Ready-reference guide for human resources (HR) professionals. Contains demographic statistics along with other valuable employee data for full time permanent (FTP) and part time permanent (PTP) SSA employees.

About MCH Data Connect The MCH Data Connect provides public health professionals, researchers, practitioners, policy makers and students with a comprehensive catalog of maternal and child health data resources. Users can access a variety of databases, data sets, interactive tools, and maps related to their area of interest. Maternal and Child Health The MCH Data Connect uses a broad definition of Maternal and Child Health, including the influence of access to health care, health, health behaviors, education, violence, environmental conditions, demographics, and policy on the health of women, men, children, youth, families and communities. Topics Topics included in the MCH Data Connect: health care policy, experience of health care, family planning, sexual and reproductive health, economics, politics, social services, violence, and health behaviors, among others. Data Resources Data resources described in this catalog include data sets, statistics, interactive tables, interactive maps, and databases. Many of the data sources are available for public consumption, though specific databases may require th e user to purchase or apply for the dataset. Basic Search Locate the "Search Studies" highlighted box above the list of resource on the MCH Data Connect homepage. Leave "Cataloging Information" as the default basic search command. To search, enter the keyword, topic or area of interest in field box (next to "Cataloging Information") to obtain a list of resources that apply to your search. Access Resource Once the search is completed, a list of resources will appea r. The first line provides a brief summary. To get more information (including producer, background, user functionality and data sources) about the specific resource, click on the underlined/ blue hyperlinked title. Once the resource description is opened, click on the link that says “Click here to access data from site” to go directly to the resource's web page. Advanced Search Click on the "Advanced Search" link located in the "Search Studies" highlighted box above the list of resources on the MCH Data Connect homepage. From the Search Scope drop down lists, enter either Keyword or Abstract (these are the most detailed fields used by the MCH Data Connect). Enter multiple search terms to use the “and” searching criterion. For example, to search for resources related to diabetes and exercise, the user would select "Keyword" from the drop d own list, "contains" and then enter "diabetes" in the field box. The user would repeat the first two steps to enter "exercise" in the next field box. Collections The Topic Folders section provides a list of broad categories that include many resources found in the MCH Data Connect. The files of the Topic Folders are on the left side of the homepage. Clicking on a file folder will result in a list of the resources that are related to the topic. The Topic Folders offer a starting place for your search. You can narrow your search further by performing either of the previous two searching techniques within a collection. Qu estions or Comments? For questions, comments, or if you think we missed a useful information tool, please contact us via email at mchdataconnect@gmail.com. Glossary Some terms you will see on this website are unique to the cataloging service, Dataverse; The MCH Data Connect uses them differently. Please see below for a glossary of terms you will find at MCH Data Connect. Please note that interactive tools, datasets, and reports are referred to as “resources.” Te rms Dataverse, the program used to develop the MCH Data Connect Study, resource containing relevant public health data and/or information Collection, broad categories into which resources have been classified How to Cite, used as the resource title by MCH Data Connect Study Global ID, unique code given to each resource Producer, the agency or entity that produces and maintains the resource< /p> Deposit Date, date when resource was added to the MCH Data Connect Provenance, will always be MCH Data Connect Abstract and Scope, contains resource summary and geographic unit information Abstract, summary of the resource Background, information about the purpose and development of the resource User Functionality, what users can do with the data (i.e. download, customize charts) Dat a Notes, information about data sources, years and samples (if applicable) Abstract Date, month and year that resource was added to MCH Data Connect Keyword, specific variables, topics, or words that the resource addresses/encompasses Geographic Unit, level at which data is available Title, name of specific resource Keyword Vocabulary, “link:” clicking on “link” will take user to an external website relate d to the keyword term. The following terms are not used by the MCH Data Connect Dataverse: Topic Cl... Visit https://dataone.org/datasets/sha256%3A6200525c71b8a813935cf3ad80014343a061959682fb7c5a7df3c15b59ff8fc5 for complete metadata about this dataset.

This dataset is a polygon coverage of counties limited to the extent of the Pond Creek coal bed resource areas and attributed with statistics on the thickness of the Pond Creek coal zone, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

Datasets excerpts from the Labour Force Survey provided by Statistics Canada. The data in these tables have additional details compared to datasets available on the Statistics Canada website. All data provided is on an annual basis. Data is available up to 2024 and current as of March 2025. Note: Files require the Beyond 20/20 Professional Browser. Information on Beyond 20/20 can be found here: https://www.statcan.gc.ca/en/public/beyond20-20. Several Canadian University sites have detailed guides and other resources for users of Beyond 20/20. Source: Statistics Canada, Labour Force Survey, March 2025. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.

