In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

This layer contains Gross Domestic Product (GDP) Per Capita - the total value of goods produced and services provided, divided by the total population in each country, from 1960 to 2016, expressed in 2016 US Dollars. Expressing the GDP in "per capita" terms allows for better comparisons across countries. Total GDP is available in an accompanying layer. GDP as a measure has been largely criticized as an incomplete measure of productivity and wealth, as it does not take into account production in the informal economy, quality of life, degradation to the environment, or income distribution. However, GDP is an internationally comparable measure, used in everything from banks setting interest rates to political campaign speeches.Source: World Bank, World Development Indicators.

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

We developed and presented a set of comparable spatially explicit global gridded gross domestic product (GDP) for both historical period (2005 as representative) and for future projections from 2030 to 2100 at a ten-year interval for all five SSPs. The DMSP-OLS nighttime light (NTL) images and the LandScan Global Population database were used to generate LitPop map, which reduces the limitations of saturation problem of using NTL images alone or the assumption of even GDP per capita within an administrative boundary of gridded data set in GDP disaggregation. We used the LitPop maps to disaggregate national GDP and over 800 provincial gross regional product (GRP, in 2005 PPP USD) across the globe in 2005 and to downscaled to a spatial resolution of 30 arc-seconds (~1 km at equator). National and supranational GDP growth rate projections in 2030-2100 under five SSPs were then downscaled to 1-km grids based on the LitPop approach, which used NPP-VIIRS product as fixed NTL image in 2015 and the population projections of 0.125 arc-degreee (Jones and O'Neill, 2016), which are downscaled to 1-km based on LandScan population distribution pattern in 2015. We then upscaled this gridded GDP dataset to 0.25 arc-degree and provided here.

There are 41 tif files (2005 and 2030 - 2100 at a ten-year interval for five SSPs) for each spatial resolution. The gridded GDP are distributed over land with value of zero filled in the Antarctica, oceans and some desert or wilderness areas (non-illuminated and depopulated zones). The spatial extents are 60S - 90N and 180E - 180W in standard WGS84 coordinate system.

For more details, please refer to the corresponding article: Global gridded GDP data set consistent with the shared socioeconomic pathways by Wang and Sun (2022).

Fayl Faylın tarixçəsi Faylın istifadəsi Faylın qlobal istifadəsi MetaməlumatlarSınaq göstərişi ölçüsü 800 348 piksel Dig

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

Throughout the Second World War, the United States consistently had the largest gross domestic product (GDP) in the world. Additionally, U.S. GDP grew significantly throughout the war, whereas the economies of Europe and Japan saw relatively little growth, and were often in decline. The impact of key events in the war is also reflected in the trends shown here - the economic declines of France and the Soviet Union coincide with the years of German invasion, while the economies of the three Axis countries experienced their largest declines in the final year of the war.

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

Graph and download economic data for Gross Domestic Product for Ethiopia (MKTGDPETA646NWDB) from 1960 to 2023 about Ethiopia and GDP.

This world map shows the member economies of Asia-Pacific Economic Cooperation (APEC).

19 of the 20 countries with the lowest estimated GDP per capita in the world in 2024 are located in Sub-Saharan Africa. South Sudan is believed to have a GDP per capita of just 351.02 U.S. dollars - for reference, Luxembourg has the highest GDP per capita in the world, at almost 130,000 U.S. dollars, which is around 400 times larger than that of Burundi (U.S. GDP per capita is over 250 times higher than Burundi's). Poverty in Sub-Saharan Africa Many parts of Sub-Saharan Africa have been among the most impoverished in the world for over a century, due to lacking nutritional and sanitation infrastructures, persistent conflict, and political instability. These issues are also being exacerbated by climate change, where African nations are some of the most vulnerable in the world, as well as the population boom that will place over the 21st century. Of course, the entire population of Sub-Saharan Africa does not live in poverty, and countries in the southern part of the continent, as well as oil-producing states around the Gulf of Guinea, do have some pockets of significant wealth (especially in urban areas). However, while GDP per capita may be higher in these countries, wealth distribution is often very skewed, and GDP per capita figures are not representative of average living standards across the population. Outside of Africa Yemen is the only country outside of Africa to feature on the list, due to decades of civil war and instability. Yemen lags very far behind some of its neighboring Arab states, some of whom rank among the richest in the world due to their much larger energy sectors. Additionally, the IMF does not make estimates for Afghanistan, which would also likely feature on this list.

