OECD Compendium of Productivity Indicators 2023

2. Cross-country comparisons of labour productivity levels

Context

Productivity is commonly defined as a ratio between the volume of output and the volume of inputs. In other words, it measures how efficiently production inputs, such as labour and capital, are being used in an economy to produce a given level of output. Productivity is a key source of economic growth and living standards and, as such, internationally comparable indicators of productivity are central for assessing economic performance.

There are different productivity measures. The choice largely reflects the policy question to be addressed, with data availability often constituting an additional constraint.

Labour productivity, measured as Gross Domestic Product (GDP) or Gross Value Added (GVA) per hour worked or per worker, is one of the most widely available measures of productivity, which is therefore widely used for international comparisons. Labour productivity measures based on hours worked better capture the volume of labour input than measures based on the number of persons employed due to cross-country differences in working time (e.g. related to part-time employment) and employment legislations (e.g. statutory working time).

Size of output

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. In order 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 (OECD, 2017; World Bank, 2020).

Key findings

• The use of PPPs to convert countries’ GDP into a common currency, as opposed to the use of nominal exchange rates, allows analysts to account for cross-country differences in price levels. Indeed, when using PPPs rather than exchange rates as currency converters to US Dollars (USD), the gap between a country’s GDP and the US GDP shrinks for the large majority of OECD countries. For example, in 2021, the GDP expressed in USD more than doubles when using PPPs rather than nominal exchange rates for Hungary, Mexico and Poland, and it is multiplied by three for Colombia and Türkiye.

• When using PPPs to convert countries’ GDP into a common currency, the United States accounts for the largest share (over one third) of GDP in 2021 in the OECD area, followed by Japan, Germany, France, the United Kingdom, Italy, Türkiye, and Mexico. The ranking and the shares differ when using nominal exchange rates.

Indicators

 

 

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 the last 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 (IP) assets are located rather than in the country where output is physically produced (e.g. in the case of contract manufacturing). This can lead to a disconnection between GDP and production factors, as well as to changes in GDP due to the relocation of IP assets from one country to another. Moreover, some of the income generated by IP assets may be ultimately transferred abroad. This can happen, for example, when IP assets are located in the balance sheets of affiliates of multinational enterprises who ultimately transfer the related benefits to their parent company (UNECE, 2015). A large gap between GDP and Gross National Income (GNI), which accounts for property income flows across countries, is likely to signal transfers of income related to IP assets.

• 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 distinction between consumers and producers, are only a few examples of the challenges currently faced by national accountants (Ahmad and Schreyer, 2016; Ahmad et al., 2017). Moreover, shorter cycles of entering and exiting of ICT products exacerbate long standing challenges on the distinction between price movements and quality increases (Aeberhardt et al., 2020).

When it comes to the measurement of GDP in volume or real terms (i.e. excluding the impact of inflation), it is important to note that most countries covered in the report derive annual estimates of real GDP using annually chain-linked volume indices1 (i.e. updating every year the prices used to measure volume indices). 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. The 2008 System of National Accounts (2008 SNA) recommends the production of estimates based on annually chain-linked volume indices.

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.

Employment and hours worked

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. 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 because of public holidays, annual paid leave, sick leave, maternity leave, strikes, bad weather, and economic conditions, among other reasons. As such, the relevant concept for measuring labour input is hours actually worked, as opposed to hours paid, contractual hours, or usual hours worked. However, the number of persons employed (i.e. total employment) is often used as a proxy for labour input, in particular, when data on total hours worked (either actually worked or paid) cannot be estimated.

Key findings

• The United States accounts for the largest share (about one fourth) of total hours worked and total employment in the OECD area. However, the ranking of countries in terms of their share in total labour input varies with the measure of labour input used, i.e. hours worked or employment. Variations in working time patterns (e.g. part-time or full time employment) and employment legislations (e.g. statutory hours) across countries and over time affect the comparability of total employment figures, justifying, when possible, the use of total hours worked as a measure of labour input in productivity analysis.

