National Household Forest Survey 2018-2019
Liberia, 2018 - 2019
Liberia Institute of Statistics and Geo-Information Services
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July 12, 2021
Last modified
July 12, 2021
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Identification
Survey ID Number
LBR_2018_NHFS_v01_M
Title
National Household Forest Survey 2018-2019
Country/Economy
| Name | Country code |
|---|---|
| Liberia | LBR |
Study type
Living Standards Measurement Study [hh/lsms]
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Household; Community
Version
Version Description
Version 1. Edited, anonymous dataset.
Coverage
Geographic Coverage
The NHFS is focused on forest proximate households. Therefore, the sample is limited to enumeration areas which fall within 2.5km of the nearest forest, as defined using Metria and Geoville (2019) land cover data. The final sample includes enumeration areas from all 15 of Liberia's counties, but excludes urban areas of Montserrado.
Universe
All EAs within 2.5 kilometers of forests except for the EAs from the urban part of the Montserrado county.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Liberia Institute of Statistics and Geo-Information Services | Government of Liberia |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Living Standards Measurment Study | World Bank Group | Technical Support |
| World Bank AFR ENR | World Bank Group |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| World Bank Forest Carbon Partnership Facility REDD+ Readiness Support | Survey Implementation | |
| Program on Forests | PROFOR | Technical Assistance |
| World Bank Environment and Natural Resource Global Practice | Technical Assistance |
Sampling
Sampling Procedure
Given the focus of the NHFS on the population living in close proximity to forests4, a first step was to clearly define forest for the purposes of the survey. Building on the national definition of forest used in Liberia, and modifying it in order to minimize the impact of small urban forests and facilitate survey operations, the NHFS employed the following definition:
Forest = area with at least 30 percent tree canopy cover, with trees higher than 5 meters and at least 50 hectares in size
The forest cover was determined using high-resolution forest cover data produced in 2019 based on satellite information on forest cover in Liberia for 2015.6 All EAs within 2.5 kilometers of forests identified with this definition were deemed eligible for inclusion in the NHFS.7 EAs from the Montserrado county (part of Greater Monrovia) were excluded from the sample universe due to the high rate of urbanization. However, rural parts of Montserrado county were included in the sample universe.
Based on the forest definition defined above, the distance from each EA in the country (except urban Montserrado) to the nearest forest was computed. That distance was subsequently used to assign each EA to one of the following strata: S1 (less than 2km from forest); S2 (two to 7 km from forest); S3 (7 to 15 km from forest).
Following strata classification, a total of 250 EAs were selected through a Probability Proportional to Size (PPS) sampling approach within each stratum, with the following purposeful allocation across strata: 90 EAs in S1; 90 EAs in S2; 70 EAs in S3.8 The measure of size for each EA was based on the total number of households listed in the 2008 PHC.
Following the selection of the 250 sample EAs, a listing of households was conducted in each sample EA to provide the sampling frame for the second stage selection of households. Random sampling was used to select 12 households from the household listing for each sample EA.
The original sample design provided a total household sample size of 3,000 (250 EAs with 12 households sampled per EA), data from 14 households are missing or unusable, representing 0.05 percent of the sample and resulting in a final sample of 2,986 households. Similarly, data from 5 of the community questionnaires were missing or unusable, resulting in a total sample of 245 community questionnaires. The final sample of 2,986 households is distributed across counties.
Upon post-data collection analysis, it was discovered that the initial variable that was used to stratify EAs by distance to forest was incorrectly computed. Despite thorough attempts to understand the nature and source of the error, it was determined that a mechanical error must have occurred during the process of the distance calculations. This error rendered the stratification incorrect. Therefore, the stratification by distance to forest has been abandoned and the sample weighted to reflect only geographic clusters, not distance to forest. This was determined to be the most appropriate way forward following consultation with sampling experts.
The resulting sample, therefore, is weighted to reflect all EAs in Liberia (with the exception of urban Montserrado) that fall within 2.5 km of the nearest forest, which was the upper bound of the distances for the selected EAs.
Forest = area with at least 30 percent tree canopy cover, with trees higher than 5 meters and at least 50 hectares in size
The forest cover was determined using high-resolution forest cover data produced in 2019 based on satellite information on forest cover in Liberia for 2015.6 All EAs within 2.5 kilometers of forests identified with this definition were deemed eligible for inclusion in the NHFS.7 EAs from the Montserrado county (part of Greater Monrovia) were excluded from the sample universe due to the high rate of urbanization. However, rural parts of Montserrado county were included in the sample universe.
Based on the forest definition defined above, the distance from each EA in the country (except urban Montserrado) to the nearest forest was computed. That distance was subsequently used to assign each EA to one of the following strata: S1 (less than 2km from forest); S2 (two to 7 km from forest); S3 (7 to 15 km from forest).
