This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

This dataset includes one dataset which was custom ordered from Statistics Canada.The table includes information on housing suitability and shelter-cost-to-income ratio by number of bedrooms, housing tenure, age of primary household maintainer, household type, and income quartile ranges for census subdivisions in British Columbia. 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 variables: Geography: Non-reserve CSDs in British Columbia - 299 geographies 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. Housing Tenure Including Presence of Mortgage (5) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero by housing tenure 2. Households who own 3. With a mortgage1 4. Without a mortgage 5. Households who rent Notes: 1) Presence of mortgage - Refers to whether the owner households reported mortgage or loan payments for their dwelling. 2015 Before-tax Household Income Quartile Ranges (5) 1. Total – Private households by quartile ranges1, 2, 3 2. Count of households under or at quartile 1 3. Count of households between quartile 1 and quartile 2 (median) (including at quartile 2) 4. Count of households between quartile 2 (median) and quartile 3 (including at quartile 3) 5. Count of households over quartile 3 Notes: 1) A private household will be assigned to a quartile range depending on its CSD-level location and depending on its tenure (owned and rented). Quartile ranges for owned households in a specific CSD are delimited by the 2015 before-tax income quartiles of owned households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. Quartile ranges for rented households in a specific CSD are delimited by the 2015 before-tax income quartiles of rented households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. 2) For the income quartiles dollar values (the delimiters) please refer to Table 1. 3) Quartiles 1 to 3 are suppressed if the number of actual records used in the calculation (not rounded or weighted) is less than 16. For cases in which the renters’ quartiles or the owners’ quartiles (figures from Table 1) of a CSD are suppressed the CSD is assigned to a quartile range depending on the provincial renters’ or owners’ quartile figures. Number of Bedrooms (Unit Size) (6) 1. Total – Private households by number of bedrooms1 2. 0 bedrooms (Bachelor/Studio) 3. 1 bedroom 4. 2 bedrooms 5. 3 bedrooms 6. 4 bedrooms Note: 1) Dwellings with 5 bedrooms or more included in the total count only. Housing Suitability (6) 1. Total - Housing suitability 2. Suitable 3. Not suitable 4. One bedroom shortfall 5. Two bedroom shortfall 6. Three or more bedroom shortfall Note: 1) 'Housing suitability' refers to whether a private household is living in suitable accommodations according to the National Occupancy Standard (NOS); that is, whether the dwelling has enough bedrooms for the size and composition of the household. A household is deemed to be living in suitable accommodations if its dwelling has enough bedrooms, as calculated using the NOS. 'Housing suitability' assesses the required number of bedrooms for a household based on the age, sex, and relationships among household members. An alternative variable, 'persons per room,' considers all rooms in a private dwelling and the number of household members. Housing suitability and the National Occupancy Standard (NOS) on which it is based were developed by Canada Mortgage and Housing Corporation (CMHC) through consultations with provincial housing agencies. Shelter-cost-to-income-ratio (4) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero 2. Spending less than 30% of households total income on shelter costs 3. Spending 30% or more of households total income on shelter costs 4. Spending 50% or more of households total income on shelter costs Note: 'Shelter-cost-to-income ratio' refers to the proportion of average total income of household which is spent on shelter costs. Household Statistics (8) 1....

