A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Denmark Number of Family: Couples: with Children data was reported at 588,412.000 Unit in 2017. This records an increase from the previous number of 587,852.000 Unit for 2016. Denmark Number of Family: Couples: with Children data is updated yearly, averaging 592,436.000 Unit from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 601,022.000 Unit in 2000 and a record low of 584,183.000 Unit in 2015. Denmark Number of Family: Couples: with Children data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.H011: Number of Family: by Family Type.

Philippines Number of Families: National Capital Region (NCR) data was reported at 3,019,000.000 Unit in 2015. This records an increase from the previous number of 2,917,000.000 Unit for 2012. Philippines Number of Families: National Capital Region (NCR) data is updated yearly, averaging 2,240,837.500 Unit from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 3,019,000.000 Unit in 2015 and a record low of 1,435,436.000 Unit in 1988. Philippines Number of Families: National Capital Region (NCR) data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H014: Family Income and Expenditure Survey: No of Families: By Income Class and Main Source of Income.

This dataset provides a comprehensive overview of 500 languages spoken around the world. It captures essential linguistic features, including language families, geographical regions, writing systems, and the estimated number of native speakers. This dataset aims to highlight the rich diversity of languages and their cultural significance, offering valuable insights for linguists, researchers, and enthusiasts interested in global language distribution.

The dataset contains real and accurate records for 500 languages across different regions and linguistic families. It covers a diverse range of languages, from widely spoken ones like English and Mandarin to less commonly known languages. The data was meticulously compiled to reflect the authentic linguistic landscape and provide a valuable resource for language studies and cultural analysis.

This dataset has been obtained from a public dataset published by United Nations: https://www.un.org/development/desa/pd/data/world-fertility-data

The original dataset in an Excel spreadsheet has been divided for indicators, re-named some columns for better reusability and grouped values from country for better Tableau integration identifying countries.

NOTE: Highly suggest checking the sheet "Database Field Descriptions" at Excel file for full field description.

Here's the original description within the Excel file: World Fertility Data 2019 presents data on age-specific fertility rates, total fertility and mean age at childbearing for 201 countries or areas of the world. The database includes data from civil registration systems, population censuses, and sample surveys available as of August 2019 and covers the time period from 1950 to the present.

The World Fertility Data database builds on the historical repository of demographic data and census and survey reports collected over the past 50 years by the Population Division and Statistics Division of the Department of Economic and Social Affairs (DESA) of the United Nations Secretariat. Data derived from censuses are generally reported by National Statistical Offices to the Statistics Division. Census data are also obtained from official census publications produced by National Statistical Offices. Estimates based on data compiled from civil registration systems are generally obtained from National Statistical Offices. Additional sources of data include the Demographic and Health Surveys (DHS), the Multiple Indicator Cluster Surveys (MICS), the Reproductive Health Surveys (RHS), the Statistical Office of the European Union (Eurostat), the Human Fertility Database (HFD), the Human Fertility Collection (HFC), the Pan-Arab Project for Child Development Surveys (PAPCHILD), the Pan-Arab Project for Family Health Survey (PAPFAM), national surveys, as well as fertility estimates produced by the Population Division of DESA.

This revision of the database was prepared by Lina Bassarsky and Kyaw Kyaw Lay, under the supervision of Victor Gaigbe-Togbe. Assistance on programming was provided by Kyaw Kyaw Lay. Giulia Gonnella assisted the team with reviewing metadata. This database builds upon a previous edition, World Fertility Data 2017, to which the following contributed: Kirill Andreev, Helena Cruz Castanheira, and Stephen Kisambira. The Population Division extends thanks to our colleagues in National Statistical Offices for providing the requested data, reports and answering our numerous questions regarding the data.

Note: The designations employed in this publication and the material presented in it do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The term “country” as used in this publication also refers, as appropriate, to territories or areas.

Australia Average Number of Dependent Children in Household: Multiple Family data was reported at 1.200 Person in 2020. This records a decrease from the previous number of 1.400 Person for 2018. Australia Average Number of Dependent Children in Household: Multiple Family data is updated yearly, averaging 1.300 Person from Jun 2004 (Median) to 2020, with 9 observations. The data reached an all-time high of 1.500 Person in 2016 and a record low of 1.200 Person in 2020. Australia Average Number of Dependent Children in Household: Multiple Family data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H039: Survey of Income and Housing: Average Number of Dependent Children in Household: by Family Composition.

Households are the fundamental units of co-residence and play a crucial role in social and economic reproduction worldwide. They are also widely used as units of enumeration for data collection purposes, with substantive implications for research on poverty, living conditions, family structure, and gender dynamics. However, reliable comparative data on households and changes and living arrangements around the world is still under development. The CORESIDENCE database (CoDB) aims to bridge the existing data gap by offering valuable insights not only into the documented disparities between countries but also into the often-elusive regional differences within countries. By providing comprehensive data, it facilitates a deeper understanding of the complex dynamics of co-residence around the world. This database is a significant contribution to research, as it sheds light on both macro-level variations across nations and micro-level variations within specific regions, facilitating more nuanced analyses and evidence-based policymaking.

