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

Kenya Integrated Household Budget Survey, 2016 Data on households in Kenya as of 2016

Abstract

The Kenya Integrated Household Budget Survey (IHBS) was designed to capture a wide range of socio-economic indicators using an integrated approach as opposed to stand alone surveys. Kenya has a rich history of conducting Household Budget Surveys (HBS) which ordinarily collect data on socio-economic indicators such as demographic, education, health, household consumption, expenditure patterns and sources of household income amongst others. The socio-economic indicators derived from the survey were a milestone in planning and policy information. The Integrated Household Budget Survey also provided statistics for monitoring and evaluating development initiatives and targeted interventions. These indicators complemented the existing baseline information from the 2009 Kenya Population and Housing Census (KPHC) and other surveys.

The survey 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. The specific objectives of the survey include:

1. Computation of poverty/welfare measures (incidence, gap and severity)
2. Updating of national accounts benchmarks e.g. private consumption, informal sector, analysis of household sector
3. Forming a basis for updating household expenditure weights to be used in the development of new consumer Price Index (CPI)
4. Providing quarterly estimates on selected indicators at national level.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey used the fifth national Sample Survey and Evaluation Program (NASSEP V) master frame based on the Kenya Population and Housing Census (KPHC) conducted in 2009. The sample took into consideration the number of households, the area of residence and the domains of analysis.

The sample was stratified and selected in two stages from the master sample frame. Stratification was achieved by separating each county into urban and rural areas; in total 92 sampling strata were created. Samples were selected independently in each sampling stratum by a two stage selection. In this regard, 2400 clusters were sampled with equal probability from 5,360 clusters in NASSEP V. The clusters served as primary sampling units for the selection of ten households per cluster, translating to 24,000 households. A combination of two methods the Paper Assisted Personal Interview (PAPI) and the Computer Assisted Personal Interview (CAPI) were used in the survey.

Sampling deviation

The IHBS was 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 counties (Each as a separate domain), urban and rural (each as a separate domain at National level), and lastly the National-level aggregate.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaires were administered during the survey: 1. Household Questionnaire 2. Community Questionnaire 3. Market Price Questionnaire

Abstract

The Kenya Integrated Household Budget Survey (KIHBS 2005/06) Project was to collect a wide spectrum of socio-economic indicators required to measure, monitor and analyse the progress made in improving living standards. Specifically, the KIHBS was designed to update and strengthen three vital aspects of the national statistical database, notably: the Consumer Price Index (CPI), poverty and inequality; and the System of National Accounts (SNA). The data collection phase of this survey took 12 months and data on demographics, housing, education, health, agriculture and livestock, enterprises, expenditure and consumption, among others, was collected.

Geographic coverage

The survey covered all the districts in Kenya. The data representativeness are at the following levels

-National -Urban/Rural -Provincial -District

Analysis unit

Households Indviduals within Households Community

Universe

The survey covered all household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

KNBS has established the NASSEP IV sampling frame based on the 1999 Population and Housing Census. The sample design of the KIHBS 2005/2006 is based on this frame. The survey drew a sample of clusters from the set of 540 urban clusters and the 1,260 rural clusters under NASSEP IV Sampling frame.

The KIHBS 2005/2006 covered a total of 1,343 clusters with a total sample of 13,430 households, stratified by district and by Urban/Rural.

In the first stage, using the KNBS Master Sample (NASSEP IV), 1,343 clusters were selected with equal probability within a district. In the second stage, 10 households were selected with equal probability in each cluster.

A total sample of 13,430 households (10 households in each of 1,430 Primary Sampling Units - called clusters,) was allocated into 136 explicit strata (the urban and rural sections of each of Kenya's 69 districts, except in Nairobi and Mombassa, which are wholly urban). The 1,343 clusters required by the KIHBS were selected from the CBS 1,800-cluster master sample. This selection was done with equal probability within each stratum, except for the six districts that contain urban areas qualified as municipalities. In these districts, the urban part of the sample was further stratified into six groups (five socio-economic classes in the municipality itself and other urban areas in the district,).

