Economic Fitness Metric

Bangladesh

iso2Code: BD

value: South Asia

capitalCity: Dhaka

country_code: BGD

country_name: Bangladesh

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


value: Economic Fitness

sourceNone: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

Bulgaria

iso2Code: BG

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Sofia

country_code: BGR

country_name: Bulgaria

value: Economic Fitness Metric

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.

schema: wb


Economic Fitness Metric

Bahrain

iso2Code: BH

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Manama

country_code: BHR

country_name: Bahrain

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Bosnia and Herzegovina

iso2Code: BA

capitalCity: Sarajevo

country_code: BIH

country_name: Bosnia and Herzegovina


Economic Fitness Metric

Belarus

iso2Code: BY

capitalCity: Minsk

country_code: BLR

country_name: Belarus


Belize

iso2Code: BZ

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Belmopan

country_code: BLZ

country_name: Belize


Economic Fitness Metric

Bolivia

iso2Code: BO

iso2code: XN

value: Lower middle income

capitalCity: La Paz

country_code: BOL

country_name: Bolivia

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


value: Economic Fitness

Brazil

iso2Code: BR

capitalCity: Brasilia

country_code: BRA

country_name: Brazil


Economic Fitness Metric

Brunei Darussalam

iso2Code: BN

value: East Asia & Pacific

capitalCity: Bandar Seri Begawan

country_code: BRN

country_name: Brunei Darussalam


Bhutan

iso2Code: BT

value: South Asia

iso2code: XI

value: IDA

capitalCity: Thimphu

country_code: BTN

country_name: Bhutan


Economic Fitness Metric

Central African Republic

iso2Code: CF

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

capitalCity: Bangui

country_code: CAF

country_name: Central African Republic

value: Economic Fitness Metric


Canada

iso2Code: CA

iso2code: XU

value: North America

iso2code: XD

capitalCity: Ottawa

country_code: CAN

country_name: Canada

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Economic Fitness Metric

Switzerland

iso2Code: CH

value: Europe & Central Asia

value: High income

iso2code: XX

value: Not classified

capitalCity: Bern

country_code: CHE

country_name: Switzerland

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


value: Economic Fitness

Chile

iso2Code: CL

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XF

value: IBRD

capitalCity: Santiago

country_code: CHL

country_name: Chile

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

China

iso2Code: CN

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Beijing

country_code: CHN

country_name: China


Cote d'Ivoire

iso2Code: CI

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XN

value: Lower middle income

value: IDA

capitalCity: Yamoussoukro

country_code: CIV

country_name: Côte d'Ivoire

value: Economic Fitness Metric


Economic Fitness Metric

Cameroon

iso2Code: CM

iso2code: XH

value: Blend

capitalCity: Yaounde

country_code: CMR

country_name: Cameroon

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


value: Economic Fitness

Congo, Dem. Rep.

iso2Code: CD

iso2code: ZG

value: Low income

iso2code: XI

capitalCity: Kinshasa

country_code: COD

country_name: Congo, Dem. Rep.

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Congo, Rep.

iso2Code: CG

iso2code: ZF

capitalCity: Brazzaville

country_code: COG

country_name: Congo, Rep.

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Colombia

iso2Code: CO

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: IBRD

capitalCity: Bogota

country_code: COL

country_name: Colombia


Economic Fitness Metric

Costa Rica

iso2Code: CR

value: Upper middle income

capitalCity: San Jose

country_code: CRI

country_name: Costa Rica


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


Economic Fitness Metric

Czechia

iso2Code: CZ

capitalCity: Prague

country_code: CZE

country_name: Czech Republic

value: Economic Fitness Metric


Germany

iso2Code: DE

capitalCity: Berlin

country_code: DEU

country_name: Germany


Economic Fitness Metric

Denmark

iso2Code: DK

capitalCity: Copenhagen

country_code: DNK

country_name: Denmark


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

iso2code: XF

capitalCity: Algiers

country_code: DZA

country_name: Algeria


Economic Fitness Metric

Ecuador

iso2Code: EC

iso2code: ZJ

iso2code: XT

capitalCity: Quito

country_code: ECU

country_name: Ecuador

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt


value: Economic Fitness

Egypt, Arab Rep.

iso2Code: EG

capitalCity: Cairo

country_code: EGY

country_name: Egypt, Arab Rep.

