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

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Global Socio-Economic & Environmental Indicators (1990-2021)

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Description: This comprehensive dataset provides a rich collection of socio-economic and environmental indicators for countries across the world. Spanning the years from 1990 to 2021, the dataset includes valuable information on Human Development Index (HDI), Life Expectancy, Gross National Income per Capita (GNI), and CO2 Production.

Key Columns: - ISO3: Three-letter country code (ISO 3166-1 alpha-3) - Country: Country name - hdicode: Identifier related to the Human Development Index (HDI) - region: Geographical region or grouping - hdi_rank_2021: HDI rank for the year 2021

Temporal Coverage: 1990 to 2021

Use Cases: - Analyze trends in HDI, life expectancy, GNI, and CO2 emissions over time. - Investigate the relationship between socio-economic development and environmental impact. - Conduct comparative studies between countries and regions. - Explore correlations and patterns in socio-economic indicators and HDI rankings.

This dataset offers a valuable resource for researchers, analysts, and policymakers interested in studying the dynamic interplay between socio-economic development and environmental factors on a global scale.

Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

A wide-ranging representative longitudinal study of private households that permits researchers to track yearly changes in the health and economic well-being of older people relative to younger people in Germany from 1984 to the present. Every year, there were nearly 11,000 households, and more than 20,000 persons sampled by the fieldwork organization TNS Infratest Sozialforschung. The data provide information on all household members, consisting of Germans living in the Old and New German States, Foreigners, and recent Immigrants to Germany. The Panel was started in 1984. Some of the many topics include household composition, occupational biographies, employment, earnings, health and satisfaction indicators. In addition to standard demographic information, the GSOEP questionnaire also contains objective measuresuse of time, use of earnings, income, benefit payments, health, etc. and subjective measures - level of satisfaction with various aspects of life, hopes and fears, political involvement, etc. of the German population. The first wave, collected in 1984 in the western states of Germany, contains 5,921 households in two randomly sampled sub-groups: 1) German Sub-Sample: people in private households where the head of household was not of Turkish, Greek, Yugoslavian, Spanish, or Italian nationality; 2) Foreign Sub-Sample: people in private households where the head of household was of Turkish, Greek, Yugoslavian, Spanish, or Italian nationality. In each year since 1984, the GSOEP has attempted to re-interview original sample members unless they leave the country. A major expansion of the GSOEP was necessitated by German reunification. In June 1990, the GSOEP fielded a first wave of the eastern states of Germany. This sub-sample includes individuals in private households where the head of household was a citizen of the German Democratic Republic. The first wave contains 2,179 households. In 1994 and 1995, the GSOEP added a sample of immigrants to the western states of Germany from 522 households who arrived after 1984, which in 2006 included 360 households and 684 respondents. In 1998 a new refreshment sample of 1,067 households was selected from the population of private households. In 2000 a sample was drawn using essentially similar selection rules as the original German sub-sample and the 1998 refreshment sample with some modifications. The 2000 sample includes 6,052 households covering 10,890 individuals. Finally, in 2002, an overrepresentation of high-income households was added with 2,671 respondents from 1,224 households, of which 1,801 individuals (689 households) were still included in the year 2006. Data Availability: The data are available to researchers in Germany and abroad in SPSS, SAS, TDA, STATA, and ASCII format for immediate use. Extensive documentation in English and German is available online. The SOEP data are available in German and English, alone or in combination with data from other international panel surveys (e.g., the Cross-National Equivalent Files which contain panel data from Canada, Germany, and the United States). The public use file of the SOEP with anonymous microdata is provided free of charge (plus shipping costs) to universities and research centers. The individual SOEP datasets cannot be downloaded from the DIW Web site due to data protection regulations. Use of the data is subject to special regulations, and data privacy laws necessitate the signing of a data transfer contract with the DIW. The English Language Public Use Version of the GSOEP is distributed and administered by the Department of Policy Analysis and Management, Cornell University. The data are available on CD-ROM from Cornell for a fee. Full instructions for accessing GSOEP data may be accessed on the project website, http://www.human.cornell.edu/che/PAM/Research/Centers-Programs/German-Panel/cnef.cfm * Dates of Study: 1984-present * Study Features: Longitudinal, International * Sample Size: ** 1984: 12,290 (GSOEP West) ** 1990: 4,453 (GSOEP East) ** 2000: 20,000+ Links: * Cornell Project Website: http://www.human.cornell.edu/che/PAM/Research/Centers-Programs/German-Panel/cnef.cfm * GSOEP ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00131