Sampling deviation

From the design sample of 1,343 clusters, only 1,339 clusters were covered the remianing 4 clusters were not covered due to Inaccessibilty and security issues.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were administered namely: -Socio-economic questionnaire and -Community questionnaire -Diary qustionares

The socio-economic module included the following sections

-Household member Information -Education -Health, Fertility and Household Deaths -Labour -Child Health and Anthropometry -Housing -Water, Sanitation and Energy use -Consumption of Food Items over the past week -Expenditre on regular Non-food items over the past month -Expenditure on Durables over the past 12 months -Agricultural holdings -Agricltral Outputs -Livestock -Hosehold Enterprises -Transfers -Other Income -Recent Shocks to Household -Credit to Household members

The Community Questionnaire was administered to capture community level information. The information collected related to; - Community facilities including access to schools, health facilities, roads, extension services and markets, - Community major events, - Land tenure,

The were two types of diaries one used to record goods and services purchased, and the other to record goods and services consumed by the household.

Cleaning operations

Data editing took place at the data collection in the field inluding; a) During data entry in the field, b) Structure checking and completeness and c) Structural checking of SPSS data files

Sampling error estimates

Estimates of sampling error was calculated for the poverty level estimates only using .............

Consumer Price Index (CPI): Weights: Kenya Integrated Household Budget Survey (KIHBS) data was reported at 1.000 Per 1 in 2018. This stayed constant from the previous number of 1.000 Per 1 for 2017. Consumer Price Index (CPI): Weights: Kenya Integrated Household Budget Survey (KIHBS) data is updated yearly, averaging 1.000 Per 1 from Dec 2009 (Median) to 2018, with 10 observations. The data reached an all-time high of 1.000 Per 1 in 2018 and a record low of 1.000 Per 1 in 2018. Consumer Price Index (CPI): Weights: Kenya Integrated Household Budget Survey (KIHBS) 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.I006: Consumer Price Index: Weights.

Abstract

The Kenya Integrated Household Budget Survey (IHBS) 2005-2006, was designed to capture data that would be used to update poverty, welfare statistics and employment statistics, derive the Consumer Price Index (CPI) and revise the national accounts information.

IHBS also aimed at providing data on socio-economic aspects of the Kenyan population including education, health, energy, housing, water and sanitation. This data is critical to the government and private sector for the purpose of guiding investment and national development policy decisions. The survey was carried out by the Kenya National Bureau of Statistics (KNBS) with technical and financial assistance from the Department for International Development (DfID), the United States Agency for International Development (USAID), the European Union (EU), the Danish International Development Agency (DANIDA), The World Bank and the United Nations Development Program (UNDP).

The survey covered all the 70 districts including rural and urban clusters with data being collected from all arid and semi-arid areas for the first time in a decade. All surveyed households were captured using the Geographical Positioning System (GPS) which made it possible to identify the precise geographical location of households. The survey was conducted over a period of 12 months, which covers all possible seasons, as in contrast to previous surveys where the longest survey conducted by the Bureau was for three (3) months.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Universe

The survey covered all household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya National Bureau of Statistics (KNBS) has established the National Sample Survey and Evaluation Program (NASSEP IV) sampling frame based on the 1999 Population and Housing Census (PHC). The sample design of the IHBS 2005-06 is based on this frame. The survey drew a sample of clusters from the set of 540 urban clusters and the 1,260 rural clusters under NASSEP IV sampling frame.

The IHBS 2005-06 covered a total of 1,343 clusters with a total sample of 13,430 households, stratified by district and by urban/rural. In the first stage, using the KNBS Master Sample (NASSEP IV), 1,343 clusters were selected with equal probability within a district. In the second stage, 10 households were selected with equal probability in each cluster.

