Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -1.180 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -1.180 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -1.180 % in 2021 and a record low of -1.180 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Kenya KE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 13.900 % in 2015. This records a decrease from the previous number of 16.800 % for 2005. Kenya KE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 16.800 % from Dec 1992 (Median) to 2015, with 5 observations. The data reached an all-time high of 21.600 % in 1992 and a record low of 13.900 % in 2015. Kenya KE: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Disposable Personal Income in Kenya increased to 14266.05 KES Billion in 2023 from 12574.84 KES Billion in 2022. This dataset provides the latest reported value for - Kenya National Disposable Income - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Kenya Average Wage Earnings data was reported at 894,232.800 KES in 2023. This records an increase from the previous number of 864,750.100 KES for 2022. Kenya Average Wage Earnings data is updated yearly, averaging 617,900.550 KES from Jun 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 894,232.800 KES in 2023 and a record low of 366,613.600 KES in 2008. Kenya Average Wage Earnings data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.G009: Average Wage Earnings: by Sector and Industry: International Standard of Industrial Classification Rev 4.

The Gross Domestic Product per capita in Kenya was last recorded at 1853.09 US dollars in 2024. The GDP per Capita in Kenya is equivalent to 15 percent of the world's average. This dataset provides the latest reported value for - Kenya GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Personal Income Tax Rate in Kenya stands at 35 percent. This dataset provides the latest reported value for - Kenya Personal Income Tax Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Kenya KE: Imports: High-Income Economies: % of Total Goods Imports data was reported at 42.934 % in 2016. This records a decrease from the previous number of 47.762 % for 2015. Kenya KE: Imports: High-Income Economies: % of Total Goods Imports data is updated yearly, averaging 73.302 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 90.813 % in 1981 and a record low of 42.934 % in 2016. Kenya KE: Imports: High-Income Economies: % of Total Goods Imports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Imports. Merchandise imports from high-income economies are the sum of merchandise imports by the reporting economy from high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

The gini index in Kenya was forecast to remain on a similar level in 2029 as compared to 2024 with 0.39 points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Mozambique and Zimbabwe.

Kenya KE: Exports: High-Income Economies: % of Total Goods Exports data was reported at 41.840 % in 2016. This records an increase from the previous number of 40.919 % for 2015. Kenya KE: Exports: High-Income Economies: % of Total Goods Exports data is updated yearly, averaging 46.898 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 75.387 % in 1960 and a record low of 34.513 % in 2001. Kenya KE: Exports: High-Income Economies: % of Total Goods Exports data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Exports. Merchandise exports to high-income economies are the sum of merchandise exports from the reporting economy to high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Abstract

The 2015/16 Kenya Integrated Household Budget Survey (KIHBS) was conducted over a 12-month period to obtain up-to-date data on a range of socioeconomic indicators used to monitor the implementation of development initiatives. The Survey collected data on household characteristics, housing conditions, education, general health characteristics, nutrition, household income and credit, household transfers, information communication technology, domestic tourism, shocks to household welfare and access to justice. The findings are presented at national, county, rural and urban domains.

Household Characteristics The findings of the 2015/16 KIHBS basic characteristics of the population show that the sex ratio is 97.5. About 70 per cent of households were headed by males and the reported average household size was 4 members. The age dependency ratio declined to 81.6 per cent in 2015/16 KIHBS as compared to 84.0 per cent recorded in 2005/06 KIHBS. Majority (54.4%) of the population aged 18 years and above are in monogamous unions. At the national level, 8.4 per cent of children were orphans.

Housing Conditions and amenities Information regarding housing conditions and ownership, access to water, energy, sanitation and waste disposal was collected in the 2015/16 KIHBS. Bungalow was the most common dwelling type of housing occupied by 55.4 per cent of the households. About 60 per cent of households reported that they owned the dwellings that they resided in. The findings show that 72.6 per cent of households use improved drinking water sources. The statistics show that six out ten households had access to improved human waste disposal methods. Overall, 41.4 per cent of households were connected to electricity from the main grid.

Education Findings on education are presented for; pre-primary, primary, secondary, middle level college and university levels; and informal education, Madrassa/Duksi. Nationally, 89.4 per cent of the population aged three years and above had ever attended school. The overall Gross Attendance (GAR) for pre-primary, primary and secondary levels was 94.4 per cent, 107.2 per cent and 66.2 per cent, respectively. The population aged 3 years and above that did not have any educational qualification was 49.7 per cent. Most of the population aged 3 years and above that had not attended school cited not being allowed to attend by parent(s) as the reason for non-attendance. The proportion of the population aged 15-24 years that was literate, based on respondents' self -assessment, was 88.3 per cent.

