An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

Data in this layer represents demographic data from the American Community Survey 5 yr estimates, 2015-2019 for Age, Disability, Education, Female Population, Limited English Proficiency, Low Income, Place of Birth, Race, and Zero Vehicle Households. Each layer contains a number of attributes pertaining to the specific topic. For additional information about the data, definitions, and source please contact NJTPA (gfausel@njtpa.org).

Frequency distribution of respondents’ socio-demographic characteristics, years of experience and working hours per day (N = 130).

This dataset presents a comprehensive overview of household and per-capita income and expenditure patterns in various demographic, geographic, and socioeconomic contexts. It encompasses three main categories:Disposable IncomeConsumption ExpenditureFinal Monetary Consumption ExpenditureWithin each category, indicators detail averages, medians, and percentages across dimensions such as administrative region, nationality of the household head, age group, educational level, marital status, type of dwelling, type of ownership, household size, and income sources. The dataset thus enables in-depth analysis of how different factors influence income and expenditure.esearchers, policymakers, and analysts can employ these indicators to:Understand how household and per-capita incomes vary by social and economic factors.Examine consumption patterns and their drivers, including demographic variables.Analyze the final monetary consumption expenditure in more detail using COICOP divisions for targeted economic and social policy insights.In doing so, users can identify disparities, assess living standards, and formulate data-driven strategies to address economic and social challenges at both the household and regional levels.Notes:For the first time the methodology for calculating household disposable income and consumption expenditure is used in Household Income and Consumption Expenditure Survey of 2023

kt estimated for males (2019–2025) by the ARIMA model (0, 2, 2).

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Surveys related to the family budget are considered one of the most important surveys types carried out by the Department Of Statistics, since it provides data on household expenditure and income and their relationship with different indicators. Therefore, most of the countries undertake periodic surveys on household income and expenditures. The Department Of Statistics, since established, conducted a series of Expenditure and Income Surveys during the years 1966, 1980, 1986/1987, 1992, 1997, 2002/2003, 2006/2007, and 2008/2009 and because of continuous changes in spending patterns, income levels and prices, as well as in the population internal and external migration, it was necessary to update data for household income and expenditure over time. Hence, the need to implement the Household Expenditure and Income Survey for the year 2010 arises. The survey was then conducted to achieve the following objectives: 1. Provide data on income and expenditure to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. 2. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 3. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 4. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty. 5. Identify consumer spending patterns prevailing in the society, and the impact of demographic, social and economic variables on those patterns. 6. Calculate the average annual income of the household and the individual, and identify the relationship between income and different socio-economic factors, such as profession and educational level of the head of the household and other indicators. 7. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the Kingdom. Where the Kingdom is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Household Expenditure and Income survey sample, for the year 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map. It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

To reach the survey objectives, 3 forms have been developed. Those forms were finalized after being tested and reviewed by specialists taking into account making the data entry, and validation, process on the computer as simple as possible.

(1) General Form/Questionnaire This form includes: - Housing characteristics such as geographic location variables, household area, building material predominant for external walls, type of tenure, monthly rent or lease, main source of water, lighting, heating and fuel cooking, sanitation type and water cycle, the number of rooms in the dwelling, in addition to providing ownership status of some home appliances and car. - Characteristics of household members: This form focused on the social characteristics of the family members such as relation to the head of the family, gender, age and educational status and marital status. It also included economic characteristics such as economic activity, and the main occupation, employment status, and the labor sector. to the additions of questions about individual continued to stay with the family, in order to update the information at the beginning of the second, third and fourth rounds. - Income section which included three parts · Family ownership of assets · Productive activities for the family · Current income sources

(2) Expenditure on food commodities form/Questionnaire This form indicates expenditure data on 17 consumption groups. Each group includes a number of food commodities, with the exception of the latter group, which was confined to some of the non-food goods and services because of their frequent spending pattern on daily basis like food commodities. For the purposes of the efficient use of results, expenditure data of the latter group was moved with the non-food commodities expenditure. The form also includes estimated amounts of own-produced food items and those received as gifts or in an in-kind form, as well as servants living with the family spending on themselves from their own wages to buy food.