A total sample of 13,430 households (10 households in each of 1,430 Primary Sampling Units (PSU) were allocated into 136 explicit strata (the urban and rural sections of each of Kenya's 69 districts, except in Nairobi and Mombasa, which are wholly urban). The 1,343 clusters required by the IHBS were selected from the Central Bureau of Statistics (CBS) 1,800-cluster master sample. This selection was done with equal probability within each stratum, except for the six districts that contain urban areas qualified as municipalities. In these districts, the urban part of the sample was further stratified into six groups (five socio-economic classes in the municipality itself and other urban areas in the district).

Sampling deviation

From the design sample of 1,343 clusters, only 1,339 clusters were covered the remaining 4 clusters were not covered due to inaccessibility and security issues.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaires were administered during the survey: 1. Household Questionnaire 2. Community Questionnaire 3. Diary Questionnaire

Cleaning operations

Data editing took place at the data collection in the field including: - During data entry in the field - Structure checking and completeness - Structural checking of SPSS data files

Response rate

98%

Sampling error estimates

Estimates of sampling error was calculated for the poverty level estimates only using .............

Kenya Integrated Household Budget Survey, 2016

Kenya Integrated Household Budget Survey, 2016

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

The KIHBS Marital Status data-set specifically looks at the results of the survey and seeks to examine trends in the living structure of households including monogamous, polygamous ,divorces, widowed, separated and come-we-stay (living together) unions.

The Kenya Integrated Household Budget Survey 2005/06 is designed to provide numerous indicators and the data needed to measure living standards and poverty in Kenya, with particular emphasis on updating the Consumer Price Index (CPI), and providing new estimates of the household accounts for the national accounting system.

This dataset contains information on County poverty rates estimates which is based on Kenya Integrated Household Budget Survey ( KIHBS) data for Constituencies in 2005/6

The KIHBS Work days lost to Sickness samples the impact of illness in over 70 administrative regions of the country.

The Kenya Integrated Household Budget Survey 2005/06 is designed to provide numerous indicators and the data needed to measure living standards and poverty in Kenya, with particular emphasis on updating the Consumer Price Index (CPI), and providing new estimates of the household accounts for the national accounting system.

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley conducted the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to track the socioeconomic impacts of the COVID-19 pandemic and the recovery from it to provide timely data to inform policy. This collection contains information from seven waves of the COVID-19 RRPS, which was part of a panel survey that targeted Kenyan nationals 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. Sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. The "WAVE" variable represents in which wave the households were interviewed in. All waves of this survey included information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. The data contain information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority were randomly selected. The samples covered urban and rural areas and were designed to be representative of the population of Kenya using cell phones. The sample size for each completed wave was: Wave 1: 4,061 Kenyan households Wave 2: 4,492 Kenyan households Wave 3: 4,979 Kenyan households Wave 4: 4,892 Kenyan households Wave 5: 5,854 Kenyan households Wave 6: 5,765 Kenyan households Wave 7: 5,633 Kenyan households The collection is organized into three levels. The first level is the Household Level Data, which contains household level information. The 'HHID' variable uniquely identifies all households. The second level is the Adult Level Data, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'ADULT_ID' variable. The third level is the Child Level Data, which contains information for every child in the household. Each child in a household is uniquely identified by the 'CHILD_ID' variable.

Abstract

The main objective of this survey is to help improve the impact of migration and remittances on the economic and social situation in Kenya. At present, our knowledge base on migration and remittances in Kenya is quite limited. By providing rich and detailed information on the impact of migration and remittances at the household level, this survey will greatly increase our ability to maximize the socio-economic impact of migration and remittances in Kenya. To these ends, the survey will collect nationally-representative information in various African countries on three types of households: non-migrant households, internal migrant households and international migrant households. Comparisons between these three types of households will help policymakers identify the socio-economic impact of migration and remittances in Kenya.