General Health Characteristics General health characteristics discussed in the report comprise: morbidity by sex, health seeking behaviour, utilization of health care services and facilities, disability and engagement in economic activities and health insurance coverage. Information on child survival such as place of delivery, assistance during delivery, immunization and incidences of diarrhoea is also presented. The results show that two out of ten individuals reported a sickness or injury over the four weeks preceding the survey. Majority of the individuals (55.5 %) with a sickness or injury visited a health worker at a health facility for diagnosis. Disabilities were reported by 2.8 per cent of the population. Slightly more than a third of persons with disabilities reported having difficulty in engaging in economic activities. moderately stunted. A higher proportion (32.4%) of children in the rural areas were moderately stunted compared to those in urban areas (24.5%). Overall, 13.0 per cent of children were moderately wasted while 6.7 per cent were moderately underweight. The statistics further indicate that 98.8 per cent of children aged 0-59 months were ever breast fed. The mean length of breastfeeding nationally stood at 16.8 months. Porridge was the most common type of first supplement given to majority (35.9%) of children aged 0-23 months. The survey findings show that eight out of ten children participated in community-based nutritional programmes.

Household Income and Credit Household income is the aggregate earnings of all household members. It includes all forms of income arising from employment, household enterprises, agricultural produce, rent, pension and financial investment. The discussion in this report focuses on income from rent, pension, financial investment and other related incomes. Information is also provided on access and sources of credit. At national level, 7.2 per cent of households reported having received income from rent, pension, financial investment and other related incomes within the 12 months preceding the survey. A third of the households sought credit and over 90 per cent successfully acquired credit.

Household Transfers Transfers constitute income, in cash or in kind, that the household receives without working for it and it augments household income by improving its welfare. Three out of ten households reported having received cash transfers within the 12 months preceding the survey period. The average amount received per household from cash transfers was KSh. 27,097. Majority of households received cash transfers through a family member. Money transfer agents were the preferred mode of transmitting money for most beneficiaries of transfers received from outside Kenya. Over half of the households gave out transfers in kind.

Information and Communication Technology The 2015/16 KIHBS collected information on ICT equipment use and ownership. Findings show that three in every four individuals aged 18 years and above owned a mobile phone with an average number of 1.3 SIM cards per person. The most commonly used ICT equipment is the radio and mobile phone, reported by 79.3 per cent and 68.5 per cent of individuals aged 3 years and above, respectively. The highest proportion (50.3%) of those that did not own a mobile phone cited its high cost as the reason. Urban areas had the highest proportion of population with ownership of a mobile phone. Nairobi City County had the highest proportion of population with a mobile phone while Turkana County had the lowest. The population aged 3 years and above that reported using internet over the last three months preceding the survey was 16.6 per cent. Three in every ten households had internet connectivity and use of internet in mobility was reported as the most common place of use of internet. The internet was used mainly for social networking. No need to use the internet was the most predominant reason for not using the internet reported by 30.1 per cent of those who did not use it.

Domestic Tourism Domestic tourism comprises activities of residents travelling to and staying at least over a night in places outside their usual environment within the country, for not more than 12 months, for leisure, business or other purposes. At national level, 13.4 per cent of individuals reported that they travelled within Kenya in the 3 months preceding the survey. Visiting friends and relatives was reported by the highest proportion (71.1%) of individuals taking trips. Majority of those who took a trip (66.4%) reported that they sponsored themselves. Transport costs accounted for the largest share (38.4%) of expenditure on domestic tourism. Majority of those who did not take a trip reported high cost as a reason.

Shocks to Household Welfare A shock is an event that may trigger a decline in the well-being of an individual, a community, a region, or even a nation. The report presents information on shocks which occurred during the five-year period preceding the survey and had a negative impact on households' economic status or welfare. Three in every five households reported having experienced at least one shock within the five years preceding the survey. A large rise infood prices was reported by the highest proportion (30.1 per cent) of households as a first severe shock. Most households reported that they spent their savings to cope with the shock(s).

Justice The survey sought information from household members on their experiences regarding grievances/disputes, resolution mechanisms, status of grievance/dispute resolution and costs incurred. Majority of households (26.2%) experienced grievances related to succession and inheritance. Approximately seven out of ten households that experienced grievances reported that they were resolved by parties from whom they sought interventions. Lawyers on average received the highest amount of money (KSh 59,849) paid to a primary organization for grievance resolution through a formal channel. Courts accounted for the highest informal costs averaging KSh 6,260 in grievance resolution.

Geographic coverage

The survey covers all the Counties in Kenya based on the following levels National, Urban, Rural and County

Analysis unit

Households Indviduals within Households and Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Design and Sample Selection The second Kenya Integrated Household Budget Survey 2015/16 will be the eighth household budget survey to be conducted in Kenya following those conducted in 1981/82, 1983/84, 1992, 1994, 1997 and 2005/06. The KIHBS 2015/16 is a multi-indicator survey in nature with the main objective of updating the household consumption patterns in all the Counties.

