Informal Business Sector Survey 2016
Zimbabwe, 2017
World Bank Group (WBG)
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February 05, 2020
Last modified
February 05, 2020
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Identification
Survey ID Number
ZWE_2017_ISBS_v01_M
Title
Informal Business Sector Survey 2016
Country
| Name | Country code |
|---|---|
| Zimbabwe | ZWE |
Study type
Informal Sector Survey [hh/iss]
Abstract
The 2016 Zimbabwe Informal Sector Business Survey (ISBS) data was collected by the World Bank Group - Enterprise Analysis Unit. The survey covers Harare city.
The primary objectives of the survey are: i) to understand the business demographics of the sector in the two cities, and ii) to describe the environment within which these businesses operate. A secondary objective of the survey is to provide an estimate of the number of informal businesses operating in these cities.
The primary objectives of the survey are: i) to understand the business demographics of the sector in the two cities, and ii) to describe the environment within which these businesses operate. A secondary objective of the survey is to provide an estimate of the number of informal businesses operating in these cities.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Unit of analysis is informal business, where informality is defined based on whether or not a business is formally registered with the government.
Version
Version Description
Version 01. Edited, anonymous dataset for public distribution.
Scope
Notes
The 2016 Zimbabwe Informal Sector Business Survey covered the following topics:
- General information
- Location and infrastructure
- Crime
- Sales and supplies
- Finance
- Labor
- Registration
- Business environment
- Assets
- General information
- Location and infrastructure
- Crime
- Sales and supplies
- Finance
- Labor
- Registration
- Business environment
- Assets
Coverage
Geographic Coverage
The survey covers Harare city.
Universe
The universe includes informal businesses, where informality is defined based on whether or not a business is formally registered with the government.
Producers and sponsors
Primary investigators
| Name |
|---|
| World Bank Group (WBG) |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| World Bank Group | WBG |
Sampling
Sampling Procedure
The 2016 Harare ISBS pilots a new way of surveying these informal sector businesses. The survey follows an area-based sampling methodology, whereby the primary sampling unit is a geographic area rather than an establishment or a business unit. To account for potential clustering of informal business, the survey uses a particular type of area-based sampling, called (stratified) Adaptive Cluster Sampling (ACS). This is a version of area-based sampling in which one selects a sample of starting grids (usually squares), which will constitute the start of the fieldwork. All informal business in selected squares will be enumerated, using a 2 to 3-minutes questionnaire (short-form questionnaire). A randomly selected subset of the enumerated businesses will be given a 20-minutes, long-form questionnaire.
The first step in the sampling approach was the construction of a spatial grid as the Primary Sampling Units (PSU) frame. The grid covered the total of municipal Harare, and each cell had a size of 200 by 200 meters. This produced a total of about 22,000 grids. The second step was to stratify each grid, based on likely concentration of informal business units. In Harare, the grids were categorized into four strata: three strata of low, medium, and high concentration of informal sector activity, and a market centre. The stratification was based on local knowledge of the survey implementing contractor. The third step in the sampling process was to select a pre-defined number of starting squares from each stratum for enumeration purposes. For Harare ISBS, a total of 226 starting squares were randomly selected for enumeration. The target number of starting squares, as well as the initial allocation across strata, was defined through a simulation. This simulation is implemented in R and uses the Shiny library.
The first step in the sampling approach was the construction of a spatial grid as the Primary Sampling Units (PSU) frame. The grid covered the total of municipal Harare, and each cell had a size of 200 by 200 meters. This produced a total of about 22,000 grids. The second step was to stratify each grid, based on likely concentration of informal business units. In Harare, the grids were categorized into four strata: three strata of low, medium, and high concentration of informal sector activity, and a market centre. The stratification was based on local knowledge of the survey implementing contractor. The third step in the sampling process was to select a pre-defined number of starting squares from each stratum for enumeration purposes. For Harare ISBS, a total of 226 starting squares were randomly selected for enumeration. The target number of starting squares, as well as the initial allocation across strata, was defined through a simulation. This simulation is implemented in R and uses the Shiny library.
Weighting
To estimate population parameters, weights are applied to survey samples. In surveys design following standard random sampling, selection probability of all units is known before the actual data collection. Hence, weights can be derived as the inverse of selection probability.
Computation of sampling weights is a bit involved for Adaptive cluster sampling since final sample size is not known apriori. In ACS, selection probabilities are not known a priori since units are adaptively added to the sample depending on the number of informal units found in a square. In adaptive sampling, one instead talks about empirically derived inclusion probabilities.
