Cyprus

iso2Code: CY

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Nicosia

country_code: CYP

country_name: Cyprus

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

value: Economic Fitness 2

sourceNone: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

Czechia

iso2Code: CZ

capitalCity: Prague

country_code: CZE

country_name: Czech Republic

value: Universal Economic Fitness Metric

value: CC BY-4.0

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

schema: wb


Germany

iso2Code: DE

capitalCity: Berlin

country_code: DEU

country_name: Germany

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Universal Economic Fitness Metric

Denmark

iso2Code: DK

capitalCity: Copenhagen

country_code: DNK

country_name: Denmark


Dominican Republic

iso2Code: DO

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Santo Domingo

country_code: DOM

country_name: Dominican Republic


Universal Economic Fitness Metric

Algeria

iso2Code: DZ

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

value: Middle East & North Africa (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Algiers

country_code: DZA

country_name: Algeria


Ecuador

iso2Code: EC

capitalCity: Quito

country_code: ECU

country_name: Ecuador


Universal Economic Fitness Metric

Egypt, Arab Rep.

iso2Code: EG

capitalCity: Cairo

country_code: EGY

country_name: Egypt, Arab Rep.


Eritrea

iso2Code: ER

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

iso2code: XI

value: IDA

capitalCity: Asmara

country_code: ERI

country_name: Eritrea


Universal Economic Fitness Metric

value: Economic Fitness 2

Spain

iso2Code: ES

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Madrid

country_code: ESP

country_name: Spain

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Estonia

iso2Code: EE

capitalCity: Tallinn

country_code: EST

country_name: Estonia

value: Universal Economic Fitness Metric

value: CC BY-4.0

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Universal Economic Fitness Metric

Ethiopia

iso2Code: ET

capitalCity: Addis Ababa

country_code: ETH

country_name: Ethiopia


Finland

iso2Code: FI

capitalCity: Helsinki

country_code: FIN

country_name: Finland


Universal Economic Fitness Metric

Fiji

iso2Code: FJ

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XH

value: Blend

capitalCity: Suva

country_code: FJI

country_name: Fiji


France

iso2Code: FR

capitalCity: Paris

country_code: FRA

country_name: France


Universal Economic Fitness Metric

Gabon

iso2Code: GA

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XF

value: IBRD

capitalCity: Libreville

country_code: GAB

country_name: Gabon


value: Economic Fitness 2

United Kingdom

iso2Code: GB

capitalCity: London

country_code: GBR

country_name: United Kingdom


Universal Economic Fitness Metric

Georgia

iso2Code: GE

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

capitalCity: Tbilisi

country_code: GEO

country_name: Georgia


Ghana

iso2Code: GH

iso2code: ZG

iso2code: ZF

iso2code: XN

value: Lower middle income

value: IDA

capitalCity: Accra

country_code: GHA

country_name: Ghana


Universal Economic Fitness Metric

Guinea

iso2Code: GN

iso2code: XI

capitalCity: Conakry

country_code: GIN

country_name: Guinea

value: Universal Economic Fitness Metric

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Gambia, The

iso2Code: GM

iso2code: XM

value: Low income

capitalCity: Banjul

country_code: GMB

country_name: Gambia, The

value: CC BY-4.0

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1


Universal Economic Fitness Metric

Guinea-Bissau

iso2Code: GW

capitalCity: Bissau

country_code: GNB

country_name: Guinea-Bissau

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Greece

iso2Code: GR

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Athens

country_code: GRC

country_name: Greece


Universal Economic Fitness Metric

Guatemala

iso2Code: GT

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Guatemala City

country_code: GTM

country_name: Guatemala


value: Economic Fitness 2

Guyana

iso2Code: GY

capitalCity: Georgetown

country_code: GUY

country_name: Guyana


Universal Economic Fitness Metric

Honduras

iso2Code: HN

capitalCity: Tegucigalpa

country_code: HND

country_name: Honduras


Croatia

iso2Code: HR

value: Europe & Central Asia

iso2code: XF

value: IBRD

capitalCity: Zagreb

country_code: HRV

country_name: Croatia


Universal Economic Fitness Metric

Haiti

iso2Code: HT

iso2code: XN

value: Lower middle income

capitalCity: Port-au-Prince

country_code: HTI

country_name: Haiti


Hungary

iso2Code: HU

capitalCity: Budapest

country_code: HUN

country_name: Hungary


Universal Economic Fitness Metric

Indonesia

iso2Code: ID

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Jakarta

country_code: IDN

country_name: Indonesia


India

iso2Code: IN

value: South Asia

capitalCity: New Delhi

country_code: IND

country_name: India

value: Universal Economic Fitness Metric

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Ireland

iso2Code: IE

capitalCity: Dublin

country_code: IRL

country_name: Ireland

value: CC BY-4.0

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1


Iran, Islamic Rep.

iso2Code: IR

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

value: Middle East & North Africa (excluding high income)

capitalCity: Tehran

country_code: IRN

country_name: Iran, Islamic Rep.

