This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

Housing Choice Vouchers by TractThis National Geospatial Data Asset (NGDA) dataset, shared as a Department of Housing and Urban Development (HUD) feature layer, displays the census tracts of those areas with residents who participate in the Housing Choice Voucher Program (HCVP) in the United States. Per HUD, "The Housing Choice Voucher Program (also known as Section 8) helps low-income families, elderly persons, veterans and disabled individuals afford housing in the private market. Program participants can choose any eligible housing unit, including single-family homes, townhouses, and apartments, with rent partially covered by a subsidy paid directly to the landlord. There are around 2,000 Local Public Housing Agencies (PHAs) across the country that administer the HCV program with funding from HUD."Census Tract 800609Data currency: current federal service (HCV by Tract)NGDAID: 121 (Assisted Housing - Housing Choice Vouchers by Tract - National Geospatial Data Asset (NGDA))OGC API Features Link: Not AvailableFor more information, please visit: HCV Applicant and Tenant Resources; Housing Choice Vouchers by TractSupport Documentation: Housing Choice Vouchers by TractFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes." For other NGDA Content: Esri Federal Datasets

The Resident Characteristics Report summarizes general information about households who reside in Public Housing, or who receive Section 8 assistance. The report provides aggregate demographic and income information that allows for an analysis of the scope and effectiveness of housing agency operations. The data used to create the report is updated once a month from IMS/PIC.

This dataset contains replication files for "The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment" by Raj Chetty, Nathaniel Hendren, and Lawrence Katz. For more information, see https://opportunityinsights.org/paper/newmto/. A summary of the related publication follows. There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes. The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing). Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success. In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move. In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market. For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015). The MTO treatments also had little or no impact on adults’ economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes. The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains. Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child’s total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing. We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvant... Visit https://dataone.org/datasets/sha256%3Aa12b8c1f14eeabc92c1d91bd0311bc4aa3ddf6d7fb69ca798ca6926e7fa292c7 for complete metadata about this dataset.

This dataset describes information related to the City of Mesa Housing Authority (MHA) which administers the Section 8 Housing Choice Voucher Program. The program assists low-income individuals or families living in Mesa with rental assistance according to their income. Information in this dataset is used to calculate the Utilization Rate (the percentage of vouchers that are leased up of the number of allocated vouchers from US Department of Housing & Urban Development (HUD) to MHA) and the Voucher Budget Authority (the percentage of the allocated funding dollars for rent payments on behalf of current housing voucher participants).

The dataset is a catalog of major residential development projects in Somerset County, NJ. This includes Affordable Housing, Senior housing options, and Market-rate rentalsAffordable Housing Options: With New Jersey having some of the highest housing costs in the county, the state government has implemented several initiatives and programs to provide housing options for low- and moderate-income eligible households. In addition, several municipalities have implemented inclusionary zoning laws, that require property developers to allocate a certain percentage of the units for affordable housing. Somerset county has several affordable housing programs to help low-and moderate-income eligible households and first-time homebuyers, including the Mt. Laurel Doctrine, New Jersey Balanced Housing Program, HUD Public Housing Program, HUD Housing Choice Voucher Program (Section 8). This dataset provides a comprehensive list of all affordable housing projects in the county. The dataset includes ‘inclusionary’ developments that are comprised of both market-rate units and affordable units. It also includes municipality-sponsored and other 100% affordable housing projects, as well as affordable housing created through the redevelopment process. The total number of market rate and affordable housing units in each project is provided. Some projects include a blend of both rental and for-purchase units. Senior Housing Options: There are several housing options in Somerset County for older adults seeking assistance with daily living or those who want to maintain their independence or those who seek to live in communities designed for older adults. These options include – Active Adult Communities: These are communities designed for older adults who can live independently but want to live in a community specifically for older adults. They typically offer amenities such as fitness centers, swimming pools, and social activities. Many independent living communities also offer additional services such as transportation, housekeeping, and meals. Assisted Living Communities: These communities aid with daily living activities such as bathing, dressing, and medication management. They offer a range of services, depending on the level of care needed. Some assisted living communities also offer memory care services for individuals with dementia or Alzheimer's disease. Continuing Care Retirement Communities: These communities offer a continuum of care that includes independent living, assisted living, and skilled nursing care. This allows residents to "age in place" and receive additional care as needed without having to move to a different community. Senior Residence: These communities are restricted to residents who are 55 years of age or older. They typically offer amenities like active adult communities and may have additional features such as golf courses, community centers, and events. Market Rate Rentals: These properties are typically owned/operated by private landlords and are not considered affordable housing and are not subject to government subsidies. These include apartments, condominiums, town homes, single-family homes. The information included in this dataset represents a point-in-time (November 2023) and is subject to change. Furthermore, new, or alternative housing projects may be proposed in future years, which will be incorporated into subsequent dataset updates. Updates to this dataset will take place on an as-needed basis.

