Abstract

The Government of Liberia and its development partners recognized agriculture as a pivotal sector in fostering economic growth, reducing poverty, and achieving food security. Since the post-war period (insert dates) , the government in collaboration with development partners, has made substantial investments to develop and expand the agriculture sector. Over the years, policymakers and data users in the agriculture sector have experienced significant challenges in obtaining the data needed to monitor and evaluate these interventions and make informed decisions on new interventions. To address these challenges, the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and the Ministry of Agriculture (MoA) conducted several ad hoc agricultural surveys. While valuable, these surveys have often been limited in scope and unable to provide the comprehensive data needed for effective policymaking and planning. To support the sector more robustly, the government decided to undertake a comprehensive agricultural census:the Liberia Agriculture Census 2024, the second agricultural census in Liberia since 1971 and the first to be conducted digitally, aimed to collect structural and reliable data on various aspects of the agricultural sector.

The main objectives of the LAC-2024 was to:

· Reduce the existing data gap in Liberia's agriculture sector.

· Provide comprehensive data on the agriculture sector for policy formulation and evaluation of existing programmes.

· Enable LISGIS to establish an agriculture master sampling frame for future agricultural surveys and research.

· Identify the structural changes in the agriculture sector over time.

· Provide information on crop, livestock, poultry, and aquaculture activities.

· Determine the size, composition, practices and related characteristics of Liberia's agricultural holdings.

· Generate disaggregated agriculture statistics.

· Provide statistics for advocacy on Liberia's agriculture sector.

· Identify agricultural practices and constraints at the community level.

To achieve these objectives, the LAC-2024 was designed to collect structural data at the household, non-household and community levels. The data provided a wealth of information on the state of agriculture in Liberia. This documentation provides information on how data was collected at the household level. The documentation also provides useful information on the household anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The universe for the Liberia Agriculture Census 2024 household level data collection is all households in Liberia having at least one member engaged in agricultural activity during the 2022/2023 farming season.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Liberia Agriculture Census 2024 (LAC-2024) was a sampled census conducted in all 15 counties of Liberia. The sampling frame used for the LAC-2024 is based on the 2022 National Population and Housing Census (2022-NPHC), conducted by the LISGIS. The sample design for the census was a stratified cluster sample with enumeration areas (EAs) as clusters and farming households as units of interest. In line with budget availability, a large sample of 4,800 EAs was considered for the LAC-2024. These EAs had a total of 269,652 agricultural households in the frame. The sample was allocated by strata (districts, urban/rural) proportional to the numbers of farming households in the frame. In total, about 78.8% of the sample was allocated to rural areas. The stratified sample of EAs was selected with a probability proportional to the number of farming households at EA level. A complete listing of all households (both agricultural and non-agricultural) was carried out in the selected EAs and detailed questions were addressed to all households that practiced agricultural activities during the 2022/2023 farming season. The results of the LAC-2024 are representative at the district level.

For more information on the LAC-2024 sampling methodology, see the methodology section of the Liberia Agriculture Census 2024 Household Report (available in the downloads tab).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The LAC-2024 employed three questionnaires: the Household Questionnaire, the Community Questionnaire and the Non-Household Questionnaire. These three questionnaires were based on the 50x2030 Initiative standard model questionnaires. The Liberia Agriculture Census Technical Working Group (LAC-TWG), comprising technical staff from LISGIS,Ministry of Agriculture (MOA), National Fishery and Aquaculture Authority (NaFAA), Cooperative Development Agency (CDA) and the Ministry of Internal Affairs (MIA) worked with technicians from the 50x2030 Initiative to adapt the questionnaires to Liberia's context and realities. Suggestions and inputs were solicited from various stakeholders representing government ministries, agencies and commissions(termed MACs by LISGIS), nongovernmental and international organizations as well as academic institutions researching agricultural issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, to ease administration. The household questionnaire included type of agricultural activities practiced, household members characteristics, housing conditions, hired labour practices, agricultural parcels and plots characteristics, types of crops and methods of crop cultivation, inputs, tools and equipment used, type and number of livestock and poultry. The household questionnaire was administered to the household head or an adult member of the household with knowledge of the household and its agricultural activities. The primary respondent (i.e., the household member that provided most of the information for the questionnaire or a given module, household member, or crop) sometimes varied across modules.

Cleaning operations

The data was edited using CSpro software, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In a few cases, manual editing was applied to recode the “other specify” category. The SPSS software was used for this purpose.

Response rate

92.8%

Abstract

The Government of Liberia and its development partners recognized agriculture as a pivotal sector in fostering economic growth, reducing poverty, and achieving food security. Since the post-war period (insert dates), the government in collaboration with development partners, has made substantial investments to develop and expand the agriculture sector. Over the years, policymakers and data users in the agriculture sector have experienced significant challenges in obtaining the data needed to monitor and evaluate these interventions and make informed decisions on new interventions. To address these challenges, the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and the Ministry of Agriculture (MoA) conducted several ad hoc agricultural surveys. While valuable, these surveys have often been limited in scope and unable to provide the comprehensive data needed for effective policymaking and planning. To support the sector more robustly, the government decided to undertake a comprehensive agricultural census: the Liberia Agriculture Census 2024, the second agricultural census in Liberia since 1971 and the first to be conducted digitally, aimed to collect structural and reliable data on various aspects of the agricultural sector.

