International dollar

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The international dollar (int'l dollar or intl dollar, symbols Int'l$., Intl$., Int$), also known as Geary–Khamis dollar (symbols G-K$ or GK$), is a hypothetical unit of currency that has the same purchasing power parity that the U.S. dollar had in the United States at a given point in time.[1][2] It is widely used in economics and financial statistics for various purposes, most notably to determine and compare the purchasing power parity and gross domestic product of various countries and markets. The year 1990 or 2000 is often used as a benchmark year for comparisons that run through time. The unit is often abbreviated, e.g. 2000 US dollars or 2000 Int'l$ (if the benchmark year is 2000).

It is based on the twin concepts of purchasing power parities (PPP) of currencies and the international average prices of commodities. It shows how much a local currency unit is worth within the country's borders. It is used to make comparisons both between countries and over time. For example, comparing per capita gross domestic product (GDP) of various countries in international dollars, rather than based simply on exchange rates, provides a more valid measure to compare standards of living. It was proposed by Roy C. Geary in 1958 and developed by Salem Hanna Khamis between 1970 and 1972.

Figures expressed in international dollars cannot be converted to another country's currency using current market exchange rates; instead they must be converted using the country's PPP exchange rate used in the study.

Exchange rate by country[edit]

According to IMF, below is the exchange rate of International dollar to local currency of respective countries in 2018:

Country Exchange rate in 2018[3]
Afghanistan 19.483
Albania 42.454
Algeria 31.034
Angola 133.623
Antigua and Barbuda 1.683
Argentina 15.95
Armenia 196.873
Aruba 1.154
Australia 1.44
Austria 0.833
Azerbaijan 0.445
Bahamas, The 1.005
Bahrain 0.191
Bangladesh 31.561
Barbados 1.904
Belarus 0.642
Belgium 0.818
Belize 1.143
Benin 211.003
Bhutan 22.711
Bolivia 3.119
Bosnia and Herzegovina 0.702
Botswana 4.531
Brazil 2.027
Brunei Darussalam 0.528
Bulgaria 0.665
Burkina Faso 200.504
Burundi 753.844
Cabo Verde 45.624
Cambodia 1399.891
Cameroon 225.503
Canada 1.207
Central African Republic 315.84
Chad 201.125
Chile 396.744
China, People's Republic of 3.499
Colombia 1314.614
Comoros 207.693
Congo, Dem. Rep. of the 976.995
Congo, Republic of 209.142
Costa Rica 393.262
Croatia 6.25
Cyprus 0.6
Czech Republic 13.45
Côte d'Ivoire 223.279
Denmark 7.356
Djibouti 93.361
Dominica 1.888
Dominican Republic 22.49
Ecuador 0.541
Egypt 3.425
El Salvador 0.488
Equatorial Guinea 255.465
Eritrea 4.865
Estonia 0.578
Eswatini 5.168
Ethiopia 10.028
Fiji 1.112
Finland 0.903
France 0.792
Gabon 247.587
Gambia, The 13.22
Georgia 0.964
Germany 0.77
Ghana 1.567
Greece 0.592
Grenada 1.846
Guatemala 4.048
Guinea 3532.387
Guinea-Bissau 235.528
Guyana 120.031
Haiti 30.474
Honduras 11.646
Hong Kong SAR 5.915
Hungary 134.826
Iceland 144.259
India 18.13
Indonesia 4240.903
Iran 17.750
Iraq 395.634
Ireland 0.833
Israel 3.943
Italy 0.732
Jamaica 73.926
Japan 98.089
Jordan 0.321
Kazakhstan 117.161
Kenya 50.041
Kiribati 1.05
Korea, Republic of 847.093
Kosovo 0.322
Kuwait 0.14
Kyrgyz Republic 22.703
Lao P.D.R. 2842.329
Latvia 0.51
Lebanon 949.423
Lesotho 5.302
Liberia 0.513
Libya 0.759
Lithuania 0.465
Luxembourg 0.919
Macao SAR 5.692
Madagascar 939.227
Malawi 213.558
Malaysia 1.427
Maldives 10.265
Mali 216.138
Malta 0.574
Marshall Islands 1.029
Mauritania 10.238
Mauritius 16.071
Mexico 9.145
Micronesia, Fed. States of 1.052
Moldova 7.337
Mongolia 736.791
Montenegro 0.387
Morocco 3.537
Mozambique 22.368
Myanmar 288.453
Namibia 7.046
Nauru 1.269
Nepal 34.978
Netherlands 0.798
New Zealand 1.483
Nicaragua 11.586
Niger 216.631
Nigeria 110.449
North Macedonia 20.231
Norway 8.919
Oman 0.152
Pakistan 30.278
Palau 1.029
Panama 0.61
Papua New Guinea 2.372
Paraguay 2538.502
Peru 1.617
Philippines 18.28
Poland 1.741
Portugal 0.612
Puerto Rico 0.802
Qatar 1.976
Romania 1.829
Russian Federation 24.572
Rwanda 296.092
Saint Kitts and Nevis 1.613
Saint Lucia 2.057
Saint Vincent and the Grenadines 1.659
Samoa 1.883
San Marino 0.687
Saudi Arabia 1.584
Senegal 217.987
Serbia 41.223
Seychelles 7.589
Sierra Leone 2647.673
Singapore 0.859
Slovak Republic 0.472
Slovenia 0.602
Solomon Islands 7.804
Somalia 0.391
South Africa 6.172
South Sudan, Republic of 33.285
Spain 0.648
Sri Lanka 49.561
Sudan 7.67
Suriname 2.863
Sweden 8.807
Switzerland 1.251
Syria no data
São Tomé and Príncipe 12.164
Taiwan Republic of China 14.212
Tajikistan 2.206
Tanzania 722.834
Thailand 12.354
Timor-Leste 0.435
Togo 212.697
Tonga 1.738
Trinidad and Tobago 3.435
Tunisia 0.731
Turkey 1.619
Turkmenistan 1.267
Tuvalu 1.252
Uganda 1085.74
Ukraine 9.115
United Arab Emirates 2.107
United Kingdom 0.697
United States 1
Uruguay 22.553
Uzbekistan 1472.271
Vanuatu 123.395
Venezuela 6.542
Vietnam 7790.097
Yemen 195.891
Zambia 3.818
Zimbabwe 1.005