Dataset

This dataset was created by Wabinab Chow

Contents

Detailed disaggregated household projections for England, by region and local authority. The 2018-based projections are the most recent available.

Abstract

The World Bank and UNHCR in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform a targeted response. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets refugee household and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection, and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing. The data is uploaded in three files. The first is the hh file, which contains household level information. The 'hhid', uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'adult_id'. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the 'child_id'. The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 1,328 refugee households Wave 2: July 16 to September 18, 2020; 1,699 refugee households Wave 3: September 28 to December 2, 2020; 1,487 refugee households Wave 4: January 15 to March 25, 2021; 1,376 refugee households Wave 5: March 29 to June 13, 2021; 1,562 refugee households Wave 6: July 14 to November 3, 2021; 1,407 refugee households Wave 7: November 15, 2021, to March 31, 2022; 1,281 refugee households Wave 8: May 31 to July 8, 2022: 1,355 refugee households The same questionnaire is also administered to nationals in Kenya, with the data available in the WB microdata library: https://microdata.worldbank.org/index.php/catalog/3774

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless, where sampling approaches differ across strata. For refugees in Kakuma and Kalobeyei, as well as for stateless people, recently conducted Socioeconomic Surveys (SES), were used as sampling frames. For the refugee population living in urban areas and the Dadaab camp, no such household survey data existed, and sampling frames were based on UNHCR's registration records (proGres), which include phone numbers. For Kakuma, Kalobeyei, Dadaab and urban refugees, a two-step sampling process was used. First, 1,000 individuals from each stratum were selected from the corresponding sampling frames. Each of these individuals received a text message to confirm that the registered phone was still active. In the second stage, implicitly stratifying by sex and age, the verified phone number lists were used to select the sample. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in. For the stateless population, all the participants of the Shona socioeconomic survey (n=400) were included in the RRPS, because of limited sample size. The sampling frames for the refugee and Shona stateless communities are thus representative of households with active phone numbers registered with UNHCR.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion

Cleaning operations

Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. Extended missing values are used to indicate why a value is missing for all variables. The following extended missing values are used in the dataset: · .a for 'Don't know' · .b for 'Refused to respond' · .c for 'Outliers set to missing' · .d for 'Inconsistency set to missing' (used for employment data as explained below) · .e for 'Field Skipped' (where an error in the survey tool caused the question to be missed) · .z for 'Not administered' (as the variable was not relevant to the observation) More detailed data on children was collected between waves 3 and 7, compared to waves 1, 2 and 8. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the 'hh' data for waves 1 and 2. Between waves 3 and 7, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the 'child' data set. The household level weights can be used for analysis of the children's data. In wave 8, detailed information on children was dropped, as the questionnaire focused on other topics. The education status of household members, except for the respondent, was imputed for rounds 1 and 2. For rounds 1 and 2, only the education status of the respondent was elicited, while for later rounds the education status for each household member was asked. In order to evaluate outcomes by the household member's education status, information on education was imputed for waves 1 and 2, using the information provided for all household members in waves 3, 4, and 5. This resulted in additional information on the education status for household members in round 1 and 2, which was not yet available for earlier versions of this data. Some questions are not asked repeatedly across waves such that their values were imputed. For some questions, answers are not possible or unlikely to change within two months between survey waves such that households were not asked about them in all waves. The questions on assets owned before March 2020 were only asked to households when they are interviewed for the first time. The questions on the dwelling's wall and floor material as well as the household's connection to the power grid was not asked for all households in wave 2 and 3, where only new households and those who moved were covered by these questions. Questions on the main source of electricity in the households and types of assets owned were not asked in wave 8. The missing values those variables have when they were not asked, are imputed from the answers given in earlier waves. Improved quality insurance algorithms lead to minor revisions to wave 1 to 5 data. Based on additional data checks, the team has made minor refinements to wave 1 to 5 data. The identification of the household members that were the respondent or the household head was refined in the rare cases where it was not possible to interview the same respondent as in previous waves for a given household such that another adult was interviewed. For this reason, for about 2 percent of observations the household head status was assigned to an incorrect household member, which was corrected. For <1 percent of households the respondent did not appear in adult level dataset. For about 1 percent of observations in wave 5 the respondent appeared twice in the adult level dataset. Data from questions on COVID-19 vaccinations from wave 7 was dropped from the dataset. Due to significantly higher self-reported vaccination rates compared to official administrative records, data on vaccinations was deemed unreliable, most likely due to social desirability bias. Consequently, questions on vaccination status and questions using the vaccination data as a validation criterion were dropped from the datasets.