Geographic coverage

Embu, Garissa, Kakamega, Kiambu, Kilifi, Kisii, Lugari, Machakos, Malindi, Migori, Mombasa, Nairobi, Nakuru, Siaya, Thika, Vihiga, Rachuonyo

Analysis unit

• Household
• Individual

Universe

17 out of 69 districts in Kenya were selected using procedures described in the methodology report

Kind of data

Sample survey data [ssd]

Sampling procedure

The study used the Kenya National Bureau of Statistics (KNBS) National Sample Survey and Evaluation Programme (NASSEP IV) sampling frame which has 69 districts as stratum comprising both urban and rural areas. The sample design for the study was multi-stage with the first stage covering the primary sampling units (PSUs) which was a sample of clusters developed during the 1999 census. The second stage was selection of households within the clusters. A re-listing of all households in sampled clusters was carried out to up-date the 1999 and also to be able to classify households into the three strata of interest in this study: international migrant households, internal migrant households, and non-migrant households. At the household level, interviews were held with the household head/spouse or other responsible adult with the requisite information about the household. The study uses a purposive survey methodology that first selected districts with the largest concentration of international migrants, and then selected clusters also with the highest concentration of international migrants. This was done based on the information of previous household surveys and the knowledge of the administrative officers, statistical officers and cluster guides.

Sampling Frame At the time of the study, the available National Census was conducted in 1999. This census did not contain questions on remittances but had questions on migration. The migration question asked then was where family members were living in the last one year. This means that the census captured either those who had come back or those who had come visiting and were to return to where they migrated to. It did not distinguish clearly the migration component. Further, the census was conducted 10 years ago which meant it does not provide the current status on aspects of migration. The Kenya Integrated Household Budget Survey (KIHBS) 2005/06 and the Financial Services Deepening survey (FSD) are two surveys that have recently been conducted with an element of migration and remittances. However, the information is not adequate for the current survey. For example, the KIHBS has a question that captures issues of remittance linking them to the transfers received from abroad. Although it has about 13,000 households, only about 125 households indicated they had received such transfers. This was a very small sample compared to what was envisaged by the current study. The Financial Services Deepening survey (FSD) (2006/07) also has a question on cash transfers from abroad but all this is related to issues of access to financial services and not to issues sought in the current study. Thus, it could not be used for the current study. The KIHBS and FSD surveys was based on the KNBS NASSEP IV and although one may have thought of revisiting the households that were covered for additional information, it is against the KNBS regulations to conduct such follow-ups and the households identities are not provided. The Kenya National Bureau of Statistics household survey sampling frame, the National Sample Survey and Evaluation Programme (NASSEP IV), is based on the 1999 population and housing census. The objective of NASSEP IV frame was to construct a national master sampling frame of clusters of households in both rural and urban areas in Kenya using a sound sampling design. This sampling frame has a total of 1,800 clusters of which 1,260 are rural and 540 are urban as indicated in Appendix Table 1. Each cluster holds about 80 to 100 households. The framework is based on the old administrative units comprising of 69 districts in 8 Provinces. Currently, the districts have been subdivided and increased to 265 but this does not distort our sampling frame based on NASSEP IV as the new districts are curved out of the old districts.

