Chart and table of population level and growth rate for the Mombasa, Kenya metro area from 1950 to 2025.

This dataset shows the Mombasa population pyramid by Age group as reported by the Kenya National Bureau of statistics during the 2009 National census

As of 2043, Nairobi was the most populated city in Kenya, with more than 2.7 million people living in the capital. The city is also the only one in the country with a population exceeding one million. For instance, Mombasa, the second most populated, has nearly 800 thousand inhabitants. As of 2020, Kenya's population was estimated at over 53.7 million people.

Kenya had a Muslim population of roughly 5.6 million people, according to the last country census conducted in 2019. Nearly 50 percent of individuals adhering to Islam lived in the Northern-East counties of Mandera (856.5 thousand people), Garissa (815.8 thousand people), and Wajir (767.3 thousand people). Overall, around 10 percent of Kenya's population identified as Muslim.

Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. The survey is the 7th KDHS implemented in the country.

The primary objective of the 2022 KDHS is to provide up-to-date estimates of basic sociodemographic, nutrition and health indicators. Specifically, the 2022 KDHS collected information on: • Fertility levels and contraceptive prevalence • Childhood mortality • Maternal and child health • Early Childhood Development Index (ECDI) • Anthropometric measures for children, women, and men • Children’s nutrition • Woman’s dietary diversity • Knowledge and behaviour related to the transmission of HIV and other sexually transmitted diseases • Noncommunicable diseases and other health issues • Extent and pattern of gender-based violence • Female genital mutilation.

The information collected in the 2022 KDHS will assist policymakers and programme managers in monitoring, evaluating, and designing programmes and strategies for improving the health of Kenya’s population. The 2022 KDHS also provides indicators relevant to monitoring the Sustainable Development Goals (SDGs) for Kenya, as well as indicators relevant for monitoring national and subnational development agendas such as the Kenya Vision 2030, Medium Term Plans (MTPs), and County Integrated Development Plans (CIDPs).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-54, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates.

The 2022 KDHS employed a two-stage stratified sample design where in the first stage, 1,692 clusters were selected from the K-HMSF using the Equal Probability Selection Method (EPSEM). The clusters were selected independently in each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households served as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, after the household listing procedure, it was found that some clusters had fewer than 25 households; therefore, all households from these clusters were selected into the sample. This resulted in 42,022 households being sampled for the 2022 KDHS. Interviews were conducted only in the pre-selected households and clusters; no replacement of the preselected units was allowed during the survey data collection stages.

For further details on sample design, see APPENDIX A of the survey report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 KDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Kenya. In addition, a self-administered Fieldworker Questionnaire was used to collect information about the survey’s fieldworkers.

Cleaning operations

CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed with a mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. Programming of questionnaires into the Android application was done by ICF, while configuration of tablets was completed by KNBS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data.

Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and identify any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the KNBS and ICF central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the survey, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Kenya Demographic and Health Survey (2022 KDHS) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 KDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2022 KDHS is a SAS program. This program used the Taylor linearisation method for variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data

Abstract

The survey of the Pemba was an attempt to reach all households in Kenya with links to Pemba in Tanzania. It was conducted in the two counties of Kilifi and Kwale on the coast, north and south of Mombasa, respectively. According to information from village elders familiar with the Pemba community in Kenya, most of the Pemba population resides in these two counties. While there are some Pemba residents in Lamu, the security situation prevented data collection there. Further, a few Pemba are believed to live in the city of Mombasa and elsewhere in the country. But due to lack of further information, no data were collected in Mombasa or elsewhere.

The objectives of the full survey, conducted in August 2016, were: 1. To establish the number and characteristics of the Pemba living in Kenya, including their arrival period in Kenya, nationality and their problems; 2. To make recommendations for the issuance of the documentation that is required for those who apply for citizenshiop by registration

Geographic coverage

Kwale and Kilifi counties, Kenya.