(3) Expenditure on non-food commodities form/Questionnaire This form indicates expenditure data on 11 groups of non-food items, and 5 sets of spending on services, in addition to a group of consumption expenditure. It also includes an estimate of self-consumption, and non-food gifts or other items in an in-kind form received or sent by the household, as well as servants living with the family spending on themselves from their own wages to buy non-food items.

Cleaning operations

Raw Data

The data collection phase was then followed by the data processing stage accomplished through the following procedures: 1- Organizing forms/questionnaires A compatible archive system, with the nature of the subsequent operations, was used to classify the forms according to different round throughout the year. This is to effectively enable extracting the forms when required for processing. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms are back to the archive system. 2- Data office checking This phase is achieved concurrently with the data collection phase in the field, where questionnaires completed in the fieldwork are immediately sent to data office checking phase. 3- Data coding A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were use, while for the rest of the questions, all coding were predefined during

This longitudinal survey was designed to add significantly to the amount of detailed information available on the economic situation of households and persons in the United States. These data examine the level of economic well-being of the population and also provide information on how economic situations relate to the demographic and social characteristics of individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules which are series of supplemental questions asked during selected household visits. No topical modules were created for the first or second waves. The Wave III Rectangular Core and Topical Module File offers both the core data and additional data on (1) education and work history and (2) health and disability. In the areas of education and work history, data are supplied on the highest level of schooling attained, courses or programs studied in high school and after high school, whether the respondent received job training, and if so, for how long and under what program (e.g., CETA or WIN). Other items pertain to the respondent's general job history and include a description of selected previous jobs, duration of jobs, and reasons for periods spent not working. Health and disability variables present information on the general condition of the respondent's health, functional limitations, work disability, and the need for personal assistance. Data are also provided on hospital stays or periods of illness, health facilities used, and whether health insurance plans (private or Medicare) were available. Respondents whose children had physical, mental, or emotional problems were questioned about the causes of the problems and whether the children attended regular schools. The Wave IV Rectangular Core and Topical Module file contains both the core data and sets of questions exploring the subjects of (1) assets and liabilities, (2) retirement and pension coverage, and (3) housing costs, conditions, and energy usage. Some of the major assets for which data are provided are savings accounts, stocks, mutual funds, bonds, Keogh and IRA accounts, home equity, life insurance, rental property, and motor vehicles. Data on unsecured liabilities such as loans, credit cards, and medical bills also are included. Retirement and pension information covers such items as when respondents expect to stop working, whether they will receive retirement benefits, whether their employers have retirement plans, if so whether they are eligible, and how much they expect to receive per year from these plans. In the category of housing costs, conditions, and energy usage, variables pertain to mortgage payments, real estate taxes, fire insurance, principal owed, when the mortgage was obtained, interest rates, rent, type of fuel used, heating facilities, appliances, and vehicles. The Wave V topical modules explore the subject areas of (1) child care, (2) welfare history and child support, (3) reasons for not working/reservation wage, and (4) support for nonhousehold members/work-related expenses. Data on child care include items on child care arrangements such as who provides the care, the number of hours of care per week, where the care is provided, and the cost. Questions in the areas of welfare history and child support focus on receipt of aid from specific welfare programs and child support agreements and their fulfillment. The reasons for not working/reservation wage module presents data on why persons are not in the labor force and the conditions under which they might join the labor force. Additional variables cover job search activities, pay rate required, and reason for refusal of a job offer. The set of questions dealing with nonhousehold members/work-related expenses contains items on regular support payments for nonhousehold members and expenses associated with a job such as union dues, licenses, permits, special tools, uniforms, or travel expenses. Information is supplied in the Wave VII Topical Module file on (1) assets and liabilities, (2) pension plan coverage, and (3) real estate property and vehicles. Variables pertaining to assets and liabilities are similar to those contained in the topical module for Wave IV. Pension plan coverage items include whether the respondent will receive retirement benefits, whether the employer offers a retirement plan and if the respondent is included in the plan, and contributions by the employer and the employee to the plan. Real estate property and vehicles data include information on mortgages held, amount of principal still owed and current interest rate on mortgages, rental and vacation properties owned, and various items pertaining to vehicles belonging to the household. Wave VIII Topical Module includes questions on support for nonhousehold members, work-related expenses, marital history, migration history, fertility history, and household relationships. Support for nonhousehold members includes data for children and adults not in the household. Weekly and annual work-related expenses are documented. Widowhood, divorce, separation, and marriage dates are part of the marital history. Birth expectations as well as dates of birth for all the householder's children, in the household or elsewhere, are recorded in the fertility history. Migration history data supplies information on birth history of the householder's parents, number of times moved, and moving expenses. Household relationships lists the exact relationships among persons living in the household. Part 49, Wave IX Rectangular Core and Topical Module Research File, includes data on annual income, retirement accounts, taxes, school enrollment, and financing. This topical module research file has not been edited nor imputed, but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