• Estimates of average hours worked per worker differ substantially across countries. While some countries record close to 2000 hours worked per worker in a given year, other countries record less than 1500. While differences in average hours worked per worker across countries partly reflect structural differences in the organisation of labour markets, there is empirical evidence that measurement can also play a role in explaining these differences (Ward, Zinni and Marianna, 2018). A comparison of official national accounts estimates of hours worked per worker in countries using a direct approach with OECD estimates points to a reduction of average hours worked per worker of around 8% compared with official national accounts estimates. In addition, there are notable changes in the international ranking of average hours worked per worker when they are estimated using the simplified component method (see How to read the indicators for further details).

Indicators

 

 

 

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, only or in addition, on establishment surveys and administrative sources. The use of different sources may affect the comparability of labour productivity levels but comparisons of labour productivity growth are less likely to be affected (Ward, Zinni and Marianna, 2018).

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 led 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).

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 should 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).

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 https://www.oecd.org/sdd/productivity-stats/OECD-Productivity-Statistics-Methodological-note.pdf.

Labour productivity

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 degree 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 assets), technical efficiency and organisational change. Labour productivity is a key dimension of economic performance and an essential driver of changes in living standards.

Intangible assets play an increasingly important role in economic growth and productivity. This comes with measurement challenges related to the potential recording of output where intellectual property assets are located rather than where output is physically produced. In addition to challenges to the measurement of output and GDP, intellectual property assets may give rise to large transfers of income between the countries where they are registered, usually for fiscal reasons (low-tax jurisdictions), and those where the ultimate owners of these assets are resident, thus leading to a large gap between GDP and GNI (see the section on the Size of output). In such cases, measures of labour productivity based on GNI are more meaningful than measures based on GDP.

Key findings

• There are large disparities in labour productivity levels across countries, including within the OECD area. When measured as GDP per hour worked in PPP terms, average labour productivity in the OECD area was slightly above USD 60 per hour in 2021, with a standard deviation across countries of about USD 24. When measured as GDP per hour worked, labour productivity was about twice the OECD average in Ireland and Luxembourg and about one third of the OECD average in Mexico and Colombia.

• Most countries with a labour productivity level below the OECD average in 2000 have seen a substantial catch-up since then, pointing to a convergence of labour productivity levels across OECD countries. However, the gap with the OECD average deepened for Greece, Israel, Japan, Mexico and New Zealand in the last 20 years.

• In most countries labour productivity measures based on GDP and GNI are similar, as the underlying income flows are relatively small or offset each other. In Ireland and Luxembourg, however, significant differences arise between labour productivity measures based on GDP and GNI, reflecting the significant role played by multinationals in output and income transfers. In such cases, labour productivity measures based on GNI are more meaningful.

Indicators

 

 

 

How to read the indicators

Following national accounts conventions, 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 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.

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

Aeberhardt, L., F. Hatier, M. Leclair, B. Pentinat and J.‑D. Zafar (2020), “Does the Digital Economy Distort the Volume‑Price Split of GDP? The French Experience” INSEE, Economics and Statistics N° 517-518-519, 2020, https://www.insee.fr/en/statistiques/4770160?sommaire=4770271.

Ahmad, N., et al. (2003), "Comparing Labour Productivity Growth in the OECD Area: The Role of Measurement", OECD Science, Technology and Industry Working Papers, No. 2003/14, OECD Publishing, Paris, https://doi.org/10.1787/126534183836.

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., 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.

OECD (2001), Measuring Productivity - OECD Manual: Measurement of Aggregate and Industry-level Productivity Growth, OECD Publishing, Paris, https://doi.org/10.1787/9789264194519-en.

OECD (2017), “Purchasing Power Parities – Not only about Big Macs”, Statistical Insights, https://www.oecd.org/sdd/statistical-insights-purchasing-power-paritiesnot-only-about-big-macs.htm

System of National Accounts (SNA) 2008, New York, http://unstats.un.org/unsd/nationalaccount/sna2008.asp.

UNECE (2015), Guide to Measuring Global Production, https://unece.org/fileadmin/DAM/stats/publications/2015/Guide_to_Measuring_Global_Production__2015_.pdf

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.

World Bank (2020), Purchasing Power Parities and the Size of World Economies – Results from the 2017 International Comparison Program, https://openknowledge.worldbank.org/bitstream/handle/10986/33623/9781464815300.pdf

Note

← 1.  The United States and Canada use chain-linked Fisher indices while other OECD countries use the chain-linked Laspeyres ones.