This dataset represents Exclusive Economic Zones (EEZ) of the world. Up to now, there was no global public domain cover available.
Therefore, the Flanders Marine Institute decided to develop its own database. The database includes two global GIS-layers: one contains polylines that represent the maritime boundaries of the world countries, the other one is a polygon layer representing the Exclusive Economic Zone of countries. The database also contains digital information about treaties. Please note that the EEZ shapefile also includes the internal waters of each country.

An exclusive economic zone (EEZ) is a sea zone prescribed by the United Nations Convention on the Law of the Sea over which a sovereign state has special rights over the exploration and use of marine resources, including energy production from water and wind. This maritime boundary is designed to be used with other marine boundaries in order to help determine areas of trade, commerce and transportation. The 200 NM zone is measured, country-by-country, from another maritime boundary, the baseline (usually but not in all cases the mean low-water mark, used is not the same thing as the coast line. For each country, obtain the official list of the baseline points from the United Nations under Maritime Space.The exclusive economic zone stretches much further into sea than the territorial waters, which end at 12 NM (22 km) from the coastal baseline (if following the rules set out in the UN Convention on the Law of the Sea). Thus, the EEZ includes the contiguous zone. States also have rights to the seabed of what is called the continental shelf up to 350 NM (648 km) from the coastal baseline, beyond the EEZ, but such areas are not part of their EEZ. The legal definition of the continental shelf does not directly correspond to the geological meaning of the term, as it also includes the continental rise and slope, and the entire seabed within the EEZ. The chart below diagrams the overlapping jurisdictions which are part of the EEZ. When the (EEZ) boundary is between countries which are separated by less than 200NM is settled by international tribunals at any arbitrary line. Many countries are still in the process of extending their EEZs beyond 200NM using criteria defined in the United Nations Convention on the Law of the Sea. Dataset Summary The data for this layer were obtained from https://www.marineregions.org/published here. Link to source metadata.Preferred Citation: Flanders Marine Institute (2023). Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 12. Available online at https://www.marineregions.org/. https://doi.org/10.14284/632This layer is a feature service, which means it can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.

This map contains Gross Domestic Product - the total value of goods produced and services provided - by country, per capita in 2016, expressed in 2016 US Dollars. Expressing the GDP in "per capita" terms allows for better comparisons across countries. Total GDP is available in an accompanying map. GDP as a measure has been largely criticized as an incomplete measure of productivity and wealth, as it does not take into account production in the informal economy, quality of life, degradation to the environment, or income distribution. However, GDP is an internationally comparable measure, used in everything from banks setting interest rates to political campaign speeches.Source: World Bank, World Development Indicators.

This map is adapted from the outstanding work of Dr. Joseph Kerski at ESRI. A map of political, social, and economic indicators for 2010. Created at the Data Analysis and Social Inquiry Lab at Grinnell College by Megan Schlabaugh, April Chen, and Adam Lauretig.Data from Freedom House, the Center for Systemic Peace, and the World Bank.Shapefile:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. 2010. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1).Field Descriptions:

Variable Name Variable Description Years Available Further Description Source

TotPop Total Population 2011 Population of the country/region World Bank

GDPpcap GDP per capita (current USD) 2011 A measure of the total output of a country that takes the gross domestic product (GDP) and divides it by the number of people in the country. The per capita GDP is especially useful when comparing one country to another because it shows the relative performance of the countries. World Bank

GDPpcapPPP GDP per capita based on purchasing power parity (PPP) 2011

World Bank

HDI Human Development Index (HDI) 2011 A tool developed by the United Nations to measure and rank countries' levels of social and economic development based on four criteria: Life expectancy at birth, mean years of schooling, expected years of schooling and gross national income per capita. The HDI makes it possible to track changes in development levels over time and to compare development levels in different countries. World Bank

LifeExpct Life expectancy at birth 2011 The probable number of years a person will live after a given age, as determined by mortality in a specific geographic area. World Bank

MyrSchool Mean years of schooling 2011 Years that a 25-year-old person or older has spent in schools World Bank

ExpctSch Expected years of schooling 2011 Number of years of schooling that a child of school entrance age can expect to receive if prevailing patterns of age-specific enrolment rates persist throughout the child’s life. World Bank

GNIpcap Gross National Income (GNI) per capita 2011 Gross national income (GNI) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI per capita is gross national income divided by mid-year population. World Bank

