Notes: Charts a and b show percentage difference between the 90th and 10th deciles approximated by log differences; Charts c and d show ratio of productivity at 90th to 10th percentile using the difference in the log levels. UK data only available from 2002, UK index  31  All speeches are available online at www.bankofengland.co.uk/speeches  31  base year = 2002. Data have been aggregated from a detailed industry level. To the extent that industry classifications in the UK and the other countries shown are slightly different this may affect the comparability of the series. Chart 21: Management practices and labour productivity Density Management score  0.6 0.5  Labour productivity  0.4 0.3  0.2 0.1  0 -4 Z score  -2  0  2  4  Source: BvD, World Management Survey and Bank calculations. Notes: The productivity kernel is based on around 280,000 firm-year observations on labour productivity (in logs) spanning the 20022013 period (source: BVD). The management kernel is based on around 1500 interviews with firm managers from the WMS database (as described in Bloom and Van Reenen, 2007, 2010). Covering the 2002-2014 period. The raw data are converted to Z-scores by removing the sample mean from each observation and dividing the demeaned values by the sample standard deviation.
Every 10 percentage point increase in a company’s export share of turnover is, on average, associated with a 3% increase in productivity. While causation could operate in either direction, this illustrates the productivity benefits of a corporate sector which is open to trade and exposed to external competition and innovation. Chart 22e classifies firms according to their spending on new products and processes. The differences here are not especially surprising, with average productivity around 20% higher among firms investing in new  52 53  For example, Haldane (2016). Newman et al (2015). 14  All speeches are available online at www.bankofengland.co.uk/speeches  14  products and processes. Nonetheless it is striking, and perhaps surprising, these differences are not larger. Clearly, they do not explain most of the productivity differences across companies. Firm size appears to matter somewhat more to productivity (Chart 22f). For example, for small firms (with fewer than 50 employees) the picture is a nuanced one. In general, their average levels of productivity are materially lower than for larger firms, on average around 7% lower. There is, in particular, a larger lower tail of small companies with low, or even negative, levels of productivity. Nonetheless, some smaller companies do inhabit the high-productivity, upper tail. Moreover, small companies tend to exhibit faster rates of productivity growth than larger firms, even when it is from a lower base. Table 3 looks at average productivity growth rates among firms of different size.