The Sample This study utilized the NASSEP IV frame to select 102 clusters (5.6% of the total clusters) in 19 districts which yielded a total sample of 2,448 households assuming an average of 24 households in each cluster. The districts were selected first, then the clusters in each district and finally the households in each cluster. Households in each cluster were re-listed (updated) and grouped into three strata--international migrant, internal migrant and non-migrant households. In the selection of clusters in each district, at least one of the targeted five clusters was urban with exception of Nairobi and Mombasa which are purely urban. The study however ended up covering 92 clusters (5.1% of the total clusters in NASSEP IV) from 17 districts. Two targeted districts-Kajiado and Baringo- were not covered due to logistical problems. First of all, the team was expected to finalize the field by 15th December so that the analysis could begin and be on time. When the fieldwork was winding up on 22nd December, the two districts were yet to be covered. Two, the two districts have more transport challenges and the team was therefore expected to use KNBS transport facilities and more research assistants to capture the households which are more widely spread on the ground. This required adequate funding and by the time the fieldwork was winding up no funds had been received from World Bank. Third, even when the funds were received in January, the team considered that the study would be capturing households in a different consumption cycle, having just gone through the festive season. Given all these factors, this saw a total of 2,123 household covered out of 2, 208 (96% of the total targeted). Of these, some households were later dropped due to a lot of missing data especially due to non response, and at the end a total of 1,942 households were cleaned up for analysis. This including 953 are urban and 989 rural drawn from 51 rural and 40 urban clusters. Selection of Districts There was a particular interest in investigating households that had international migrants and which may have received transfers from abroad. A random sample of the population would not produce adequate number of households that had received transfers or had international migration, as we learnt from the KIHBS data set. As indicated earlier, out of 13,000 households surveyed under KIHBS only 125 households receiving remittances from abroad. With this experience and information, this study selected the top nineteen districts from KIHBS (2005/07) that showed households with migration characteristics. The key factor used was that the households indicated they received cash transfers from abroad. Districts with more than one household fulfilling this criterion of having received transfers from abroad were considered. In addition, Financial Services Deepening survey (FSD) survey results were used to confirm that the selected districts had reported having received money from abroad. In addition, since this is a relatively rare phenomenon in Kenya, the selection of districts is designed such that households with the relevant characteristics have a high probability of being selected. As such those districts with a presence of cash transfers mechanisms such as M-PESA, Western Union, or Money Gram services were considered. All these information was used to update the information from KIHBS.

Selection of Clusters In each district, 5 clusters were selected of which at least one cluster was an urban cluster as defined by KNBS, except for Nairobi and Mombasa which are purely urban. Some other district had more than one urban cluster selected based on their number of clusters and accessibility to rural clusters for example Garissa. The study covered 10 clusters in Nairobi and 6 in Mombasa with an attempt made to capture this across various income group levels.
In selection of the clusters, the supervisors sat down with the KNBS statistics officers, cluster guides, village elders, administrative officers (Chiefs and sub-chiefs) to map out clusters where the probability of getting an international migrant was high. Of this probabilities were very subjective as it was based on how well these people understood the composition of the households in the areas they represent. This helped to identify the five clusters targeted for study.

Selection of Households The selection process involved re-listing of the households in each cluster so as to update the list of occupied households and identify the three groups of households. Each group or stratum was treated as an independent sub-frame and random sampling was used to select households in each group. The listing exercise was

2005/6 Kenya Integrated Household Budget Survey. Poverty headcount (percent below poverty line) by District in Swahili. Takwimu Ya Umasikini Katika Wilaya.

Abstract

Kenya hosts over half a million refugees, who, along with their hosts in urban and camp areas, face difficult living conditions and limited socioeconomic opportunities. Most refugees in Kenya live in camps located in the impoverished counties of Turkana (40 percent) and Garissa (44 percent), while 16 percent inhabit urban areas—mainly in Nairobi but also in Mombasa and Nakuru. Refugees in Kenya are not systematically included in national surveys, creating a lack of comparable socioeconomic data on camp-based and urban refugees, and their hosts. As the third of a series of surveys focusing on closing this gap, this Socioeconomic Survey of Urban Refugees's aim is to understand the socioeconomic needs of urban refugees in Kenya, especially in the face of ongoing conflicts, environmental hazards, and others shocks, as well as the recent government announcement to close Kenya’s refugee camps, which highlights the potential move of refugees from camps into urban settings. The SESs are representative of urban refugees and camp-based refugees in Turkana County. For the Kalobeyei 2018 and Urban 2020–21 SESs, households were randomly selected from the UNHCR registration database (proGres), while a complete list of dwellings, obtained from UNHCR’s dwelling mapping exercise, was used to draw the sample for the Kakuma 2019 SES. The Kalobeyei SES and Kakuma SES were done via Computer-Assisted Personal Interviews (CAPI). Due to COVID-19 social distancing measures, the Urban SES was collected via Computer Assisted Telephone Interviewing (CATI). The Kalobeyei SES covers 6,004 households; the Kakuma SES covers 2,127 households; and the Urban SES covers 2,438 households in Nairobi, Nakuru, and Mombasa. Questionnaires are aligned with national household survey instruments, while additional modules are added to explore refugee-specific dynamics. The SES includes modules on demographics, household characteristics, assets, employment, education, consumption, and expenditure, which are aligned with the Kenya Integrated Household Budget Survey (KIHBS) 2015–16 and the recent Kenya Continuous Household Survey (KCHS) 2019. Additional modules on access to services, vulnerabilities, social cohesion, mechanisms for coping with lack of food, displacement trajectories, and durable solutions are administered to capture refugee-specific challenges.