KIHBS 2015/16 is designed to provide estimates for various indicators at the County-level. A total of 50 study domains are envisaged. These are; all the forty-seven (47) counties (Each as a separate domain), urban and rural (each as a separate domain at National level), and lastly the National-level aggregate.

Sampling frame The sampling frame used for KIHBS 2015/16 is the fifth National Sample Survey and Evaluation Program (NASSEP V) master frame developed from the Population and Housing Census (KPHC) conducted in

Abstract

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

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

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

Cleaning operations

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

Abstract

In an effort to address the ICT data challenges, the Communications Commission of Kenya (CCK) partnered with Kenya National Bureau of Statistics (KNBS) to undertake a comprehensive National ICT Survey. This was planned and executed during the months of May and June 2010.

The main objective of the study was to collect, collate and analyse data relating to ICT access and usage by various categorizations in Kenya. The survey captured data and information on critical ICT indicators as defined by international bodies such as the International Telecommunications Union (ITU). These indicators focused on household and individuals; and the data was be disaggregated by age, gender, administrative regions, rural and urban locations.

The specific objectives of the study were to; Obtain social economic information with a view of understanding usage patterns of ICT services; (a) Obtain social economic information with a view of understanding usage patterns of ICT services; (b) Collect, collate and analyze ICT statistics in line with ICT indicators; (c) Evaluate the factors that will have the greatest impact in ensuring access and usage of ICTs and; (d) Develop a database on access and usage of ICT in Kenya

Geographic coverage

National coverage

Analysis unit

District, Household, Individual

Universe

Households from the sampled areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The National Sample Survey and Evaluation Programme (NASSEP IV) maintained by the Bureau was used as the sampling frame. The frame has 1,800 clusters spread all over the country, and covers all socio-economic classes and hence able to get a suitable and representative sample of the population. The survey was distributed into four domains, namely: 1. National, 2. Major Urban areas, 3. Other Urban areas, and 4. Rural areas.

The major urban towns included Nairobi, Thika, Mombasa, Kisumu, Nakuru and Eldoret. All other areas defined as urban by KNBS but fall outside the major municipalities above were categorized as 'other urban areas'. The rural domain was further sub-divided into their respective provinces, excluding Nairobi which is purely urban. For the 'rural' component, the districts that display identical socio-cultural and economic conditions have been pooled together to create strata from which a representative set of districts is selected to represent the group of such districts. A total of 42 such stratifications were done and one district in each categorization was selected. The major urban areas of the country namely Nairobi, Mombasa, Kisumu, Nakuru, Eldoret and Thika were all sub-stratified into five sub-strata based on perceived levels of income into the: 1. Upper income 2. Lower Upper 3. Middle 4. Lower Middle and 5. Lower.

In this survey, all the six 'major urban' are included while just a few of the 'other urban areas' are selected depending on their population (household) distribution.

Selection of the Clusters for the Survey The selection of the sample clusters was done systematically using the Equal Probability Selection method (EPSEM). Since NASSEP IV was developed using Probability Proportional to Size (PPS) method, the resulting sample retains its properties. The selection was done independently within the districts and the urban /rural sub-stratum.

Selection of the Households From each selected cluster, an equal number of 15 households were selected systematically, with a random start. The systematic sampling method was adopted as it enables the distribution of the sample across the cluster evenly and yields good estimates for the population parameters. Selection of the households was done at the office and assigned to the Research Assistants, with strictly no allowance for replacement of non-responding households.

Sampling deviation

Owing to the some logistical challenges the following clusters were partially or not covered at all: • One cluster in Tana River due to floods. • Two clusters in Molo where households shifted to safer areas after the Post Election Violence (PEV). As a result, fewer than the expected households were covered. • One cluster in Koibatek was covered halfway due to relocation of households to pave way for a large plantation.

Where there was no school found within the cluster, Research Assistant was allowed to sample an institution from a neighbouring cluster. In some districts, the schools were found to be very far from the cluster and therefore could not be covered. Where a cluster was to be covered over a weekend, it was often not possible to find a responsible person in institutions to respond to the questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household questionnaire: This will be used to collect background information pertaining to the members of the household and businesses operated by household members. It will collect information about each person in the household such as name, sex, age, education, and relationship to household head etcetera. This information is vital for calculating certain socio-demographic characteristics of the household. The Business module in the household questionnaire will be used to collect information pertaining to usage of ICT in businesses identified in the household. To estimate the magnitude, levels and distribution of ICT usage in the country, all the selected respondents 15 years and above will be subjected to business questionnaire.

Institutional Questionnaire: This will collect information pertaining to institutions providing ICT related programmes in the country. This information will be analyzed to identify gaps and other issues of concern, which need to be addressed in the promotion ICT provision in the country.

