{"type":"survey","doc_desc":{"idno":"DDI_LBR_2018_NHFS_v01_M_WB","producers":[{"name":"Development Economics Data Group","abbreviation":"DECDG","affiliation":"The World Bank Group","role":"Documentation of the DDI"}],"prod_date":"2020-09-18","version_statement":{"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","version_date":"2021-05-27"}},"study_desc":{"title_statement":{"idno":"LBR_2018_NHFS_v01_M","title":"National Household Forest Survey 2018-2019","alt_title":"NHFS 2018-2019"},"authoring_entity":[{"name":"Liberia Institute of Statistics and Geo-Information Services","affiliation":"Government of Liberia"}],"production_statement":{"producers":[{"name":"Living Standards Measurment Study","affiliation":"World Bank Group","role":"Technical Support"},{"name":"World Bank AFR ENR","affiliation":"World Bank Group","role":""}],"funding_agencies":[{"name":"World Bank Forest Carbon Partnership Facility REDD+ Readiness Support","abbreviation":"","role":"Survey Implementation"},{"name":"Program on Forests","abbreviation":"PROFOR","role":"Technical Assistance"},{"name":"World Bank Environment and Natural Resource Global Practice","abbreviation":"","role":"Technical Assistance"}]},"distribution_statement":{"contact":[{"name":"Neeta Hooda","affiliation":"World Bank","email":"nhooda@worldbank.org","uri":""},{"name":"Sydney Gourlay","affiliation":"World Bank","email":"sgourlay@worldbank.org","uri":""}]},"series_statement":{"series_name":"Living Standards Measurement Study [hh\/lsms]"},"version_statement":{"version":"Version 1. Edited, anonymous dataset."},"study_info":{"coll_dates":[{"start":"2018-09","end":"2019-01","cycle":""}],"nation":[{"name":"Liberia","abbreviation":"LBR"}],"geog_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.","analysis_unit":"Household; Community","universe":"All EAs within 2.5 kilometers of forests except for the EAs from the urban part of the Montserrado county.","data_kind":"Sample survey data [ssd]"},"method":{"data_collection":{"data_collectors":[{"name":"Liberia Institute of Statistics and Geo-Information Services","abbreviation":"LISGIS","affiliation":"Government of Liberia"}],"collector_training":{"training":"The training of field staff, including enumerators, mappers, listers, supervisors and monitors, was held from August 14-23, 2018, at LISGIS HQ. Phase I of the training focused on familiarizing the participants with the paper versions of the household and community questionnaires; Phase II focused on familiarizing participants with the CAPI version of the questionnaires. Training also included a field-day for pilot-testing the questionnaires, to gather inputs from respondents and adjust the questionnaire based on feedback from the piloting. The World Bank team worked closely with LISGIS and with two designated staff members from FDA on the training and on the refinement of the questionnaire in CAPI. To be able to accommodate the CAPI changes, the training was paused for two days and it finally concluded on August 25th, 2018.\n\nFrom the pool of 44 enumerators invited for the training, a final group of the top 35 were selected competitively. This selection was based on their scores on a written test and on an assessment of their performance in the field during the piloting."},"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:\n\nForest = area with at least 30 percent tree canopy cover, with trees higher than 5 meters and at least 50 hectares in size\n\nThe 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.\n\nBased 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).\n\nFollowing 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.\n\nFollowing 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.\n\nThe 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.\n\nUpon 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.\n\nThe 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.","sampling_deviation":"Please refer to the Basic Information Document found in the External Resources section.","coll_mode":"Computer Assisted Personal Interview [capi]","research_instrument":"The NHFS survey consisted of:\n1. A HH questionnaire, administered to 12 selected HHs in each enumeration area, and\n2. A community questionnaire, administered to a group of members from the EA.\n\nEach questionnaire was administered using computer-assisted personal interviewing (CAPI) with CSPro3 software.","instru_development":"Liberia NHFS questions are derived from the National Socioeconomic Surveys in Forestry guidebook and set of specialized forestry modules (FAO, CIFOR, IFRI, and World Bank, 2016).\n\nThe guidebook modules, originally designed for universal adoption, were adapted to the Liberian context. Borrowing from the Liberia Household Income and Expenditure Survey (HIES) instruments, they were then supplemented with several modules on income to allow for computation of total HH income. In addition, the NHFS team developed a questionnaire module on gender-related aspects of forest enterprises and forest-related community participation.","coll_situation":"Data was collected using CSPro. Data collection was implemented by LISGIS.","weight":"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.\n\nFor more information on weighting please refer to the Basic Information Document in the External Resources","cleaning_operations":"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.\n\nThe 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.\n\nThe 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.\n\nThe 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."}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"","required":"yes","form_no":"","uri":""}],"cit_req":"Use of the dataset must be acknowledged using a citation which would include:\n- the Identification of the Primary Investigator\n- the title of the survey (including country, acronym and year of implementation)\n- the survey reference number\n- the source and date of download.\n\nExample:\n\nLiberia 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":"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."}}},"schematype":"survey","data_files":[{"file_id":"F1","file_name":"comm_sect1_public.dta","description":"National Household Forest Survey 2018-2019 community datafile:\n- Section 1 (Identification of Community)","case_count":245,"var_count":7,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F2","file_name":"comm_sect2_public.dta","description":"National Household Forest Survey 2018-2019 community datafile:\n- Section 2 (List of participants at community 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