Abstract

The GHS is an annual household survey specifically designed to measure the living circumstances of South African households. The GHS collects data on education, employment, health, housing and household access to services.

Geographic coverage

The survey is representative at national level and at provincial level.

Analysis unit

Households and individuals

Universe

The survey covered all de jure household members (usual residents) of households in the nine provinces of South Africa and residents in workers' hostels. The survey does not cover collective living quarters such as students' hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data

Sampling procedure

A multi-stage, stratified random sample was drawn using probability-proportional-to-size principles. First level stratification was based on province and second-tier stratification on district council.

Mode of data collection

Face-to-face [f2f]

Research instrument

GHS uses questionnaires as data collection instruments

Data appraisal

In GHS 2009-2010:

The variable on care provision (Q129acre) in the GHS 2009 and 2010 should be used with caution. The question to collect the data (question 1.29a) asks:

"Does anyone in this household personally provide care for at least two hours per day to someone in the household who - owing to frailty, old age, disability, or ill-health cannot manage without help?"

Response codes (in the questionnaire, metadata, and dataset) are:

1 = No 2 = Yes, 2-19 hours per week 3 = Yes, 20-49 hours per week 4 = Yes, 50 + hours per week 5 = Do not know

There is inconsistency between the question, which asks about hours per day, and the response options, which record hours per week. The outcome that a respondent who gives care for one hour per day (7 hours/week) would presumably not answer this question. Someone giving care for 13 hours a week would also be excluded as though they do that do serious caregiving, which is incorrect.

In GHS 2009-2015:

The variable on land size in the General Household Survey questionnaire for 2009-2015 should be used with caution. The data comes from questions on the households' agricultural activities in Section 8 of the GHS questionnaire: Household Livelihoods: Agricultural Activities. Question 8.8b asks:

“Approximately how big is the land that the household use for production? Estimate total area if more than one piece.” One of the response category is worded as:

1 = Less than 500m2 (approximately one soccer field)

However, a soccer field is 5000 m2, not 500, therefore response category 1 is incorrect. The correct category option should be 5000 sqm. This response option is correct for GHS 2002-2008 and was flagged and corrected by Statistics SA in the GHS 2016.

Abstract

In 2007, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The sixth revision of the 2007 Cross-Sectional User Database as released in May 2014 is documented here.

Geographic coverage

National

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Documentation.

Mode of data collection

Mixed

Abstract

EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Longitudinal data is limited to income information and a limited set of critical qualitative, non-monetary variables of deprivation, aimed at identifying the incidence and dynamic processes of persistence of poverty and social exclusion among subgroups in the population. The longitudinal component is also more limited in sample size compared to the primary, cross-sectional component. Furthermore, for any given set of individuals, microlevel changes are followed up only for a limited duration, such as a period of four years. For both the cross-sectional and longitudinal components, all household and personal data are linkable. Furthermore, modules providing updated information in the field of social exclusion is included starting from 2005.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labour, education and health observations only apply to persons 16 and older. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

This is the 1st release of 2012 Longitudinal Dataset, as published by Eurostat in September 2014.

Geographic coverage

The survey covers following countries: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Greece, Spain, France, Ireland, Italy, Cyprus, Croatia, Latvia, Lithuania, Luxembourg, Hungary, Netherlands, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Iceland, Norway.