Analysis unit

Households, individuals

Universe

The total number of households with links to Pemba in Tanzania, in Kilifi and Kwale counties.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A household mapping exercise was conducted in Kilifi and Kwale to identify Pemba households and to make it easier to locate them on the ground. The mapping was done from 4 to 12 August 2016 by a team from UNHCR Kenya office and KNBS.

The mapping in each village commenced with a visit to the chief's office, who put the team in touch with the village chair. The team explained the purpose of its visit to the village chair and began the mapping exercise. The importance of involving the chiefs and village chairpersons is that they are well connected, recognised and trusted by residents in their communities. The same procedure is followed by KNBS when they are mapping for sample surveys and censuses.

The team established physical boundaries of the area to be mapped, located the boundaries on the map and then identified and listed the Pemba households within the enumeration boundary. A Pemba household, in this context, is one identified by the informants as having at least one person with origins or links to Pemba. The links may include a person's spouse, parents or grandparents, who migrated to Kenya from Pemba or where a person has migrated from Pemba to Kenya.

The mapping team was followed by the village chair to the Pemba households, where the UNHCR and Haki Centre staff listed number of persons in each, while the KNBS staff marked the location of the household on the map. The entrances of identified Pemba households were marked in chalk with the letters HCR and a number starting at 001 to make it easier to find the houses during the enumeration. Since it seems to be generally well known where the Pemba live it was not considered stigmatising to mark their doors. During the feedback forums with the Pemba after the survey, there was no mention of stigmatization due to marking the door with chalk.

The maps were from the 2009 national housing and population census, purchased from KNBS. The team made lists with information about the location, number and size of each household. The mapping team visited 17 villages in Kilifi and Kwale (see Table 1 in Section 2.7). All villages visited were identified before the mapping exercise by key informants as locations being home to the Pemba of Kenya. The key informants were Pemba elders in different sub-counties previously identified for providing background information on the Pemba arrival and history in Kenya. In each sub-country, the chief, the assistant chief or the village chair also accompanied the team. In Kwale, 358 households were identified with 2,220 persons, and in Kilifi, 86 households with 558 persons.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed before the pilot survey and revised during and after the pilot survey, based on the experience gained. The pilot survey was used to test the questions and to check for inconsistences and misinterpretations due to unclear concepts and definitions. The testing process also revealed some important themes that had been left out.

The structure of the questionnaire was altered, including the order of the questions and the introductory pages, to facilitate administration of the questionnaire.

Finally, the questionnaire was translated into Swahili. Both the English and Swahili versions were used in the survey, even though the English version was preferred by almost all interviewers. The two versions of the questionnaire are attached in Annex 4 and 5. Enumerators used the English questionnaire to frame the questions in the local and less academic version of Swahili.

Cleaning operations

The data were imported into a Statistics Analysis Software (SAS) file and validated. Several errors were identified during the validation process, both on how the data had been recorded by the interviewers in the field and how the data had been entered by the clerks. There were particularly many errors in the entry of the variable “Relation to the household head” (Q.2). There were also many errors in the entry of the age of the household head, which was mostly due to errors in recording the right codes. A substantial amount of time was spent cleaning the data after the data had been entered, which included consulting many paper questionnaires. The quality of the survey data was significantly improved after the data entry revision. The data were analysed using both SAS software and Excel spreadsheets.

Response rate

The rate of non-response was low. Of the 452 households visited, visits to only 23 households can be categorised as non-response. A lot of effort was made to revisit non-responding households, using interviewers living nearby. Out of the 23 non-responsive households, 12 were not at home or there was no adult at home. There were 2 interrupted interviews, 7 refusals and 2 with no links to Pemba. In one household the respondent was not mentally stable enough to be interviewed, according to the enumerator.

