This statistic represents the estimated population per U.S. House of Representatives seat in 2015, by state. As of 2015, the rate in Montana was at *********** population per House of Representatives seat.

As of 2020, Malta was the country with the highest number of parliament members per 100,000 inhabitants. This Southern European country counted 14.3 members in the parliament. By contrast, the Spanish parliament had the lowest number of members in proportion to the population.

In September 2020, a constitutional referendum was held in Italy on the number of parliament members. The Italian Parliament consists of the Chamber of Deputies and Senate of the Republic. The data depicted in the chart show the number of deputies before the referendum, which amounted to 630 members. For every 100,000 individuals, Italy had one deputy, one of the lowest number in the European Union in proportion to country's population. After the referendum, Italy could have just 0.7 members in the Chamber of Deputies per 100,000 population, ranking last in the EU.

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.

Per Capita Net State Domestic Product (Per Capita NSDP) measures the average economic output generated per person within a specific state or region after accounting for the depreciation of capital assets. It is calculated by dividing the Net State Domestic Product (NSDP), which represents the total value of goods and services produced within a state, by its population. Unlike Gross State Domestic Product (GSDP), which reflects the overall production without considering depreciation, NSDP provides a more accurate representation of the state's sustainable economic output. The per capita element offers insight into the average living standards and productivity of individuals within the state, making it a valuable indicator for comparing economic performance across regions and assessing the financial well-being of residents. Higher Per Capita NSDP typically indicates better economic conditions and higher living standards, though it doesn't account for income inequality or distribution within the population.

This dataset was created in order to document self-reported life evaluations among small-scale societies that exist on the fringes of mainstream industrialized socieities. The data were produced as part of the LICCI project, through fieldwork carried out by LICCI partners. The data include individual responses to a life satisfaction question, and household asset values. Data from Gallup World Poll and the World Values Survey are also included, as used for comparison. TABULAR DATA-SPECIFIC INFORMATION --------------------------------- 1. File name: LICCI_individual.csv Number of rows and columns: 2814,7 Variable list: Variable names: User, Site, village Description: identification of investigator and location Variable name: Well.being.general Description: numerical score for life satisfaction question Variable names: HH_Assets_US, HH_Assets_USD_capita Description: estimated value of representative assets in the household of respondent, total and per capita (accounting for number of household inhabitants) 2. File name: LICCI_bySite.csv Number of rows and columns: 19,8 Variable list: Variable names: Site, N Description: site name and number of respondents at the site Variable names: SWB_mean, SWB_SD Description: mean and standard deviation of life satisfaction score Variable names: HHAssets_USD_mean, HHAssets_USD_sd Description: Site mean and standard deviation of household asset value Variable names: PerCapAssets_USD_mean, PerCapAssets_USD_sd Description: Site mean and standard deviation of per capita asset value 3. File name: gallup_WVS_GDP_pk.csv Number of rows and columns: 146,8 Variable list: Variable name: Happiness Score, Whisker-high, Whisker-low Description: from Gallup World Poll as documented in World Happiness Report 2022. Variable name: GDP-PPP2017 Description: Gross Domestic Product per capita for year 2020 at PPP (constant 2017 international $). Accessed May 2022. Variable name: pk Description: Produced capital per capita for year 2018 (in 2018 US$) for available countries, as estimated by the World Bank (accessed February 2022). Variable names: WVS7_mean, WVS7_std Description: Results of Question 49 in the World Values Survey, Wave 7.

Does reapportionment in a legislature affect policy outcomes? We examine this question from a comparative perspective by focusing on reapportionment associated with the electoral reform in Japan. First, we show that the reform of 1994 resulted in an unprecedented degree of equalization in legislative representation. Second, municipal-level data, we present evidence that municipalities in overrepresented districts received significantly more subsidies per capita, as compared to those in underrepresented districts, in both pre reform and post reform years. Third, by examining the relationship between the change in the number os seats per capita and the change in the amount of subsidies per capita at the municipal level, we show that the equalization in voting strength resulted in an equalization of total transfers per person.

Content

A dataset of counties that are representative for Germany with regard to

the average disposable income,

the quota of divorces,

the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,

the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

To quantitatively assess the capacity of POI in representing demographic data, this study proposes the Population Representation Index (PRI), defined as the population size mapped by per unit of POI.This index reveals the population base corresponding to each unit of POI, directly reflecting the relationship between population and POI. Furthermore, it measures the population service capacity per POI unit, indicating supply-demand between infrastructure and demographics need. A higher PRI value indicates that each POI unit corresponds to a larger population, suggesting a potential mismatch between POI and population demand in the area, thereby indicating weaker POI representativeness. A greater distribution range and higher dispersion of RPI values signify more pronounced regional variation in the representativeness of POI.

