Agriculture Household Listing Survey 2023
Sierra Leone, 2023
Get MicrodataIdentification
SLE_2023_SLLIST_v01_M_v01_A_ESS
Agriculture Household Listing Survey 2023
SLLIST 2023
| Name | Country code |
|---|---|
| Sierra Leone | SLE |
In recognition of the critical role the agricultural sector plays in national development, Sierra Leone joined the 50x2030 Initiative in early 2023. This partnership focuses on establishing a sustainable annual agricultural survey program. The program's primary aim is to generate high-quality, timely, and relevant agricultural data that directly addresses the country's needs. The implementation of the 50x2030 activities in Sierra Leone relies on a variety of statistical undertakings, one of which is the Sierra Leone Listing Survey (SLLIST) conducted in 2023. This survey plays a vital role in monitoring and achieving the goals of the 50x2030 initiative. It serves as a foundational element for subsequent surveys, establishing a comprehensive sampling frame that will be utilized in future data collection efforts. By meticulously gathering data on various aspects, SLLIST lays the groundwork for further analysis and ensures the accuracy of subsequent surveys.
The other specific objectives for the design of the Listing Survey were to:
- Accurately enumerate and document all dwelling units/structures and households within the selected enumeration areas (EAs) in preparation for agricultural holdings/households' selection
- Record detailed description of every structure and identify the heads of agricultural holdings/households
- Collect information in each of the 520 selected EAs, ensuring effective supervision and monitoring of the agricultural holdings/households to be selected for data collection.
Sample survey data [ssd]
Households
Scope
This survey focused on collecting data from agricultural households, covering topics such as household demographics, land ownership, agricultural activities, livestock rearing, labor force composition, and participation in off-farm activities.
| Topic | Vocabulary | URI |
|---|---|---|
| Agricultural workers | ELSST Thesaurus | https://thesauri.cessda.eu/elsst-5/en/ |
| Agricultural production | ELSST Thesaurus | https://thesauri.cessda.eu/elsst-5/en/ |
Coverage
National coverage, with the exception of the Western Urban district
Producers and sponsors
| Name | Affiliation |
|---|---|
| Statistics Sierra Leone | Government of Sierra Leone |
| Ministry of Agriculture and Food Security | Government of Sierra Leone |
| Name | Affiliation | Role |
|---|---|---|
| Food and Agriculture Organization | United Nations | Technical Assistance |
| Name | Abbreviation | Role |
|---|---|---|
| World Bank | WB | Financial assistance through the HISWA Project |
Sampling
The survey employed a stratified random sampling technique to ensure a representative sample of agricultural households across all five regions and fifteen districts of Sierra Leone with the exception of the Western Urban district. From 514 Enumeration Areas, a total of 42990 agricultural households were interviewed.
The response rate was 99.9%.
Sample weights were calculated for the data file. It was computed as the inverse of the probability of selection of agricultural household, the latter bieng the product of the probability of selection of the EA in which the household is located and the probability of selection of the EA.
The weight variable is called "SampleWeight".
Survey instrument
The questionnaire was administered in each household, preferably to the head of household. It includes questions on household demographic characteristics and agricultural activities practiced.
The questionnaire is provided as external resource.
The dataset was anonymized using statistical disclosure methods. To start the process, variables were classified into main categories as variables to delete, quasi identifiers, direct identifiers, linked variables. All direct identifiers and unnecessary variables were removed. Then, quasi-identifiers (and linked variables) were considered to formulate disclosure scenarios. Disclosure risk was measured using k-anonymity and probabilistic risk.
Quasi identifiers have been anonymized using top coding (which was especially applied to categorical varaibles ), shuffling (which was applied to enumeration area codes) and local suppression which was applied to both the quasi identifiers and the linked variables. In addition, information loss was mainly measured based on number of missing values introduced during anonymization and dependency coefficient between categorical variables.
The software used was R.
Data collection
| Start | End |
|---|---|
| 2023-08-28 | 2023-09-18 |
- Computer Assisted Personal Interview [capi]
Data processing
The listing questionnaire was implemented on CSPRO as CAPI tool. During data collection, some validation controls were integrated into the app to minimize mistakes when typing households’ answers. After data collection, a processing program designed under SPSS software permitted to clean both cases and variables. Duplicated cases were deleted and then the sampling weights adjusted to take the two non-covered EAs into account. Missing, illegal, unlike and incoherent values were detected and then locally imputed objectively in respecting filters. Finally, the necessary tabulation variables were created and then tables were produced according to the tabulation plan designed earlier.
Data appraisal
To appreciate the data quality, some tables were supported by sampling errors estimates. Especially, coefficients of variations and standard errors were estimated for a set of indicators in open data publishing purposes.
Data Access
| Confidentiality declaration text |
|---|
| The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO. |
Licensed datasets, accessible under conditions.
Disclaimer and copyrights
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_SLE_2023_SLLIST_v01_M_v01_A_ESS
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Statistics Sierra Leone | Stats SL | Government of Sierra Leone | Metadata producer |
| Ministry of Agriculture and Food Security | MAFS | Government of Sierra Leone | Metadata producer |
| Statistics Division | ESS | FAO | Metadata producer and metadata adapted for FAM |
| Development Data Group | DECDG | The World Bank | Metadata adapted for World Bank Microdata Library |
Metadata version
Identical to a metadata (SLE_2023_SLLIST_v01_EN_M_v01_A_ESS) published on FAO microdata repository (https://microdata.fao.org/index.php/catalog). Some of the metadata fields have been edited.