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

The national gross income per capita in Kenya stood at ***** U.S. dollars in 2023. Between 1962 and 2023, the national gross income rose by ***** U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend.

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

Key information about Kenya Monthly Earnings

• Kenya Monthly Earnings stood at 590 USD in Jun 2023, compared with the previous figure of 639 USD in Jun 2022
• Kenya Monthly Earnings data is updated yearly, available from Jun 1997 to Jun 2023, with an average number of 420 USD
• The data reached the an all-time high of 651 USD in Jun 2020 and a record low of 157 USD in Jun 1997

CEIC calculates Monthly Earnings from annual Average Wage Earnings divided by 12 and converts it into USD. The Kenya National Bureau of Statistics provides Average Wage Earnings in local currency. The Central Bank of Kenya average market exchange rate is used for currency conversions. Monthly Earnings are in annual frequency, ending in June of each year. Monthly Earnings prior to 2008 are based on ISIC Rev. 2.


Further information about Kenya Monthly Earnings

• In the latest reports, Kenya Population reached 49 million people in Dec 2021
• Unemployment Rate of Kenya increased to 3 % in Dec 2020
• The country's Labour Force Participation Rate increased to 74 % in Dec 2023

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

Kenya Consumer Price Index (CPI): Nairobi: Middle Income Group data was reported at 165.960 Feb2009=100 in Sep 2018. This records an increase from the previous number of 164.100 Feb2009=100 for Aug 2018. Kenya Consumer Price Index (CPI): Nairobi: Middle Income Group data is updated monthly, averaging 131.010 Feb2009=100 from Feb 2009 (Median) to Sep 2018, with 116 observations. The data reached an all-time high of 165.960 Feb2009=100 in Sep 2018 and a record low of 100.000 Feb2009=100 in Feb 2009. Kenya Consumer Price Index (CPI): Nairobi: Middle Income Group 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.I002: Consumer Price Index: Feb2009=100.

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

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

Kenya Consumer Price Index (CPI): Nairobi: Middle and Upper Income Group: Others data was reported at 184.960 Oct1997=100 in Jun 2009. This records an increase from the previous number of 184.250 Oct1997=100 for May 2009. Kenya Consumer Price Index (CPI): Nairobi: Middle and Upper Income Group: Others data is updated monthly, averaging 136.940 Oct1997=100 from Jan 2000 (Median) to Jun 2009, with 114 observations. The data reached an all-time high of 184.960 Oct1997=100 in Jun 2009 and a record low of 113.100 Oct1997=100 in Jan 2000. Kenya Consumer Price Index (CPI): Nairobi: Middle and Upper Income Group: Others 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.I004: Consumer Price Index: Oct1997=100.

Abstract

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

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

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

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

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

Cleaning operations

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

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

Smallholder farming in sub-Saharan Africa keeps many rural households trapped in a cycle of poor productivity and low incomes. Two options to reach a decent income include intensification of production and expansion of farm areas per household. In this study, we explore what is a “viable farm size,” i.e., the farm area that is required to attain a “living income,” which sustains a nutritious diet, housing, education and health care. We used survey data from three contrasting sites in the East African highlands—Nyando (Kenya), Rakai (Uganda), and Lushoto (Tanzania) to explore viable farm sizes in six scenarios. Starting from the baseline cropping system, we built scenarios by incrementally including intensified and re-configured cropping systems, income from livestock and off-farm sources. In the most conservative scenario (baseline cropping patterns and yields, minus basic input costs), viable farm areas were 3.6, 2.4, and 2.1 ha, for Nyando, Rakai, and Lushoto, respectively—whereas current median farm areas were just 0.8, 1.8, and 0.8 ha. Given the skewed distribution of current farm areas, only few of the households in the study sites (0, 27, and 4% for Nyando, Rakai, and Lushoto, respectively) were able to attain a living income. Raising baseline yields to 50% of the water-limited yields strongly reduced the land area needed to achieve a viable farm size, and thereby enabled 92% of the households in Rakai and 70% of the households in Lushoto to attain a living income on their existing farm areas. By contrast, intensification of crop production alone was insufficient in Nyando, although including income from livestock enabled the majority of households (73%) to attain a living income with current farm areas. These scenarios show that increasing farm area and/or intensifying production is required for smallholder farmers to attain a living income from farming. Obviously such changes would require considerable capital and labor investment, as well as land reform and alternative off-farm employment options for those who exit farming.