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Universal Economic Fitness Metric

Iraq

iso2Code: IQ

capitalCity: Baghdad

country_code: IRQ

country_name: Iraq


Iceland

iso2Code: IS

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Reykjavik

country_code: ISL

country_name: Iceland


Universal Economic Fitness Metric

value: Economic Fitness 2

Israel

iso2Code: IL

country_code: ISR

country_name: Israel


Italy

iso2Code: IT

value: Europe & Central Asia

capitalCity: Rome

country_code: ITA

country_name: Italy


Universal Economic Fitness Metric

Jamaica

iso2Code: JM

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: Upper middle income

value: IBRD

capitalCity: Kingston

country_code: JAM

country_name: Jamaica


Jordan

iso2Code: JO

iso2code: XN

value: Lower middle income

iso2code: XF

capitalCity: Amman

country_code: JOR

country_name: Jordan


Universal Economic Fitness Metric

Japan

iso2Code: JP

value: East Asia & Pacific

capitalCity: Tokyo

country_code: JPN

country_name: Japan

value: Universal Economic Fitness Metric


Kazakhstan

iso2Code: KZ

value: Europe & Central Asia (excluding high income)

capitalCity: Astana

country_code: KAZ

country_name: Kazakhstan

value: CC BY-4.0

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Kenya

iso2Code: KE

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XH

value: Blend

capitalCity: Nairobi

country_code: KEN

country_name: Kenya

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1


Kyrgyz Republic

iso2Code: KG

iso2code: XI

value: IDA

capitalCity: Bishkek

country_code: KGZ

country_name: Kyrgyz Republic

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Universal Economic Fitness Metric

value: Economic Fitness 2

Cambodia

iso2Code: KH

value: East Asia & Pacific (excluding high income)

capitalCity: Phnom Penh

country_code: KHM

country_name: Cambodia


Korea, Rep.

iso2Code: KR

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Seoul

country_code: KOR

country_name: Korea, Rep.


Universal Economic Fitness Metric

Kuwait

iso2Code: KW

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Kuwait City

country_code: KWT

country_name: Kuwait


Lao PDR

iso2Code: LA

value: East Asia & Pacific

value: Lower middle income

capitalCity: Vientiane

country_code: LAO

country_name: Lao PDR


Universal Economic Fitness Metric

Lebanon

iso2Code: LB

iso2code: XQ

value: Middle East & North Africa (excluding high income)

iso2code: XN

value: IBRD

capitalCity: Beirut

country_code: LBN

country_name: Lebanon

value: Universal Economic Fitness Metric


Liberia

iso2Code: LR

value: Sub-Saharan Africa

iso2code: XM

value: Low income

capitalCity: Monrovia

country_code: LBR

country_name: Liberia

value: CC BY-4.0

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Libya

iso2Code: LY

iso2code: XT

value: Upper middle income

iso2code: XF

capitalCity: Tripoli

country_code: LBY

country_name: Libya

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1


Sri Lanka

iso2Code: LK

value: South Asia

value: IDA

capitalCity: Colombo

country_code: LKA

country_name: Sri Lanka

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Universal Economic Fitness Metric

value: Economic Fitness 2

Lesotho

iso2Code: LS

iso2code: ZG

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XI

capitalCity: Maseru

country_code: LSO

country_name: Lesotho


Lithuania

iso2Code: LT

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Vilnius

country_code: LTU

country_name: Lithuania


Universal Economic Fitness Metric

Luxembourg

iso2Code: LU

capitalCity: Luxembourg

country_code: LUX

country_name: Luxembourg


Latvia

iso2Code: LV

capitalCity: Riga

country_code: LVA

country_name: Latvia


Universal Economic Fitness Metric

Morocco

iso2Code: MA

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

value: Middle East & North Africa (excluding high income)

value: Lower middle income

value: IBRD

capitalCity: Rabat

country_code: MAR

country_name: Morocco


Moldova

iso2Code: MD

value: Europe & Central Asia (excluding high income)