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the cross-sectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Mode of data collection

Mixed

California State Income Limits reflect updated median income and household income levels for acutely low-, extremely low-, very low-, low- and moderate-income households for California’s 58 counties (required by Health and Safety Code Section 50093). These income limits apply to State and local affordable housing programs statutorily linked to HUD income limits and differ from income limits applicable to other specific federal, State, or local programs.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

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

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Abstract

The NLSS 1995/96 is basically limited to the living standards of households.

The basic objectives of this survey was to provide information required for monitoring the progress in improving national living standards and to evaluate the impact of various government policies and program on living condition of the population. This survey captured comprehensive set of data on different aspects of households welfare like consumption, income, housing, labour markets, education, health etc.

Geographic coverage

National coverage The 4 strata of the survey: - Mountains - Hills (Urban) - Hills (Rural) - Terai

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all modified de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

Sample Frame: A complete list of all wards in the country, with a measure of size, was developed in order to select from it with Probability Proportional to Size (PPS) the sample of wards to be visited. The 1991 Population Census of Nepal was the best starting point for building such a sample frame. The Central Bureau of Statistics (CBS) constructed a data set with basic information from the census at the ward level. This data set was used as a sample frame to develop the NLSS sample.

Sample Design: The sample size for the NLSS was set at 3,388 households. This sample was divided into four strata based on the geographic and ecological regions of the country: (i) mountains, (ii) urban Hills, (iii) rural Hills, and (iv) Terai.

The sample size was designed to provide enough observations within each ecological stratum to ensure adequate statistical accuracy, as well as enough variation in key variables for policy analysis within each stratum, while respecting resource constraints and the need to balance sampling and non-sampling errors.

A two-stage stratified sampling procedure was used to select the sample for the NLSS. The primary sampling unit (PSU) is the ward, the smallest administrative unit in the 1991 Population Census. In order to increase the variability of the sample, it was decided that a small number of households - twelve - would be interviewed in each ward. Thus, a total of275 wards was obtained.

In the first stage of the sampling, wards were selected with probability proportional to size (PPS) from each of the four ecological strata, using the number of household in the ward as the measure of size. In order to give the sample an implicit stratification respecting the division of the country into Development Regions, the sample frame was sorted by ascending order of district codes, and these were numbered from East to West. The sample frame considered all the 75 districts in the country, and indeed 73 of them were represented in the sample. In the second stage of the sampling, a fixed number of households were chosen with equal probabilities from each selected PSU.

The two-stage procedure just described has several advantages. It simplified the analysis by providing a self-weighted sample. It also reduced the travel time and cost, as 12 or 16 households are interviewed in each ward. In addition, as the number of households to be interviewed in each ward was known in advance, the procedure made it possible to plan an even workload across different survey teams.

Mode of data collection

Face-to-face [f2f]

Research instrument

A preliminary draft of the questionnaire was first prepared with several discussions held between the core staff and the consultant to the project. Several documents both received from the world bank as well as from countries that had already conducted such surveys in the past were referred during this process. Subsequently the questionnaire was translated into NepalI.

After a suitable draft design of the questionnaire, a pre-test was conducted in five different places of the country. The places selected for the pre-test were Biratnagar, Rasuwa, Palpa, Nepalganj and Kathmandu Valley. The entire teams created for the pre-test were also represented by either a consultant or an expert from the bank. Feedback received from the field was utilized for necessary improvements in finalizing the seventy page questionnaire.

The content of each questionnaire is as follows:

HOUSEHOLD QUESTIONNAIRE

Section 1. HOUSEHOLD INFORMATION This section served two main purposes: (i) identify every person who is a member of the household, and (ii) provide basic demographic data such as age, sex, and marital status of everyone presently living in the household. In addition, information collected also included data on all economic activities undertaken by household members and on unemployment.

Section 2. HOUSING This section collected information on the type of dwelling occupied by the household, as well as on the household's expenditures on housing and amenities (rent, expenditure on water, garbage collection, electricity, etc.).

Section 3. ACCESS TO FACILITIES This section collected information on the distance from the household's residence to various public facilities and services.

Section 4. MIGRATION This section collected information from the household head on permanent migration for reasons of work or land availability.

Section 5. FOOD EXPENSES AND HOME PRODUCTION This section collected information on all food expenditures of the household, as well as on consumption of food items that the household produced.

Section 6. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS This section collected information on expenditure on non-food items (clothing, fuels, items for the house, etc.), as well as on the durable goods owned by the household.

