Productivity and economic growth
Prior to the COVID-19 crisis, considerable attention focused on the long-term productivity slowdown observed across countries. This was referred to as the productivity paradox, as the productivity slowdown occurred at a time of significant technological change. The focus on productivity is expected to resurface and gain prominence, once the recovery from the COVID-19 crisis is fully underway.
The increasing diffusion of digital technologies in the 2000s was expected to spark a new wave of productivity growth, similar to those seen in the past, e.g. as a result of electrification (from the mid-1880s) and, to a lesser extent, ICT investments (in the 1990s). However, this has not, yet, materialised, raising a number of still largely open questions, ranging from the potential lagged effects of these new technologies, to structural factors, right through to measurement.
Indeed, a number of views have been put forward to address the paradox:
The transformative nature and scale of today’s technological breakthroughs pale into insignificance compared with those that took place in the past. The benefits from electricity, internal combustion engines, the invention of telephone and radio, spread out through the economy over many years. Recent innovations, such as ICT, although also revolutionary, have shown more rapid adoption and a shorter-lived impact on productivity and economic growth (Cowen, 2011; Gordon, 2012).
The pace of technological progress has not slowed but adoption requires parallel innovation in organisational structures and business models. The next wave of productivity growth driven by technology breakthroughs in artificial intelligence, robotics, the Internet of Things, Big Data, 3D printing, nanotechnology and biotechnology, may lag the innovations and take time to be fully deployed (Brynjolfsson and McAfee, 2011; Baily, Manyika and Gupta, 2013).
A breakdown of the diffusion machine. Some studies suggest that an important explanation for the productivity slowdown is the slowing pace at which innovations spread from the most globally advanced firms to the rest of the economy (OECD, 2015; Andrews, Criscuolo and Gal, 2016). More recent work has analysed the drivers of differences in firms’ ability to achieve productivity gains focusing, in particular, on managerial quality and workers’ skills. Preliminary evidence suggests that low managerial quality and the lack of ICT skills curb the adoption of digital technologies and the rate of diffusion (Andrews, Nicoletti and Timiliotis, 2018), and that more productive firms tend to employ a larger share of skilled employees and operate with a larger share of managerial roles (OECD, 2019).
Other structural changes. Another factor that may explain the longer-term decline in productivity growth across (developed) economies is the long-term shift from manufacturing to services, in particular the shift to lower productivity personal services. Demographic changes and more service orientated consumption patterns, notably from ageing populations, may exacerbate this effect. Nevertheless, a number of converging studies conclude that the impact of this phenomenon is limited so far (Barnett et al., 2014; Kierzenkowski et al., 2018; Riley et al., 2018; Sorbe et al. 2018, Mourougane and Kim, 2020).
Measurement. Several measurement challenges can limit the analysis of recent productivity trends. Many of these challenges concern longstanding issues relating to the measurement of factors of production and output, and especially the distinction between price and volume changes. New forms of doing business, driven in particular by digitalisation and the sharing economy, as well as the increasing importance of knowledge-based assets, have added new measurement challenges and exacerbated the long-standing ones. While the jury remains out on the underlying causes, a growing body of evidence has suggested that measurement, or rather “mismeasurement”, is not the cause (Syverson, 2017; Byrne, Fernald and Reinsdorf, 2016; Ahmad and Schreyer, 2016; Ahmad, Ribarsky and Reinsdorf, 2017).
“Productivity isn’t everything, but in the long run it is almost everything” (Krugman, 1994). Productivity gains reflect the ability to produce more output by better combining inputs, owing to new ideas, technological breakthroughs and augmented business models. These transform the production of goods and services, fostering economic growth and rising living standards and well-being.
Key findings
The recovery in GDP growth after the 2007-2009 financial crisis remained modest in most advanced economies and was largely sustained by increased employment.
The slowdown in labour productivity growth is a common feature among advanced economies, weighing down on GDP growth rates.
While the financial crisis may have exacerbated the slowdown in GDP and labour productivity growth, this slowdown was underway prior to the crisis.
