Quantitative analysis
in M&As evaluation
Ha Long, Vietnam – 13&14 August 2005
Dr. Patrick Krauskopf, Vice-Director,
Fanny Raess,
Swiss Competition Commission
Structure of Presentation
1. Introduction
2. Role of quantitative techniques
3. Frequently used quantitative analysis tools
a)
b)
c)
d)
e)
Price correlation analysis
Own-price and Cross-price elasticity analysis
SSNIP Test
HHI
Analysis of the modification of the competition structure
4. Conclusion
1. Introduction
• Objectives of the presentation
• Preliminary comment: Description of the
pre-merger situation to analyse the behavior
of the competitors
–
–
–
–
Perfect competition
Monopolistic/Oligopolistic competition
Contestable markets
Dominance assessment (single vs collective)
2. Role of quantitative analysis
• Quantitative analyses can (and should)
provide important evidence in an
merger evaluation
• Quantitative evidence should be viewed
as complementary to the qualitative
evidence
3. Frequently-used quantitative
analysis tools
• Using quantitative approach to:
–
–
Define the relevant market
Evaluate the merger impact
• The choice of technique will depend on
–
–
the specific circumstances of the case
the availability of data
• It is possible that more than one technique could
be usefully applied to any particular case
a) Price Correlation - definition
•
•
•
•
Standard tool in evaluating product market definition
Prices of products in the same market are expected to move
together over time
Basis of the approach:
1. Examine whether price levels move together
2. Quantify the extent to which price levels move
together over time. This is measured by the
correlation coefficient
The correlation coefficient can vary between + 1 and –1
–
–
–
correlation of +1 => prices moving perfectly together
correlation of -1 => prices moving perfecty inversely to one another
correlation of 0 => no statistical association between the two series
a) Price Correlation - Example
• Are still water, sparkling water and soft drinks in the same relevant
product market?
• The price correlation analysis showed that:
o Panel A: Correlation between prices of water brands and soft drinks is weak
o Panel B: Correlation between prices of still and sparkling water brands is high
=> the market should include still and sparkling waters but should exclude
soft drinks.
Illustration
(source: Lexecon)
Panel A: Low
correlation coefficient
(< 0.3)
Panel B: High
correlation coefficient
(> 0.9)
b) Own and cross-price elasticities (ε) definitions
• Standard tools in evaluating product market definition
• The own-price elasticity measures the percentage change
in quantity (Q) of good X for each percentage change in
the price (P) of good X
– If ε is high => P increase is not likely to be profitable (ex: Pepsi)
– If ε is low => P increase is likely to be profitable (ex: Petrol)
• The cross-price elasticity measures the percentage change
in quantity (Q) of good X for each percentage change in
the price (P) of good Y
b) Own and cross-price elasticities –
example (1)
Market: Petrol
Companies: Shell, Agip, Esso, BP, Totalfina
– If Pshell increases => Qshell decreases and
Qotherbrands increases => εShell is high
– If Pmarket increases => Qmarket is only sligthly
decreased => ε market is low
b) Own and cross-price elasticities –
example (2)
• Pepsi - Coca-Cola
If Ppepsi increases => consumers will switch to
Coca-Cola => Q Coca-cola will increase
=> Pepsi and Coca-Cola are substitutes (ε qp > o)
• Wine - Water
If P Wine increases => consumer will not change
their water consumption => Q water will stay
constant
=> Wine and Water are not substitutes (ε qp = o)
c) SSNIP Test (small but significant nontransitory increase in price) - definition
• Standard tool in evaluating product market definition
• It is a « hypothetical monopolist test »
• Tests whether a good or a set of goods define a relevant
product market
• The question is: « what type of constraints does the
presence of other goods place on the producer of the good
in question, when he decides to increase his price by 510%? »
c) SSNIP Test - example
• 3 goods: Apple (A), Pear (P) and Banana (B)
Are they part of the same relevant product market?
• If all the producers of A can profitably introduce a small
but significant and non-transitory increase in price, despite
the existence of P and B => the relevant market is made of
A only
• However, if consumers, on reaction to this increase of
price of A, significantly shift to P => P are added to the
relevant market, as the producers of A have no market
power (unprofitable price increase)
• Same process with A and P together, until a profitable
price increase is found
d) HHI - Herfindahl-Hirschman index –
defintion (1)
• Measure of market concentration
HHI = Sum of the squares of the market shares
• The closer the market structure is to a monopoly,
the higher the market concentration (and the lower
the assumed degree of competition)
• In many juridictions, HHI is not considered as
direct evidence, but as an indicator for further
analysis
d) HHI as a screening device (EU)
• HHI < 1000 => Unlike to challenge
• 1000 < HHI < 1800 and ΔHHI < 100 => Unlike to challenge
1000 < HHI < 1800 and ΔHHI > 100 => Likely to challenge =>
Further analysis needed
• HHI > 1800 and ΔHHI < 50 => Unlike to challenge
HHI > 1800 and < 50 ΔHHI < 100 => Likely to challenge =>
Further analysis needed
HHI > 1800 and ΔHHI > 100 => Likely to challenge
d) HHI - Example
• Pre-merger: 5 firms
A 30%, B 25%, C 25%, D 10%, E 10%
The HHI is: 302 + 252 + 252 + 102 + 102 = 2350
• If B mergers with D, 4 firms
A 30%, B/D 35%, C 25 %, E 10%
The HHI is 352 + 302 + 252 + 102 = 2850
Δ HHI is 2850-2350 = 500
• Interpretation:
HHI > 1800 and ΔHHI > 100 => Likely to challenge
e) Analysis of the modification of
the competition structure
• Based on the market shares, is the merger
likely to induce a structural change in the
market structure?
• Examples:
– Perfect → Oligopolistic competition?
– Increased probability of collective dominance
or cartellization?
4. Conclusion
• Empirical economic analysis lies at the
heart of modern competition policy
• Econometric analyses does not “come
from out of the blue”
• Potential weakness of the tools: lack of
data, asymmetry of data (firm/authority)
• Role of economists is complementary to
the role of lawyers