The main objectives of the LAC-2024 was to: · Reduce the existing data gap in Liberia's agriculture sector. · Provide comprehensive data on the agriculture sector for policy formulation and evaluation of existing programmes. · Enable LISGIS to establish an agriculture master sampling frame for future agricultural surveys and research. · Identify the structural changes in the agriculture sector over time. · Provide information on crop, livestock, poultry, and aquaculture activities. · Determine the size, composition, practices and related characteristics of Liberia's agricultural holdings. · Generate disaggregated agriculture statistics. · Provide statistics for advocacy on Liberia's agriculture sector. · Identify agricultural practices and constraints at the community level.

To achieve these objectives, the LAC-2024 was designed to collect structural data at the household, non-household and community levels. The data provided a wealth of information on the state of agriculture in Liberia. This documentation provides information on how data was collected at the community level. The documentation also provides useful information on the community anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Agricultural Communities

Universe

The universe for the Liberia Agriculture Census 2024 community operations is: all communities (localities) in Liberia that are located within an agricultural enumeration area.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Focus group interviews were conducted in communities in the EAs selected for the sample census. A sampled community had the same probability of selection and sample weight as the EA. If a community was linked to many EAs, additional adjustment for multiplicity was performed. The LAC-2024 community operations engaged 61,600 respondents across 7,193 sampled communities. Nationally, the distribution of respondents shows that males were 66.1% of the total 61,600 participants, while females were 33.9%.

Mode of data collection

Focus Group [foc]

Research instrument

The LAC-2024 employed three questionnaires: the Household Questionnaire, the Community Questionnaire and the Non-Household Questionnaire. These three questionnaires were based on the 50x2030 Initiative standard model questionnaires. The Liberia Agriculture Census Technical Working Group (LAC-TWG), comprising technical staff from LISGIS,Ministry of Agriculture (MOA), National Fishery and Aquaculture Authority (NaFAA), Cooperative Development Agency (CDA) and the Ministry of Internal Affairs (MIA) worked with technicians from the 50x2030 Initiative to adapt the questionnaires to Liberia's context and realities. Suggestions and inputs were solicited from various stakeholders representing government ministries, agencies and commissions (termed MACsby LISGIS), nongovernmental and international organizations as well as academic institutions researching agricultural issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, to ease administration.

The community questionnaire included the following sections: 1- respondents characteristics; 2- production and processing activities in the community; 3- land characteristics and irrigation in the community; 4- markets to sell agriculture products; 5- access to agricultural inputs, services and credits in the community; 6- social cohesion; 7- difficulties in agricultural activities; 8- livestock and Poultry Production; 9- environment; 10- disasters and shocks; 11- community infrastructure and transportation; 12- community organizations; 13- community resource management; 14- land prices and credit; 15- community key events; 16- labour and producer prices.

Cleaning operations

The data was edited using CSpro software, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In a few cases, manual editing was applied to recode the "other specify" category. The SPSS software was used for this purpose.

Response rate

100%

The STRIVE project, funded by USAID's Displaced Children and Orphans Fund (DCOF) and managed by FHI 360, used market-led economic strengthening initiatives to improve the well-being of vulnerable children. Through STRIVE, ACDI/VOCA implemented the Agriculture for Children’s Empowerment (ACE) Project in Liberia, which is founded on the premise that increased household economic security will stimulate more consistent investments in children’s well being via longer term social investments in education and nutrition. ACE’s primary focus was on the horticulture value chain (VC) — the production and marketing of vegetables by smallholder farmers in Montserrado, Bong, and Nimba counties of Liberia. ACE also strengthened smallholder rice farming to increase household food security using a market-sensitive approach to rice seed lending and cultivation. This dataset contains endline information about each plot the household owns.

Abstract

The continued development of Liberia’s agricultural sector is crucial to Liberia’s economic growth and food security. A focus on smallholder farmers helps to ensure pro-poor growth; over 70% of Liberia’s population is involved in farming and the vast majority of this population practice cultivation at the subsistence level, utilizing traditional techniques. The Smallholder Agricultural Productivity Enhancement and Commercialization project (SAPEC) aims to improve the productivity, income and nutritional outcomes of beneficiary farmers in 12 of Liberia’s 15 counties. SAPEC provides farmers with agricultural technologies, constructs and rehabilitates infrastructure to support value-chains and market linkages, as well is working to improve the institutional capacity of the Ministry of Agriculture and associated research institutions. The impact evaluation focuses most directly through the most rigorous methods on the input delivery component. SAPEC’s design incorporates a focus on women, youth and the disabled to better integrate these groups into the agricultural sector and improve their capacity. Given Liberia’s relatively low life expectancy and high youth population (42% below age 15; LISGIS 2011), it is particularly important to encourage youth participation in agriculture. Declining youth participation in the agriculture sector across Africa prompts concerns that if youth are the most open to new technologies, programs promoting new agricultural methods and varieties may struggle to convince farmers to try these new methods unless they can recruit young farmers.

