Over ******* entities were registered in Kenya in the period from July 2022 to June 2023. Of these, local private businesses made up the majority, some ******. Additionally, other ** local public companies and *** foreign businesses had registration in the country. As of August 2023/2024, ****** private and ** public companies, as well as ** foreign businesses were registered.

Graph and download economic data for Small Firms with a Bank Loan or Line of Credit to Total Small Firms for Kenya (DDAI04KEA156NWDB) from 2007 to 2018 about Kenya, small, credits, and business.

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.

Abstract

The MSME sector in Kenya has over the years been recognized for its role in provision of goods and services, enhancing competition, fostering innovation, generating employment and in effect, alleviation of poverty. The crucial role of MSMEs is underscored in Kenya's Vision 2030 - the development blueprint which seeks to transform Kenya into an industrialized middle-income country, providing a high-quality life to all its citizens by the year 2030. The MSME sector has been identified and prioritized as a key growth driver for achievement of the development blue print.

The measurement of the size of the sector in terms of employment as well as its contribution to Gross Domestic Product [GDP] and the generation of income is of major importance. This is not only because of their usefulness in the design of appropriate policies and programmes but also in understanding their dynamics in terms of income, wages, growth patterns, sector and their evolving nature among others. MSMEs tend to be dynamic: the structure and their operations change considerably within a short time. The last comprehensive study is the 1999 National Micro and Small Enterprise (MSE) Baseline Survey. The 2016 National MSME Survey was therefore, designed to respond to the existing data gap and sought to provide data at national and county levels. The unit of observation was the establishments and the survey targeted those that engaged at most 99 persons. The terms establishment, enterprise and business are however, used interchangeably in this report.

Geographic coverage

i) National ii) Counties and iii) Urban and rural residence

Analysis unit

i) National ii) Counties and iii) Urban and rural residence

Kind of data

Census/enumeration data [cen]

Sampling procedure

Survey Design The previous MSE studies used the household-based approach to identify businesses/establishments. However, the 2016 MSME survey, in addition to the household-based approach, interviewed businesses/establishments identified from business registers maintained by county governments. The 2016 MSME survey was cross-sectional and was designed to provide estimates at national and county levels. The survey used a representative probability sample design aimed at producing estimates at the following domains; · National · Counties and · Urban and rural residence (For Unlicensed businesses only.

The survey adopted a stratified random sampling method for the establishment-based sample in which a systematic random sample of establishments was drawn using equal probability selection method. For the household-based sample, a two-stage stratified cluster sampling design was used where the first stage involved selection of 600 clusters (354 in rural and 246 in urban) with equal probability. In the second stage, a uniform random sample of 24 households in each cluster was selected using systematic random sampling method.

Mode of data collection

Face-to-face [f2f]

Research instrument

One Enterprise questionnaire

A common concern with efforts to directly help some small businesses to grow is that their growth comes at the expense of their unassisted competitors. We test this possibility using a two-stage randomized experiment in Kenya which randomizes business training at the market level, and then within markets to selected businesses. Three years after training, the treated businesses are selling more, earn higher profits, and their owners have higher well-being. Point estimates of the spillovers on the competing businesses are small and not statistically significant, and the markets as a whole have grown in terms of sales volume.This archive contains the original survey data, and Stata replication code needed to reproduce this paper.

Abstract

The Kenya Enterprise Survey was conducted between May and July 2007. The research is based on a representative sample of 657 formal firms and 124 informal establishments. The sample was drawn in four locations (Nairobi, Mombasa,Nakuru, and Kisumu) and covered both manufacturing and services sectors.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

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

Geographic coverage

National

Analysis unit

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

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The study used stratified simple random sampling for the formal economy (registered establishments with more than 4 workers), and simple random sampling for the micro firms (non-registered establishments with less than 5 employees). Close to 60% of the formal sample is represented by manufacturing firms. Within manufacturing food (17 percent), garments (12 percent) and other manufacturing (31 percent) represent individual strata. Outside the manufacturing sector, the retail sector account for 19 percent of the sample and less than a quarter of the firms belong to the rest of services stratum.

The sample was drawn in four locations: Nairobi, Mombasa, Nakuru, and Kisumu. Size stratification for formal firms was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

For establishments with five or more full-time permanent paid employees, the universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15); 2. Manufacturing: Garment (Group D, sub group 18); 3. Manufacturing: Other Manufacturing (Group D excluding sub-groups 15 and 18); 4. Retail Trade: (Group G, sub-group 52); 5. Rest of the universe, including: • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

The sampling frame was obtained from the Kenya National Bureau of Statistics, the Kenya Association of Manufacturers, the Kenya National Chamber of Commerce, the Kenya Private Sector Alliance, and from the Federation of Kenya Employers. The lists were merged together into a master list which was validated, updated where possible, and then used to establish the initial population size for each stratum. The final population size in all strata and locations was 6562 with the vast majority of establishments operating in the rest of the universe, and manufacturing strata.

