Low & middle income

iso2Code: XO

country_code: LMY

country_name: Low & middle income

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


name: Organic water pollutant (BOD) emissions (kg per day per worker)

value: WDI Database Archives

sourceNone: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

country_code: LSA

country_name: Low-income Africa

value: CC BY-4.0

value: Annual

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.

schema: wb


Lesotho

iso2Code: LS

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XN

value: Lower middle income

iso2code: XI

value: IDA

capitalCity: Maseru

country_code: LSO

country_name: Lesotho

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Late-demographic dividend

value: Aggregates

country_code: LTE

country_name: Late-demographic dividend


Lithuania

iso2Code: LT

value: Europe & Central Asia

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Vilnius

country_code: LTU

country_name: Lithuania


value: WDI Database Archives

Luxembourg

iso2Code: LU

capitalCity: Luxembourg

country_code: LUX

country_name: Luxembourg

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Latvia

iso2Code: LV

capitalCity: Riga

country_code: LVA

country_name: Latvia

value: CC BY-4.0

value: Annual


Macao SAR, China

iso2Code: MO

value: East Asia & Pacific

country_code: MAC

country_name: Macao SAR, China

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


St. Martin (French part)

iso2Code: MF

iso2code: ZJ

value: Latin America & Caribbean

capitalCity: Marigot

country_code: MAF

country_name: St. Martin (French part)

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Morocco

iso2Code: MA

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

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

value: Lower middle income

iso2code: XF

value: IBRD

capitalCity: Rabat

country_code: MAR

country_name: Morocco


Monaco

iso2Code: MC

value: Europe & Central Asia

capitalCity: Monaco

country_code: MCO

country_name: Monaco

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


value: WDI Database Archives

Moldova

iso2Code: MD

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Chisinau

country_code: MDA

country_name: Moldova


Middle East (developing only)

value: Aggregates

country_code: MDE

country_name: Middle East (developing only)

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


Madagascar

iso2Code: MG

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XM

value: Low income

value: IDA

capitalCity: Antananarivo

country_code: MDG

country_name: Madagascar

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


Maldives

iso2Code: MV

value: South Asia

iso2code: XI

capitalCity: Male

country_code: MDV

country_name: Maldives

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


country_code: MEA

country_name: Middle East & North Africa

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


value: WDI Database Archives

Mexico

iso2Code: MX

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: IBRD

capitalCity: Mexico City

country_code: MEX

country_name: Mexico


Marshall Islands

iso2Code: MH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

value: Upper middle income

capitalCity: Majuro

country_code: MHL

country_name: Marshall Islands

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Middle income

iso2Code: XP

value: Aggregates

country_code: MIC

country_name: Middle income

value: Annual


North Macedonia

iso2Code: MK

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

capitalCity: Skopje

country_code: MKD

country_name: North Macedonia

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


Mali

iso2Code: ML

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

value: Low income

value: IDA

capitalCity: Bamako

country_code: MLI

country_name: Mali

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


value: WDI Database Archives

Malta

iso2Code: MT

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Valletta

country_code: MLT

country_name: Malta

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Myanmar

iso2Code: MM

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Naypyidaw

country_code: MMR

country_name: Myanmar

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


iso2Code: XQ

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

value: Aggregates

country_code: MNA

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


Montenegro

iso2Code: ME

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Podgorica

country_code: MNE

country_name: Montenegro

value: Organic water pollutant (BOD) emissions (kg per day per worker)


value: WDI Database Archives

Mongolia

iso2Code: MN

capitalCity: Ulaanbaatar

country_code: MNG

country_name: Mongolia

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Northern Mariana Islands

iso2Code: MP

value: East Asia & Pacific

value: High income

value: Not classified

capitalCity: Saipan

country_code: MNP

country_name: Northern Mariana Islands

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Mozambique

iso2Code: MZ

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: Maputo

country_code: MOZ

country_name: Mozambique

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Mauritania

iso2Code: MR

value: Lower middle income

capitalCity: Nouakchott

country_code: MRT

country_name: Mauritania

value: Annual

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


value: WDI Database Archives

country_code: MTQ

country_name: Martinique

value: Organic water pollutant (BOD) emissions (kg per day per worker)