In 2023, around 20 million people were employed in Kenya, this was an increase of some 900,000 individuals from the previous year. The employees belonged mostly to the informal sector. Roughly 16.7 million worked in informal conditions, whereas close to 3.3 million were employed in the formal sector. The informal sector constitutes an important part of the Kenyan economy, being related to employment creation, production, and income generation. Trends in the informal labor market and economic sectors The largest employment activities for people in the informal sector were in wholesale and retail trade, as well as hotels and restaurants, with 9.32 million people employed in these areas in 2022. Moreover, the hospitality sector in the country was the fastest-growing economic sector with a quarterly growth rate of 21.5 percent of the GDP. However, the largest economic sector as an added value to the GDP was the agricultural sector. Navigating unemployment challenges in Kenya Kenya’s unemployment rate is following a decreasing trend, which dropped below five percent at the end of 2022. However, unemployment among the youth in the same period was fairly high at 13.4 percent. The cohort with the highest level of unemployment was among the age group between 20 to 24 years old, with an unemployment rate of over 15 percent.

The agricultural sector accounted for 53.8 percent of the total employment in Kenya as of 2020. Despite being the industry with a higher employment level, the services sector is growing steadily as well, reaching a share of 38.7 percent in 2020. A similar tendency was registered in the manufacturing sector, which accounted for 7.4 percent of the employment in the same period.

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

The 2007 Kenya National Survey for Persons with Disabilities (KNSPWD) was a national sample survey - the first of its kind to be conducted in Kenya - designed to provide up-to-date information for planning, monitoring and evaluating the various activities, programmes and projects intended to improve the wellbeing of persons with disabilities. The survey covered more than 14,000 households in a total of 600 clusters (436 rural and 164 urban).

The survey interviewed persons with disabilities of all ages in sampled areas to get estimates of their numbers; distribution; and demographic, socio-economic and cultural characteristics. The survey also sought to know the nature, types and causes of disabilities; coping mechanisms; nature of services available to them; and community perceptions and attitudes towards PWDs.

The survey was undertaken by the National Coordinating Agency for Population and Development (NCAPD) in collaboration with the Kenya National Bureau of Statistics (KNBS); Ministry of Gender, Sports, Culture and Social Services (MGSCSS); Ministry of Health (MOH); and the Ministry of Education Science and Technology (MOEST). Other participants were United Disabled Persons of Kenya (UDPK); Kenya Programmes of Disabled Persons (KPDP); Association for the Physically Disabled of Kenya (ADPK); and Africa Mental Health Foundation (AMHF). Technical and financial support came from the Department for International Development (DFID), the World Bank and the United States Agency for International Development (USAID) under the Statistical Capacity Building Project (STATCAP) project. The United Nations Population Fund (UNFPA) provided support for the design of survey instruments.

Geographic coverage

National

Analysis unit

Households and individuals

Universe

The survey covered all de jure household members (usual residents) and all women aged between 12-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the survey intended to estimate the number of PWDs, it was realized that a significant proportion of these individuals reside in institutions, which are not part of the household sampling frame. However, a comprehensive list of institutions that existed did not form sufficient sampling frame for estimation of numbers of institution-based PWDs for the entire country. A mechanism had to be devised for incorporating these persons into the survey to supplement the data derived from the household-based survey.

The targeted survey population for the institutional based survey was defined as all people living in homes and occupying long-stay beds in public or private hospitals; or living in long-stay residential units for people with an intellectual, psychiatric/physical disability, vision or hearing impairments, or with multiple disabilities. The following types of institutions were covered: · Hospitals (acute care, chronic care hospitals, nursing homes) · Psychiatric institutions · Treatment centres for persons with physical disabilities · Residential special schools · Private and non-private group homes · Private and non-private children's homes · Orphanages · Private and non-private residences for senior citizens (Mji wa wazee) · Other residential institutions with people with disabilities

The sampling frame compiled for the institutional survey comprised all institutions indicated above. The frame included the name of the institution, type, number of individuals, location and type of disability. The frame was compiled from various sources, including MOH, MOEST, MSGSS and various organizations dealing with disabilities, among others.

In order to achieve representation, the institutions were first stratified according to location (provinces) and then by nature of disability. The institutions were further classified into two broad categories depending on nature and size (number of PWDs). All key institutions were sampled with certainty (that is, all selected in the sample). The remaining institutions within a province were arranged and serially listed by disability type and a systematic random sampling procedure used to select the sample.

