Micro-Enterprise Survey 2016
Zimbabwe, 2016 - 2017
World Bank
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April 28, 2017
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Identification
IDNO
ZWE_2016_MS_v01_M
Title
Micro-Enterprise Survey 2016
Countries
| Name | Code |
|---|---|
| Zimbabwe | ZWE |
Study notes
This research of registered businesses with one to four employees was conducted in Zimbabwe between October 2016 and February 2017, at the same time with Zimbabwe Enterprise Survey 2016. Data from 360 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The objective of the survey was to obtain feedback from enterprises on the state of the private sector and constraints to its growth.
Micro-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 percent 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.
Micro-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 percent 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.
Kind of data
Sample survey data [ssd]
Version
Version
v01, edited, anonymous dataset for public distribution
All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1 (some exceptions apply due to comparability reasons). Variable names preceded by the prefix "ZIM" indicate questions specific to Zimbabwe, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric.
The last complete fiscal year varies by a firm, although it is January to December 2015 for majority of firms. Variables fymone (month of the last complete fiscal year) and fyyeare (last complete fiscal year) in the dataset provide the month and year of the last complete fiscal year for each firm.
All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1 (some exceptions apply due to comparability reasons). Variable names preceded by the prefix "ZIM" indicate questions specific to Zimbabwe, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric.
The last complete fiscal year varies by a firm, although it is January to December 2015 for majority of firms. Variables fymone (month of the last complete fiscal year) and fyyeare (last complete fiscal year) in the dataset provide the month and year of the last complete fiscal year for each firm.
Coverage
Geographic coverage
Bulawayo, Harare, Manicaland, and Midlands
Geographic unit
Regions covered are selected based on the number of establishments, contribution to employment, and value added. In most cases these regions are metropolitan areas and reflect the largest centers of economic activity in a country.
Unit of analysis
The primary sampling unit of the study is an 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.
Micro-enterprises are formal firms with less than five employees.
Micro-enterprises are formal firms with less than five employees.
Universe
The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private 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. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.
Producers and sponsors
Authoring entity
| Name |
|---|
| World Bank |
Funding agencies
| Name | Abbreviation |
|---|---|
| World Bank | |
| The Department for International Development | DFID |
Sampling
Sampling procedure
The sample was selected using stratified random sampling. Two levels of stratification were used: firm sector and geographic region.
For industry stratification, the universe was stratified into three manufacturing and two services industries - Food and Beverages (ISIC Rev. 3.1 code 15), Textile and Garments (ISIC codes 17 and 18), Other Manufacturing (16, 19 - 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).
There is no further breakdown by firm size for the Micro-Enterprise Survey for sampling purpose and all firms are classified in to one size group (1 to 4 employees).
Regional stratification for the Zimbabwe Micro Survey was done across four regions: Bulawayo, Harare, Manicaland, and Midlands.
The sample frame used for the survey was generated through a block enumeration because available business registry data could not be secured from Zimbabwe National Statistical Agency. Therefore, a listing of firms was generated through block enumeration conducted from August to September 2016. The contractor physically created a list of establishments in the four regions covered in the survey, from which samples were then drawn.
The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them.
Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 14.9% (73 out of 489 establishments).
For industry stratification, the universe was stratified into three manufacturing and two services industries - Food and Beverages (ISIC Rev. 3.1 code 15), Textile and Garments (ISIC codes 17 and 18), Other Manufacturing (16, 19 - 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).
There is no further breakdown by firm size for the Micro-Enterprise Survey for sampling purpose and all firms are classified in to one size group (1 to 4 employees).
Regional stratification for the Zimbabwe Micro Survey was done across four regions: Bulawayo, Harare, Manicaland, and Midlands.
The sample frame used for the survey was generated through a block enumeration because available business registry data could not be secured from Zimbabwe National Statistical Agency. Therefore, a listing of firms was generated through block enumeration conducted from August to September 2016. The contractor physically created a list of establishments in the four regions covered in the survey, from which samples were then drawn.
The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them.
Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 14.9% (73 out of 489 establishments).
Response rate
Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.
Item non-response was addressed by two strategies:
a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9).
b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.
Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.
The number of interviews per contacted establishments was 0.74. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 0.11.
Item non-response was addressed by two strategies:
a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9).
b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.
Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.
The number of interviews per contacted establishments was 0.74. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 0.11.
Weighting
For some units it was impossible to determine eligibility because the contact was not successfully completed. Consequently, different assumptions as to their eligibility result in different universe cells' adjustments and in different sampling weights. Three sets of assumptions were considered:
a- Strict assumption: eligible establishments are only those for which it was possible to directly determine eligibility.
b- Median assumption: eligible establishments are those for which it was possible to directly determine eligibility and those that rejected the screener questionnaire or an answering machine or fax was the only response. Median weights are used for computing indicators on the www.enterprisesurveys.org website.
c- Weak assumption: in addition to the establishments included in points a and b, all establishments for which it was not possible to finalize a contact are assumed eligible. This includes establishments with dead or out of service phone lines, establishments that never answered the phone, and establishments with incorrect addresses for which it was impossible to find a new address. Note that under the weak assumption only observed non-eligible units are excluded from universe projections.
a- Strict assumption: eligible establishments are only those for which it was possible to directly determine eligibility.
b- Median assumption: eligible establishments are those for which it was possible to directly determine eligibility and those that rejected the screener questionnaire or an answering machine or fax was the only response. Median weights are used for computing indicators on the www.enterprisesurveys.org website.
c- Weak assumption: in addition to the establishments included in points a and b, all establishments for which it was not possible to finalize a contact are assumed eligible. This includes establishments with dead or out of service phone lines, establishments that never answered the phone, and establishments with incorrect addresses for which it was impossible to find a new address. Note that under the weak assumption only observed non-eligible units are excluded from universe projections.
Data Collection
Dates of collection
| Start | End |
|---|---|
| 2016-10 | 2017-02 |
Mode of data collection
Face-to-face [f2f]
Data collection supervision
Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Kenya ES 2013 Implementation."
Questionnaires
The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module), and additional manufacturing and services specific questions.
The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).
Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.
The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).
Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.
Data collector(s)
| Name |
|---|
| Probe Market Research |
Data Processing
Data editing
Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.
Data access
metadata.study_desc.data_access.dataset_use.conf_dec
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 Micro-Enterprise Survey (MS) 2016, Ref. ZWE_2016_MS_v01_M. Dataset downloaded from [URL] 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 Micro-Enterprise Survey (MS) 2016, Ref. ZWE_2016_MS_v01_M. Dataset downloaded from [URL] on [date].
Original archive where collection 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.
Contacts
Contact(s)
| Name | |
|---|---|
| Enterprise Analysis Unit | enterprisesurveys@worldbank.org |
Metadata production
IDNo
DDI_ZWE_2016_MS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | World Bank | DDI Documentation |
Production date
2014-04-28
Version
v01 (April 2014)