Bahrain

iso2Code: BH

iso2code: ZQ

value: Middle East & North Africa

iso2code: XX

capitalCity: Manama

country_code: BHR

country_name: Bahrain

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 Policy & Debt

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


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

value: Economic Fitness

Bosnia and Herzegovina

iso2Code: BA

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Sarajevo

country_code: BIH

country_name: Bosnia and Herzegovina

value: Annual

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


Belarus

iso2Code: BY

capitalCity: Minsk

country_code: BLR

country_name: Belarus

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


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


Bolivia

iso2Code: BO

iso2code: XN

value: Lower middle income

capitalCity: La Paz

country_code: BOL

country_name: Bolivia


Brazil

iso2Code: BR

capitalCity: Brasilia

country_code: BRA

country_name: Brazil


Brunei Darussalam

iso2Code: BN

value: East Asia & Pacific

iso2code: XD

value: High income

value: Not classified

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

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

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 Policy & Debt

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


Canada

iso2Code: CA

iso2code: XU

value: North America

iso2code: XX

capitalCity: Ottawa

country_code: CAN

country_name: Canada

value: Annual

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.


Switzerland

iso2Code: CH

value: Europe & Central Asia

capitalCity: Bern

country_code: CHE

country_name: Switzerland

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


Chile

iso2Code: CL

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XF

value: IBRD

capitalCity: Santiago

country_code: CHL

country_name: Chile


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

iso2code: XN

value: Lower middle income

value: IDA

capitalCity: Yamoussoukro

country_code: CIV

country_name: Côte d'Ivoire

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

Cameroon

iso2Code: CM

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XH

value: Blend

capitalCity: Yaounde

country_code: CMR

country_name: Cameroon


Congo, Dem. Rep.

iso2Code: CD

value: Low income

iso2code: XI

capitalCity: Kinshasa

country_code: COD

country_name: Congo, Dem. Rep.

value: Economic Policy & Debt

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


Congo, Rep.

iso2Code: CG

iso2code: ZG

iso2code: ZF

capitalCity: Brazzaville

country_code: COG

country_name: Congo, Rep.

value: Annual

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.


Colombia

iso2Code: CO

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Bogota

country_code: COL

country_name: Colombia

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


Costa Rica

iso2Code: CR

value: IBRD

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


Czechia

iso2Code: CZ

capitalCity: Prague

country_code: CZE

country_name: Czech Republic

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

Germany

iso2Code: DE

capitalCity: Berlin

country_code: DEU

country_name: Germany


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


Ecuador

iso2Code: EC

iso2code: ZJ

iso2code: XT

value: Upper middle income

capitalCity: Quito

country_code: ECU

country_name: Ecuador


Egypt, Arab Rep.

iso2Code: EG

capitalCity: Cairo

country_code: EGY

country_name: Egypt, Arab Rep.

value: Economic Policy & Debt


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: 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


Spain

iso2Code: ES

iso2code: XD

iso2code: XX

capitalCity: Madrid

country_code: ESP

country_name: Spain

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

value: Annual

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.


Estonia

iso2Code: EE

value: Europe & Central Asia

value: High income

capitalCity: Tallinn

country_code: EST

country_name: Estonia


Ethiopia

iso2Code: ET

capitalCity: Addis Ababa

country_code: ETH

country_name: Ethiopia

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

Finland

iso2Code: FI

value: Not classified

capitalCity: Helsinki

country_code: FIN

country_name: Finland


France

iso2Code: FR

capitalCity: Paris

country_code: FRA

country_name: France


Gabon

iso2Code: GA

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


Georgia

iso2Code: GE

value: Europe & Central Asia (excluding high income)

iso2code: XT

capitalCity: Tbilisi

country_code: GEO

country_name: Georgia


Ghana

iso2Code: GH

iso2code: XN

value: Lower middle income

capitalCity: Accra

country_code: GHA

country_name: Ghana


Guinea

iso2Code: GN

iso2code: ZG

iso2code: ZF

capitalCity: Conakry

country_code: GIN

country_name: Guinea

value: Economic Policy & Debt


Gambia, The

iso2Code: GM

value: Sub-Saharan Africa

iso2code: XM

iso2code: XI

capitalCity: Banjul

country_code: GMB

country_name: Gambia, The


Guinea-Bissau

iso2Code: GW

value: Sub-Saharan Africa (excluding high income)

