OECD Compendium of Productivity Indicators 2023

3. Productivity and economic growth

Context

Productivity growth over the last two decades has been sluggish in many OECD economies, with the slowdown in productivity preceding the global financial crisis in some countries. The COVID-19 pandemic and the war in Ukraine that started in February 2022 have triggered an energy crisis and increased uncertainties around economic developments, threatening the economic recovery. Concerns have risen that these developments may lead to a pronounced and long-standing fall in productivity growth.

Prior to the COVID-19 pandemic, 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 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 open questions, ranging from the potential lagged effects of these new technologies to structural factors and statistical measurement issues.

Indeed, several 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, particularly focusing 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 more recent OECD work on The Human Side of Productivity shows that more productive firms tend to employ a larger share of skilled employees and operate with a larger share of managerial roles (OECD, 2019; Criscuolo et al., 2021).

• Sectoral 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, several 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). See Chapter 4 on Industry contributions to labour productivity growth in this publication for a more detailed discussion on the impact of reallocations across industries on aggregate labour productivity developments.

• 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 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 is still out on the underlying causes, a growing body of evidence has suggested that measurement, or rather “mismeasurement”, is not the cause of the observed productivity slowdown (Syverson, 2017; Byrne, Fernald and Reinsdorf, 2016; Ahmad and Schreyer, 2016; Ahmad, Ribarsky and Reinsdorf, 2017).

GDP growth: contributions from employment, hours worked per worker, and labour productivity

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 COVID-19 pandemic has triggered a global recession. GDP shrank in all OECD countries and G20 economies during the first wave of the COVID-19 outbreak (second quarter of 2020), with the downturn in 2020 being even more severe than the 2007-2009 recession in most countries. Nevertheless, not all countries were affected equally. In 2020, the largest contraction in GDP growth was recorded in Spain, at around minus 12%, followed by those observed in the United Kingdom, Italy and Greece. By contrast, output losses have been rather small in a few countries including Estonia, Lithuania, and New Zealand.

• GDP rebounded swiftly in 2021 in all countries despite the second wave of COVID-19 infections. Chile, Colombia, Ireland and Türkiye are those that experienced the highest growth in 2021, while the recovery was subdued in Germany and Japan.

• The decomposition of GDP growth into growth in labour productivity per hour worked, growth in the number of persons employed, and growth in hours worked per worker, shows a large positive contribution of growth in labour productivity per hour worked in 2020, followed by a negative contribution in 2021 in most countries. As explained in more detail in Chapter 4 on Industry contributions to labour productivity growth, the 2020 upsurge in aggregate labour productivity was largely driven by the fact that hours worked declined more in contact-intensive sectors with lower productivity levels. These movements were themselves related to temporary mobility restrictions and lockdowns of economic activities to contain the pandemic, thus explaining opposite aggregate labour productivity developments in 2020 and 2021.

• Depending on countries, the number of persons employed or hours worked per worker were the main drivers of the GDP contraction in 2020. This contrasted picture is largely related to differences in the statistical treatment of workers in temporary lay-off in official labour statistics. These workers were recorded as employed in European countries, while they were registered as unemployed in Canada and the United States (see the section Measuring labour input in Chapter 1). This largely explains why, in Canada and the United States, the main contributor to the 2020 decline in GDP was the number of persons employed, while it was hours worked per worker in European countries.

Indicators

 

Compare

How to read the indicators

In the charts above, 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 of Chapter 2. 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.

GDP growth: contributions from labour, capital and multifactor productivity

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 accounting decomposes total output growth, measured here as GDP growth, into these three components. As such, they provide a useful 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 the productive capital stock and capital quality is the overall contribution of capital services to GDP growth.

Key findings

• When contributions to GDP growth are analysed in the growth accounting framework, changes in labour input, measured as total hours worked, stand out as the driving force of the GDP contraction in almost all OECD countries in 2020. Total hours worked are also the key driver of the recovery in 2021.

• The deterioration of multifactor productivity (MFP) performance was a striking feature of both the Global Financial Crisis and the first year of the COVID-19 pandemic. Indeed, all countries recorded negligible or negative MFP growth in 2007-2010 and 2020. However, MFP contributed positively to the 2021 recovery of GDP growth in most countries for which 2021 data are available.