Note: Refer to Sampling Weight section in "The 2016 Zimbabwe Informal Sector Business Survey Dataset" document for further details on sampling weight.
Computation of sampling weights is a bit involved for Adaptive cluster sampling since final sample size is not known apriori. In ACS, selection probabilities are not known a priori since units are adaptively added to the sample depending on the number of informal units found in a square. In adaptive sampling, one instead talks about empirically derived inclusion probabilities.
Note: Refer to Sampling Weight section in "The 2016 Zimbabwe Informal Sector Business Survey Dataset" document for further details on sampling weight.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2017-04 | 2017-07 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Since the primary sampling unit is a set of grids/squares, enumerators were assigned to starting squares. All informal business units in selected squares were enumerated using a 2 to 3-minutes questionnaire, (called short-form questionnaire). A randomly selected subset of the enumerated businesses were given a 20-minutes, long-form questionnaire, essentially the main questionnaire of the survey. This survey was fully implemented into the World Banks’ Survey Solutions CAPI system. The selection for long-form (man) questionnaire was conducted in real time using the CAPI system; these minimizes the issued stemming from the transitory nature of many informal activities. An important feature of the implementation is that enumerators did not have control over who gets selected for an interview with the long-form (main) questionnaire. All respondents that were not selected for the long-form were given a short-form questionnaire, which captured information on the type of activity, physical location, and the number of workers. Outright refusals were also recorded, using enumerator observation of the activity and workers observed.
The survey is adaptive in the sense that if the number of informal units in a square exceeds a predefined threshold, all the squares surrounding the starting square are surveyed, following the same approach of enumeration and randomly conducting the main interview. If one of the surrounding squares exceed the threshold, then the squares surrounding that square in turn are also surveyed. This process continues until either the network is exhausted, or an arbitrary cut-off point is defined. We defined this cut-off for Harare ISBS to be the 4th expansion, though it was never reached in fieldwork. Overall, the enumeration started with a total of 226 starting squares and a total of 439 squares were enumerated in the end. About 3700 informal business units were listed. Out of the 3700 informal units, about 515 were randomly selected to the main questionnaire (i.e., the long-form). The main data file therefore contains 515 observations.
The survey is adaptive in the sense that if the number of informal units in a square exceeds a predefined threshold, all the squares surrounding the starting square are surveyed, following the same approach of enumeration and randomly conducting the main interview. If one of the surrounding squares exceed the threshold, then the squares surrounding that square in turn are also surveyed. This process continues until either the network is exhausted, or an arbitrary cut-off point is defined. We defined this cut-off for Harare ISBS to be the 4th expansion, though it was never reached in fieldwork. Overall, the enumeration started with a total of 226 starting squares and a total of 439 squares were enumerated in the end. About 3700 informal business units were listed. Out of the 3700 informal units, about 515 were randomly selected to the main questionnaire (i.e., the long-form). The main data file therefore contains 515 observations.
Data Collectors
| Name |
|---|
| Probe Market Research |
Questionnaires
Questionnaires
The survey data was collected using a standardized questionnaire, i.e., the long-form questionnaire. The questionnaire was developed building on previous modules used by the Enterprise Analysis Unit of the World Bank to survey informal businesses.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Enterprise Analysis Unit | World Bank Group | enterprisesurveys@worldbank.org |
Confidentiality
Confidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.
Citation requirements
The use of this dataset must be acknowledged using a citation which would include:
- the identification of the Primary Investigator (including country name)
- the full title of the survey and its acronym (when available), and the year(s) of implementation
- the survey reference number
- the source and date of download (for datasets disseminated online).
Example:
The World Bank. Zimbabwe - Informal Sector Business Survey 2016, Ref. ZWE_2017_ISBS_v01_M. Dataset downloaded from https://www.enterprisesurveys.org/portal/login.aspx on [date].
- the identification of the Primary Investigator (including country name)
- the full title of the survey and its acronym (when available), and the year(s) of implementation
- the survey reference number
- the source and date of download (for datasets disseminated online).
Example:
The World Bank. Zimbabwe - Informal Sector Business Survey 2016, Ref. ZWE_2017_ISBS_v01_M. Dataset downloaded from https://www.enterprisesurveys.org/portal/login.aspx on [date].
Archive where study is originally stored
Enterprise Surveys
https://www.enterprisesurveys.org/Portal/
Cost: None
https://www.enterprisesurveys.org/Portal/
Cost: None
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_ZWE_2017_ISBS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank Group | Documentation of the survey |
Date of Metadata Production
2020-02-04
DDI Document version
Version 01 (February 2020)