The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Australia GDP per Capita: Chain Volume: 2007-08p: sa data was reported at 13,899.000 AUD in Jun 2010. This records an increase from the previous number of 13,799.000 AUD for Mar 2010. Australia GDP per Capita: Chain Volume: 2007-08p: sa data is updated quarterly, averaging 9,623.500 AUD from Sep 1973 (Median) to Jun 2010, with 148 observations. The data reached an all-time high of 13,902.000 AUD in Mar 2008 and a record low of 7,267.000 AUD in Jun 1974. Australia GDP per Capita: Chain Volume: 2007-08p: sa data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A170: SNA08: Gross Domestic Product per Capita.

Lesotho's GDP per employed person is US$7,746 which is the 148th highest in the world ranking. Transition graphs on GDP per employed person in Lesotho and comparison bar charts (USA vs. China vs. Japan vs. Lesotho), (Gabon vs. Slovenia vs. Lesotho) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Graph and download economic data for Total Gross Domestic Product for Salt Lake City, UT (MSA) (NGMP41620) from 2001 to 2023 about Salt Lake City, UT, industry, GDP, and USA.

Slovakia SK: GDP: Gross Capital Formation: Gross Fixed Capital Formation data was reported at 30.501 EUR bn in Dec 2026. This records an increase from the previous number of 30.260 EUR bn for Sep 2026. Slovakia SK: GDP: Gross Capital Formation: Gross Fixed Capital Formation data is updated quarterly, averaging 15.186 EUR bn from Mar 1990 (Median) to Dec 2026, with 148 observations. The data reached an all-time high of 30.501 EUR bn in Dec 2026 and a record low of 3.095 EUR bn in Mar 1991. Slovakia SK: GDP: Gross Capital Formation: Gross Fixed Capital Formation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovakia – Table SK.OECD.EO: GDP by Expenditure: Forecast: OECD Member: Quarterly. IT - Gross fixed capital formation, total, nominal value

The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

Richness index (2010)

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

Poland GDP: Gross Capital Formation data was reported at 762.913 PLN bn in Dec 2026. This records an increase from the previous number of 752.244 PLN bn for Sep 2026. Poland GDP: Gross Capital Formation data is updated quarterly, averaging 290.617 PLN bn from Mar 1990 (Median) to Dec 2026, with 148 observations. The data reached an all-time high of 762.913 PLN bn in Dec 2026 and a record low of 14.510 PLN bn in Mar 1990. Poland GDP: Gross Capital Formation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Poland – Table PL.OECD.EO: GDP by Expenditure: Forecast: OECD Member: Quarterly. ITISK - Gross capital formation, total, nominal value; Quarterly series are benchmarked on annual data

Hong Kong GDP: SNA93: 2007p: CL: Gross Domestic Fixed Capital Formation data was reported at 83,317.000 HKD mn in Dec 2009. This records a decrease from the previous number of 86,437.000 HKD mn for Sep 2009. Hong Kong GDP: SNA93: 2007p: CL: Gross Domestic Fixed Capital Formation data is updated quarterly, averaging 47,864.000 HKD mn from Mar 1973 (Median) to Dec 2009, with 148 observations. The data reached an all-time high of 89,145.000 HKD mn in Dec 1997 and a record low of 12,147.000 HKD mn in Dec 1974. Hong Kong GDP: SNA93: 2007p: CL: Gross Domestic Fixed Capital Formation data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR – Table HK.A012: SNA 1993: GDP: by Expenditure: 2007 Price: Chain Linked. Rebased from 2007p to 2008p. Replacement Series ID: 293057201

Ref. Year = 2022: GDP: Volume: Gross Capital Formation data was reported at 101.996 EUR bn in Dec 2026. This records an increase from the previous number of 101.268 EUR bn for Sep 2026. Ref. Year = 2022: GDP: Volume: Gross Capital Formation data is updated quarterly, averaging 51.949 EUR bn from Mar 1990 (Median) to Dec 2026, with 148 observations. The data reached an all-time high of 375.151 EUR bn in Dec 2019 and a record low of 18.010 EUR bn in Mar 1992. Ref. Year = 2022: GDP: Volume: Gross Capital Formation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Ireland – Table IE.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Quarterly.