capitalCity: Chisinau

country_code: MDA

country_name: Moldova

value: Universal Economic Fitness Metric

value: CC BY-4.0


Universal Economic Fitness Metric

Madagascar

iso2Code: MG

value: Sub-Saharan Africa

iso2code: XM

value: Low income

capitalCity: Antananarivo

country_code: MDG

country_name: Madagascar

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Maldives

iso2Code: MV

value: South Asia

iso2code: XT

value: Upper middle income

value: IDA

capitalCity: Male

country_code: MDV

country_name: Maldives

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Universal Economic Fitness Metric

value: Economic Fitness 2

Mexico

iso2Code: MX

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XF

capitalCity: Mexico City

country_code: MEX

country_name: Mexico


North Macedonia

iso2Code: MK

value: Europe & Central Asia

capitalCity: Skopje

country_code: MKD

country_name: North Macedonia


Universal Economic Fitness Metric

Mali

iso2Code: ML

iso2code: ZG

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XI

capitalCity: Bamako

country_code: MLI

country_name: Mali


Malta

iso2Code: MT

value: Middle East & North Africa

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Valletta

country_code: MLT

country_name: Malta


Universal Economic Fitness Metric

Myanmar

iso2Code: MM

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Naypyidaw

country_code: MMR

country_name: Myanmar

value: Universal Economic Fitness Metric

value: CC BY-4.0


Montenegro

iso2Code: ME

value: Europe & Central Asia (excluding high income)