Appendix A includes an analysis of conclusion and amendment dates of tax treaties. It is part of the thesis "A Multilateral Agreement for International Taxation: Designing an instrument to modernise international tax law.'The point of Appendix A is to calculate the average time tax treaties are updated. Data from the IBFD’s tax treaty database on tax treaties, in force on 1 January 2013, was used to calculate the average time (i.e., the ‘estimated mean’) in which an OECD-member country’s tax treaty was updated. In the database, the date of conclusion of each selected tax treaty was coded, as well as the date of: (1) any new bilateral tax treaty concluded within the same tax treaty relationship; (2) a protocol to a treaty or (3) an exchange of notes or any other mutual agreement, provided that this agreement changed the wording of the tax treaty in question.Only the treaties of the founding states of the OECD were taken into account. These are: the Republic of Austria, the Kingdom of Belgium, the Dominion of Canada, the Kingdom of Denmark, the French Republic, the Federal Republic of Germany, the Kingdom of Greece, the Republic of Iceland, the Republic of Ireland, the Italian Republic, the Grand Duchy of Luxembourg, the Kingdom of the Netherlands, the Kingdom of Norway, the Portuguese Republic, the Kingdom of Spain, the Kingdom of Sweden, the Swiss Confederation, the Turkish Republic, the United Kingdom of Great Britain and Northern Ireland, and the United States of America. The count includes tax treaties of these countries with non-OECD member countries. In counting the amendments to tax treaties in force, terminated treaties which have not been followed up by a new treaty, abandoned treaties as well as treaties that were not ratified before 1-1-2013, were excluded. Moreover, all pre-war (1940) treaties were not considered.----------------------------------------------------------------------------------------------------------------------------------------------------------------Appendix B includes the tax treaty case law analysis to the thesis "A Multilateral Agreement for International Taxation, Designing an instrument to modernise international tax law". Appendix B consists of a code book (description of coding per case) as well as a summary Excel file.Appendix B comprises an analysis of relevant case law decided by courts in OECD member countries. The point of the analysis is to provide some (factual) insight in the way the OECD Commentary is used to ‘modernise’ the terms of a tax treaty through interpretive rule-stretching. To circumvent discussions about the ‘status’ of the OECD Commentary under international law and in the process of tax treaty interpretation – this issue is still under debate in tax law doctrine –the reasoning of the courts as to the interpretive relevance of the OECD Commentary was not considered in the analysis. Instead, it focuses on the Commentary’s effects under five different groups of circumstances. The selection of cases as well as the relevant circumstances can be described as follows:Case selectionFirst, a group of relevant cases, all settled within the jurisdiction of OECD member countries, was selected by using a specific search query within the database of the International Tax Law Reports. The search function was applied on 1 January 2013 and brought up about 150 judgements in which the word ‘interpretation’ was found within the same paragraph as the words ‘tax treaty’, ‘tax agreement’ or ‘tax convention’.If a case included more than one interpretative issue, each issue was assessed as if it were an individual case. This was for instance the case when a judge clearly interpreted two distinct treaty terms, or when a judgement dealt with the application of more than one tax treaty.From these ‘cases’, an additional selection excluded those which clearly fell outside the ambit of this research. Cases deselected were those in which the Commentary could clearly not have played a role, i.e.,(1) those that dealt with the interpretation of domestic tax law rather than the term of a tax treaty; (2) those not related to a tax treaty on income and capital, but e.g., to inheritance tax treaties; (3) those that dealt with tax treaty provisions that were clearly not in conformity with the OECD MTC and (4) those that dealt with the interpretation of treaty terms such as ‘profits’, ‘income’ or ‘gains’, which require domestic law rules to be calculated or determined, rather than the interpretative rules of the OECD Commentary.The use of the search function, in combination with the additional selection, resulted in a sample of ‘neutral’ cases (i.e., cases on tax treaty interpretation in which the Commentary could have been, but also in which it could not have been, of relevance). This allowed for the generalisation of the Commentary’s relevance under a set of varying circumstances.Coding and grouping of circumstancesIn accordance with the facts of a case, each case first coded on and then grouped within five categories of circumstances. The circumstances, here formulated in the form of questions, are:1. Was a reservation on the provisions of the OECD MTC or an observation on the Commentary submitted?2. Was the relevant treaty term not defined in the treaty?3. Was one of the treaty parties not a member of the OECD?4. Did the Commentary exist before the conclusion of the relevant bilateral tax treaty?5. If question 4 was answered with no (i.e. the commentary was adopted after the conclusion of the relevant bilateral tax treaty), was that Commentary:a. similar to;b. expounding on;c. gap-filling in relation to;d. or contradictory to Commentary existing at the time of conclusion of a treaty?For each case, questions 1 to 4 were answered with either ‘yes’ or ‘no’, question 5 with ‘a’; ‘b’; ‘c’ and ‘d’. The answers were coded per case in a codebook and its related Excel database, which can be found in Appendix B. If a question could be answered with ‘yes’, that case was placed in that group. If a case could be placed in more than one group, it was.AnalysisSubsequently, the influence of the Commentaries in all of the cases selected was determined, distinguishing between three possible entries: either the Commentary was used by the court, and therefore of relevance, or it was not used or disregarded by the court, and therefore not of relevance. In some cases, the Commentary’s influence could not be determined or established. These cases were coded ‘N.A (‘not applicable’). For each circumstance, this then resulted in a list of cases in which the Commentary was of relevance, a list of cases in which the Commentary was not of relevance, and a list of cases in which the relevance of the Commentary could not be determined.The gradual normative influence of the Commentary on each decision (e.g., decisive, supplementary, etc.) was not considered: coding the relative influence of the Commentary proved too problematic (one of the problems was that in most decisions, courts do not give explicit reasons for their use of Commentary).The research setup is also included in a separate PDF file. The .docx format of the files ‘Appendix B code book of tax treaty case law analysis on the status of the OECD Commentary 2016.docx’ and ‘Research setup of Appendixes A and B.docx’ were provided by the depositor. DANS added the .pdf format of these files for preservation purposes.

