# Presentation: 475797

## 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
***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

## 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

## ...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

## 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

## 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)

## ... 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

## ... 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

## ... 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**

## 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

## 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