Self-reported data from approximately 380 public libraries, First Nation public libraries and contracting organizations. The data includes: general information including address financial information holdings information staffing information facilities information activities information including typical week data partnership information (2011 onwards) Data from 2011 and onwards is from a refreshed database. New fields were added for: provincial funding types project grant types special collections holdings circulation of E-resources including E-books lending laptops program types readers advisory transactions information technology support In 2012, new fields were added for: E-readers requests for accessible format materials business and economic partnerships. In 2013 more fields were added for social media visits and other professional staff. In 2016 a field was added for indigenous language training and retention, while circulating and reference holdings information was combined. In 2017 fields were added for e-learning services, students hired for a summer or semester, circulating wireless hot spots, and library service visits to residence-bound people. In 2019 fields were added for Facility Rentals and Bookings, ‘Pop-up’ Libraries, Extended Services and Facilities, Government Services Partnerships, and Business and Economic Sector Partnerships. The database uses the common name "LibStats".

This dataset is a polygon coverage of counties limited to the extent of the Upper Freeport coal bed resource areas and attributed with statistics on these coal quality parameters: ash yield (percent), sulfur (percent), SO2 (lbs per million Btu), calorific value (Btu/lb), arsenic (ppm) content and mercury (ppm) content. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. The attributes were generated from public data found in the geochemical dataset found in Chap. D, Appendix 8, Disc 1, as well as some additional proprietary data. Please see the metadata file found in Chap. D, Appendix 9, Disc 1, for more detailed information on the geochemical attributes. The county statistical data used for this data set are found in Tables 2-5 and 17-18, Chap. D, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 6-16, Chap. D, Disc 1.

This dataset is a polygon coverage of counties limited to the extent of the Fire Clay coal zone resource areas and attributed with statistics on the thickness of the Fire Clay coal bed, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. This resource model for the Fire Clay coal zone must be considered provisional, because the correlation of the zone continues to be evaluated in West Virginia.

This dataset is a polygon coverage of counties limited to the extent of the Upper Freeport coal bed resource areas and attributed with statistics on the thickness of the Upper Freeport coal bed, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

This dataset is a polygon coverage of counties limited to the extent of the Pittsburgh coal bed resource areas and attributed with statistics on the thickness of the Pittsburgh coal bed, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

Through its Employment and Financial Services (EFS) division, Assisted Living and Social Services (ALSS) programs form a strong foundation of support to help many Albertans find and keep jobs. The ministry provides financial support, employment services, career resources, referrals, information on job fairs and workshops, and local labor market information. The goal is to help individuals and families gain independence by providing opportunities to enhance their skills to get jobs. The alis.alberta.ca website provides employment resources to help Albertans enhance their employability, plan for education and training, make informed career choices, and connect to and be successful in the labour market. This dataset provides information on web traffic statistics for the alis website, including information on pageviews and web sessions, demographic information for web sessions, and traffic information for the alis YouTube channel at: https://www.youtube.com/user/ALISwebsite.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society. The survey is created for both individuals and businesses. It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

Description of the data in this data set: structure of the survey and pre-defined answers (if any) 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed} 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question) 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility} 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 8. How would you assess the value of the following data categories? 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)} 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)} 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company} 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”} 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

Format of the file .xls, .csv (for the first spreadsheet only), .odt

Licenses or restrictions CC-BY

This dataset is a polygon coverage of counties limited to the extent of the Pocahontas No. 3 coal bed resource areas and attributed with statistics on the thickness of the Pocahontas No. 3 coal bed, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

This dataset is a polygon coverage of counties limited to the extent of the Pittsburgh coal bed resource areas and attributed with statistics on these coal quality parameters: ash yield (percent), sulfur (percent), SO2 (lbs per million Btu), calorific value (Btu/lb), arsenic (ppm) content and mercury (ppm) content. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. The attributes were generated from public data found in the geochemical dataset found in Chap. C, Appendix 8, Disc 1, as well as some additional proprietary data. Please see the metadata file found in Chap. C, Appendix 9, Disc 1, for more detailed information on the geochemical attributes. The county statistical data used for this data set are found in Tables 2-5 and 17-18, Chap. C, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 6-16, Chap. C, Disc 1.