Mauritius

iso2Code: MU

value: Upper middle income

value: IBRD

capitalCity: Port Louis

country_code: MUS

country_name: Mauritius

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Malawi

iso2Code: MW

capitalCity: Lilongwe

country_code: MWI

country_name: Malawi

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Malaysia

iso2Code: MY

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

capitalCity: Kuala Lumpur

country_code: MYS

country_name: Malaysia


country_code: MYT

country_name: Mayotte


North America

iso2Code: XU

value: Aggregates

country_code: NAC

country_name: North America


Namibia

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

capitalCity: Windhoek

country_code: NAM

country_name: Namibia


New Caledonia

iso2Code: NC

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Noum'ea

country_code: NCL

country_name: New Caledonia

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Niger

iso2Code: NE

value: Low income

value: IDA

capitalCity: Niamey

country_code: NER

country_name: Niger

value: Annual

value: World Bank and UNIDO's industry database.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


value: WDI Database Archives

Nigeria

iso2Code: NG

iso2code: XN

value: Lower middle income

iso2code: XH

value: Blend

capitalCity: Abuja

country_code: NGA

country_name: Nigeria

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Environment: Water pollution


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: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Netherlands

iso2Code: NL

value: Europe & Central Asia

value: High income

value: Not classified

capitalCity: Amsterdam

country_code: NLD

country_name: Netherlands


country_code: NOC

country_name: High income: nonOECD

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Norway

iso2Code: NO

capitalCity: Oslo

country_code: NOR

country_name: Norway

value: Annual


Nepal

iso2Code: NP

value: South Asia

iso2code: XI

value: IDA

capitalCity: Kathmandu

country_code: NPL

country_name: Nepal

value: World Bank and UNIDO's industry database.


value: WDI Database Archives

Nauru

iso2Code: NR

value: East Asia & Pacific

iso2code: XF

value: IBRD

capitalCity: Yaren District

country_code: NRU

country_name: Nauru

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


New Zealand

iso2Code: NZ

capitalCity: Wellington

country_code: NZL

country_name: New Zealand

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Environment: Water pollution


country_code: OEC

country_name: High income: OECD

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


OECD members

iso2Code: OE

value: Aggregates

country_code: OED

country_name: OECD members

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Oman

iso2Code: OM

iso2code: ZQ

value: Middle East & North Africa

iso2code: XD

value: High income

capitalCity: Muscat

country_code: OMN

country_name: Oman


country_code: OSS

country_name: Other small states

value: CC BY-4.0


Pakistan

iso2Code: PK

value: Lower middle income

value: Blend

capitalCity: Islamabad

country_code: PAK

country_name: Pakistan

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Panama

iso2Code: PA

value: Latin America & Caribbean

capitalCity: Panama City

country_code: PAN

country_name: Panama

value: Annual


value: WDI Database Archives

Peru

iso2Code: PE

value: Latin America & Caribbean (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Lima

country_code: PER

country_name: Peru

value: World Bank and UNIDO's industry database.


Philippines

iso2Code: PH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XN

value: IBRD

capitalCity: Manila

country_code: PHL

country_name: Philippines

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


Palau

iso2Code: PW

capitalCity: Koror

country_code: PLW

country_name: Palau

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Papua New Guinea

iso2Code: PG

value: Lower middle income

iso2code: XH

capitalCity: Port Moresby

country_code: PNG

country_name: Papua New Guinea

value: CC BY-4.0


Poland

iso2Code: PL

value: Europe & Central Asia

value: High income

capitalCity: Warsaw

country_code: POL

country_name: Poland

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


value: WDI Database Archives

Pre-demographic dividend

value: Aggregates

country_code: PRE

country_name: Pre-demographic dividend

value: Annual


Puerto Rico

iso2Code: PR

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XX

value: Not classified

capitalCity: San Juan

country_code: PRI

country_name: Puerto Rico

value: World Bank and UNIDO's industry database.