Thailand Avg Monthly Household Income: Northeastern data was reported at 20,271.000 THB in 2017. This records a decrease from the previous number of 21,094.000 THB for 2015. Thailand Avg Monthly Household Income: Northeastern data is updated yearly, averaging 8,413.500 THB from Dec 1981 (Median) to 2017, with 20 observations. The data reached an all-time high of 21,094.000 THB in 2015 and a record low of 2,512.000 THB in 1981. Thailand Avg Monthly Household Income: Northeastern data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H013: Household Socio Economic Survey: Household Income and Expenditure.

Description:

This dataset combines data from three sources to provide a comprehensive overview of county-level socioeconomic indicators, educational attainment, and voting outcomes in the United States. The dataset includes variables such as unemployment rates, median household income, urban influence codes, education levels, and voting percentages for the 2020 U.S. presidential election. By integrating this data, the dataset enables analysis of how factors like income, education, and unemployment correlate with political preferences, offering insights into regional voting behaviors across the country.

References:

The following reference datasets were used to construct this dataset.

[1] Harvard Dataverse, Voting Data Set by County. Available: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi: 10.7910/DVN/VOQCHQ

[2] USDA Economic Research Service, Educational Attainment and Un- employment Data. Available: https://www.ers.usda.gov/data-products/ county-level-data-sets/county-level-data-sets-download-data/

Get statistical data for rural and urban Ontario on key socioeconomic variables. The data identifies: * demographic information for rural and urban Ontario by census year (population change and age breakdown, immigrants, visible minorities, educational attainment, average household income) * economic indicators for rural and urban Ontario (monthly and annual employment by industry, monthly and annual labour force characteristics, number of businesses by industries and employee number) * rural and urban Ontario census profiles (for all census of population variables starting with 2001) Find more resources with socioeconomic data and information about Rural Ontario

Abstract

The Iraq Household Socio-Economic Survey conducted in 2006-2007 (IHSES 2007), was Iraq's first nationwide income and expenditure survey since 1988. Based on the model of the Living Standards Measurement Surveys, it covered more than 18,000 households, collected detailed data on all aspects of household income and expenditure and generated information on a wide variety of socio-economic indicators. It also formed the basis for updating the Consumer Price Index (CPI), from an outdated index based in 1990 to a revised index with the base year of 2007. Detailed analysis of poverty, its incidence, characteristics, determinants and consequences, was undertaken using this comprehensive survey. Under the overall guidance of the Poverty Reduction Strategy High Committee (PRSHC) and a technical sub-committee, a poverty line was defined and adopted by the Council of Ministers.

Six years later, in 2012, the second round of the IHSES was completed. Learning from past and international experience on survey design, implementation and sampling, IHSES 2012 also incorporated additional modules on areas of evolving interest. It is the most comprehensive socio-economic survey as yet undertaken in Iraq.

Objectives of the survey: 1) to provide data to help measure and analyze poverty and monitor the implementation of the national strategy to alleviate poverty (issued in 2009) and update it with a new strategy, 2) to provide an integrated system of data to assess the social and economic situation of families and develop indicators related to human development, 3) to provide data meeting the requirements and needs of the national accounts, 4) to provide detailed indicators of consumer spending and the impact of various changes in it to serve the production, consumption, export and import decision-making, 5) to provide detailed indicators of the incomes of individuals and families by source, 6) to provide the data required for creating a new index record of consumer prices beyond 2012.

Geographic coverage

National coverage

Analysis unit

Households and individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHSES intends to provide estimators of comparable quality for each of Iraq's 118 gadahs (districts). This implies that the sample should be explicitly stratified by gadah, with a similar sample size allocated to each gadah, regardless of its size. A sample size of 216 households per gadah is proposed, equivalent to a total sample of 25,488 households for the country.