Abstract

The Kilifi Health and Demographic Surveillance System (KHDSS), located on the Indian Ocean coast of Kenya, was established in 2000 as a record of births, pregnancies, migration events, deaths and cause of deaths and is maintained by 4-monthly household visits. The study area was selected to capture the majority of patients admitted to Kilifi District Hospital. The KHDSS has 284 000 residents and covers 891 km2 and the hospital admits 4400 paediatric patients and 3400 adult patients per year. At the hospital, morbidity events are linked in real time by a computer search of the population register. Linked surveillance was extended to KHDSS vaccine clinics in 2008.

KHDSS data have been used to define the incidence of hospital presentation with childhood infectious diseases (e.g. rotavirus diarrhoea, pneumococcal disease), to test the association between genetic risk factors (e.g. thalassaemia and sickle cell disease) and infectious diseases, to define the community prevalence of chronic diseases (e.g. epilepsy), to evaluate access to health care and to calculate the operational effectiveness of major public health interventions (e.g. conjugate Haemophilus influenzae type b vaccine). Rapport with residents is maintained through an active programme of community engagement. A system of collaborative engagement exists for sharing data on survival, morbidity, socio-economic status and vaccine coverage.

Geographic coverage

Kilifi District is situated 60km to the north of Mombasa on the Kenyan coast. It has an area of approximately 2,500 square kilometres and a population of 650,000. A flat coastal strip extends approximately 10km inland to low hills rising to an altitude of 250 metres

An area of 891 km2 was selected as the smallest number of administrative sublocations that collectively included the stated sublocation of residence of at least 80% of paediatric inpatients in the preceding 3 years (1998-2000). KDH is located in Kilifi town, 3° south of the equator and KHDSS extends up and down the coastal strip for 35 km from Kilifi. KDH is the only inpatient facility offering paediatric services in the KHDSS area. The local economy is based on subsistence farming of maize, cassava, cashew nuts and coconuts as well as goats and dairy cows. Two large agricultural estates, two research institutes and several tourist hotels contribute to local employment.

Analysis unit

Individual

Universe

All individuals in the HDSS area

Frequency of data collection

Three rounds in a year

Sampling procedure

No sampling, complete population surveyed

Sampling deviation

Not Applicable

Mode of data collection

Proxy Respondent [proxy]

Research instrument

1. Enumeration of persons

The Enumeration of People Data Entry Form has all names of residents within an homestead (Hm). This form bears the Enumeration Zone ( EZ) and Hm numbers, Hm name and name of homesteadhead. Also, it has details of each individual such as name, sex, ethinicity, pregnancy status, Kenya national identification number, Mother's national identification card number as well as the BU where an individual sleeps. A Fw uses this form to up-date the residence status of people.

1. Enumeration of buildings

This form has a list of all homesteads and existing buildings in each homestead (Hm). The form indicates: Hm name, Hm number and building units(BUs) in alphabet numbers. The geographical co-ordinates and materials used to make each building are also included. The census FWs update this form to show if the building unit still exists or if the BU has been demolished.

1. Listing of all registered Homesteads The Listing of All Registered Homesteads form has all active Hms in a sub-enumeration zone (sub- EZ) according to the previous census round. It is used to confirm number and specific HMs in a sub-EZ with the records of Building Structure (BS) Data Entry Form

2. In migrants

This form is used to record new people who have moved into an existing or a new homestead, or people who have been present but missed in the previous census rounds and intend to stay for the next three or more months.

5 .Births

This form is used to record all new born babies by resident mothers. In this form, all personal details of the baby are recorded and linked to those of the mother if she is a resident.

1. Pregnancy All resident women within the reproductive age bracket i.e., between 15 and 49 years, are usually flagged in the Enumeration Data Entry form to be asked about their pregnancy status.

2. Change person details Change Personal Details Data Entry form is designed to record changes of personal details.The Change Personal Details Data Entry form provides fields and codes used to effect such changes or corrections. Accuracy of the new value must be supported by evidence, preferrably documented evidence for example, a national identification card for date of birth.

