Approximating diversion ratios for retail chain mergersmergersPresentation to CRESSE European Conference on Competition & Regulation 5 July 2008on Competition & Regulation, 5 July 2008
Chris Walters*
Assistant Director, Mergers
*Views expressed are mine only*Views expressed are mine only
OverviewOverview
E t i th d l b d● Econometric methodology based on Somerfield/Morrisons (2005) for approximating diversion ratios to measure competitive effects ofdiversion ratios to measure competitive effects of local retail mergers
● Apply methodology to two recent (2006) local retail mergers examined by OFT and CC: Vue/Ster (cinemas) and Waterstone’s/Ottakar’sVue/Ster (cinemas) and Waterstone s/Ottakar s(book stores)
● Suggest some thresholds against which predicted diversion ratios can be judged
BackgroundBackground
L l t il t d t h b● Local retail mergers tend to have been examined by OFT/CC using isochrone/fascia count/market share rulescount/market share rules
● E.g. supermarkets, book stores, cinemas, pharmacies, bingo halls, off-licences, licensed betting offices, funeral homes, pubs
● This methodology is convenient but may be unrealistic. Alternative?
● Diversion ratios—closeness of competition
Diversion ratiosDiversion ratios
● Diversion ratio from A to B represents proportion● Diversion ratio from A to B represents proportion of revenue from A’s customers who would choose B as their second choice as opposed to C ppor D (cross-pB to A/own-pA)
● In an undifferentiated/equidistant market● In an undifferentiated/equidistant market, diversion ratios match market shares: if A, B and C each have 33% shares, the diversion ratio from A t B i 50% (33%/66%)A to B is 50% (33%/66%)
● In differentiated single-good Bertrand model, g gdiversion ratios may be combined with margins to ‘predict’ post-merger price increases
Somerfield/Morrisons (2005)Somerfield/Morrisons (2005)
C l t d i iti b S fi ld f 115● Completed acquisition by Somerfield of 115 geographically non-contiguous, mostly mid-range supermarkets from Morrisonsrange supermarkets from Morrisons
● Isochrone/fascia count/market share rules suggested acquisition of 56 of these supermarkets potentially problematic
● Survey of 5,400 shoppers at these stores asking for second choice supermarket
Survey resultsSurvey results
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Approximating diversion ratiosApproximating diversion ratios
Di i ti b l b i t● Diversion ratios can be laborious to measure, e.g. with customer surveys, especially at phase 1
● Can they be approximated using the traditional isochrone/fascia count/market share methodology?
● Econometric model relating diversion ratios to● Econometric model relating diversion ratios to such local market characteristics
Pseudo ML fractional logit modelPseudo-ML fractional logit model● Diversion ratios (d) are 80%
90%
100%
● Diversion ratios (d) are non-negative and cannot exceed 1, therefore usual to assume they are 40%
50%
60%
70%
80%
1/[1
+ex
p(-X
ygenerated by a model d=1/[1+exp(-xβ)]
● Logit transformation 0%
10%
20%
30%
40%
d=1
β)]
● Logit transformation gives ln[d/(1-d)]=xβ, which can be estimated by OLS
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Xβ
4
6
8
10
y
● However, 8 diversion ratios in sample are 0, so preserve these by using -4
-2
0
2
4
ln[d
/(1-d
)]
preserve these by using pseudo-ML estimator instead of OLS (Papke & Wooldridge, 1996)
-10
-8
-6
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Xβ
Pre merger independent variablesPre-merger independent variables
● Di i ti rvey
● Diversion ratios implied by market shares .5
.6ra
tio fr
om C
C s
u
● Number of proximity stores in isochrone .3
.4om
er d
iver
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r● Drive time to nearest
proximity store .1.2
-Som
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● Number of competing fascias
0M
orris
ons -
0 .1 .2 .3 .4 .5 .6Morrisons-Somerfield diversion ratio implied by pre-merger market shares
Results (marginal/partial effects)Results (marginal/partial effects)Independent variable dy/dx Std Err z P>z X*Independent variable dy/dx Std. Err. z P>z X
Number of proximity stores 0.027 0.008 3.25 0.001 1
U b d † 0 048 0 022 2 23 0 026 1Urban dummy† 0.048 0.022 2.23 0.026 1
Drive time to closest proximity store -0.000 0.003 -0.13 0.899 4.8
Urban dummy X proximity drive time -0 014 0 006 -2 37 0 018 3 0Urban dummy X proximity drive time 0.014 0.006 2.37 0.018 3.0
Diversion ratio from pre-merger market shares 0.183 0.067 2.73 0.006 0.1
Number of pre-merger competing fascias -0.030 0.012 -2.46 0.014 3Number of pre merger competing fascias 0.030 0.012 2.46 0.014 3
†For discrete change from 0 to 1.
*Value of independent variable at which marginal effect is calculated (generally mean or median of independent variable).
Predicted and actual diversion ratios
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0 .1 .2 .3 .4Surveyed diversion ratio
DiscussionDiscussion
M d l t di t l di i● Model appears to over-predict low diversion ratios (<20%) and under-predict high diversion ratios (>20%)ratios (>20%)
- Could add other independent variables but o ld competition a thorit ha e these ewould competition authority have these ex
ante?
