CCEMG - EPPI-Centre Cost Converter  

This ‘CCEMG–EPPI Centre Cost Converter’ (v.1.7 last update: January 2024) is an open access 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 ‘Information for users’, located beneath the tool on 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 9.49100017547607   39.4179992675781 | 39.4179992675781 1.00
Target Afghanistan 2002 9.49100017547607  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 (see FAQs ‘3) Specific Data Series > For the countries that adopted the euro, how did you convert the series expressed in national currency?’). 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.

Information for users

This web-based tool adjusts estimates of costs from their original currency and price year to a target currency and/or price year.


The current version of this tool (Version 1.7) was released during January 2024. Version 1.7 is based on the latest available International Monetary Fund (IMF) (October 2023) [1] and Organisation for Economic Cooperation and Development (OECD) (2022) [2] datasets (see ‘How the tool works’, below, for further details).

The next update of this tool is scheduled for release during November 2024 (using October 2024 and 2023 datasets) and we will aim to release an updated version annually thereafter, during each November. We also plan to launch a refreshed version of this web page (user interface) during 2024.

We are also currently working on a new open access journal article to report updated details of the development, underlying methods and data, user interface and applications of this web-based tool. The new article will include links to the open access datasets that underpin this tool (currently available on request – see below).

This web-based tool was originally developed as a joint initiative between the Campbell & Cochrane Economics Methods Group (CCEMG) and the EPPI Centre, University College London. It is currently being maintained by researchers at the EPPI Centre. Please send any questions or comments via e-mail to Ian Shemilt (EPPI Centre).

Journal article

Further details of this web-based tool are described in this journal article published in Evidence and Policy Volume 6, Number 1, 2010, pp. 51-59. DOI: (£). Key information for users is also summarised below.

How the tool works…

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, as 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 [1]. This dataset contains GDPD values for 196 countries (currencies) from 1980 onwards. It is updated biennially in April and October and each new October release of the 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 [1]. This dataset contains PPP values for 196 countries (currencies) from 1980 onwards, updated biennially in April and October, and each new October release of the dataset is imported into the database underlying this web-based tool.  The second is the OECD ‘Purchasing Power Parities for GDP’ dataset, published as part of the OECD.Stat series [2]. This dataset contains PPP values for all current OECD countries (plus selected non-OECD member economies), updated annually, and we import values from 1980 to present into the database underlying this web-based tool. Note that PPP values differ between the IMF and OECD datasets with respect to OECD countries in all price years, due to variations in the detailed methods used to generate these data. Therefore, different results are produced by this tool depending on which dataset is selected for PPP values. There is no normative basis for the choice between these two datasets.

Applications of the tool…

International comparisons of the costs of health care, public health, crime and justice, education, environmental, international development, social welfare and other interventions are increasingly important for a number of different applications [3]. 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 the 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 between studies.

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, to enable 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 [4].

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 outcomes under conditions of uncertainty [5]. Decision models synthesise data from several different sources to inform specific decisions. Unit costs are one of the data inputs 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 cost data inputs for use in decision models [6]. However, if the latter 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 being developed, these data inputs will need to be adjusted for currency and/or price year.

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 [7], technology-specific PPPs [8] and episode-specific PPPs [3] 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 considered.

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 crime and justice, education, environment, international development, 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.


[1International Monetary Fund. World Economic Outlook Database (October 2023). Available from: (Downloaded 12 January 2024).

[2] Organisation for Economic Co-operation and Development. Purchasing Power Parities for GDP dataset (2022). Available from: (Downloaded 12 January 2024).

[3] 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.

[4] Aluko P, Graybill E, Craig D, Henderson C, Drummond M, Wilson ECF, Robalino S, Vale L; on behalf of the Campbell and Cochrane Economics Methods Group. Chapter 20: Economic evidence. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from

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

[6] 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.

[7] 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.

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