Income limits used to determine the income eligibility of applicants for assistance under three programs authorized by the National Housing Act. These programs are the Section 221(d)(3) Below Market Interest Rate (BMIR) rental program, the Section 235 program, and the Section 236 program. These income limits are listed by dollar amount and family size, and they are effective on the date issued. Due to the Housing and Economic Recovery Act of 2008 (Public Law 110-289), Income Limits used to determine qualification levels as well as set maximum rental rates for projects funded with tax credits authorized under section 42 of the Internal Revenue Code (the Code) and projects financed with tax exempt housing bonds issued to provide qualified residential rental development under section 142 of the Code (hereafter referred to as Multifamily Tax Subsidy Projects (MTSPs)) are now calculated and presented separately from the Section 8 income limits.
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License information was derived automatically
Text source: https://www.huduser.gov/portal/datasets/HOME-Income-limits.htmlLanding page description:HOME Income Limits data are available from FY 1998 to the present. The HOME Income Limits are calculated using the same methodology that HUD uses for calculating the income limits for the Section 8 program, in accordance with Section 3(b)(2) of the U.S. Housing Act of 1937, as amended. These limits are based on HUD estimates of median family income, with adjustments based on family size. Please note that the 30 percent income limits for the HOME program have been calculated based on the definition of Extremely Low–Income Family (ELI) as described in Consolidated Submission for CPD Programs section of 24 CFR part 91.5. Therefore, the ELI Limit is calculated as 30 percent of median family income for the area and may not be the same as the Section 8 ELI Limit for your jurisdiction. The Section 8 Limit is calculated based on the definition of ELI as described in The 2014 Consolidated Appropriations Act, (Section 238 on page 128 Stat 635) which defines ELI as very low–income families whose incomes do not exceed the higher of the Federal poverty level or 30% of area median income. Family sizes in excess of 8 persons are calculated by adding 8% of the four-person income limit for each additional family member. That is, a 9-person limit should be 140% of the 4-person limit, the 10-person limit should be 148%.The HOME income limit values for large households (9-12 persons) must be rounded to the nearest $50. Therefore, all values from 1 to 24 are rounded down to 0, and all values from 25 to 49 are rounded up to 50.Note: The FY 2024 HOME Income Limits effective date is June 01, 2024.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in Lower Frederick Township, Pennsylvania, 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
https://i.neilsberg.com/ch/lower-frederick-township-pa-median-household-income-by-household-size.jpeg" alt="Lower Frederick Township, Pennsylvania median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Lower Frederick township median household income. You can refer the same here
Low income cut-offs (LICOs) before and after tax by community size and family size, in current dollars, annual.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in Lower Milford Township, Pennsylvania, 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
https://i.neilsberg.com/ch/lower-milford-township-pa-median-household-income-by-household-size.jpeg" alt="Lower Milford Township, Pennsylvania median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Lower Milford township median household income. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘3.05 Subsidized Housing Funding Usage (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/6c97f668-67da-4ae8-81a9-b35b7406c076 on 11 February 2022.
--- Dataset description provided by original source is as follows ---
This dataset provides information on Tempe's subsidized housing program, including monthly voucher and funding budgets and expenditures.
The City of Tempe Housing Services Division receives federal funds through Housing and Urban Development Department (HUD) to subsidize housing for low-income families that is decent, safe, sanitary and affordable. Families served by the program must live at or below 50% of the area median income.
Tempe has a fixed number of Housing Choice Vouchers (HCVs) based on our HUD contract, which represents the maximum number of families that the Housing Authority could assist. Congress and HUD do not fund the program to assist all of the families we are allotted to assist. We can only assist the number of families we have the budget to assist.
HUD provides an initial funding amount based on what they anticipate they will allocate to housing assistance payments. The actual amount of funding received is subject to change depending on Federal Budget priorities, Congressional approval and many other factors.