The sample also includes panel data collected from establishments surveyed in the 2003 Kenya Investment Climate Survey (ICS). That survey included establishments in all three manufacturing strata distributed across the entire country. In order to collect the largest possible set of panel data, an attempt was made to contact and survey every establishment in the panel, provided it was located in one of the four cities covered by this survey and operated in the universe under study.

The remainder of the sample (including the entire rest of universe and retail sample in each city) was selected at random from the master list by a computer program.

In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees. The implementing agency, EEC Canada, selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all regions of the survey. The following procedure was followed for the sampling of micro establishments: 1. Step 1: districts and specific zones of each district with a high concentration of micro establishments were identified; 2. Step 2: a count of all micro establishments in these specific zones was conducted; 3. Step 3: the count by zone was converted into one list of sequential numbers for the whole survey region and a virtual list was created with establishments numbers; 4. Step 4: a computer program performed a random selection of establishments numbers from that virtual list; 5. Step 5: based on the ratio between the number selected in each specific zone and the total population in that zone, a skip rule was created and applied for selecting the corresponding establishments in each zone.

Enumerators applied the skip rule defined for that zone as well as how to select replacements in the event of refusal or other cause of non-participation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro Establishments Questionnaire (for establishments with 1 to 4 employees).

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, registration, and performance measures. The questionnaire also assesses the survey respondents' opinions on what are the obstacles to firm growth and performance.

Abstract

The Hunger Safety Net Programme (HSNP) is a social protection project being conducted in the Arid and Semi-Arid Lands (ASALs) of northern Kenya. The ASALs are extremely food-insecure areas highly prone to drought, which have experienced recurrent food crises and food aid responses for decades. The HSNP is intended to reduce dependency on emergency food aid by sustainably strengthening livelihoods through cash transfers. The pilot phase ran from 2009 to 2013. The second phase has been launched in July 2013 and contracted to run until March 2018. Oxford Policy Management (OPM) was responsible for the monitoring and evaluation (M&E) of the programme under the pilot phase, as well as the second phase of implementation. Within the impact evaluation component for Phase 2, OPM used a range of analytical methods within an overarching mixed-method approach. The quantitative impact evaluation of HSNP Phase 2 compares the situation of HSNP2 beneficiaries and control households, relying on the Regression Discontinuity approach, integrated by a targeted Propensity Score Matching approach. In addition to the analysis at the household level, a Local Economy-Wide Impact Evaluation (LEWIE) was conducted to investigate the impact of the HSNP2 on the local economy, including on the production activities of both beneficiary and non-beneficiary households. A single round of data collection based on a household and business survey underpins the household quantitative impact evaluation and the LEWIE study. The objective of the survey is to collect household and business data to provide an assessment of the programme's impact on the local economy, as well as beneficiary households. The household survey is a survey of 5,979 people, carried out between 13 February and 29 June 2016 in 187 sub-locations across the four counties of Mandera, Marsabit, Turkana and Wajir. The survey covered modules on household demographic characteristics, livestock, assets, land, transfers, food and non-food consumption, food security, saving and borrowing, jobs, business, livestock trading and subjective poverty. In addition to the household survey, a business questionnaire was conducted in the three main commercial hubs of each county. Overall, 282 business questionnaires were administered in the four counties. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. The aim was to capture information on three main sectors of the local economy:

1. Retailing - shops that sell retail goods on which a price mark-up is applied
2. Services
3. Producers - businesses that transform inputs into outputs

Lastly, since livestock trading is a very important activity in the HSNP counties, livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews.

Geographic coverage

Regional

Analysis unit

Households

Universe

(a) At the household level, the study population consists of all the households in the four HSNP counties (i.e. Mandera, Marsabit, Turkana and Wajir). Within a household, the survey covered all de jure household members (usual residents).

