Micro, Small and Medium Enterprises Survey 2016
Kenya, 2016
Get Microdata
Reference ID
ken-knbs-msmes-2016-v1
Producer(s)
Kenya National Bureau of Statistics
Created on
Nov 17, 2017
Last modified
Aug 15, 2022
Page views
906411
Downloads
880266
Identification
Survey ID Number
ken-knbs-msmes-2016-v1
Title
Micro, Small and Medium Enterprises Survey 2016
Country
| Name | Country code |
|---|---|
| Kenya | KEN |
Study type
Enterprise/Establishment 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 SMEs 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 SME 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 Micro and Small Enterprise (MSE) Baseline Survey. The 2016 National SME 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 Micro and Small Enterprise (MSE) Baseline Survey. The 2016 National SME 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
Individuals and institutions
Version
Version Description
Version 1: Edited, anonymised data for public distribution
Version Date
2016-06-27
Scope
Notes
Small and Medium Enterprises are considered as sources of employment generation, economic growth, and social transformation. A significant proportion of the SMEs 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 SME policy formulation and implementation.
Coverage
Geographic Coverage
The census had national coverage.
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
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Director General | KNBS | directorgeneral@knbs.go.ke | www.knbs.go.ke |
Confidentiality
B
Citation requirements
Kenya National Bureau of Statistics. Micro, Small and Medium Establishment Survey 2016 [dataset] Version 1. Nairobi: KNBS [producer and distributor], 2016.
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Kenya National Bureau of Statistics | Ministry of Planning and National Development | director@knbs.go.ke | www.knbs.go.ke |
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
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
v2