This presentation by Amelia Fletcher, Professor of Competition Policy, Norwich Business School and Non-Executive Director, UK Financial Conduct Authority ; was made during the Workshop on market studies selection and prioritisation of sectors and industries held on 9 March 2017 at the OECD Headquarters. More papers and presentations on the topic can be found out at http://www.oecd.org/daf/competition/market-studies-workshop-on-selection-prioritisation-of-sectors-industries.htm

1. Bea
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.

2. 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.

3. 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.

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 Further unpublished work on productivity and competition (CMA, 2016)

5. "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

6. "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

7. "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).

8. "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.

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

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

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.

12. "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.

13. "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).

14. 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.

15. 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).

16. Bea
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.