7 datasets found
  1. o

    Kenya Facts and Figures 2014 - Dataset - openAFRICA

    • open.africa
    Updated Mar 27, 2016
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    (2016). Kenya Facts and Figures 2014 - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/kenya-facts-and-figures-2014
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    Dataset updated
    Mar 27, 2016
    Area covered
    Kenya
    Description

    Kenya Facts & Figures is your direct route to Kenya’s economy through reliable statistics. It enables you to have a complete picture of the Kenyan economy from a single source. This booklet provides, at a glance, a comprehensive picture of Kenya’s economy covering all sectors as well as reflecting the various trends over the last four years.

  2. e

    Kenya - Solar Radiation Measurement Data - Dataset - ENERGYDATA.INFO

    • energydata.info
    Updated Jan 31, 2023
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    (2023). Kenya - Solar Radiation Measurement Data - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/kenya-solar-radiation-measurement-data
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    Dataset updated
    Jan 31, 2023
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kenya
    Description

    Ground measured solar irradiation and meteorological data for Laisamis, Narok and Homa Bay.

  3. data kenya how to get info

    • data.wu.ac.at
    csv, json, xml
    Updated Feb 1, 2015
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    World Bank Group Open Finances (2015). data kenya how to get info [Dataset]. https://data.wu.ac.at/schema/finances_worldbank_org/bTljZi1nbjg3
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    csv, xml, jsonAvailable download formats
    Dataset updated
    Feb 1, 2015
    Dataset provided by
    World Bankhttp://worldbank.org/
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    This dataset contains raw response data to a nano-survey that was conducted in Indonesia and Kenya on the demand for open financial data. You can read more about the project here: (http://bit.ly/OpenDemand). A nano-survey is an innovative technology that extends a brief survey to a random sampling of internet users. Note: "NA" indicates "No Answer."

  4. a

    Understanding the Dynamics of Access, Transition and Quality of Education in...

    • microdataportal.aphrc.org
    Updated Nov 25, 2014
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    African Population and Health Research Center (2014). Understanding the Dynamics of Access, Transition and Quality of Education in six urban sites in Kenya (ERP III) - KENYA [Dataset]. https://microdataportal.aphrc.org/index.php/catalog/62
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    Dataset updated
    Nov 25, 2014
    Dataset authored and provided by
    African Population and Health Research Center
    Time period covered
    2012
    Area covered
    Kenya
    Description

    Abstract

    African Population and Health Research Center (APHRC) had from 2005 to 2010, conducted a longitudinal survey in two formal settlements (Harambee and Jericho) and two informal (slum) settlements (Korogocho and Viwandani) in Nairobi to understand the uptake and patterns of school enrolment after the introduction of the Free Primary Education (FPE) in Kenya. The results of the study showed increased utilization of private informal schools among slum households as compared to the formal settlements.

    That is, by 2010, almost two thirds of pupils in the slum settlements were enrolled in private informal schools while in Harambee and Jericho, more than three quarters of the pupils were enrolled in government primary schools with the remaining portion attending high-end private schools.

    In 2012, ERP conducted a cross-sectional survey across six major urban centers to investigate, within the context of FPE, if the pattern of school enrolment observed in Korogocho and Viwandani slums could also be observed in other urban slums in Kenya. Below are some key facts from this study. Data is manly disaggregated by school type - government schools (FPE schools), and non-government schools, specifically the formal private schools and low-cost schools.

    The study tried to answer four broad questions: What is the impact of free primary education (FPE) on schooling patterns among poor households in urban slums in Kenya? What are the qualitative and quantitative explanations of the observed patterns? Is there a difference in achievement measured by performance in a standardized test on literacy and numeracy administered to pupils in government schools under FPE and non-government schools?

    Geographic coverage

    Kenya - in six urban slums of Nairobi spread across 6 towns - Nairobi, Mombasa, Nyeri, Eldoret, Nakuru and Kisumu. In total 5854 households and 230 schools were covered.

    Analysis unit

    A cross-sectional survey focusing on households with individuals aged between 5 and 19, as well as schools and pupils in grades 3 and 6. Data therefore exits at household, individuals, schools and student levels.

    Universe

    This is a cross sectional study that was conducted in seven slum sites spread across six towns namely Nairobi, Mombasa, Kisumu, Eldoret, Nakuru and Nyeri and targetted hoseholds with individuals aged between 5 and 19 years and schools located within the study site or within a 1KM radius. For the schools to be included in the study they had to have both grade 3 and 6, which were target grades for this study.

