Practical use of microdata to

inform policy:

Firm level competition data

Chris Jenkins

Economics Director, CMA

14 October 2016

1

Introduction

● Can we use cross-economy microdata to identify

markets where there might be competition problems?

● Approach = use ONS and FAME microdata to construct

sectoral competition indicators

- Also productivity indicators?

● Outline

- Methodology

- Initial findings

- Productivity indicators

- Where next?

2

Indicators and data sources

3

Area Indicator Years Database

Concentration

Number of firms 2009-13 ONS Business Structure

Database (BSD)

HHI 2009-13 BSD

Market share of largest firm 2011-13 BSD

Profitability EBIT margin 2011-13 FAME

Dynamics

Churn - (entry+exit)/ turnover 2009-12 BSD

Coefficient of variation of

market leader

2008-13 BSD

Coefficient of variation of the C3 2008-13 BSD

Productivity

Labour productivity by sector

compared with productivity

of related sectors

2009-12 ONS Annual Business

Survey (ABS) and ONS

BRES

Change in dispersion of labour

productivity

2008-13 ABS and BRES

Market size Total turnover by sector 2009-13 BSD

Pros and cons of competition indicators

● Pros:

- Comparable indicators

across whole economy - 728

sectors at 4/5 SIC code level

- Results can be updated over

time

- Provides top-down screen to

use alongside other sources

of intelligence

● Cons:

- SIC code often not a good match

for economic product markets

- Data is UK-wide – in practice

markets may be local, or

supranational

- Robustness of data may be

limited at a narrow sectoral level

4

● Overall: competition indicators will never be sufficient in themselves to

identify competition problems, but could potentially provide valuable

information to put alongside other sources of intelligence

5

Illustrative findings (1) – previous

market study sectors

6

Illustrative findings (2) - selected sectors

● Sectors score highly

across a number of

indicators

● However, in some

cases likely to span

many markets – (eg

‘organic basic

chemicals’)

7

Illustrative findings (3) - Financial sectors

Useful results?

● Results generally match our expectations of competition in these

sectors

- Useful source of information alongside other metrics

● Limit to how far we can take the analysis given difficulty of matching

SIC codes with economic markets

● One extension would be to consider imports/exports data as a proxy

for geographic scope

- Significant imports and exports might suggest competitive constraints

wider than UK

● We could also update indicators over time and look at movements in

indicators over a longer period

8

Can productivity be used as an

indicator to identify problem markets?

● In theory productivity could also be a useful measure – target

intervention on low-productivity sectors

- Rationale = competition is a driver of productivity, so low productivity

might indicate competition concerns

- However, low productivity might have nothing to do with lack of

competition – only a filter

● Key challenge is in developing a meaningful indicator which can be

compared across sectors

● Labour productivity varies widely between sectors, primarily because

of differences in capital-labour ratio (ie capital intensity) and quality of

capital

● We have therefore examined the productivity of a sector relative to its

industry average e.g. glues to all chemicals

9

Worst-ranked sectors based on relative

productivity – BUT note significant caveats

set out in following slides

10

Sector Relative

labour

productivity,

average 2008-

12 (£ 000’s)*

Absolute

sector

productivity,

average 2008-

12 (£ 000’s)

Change in

absolute

sector labour

productivity,

2008-2012 (£

000’s)

Strength of

competition

(high ranking

= less

competitive)

Satellite telecommunications activities -186 -64 NA 444

Inland passenger water transport -122 26 +7 384

Radio broadcasting -115 93 NA 471

Manufacture of basic pharmaceutical products -110 64 NA 310

Wholesale of petroleum and petroleum products -90 -39 -962 404

Renting of video tapes and disks -77 17 -20 451

Wired telecommunications activities -64 59 NA 243

Renting and leasing of recreational and sports goods -58 36 +42 291

Other treatment of petroleum products (excluding mineral

oil refining/petrochemicals manufacture)

-56 142 NA 377

Renting and leasing of personal and household goods -48 46 +41 298

* Relative labour productivity is obtained by subtracting productivity of the industry (2 digit SIC) from productivity of

the sector (4/5 digit SIC)

11

Estimated productivity based on GVA is likely to fall in

the short-run as competition increases

● Example competitive market:

- Workers produce 100 units

- Prices are competitive at £1

- Productivity ≈ £100

● In an uncompetitive market:

- Workers produce only 70 units

- The competitive price level is also £1,

so true productivity ≈ £70

- But price are excessive at £2

- So apparent productivity ≈ £140

● Is low productivity just a signal

of low profits i.e. effective

competition?!

Limitations of relative

productivity (1/3)

Robustness of survey evidence

● Worst-ranked sector on basis

of relative productivity is

satellite communications

● But chart suggests significant

data problems

- Negative productivity? (Driven

by negative GVA estimates)

- Very unstable productivities

ranging from £100 to -£250

(economy average ≈ £50)

- Is the industry benchmark really

comparable?

● Similar data problems affect

other sectors

12

Limitations of relative

productivity (2/3)

● Based on empirical

literature, would expect

low relative productivity to

be correlated with low

levels of competition

(upwards sloping line)

● Lack of any relationship

suggests relative

productivity measure is

not informative

● Question is whether we

could come up with any

better measures?

13

We do not find any relationship between our measures of

competition and relative productivity

Limitations of relative

productivity (3/3)

14

Possible next steps

● Longer time series data to look for trends?

● Use imports/exports data as a proxy for

geographic scope?

● TFP rather than labour productivity?

● Cross-country comparisons?

● Use indicators as an evaluation tool?