Korea, Dem. People's Rep.

iso2Code: KP

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XM

value: Low income

capitalCity: Pyongyang

country_code: PRK

country_name: Korea, Dem. People's Rep.


Portugal

iso2Code: PT

capitalCity: Lisbon

country_code: PRT

country_name: Portugal

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


Paraguay

iso2Code: PY

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: Asuncion

country_code: PRY

country_name: Paraguay

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


value: WDI Database Archives

West Bank and Gaza

iso2Code: PS

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

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

country_code: PSE

country_name: West Bank and Gaza

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Pacific island small states

value: Aggregates

country_code: PSS

country_name: Pacific island small states

value: Annual

value: World Bank and UNIDO's industry database.


country_code: PST

country_name: Post-demographic dividend


French Polynesia

iso2Code: PF

value: East Asia & Pacific

iso2code: XD

value: High income

value: Not classified

capitalCity: Papeete

country_code: PYF

country_name: French Polynesia


Qatar

iso2Code: QA

capitalCity: Doha

country_code: QAT

country_name: Qatar

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


country_code: REU

country_name: Reunion


country_code: ROM

country_name: Romania


Romania

iso2Code: RO

value: Europe & Central Asia

value: IBRD

capitalCity: Bucharest

country_code: ROU


Russian Federation

iso2Code: RU

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Moscow

country_code: RUS

country_name: Russian Federation

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


value: WDI Database Archives

Rwanda

iso2Code: RW

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

value: Low income

iso2code: XI

value: IDA

capitalCity: Kigali

country_code: RWA

country_name: Rwanda

value: CC BY-4.0


South Asia

value: Aggregates

country_code: SAS

country_name: South Asia

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual

value: World Bank and UNIDO's industry database.


Saudi Arabia

iso2Code: SA

value: Middle East & North Africa

value: High income

iso2code: XX

value: Not classified

capitalCity: Riyadh

country_code: SAU

country_name: Saudi Arabia

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.


Sudan

iso2Code: SD

iso2code: XM

capitalCity: Khartoum

country_code: SDN

country_name: Sudan

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


value: WDI Database Archives

Senegal

iso2Code: SN

value: Sub-Saharan Africa

iso2code: XN

value: Lower middle income

capitalCity: Dakar

country_code: SEN

country_name: Senegal


Singapore

iso2Code: SG

value: East Asia & Pacific

capitalCity: Singapore

country_code: SGP

country_name: Singapore


Solomon Islands

iso2Code: SB

value: East Asia & Pacific (excluding high income)

value: IDA

capitalCity: Honiara

country_code: SLB

country_name: Solomon Islands

value: CC BY-4.0


Sierra Leone

iso2Code: SL

value: Sub-Saharan Africa (excluding high income)

value: Low income

capitalCity: Freetown

country_code: SLE

country_name: Sierra Leone

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


El Salvador

iso2Code: SV

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

iso2code: XF

value: IBRD

capitalCity: San Salvador

country_code: SLV

country_name: El Salvador

value: World Bank and UNIDO's industry database.


San Marino

iso2Code: SM

value: Europe & Central Asia

iso2code: XD

value: High income

value: Not classified

capitalCity: San Marino

country_code: SMR

country_name: San Marino

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


value: WDI Database Archives

Somalia

iso2Code: SO

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

value: IDA

capitalCity: Mogadishu

country_code: SOM

country_name: Somalia

value: CC BY-4.0


Serbia

iso2Code: RS

value: Europe & Central Asia (excluding high income)

iso2code: XT

capitalCity: Belgrade

country_code: SRB

country_name: Serbia

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


name: Sub-Saharan Africa (excluding high income)

value: Aggregates

country_code: SSA

value: World Bank and UNIDO's industry database.


