Identifying Neighborhood Eects among Firms: Evidence from the Location Lotteries of the Tokyo Tsukiji Fish Market

Transcription

1 Identifying Neighborhood Eects among Firms: Evidence from the Location Lotteries of the Tokyo Tsukiji Fish Market Kentaro Nakajima 1 Kensuke Teshima 2 1 Hitotsubashi University 2 ITAM February, / 93

2 Motivation Access to a variety of goods is the heart of urban attractiveness (Glaeser, Kolko, and Saiz 2001, Handbury and Weinstein, 2015). Access needs purchase. Purchase of these goods take place in retail clusters, such as shopping districts and shopping malls. What is the mechanism behind the formation of shopping districts or clusters of service sector rms? Understanding the machanism is important also because many urban agglomerations take form of retail clusters or more generally clusters of service sector rms (restaurants etc). 2 / 93

3 Motivation Researchers have modeled these shopping districts or retail clusters, based on the idea that the diversity of neighboring stores saves trip cost of buyers with love of variety preferences (Henkel et al., 2000; Tabuchi, ). Consumers with love of variety preference need to visit multiple stores to obtain variety of goods. Clustering of a diverse set of shops is attractive for buyers because it reduces trip cost. Thus, it is in turn attractive for shops, too. Thus, clusters are formed to facilitate access to varieties. This mechanism generates shopping externality from complementarity of products: a store's sales increases when surrounded by neighboring shops of other diverse products because of shopping behavior of buyers. 3 / 93

4 Research questions We ask the following fundamental questions for retail cluster formation: existence of complementarity from diversity. Does the diversity of surrounding stores improve the performance of stores? If so, are they because such clusters facilitate saving of buyers' trip cost? 4 / 93

5 Research questions We ask the following fundamental questions for retail cluster formation: existence of complementarity from diversity. Does the diversity of surrounding stores improve the performance of stores? If so, are they because such clusters facilitate saving of buyers' trip cost? Identifying externality, or neighborhood eects among rms in general, are very challenging because of endogeneity of neighboring rms(combes, Duranton, Gobilon, 2011). Ex. Higher consumer demand nothing to do with diversity can induce diverse stores to locate each other in certain locations. This challenge is bigger when analyzing diversity, as we would need exogenous variation in the location of many rms. 5 / 93

6 Our paper Estimate the impact of characteristics of neighboring rms, in particular the diversity of neighborhood types, on rm performance. Analyze whether the eects are due to shopping behavior or to other mechanisms, such as knowledge spillover. Using data on around 1000 intermediate wholesalers (Naka-oroshi) of the Tokyo-Tsukiji sh market from 1990 to 1995 (to be expanded). Exploiting the institutional setting where their locations are determined by relocation lotteries, which generates exogenous variations in the characteristics of neighboring rms. 6 / 93

7 Tsukiji market as a laboratory 1 Cluster of heterogeneous sh intermediaries. They are heterogeneous in the types of sh they deal with, in their size, and in the degree of specialization. 2 Location lotteries that reshue them. 3 1&2 imply that the lotteries exogenously form neighborhood with higher or lower diversity, those with higher or lower fraction of the some type of rms and that we can analyze what type of rms benet from such neighborhood characteristics. 7 / 93

8 Tsukiji market as a laboratory 1 Cluster of heterogeneous sh intermediaries. They are heterogeneous in the types of sh they deal with, in their size, and in the degree of specialization. 2 Location lotteries that reshue them. 3 1&2 imply that the lotteries exogenously form neighborhood with higher or lower diversity, those with higher or lower fraction of the some type of rms and that we can analyze what type of rms benet from such neighborhood characteristics. Setting to distinguish shopping externality from other mechanisms such as knowledge spillovers: neighbors facing across buyer corridor (front neighbors), neighbors back to back without a buyer corridor in between (back neighbors). 8 / 93