This dataset is a polygon coverage of counties limited to the extent of the Pond Creek coal zone resource areas and attributed with statistics on these coal quality parameters: ash yield (percent), sulfur (percent), SO2 (lbs per million Btu), calorific value (Btu/lb), arsenic (ppm) content and mercury (ppm) content. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. The attributes were generated from public data found in geochemical dataset found in Chap. G, Appendix 7, Disc 1. Please see the detailed information on the geochemical attributes. The county statistical data used for this data set are found in Tables 2-5 and 17-18, Chap. G, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 6-16, Chap. G, Disc 1.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: A dwelling unit is an area or a collection of areas used for housing purposes by a household at the time of the census. Note that dwelling units in the same building as other establishments such as a hospital, a hotel, etc., must be considered separately and counted as dwelling units. For example, an independent and separate house situated within the enclosure of a hospital building or a school to be used by the director and his family must be considered a dwelling unit. Similarly, independent apartments in the same building as a hospital or a school must be considered dwelling units if they have a separate entrance. - Households: This is a grouping of persons, related or not, who recognize the authority of the same individual called "Head of Household," and whose resources and spending are communal. More than often, these persons live under the same roof, in the same courtyard, or the same concession. - Group quarters: A collective household is defined as a group of persons, generally unrelated, who live together in an establishment for discipline, travel, health, education, or professional purposes. Establishments in which collective households are found are: barracks, boarding schools, prisons, monasteries, convents and religious communities, orphanages, mental health institutions, hotels, temporary work sites' barracks. If an ordinary household lives in one of the above-mentioned establishments (household of a prison director, hospital director), it shall be counted as an ordinary household.

Universe

All persons present on the territory of the Republic of Benin on census day

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Institute for Statistics and Economic Analysis

SAMPLE SIZE (person records): 498419.

SAMPLE DESIGN: Systematic sample of every tenth household drawn by IPUMS from 100% microdata

Mode of data collection

Face-to-face [f2f]

Research instrument

A single census form for persons and dwellings

Regions with professional boards or boards, yes=1, no=0 by region, table content and annual range

Abstract

The Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey 2002-2003 is one of the biggest national surveys carried out in accordance with an international methodology with technical and financial support from the World Bank and United Nations Development Programme.

Background This survey was developed in response to provide the picture of the current situation of poverty in Mongolia in relation to social and economic indicators and contribute toward implementation and progress on National Millennium Development Goals articulated in the National Millennium Development Report and monitoring of the Economic Growth Support and Poverty Reduction Strategy, as well as toward developing and designing future policies and actions. Also, the survey enriched the national database on poverty and contributed in improving the professional capacity of experts and professionals of the National Statistical Office of Mongolia.

Purpose Since the onset of the transition to a market economy of Mongolia our country the need to study changes in people's living standards in relation to household members' demographic situation, their education, health, employment and household engagement in private enterprises has become extremely important. With that purpose and with the support of the World Bank and the United Nations Development Programme, the National Statistical Office of Mongolia conducted the Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey-like features between 2002 and 2003. In conjunction with LSMS household interviews the NSO also collected a price and a community questionnaire in each selected soum. The latter collected information on the quality of infrastructure, and basic education and health services.

Main importance of the survey is to provide policy makers and decision makers with realistic information about poverty and will become a resource for experts and researchers who are interested in studying poverty as well as social and economic issues of Mongolia.

In July 2003 the Government of Mongolia completed the Economic Growth and Poverty Reduction Strategy Paper in which the Government gave high priority to the fight against poverty. As part of that commitment this paper is a study that intends to monitor poverty and understand its main causes in order to provide policy-makers with useful information to improve pro-poor policies.

Content The Integrated HIES with LSMS design has the peculiarity of being a sub-sample of a larger survey, namely the Household Income and Expenditure Survey 2002. Instead of administering an independent consumption module, the Integrated HIES with LSMS 2002-2003 depends on the HIES 2002 information on household consumption expenditure. This is why the survey is referred as Integrated HIES with LSMS 2002-2003. This survey is the only source of information of income-poverty, and the questionnaire is designed to provide poverty estimates and a set of useful social indicators that can monitor more in general human development, as well as more specific issues on key sectors, such as health, education, and energy. And, the price and social survey, in conjunction with LSMS household interviews, collected information on the quality of infrastructure, and basic education and health services of each selected soum.

