This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

Analysis of ‘Country Socioeconomic Status Scores: 1880-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sdorius/globses on 30 September 2021.

--- Dataset description provided by original source is as follows ---

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the world’s people live in a country with a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS: UNID: ISO numeric country code (used by the United Nations) WBID: ISO alpha country code (used by the World Bank) SES: Socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174) country: Short country name year: Survey year SES: Socioeconomic status score (1-99) for each of 174 countries gdppc: GDP per capita: Single time-series (imputed) yrseduc: Completed years of education in the adult (15+) population popshare: Total population shares

DATA SOURCES: The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita: 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. Maddison population data in 000s; GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls. 2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm 3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/ Total Population 1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.
2. United Nations Population Division. 2009.

--- Original source retains full ownership of the source dataset ---

Socioeconomic indicators like the poverty rate, population change, unemployment rate, and education levels vary across the nation. ERS has compiled the latest data on these measures into a mapping and data display/download application that allows users to identify and compare States and counties on these indicators.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Poverty Population Unemployment Education Web page with links to Excel files For complete information, please visit https://data.gov.

The Personal and Family Income Statistics operation computes the income of people aged 18 years or older residing in the Basque Country and calculates magnitudes such as gross income per capita or average personal income; constitutes the basis for the knowledge of the distribution of individual and family wealth by relying on fiscal data, linked to census variables of the population. The breakdown of income according to population strata such as sex, age or relationship with productive activity (working, unemployed, retired population) allows an approximation to the knowledge of various groups.

Main effects of individual socioeconomic status (SES) and countries’ socioeconomic development (SED) on social relationships: Crude and adjusted results from multi-level models, Odds ratios (OR) or coefficients (Coeff) and 95% confidence intervals (CI).

Additional file of Development of the Global Network for Women’s and Children’s Health Research’s socioeconomic status index for use in the network’s sites in low and lower middle-income countries

The Personal and Family Income Statistics operation computes the income of people aged 18 years or older residing in the Basque Country and calculates magnitudes such as gross income per capita or average personal income; constitutes the basis for the knowledge of the distribution of individual and family wealth by relying on fiscal data, linked to census variables of the population. The breakdown of income according to population strata such as sex, age or relationship with productive activity (working, unemployed, retired population) allows an approximation to the knowledge of various groups.

Data across all counties in five states (Arizona, Colorado, New Mexico, Oklahoma, and Texas) in the U.S. were collected for the study on the impact of the socio-economic and political status on the county-level COVID-19 vaccination rates. Variables were obtained from various data sources; the Bureau of Labor Statistics, Bureau of Economic Analysis, 2010 US Census, Politico, and Centers for Disease Control and Prevention (CDC). It was found that county-level vaccination rates were significantly associated with the percentage of Democrat votes, the elderly population, and per capita income of the county. In addition, the results revealed racial and ethnic disparities in COVID-19 vaccination. The manuscript entitled “Socio-political and Economic Impact on the COVID-19 Vaccination: Southwest Regional Study” was submitted for publication.

Additional data to complement the Childbearing by socio-economic status and country of birth of mother, England and Wales, 2014 publication

Objectives: Evidence on social inequalities in mental health of persons with physical impairments is limited. We therefore investigate associations of individual-level socioeconomic status (SES) and the country-level socioeconomic development (SED) with mental health in persons with spinal cord injury (SCI).Methods: We analyzed data from 12,588 participants of the International SCI Community Survey from 22 countries. To investigate individual-level inequalities, SES indicators (education, income, financial hardship, subjective status) were regressed on the SF-36 mental health index (MHI-5), stratified by countries. Country-level inequalities were analyzed with empirical Bayes estimates of random intercepts derived from linear mixed-models adjusting for individual-level SES.Results: Financial hardship and subjective status consistently predicted individual-level mental health inequalities. Country-level SED was inconsistently related to mental health when adjusting for individual-level SES. It however appeared that higher SED was associated with better mental health within higher-resourced countries.Conclusion: Reducing impoverishment and marginalization may present valuable strategies to reduce mental health inequalities in SCI populations. Investigations of country-level determinants of mental health in persons with SCI should consider influences beyond country-level SED, such as cultural factors.

