This document discusses using firm-level microdata to identify markets that may have competition problems and examines productivity as a potential indicator. It outlines competition indicators constructed from UK business data and finds that some match sectors previously studied. However, limitations include SIC codes not perfectly matching markets. The document also explores using relative productivity between sectors as a indicator but finds the measure has limitations like unstable survey data and no clear relationship with competition measures. Potential next steps discussed are longer time series, imports/exports as scope proxy, and total factor productivity.

1. Practical use of microdata to
inform policy:
Firm level competition data
Chris Jenkins
Economics Director, CMA
14 October 2016
1

2. 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

3. 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

4. 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. 5
Illustrative findings (1) – previous
market study sectors

6. 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. 7
Illustrative findings (3) - Financial sectors

8. 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

9. 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

10. 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. 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)

12. 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)

13. ● 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. 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?