A sample size of 102 institutions catering for different population sizes of PWDs was covered. Once the institutions were sampled, the next exercise involved selection of individuals for the survey. Five bands were created depending on the size of the sampled institution. The bands were: less than or equal to 30; 31-50; 51-100; 101-200; and above 200. A listing of all residents was compiled during the day of the interview and a systematic random sample drawn. Five respondents were selected from each of the sampled institutions with up to 30 PWDs, eight from those having 31-50, and ten from those having 51-100. For institutions having 100-200 PWDs, 15 were chosen, and from those having 201 and above, 20.

The KNSPWD household sample was constructed to allow for estimation of key indicators at the provincial level as well as of the urban and rural components separately. The survey utilized a multi-stage cluster sample design and was based on a master sample frame developed and maintained by KNBS. The master sampling frame is the National Sample Survey and Evaluation Programme (NASSEP) IV. It has 1,800 clusters (data collection area points) that were developed with probability proportional to size (PPS) from the enumeration areas (EAs) delineated during the 1999 Kenya Population and Housing Census. Of the 1,800 clusters, 1,260 are rural based and the other 540 are located in urban areas.

In the frame, the first stage involved selecting the census EAs using PPS and developing them into clusters. The process involved quick counting of the selected EA and dividing into segments depending on the measure of size (MOS). The MOS was defined as an average of 100 households, with lower and upper bounds of 50 and 149 households, respectively. The EAs that were segmented had only one segment selected randomly to form a cluster. The EAs that had fewer than 50 households were merged prior to the selection process. During the creation of NASSEP IV, other than each of the 69 districts being a stratum, the six major urban areas (Nairobi, Mombasa, Kisumu, Nakuru, Eldoret and Thika) were further stratified into five income classes: upper, lower upper, middle, lower middle and lower. The aim was to ensure that different social classes within these areas were well represented in any time sample that was drawn.

The second sampling stage involved selecting clusters for the KNSPWD from all the clusters in the NASSEP IV master sampling frame. A total of 600 clusters (436 rural and 164 urban) was sampled from all the districts in the country with boundaries as defined in the 1999 Kenya population and housing census. The third stage of selection involved systematically sampling 25 households from each cluster, hence producing 15,000 households in total.

Mt. Elgon district was excluded from the survey because of persistent insecurity in the area. The effect of exclusion of the district in the sample is minimal since it contributes 0.5% of the population according to 1999 census.

Mode of data collection

Face-to-face [f2f]

Research instrument

Models of questionnaires and survey instruments developed by the World Health Organization (WHO), Washington Group Consortium and organizations in other countries were tailored to the Kenyan context. The purpose was not only to make the instruments responsive to the country situation, but also to ensure that the results would be comparable to those from other countries.

With input from a wide range of people who have worked in the area of disability, and who have conducted national surveys, a workshop was held to develop and adopt the following instruments for Kenya:

· Household questionnaire: Designed to collect background information at the household level for all the usual members as well as any visitors who slept in the household the night before the interview. This questionnaire was also used to screen PWDs by type to identify those who were eligible for the individual disability questionnaire. This instrument was administered to the most knowledgeable person in the household on the day of the visit. · Individual questionnaire: Administered to any PWDs who had been identified using the household questionnaire. The questionnaire included the following key sections: activity limitation; environmental factors; situation analysis; support services; education; employment and income; immediate surroundings; assistive devices; attitudes towards disability; and health and general well-being · Reproductive health questionnaire: Administered to all eligible females aged 12 to 49 who were living with any form of disability. It collected information on reproductive health. · Institutional questionnaire: Administered to the heads of the various categories of institutions serving PWDs. Randomly selected PWDs in these institutions were interviewed using the individual questionnaire. · Focus group discussion guide: Used to collect qualitative information from a group of 6-10 members within each of the sampled clusters. The groups comprised PWDs, community leaders, service providers, opinion leaders and teachers. The focus group discussions collected information on knowledge, attitudes and beliefs of community members about PWDs and the different services available for PWDs in the different communities. Likewise, focus group discussions were used to collect qualitative information about problems faced by PWDs, their coping mechanisms and their access to essential basic services, as well as an overview of community perceptions of PWDs and views on how best to