The CoDB is composed of three datasets covering 155 countries (National Dataset), 3563 regions (Subnational Dataset), and 1511 harmonized regions (Subnational-Harmonized Dataset) for the period 1960 to 2021, and it provides 146 indicators on household composition and family arrangements across the world.

This repository is composed of the following elements: a RData file named CORESIDENDE_DATABASE containing the CoDB in the form of a List.

The CORESIDENDE_DB list object is composed of six elements:

1. NATIONAL: a data frame with the household composition and living arrangements indicators at the national level.
2. SUBNATIONAL: a data frame with the household composition and living arrangements indicators at the subnational level computed over the original subnational division provided in each sample and data source.
3. SUBNATIONAL_HARMONIZED: a data frame with the household composition and living arrangements indicators computed over the harmonized subnational regions.
4. SUBNATIONAL_BOUNDARIES_CORESIDENCE: a spatial data frame (a sf object) with the boundary’s delimitation of the subnational harmonized regions created for this project.
5. CODEBOOK: a data frame with the complete list of indicators, their code names and description.
6. HARMONIZATION_TABLE: a data frame with the full list of individual country-year samples employed in this project and their state of inclusion in the 3 datasets composing the CoDB.

Elements 1, 2, 3, 5 and 6 of the R list are also provided as csv files under the same names. Element 4, the harmonized boundaries, is at disposal as gpkg (Geopackage) file.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Blue Earth County, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in Blue Earth County. Across the different household sizes in Blue Earth County the mean income is $87,806, and the standard deviation is $24,832. The coefficient of variation (CV) is 28.28%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $36,307. It then further increased to $95,561 for 7-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blue Earth County median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Globe, AZ, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Globe did not include 6, or 7-person households. Across the different household sizes in Globe the mean income is $68,554, and the standard deviation is $29,234. The coefficient of variation (CV) is 42.64%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $27,069. It then further increased to $97,625 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/globe-az-median-household-income-by-household-size.jpeg" alt="Globe, AZ median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Globe median household income. You can refer the same here

Dear Partners,

This month, the Administration for Children and Families (ACF) observed World Day Against Child Labor by spotlighting and encouraging those, who could, to join the Within and Beyond Our Borders: Collective Action to Address Hazardous Child Labor organized by the U.S. Department of Labor (DOL) on June 12, 2023. If you missed it, or would like to rewatch it, you can find it here

.

Since 2018, the DOL has seen a 69 percent increase in children being employed illegally by companies. In the last fiscal year, the department found that 835 companies it investigated had employed more than 3,800 children in violation of labor laws. There has been a 26 percent increase in children employed in hazardous occupations. These numbers tell us that we have work to do as the human services sector to learn more and become engaged in preventing unlawful child labor and supporting youth.

As I have said before, child labor exploitation can disrupt a youth’s health, safety, education, and overall well-being, which are unacceptable consequences for any child. The Administration for Children and Families (ACF) supports a broad network of resources for vulnerable youth. We know that migrant and immigrant youth are especially vulnerable to exploitation, and it is often youth in or exiting the child welfare system who are targeted for various forms of exploitation. Child labor exploitation can impact children and youth across demographics.

On March 24, 2023, the DOL and the U.S. Department of Health and Human Services (HHS) announced a Memorandum of Agreement - PDF

to advance ongoing efforts to address child labor exploitation. In addition, DOL and HHS are collaborating on training and educational materials.

As we expand this work, we know how important our partners throughout the country are in this effort. The Administration for Children and Families (ACF) is committed to addressing the increased presence of child labor exploitation through a variety of actions including equipping partners with materials and educational resources to build knowledge about child labor laws and rights, and remedies. This information is important for our human services sector and the children and families who may be most at risk.

Please join ACF in increasing awareness and distributing resources to address child labor exploitation including the following:

ACF resources may be also useful when working with a youth who has concerns about their safety. This includes the Family and Youth Services Bureau (FYSB)’s program on Runaway and Homeless Youth which provides a hotline for youth, concerned adults, and providers to access resources. At, www.1800runaway.org

, their 24/7 crisis connection allows for calls, texts, live chat, and email to get information and resources.

In addition, ACF’s Office of Trafficking In-Persons (OTIP) is an important resource for identifying and supporting survivors of trafficking. The National Human Trafficking Hotline

provides a 24/7, confidential, multilingual hotline for victims, survivors, and witnesses of human trafficking. While labor exploitation should not be conflated with labor trafficking, in some cases labor exploitation may rise to meet the legal definitions of trafficking. The OTIP website

contains many resources for grantees and communities on labor trafficking.

Again, I hope you will continue to build awareness for yourself, your organization, or your community on child labor exploitation. It takes a whole community effort to support our children and youth.

Most sincerely,

January Contreras

Metadata-only record linking to the original dataset. Open original dataset below.

