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.

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.

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.

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.

This dataset is a polygon coverage of counties limited to the extent of the Lower Kittanning 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. E, Appendix 2, Disc 1, as well as some additional proprietary data. Please see the metadata file found in Chap. E, 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 2-3, 12-13 and 25-26 in Chap. E, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 14-24, Chap. E, Disc 1.

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.

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 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. F, Appendix 7, Disc 1. Please see the metadata file found in Chap. F, Appendix 8, 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. F, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 6-16, Chap. F, 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 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.

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.

Statistics Canada, in collaboration with the Public Health Agency of Canada and Natural Resources Canada, is presenting selected Census data to help inform Canadians on the public health risk of the COVID-19 pandemic and to be used for modelling analysis. The data provided here show the counts of the population in nursing homes and/or residences for senior citizens by broad age groups (0 to 79 years and 80 years and over) and sex, from the 2016 Census. Nursing homes and/or residences for senior citizens are facilities for elderly residents that provide accommodations with health care services or personal support or assisted living care. Health care services include professional health monitoring and skilled nursing care and supervision 24 hours a day, 7 days a week, for people who are not independent in most activities of daily living. Support or assisted living care services include meals, housekeeping, laundry, medication supervision, assistance in bathing or dressing, etc., for people who are independent in most activities of daily living. Included are nursing homes, residences for senior citizens, and facilities that are a mix of both a nursing home and a residence for senior citizens. Excluded are facilities licensed as hospitals, and facilities that do not provide any services (which are considered private dwellings).

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.

Abstract

Niger is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program. This program has developed a household level survey with a view to enhancing our knowledge of agriculture in Sub-Saharan Africa, in particular, its role in poverty reduction and the techniques for promoting efficiency and innovation in this sector. To achieve this objective, an innovative model for agricultural data collection in this region will need to be developed and implemented. To this end, activities conducted in the future will be supported by four main pillars - a multisectoral framework, institutional integration, analytical capacity building, and active dissemination.

• First, agricultural statistical data collection must be part of an expanded and multisectoral framework that goes beyond the rural area. This will facilitate generation of the data needed to formulate effective agricultural policies throughout Niger and in the broader framework of the rural economy.

• Second, agricultural statistical data collection must be supported by a well-adapted institutional framework suited to fostering collaboration and the integration of data sources. By supporting a multi-pronged approach to data collection, this project seeks to foster intersectoral collaboration and overcome a number of the current institutional constraints.

• Third, national capacity building needs to be strengthened in order to enhance the reliability of the data produced and strengthen the link between the producers and users of data. This entails having the capacity to analyze data and to produce appropriate public data sets in a timely manner. The lack of analytical expertise in developing countries perpetuates weak demand for statistical data.

• Consequently, the foregoing has a negative impact on the quality and availability of policy-related analyses. Scant dissemination of statistics and available results has compounded this problem.

In all countries where the LSMS-ISA project will be executed, the process envisioned for data collection will be a national household survey, based on models of LSMS surveys to be conducted every three years for a panel of households. The sampling method to be adopted should ensure the quality of the data, taking into account the depth/complexity of the questionnaire and panel size, while ensuring that samples are representative.

The main objectives of the ECVM/A are to:

• Gauge the progress made with achievement of the Millennium Development Goals (MDGs);

• Facilitate the updating of the social indicators used in formulating the policies aimed at improving the living conditions of the population;

• Provide data related to several areas that are important to Niger without conducting specific surveys on individual topics ;

• Provide data on several important areas for Niger that are not necessarily collected in other more specific surveys.

Geographic coverage

The ECVM/A 2014 is a panel survey with the ECVM/A 2011. The ECVM/A 2011 was designed to have national coverage, including both urban and rural areas in all the regions of the country. The domains are defined as the entire country, the city of Niamey; and other urban areas, rural areas, and in the rural areas, agricultural zones, agro-pastoral zones and pastoral zones.