We propose to study the impact of seed and tool distribution on the take-up of modern farming inputs and the use of productivity enhancing tools, thereby resulting in higher agricultural yields and improved nutritional outcomes, as measured by dietary diversity scores. The wide geographic scope of SAPEC and its focus on smallholder farmers offer a unique opportunity to generate data that can be more robustly extrapolated to the wider Liberian population. We will use data from a 2016 registration of Liberian farmers to randomly select 1,000 Liberian farmers from 100 randomly selected communities in Liberian districts serviced by SAPEC.

Using a randomization at multiple levels, we seek to determine whether the provision of 91%-subsidized improved seeds, tools, and fertilizer promote the take-up of modern farming inputs and improve diets. We will also study whether particular beneficiary sub-groups (by age and gender) are more likely to respond to SMS messaging with an agricultural focus and whether small adjustments to the content of these messages can result in relatively greater improvements in take-up by youth.

Geographic coverage

The Smallholder Agriculture Productivity Enhancement and Comercialization (SAPEC) project was conducted in 12 of Liberia's counties and across 97 communities. Counties included in the sample are Bomi, Gbarpolu, Grand Bassa , Grand Cape Mount, Grand Gedeh, Grand Kru, Margibi, Maryland, Montserrado, River Cess, River Gee and Sinoe.

Analysis unit

This study describes:

• Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The SAPEC project was conducted in 12 of Liberia's counties and across 97 communities. In most of the communities, 10-11 farmers were randomly selected to receive the SAPEC benefits while another 10 farmers were randomly chosen to not receive benefits during the 2017 round of distribution. Randomization was done at the community level for which communities will receive the SAPEC benefits then randomly chosen at the farmer level.

Before the baseline survey was launched, the study was piloted extensively in the field based on a rapid response survey that was commissioned by SAPEC, designed by DIME, and implemented with 570 households in 2016. The e-platform developed by LATA was used to compose the sample frame. For each community a sample of 10 farmers were randomly selected in the 50 randomly selected communities to receive the SAPEC benefits. The sampling frame was coordinated closely with the focal SAPEC official in each community in order to ensure that sampled households were able to receive the SAPEC benefits.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is in English and it is provided as a Related Material.

This interactive map of Liberia is divided into 15 counties and highlights the 12 counties targeted by the Smallholder Agricultural Productivity Enhancement and Commercialization Project (SAPEC). These 12 targeted counties account for approximately 60% of Liberia’s total land area, 67% of the population, and 51% of its rice and cassava producing households. The other three counties have been selected for similar projects funded by other donors. The project intends to increase the productivity of 4,000 hectares including 1,000 hectares of upland, which will be dedicated to cassava and rice cultivation respectively. More than 50% percent of the population of these counties whose income depends heavily on agricultural activities, lives below the poverty line. SAPEC will also make more land and water available for cropping through the rehabilitation of 1,000 hectares of community-owned lowland in the four rice-producing counties of Grand Gedeh (438 ha), River Gee (424 ha), Maryland (25 ha) and Grand Kru (113 ha). Additionally, SAPEC will rehabilitate twelve market centers (1 per country), construct nine agribusiness centers, and rehabilitate 270 kilometers of all-weather roads (35 km in Maryland, 60 km in Grand Gedeh, 50 km in Grand Kru, 40 km in River Cess and 40 km in Sinoe. Data Sources: SAPEC Selected CountiesSource: GAFSP Documents SAPEC Selected Counties by CropSource: African Development Bank Group Documents Poverty (Proportion of population below the poverty line) (2007): Proportion of the population living on less than $21,424 LD a year in rural areas and $30.224 LD a year in urban areas. Source: Liberia Institute of Statistics and Geo-Information Services (LISGIS). CWIQ 2007 (Core Welfare Indicators Questionnaire). “Liberia Joint IDA-IMP Staff Advisory Note on the Poverty Reduction Strategy Paper - June 24 2008”. Poverty (Proportion of population below the poverty line) (2010): Proportion of the population living on less than $30,224 per year per adult equivalent.Source: World Bank. “Liberia Poverty Note - Tracking the Dimensions of Poverty 2012.” Poverty (Proportion of population below the poverty line) (2014): Proportion of the population living on less than 62,963.63 LD per adult equivalent per year.Source: Liberia Institute of Statistics and Geo-Information Services (LISGIS). “Household Income and Expenditure Survey 2014 - Agenda for Transformation: Baseline Indicators February 2016.” Total Population (Thousands) (2008): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Source: Liberia Institute of Statistics and Geo-Information Services (LISGIS). “2008 National Population and Housing Census - Preliminary Results, June 2008”. Table 5: Population, Surface, Area and Density (1984 and 2008). Population Density (Persons per 1 square kilometer) (2008): Population divided by land area in square kilometers.Source: Liberia Institute of Statistics and Geo-Information Services (LISGIS). “2008 National Population and Housing Census - Preliminary Results, June 2008." Table 5: Population, Surface, Area, and Density (1984 and 2008). Malnutrition (Proportion of underweight children under 5 years) (2007): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight-for-age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: Liberia Institute of Statistics and Geo-Information Services (LISGIS). “Liberia Demographic and Health Survey 2007” Measure DHS. Malnutrition (Proportion of underweight children under 5 years) (2013): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: Liberia Institute of Statistics and Geo-Information Services (LISGIS). “Liberia Demographic and Health Survey 2013” Measure DHS. Market Centers: Key market centers for retail, assembly and/ or wholesale of agricultural products. FEWS NET Reference markets.Source: FEWS Net. The Famine Early Warning Systems Network (FEWS NET) is a USAID-funded activity that collaborates with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues. Rice Area (2008): Area in hectares of agriculture land used for rice.Source: Ministry of Agriculture (MOA), Liberia Institute of Statistics & Geo-information Services (LISGIS), United Nations Food and Agriculture Organization (FAO), Catholic Relief Services (CRS) and Samaritan Purse. “Production estimates of major crops and animals 2008.” Rice Production (2008): Rice harvested expressed in tons.Source: Ministry of Agriculture (MOA), Liberia Institute of Statistics & Geoinformation Services (LISGIS), United Nations Food and Agriculture Organization (FAO), Catholic Relief Services (CRS) and Samaritan Purse. “Production estimates for major crops and animals 2008.” Cassava Area (2008): Area in hectares of agriculture land used for Cassava.Source: Ministry of Agriculture (MOA), Liberia Institute of Statistics & Geoinformation Services (LISGIS), United Nations Food and Agriculture Organization (FAO), Catholic Relief Services (CRS) and Samaritan Purse. “Production estimates for major crops and animals 2008.” Cassava Production (2008): Cassava harvested expressed in tons.Source: Ministry of Agriculture (MOA), Liberia Institute of Statistics & Geoinformation Services (LISGIS), United Nations Food and Agriculture Organization (FAO), Catholic Relief Services (CRS) and Samaritan Purse. “Production estimates for major crops and animals 2008.” Livelihood Zones (2010): FEWS NET uses the Household Economy Approach (HEA) as the framework for its livelihoods work. For early warning of food insecurity, livelihoods analysis provides invaluable insight into the ability of households such as these to contend with shocks. The analysis also provides detailed information for humanitarian assistance planning and ongoing monitoring.Source: FEWS NET - USAID. “Livelihood zoning “plus” Liberia 2010.”

