4. Productivity and economic growth
A number of shocks have hit the global economy in the past few years. They have led to a business environment of heightened inflation, tightened financial conditions, weakened trade linkages and increased uncertainties, threatening economic and productivity growth.
This is happening in a context of sluggish productivity growth over the last two decades in many OECD economies. The slowdown in productivity preceded the global financial crisis in some countries and occurred at a time of significant technological change, with increasing diffusion of digital technologies in the 2000s. This has been referred to as the productivity paradox and several views have been put forward to explain it:
Limited transformative nature and scale of today’s technological breakthroughs 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[1]) (Gordon, 2012[2]).
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[3]) (Andrews, Criscuolo and Gal, 2016[4]). In addition, low managerial quality and the lack of ICT skills can curb the adoption of digital technologies and the rate of diffusion (Andrews, Nicoletti and Timiliotis, 2018[5]), and 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[6]) (Criscuolo et al., 2021[7]). Financing constraints specific to intangible assets, that help to enable the adoption and diffusion of technologies, may also play a role (Demmou and Franco, 2021[8]).
Sectoral changes. The long-term shift from manufacturing to services, in particular the shift to lower-productivity personal services, may help explain the longer-term decline in productivity growth across (developed) economies. Demographic changes and more service-oriented consumption patterns, notably from ageing populations, may exacerbate this effect. Nevertheless, several studies conclude that the impact of this phenomenon is limited so far (Barnett et al., 2014[9]) (Kierzenkowski, Machlica and Fulop, 2018[10]) (Riley, Rincon-Aznar and Samek, 2018[11]) (Sorbe, Gal and Millot, 2018[12]) (Mourougane and Kim, 2020[13]). See Chapter 5 on Industry contributions to aggregate 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 them relate 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, the sharing economy, and 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[14]) (Byrne, Fernald and Reinsdorf, 2016[15]) (Ahmad and Schreyer, 2016[16]) (Ahmad, Ribarsky and Reinsdorf, 2017[17]), though there are also studies suggesting some form of mismeasurement related in particular to intangible assets may indeed exist (Brynjolfsson, Rock and Syverson, 2021[18]).
Looking ahead, several megatrends such as ageing, and the green and digital transitions may impact productivity in the medium term. Their effects on economic performance remain to be seen. For instance, the green transition and policies underpinning it may impede economic performance over the medium term, but they could also boost it by inducing innovation in clean technologies (Dechezleprêtre and Kruse, 2018[19]).
The surge in generative Artificial Intelligence has also opened up new prospects for the future of productivity, but its economic impact and how it will affect different groups of workers and sectors, are uncertain (Autor, 2022[20]) (OECD, 2023[21]).
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
While 2021 saw a rapid recovery in GDP, the majority of countries experienced a growth slowdown in 2022, with an OECD average of 2.9% compared to 5.7% in the previous year. Chile, Estonia and Luxembourg had the sharpest downswing relative to 2021, and in Estonia GDP growth even turned negative. Only Austria, Iceland and Portugal experienced faster GDP growth in 2022 than in 2021.
Growth in the number of persons employed was the main positive contributor to GDP growth in most OECD countries in 2022 (Figure 4.1).
The contribution to GDP growth from labour productivity fell in 20 OECD economies in 2022. This suggests that some proportion of the new jobs created in 2022 were in lower-productivity jobs. As described in more detail in Chapter 5, this is to a certain extent due to reversal of reallocation effects that took place during the pandemic. Contact-intensive and typically less productive sectors, such as hospitality services, have recovered, while some other low-productivity activities, such as mining and utilities, also expanded, especially in Europe.
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 Hours worked and employment of Chapter 3. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018[22]).
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 either stem from raising the labour and capital inputs used in production, or from 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 and provides a useful tool 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 main driving force of GDP growth in almost all OECD countries in 2022 (Figure 4.2). However, the contribution of hours worked was in many countries lower than in 2021.This was particularly the case in Belgium, Canada, Greece, Italy and the United Kingdom.
Productive capital stock and capital quality contributed relatively little to GDP growth in 2022, with a few exceptions, such as Israel, Korea, Norway, New Zealand, Sweden and the United States.
The post-COVID recovery in multifactor productivity observed in 2021 was not sustained, as it contributed significantly less to GDP growth in 2022 in most OECD countries. In some countries, the contribution of multifactor productivity to growth was even negative, as in Australia, Belgium, Canada, Denmark, France, Korea, Luxembourg, Norway, New Zealand, Sweden and the United States.
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 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[23]). 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 (Pionnier, Zinni and Baret, 2023[24]). 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[25]) (Schreyer, Bignon and Dupont, 2003[23]).
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 technological change but, in fact, technological 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 technological 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 Hours worked and employment of Chapter 3. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018[22]).