HIES - food expenditure and consumption, non-food expenditure, other expense, income LSMS - general information, household roster, housing, education, employment, health, fertility, migration, agriculture, livestock, non-farm enterprises, other souces of income, savings and loans, remittances, durable goods, energy PRICE SURVEY - prices of household consumer goods and services SOCIAL SURVEY - population and households, economy and infrastructure, education, health, agriculture and livestock, and non-agricultural business

Survey results The final report of this survey has main results on key poverty indicators, used internationally, as they relate to various social sectors. Its annexes contain information regarding the consumption structure, poverty lines along with the methodology used, as well as some statistical indicators.

The main contributions of this survey report are: - new poverty estimates based on the latest available household survey, the Integrated HIES with LSMS 2002-2003 - the implementation of appropriate, and internationally accepted, methodologies in the calculation of poverty and its analysis (these methodologies may constitute a reference for the analysis of future surveys) - a 'poverty profile' that describes the main characteristics of poverty

The first section of the report provides information on the Mongolian economic background, and presents the basic poverty measures that are linked to the economic performance to offer an indication of what happened to poverty and inequality in recent years. A second section goes in much more detail in generating and describing the poverty profile, in particular looking at the geographical distribution of poverty, poverty and its correlation with household demographic characteristics, characteristics of the household head, employment, and assets. A final section looks at poverty and social sectors and investigates various aspects of education, health and safety nets. The report contains also a number of useful, but more technical appendixes with information about the HIES-LSMS 2002-2003 (sample design and data quality), on the methodology used to construct the basic welfare indicator, and set the poverty line, some sensitivity analysis, and additional statistical information.

Geographic coverage

The survey is nationally representative and covers the whole of Mongolia.

Analysis unit

• Household (defined as a group of persons who usually live and eat together)
• Household member (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)
• Selected soums (for collecting prices of household consumer goods and services and information on quality of infrastructure, basic education, health services and so on)

Universe

The survey covered selected households and all members of the households (usual residents). And the price and social surveys covered all selected soums.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Integrated HIES with LSMS 2002-2003 households are a subset of the household interviewed for the HIES 2002. One third of the HIES 2002 households were contacted again and interviewed on the LSMS topics. The subset was equally distributed among the four quarters.

The HIES 2002, and consequently the Integrated HIES with LSMS 2002-2003, used the 2000 Census as sample frame. 1,248 enumerations areas were part of the sample, which is a two-stage stratified random sample. The strata, or domains of estimation, are four: Ulaanbaatar, Aimag capitals and small towns, Soum centres, and Countryside. At a first stage a number of Primary Sampling Units (PSUs) were selected from each stratum. In the selected PSUs enumerators listed all the households residing in the area, and in a second stage households were randomly selected from the list of households identified in that PSU (10 households were selected in urban areas and 8 households in rural areas).

It should be noted that non-response case of households once selected for the survey exerts unfavorable influence on the representativeness of the survey. Therefore an enumerator should take every step to avoid that. To obtain true and timely survey results a proper agreement should be reached with a selected household before a survey starts. One of the main reasons of non-response is that an enumerator doesn't meet with the household members who are able to give the required information. An enumerator should visit a household at least 3 times within the given period to take the questionnaire.

Another common reason is that a household refuses to participate in the survey. In this case an enumerator should explain the purpose of the survey again, explain that the private data will be kept strictly confidential according to the corresponding law. If necessary an enumerator can ask local statistical division or local administration for the help. However this practice is very seldom.

If there is no possibility to take the questionnaires from the selected households due to weather conditions or disasters, reserved households with numbers 11, 12, 13 respectively from the list provided by the NSO should replace the omitted ones. However the reasons of replacements are to be declared in detail on the form.

Sampling deviation

At the planning stage the time lag between the HIES and LSMS interviews was expected to be relatively short. However, for various reasons it is on average of about 9 months, and for some households more than one year. Households interviewed in the first and second quarter of 2002 were generally re-interviewed in March and April 2003, while households of the third and fourth quarter of 2002 were re-interviewed in May, June and July of 2003. The considerable time lag between HIES and LSMS interviews was the main responsible for a considerable loss of households in the LSMS sample, households that could not be easily relocated and therefore re-interviewed. Due also to some incomplete questionnaires, the number of households that were used for the final poverty analysis is 3,308.