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Economic Fitness Metric

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

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Spain

iso2Code: ES

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

capitalCity: Madrid

country_code: ESP

country_name: Spain


Economic Fitness Metric

Estonia

iso2Code: EE

value: Not classified

capitalCity: Tallinn

country_code: EST

country_name: Estonia

value: Economic Fitness Metric


Ethiopia

iso2Code: ET

capitalCity: Addis Ababa

country_code: ETH

country_name: Ethiopia


Economic Fitness Metric

Finland

iso2Code: FI

capitalCity: Helsinki

country_code: FIN

country_name: Finland


France

iso2Code: FR

capitalCity: Paris

country_code: FRA

country_name: France


Economic Fitness Metric

Gabon

iso2Code: GA

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Libreville

country_code: GAB

country_name: Gabon


United Kingdom

iso2Code: GB

capitalCity: London

country_code: GBR

country_name: United Kingdom

value: Annual


Economic Fitness Metric

Georgia

iso2Code: GE

value: Europe & Central Asia (excluding high income)

iso2code: XT

capitalCity: Tbilisi

country_code: GEO

country_name: Georgia

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth


value: Economic Fitness

Ghana

iso2Code: GH

iso2code: XN

value: Lower middle income

capitalCity: Accra

country_code: GHA

country_name: Ghana

value: Economic Policy & Debt


Economic Fitness Metric

Guinea

iso2Code: GN

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

capitalCity: Conakry

country_code: GIN

country_name: Guinea

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Gambia, The

iso2Code: GM

iso2code: XM

value: Low income

iso2code: XI

value: IDA

capitalCity: Banjul

country_code: GMB

country_name: Gambia, The

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


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 products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Greece

iso2Code: GR

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Athens

country_code: GRC

country_name: Greece


Economic Fitness Metric

Greenland

iso2Code: GL

capitalCity: Nuuk

country_code: GRL

country_name: Greenland

value: Economic Fitness Metric


Guatemala

iso2Code: GT

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Guatemala City

country_code: GTM

country_name: Guatemala

value: Annual


Economic Fitness Metric

value: Economic Fitness

Guyana

iso2Code: GY

capitalCity: Georgetown

country_code: GUY

country_name: Guyana

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt


Honduras

iso2Code: HN

iso2code: XN

value: Lower middle income

capitalCity: Tegucigalpa

country_code: HND

country_name: Honduras

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Economic Fitness Metric

Croatia

iso2Code: HR

capitalCity: Zagreb

country_code: HRV

country_name: Croatia


Hungary

iso2Code: HU

capitalCity: Budapest

country_code: HUN

country_name: Hungary


Economic Fitness Metric

Indonesia

iso2Code: ID

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

capitalCity: Jakarta

country_code: IDN

country_name: Indonesia


India

iso2Code: IN

value: South Asia

capitalCity: New Delhi

country_code: IND

country_name: India

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Ireland

iso2Code: IE

value: Europe & Central Asia

value: High income

value: Not classified

capitalCity: Dublin

country_code: IRL

country_name: Ireland


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: Economic Fitness Metric


Economic Fitness Metric

Iraq

iso2Code: IQ

value: Upper middle income

value: IBRD

capitalCity: Baghdad

country_code: IRQ

country_name: Iraq

value: Annual


value: Economic Fitness

Iceland

iso2Code: IS

iso2code: XD

iso2code: XX

capitalCity: Reykjavik

country_code: ISL

country_name: Iceland

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt


Economic Fitness Metric

Israel

iso2Code: IL

country_code: ISR

country_name: Israel

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Italy

iso2Code: IT

capitalCity: Rome

country_code: ITA

country_name: Italy


Economic Fitness Metric

Jordan

iso2Code: JO

value: Lower middle income

iso2code: XF

capitalCity: Amman

country_code: JOR

country_name: Jordan


Japan

iso2Code: JP

value: East Asia & Pacific

capitalCity: Tokyo

country_code: JPN

country_name: Japan


Economic Fitness Metric

Kazakhstan

iso2Code: KZ

value: Europe & Central Asia (excluding high income)

capitalCity: Astana

country_code: KAZ

country_name: Kazakhstan

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Kenya

iso2Code: KE

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XN

iso2code: XH

value: Blend

capitalCity: Nairobi

country_code: KEN

country_name: Kenya

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Kyrgyz Republic

iso2Code: KG

value: Europe & Central Asia

iso2code: XI

value: IDA

capitalCity: Bishkek

country_code: KGZ

country_name: Kyrgyz Republic


Cambodia

iso2Code: KH

value: East Asia & Pacific (excluding high income)

capitalCity: Phnom Penh

country_code: KHM

country_name: Cambodia

value: Economic Fitness Metric


Economic Fitness Metric

Korea, Rep.

iso2Code: KR

value: High income

value: Not classified

capitalCity: Seoul

country_code: KOR

country_name: Korea, Rep.