    Sampling procedure

    This was a cross-sectional study involving schools and households. The study covered six purposively selected major towns (Eldoret, Kisumu, Mombasa, Nairobi, Nakuru and Nyeri) in different parts of Kenya (see Map 1) to provide case studies that could lead to a broader understanding of what is happening in urban informal settlements. The selection of a town was informed by presence of informal settlements and its administrative importance, that is, provincial headquarter or regional business hub. A three-stage cluster sampling procedure was used to select households in all towns with an exception of Nairobi. At the first stage, major informal settlement locations were identified in each of the six towns. The informal settlement sites were identified based on enumeration areas (EAs) designated as slums in the 2009 National Population and Housing Census conducted by the Kenya National Bureau of Statistics (KNBS). After identifying all slum EAs in each of the study towns, the location with the highest number of EAs designated as slum settlements was selected for the study. At the second stage of sampling, 20% of EAs within each major slum location were randomly selected. However, in Nakuru we randomly selected 67% (10) EAs while in Nyeri all available 9 EAs were included in the sample. This is because these two towns had fewer EAs and therefore the need to oversample to have a representative number of EAs. In total, 101 EAs were sampled from the major slum locations across the five towns. At the third stage, all households in the sampled EAs were listed using the 2009 census' EA maps prepared by KNBS. During the listing, 10,388 households were listed in all sampled EAs. Excluding Nairobi, 4,042 (57%) households which met the criteria of having at least one school-going child aged 5-20 years were selected for the survey. In Nairobi, 50% of all households which had at least one school-going child aged between 5 and 20 years were randomly sampled from all EAs existing in APHRC schooling data collected in 2010. A total of 3,060 households which met the criteria were selected. The need to select a large sample of households in Nairobi was to enable us link data from the current study with previous ones that covered over 6000 households in Korogocho and Viwandani. By so doing, we were able to get a representative sample of households in Nairobi to continue observing the schooling patterns longitudinally. In all, there were 7,102 eligible households in all six towns. A total of 14,084 individuals within the target age bracket living in 5,854 (82% of all eligible households) participated in the study. The remaining 18% of eligible households were not available for the interview as most of them had either left the study areas, declined the interview, or lacked credible respondents in the household at the time of the data collection visit or call back.

    For the school-based survey, schools in each town were listed and classified into three groups based on their location: (i) within the selected slum location; (ii) within the catchment area of the selected slum area - about 1 km radius from the border of the study locations; and (iii) away from a selected slum. In Nairobi, schools were selected from existing APHRC data. During the listing exercise, lists of schools were also obtained from Municipality/City Education Departments in selected towns. The lists were used to counter-check the information obtained during listing. All schools located within the selected slum areas and those situated within the catchment area (1 km radius from the border of the slum) were included in the sample as long as they had a grade 6 class or intended to have one in 2012. The selection of schools within an informal settlement and those located within 1 km radius was because they were more likely to be accessed by children from the target informal settlement. Two hundred and forty-five (245) schools met the selection criteria and were included in the sample. Two hundred and thirty (230) primary schools (89 government schools, 94 formal private, and 47 low-cost schools) eventually participated in the survey. A total of 7,711 grade 3, 7,319 grade 6 pupils and 671 teachers of the same grades were reached and interviewed. All 230 head teachers (or their deputies) were interviewed on school characteristics.

    Mode of data collection

    Face-to-face [f2f]; Focus groups; Assessment; Filming (classroom observation).

    Research instrument

    Five survey questionnaires were administered at household level:

    (i). An individual schooling history questionnaire was administered to individuals aged 5 - 20. The questionnaire was directly administered to individuals aged 12 - 20 and administered to a proxy for children younger than 12 years. Ideally, the proxy was the child's parent or guardian, or an adult familiar with the individual's schooling history and who usually resides in the same household. The questionnaire had two main sections: school participation for the current year (year of interview), and school participation for the five years preceding the year of interview (i.e. 2007 to 2011). The section on schooling participation on the current year collected information on the schooling status of the individual, the type, name and location of the school that the individual was attending, grade, and whether the individual had changed schools or dropped out of school in the current year. Respondents also provided information on the reasons for changing schools and dropping out of school, where applicable. The section on school participation for previous years also collected similar information. The questionnaire also collected information on the individual's year of birth and when they joined grade one.

    (ii). A household schedule questionnaire was administered to the household head or the spouse. It sought information on the members of the household, their relationship to the household head, their gender, age, education and parental survivorship.