Short description of Geary-Khamis system[edit]

This system is valuing the matrix of quantities using the international prices vector. The vector is obtained by averaging the national prices in the participating countries after their conversion into a common currency with PPP and weighing quantities. PPPs are obtained by averaging the shares of national and international prices in the participating countries weighted by expenditure. International prices and PPPs are defined by a system of interrelated linear equations that need to be solved simultaneously. The GK method produces PPPs that are transitive and actual final expenditures that are additive.

Inflation adjusting[edit]

When comparing between countries and between years, the international dollar figures may be adjusted to compensate for inflation. In that case, the base year is chosen, and all figures will be expressed in constant international dollars for that specified base year. Researchers must understand, which adjustments are reflected in the data (Marty Schmidt):

•Population adjustments (In which case, figures represent per capita monies)

•Currency exchange rate adjustments (In which case, figures will be expressed in one currency unit (typically US$, International $, € £ or ¥)

•Purchasing power parity adjustments and/or average commodity prices (in which case, figures are typically expressed as International $)

•Inflation adjustments (in which case, figures have been adjusted, based on changes in an inflation index such as the consumer price index, to represent currency for a "base" year, such as 2000).

Description of Geary-Khamis system[edit]

Suppose PPPj is the parity of j-th currency with a currency called international dollars, which may reflect any currency, however, US dollar is the most commonly used. Then the international price Pi is defined as an international average of prices of i-th commodity in various countries. Prices in these countries are expressed in their national currencies. Geary-Khamis method solves this by using national prices after conversion into a common currency using the purchasing power parities (PPP). Hence, the international price, Pi of i-th commodity is defined as:

This equation implies that the international price of i-th commodity is calculated by dividing the total output of i-th commodity in all selected countries, converted in international dollars, using purchasing power parities, by the total quantity produced of i-th commodity. Previous equation can be rewritten as follows:

This equation suggests that Pi is weighted average of international prices pij after conversion into international dollars using PPPj. PPPj is by Geary-Khamis system defined through this equation:

The numerator of the equation represents the total value of output in j-th country expressed in national currency, and the denominator is the value of j-th country output evaluated by repricing at international prices Pi in international dollars. Then PPPj gives the number of national currency units per international dollar.

