Key information about Kenya Real GDP Growth

• The Gross Domestic Product (GDP) in Kenya expanded 5.2 % YoY in Sep 2022, following a growth of 5.3 % in the previous quarter.
• Real GDP Growth YoY data in Kenya is updated quarterly, available from Mar 2010 to Sep 2022, with an average rate of 5.4 %.
• The data reached an all-time high of 13.6 % in Mar 2011 and a record low of -4.1 % in Jun 2020.

CEIC calculates quarterly Real GDP Growth from quarterly Real GDP. The Kenya National Bureau of Statistics provides Real GDP in local currency, at 2016 prices. Real GDP Growth prior to Q1 2017 is calculated from Real GDP at 2009 prices.


Related information about Kenya Real GDP Growth

• In the latest reports, Nominal GDP of Kenya reached 26.9 USD bn in Sep 2022.
• Its GDP deflator (implicit price deflator) increased 4.0 % in Sep 2022.
• GDP Per Capita in Kenya reached 2,236.0 USD in Dec 2021.
• Its Gross Savings Rate was measured at 13.3 % in Dec 2021.
• For Nominal GDP contributions, Investment accounted for 20.3 % in Dec 2021.
• Public Consumption accounted for 12.1 % in Dec 2021.
• Private Consumption accounted for 74.6 % in Dec 2021.

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

Economic Survey Data Tables in Excel format showing specific indicators for the years 2018-2022. Money shown in KSh Million. 2022 figures are provisional. The Economic Survey report is an annual publication prepared by the Kenya National Bureau of Statistics that provides socio-economic information covering a five-year period. Statistics presented in Economic Survey reports are produced in line with internationally sound and scientific methods that are anchored on the fundamental principles of producing official statistics.

The Economic Survey report is an annual publication prepared by the Kenya National Bureau of Statistics that provides socio-economic information covering a five-year period. Statistics presented in Economic Survey reports are produced in line with internationally sound and scientific methods that are anchored on the fundamental principles of producing official statistics.

The Kenya National Bureau of Statistics has released the 2019 Economic Survey report which highlights the country’s economic performance for the year 2018. The report shows the economy has expanded by 6.3%, compared to 4.9% in 2017.

The Survey has other information spanning Kenya's economy such as employment statistics, public finance, agriculture, education, energy, manufacturing and more.

Key information about Kenya Private Consumption: % of GDP

• Kenya Private Consumption accounted for 76.2 % of its Nominal GDP in Dec 2023, compared with a ratio of 74.9 % in the previous year.
• Kenya Private Consumption contribution to Nominal GDP ratio is updated yearly, available from Dec 1969 to Dec 2023, with an average share of 77.0 %.
• The data reached an all-time high of 87.6 % in Dec 1995 and a record low of 67.1 % in Dec 2010.

CEIC calculates Private Consumption as % of Nominal GDP from annual Private Consumption Expenditure and annual Nominal GDP. The Kenya National Bureau of Statistics provides Private Consumption Expenditure in local currency and Nominal GDP in local currency. Private Consumption as % of Nominal GDP prior to 2006 is sourced from the International Monetary Fund.


Related information about Kenya Private Consumption: % of GDP

• In the latest reports, Kenya GDP expanded 5.2 % YoY in Sep 2022.
• Its Nominal GDP reached 26.9 USD bn in Sep 2022.
• Kenya GDP Per Capita reached 2,236.0 USD in Dec 2021.
• Its Gross Savings Rate was measured at 11.9 % in Dec 2023.

The Economic Survey report is an annual publication prepared by the Kenya National Bureau of Statistics that provides socio-economic information covering a five-year period. Statistics presented in Economic Survey reports are produced in line with internationally sound and scientific methods that are anchored on the fundamental principles of producing official statistics.