Expenditures are reported monthly, as HUD requires expenses to be posted in the month they were incurred rather than the month the expense was paid.
The performance measure dashboard is available at 3.05 Subsidized Housing Funding Usage.
Additional Information
Source: Manually maintained data, Housing Pro and Quickbooks
Contact: Levon Lamy
Contact E-Mail: Levon_Lamy@tempe.gov
Data Source Type: CSV
Preparation Method: Monthly values are calculated by determining the month each of the expenditures was for and retroactivelly accruing the funding use to the appropriate period. There are multiple, multistep excel worksheets that are used to balance between the specialty Housing Software, City Financial System and the HUD mandated reporting system. Additionally, it is important to note that Funding is allocated by Congress on the Federal Fiscal Year (October - September), the City operates on a Fiscal Year (July - June) and HUD provides funding on the Housing Authority in Calendar Year (January - December) funding increments. Therefore, the City must cross balance between three funding years.
Publish Frequency: Quarterly
Publish Method: Manual
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in Show Low, 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
https://i.neilsberg.com/ch/show-low-az-median-household-income-by-household-size.jpeg" alt="Show Low, AZ median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Show Low median household income. You can refer the same here
This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.
The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.
The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.
Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.
As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.
Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.
For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.
*According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."
Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).
Low income measure (LIM) thresholds by household size for market income, total income and after-tax income, in current and constant dollars, annual.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in Lower Township, New Jersey, 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
https://i.neilsberg.com/ch/lower-township-nj-median-household-income-by-household-size.jpeg" alt="Lower Township, New Jersey median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Lower township median household income. You can refer the same here
LOW TRANSPORTATION COST INDEXSummaryThe Low Transportation Cost Index is based on estimates of transportation expenses for a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e. CBSA). The estimates come from the Location Affordability Index (LAI). The data correspond to those for household type 6 (hh_type6_) as noted in the LAI data dictionary. More specifically, among this household type, we model transportation costs as a percent of income for renters (t_rent). Neighborhoods are defined as census tracts. The LAI data do not contain transportation cost information for Puerto Rico.InterpretationValues are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the transportation cost index, the lower the cost of transportation in that neighborhood. Transportation costs may be low for a range of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community.
Data Source: Location Affordability Index (LAI) data, 2012-2016.Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 11.
References: www.locationaffordability.infohttps://lai.locationaffordability.info//lai_data_dictionary.pdf
To learn more about the Low Transportation Cost Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020
https://www.icpsr.umich.edu/web/ICPSR/studies/38908/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38908/terms
The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, States and Territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 states, the District of Columbia, and the United States territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the data files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Most variables have a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. Beginning with the 2020 files, the analytic data files are supplemented by four additional data files containing select policy information featured in the annual reports (prior to 2020, the full detail of the annual reports was reproduced as data files). The supplemental data files are available as 4 datasets (Datasets 33-36) and present key aspects of the differences in CCDF-funded programs across all states and territories as of October 1 of each year (2009-2022). The files include variables that are calculated using several variables from the analytic data files (Datasets 1-32) (such as copayment amounts for example family situations) and information that is part of the annual project reports (the annual Book of Tables) but not stored in the full database (such as summary market rate survey information from the CCDF plans). 2. The project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in Lower Allen Township, Pennsylvania, 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
https://i.neilsberg.com/ch/lower-allen-township-pa-median-household-income-by-household-size.jpeg" alt="Lower Allen Township, Pennsylvania median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Lower Allen township median household income. You can refer the same here
Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.