While GDP growth in the OECD area as a whole was higher between 2016 and 2019 than between 2010 and 2015, this mainly reflected an increase in the number of persons employed. The contribution of labour productivity growth remained broadly stable from 2010 to 2019, at a lower level than before the financial crisis. Looking ahead, a key question concerns the impact of the COVID-19 crisis on long-term (labour) productivity developments (see Chapter on Productivity and the COVID-19 pandemic).
How to read the indicators
In the above charts, national accounts figures on hours worked for Austria, Estonia, Finland, Greece, Latvia, Lithuania, Poland, Portugal, Sweden and the United Kingdom have been replaced with estimates obtained with the OECD simplified component method described in the Section on Employment and hours worked. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018).
For further methodological information, consult the OECD Productivity Statistics – Methodological notes at https://www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf.
Economic growth can be fostered either by raising the labour and capital inputs used in production, or by improving the overall efficiency with which these inputs are combined, meaning higher multifactor productivity (MFP) growth. Growth accounts decompose total output growth, measured here as GDP growth, into these three components. As such, they provide an essential tool for policy makers to identify the underlying drivers of economic growth.
The contribution of labour (capital) to GDP growth is measured as the growth in labour (capital) input, multiplied by the share of labour (capital) in total costs of production. In the figures below, the contribution of capital to GDP growth is further broken down to highlight the contribution made by changes in the volume of the productive capital stock used in production and gains from changes in the composition of capital (i.e. capital quality). The sum of the contributions from productive capital stock and capital quality results in the overall contribution of capital services to GDP growth.
Key findings
When contributions to GDP growth are analysed before and after the 2007-2009 financial crisis, important differences arise. In the last decade, most countries have seen an increase in the contribution of labour input (total hours worked) to GDP growth as compared with the pre-financial crisis period. However, the contributions of productive capital stock and capital quality have declined almost across the board. The largest drops in the contribution of productive capital stock have been observed in Greece, Italy, Portugal and Spain, and declines in the contribution of capital quality have been more pronounced in Australia, Canada and Greece.
In most countries, the contribution of MFP to GDP growth in the last decade has been lower than before the 2007-2009 financial crisis. Finland, Greece, Korea, Sweden, the United Kingdom and the United States experienced the sharpest declines, which in turn contributed to drive the slowdown observed in GDP growth during that period.
How to read the indicators
For productivity analysis, the appropriate measure of capital input is the flow of capital services, i.e. the flow of productive services that can be drawn from the productive capital stock. This productive capital stock is the cumulative stock of past investments in capital assets adjusted for the losses in their productive capacity (or efficiency) and retirement (Schreyer, Bignon, and Dupont, 2003). Conceptually, capital services reflect a quantity, not to be confused with the value of capital, as measured by the net wealth capital stock. To illustrate this, take the example of a taxi. The capital services provided by this taxi relate to the number of trips, distance driven, and comfort of the taxi, rather than the market value of the vehicle, which would instead relate to the net wealth capital stock concept. These services are estimated using the rate of change of the productive capital stock of different capital goods and aggregated using rental prices or user costs shares as weights (as opposed to market price shares used to aggregate net wealth capital stocks).
Countries use different approaches to deflate investment in information and communication technologies (ICT) assets (i.e. computer hardware, telecommunications equipment, and computer software and databases), where constant-quality price changes are particularly important but difficult to measure, and they also assume different depreciation rates and retirement profiles. To adjust for these differences, the OECD estimates productive capital stocks and computes aggregate measures of capital services using a set of harmonised ICT investment deflators as well as common depreciation rates and retirement profiles for the different assets across countries (Schreyer, 2002).
MFP growth is measured as a residual, i.e. that part of GDP growth that cannot be explained by the contributions of labour and capital inputs to GDP growth. Traditionally, MFP growth is seen as a measure of technical change but, in fact, technical change can also be embodied in factor inputs, e.g. improvements in design and quality between two vintages of the same capital asset. In practice, MFP only captures disembodied technical change, e.g. network effects or spillovers from production factors, the effects of better management practices, organisational change and improvements in the knowledge base. Moreover, MFP picks up other factors such as adjustment costs, economies of scale, effects from imperfect competition, variations in capacity utilisation (if not captured by the capital input measures) and errors in the measurement of output, inputs and input weights. For instance, increases in educational attainment or a shift towards a more skill-intensive production process, if not captured in the form of composition (or quality) adjusted labour input (i.e. labour services) will end up in measured MFP. Therefore, accurate estimates of output and input measures is key to get a reliable measure of MFP.