capitalCity: Bissau

country_code: GNB

country_name: Guinea-Bissau


Greece

iso2Code: GR

iso2code: XD

iso2code: XX

capitalCity: Athens

country_code: GRC

country_name: Greece


Greenland

iso2Code: GL

capitalCity: Nuuk

country_code: GRL

country_name: Greenland


Guatemala

iso2Code: GT

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

iso2code: XF

capitalCity: Guatemala City

country_code: GTM

country_name: Guatemala


Guyana

iso2Code: GY

value: IDA

capitalCity: Georgetown

country_code: GUY

country_name: Guyana

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

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


Honduras

iso2Code: HN

iso2code: XN

capitalCity: Tegucigalpa

country_code: HND

country_name: Honduras

value: Annual

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.


Croatia

iso2Code: HR

value: Europe & Central Asia

value: High income

capitalCity: Zagreb

country_code: HRV

country_name: Croatia

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

Hungary

iso2Code: HU

value: Not classified

capitalCity: Budapest

country_code: HUN

country_name: Hungary


Indonesia

iso2Code: ID

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

value: IBRD

capitalCity: Jakarta

country_code: IDN

country_name: Indonesia


India

iso2Code: IN

value: South Asia

value: Lower middle income

capitalCity: New Delhi

country_code: IND

country_name: India

value: Economic Policy & Debt


Ireland

iso2Code: IE

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.


Iraq

iso2Code: IQ

value: Upper middle income

capitalCity: Baghdad

country_code: IRQ

country_name: Iraq

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


Iceland

iso2Code: IS

iso2code: XD

iso2code: XX

capitalCity: Reykjavik

country_code: ISL

country_name: Iceland

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


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

value: Europe & Central Asia

value: High income

capitalCity: Rome

country_code: ITA

country_name: Italy


value: Economic Fitness

Jordan

iso2Code: JO

iso2code: XF

capitalCity: Amman

country_code: JOR

country_name: Jordan

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.


Japan

iso2Code: JP

value: East Asia & Pacific

value: Not classified

capitalCity: Tokyo

country_code: JPN

country_name: Japan


Kazakhstan

iso2Code: KZ

value: Europe & Central Asia (excluding high income)

value: IBRD

capitalCity: Astana

country_code: KAZ

country_name: Kazakhstan


Kenya

iso2Code: KE

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XN

value: Lower middle income

iso2code: XH

value: Blend

capitalCity: Nairobi

country_code: KEN

country_name: Kenya


Kyrgyz Republic

iso2Code: KG

iso2code: XI

value: IDA

capitalCity: Bishkek

country_code: KGZ

country_name: Kyrgyz Republic

value: Economic Policy & Debt


Cambodia

iso2Code: KH

value: East Asia & Pacific (excluding high income)

capitalCity: Phnom Penh

country_code: KHM

country_name: Cambodia

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

value: Annual


Korea, Rep.

iso2Code: KR

capitalCity: Seoul

country_code: KOR

country_name: Korea, 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.

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

Kuwait

iso2Code: KW

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

value: High income

capitalCity: Kuwait City

country_code: KWT

country_name: Kuwait


Lao PDR

iso2Code: LA

value: East Asia & Pacific

capitalCity: Vientiane

country_code: LAO

country_name: Lao PDR

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.


Lebanon

iso2Code: LB

iso2code: XQ

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

value: IBRD

capitalCity: Beirut

country_code: LBN

country_name: Lebanon


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


Libya

iso2Code: LY

iso2code: XT

value: Upper middle income

iso2code: XF

capitalCity: Tripoli

country_code: LBY

country_name: Libya


Lithuania

iso2Code: LT

value: Europe & Central Asia

iso2code: XX

value: Not classified

capitalCity: Vilnius

country_code: LTU

country_name: Lithuania

value: Economic Policy & Debt


Latvia

iso2Code: LV

capitalCity: Riga

country_code: LVA

country_name: Latvia

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

value: Annual


Morocco

iso2Code: MA

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.

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

Madagascar

iso2Code: MG

iso2code: ZG

iso2code: ZF

iso2code: XI

value: IDA

capitalCity: Antananarivo

country_code: MDG

country_name: Madagascar


Mexico

iso2Code: MX

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Mexico City

country_code: MEX

country_name: Mexico

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.