Sociodemographic characteristics by BMI subgroups in n (% cumulated).

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

In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Abstract

This survey provides information on household income and expenditure to be able to measure the levels and changes in the living condition of the people and to observe the consumption patterns .

Key objectives of the survey - To identify the income patterns in Urban, Rural and Estate Sectors & provinces. - To identify the income patterns by income levels. - Average consumption of food items and non food items - Expenditure patterns by sector and by differnt income levels.

Geographic coverage

National coverage.

Analysis unit

Household, Individuals

Universe

For this survey a sample of buildings and the occupants therein was drawn from the whole island

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage stratified random sample design was used in this survey. Sectors of the District are the domains for stratification. The Master sample frame prepared for the Demographic Survey 1994 was used in this survey. There were about 4000 Primary Sampling Units ( PSUs) in the frame. From this frame, a sample of 1061 PSU's were drawn for Income and Expenditure Survey 1995/96. From each sampled PSU, 20 numbers of housing units ( final sampling units ) were drawn to reach a sample of 21,220 housing units. Therefore the weighting factors calculated for Income and Expenditure Survey 1995/96 were based on the corresponding factors of the Demographic Survey 1994.

Sample allocation

The District and Sector allocation of the number of housing units to be surveyed was done proportionate to the square root of the total number of housing units in the District and Sector. However selected areas under the urban sector of the Colombo District and the Gampaha District were over-sampled to obtain a better representative sample and to obtain weighting factors for computation of Consumer Price Indices.

( Refer section 1.3 of the Final Report attached in the external resources section )

Estimation Procedure ( Refer section 1.4 of the Final Report attached in the external resources section )

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionaires

The survey schedule was designed to collect data on household basis and separate schedules were used for each household (identified according to the definition of the household) within the housing units selected for the survey.The survey Schedule consists of three main sections .

       1. Demographic section 
       2. Expenditure
       3. Income

The demographic characteristics and usual activities of the inmates belonging to the household are reported in the Demographic section of the schedule and close relatives temporarily living away are also listed in the section. Expenditure section has two sub sections to report food and non-food consumption data separately. Expenditure incurred on their own decisions by boarders and servants are recorded in the sub section under the expenditure section. The income has seven sub sections categorized according to the main sources of income.

Response rate

The all Island response rate was

Sampling error estimates

Refer the section 1.4 of the final report attached in the External Resources section.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demograohic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor chracteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Household/families
• Individuals

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2008 Household Expenditure and Income Survey sample was designed using two-stage cluster stratified sampling method. In the first stage, the primary sampling units (PSUs), the blocks, were drawn using probability proportionate to the size, through considering the number of households in each block to be the block size. The second stage included drawing the household sample (8 households from each PSU) using the systematic sampling method. Fourth substitute households from each PSU were drawn, using the systematic sampling method, to be used on the first visit to the block in case that any of the main sample households was not visited for any reason.