The maps displayed on the GAFSP website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). This third-round survey was representative at national level, covering Liberia’s 15 counties. Data were collected through face-to-face interviews conducted between 9 September and 4 October 2021. The sampling approach was based on random sampling for household questionnaires. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

This round 3 survey was representative at national level, covering Liberia's 15 counties. Data were collected through face-to-face interviews conducted between 9 September and 4 October 2021. The sampling approach was based on random sampling for household questionnaires. The overall sampling included 1 800 households, 45 key informants, 45 agro-input vendors and 45 agri-input traders, totalling 1 935 interviews.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries

Abstract

The main purpose of the Household Income Expenditure Survey (HIES) 2016 was to offer high quality and nationwide representative household data that provided information on incomes and expenditure in order to update the Consumer Price Index (CPI), improve National Accounts statistics, provide agricultural data and measure poverty as well as other socio-economic indicators. These statistics were urgently required for evidence-based policy making and monitoring of implementation results supported by the Poverty Reduction Strategy (I & II), the AfT and the Liberia National Vision 2030. The survey was implemented by the Liberia Institute of Statistics and Geo-Information Services (LISGIS) over a 12-month period, starting from January 2016 and was completed in January 2017. LISGIS completed a total of 8,350 interviews, thus providing sufficient observations to make the data statistically significant at the county level. The data captured the effects of seasonality, making it the first of its kind in Liberia. Support for the survey was offered by the Government of Liberia, the World Bank, the European Union, the Swedish International Development Corporation Agency, the United States Agency for International Development and the African Development Bank. The objectives of the 2016 HIES were:

1. Update the Consumer Price Index (CPI): To obtain a new set of weights for the basket of goods and services that upgrade the Monrovia Consumer Price Index (MCPI) and the National Consumer Price Index (NCPI) and to revise the CPI basket of goods and services in Liberia to reflect the current consumption pattern of residence.
2. Improve National Accounts Statistics: To get information on annual household expenditure patterns in order to update the household component of the National Accounts.
3. Measure Poverty: To prepare robust poverty indices that enable the understanding of poverty dynamics across the country and of the factors influencing them.
4. Improve Agricultural Statistics: To obtain nationally representative and policy relevant agricultural statistics in order to undertake in-depth analysis of agricultural households.
5. Capture Socio-economic Impact of Ebola Virus Disease (EVD): To obtain a post-EVD dataset which allows for an in-depth analysis of the socioeconomic impact of EVD on households.
6. Benchmark Agenda for Transformation Indicators: To provide an update on selected socioeconomic indicators used to benchmark the government’s policies embedded within the Agenda for Transformation.
7. Develop Statistical Capacity: Emphasize capacity building and development of sustainable statistical systems through every stage of the project to produce accurate and timely information about Liberia.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The original sample design for the HIES exploited two-phased clustered sampling methods, encompassing a nationally representative sample of households in every quarter and was obtained using the 2008 National Housing and Population Census sampling frame. The procedures used for each sampling stage are as follows:
i. First stage
Selection of sample EAs. The sample EAs for the 2016 HIES were selected within each stratum systematically with Probability Proportional to Size from the ordered list of EAs in the sampling frame. They are selected separately for each county by urban/rural stratum. The measure of size for each EA was based on the number of households from the sampling frame of EAs based on the 2008 Liberia Census. Within each stratum the EAs were ordered geographically by district, clan and EA codes. This provided implicit geographic stratification of the sampling frame.

