# Quantitative analysis in M&As evaluation Ha Long, Vietnam – 13&14 August 2005 Dr. Patrick Krauskopf, Vice-Director, Fanny Raess, Swiss Competition Commission.

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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)Price correlation analysis b)Own-price and Cross-price elasticity analysis c)SSNIP Test d)HHI e)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: oPanel A: Correlation between prices of water brands and soft drinks is weak oPanel 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 P shell increases => Q shell decreases and Q otherbrands increases => ε Shell is high –If P market increases => Q market is only sligthly decreased => ε market is low

b) Own and cross-price elasticities – example (2) Pepsi - Coca-Cola If P pepsi 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 non- transitory 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 5- 10%? »

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 Unlike to challenge 1000 Unlike to challenge 1000 100 => Likely to challenge => Further analysis needed HHI > 1800 and ΔHHI Unlike to challenge HHI > 1800 and 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: 30 2 + 25 2 + 25 2 + 10 2 + 10 2 = 2350 If B mergers with D, 4 firms A 30%, B/D 35%, C 25 %, E 10% The HHI is 35 2 + 30 2 + 25 2 + 10 2 = 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

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