GNIpcapHDI GNI per capita rank minus HDI rank 2011

World Bank

NaIncHDI Nonincome HDI
2011

World Bank

15+LitRate Adult (15+) literacy rate (%). Total 2010

UNESCO

EmplyAgr Employment in Agriculture 2009

World Bank

GDPenergy GDP per unit of energy use 2010 The PPP GDP per kilogram of oil equivalent of energy use. World Bank

GDPgrowth GDP growth (annual %) 2011

World Bank

GDP GDP (current USD) 2011

World Bank

ExptGDP Exports of Goods and Service (% GDP) 2011 The value of all goods and other market services provided to the rest of the world World Bank

ImprtGDP Imports of Goods and Service (% GDP) 2011 The value of all goods and other market services received from the rest of the world. World Bank

AgrGDP Agriculture, Value added (% GDP) 2011 Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. World Bank

FDI Foreign Direct Investment, net (current USD) 2011 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. World Bank

GNIpcap GNI per capita PP 2011 GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. World Bank

Inflatn Inflation, Consumer Prices (annual %) 2011 Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. World Bank

InfltnGDP Inflation, GDP deflator (annual %) 2011 Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. World Bank

PctWomParl % women in national parliament 2010

United Nations

IntnetUser Internet Users, per 100 peple 2011 Internet users are people with access to the worldwide network. World Bank

HIVPrevlnc Estimated HIV Prevalence% - (Ages 15-49) 2009 Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV. UNAIDS estimates. UNAIDS

AgrLand Agricultural land (% of land area) 2009 Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. World Bank

AidRecPP Aid received per person (current US$) 2010 Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). World Bank

AlcohAdul Alcohol consumption per adult (15+) in litres 2008 Liters of pure alcohol, computed as the sum of alcohol production and imports, less alcohol exports, divided by the adult population (aged 15 years and older). World Health Organization

ArmyPct Military expenditure (% of central government expenditure) 2008 Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). World Development Indicators (World Bank)

TFR Total Fertility Rate 2011 The average number of children that would be born per woman if all women lived to the end of their childbearing years and bore children according to a given fertility rate at each age. This indicator shows the potential for population change in a country. World Bank

CO2perUSD CO2 kg per USD 2008 Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. World Bank

ExpdtrPrim Expenditure per student, primary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Primary is the total public expenditure per student in primary education as a percentage of GDP per capita. Public expenditure (current and capital) includes government spending on educational institutions (both public and private), education administration as well as subsidies for private entities (students/households and other privates entities). World Bank

ExpdtrSecd Expenditure per student, secondary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Secondary is the total public expenditure per student in secondary education as a percentage of GDP per capita. World Bank

ExpdtrTert Expenditure per student, tertiary (% of GDP per capita) 2008 Public expenditure per pupil as a % of GDP per capita. Tertiary is the total public expenditure per student in tertiary education as a percentage of GDP per capita. World Bank

FDIoutf Foreign direct investment, net outflows (% of GDP) 2010 Foreign direct investment are the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net outflows of investment from the

Mapping Our World Using GIS is a 1:1 set of instructional materials for teaching basic concepts found in middle school world geography. Each module consists of multiple files.

        Economists generally classify a country as “developing” or “developed” by determining the percentage

of gross domestic product (GDP) engaged in each of three sectors of the economy — agriculture, industry, and services. A country with a high percentage of its GDP in agriculture is categorized as developing, while a country with a high percentage of its GDP in services and industry is categorized as developed.

        In this activity, you will use maps of percentages of GDP in the three sectors to explore patterns of

development around the world. You will also examine two other economic indicators — energy use and GDP per capita — and compare the maps of GDP in economic sectors to the maps of GDP per capita and energy use. You will evaluate whether or not the economic sector criteria are good indicators of a country’s economic status.

  The Mapping Our World collection is at: http://esriurl.com/MOW. All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

Abstract

The Partnership for Economic Inclusion (PEI) Landscape Survey 2019 - 2020 aimed to provide a comprehensive inventory of ongoing economic inclusion programs, or those that are in the development pipeline. For the purpose of the PEI Landscape Survey 2019 - 2020, the PEI management team (PEIMT) defined economic inclusion programs as multidimensional interventions that support and enable households to achieve sustainable livelihoods and increase their incomes and assets, while building human capital and promoting social inclusion.