Content

Daily report of how many Single Adults and Families are served

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Jelleke Vanooteghem on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Philippines Number of Families: Region X, Northern Mindanao data was reported at 1,029,000.000 Unit in 2015. This records an increase from the previous number of 976,000.000 Unit for 2012. Philippines Number of Families: Region X, Northern Mindanao data is updated yearly, averaging 736,597.500 Unit from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 1,029,000.000 Unit in 2015 and a record low of 528,138.000 Unit in 1997. Philippines Number of Families: Region X, Northern Mindanao data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H014: Family Income and Expenditure Survey: No of Families: By Income Class and Main Source of Income.

Abstract

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS3 drew its sample from IFLS1, IFLS2, IFLS2+. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly costeffectiveness reasons, 14 of the then existing 27 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi).

Household Survey:

Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA.This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

IFLS3 Re-Contact Protocols The sampling approach in IFLS3 was to re-contact all original IFLS1 households having living members the last time they had been contacted, plus split-off households from both IFLS2 and IFLS2+, so-called target households (8,347 households-as shown in Table 2.1*) Main field work for IFLS3 went on from June through November, 2000. A total of 10,574 households were contacted in 2000; meaning that they were interviewed, had all members died since the last time they were contacted, or had joined another IFLS household which had been previously interviewed (Table 2.1*). Of these, 7,928 were IFLS3 target households and 2,646 were new split-off households. A 95.0% re-contact rate was thus achieved of all IFLS3 "target" households. The re-contacted households included 6,800 original 1993 households, or 95.3% of those. Of IFLS1 households, somewhat lower re-contact rates were achieved in Jakarta, 84.5%, and North Sumatra,

Philippines No. of Families: IC: Annual: PhP 250,000 and over data was reported at 7,888,000.000 Unit in 2015. This records an increase from the previous number of 6,228,000.000 Unit for 2012. Philippines No. of Families: IC: Annual: PhP 250,000 and over data is updated yearly, averaging 7,058,000.000 Unit from Dec 2012 (Median) to 2015, with 2 observations. The data reached an all-time high of 7,888,000.000 Unit in 2015 and a record low of 6,228,000.000 Unit in 2012. Philippines No. of Families: IC: Annual: PhP 250,000 and over data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H014: Family Income and Expenditure Survey: No of Families: By Income Class and Main Source of Income.

United States Inventory: Multi-Family: West Virginia data was reported at 10.000 Unit th in Jun 2020. This stayed constant from the previous number of 10.000 Unit th for May 2020. United States Inventory: Multi-Family: West Virginia data is updated monthly, averaging 10.000 Unit th from May 2012 (Median) to Jun 2020, with 28 observations. The data reached an all-time high of 19.000 Unit th in May 2013 and a record low of 5.000 Unit th in Jul 2019. United States Inventory: Multi-Family: West Virginia data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB024: Inventory of Home for Sale: by States.

United States Existing Home Sales: Single Family: Inventory data was reported at 1,740,000.000 Unit in Jun 2018. This records an increase from the previous number of 1,660,000.000 Unit for May 2018. United States Existing Home Sales: Single Family: Inventory data is updated monthly, averaging 2,090,000.000 Unit from Jun 1982 (Median) to Jun 2018, with 433 observations. The data reached an all-time high of 3,400,000.000 Unit in Jul 2007 and a record low of 1,290,000.000 Unit in Dec 2017. United States Existing Home Sales: Single Family: Inventory data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s USA – Table US.EB005: Existing Home Sales.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Black Earth, WI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Black Earth did not include 6, or 7-person households. Across the different household sizes in Black Earth the mean income is $98,348, and the standard deviation is $38,725. The coefficient of variation (CV) is 39.38%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $37,082. It then further increased to $99,984 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/black-earth-wi-median-household-income-by-household-size.jpeg" alt="Black Earth, WI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth median household income. You can refer the same here

United States Inventory: Multi-Family: Hawaii data was reported at 23.000 Unit th in May 2020. This records an increase from the previous number of 8.000 Unit th for Apr 2020. United States Inventory: Multi-Family: Hawaii data is updated monthly, averaging 17.000 Unit th from Mar 2012 (Median) to May 2020, with 73 observations. The data reached an all-time high of 36.000 Unit th in Feb 2018 and a record low of 6.000 Unit th in Aug 2019. United States Inventory: Multi-Family: Hawaii data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB024: Inventory of Home for Sale: by States.

United States Months of Supply: Multi-Family: California, MD data was reported at 1.000 Month in May 2020. This stayed constant from the previous number of 1.000 Month for Nov 2019. United States Months of Supply: Multi-Family: California, MD data is updated monthly, averaging 1.000 Month from Apr 2013 (Median) to May 2020, with 6 observations. The data reached an all-time high of 2.000 Month in Oct 2019 and a record low of 1.000 Month in May 2020. United States Months of Supply: Multi-Family: California, MD data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB029: Months of Supply: by Metropolitan Areas.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Black Earth Town, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Black Earth town did not include 5, 6, or 7-person households. Across the different household sizes in Black Earth town the mean income is $129,146, and the standard deviation is $42,288. The coefficient of variation (CV) is 32.74%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $85,122. It then further increased to $185,106 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/black-earth-town-wi-median-household-income-by-household-size.jpeg" alt="Black Earth Town, Wisconsin median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth town median household income. You can refer the same here