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Project 7 Understanding local Productivity Disparities

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ERC3 - Project 7 - Understanding Local Productivity Disparities

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Project 7 Understanding local Productivity Disparities

  1. 1. Understanding Local Productivity Disparities Mark Hart, Michael Anyadike-Danes, Karen Bonner, Jun Du and Neha Prashar
  2. 2. 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.
  3. 3. 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 population. • 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
  4. 4. 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.
  5. 5. Research Focus • RQ1: What explains productivity differences between local areas? • Is this due to sectoral composition; firm size- distribution or differently shaped productivity distributions?
  6. 6. 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 LEP-level too. • 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.
  7. 7. 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 area. • 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 challenge.
  8. 8. 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 year: – Cut it first by industry: how many observations are there for each (2-digit SIC) industry? – 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?
  9. 9. Future Developments? • 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.

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