Spatial competition in the retail industry: evidence from Italy

Transcription

1 Oxford Retail Futures Conference: Public Policy in Retail and Supply Chain Management Spatial competition in the retail industry: evidence from Italy D. Berardi 1, F. Bersanetti 1, G. Fraquelli 2, A. Menozzi 2, L. Nocciola 1 1 REF Ricerche, Italy; 2 UPO, Italy Saïd Business School, Oxford, 9 December 2015

2 Overview Competition authorities use a definition of relevant market and market power based on market share 2014 Italian Antitrust authority (AGCM) s inquiry toward the jointpurchasing group Centrale Italiana led to its termination Is the definition of relevant market applied by the (Italian) Antitrust authority consistent with the spatial analysis of competition? In this paper: analysis of the demand faced by grocery stores localized in a well-defined area in the North of Italy, applying spatial econometric techniques on store-level data Results: retail stores exert a spatial influence on each other through their format and their dimension while other demographic and macroeconomic factors also affect the price

3 Spatial competition The role of demand factors: The degree of heterogeneity of consumers may favour the vicinity of competitors despite profits are growing in the distance between them (Chisholm and Norman, 2004; Orhun, 2013) Local competition: Intra-format competition at local level (Gonzales-Benito and Gonzales- Benito, 2005) Local effects on employment and revenues of a large format retailer s entry (Ellickson and Grieco, 2013) Market entry and firm geography: The entry of a large supermarket in one area increases the chance of exit of larger stores in the neighbourhood and increases (Igami, 2011) or decreases (Borraz et al., 2013) the probability of survival of smaller incumbents Italy: growth rates are higher for small stores located in the proximity of big supermarkets (Arbia et al., 2015); the higher the number of traditional retailers in the neighbourhood, the lower the large format retailers price level (Fraquelli and Menozzi, 2014)

4 Data 5 prices for each one of 28 grocery products handcollected from April 2008 to October 2013 (T=12 semesters) N=30 retail stores located in a relevant market : 5 spatially close municipalities in the Italian region Lombardy Stores are in the discount, superette, supermarket or hypermarket format, sampled among 29 different chains and 12 different business groups Retailers physical characteristics and their geographic and projected coordinates (source: AC Nielsen and REF Ricerche)

5 Descriptive statistics Variables # obs Min 25% Mean 75% Max SD p p discount p hypermarket p supermarket p superette q (units) size (sqm) ,024 3,500 16,000 4,201 sales (Keuro) ,209 11,200 18,300 60,100 14,200 cash register grate children agepop net migration

6 Methodology In each period t = 1,,T=12 and for each retail store i = 1,, N=30, a store price index p it is computed Spatial matrix W whose elements are the geographic distance based spatial weights w ij for each pair of stores i and j to obtain the spatially lagged variable Wp it Basic multiple regression (OLS) model: p = awp + Xb + e Absence of any spatial correlation between p it (of different stores) for bands farther than 9,000 meters (the greatest distance between two different stores is kilometres) For superettes, the spatial correlation disappears farther than 2,750 meters, for supermarkets farther than 4,750 meters

7 Results Dependent variable: p Variables Spatial lag model (1) Spatial error model (1) Spatial lag model (2) Spatial error model (2) Spatial lag model (3) Spatial error model (3) Spatial lag model (4) Spatial error model (4) Wp *** *** *** *** *** *** *** *** q *** *** *** *** *** *** *** *** traditional *** *** *** *** *** *** *** *** discount *** *** *** *** *** *** *** *** superette 0.020** 0.032*** 0.058*** 0.088*** hypermarket *** *** size 0.069*** 0.105*** grate *** *** *** *** *** *** *** *** agepop *** *** *** *** *** *** *** *** LM 0.169*** 0.646*** 0.132** 0.643*** 0.094* 0.743*** 0.192*** 0.676*** # obs *** p<0.01, ** p<0.05, * p<0.10

8 Conclusions The Antitrust criteria for the identification of a relevant market provide a good approximation of the competitive arena but the application of spatial econometric techniques gives a clear-cut evidence of competition at a local level Stores exert a spatial influence on each other through their format and their dimension: bigger stores and superettes apply higher prices but stores with different price patterns cluster together The importance of demand factors: retail stores apply lower prices in areas with higher average population age and growth rate (immigration) Traditional shops have a calming effect on the retail stores prices (Binkley and Connor, 1998; Fraquelli and Menozzi, 2014)