The documented dataset covers Enterprise Survey (ES) panel data collected in Sierra Leone in 2009 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.
The objective of the 2009-2017 survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. 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.
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.
Questionnaire topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, land and permits, taxation, business-government relations, performance measures, AIDS and sickness. The mode of data collection is face-to-face interviews.
Kind of data
Sample survey data [ssd]
v01, edited, anonymous dataset for public distribution
The Enterprise Surveys panel datasets have the following common format:
- Variable panel allows easy identification of panel observations
- Variable panel id is the same across the waves for the same firm
- Variable eligibility<year> reports eligibility status of all firms interviewed in the previous wave as of the <year> of the latest wave, e.g. in 2009-2016 panel, eligibility2016 reports status as of 2016 of all firms interviewed in 2009
- Wherever possible variables are matched across waves. If needed, matches are made by converting variable names in older waves to variable names in the most recent wave
- Due to methodological changes and evolution of the survey instrument it is not possible to match all variables in the datasets
- Variables that are not matched across waves are named as _<year>_<variable>, with the year in which the variable was collected (e.g. _2009_date)
- It is recommended that users thoroughly familiarize themselves with the questionnaires from each of the years contained in the dataset before proceeding with analysis
- Some monetary unit variables in 2002 and 2005 surveys (in U.S. currency) are converted into the local currency units (LCU) using the market, period average, exchange rates. The sources of the exchange rates are the International Financial Statistics (IFS - IMF) websites
- Weights are representative of the universe for the year that the firm was interviewed. They are not panel weights.
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 the 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.
The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural 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.
Producers and sponsors
The World Bank
The World Bank
Kantar Belgium SA (previously TNS OPINION)
Kantar TNS Senegal
The sample for registered establishments in Sierra Leone 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:
a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision.
b. 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 sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.
c. 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.
d. 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.)
e. 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.
f. 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 the Sierra Leone sample: firm sector, firm size, and geographic region.
Industry stratification was designed as follows: the universe was stratified into one manufacturing industry and one services industry (retail).
Size stratification was defined following the standardized definition used for the Indicator Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.
Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Kenema and W/A Urban. In 2017, regional stratification was done across four regions: Bo, Western Urban, Kenema, and Bombali.
Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample. Great efforts were made to obtain the best source for these listings.
The sample frame consisted of listings of firms from two sources: For panel firms the list of 150 firms from the Sierra Leone 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from 2016 Business Establishment Census and Dun & Bradstreet Global database (June 2017), was used.
Necessary measures were taken to ensure the quality of the frame; however, the sample frame was not immune to the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.
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 8.9% (18 out of 202 establishments).
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. It is period in which a lot of statistics is being collected by the Sierra Leone Statistics for reconstruction thus most respondents were enlightened on research benefits.
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.
Weights are representative of the universe for the year that the firm was interviewed. They are not panel weights.
Dates of collection
Mode of data collection
The current survey instruments are available:
- Services and Manufacturing Questionnaire
- Screener Questionnaire.
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.
Kantar TNS Senegal
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.
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.
Aggregate indicators based on Enterprise Survey data are available to the public at https://www.enterprisesurveys.org
Firm-level data is also available to the public free-of-charge. In order to access the firm-level data, users must agree to abide by a strict confidentiality agreement available through Enterprise Analysis Unit website by clicking on "External users register here" at https://www.enterprisesurveys.org/Portal.
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)
The World Bank. Sierra Leone Enterprise Survey (ES-P) 2009-2017, Panel Data, Ref. SLE_2009-2017_ES-P_v01_M. Dataset downloaded from [URL] on [date].
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.