Graph and download economic data for Constant GDP per capita for India (NYGDPPCAPKDIND) from 1960 to 2024 about India, per capita, real, and GDP.

There are 435 seats in the U.S. House of Representatives, of which ** are allocated to the state of California. Seats in the House are allocated based on the population of each state. To ensure proportional and dynamic representation, congressional apportionment is reevaluated every 10 years based on census population data. After the 2020 census, six states gained a seat - Colorado, Florida, Montana, North Carolina, and Oregon. The states of California, Illinois, Michigan, New York, Ohio, Pennsylvania, and West Virginia lost a seat.

NSDP Per Capita: Uttarakhand data was reported at 274,064.471 INR in 2025. This records an increase from the previous number of 246,178.490 INR for 2024. NSDP Per Capita: Uttarakhand data is updated yearly, averaging 177,691.982 INR from Mar 2012 (Median) to 2025, with 14 observations. The data reached an all-time high of 274,064.471 INR in 2025 and a record low of 100,314.463 INR in 2012. NSDP Per Capita: Uttarakhand data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GEI004: Memo Items: State Economy: Net State Domestic Product per Capita.

Abstract

The Integrated Living Conditions Survey (ILCS), conducted annually by the NSS National Statistical Service of the Republic of Armenia, formed the basis for monitoring living conditions in Armenia. The ILCS is a universally recognized best-practice survey for collecting data to inform about the living standards of households. The ILCS comprises comprehensive and valuable data on the welfare of households and separate individuals which gives the NSS an opportunity to provide the public with up to date information on the population’s income, expenditures, the level of poverty and the other changes in living standards on an annual basis.

Geographic coverage

Urban and rural communities

Analysis unit

• Households;
• Individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

During the 2001-2003 surveys two-stage random sample was used; the first stage covered the selection of settlements - cities and villages, while the second stage was focused on the selection of households in these settlements. The surveys were conducted on the principle of monthly rotation of households by clusters (sample units). In 2002 and 2003 the number of households was 387 with the sample covering 14 cities and 30 villages in 2002 and 17 cities and 20 villages in 2003.

During the 2004-2006 surveys the sampling frame for the ILCS was built using the database of addresses for the 2001 Population Census; the database was developed with the World Bank technical assistance. The database of addresses of all households in Armenia was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to the following three categories: big towns with 15,000 and more population; villages, and other towns. Big towns formed 16 strata (the only exception was the Vayots Dzor marz where there are no big towns). The villages and other towns formed 10 strata each. According to this division, a random, two-step sample stratified at marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of population residing in those settlements as percent to the total population in the country. In the first step, the settlements, i.e. primary sample units, were selected: 43 towns out of 48 or 90 percent of all towns in Armenia were surveyed during the year; also 216 villages out of 951 or 23 percent of all villages in the country were covered by the survey. In the second step, the respondent households were selected: 6,816 households (5,088 from urban and 1,728 from rural settlements). As a result, for the first time since 1996 survey data were representative at the marz level.

During the 2007-2012 surveys the sampling frame for ILCS was designed according to the database of addresses for the 2001 Population Census, which was developed with the World Bank technical assistance. The sample consisted of two parts: core sample and oversample.

1) For the creation of core sample, the sample frame (database of addresses of all households in Armenia) was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to three categories: large towns (with population of 15000 and higher), villages and other towns. Large towns formed by 16 groups (strata), while the villages and towns formed by 10 strata each. According to that division, a random, two-step sample stratified at the marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of households residing in those settlements as percent to the total households in the country. In the first step, using the PPS method the enumeration units (i.e., primary sample units to be surveyed during the year) were selected. 2007 sample includes 48 urban and 18 rural enumeration areas per month. 2) The oversample was drawn from the list of villages included in MCA-Armenia Rural Roads Rehabilitation Project. The enumeration areas of villages that were already in the core sample were excluded from that list. From the remaining enumeration areas 18 enumeration areas were selected per month. Thus, the rural sample size was doubled. 3) After merging the core sample and oversample, the survey households were selected in the second step. 656 households were surveyed per month, from which 368 from urban and 288 from rural settlements. Each month 82 interviewers had conducted field work, and their workload included 8 households per month. In 2007 number of surveyed households was 7,872 (4,416 from urban and 3,456 from rural areas).

