This chapter focuses on labour productivity, which is often used to assess convergence across countries. Labour productivity, as used in this chapter, refers to GDP divided by hours worked, or by employment when data on hours are unavailable.
OECD Compendium of Productivity Indicators 2025
Gross Domestic Product (GDP) is a widely used measure of output in the compilation of productivity indicators. It measures the value added generated by an economy, i.e. the value of goods and services produced during a given period, minus the value of intermediate consumption used in the production process. Countries measure GDP in their own currencies. To compare these estimates across countries, they have to be converted into a common currency. The conversion is often made using nominal exchange rates, but these can provide a misleading comparison of the true volume of goods and services produced across countries. A better approach is to use Purchasing Power Parities (PPPs), which are currency converters that control for differences in price levels between countries and allow for international comparisons of the volume of GDP and of the size of economies (European Union/OECD, 2024[1]).
Key findings
Using Purchasing Power Parities (PPPs) instead of nominal exchange rates to convert GDP into US dollars (USD) narrows the gap between most OECD countries and the United States. In 2023, PPP conversion more than doubled the USD value of GDP in Colombia, Hungary, and Poland, and nearly tripled it in Türkiye. In contrast, Iceland and Switzerland saw slightly lower GDP values in USD when using PPPs, with differences of less than 5% compared to nominal exchange rates.
When using PPPs to convert countries’ GDP into a common currency, the United States represented the largest share of total OECD GDP in 2023, accounting for over one-third of all OECD countries’ combined output. It was followed by Japan, Germany, France, the United Kingdom, Korea, Mexico, and Canada. The remaining 19 OECD countries together contributed nearly one-quarter of the total OECD GDP.
When using nominal exchange rates, the ranking of countries and their shares in the total OECD GDP remain broadly consistent with those obtained using PPPs. The United States, Japan, and Germany consistently hold the top three positions regardless of the conversion method. However, there are some shifts in the rankings: Germany and Japan swap second and third places, while the United Kingdom and France switched between fourth and fifth, depending on whether PPPs or nominal exchange rates are used.
Indicators
% of OECD total, 2023
Source: OECD Productivity Database (2025).
% of OECD total, 2023
Source: OECD Productivity Database (2025).
How to read the indicators
The compilation of GDP is based on harmonised accounting concepts and definitions that ensure its comparability across countries. In practice, however, the measurement of GDP can be affected by three main issues:
The measurement of the non-observed economy. An exhaustive coverage of production activities can be difficult to achieve in some countries and national estimates may differ in their coverage of non-observed activities. The size of the non-observed economy is generally larger in emerging-market and developing economies reflecting, in part, the higher degree of informal activities and employment.
International production arrangements. In recent decades, globalisation has led to a fragmentation of production processes across countries. In some cases, national accounts record output in the country where intellectual property products (IPP) are located rather than in the country where output is physically produced (e.g. in the case of contract manufacturing). This can create a disconnection between GDP and production factors, as well as to changes in GDP due to the relocation of IPP from one country to another. Moreover, some of the income generated by IPP may be ultimately transferred abroad - for instance, when affiliates of multinational enterprises hold these assets on their balance sheets and transfer the related earnings to their parent companies (UNECE, 2015[2]). These arrangements can contribute to a growing divergence between GDP and Gross National Income (GNI), as GNI accounts for cross-border property income flows. The 2025 System of National Accounts explicitly highlights how these IPP relocations can change macroeconomic indicators such as GDP and GNI by disconnecting economic ownership from physical production (SNA, 2025[3]).
The measurement of the digital economy. The digital transformation also poses many challenges to the measurement of the production of goods and services and hence GDP. The emergence of new digital services, the increasing scale of peer-to-peer interactions through digital intermediary platforms, the development of “free” services blurring the distinction between consumers and producers, are only a few examples of the challenges currently faced by national accountants (Ahmad and Schreyer, 2016[4]; Ahmad, Ribarsky and Reinsdorf, 2017[5]; UNECE, 2023[6]). Moreover, shorter life cycles of ICT products exacerbate long standing challenges regarding the distinction between price movements and quality increases (Aeberhardt et al., 2020[7]).