Data are based on a study on the systemic factors correlated with the transnational media coverage of foreign news. The dataset matches information provided by various data sources in order to performs a quantitative assessment of the systemic determinants of the network of EU transnational citations through the articles published in the main national media. The main source consists of a large dataset created by Economisti Associati on the transnational coverage of foreign news based on 148 national media and 1.96 million articles published between 16 August and 15 November 2010 by the main general and business newspapers in the EU countries. For each European country the foreign news coverage was measured in terms of the presence of references to other EU countries (target countries) in articles published in national media. References to foreign EU countries are sought in the title and body of every article through search strings containing the name of the target countries, translated into various languages. For each European country, the number of articles published in reference to each target country has been normalized on the total number of articles published in the country itself. Starting from this information, the unit of analysis of the dataset is the probability that an article published in a given European country refers to each of the target countries. The dataset combines information about media coverage with some indicators produced by other European sources. In particular: Eurostat: average population of the countries over the period 2000-2010; average GDP per capita in PPS over the period 2000-2010; bilateral trade flows as a fraction of the total international trade flows averaged over the period 1999-2001; CEPII (Center d'études prospectives et d'informations internationales): bilateral weighted distances and language commonality); CHES (Chapel Hill Expert Survey): political orientation in 2010; ECB (European Central Bank): average long-term interest rates over the period August-November 2010. The dataset is organized in 729 cases and 25 variables. 729 media relations. No sampling data aggregation other

Ireland Ref. Year = 2022: GDP: Volume: Gross Capital Formation: GFCF: Housing data was reported at 16.829 EUR bn in Dec 2026. This records an increase from the previous number of 16.625 EUR bn for Sep 2026. Ireland Ref. Year = 2022: GDP: Volume: Gross Capital Formation: GFCF: Housing data is updated quarterly, averaging 12.051 EUR bn from Mar 1990 (Median) to Dec 2026, with 148 observations. The data reached an all-time high of 28.400 EUR bn in Dec 2005 and a record low of 4.554 EUR bn in Sep 2012. Ireland Ref. Year = 2022: GDP: Volume: Gross Capital Formation: GFCF: Housing data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Ireland – Table IE.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Quarterly.

Ref. Year = 2020: GDP: Volume: Gross Capital Formation: Gross Fixed Capital Formation (GFCF) data was reported at 612.786 PLN bn in Dec 2026. This records an increase from the previous number of 606.719 PLN bn for Sep 2026. Ref. Year = 2020: GDP: Volume: Gross Capital Formation: Gross Fixed Capital Formation (GFCF) data is updated quarterly, averaging 299.640 PLN bn from Mar 1990 (Median) to Dec 2026, with 148 observations. The data reached an all-time high of 612.786 PLN bn in Dec 2026 and a record low of 90.294 PLN bn in Mar 1991. Ref. Year = 2020: GDP: Volume: Gross Capital Formation: Gross Fixed Capital Formation (GFCF) data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Poland – Table PL.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Quarterly.

Ref. Year = 2020: GDP: Volume: Gross Capital Formation data was reported at 550.013 PLN bn in Dec 2026. This records an increase from the previous number of 544.491 PLN bn for Sep 2026. Ref. Year = 2020: GDP: Volume: Gross Capital Formation data is updated quarterly, averaging 307.700 PLN bn from Mar 1990 (Median) to Dec 2026, with 148 observations. The data reached an all-time high of 614.638 PLN bn in Mar 2022 and a record low of 78.610 PLN bn in Mar 1992. Ref. Year = 2020: GDP: Volume: Gross Capital Formation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Poland – Table PL.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Quarterly.

Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Non Dwelling Construction: Net Purchase of Second Hand Assets data was reported at -0.200 Index Point in Jun 2023. This records a decrease from the previous number of 0.000 Index Point for Mar 2023. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Non Dwelling Construction: Net Purchase of Second Hand Assets data is updated quarterly, averaging 0.000 Index Point from Sep 1986 (Median) to Jun 2023, with 148 observations. The data reached an all-time high of 1.600 Index Point in Mar 2000 and a record low of -1.300 Index Point in Dec 2012. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Non Dwelling Construction: Net Purchase of Second Hand Assets data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A054: SNA08: Gross Domestic Product: by Expenditure: Chain Linked: 2020-21 Price: Seasonally Adjusted: Contribution to Growth.