For the survey 2013 the sample frame for ILCS was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2001 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample. For the purpose of drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2013 sample included 32 enumeration areas in urban and 16 enumeration areas in rural communities per month. The households to be surveyed were selected in the second round. A total of 432 households were surveyed per month, of which 279 and 153 households from urban and rural communities, respectively. Every month 48 interviewers went on field work with a workload of 9 households per month.

The sample frame for 2014-2016 was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2011 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample.
For drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2014 sample included 30 enumeration areas in urban and 18 enumeration areas in rural communities per month. The method of representative probability sampling was used to frame the sample. At regional level, all communities were grouped into two categories - towns and villages. According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all rural and urban communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration districts - that is primary sample units to be surveyed during the year - were selected. The ILCS 2015 sample included 30 enumeration districts in urban and 18 enumeration districts in rural communities per month.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire is filled in by the interviewer during the least five visits to households per month. During face-to-face interviews with the household head or another knowledgeable adult member, the interviewer collects information on the composition and housing conditions of the household, the employment status, educational level and health condition of the members, availability and use of land, livestock, and agricultural machinery, monetary and commodity flows between households, and other information.

The 2011 survey questionnaire had the following sections: (1) "List of Household Members", (2) "Migration", (3) "Housing and Dwelling Conditions", (4) "Employment", (5) "Education", (6) "Agriculture", (7) "Food Production", (8) "Monetary and Commodity Flows between Households", (9) "Health (General) and Healthcare", (10) "Debts", (11) "Subjective Assessment of Living Conditions", (12) "Provision of Services", (13) "Social Assistance", (14) "Households as Employers for Service Personnel", and (15) "Household Monthly Consumption of Energy Resources".

The Diary is completed directly by the household for one month. Every day the household would record all its expenditures on food, non-food products and services, also giving a detailed description of such purchases; e.g. for food products the name, quantity, cost, and place of purchase of the product is recorded. Besides, the household records its consumption of food products received and used from its own land and livestock, as well as from other sources (e.g. gifts, humanitarian aid). Non-food products and services purchased or received for free are also recorded in the diary. Then, the household records its income received during the month. At the end of the month, information on rarely used food products, durable goods and ceremonies is recorded, as well. The records in the diary are verified by the interviewer in the course of 5

Part of the Integrated Vulnerability Assessment in the Arab Region, this 1km pixel resolution raster dataset provides a representation of Adaptive Capacity to climate change, for the Economic Resources dimension indicator - GDP per Capita - in the Middle East and North Africa Region. Vulnerability is a concept used to express the complex interaction of climate change effects and the susceptibility of a system to its impacts. The integrated vulnerability assessment methodology is based on an understanding of vulnerability as a function of a system’s climate change exposure, sensitivity and adaptive capacity to cope with climate change effects, consistent with the approach put forward by the Intergovernmental Panel on Climate Change (IPCC) in its Fourth Assessment Report (AR4). Combining exposure, sensitivity and adaptive capacity allows assessing the vulnerability of a system to climate change. Within this conceptual framework, Adaptive Capacity refers to “the ability of a system to adjust to climate change (including climate variability and extremes), to moderate potential damages, to take advantage of opportunities, or to cope with the consequences” as defined in the IPCC AR4. Adaptive Capacity was categorized into six dimensions. The Economic Resources dimension, together with the institutions can be classified as action devices that describe the enabling environment that allow a society to adapt. Economic Resources indicators were assumed to retain the same values for the reference period and future periods, and raster grid pixel values were classified according to level of Adaptive Capacity, from low 1 to high 10.

The Gross Domestic Product per capita in Kenya was last recorded at 1853.09 US dollars in 2024. The GDP per Capita in Kenya is equivalent to 15 percent of the world's average. This dataset provides the latest reported value for - Kenya GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This child item describes R code used to determine whether public-supply water systems buy water, sell water, both buy and sell water, or are neutral (meaning the system has only local water supplies) using water source information from a proprietary dataset from the U.S. Environmental Protection Agency. This information was needed to better understand public-supply water use and where water buying and selling were likely to occur. Buying or selling of water may result in per capita rates that are not representative of the population within the water service area. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature variable in the public supply water use machine learning model. This page includes the following files: ID_WSA_04062022_Buyers_Sellers_DR.R - an R script used to determine whether a public-supply water service area buys water, sells water, or is neutral BuySell_readme.txt - a README text file describing the script