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 measures 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 contributions 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 capital stock-to-GDP ratio and in the composition of capital, often referred to as 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. Changes in capital quality (i.e. change in the composition of capital) tend 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 Greece, Ireland, Italy, Spain, the United Kingdom and the United States.
During and after the COVID pandemic, over 2020-2022, many countries recorded a hump-shaped evolution of MFP contributions to labour productivity growth: a negative contribution in 2020, followed by an increase in 2021, and a decrease (or even a negative contribution again) in 2022. However, several countries, including Australia or the United States, recorded positive MFP contributions during 2020 or even 2021, before diminishing or turning negative in 2022.
Tighter monetary policy and higher real interest rates, higher energy prices, weak household income growth and declining confidence have discouraged companies from making longer-term investments in 2022 (OECD, 2022[26]), which is reflected in a negative contribution of capital stock-to-output ratio in OECD countries for the second consecutive year.
Capital quality made a relatively small but positive contribution to labour productivity growth in most countries in 2022 (Figure 4.3). Sweden, the United States, Switzerland, Denmark, New Zealand and France were the OECD countries with the highest capital quality contributions in 2022. Finland and Ireland recorded a small negative contribution from capital quality in 2022.
Ireland is an exception among OECD countries, with a positive contribution from multifactor productivity to labour productivity growth each year between 2020 and 2022. During the same period, labour productivity growth slowed down in Ireland, reaching 0.8% in 2022, with large negative capital stock-to-output contributions over 2021-22. This evolution seems to be driven by multinational enterprises, as the MFP of the domestic sector in Ireland was relatively flat during 2020-22, while the foreign one kept rising (Central Statistics Office, 2023[27]). This is in line with documented labour productivity gaps between the domestic and foreign sectors in Ireland (Papa, Rehill and O’Connor, 2018[28]), with so far limited productivity spillovers from the latter to the former (Di Ubaldo, Lawless and Siedschlag, 2018[29]).
How to read the indicators
As explained in the previous section, the OECD estimates capital stocks and computes 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[25]) (Schreyer, Bignon and Dupont, 2003[23]). OECD also applies a consistent methodology to estimate MFP growth.
In the above charts, national accounts data 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 Hours worked and employment in Chapter 3. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018[22]).
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
[17] 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. 2017/9, OECD Publishing, Paris, https://doi.org/10.1787/a8e751b7-en.
[16] Ahmad, N. and P. Schreyer (2016), “Measuring GDP in a Digitalised Economy”, OECD Statistics Working Papers, No. 2016/7, OECD Publishing, Paris, https://doi.org/10.1787/5jlwqd81d09r-en.
[4] 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.
[5] 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.
[20] Autor, D. (2022), “The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm, To Qualified Optimism, to Vast Uncertainty”, NBER Working Paper No. 30074, https://www.nber.org/system/files/working_papers/w30074/w30074.pdf.
[9] Barnett, A. et al. (2014), “The UK Productivity Puzzle”, Bank of England Quarterly Bulletin, 2014 Q2.
[18] Brynjolfsson, E., D. Rock and C. Syverson (2021), “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies”, American Economic Journal: Macroeconomics, Vol. 13/1, pp. 333-372, https://doi.org/10.1257/mac.20180386.
[15] 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.
[27] Central Statistics Office (2023), “Productivity in Ireland 2021”, CSO Statistical Publication, https://www.cso.ie/en/releasesandpublications/ep/p-pii/productivityinireland2021/ and https://www.nerinstitute.net/sites/default/files/2023-06/Yvonne%20Hayden%20CSO%20presentation%20May%2023.pdf.
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[29] Di Ubaldo, M., M. Lawless and I. Siedschlag (2018), “Productivity spillovers from multinational activity to local firms in Ireland”, OECD Productivity Working Papers, No. 16, OECD Publishing, Paris, https://doi.org/10.1787/58619717-en.
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[13] Mourougane, A. and E. Kim (2020), “Boosting productivity in the United Kingdom’s service sectors”, OECD Economics Department Working Papers, No. 1629, OECD Publishing, Paris, https://doi.org/10.1787/78f4022e-en.
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[3] OECD (2015), The Future of Productivity, OECD Publishing, Paris, https://doi.org/10.1787/9789264248533-en.
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[24] 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.
[11] 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.
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[12] 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.
[14] Syverson, C. (2017), “Challenges to Mismeasurement Explanations for the US Productivity Slowdown”, Journal of Economic Perspectives, Vol. 31/2, pp. 165-186, https://doi.org/10.1257/jep.31.2.165.
[22] Ward, A., M. Zinni and P. Marianna (2018), “International productivity gaps: Are labour input measures comparable?”, OECD Statistics Working Papers, No. 2018/12, OECD Publishing, Paris, https://doi.org/10.1787/5b43c728-en.