Geographic coverage

Nairobi, Mombasa, Nakuru

Analysis unit

Households and individuals

Universe

All refugees registered with UNHCR via ProGres, verified via the Verification Exercise conducted in 2021

Kind of data

Sample survey data [ssd]

Sampling procedure

– The survey was conducted using the UNHCR proGres data as the sampling frame. Due to the COVID-19 lockdown, the survey data was collected via telephone. Hence, the survey is representative of households with active phone numbers registered by UNHCR in urban Kenya – Nairobi, Mombasa and Nakuru. A sample size of 2,500 was needed to ensure a margin of error of less than 5 percent at a confidence level of 95 percent for groups represented by at least 50 percent of the population. The sample for the urban SES is designed to estimate socioeconomic indicators, such as food insecurity, for groups whose share represents at least 50 percent of the population. Considering the total urban refugee population as of August 2020 and the proportions of main countries of origin, as well as a 10 percent nonresponse rate, the target sample size is 2,500 households in total, with 1,250 in Nairobi, 700 in Nakuru, and 550 in Mombasa. A total of 2,438 households were reached: 1,300 in Nairobi, 409 in Nakuru, and 729 in Mombasa. The units in ProGres list are UNHCR proGres families, which are different from households as defined in standard household surveys. Upon registration, UNHCR groups individuals into ‘proGres’ families which do not necessarily meet the criteria to be considered a household. A proGres family is usually comprised by no more than one household. In turn, a household can be integrated by one or more proGres families. Households were selected as the unit of observation to ensure comparability with national household surveys. Households are a set of related or unrelated people (either sharing the same dwelling or not) who pool ration cards and regularly cook and eat together. As proGres families were sampled, the identification of households was done by an introductory section that confirms that each member of the selected proGres family is a member of the household and whether there are other members in the households that belong to other ProGres families. Thus, the introductory section documents the number of proGres families present in the household under observation. Before selecting the survey strata, the team attempted to better understand the type of bias observed by focusing on refugees with access to phones. From the proGres data, phone penetration in urban areas is high (Nairobi and Mombasa: 93 percent, Nakuru: 95 percent). To understand the type of bias observed by focusing on refugees with access to phone, we looked at socio-economic outcomes for proGres family refugees with access to a phone number and those without

Mode of data collection

Computer Assisted Telephone Interview [cati]

In order to estimate the nutrients intake from the food consumption module of the 2015/2016 Kenya Integrated Household Budget Survey (KIHBS), each of the food items reported in that module is matched with a food item in the Food Composition Table (FCT) or Food Database (FDB) of choice, and nutrients composition are then compiled in what is called the nutrition conversion table (NCT).

居民消费价格指数（CPI）：加权：肯尼亚综合住户预算调查（KIHBS）在12-01-2018达1.000Per 1，相较于12-01-2017的1.000Per 1保持不变。居民消费价格指数（CPI）：加权：肯尼亚综合住户预算调查（KIHBS）数据按年更新，12-01-2009至12-01-2018期间平均值为1.000Per 1，共10份观测结果。该数据的历史最高值出现于12-01-2018，达1.000Per 1，而历史最低值则出现于12-01-2018，为1.000Per 1。CEIC提供的居民消费价格指数（CPI）：加权：肯尼亚综合住户预算调查（KIHBS）数据处于定期更新的状态，数据来源于Kenya National Bureau of Statistics，数据归类于Global Database的肯尼亚 – 表 KE.I005：居民消费价格指数：加权。