South Sudan

iso2Code: SS

iso2code: XM

value: Low income

iso2code: XI

capitalCity: Juba

country_code: SSD

country_name: South Sudan


country_code: SSF

country_name: Sub-Saharan Africa


country_code: SST

country_name: Small states


Sao Tome and Principe

iso2Code: ST

iso2code: XN

value: Lower middle income

capitalCity: Sao Tome

country_code: STP

country_name: São Tomé and Principe


Suriname

iso2Code: SR

value: Latin America & Caribbean

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

value: IBRD

capitalCity: Paramaribo

country_code: SUR

country_name: Suriname

value: Organic water pollutant (BOD) emissions (kg per day per worker)


value: WDI Database Archives

Slovak Republic

iso2Code: SK

value: Europe & Central Asia

value: High income

iso2code: XX

value: Not classified

capitalCity: Bratislava

country_code: SVK

country_name: Slovak Republic

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Slovenia

iso2Code: SI

capitalCity: Ljubljana

country_code: SVN

country_name: Slovenia

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


Sweden

iso2Code: SE

capitalCity: Stockholm

country_code: SWE

country_name: Sweden

value: World Bank and UNIDO's industry database.


Eswatini

iso2Code: SZ

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

value: Lower middle income

iso2code: XF

capitalCity: Mbabane

country_code: SWZ

country_name: Eswatini


Sint Maarten (Dutch part)

iso2Code: SX

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XD

capitalCity: Philipsburg

country_code: SXM

country_name: Sint Maarten (Dutch part)


country_code: SXN

country_name: SUB-SAHARAN AFRICA (excl. Nigeria)

value: Organic water pollutant (BOD) emissions (kg per day per worker)


value: WDI Database Archives

Seychelles

iso2Code: SC

iso2code: ZG

value: IBRD

capitalCity: Victoria

country_code: SYC

country_name: Seychelles


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

value: IDA

capitalCity: Damascus

country_code: SYR

country_name: Syrian Arab Republic


Turks and Caicos Islands

iso2Code: TC

value: High income

value: Not classified

capitalCity: Grand Turk

country_code: TCA

country_name: Turks and Caicos Islands

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution


Chad

iso2Code: TD

iso2code: ZF

iso2code: XM

iso2code: XI

capitalCity: N'Djamena

country_code: TCD

country_name: Chad

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual

value: World Bank and UNIDO's industry database.

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


name: East Asia & Pacific (IDA & IBRD countries)

value: Aggregates

country_code: TEA

value: Organic water pollutant (BOD) emissions (kg per day per worker)


value: WDI Database Archives

name: Europe & Central Asia (IDA & IBRD countries)

country_code: TEC


Togo

iso2Code: TG

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

capitalCity: Lome

country_code: TGO

country_name: Togo


Thailand

iso2Code: TH

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XT

value: Upper middle income

value: IBRD

capitalCity: Bangkok

country_code: THA

country_name: Thailand


Tajikistan

iso2Code: TJ

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XN

value: Lower middle income

value: IDA

capitalCity: Dushanbe

country_code: TJK

country_name: Tajikistan

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Turkmenistan

iso2Code: TM

iso2code: XF

capitalCity: Ashgabat

country_code: TKM

country_name: Turkmenistan

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution


value: WDI Database Archives

name: Latin America & the Caribbean (IDA & IBRD countries)

value: Aggregates

country_code: TLA

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: World Bank and UNIDO's industry database.