Looking forward, harmonisation in these three areas is expected to be enhanced through the implementation of the 2025 System of National Accounts.
With respect to GDP measurement in volume or real terms (i.e. excluding the impact of inflation), the System of National Accounts recommends the production of estimates based on annually chain-linked volume indices. Most countries covered in the report derive annual estimates of real GDP using annually chain-linked volume indices (i.e. updating every year the prices used to measure volume indices). The United States and Canada use chain-linked Fisher indices while other OECD countries use the chain-linked Laspeyres ones. However, Mexico and South Africa currently produce fixed-base volume indices (i.e. measuring volume indices at the prices of a fixed given period) with the base year updated less frequently.
For further methodological information, consult the OECD Productivity Statistics – Methodological notes at www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf and the update of the sources and methodologies used to construct the new OECD Productivity Database (OECD, 2025[8]).
In productivity analysis, the volume of labour input is most appropriately measured by the total number of hours actually worked, i.e. hours effectively used in production, whether paid or not.
In theory, it is preferable to use total hours worked as a measure of labour input in productivity analysis, as variations in working time patterns (e.g. part-time or full-time employment) and employment legislation (e.g. statutory working hours) across countries and over time affect the comparability of total employment figures. However, total employment (i.e. the number of persons employed) is often used as a proxy for labour input, particularly when data on total hours worked are not available.
The relevant concept for measuring labour input is hours actually worked, as opposed to hours paid, contractual hours, or usual hours worked. Hours actually worked reflect regular hours worked by full-time and part-time workers, paid and unpaid overtime, hours worked in additional jobs, excluding time not worked for reasons such as public holidays, annual paid leave, sick leave, maternity leave, strikes, bad weather, and economic conditions.
Key findings
The United States accounted for one quarter of total hours worked and total employment in 2023, both the largest shares in the OECD area. However, the ranking of countries in terms of their share in total labour input varies depending on the measure of labour input used, i.e. hours worked or employment.
Estimates of average hours worked per worker differ substantially across countries. While some countries recorded more than 2 000 hours worked per worker in 2023 (such as Colombia, Costa Rica and Mexico), others recorded less than 1 500 hours (Germany, Denmark, Norway, Austria, Sweden, Netherlands, Iceland, Luxembourg, France and Finland). In Germany and Denmark, this figure is below 1 400 hours.
Differences in average hours worked per worker across countries partly reflect structural differences in the organisation of labour markets and differences in the method used to measure hours (Ward, Zinni and Marianna, 2018[9]) (see How to read the indicators for further details).
Indicators
Source: OECD Productivity Database (2025).
Source: OECD Productivity Database (2025).
Average annual hours worked per worker
Source: OECD Productivity Database (2025).
How to read the indicators
In most countries, the main source to construct measures of hours actually worked is the labour force survey. However, many countries rely on establishment surveys and administrative sources as either primary or auxiliary sources. While the use of different sources may affect the comparability of labour productivity levels, comparisons of labour productivity growth are less likely to be affected (Ward, Zinni and Marianna, 2018[9]).
Computing estimates of hours worked also implies adjusting the activities covered by the labour input measures (employment and hours worked) to those covered by the output measure. This requires adapting the geographical and economic boundaries of employment and hours worked to the national accounts production boundary, in order to exclude resident persons working in non-resident production units and include non-resident persons working in resident production units.
In practice, countries adopt one of two methods to estimate actual hours worked for productivity analysis:
The direct method, which takes actual hours worked self-reported by respondents in surveys, generally labour force surveys (LFS).
The component method, which starts from contractual, paid or usual hours per week from establishment surveys, administrative sources or the LFS, with subsequent adjustments for absences and overtime, and other adjustments to align hours worked with the concepts of hours actually worked and the concept of domestic output.