Analysis unit

• Individuals

• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

2011 Survey

The ECVM/A 2011 was been designed to have national coverage, including both urban and rural areas in all the regions of the country. The domains are defined as the entire country, the city of Niamey; and other urban areas, rural areas, and in the rural areas, agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 26 explicit sampling strata were selected: Niamey, and urban, agriculture, agro-pastoral and pastoral zones of the seven regions other than Niamey. The target population was drawn from households in all 8 regions of the country with the exception of certain strata found in Arlit (Agadez Region) because of difficulties in going there, the very low population density, and collective housing. The portion of the population excluded from the sample represents less than 0.4% of the total population of Niger. Of a total of 36,000 people not included in the sample design, about 29,000 live in Arlit and 7,000 in collective housing.

The sample was chosen through a random two stage process:

• In the first stage a certain number of Enumeration Areas (known as Zones de Dénombrement or ZDs) was selected with Probability Proportional to Size (PPS) using the 2001 General Census of Population and Housing as the base for the sample, and the number of households as a measure of size.

• In the second stage, 12 or 18 households were selected with equal probability in each urban or rural ZD respectively. The base for the sample was an exhaustive listing of households that would be done before the start of the survey.

The total estimated size of the 2011 sample was 4,074 households. The fact that this was the first survey with panel households to be revisited in the future was taken into account in the design, making it possible to lose households between the two surveys with minimal adverse effects on the analyses.

2014 Survey

The ECVM/A 2014 is a panel survey with the ECVM/A 2011. All households are identified by three variables - GRAPPE, MENAGE and EXTENSION. GRAPPE is the cluster in which the household is located and MENAGE is the household number within that cluster. The GRAPPE and MENAGE identifiers of the households in 2014 are identical with the grappe and menage identifiers in 2011.

In the ECVM/A 2014, all households that had been interviewed in 2011 were tracked. Households that did not move were interviewed in their existing location. Households that had moved to other locations in Niger were followed and interviewed in their new locations if they could be found in the new location. Households that moved outside of Niger were not followed. Households are identified by the GRAPPE and MENAGE identifiers from 2011 even if they moved to a new location.

Individuals who moved from households, for example women who married and moved to their husband's household or men who moved out to form their own household, were also tracked. In the new location, the individual and all members in the new household were supposed to be interviewed. However in the final data set it is difficult to determine among the households of tracked individuals which one was in the original household and which are the new participants in the survey. While the GRAPPE and MENAGE are identical between the 2011 household and the movers from the 2014 survey, the individual identifiers within the household cannot be matched for these households.

Households that did not move are identified as code "0" in the variable EXTENSION. Households that moved as an entire household are identified as code "1" in variable EXTENSION. Households with an individual who moved from an original household and resided in a new household in 2014 are identified as code "2" in variable EXTENSION.

Within households, individuals should have the same identification numbers as they had in 2011. The variable MS01Q00 in the 2014 data contains the individual identification number within the household. In 2011, the variable is ms01q00. The identification numbers for members who left the household between 2011 and 2014 should not be found in the 2014 data. Their identification numbers should not have been reassigned to any other members. New members who joined the household after the 2011 survey will have identification numbers starting after the highest identification number found in the 2011 data. It is always possible that there were mistakes made in the identification of individuals in the households and the data may not be perfectly matched.

The households that moved maintain the GRAPPE (cluster) and MENAGE (household within the cluster) identification information from 2011 so that they can be matched back to information from the 2011 survey. They may have moved to a different region in the country, but are identified with their original location.

Mode of data collection

Face-to-face [f2f]

Research instrument

HOUSEHOLD QUESTIONNAIRES - FIRST VISIT

The ECVM/A involves two visits, which means that each household is visited twice. The first visit takes place during the planting season. The second visit takes place during the harvest season. The household and agriculture/livestock, as well as the community/price questionnaire are administered during the first visit. During the second visit, the household and agriculture/livestock questionnaires are administered in full, but the community questionnaire only collects price information.