ii. Second stage
Selection of sample households within a sample EA. A random systematic sample of 10 households were selected from the listing for each sample EA. Using this type of table, the supervisor only has to look up the total number of households listed, and a specific systematic sample of households is identified in the corresponding row of the table.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were three questionnaires administered for this survey: 1. Household and Individual Questionnaire 2. Market Price Questionnaire 3. Agricultural Recall Questionnaire

Cleaning operations

The data entry clerk for each team, using data entry software called CSPro, entered data for each household in the field. For each household, an error report was generated on-site, which identified key problems with the data collected (outliers, incorrect entries, inconsistencies with skip patterns, basic filters for age and gender specific questions etc.). The Supervisor along with the Data Entry Clerk and the Enumerator that collected the data reviewed these errors. Callbacks were made to households if necessary to verify information and rectify the errors while in that EA.

Once the data were collected in each EA, they were sent to LISGIS headquarters for further processing along with EA reports for each area visited. The HIES Technical committee converted the data into STATA and ran several consistency checks to manage overall data quality and prepared reports to identify key problems with the data set and called the field teams to update them about the same. Monthly reports were prepared by summarizing observations from data received from the field alongside statistics on data collection status to share with the field teams and LISGIS Management.

Abstract

The Government of Liberia considered the 2008 National Population and Housing Census (NPHC) a necessary prerequisite for assessing the socio-economic needs of its population and, hence, it attaches great importance to the determination of the current numbers and distribution of the population in pursuance of its program for national development. Therefore, the census organization provided for participation at all levels of Government, civil society and non-governmental organizations through the formation of committees, working in close collaboration with and under the direct supervision of authority from the Census Commission.

The mapping exercise that preceded the census canvassed the whole country and drew all boundaries of the administrative hierarchy and geographically positioned the various localities. Hence, the 2008 Population and Housing Census (NPHC) will, to a certain extent, bridge the statistics gaps mentioned above by offering national and sub-national baseline statistics and updated demographic indicators.

Globally, the methodology of census taking has been improving over the years and the 2008 NPHC portrays these improvements. However, there are two basic additions to this census; foremost, the shift from the de jure censuses of 1962, 1974 and 1984 to a de facto census in 2008 and, secondly, the inclusion of an Agricultural Module. The de jure census records usual residents of the household while the de facto one records persons who spent a reference night in the household. De facto censuses are easier to conduct and, hence, most countries adopt them. Liberia being a predominantly agricultural country, the 'Agricultural Module' was introduced with the aim of generating a sampling frame that will be used to design and implement agricultural surveys in the future.

The enumeration started on the morning of 21st of March, 2008 and ended in the evening of 30th March, 2008. It was done by trained enumerators who administered a standard questionnaire to the household heads or any other knowledgeable household members. Arrangements were made to ensure that special categories of the population were enumerated; for example, street children who do not live in formal households, in-mates in hotels and transients at air and sea ports.

Geographic coverage

National

Analysis unit

• Individuals;
• Households.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The processing of data collected in a census constitutes one of the most important and challenging activities that have to be undertaken efficiently and expeditiously in order to justify the immense resources invested in a census. This activity entailed several processes: manual editing of the questionnaires after enumeration, data capture, data cleaning and validation, and finally tabulation. Intelligence character recognition (ICR) technology will be employed for data capture.

Government’s commitment to provide provisional results within two and half months after enumeration and final results within another six months greatly influenced the strategies and actions adopted at every stage of data processing in order to adhere to the commitment.

Abstract

The Global Agriculture and Food Security Program Impact Evaluation proposes to study the impact of seed and tool distribution on the take-up of modern farming inputs and the use of productivity enhancing tools, thereby resulting in higher agricultural yields and improved nutritional outcomes, as measured by dietary diversity scores. The wide geographic scope of SAPEC and its focus on smallholder farmers offer a unique opportunity to generate data that can be more robustly extrapolated to the wider Liberian population. We will use data from a 2016 registration of Liberian farmers to randomly select 1,000 Liberian farmers from 100 randomly selected communities in Liberian districts serviced by SAPEC.

Using a randomization at multiple levels, we seek to determine whether the provision of 91%-subsidized improved seeds, tools, and fertilizer promote the take-up of modern farming inputs and improve diets. We will also study whether particular beneficiary sub-groups (by age and gender) are more likely to respond to SMS messaging with an agricultural focus and whether small adjustments to the content of these messages can result in relatively greater improvements in take-up by youth.