Section 7. EDUCATION This section collected information on literacy for all household members aged 5 years and above, on the level of education for those members who have attended school in the past, and on levelof education and expenditures on schooling for those currently attending an educational institution.

Section 8. HEALTH This section collected information on illnesses, use of medical facilities, expenditure on health care, children's immunization, and diarrhea.

Section 9. ANTHROPOMETRICS This section collected weight and height measurements for all children 3 years or under.

Section 10. MARRIAGE AND MATERNITY HISTORY This section collected information on maternity history, pre/post-natal care, and knowledge/use of family planning methods.

Section 11. WAGE EMPLOYMENT This section collected information on wage employment in agriculture and in non-agricultural activities, as well as on income earned through wage labor.

Section 12. FARMING AND LIVESTOCK This section collected information on all agricultural activities -- land owned or operated, crops grown, use of crops, income from the sale of crops, ownership of livestock, and income from the sale of livestock.

Section 13. NON-FARM ENTERPRISES/ACTIVITIES This section collected information on all non-agricultural enterprises and activities -- type of activity, revenue earned, expenditures, etc.

Section 14. CREDIT AND SAVINGS This section collected information on loans made by the household to others, or loans taken from others by household members, as well as on land, property, or other fixed assets owned by the household.

Section 15. REMITTANCES AND TRANSFERS This section collected information on remittances sent by members of the household to others and on transfers received by members of the household from others.

Section 16. OTHER ASSETS AND INCOME This section collected information on income from all other sources not covered elsewhere in the questionnaire.

Section 17. ADEQUACY OF CONSUMPTION This section collected information on whether the household perceives its level of consumption to be adequate or not.

RURAL COMMUNITY QUESTIONNAIRE

Section 1. POPULATION CHARACTERISTICS AND INFRASTRUCTURES This section collected information on the characteristics of the community, availability of electricity and its services and water supply and sewerage.

Section 2. ACCESS TO FACILITIES Data on services and amenities, education status and health facilities was collected.

Section 3. AGRICULTURE AND FORESTRY Information on the land situation, irrigation systems, crop cycles, wages paid to hired labor, rental rates for cattle and machinery and forestry use were asked in this section.

Section 4. MIGRATION This section collected information on the main migratory movements in and out.

Section 5. DEVELOPMENT PROGRAMS, USER GROUPS, etc. In this section, information on development programs, existence user groups, and the quality of life in the community was collected.

Section 6. RURAL PRIMARY SCHOOL This section collected information on enrollment, infrastructure, and supplies.

Section 7. RURAL HEALTH FACILITY This section collected information on health facilities, equipment and services available, and health personnel in the community.

Section 8. MARKETS AND PRICES This section collected information on local shops, Haat Bazaar, agricultural inputs, sale of crops and the conversion of local units into standard units.

URBAN COMMUNITY QUESTIONNAIRE

Section 1. POPULATION CHARACTERISTICS AND INFRASTRUCTURE Information was collected on the characteristics of the community, availability of electricity, water supply and sewerage system in the ward.

Section 2. ACCESS TO FACILITIES This section collected information on the distance from the community to the various places and public facilities and services.

Section 3. MARKETS AND PRICES This section collected information on the availability and prices of different goods.

Section 4. QUALITY OF LIFE Here the notion of the quality of life in the community was

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (StatsSA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Since 2008, StatsSA have produced an annual dataset based on the QLFS data, "Labour Market Dynamics in South Africa". The dataset is constructed using data from all four QLFS datasets in the year. The dataset also includes a number of variables (including income) that are not available in any of the QLFS datasets from 2010.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The QLFS sample covers the non-institutional population except for those in workers' hostels. However, persons living in private dwelling units within institutions are enumerated. For example, within a school compound, one would enumerate the schoolmaster's house and teachers' accommodation because these are private dwellings. Students living in a dormitory on the school compound would, however, be excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Quarterly Labour Force Survey (QLFS) uses a master sample frame which has been developed as a general-purpose household survey frame that can be used by all other Stats SA household surveys that have reasonably compatible design requirement as the QLFS. The 2013 master sample is based on information collected during the 2011 population Census conducted by Stats SA. In preparation for Census 2011, the country was divided into 103 576 enumeration areas (EAs). The census EAs, together with the auxiliary information for the EAs were used as the frame units or building blocks for the formation of primary sampling units (PSUs) for the master sample since they covered the entire country and had other information that is crucial for stratification and creation of PSUs. There are 3 324 primary sampling units (PSUs) in the master sample with an expected sample of approximately 33 000 dwelling units (DUs). The number of PSUs in the current master sample (3 324) reflects an 8,0% increase in the size of the master sample compared to the previous (2007) master sample (which had 3 080 PSUs). The larger master sample of PSUs was selected to improve the precision (smaller CVs) of the QLFS estimates.

