Measuring Competition

1. Measuring Competition Jan Boone Tilburg University, TILEC, ENCORE Rachel Griffith IFS and University College London Rupert Harrison IFS and University College London

2. Motivation • In both empirical research and competition policy price cost margin plays an important role as measure of competition • Standard interpretation is: high PCM indicates low intensity of competition • Several theoretical papers have questioned this use of PCM as a measure of competition • See, for instance, Amir and Lambson (2000), Bulow and Klemperer (2002) and Stiglitz (1987) • for cases where competition goes up and PCM goes up as well • How can we assess the importance of such counterexamples in practice? • Approach taken here: derive a measure that is theoretically more robust and see how it is correlated with measures like PCM and Herfindahl in the data

3. Relative Profits measure • The measure that we use estimates how fast a firm's profits fall as it becomes less efficient • In a more competitive market, a firm is punished more harshly in terms of profits for being inefficient • Put differently, in a more competitive market, gains in efficiency are rewarded more • This RP measure turns out to be theoretically robust

4. Results • RP measure and PCM are correlated both over time (within a given industry) and across industries (for a given year) • But they are not perfectly correlated: • In some instances PCM says competition went up, while RP says it went down • Further research needed to see which measure is 'right' but theory suggests that RP is more robust • Neither RP and Herfindahl nor PCM and Herfindahl are strongly correlated

5. Overview • Theoretical model to introduce • Two parametrizations of competition • Three measures of competition • Data and empirical implementation of the measures • Comparing the three measures • In pharmaceutical industry • Across industries • Conclusion

6. Model • p(qi,q-i) = a – bqi – dji qj • a = 20, b = 2, d = 1.5 • Firm i has marginal costs ci = i/10 with i =1,…,20 • Entry cost  = 0.02 • Firms play Cournot competition • Fall in competition through rise in entry cost to 0.4 • Increase in competition through rise in d to d = 2: goods become perfect substitutes • Equilibrium variable profits denoted by i = (pi – ci)qi • Thus profits measure is not i -  • Relative variable profits i/1 • Relative marginal costs ci/c1

7. relative profits high entry cost low entry cost relative marginal costs

8. 1 relative profits 0.8 0.6 d = 1.5 0.4 d = 2 0.2 5 10 15 20 relative marginal costs

9. Things to note • To estimate the slope of these relationships we do not need to observe all firms in the industry • Contrast this with Herfindahl and PCM which do not make much sense if only a sample of firms is observed • If entry costs are lowered, more firms enter the industry • If the interaction between firms becomes more aggressive, some inefficient firms leave the industry • In both cases the slope becomes steeper, correctly pointing out that competition has intensified

10. Theoretical robustness • We get the same intuitive result if we model intensity of competition by • Travel cost on an Hotelling beach • Conjectural variation parameter • Elasticity of substitution of a CES utility function (Dixit-Stiglitz framework) • Switch from Cournot to Bertrand competition • Basically, in every model where more competition reallocates output from less efficient to more efficient firms we find that more competition leads to an increase in profits of a firm relative to a less efficient firm.

11. Empirical implementation • We estimate the following relation for each industry for every year t • lnit = t + tAVCit + it where i indexes firms •  denotes variable profits = revenue – (labor costs + intermediates) • AVC denotes (labor costs + intermediates)/renevue • The measure of competition is the slope  < 0 • In the example above where d is raised from 1.5 to d = 2, estimated  falls from –3.70 to –4.37. • We also calculate industry average PCM, where price cost margin is defined as: /revenue • Herfindahl index

12. Data • Annual reports and accounts filed by firms on London Stock exchange • Period: 1986-1999 • 43 industries defined as SIC codes at three digit level • Each industry has at least 5 firms in every year

13. Example: Pharmaceuticals

14. Pharmaceuticals, 1986

15. Pharmaceuticals, 1994

16. Pharmaceuticals: comparing PCM and beta over time

17. Findings • Beta negative in all years and in 11 of the 14 years significantly negative • PCM and beta are positively correlated over time (coefficient 0.54) • This correlation is significantly different from zero • As beta becomes more negative () then PCM tends to fall • This is not only true for pharmaceuticals, it holds for 20 out of 43 industries • Both measures indicate that the industry has become more competitve over time • Beta is significantly lower (more negative) in the period 1995-1999 than in 1986-1990

18. Cross industry comparison • Next we look at cross industry correlations • Is it the case that industries which are competitive according to PCM in a certain year are also seen as competitive using the slope parameter beta? • Is it the case that industries with low PCM are also industries with low (more negative) beta?

19. Cross-industry correlations between the three measures

20. Findings • Beta and PCM are significantly positively correlated across industries in all years, except 1991 and 1992 • Neither of these two measures is particularly correlated with Herfindahl across industries

21. Conclusion • Herfindahl index seems 'odd man out' when comparing competition measures: not correlated with either PCM or Relative profits • Relative profits is positively correlated with PCM but not perfectly correlated • This may suggest that the theoretical objections against PCM could have practical importance • Although RP is more robust theoretically than PCM more empirical evidence is needed to show that when RP and PCM differ, RP actually has it 'right' • But RP certainly seems a useful complement to PCM in both policy and empirical research