value: Upper middle income

value: IBRD

capitalCity: Podgorica

country_code: MNE

country_name: Montenegro

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

value: Economic Fitness 2

Mongolia

iso2Code: MN

capitalCity: Ulaanbaatar

country_code: MNG

country_name: Mongolia

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Mozambique

iso2Code: MZ

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

value: IDA

capitalCity: Maputo

country_code: MOZ

country_name: Mozambique


Universal Economic Fitness Metric

Mauritania

iso2Code: MR

capitalCity: Nouakchott

country_code: MRT

country_name: Mauritania


Mauritius

iso2Code: MU

iso2code: XT

capitalCity: Port Louis

country_code: MUS

country_name: Mauritius


Universal Economic Fitness Metric

Malawi

iso2Code: MW

capitalCity: Lilongwe

country_code: MWI

country_name: Malawi


Malaysia

iso2Code: MY

value: East Asia & Pacific

iso2code: XF

capitalCity: Kuala Lumpur

country_code: MYS

country_name: Malaysia


Universal Economic Fitness Metric

Namibia

iso2code: ZG

iso2code: ZF

capitalCity: Windhoek

country_code: NAM

country_name: Namibia


Niger

iso2Code: NE

iso2code: XI

capitalCity: Niamey

country_code: NER

country_name: Niger


Universal Economic Fitness Metric

Nigeria

iso2Code: NG

iso2code: XH

value: Blend

capitalCity: Abuja

country_code: NGA

country_name: Nigeria


Nicaragua

iso2Code: NI

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Managua

country_code: NIC

country_name: Nicaragua


Universal Economic Fitness Metric

Netherlands

iso2Code: NL

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Amsterdam

country_code: NLD

country_name: Netherlands

value: Universal Economic Fitness Metric

value: CC BY-4.0


value: Economic Fitness 2

Norway

iso2Code: NO

capitalCity: Oslo

country_code: NOR

country_name: Norway

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Nepal

iso2Code: NP

value: South Asia

capitalCity: Kathmandu

country_code: NPL

country_name: Nepal

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


New Zealand

iso2Code: NZ

capitalCity: Wellington

country_code: NZL

country_name: New Zealand


Universal Economic Fitness Metric

Oman

iso2Code: OM

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Muscat

country_code: OMN

country_name: Oman


Pakistan

iso2Code: PK

capitalCity: Islamabad

country_code: PAK

country_name: Pakistan


Universal Economic Fitness Metric

Panama

iso2Code: PA

iso2code: XF

value: IBRD

capitalCity: Panama City

country_code: PAN

country_name: Panama


Peru

iso2Code: PE

iso2code: XT

value: Upper middle income

capitalCity: Lima

country_code: PER

country_name: Peru


Universal Economic Fitness Metric

Philippines

iso2Code: PH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Manila

country_code: PHL

country_name: Philippines


Papua New Guinea

iso2Code: PG

value: Lower middle income

iso2code: XH

value: Blend

capitalCity: Port Moresby

country_code: PNG

country_name: Papua New Guinea


Universal Economic Fitness Metric

Poland

iso2Code: PL

value: Europe & Central Asia

value: High income

capitalCity: Warsaw

country_code: POL

country_name: Poland


Portugal

iso2Code: PT

value: Not classified

capitalCity: Lisbon

country_code: PRT

country_name: Portugal

value: Universal Economic Fitness Metric


Universal Economic Fitness Metric

value: Economic Fitness 2

Paraguay

iso2Code: PY

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Asuncion

country_code: PRY

country_name: Paraguay

value: CC BY-4.0


French Polynesia

iso2Code: PF

iso2code: XD

iso2code: XX

capitalCity: Papeete

country_code: PYF

country_name: French Polynesia

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Qatar

iso2Code: QA

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Doha

country_code: QAT

country_name: Qatar

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Romania

iso2Code: RO

value: IBRD

capitalCity: Bucharest

country_code: ROU

country_name: Romania


Universal Economic Fitness Metric

Russian Federation

iso2Code: RU

value: Europe & Central Asia (excluding high income)

value: Upper middle income

iso2code: XF

capitalCity: Moscow

country_code: RUS

country_name: Russian Federation


Rwanda

iso2Code: RW

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

iso2code: XI

value: IDA

capitalCity: Kigali

country_code: RWA

country_name: Rwanda


Universal Economic Fitness Metric

Saudi Arabia

iso2Code: SA

value: High income

value: Not classified

capitalCity: Riyadh

country_code: SAU

country_name: Saudi Arabia

value: Universal Economic Fitness Metric


value: Economic Fitness 2

Sudan

iso2Code: SD

capitalCity: Khartoum

country_code: SDN

country_name: Sudan


Universal Economic Fitness Metric

Senegal

iso2Code: SN

iso2code: XN

value: Lower middle income

capitalCity: Dakar

country_code: SEN

country_name: Senegal

value: CC BY-4.0


Singapore

iso2Code: SG

value: East Asia & Pacific

capitalCity: Singapore

country_code: SGP

country_name: Singapore

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.


Universal Economic Fitness Metric

Sierra Leone

iso2Code: SL

capitalCity: Freetown

country_code: SLE

country_name: Sierra Leone

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


El Salvador

iso2Code: SV

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: IBRD

capitalCity: San Salvador

country_code: SLV

country_name: El Salvador


Universal Economic Fitness Metric

Serbia

iso2Code: RS

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

value: Upper middle income

capitalCity: Belgrade

country_code: SRB

country_name: Serbia


Suriname

iso2Code: SR

iso2code: XF

capitalCity: Paramaribo

country_code: SUR

country_name: Suriname


Universal Economic Fitness Metric

Slovak Republic

iso2Code: SK

iso2code: XD

value: High income

iso2code: XX

capitalCity: Bratislava

country_code: SVK

country_name: Slovak Republic


Slovenia

iso2Code: SI

value: Not classified

capitalCity: Ljubljana

country_code: SVN

country_name: Slovenia


Universal Economic Fitness Metric

Sweden

iso2Code: SE

capitalCity: Stockholm

country_code: SWE

country_name: Sweden

value: Universal Economic Fitness Metric


value: Economic Fitness 2

Seychelles

iso2Code: SC

iso2code: ZG

value: Sub-Saharan Africa

capitalCity: Victoria

country_code: SYC

country_name: Seychelles


Universal Economic Fitness Metric

Syrian Arab Republic

iso2Code: SY

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

value: Middle East & North Africa (excluding high income)