value: Annual


value: Economic Fitness

Kuwait

iso2Code: KW

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

capitalCity: Kuwait City

country_code: KWT

country_name: Kuwait

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt


Economic Fitness Metric

Lao PDR

iso2Code: LA

value: East Asia & Pacific

value: Lower middle income

capitalCity: Vientiane

country_code: LAO

country_name: Lao PDR

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Lebanon

iso2Code: LB

iso2code: XQ

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

value: IBRD

capitalCity: Beirut

country_code: LBN

country_name: Lebanon

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Liberia

iso2Code: LR

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

capitalCity: Monrovia

country_code: LBR

country_name: Liberia

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Libya

iso2Code: LY

iso2code: XT

value: Upper middle income

iso2code: XF

capitalCity: Tripoli

country_code: LBY

country_name: Libya

value: Economic Fitness Metric


Economic Fitness Metric

Lithuania

iso2Code: LT

value: Europe & Central Asia

value: High income

iso2code: XX

value: Not classified

capitalCity: Vilnius

country_code: LTU

country_name: Lithuania

value: Annual


value: Economic Fitness

Latvia

iso2Code: LV

capitalCity: Riga

country_code: LVA

country_name: Latvia

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Policy & Debt


Economic Fitness Metric

Morocco

iso2Code: MA

value: Middle East & North Africa

iso2code: XN

value: Lower middle income

capitalCity: Rabat

country_code: MAR

country_name: Morocco

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Madagascar

iso2Code: MG

iso2code: ZG

iso2code: ZF

iso2code: XI

value: IDA

capitalCity: Antananarivo

country_code: MDG

country_name: Madagascar

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Mexico

iso2Code: MX

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: IBRD

capitalCity: Mexico City

country_code: MEX

country_name: Mexico


North Macedonia

iso2Code: MK

value: Europe & Central Asia (excluding high income)

value: Upper middle income

capitalCity: Skopje

country_code: MKD

country_name: North Macedonia

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Mali

iso2Code: ML

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

capitalCity: Bamako

country_code: MLI

country_name: Mali

value: Economic Fitness Metric


value: Economic Fitness

Malta

iso2Code: MT

iso2code: ZQ

iso2code: XD

value: High income

value: Not classified

capitalCity: Valletta

country_code: MLT

country_name: Malta

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth


Economic Fitness Metric

Myanmar

iso2Code: MM

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

value: Lower middle income

value: IDA

capitalCity: Naypyidaw

country_code: MMR

country_name: Myanmar

value: Economic Policy & Debt

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Montenegro

iso2Code: ME

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Podgorica

country_code: MNE

country_name: Montenegro

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Mongolia

iso2Code: MN

iso2code: XN

capitalCity: Ulaanbaatar

country_code: MNG

country_name: Mongolia

value: Economic Fitness Metric

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


value: Economic Fitness

Mozambique

iso2Code: MZ

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

value: Low income

capitalCity: Maputo

country_code: MOZ

country_name: Mozambique


Economic Fitness Metric

Mauritania

iso2Code: MR

iso2code: XI

capitalCity: Nouakchott

country_code: MRT

country_name: Mauritania

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth


Malawi

iso2Code: MW

capitalCity: Lilongwe

country_code: MWI

country_name: Malawi


Economic Fitness Metric

Malaysia

iso2Code: MY

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Kuala Lumpur

country_code: MYS

country_name: Malaysia


Niger

iso2Code: NE

iso2code: XM

value: IDA

capitalCity: Niamey

country_code: NER

country_name: Niger

value: Economic Policy & Debt


Economic Fitness Metric

Nigeria

iso2Code: NG

value: Lower middle income

iso2code: XH

value: Blend

capitalCity: Abuja

country_code: NGA

country_name: Nigeria

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Nicaragua

iso2Code: NI

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Managua

country_code: NIC

country_name: Nicaragua


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: Economic Fitness Metric

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


value: Economic Fitness

Norway

iso2Code: NO

capitalCity: Oslo

country_code: NOR

country_name: Norway

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Nepal

iso2Code: NP

value: South Asia

iso2code: XN

capitalCity: Kathmandu

country_code: NPL

country_name: Nepal

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth


New Zealand

iso2Code: NZ

value: East Asia & Pacific

capitalCity: Wellington

country_code: NZL

country_name: New Zealand


Economic Fitness Metric

Oman

iso2Code: OM

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Muscat

country_code: OMN

country_name: Oman

value: Economic Policy & Debt


Pakistan

iso2Code: PK

value: Lower middle income

value: Blend

capitalCity: Islamabad

country_code: PAK

country_name: Pakistan


Economic Fitness Metric

Panama

iso2Code: PA

value: Latin America & Caribbean

iso2code: XF

value: IBRD

capitalCity: Panama City

country_code: PAN

country_name: Panama

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Peru

iso2Code: PE

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Lima

country_code: PER

country_name: Peru


Economic Fitness Metric

Philippines

iso2Code: PH

value: East Asia & Pacific (excluding high income)

capitalCity: Manila

country_code: PHL

country_name: Philippines

value: Economic Fitness Metric


value: Economic Fitness

Poland

iso2Code: PL

value: Europe & Central Asia

value: High income

capitalCity: Warsaw

country_code: POL

country_name: Poland

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Korea, Dem. People's Rep.