While the direct approach appeals due to its simplicity, it depends heavily on respondent recall, cannot account for response bias, and assumes a perfect alignment of measures of workers and output. The component approach is more complex, but it systematically attempts to address these issues. There is evidence that response bias and insufficient adjustments to align with the concept of domestic output lead to systematic upward biases in estimates of average hours worked per worker based on the direct approach, as compared to the component approach (Ward, Zinni and Marianna, 2018[9]).
Admittedly, the OECD simplified component method necessarily relies on available data sources. In particular, it assumes that workers in all countries take, on average, all the leave to which they are entitled. However, actual take-up leave rates are likely to reflect differences in working cultures across countries. For this and other reasons, like the access of national statistics offices to a variety of national data sources, the OECD simplified component method estimates can be considered only as a stopgap for those countries currently using a direct approach with minimal or no adjustments, while these countries work towards improving their methodologies (Ward, Zinni and Marianna, 2018[9]).
Finally, the effective quantity of labour input depends not only on the total number of hours actually worked but also on the education, working experience, business functions and other workers’ characteristics. The measure of labour input used in this publication, i.e. total hours worked, does not account for the composition or “quality” of the workforce and likely underestimates the effective contribution of labour to production.
For further methodological information, consult the OECD Productivity Statistics – Methodological notes at www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf and the update of the sources and methodologies used to construct the new OECD Productivity Database (OECD, 2025[8]).
Labour productivity is the most frequently computed productivity indicator. It represents the volume of output produced per unit of labour input. The ratio between output and labour input depends to a large extent on the presence of other inputs, such as physical capital (e.g. buildings, machinery and transport vehicles) and intangible assets used in production (e.g. intellectual property products), technical efficiency and organisational change.
Intangible assets play an important role in economic growth and productivity. This raises measurement challenges related to the potential recording of output where intellectual property products are located, rather than where output is physically produced. In addition, IPP may give rise to large income transfers between the countries where they are registered, often for fiscal reasons, such as low-tax jurisdictions, and those where the ultimate owners reside. This can result in a significant gap between GDP and GNI (Gross National Income). In such cases, measures of labour productivity based on GNI are more meaningful than those based on GDP.
The 2025 System of National Accounts (SNA, 2025[3]) gives more prominence to intangible assets through dedicated chapters on digitalisation and globalisation. The former enhances the visibility of digital activities in macroeconomic accounts and expands the scope of intangible assets by classifying data as productive assets within a newly established category named “data and databases”, and providing statistical definitions of cloud services, Artificial Intelligence, and digital platforms. The latter clarifies the classification, valuation, and ownership of intellectual property products within multinational enterprises, including cross-border flows of output and income.
Key findings
There are large disparities in labour productivity levels across OECD countries. Measured as GDP per hour worked in PPP terms, average labour productivity across OECD countries was around USD 70 per hour in 2023. The highest-performing economies recorded labour productivity levels nearly twice the OECD average, while those at the lower end were around one third of the average (Figure 4.6). While patterns are broadly similar, the difference is less marked when using GNI per hour worked, which better reflects the role of multinational enterprises (MNEs) (Figure 4.8). In terms of disparities, the maximum-to-minimum labour productivity ratio reveals that the most productive countries in the OECD required seven times less labour to produce the same output as the least productive one on average.
Labour productivity levels across OECD countries have continued to converge since 2000, especially due to gains in countries that initially had the lowest productivity levels. Since 2000, most countries with labour productivity levels below the OECD average have narrowed the gap (Figure 4.7). However, by 2023, Israel, Japan, and Greece experienced further declines in relative productivity, moving even further below the OECD average - showing a diverging pattern. Among countries that initially had above-average productivity, most have moved closer to the OECD average, with the exception of Ireland and Iceland, where productivity rose even further above the average.