Mode of data collection

Face-to-face [f2f]

Research instrument

A

Abstract

The 2023/24 Kenya Housing Survey (2023/24 KHS) provides a comprehensive analysis of housing conditions, affordability and tenure across the country. The survey was conducted by the Kenya National Bureau of Statistics (KNBS) in collaboration with the State Department for Housing and Urban Development, the State Department for Labour and Skills Development, the Kenya Space Agency, the Directorate of Resource Survey and Remote Sensing and the Central Bank of Kenya. The primary objective of the 2023/24 KHS was to provide up-to-date housing statistics to facilitate evidence-based planning and decision making in the housing sector. In addition, the survey addressed housing challenges in line with national development goals and international commitments such as the Sustainable Development Goals (SDGs). Data collection was carried out from 7th March to 10th May 2024 in all the 47 counties and targeted both households and institutions. The survey collected data on various aspects of housing, including the stock of dwellings, household spending on housing, land and dwelling ownership, access to utilities, housing affordability, overcrowding, durability of construction materials, and economic and financial statistics related to housing. Additionally, it collected information on the age, size, and characteristics of dwellings. Satellite imagery analysis was also used to assess changes in built-up areas and green spaces in Nairobi City, Mombasa, Kisumu, and Nakuru counties. Different players in the housing sector including tenants and home owners, Housing Financiers, Developers, Water Service Providers, Built Environment Professionals and Housing Regulators (County Government Physical Planning Department, Lands Department and National Environmental Management Authority) were interviewed.

SURVEY DESIGN The survey employed a cross-sectional study design to collect data for estimating housing indicators at national, rural, urban and county levels. To achieve this, a hybrid data collection system was incorporated, targeting both households and institutions. The household component of the survey was designed independently from that of the institutions. A sample survey was conducted for the households, while a census was carried out for all identified institutions key in the housing sector.

SCOPE AND TARGET POPULATION The survey covered all 47 counties to ensure that the coverage was comprehensive and representative of the entire country. The household component targeted residential housing units in both urban and rural areas while the institutional component targeted housing developers, real estate firms, Water Service Providers, County Governments-Physical Planning Departments, NEMA and Land Administration Department. The professional component targeted members from Engineers Board of Kenya (EBK), Kenya Institute of Planners (KIPs) and Board of Registration of Architects and Quantity Surveyors (BORAQS).

DATA QUALITY The quality of data for the Housing Survey was ensured through a multi-step approach. This began with defining the survey's content and scope, designing survey instruments, conducting a pre-test and pilot survey, training survey personnel, and incorporating technology for data collection and transmission. Additionally, data validation, analysis, creation of final report tables, and stakeholder engagement were all integral parts of the process. A thorough process was undertaken to review and refine the survey instruments aimed at eliminating redundancies and ensuring the questions were accurate and relevant to the current housing development programs and addressed user needs. The data collection tools were integrated into CAPI with in-built checks and controls to ensure consistency and flag out any outliers in the data. A multilevel supervision of the data collection exercise also ensured that the probability of any errors going unnoticed was minimized significantly. To further support the data quality assurance, a dashboard based at the headquarters was also used to monitor the data as fieldwork continued. Upon completion of the data collection, edit specifications were developed by subject matter specialists to provide a basis for cleaning and editing of the data. The specifications were subsequently coded into programs using statistical applications and subjected on the raw data to derive a cleaned dataset that developed the tables in the report.

THE KENYA HOUSING SURVEY DATA COLLECTION TOOLS

I. Household Questionnaire The Household Questionnaire for the 2023/24 Kenya Housing Survey is structured into multiple sections, covering different aspects of housing and household characteristics. The key sections included; Information for Household Members; Household composition, age, gender, relationship to the head and the Socio-economic characteristics such as education and employment status. Household Amenities; Access to essential services (water, electricity, sanitation, internet), Cooking fuel and lighting sources. Dwelling Unit Characteristics; Type of dwelling unit (permanent, semi-permanent, informal), Construction materials (walls, floors, roofing), Number of rooms and occupancy. Environmental and Location Aspects; Waste disposal methods, Drainage and pollution concerns in the neighborhood. Transport and Infrastructure; Accessibility to roads, public transport, and major services (schools, hospitals, markets). Disability; the Accessibility of housing and services for persons with disabilities. Land Ownership and Tenure; Land ownership status, size, tenure system (freehold, leasehold, informal). Household Individual Integrated Module; Employment and economic activities of household members, Income sources and levels. Tenants' information; Rent payment details, lease agreements, landlord-tenant relationships. Owners' information; Mortgage details, home-ownership financing sources and common Challenges in acquiring housing.