F h i hi d d di i● Further, is this over- and under-prediction an issue from the perspective of the competition authorities?authorities?
ExtensionExtension
Si lt it b t di i ti d b● Simultaneity between diversion ratio and number of competitors?
- Customers come to supermarkets but supermarkets locate where customers are
● Instrument number of proximity stores and fascia count with population in isochrone, number of
ki d i i 2car parking spaces and store size, using 2-step procedure of Wooldridge (2005)
● Results essentially unaltered
Sensitivity analysisSensitivity analysis
A l d l t d t l t i bl● Apply model to data on explanatory variables from Vue/Ster (cinemas) and Waterstone’s/Ottakar’s (book stores)Waterstone s/Ottakar s (book stores)
● Both comparable to supermarkets in terms of multi-product nature, differences in store size, small number of large players
● But unitary demand for cinemas, and book stores are not destinations
Vue/Ster (2006)Vue/Ster (2006)● In April 2005 Vue acquired 6 Ster multiplex cinemas● In April 2005, Vue acquired 6 Ster multiplex cinemas
- No national concerns- Local overlaps in Basingstoke, Edinburgh, Leeds and p g , g ,Romford
● OFT examined fascia counts of multiplexes and market shares in 20-minute drivetime isochronesshares in 20 minute drivetime isochrones- Found possible problems in Basingstoke, Leeds and
Romford● CC examined 4 overlaps on a case-by-case basis
- Found problem only in Basingstoke and Vue divested the acquired cinemaacquired cinema
Results for Vue/SterResults for Vue/Ster
Market shares Implied Predicted
Local National diversion ratio Proximity diversion
Locality Ster Vue Ster Vue Local National Number Drivetime Fascias ratio
Basingstoke (urban) 47 53 14 2 100.0 2.6 1 5 2 65.2
Cardiff (urban) 25 0 14 2 0.0 2.6 0 30 5 2.6
Edinburgh (urban) 15 16 14 2 18.8 2.6 1 5 5 4.9
Leeds (urban) 13 9 14 2 10.3 2.6 1 6 5 3.9
Norwich (rural) 31 0 14 2 0.0 2.6 0 30 3 4.7
Romford (urban) 100 0 14 2 0.0 2.6 0 22 1 2.7
Waterstone’s/Ottakar’s (2005)Waterstone s/Ottakar s (2005)
● In September 2005 Waterstone’s announced it intended to● In September 2005, Waterstone’s announced it intended to acquire 141 Ottakar’s book stores
- No national concerns- 33 local overlaps (within 1 mile on high street)
● OFT analysis emphasized degree of direct competition between Waterstone’s and Ottakar’s on non-price factors
● CC cleared merger unconditionally given no difference in range or service quality between overlap and non overlaprange or service quality between overlap and non-overlap stores
- CC surveyed customers at 33 overlap locations and 40CC surveyed customers at 33 overlap locations and 40 comparable non-overlap locations
- Obtained diversion ratios at 33 overlap locations
Results for Waterstone’s/Ottakar’sResults for Waterstone s/Ottakar s1
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.1.2
.1 .2 .3 .4 .5 .6 .7 .8 .9 1Surveyed diversion ratio
Thresholds for diversion ratios (1/3)● Three sources of possible thresholds● Three sources of possible thresholds
● First, from increment to HHI (Δ) per OFT Merger Guidelines
● For HHI of 1,000, Δ of 100 problematicFor HHI of 1,000, Δ of 100 problematic
- Implies 7.6% threshold (7.1%/92.9%)F HHI f 1 800 f 0 bl i● For HHI of 1,800, Δ of 50 problematic
- Implies 5.3% threshold (5%/95%)
Thresholds for diversion ratios (2/3)
S d f i l d f i t l● Second, from previously used fascia count rules
● OFT and CC previously have used 5-to-4 and 4-p yto-3
● S mmetric 5 to 4 merger implies 25% threshold● Symmetric 5-to-4 merger implies 25% threshold (20%/80%)
● Symmetric 4-to-3 merger implies 33% threshold (25%/75%)
Thresholds for diversion ratios (3/3)
Thi d f CC M G id li bi d● Third, from CC Merger Guidelines, combined market share of 25% potentially problematic
● Combined symmetric 25% market share implies 14.3% threshold (12.5%/87.5%)
● Not equivalent to 8-to-7 fascia count as nothing assumed about market shares non-merging firmsassumed about market shares non merging firms
Further extensionsFurther extensions
L t t l d l?● Latent class model?
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Pred
● Pool samples for Somerfield/Morrisons and
0 .1 .2 .3 .4Surveyed diversion ratio
● Pool samples for Somerfield/Morrisons and Waterstone’s/Ottakar’s?
ConclusionsConclusions
● Di i ti b i t d i t diti l l● Diversion ratios can be approximated using traditional rule-of-thumb isochrone/fascia count/market share rules
● M d l di t ibl di i ti i 25 t f● Model predicts sensible diversion ratios in 25 cases out of 37, when applied to Vue/Ster and Waterstone’s/Ottakar’s
● Se eral important ca eats and possible e tensions● Several important caveats and possible extensions, however