In most countries, labour productivity measures based on GDP and GNI are similar, as cross-border income flows are relatively small or tend to offset each other. However, in countries such as Ireland and Luxembourg, significant differences emerge due to income generated abroad, particularly by multinational enterprises. In these cases, GNI-based measures provide a more accurate reflection of national productivity. Finally, labour productivity measures based on Gross Value Added (GVA) yield lower values than those based on GDP, as they exclude product taxes, and subsidies. Overall, GVA-based measures generally follow a distribution that mirrors GDP-based metrics (Figure 4.8).
Indicators
GDP per hour worked in current prices and current PPPs
Source: OECD Productivity Database (2025).
GDP per hour worked in current prices and current PPPs
Source: OECD Productivity Database (2025).
GDP, GNI and GVA per hour worked in current prices and current PPPs
Note: GNI per hour worked data for Norway refer to 2022.
Source: OECD Productivity Database (2025).
How to read the indicators
Following national accounts standards, and consistently with the measure of output, the measure of labour input in an economy includes the contribution of cross-border workers working in resident production units. Conversely, it excludes all persons working in non-resident production units. Depending on the original data sources used to estimate employment (e.g. labour force survey, administrative data, business statistics), various adjustments are needed to ensure consistency between labour and output measures.
In Figures 4.6 to 4.8, 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 on Hours worked. However, the impact of this correction on labour productivity growth rates is marginal (Ward, Zinni and Marianna, 2018[9]).
The difference between GDP and GNI depends on where headquarters are located. If an affiliate is established in an investment hub but headquarters remain abroad, the profits shifted to that affiliate are imputed back to the country of the parent company, and GNI remains unaffected. Conversely, if headquarters are set up in the investment hub where profits are artificially inflated, no such imputation takes place. In that case, GNI remains close to GDP unless the profits are actually transferred abroad through dividend payments, which would then reduce GNI (Deaton and Schreyer, 2021[10]).
For further methodological information, consult the OECD Productivity Statistics – Methodological notes at www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf and the update of the sources and methodologies used to construct the new OECD Productivity Database (OECD, 2025[8]).
OECD Economic Outlook: Statistics and Projections (database), http://data-explorer.oecd.org/s/1v5.
OECD Employment and Labour Market Statistics (database), http://data-explorer.oecd.org/s/1v6.
OECD National Accounts Statistics (database), http://data-explorer.oecd.org/s/1v7.
OECD Productivity Statistics (database), http://data-explorer.oecd.org/s/1v8.
OECD Productivity Dashboard, www.oecd.org/en/data/dashboards/oecd-dashboard-of-productivity-indicators.html.
References
[7] Aeberhardt, L. et al. (2020), “Does the digital economy distort the volume-price split of GDP? The French experience”, Economie et Statistique / Economics and Statistics 517-518-519, pp. 139-156, https://doi.org/10.24187/ecostat.2020.517t.2027.
[5] 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.
[4] 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.
[10] Deaton, A. and P. Schreyer (2021), “GDP, Wellbeing, and Health: Thoughts on the 2017 Round of the International Comparison Program”, Review of Income and Wealth, Vol. 68/1, pp. 1-15, https://doi.org/10.1111/roiw.12520.
[1] European Union/OECD (2024), Eurostat-OECD Methodological Manual on Purchasing Power Parities (2023 edition), OECD Publishing, Paris, https://doi.org/10.1787/c9829192-en.
[8] OECD (2025), The revamp of the OECD Productivity Database, OECD Publishing, Paris, https://doi.org/10.1787/f07c55d4-en.
[3] SNA (2025), System of National Accounts 2025, United Nations, https://doi.org/10.18356/9789211073225.
[6] UNECE (2023), BPM7 Chapter 16/2025 SNA Chapter 22. Digitalization: Annotated Outline, https://unstats.un.org/unsd/nationalaccount/aeg/2022/M21/SNA_AO_Ch22_BPM_Ch16.pdf.
[2] UNECE (2015), Guide to Measuring Global Production, https://unece.org/fileadmin/DAM/stats/publications/2015/Guide_to_Measuring_Global_Production__2015_.pdf.
[9] 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.