Geographic coverage

National

Sampling procedure

Sample size: 570 households

The core strategy for the evaluation is a cluster-randomized phase in of subsidy offers with individual assignment of input deliveries within treatment communities. What this means is that, because SAPEC can only provide a fixed quantity of inputs in a particular season due to capacity constraints, the study aims to randomly assign which communities are first in line to receive the inputs and which farmers in these communities are first in line to receive the inputs. Random selection of farmers and communities is a fair way to decide who receives inputs first that allows us to compare farmers who have been offered inputs with those who have not been offered yet without ultimately affecting which farmers receive benefits at the project closing date. Including some communities and not others in the first round allows to rule out spillovers of farmers who get inputs to other farmers in the same community by comparing to communities where no one has received any inputs. Randomly selecting farmers within communities in contrast also allows us to make comparisons between farmers who are very similar to each other. Finally, strategically make offers of inputs to youth vs older farmers in order to also make careful comparisons of impacts on these groups of particular interest.

There are two dimensions on which the offer of improved seeds, cuttings, and tools will be made in order to assess the impact of this offer on agricultural practices and outcomes, both in general and differentially by the age of the farmer.

First, we propose to compare households in communities where SAPEC will offer subsidized inputs against communities where subsidized inputs will not be offered during the evaluation year. Assignment to SAPEC input provision (treatment communities) or no provision (non-treatment) occurs at two levels. First, 100 communities in SAPEC treatment and control districts will be randomly selected to take part in the study, using the list of SAPEC-eligible communities in the LATA database as a sampling frame. Each community will have a minimum of 25 farmers. Of the selected communities, 50 will be treatment communities and 50 will be control communities. Since SAPEC can deliver inputs and technical assistance to a maximum of 5,000 farmers in a given year and the potential number of beneficiaries in all SAPEC-eligible communities is much larger than this 5,000, the random assignment of potential treatment communities to the set of controls only changes the order of who receives the benefits next, rather than withholding benefits from anyone in particular.

The second dimension to create a counterfactual for beneficiaries will be the random selection of specific beneficiaries within SAPEC treatment communities. From the list of all farmers from the randomly-selected treatment and control communities that have been registered in the e-platform system, we will randomly select an average of 10 per community to be SAPEC beneficiaries (or survey respondents, for control communities) in the upcoming round. This will allow us to sample farmers within treatment communities as well as farmers who were randomly selected to not receive them this year, allowing us to see the causal impact of this input provision on the delivery of tools.

Within the communities to be treated this year, we will start from the list of all households listed in the mobile phone registries who are eligible to receive SAPEC benefits in this year’s wave of benefit delivery. In advance of distribution, we will stratify these eligible farmers by age and gender, and randomly assign 1,500 of them to be invited to report to a SAPEC office in order to be registered as beneficiaries, with equal proportions of male and female and under- and over-35-year-old farmers receiving invitations. All of the farmers who appear to be registered will receive the package of benefits from SAPEC. If less than 1,500 farmers reply, a corresponding number of additional invitations will be sent to randomly selected households who were not selected in the first round until 1,500 beneficiaries have been registered. When invitations are sent, the invitee will be randomly assigned to receive one of 5 different types of messages which emphasis different features such as the potential to earn higher income or to meet other farmers the invitee as described in section 5. This will allow us to test whether different types of farmers (young vs. older) respond to different aspects of program benefits or design.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is structured and available in English. It can be downloaded under the "Resources" tab.

The Household Income Expenditure Survey (HIES) collected detailed information at the household level on the following topics: education, health, employment, water and sanitary practices, household resources, grants, crime, conflicts and recent shocks to household wealth. The survey was also expected to provide reliable and policy relevant agricultural statistics and served as a baseline of information for the “Agenda for Transformation” set by the Government of Liberia. Other components of the HIES included capacity building and cross-country knowledge, sharing alongside efforts to improve survey methodologies in Liberia. Among other features were the design and implementation of a household survey that focused on the household income and expenditure that fed into Consumer Price Index (CPI) construction. The data collection exercise for the survey was conducted from January to December 2014.The survey covered 8,360 randomly selected households over the 12-month period. The objectives of the HIES were: 1. Evaluation and analysis of poverty levels and quality of life at the household level. 2. Analysis of primary indicators on economic productivity, employment, and social welfare. 3. Preparation of a 'weighting system' for a Consumer Price Index. 4. Generation of general economic (macroeconomic) indicators; e.g. estimates of national income. (Gross Domestic Product GDP) 5. Analysis of household ownership of productive assets and their linkages with household income activities.

Abstract

This document provides additional information on the data collected in Liberia between July 2017 and September 2017. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. 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.

The sample frame consisted of listings of firms from two sources: For panel firms the list of 150 firms from the Liberia 2009 ES was used and for fresh firms (i.e., firms not covered in 2009) firm data from the 2014 Liberia Business Registry, was used.

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.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an 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.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private 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. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - 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 subsector 72, IT, which was added to the population under study), and all public or utilities sectors.

• 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.
• 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.)
• 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.

• 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 this country: industry, establishment size, and region. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries. Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Liberia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
  Regional stratification for the Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used during the survey namely; 1. Manufacturing Module Questionnaire 2. Services Module Questionnaire

The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing Questionnaire (includes the core module, plus manufacturing specific questions). Retail firms were interviewed using the Services Questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services Questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

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.

Response rate

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.There was also very positive attitude towards World Bank initiatives.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Liberia 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 Enterprise 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.

Geographic coverage

National

Analysis unit

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.