Within each gadah, the sample will be selected in two stages, as follows:

• First, using Census Enumeration Areas (EAs) as Primary Sampling Units (PSUs), select 24 EAs with Probability Proportional to Size (PPS), using the number of households as a Measure of Size (MoS), and with implicit stratification by urban/rural and the subsequent geographical codes (nahya, mahala, village, mukataa and census block).

• Second, using households as secondary Sampling Units (SSUs), select a cluster of 9 households by systematic, equal probability sampling (SEPS) in each of the selected EAs.

The sample frames for both stages can be developed from the 2010 Census enumeration, with no updating of the household lists.

In some of the smallest gadahs, the standard PPS procedure may result in the selection of fewer than 24 EAs, with some of the larger EAs selected more than once. In those cases, two or more clusters will be taken in the EA, as needed. 2,832 EAs were selected in total. 33 of them had less than the 9 households nominally required in the second stage and were merged ex-post with neighboring EAs.

Mode of data collection

The data were collected using paper questionnaires with concurrent data entry in the field using Computer Assisted Field Entry (CAFE)

Research instrument

The survey questionnaire has four parts: Part 1 - Socio Economic Part 2 - Expenditure Part 3 - Income and other Data Part 4 - Household Diary

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and county levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsPolicy Development: Helps policymakers develop targeted interventions to address the needs of vulnerable populations.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability.Research: Provides a robust foundation for academic and applied research in socio-economic and demographic studies.Community Planning: Aids in the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities.Note: Due to limitations in the ArcGIS Pro environment, the data variable names may be truncated. Refer to the provided table for a clear understanding of the variables.CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2013-2017 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computer

Thailand Avg Monthly Household Income: Greater Bangkok data was reported at 41,897.000 THB in 2017. This records an increase from the previous number of 41,002.000 THB for 2015. Thailand Avg Monthly Household Income: Greater Bangkok data is updated yearly, averaging 25,992.000 THB from Dec 1981 (Median) to 2017, with 20 observations. The data reached an all-time high of 43,058.000 THB in 2013 and a record low of 5,962.000 THB in 1981. Thailand Avg Monthly Household Income: Greater Bangkok data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H013: Household Socio Economic Survey: Household Income and Expenditure.

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

This study examines the relationship between socio-economic factors and crime distribution using a dataset that includes variables such as unemployment rates, literacy rates, per capita income, and population density. The analysis explores how these factors influence crime rates across different regions, comparing urban and rural areas to identify variations in crime patterns due to economic and social disparities. Additionally, the study investigates cultural and psychological influences on criminal activities. The findings offer valuable insights for policymakers to develop more effective crime prevention strategies.This dataset supports the manuscript ‘Crime and Socio-Economic Inequalities: Leveraging Deep Learning and Generative AI for Comprehensive Analysis.’ It includes:- CrimeEconomicsData.csv: Original dataset with 114 observations across 10 socio-economic variables (Per Capita Income, Population Density, Unemployment, Literacy Rate, Happiness Index, Crime Rate).- supplementary_data.zip: Contains: - table_ii_metrics.csv: Performance metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC) for machine learning and deep learning models in Table II. - figure_2_confusion_matrices.csv: Confusion matrix data for each model, supporting Figure 2’s visualizations. - README.txt: Description of the files and their purpose.Preprocessed datasets are not included, as preprocessing steps (e.g., mean imputation, standardization, PCA) are detailed in the manuscript and can be replicated using CrimeEconomicsData.csv.

Health Outcomes and Socioeconomic Factors

A Study of US County Data

By Data Exercises [source]

About this dataset

This dataset contains a wealth of health-related information and socio-economic data aggregated from multiple sources such as the American Community Survey, clinicaltrials.gov, and cancer.gov, covering a variety of US counties. Your task is to use this collection of data to build an Ordinary Least Squares (OLS) regression model that predicts the target death rate in each county. The model should incorporate variables related to population size, health insurance coverage, educational attainment levels, median incomes and poverty rates. Additionally you will need to assess linearity between your model parameters; measure serial independence among errors; test for heteroskedasticity; evaluate normality in the residual distribution; identify any outliers or missing values and determine how categories variables are handled; compare models through implementation with k=10 cross validation within linear regressions as well as assessing multicollinearity among model parameters. Examine your results by utilizing statistical agreements such as R-squared values and Root Mean Square Error (RMSE) while also interpreting implications uncovered by your analysis based on health outcomes compared to correlates among demographics surrounding those effected most closely by land structure along geographic boundaries throughout the United States

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How to use the dataset

This dataset provides data on health outcomes, demographics, and socio-economic factors for various US counties from 2010-2016. It can be used to uncover trends in health outcomes and socioeconomic factors across different counties in the US over a six year period.