To estimate the sample size, the coefficient of variation and design effect in each subdistrict were calculated for the expenditure variable from data of the 2006 Household Expenditure and Income Survey. This results was used to estimate the sample size at sub-district level, provided that the coefficient of variation of the expenditure variable at the sub-district level did not exceed 10%, with a minimum number of clusters that should not be less than 6 at the district level, that is to ensure good clusters representation in the administrative areas to enable drawing poverty pockets.

It is worth mentioning that the expected non-response in addition to areas where poor families are concentrated in the major cities were taken into consideration in designing the sample. Therefore, a larger sample size was taken from these areas compared to other ones, in order to help in reaching the poverty pockets and covering them.

Mode of data collection

Face-to-face [f2f]

Research instrument

List of survey questionnaires: (1) General Form (2) Expenditure on food commodities Form (3) Expenditure on non-food commodities Form

Cleaning operations

Raw Data The design and implementation of this survey procedures were: 1. Sample design and selection 2. Design of forms/questionnaires, guidelines to assist in filling out the questionnaires, and preparing instruction manuals 3. Design the tables template to be used for the dissemination of the survey results 4. Preparation of the fieldwork phase including printing forms/questionnaires, instruction manuals, data collection instructions, data checking instructions and codebooks 5. Selection and training of survey staff to collect data and run required data checkings 6. Preparation and implementation of the pretest phase for the survey designed to test and develop forms/questionnaires, instructions and software programs required for data processing and production of survey results 7. Data collection 8. Data checking and coding 9. Data entry 10. Data cleaning using data validation programs 11. Data accuracy and consistency checks 12. Data tabulation and preliminary results 13. Preparation of the final report and dissemination of final results

Harmonized Data - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets - The harmonization process started with cleaning all raw data files received from the Statistical Office - Cleaned data files were then all merged to produce one data file on the individual level containing all variables subject to harmonization - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables - A post-harmonization cleaning process was run on the data - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format

(1) This hierarchical file contains 202,112 records. There are approximately 157 variables and two record types: family and person. Family records contain approximately 58 variables, and person records contain approximately 99 variables. (2) Each family and person record contains a weight, which must be used in any analysis. (3) This data file was obtained from the Data Program and Library Service (DPLS), University of Wisconsin. Some data management operations intended to store the data more efficiently were performed by DPLS. That organization also revised the original Census Bureau documentation. (4) The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site. This data collection supplies standard monthly labor force data as well as supplemental data on work experience, income, and migration. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Information on demographic characteristics, such as age, sex, race, educational attainment, marital status, veteran status, household relationship, and Hispanic origin, is available for each person in the household enumerated. Persons in the civilian noninstitutional population of the United States living in households and members of the armed forces living in civilian housing units in 1969. Datasets: DS1: Current Population Survey: Annual Demographic File, 1969 A national probability sample was used in selecting housing units.

The deployment of public electric vehicle charging stations (EVCS) is a critical component of transportation electrification. Recent studies have highlighted growing concerns about disparities in accessibility to public chargers between different demographic groups. This research expands ongoing equity concerns by contextualizing existing transportation equity discourse and analyzing public charger access disparities in Austin, Texas. Using threshold equity toolkits, we investigated public EVCS access disparity across different races and income groups. We conducted a generalized additive model regression to measure and visualize the effects of possible determinants on public EVCS access. The analysis results revealed that a public EVCS access disparity exists in Austin, with most chargers being installed in areas where the majority of the population is Non-Hispanic White. There was a more equal distribution of public EVCSs across income quartiles when compared with race. However, middle- and high-income groups had better access than lower-income communities in terms of distance to the nearest public EVCSs. Our regression analysis found that regional and socio-demographic factors, such as race and income, have a statistically significant impact on public charger access. The regression analysis also revealed that Austin’s current public EVCS deployment seems to favor communities above the poverty level and with higher numbers of registered electric vehicles. Local policymakers should reflect on the findings of this study to develop an equitable transportation electrification plan. Federal environmental justice plans such as the Justice40 initiative can benefit from incorporating more local contexts to better invest in disadvantaged communities.

