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Identifying Candidate Sectors for Market Studies: The UK Experience OECD Workshop 9 March 2017 Professor Amelia Fletcher Centre for Competition Policy University of East Anglia Disclaimer: These are not necessarily the views of any organisation with which I am associated.

Introduction

Focus of talk:

Attempts by OFT and CMA to develop a holistic data-intensive methodology to identify candidate sectors for market studies.

Spoiler alert!

These have not been terribly successful…

…albeit they can provide useful data and insights.

Punchline:

Collecting this data can be useful. But primarily as an element of wider processes for identifying/prioritising candidate sectors.

Alternative approaches to ideas generation

"Top-down"

Analysis of economy-wide data sets in an attempt to identify sectors that have characteristics which could suggest suitability for the use of competition or consumer tools.

"Bottom-up"

Information and intelligence gathering from a variety of sources, with a focus on identifying sectoral characteristics or firm behaviour that might point to competition or consumer concerns

NB Recall that UK market studies are not typically purely focussed on identifying competition enforcement cases.

Top-down approaches explored by OFT/CMA

1. "Predicting Cartels", Paul Grout and Silvia Sonderegger (OFT, 2005)

Analysis of sector-specific data to predict sectors in which cartels were most likely to occur

2. "Empirical indicators for Market Investigations", NERA (OFT, 2004)

Analysis of a wider set of sector-specific data to identify sectors likely to give rise to competition or consumer concerns

3. "Productivity and Competition – and OFT perspective on the productivity debate" (OFT, 2007)

Comparison of competition and productivity of UK to EU sectors

4. Further unpublished work on productivity and competition (CMA, 2016)

"Predicting Cartels" (OFT, 2005)

Methodology based on 3-digit standard industrial classification (SIC) sectors. Dataset of structural characteristics identified as potentially relevant to cartel formation/stability.

Step one: Use regression analysis and existing EC/US evidence on detected cartels to identify structural characteristics that seem to be important for the formation of (formerly workable) cartels.

Step two: Use this analysis to predict the probability of cartels in sectors where they haven't previously been identified.

Cartel likelihood found to:

increase with: total turnover, growth in turnover per firm, C3 concentration ratio, per employee costs

decrease with: variability of per firm growth, economies of scale

"Predicting Cartels" - The Top Ten!

SIC-3 Sector Likelihood

1 Telecommunications 0.84

2 Manufacture of aircraft and spacecraft 0.65

3 Manufacture of grain mill products, starches and starch products 0.61

4 Legal, accounting, bookkeeping and auditing activities; tax consultancy; market research and public opinion polling; business and management consultancy

0.55

5 Cargo handling and storage 0.50

6 Activities of travel agencies and tour operators; tourist assistance activities

0.46

7 Publishing 0.44

8 Manufacture of railway and tramway locomotives and rolling stock 0.44

9 Other land transport 0.43

10 Recycling of metal waste and scrap 0.40

"Predicting Cartels" - Comments

3-digit SIC sectors poor proxies for markets, and in particular C3 concentration a poor proxy for market concentration.

No data on some potentially important factors, such as product homogeneity or industry transparency. Noteworthy that the (logicstic) regressions only explain 14-24 % of variability in cartel activity.

Implicitly explaining both cartel creation and cartel detection. Some factors (such as per employee costs) may be more about the latter.

Nevertheless, since this study, competition issues have been investigated by the EC in respect of:

telecommunications (1), cargo handling (5), travel agencies (6), publishers (7), and recycling (10); JFTC has fined a starch cartel (3); while the UK Monopolies and Mergers Commission has reviewed both audit (4) and the leasing of rail rolling stock (8/9).

"Empirical indicators for Market Investigations" (OFT, 2004)

Methodology based on 4-digit standard industrial classification (SIC).

Step one: Collect data on 32 empirical indicators of problems in markets, grouped into 8 categories (barriers to entry, productivity, concentration, profitability, prices, consumer complaints, innovation, switching costs and others). Consider worst ranked sectors in respect of each indicator.