8 .Change buildings details The change buildings details data entry form is designed to record changes relating to building materials, category and coordinates of a building unit as well as change of homestead names. Potential areas for changes and corrections include the Hm name, roof, wall, storey, longitudes, latitudes and elevation. Specific codes are used to describe the type of a building characteristic to be changed.

1. Maps KHDSS enumerators use EZ maps with a list of Hms that bears coresponding Hm numbers. Vital landmarks, roads and other features are displayed on a map to assist locate and identify Hms. These maps are up-dated every census round by the mapping team and enumerators. Global Positioning System (GPS) and Geographic Information System (GIS) technologies are used to develop and maintain a mapping database. ETrex garmin GPS receivers are used to collect spatial data and ArcGIS 10 is used to manipulate, edit, store and generate maps.

10.Verbal autopsy

11.Extra Questions

Cleaning operations

Manual editing A manual editor on daily basis checks completed tools for completeness and consistency. Those that have issues are returned to the responsible fieldworkers for correction and/or follow ups. Manual editor’s reports are instrumental in evaluating fieldworkers after every two weeks.

Complementary nature of KEMRI studies Kemri-Wellcome Trust Programme has a number of research studies being conducted in the same KHDSS census area. Some of these studies are nested within the KHDSS and have proved useful in improving data quality. For example, issues have been raised concerning some details such as date of birth and sex, which prompted verifications in the field and corrections.

The following processing checks are done during the ETL process.

1. If the first event is legal. Like the first event must beenumeration, birth or inmigration.
2. If the last event is legal. Like the last event must be end of observtion, death or outmigration.

3. If the transition events are legal. The list of legal transitions:

   Birth followed by death Birth followed by exit Birth followed by end of observation Birth followed by outmigration

   Death followed by none

   Entry followed by death Entry followed by exit Entry followed by end of observation Entry followed by outmigration Enumeration followed by death Enumeration followed by exit Enumeration followed by outmigration

   Exit followed by entry

   Inmigration followed by Death Inmigration followed by exit Inmigration followed by end of observation Inmigration followed by outmigration

   End of observation followed by none

   Outmigration followed by none Outmigration followed by enumeration Outmigration followed by inmigration

   The list of illegal transitions:

   Birth followed by none Birth followed by birth Birth followed by entry Birth followed by enumeration Birth followed by inmigration

   Death followed by birth Death followed by death Death followed by entry Death followed by enumeration Death followed by exit Death followed by inmigration Death followed by outmigration Death followed by end of observation

   Entry followed by none Entry followed by birth Entry followed by entry Entry followed by enumeration Entry followed by inmigration

   Enumeration followed by none Enumeration followed by birth Enumeration followed by entry Enumeration followed by enumeration Enumeration followed by inmigration

   Exit followed by birth Exit followed by death Exit followed by exit Exit followed by end of observation Exit followed by outmigration

   Inmigration followed by none Inmigration followed by birth Inmigration followed by entry Inmigration followed by enumeration Inmigration followed by inmigration

   End of observation followed by birth End of observation followed by death End of observation followed by entry End of observation followed by enumeration End of observation followed by exit End of observation followed by inmigration End of observation followed by end of observation End of observation followed by outmigration

   Outmigration followed by birth Outmigration followed by death Outmigration followed by exit Outmigration followed by end of observation Outmigration followed by outmigration

   List of edited events:

   Exit followed by none Exit followed by enumeration Exit followed by inmigration Outmigration followed by entry

Response

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]

Saliva-positive drug use based on various demographics sub-groups among commercial sex workers visiting a drop in centre in Mombasa, Kenya.