Abstract

The Kenya National Adult Literacy Survey was conducted throughout the country between June 8 to August 8, 2006 by the Kenya National Bureau of Statistics (KNBS) and the Department of Adult Education (DAE). The purpose was to generate accurate and up-to-data on the status of adult literacy with a view of using that information to expand and strengthen literacy programmes, and also plan for general national development. Underlying this is the fact literacy is fundamental to socio-economic development and poverty alleviation. The specific objectives were: Determine the magnitude, levels and distribution of adult literacy for persons aged 15 and above. Obtain comprehensive data and information on adult literacy from literacy providers and stakeholders both in the private and public sectors. Identify issues of concern, which need to be addressed in the promotion of adult literacy. About 18,000 households were sampled for the survey and out of that, 15,696 were occupied in 4,782 in urban and 10,914 in rural areas. Out of the occupied households, 15,504 were successfully interviewed, which gave a response rate of 98.8 per cent. In addition, eight adult education centers or classes in each district were sampled and their teachers of managers interviewed to generate information on the perspective of the service providers. The study used various instruments to collect the data. Four questionnaires were developed for the survey and targeted the following: households, individuals, institutions providing literacy, and literacy (assessment) tests. The survey was conducted in English, Kiswahili and 18 other local languages, which provided the respondents with the opportunity to respond in a language that they were quite comfortable with. Significantly, 70 per cent of the respondents took the literacy assessments tests in either English or Kiswahili. To arrive at the adult literacy levels, two methods were used: self reporting (one's ability to read and write) and actual testing (assessment of literacy skills) of the population. Unlike previous surveys that relied on self confessions, this time round, tests were administered to examine the respondents' mastery levels in literacy and numeracy. The competency levels in either literacy or numeracy were graded on a scale of one to five, with those who attained Levels Four and Five being considered as having the desirable levels of mastery of the skills. Those who attained Levels Three, Four and Five were considered to have attained the minimum mastery level.

Geographic coverage

The sample for the KNALS covered the population residing in households across the country

Analysis unit

population residing in households across the country

Universe

All members of the household selected aged 15 years and above were eligible for inclusion in the literacy survey. However, only one eligible member from each household was selected during the administration of individual questionnaire and test items.

Kind of data

Sample survey data [ssd]

Sampling procedure

A probability sample of about 18,000 households was selected for the survey to allow for separate estimates for key indicators for each of the provinces and districts in the country and for urban and rural areas separately. The survey utilised a two-stage sample design. The first stage involved selecting clusters from the national master sample maintained by KNBS.A total of 1,200 clusters comprising 377 urban and 823 rural were selected from this master frame. The second stage of selection involved the systematic sampling of households from a list of all households. Fifteen households were sampled from each of the sampled clusters. The household listing was updated recently while preparing for the Kenya Integrated and Household Budget Survey (KIHBS). Selection of clusters and households for the survey was done by KNBS experts in Nairobi and the sample lists were given to survey supervisors. All members of the household selected aged 15 years and above were eligible for inclusion in the literacy survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

These included: a household questionnaire, an individual questionnaire, a literacy assessment instrument and an institutional questionnaire.

Response rate

A total of 17,892 households were sampled of which 15,695 (4,781 urban and 10,914 rural) were occupied at the time of the survey and therefore eligible for interviews. Some of the sampled households could not be accessed because they were no longer inhabited thus contributing to a large extent to the recorded shortfall. Out of a total of 15,695households occupied at the time of the survey, 15,504 were successfully interviewed yielding a response rate of 98.8 per cent. Rural households realized a 99.7 per cent response rate compared to a response rate of 96.6 per cent in urban areas. Members of households aged 15 years and above were eligible for the individual interviews. Of the total 15,695 respondents identified, 15,473 were successfully interviewed, giving an individual response rate of 98.6 per cent. Response rates are higher in rural areas compared to the response rates in urban areas. Response rates for the literacy assessment test were lower than those for the individual interviews. Out of15, 695 eligible respondents, 14,761 took the literacy test giving a response rate of94.0 per cent (91.3 per cent urban and 95.3 per cent rural)