The dataset contains a variety of information including statefips (a two digit code that identifies the state), countyfips (a three digit code that identifies the county), avg household size, avg annual count of cancer cases, average deaths per year, target death rate, median household income, population estimate for 2015, poverty percent study per capita binned income as well as demographic information such as median age of male and female population percent married households adults with no high school diploma adults with high school diploma percentage with some college education bachelor's degree holders among adults over 25 years old employed persons 16 and over unemployed persons 16 and over private coverage available private coverage available alone temporary private coverage available public coverage available public coverage available alone percentages of white black Asian other race married households and birth rate.

Using this dataset you can build a multivariate ordinary least squares regression model to predict “target_deathrate”. You will also need to implement k-fold (k=10) cross validation to best select your model parameters. Model diagnostics should be performed in order to assess linearity serial independence heteroskedasticity normality multicollinearity etc., while outliers missing values or categorical variables will also have an effect your model selection process. Finally it is important to interpret the resulting models within their context based upon all given factors associated with it such as outliers missing values demographic changes etc., before arriving at a meaningful conclusion which may explain trends in health outcomes and socioeconomic factors found within this dataset

Research Ideas

• Analysis of factors influencing target deathrates in different US counties.
• Prediction of the effects of varying poverty levels on health outcomes in different US counties.
• In-depth analysis of how various socio-economic factors (e.g., median income, educational attainment, etc.) contribute to overall public health outcomes in US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. -...

Ukraine Households Income: SB: Social Benefits data was reported at 393,300.000 UAH mn in 2017. This records an increase from the previous number of 337,773.000 UAH mn for 2016. Ukraine Households Income: SB: Social Benefits data is updated yearly, averaging 204,101.000 UAH mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 393,300.000 UAH mn in 2017 and a record low of 23,978.000 UAH mn in 2001. Ukraine Households Income: SB: Social Benefits data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.H009: Household Income and Expenditure: Annual.

Thailand Avg Monthly Household Income: Southern data was reported at 26,913.000 THB in 2017. This records an increase from the previous number of 26,286.000 THB for 2015. Thailand Avg Monthly Household Income: Southern data is updated yearly, averaging 11,323.500 THB from Dec 1981 (Median) to 2017, with 20 observations. The data reached an all-time high of 27,504.000 THB in 2013 and a record low of 3,256.000 THB in 1981. Thailand Avg Monthly Household Income: Southern data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H013: Household Socio Economic Survey: Household Income and Expenditure.

Thailand Avg Monthly Household Expenditure: Greater Bangkok data was reported at 33,126.000 THB in 2017. This records an increase from the previous number of 32,091.000 THB for 2016. Thailand Avg Monthly Household Expenditure: Greater Bangkok data is updated yearly, averaging 21,716.000 THB from Dec 1981 (Median) to 2017, with 25 observations. The data reached an all-time high of 33,126.000 THB in 2017 and a record low of 5,737.000 THB in 1981. Thailand Avg Monthly Household Expenditure: Greater Bangkok data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H013: Household Socio Economic Survey: Household Income and Expenditure.

Thailand Monthly Income per Capita: Northeastern data was reported at 6,743.000 THB in 2017. This records an increase from the previous number of 6,696.000 THB for 2015. Thailand Monthly Income per Capita: Northeastern data is updated yearly, averaging 2,113.500 THB from Dec 1981 (Median) to 2017, with 20 observations. The data reached an all-time high of 6,743.000 THB in 2017 and a record low of 493.000 THB in 1981. Thailand Monthly Income per Capita: Northeastern data remains active status in CEIC and is reported by National Statistical Office. The data is categorized under Global Database’s Thailand – Table TH.H013: Household Socio Economic Survey: Household Income and Expenditure.