https://borealisdata.ca/api/datasets/:persistentId/versions/3.1/customlicense?persistentId=doi:10.5683/SP3/LCXVCRhttps://borealisdata.ca/api/datasets/:persistentId/versions/3.1/customlicense?persistentId=doi:10.5683/SP3/LCXVCR
Note: Data on gender diverse households (formerly "2SLGBTQ+" households) has been added as of March 28th, 2025. For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This dataset contains 18 tables which draw upon data from the 2021 Canadian Census of Population. The tables are a custom order and contain data pertaining to core housing need and characteristics of households and dwellings. This custom order was placed in collaboration with Housing, Infrastructure and Communities Canada to fill data gaps in their Housing Needs Assessment Template. 17 of the tables each cover a different geography in Canada: one for Canada as a whole, one for all Canadian census divisions (CD), and 15 for all census subdivisions (CSD) across Canada. The 18th table contains the median income for all geographies. Statistics Canada used these median incomes as the "area median household income (AMHI)," from which they derived some of the data fields within the Shelter Costs/Household Income dimension. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada, all CDs & Country as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia), all CSDs & each Province as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), all CSDs & each Territory as a whole *- Data on gender diverse households is only available for geographies (provinces, territories, CDs, CSDs) with a population count greater than 50,000. Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts greater than 10 are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Counts less than 10 are rounded to a base of 10, meaning they will be rounded to either 10 or Zero. Universe: Private Households in Non-farm Non-band Off-reserve Occupied Private Dwellings with Income Greater than zero. Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Tenure Including Presence of Mortgage and Subsidized Housing; Household size (7) 1. Total - Private households by tenure including presence of mortgage payments and subsidized housing 2. Owner 3. With mortgage 4. Without mortgage 5. Renter 6. Subsidized housing 7. Not subsidized housing Housing indicators in Core Housing Universe (12) 1. Total - Private Households by core housing need status 2. Households examined for core housing need 3. Households in core...
(StatCan Product) Counts of families with children and number of children in these families by family type, age of children and level of after-tax LICO. Counts of persons regardless of family type by level of after-tax LICO. Customization details: Counts of families with children (0-17 years) and number of children in these families by family type, age of children and level of after-tax Low Income Cut-Off (LICO) and counts of persons (18-21 years) regardless of family type by level of after-tax LICO for 2009. This information product has been customized to present information by the 10Children and Family Services Authority Areas (CFSA) (map on page 2). The after-tax income data has been presented in Low-Income Cut-Off (LICO) instead of Low Income Measures (LIM). There are 10 tables all together in 5 excel worksheets: Table 1: Children aged 0-5. Number of children by family type and LICO. Table 2: Families with children aged 0-5. Number of families by family type and LICO. Table 3: Children aged 6-12. Number of children by family type and LICO. Table 4: Families with children aged 6-12. Number of families by family type and LICO. Table 5: Children aged 13-17. Number of children by family type and LICO. Table 6: Families with children aged 13-17. Number of families by family type and LICO. Table 7: Children aged 0-17. Number of children by family type and LICO. Table 8: Families with children aged 0-17. Number of families by family type and LICO. Table 9: Persons aged 18-21. Number of persons aged 18-21 by LICO. Table 10: Persons aged 18-21. Number of families with persons aged 18-21 by LICO.