The household questionnaire comprises 18 sections, not including the cover page which covers information of a general nature (identity, name of household head) and Section 0 which covers detailed information on household identification and the results of the survey. In the first visit, 16 of the sections were administered.

• Section 1 focuses on the socio-demographic characteristics of household members (gender, age, relations with household head, survival of relations);

• Sections 2 and 3 focus respectively on the education and health of household members;

• Section 4 focuses on the characteristics of the labor market and seeks to determine whether the subject is inactive (retirees, for example), unemployed or employed; and in the case of those in employment, to identify the characteristics of their employment (socio-professional category, seniority, working hours,

Abstract

The 1998/99 Integrated Labour Force Survey (ILFS) was the first of its kind to integrate three related surveys (labour force, informal sector and child labour modular surveys) into a single cost-effective survey. It was conducted over the whole country on the household-based NASSEP III sample frame, and covered 11,049 households giving a response rate of 86.2 per cent. As such, the survey collected a wide range of representative information that can be used in the design, implementation, monitoring and evaluation of various policies and programmes. In particular, it provides indicators such as school enrolments rates, housing conditions, access to amenities and facilities, income and expenditures, unemployment rates, and income and expenditure levels which should provide invaluable inputs into the monitoring and evaluation of the economic reforms and poverty reduction programmes that are being implemented by the Government.

The key objectives of the survey were to update data on the labour force, determine the size and output of the informal sector, and estimate the extent of child labour. A rich data bank has been created as a by-product of data processing exercise, which can be used to carry out further analysis of the information collected by the survey.

In designing and implementing the survey, CBS worked closely with other stakeholders through the Inter-Ministerial Steering Committee (IMSC) that was formed to provide overall guidance on the implementation of the survey. The committee was composed of representatives from Ministry of Labour and Human Resource Development, Ministry of Education Science and Technology, and the Macro Planning and Human Resources and Social Services departments in the Ministry of Finance and Planning. A Technical Working Group (TWG) was formed as the survey's secretariat that undertook day-to-day activities on the implementation of the survey.

The Surveyed Population

Age-sex Structure The age-sex pyramid of the surveyed population depicts a youthful population, with those aged below 15 years absorbing 42.3 per cent of the population, leading to a dependency ration of 85.3 per cent. The sex ratio was 0.997 for the whole population and 1.06 at birth (age 0-4). The average household size was 4.2 persons (3.3 persons in urban areas and 4.7 persons in rural areas).

Marital status and migration patterns An estimated 42.7 per cent of the population aged over 12 years had never married. Of those ever married, 51.3 per cent were in current marriage, 3.5 per cent widowed and 3.6 per cent separated or divorced. There was evidence of early marriages where 5.0 percent of the population aged 13-17 reported they were currently married.

Education and Literacy There were 3.6 million children in primary and 0.9 million children in secondary schools, giving gross enrolment ratios of 89.1 percent and 30.7 percent respectively. Student sex ratio, or ratio of males for females, in primary schools was 1.08, while that for secondary schools was 1.20. About 16.4 percent of the Kenyan population aged over 5 years and over reported to have had no formal education at all. Those with primary education constituted 59.0 per cent of the referenced population while 19.7 percent had attained secondary education. Only 1.1 per cent had attained university education.

Housing and amenities About 31.0 per cent of the households had a permanent dwelling unit. Majority of the rural households reported that they owned both the dwelling units they lived in and the land on which it was built, while almost all the urban residents lived in rented dwelling units. About 12.5 per cent of households, mainly in the rural areas, reported they had no toilet facilities. The commonest type of waste disposal was pit latrine, but flush toilet was prevalent in urban areas. Most of the rural households travelled long distances to fetch water, while 80.4 percent of the urban households had water within 50 meters. Firewood was the commonest type of cooking fuel in rural areas, while paraffin (53.3 per cent) and charcoal (22.6 per cent) were the main types of cooking fuels in urban areas. About 77.2 per cent of responding households were using paraffin to light their houses, with 90.5 per cent in rural areas. Urban areas mainly relied on paraffin (50.7 per cent) and electricity (41.8 per cent) as the chief sources of lighting.

