To make valid cross-country comparisons, economic indicators which are compiled in local currencies must be converted to a common and comparable value. In many cases, the currency chosen for reference is the US dollar. How this conversion is undertaken can impact the relative positions of countries in terms of the size of their economies or national income level, and how their evolution looks over time. The most common way is using exchange rates. In this case, economic time series expressed in US dollars can be influenced by exchange rate fluctuations, which can have an important impact.
As one example, World Bank classifications of countries by income level are based on comparable measures of Atlas Gross National Income per Capita, a measure of aggregate income derived from macroeconomic data. This measure makes specific adjustments to account for short term volatility in exchange rates along with parallel exchange rate activity. When parallel activity exists, the classification a country receives can depend greatly on the choice of exchange rate.
Importance of accounting for parallel exchange rates
While currency conversions to US dollars are generally undertaken with official exchange rates, in some cases these are not reflective of the economic reality. In certain countries, multiple or dual exchange rate activity exist and should be appropriately accounted for in underlying statistics and resulting classifications. Parallel exchange rate markets can arise because of exchange rate regulations or controls introduced by governments or central banks, or due to economic conditions impacting international transactions that lead to a significant informal or unofficial market. For example, Lebanon introduced preferential exchange rate controls to secure access to critical products, while in Sudan, a large parallel market for foreign exchange developed in response to ongoing shortages.
This phenomenon is not static and can emerge and disappear at different points in time, as countries gain access to foreign exchange reserves and effective policies are introduced to prevent market distortions. There is evidence that parallel markets have become more prevalent in low-income countries over the past several years, intensifying more recently as the COVID-19 pandemic has impacted commodity prices and led to supply chain disruptions for critical goods and services. 1
While information is sometimes incomplete and measurement is challenging, economic time series in the World Bank’s World Development Indicators (WDI) and the operational and income classifications of countries which draw on these data, aim to account for the emergence of parallel markets to the best extent possible. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level.
Impacts in countries where adjustments are made
There must be significant parallel activity, enough to have an impact on a national scale, to warrant adjustments to official exchange rates. Currently adjustments are made for known quantifiable parallel market activity in Iran, Lebanon, Nigeria, Sudan, Yemen, and Zimbabwe. Adjustments are also under consideration for Angola and Argentina. The following charts show the impact of accounting for parallel exchange rates in Atlas GNI per capita, in comparison with a baseline scenario derived with the official exchange rate. In the former case, weighted averages of parallel and official rates are used in the conversion, with the weights depending on specific local circumstances. This is further illustrated with examples below.
Two examples of how to account for parallel exchange rates
To reflect economic reality, the conversion of estimates in local currency to US dollars should be undertaken with a factor that reflects exchange rates in which transactions have occurred; therefore reflect a weighted average of exchange rates effectively in use, including official rates and parallel rates accessible through unofficial channels. To derive this weighted average, information is needed on the exchange rates themselves, along with an understanding of the nature of transactions to which they apply.
Since local circumstances can differ significantly, the appropriate method to account for parallel exchange rate activity should be determined on a case-by-case basis. Two illustrative examples are provided below. The first pertains to a case where the central bank has introduced a regime of multiple official rates which apply to goods and services in specific categories and the second to a more standard scenario where economic conditions have led to an important informal or unofficial market that differs substantially from the official rate.
Example 1: Accounting for imports of critical goods and services method
In this example, there are three categories of imported goods and services: highly critical, critical, and other. Each category is subject to different exchange rates, sanctioned officially in policies or regulations:
Based on the above, the average exchange rate becomes:
|AER =||RC1 * Eo + RC2 * Ec + RC3 * Eb|
The conversion factor for the Atlas method is then calculated as a three-year average of the average exchange rates (see: Methodology).
Example 2: GDP component shares method
In this example, local information can be leveraged to understand the nature of transactions undertaken at official vs. parallel market exchange rates. This, in turn, can be used to estimate expenditure shares that can be applied to the components of Gross Domestic Product (GDP) which, when adjusted for property income flows to and from non-residents, gives Gross National Income (GNI).
||Final consumption expenditure|
||Gross Capital Formation|
||Net Exports of Goods and Services|
||Gross domestic product|
||Official exchange rate|
||Parallel market exchange rate|
||Share of imported content of XXX (FCE, GCF, NEX) imported at exchange rate i (Eo, Eb)|
Based on the above, the average exchange rate becomes:
|AER =||(SFCEEo * Eo + SFCEEb * Eb) * FCE/GDP +|
| (SGCFEo * Eo + SGCFEb * Eb) * GCF/GDP +
(SNEXEo * Eo + SNEXEb * Eb) * NEX/GDP
Challenges and next steps
Accurately accounting for parallel exchange rate activity presents several statistical and measurement challenges. Concerted efforts must be made to address them to ensure ongoing data quality. Countries where parallel markets emerge should first be identified and then data sources explored to obtain information on the parallel exchange rates. These could include, for example, central banks or commercial exchange vendors at the local level. While the IMF regularly monitors exchange restrictions and multiple currency practices by country, no comprehensive cross-national data source currently exists for this information.
Methodologies need to be developed, and assumptions refined and tested to develop the weights that apply to transactions at different exchange rates effectively in use, relying on strong local insights from World Bank country teams and other sources. With further assessments, adjustments may be made to account for parallel exchange rate activity in additional countries. Future examination may also reveal needed improvements to previously published historical time series if past parallel exchange rate conditions were not accurately reflected.
For this purpose, the Development Data Group of the World Bank will conduct a survey in a set of countries where multiple exchange rates are in use in August 2021. The survey aims to: (i) identify information sources to monitor dual, multiple, or parallel exchange rates, (ii) gather economic intelligence on the nature and scope of transactions undertaken with different exchange rate mechanisms, and (iii) collect data for any available time series on parallel exchange rates. The results of the survey will be used to improve the methodologies for calculating average exchange rates for countries where parallel exchange rates are prevalent.
As a longer-term objective, the World Bank will continue to improve the current Atlas method used to classify countries by income level. One area of research is the potential use of measures of Purchasing Power Parity (PPPs), as these measures can be more reflective of material wellbeing. Any future decision to change the methodology underlying the World Bank income classifications would be subject to careful research, analysis, and broad consultation.