9 Example: Octopus / 93

10 Example: Octopus / 93

11 Example: Tuna / 93

12 Example: Tuna / 93

13 Example: Tuna High density area. 13 / 93

14 Example: Tuna Low density area 14 / 93

15 Review of the results Increases in the diversity in neighboring shops have Positive eects for small-sized and more specialized rms. No, or negative eects for other rms. Such eects from neighborhood come from stores facing across buyer corridor (front neighbors), not stores in the back (back neighbors), thus proving the eect of diversity coming from shopping behavior of buyers. 15 / 93

16 Review of the results Increases in the diversity in neighboring shops have Positive eects for small-sized and more specialized rms. No, or negative eects for other rms. Such eects from neighborhood come from stores facing across buyer corridor (front neighbors), not stores in the back (back neighbors), thus proving the eect of diversity coming from shopping behavior of buyers. Outside the laboratory (work in progress): the data of all the retailers in Tokyo. Smaller and more specialized retailers are more likely to be located together in shopping districts while larger and diversied ones are isolated. The complementarity of products among specialized diverse stores is an important factor behind urban agglomeration. 16 / 93

17 Literature Gains from variety and cities: Glaeser, Kolko, Saiz (2001), Handbury and Weinstein (2015), Schi (2015), Cosman (2014), Couture (2015). Causal evidence that diversity positively aects (some) rms and suggestive evidence that love of variety can be a source for retail cluster formation within city. 17 / 93

18 Literature Gains from variety and cities: Glaeser, Kolko, Saiz (2001), Handbury and Weinstein (2015), Schi (2015), Cosman (2014), Couture (2015). Causal evidence that diversity positively aects (some) rms and suggestive evidence that love of variety can be a source for retail cluster formation within city. Our study is complementary to growing literature using rich micro data of rm locations within cities. Knowledge spillovers: Arzaghi & Henderson (2007). Urban consumption: Davis et al. (2016), Jardim (2015), Pasidis et al. (2017). Our the study is the rst to use exogenous variation in rm location due to a lottery. 18 / 93

19 Literature: Natural experiment in economic geography Destruction: Davis and Weinstein (2002), Hornbeck and Keniston (2017), Redding and Sturm (2016). In addition, many others to study earthquakes, in particular in Japan. Changes in border: Redding and Sturm (2008), Nakajima (2008), Burchardi and Hassan (2013). Creation and closure of transportation: Redding, Sturm, and Wolf (2011), Donaldson (forthcoming), Faber (2014), Bernard, Moxnes, and Saito (2017). Many papers from Japan! 19 / 93

20 Literature: Natural experiment in economic geography Destruction: Davis and Weinstein (2002), Hornbeck and Keniston (2017), Redding and Sturm (2016). In addition, many others to study earthquakes, in particular in Japan. Changes in border: Redding and Sturm (2008), Nakajima (2008), Burchardi and Hassan (2013). Creation and closure of transportation: Redding, Sturm, and Wolf (2011), Donaldson (forthcoming), Faber (2014), Bernard, Moxnes, and Saito (2017). Many papers from Japan! 20 / 93

21 Tsukiji market 21 / 93

22 Tsukiji market 22 / 93

23 Tsukiji market structure 23 / 93

24 Fish specialties of intermediate wholesalers 24 / 93

25 More on them There is right to operate one shop. Firms can own multiple shops, by buying right from other shops. When they do, they almost always have their shops next to each other because one shop space is small. They can apply to a lottery, combining all the shops to next to each other (the details of the lottery to follow). The tradings of the rights mostly happen right before the lottery because the lottery allows them to expand the number of shops space. Well before lottery, rms need to buy the right of direct neighbor to expand. Firms can belong to more than one trade associations. It is generally thought to be dicult to change sh specialty. 25 / 93