Abstract

The Census of Industrial Production (CIP) and construction was carried out from July to October 2018. The census aimed at providing key statistics to be used for development of the structure of the industrial sector; rebasing of the producer price index, compilation of Supply and Use tables, Input-Output tables, Value Added and other National Accounts statistics and industrial exports and imports for the balance of payments statistics. Whereas, the last CIP was conducted in 2010 with 2009 as the reference year, the 2018 CIP reference year was 2017.

Data collected in the census included general particulars of establishments/enterprises, employment, labour costs, income, expenditure, goods and materials consumed, goods produced, fixed assets, imports, exports, waste management, ICT usage and the general business environment. The Census therefore sought to: i. Provide information for mining and quarrying, manufacturing, electricity and gas supply, water and sewerage, and construction sectors to be used in revision and rebasing of the National Accounts. ii. Form the basis for revisions and rebasing of key indices such as the Index of Industrial Production (IIP) and the Production Price Index (PPI). iii. Collect data to be used to update the sampling register for the annual Survey of Industrial Production, Monthly Survey of Industrial Production and rebasing of the Construction Input Price Index (CIPI). iv. Provide data for computing baseline export/import price indices for the industrial sector. v. Provide updated information to monitor the growth and the gains in fish processing, agro-Processing, leather and textiles sub-sectors which have been put on focus in realization of the Government increase of manufacturing contribution to GDP which is one of its big four action plans.

Specifically, the CIP 2018 set to;

i. Provide benchmark data to update economic structure of the industrial sector from the CIP 2010 level, To provide data on Industrial Structure; Update the current frame which was last developed in 2009; Improve quality of industrial data through broadening of statistical database to cater for changes that have taken place since 2009. ii. Establish an industrial database and update the register which will monitor and reflect changes in the structure of industry and provide a frame for industrial surveys. iii. Provide data for use in compilation of Supply and Use Tables, input-output tables and other national accounts statistics. iv. Provide data for industrial output, and capacity utilization. v. Provide measures of key statistics and the economic structure for the construction sector Provide data for computing baseline export/import price indices for the industrial sector. vi. Provide data for rebasing the Producer Price Index (PPI), the Index of Industrial Production (IIP) and the Construction Input Price Indices (CIPI) vii. Provide a basis for assessing trends in the economy and the contribution of industrial activities to the national economy.

Geographic coverage

National coverage

Analysis unit

Formal establishments that were involved in Industrial production activities across all the 47 counties

Abstract

In 1999, the International Center for Economic Growth (ICEG) organised a national baseline survey of micro and small enterprises in Kenya, in collaboration with the Central Bureau of Statistics (CBS) and K-Rep Holdings Limited. The survey was conducted from March 1999 through October 1999. The primary objectives of the survey were two-fold: First, to update and expand on the information generated in the 1993 and 1995 surveys. And second, to improve the reliability of estimates on the MSE sectors contribution to the Kenyan economy in terms of employment incomes, and gross domestic product.

The first specific objective of the study was to measure the size and magnitude of the sector by estimating the total number of micro and small enterprises in the country. Estimates of the overall magnitude of the MSE sector become critical in analyzing the structure of the MSE sector in Kenya in order to understand the various distribution aspects of type of activity, rural-urban distribution, enterprise size and gender composition. This information is important for the appropriate design of policy instruments as well as in targeting various support interventions for the sector.

In addition, the survey assesses the contribution of the sector to income and analyses production dynamics through an estimation of wages, entrepreneurs income value added and accounts by activity size, gender distribution etc. This assessment is particularly useful considering the prominent role attributed to the sector in terms of income generation for the poor (poverty alleviation). The measurement of value added should establish the extent to which the sector generates profits for re-investment, while an estimation of wages informs about the cost of labour, and by implication, the sector's competitiveness.