iso2Code: KP

iso2code: XM

value: Low income

value: Not classified

capitalCity: Pyongyang

country_code: PRK

country_name: Korea, Dem. People's Rep.

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Portugal

iso2Code: PT

iso2code: XD

capitalCity: Lisbon

country_code: PRT

country_name: Portugal

value: Economic Policy & Debt


Economic Fitness Metric

Paraguay

iso2Code: PY

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

capitalCity: Asuncion

country_code: PRY

country_name: Paraguay


Qatar

iso2Code: QA

value: Middle East & North Africa

iso2code: XX

capitalCity: Doha

country_code: QAT

country_name: Qatar


Economic Fitness Metric

Romania

iso2Code: RO

value: IBRD

capitalCity: Bucharest

country_code: ROU

country_name: Romania

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Russian Federation

iso2Code: RU

value: Europe & Central Asia (excluding high income)

value: Upper middle income

capitalCity: Moscow

country_code: RUS

country_name: Russian Federation


Economic Fitness Metric

Rwanda

iso2Code: RW

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XI

value: IDA

capitalCity: Kigali

country_code: RWA

country_name: Rwanda

value: Economic Fitness Metric


value: Economic Fitness

Saudi Arabia

iso2Code: SA

iso2code: ZQ

value: High income

capitalCity: Riyadh

country_code: SAU

country_name: Saudi Arabia

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Sudan

iso2Code: SD

value: Low income

capitalCity: Khartoum

country_code: SDN

country_name: Sudan

value: Annual


Senegal

iso2Code: SN

iso2code: XN

value: Lower middle income

capitalCity: Dakar

country_code: SEN

country_name: Senegal

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Singapore

iso2Code: SG

value: East Asia & Pacific

iso2code: XD

value: Not classified

capitalCity: Singapore

country_code: SGP

country_name: Singapore

value: Economic Policy & Debt


Sierra Leone

iso2Code: SL

iso2code: XM

capitalCity: Freetown

country_code: SLE

country_name: Sierra Leone

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Economic Fitness Metric

El Salvador

iso2Code: SV

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: San Salvador

country_code: SLV

country_name: El Salvador


Somalia

iso2Code: SO

iso2code: ZG

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

value: IDA

capitalCity: Mogadishu

country_code: SOM

country_name: Somalia

value: Economic Fitness Metric


Economic Fitness Metric

value: Economic Fitness

Serbia

iso2Code: RS

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

capitalCity: Belgrade

country_code: SRB

country_name: Serbia

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Suriname

iso2Code: SR

capitalCity: Paramaribo

country_code: SUR

country_name: Suriname

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Slovak Republic

iso2Code: SK

value: High income

iso2code: XX

value: Not classified

capitalCity: Bratislava

country_code: SVK

country_name: Slovak Republic

value: Economic Policy & Debt


Slovenia

iso2Code: SI

capitalCity: Ljubljana

country_code: SVN

country_name: Slovenia

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Economic Fitness Metric

Sweden

iso2Code: SE

capitalCity: Stockholm

country_code: SWE

country_name: Sweden


Syrian Arab Republic

iso2Code: SY

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

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

value: Low income

iso2code: XI

capitalCity: Damascus

country_code: SYR

country_name: Syrian Arab Republic


Economic Fitness Metric

Chad

iso2Code: TD

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

capitalCity: N'Djamena

country_code: TCD

country_name: Chad


Togo

iso2Code: TG

value: IDA

capitalCity: Lome

country_code: TGO

country_name: Togo

value: Economic Fitness Metric


Economic Fitness Metric

value: Economic Fitness

Thailand

iso2Code: TH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Bangkok

country_code: THA

country_name: Thailand


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: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.


