We-Fi WeTour Women in Tourism Enterprise Survey 2019
Sierra Leone, 2019
Get MicrodataIdentification
SLE_2019_WE-FI_v01_M
We-Fi WeTour Women in Tourism Enterprise Survey 2019
| Name | Country code |
|---|---|
| Sierra Leone | SLE |
The World Bank We-Fi WeTour Women in Tourism Enterprise Survey was conducted in Sierra Leone and Ghana.
Sample survey data [ssd]
Micro, Small and Medium Tourism and tourism-related enterprises
Version
Edited, anonymous dataset for public distribution.
Scope
The survey collected information from Micro, Small and Medium Tourism and tourism-related enterprises on the following thematic areas:
• business characteristics
• investment climate
• marketing and sales
• production and operations
• human resources/ workforce
• finance and accounting
• business strategy
• ICT usage
The survey use in Sierra Leone and Ghana was a portion of BESTIN-OPMes, (for Benchmarking Strategy and Innovation – Operations People Money – enterprise survey) a larger global enterprise survey that belongs to EECi with additional information available at www.groupeeci.com
Coverage
In Sierra leone, two destination areas were identified as Freetown and the Western Area.
The universe of MSMEs in Tourism and Tourism-related sectors of Freetown and the Western Area in Sierra Leone comprises 1,067 entities identified individually in the sampling frame.
Producers and sponsors
| Name |
|---|
| Louise Twining-Ward (World Bank - Finance, Competitiveness and Innovation Global Practice, Markets & Technology Unit) |
| Name | Role |
|---|---|
| World Bank | Financing |
| Name | Affiliation | Role |
|---|---|---|
| Abhishek Saurav | World Bank Group | Economist |
| Souleima Hilal | World Bank Group | Analyst |
| Wendy Li | World Bank Group | Coordinator |
| Fares Khoury | EECI | Data Collection |
Sampling
The universe of tourism and tourism related SMEs was constructed in each country using all available sources. For both countries the original sample frame of SMEs was compiled from previous sample frames developed for enterprise surveys by EEC International, the amalgamation of past listings of SMEs from the NSO and other public registries, as well as numerous other sources collated from business associations and other publicly available sources of tourism-related information portals, namely: travel agent reservation systems such as Amadeus and Sabre, tourism and tourism-related websites such as Expedia and TripAdvisor, as well as establishments referenced on Google Maps and appearing on Google Street View. The sample frame for micro enterprises was planned to result from systematic block enumeration in the targeted locations. During the block enumeration, entities were identified by a number on a list and a geographical reference (map or other description of the location), information on its apparent activity (tourism or tourism-related), as well as visible gender composition (no apparent female, no apparent male, mixed presence). Neither the activity composition nor the gender composition were known at inception. The validation of the sample frame consisted in ensuring that there were no foreign elements (activities not included in the universe under study).
The sampling strategy that EECI applied for the Tourism and Tourism related Sectors applying consisted in randomly drawing from the frame of MSMEs a screened sample until the minimum number of male and female respondents targeted was obtained, inclusive of the expected non-response.
For Sierra Leone, the frame contained a total of 1,067 entities, of which 705 micros and 362 SMEs. A random draw of 323 entities, consisting of 212 micros and 111 SMEs generated through a screening 125 female entities and 198 male entities. The entire group of 125 female entities was directed to interviewing, and the first 125 male entities that were screened, were directed to interviewing, with an expected 120 respondents by genre. For more details see Methodology Note provided under Related Documents.
The response rate was 96.6% for Sierra Leone. There are slight variations of these indicators by sub-groups of businesses.
The final dataset contains three of weight estimations according to sub-groups of businesses:
• by size (two categories - Micro or SME),
• by gender (two categories - Male or Female enterprises)
• and by size-gender (four categories - Micro-male, Micro-female, SME-male and SME-female).
The weight of each category, in each one of the sub-groups of businesses, is the ratio between the actual population in the category and the effective number of respondents in this same category.
Population distribution by size was a known characteristic, while gender distribution was unknown. In order to obtain population composition by gender, screening proportions were used as a proxy.
Data collection
| Start | End |
|---|---|
| 2019-04-22 | 2019-05-31 |
| Name |
|---|
| Economic Expertise & Consulting International |
World Bank Team - Finance, Competitiveness and Innovation Global Practice, Markets & Technology Unit
Data processing
Data entry and quality controls were implemented by the contractor then data was delivered to the World Bank. The World Bank validated data were validated for logical consistency, flagging problems that were then corrected by the implementing contractor.
Data appraisal
According to sample design, it is possible to generalize survey results (at a precision of 7.5% and a confidence level of 90%) at the sector level, and the respective gender sub-groups of businesses.
Data Access
| Name | Affiliation | URL |
|---|---|---|
| Microdata Library | World Bank | microdata.worldbank.org |
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | Before being granted access to the dataset, all users have to formally agree: 1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor. 2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files. 3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor. |
- Public use files, accessible to all
Twining-Ward, L, Saurav, A. and Hial S.E. (World Bank Group). (2019). Women in Tourism Enterprise Survey for Sierra Leone (WE-FI). Ref (SLE_2019_WE-FI_v01_M). Downloaded from [url] on [date].
Disclaimer and copyrights
The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Directors or Executive Directors of the respective institutions of the World Bank Group or the governments they represent. The World Bank Group does not guarantee the accuracy of the data included in this work.
© 2019 The World Bank Group
Contacts
| Name | Affiliation | |
|---|---|---|
| Louiset Twining-Ward | World Bank Group | ltwiningward@worldbank.org |
| Abhishek Saurev | World Bank Group | asaurav@ifc.org |
Metadata production
DDI_SLE_2019_WE-FI_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Economics Data Group | The World Bank | Documentation of the DDI |
2019-11-05
Metadata version
Version 01 (November 2019)