Migration Patterns The overall out-migration rate was 13.2 percent, with rural areas losing a large portion of its population to urban areas. Among the eight provinces, Nairobi, Western and Central experienced significant out-migration of over 15.0 percent. Overall, urban areas were net gainers in population flows within the country.

Household expenditure Overall mean monthly expenditure per household amounted to Kshs 6,343. Monthly mean expenditures for rural households were estimated at Kshs 4,101, while the urban equivalent was Kshs 10,826. There were expenditure differentials between male- and female-headed households, where mean monthly expenditures for female-headed households in rural areas was Kshs 2,986, quite below he monthly expenditure of Kshs 4,620 for male-headed households. Similarly, mean expenditure for male-headed households in urban areas was almost twice that of female-headed households.

The Labour Force Participation

Economic activity The results show that there were 15.9 million persons aged 15-64 (the working population) of which 77.4 per cent reported to be economically active. Most of the active population was youth between 24-34 years of age. About 14.6 percent of the economically active were unemployed. Some 3.6 million persons reported to be economically inactive, representing 22.6 per cent of the population aged 15-64 years. Majority of the inactive population was full time students (47.3 per cent). Only 2.0 per cent of the inactive population reported they were out of the labour force because they were retired.

Participation Rates The overall labour force participation rate for the population aged 15 - 64 years stood at 73.6 per cent. Urban areas had higher labour force participation rate of 86.4 per cent compared to rural areas with a rate of 73.8 per cent. Males had a slightly higher participation rate of 74.7 per cent compared to that of females at 72.6 per cent. The results show that participation rates increase along the age spectrum to about 95.2 for the age group 40 - 44 before levelling to 80. 1 per cent for the age cohort 60 - 64. Also, participation rates tend to rise with the level of formal education, rising from 83.7 per cent for those with no education to over 98.8 per cent for those who have completed post-graduate education.

Employment The number of employed persons aged 15-64 years stood at 10.5 million persons, giving employment rate of 85.4 per cent. The overall employment sex ratio was 1.08, but females dominated rural based small-scale farming and pastoralist activities, with a sex ratio of 0.67. Rural area absorbed 70.1 per cent of the employed persons. The working population was largely made up of unpaid family workers (39.6 per cent), mostly working in the rural areas and paid employees, largely concentrated in urban areas (33.4 per cent). Self-employed persons constituted 23.8 per cent of the employed. Of the three sectors of the economy, small-scale farming and pastoralist activities engaged 42.1 per cent of workers. Informal sector and formal or modern sector absorbed 31.6 per cent and 26.3 per cent of the total workforce.

Occupations and industry Most of the employed persons reported to be skilled agricultural and fishery workers (37.3 per cent), largely self-employed based in rural areas. Professionals were mainly in paid employment, and accounted for only 1.2 per cent of the employed persons. The agricultural activities absorbed 63.1 per cent of the employed persons. The other major employers were the service industries with community, social and personal services accounting for 6.1 per cent of the employed. The least popular industries were private households with employed persons, and electricity and water supply. The number of females employed in activities traditionally dominated by males such as construction, mining and quarrying was notably low. However, females were concentrated in agricultural activities, trades, and educational services.

Hours of work Most workers reported 40 working hours per week with a significant proportion of the urban population working above the average hours. Urban workers generally reported to have worked for longer hours than workers in rural areas. Gender analysis shows that females worked for fewer hours than males, particularly in the rural areas. However, females who worked in urban areas (in private households as housemaids) were working quite above 40 hours in a week.

Wage levels Average earnings amounted to KShs 7,766 per month, with the main source of employee's remuneration being basic salary, which formed 81.3 per cent of the overall earnings per person. Earnings in urban were almost double the average earnings in rural areas. There were significant disparities in earnings by gender as females were earnings wages quite below their male counter parts in both rural and urban areas.