To map the universe of economic inclusion programs, the PEIMT reviewed the World Bank financing portfolio as well as external sources. The first stage of the World Bank portfolio scan involved manually reviewing ongoing and pipeline programs from the Social Protection and Jobs (SPJ) Global Practice, listed in the World Bank Operations Portal, across all geographical regions. To determine whether a program focused on economic inclusion, the PEIMT reviewed each program's development objective and the component description included in its Project Appraisal Document (PAD) or, when a PAD was not available, its Project Information Document (PID), Project Paper (PP), or Project Information and Integrated Safeguards Data Sheet (PSDS).

Kind of data

Administrative records data [adm]

Sampling procedure

To map the universe of economic inclusion programs, the PEIMT reviewed the World Bank financing portfolio as well as external sources. The first stage of the World Bank portfolio scan involved manually reviewing ongoing and pipeline projects from the Social Protection and Jobs (SPJ) Global Practice, listed in the World Bank Operations Portal, across all geographical regions. To determine whether a program focused on economic inclusion, the PEIMT reviewed each project's development objective and the component description included in its Project Appraisal Document (PAD) or, when a PAD was not available, its Project Information Document (PID), Project Paper (PP), or Project Information and Integrated Safeguards Data Sheet (PSDS).

As a second stage, in order to validate each economic inclusion program and to speed up the mapping process, the PEIMT worked with the Text and Data Analytics (TDA) team from the Development Economics (DEC) department of the World Bank. Using a predefined set of keywords , the TDA team applied advanced text analytics to projects' summaries as well as to their PADs, PIDs, PPs, or PSDSs. They applied this technique to a total sample of approximately 1,200 projects (both active and pipeline) across all geographical regions under these Global Practices: Urban Resilience and Land; Social Development; Social Protection and Jobs; Finance, Competitiveness and Innovation; and Agriculture and Food. The team then ranked projects based on the number of keywords found. Any project that had at least one keyword could be considered an economic inclusion project. The PEIMT then compared the TDA-assisted selection with the manual selection for the SPJ projects and found that the results were accurate in correctly excluding projects. The TDA-assisted selection, however, also included far more projects than the manual review did.

To finalize the mapping of World Bank-financed economic inclusion projects, the PEIMT team manually reviewed the TDA-assisted selection of economic inclusion projects for the remaining Global Practices. The team assessed the relevance of a project based on project summaries, the types of words identified through the TDA techniques, and the frequency with which keywords came up in the project documents. In some cases, when a summary did not provide enough information, the PAD was reviewed to make a final decision. Overall, the TDA methods allowed the PEIMT to trim the number of projects for review by half. In total, the PEIMT identified 149 World Bank economic inclusion projects (representing 92 individual government programs in 57 countries ). Surveys were sent to these 92 unique identified programs, and responses were received back from 77 of them. The mapping of World Bank-supported projects was updated in June 2020 through a full manual review of nearly 50 projects from the Environment and Natural Resources Global Practice, which resulted in 17 additional projects and a total of 166 economic inclusion projects supported by the World Bank.

To map projects outside of World Bank operations, the PEIMT used the PEI's 2017 survey dataset to identify projects that were still ongoing as well as partners, including governments, NGOs, regional organizations, multilaterals, and other development partners involved in economic inclusion programming. Organizations were approached to self-identify programs that met a prescribed set of criteria, which had been developed based on the working definition of economic inclusion programs. Since the 2017 survey captured mostly non-government programs, in order to map other relevant economic inclusion interventions the PEIMT scanned several databases and inventories of social protection and productive inclusion programs, including ECLAC's database of labor and productive inclusion programs in Latin America and the Caribbean and Manchester's Social Assistance database. The number of projects identified outside of the World Bank portfolio totaled 146, from which 140 responses were expected and 127 responses were received.

Mode of data collection

Internet [int]

This dataset represents the polygons of the Exclusive Economic Zones (EEZ) of the world, in a high resolution: the coastline is based on GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database) The data set of the Exclusive Economic Zones can be used in many applications. In biogeography for example, it is possible to create for instance species distribution lists per country.

Mapping Our World Using GIS is a 1:1 set of instructional materials for teaching basic concepts found in middle school world geography. Each module consists of multiple files.

        Economists generally classify a country as “developing” or “developed” by determining the percentage

of gross domestic product (GDP) engaged in each of three sectors of the economy — agriculture, industry, and services. A country with a high percentage of its GDP in agriculture is categorized as developing, while a country with a high percentage of its GDP in services and industry is categorized as developed.