The master sample is designed to be representative at provincial level and within provinces at metro/non-metro levels. Within the metros, the sample is further distributed by geographical type. The three geography types area: urban, tribal and farms. This implies, for example, that within a metropolitan area, the sample is representative of the different geography types that may exist within that metro. It is divided equally into four sub-groups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one (1) to four (4) and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group.

There are a number of aspects in which the 2013 version of the master sample differs from the 2007 version. In particular, the number of primary sample units increased. Mining strata were also introduced which serves to improve the efficiency of estimates relating to employment in mining. The number of geo-types was reduced from 4 to 3 while the new master sample allows for the publication of estimates of the labour market at metro level. The master sample was also adjusted Given the change in the provincial distribution of the South African population between 2001 and 2011. There was also an 8% increase in the sample size of the master sample of PSUs to improve the precision of the QLFS estimates. The sample size increased most notable in Gauteng, the Eastern Cape and KwaZulu-Natal. For more details on the differences between the two master samples please consult the section 8 (technical notes) of the QLFS 2015 Q3 release document (P0211).

From the master sample frame, the QLFS takes draws employing a stratified two-stage design with probability proportional to size (PPS) sampling of PSUs in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage. The primary stratification occurred at provincial, metro/non-metro, mining and geography type while the secondary strata were created within the primary strata based on the demographic and socio-economic characteristics of the population.

For each quarter of the QLFS, a ¼ of the sampled dwellings is rotated out of the sample. These dwellings are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings are expected to remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, two quarters and a new household moves in, the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (or unoccupied).

Mode of data collection

Face-to-face [f2f]

The United States Department of Agriculture's (USDA), Rural Development (RD) Agency operates a broad range of programs that were formally administered by the Farmers Home Administration to support affordable housing and community development in rural areas. RD helps rural communities and individuals by providing loans and grants for housing and community facilities. RD provides funding for single family homes, apartments for low-income persons or the elderly, housing for farm laborers, childcare centers, fire and police stations, hospitals, libraries, nursing homes and schools. To learn more, visit: https://www.rd.usda.gov/about-rd/agencies/rural-housing-service, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_USDA_Rural_Housing_by_TractDate of Coverage: 2018

Plan Bay Area 2050 utilized this single data layer to inform the Plan Bay Area 2050 Equity PriorityCommunities (EPC).

This data set was developed using American Community Survey (ACS) 2014-2018 data for eight variables considered.

This data set represents all tracts within the San Francisco Bay Region and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern.

Plan Bay Area 2050 Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:

People of Color (70% threshold) Low-Income (less than 200% of Federal poverty level, 28% threshold) Level of English Proficiency (12% threshold) Seniors 75 Years and Over (8% threshold) Zero-Vehicle Households (15% threshold) Single-Parent Households (18% threshold) People with a Disability (12% threshold) Rent-Burdened Households (14% threshold)

If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds thethreshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.

Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools. The school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency 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

Utilising a regression analysis we created a correlation matrix utilising a number of demographic indicators from the Local Insight platform. This application is showing the distribution of the datasets that were found to have the strongest relationships, with the base comparison dataset of Indices of Deprivation 2019 income deprivation affecting older people. This app contains the following datasets: proportion of single pension credit claimants, proportion of retirement age people receiving pension credit guarantee element, proportion of benefit claimants aged 50 to 64, proportion of people with numeracy skills at entry level 1 or below, Indices of Deprivation 2015 housing affordability indicator, proportion of people in the Social Grade (N-SEC) 8 never worked and long-term unemployed, female healthy life expectancy at birth, proportion of people part of Sport England Market Segmentation Pub League Team Mates, Indices of Deprviation 2010 income domain score and proportion of people over the age of 65 with 'bad' or 'very bad' health.