Unemployment There were 1.8 million unemployed persons aged 15-64 years, giving an overall unemployment rate of 14.6 per cent. The urban unemployment rate had risen from -- per cent in 1989 to 25.1 per cent by 1999. Like wise, unemployment in the rural areas was high at 9.4 per cent, but less acute then in urban areas. Most of the unemployed were youth and females. Most of the unemployed persons (94.2 per cent) were looking for paid employment during the one-week reference period. It is also worth noting the shift from subsistence farming, as more jobs searchers were ready to start self-employment (mainly found in mostly in the expanding informal sector) than farming activities

Educational Technology in Public School Districts, 2008 (FRSS 93), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 93 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology in public school districts during Fall 2008. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. The study was conducted by having school superintendents fill out surveys via the web or by mail. Public school districts were sampled. The study's weighted response rate was 90 percent. Key statistics produced from FRSS 93 were information on networks and internet capacity, technology policies, district-provided resources, teacher professional development, and district-level leadership for technology. Respondents reported the number of schools in the district with a local area network and the number of schools with each type of district network connection. The survey collected information on written district policies on acceptable student use of various technologies. Other survey topics included employment of staff responsible for educational technology leadership and the type of teacher professional development offered or required by districts for educational technology. Respondents gave their opinions on statements related to the use of educational technology in the instructional programs in their districts.

Abstract

The Annual Agriculture Sample Survey (AASS 2023/24) was conducted to generate up-to-date and precise data on crops, livestock and aquaculture activities. Accurate crop production figures are essential for a wide range of stakeholders in the agriculture sector. The data from this survey will provide critical insights for farmers, agricultural businesses, government policymakers, and other key players to inform their decisions in both the short and long term.

The specific objectives of the AASS 2023/24 include:

1. To collect timely data on agricultural production and productivity at both national and regional levels;

2. To gather core data to help develop and review agricultural policies and to guide the implementation of agricultural plans at national and regional levels between agricultural census periods;

3. To compile fundamental statistics that facilitate comparisons in the development of the agriculture sector across the country; and

4. To collect data on agricultural machinery, equipment, and structures, as well as information on women’s empowerment and nutrition.

The Women's empowerment and nutrition was an additional module that was integrated into the AASS 2023/24 to generate nationally representative statistics on empowerment and women's dietary diversity among agricultural households. This module is useful in generating the Women Empowerment Metric for National Statistical Systems (WEMNS) indicator (https://weai.ifpri.info/wemns/) and the Women's Dietary Diversity (MDD-W) indicator.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS 2023/24) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2023/24 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, 1,504 EAs were selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the District and Council codes which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection without replacement was conducted, for the selection of 12 SSUs (agricultural households) in each selected EAs. A total sample of 18,048 agricultural holdings across 1504 EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2023/24 Annual Agricultural Survey used two main questionnaires, Smallholder Farmers and Large-Scale Farms Questionnaire, consolidated into a single questionnaire within the CAPI System. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The Questionnaire is attached as an external resource in the downloads tab.

Cleaning operations

The data processing and data editing phases were critical components of the Annual Agriculture Sample Survey for the agricultural year 2023/24. These phases ensure that the collected data is of high quality, consistent, coherent, and ready for analysis and reporting. The technical team responsible for these tasks included members from the National Bureau of Statistics (NBS), the Office of the Chief Government Statistician (OCGS), Agricultural Sector Lead Ministries (ASLMs), and academia, with technical support from FAO experts at various levels.

A. Data Processing

A.1. Data Entry: - Enumerators entered data directly into tablets during interviews, eliminating the need for a separate data entry activity. This method minimized errors associated with manual data entry. Data collected in the field was periodically synchronized with a central database, ensuring that the information was securely stored and readily accessible for processing.

A.2. Data Cleaning: - Upon synchronization, the data underwent initial automated checks to identify and flag obvious errors, such as missing values, out-of-range responses, and inconsistencies. - Technical staff conducted a manual review of flagged entries, correcting errors based on predefined rules and protocols. This step ensured that all data was accurate and complete before further processing.