iso2code: XM

value: Low income

iso2code: XI

value: IDA

capitalCity: Damascus

country_code: SYR

country_name: Syrian Arab Republic

value: CC BY-4.0


Chad

iso2Code: TD

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

capitalCity: N'Djamena

country_code: TCD

country_name: Chad

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Togo

iso2Code: TG

capitalCity: Lome

country_code: TGO

country_name: Togo


Thailand

iso2Code: TH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

value: IBRD

capitalCity: Bangkok

country_code: THA

country_name: Thailand


Universal Economic Fitness Metric

Tajikistan

iso2Code: TJ

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Dushanbe

country_code: TJK

country_name: Tajikistan


value: Economic Fitness 2

Tunisia

iso2Code: TN

iso2code: XF

capitalCity: Tunis

country_code: TUN

country_name: Tunisia

value: Universal Economic Fitness Metric


Universal Economic Fitness Metric

Turkiye

iso2Code: TR

value: Upper middle income

capitalCity: Ankara

country_code: TUR

country_name: Turkey


Tanzania

iso2Code: TZ

value: Sub-Saharan Africa

capitalCity: Dodoma

country_code: TZA

country_name: Tanzania


Universal Economic Fitness Metric

Uganda

iso2Code: UG

iso2code: ZG

value: Low income

value: IDA

capitalCity: Kampala

country_code: UGA

country_name: Uganda


Ukraine

iso2Code: UA

capitalCity: Kiev

country_code: UKR

country_name: Ukraine

value: CC BY-4.0


Universal Economic Fitness Metric

Uruguay

iso2Code: UY

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XD

value: High income

capitalCity: Montevideo

country_code: URY

country_name: Uruguay

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1


United States

iso2Code: US

iso2code: XU

value: North America

iso2code: XX

value: Not classified

capitalCity: Washington D.C.

country_code: USA

country_name: United States

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

Uzbekistan

iso2Code: UZ

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XH

value: Blend

capitalCity: Tashkent

country_code: UZB

country_name: Uzbekistan


value: Economic Fitness 2

Venezuela, RB

iso2Code: VE

iso2code: XY

value: IBRD

capitalCity: Caracas

country_code: VEN

country_name: Venezuela, RB

value: Universal Economic Fitness Metric


Universal Economic Fitness Metric

Viet Nam

iso2Code: VN

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Hanoi

country_code: VNM

country_name: Vietnam


Yemen, Rep.

iso2Code: YE

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

value: Middle East & North Africa (excluding high income)

iso2code: XM

value: Low income

iso2code: XI

value: IDA

capitalCity: Sana'a

country_code: YEM

country_name: Yemen, Rep.

value: CC BY-4.0


Universal Economic Fitness Metric

South Africa

iso2Code: ZA

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

capitalCity: Pretoria

country_code: ZAF

country_name: South Africa

value:
World Bank, Economic Fitness project. For more details please visit the following links.

The Fitness and Complexity algorithm has been introduced in:
https://www.nature.com/articles/srep00723

For a detailed discussion of the data preprocessing, the computation of the universal fitness, and the economical implications:
http://documents.worldbank.org/curated/en/309521529586431853/Integrating-services-in-the-economic-fitness-approach

The convergence criterion of the Fitness and Complexity algorithm is discussed in:
https://link.springer.com/article/10.1140/epjst/e2015-50118-1

value: The trade data are necessary to define a coherent network for all countries and all traded sectors (products and services). This may have some limitations for countries in which the exported products and services are not a good proxy of their industrial competitiveness. While the fitness analysis usually refers to manufacturing only, in the Universal Fitness also services are included. However, being the corresponding database less granular (i.e., more aggregated), also the products database has been aggregated and this can mildly impact on the algorithm outputs. In the final, universal dataset the relative weight of products and services reflects the respective importance in the international trade flow. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered. Finally, some countries have well known reporting issues in both products and services. For instance, some countries report only at some aggregation levels. Two notable examples are Ireland and China. In order to obtain a complete and more diversified database we adopt an interpolation procedure for such misreporting countries using the average declaration of the other countries in that year. We can expect the final results to mildly change if export records will change in the future.


Zambia

iso2Code: ZM

capitalCity: Lusaka

country_code: ZMB

country_name: Zambia

value: The Universal Economic Fitness (UEF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis.  Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries.   Countries with low UEF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries.  The starting data is the COMTRADE list of products and the IMF-BOP list of services exported by each country. This data defines a bipartite network of countries and sectors, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Universal Economic Fitness of all countries and the Complexity of all sectors. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods and, in the case of Universal Fitness, services.  The interaction among products and services traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework (e.g, science, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested, and consists in two coupled equations to be iterated up to convergence. The iterations are stopped when a rank-based criterion is met, that is, when we estimate that the next change in ranking will be in a number of iterations higher than 10^6. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data. While the Economic Fitness is computed considering the RCA values, the Universal Economic Fitness is computed using the market shares and so while the former is intensive, the latter is extensive, that is more correlated with countries size. As a consequence, while the natural counterpart of the Economic Fitness is GDP per capita, the Universal Fitness is more comparable with GDP.


Universal Economic Fitness Metric

value: Economic Fitness 2

Zimbabwe

iso2Code: ZW

value: Blend

capitalCity: Harare

country_code: ZWE

country_name: Zimbabwe