Universe

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.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2009-2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - 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 subsector 72, IT, which was added to the population under study), and all public or utilities sectors.

• 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.
• 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.)
• 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.

• 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 this country: industry, establishment size, and region. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries. Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Liberia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
  Regional stratification for the Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.

Mode of data collection

Face-to-face [f2f]

Research instrument

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.

Cleaning operations

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.

Response rate

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.There was also very positive attitude towards World Bank initiatives.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). This third-round survey was representative at national level, covering Liberia’s 15 counties. Data were collected through face-to-face interviews conducted between 9 September and 4 October 2021. The sampling approach was based on random sampling for household questionnaires. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

This round 3 survey was representative at national level, covering Liberia's 15 counties. Data were collected through face-to-face interviews conducted between 9 September and 4 October 2021. The sampling approach was based on random sampling for household questionnaires. The overall sampling included 1 800 households, 45 key informants, 45 agro-input vendors and 45 agri-input traders, totalling 1 935 interviews.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries

Abstract

Landmine Action (LMA) has two sites in Liberia for their agricultural reintegration program-the Sinoe Agricultural Training Program (SATP) in Panama, Sinoe County, which accommodates 200 trainees, and the Tumutu Agricultural Training Program (TATP) in Salala, Bong County, which accommodates 400 trainees. A baseline survey was conducted in the Sinoe region in August 2009 before implementation of the program in that location. A follow-up survey is planned for December 2010, approximately one year after the training sessions are complete. Another baseline survey was conducted in Gbarpolu, Nimba, Bong, and Margibi Counties in September and October 2009 for the TATP evaluation. As with the Sinoe baseline survey, the results summarize the average characteristics of the target population and verify balance of means between treatment and control groups. A follow-up survey is planned for March 2011, approximately a year after the completion of the program. The impact analysis will be conducted at the conclusion of the TATP follow-up survey, combining the results from the two sites.

Geographic coverage

Landmine Action (LMA) has two sites in Liberia for their agricultural reintegration program-the Sinoe Agricultural Training Program (SATP) in Panama, Sinoe County, which accommodates 200 trainees, and the Tumutu Agricultural Training Program (TATP) in Salala, Bong County, which accommodates 400 trainees. A baseline survey was conducted in the Sinoe region in August 2009 before implementation of the program in that location. Another baseline survey was conducted in Gbarpolu, Nimba, Bong, and Margibi Counties in September and October 2009 for the TATP evaluation.

Analysis unit

Person

Universe

With more demand for the training program than can be accommodated in a single round of the program, youth who express interest in participating in the program were registered by the Landmine Action field team and then surveyed by the IPA research team.

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sinoe

Registration. The Landmine Action Field Team carried out registration during June 23 - July 15, 2009. 440 youth were registered by LMA.

Baseline survey. After registration, in August 2009, registrants were approached to participate in the baseline survey. Of the original 440, 22 (5 percent) could not be located again. Some of these had left their communities; a few appeared to be hiding. In general, the baseline survey effort was highly successful. During the survey, 16 registrants communicated that they were no longer interested in the training; these individuals were not surveyed, and (at their request) their names were removed from the registration list, randomization, and program eligibility. Full and complete baseline data were successfully collected on all interested individuals.

B. Tumutu

For the TATP survey, Landmine Action registered approximately 900 individuals in Gbarpolu, 400 of whom were to be offered a spot in the program. The Landmine Action field team began registration in Gbarpolu County around September 10, 2009. The IPA research team arrived in Gbarpolu on September 17, 2009 and followed close behind the LMA field team.

LMA felt it was important to include some people from the local community around the TATP site to maintain positive community relations, so also planned to reserve 50 randomized training spots for residents of Bong County.

During the pickup process, however, it became clear that Gbarpolu would have high non-compliance, After the experience of high rates of non-compliance in Sinoe, IPA wanted to ensure a large enough buffer to maintain a sizeable control group for the TATP evaluation-neither team wanted to draw replacements from the control group, as had been done in Sinoe. To accommodate these needs, the LMA and IPA teams performed registration in five phases:

Phase 1: Gbarpolu. To try to streamline efforts to locate registrants for the baseline survey, LMA and IPA management agreed that the LMA field team and the IPA research team would carry out their work simultaneously in Gbarpolu. The IPA team planned to travel alongside the LMA team and survey individuals immediately after they were registered. However, the LMA field team began registration during their sensitization trip to Gbarpolu around September 10 2009, and returned to Monrovia with approximately 200 individuals already registered. IPA began surveying one week later and had to readjust its plans of traveling alongside the LMA field team to accommodate the condensed time frame in which all surveys needed to be completed.

The IPA research team arrived in Gbarpolu on September 17 2009 and the IPA and LMA teams on the ground coordinated in order to keep the work moving along smoothly. Each evening, IPA was given LMA's recently completed long forms, and then IPA would survey the new registrants over the following days. IPA was able to survey most of the individuals surveyed by LMA, although a small percentage of registrants who left their communities immediately after meeting with the LMA field team could not be located despite numerous attempts by the IPA team. There were also some registrants who changed their minds and decided they were no longer interested in the program. These individuals were dropped from the list of registrants and were not included in the randomization.