A.3. Data Integration: - Data from different sections of the questionnaire (e.g., household information, crop production, livestock data) were integrated into a unified dataset. This process involved matching and merging records to ensure consistency across all sections by data scientists/ data programmers. - The technical team harmonized data formats and units of measurement to ensure consistency. This step was important for maintaining coherence in subsequent analyses.

B. Data Editing

B.1. Consistency Checks: - The data editing phase included rigorous checks for internal consistency within the dataset. This involved ensuring that related variables were logically consistent (e.g., the number of chicken reported matched the eggs production data). - The team conducted cross-sectional checks to verify consistency across different sections of the questionnaire. For example, crop production data were cross-referenced with input use and labor data to identify and correct discrepancies.

B.2. Outlier Detection and Treatment: - Statistical techniques were employed to identify outliers in the dataset. Outliers could indicate data entry errors or exceptional cases that required further investigation. - Identified outliers were validated through additional checks by using STATA program or, if necessary, follow-up with the respondents. This ensured that the outliers were genuine and not due to errors.

B.3. Imputation of Missing Data: - For instances where data was missing, the team used imputation techniques to estimate the missing values. Imputation methods included statistical techniques such as mean substitution, regression imputation, or hot-deck imputation, where necessary. All imputed values were documented by do files (STATA files). This transparency ensured that subsequent analyses accounted for the imputed data appropriately.

B.4. Data Validation: - The dataset was validated against external data sources, such as previous surveys, administrative records, and satellite imagery (limited), to ensure accuracy and reliability. - The validation process included a feedback loop where any identified issues were communicated back to the data collection teams for clarification and correction. - Technical online meetings between FAO, NBS, OCGS and ASLMs related to data validation were conducted professionally to ensure accountability of data along the value chain.

C. Continuous Improvement - After the completion of the survey, the entire process was reviewed to identify areas for improvement. Feedback from all team members and stakeholders was gathered to refine the methodologies and protocols for future agriculture surveys in series under 50x20230 initiatives. - Detailed documentation of all processes, decisions, and methodologies was maintained. This documentation served as a reference for future surveys and contributed to the transparency and reproducibility of the survey process.

STATISTICAL DISCLOSURE CONTROL (SDC)

Microdata are disseminated as Public Use Files under the terms indicated in Appendix A of the NBS Dissemination and Pricing Policy (https://www.nbs.go.tz/publications/policies-and-legislations). These access conditions are also indicated in the "data access" section below.

Statistical Disclosure Control (SDC) methods have been applied to the microdata, to protect the confidentiality of the individuals that data was collected from. These methods include: i) removal of information that may directly identify a respondent (name, address, etc.), ii) grouping values of some variables into categories (e.g. age), iii) limiting geographical information to the region level or higher, iv) suppression of some data points for variables that, in combination with others, may pose a relevant risk of identification of a statistical unit, v) adding noise to continuous

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.

The National Reporting System (NRS) for Adult Education, 2017-18 (NRS 2017-18) is a performance accountability system for the national adult education program that is authorized under the Adult Education and Family Literacy Act (AEFLA), title II of the Workforce Innovation and Opportunity Act (WIOA) of 2014. More information about the program is available at https://www2.ed.gov/about/offices/list/ovae/resource/index.html. NRS 2017-18 is a cross-sectional data collection that is designed to monitor performance accountability for the federally funded, state-administered adult education program. States are required to submit their progress in adult education and literacy activities by reporting data on the WIOA primary indicators of performance for all AEFLA program participants who receive 12 or more hours of service, as well as state expenditures on the adult education program. States may also report on additional, optional secondary measures that include outcomes related to employment, family, and community. The data collection is conducted using a web-based reporting system. NRS 2017-18 is a universe data collection activity, and all states are required to submit performance data. Key statistics that are produced from the data collection include student demographics, receipt of secondary school diploma or a high school equivalency (HSE) credential, placement in postsecondary education or training, measurable skill gain, and employment outcomes.