Us House Congressional Representatives serving Macon-Bibb County.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

The scholars comprising journal editorial boards play a critical role in defining the trajectory of knowledge in their field. Nevertheless, studies of editorial board composition remain rare, especially those focusing on journals publishing research in the increasingly globalized fields of science, technology, engineering, and math (STEM). Using metrics for quantifying the diversity of ecological communities, we quantified international representation on the 1985–2014 editorial boards of 24 environmental biology journals. Over the course of 3 decades, there were 3,827 unique scientists based in 70 countries who served as editors. The size of the editorial community increased over time—the number of editors serving in 2014 was 4-fold greater than in 1985—as did the number of countries in which editors were based. Nevertheless, editors based outside the “Global North” (the group of economically developed countries with high per capita gross domestic product [GDP] that collectively concentrate most global wealth) were extremely rare. Furthermore, 67.18% of all editors were based in either the United States or the United Kingdom. Consequently, geographic diversity—already low in 1985—remained unchanged through 2014. We argue that this limited geographic diversity can detrimentally affect the creativity of scholarship published in journals, the progress and direction of research, the composition of the STEM workforce, and the development of science in Latin America, Africa, the Middle East, and much of Asia (i.e., the “Global South”).

This database automatically captures metadata sourced from the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL OFFICE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled "Gross domestic product and actual individual consumption per capita in purchasing power standards, volume indices, EU-27 Member States, annually".

The actual data is available in PC-Axis format (.px). Among the additional links, you can access the pages of the source portal for insight and selection of data, and there is also the PX-Win program, which can be downloaded for free. Both allow you to select data for display, change the format of the printout and save it in different formats, as well as view and print tables of unlimited size and some basic statistical analyses and graphical representations.

Administrative unitsRepresents the administrative units used for GDP per capita (PPP) and HDI data products. National administrative units have id 1-999, sub-national ones 1001-admin_areas_GDP_HDI.ncGDP_per_capita_PPP_1990_2015The GDP per capita (PPP) dataset represents average gross domestic production per capita in a given administrative area unit. GDP is given in 2011 international US dollars. Gap-filled sub-national data were used, supplemented by national data where necessary. Datagaps were filled by using national temporal pattern. Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.GDP_PPP_1990_2015_5arcminThis global dataset represents the gross domestic production (GDP) of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP) which is multiplied by gridded population data HYDE 3.2 (the years of population data not available (1991-1999) were linearly interpolated at grid scale based on data from years 1990 and 2000). Dataset has global extent at 5 arc-min resolution for the 26-year period of 1990-2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.HDI_1990_2015HDI is a composite index of average achievement in key dimensions of human development (dimensionless indicator between 0 and 1). This index is based on method introduced 2010 and updated 2011. The subnational data for HDI were collected from multiple national-level datasets, and national-level HDI was collected from UNDP. Years with missing data were interpolated over time thin plate spines, assuming smooth trend over time. The dataset has a global extent at 5 arc-min resolution, and the annual data is available for each year over 1990-2015. HDI sub-national data covers 39 countries and 66% of global population in 2015.pedigree_GDP_per_capita_PPP_1990_2015This is the source data for GDP per capita (PPP), published as an indication of accuracy and precision. Reports the scale (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.pedigree_HDI_1990_2015This is the source data for Human Development Index (HDI), published as an indication of accuracy and precision. Reports the scale (national, sub-national) and type (reported, interpolated, extrapolated) of each year of data. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.GDP_PPP_30arcsecThe GDP (PPP) data represents average gross domestic production of each grid cell. GDP is given in 2011 international US dollars. The data is derived from GDP per capita (PPP), which is multiplied by gridded population data from Global Human Settlement (GHS). Dataset has a global extent at 30 arc-second resolution for three time steps: 1990, 2000, and 2015. Detail description is given in a linked article and metadata is provided as an attribute in the NetCDF file itself.kummu_etal_scidata_codeThis file contains the scripts for data handling and production An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key economic and human development indicators are still mainly provided only at national level, and downscaled by users for gridded spatial analyses. Instead, it would be beneficial to adopt data for sub-national administrative units where available, supplemented by national data where necessary. To this end, we present gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5 arc-min resolution for the 25-year period of 1990–2015. Additionally, total GDP (PPP) is provided with 30 arc-sec resolution for three time steps (1990, 2000, 2015).