Mart, GS, HomePlus, and Lotte. 12

14 Figure 2. Retail Grocery Industry Trends in Seoul (a) Big-box stores (b) Small and Medium-sized supermarkets (c) Local grocery stores excluding supermarkets (d) Comparison between small-formats and supermarkets Medium-sized supermarkets Small-format chain stores Small-sized supermarkets Expansion of Big-box Stores Expansion of Small-formats Source: Statistics of Korean Businesses, Statistics Korea Note: The unit is the number of stores. 13

15 Figure 3. The Locations of the Incumbents, Big-Box, and Small-Format Chain Stores 14

16 Table 1. List of Notations in the Theoretical Model Notations Explanation Goods 1 Raw agricultural product available at the incumbent and big-box stores (A and B). 2 Fresh-cut produce available at all stores (A, B, and C). 3 Processed food available at the big-box and small-format chain stores (B and C). Stores A The incumbent store that carries goods 1 and 2. B The big-box store that carries all goods 1, 2, and 3. C The small-format chain store that carries goods 2 and 3. Prices pp iiii The price of good ii sold at store jj. Consumers vv ii > 0 The reservation price of good ii. tt > 0 Transportation cost. cc > 0 Lump-sum cost for the big-box store incurred by congestion. 0 < ττ < tt Additional transportation cost when a one-stop shopping is not available. 15

17 Table 2. Summary Statistics Region Variables All areas Seoul Gyeonggi & Incheon The nearest small-format chain store Within 500 meters (0.493) (0.499) (0.487) Between 500 and 1000 meters (0.458) (0.477) (0.445) Expenditure ratio All foods At big-box stores (0.206) (0.206) (0.206) At incumbent stores (0.159) (0.178) (0.146) Whole foods At big-box stores (0.207) (0.203) (0.209) At incumbent stores (0.186) (0.203) (0.175) Processed foods At big-box stores (0.222) (0.228) (0.218) At incumbent stores (0.104) (0.121) (0.091) Population (in thousands) (12.495) (9.263) (13.987) Note: The standard deviations are in parentheses. Whole foods consist of raw agricultural produces and fresh-cut products such as meats, fish/seafood, grains, fruits, vegetables, and so on. 16

18 Table 3. Estimation Results for the SCA Dependent Variable: Yearly Expenditure Proportion Big-box Stores. Local Incumbents. All Whole Processed All Whole Processed Variables (1) (2) (3) (4) (5) (6) The nearest small-format chain store Within 500 meters ** ** ** 0.025* 0.035** (0.024) (0.023) (0.031) (0.014) (0.018) (0.013) Between 500 and 1000 meters ** 0.041** (0.024) (0.022) (0.035) (0.015) (0.019) (0.017) Other control variables Expenditure ratio of whole foods *** *** *** 0.284*** 0.166*** (0.055) (0.054) (0.068) (0.041) (0.053) (0.035) Log of population (0.052) (0.056) (0.059) (0.038) (0.052) (0.029) Constant *** ** *** *** *** (5.997) (6.306) (7.100) (4.569) (6.020) (3.507) Panel FE Yes Yes Yes Yes Yes Yes Local FE Yes Yes Yes Yes Yes Yes Yearly FE Yes Yes Yes Yes Yes Yes No. of households Obs. 1,908 1,908 1,908 1,908 1,908 1,908 R-squared Note: The clustered robust standard errors for the panel fixed effects are in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively. 17

19 Local Table 4. Estimation Results for Seoul Only.. Dependent Variable: Yearly Expenditure Proportion Big Box Stores Incumbents All Whole Processed All Whole Processed Variables (1) (2) (3) (4) (5) (6) The nearest small-format chain store Within 500 meters * * * * (0.053) (0.053) (0.057) (0.023) (0.030) (0.024) Between 500 and 1000 meters ** 0.066** (0.041) (0.039) (0.053) (0.025) (0.030) (0.035) Other controls Expenditure ratio of whole foods ** ** *** 0.363*** 0.203*** (0.100) (0.091) (0.126) (0.066) (0.084) (0.055) Log of population (0.166) (0.161) (0.209) (0.111) (0.148) (0.086) Constant *** *** *** *** *** *** (10.337) (9.502) (13.095) (7.726) (9.935) (6.199) Panel FE Yes Yes Yes Yes Yes Yes Local FE Yes Yes Yes Yes Yes Yes Yearly FE Yes Yes Yes Yes Yes Yes No. of households Obs R-squared Note: The clustered robust standard errors for the panel fixed effects are in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively. 18

