CCEMG - EPPI-Centre Cost Converter  

The ‘CCEMG – EPPI-Centre Cost Converter’ (v.1.6 last update: 29 April 2019) is a free web-based tool for adjusting estimates of cost expressed in one currency and price year to a specific target currency and price year. Before using the tool, please read the Important information for users located at the foot of this web-page. To use the tool please enter and select your data using Steps 1-6, below.

1. Input cost estimate (value) reported in original study (e.g. 123.45) Recalculate    
2. Select source dataset for PPP values  
3. Select currency (country) reported in original study (e.g. United States)
4. Select target currency (country) (e.g. United Kingdom)
5. Select price year reported in original study (e.g. 1997)
6. Select target price year (e.g. 2010)

Currency (country) Price year PPP values ICF** GDPD values IIF*** Results
Original Afghanistan 2002 9.491   97.765 | 97.765 1.00
Target Afghanistan 2002 9.491  1.00  
Final result: original cost estimate converted to target currency and price year     123.45

* Cost estimate in original study is reported in a pre-Euro currency. Further information on the IMF website. (Irrevocable Euro conversion rates apply to both IMF and OECD PPP values.)
** ICF = Implied Conversion Factor
*** IIF = Implied Inflation Factor

NB. If a ‘PPP value’ or ‘GDPD value’ is displayed in red type in the ‘Results’ panel, this indicates that the value is based on IMF staff estimates. In this case, the ‘Final result’ will be based partly or wholly on value(s) based on IMF staff estimates.

Important information for users

Full details of the development, underlying methods and data, user interface and applications of this web-based tool are described in a paper published in the journal Evidence and Policy (Volume 6, Number 1, 2010, pp. 51-59). This paper is available electronically via IngentaConnect via this link. Important information for users is summarised below.

International comparisons of the costs of health care, social and behavioural interventions are increasingly important for a number of different applications [1]. One specific application relates to systematic reviews of interventions that include an aim to incorporate evidence on costs drawn from published or unpublished studies. Since studies included in such reviews are often conducted in different countries and/or at different times, estimates of costs are often expressed in different currencies and/or price years.

For example, ‘Study A’, conducted in the United States in 2000, reports an estimate of the mean total direct costs of a drug rehabilitation programme per participant that is expressed in 2000 US Dollars ($), whilst ‘Study B’, conducted in the United Kingdom in 2005, reports an estimate of the same cost measure for a comparable programme that is expressed in 2005 UK Pounds Sterling (£). In these circumstances, in order for end users of reviews to make meaningful comparisons between two (or more) estimates of the costs of a programme, technology or intervention, it is preferable for these estimates to be expressed using a ‘common metric’, which requires their adjustment to a common currency and price year [2, 3].

Cost adjustments are also sometimes applied in decision models for economic evaluation. A decision model is an analytic tool used to support systematic approaches to evaluating the impact of alternative interventions on costs and other outcomes under conditions of uncertainty [4]. Decision models synthesise data that may be collected from several different sources to inform specific decisions. Ideally, these data should be assembled using a systematic but necessarily iterative approach to searching, with the aim of identifying the ‘best available’ source(s) of data to estimate each model parameter. Unit costs are one of the data components needed to estimate model parameters. Cost calculations based on reliable databases or data sources conducted for the specific study and in the same jurisdiction have been proposed as the preferred source of unit costs data for use in decision models [5]. However, if the preferred source is not available, it may be necessary to utilise unit costs obtained from previously published sources. In these circumstances, if the source unit costs are expressed in a different currency and/or price year from those applicable to the decision model, some adjustment of these data is required.

This web-based tool adjusts estimates of costs for currency and/or price year using a two-stage computation. Stage 1 adjusts the original estimate of cost from the original price year to a target price year, using a Gross Domestic Product deflator index (‘GDPD values’). ‘GDPD values’ are a measure of the change over time in prices of all new, domestically produced, final goods within that economy. This can be viewed as a measure of general inflation within an economy over time, which takes account of inflation across a broad range of economic sectors.

Stage 2 converts the price-year adjusted cost estimate from the original currency to a target currency, using conversion rates based on Purchasing Power Parities for GDP (‘PPP values’). ‘PPP values’ adjust appropriately for differences in current price levels between countries, thus allowing comparisons based on a common set of average international prices; this is an advantage over pure exchange-rate conversion and GDP per capita approaches. In other words, PPPs eliminate differences in price levels between countries in the process of conversion, whereas pure exchange-rate conversion and GDP per capita approaches do not. The price levels underpinning ‘PPP values’ are measured based on a general ‘basket’ of goods and services covering a broad range of economic sectors.

‘GDPD values’ used in Stage 1 of the computation are obtained from the International Monetary Fund (IMF) World Economic Outlook Database ‘GDP deflator index’ dataset [6]. This dataset contains ‘GDPD values’ for 184 countries (currencies) from 1980 onwards. It is updated biennially in April and October and each new release dataset is imported into the database underlying this web-based tool.