标题：全球财富分配洞察报告高收入人群收入占比分析 数据内容：本数据集包含了全球多个国家和地区在过去多年中，最富有的1％人口的收入份额（税前）数据。数据集中的字段包括国家（Country）、年份（Year）、最富有的1%人口的收入份额（Top 1% (before tax)）以及具体的收入份额百分比（Income share of the richest 1% (before tax)）。通过这些数据，可以分析全球财富分配的不平等状况及其随时间的变化趋势。 数据来源：互联网公开数据 数据用途：该数据集可用于经济学、公共政策、金融投资和社会研究等多个领域。具体来说，它可以用于分析财富不平等的现状及其驱动因素，评估税收政策对收入分配的影响，支持财富管理与投资决策，以及为制定更加公平的经济政策提供数据支持。 行业分类：经济学, 公共政策, 金融投资, 社会研究, 数据科学 标签：全球收入分配, 高收入人群, 财富不平等, 税前收入, 经济研究, 政策分析, 投资决策, 社会公平, 数据可视化, 统计分析

Abstract

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

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

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

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

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

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

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

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

Cleaning operations

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

Response rate

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

Note: The data release is complete as of August 14th, 2023. 1. (Added April 4th) Canada and Census Divisions = Early April 2023 2. (Added May 1st) Ontario, British Columbia, and Alberta Census Subdivisions (CSDs) = Late April 2023 3a. (Added June 8th) Manitoba and Saskatchewan CSDs 3b. (Added June 12th) Quebec CSDs = June 12th 2023 4. (Added June 30th) Newfoundland and Labrador, Prince Edward Island, New Brunswick, and Nova Scotia CSDs = Early July 2023 5. (Added August 14th) Yukon, Northwest Territories, and Nunavut CSDs = Early August 2023. 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 Census of Canada. The tables are a custom order and contains data pertaining to core housing need and characteristics of households. 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 last 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. Included alongside the data tables is a guide to HART's housing need assessment methodology. This guide is intended to support independent use of HART's custom data both to allow for transparent verification of our analysis, as well as supporting efforts to utilize the data for analysis beyond what HART did. There are many data fields in the data order that we did not use that may be of value for others. 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 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 of 10 or less are rounded to a base of 10, meaning they will be rounded to either 10 or zero. Universe: Full 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...

Low income cut-offs (LICOs) before and after tax by community size and family size, in current dollars, annual.

Housing Assessment Resource Tools (HART) This dataset contains 2 tables and 5 files which draw upon data from the 2021 Census of Canada. The tables are a custom order and contain data pertaining to older adults and housing need. The 2 tables have 6 dimensions in common and 1 dimension that is unique to each table. Table 1's unique dimension is the "Ethnicity / Indigeneity status" dimension which contains data fields related to visible minority and Indigenous identity within the population in private households. Table 2's unique dimension is "Structural type of dwelling and Period of Construction" which contains data fields relating to the structural type and period of construction of the dwelling. Each of the two tables is then split into multiple files based on geography. Table 1 has two files: Table 1.1 includes Canada, Provinces and Territories (14 geographies), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); and Table 1.2 includes Canada and the CMAs of Canada (44). Table 2 has three files: Table 2.1 includes Canada, Provinces and Territories (14), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); Table 2.2 includes Canada and the CMAs of Canada excluding Ontario and Quebec (20 geographies); and Table 2.3 includes Canada and the CMAs of Canada that are in Ontario and Quebec (25 geographies). 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 as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia) as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), as a whole as well as all census divisions (CDs) within the 3 territories - All 43 census metropolitan areas (CMAs) in Canada 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 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. Universe: Full Universe: Population aged 55 years and over in owner and tenant households with household total income greater than zero in non-reserve non-farm private dwellings. Definition of 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: Table 1: Age / Gender (12) 1. Total – Population 55 years and over 2. Men+ 3. Women+ 4. 55 to 64 years 5. Men+ 6. Women+ 7. 65+ years 8. Men+ 9. Women+ 10. 85+ 11. Men+ 12. Women+ Housing indicators (13) 1. Total – Private Households by core housing need status 2. Households below one standard only...