Kind of data

Sample survey data [ssd]

Sampling procedure

The household survey sample was drawn from the population residing in the five cities/urban centers. The most recent Population and Housing Census (2009) was used to identify clusters from which a representative sample of households for each city/urban center was drawn. A total of 13,140 households were selected for interviewing, ensuring that the sample was sufficient to allow analysis of the findings by each of the five intervention sites. Nairobi was intentionally oversampled (4,260 vs. 2,220 households) due its significantly larger size. With the exception of Machakos and Kakamega, the sample in each urban area was apportioned equally between formal and informal localities. A two-stage cluster sampling design was used for each urban area. Stage one involved selecting a random sample of clusters in each urban area. In Nairobi, 71 clusters were randomly selected in each of the formal and informal areas (domains), for a total of 142. In Mombasa and Kisumu, 37 clusters were randomly drawn from each domain, for a total 74 per urban area. In Machakos and Kakamega, 74 clusters were randomly selected per urban area. In the second stage, a random sample of 30 households was selected within each selected cluster. Interviews with women took place in all households selected. In Nairobi, Mombasa and Kisumu, half of the households (15) in each of the selected clusters were also selected to interview men. For each household selected, an interview with the head of the household (or his or her representative) was conducted. Each interview assessed household assets and environmental circumstances such as sanitation and housing materials, obtained a listing of usual residents of the household and asked about demographic characteristics of the head of the household. All women aged 15-49 years who were either usual residents or visitors present in the sampled households on the night prior to the survey were eligible for a detailed interview. In addition, in half of the sampled households in Nairobi, Mombasa and Kisumu, all men aged 15-59 years were asked to participate in a detailed interview. Interviews took place in a location where the respondent could be assured some level of privacy and were conducted by a same-sex interviewer using a paper-and-pencil questionnaire following the receipt of informed consent. Respondents were asked about demographic characteristics, for information on current and past FP method use, fertility desires and intentions, health-seeking behaviors for themselves and their children, how they pay for health care services, exposure to FP messages, and migration patterns, using a structured questionnaire. At mid-term and end line, the objective will be to find the same female respondents, so contact information was requested to permit extensive tracking procedures at each follow -up round of data collection. Repeated cross-sections of men (not necessarily the same ones) will be interviewed at mid and end line.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect baseline information-one for each of the households, one for women and one for men. In Machakos and Kakamega, only women were interviewed. Questionnaires were based on the questionnaires used by the Demographic and Health Survey program in Kenya but were modified and expanded by all in-country partners to reflect MLE and Tupange objectives. Questionnaires were translated from English into Kiswahili, Luhya, Kamba and Dholuo-the four most commonly spoken languages in the five cities. Final revisions were made to the questionnaires following extensive pre-testing and training of field staff. The household questionnaire was administered prior to the women's and men's questionnaires to facilitate the identification of eligible household members. The methodology and questionnaires were tested in Kisumu and Nairobi August 5-8, 2010, in clusters outside the planned intervention areas to minimize chances of contamination. Survey instruments were finalized based on feedback from and lessons learned during the pre-test.

Socio- demographic characteristics of commercial sex workers visiting a drop in centre in Mombasa, Kenya.

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

Abstract

This research of registered businesses with one to four employees was conducted in Kenya between April 2013 and January 2014, at the same time with Kenya Enterprise Survey 2013. Data from 360 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The objective of the survey was to obtain feedback from enterprises on the state of the private sector and constraints to its growth.

Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Central, Nyanza, Mombasa, Nairobi, and Nakuru regions

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ethiopia was selected using stratified random sampling. Two levels of stratification were used: firm sector and geographic region.

For industry stratification, the universe was divided into four manufacturing industries (food, textiles and garments, chemicals and plastics, other manufacturing) and two service sectors (retail and other services).

Regional stratification was defined in five regions: Central, Nyanza, Mombasa, Nairobi, and Nakuru.

2012 Census of Business Establishments of the Kenya National Bureau of Statistics was used as a sample frame for the survey of micro firms.