In the above charts, national accounts figures on hours worked for Austria, Estonia, Finland, Greece, Latvia, Lithuania, Poland, Portugal, Sweden and the United Kingdom have been replaced with estimates obtained with the OECD simplified component method described in the Section on Employment and hours worked. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018).
For further methodological information, consult the OECD Productivity Statistics – Methodological notes at https://www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf.
Labour productivity growth points to changes in the volume of output for a given volume of hours worked. Higher levels of labour productivity can be achieved if more capital is used in production, if capital quality increases, and if labour and capital are used together more efficiently, which means higher multifactor productivity growth (MFP).
By reformulating the growth accounting framework described in the previous section, labour productivity growth can be decomposed into the contribution of capital and multifactor productivity (MFP). In the figures below, the contribution of capital to labour productivity growth is further broken down to highlight the contribution of changes in the productive capital stock to GDP ratio (i.e. capital-output ratio) and in the composition of capital (i.e. capital quality).
Key findings
In most countries, labour productivity growth has been lower in the last decade than before the 2007-2009 financial crisis. The slowdown has been particularly marked in Finland, Greece, Korea, Sweden, the United Kingdom, and the United States. Some caution is needed when analysing the increase in labour productivity growth in certain countries, for example in Spain and Italy, as it can mask significant declines in employment, and hence, total hours worked during and after the financial crisis.
With some exceptions, the contribution of the capital-output ratio to labour productivity growth is generally limited. However, over the last decade, the capital-output ratio was the largest contributor to labour productivity growth in a few countries, e.g. Belgium, Ireland, Portugal and the United Kingdom.
In most countries, capital quality and MFP contributed less to labour productivity growth in the last decade than in the period before the 2007-2009 financial crisis. Indeed, the contribution of MFP fell sharply in some countries, e.g. Finland, Greece, Korea, Sweden, the United Kingdom and the United States.
How to read the indicators
As explained in the previous section, the OECD estimates productive capital stocks and computes aggregate measures of capital services using a set of harmonised ICT investment deflators as well as common depreciation rates and retirement profiles for the different assets across countries (Schreyer, 2002; Schreyer, Bignon and Dupont, 2003). It also applies a consistent methodology to estimate MFP growth.
While MFP growth contributes to both GDP and labour productivity growth, the corresponding contributions are different. MFP growth contributes to GDP growth with a weight equal to one, but it is weighted by the inverse of the labour cost share to calculate its contribution to labour productivity growth.
In the above charts, and as before, national accounts figures on hours worked for Austria, Estonia, Finland, Greece, Latvia, Lithuania, Poland, Portugal, Sweden and the United Kingdom have been replaced with estimates obtained with the OECD simplified component method described in the Section on Employment and hours worked. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018).
For further methodological information, consult the OECD Productivity Statistics – Methodological notes at https://www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf.
OECD Economic Outlook: Statistics and Projections (database), https://doi.org/10.1787/eo-data-en.
OECD Employment and Labour Market Statistics (database), https://doi.org/10.1787/lfs-data-en.
OECD National Accounts Statistics (database), https://doi.org/10.1787/na-data-en.
OECD Productivity Statistics (database), https://doi.org/10.1787/pdtvy-data-en.
References and further reading
Ahmad, N. and P. Schreyer (2016), “Measuring GDP in a Digitalised Economy”, OECD Statistics Working Papers, 2016/07, OECD Publishing, Paris. https://doi.org/10.1787/5jlwqd81d09r-en.
Ahmad, N., Reinsdorf M., and J. Ribarsky. (2017), “Can Potential Mismeasurement of the Digital Economy Explain the Post-Crisis Slowdown in GDP and Productivity growth?”, OECD Statistics Working Papers, OECD Publishing, Paris.