Sweden GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: Central Govt data was reported at 27,874.000 SEK mn in Dec 2017. This records an increase from the previous number of 20,715.000 SEK mn for Sep 2017. Sweden GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: Central Govt data is updated quarterly, averaging 18,370.500 SEK mn from Mar 1981 (Median) to Dec 2017, with 148 observations. The data reached an all-time high of 32,272.000 SEK mn in Dec 2010 and a record low of 9,631.000 SEK mn in Sep 1981. Sweden GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: Central Govt data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.A006: ESA 2010: GDP: by Expenditure: Chain Linked 2016 Price. Rebased from Chain Linked 2016p to Chain Linked 2017p Replacement series ID: 403916497

GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: NPISH data was reported at 2,125.000 SEK mn in Dec 2017. This records an increase from the previous number of 1,073.000 SEK mn for Sep 2017. GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: NPISH data is updated quarterly, averaging 766.000 SEK mn from Mar 1981 (Median) to Dec 2017, with 148 observations. The data reached an all-time high of 2,125.000 SEK mn in Dec 2017 and a record low of 307.000 SEK mn in Mar 1999. GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: NPISH data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.A006: ESA 2010: GDP: by Expenditure: Chain Linked 2016 Price. Rebased from Chain Linked 2016p to Chain Linked 2017p Replacement series ID: 403916487

Sweden GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: Market Producers data was reported at 246,522.000 SEK mn in Dec 2017. This records an increase from the previous number of 217,591.000 SEK mn for Sep 2017. Sweden GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: Market Producers data is updated quarterly, averaging 127,147.000 SEK mn from Mar 1981 (Median) to Dec 2017, with 148 observations. The data reached an all-time high of 246,564.000 SEK mn in Jun 2017 and a record low of 69,181.000 SEK mn in Sep 1982. Sweden GDP: Chain Linked 2016p: Uses: GD: GCF: Gross Fixed Capital Formation: Market Producers data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.A006: ESA 2010: GDP: by Expenditure: Chain Linked 2016 Price. Rebased from Chain Linked 2016p to Chain Linked 2017p Replacement series ID: 403916477

Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Dwellings: New & Used data was reported at 0.000 Index Point in Jun 2023. This stayed constant from the previous number of 0.000 Index Point for Mar 2023. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Dwellings: New & Used data is updated quarterly, averaging 0.000 Index Point from Sep 1986 (Median) to Jun 2023, with 148 observations. The data reached an all-time high of 0.500 Index Point in Mar 2000 and a record low of -0.900 Index Point in Sep 2000. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Dwellings: New & Used data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A054: SNA08: Gross Domestic Product: by Expenditure: Chain Linked: 2020-21 Price: Seasonally Adjusted: Contribution to Growth.

Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Public: General Government data was reported at 0.300 Index Point in Jun 2023. This records an increase from the previous number of 0.100 Index Point for Mar 2023. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Public: General Government data is updated quarterly, averaging 0.000 Index Point from Sep 1986 (Median) to Jun 2023, with 148 observations. The data reached an all-time high of 0.800 Index Point in Sep 1995 and a record low of -0.700 Index Point in Mar 2012. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Public: General Government data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A054: SNA08: Gross Domestic Product: by Expenditure: Chain Linked: 2020-21 Price: Seasonally Adjusted: Contribution to Growth.

Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Non Dwelling Construction: New Engineering Construction data was reported at 0.000 Index Point in Jun 2023. This records a decrease from the previous number of 0.100 Index Point for Mar 2023. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Non Dwelling Construction: New Engineering Construction data is updated quarterly, averaging 0.000 Index Point from Sep 1986 (Median) to Jun 2023, with 148 observations. The data reached an all-time high of 1.300 Index Point in Sep 2011 and a record low of -0.600 Index Point in Sep 2014. Australia GDP: 2020-21p: sa: Contribution to Growth: Gross Fixed Capital Formation: Private: Non Dwelling Construction: New Engineering Construction data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A054: SNA08: Gross Domestic Product: by Expenditure: Chain Linked: 2020-21 Price: Seasonally Adjusted: Contribution to Growth.