Step two: Where possible (for 8 indicators), apply weights to gain a weighted average indicator. Consider worst 15 sectors on this basis.

Step three: Add back 3-5 worst sectors on key non-included indicators (complaints, advertising-to-sales ratios, innovation.

Step four: Consider the 26 sectors thus identified in more detail.

"Empirical indicators for Market Investigations" – The Weighted Top 15!

"Empirical indicators for Market Investigations" – The Other 11!

"Empirical indicators for Market Investigations" - Comments

Very difficult to consider indicators that may give rise to competition problems alongside indicators that may give rise to consumer problems.

There are significant data/methodology issues:

4-digit standard industrial classification (SIC) sectors get closer to markets, but could still be too wide/narrow and can be hard to interpret. Also, firms active across more than one SIC code have all their info allocated to a single 'primary' SIC code .

There are lots of data gaps and not all data is collected on basis of SIC sectors (and translation can be hard).

Weighting is of apples and pears. Any weighting is subjective.

Overall, competition indicators not as good as in Grout and Sonderegger. Complaints more useful, but deserve more sophisticated analysis.

"Productivity and Competition" (OFT, 2007)

Methodology based on 4-digit standard industrial classification (SIC).

Step one: Calculate growth in labour productivity and total factor productivity for UK and EU.

Step two: Calculate the following competition measures for UK: Market share variance, entry and exit, persistence, productivity dispersion.

Step three: Identify candidate sectors on the basis of (i) differences in productivity growth between UK and EU and (ii) differences in productivity growth through time. Check competition measures.

Identify shortlist of 18 sectors. Results (apparently) never published.

Again, significant concerns re data/methodology.

"Practical use of microdata to inform policy" (CMA, 2016)

Ongoing work, so not yet published. Uses data at 4/5-digit SIC level. Considers competition measures and also 'relative' labour productivity of SIC-5 sectors to SIC-2 industry average. Aim of latter is to allow for differences in capital intensity and quality of capital across industries.

Early findings interesting. Good links between competition measures and competition-focussed market investigations (such as audit), albeit less good links with consumer-focussed ones (such as private motor insurance or payday lending). But similar data issues to before.

Little apparent value (so far) from productivity measures. Work notes that high measured productivity could be due to poor competition (and thus high prices). So not necessarily good! Short-term productivity could also be reduced by other benign factors, such as prudential regulation in banks, or pro-competitive process of industry change).

Two non-UK approaches

ACM Economic Detection Instrument. Uses competition indicators to identify sectors at risk of cartel behaviour. Includes: Number of trade associations; Prices (NL versus EU); Concentration (HHI, number of firms and import rate); and Market dynamics (Market growth, churn rate, survival rate and R&D). Results pretty good, despite exogenous weightings.

EC Consumer Markets Scoreboard. Annual analysis of the functioning of key consumer markets. Market Performance Indicator (MPI) is a composite index made of 5 components: comparability of offers; business trust in consumer protection rules; extent to which markets meet consumers expectations; choice of retailers/suppliers; and the degree to which problems experienced in the market cause detriment.

Top-down approaches – in summary

May have some value, albeit more so for competition-focused work.

In practice, relatively little used in UK so far, despite several attempts!

Perhaps because, even if indicators (eg high concentration, high barriers to entry) are reasonably good indicators of weak competition, it is not clear that they are breaches of the law or otherwise remediable.

Overall, it may be unrealistic to expect a mechanistic top-down exercise to do more than prompt ideas. Wider intelligence remains crucial.

Other top-down measures may be worth considering: eg expenditure by income decile (what issues are especially harming the poor?).

That said, data created is potentially useful for prioritisation work in thinking about relative strength of possible candidate market studies. (And may also be useful for ex post evaluation of interventions).

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Identifying Candidate Sectors for Market Studies: The UK Experience Discussion! Professor Amelia Fletcher Centre for Competition Policy University of East Anglia Disclaimer: These are not necessarily the views of any organisation with which I am associated.