26 Spaces for intermediate wholesalers 26 / 93

27 Horizontal Corridor 27 / 93

28 Horizontal corridor 28 / 93

29 Horizontal corridor (morning) 29 / 93

30 Vertical street 30 / 93

31 Small vertical street 31 / 93

32 Large vertical street 32 / 93

33 Corner 33 / 93

34 Corner 34 / 93

35 How we started the project 35 / 93

36 Lotteries

37 Blocks 37 / 93

38 Lottery Scene 38 / 93

39 Lottery Scene 39 / 93

40 Lottery Scene 40 / 93

41 Within block assignment 41 / 93

42 Within block assignment 42 / 93

43 Within block assignment 43 / 93

44 Within block assignment 44 / 93

45 Within block assignment 45 / 93

46 Within block assignment 46 / 93

47 Within block assignment 47 / 93

48 Threat to identication: joint applications Joint applications of more than one rm are allowed, creating potential threat to our identication strategy. Our reaction: Joint applications of more than two rm are not common. The neighbors across the corridor cannot be selected because they are typically far apart in lottery numbers. We will analyze front neighbors in a robustness check. 48 / 93

49 Anecdotes of the benets of diversity There is a jargon Tsuide-gai, used in Tsukiji sh market. Purchase of sh at one shop after a planned purchase of another type of sh nearby. Specialized and small-sized shops tend to say that this is important. Non-specialized and big-sized shops tend to say that this is not important. 49 / 93

50 Remarks on the Mechanism The eect of neighborhood diversity on store sales can be derived in a standard model with love of variety preference with some assumptions about the trip cost that prevent customers to go all the neighborhoods. In the paper, we present such a model in the appendix, borrowing from Matsuyama (1992) of 3 tiers of varieties, groups, and regions. In Matsuyama's model, under some assumptions about elasticities among them, rms cluster together. Under the same condition, we show that more balanced group composition within region increases each store's sale. In our empirical specication, based on our understanding of the context, we add a layer of rm-level heterogeneity (big vs small, specialized vs non-specialized). 50 / 93

51 Data and Specications

52 Data Location, sh specialty, and number of shops of intermediate wholesalers. Map (Nikkan Shokuryo Shinbun, Minato Shinbun) Directories (Nikkan Shokuryo Shinbun, Union) Details of the lotteries. Industry newspapers, documents of the union. Periods: 1990 and / 93

53 Hypothesis Firms should benet more from a more diverse set of other shops in their neighborhood. This eects should be stronger for smaller rms. Regress the change of the performance from 1990 to 1995 on neighborhood characteristics generated by 1990 lottery, with interaction terms of rm-level size. y igrb = β 0 + β 1 Diverstiy r( i) + β 2 Size ig + β 3 Diversity r( i) Size ig + γx igrb + α g + θ b + ε igrb We need to dene, neighborhood, neighborhood characteristics such as diversity, performance and rm-size. 53 / 93

54 Neighborhood 54 / 93

55 Neighborhood 55 / 93

56 Neighborhood 56 / 93

57 Key neighborhood characteristics Diversity: Total number of unique trade groups to which neighborhood rms belong. Other neighborhood characteristics: Fraction of neighborhood rms belonging to the same trade group. Fraction of multi-store rms. The reasons for including them: they may be correlated with diversity and they may be interesting on its own. Note: Diversity and the fraction of neighborhood rms belonging to the same trade group are not perfectly mechanically negatively correlated. For example, a shrimp shop surrounded by tuna shops would have zero for both. 57 / 93

58 Performance and rm-size Measure of rm performance: Number of shops the rm owns. Firms can extend their shops by buying right of stall-holding from other rms. Actually, positive correlation exists between number of shops and labor productivity (by Census of Commerce). Specically, we use the following variables. Changes in number of shops from 1990 to 1995 conditional on survival. Exit. Shop increasing dummy (=1 if a rm increases its shop from 1990 to 1995). Measure of rm-size: multi-shop dummy in / 93

59 Validity of Randomization

60 Validity of randomization Check 1: These neighborhood variables should not be systematically correlated with rm characteristics (balance test). Check 2: The allocation of the shops should be indistinguishable from the one generated from the Dartboard approach. Ellison and Glaeser (1997). K-density approach: Duranton and Overman (2005). 60 / 93