Cleaning operations

As a matter of procedure initial manual editing was done in the field by the RAs. The supervisors further checked the questionnaires and validated the data in the field by randomly sampling 20 per cent of the filled questionnaires. After the questionnaires were received from the field, an office editing team was constituted to do office editing.

Data was captured using Census and Survey Processing System (CSPRO) version 4.0 through a data entry screen specially created with checks to ensure accuracy during data entry. All questionnaires were double entered to ensure data quality. Erroneous entries and potential outliers were then verified and corrected appropriately. A total of 20 data entry personnel were engaged during the exercise.

The captured data were exported to Statistical Package for Social Sciences (SPSS) for cleaning and analysis. The cleaned data was weighted before final analysis. The weighting of the data involved application of inflation factors derived from the selection probabilities of the EAs and households detailed in section 2.2.7, on weighting the Sample Data.

Response rate

The overall response rate stood at 85.9 per cent. Nairobi had the lowest response rate at 69.4 per cent while the highest (94.6 per cent) was realized in North Eastern. More than 95.5 per cent of all the sampled households were occupied out of which 85.9 per cent were interviewed.

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

In developing countries, unexpected income shocks are common but informal insurance is typically incomplete. An important question is therefore whether risk-sharing within the household is effective. This paper presents results from a field experiment with 142 married couples in Kenya in which individuals were given random income shocks. Even though the shocks were small relative to lifetime income, men increase private consumption when they receive the shock but not when their wives do, a rejection of efficiency. Such behavior is not specific to the experiment - both spouses spend more on themselves when their labor income is higher.

Background The prevalence of non-communicable diseases is increasing in lower-middle-income countries as these countries transition to unhealthy lifestyles. The transition is mostly predominant in urban areas. We assessed the association between wealth and obesity in two sub-counties in Nairobi City County, Kenya, in the context of family and poverty. Results A total of 149 households, response rate of 93%, participated, 72 from Embakasi and 77 from Langata. Most of the participants residing in Embakasi belonged to the lower income and education groups whereas participants residing in Langata belonged to the higher income and education groups. About 30% of the pre-adolescent participants in Langata were with at least overweight, whereas the respective number in Embakasi was only 6% (p<0.001). In contrast, the prevalence of adults (mostly mothers) with overweight and obesity was high (65%) and similar in the two study areas. Wealth (b = 0.01; SE 0.0; p=0.003) and income (b = 0.29; SE 0.11; p=0.009) predicted higher BMI z-score in pre-adolescents. Conclusions In Nairobi, pre-adolescent overweight was already highly prevalent in the middle-income area, while the proportion of women with overweight/obesity was high also in the low-income area. These results suggest that a lifestyle promoting obesity is prevalent even in lower income areas in urban Kenya, and this is a strong justification for promoting healthy lifestyles across all socio-economic classes. This cross-sectional study was conducted among of 9-14 years old pre-adolescents and their guardians living in low- (Embakasi) and middle-income (Langata) sub-counties. The sociodemographic characteristics were collected using a validated questionnaire. Weight, height, mid-upper arm circumference, and waist circumference were measured using standard approved protocols. Socioeconomic characteristics of the residential sites were accessed using Wealth Index, created by using Principal Component Analysis. Statistical analyses were done by analysis of variance (continuous variables, comparison of areas) and with logistic and linear regression models.

Kenya KE: GDP: Gross National Income data was reported at 2,538,668.317 KES mn in 2010. This records an increase from the previous number of 2,362,526.591 KES mn for 2009. Kenya KE: GDP: Gross National Income data is updated yearly, averaging 119,713.000 KES mn from Dec 1963 (Median) to 2010, with 48 observations. The data reached an all-time high of 2,538,668.317 KES mn in 2010 and a record low of 6,429.100 KES mn in 1963. Kenya KE: GDP: Gross National Income data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Kenya – Table KE.IMF.IFS: Gross Domestic Product: by Expenditure: Annual.

This dataset provides values for PERSONAL INCOME TAX RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 45.700 % in 2021. This records an increase from the previous number of 44.800 % for 2020. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 42.400 % from Dec 1992 (Median) to 2021, with 7 observations. The data reached an all-time high of 45.700 % in 2021 and a record low of 40.000 % in 1994. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Kenya KE: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data was reported at 36.100 % in 2021. This records an increase from the previous number of 35.000 % for 2020. Kenya KE: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data is updated yearly, averaging 29.400 % from Dec 1992 (Median) to 2021, with 7 observations. The data reached an all-time high of 36.700 % in 2005 and a record low of 25.400 % in 1997. Kenya KE: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Kenya Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at -3.090 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging -3.090 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -3.090 % in 2021 and a record low of -3.090 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.