The enumerated establishments with less than five employees (micro establishments) were used as sample frame for the Kenya micro survey with the aim of obtaining interviews at 360 establishments.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 5.2% (39 out of 756) for micro firms.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

Abstract

National Health Accounts (NHA) is an internationally recognised method used to track expenditures in a health system for a specified period of time. Specifically, NHA details the flow of funding from financial sources (e.g., donors, Ministry of Finance), to financing agents (i.e., those who manage the funds, such as the Ministry of Health [MoH] or nongovernmental organisations [NGOs]), to providers (e.g., public and private facilities) and finally to end users (e.g., inpatient and outpatient care, pharmaceuticals).

Actual expenditures, rather than budget inputs, are used to fill a series of tables that show the flow of funding through the health sector. NHA also provides detailed breakdowns of disease-specific expenditures such as those for HIV/AIDS and reproductive health (RH). NHA is designed to be used as a policy tool to facilitate the implementation of health system goals.

The 2007 National Health Accounts study is the third round of NHA in Kenya. The first two estimations covered financial years (FYs) 1994/95 and 2001/02, respectively. This third round, undertaken in 2007 and covering 2005/06 was implemented by the MoH and Kenya National Bureau of Statistics (KNBS) with financial support from the United States Agency for International Development (USAID). USAID's Health Systems 20/20 Project, led by Abt Associates Inc., provided technical support. The findings will be used as a platform for informing policy decisions concerning resource allocation and will also be used by stakeholders in the sector.

Geographic coverage

National coverage

Analysis unit

Households and institutions

Universe

Household health expenditure covered all households in the country whereas the institutional surveys covered firms selected under the review.

Kind of data

Administrative records data [adm]

Sampling procedure

Kenya is divided into eight administrative provinces. The provinces are in turn subdivided into 70 districts. Each district is subdivided into divisions while the divisions are split into locations and finally each location into sublocations.

During the 1999 population census, each sublocation was subdivided into smaller units called enumeration areas (EAs). Kenya has about 62,000 EAs. The EAs provided census information on households and population. This information was used in the design of the National Sample Survey Evaluation Programme (NASSEP) IV master sample with 1,800 selected EAs.

The cartographic records for each EA in the master sample were updated in the field, one year preceding the NHA survey. The 1,800 clusters were distributed into 540 urban and 1,260 rural clusters.

The province provided a natural stratification of the population. The six major urban centres Nairobi, Mombasa, Kisumu, Nakuru, Eldoret, and Thika were further substratified into five socioeconomic classes based on incomes to circumvent the extensive socioeconomic diversity inherent in them as follows: upper, lower upper, middle, lower middle and lower; this improved the precision of estimates due to reduced sampling variation.

It was estimated that 8,844 households would be sufficient to provide estimates both at provincial and national levels as well as disaggregation to urban and rural components of the country. This sample was to yield 6,060 interviews in the rural and 2,784 in the urban clusters (Table 2.2). This was to be achieved through coverage of 737 clusters (505 rural and 232 urban clusters).

Twelve households were to be covered in each cluster. The method of proportional allocation was used in assigning the sample households to the provinces and districts. The count of the households was transformed to the square root of the census households to avoid under-representing the smaller districts.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

To expedite data entry and monitor data quality, all completed questionnaires were sent to a data management unit at the MoH Planning Department, which was the designated secretariat for the activity.

This approach helped in standardizing and speeding up data entry and reducing errors. Questionnaires were also checked for completeness before entry.

Data were entered in a Census and Survey Processing System (CSPro) by a team of data entry clerks under the supervision of data entry supervisors. The data were reentered for validation.

The data files were then converted into SPSS, the software used for data analysis. Much of the analysis was replicated using Stata, to confirm that weighted estimates were correct. Stata was also used to perform analysis that could not be undertaken using SPSS.

Response rate

A total of 8,844 households were selected for the survey. Of these, 8,453 were successfully interviewed, giving a response rate of 95.6 percent, and the survey reported observations on 38,235 individuals living in these households.