North Macedonia

iso2Code: MK

value: Europe & Central Asia (excluding high income)

value: IBRD

capitalCity: Skopje

country_code: MKD

country_name: North Macedonia


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


Malta

iso2Code: MT

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

value: High income

value: Not classified

capitalCity: Valletta

country_code: MLT

country_name: Malta


Myanmar

iso2Code: MM

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Naypyidaw

country_code: MMR

country_name: Myanmar

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

value: Annual

value: Economic Policy & Debt


value: Economic Fitness

Montenegro

iso2Code: ME

value: Europe & Central Asia

iso2code: XT

value: Upper middle income

iso2code: XF

capitalCity: Podgorica

country_code: MNE

country_name: Montenegro

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: 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


Mongolia

iso2Code: MN

iso2code: XN

value: Lower middle income

value: IBRD

capitalCity: Ulaanbaatar

country_code: MNG

country_name: Mongolia

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.


Mozambique

iso2Code: MZ

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

value: Low income

value: IDA

capitalCity: Maputo

country_code: MOZ

country_name: Mozambique


Mauritania

iso2Code: MR

iso2code: XI

capitalCity: Nouakchott

country_code: MRT

country_name: Mauritania


Malawi

iso2Code: MW

capitalCity: Lilongwe

country_code: MWI

country_name: Malawi


Malaysia

iso2Code: MY

value: East Asia & Pacific

capitalCity: Kuala Lumpur

country_code: MYS

country_name: Malaysia


Niger

iso2Code: NE

iso2code: XM

capitalCity: Niamey

country_code: NER

country_name: Niger


Nigeria

iso2Code: NG

iso2code: XH

value: Blend

capitalCity: Abuja

country_code: NGA

country_name: Nigeria


Nicaragua

iso2Code: NI

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Managua

country_code: NIC

country_name: Nicaragua

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


value: Economic Fitness

Netherlands

iso2Code: NL

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Amsterdam

country_code: NLD

country_name: Netherlands

value: Annual

value: Economic Policy & Debt


Norway

iso2Code: NO

value: Europe & Central Asia

capitalCity: Oslo

country_code: NOR

country_name: Norway

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: 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


Nepal

iso2Code: NP

value: South Asia

iso2code: XN

value: Lower middle income

capitalCity: Kathmandu

country_code: NPL

country_name: Nepal

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.


New Zealand

iso2Code: NZ

capitalCity: Wellington

country_code: NZL

country_name: New Zealand


Oman

iso2Code: OM

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Muscat

country_code: OMN

country_name: Oman


Pakistan

iso2Code: PK

capitalCity: Islamabad

country_code: PAK

country_name: Pakistan


Panama

iso2Code: PA

iso2code: XF

value: IBRD

capitalCity: Panama City

country_code: PAN

country_name: Panama


Peru

iso2Code: PE

iso2code: XT

value: Upper middle income

capitalCity: Lima

country_code: PER

country_name: Peru


Philippines

iso2Code: PH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Manila

country_code: PHL

country_name: Philippines


Poland

iso2Code: PL

capitalCity: Warsaw

country_code: POL

country_name: Poland

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


value: Economic Fitness

Korea, Dem. People's Rep.

iso2Code: KP

iso2code: XM

value: Low income

capitalCity: Pyongyang

country_code: PRK

country_name: Korea, Dem. People's Rep.


Portugal

iso2Code: PT

iso2code: XD

value: High income

value: Not classified

capitalCity: Lisbon

country_code: PRT

country_name: Portugal


Paraguay

iso2Code: PY

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

capitalCity: Asuncion

country_code: PRY

country_name: Paraguay

value: Annual

value: Economic Policy & Debt


Qatar

iso2Code: QA

iso2code: XX

capitalCity: Doha

country_code: QAT

country_name: Qatar

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: 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


Romania

iso2Code: RO

value: Europe & Central Asia

capitalCity: Bucharest

country_code: ROU

country_name: Romania

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.