The 1999 survey also assesses the overall size and contribution of the MSE sector to the national economy by conducting a macroeconomic estimation of the total labour force and contribution to GDP. The survey analyses issues of entrepreneurship and business characteristics in the context of demand and supply of business support services including credit, infrastructure (water, electricity, roads and telephone), training, and technology Finally, the 1999 survey assesses business constraints, business entry and closures and conclusions.

Geographic coverage

The survey covered all the districts in Kenya. The data representativeness are at the following levels -National -Urban/Rural -Provincial -District

Analysis unit

Households Indviduals within Households Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The usual sampling procedure m Kenya consists of a randomized selection of clusters corresponding to enumeration areas (or a division of them) within the master sample with a probability equivalent to the size m number of households in the selected clusters all households are interviewed The sample for the 1999 survey was based on the National Sample Survey and Evaluation Programme (NASSEP) III sampling frame of the Central Bureau of Statistics developed from the 1989 Population and Housing Census The NASSEP III sampling frame is a two-stage stratified cluster sample design with individual districts forming the strata.

In the creation of the NASSEP I11 sampling frame the first stage of sampling involved selection of enumeration areas (EAs) from the 1989 population census within the stratum forming the primary sampling units (PSUs) This master sample corresponds to the task of one single enumerator during the population census For sampling purposes the EAs are split into several clusters of approximately 100 households The master sample is made of 1,300 clusters and the 146 selected clusters for the 1999 National MSE Baseline Survey represent 11 2% of the master sample.

While planning for the sample selection for the 1999 survey consideration was given to combining the features of the previous two surveys (see Annex V) with provisions for possible modification to formulate a sampling scheme that would provide accurate estimates of the characteristics of the MSEs in the country. From the objectives of this survey it was expected that the clusters covered in the 1993 MSE survey would be included (for follow up purposes) as well as the industrial and commercial areas of the major towns for a more appropriate coverage of small and medium enterprises However it was finally decided not to follow these orientations because sample selection would not then meet the statistical requirements of randomization it was then decided to do a fresh random sample to avoid problems of coherence aggregation at national level and respondent fatigue.

Usually the selection of clusters (or EAs) is based on a preliminary stratification to distinguish the several strata m the country The need for stratification arises from the &verse economic and demographic characteristics in the various parts of the country The grouping of identical units into one stratum results in a homogeneous set, the strata differing from each other as much as possible This results in increased precision of the estimates of the characteristics of the population as the variance is substantially reduced.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The 1999 survey questionnaire collected information on revenue, value added and income by reconstituting simplified accounts for the enterprise, in conformity with the System of National Accounts (SNA). Recording expenditures in parallel with revenues and income opens the door to the possibility for cross-checking of responses in the field as well as once the questionnaire is being supervised or at data entry where purchases of raw materials or goods cannot exceed the revenues unless stocks at end of year are much higher than at start. Furthermore extreme values for revenues and incomes were thoroughly examined during data cleaning and appropriately corrected for by returning to the questionnaire and confronting the responses to other information given by the respondent (in particular responses to total sales net income and normal returns in section 7 of the questionnaire giving room to comparisons between indirect and direct responses which proved to be under-estimated by a factor 2 in Tunisian surveys for example) In addition, the reference to standard deviation and median values has been made as often as possible in the report.

Kenya’s Statistical Abstract is the single source of data covering a many areas of Kenya’s Economic, Political, Geographic, Financial and Educational Data. It is a compilation of statistical information from KNBS Censuses and Surveys. Broadly the topics covered in this publication touch on the Constitution, land, climate, population, migration, tourism, national accounts(GDP), External trade, domestic exports, imports, agriculture, forestry, fishing, manufacturing, building, construction, housing, mining engery , electricity, fuel, currency, banking, insurance, stock exchange, transportation and telecommunications, public health, public finance and retail sectors.

Key information about Kenya Nominal GDP

• Kenya Nominal GDP reached 26.9 USD bn in Sep 2022, compared with 28.6 USD bn in the previous quarter.
• Nominal GDP in Kenya is updated quarterly, available from Mar 2009 to Sep 2022, with an average number of 17.2 USD bn.
• The data reached an all-time high of 29.2 USD bn in Mar 2022 and a record low of 8.8 USD bn in Mar 2009.