    (iii). A parental/guardian perception questionnaire was administered to the household head or the parent/guardian of the child. It collected information on the parents/guardians' perceptions on Free Primary Education since its implementation, household support to school where child(ren) attends and household schooling decision.

    (iv). A parental/guardian involvement questionnaire was strictly administered to a parent or guardian who usually lives in the household and who was equipped with adequate knowledge of the individual's schooling information (i.e. credible respondent). The questionnaire was completed for each individual of the targeted age bracket (5-20 years). The information on the child comprised questions on the gender of the child, parental/guardian aspirations for the child's educational attainment, and parental beliefs about the child's ability in school and their chances of achieving the aspired level.

    (v). A household amenities and livelihood questionnaire was administered to the household head or the spouse or a member of the household who could give reliable information. The questionnaire collected information on duration of stay in the

  5. Extreme poverty rate in Kenya 2016-2030

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Extreme poverty rate in Kenya 2016-2030 [Dataset]. https://www.statista.com/statistics/1227076/extreme-poverty-rate-in-kenya/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Kenya
    Description

    In 2025, *** percent of Kenya’s population live below **** U.S. dollars per day. This meant that over 8.9 million Kenyans were in extreme poverty, most of whom were in rural areas. Over *** million Kenyans in rural communities lived on less than **** U.S. dollars daily, an amount *** times higher than that recorded in urban regions. Nevertheless, the poverty incidence has declined compared to 2020. That year, businesses closed, unemployment increased, and food prices soared due to the coronavirus (COVID-19) pandemic. Consequently, the country witnessed higher levels of impoverishment, although improvements were already visible in 2021. Overall, the poverty rate in Kenya is expected to decline to ** percent by 2025. Poverty triggers food insecurity Reducing poverty in Kenya puts the country on the way to enhancing food security. As of November 2021, *** million Kenyans lacked sufficient food for consumption. That corresponded to **** percent of the country's population. Also, in 2021, over one-quarter of Kenyan children under five years suffered from chronic malnutrition, a growth failure resulting from a lack of adequate nutrients over a long period. Another *** percent of the children were affected by acute malnutrition, which concerns a rapid deterioration in the nutritional status over a short period. A country where prosperity and poverty walk side by side The poverty incidence in Kenya contrasts with the country's economic development. In 2021, Kenya ranked among the ten highest GDPs in Africa, at almost *** billion U.S. dollars. Moreover, its gross national income per capita has increased to ***** U.S. dollars over the last 10 years, a growth of above**** percent. Generally, while poverty decreased in the country during the same period, Kenya still seems to be far from reaching the United Nation's Sustainable Development Goals (SDGs) to eliminate extreme poverty by 2030.

  6. e

    Africa - Water Bodies - Dataset - ENERGYDATA.INFO

    • energydata.info
    Updated Oct 4, 2024
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    (2024). Africa - Water Bodies - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/africa-water-bodies
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    Dataset updated
    Oct 4, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Africa
    Description

    This dataset shows water bodies in Africa including lakes, reservoir, and lagoon. Data is curated from RCMRD Geoportal. The Regional Centre for Mapping of Resources for Development (RCMRD) was established in Nairobi – Kenya in 1975 under the auspices of the United Nations Economic Commission for Africa (UNECA) and the then Organization of African Unity (OAU), today African Union (AU). RCMRD is an inter-governmental organization and currently has 20 Contracting Member States in the Eastern and Southern Africa Regions; Botswana, Burundi, Comoros, Ethiopia, Kenya, Lesotho, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Somali, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe. To learn more about RCMRD, please visit http://www.rcmrd.org/

  7. Fairley kenya USA Import & Buyer Data

    • seair.co.in
    Updated Sep 12, 2016
    + more versions
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    Seair Exim (2016). Fairley kenya USA Import & Buyer Data [Dataset]. https://www.seair.co.in
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    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Sep 12, 2016
    Dataset provided by
    Seair Exim Solutions
    Authors
    Seair Exim
    Area covered
    Kenya, United States
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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(2016). Kenya Facts and Figures 2014 - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/kenya-facts-and-figures-2014

Kenya Facts and Figures 2014 - Dataset - openAFRICA

Explore at:
Dataset updated
Mar 27, 2016
Area covered
Kenya
Description

Kenya Facts & Figures is your direct route to Kenya’s economy through reliable statistics. It enables you to have a complete picture of the Kenyan economy from a single source. This booklet provides, at a glance, a comprehensive picture of Kenya’s economy covering all sectors as well as reflecting the various trends over the last four years.

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