Strong swings in Kenya's economic performance and slowing down in overall growth reflect the fundamental structural problems of the economy. Kenya now faces the necessity of making difficult and far-reaching policy decisions to remedy these structural weaknesses in the context of a generally unfavorable world economic environment. Efforts must be made to revitalize the agricultural sector, restructure the industrial sector to make it more internationally competitive, design government expenditure plans which are consistent with resource availablities, support growth of the productive sectors and contribute to meeting basic needs, and reduce the rate of population growth. The Fourth Plan makes these efforts, but the program of structural adjustment must be executed first. The structural adjustment program should result in a more equitable pattern of income distribution and a manageable balance of payments situation. A second phase of the program is under consideration. Fundamental restructuring of the pattern of development will be difficult but is essential. Agricultural issues are discussed in the annex.

Apnea of prematurity (AOP) is a common complication among preterm infants (<37 weeks gestation), globally. However, access to caffeine citrate (CC) that is a proven safe and effective treatment in high income countries is largely unavailable in low-and-middle income countries, where most preterm infants are born. Therefore, the overall aim of this study was to describe the demand, policies, and supply factors affecting the availability and clinical use of CC in LMICs. A mixed methods approach was used to collect data from diverse settings in LMICs including Ethiopia, Kenya, Nigeria, South Africa, and India. Qualitative semi-structured interviews and focus group discussions were conducted with different health care providers, policymakers, and stakeholders from industry. Additional data was collected using standard questionnaires. A thematic framework approach was used to analyze the qualitative data and descriptive statistics were used to summarize the quantitative data. The findings indicate that there is variation in in-country policies on the use of CC in the prevention and treatment of AOP and its availability across the LMICs. As a result, the knowledge and experience of using CC also varied with clinicians on Ethiopia having no experience of using it while those in India have greater knowledge and experience of using it. The in turn influenced the demand and our findings show that only 29% of eligible preterm infants are receiving CC in these countries. There is an urgent need to address the multilevel barriers to accessing CC for management of AOP in Africa. These include cost, lack of national policies and therefore lack of demand stemming from its clinical equivalency with aminophylline. Practical ways to reduce the cost of CC in LMICs could potentially increase its availability and use. Methods Study design, setting, population, sampling We conducted a landscape evaluation involving stakeholders in Africa (Ethiopia, Kenya, Nigeria, South Africa) and South Asia (India – five states of Delhi; Bihar, Uttar Pradesh, Telangana and Madhya Pradesh) on CC availability and use from 1 July 2022 to 31 December 2022. We used a mixed methods study design to understand the complexity of CC availability and use across these LMICs. We selected a geographically and culturally diverse countries with high annual preterm births (~200,000). The selection of stakeholders within each focus country was by convenience and/or purposive sampling. We selected health facilities providing care for preterm infants and were able to provide the data required to achieve the study’s objectives. Proximity and ease of data collection was also factored into selection by research teams. Data collection Qualitative The research teams conducted key informant interviews and focus group discussions (FGD’s) with stakeholders in newborn health. The interviews with healthcare providers sought to explore their experience of using CC as a treatment for AOP. Interviews with WHO and Ministry of Health officials sought to understand current global and national health policies and CC’s inclusion in the essential drug list for using CC to treat AOP. Interviews with major drug suppliers and distributors of CC aimed to determine the current local market pricing of CC and its alternatives within and between countries. Also, to evaluate the factors determining the end-customer price of CC. The available average end-customer price per country was used to determine the daily cost of managing AOP for aminophylline and CC. We compared the average daily cost between aminophylline and cc for both public and private hospitals in each country. The dosing regimen for CC was a loading dose of 20 mg/kg/dose and a daily maintenance dose of between 5 to 10 mg/kg/day. The dosing regimen for aminophylline was a loading dose of 6 mg/kg administered intravenously (IV), followed by a maintenance dose of 2.5 mg/kg/dose/IV administered every 8 hours. Interviews and FGD’s were done in person or virtually over video or audio teleconferencing based on the preferences of the participants. All interviews were conducted in English. teams were situated in each country of focus and had previous training and experience conducting qualitative interviews and FGDs and in qualitative data analysis. The interviews and FGDs were semi structured using guide with a set of open-ended questions, in a set order and allowing for in-depth insights into the subject area. These guides were pilot tested across the 3 countries prior to data collection. Quantitative Additional interviews were conducted using standard questionnaires and had been piloted and refined in these settings prior to being used for data collection.The research team surveyed 107 providers: 20 from Ethiopia, 18 from India, 23 from Kenya, 28 from Nigeria, and 18 from South Africa. Providers were from 45 private or public health facilities across the five study countries. Of these, 12 (27%) were primary or secondary public, 7 (16%) were primary or secondary private, 25 (56%) were tertiary public, and 1 (2%) tertiary private Demand forecast for caffeine citrate. A demand forecast was conducted to determine the amount of CC needed per country. Using data from demographic health survey data from each country, we estimated the proportion of infants who would be eligible for CC treatment. Given AOP risk can be as high as 80% in preterm infants with birthweight ≤1500g (very low birth weight (VLBW)), we estimated that all VLBW infants met eligibility criteria for treatment with CC. We limited this forecast to public facilities where limited government funding constrains drug availability. We applied country-specific policies and assumptions to determine the percentage of VLBW infants who received or had a missed opportunity for CC treatment. These assumptions included, availability of CC, VLBW infants born in secondary facilities will be transferred to a tertiary center capable of providing AOP treat; some transfers will be unsuccessful and even when successful, AOP treatment will be unavailable. Data management and analysis All interviews were transcribed verbatim by an experienced transcriber. Authors reviewed the interview transcripts for errors. A coding framework was generated, and an emergent thematic analysis approach was used to analyze the data, to identify patterns and themes. Descriptive statistics were used to summarize the quantitative data.