You can select one of two alternative source datasets for ‘PPP values’ for use in Stage 2 of the computation. The first is the ‘Implied PPP conversion rate’ dataset, obtained from the IMF World Economic Outlook Database [6]. This dataset contains ‘PPP values’ for 191 countries (currencies) from 1980 onwards, updated biennially in April and October.  The second is the OECD ‘Purchasing Power Parities for GDP’ dataset, published as part of the OECD.Stat series [7]. This dataset contains ‘PPP values’ for all current OECD countries (plus selected non-OECD member economies) from 1980 to the present. It is updated triennially in February, June and December. As with ‘GDPD values’, each new release of these two ‘PPP values’ datasets is imported into the database underlying this web-based tool. Note that the ‘PPP values’ contained in the IMF and OECD datasets differ slightly with respect to the OECD countries in all price years, due to variations in the detailed methodologies used to generate these data. Therefore, different results are produced by this tool depending on the choice of dataset. There is no normative basis for the choice between these two datasets.

This web-based tool is a generic tool intended to be applicable across a large number of different countries and all sectors, including (but not limited to) health care, social welfare, education and criminal justice.  It is important to be aware that the tool utilises one of several methods currently available to convert costs to a target (common) currency (that is, use of Purchasing Power Parities for GDP). As well as the alternative methods mentioned above (that is, pure exchange-rate conversion and GDP per capita approaches), health economists have developed and applied health care-specific PPPs [8], technology-specific PPPs [9] and episode-specific PPPs [1] for this purpose. An advantage of using these ‘context-specific’ methods in health care applications is that they derive conversion rates based on cross-country comparisons of the relative prices of, respectively, baskets of health care services, specific health care technologies and specific health care episodes, whereas Purchasing Power Parities for GDP derive conversion rates based on comparisons of the prices of a larger and more diverse basket of goods and services covering a broad range of economic sectors. Authors of systematic reviews of health care interventions aiming to summarise evidence on costs, and researchers developing decision models for health economic evaluation, should therefore consider whether it is feasible to apply these more sophisticated ‘context-specific methods’ in preference to those underpinning this web-based tool. The availability of detailed descriptive data on health care interventions being examined (which are needed to inform calculation of technology and episode-specific PPPs) is likely to be a key factor to be taken into account in making this assessment.

No equivalent alternative ‘context-specific’ methods have yet been developed based on cross-country comparisons of the relative prices of non-health technologies, programmes, policies or services, such as those implemented within and across the education, crime and justice or social welfare sectors. Therefore, methods based on Purchasing Power Parities for GDP, including those underlying this web-based tool, may currently be regarded as the first-line approach for use in applications outside health care. Such methods may also be viewed as a more methodologically straightforward alternative for use in applications within the health care sector, for use in circumstances in which context-specific methods are not judged feasible.

This web-based tool is developed as a joint initiative between The Campbell and Cochrane Economics Methods Group (CCEMG) and the Evidence for Policy and Practice Information and Coordinating Centre (EPPI-Centre). Please direct any questions or comments to Ian Shemilt, CCEMG Co-convenor (e-mail: ).


[1] Schreyogg J, Tiemann O, Stargard T, Busse R. Cross-country comparisons of costs: the use of episode-specific transitive purchasing power parities with standardised cost categories. Health Economics 2008: 17(S): S95-S103.

[2] Shemilt I, Mugford M, Byford S, Drummond M, Eisenstein E, Knapp M, Mallender J, McDaid D, Vale L, Walker D (2008a). Chapter 15: Incorporating economics evidence, in JPT. Higgins and S. Green (eds) Cochrane Handbook for Systematic Reviews of Interventions.Chichester : John Wiley & Sons. Available from:

[3] Shemilt I, Mugford M, Byford S, Drummond M, Eisenstein E, Knapp M, Mallender J, Marsh K, McDaid D, Vale L, Walker D (2008b). Campbell Collaboration Methods Policy Brief: Economics Methods (updated April 2008). Oslo : The Campbell Collaboration.

[4] Briggs, A., Claxton, K. and Sculpher, M (2006) Decision modelling for health economic evaluation, Oxford: Oxford University Press.

[5] Cooper NJ, Sutton AJ, Ades AE, Paisley S, Jones DR. Use of evidence in economic decision models: practical and methodological issues. Health Economics 2007; 16: 1277–86.

[6] International Monetary Fund. World Economic Outlook Database (October 2018). Available from: (Downloaded 23 April 2019).

[7] Organisation for Economic Co-operation and Development. Purchasing Power Parities for GDP dataset (April 2019). Available from: (Downloaded 23 April 2019).

[8] Busse R, Schreyogg J, Smith PC. Variability in healthcare treatment costs amongst nine EU countries - results from the HealthBASKET project. Health Economics 2008; 17: S1-S8.

[9] Wordsworth S, Ludbrook A. Comparing costing results in across country economic evaluations: the use of technology specific purchasing power parities. Health Economics/i> 2005; 14: 93-99.