Advantages of Geary-Khamis method[edit]

Geary-Khamis international dollar is widely used by foreign investors and institutions such as IMF, FAO and World Bank. It has become so widely used because it made possible to compare living standards between countries. Thanks to the international dollar they can see more trustworthy economic situation in the country and decide whether to provide additional loans (or any other investments) to said country, or not. It also offers some comparison of purchasing power parities all around the world (developing countries tend to have higher PPPs). Some traders even use Geary-Khamis method to determine if country´s currency is undervalued or overvalued. Exchange rates are frequently used for comparing currencies, however, this approach does not reflect real value of currency in said country. It is better to include PPP or prices of goods in said country. International dollar solves this by taking into account exchange rates, PPP and average commodity prices. Geary-Khamis method is the best method for comparisons of agricultural outputs.

Criticism of using 1990 US dollars for long run comparisons[edit]

Economists and historians use many methods when they want to research economic development in the past. For example, if we take the United States of America and United Kingdom (these two examples were compared many times in various researches), someone may use nominal exchange rates, Lindert and Williamson (2016) used PPP exchange rates and Broadberry (2003) used growth rates using own-country price indices. However, none of them is somehow better than the others (or theoretically justifiable). There is a high probability that these three methods will give three different answers, and, in fact, Brunt and Fidalgo (2018)[4] showed in their paper that: “these three approaches do give three different answers when estimating output levels and growth rates in the US and UK – and they are not only different to one another, but also different to a comparison using the (more theoretically justifiable) chained GK prices.” Even though it is more theoretically justifiable, it does not mean it should be used without considering every aspect of this method. For example, Maddison (2001) used the 1990 international dollar when he examined prices during the time of Christ. Ideally, we would use a price benchmark which is significantly closer to the time of Christ. However, there are no such benchmarks. Another problem is that there is no set of international prices, which we could use for valid cross-country comparisons. Comparing GDP levels across countries using their own prices converted at the nominal exchange rate has no value whatsoever. This approach is quite arbitrary because the exchange rate is determined simply by the supply and demand for currency and these metrics are greatly dependent on the volumes of trade balances. It makes little (or no) sense to value all goods (both traded and non-traded at the nominal exchange rate, especially since the absolute volumes of trades may be small compared to total output in both countries. Economists therefore create PPP exchange rates, deriving the exchange rate by valuing a basket of goods in the two countries at two sets of prices (and expressing them as a ratio afterwards). This allows us to see how much it actually costs to live in said country. Although with this approach emerges another problem. What should we choose to be in the basket? Brunt and Fidalgo (2018) use examples of an English basket in 1775 and Chinese basket in 1775. While the English one would have a lot of wheat, the Chinese one would have a lot of rice. Wheat was quite affordable in England and rice was quite affordable in China, however, if we switch these goods, they both would be relatively expensive. This nicely illustrates how choice of the content of the basket will influence the comparison. Simply by using English basket, China would seem like an expensive place to live and vice versa. Geary-Khamis tries to solve this by estimating a weighted average price of each commodity using the shares of countries in world production to weight the country prices. Another problem emerges when researchers compare countries which have different price structure than the international price structure. Brunt and Fidalgo (2018) show examples of Ireland (which has really similar price structure to the international) and South Africa (which has really different price structure to the international). So, when using domestic and international price indices, Ireland´s growth rates move in very similar direction, but when domestic and international prices are applied to South Africa, they, in fact, move in opposite directions. It is worth noting, that bigger countries tend to have a price index that moves more similarly to the international price index. It is simply because bigger countries have a bigger weight in creation of this index.

See also[edit]


  1. ^ "International Dollar Geary-Khamis Defined, Examples Explained". Business Case Web Site. 24 February 2016. Retrieved 13 April 2019.
  2. ^ "What is an "international dollar"?". World Bank Data Help Desk. Retrieved 13 April 2019.
  3. ^ Implied PPP conversion rate. National currency per international dollar, IMF DataMapper
  4. ^

External links[edit]