Abstract

This was a prospective population based study comparing education outcomes and education services among slum and non-slum settlements in Nairobi. The study was being conducted in two slum settlements of Korogocho and Viwandani, and two non-slum settlements of Jericho and Harambee. Korogocho is situated within Korogocho administrative location, Viwandani in Viwandani administrative location, and Jericho and Harambee in Makadara administrative location. The study identified households who had children aged between 5 and 19 years old and living within the boundaries of the study sites. The households were followed untl 2010. New households fitting the inclusion criteria were enrolled each year, while the upper age limit increased by a single year for each additional year. By 2010, the study wa following about 16400 individuals aged bewteen 5 and 24 years. The study targetted also schools where the idenfied pupils attended. Several questionnaires were administered and included schooling history to capture schooling information for the current schooling years and 5 years backwards. Afterwards, an update questionnaire was introduced to capture prospective schooling information. The second questionnaire captured information from the parents on their perceptions towards free primary education and support for their children schooling. In addition, individuals who were 12 years and above responded to a behvaior questionnaires. In the schools, a school characteristics questionniare was administred.

The objectives of the ERP I were:

· What is the impact of free primary education on school enrolment patterns and dropout rates among urban slum and non-slum children?

· What factors are associated with school participation (enrolment, attendance, repetition, performance and dropout) among urban slum and non-slum children?

· What are the (causal) linkages between school participation and the onset and extent of indulgence in risky behaviors in children?

Geographic coverage

Two slums of Nairobi (Korogocho and Viwandani) and two non-slums of Nairobi (Harambee and Jericho)

Analysis unit

1. HOUSEHOLDS

2. INDIVIDUALS WITHIN THE AGE OF STUDY. AVERAGE OF 2.7 INDIVIDIDUALS PER HOUSEHOLD

3. SCHOOLS

Universe

The data covers individuals aged 5 and 19 years in 2005 who were by 2010 aged between 5 and 24 years. It also covered primary schools within Nairobi, where majority of the pupils were reported to be enrolled.

Sampling procedure

Selection of study sites

Using the Kenya 1999 housing and population census, and the 1997 Welfare Monitoring Survey III collected by the Central Bureau of Statistics (Government of Kenya 2000), all the 49 locations of Nairobi were ranked into five groups according to the percentage of the population below the poverty line. NUHDSS slum locations of both Viwandani and Korogocho were in the poorest percentile (ranked 48th and 44th, respectively). Those in the richest quintile were excluded because most children in the wealthy communities go to formal private schools which are scattered all over the city. The majority of the locations in the 4th quintile have a mixture of formal and informal settlement features. In order to have a formal residential area in the middle income category where most children are likely to go to public schools, three locations were explored in the second quintile (i.e. the second richest set of locations). During discussion of the project's design, participants, who were mainly Kenyans with comprehensive knowledge of the areas, recommended carrying out the study in Bahati, as opposed to Umoja or Kariokor, the other locations in the second quintile. APHRC researchers visited the three communities to assess their suitability as a comparison site for the project. Bahati (Harambee and Jericho) was chosen because it is relatively stable, is mostly inhabited by middle-income parents with school-going children who mostly go to public schools in the area. In Bahati, 26% of the population lived below the poverty line while in Korogocho and Viwandani, the corresponding percentages were 60% and 76%, respectively. Having Bahati as the comparison area was therefore to enable the study to assess factors affecting schooling among some relatively poor households that did not reside in slum settlements.