Data on county socioeconomic status for 2,132 US counties and each county’s average annual cardiovascular mortality rate (CMR) and total PM2.5 concentration for 21 years (1990-2010). County CMR, PM2.5, and socioeconomic data were obtained from the U.S. National Center for Health Statistics, U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system, and the U.S. Census, respectively. A socioeconomic index was created using seven county-level measures from the 1990 US census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This dataset is associated with the following publication: Wyatt, L., G. Peterson, T. Wade, L. Neas, and A. Rappold. The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 136: 105430, (2020).

The American Community Survey (ACS) 5 Year 2016-2020 socioeconomic estimate data is a subset of information derived from the following census tables:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By Tenure;B17021 - Poverty Status Of Individuals In The Past 12 Months By Living Arrangement;B19001 - Household Income In The Past 12 Months;B19013 - Median Household Income In The Past 12 Months;B19025 - Aggregate Household Income In The Past 12 Months;B19113 - Median Family Income In The Past 12 Months;B19202 - Median Non-family Household Income In The Past 12 Months;B23001 - Sex By Age By Employment Status For The Population 16 Years And Over;B25014 - Tenure By Occupants Per Room;B25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into Unit;B25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 Months;C24010 - Sex By Occupation For The Civilian Employed Population 16 Years And Over;B20004 - Median Earnings In the Past 12 Months (In 2015 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and Over;B23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, and;B24021 - Occupation By Median Earnings In The Past 12 Months (In 2015 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Socioeconomic Estimate Data by CountyDate of Coverage: 2016-2020

Description:

*The World Development Indicators (WDI) is a premier compilation of cross-country comparable data about development. It provides a broad range of economic, social, environmental, and governance indicators to support analysis and decision-making for development policies. The dataset includes indicators from different countries, spanning multiple decades, enabling researchers and policymakers to understand trends and progress in development goals such as poverty reduction, education, healthcare, and infrastructure.*

*The dataset is a collection of multiple CSV files providing information on global indicators, countries, and time-series data. It is structured as follows:*

1. series:
Contains metadata for various indicators, including their descriptions, definitions, and other relevant information. This file acts as a reference for understanding what each indicator represents.

2. country_series:
Establishes relationships between countries and specific indicators. It provides additional metadata, such as contextual descriptions of indicator usage for particular countries.

3. countries:
Includes detailed information about countries, such as country codes, region classifications, income levels, and other geographical or socio-economic attributes.

4. footnotes:
Provides supplementary notes and additional context for specific data points in the main dataset. These notes clarify exceptions, limitations, or other special considerations for particular entries.

5. main_data:
The core dataset containing the actual indicator values for countries across different years. This file forms the backbone of the dataset and is used for analysis.

6. series_time:
Contains time-related metadata for indicators, such as their start and end years or periods of data availability.

*This dataset is ideal for analyzing global development trends, comparing country-level statistics, and studying the relationships between different socio-economic indicators over time.*

Columns and Examples:

Series Code:

Description: Unique code identifying the data series.

Example: AG.LND.AGRI.K2 (Agricultural land, sq. km).

Topic:

Description: Category under which the indicator is classified.

Example: Environment: Land use.

Indicator Name:

Description: Full name describing what the indicator measures.

Example: Agricultural land (sq. km).

Short Definition:

Description: A brief explanation of the indicator (if available).

Example: Not applicable for all indicators.

Long Definition:

Description: Detailed explanation of the indicator’s meaning and methodology.

Example: "Agricultural land refers to the share of land area that is arable, under permanent crops, or under permanent pastures."

Unit of Measure:

Description: Unit in which the data is expressed.

Example: Square kilometers.

Periodicity:

Description: How frequently the data is collected or reported.

Example: Annual.

Base Period:

Description: The reference period used for comparison, if applicable.

Example: Often not specified.

Other Notes:

Description: Additional context or remarks about the data.

Example: "Data for former states are included in successor states."

Aggregation Method:

Description: Method used to combine data for groups (e.g., regions).

Example: Weighted average.

Limitations and Exceptions:

Description: Constraints or exceptions in the data.

Example: "Data may not be directly comparable across countries due to different definitions."

