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Project 7 Understanding local Productivity Disparities
Understanding Local Productivity
Mark Hart, Michael Anyadike-Danes,
Karen Bonner, Jun Du and Neha Prashar
Background – a well trodden Path
• Recent analysis by the OECD also suggests that labour
productivity growth in firms at the global productivity frontier
has also been significantly more rapid than that in non-
frontier firms (OECD 2015).
• In the UK, analysis using the ONS BSD (2008-2015) suggests
that, in a population of survivor firms, firms at the top 10%
mark of the productivity distribution are ten times more
productive than those at the bottom 10% mark.
• And, just as important, this dispersion is persistent: about a
decade later the 90/10 ratio was still around ten.
What we also know – 2008-2015 panel
• On average, productivity growth is inversely related to job growth
and directly related to turnover growth.
• Positive productivity growth is more commonly recorded alongside
positive turnover growth.
• Firms which record both positive job growth and positive
productivity growth are relatively rare, less than 10% of the
• It is not possible to tell, without further investigation, what
proportion of that 10% were above median productivity in 2008,
however we do know that only a small proportion of above median
productivity firms record productivity growth.
Anyadike-Danes, M and Hart, M (2018) “Seeing the trees for the wood: going with the grain of the
extraordinary heterogeneity of firm-level productivity”, ERC Research Paper, in preparation
Taking this a little further…
• These properties of the UK productivity distribution –
dispersion plus persistence – have important implications
both for the understanding of productivity across areas, and
the importance of diffusion processes which may change the
shape of the productivity distribution.
• When the distribution of a measure – in this case productivity
– is very dispersed (and persistently so) then its mean – the
average level of productivity – is essentially meaningless as a
summary performance indicator.
• Rather, if we wish to compare areas, or distributions through
time to capture diffusion effects, we need to consider a
selection of points across the distribution.
• RQ1: What explains productivity differences
between local areas?
• Is this due to sectoral composition; firm size-
distribution or differently shaped productivity
In detail……size & sector
• We also know from our earlier work that there is
considerable variation in productivity within firm size-
bands: it is not case that, for example, that larger firms
have uniformly higher productivity than smaller firms.
• So, we propose to investigate size-related differences at
• Finally, it also seems worth exploring whether productivity
by sector varies across LEPs or not. Whilst data limitations
may mean that we may have to limit ourselves to a handful
of aggregated sectors, the sectoral detail will help to flesh
out the picture of LEP productivity.
Data & Method
• Using data from the ONS IDBR (BSD and/or the refreshed BEIS
version), we will compare productivity at the: 10%; 25%; 50%;
75%; and 90%; points of the distribution for firms in each LEP
• Whilst such an approach will generate a considerable volume
of data, the gains from taking a more nuanced view will allow
us to form a more accurate and robust picture of the extent of
productivity differences between LEPs.
• Firm vs local unit data at this level of spatial analysis will be a
Data Issues and the ‘Long Tail’?
• What UK firm-level data might produce a long-tailed distribution?
• IDBR/BSD – timing issues for turnover and employment – once we
can ring-fence the ‘quality’ data in the overall dataset population
dataset. Considerable progress made by BEIS.
• Why not use ARDx? - we have about 60,000 observations for each
– Cut it first by industry: how many observations are there for each (2-digit
– Then of course you need to think about size – 3-4 broad size-bands,
– …..and age (we believe in that so, 2-3 broad age categories?), and
obviously place (defined as?).
– How much of an actual distribution do you end up with?
• This first-stage project on local productivity disparities will be
completed by January 2019
• Need to use the results to identify SMEs which are at the
national frontier of productivity within their sector, identify
the correlates or causes of that performance and consider the
potential for improving diffusion.
• Analysis will adopt a mixed-methods approach combining
data analysis of the correlates of high productivity growth
among SMEs using survey data (e.g. UK Innovation Survey, IP
data, ESS, LSBS) along with semi-structured interviews with
around 25 high productivity SMEs.