61 K-density Approach: bilateral distances 61 / 93

62 K-density Approach: bilateral distances 62 / 93

63 K-density approach: density of actual bilateral distances 63 / 93

64 K-density Approach: counter-factual assignment Now, lets randomly allocate these rms into locations. 64 / 93

65 K-density Approach: counter-factual assignment Again, and again, many times. 65 / 93

66 K-density Approach: counter-factual distribution We can calculate the distribution of bilateral distance each time. 66 / 93

67 K-density Approach: counter-factual distribution We can calculate the distribution of bilateral distance each time. 67 / 93

68 K-density Approach: condence interval We can calculate the condence interval from random assignment. 68 / 93

69 K-density Approach: results The distributions of the bilateral distances of (a) rms belonging to a same trade group (left) and (b) rms belonging to pairs of two dierent trade groups (right). We do by each block and stack across blocks. The actual distribution is within the condence interval calculated from our counterfactual random assignment. 69 / 93

70 Results

71 Result: Baseline 71 / 93

72 Result: neighborhood diversity 72 / 93

73 Result: fraction of same trade group 73 / 93

74 Front and Back 74 / 93

75 Front 75 / 93

76 Back 76 / 93

77 Result: Front neighbor only 77 / 93

78 Result: Front neighbor only 78 / 93

79 Result: Back neighbor only 79 / 93

80 Result: Back neighbor only 80 / 93

81 Specialization Are the results bigger for specialized rms? Anecdotes suggest specialized rms are more likely to be aected from neighborhood characteristics. 81 / 93

82 Specialization 82 / 93

83 Specialization 83 / 93

84 Census of Commerce (preliminary) As an alternative measure of performance, we use sales from the Census of Commerce in Caution: We cannot see the changes from 1990 to 1997, because there is no data before the 1995 lottery. However, if the 1990 neighborhood characteristics generated by the lottery are random, we could attribute the dierences in sales 1997 to the neighborhood characteristics This analysis checks.. robustness of our results using changes in the number of shops as a measure of rm performance. persistence of the neighborhood eects. 84 / 93

85 Specialization 85 / 93

86 Result: Census of Commerce (Sales) 86 / 93

87 Other results Robust to Alternative denition of neighborhood diversity (inverse of Herndahl index). Alternative denition of neighborhood region (rm-specic neighboring shops, taking N closest front neighbors to the left and right). 87 / 93

88 Outside Laboratory

89 Implications for outside our context (preliminary) Our results from the Tsukiji market: Diversity benets only small-sized and specialized rms. Small-sized and specialized rms have incentive to form a cluster of diverse stores, while large-sized and non-specialized rms do not. We explore this implication for the data set of all the retailers in Tokyo, using the census of commerce. Are stores small and more specialized (less diversied) in an area with diverse set of stores? LogTotalSales ijz = β 0 + β 1#Firms z + β 2#ProductTypes z + β 3LogTotalSales z + λ j + ε ijz #ProductTypes ijz = β 0 + β 1#Firms z + β 2#ProductTypes z + β 3LogTotalSales z + β 4LogTotalSales ijz + λ j + ε ijz i, store, j main product, z zip-code. 89 / 93

90 Result: Census of Commerce (Retailers in all Tokyo) 90 / 93

91 Result: Census of Commerce (Retailers in all Tokyo) 91 / 93

92 Conclusion The Tsukiji market provides a unique and ideal setting to test explore the mechanism of retail cluster formation: complementary of diverse products. Heterogeneity in intermediate wholesalers. Location lotteries. Increases in the diversity in neighboring shops has... Positive eects for small-sized and specialized rms. No eects for large-sized and non-specialized rms. Small-sized and specialized rms have incentive to form a cluster of diverse stores, while large-sized and non-specialized rms do not. We nd a set of consistent correlations for all the retailers in Tokyo. These provide rst randomization-based evidence of the eect of diversity of neighboring stores on store performance, whose implication seems to hold more generally. Love of variety can be a source of retail cluster formation. 92 / 93

93 Thank you!