Attribution-ShareAlike 2.0 (CC BY-SA 2.0)https://creativecommons.org/licenses/by-sa/2.0/
License information was derived automatically
The CLARISSA Cash Plus intervention represented an innovative social protection scheme for tackling social ills, including the worst forms of child labour (WFCL). A universal and unconditional ‘cash plus’ programme, it combined community mobilisation, case work, and cash transfers (CTs). It was implemented in a high-density, low-income neighbourhood in Dhaka to build individual, family, and group capacities to meet needs. This, in turn, was expected to lead to a corresponding decrease in deprivation and community-identified social issues that negatively affect wellbeing, including WFCL. Four principles underpinned the intervention: Unconditionality, Universality, Needs-centred and people-led, and Emergent and open-ended.The intervention took place in Dhaka – North Gojmohol – over a 27-month period, between October 2021 and December 2023, to test and study the impact of providing unconditional and people‑led support to everyone in a community. Cash transfers were provided between January and June 2023 in monthly instalments, plus one investment transfer in September 2023. A total of 1,573 households received cash, through the Upay mobile financial service. Cash was complemented by a ‘plus’ component, implemented between October 2021 and December 2023. Referred to as relational needs-based community organising (NBCO), a team of 20 community mobilisers (CMs) delivered case work at the individual and family level and community mobilisation at the group level. The intervention was part of the wider CLARISSA programme, led by the Institute of Development Studies (IDS) and funded by UK’s Foreign, Commonwealth & Development Office (FCDO). The intervention was implemented by Terre des hommes (Tdh) in Bangladesh and evaluated in collaboration with the BRAC Institute of Governance and Development (BIGD) and researchers from the University of Bath and the Open University, UK.The evaluation of the CLARISSA Social Protection pilot was rooted in contribution analysis that combined multiple methods over more than three years in line with emerging best practice guidelines for mixed methods research on children, work, and wellbeing. Quantitative research included bi-monthly monitoring surveys administered by the project’s community mobilisers (CMs), including basic questions about wellbeing, perceived economic resilience, school attendance, etc. This was complimented by baseline, midline, and endline surveys, which collected information about key outcome indicators within the sphere of influence of the intervention, such as children’s engagement with different forms of work and working conditions, with schooling and other activities, household living conditions and sources of income, and respondents’ perceptions of change. Qualitative tools were used to probe topics and results of interest, as well as impact pathways. These included reflective diaries written by the community mobilisers; three rounds of focus group discussions (FGDs) with community members; three rounds of key informant interviews (KIIs) with members of case study households; and long-term ethnographic observation.Quantitative DataThe quantitative evaluation of the CLARISSA Cash Plus intervention involved several data collection methods to gather information about household living standards, children’s education and work, and social dynamics. The data collection included a pre-intervention census, four periodic surveys, and 13 rounds of bi-monthly monitoring surveys, all conducted between late 2020 and late 2023. Details of each instrument are as follows:Census: Conducted in October/November 2020 in the target neighbourhood of North Gojmohol (n=1,832) and the comparison neighbourhood of Balurmath (n=2,365)Periodic surveys: Baseline (February 2021, n=752 in North Gojmohol), Midline 1 (before cash) (October 2022, n=771 in North Gojmohol), Midline 2 (after 6 rounds of cash) (July 2023, n=769 in North Gojmohol), and Endline (December 2023, n=750 in North Gojmohol and n=773 in Balumath)Bi-monthly monitoring data (13 rounds): Conducted between December 2021 and December 2023 in North Gojmohol (average of 1,400 households per round)The present repository summarizes this information, organized as follows:1.1 Bimonthly survey (household): Panel dataset comprising 13 rounds of bi-monthly monitoring data at the household level (average of 1,400 households per round, total of 18,379 observations)1.2 Bimonthly survey (child): Panel dataset comprising 13 rounds of bi-monthly monitoring data at the child level (aged 5 to 16 at census) (average of 940 children per round, total of 12,213 observations)2.1 Periodic survey (household): Panel dataset comprising 5 periodic surveys (census, baseline, midline 1, midline 2, endline) at the household level (average of 750 households per period, total of 3,762 observations)2.2 Periodic survey (child): Panel dataset comprising 4 periodic surveys (baseline, midline 1, midline 2, endline) at the child level (average of 3,100 children per period, total of 12,417 observations)3.0 Balurmat - North Gojmohol panel: Balanced