(b) At the market level, the survey covered a random sample of businesses in the three main commercial hubs of each county. The following categories of businesses were excluded from the listing:

• Temporary stalls or mobile sellers located outside permanent kiosks
• Banks
• Education institutions (schools, universities etc.)
• Health facilities

(c) The livestock trader survey was conducted in the three main livestock markets of each county. To the extent possible, livestock traders have been sampled in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) HOUSEHOLD SURVEY The household survey used a two-stage sampling approach, for which the sample frame was defined by sub-locations and households in the HSNP Management Information System (MIS) data. The MIS data are data from a census of nearly all households in the four HSNP counties. The census contains the information that was gathered in respect of these households during the registration for the HSNP programme, their Proxy Means Test (PMT) score and their assignment to the HSNP cash transfers, as well as information about all payments received by all households since the start of Phase 2. The HSNP acknowledges that a small number of the population was recognised to be missed and was registered at a later date. The sampling procedure was intended to cover the different sample requirements of the impact evaluation approaches, including the Local Economy-Wide Impact Evaluation (LEWIE), the quantitative impact evaluation based on the Regression Discontinuity (RD) approach, and the Propensity Score Matching (PSM) back-up.

Drawing the sample consisted of two stages: 1. First stage: sampling of sub-locations 2. Second stage: sampling of households within a sub-location.

The sampling process yielded a sample of 187 sub-locations, including the 24 that were sampled with certainty. 11 sub-locations were sampled twice, and one sub-location was sampled three times. 44 sub-locations were selected in Mandera, 46 in Wajir, 48 in Marsabit and 49 in Turkana. In each sub-location 32 households were sampled. In a few sub-locations there were insufficient households to select the desired LEWIE sample, resulting in fewer than 32 households sampled. Overall, 6,384 households were sampled.

(b) BUSINESS SURVEY A business questionnaire was conducted in the three main commercial hubs of each county. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. In each sub-location, a sample of at least seven businesses from each category was targeted. Since no sampling frame for local businesses was available, the survey research teams in each county undertook a listing exercise of all businesses on the main commercial centre of the selected sub-locations. Once the listing was completed, the team leader sampled the required number of businesses using a step sampling approach. Overall, 282 business questionnaires were administered in the four counties. The business survey is not representative of any commercial hubs.

(c) LIVESTOCK TRADER SURVEY Since livestock trading is a very important activity in the HSNP counties, a number of livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews. Each enumerator team was asked to interview four traders in each of the sub-locations, leading to a total sample size of 12 livestock trader interviews per county. Sampling of livestock traders was mostly done purposively. To the extent possible, team leaders sampled livestock traders in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties. The livestock trader survey is not representative of any livestock markets.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

(a) QUALITY CHECKS

Given the data was electronically collected, it was continually checked, edited and processed throughout the survey cycle. A first stage of data checking was done by the survey team which involved: (i) checking of all IDs (ii) checking for missing observations (iii) checking for missing item responses where none should be missing (iv) first round of checks for inadmissible/out of range and inconsistent values.

(b) DATA PROCESSING Additional data processing activities were performed at the end of data collection in order to transform the collected cleaned data into a format that is ready for analysis. The aim of these activities was to produce reliable, consistent and fully-documented datasets that can be analysed throughout the survey and archived at the end in such a way that they can be used by other data users well into the future. Data processing activities involved:

• Computing and merging in the sampling weights
• Reshaping datasets in order to produce data files for each unit of observation (households, household members, and businesses)
• Anonymising data by removing all variables that identify respondents such as names, address, GPS coordinates, etc.
• Classifying non-response and coding them using a pre-determined classification scheme
• Properly naming and labelling the variables in each dataset

Response rate

Household survey response rate was 88.9 percent. For business survey and livestock trader survey, the response rate was 100 percent.

Data appraisal

The datasets were then sent to the analysis team where they were subjected to a second set of checking and cleaning activities. This included checking for out of range responses and inadmissible values not captured by the filters built into the CAPI software or the initial data checking process by the survey team. A comprehensive data checking and analysis system was created including a logical folder structure, the development of template syntax files (in Stata), to ensure data checking and cleaning activities were recorded, that all analysts used the same file and variable naming conventions, variable definitions,

In 2020, tea factories managed by KTDA in Kenya reached in total a revenue of approximately ** billion Kenyan shillings (KSh), around ***** million U.S. dollars. KTDA Management Services is a private company and administers over ** small tea farms in the country. Among the selected manufacturers, it accumulated the highest market share in Kenya's tea industry. Williamson Tea Kenya Plc had the second largest market share, with sales revenue exceeding three billion KSh, roughly **** million U.S. dollars. Additionally, the company is the major shareholder from Kapchorua Tea Kenya Plc, which recorded, in its turn, *** billion Ksh (**** million U.S. dollars) in revenue.