2. Belief: UK Productivity is being held back
by deficiencies in management and
• Productivity decompositions attribute much of the
productivity gap against G7 competitors to TFP, rather
than labour quality or capital investment. Management &
leadership capability would appear in TFP.
• International comparisons place the UK in mid-table for
quality of its management and leadership.
• Deficiencies in UK management skills are a key driver of
the productivity gap
• Bloom et al (2014) estimate that about a quarter of
the UK’s productivity gap with the United States
could be down to poor management
• Recent research with the ERC has expanded analysis to
SMEs in the UK and finds shows similar links between
skills, practices and performance (lower diagram), and a
‘long tail’ of firms with room for improvement
• UK Government invests extensively in improving
workforce skills (schools, University, vocational quals).
Likewise in improving capital markets, R&D, innovation.
These are the ingredients of productivity.
• But investment in how the ingredients are combined –
the recipe and the culinary skills – in UK is low,
suggesting potential for intervention to valuably improve
how people and resources are used more effectively.
How management skills and practices lead to improved outcomes
Source: Constraints on Developing UK Management Practices, BIS 2011
Source: BIS (2015) Leadership and management skills in small and medium-sized businesses.
Average management and leadership scores by country,
ranked from low to high
3. Hypothesis: Business-Level intervention can
Improve Whole Economy Productivity
• Adding fertiliser to a plant to improve the ecology of a habitat.
• …Yet, so many processes between intervention and impact,
and spanning big differences in scale
4. Where to start?!
Remains extremely difficult to pick future HGFs
• Predicting future high-growth prone to Type I & Type II errors
• Only know the characteristics that improve the chances of
future high growth:
• E.g. capabilities of owner/managers, level of ambition,
• …but often unobservable and even then hardly high precision.
• If hard to pick winning businesses, even harder to pick proven
winning policy interventions!
5. Logic Model with Huge Black Box
Selection effects? Purity of treatment
6. There is scarce robust evidence to measure
impacts or improve design
• Evaluations typically focus on the beneficiaries’ assessment of benefits and a
single policy design
• This approach does not provide a robust assessment
• The UK What Works Centre conducted a review of impact evaluations from
across the OECD
• Just 1% provided credible evidence of growth impacts
Business Advice 730 23 14
Access to Finance 1450 27 17
Innovation 1700 63 9
7. Randomised Control Trials (RCTs) of
business advice in BEIS
• Using RCTs to address specific
questions in our hypothetical
• If one link in the circuit doesn’t
work, then whole black box
• Four RCTs on Business Advice:
- Main Growth Vouchers Programme (GVP)
- Growth Vouchers Additional Target group
- Business Schools RCT
- Growth Impact Pilot RCT
8. Growth Vouchers Programme - An
example of a large scale trial
• UK launched the Growth
Vouchers Programme as a
research trial testing the
benefits of supporting
businesses to use external
• Between Jan 2014 and
• 38,000 applications
• 28,000 diagnostics
• 20,000 vouchers issued,
worth up to £39.4 million
• 6,400 vouchers used,
• RCT implemented
9. Growth Vouchers Programme – Early Evidence
• Subsidising cost of Business Advice led to increased use of
advice in treatment group (no evidence of substitution).
• Positive initial perception evidence from treatment group in
terms of future business actions needed for growth.
• But too early for business performance metrics (and take a long
time to measure in admin data because of reporting lags).
• There might be an adverse impact on self-assessment of
business skills within treatment as they are exposed to what
they don’t know, and consider focus (tackling ‘how’, not ‘what’).
10. Emerging lessons from wider BEIS evaluation
• Can’t pick likely future winners from data. We cannot identify future
HGFs from standard business data on current and past characteristics
(e.g. growth trajectories, size, age, sector, geography).
• …yet businesses receiving intervention grow quicker. Businesses
participating on GrowthAccelerator (part of Business Growth Service)
grew 3x faster than peers – so something going on – either successful
selection based on richer data (inc. soft data), or an indicator of
• Perception data from managers may not align to business
• How businesses act on advice varies. We don’t know what changed
behaviour will produce the most improvement.
• Time lags. Our wider evaluation evidence suggests that impacts on
business metrics might take 3-5 years to reach full effect – just as
many evaluations stop listening.
11. Many Questions remain unanswered
We don’t know:
• Improvement in SME-level productivity. [We measure turnover/jobs,
• Improvement in wider economy productivity.
• Rather than measure productivity directly, past interventions seek to
minimise substitution or displacement through eligibility and targeting.
• Return of investment unlikely to be scalable to offer impacts
measureable at economy-wide level.
• …but they are likely to be a helpful investment with a speedy return
because they engage currently economically active and ambitious
FIRM-LEVEL INTERVENTION &
Dr Tony Moody | Business & Science Group | BEIS
Our research, evaluations and surveys are published here: