Gross Domestic Product (GDP) - Outlook from the Organisation for Economic Co-operation and Development (OECD)

Indicator definition

GDP can be calculated in three ways, providing different perspectives on the balance of economic activity. Essentially these three approaches consist of adding up the total value of incomes, spending or production in a country or region during a period of time.

More formally they can be defined as follows (OECD, 2015b):

1. the ‘income approach’ calculates GDP based on the sum of primary incomes distributed by resident producer unit;
2. the ‘expenditure approach’ calculates GDP based on the sum of the final uses of goods and services (all uses except intermediate consumption) measured in purchasers' prices, less the value of imports of goods and services;
3. the ‘output approach’ calculates GDP as the sum of the gross values added of all resident institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs).

Applying these approaches using current price data will deliver an estimate of nominal GDP. Nominal GDP data are often adjusted to facilitate meaningful comparisons of economic output between different time periods and between different countries.

For example, nominal GDP is adjusted to remove the effects of price inflation in order to provide a more realistic measure of changes in the volume of economic production. Constant price estimates of GDP are obtained by calculating the value of production in different periods using the price levels from a single base period. Similarly, nominal GDP growth is converted into real GDP growth using the ‘GDP deflator’ (OECD, 2015a).

Price differences between countries can likewise make it hard to compare the volume of national production using nominal price data at market exchange rates. Purchasing power parity (PPP) estimates of GDP compensate for differences in prices between countries to provide a better comparison of the volume of goods and services produced in different countries or regions. They thereby support better comparisons of living standards (OECD, 2015c).

The data shown in this indicator are expressed in 2005 US dollars in PPP terms.

References

OECD, 2015a: 'OECD Glossary of Statistical Terms - Gross domestic product (GDP) – constant prices Definition' (http://stats.oecd.org/glossary/detail.asp?ID=1164) accessed 19 Jan 2015.

OECD, 2015b: 'OECD Glossary of Statistical Terms - Gross domestic product (GDP) Definition' (http://stats.oecd.org/glossary/detail.asp?ID=1163) accessed 19 Jan 2015.

OECD, 2015c: 'OECD Glossary of Statistical Terms - Purchasing power parities (PPPs) – OECD Definition' (http://stats.oecd.org/glossary/detail.asp?ID=2205) accessed 19 Jan 2015.

Units

Varying, depending on figure:

Figure 1: Billion 2005 USD PPP

Figure 2:  2005 USD PPP

Figure 3: Percentage (%)

Policy context and targets

Context description

GDP is often cited as a measure of economic performance and of living standards. Certain countries (e.g. China and India) have established GDP growth targets as a component of their national development planning, while many others use GDP projections to guide financial and economic policy because of the strong links between economic growth and fiscal revenues, debt sustainability and employment rates.

In Europe, the EU and its Member States responded to the 2008 economic crisis by putting in place a number of strategic policies to increase economic growth across the Union. The resulting ‘Europe 2020’ strategy includes Europe wide targets and specific recommendations for each Member State (EC, 2010).

European regional policy is also directed at improving the economic development of the least wealthy regions in Europe. These are defined as regions where GDP is less than 75% of the EU’s average GDP. Regional policy includes targeted investments intended to develop these areas and to increase their economic growth (EC, 2013).

The link between GDP and environmental policy is particularly evident in the area of resource efficiency. The ‘Roadmap to a resource-efficient Europe’ (EC, 2011) established resource productivity as the EU’s provisional lead indicator in its resource efficiency scoreboard. Resource productivity is defined as Euros of GDP per kg of domestic material consumption (GDP/DMC). According to the same logic, the resource efficiency trends of particular sectors can be estimated by comparing resource consumption or emissions to sectoral gross value added.

Within Europe there is some debate about the appropriateness of GDP as the primary measure for national development. One major concern is that GDP (or GDP per capita) excludes many important dimensions of human well-being, including local environmental conditions. It thereby provides a rather distorted picture of changes in living standards.

Another concern is that focusing on the amount of goods and services that a country produces in a period of time potentially provides a misleading indication of the status of the capital stocks (including ecosystems) that will determine future production. Indeed, focusing solely on GDP creates incentives to deplete capital stocks because the returns are treated as income.

The need for better measures of economic performance and living standards is now widely recognised. In Europe, the European Commission’s ‘Beyond GDP’ initiative aims to support the development of alternative indicators and their integration into decision making (EC, 2009; EC, 2014).

Targets

No targets have been specified

Related policy documents

• COM(2009) 433 final - GDP and beyond measuring progress in a changing world
  COMMUNICATION FROM THE COMMISSION TO THE COUNCIL AND THE EUROPEAN PARLIAMENT
• COM(2010) 2020 final, Europe 2020: A strategy for smart, sustainable and inclusive growth
  European Commission, 2010. Europe 2020: A strategy for smart, sustainable and inclusive growth. COM(2010) 2020 final. 
• MEMO/13/878 - Refocusing EU Cohesion Policy for Maximum Impact on Growth and Jobs: The Reform in 10 points
  The reform of Cohesion Policy will ensure maximum impact for investments, adapted to the individual needs of regions and cities.
• Roadmap to a Resource Efficient Europe COM(2011) 571
  Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Roadmap to a Resource Efficient Europe.  COM(2011) 571  

Methodology

Methodology for indicator calculation

Methodology for indicator selection (including description of data used)

Methodology for compilation of historic economic data:

The OECD’s projections combine expert judgement with historical data provided by countries. Sources for the historical data are publications of national statistical agencies and OECD statistical publications such as the Annual National Accounts, the International Monetary Fund, the United Nations and Eurostat.

Methodology for making long-term projections:

The OECD’s projections are produced by its country experts, working with OECD topic specialists. Their forward-looking assessments refer to historic data and take into account current and prospective developments, including officially mandated policies, historical relationships between key variables and new information and indicators related to domestic and global conditions.

The OECD uses the NIGEM model of the British National Institute of Economic and Social Research, which uses a ‘New-Keynesian’ framework. As a policy-advice model, NIGEM is also designed to be flexible, allowing assumptions on behaviour and policy to be changed. Expert judgement is used in determining these assumptions.

The effects of new elements and revised judgments are typically assessed at the start of each projection round; these are taken in conjunction with simulations of the effects of revised assumptions. In making the projections, particular attention is paid to consistency at domestic and world levels, to ensure that key accounting identities and relationships are observed, notably with respect to international trade and the balance of payments, a process assisted by the OECD’s international trade model (Murata et al., 2000; Pain et al., 2005) and a variety of other estimated relationships between key variables. 

Outputs from this process are collated and uploaded to the OECD Long Term Baseline projection which is the primary information source for this indicator.

Methodology for gap filling

see ‘Methodology for indicator selection’

Methodology references

No methodology references available.

Uncertainties

Methodology uncertainty

The OECD periodically reviews its projections for predictive accuracy. These analyses try to distinguish between errors arising from projection revisions, changes in underlying assumptions and errors of judgement about economic conditions and forces shaping the outlook (OECD, 2011).

Typically, larger projection errors occur around major turning points in economic activity. The reasons for these errors may include errors of judgement or a decline in the predictive power of standard economic relationships, for example, through unexpected and significant events. These so called ‘black swan’ events can cause radical and unexpected changes in economic and social circumstances and all projections are vulnerable to these.

There are also challenges relating to the quality of information available in and around cyclical turning points. Assessments of accuracies related to past OECD projections are provided by Koutsogeorgopoulou (2000) and Vogel (2007).

Projection uncertainty

The OECD projections are subject to a number of common assumptions. These include: macroeconomic policies, fiscal policies, domestic monetary policies, oil and non-oil commodity prices and exchange rates.

Population is a key determinate of future GDP and the OECD’s projections are based on the United Nations medium scenario (UN, 2013). Information on the nature of other assumptions can be found on the OECD’s website (OECD, 2011).

References

Koutsogeorgopoulou, V., 2000, 'A Post-Mortem on Economic Outlook Projections', OECD Economics Department Working Papers, Organisation for Economic Co-operation and Development, Paris.

OECD, 2011, 'Economic outlook, analysis and forecasts - OECD' accessed 10 November 2014.

UN, 2013, 'World population prospects: the 2012 revision', United Nations Department of Economic and Social Affairs, New York, US.

Vogel, L., 2007, 'How do the OECD Growth Projections for the G7 Economies Perform?', OECD Economics Department Working Papers, Organisation for Economic Co-operation and Development, Paris.

Data sets uncertainty

No uncertainty has been specified

Rationale uncertainty

As noted above, there are a number of criticisms of GDP. These relate to the fact that GDP is a measure of market activity alone. As a result, many activities that contribute to well-being are excluded, such as time spent caring for vulnerable groups, home maintenance and cleaning, food preparation, and voluntary service for neighbourhood, church, and civic groups.

GDP also treats the depletion of natural capital assets as current income. This means that the loss of natural capital may augment GDP, even though it may lead to losses in public goods and reductions in ecosystem services, which may undermine future living standards.

The European Commission’s ‘Beyond GDP’ initiative (EC, 2009) has been addressing these criticisms. The initiative’s work has continued (EC, 2014), notably in the development of alternative indicators and attempts to include these indicators into key policy areas, such Sustainable Development Goals (which will replace the Millennium Development Goals) and the OECD’s regional wellbeing assessment (OECD, 2014b) developed under the umbrella of the OECD Better Life Initiative (OECD, 2014a).

References

EC, 2009, Communication from the Commission to the Council and the European Parliament - GDP and beyond : measuring progress in a changing world, COM/2009/0433 final.

EC, 2014, 'European Commission - Beyond GDP', accessed 10 Nov 2014.