CEIC converts quarterly Nominal GDP into USD. The Kenya National Bureau of Statistics provides Nominal GDP in local currency. The Central Bank of Kenya average market exchange rate is used for currency conversions.


Related information about Kenya Nominal GDP

• In the latest reports, Kenya GDP expanded 5.2 % YoY in Sep 2022.
• Its GDP deflator (implicit price deflator) increased 4.0 % in Sep 2022.
• Kenya GDP Per Capita reached 2,236.0 USD in Dec 2021.
• Its Gross Savings Rate was measured at 13.3 % in Dec 2021.
• For Nominal GDP contributions, Investment accounted for 20.3 % in Dec 2021.
• Public Consumption accounted for 12.1 % in Dec 2021.
• Private Consumption accounted for 74.6 % in Dec 2021.

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

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

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

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

Geographic coverage

National coverage

Analysis unit

District, Household, Individual

Universe

Households from the sampled areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

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

Sampling deviation

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

Cleaning operations

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

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

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

Response rate

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

Levels of household food insecurity in Nairobi by household structure.

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.

Abstract

In Kenya, economic analysts have held the view that emerging markets and liberalization of economies prompted large inflows of private capital into the country. These flows have macroeconomic effects that demand urgent policy responses. With limited data on the level and composition of these flows, policy makers are constrained in making timely and appropriate policy responses. This data gap motivated the Kenya National Bureau of Statistics (KNBS) in partnership with other key stakeholders to carry out the survey on foreign investment in Kenya. A sample of 900 enterprises was taken out of an estimated population of 3,500 enterprises with foreign transactions/positions.

The specific objectives of the survey was to collect data necessary to improve the quality of Balance of Payments (BOP) statistics and initiate compilation of International Investment Position (IIP) statistics; collect data necessary for assessment of investors' perceptions of the investment climate in the country, with a view to identifying ways to improve it, and comply with international standards of compilation and reporting of BOP and IIP statistics.

The main analytical tool for Foreign Investment Survey was a questionnaire administered to companies with foreign assets and liabilities. Additional information was sought from banks and other financial institutions to collect data on foreign exchange transactions through the financial institutions. The survey was designed to capture data on foreign capital for the reference period 2007 and 2008 as well as investor perceptions on the business environment in Kenya.

The specific objectives of the survey, therefore, include: a) To collect data necessary to improve the quality of BOP statistics and initiate compilation of IIP statistics. b) To collect data necessary for assessment of investors' perceptions of the investment climate in the country, with a view to identifying ways to improve it. c) To comply with international standards of compilation and reporting of BOP and IIP statistics.

Geographic coverage

National coverage

Analysis unit

Organization, institution

Kind of data

Sample survey data [ssd]

Sampling procedure

Surveying of enterprises with FAL was a challenge mainly due to lack of a comprehensive enterprise register. In order to surmount this challenge, a list of enterprises with FAL (enterprise frame) was generated by harmonizing various lists. These lists include register of enterprises maintained by KNBS, tax records of Kenya Revenue Authority (based on size of turnover on the assumption that there is some correlation between turnover and the possibility of having foreign assets and liabilities, and Export Processing Zones Authority (EPZA) enterprises. More enterprises were obtained in consultation with Export Promotion Council (EPC), Kenya Investment Authority (KenInvest), Central Bank of Kenya, Commissioner of Insurance, some foreign embassies, the Nairobi Stock Exchange and Communication Commission of Kenya. The harmonized list then formed the FAL sampling frame of 3,500 enterprises.In carrying out the Foreign Investment Survey(FIS), a purposive sample of 900 enterprises was drawn on the basis of gross turnover, and enterprises known (through other KNBS surveys) to have foreign exchange transactions. The list of enterprises comprised leading companies across different sectors of the economy based on information from regulatory institutions. This procedure was adopted, firstly due to the insufficient funding to carry out a complete census of FAL enterprises, and secondly, it proved to be the most efficient technique to obtain a representative sample. Because of the limitations of the purposive sampling technique so adopted, up rating mechanism was not applied to the results of the survey. After undertaking about two more cycles of FIS, KNBS will then systematically determine total population size of FAL enterprises in the country, for use in probability sampling and up-rating of survey results.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire: The design of the FIS questionnaire was guided by the information to be required and the format closely follows of questionnaires of MEFMI member countries undertaking foreign private capital surveys. The information sought through FIS questionnaire was general information of the enterprises, data on foreign liabilities and assets, international trade in services for the period 2007 and 2008 and investor perceptions on the investment climate in the countryNBS