        In this activity, you will use maps of percentages of GDP in the three sectors to explore patterns of

development around the world. You will also examine two other economic indicators — energy use and GDP per capita — and compare the maps of GDP in economic sectors to the maps of GDP per capita and energy use. You will evaluate whether or not the economic sector criteria are good indicators of a country’s economic status.

  The Mapping Our World collection is at: http://esriurl.com/MOW. All Esri GeoInquiries can be found at: http://www.esri.com/geoinquiries

Consolidated Open Source Global Development Dataset (COSGDD)

Executive Summary

Motivation

Welcome to the Consolidated Open Source Global Development Dataset (COSGDD)!

The Consolidated Open Source Global Development Dataset (COSGDD) was created to address the growing need for accessible, consolidated, and diverse global datasets for education, research, and policy-making. By combining data from publicly available, open-source datasets, COSGDD provides a one-stop resource for analyzing key socio-economic, environmental, and governance indicators across the globe.

Streamlit Dashboard Link (The LIME explanation graph will take time to load) - https://cosgdd.streamlit.app/ Github Code Repo Link - https://github.com/AkhilByteWrangler/Consolidated-Open-Source-Global-Development-Dataset

Overview

Imagine having a magical map of the world that shows you not just the roads and mountains but also how happy people are, how much money they make, how clean the air is, and how fair their governments are. This dataset is that magical map - but in the form of organized data!

It combines facts and figures from trusted sources to help researchers, governments, companies, and YOU understand how the world works and how to make it better.

Why Does This Dataset Exist?

The world is complicated. Happiness doesn’t depend on just one thing like money; it’s also about health, fairness, relationships, and even how clean the air is. But these pieces of the puzzle are scattered across many places. This dataset brings everything together in one place, making it easier to:
- Answer big questions like:
- What makes people happy?
- Is wealth or freedom more important for well-being?
- How does urbanization affect happiness?
- Find patterns and trends across countries.
- Make smart decisions based on real-world data.

Who Should Use This Dataset?

This dataset is for anyone curious about the world, including:
- Researchers: Study connections between happiness, governance, and sustainability.
- Policy Makers: Design better policies to improve quality of life.
- Data Enthusiasts: Explore trends and patterns using statistics or machine learning.
- Businesses: Understand societal needs to improve Corporate Social Responsibility (CSR).

Description of Data

This dataset consolidates data from well-established sources such as the World Happiness Report, The Economist Democracy Index, environmental databases, and more. It includes engineered features to deepen understanding of well-being and sustainability.

Core Features

• Happiness Metrics:
  • Life Ladder: Self-reported happiness scores.
• Economic Indicators:
  • Log GDP per capita: Log-transformed measure of wealth.
  • Tax Revenue: Government revenue as a share of GDP.
• Social Indicators:
  • Social support: Proportion of people with reliable social networks.
  • Freedom to make life choices: Self-reported freedom levels.
• Environmental Metrics:
  • Total Emissions: Aggregated greenhouse gas emissions.
  • Renewables Production: Share of renewable energy production.
• Governance Indicators:
  • Democracy_Index: Quantitative measure of democratic governance.
  • Rule_of_Law_Index: Assessment of the legal system’s strength.
• Engineered Features:
  • Freedom_Index: Combines wealth and freedom.
  • Generosity_Per_Dollar: Normalized generosity against GDP.
  • Environmental_Bonus: Evaluates environmental efficiency relative to economic output.
  • See full documentation for more.

Core Columns

1. Country

• Unit: Country name as a string.
• Source: Sourced from all contributing datasets (e.g., World Happiness Report, UN datasets).
• Significance:
  Identifies the geographic region for the data. Essential for country-specific analyses, comparisons, and aggregations.

2. Year

• Unit: Year as an integer (e.g., 2024).
• Source: Included across all datasets.
• Significance:
  Indicates the time frame of the data. Vital for studying trends, changes over time, and time-series modeling.

Happiness Metrics

3. Life Ladder

• Unit: Scale from 0 (worst possible life) to 10 (best possible life).
• Source: World Happiness Report.
• Significance:
  Captures subjective well-being based on self-reported happiness. A central measure for studying the quality of life globally.

4. Log GDP per Capita

• Unit: Logarithmic transformation of GDP per capita in constant international dollars.
• Source: World Happiness Report, based on World Bank data.
• Significance:
  Provides a no...