Indicators

 

Compare

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 should not be confused with the value of capital that is measured by the net wealth capital stock. For example, the capital services provided by a 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. Moreover, they tend to use different depreciation and retirement profiles for all assets. Unexplained differences in depreciation and retirement profiles across countries may harm the cross-country comparability of capital stocks and macroeconomic indicators and recent analyses highlighted the need for national statistical agencies to regularly review and update these profiles (Bennet et al., 2020; Giandrea et al., 2021; Kornfeld and Fraumeni, 2022; Pionnier et al., 2023). To adjust for potential measurement 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 all assets across countries (Schreyer, 2002; Schreyer, Bignon and Dupont, 2003).

MFP growth is measured as a residual, i.e. by the 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 by labour input measures (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 of Chapter 2. 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: contributions from capital and multifactor productivity

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 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 stock-output ratio) and in the composition of capital (i.e. capital quality).

Key findings

• The evolution of the capital-stock-to-output ratio contributes to aggregate labour productivity growth in a countercyclical way. It increases during economic downturns and declines during economic rebounds, as capital stock moves more slowly than GDP. During periods of stable economic growth, its contribution is typically small. Capital quality tends to have a small and stable contribution to aggregate labour productivity growth.

• MFP growth is usually the main driver of labour productivity growth, but, over the course of the last two decades prior to the COVID-19 crisis, its contribution has been declining in most countries, particularly in Finland, Greece, Korea, Sweden, the United Kingdom and the United States.

• Most countries recorded a decline in MFP growth in 2020, followed by an increase in 2021. This reflected the large fluctuations in GDP growth and the overall stability of the measured capital input. Accounting for fluctuations in capital utilisation during the business cycle may result in smoother MFP developments in 2020 and 2021 (see the section Measuring investment and capital utilisation in Chapter 1).

• The United States currently stands out as an exception, with an increase in MFP growth in 2020, and an even more significant increase in 2021. While national accounts data for these two years are still subject to revisions, these developments should be put in relation with positive and increasing contributions of within-industry contributions to aggregate labour productivity growth in the United States in both years (see Chapter 4).

Indicators

 

Compare

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 the same depreciation rates and retirement profiles for the different assets across countries (Schreyer, 2002; Schreyer, Bignon and Dupont, 2003). OECD 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, 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 in Chapter 2. 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.

Data sources

OECD Economic Outlook: Statistics and Projections (database), http://dx.doi.org/10.1787/eo-data-en.

OECD Employment and Labour Market Statistics (database), http://dx.doi.org/10.1787/lfs-data-en.

OECD National Accounts Statistics (database), http://dx.doi.org/10.1787/na-data-en.

OECD Productivity Statistics (database), http://dx.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. http://dx.doi.org/10.1787/5jlwqd81d09r-en.

Ahmad, N., J. Ribarsky and M. Reinsdorf (2017), "Can potential mismeasurement of the digital economy explain the post-crisis slowdown in GDP and productivity growth?", OECD Statistics Working Papers, No. 2017/09, OECD Publishing, Paris, https://doi.org/10.1787/a8e751b7-en.

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.

Bennett, J. and Kornfeld, R. and Sichel, D. and Wasshausen, D. (2020), “Measuring infrastructure in the Burea of Economic Analysis National Economic Accounts”, BEA working paper series WP 2020-12, https://www.bea.gov/system/files/papers/BEA-WP2020-12.pdf.

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.

Criscuolo, C., et al. (2021), "The Role of Telework for Productivity during and post-COVID-19: Results from an OECD Survey among Managers and Workers", OECD Productivity Working Papers, No. 31, OECD Publishing, Paris, https://doi.org/10.1787/7fe47de2-en.

Giandrea, M., R. Kornfeld, P. Meyer and S. Powers (2021), “Alternative Capital Asset Depreciation Rates for U.S. Capital and Multifactor Productivity Measures”, BLS Working Papers, Working Paper 539.

Gordon, R. (2012), “Is US Economic Growth Over? Faltering Innovation Confronts the Six Headwinds”, NBER Working Papers, No. 18315.

Kornfeld, R. and B. Fraumeni (2022), “How Should We Measure Infrastructure? The Case of Highways and Streets”, NBER Working Paper no. 30045, https://www.nber.org/papers/w30045.

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

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, http://dx.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.

Pionnier, P., M. Zinni and K. Baret (2023), "Sensitivity of capital and MFP measurement to asset depreciation patterns and initial capital stock estimates", OECD Statistics Working Papers, No. 2023/01, OECD Publishing, Paris, https://doi.org/10.1787/92498395-en.

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, http://dx.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, http://dx.doi.org/10.1787/5b43c728-en.