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Geographic market definition in UK grocery and the chain of substitution

Geographic market definition in UK grocery and the chain of substitution FTC CONFERENCE ON GROCERY STORE ANTITRUST, MAY 2007 DAVID PARKER History and context Market definition part of the legal process in UK Similar approach to US - SSNIP, demand and supply side substitution

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Geographic market definition in UK grocery and the chain of substitution

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  1. Geographic market definition in UK grocery and the chain of substitution FTC CONFERENCE ON GROCERY STORE ANTITRUST, MAY 2007 DAVID PARKER

  2. History and context • Market definition part of the legal process in UK • Similar approach to US - SSNIP, demand and supply side substitution • Safeway Merger Inquiry (2003) came up with choice rules for divestments • Local market – 10 minutes urban, 15 rural • Based on customer shopping distances • Divest if “4 to 3” or less in local markets • Arguments about GIS systems and drivetimes • Groceries Inquiry (current) has seen suggestions of similar test on organic growth • Definition of local market(s) matters • Ongoing debate about how far customers travel

  3. Great Britain is crowded… • Overall population density • UK: c. 250 per sq. km • US: c. 33 sq km • Source: CIA Factbook • Urban population density (areas over 500,000 people) • UK: 4,100 per urban sq. km • US: 1,150 per urban sq. km • Source: Demographia

  4. …so chain of substitution may be relevant (market wider than store catchment) Customers Isochrones (10/20 minutes) Tesco one-stop shop Rival one-stop shop

  5. Simple theoretical framework for SSNIP test • All customers in hypothesised local market (HLM) travel to nearest store • All stores in HLM raise prices by 5% (CC Market Investigation Guidelines) • Customers face choice: • Remain with current store choice at 5% extra prices • Switch to nearest store outside isochrone – incur greater transport cost • If enough customers switch (estimated revenue loss greater than critical loss) then not profitable • Widen HLM and test again

  6. Four main data requirements – locations… • Locations of stores and customers • Store locations from Tesco database (but also publicly available) • Customer locations from UK Census – use Census Output Areas (COAs) with c. 100 households in each • Highly accurate location information • GIS software with drivetimes calculates distance from home to store for each combination • Uses client software, but off-the-shelf packages exist (e.g. MapInfo)

  7. … cost of travel time… • Whether a customer switches depends on cost of travel time • Derive econometrically – conditional logit model based on customer shopping decisions • TNS Worldpanel data (similar to AC Nielsen Homescan data used by Hausman in recent Wal-Mart paper) • TNS data records actual choices – so expand dataset with information on other stores available to customers to generate customer choice set • Populate with store characteristics (distance, relative price, size) • Logit model gives price/distance trade-off

  8. … basket distribution… • Using average basket size systematically underestimates switching • Large basket customers more likely to switch • Higher cost from price increase but same extra travel cost • Previous Inquiries looked at “weekly one-stop shop” market • Define one-stop shop to be a trip that accounts for (say) 60% or more of average weekly spend • Can get this from TNS Worldpanel data – contains all customer shopping trips in a panel • Allows size of one-stop basket to vary across customers • Calculate distribution of one-stop shops • Assume same distribution in each Census Output Area

  9. … and store margins • If customers switch, firms lose sales • Profit loss lower than sales loss as some costs are saved • Look for costs that could be saved for a sales loss over relevant time period (e.g. 1 year) • Cost of goods sold - fully • Promotional spend - mostly • Staff costs – partially • Distribution costs - partially • Property costs – limited • Use client assumptions on fixed/variable proportions for each cost item

  10. Then calculate across all customers and stores – example In each Census Output Area there is a “critical basket size” above which customers will switch outside the area The darker the area, the lower is the “critical basket size” and hence more switching Overall we find 97% of stores have wider markets than previous findings… and 87% of stores in markets at least 30 minutes wide “Chain of substitution” really matters, especially in urban areas

  11. Conclusions • Aim to quantify empirical observation that customers at edge of catchment areas can switch • Stores outside catchment area constrain stores inside • Questions – how much, and where does chain end? • Potentially relevant to merger analysis even if no market definition stage • At some point need to make assessment of which stores are in analysis and which are part of outside option • UK grocery has masses of locational and customer data • Allows for application of simple theoretical framework

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