Sierra Leone - Enterprise Survey 2017

The survey was conducted in Sierra Leone between July and September 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the Enterprise Survey is to gain an understanding of what firms experience in 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 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. Only registered businesses are surveyed in the Enterprise Survey. Data from 150 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews. 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.

Acronym: 
ES 2017
Type: 
Microdata
Topics: 
Topic not specified
Languages Supported: 
English
Geographical Coverage: 
Sierra Leone
Reference ID: 
SLE_2017_ES_v01_M
Release Date: 
March 7, 2018

Harvest Source

Harvest Source: 
Microdata

Harvest Source ID

Harvest Source ID: 
9715

Last Updated

Last Updated: 
March 7, 2018
Data Collector(s) Name: 
Kantar TNS Senegal
Data Collector(s) Name: 
Kantar TNS Senegal
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.
Funding Name, Abbreviation, Role: 
The World Bank
Primary Investigator Name, Affiliation: 
The World Bank
Questionnaires: 
Two types of questionnaires were used during the survey namely; 1. Manufacturing Module Questionnaire 2. Services Module Questionnaire The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully 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 were 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.
Response Rates: 
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: - 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). - 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 0.75.9 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 0.06.
Sampling Procedure: 
The sample for 2017 Sierra Leone ES was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except subsector 72, IT, which was added to the population under study), and all public or utilities sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) - Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is - The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings. Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in Appendix C of the Survey Report. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Sierra Leone ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification for the Sierra Leone ES was done across four regions: Bo, Western Urban, Kenema, and Bombali.
Series Information: 
As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving business environments as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate. An Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. Firm-level surveys have been conducted since 1998 by different units within the World Bank. Since 2005-2006, most data collection efforts have been centralized within the Enterprise Analysis Unit. The Enterprise Surveys are conducted across all geographic regions and cover small, medium, and large companies. The surveys are administered to a representative sample of firms in the non-agricultural formal private economy. Data are used to create indicators that benchmark the quality of the business and investment climate across countries.
Study Type: 
Enterprise Survey
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.
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.
Version Description: 
v01, edited anonymized dataset for public distributionAll 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 "MYA" indicate questions specific to Myanmar, 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.
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:- Strict assumption: eligible establishments are only those for which it was possible to directly determine eligibility. - 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.- 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.

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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. Sierra Leone Enterprise Survey (ES) 2017, Ref. SLE_2017_ES_v01_M. Dataset downloaded from [URL] on [date].

The survey was conducted in Sierra Leone between July and September 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the Enterprise Survey is to gain an understanding of what firms experience in 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 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. Only registered businesses are surveyed in the Enterprise Survey. Data from 150 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews. 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.

Dataset Info

These fields are compatible with DCAT, an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web.
FieldValue
Modified Date
2018-03-07
Release Date
December 31,1969
Identifier
8a619a5c-f652-428b-8441-e816e13437dd
License
License Not Specified
Rating: 
0
No votes yet
Reference ID: 
SLE_2017_ES_v01_M
Acronym: 
ES 2017
Type: 
Languages Supported: 
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.
Response Rates: 
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: - 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). - 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 0.75.9 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 0.06.
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:- Strict assumption: eligible establishments are only those for which it was possible to directly determine eligibility. - 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.- 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.
Time Periods: 
March, 2018
Data Collector(s) Name: 
Kantar TNS Senegal
Primary Investigator Name, Affiliation: 
The World Bank
Version Description: 
v01, edited anonymized dataset for public distributionAll 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 "MYA" indicate questions specific to Myanmar, 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.
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.
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.
Geographical Coverage: 
Data Classification of a Dataset: 
Series Information: 
As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving business environments as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate. An Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. Firm-level surveys have been conducted since 1998 by different units within the World Bank. Since 2005-2006, most data collection efforts have been centralized within the Enterprise Analysis Unit. The Enterprise Surveys are conducted across all geographic regions and cover small, medium, and large companies. The surveys are administered to a representative sample of firms in the non-agricultural formal private economy. Data are used to create indicators that benchmark the quality of the business and investment climate across countries.
Sampling Procedure: 
The sample for 2017 Sierra Leone ES was selected using stratified random sampling, following the methodology explained in the <a href="http://www.enterprisesurveys.org/~/media/GIAWB/EnterpriseSurveys/Documents/Methodology/Sampling_Note.pdf">Sampling Note</a>. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except subsector 72, IT, which was added to the population under study), and all public or utilities sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.) - Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is - The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings. Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in Appendix C of the Survey Report. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries- Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Sierra Leone ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification for the Sierra Leone ES was done across four regions: Bo, Western Urban, Kenema, and Bombali.
Release Date: 
Wednesday, March 7, 2018
Last Updated Date: 
Wednesday, March 7, 2018
Questionnaires: 
Two types of questionnaires were used during the survey namely; 1. Manufacturing Module Questionnaire 2. Services Module Questionnaire The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully 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 were 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 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.
Harvest Source: 
Harvest Source ID: 
9715
Citation Text: 
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. Sierra Leone Enterprise Survey (ES) 2017, Ref. SLE_2017_ES_v01_M. Dataset downloaded from [URL] on [date].
Modified date: 
17597
Study Type: 
Enterprise Survey
Primary Dataset: 
Yes

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