Small and Medium Enterprises (MSME) Survey 2016
Kenya, 2016
Kenya National Bureau of Statistics
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June 01, 2022
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June 01, 2022
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Identification
Survey ID Number
DDI-KEN-KNBS-MSME-2016-v1.0
Title
Small and Medium Enterprises (MSME) Survey 2016
Country
| Name | Country code |
|---|---|
| KENYA | KEN |
Study type
Enterprise Census [en/census]
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.
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.
Kind of Data
Census/enumeration data [cen]
Unit of Analysis
i) National
ii) Counties and
iii) Urban and rural residence
ii) Counties and
iii) Urban and rural residence
Version
Version Date
2016-06-27
Scope
Notes
The Micro, Small and Medium Enterprises (MSMEs) are considered as sources of employment generation, economic growth, and social transformation. A significant proportion of the MSMEs are formal, while majority fall within the informal economy based on their size, location, ownership, status of formality and economic activity, together, as major job providers, they produce a significant share of total value added, and provide a large segment of the poor and middle-income populations with affordable goods and services. There is however limited or outdated data to inform MSME policy formulation and implementation.
Coverage
Geographic Coverage
i) National
ii) Counties and
iii) Urban and rural residence
ii) Counties and
iii) Urban and rural residence
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Kenya National Bureau of Statistics | Ministry of Planning and National development |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Kenya National Bureau of Statistics | KNBS | Funding of Operational Cost |
Sampling
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.
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.
Weighting
Data Weighting
Weighting of the data was necessary since the selected samples were not self-weighting due to varying probabilities of selection across different strata. Separate weights were, therefore, computed for the various sets of data. The design weights for the licensed establishments incorporated the probabilities of selection of the establishments from the establishments sampling frame. The weights were further adjusted to cater for nonresponses.
Household weights were used for the unlicensed establishments as the latter were operating from the household. These weights incorporated the probabilities of selection of the clusters from the census EAs database into the NASSEP V sampling frame, the probabilities of selection of the MSME clusters from the frame and the probabilities of selection of the households from each of the sampled clusters. These weights were then adjusted to cater for household and individual non-response.
Weighting of the data was necessary since the selected samples were not self-weighting due to varying probabilities of selection across different strata. Separate weights were, therefore, computed for the various sets of data. The design weights for the licensed establishments incorporated the probabilities of selection of the establishments from the establishments sampling frame. The weights were further adjusted to cater for nonresponses.
Household weights were used for the unlicensed establishments as the latter were operating from the household. These weights incorporated the probabilities of selection of the clusters from the census EAs database into the NASSEP V sampling frame, the probabilities of selection of the MSME clusters from the frame and the probabilities of selection of the households from each of the sampled clusters. These weights were then adjusted to cater for household and individual non-response.
Data Collection
Dates of Data Collection
| Start |
|---|
| 2016 |
Data Collection Mode
Face-to-face [f2f]
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Kenya National Bureau of Statistics | KNBS | Ministry of Planning and National Development |
Questionnaires
Questionnaires
One Enterprise questionnaire
Data Processing
Other Processing
Data Capture and Processing
The 2016 MSME survey data was collected using tablets/CAPI. The data capture program was developed using SurveyCTO. This software was considered mainly due to its simplified user interface. It also has a random audio audit which records surveys as they are being conducted to ensure collection of high quality data. In addition, SurveyCTO allows export of data directly to spreadsheets and other statistical packages. The designed program also incorporated inbuilt data skips and check procedures to minimize data collection errors. The tablets were internet-enabled for real time data transmission to a central server. Once all the data was transmitted to the server, it was downloaded and merged into two distinct data files; the establishment-based and household-based data files. In each of the files, data cleaning such as checking for duplicates, missing records and outliers was carried out based on the developed editing specifications.
The final phase of processing was data outputs generation guided by a tabulation plan. This document guided the data processing team to produce outputs which sought to address survey objectives. Both STATA and SPSS softwares were used for data analysis.
The 2016 MSME survey data was collected using tablets/CAPI. The data capture program was developed using SurveyCTO. This software was considered mainly due to its simplified user interface. It also has a random audio audit which records surveys as they are being conducted to ensure collection of high quality data. In addition, SurveyCTO allows export of data directly to spreadsheets and other statistical packages. The designed program also incorporated inbuilt data skips and check procedures to minimize data collection errors. The tablets were internet-enabled for real time data transmission to a central server. Once all the data was transmitted to the server, it was downloaded and merged into two distinct data files; the establishment-based and household-based data files. In each of the files, data cleaning such as checking for duplicates, missing records and outliers was carried out based on the developed editing specifications.
The final phase of processing was data outputs generation guided by a tabulation plan. This document guided the data processing team to produce outputs which sought to address survey objectives. Both STATA and SPSS softwares were used for data analysis.
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Kenya National Bureau of Statistics | Ministry of Planning and National Development | director@knbs.go.ke | www.knbs.go.ke |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Director General | KNBS | directorgeneral@knbs.go.ke | www.knbs.go.ke |
Confidentiality
Before being granted access to the dataset, all users have to formally agree:
1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Citation requirements
"Micro, Small and Medium Establishment(MSME) Survey 2016, Version 1.0 of the KNBS dataset "
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Copyright
Copyright 2016, Kenya National Bureau of Statistics
Metadata production
DDI Document ID
DDI-KEN-KNBS-MSME-2016-v1.0
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Kenya National Bureau of Statistics | KNBS | Ministry of Planning and National Development | Documentation of the Study |
Date of Metadata Production
2017-06-27
DDI Document version
MSME 2016 Version 1.0