Russian Federation

iso2Code: RU

value: Europe & Central Asia (excluding high income)

value: IBRD

capitalCity: Moscow

country_code: RUS

country_name: Russian Federation


Rwanda

iso2Code: RW

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XI

value: IDA

capitalCity: Kigali

country_code: RWA

country_name: Rwanda


value: Economic Fitness

Saudi Arabia

iso2Code: SA

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Riyadh

country_code: SAU

country_name: Saudi Arabia

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


Sudan

iso2Code: SD

value: Low income

capitalCity: Khartoum

country_code: SDN

country_name: Sudan


Senegal

iso2Code: SN

iso2code: XN

value: Lower middle income

capitalCity: Dakar

country_code: SEN

country_name: Senegal


Singapore

iso2Code: SG

value: East Asia & Pacific

iso2code: XD

value: High income

value: Not classified

capitalCity: Singapore

country_code: SGP

country_name: Singapore


Sierra Leone

iso2Code: SL

iso2code: XM

capitalCity: Freetown

country_code: SLE

country_name: Sierra Leone

value: Annual

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.


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

capitalCity: San Salvador

country_code: SLV

country_name: El Salvador

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: 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


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

Serbia

iso2Code: RS

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

value: IBRD

capitalCity: Belgrade

country_code: SRB

country_name: Serbia

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


Suriname

iso2Code: SR

capitalCity: Paramaribo

country_code: SUR

country_name: Suriname


Slovak Republic

iso2Code: SK

value: High income

iso2code: XX

value: Not classified

capitalCity: Bratislava

country_code: SVK

country_name: Slovak Republic


Slovenia

iso2Code: SI

capitalCity: Ljubljana

country_code: SVN

country_name: Slovenia

value: Annual

value: Economic Policy & Debt


Sweden

iso2Code: SE

capitalCity: Stockholm

country_code: SWE

country_name: Sweden

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.


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


Chad

iso2Code: TD

iso2code: ZF

iso2code: XM

capitalCity: N'Djamena

country_code: TCD

country_name: Chad

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: 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


Togo

iso2Code: TG

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

capitalCity: Lome

country_code: TGO

country_name: Togo


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

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

value: IDA

capitalCity: Dushanbe

country_code: TJK

country_name: Tajikistan

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


Turkmenistan

iso2Code: TM

value: IBRD

capitalCity: Ashgabat

country_code: TKM

country_name: Turkmenistan

value: Annual

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

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.


Turkiye

iso2Code: TR

capitalCity: Ankara

country_code: TUR

country_name: Turkey

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

Tanzania

iso2Code: TZ

iso2code: ZG

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

capitalCity: Dodoma

country_code: TZA

country_name: Tanzania


Uganda

iso2Code: UG

value: Low income

capitalCity: Kampala

country_code: UGA

country_name: Uganda


Ukraine

iso2Code: UA

value: Europe & Central Asia

capitalCity: Kiev

country_code: UKR

country_name: Ukraine


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


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 Ranking (1 = high, 149 = low)


Venezuela, RB

iso2Code: VE

iso2code: XY

iso2code: XF

value: IBRD

capitalCity: Caracas

country_code: VEN

country_name: Venezuela, RB

value: Annual

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

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

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: 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


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.


