Enterprise Survey 2017
Liberia, 2017
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Reference ID
LBR_2017_ES_v01_M
Producer(s)
World Bank
Collection(s)
Created on
Mar 08, 2018
Last modified
Mar 08, 2018
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Identification
Survey ID Number
LBR_2017_ES_v01_M
Title
Enterprise Survey 2017
Country/Economy
| Name | Country code |
|---|---|
| Liberia | LBR |
Study type
Enterprise Survey [en/oth]
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.
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.
Abstract
This document provides additional information on the data collected in Liberia between July 2017 and September 2017. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.
The sample frame consisted of listings of firms from two sources: For panel firms the list of 150 firms from the Liberia 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from the 2014 Liberia Business Registry, was used.
As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment 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.
The sample frame consisted of listings of firms from two sources: For panel firms the list of 150 firms from the Liberia 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from the 2014 Liberia Business Registry, was used.
As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment 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.
Kind of Data
Sample survey data [ssd]
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.
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.
Version
Version Description
v01, edited anonymized 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 "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.
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 "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.
Scope
Notes
The scope of the study includes:
- Characteristics of establishment
- Infrastructure and services
- Sales and supplies
- Degree of competition
- Capacity
- Land and permits
- Innovation
- Crime
- Finance
- Business-government relations
- Labor
- Business environment
- Performance
- Characteristics of establishment
- Infrastructure and services
- Sales and supplies
- Degree of competition
- Capacity
- Land and permits
- Innovation
- Crime
- Finance
- Business-government relations
- Labor
- Business environment
- Performance
Coverage
Geographic Coverage
National
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.
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
Primary investigators
| Name |
|---|
| World Bank |
Funding Agency/Sponsor
| Name |
|---|
| World Bank |
Sampling
Sampling Procedure
The sample for the 2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random 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 particularly true if measurements within strata are homogeneous.
- 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. 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 Liberia 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 Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.
- 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 particularly true if measurements within strata are homogeneous.
- 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. 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 Liberia 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 Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.
Response Rate
There was a high response rate especially as a result of positive attitude towards the international community in collaboration with the government in their reconstruction efforts after a period of civil strife.There was also very positive attitude towards World Bank initiatives.
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.
- 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.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2017-07 | 2017-09 |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
Private contractors conduct the Enterprise Surveys on behalf of the World Bank. Due to sensitive survey questions addressing business-government relations and corruption-related topics, private contractors are preferred over any government agency or an organization/institution associated with government, and are hired by the World Bank to collect the data.
The Enterprise Survey was implemented following a 2-stage procedure. In the first stage a screener questionnaire is applied over the phone to determine eligibility and to make appointments. In the second stage a face-to-face interview takes place with the Manager/Owner/Director of each establishment. However, sometimes the phone numbers were unavailable in the sample frame, and thus the enumerators applied the screeners in person.
Kantar Belgium SA (previously TNS OPINION) was the main contractor and Kantar TNS Senegal (TNS RMS Senegal) was the subcontractor that implemented the Sierra Leone ES.
The Enterprise Survey was implemented following a 2-stage procedure. In the first stage a screener questionnaire is applied over the phone to determine eligibility and to make appointments. In the second stage a face-to-face interview takes place with the Manager/Owner/Director of each establishment. However, sometimes the phone numbers were unavailable in the sample frame, and thus the enumerators applied the screeners in person.
Kantar Belgium SA (previously TNS OPINION) was the main contractor and Kantar TNS Senegal (TNS RMS Senegal) was the subcontractor that implemented the Sierra Leone ES.
Data Collectors
| Name |
|---|
| Kantar Belgium SA (Previously TNS Opinion) |
| Kantar TNS Senegal (TNS RMS Senegal) |
Questionnaires
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.
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 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.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Enterprise Analysis Unit | The World Bank | enterprisesurveys@worldbank.org | Link |
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. Liberia Enterprise Survey (ES) 2017, Ref. LBR_2017_ES_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. Liberia Enterprise Survey (ES) 2017, Ref. LBR_2017_ES_v01_M. Dataset downloaded from [URL] on [date].
Archive where study is originally stored
Enterprise Surveys Data Portal - https://www.enterprisesurveys.org/portal
Cost: None
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_LBR_2017_ES_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | The World Bank | Documentation of the study |
Date of Metadata Production
2018-03-02
DDI Document version
v01 (February 2018)