Economic Fitness Metric

Turkmenistan

iso2Code: TM

capitalCity: Ashgabat

country_code: TKM

country_name: Turkmenistan

value: Economic Policy & Debt

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


Tunisia

iso2Code: TN

value: Middle East & North Africa

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

capitalCity: Tunis

country_code: TUN

country_name: Tunisia


Economic Fitness Metric

Turkiye

iso2Code: TR

capitalCity: Ankara

country_code: TUR

country_name: Turkey

value: Economic Fitness Metric


value: Economic Fitness

Tanzania

iso2Code: TZ

iso2code: ZG

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

value: IDA

capitalCity: Dodoma

country_code: TZA

country_name: Tanzania


Economic Fitness Metric

Uganda

iso2Code: UG

value: Low income

capitalCity: Kampala

country_code: UGA

country_name: Uganda


Ukraine

iso2Code: UA

value: Europe & Central Asia

value: IBRD

capitalCity: Kiev

country_code: UKR

country_name: Ukraine


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


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: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth


Economic Fitness Metric

Uzbekistan

iso2Code: UZ

value: Europe & Central Asia (excluding high income)

value: Lower middle income

iso2code: XH

value: Blend

capitalCity: Tashkent

country_code: UZB

country_name: Uzbekistan

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

value: Economic Policy & Debt

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


value: Economic Fitness

Venezuela, RB

iso2Code: VE

iso2code: XY

iso2code: XF

capitalCity: Caracas

country_code: VEN

country_name: Venezuela, RB

value: Economic Fitness Metric


Economic Fitness Metric

Viet Nam

iso2Code: VN

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XN

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.


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

value: IBRD

capitalCity: Pretoria

country_code: ZAF

country_name: South Africa

value: Annual


Zambia

iso2Code: ZM

value: Lower middle income

capitalCity: Lusaka

country_code: ZMB

country_name: Zambia

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth

value: Economic Fitness (EF) 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 EF 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 exported by each country. This data defines a bipartite network of countries and products, or goods and services. A suitably designed mathematical algorithm applied to this network leads to the Economic Fitness of all countries and the Complexity of all products. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.


Economic Fitness Metric

Zimbabwe

iso2Code: ZW

value: Blend

capitalCity: Harare

country_code: ZWE

country_name: Zimbabwe

value: Economic Policy & Debt

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


name: Economic Fitness Ranking (1 = high, 149 = low)

value: Economic Fitness

Afghanistan

iso2Code: AF

value: South Asia

capitalCity: Kabul

country_code: AFG

country_name: Afghanistan


Angola

iso2Code: AO

capitalCity: Luanda

country_code: AGO

country_name: Angola

value: Economic Fitness Ranking (1 = high, 149 = low)


Albania

iso2Code: AL

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

capitalCity: Tirane

country_code: ALB

country_name: Albania


Andorra

iso2Code: AD

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Andorra la Vella

country_code: AND

country_name: Andorra


United Arab Emirates

iso2Code: AE

value: Middle East & North Africa

capitalCity: Abu Dhabi

country_code: ARE

country_name: United Arab Emirates


Argentina

iso2Code: AR

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Buenos Aires

country_code: ARG

country_name: Argentina


Armenia

iso2Code: AM

capitalCity: Yerevan

country_code: ARM

country_name: Armenia

value: Annual

value: World Bank, Economic Fitness project. For more details, please visit https://www.nature.com/articles/srep00723 and http://documents.worldbank.org/curated/en/632611498503242103/On-the-predictability-of-growth


Australia

iso2Code: AU

value: East Asia & Pacific

capitalCity: Canberra

country_code: AUS

country_name: Australia

value: Economic Policy & Debt


Austria

iso2Code: AT

capitalCity: Vienna

country_code: AUT

country_name: Austria

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. 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.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.

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.  The interaction among products 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 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. 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.


value: Economic Fitness

Azerbaijan

iso2Code: AZ

iso2code: XT

capitalCity: Baku

country_code: AZE

country_name: Azerbaijan


Burundi

iso2Code: BI

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: Bujumbura

country_code: BDI

country_name: Burundi

value: Economic Fitness Ranking (1 = high, 149 = low)


Belgium

iso2Code: BE

value: Europe & Central Asia

iso2code: XD

value: High income

value: Not classified

capitalCity: Brussels

country_code: BEL

country_name: Belgium


Benin

iso2Code: BJ

iso2code: XN

value: Lower middle income

capitalCity: Porto-Novo

country_code: BEN

country_name: Benin


Burkina Faso

iso2Code: BF

capitalCity: Ouagadougou

country_code: BFA

country_name: Burkina Faso


Bangladesh

iso2Code: BD

value: South Asia

capitalCity: Dhaka

country_code: BGD

country_name: Bangladesh


Bulgaria

iso2Code: BG

value: Europe & Central Asia (excluding high income)

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Sofia

country_code: BGR

country_name: Bulgaria

value: Annual