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


Timor-Leste

iso2Code: TL

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XH

value: Blend

capitalCity: Dili

country_code: TLS

country_name: Timor-Leste


name: Middle East & North Africa (IDA & IBRD countries)

country_code: TMN


country_code: TMP


Tonga

iso2Code: TO

value: Upper middle income

capitalCity: Nuku'alofa

country_code: TON

country_name: Tonga


country_code: TSA

country_name: South Asia (IDA & IBRD)


name: Sub-Saharan Africa (IDA & IBRD countries)

country_code: TSS


Trinidad and Tobago

iso2Code: TT

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XD

value: High income

value: IBRD

capitalCity: Port-of-Spain

country_code: TTO

country_name: Trinidad and Tobago


Tunisia

iso2Code: TN

iso2code: ZQ

value: Middle East & North Africa

iso2code: XQ

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

value: Lower middle income

capitalCity: Tunis

country_code: TUN

country_name: Tunisia

value: CC BY-4.0

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


value: WDI Database Archives

Turkiye

iso2Code: TR

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

capitalCity: Ankara

country_code: TUR

country_name: Turkey

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution


Tuvalu

iso2Code: TV

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XI

value: IDA

capitalCity: Funafuti

country_code: TUV

country_name: Tuvalu

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: World Bank and UNIDO's industry database.

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


country_code: TWN

country_name: Taiwan, China


Tanzania

iso2Code: TZ

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

capitalCity: Dodoma

country_code: TZA

country_name: Tanzania


Uganda

iso2Code: UG

iso2code: XM

value: Low income

capitalCity: Kampala

country_code: UGA

country_name: Uganda

value: CC BY-4.0


value: WDI Database Archives

Ukraine

iso2Code: UA

iso2code: XN

value: Lower middle income

iso2code: XF

value: IBRD

capitalCity: Kiev

country_code: UKR

country_name: Ukraine

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Upper middle income

iso2Code: XT

value: Aggregates

country_code: UMC

country_name: Upper middle income

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


Uruguay

iso2Code: UY

iso2code: ZJ

value: Latin America & Caribbean

value: High income

capitalCity: Montevideo

country_code: URY

country_name: Uruguay

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution


United States

iso2Code: US

iso2code: XU

value: North America

iso2code: XD

iso2code: XX

value: Not classified

capitalCity: Washington D.C.

country_code: USA

country_name: United States

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: World Bank and UNIDO's industry database.


Uzbekistan

iso2Code: UZ

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XH

value: Blend

capitalCity: Tashkent

country_code: UZB

country_name: Uzbekistan

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


value: WDI Database Archives

St. Vincent and the Grenadines

iso2Code: VC

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

value: Upper middle income

capitalCity: Kingstown

country_code: VCT

country_name: St. Vincent and the Grenadines

value: CC BY-4.0


Venezuela, RB

iso2Code: VE

iso2code: XY

value: IBRD

capitalCity: Caracas

country_code: VEN

country_name: Venezuela, RB

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


British Virgin Islands

iso2Code: VG

value: Latin America & Caribbean

value: High income

capitalCity: Road Town

country_code: VGB

country_name: British Virgin Islands

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution


Virgin Islands (U.S.)

iso2Code: VI

value: Not classified

capitalCity: Charlotte Amalie

country_code: VIR

country_name: Virgin Islands (U.S.)

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: World Bank and UNIDO's industry database.


Viet Nam

iso2Code: VN

value: East Asia & Pacific

value: East Asia & Pacific (excluding high income)

iso2code: XN

value: Lower middle income

capitalCity: Hanoi

country_code: VNM

country_name: Vietnam


value: WDI Database Archives

Vanuatu

iso2Code: VU

iso2code: XI

value: IDA

capitalCity: Port-Vila

country_code: VUT

country_name: Vanuatu

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


country_code: WBG

country_name: West Bank and Gaza

value: CC BY-4.0


World

value: Aggregates

country_code: WLD

country_name: World

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.