Following strata classification, a total of 250 EAs were selected through a Probability Proportional to Size (PPS) sampling approach within each stratum, with the following purposeful allocation across strata: 90 EAs in S1; 90 EAs in S2; 70 EAs in S3.8 The measure of size for each EA was based on the total number of households listed in the 2008 PHC.
Following the selection of the 250 sample EAs, a listing of households was conducted in each sample EA to provide the sampling frame for the second stage selection of households. Random sampling was used to select 12 households from the household listing for each sample EA.
The original sample design provided a total household sample size of 3,000 (250 EAs with 12 households sampled per EA), data from 14 households are missing or unusable, representing 0.05 percent of the sample and resulting in a final sample of 2,986 households. Similarly, data from 5 of the community questionnaires were missing or unusable, resulting in a total sample of 245 community questionnaires. The final sample of 2,986 households is distributed across counties.
Upon post-data collection analysis, it was discovered that the initial variable that was used to stratify EAs by distance to forest was incorrectly computed. Despite thorough attempts to understand the nature and source of the error, it was determined that a mechanical error must have occurred during the process of the distance calculations. This error rendered the stratification incorrect. Therefore, the stratification by distance to forest has been abandoned and the sample weighted to reflect only geographic clusters, not distance to forest. This was determined to be the most appropriate way forward following consultation with sampling experts.
The resulting sample, therefore, is weighted to reflect all EAs in Liberia (with the exception of urban Montserrado) that fall within 2.5 km of the nearest forest, which was the upper bound of the distances for the selected EAs.
Deviations from the Sample Design
Please refer to the Basic Information Document found in the External Resources section.
Weighting
Sample weights are constructed to reflect the population in EAs within 2.5 kms of the forests, for three geographic clusters. The weights are also adjusted to reflect population estimates as of 2016.
For more information on weighting please refer to the Basic Information Document in the External Resources
For more information on weighting please refer to the Basic Information Document in the External Resources
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2018-09 | 2019-01 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Data was collected using CSPro. Data collection was implemented by LISGIS.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Liberia Institute of Statistics and Geo-Information Services | LISGIS | Government of Liberia |
Questionnaires
Questionnaires
The NHFS survey consisted of:
1. A HH questionnaire, administered to 12 selected HHs in each enumeration area, and
2. A community questionnaire, administered to a group of members from the EA.
Each questionnaire was administered using computer-assisted personal interviewing (CAPI) with CSPro3 software.
1. A HH questionnaire, administered to 12 selected HHs in each enumeration area, and
2. A community questionnaire, administered to a group of members from the EA.
Each questionnaire was administered using computer-assisted personal interviewing (CAPI) with CSPro3 software.
Data Processing
Data Editing
The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted by the field-based teams during the interview itself utilizing error messages generated by the CSPro application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent.
The second stage occurred during the review of the questionnaire by the supervisors. Prior to sharing data with LISGIS HQ, the supervisor was to review the interviewers. Depending on the outcome, the supervisors can either approve or reject the case. If rejected, the case goes back to the respective enumerator and a re-visit to the household may be necessary. Additional errors were compiled into error reports by the World Bank and LISGIS HQ that were regularly sent to the teams and then corrected based on re-visits to the household.
The last stage involved a comprehensive review of the final raw data following the first and second stage cleaning, after data collection completion. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.
The first and the second stage of the cleaning activities were led by LISGIS and the World Bank provided technical assistance. The third stage of data cleaning was performed by the World Bank team exclusively.
The second stage occurred during the review of the questionnaire by the supervisors. Prior to sharing data with LISGIS HQ, the supervisor was to review the interviewers. Depending on the outcome, the supervisors can either approve or reject the case. If rejected, the case goes back to the respective enumerator and a re-visit to the household may be necessary. Additional errors were compiled into error reports by the World Bank and LISGIS HQ that were regularly sent to the teams and then corrected based on re-visits to the household.
The last stage involved a comprehensive review of the final raw data following the first and second stage cleaning, after data collection completion. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.
The first and the second stage of the cleaning activities were led by LISGIS and the World Bank provided technical assistance. The third stage of data cleaning was performed by the World Bank team exclusively.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Neeta Hooda | World Bank | nhooda@worldbank.org |
| Sydney Gourlay | World Bank | sgourlay@worldbank.org |
Confidentiality
Citation requirements
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download.
Example:
Liberia Institute of Statistics and Geo-Information Services. Liberia National Household Forest Survey 2018-2019. Ref. LBR_2018_NHFS_v01_M. Dataset downloaded from [url] on [date].
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download.
Example:
Liberia Institute of Statistics and Geo-Information Services. Liberia National Household Forest Survey 2018-2019. Ref. LBR_2018_NHFS_v01_M. Dataset downloaded from [url] on [date].
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_2018_NHFS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank Group | Documentation of the DDI |
Date of Metadata Production
2020-09-18
DDI Document version
Version 2 (May 2021). This version is similar to version 1 except for the addition of the data description section and the availability of the microdata