II. Kenya Housing Survey Institutional Questionnaire The 2023/24 Kenya Housing Survey Institutional Questionnaire related to real estate development is structured into multiple sections. This Questionnaire was administered to developers and real estate firms and the key sections included: Types of real estate projects undertaken, Number of completed and ongoing projects, Challenges faced in real estate development, Information on specific housing projects (location, type, cost), Financing sources and ownership structure, Construction materials and environmental considerations, Details on commercial, industrial, and institutional buildings, Occupancy rates and rental/sale prices. Questions about market trends, demand, and pricing, Factors affecting property transactions, Prices, unit sizes, and buyer demand trends, Rental prices, occupancy rates, and tenancy duration, Market conditions for office spaces, retail, and mixed-use developments, Information on warehouse developments, rental prices, and usage.

III. County Government questionnaire This Questionnaire captured about basic details about Counties and Questions related to building applications and approvals (e.g., number of residential building applications received and approved in different years). Factors considered in approval of construction permits, such as existing use, visual impact, and emerging technologies. There are also Questions about urban planning and land use, including Number of urban centers classified as towns, municipalities, and cities. Finally, the number of approved and pending physical and land use development plans.

IV. Financiers' Questionnaire The 2023/24 KHS collected information on housing development financing with a focus on respondents within the housing development sector. These included commercial banks, microfinance banks, SACCOS and other institutions that provide finance for housing development, including financial details, funding information, and related metrics.

V. Lands Department Questionnaire This Questionnaire aimed at collecting data related to land administration and management. specific data related to land management, policies, financial data, or other related metrics.

VI. State Department for Housing and Urban Development Questionnaire This questionnaire was used to collect information from the State Department for Housing and Urban Development targeting policy housing and urban development issues.

VII. Built Environment Professionals Questionnaire This questionnaire collected information from built environment professionals involved in the planning, design, and construction of housing in Kenya. The data collected was be used to assess the state of the housing sector, challenges faced, and trends in building and urban development from the perspective of Built Environment Professionals Questionnaire. The Built Environment Professionals interviewed are Valuers, Architects, Planners, Engineers (Civil/Structural/Mechanical/Electrical), Building Surveyors, Land Surveyors, and Quantity Surveyors involved in the planning, design, construction, and maintenance of the built environment.

VIII. National Environment Management Authority Questionnaire This survey data collection tool targeted all the National Environment Management Authority offices (NEMA) to gather insights into their licensing process for housing development projects and related environmental regulations.

IX. Water Sewerage & Service Providers Questionnaire The Water Sewerage & Service Providers (WSSP) section - this was a structured data collection tool in the delivery of water and sanitation services and within the context of housing and urban development. The survey tool or a research questionnaire targeting WSSPs to collect data on Water and sewer connection applications, Types of developments being connected (residential vs. mixed-use), Sewer coverage percentages, Costs, timelines, and challenges in providing services and Plans for future infrastructure

This dataset is a polygon coverage of counties limited to the extent of the Pocahontas No. 3 coal bed resource areas and attributed with statistics on these coal quality parameters: ash yield (percent), sulfur (percent), SO2 (lbs per million Btu), calorific value (Btu/lb), arsenic (ppm) content and mercury (ppm) content. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. The attributes were generated from public data found in the geochemical dataset found in Chap. H, Appendix 2, Disc 1. Please see the metadata file found in Chap. H, Appendix 3, Disc 1, for more detailed information on the geochemical attributes. The county statistical data used for this data set are found in Tables 6-9 and 21-22 in Chap. H, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 10-20, Chap. H, Disc 1.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: A compound is a set of one or more buildings, enclosed or not. Generally, it is placed under the authority of a compound head. A compound may include one or several household. - Households: A household is generally defined as a group of people, whether related or not, living together under the same roof and pooling all or part of their resources to meet their basic needs, in particular housing and food. Those persons called household members generally take their meals together and recognize the authority of a single person, the head of household. - Group quarters: A collective household is a group of people who, for extra-familial reasons, notably professional, health, educational, denominational, or deprivation of liberty, live together in an establishment or specialized institution.

Universe

The census covers the entire population present on the national territory at the time of the operation. Floating population [Population flottante]: these are the homeless people, who live anywhere, near the market places, in the factories, in shacks or even on the pavement, etc.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: National Agency of Statistics and Demography

SAMPLE SIZE (person records): 1245551.

SAMPLE DESIGN: Sample drawn by the National Agency of Statistics and Demography Floating population [Population flottante]: these are the homeless people, who live anywhere, near the market places, in the factories, in shacks or even on the pavement, etc.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is divided into six sections. Section A is geographic identification of the household. Section B includes questions on the individual characteristics. Section C includes questions on the deads the occurred in the household last year. Section D includes questions on out-migrants who left the household in the past five years. Section D contains housing questions, and Section F contains questions on poverty.