Samoa

iso2Code: WS

capitalCity: Apia

country_code: WSM

country_name: Samoa

value: Annual

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Kosovo

iso2Code: XK

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

iso2code: XT

value: Upper middle income

capitalCity: Pristina

country_code: XKX

country_name: Kosovo


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

capitalCity: Sana'a

country_code: YEM

country_name: Yemen, Rep.

value:
Data on water pollution are more readily available than are other emissions data because most industrial pollution control programs start by regulating emissions of organic water pollutants. Such data are fairly reliable because sampling techniques for measuring water pollution are more widely understood and much less expensive than those for air pollution.

The data comes from an international study of industrial emissions that may have been the first to include data from developing countries (Hettige, Mani, and Wheeler 1998). These data were updated through 2007 by the World Bank's Development Research Group.

Unlike estimates from earlier studies based on engineering or economic models, these estimates are based on actual measurements of plant-level water pollution. The focus is on organic water pollution caused by organic waste, measured in terms of biochemical oxygen demand (BOD), because the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treatment, by contrast, reduces BOD.

value: Environment: Water pollution


country_code: YMD

country_name: Yemen, PDR

value: Organic water pollutant (BOD) emissions (kg per day per worker)

value: World Bank and UNIDO's industry database.


value: WDI Database Archives

country_code: YUG

country_name: Serbia and Montenegro


South Africa

iso2Code: ZA

iso2code: ZG

value: Sub-Saharan Africa

iso2code: ZF

value: Sub-Saharan Africa (excluding high income)

iso2code: XF

value: IBRD

capitalCity: Pretoria

country_code: ZAF

country_name: South Africa

value:
Hettige, Mani, and Wheeler (1998) used plant- and sector-level information on emissions and employment from 13 national environmental protection agencies and sector-level information on output and employment from the United Nations Industrial Development Organization (UNIDO). Their econometric analysis found that the ratio of BOD to employment in each industrial sector is about the same across countries. This finding allowed the authors to estimate BOD loads across countries and over time.

The estimated BOD intensities per unit of employment were multiplied by sectoral employment numbers from UNIDO's industry database for 1980-98. These estimates of sectoral emissions were then used to calculate kilograms of emissions of organic water pollutants per day for each country and year. The data were derived by updating these estimates through 2007.

BOD refers to biochemical oxygen demand.


country_code: ZAR

country_name: Congo, Dem. Rep.

value: CC BY-4.0

value: Emissions of organic pollutants from industrial activities are a major cause of degradation of water quality. Water quality and pollution levels are generally measured as concentration or load - the rate of occurrence of a substance in an aqueous solution. Polluting substances include organic matter, metals, minerals, sediment, bacteria, and toxic chemicals. Because water pollution tends to be sensitive to local conditions, the national-level data may not reflect the quality of water in specific locations.

value: Annual


Zambia

iso2Code: ZM

value: Lower middle income

value: IDA

capitalCity: Lusaka

country_code: ZMB

country_name: Zambia

value: Emissions per worker are total emissions of organic water pollutants divided by the number of industrial workers. Organic water pollutants are measured by biochemical oxygen demand, which refers to the amount of oxygen that bacteria in water will consume in breaking down waste. This is a standard water-treatment test for the presence of organic pollutants.


Zimbabwe

iso2Code: ZW

iso2code: XH

value: Blend

capitalCity: Harare

country_code: ZWE

country_name: Zimbabwe


Economic Fitness Metric

value: Economic Fitness

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

Afghanistan

iso2Code: AF

value: South Asia

value: Low income

capitalCity: Kabul

country_code: AFG

country_name: Afghanistan

value: Economic Fitness Metric

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

value: Economic Policy & Debt

value: The trade data are necessary to define a coherent network for all countries and all products. This may have some limitations for countries in which the exported products are not a good proxy of its industrial competitiveness. Also export refers generally to manufacturing. Services can be included but the corresponding database trade in services are less granular. In principle the approach could use other data like the labor statistics which automatically include all services. A basic concept of the algorithm is the importance of diversification. This is correct at the level of countries but it becomes gradually problematic if one moves to smaller scales like regions, cities up to individual firms where specialization becomes dominant. In these cases suitable modifications should be considered.  The COMTRADE dataset comes at different levels of granularity. Each level has advantages and disadvantages which should be considered in relation to the problem addressed.

