Large-scale firm surveys and administrative data facilitate analysis of linkages between the behaviour of businesses and macroeconomic developments in productivity. Syverson (2011) notes the significant and unexplained variation in productivity across businesses within narrowly defined industries that is evident in large scale firm-level datasets. It would seem that some businesses are able to outperform others by a very large margin, despite using the same factors of production. Understanding why these differences in measured productivity performance exist between businesses, whether and why they persist, and how the composition of the business population changes over time, can help us understand the drivers of developments in aggregate productivity and provides us with clues as to where a growth strategy might usefully be focussed.
As large-scale firm-level datasets are developed and become available and accessible to researchers in universities and research institutions, as well as analysts in government departments, progress has been made in establishing the micro foundations underlying aggregate productivity developments. Examples of such efforts include the cross-country comparative work by OECD and others, documenting trends in the dispersion of productivity and markups across firms since the start of the century and the emergence of superstar firms (Andrews et al., 2016; De Loecker et al., 2020). Jacob and Mion (2020) consider UK firms around the Great Financial Crisis, and suggest that the sharp slowdown in measured productivity growth after the crisis was much related to demand weakness pushing down markups. Others have used large-scale firm-level datasets to develop shift-share decompositions of aggregate productivity growth to ascertain the importance of business churn (see Riley et al., 2015, for an example of implementation in the UK data) and sources of input misallocation (Anderson et al., 2019; Besley et al., 2020).
Improving the usability of data
A key issue for these types of study is that the data available is rarely designed for the purpose to which it is being applied. This has important implications for the use of and conclusions drawn from these data in research, as is increasingly recognised in the literature (White et al., 2018; Bartelsman and Wolf, 2018), and generates significant additional costs to individual researchers. In the UK there is much scope to improve the usability of key large-scale firm-level datasets and for enriching productivity analysis, its granularity and complexity, for example through data linkage, as demonstrated in recent work by Lui et al. (2020) and Ardanaz-Badia et al. (2022).
Researchers from The Productivity Institute and partners are analysing and developing firm-level datasets to establish stylised facts about business performance and aggregate productivity in the UK. Addressing gaps in the measurement of UK productivity, intangibles, innovation and business dynamism, these efforts will help to resolve some outstanding productivity puzzles by addressing three overarching research questions:
- What can we learn from the recent period of accelerated structural change to advance productivity? Were better-managed firms more innovative than others? What real-time or survey improvement on domestic innovation, supply chain and trade patterns are required to answer this? How has greater use of potential capital (a missing capital) affected productivity?
- What are the levels of innovation-related intangibles in UK firms, and what is their relationship with technology adoption and productivity in firms, sectors and places? How does this relate to specific examples, e.g., skills accumulation and the value of open-source software?
- What has happened to dynamism and market structures in the UK, and how has this affected productivity? How does the UK compare to other countries? Can we develop real-time measures of dynamism, related to market power and shifting trade patterns?
Importantly, the research also contributes to the development of UK data infrastructure. Working with key users, academic partners and data developers, the aim is to support better documentation of key business datasets through use examples and new data linkage. The Productivity Lab aims to build a one-stop shop for these endeavours.
References:
- Anderson, G., Riley, R., and Young, G., 2019. “Distressed Banks: Distorted Decisions?”, Centre for Macroeconomics DP 2019-08.
- Andrews, D., Criscuolo, C. and Gal, P., 2016 “The best versus the rest: the global productivity slowdown, divergence across firms and the role of public policy”, OECD Productivity Working Papers, 2016-05, OECD Publishing, Paris.
- Bartelsman, E. and Wolf, Z., 2018. “Measuring Productivity Dispersion”, in Grifell-Tatjé, E., Knox Lovell, C. A., and Sickles, R., 2018. (Eds.) The Oxford Handbook of Productivity Analysis, Oxford University Press.
- Besley, T., Roland, I., and Van Reenen, J., 2020. “The Aggregate Consequences of Default Risk: Evidence from Firm-level Data”, NBER Working Papers No. w26686.
- De Loecker, J., Eeckhout, J., and Unger, G., 2020. “The rise of market power and the macroeconomic implications”, Quarterly Journal of Economics, 135(2), p. 561–644.
- Jacob, N. and Mion, G., 2020. “The UK’s Great Demand and Supply Recession”, CEPR Discussion Paper No. DP15516.
- Riley, R., Rosazza Bondibene, C., and Young, G., 2015. “The UK Productivity Puzzle 2008-2013: Evidence from British Businesses”, Bank of England Working Paper No. 531.
- Syverson, C., 2011. “What Determines Productivity”, Journal of Economic Literature, Vol. 49, No. 2, p. 326-365.
- White, T. K., Reiter, J., and Petrin, A., 2018. “Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion”, Review of Economics and Statistics, vol. 100 (3), p. 502-509.
Blog written by Professor Rebecca Riley