panel dataset comprising 558 households in North Gojmohol and 773 households in Balurmath, observed both at 2020 census and 2023 endline (total of 2,662 observations)4.0 Questionnaires: Original questionnaires for all datasetsAll datasets are provided in Stata format (.dta) and Excel format (.xlsx) and are accompanied by their respective dictionary in Excel format (.xlsx).Qualitative DataThe qualitative study was conducted in three rounds: the first round of IDIs and FGDs took place between December 2022 and January 2023; the second round took place from April to May 2023; and the third round took place from November to December 2023. KIIs were taken during the 2nd round of study in May 2023.The sample size by round and instrument type is shown below:RoundsIDIs with childrenIDIs with parentsIDIs with CMsFGDsKIIs1st Round (12/2022 – 01/2023)3026-06-2nd Round ( 04/2023 – 05/2023)3023-06053rd Round (11/2023 – 12/2023)26250307-The files in this archive contain the qualitative data and include six types of transcripts:· 1.1 Interviews with children in case study households (IDI): 30 families in round 1, 30 in round 2, and 26 in round 3· 1.2 Interviews with parents in case study households (IDI): 26 families in round 1, 23 in round 2, and 25 in round 3· 1.3 Interviews with community mobiliser (IDI): 3 CM in round 3· 2.0 Key informant interviews (KII): 5 in round 2· 3.0 Focus group discussions (FGD): 6 in round 1, 6 in round 2, and 7 in round 3· 4.0 Community mobiliser micro-narratives (556 cases)Additionally, this repository includes a comprehensive list of all qualitative data files ("List of all qualitative data+MC.xlsx").
(StatCan Product) Counts of families with children and number of children in these families by family type, age of children and level of after-tax LICO. Counts of persons regardless of family type by level of after-tax LICO. Customization details: Counts of families with children (0-17 years) and number of children in these families by family type, age of children and level of after-tax Low Income Cut-Off (LICO) and counts of persons (18-21 years) regardless of family type by level of after-tax LICO for 2008. This information product has been customized to present information by the 10Children and Family Services Authority Areas (CFSA) (map on page 2). The after-tax income data has been presented in Low-Income Cut-Off (LICO) instead of Low Income Measures (LIM). There are 10 tables all together in 5 excel worksheets: Table 1: Children aged 0-5. Number of children by family type and LICO. Table 2: Families with children aged 0-5. Number of families by family type and LICO. Table 3: Children aged 6-12. Number of children by family type and LICO. Table 4: Families with children aged 6-12. Number of families by family type and LICO. Table 5: Children aged 13-17. Number of children by family type and LICO. Table 6: Families with children aged 13-17. Number of families by family type and LICO. Table 7: Children aged 0-17. Number of children by family type and LICO. Table 8: Families with children aged 0-17. Number of families by family type and LICO. Table 9: Persons aged 18-21. Number of persons aged 18-21 by LICO. Table 10: Persons aged 18-21. Number of families with persons aged 18-21 by LICO.
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Context
The dataset presents median household incomes for various household sizes in Lower Chichester Township, Pennsylvania, 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
https://i.neilsberg.com/ch/lower-chichester-township-pa-median-household-income-by-household-size.jpeg" alt="Lower Chichester Township, Pennsylvania median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
Variables / Data Columns
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
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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/.
This dataset is a part of the main dataset for Lower Chichester township median household income. You can refer the same here
A feature service prepared by U.S. Department of Housing and Urban Development (U.S. HUD) that displays Difficult Development Areas (DDA) for the Low Income Housing Tax Credit program. DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI).
Income limits used to determine the income eligibility of applicants for assistance under three programs authorized by the National Housing Act. These programs are the Section 221(d)(3) Below Market Interest Rate (BMIR) rental program, the Section 235 program, and the Section 236 program. These income limits are listed by dollar amount and family size, and they are effective on the date issued. Due to the Housing and Economic Recovery Act of 2008 (Public Law 110-289), Income Limits used to determine qualification levels as well as set maximum rental rates for projects funded with tax credits authorized under section 42 of the Internal Revenue Code (the Code) and projects financed with tax exempt housing bonds issued to provide qualified residential rental development under section 142 of the Code (hereafter referred to as Multifamily Tax Subsidy Projects (MTSPs)) are now calculated and presented separately from the Section 8 income limits.