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods.  The interaction among products traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data.


Angola

iso2Code: AO

value: Sub-Saharan Africa

value: Sub-Saharan Africa (excluding high income)

iso2code: XN

value: IBRD

capitalCity: Luanda

country_code: AGO

country_name: Angola

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


Economic Fitness Metric

Albania

iso2Code: AL

value: Europe & Central Asia

value: Europe & Central Asia (excluding high income)

value: Upper middle income

capitalCity: Tirane

country_code: ALB

country_name: Albania

value: Annual


Andorra

iso2Code: AD

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Andorra la Vella

country_code: AND

country_name: Andorra


Economic Fitness Metric

value: Economic Fitness

United Arab Emirates

iso2Code: AE

iso2code: ZQ

value: Middle East & North Africa

capitalCity: Abu Dhabi

country_code: ARE

country_name: United Arab Emirates


Argentina

iso2Code: AR

iso2code: ZJ

value: Latin America & Caribbean

iso2code: XJ

value: Latin America & Caribbean (excluding high income)

iso2code: XT

capitalCity: Buenos Aires

country_code: ARG

country_name: Argentina

value: Economic Fitness Metric


Economic Fitness Metric

Armenia

iso2Code: AM

iso2code: XF

capitalCity: Yerevan

country_code: ARM

country_name: Armenia

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

value: Economic Policy & Debt

value: The 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.


Australia

iso2Code: AU

value: East Asia & Pacific

capitalCity: Canberra

country_code: AUS

country_name: Australia

value: The new literature of Economic Fitness uses techniques which, differently from traditional index construction approaches, do not try to average out the complexity of the system, but embraces it by explicitly building on the heterogeneity of individual actors, activities and interactions to extract relevant parameters to characterize the system.  In this way, information about production capabilities may be extracted from trade in goods.  The interaction among products traded, and the relatively unique combinations are a precursor to future competitiveness and long-term growth.   A basic characteristic of Economic Fitness is being parameter free. The standard methods of analysis consider many elements and sum them up in some suitable way. This sum of incommensurate elements leads to a major problem of controlling noise while increasing signal. The Fitness approach starts by considering a single dataset to control noise problems.  Other data can then be added later in a controlled hierarchical framework 9e.g, services, technologies). The algorithm is designed on simple and transparent economical concepts which have a clear meaning and have been extensively tested. The evolution of each country is defined in the GDP-Fitness space which shows a strong heterogeneity in the dynamics. There is zone characterized by regular flow and another one which is more chaotic. This implies that growth forecasting should consider this heterogeneity and go beyond standard regressions. This novel approach to the analysis and long-term forecasting has been shown to outperform the standard methods even if it requires much less data.


Economic Fitness Metric

Austria

iso2Code: AT

capitalCity: Vienna

country_code: AUT

country_name: Austria

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


Azerbaijan

iso2Code: AZ

value: Europe & Central Asia

value: IBRD

capitalCity: Baku

country_code: AZE

country_name: Azerbaijan


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


value: Economic Fitness

Belgium

iso2Code: BE

iso2code: XD

value: High income

iso2code: XX

value: Not classified

capitalCity: Brussels

country_code: BEL

country_name: Belgium

value: Annual


Economic Fitness Metric

Benin

iso2Code: BJ

iso2code: XN

value: Lower middle income

capitalCity: Porto-Novo

country_code: BEN

country_name: Benin

value: Economic Fitness Metric


Burkina Faso

iso2Code: BF

capitalCity: Ouagadougou

country_code: BFA

country_name: Burkina Faso

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

value: Economic Policy & Debt