Sampling of households

All households included in the NUHDSS database and with individuals aged between 5 and 19 years in 2005 were included in the study. thereafter they were followed until 2010. In between those who entered into the system or reach the aged of 5 years were also included and followed prospectively.

Sampling of schools

Schools were the pupils under serveillance were reported to be enrolled formed part of the sampling frame for schools. The inclusion criteria for the schools survey was that the school should be located within Nairobi and that it should have a minimum of five pupils in oyr household survey enrolled in it.

Mode of data collection

Face-to-face [f2f]; FGD

Research instrument

The Questionnaires

The questionnaires hereafter are referred to as modules. There are several modules since the beginning of the education project:

1) Household module

2) Primary school module

3) Parent guardian module

4) Education child update (schooling history) module

5) Child school status update questionnaire

6) Education child update module

7) Primary school questionnaire

8) Child Behavior Questionnaire

9) Supplementary primary school module

The household module

The household module served as a starting point of the interview. It identified the respondent's household. The module was administered to the owner of the household or any other adult who was credible and who usually lives in the household. It served to identify individual households and its occupants and thus served as a basis for the other modules to be administered. It contains a complete list of the household members and some basic information on age, sex, parental survivorship, education, and labor force participation.

For each of the household, information on water source and trash disposal methods, type of toilet facility used by the household, materials for the house (roof, floor, and walls), fuel for lighting and cooking, and ownership of assets was collected.

The Primary schools module:

This module serves to generate indicators on schooling participation. The module is meant for headmasters or teachers knowledgeable enough to provide information on the school. It comprises of the following sections:

Background

This serves to identify the name of the school, the date and time of the interview and the location of the school.

Particulars of respondent

This section of the module collected information on the respondent and establishes the respondent's full names, position held by respondent in the school and how long the respondent has been working in the school.

School background

This section sets to establish whether the school is registered, if registered under which ministry the school is registered(ministry of culture or ministry of education), its registration number, the type of curriculum followed by the school and the management of the school. The understanding is that the name of the school being used maybe different from the one under which the school is registered. The information is important especially if we are to link the school to the Ministry of Education or Ministry of Culture records. This information will most probably be obtained from the school records (if they exist).

School facilities

This section sets to collect information on the school facilities, such as textbooks provided by the school to the pupils in each grade they include Mathematics, Science ,Kiswahili and English, a library, science lab for pupils use ,a playground for outdoor sports pupils use and inventory of all school's equipment e.g. desks. For purposes of this project, a library is considered to be a room which has reference books where pupils can go to read.

This section also offers information on the school ownership of a toilet facility for use by the pupils and whether there are separate toilets provided for boys and girls. It also offers information on the school's water source and the availability of electricity in the school.

In addition the module in this section probes the respondent on whether the inspector of schools has visited the school in the current schooling year and requests for the date and year of visit. The inspector of school is from the City Council education department or from the Ministry of Education.

Enrollment for the current school year

Enrollment refers to children who are current registered for specific grades/classes in the school.

The objective of this section is to provide information on the number of boys and girls in each of the streams in the school in the current school year. It also sets to establish whether there were any pupils who were turned away during enrollment in the current year and the approximate number of pupils who were turned away from enrolling in the school.

Expenditure on schooling

The module here asks questions on the school fee structure, it seeks to establish whether the pupils are required to pay fees and how much (Kenya shillings) they pay for the following: tuition, construction fund, Parents Teachers Association (PTA), extra classes, examination fees, school meals and other items.

This was required to be filled for all grades in the school and whether paid annually, termly, monthly and weekly.

It also provides information on whether the pupils are required to wear uniform in order to be allowed in class and the source of purchase of the

Kenya's Gross Domestic Product (GDP) grew by 4.6 percent in the second quarter of 2024. Among sectors, accommodation and food services had the strongest performance, with quarterly growth of 26.6 percent. Financial and insurance sectors followed, registering a 5.1 percent growth rate. On the other hand, the construction sector had a negative growth rate of -2.9 percent.