Notes from Original Source:

Description: Remarks provided by the data source.

Example: Not specified for all indicators.

General Comments:

Description: Broad remarks about the dataset or indicator.

Example: Not available in all cases.

Source:

Description: Organization providing the data.

Example: Food and Agriculture Organization.

Statistical Concept and Methodology:

Description: Explanation of how the data was generated.

Example: "Agricultural land is calculated based on land area classified as arable."

Development Relevance:

Description: Importance of the indicator for development.

Example: "Agricultural land availability impacts food security and rural livelihoods."

Related Source Links:

Description: URLs to related information sources (if any).

Example: Not specified.

Other Web Links:

Description: Additional web resources.

Example: Not specified.

Related Indicators:

Description: Indicators conceptually related...

What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S Census Data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Theme 1 - Socioeconomic StatusTheme 2 - Household CharacteristicsTheme 3 - Racial & Ethnic Minority StatusTheme 4 - Housing Type & Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2020 Documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2020 Full DocumentationSVI Home PageContact the SVI Coordinator

Purpose: The article examines specific factors of socio-economic development in the context of national security. The purpose of the current work is to identify the connections between selected indicators of socio-economic development and the level of national security, in the context of improving the quality of life for all citizens. Design/Methodology/Approach: The process of globalization has caused changes that manifest in various spheres of human life. One of the main positive aspects of the globalization process, which affects the quality of human life, is regional development. This can be measured using selected socio-economic indicators. Findings: Regional development is not the only factor that affects the quality of human life. Negative aspects of the socio-economic development process, such as illegal migration and the overall increase in crime, have made the issue of national security extremely relevant for every country. Practical Implications: One of the numerous aspects of national security that negatively affects the quality of human life, as well as the overall perception of the globalization process, is migration and the crimes committed by foreigners in certain countries. Originality/Value: Since the goal of socio-economic development is to improve the quality of human life, it is also necessary in this context to address the security issues of each citizen in a given country, which, overall, is a component of national security.

Logistic regression models of socioeconomic status and psychosocial resources, adjusted for age, sex, country of birth, employment status and other measures of SES.

Why do citizens vary in their perceptions about the frequency of corruption? We hypothesize that those most harmed by corruption—the socioeconomically disadvantaged—should perceive corruption to be more frequent. Using multiple cross-national surveys, we find that the poor and the uneducated tend to perceive higher levels of corruption than the wealthy and the well educated. However, this relationship only holds in countries at high levels of economic development. In poorer countries, the statistical relationship is much weaker and sometimes runs in the opposite direction.

Factor loading values for SES.

BackgroundThe association between socioeconomic status (SES) and health is well-documented; however limited evidence on the relationship between SES and gastrointestinal (GI) infections exists, with published studies producing conflicting results. This systematic review aimed to assess the association between SES and GI infection risk, and explore possible sources of heterogeneity in effect estimates reported in the literature.MethodsMEDLINE, Scopus, Web of Science and grey literature were searched from 1980 to October 2015 for studies reporting an association between GI infections and SES in a representative population sample from a member-country of the Organisation for Economic Co-operation and Development. Harvest plots and meta-regression were used to investigate potential sources of heterogeneity such as age; level of SES variable; GI infection measurement; and predominant mode of transmission. The protocol was registered on PROSPERO: CRD42015027231.ResultsIn total, 6021 studies were identified; 102 met the inclusion criteria. Age was identified as the only statistically significant potential effect modifier of the association between SES and GI infection risk. For children, GI infection risk was higher for those of lower SES versus high (RR 1.51, 95% CI;1.26–1.83), but there was no association for adults (RR 0.79, 95% CI;0.58–1.06). In univariate analysis, the increased risk comparing low and high SES groups was significantly higher for pathogens spread by person-to-person transmission, but lower for environmental pathogens, as compared to foodborne pathogens.ConclusionsDisadvantaged children, but not adults, have greater risk of GI infection compared to their more advantaged counterparts. There was high heterogeneity and many studies were of low quality. More high quality studies are needed to investigate the association between SES and GI infection risk, and future research should stratify analyses by age and pathogen type. Gaining further insight into this relationship will help inform policies to reduce inequalities in GI illness in children.

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...