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

capitalCity: Pretoria

country_code: ZAF

country_name: South Africa


Zambia

iso2Code: ZM

value: Lower middle income

capitalCity: Lusaka

country_code: ZMB

country_name: Zambia

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


Zimbabwe

iso2Code: ZW

value: Blend

capitalCity: Harare

country_code: ZWE

country_name: Zimbabwe

value: Annual


Universal Economic Fitness Metric

value: Economic Fitness 2

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

Afghanistan

iso2Code: AF

value: South Asia

capitalCity: Kabul

country_code: AFG

country_name: Afghanistan

value: Universal Economic Fitness Metric

value: CC BY-4.0

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

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

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

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

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

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


Angola

iso2Code: AO

value: IBRD

capitalCity: Luanda

country_code: AGO

country_name: Angola

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


Universal Economic Fitness Metric

Albania

iso2Code: AL

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

capitalCity: Tirane

country_code: ALB

country_name: Albania


Argentina

iso2Code: AR

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XF

capitalCity: Buenos Aires

country_code: ARG

country_name: Argentina


Universal Economic Fitness Metric

Armenia

iso2Code: AM

value: Upper middle income

capitalCity: Yerevan

country_code: ARM

country_name: Armenia


Australia

iso2Code: AU

value: East Asia & Pacific

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Canberra

country_code: AUS

country_name: Australia


Universal Economic Fitness Metric

value: Economic Fitness 2

Austria

iso2Code: AT

capitalCity: Vienna

country_code: AUT

country_name: Austria


Azerbaijan

iso2Code: AZ

capitalCity: Baku

country_code: AZE

country_name: Azerbaijan


Universal Economic Fitness Metric

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


Belgium

iso2Code: BE

capitalCity: Brussels

country_code: BEL

country_name: Belgium

value: Universal Economic Fitness Metric

value: CC BY-4.0

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

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

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

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

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


Universal Economic Fitness Metric

Benin

iso2Code: BJ

iso2code: XN

value: Lower middle income

capitalCity: Porto-Novo

country_code: BEN

country_name: Benin

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

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


Burkina Faso

iso2Code: BF

capitalCity: Ouagadougou

country_code: BFA

country_name: Burkina Faso


Universal Economic Fitness Metric

Bangladesh

iso2Code: BD

value: South Asia

capitalCity: Dhaka

country_code: BGD

country_name: Bangladesh


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


Universal Economic Fitness Metric

value: Economic Fitness 2

Bahrain

iso2Code: BH

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

value: High income

value: Not classified

capitalCity: Manama

country_code: BHR

country_name: Bahrain


Bosnia and Herzegovina

iso2Code: BA

capitalCity: Sarajevo

country_code: BIH

country_name: Bosnia and Herzegovina


Universal 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


Universal Economic Fitness Metric

Bolivia

iso2Code: BO

capitalCity: La Paz

country_code: BOL

country_name: Bolivia

value: Universal Economic Fitness Metric

value: CC BY-4.0


Brazil

iso2Code: BR

capitalCity: Brasilia

country_code: BRA

country_name: Brazil

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

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

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

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

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


Universal Economic Fitness Metric

Brunei Darussalam

iso2Code: BN

value: East Asia & Pacific

iso2code: XX

capitalCity: Bandar Seri Begawan

country_code: BRN

country_name: Brunei Darussalam

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


Bhutan

iso2Code: BT

value: South Asia

iso2code: XN

value: Lower middle income

iso2code: XI

value: IDA

capitalCity: Thimphu

country_code: BTN

country_name: Bhutan

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


Universal Economic Fitness Metric

Botswana

iso2Code: BW

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XT

value: Upper middle income

value: IBRD

capitalCity: Gaborone

country_code: BWA

country_name: Botswana


value: Economic Fitness 2

Central African Republic

iso2Code: CF

iso2code: XM

value: Low income

capitalCity: Bangui

country_code: CAF

country_name: Central African Republic


Universal Economic Fitness Metric

Canada

iso2Code: CA

iso2code: XU

value: North America

iso2code: XD

value: High income

value: Not classified

capitalCity: Ottawa

country_code: CAN

country_name: Canada


Switzerland

iso2Code: CH

value: Europe & Central Asia

capitalCity: Bern

country_code: CHE

country_name: Switzerland

value: Universal Economic Fitness Metric

value: CC BY-4.0

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

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

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

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


Universal Economic Fitness Metric

Chile

iso2Code: CL

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XF

capitalCity: Santiago

country_code: CHL

country_name: Chile

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

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


China

iso2Code: CN

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Beijing

country_code: CHN

country_name: China

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


Universal Economic Fitness Metric

Cote d'Ivoire

iso2Code: CI

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

value: Lower middle income

value: IDA

capitalCity: Yamoussoukro

country_code: CIV

country_name: Côte d'Ivoire


value: Economic Fitness 2

Cameroon

iso2Code: CM

iso2code: XN

iso2code: XH

value: Blend

capitalCity: Yaounde

country_code: CMR

country_name: Cameroon


Universal Economic Fitness Metric

Congo, Dem. Rep.

iso2Code: CD

iso2code: ZG

value: Low income

iso2code: XI

capitalCity: Kinshasa

country_code: COD

country_name: Congo, Dem. Rep.


Congo, Rep.

iso2Code: CG

iso2code: ZF

capitalCity: Brazzaville

country_code: COG

country_name: Congo, Rep.


Universal Economic Fitness Metric

Colombia

iso2Code: CO

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: Upper middle income

value: IBRD

capitalCity: Bogota

country_code: COL

country_name: Colombia


Cabo Verde

iso2Code: CV

capitalCity: Praia

country_code: CPV

country_name: Cabo Verde

value: Universal Economic Fitness Metric

value: CC BY-4.0

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

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

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

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


Universal Economic Fitness Metric

Costa Rica

iso2Code: CR

value: Latin America & Caribbean

capitalCity: San Jose

country_code: CRI

country_name: Costa Rica

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

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