Cleaning operations

Field eiting was done by the Research Assistants prior to submitting the completed questionnaires to the Supervisor.

Data Entry: Involved capturing all the information from paper questionnaires and storing in electronic format.This was done using the Private Capital Monitoring System (PCMS), computer software developed by MEFMI.The system facilitates real-time processing of Private Capital survey and non-survey information in line with Balance of Payments Manual fifth edition (BPM5).

Response rate

Out of the 900 enterprises surveyed, 500 or 56 per cent indicated that they had FAL. The 400 enterprises without FAL were asked to complete the Investor Perception section. Of the 500 enterprises with FAL, 393 completed the questionnaire (a response rate of 78.6 per cent).

Key information about Kenya Investment: % of GDP

• Kenya Investment accounted for 16.4 % of its Nominal GDP in Dec 2023, compared with a ratio of 19.1 % in the previous year.
• Kenya investment share of Nominal GDP data is updated yearly, available from Dec 1964 to Dec 2023, with an average ratio of 20.1 %.
• The data reached an all-time high of 29.8 % in Dec 1978 and a record low of 13.1 % in Dec 1964.

CEIC calculates Investment as % of Nominal GDP from annual Nominal Gross Capital Formation and annual Nominal GDP. Gross Capital Formation is calculated as the sum of Gross Fixed Capital Formation and Changes in Inventories. The Kenya National Bureau of Statistics provides Nominal Gross Capital Formation in local currency and Nominal GDP in local currency. Investment as % of Nominal GDP prior to 2006 is sourced from the World Bank.


Related information about Kenya Investment: % of GDP

• In the latest reports, Kenya GDP expanded 5.2 % YoY in Sep 2022.
• Kenya Nominal GDP reached 26.9 USD bn in Sep 2022.
• Its GDP deflator (implicit price deflator) increased 4.0 % in Sep 2022.
• Kenya GDP Per Capita reached 2,236.0 USD in Dec 2021.
• Its Gross Savings Rate was measured at 11.9 % in Dec 2023.

Key information about Kenya Visitor Arrivals

• Kenya Visitor Arrivals grew 51.8 % in Dec 2021, compared with a decrease of 70.9 % in the previous year
• Kenya Visitor Arrivals Growth rate data is updated yearly, available from Dec 1992 to Dec 2021
• The data reached an all-time high of 51.8 % in Dec 2021 and a record low of -70.9 % in Dec 2020

CEIC calculates annual Tourist Arrivals Growth from Tourist Arrivals. Tourist Arrivals are calculated by subtracting Transit Visitors from Total Visitors. The Kenya National Bureau of Statistics provides Total Visitors and Transit Visitors. Tourist Arrivals include Same-Day Visitors.

Further information about Kenya Visitor Arrivals

• In the latest reports, Kenya Visitor Arrivals recorded 823,312.0 person in the year of Dec 2021
• Tourism Revenue of Kenya reached 1.8 USD bn in Dec 2019, an increase of 15.3 % change from the previous year