From September 17 to October 11 2009, IPA surveyed 708 individuals in Gbarpolu, including 622 men and 86 women. IPA selected 312 men and 38 women to participate in the program through the randomization process. 13 of these individuals were automatically included in the program because they were generals, and 30 went into a special randomization for former commanders. Half of these commanders were randomly selected to go into the program.

Out of the 350 individuals selected through the randomization, 242 agreed to come on the program, including 17 women and 225 men.

· Surveyed: 90 women and 624 men (714 total) · Randomization: 81 women and 603 men (684 total) · 30 went into commanders' randomization (half were selected for the program) · 13 automatically included generals · Selected: 38 women and 312 men · Entered program: 17 women and 225 men (242 total)

Phase 2: Bong round 1. After the start of the baseline survey, Landmine Action decided to take 50 individuals from Salala and the surrounding communities in Bong County, reducing the number of program spots for Gbarpolu residents to 350. The District Commissioner in Salala, James Kerkula, asked LMA if he could select some community members to go into the program. LMA and IPA agreed that the District Commissioner would fill 15 of the 50 spots with any people of his choice, and that the remaining 35 spots would be filled through the process of registration by LMA and surveying and random selection by IPA. LMA agreed to register around 85 people for the 35 spots. IPA surveyed 84 individuals (59 men and 25 women) from October 7 to October 9 2009.

Near the end of the registration process, it came to the attention of some members of the IPA team that the District Commissioner had allegedly taken money from community members in exchange for a spot in the program. After the Commissioner released his list of 15 names, a group of Salala residents approached the IPA team and complained that they had given the District Commissioner money but their names didn't come out on the list. They were extremely angry and refused to leave the registration area.

· Surveyed: 25 women and 59 men (84 total) · Randomization: none

Phase 3: Bong round 2. The management of LMA confirmed the allegations and decided to redo the registration exercise in the Salala area. The second time, LMA planned to fill all 50 spots through the registration and survey and random selection process. They agreed to register around 115 individuals to fill the 50 spots in addition to a control group.

LMA re-registered some individuals who had previously registered during the first exercise in Salala (23 individuals, including 10 women and 13 men), and then registered a number of new individuals (97, including 16 women and 81 men). The registration period lasted from October 10 to October 12, 2009. IPA surveyed a total of 26 women and 95 men, and 7 women and 43 men were selected through the randomization process. In the end, 37 men, 7 women, and 3 generals entered the program.

· Surveyed: 26 women and 94 men (120 total, 10 women and 13 men were surveyed during phase 2) · Randomization: 24 women and 89 men (113 total) · Selected: 7 women and 43 men (50 total) · Entered program: 7 women and 37 men (44 total)

Phase 4: Guthrie. Out of the 350 individuals selected to participate in TATP through the first three phases, 108 decided that they were no longer interested in the program. In order to maintain the control group in Gbarpolu so as not to compromise the evaluation, it was decided that non-compliant individuals would not be replaced with others from the control list for Gbarpolu but that the target participant size would be achieved by expanding registration into other regions of the country. LMA and IPA agreed to carry out an additional registration exercise in a new area, and since LMA worked extensively with individuals from Guthrie during the first two phases of TATP, LMA headquarters decided that the teams should conduct this new registration exercise in Guthrie. Between October 16 and October 19, IPA surveyed 89 individuals. None of these individuals ended up entering the program.

· Surveyed: 89 individuals · Randomization: none

Phase 5: Nimba. After the baseline survey was completed in Guthrie, LMA decided that they did not want to take anyone from Guthrie into

This dataset contains endline information about harvests for each plot the household owns. The STRIVE project, funded by USAID's Displaced Children and Orphans Fund (DCOF) and managed by FHI 360, used market-led economic strengthening initiatives to improve the well-being of vulnerable children. Through STRIVE, ACDI/VOCA implemented the Agriculture for Children’s Empowerment (ACE) Project in Liberia, which is founded on the premise that increased household economic security will stimulate more consistent investments in children’s well being via longer term social investments in education and nutrition. ACE’s primary focus was on the horticulture value chain (VC) — the production and marketing of vegetables by smallholder farmers in Montserrado, Bong, and Nimba counties of Liberia. ACE also strengthened smallholder rice farming to increase household food security using a market-sensitive approach to rice seed lending and cultivation. This dataset contains endline information about each plot the household owns, their size, the crops grown on them, and the methods used to grow plants on those plots.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Liberia 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 Enterprise 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.

Geographic coverage

National

Analysis unit

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.

Universe

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.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2009-2017 Liberia Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Note. Stratified random was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - 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 subsector 72, IT, which was added to the population under study), and all public or utilities sectors.

• 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.
• 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.)
• 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.

• 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 this country: industry, establishment size, and region. Industry stratification was designed as follows: the universe was stratified as into manufacturing and services industries. Manufacturing (ISIC Rev. 3.1 codes 15 - 37), and Services (ISIC codes 45, 50-52, 55, 60-64, and 72). For the Liberia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
  Regional stratification for the Liberia ES was done across three regions: Montserrado, Margibi, and Nimba.

Mode of data collection

Face-to-face [f2f]

Research instrument

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.

Cleaning operations

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.

Response rate

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.There was also very positive attitude towards World Bank initiatives.