Andrews, D., C. Criscuolo and P. Gal (2016), "The Best versus the Rest: The Global Productivity Slowdown, Divergence across Firms and the Role of Public Policy", OECD Productivity Working Papers, No. 5, OECD Publishing, Paris, https://doi.org/10.1787/63629cc9-en.
Andrews, D., G. Nicoletti and C. Timiliotis (2018), "Digital Technology Diffusion: A Matter of Capabilities, Incentives or Both?", OECD Economics Department Working Papers, No. 1476, OECD Publishing, Paris, https://doi.org/10.1787/7c542c16-en.
Barnett, A., S. Batten, A. Chiu, J. Franklin and M. Sebastian-Barriel (2014b), "The UK Productivity Puzzle", Bank of England Quarterly Bulletin, 2014 Q2
Baily, M. N., J. Manyika and S. Gupta (2013), “U.S. Productivity Growth: An Optimistic Perspective”, International Productivity Monitor, No. 25, Spring.
Brynjolfsson, E. and A. McAfee (2011), Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy, Digital Frontier Press.
Byrne D., J. Fernald and M. Reinsdorf (2016), “Does the United States have a Productivity Slowdown or a Measurement Problem?”, Brookings Papers on Economic Activity, http://www.brookings.edu/~/media/projects/bpea/spring-2016/byrneetal_productivitymeasurement_conferencedraft.pdf.
Cowen, T. (2011), The Great Stagnation: How America Ate All The Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better, Dutton.
Gordon, R. (2012), “Is US Economic Growth Over? Faltering Innovation Confronts the Six Headwinds”, NBER Working Papers, No. 18315.
Kierzenkowski, R., G. Machlica and G. Fulop (2018), "The UK Productivity Puzzle through the Magnifying Glass: A Sectoral Perspective", OECD Economics Department Working Papers, No. 1496, OECD Publishing, Paris, https://doi.org/10.1787/e704ee28-en
Krugman, P. (1994), The Age of Diminished Expectations, Revised And Updated Edition, MIT Press, Cambridge, Massachusetts.
Mourougane A., E.J. Kim (2020), “Boosting Productivity in the United Kingdom’s Service Sector”, OECD Economic Department Working Paper, No. 1629, OECD Publishing, Paris, https://doi.org/10.1787/78f4022e-en
OECD (2015), The Future of Productivity, OECD Publishing, Paris, https://doi.org/10.1787/9789264248533-en.
OECD (2019), “The Human Side of Productivity: Setting the Scene”, Background paper, Preliminary version, OECD Global Productivity Forum, Fourth Annual Conference in Sydney, Australia, https://www.oecd.org/global-forum-productivity/events/Human-side-of-productivity-background-paper.pdf.
Riley, R., A. Rincon-Aznar and L. Samek (2018), “Below the Aggregate: A Sectoral Account of the UK Productivity Puzzle”, ESCoE Discussion Paper, 2018-06
Schreyer, P. (2002), “Computer Prices and International Growth and Productivity Comparisons”, Review of Income and Wealth, Series 48, Number 1.
Schreyer, P., P. Bignon and J. Dupont (2003), "OECD Capital Services Estimates: Methodology and a First Set of Results", OECD Statistics Working Papers, No. 2003/06, OECD Publishing, Paris, https://doi.org/10.1787/658687860232.
Sorbe, S., P. Gal and V. Millot (2018), "Can Productivity still Grow in Service-Based Economies?: Literature Overview and Preliminary Evidence from OECD countries", OECD Economics Department Working Papers, No. 1531, OECD Publishing, Paris, https://doi.org/10.1787/4458ec7b-en
System of National Accounts (SNA) 2008, New York, http://unstats.un.org/unsd/nationalaccount/sna2008.asp.
Syverson, C. (2017), "Challenges to Mismeasurement Explanations for the US Productivity Slowdown", Journal of Economic Perspectives, 31 (2): 165-86.
Ward, A., M. Zinni and P. Marianna (2018), “International Productivity Gaps: Are Labour Input Measures Comparable?”, OECD Statistics Working Papers, 2018/12, OECD Publishing, Paris, https://doi.org/10.1787/5b43c728-en.