Abstract (en): This data collection supplies standard monthly labor force data as well as supplemental data on work experience, income, and migration. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Information on demographic characteristics, such as age, sex, race, educational attainment, marital status, veteran status, household relationship, and Hispanic origin, is available for each person in the household enumerated. Persons in the civilian noninstitutional population of the United States living in households and members of the armed forces living in civilian housing units in 1969. A national probability sample was used in selecting housing units. (1) This hierarchical file contains 202,112 records. There are approximately 157 variables and two record types: family and person. Family records contain approximately 58 variables, and person records contain approximately 99 variables. (2) Each family and person record contains a weight, which must be used in any analysis. (3) This data file was obtained from the Data Program and Library Service (DPLS), University of Wisconsin. Some data management operations intended to store the data more efficiently were performed by DPLS. That organization also revised the original Census Bureau documentation. (4) The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Climate change, urbanization, and global trade have contributed to the recent spread of dengue viruses. In this study, we investigate the relationship between dengue occurrence in humans, climate factors (temperature and minimum quarterly rainfall), socio-economic factors (such as household income, regional rates of education, regional unemployment, housing overcrowding, life expectancy, and medical resources), and demographic factors (such as migration flows, age structure of the population, and population density). From a geographical perspective, this study focuses on Mexico and parts of the United States to exploit similarity in climate conditions and differences in socio-economic and demographic factors, so as to try to isolate the role of the latter. Areas at risk of dengue are first selected based on the predicted presence of at least one of the two mosquito vectors responsible for dengue's transmission: Aedes aegypti and Aedes albopictus. The presence of the mosquito in a region is predicted by the above mentioned climate variables. In those regions where the vectors had a high probability of presence, we assess the impact of one composite socio-economic indicator (derived through factor analysis to account for collinearity), and three composite demographic indicators (also derived from factor analysis) on the regional distribution of dengue cases, controlling for climate, and allowing for spatial correlation. We found that an increase of one unit in the socio-economic indicator is related to a drop in the occurrence of dengue, whereas the demographic indicators showed no significant impact after taking climate into account. The significant impact of the socio-economic indicator also persists when looking at differences in the occurrence of dengue in Mexico only.

Wildlife communities are structured by numerous ecological filters in cities that influence their populations, and some species even manage to thrive in urban landscapes. CAP researchers were the first to observe “the luxury effect”, the hypothesis that biodiversity is positively related to income of residents. The luxury effect is still being tested worldwide twenty years later and has led to important new research on other socio-demographic factors that shape biodiversity but are vastly understudied, such as race and ethnicity, as well as the interaction of these factors with urban structural inequalities that may be hidden by income. This research aims to unpack the luxury effect by considering other landscape and socio-demographic factors that may influence wildlife communities across neighborhoods of metro Phoenix. Specifically, we are investigating if neighborhood income and ethnicity independently influence mammal occupancy in neighborhoods across the CAP ecosystem. To answer this question, we leveraged a wildlife camera array across CAP within community parks, in which cameras are placed across a gradient of average median household income and percent Latinx of residents. Incorporating socioeconomic data into urban mammal research will allow for the advancement in the understanding of socio-ecological patterns.

IntroductionThe introduction of the Income Index constructed by authors as well as the identification of demographic, socio-economic and occupation-related factors influencing the income of individuals in EU countries is the main contribution of the paper. The Income Index makes it possible to analyze data of individuals from all EU countries.MethodsThe multiple hierarchical regression of EU-SILC microdata provides the factors that influence individuals’ income.ResultsOutcomes show through which factors can be intervened in social policy settings to reduce income inequality. Factors significantly affecting the Income Index are the household composition, occupation sector (typically agriculture and accommodation and services are related to low incomes) and the degree of urbanization (rural areas with the lowest incomes of individuals).DiscussionFindings confirm ongoing discussions about the specific position of single parent households in the labour market and their need for social support.