20 Local Table 5. Estimation Results for Incheon and Gyeonggi.. Dependent Variable: Yearly Expenditure Proportion Big Box Stores Incumbents All Whole Processed All Whole Processed Variables (1) (2) (3) (4) (5) (6) The nearest small-format chain store Within 500 meters * * (0.024) (0.022) (0.036) (0.017) (0.021) (0.015) Between 500 and 1000 meters (0.029) (0.026) (0.044) (0.019) (0.023) (0.018) Other controls Expenditure ratio of whole foods *** *** *** 0.235*** 0.149*** (0.064) (0.067) (0.076) (0.052) (0.067) (0.045) Log of population 0.092* (0.049) (0.056) (0.055) (0.040) (0.054) (0.031) Constant *** ** ** *** *** ** (7.736) (7.850) (9.532) (5.316) (7.047) (4.223) Panel FE Yes Yes Yes Yes Yes Yes Local FE Yes Yes Yes Yes Yes Yes Yearly FE Yes Yes Yes Yes Yes Yes No. of households Obs. 1,218 1,218 1,218 1,218 1,218 1,218 R-squared Note: The clustered robust standard errors for the panel fixed effects are in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively. 19

21 Local Table 6. Robustness Check.. Dependent Variable: Yearly Expenditure Proportion Big Box Stores Incumbents All Whole Processed All Whole Processed Variables (1) (2) (3) (4) (5) (6) The nearest small-format chain store Within 500 meters * * (0.035) (0.035) (0.042) (0.016) (0.022) (0.019) Between 500 and 1000 meters ** 0.052** (0.022) (0.019) (0.045) (0.017) (0.025) (0.023) Other controls Expenditure ratio of whole foods *** *** * 0.364*** 0.290*** 0.155** (0.096) (0.091) (0.138) (0.075) (0.094) (0.068) Log of population * (0.080) (0.116) (0.071) (0.095) (0.113) (0.092) Constant *** ** ** * (21.470) (17.651) (43.941) (16.550) (23.416) (23.130) Panel FE Yes Yes Yes Yes Yes Yes Local FE Yes Yes Yes Yes Yes Yes Yearly FE Yes Yes Yes Yes Yes Yes No. of households Obs R-squared Note: The clustered robust standard errors for the panel fixed effects are in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively. 20

22 Difference Appendix A. Table A1 presents the shopping distance to the nearest big box store from a household residence. Most households living in the SCA could go to big box stores within 5 kilometers distance from their residence, throughout the entire period. In particular, as of December 2009, 462 out of 636 households (72.6%) could access a big box store even within 2 kilometers distance and this tendency continued to following years. Table A1. The Locations of Big Box Stores Year Variables The nearest big-box store from residence Within 2 kilometers Between 2 and 5 kilometers Over 5 kilometers Obs Note: The units are the number of households in our Consumer Panel Data set. As seen in Table A2, the log-distances from households to their nearest big-box stores hardly change from 2009 to That is, during that period, not big-box but small-format chain stores rapidly entered into local grocery markets. Table A2. The Distances of Big Box Stores Mean of Log-Distance.. Variables The nearest big-box store from residence Within 2 kilometers (0.646) (0.660) (0.660) Between 2 and 5 kilometers (0.275) (0.272) (0.272) Over 5 kilometers (0.538) (0.507) (0.507) All (0.936) (0.940) (0.940) Obs Note: The standard deviations are in parentheses. The units are the means of log-distances from households and their nearest big-box stores in our Consumer Panel Data set. 21