Abstract

Niger is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program. This program has developed a household level survey with a view to enhancing our knowledge of agriculture in Sub-Saharan Africa, in particular, its role in poverty reduction and the techniques for promoting efficiency and innovation in this sector. To achieve this objective, an innovative model for agricultural data collection in this region will need to be developed and implemented. To this end, activities conducted in the future will be supported by four main pillars: a multisectoral framework, institutional integration, analytical capacity building, and active dissemination.

First, agricultural statistical data collection must be part of an expanded and multisectoral framework that goes beyond the rural area. This will facilitate generation of the data needed to formulate effective agricultural policies throughout Niger and in the broader framework of the rural economy.

Second, agricultural statistical data collection must be supported by a well-adapted institutional framework suited to fostering collaboration and the integration of data sources. By supporting a multi-pronged approach to data collection, this project seeks to foster intersectoral collaboration and overcome a number of the current institutional constraints.

Third, national capacity building needs to be strengthened in order to enhance the reliability of the data produced and strengthen the link between the producers and users of data. This entails having the capacity to analyze data and to produce appropriate public data sets in a timely manner. The lack of analytical expertise in developing countries perpetuates weak demand for statistical data.

Consequently, the foregoing has a negative impact on the quality and availability of policy-related analyses. Scant dissemination of statistics and available results has compounded this problem.

In all countries where the LSMS-ISA project will be executed, the process envisioned for data collection will be a national household survey, based on models of LSMS surveys to be conducted every three years for a panel of households. The sampling method to be adopted should ensure the quality of the data, taking into account the depth/complexity of the questionnaire and panel size, while ensuring that samples are representative.

The main objectives of the ECVMA are to:

• Gauge the progress made with achievement of the Millennium Development Goals (MDGs);
• Facilitate the updating of the social indicators used in formulating the policies aimed at improving the living conditions of the population;
• Provide data related to several areas that are important to Niger without conducting specific surveys on individual topics ;
• Provide data on several important areas for Niger that are not necessarily collected in other more specific surveys.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The data entry was done in the field simultaneously with the data collection. Each data collection team included a data entry operator who key entered the data soon after it was collected. The data entry program was designed in CSPro, a data entry package developed by the US Census Bureau. This program allows three types of data checks: (1) range checks; (2) intra-record checks to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies between the different modules of the questionnaire.

The data as distributed represent the best effort to provide complete information. The data were collected and cleaned prior to the construction of the consumption aggregate. Using the same guidelines as were used in 2011, the households that are provided in the data set should have consumption data for both visits. This may not be the case. During the cleaning process, it was found that households had been misidentified which allowed more households to be included in the final consumption aggregate file (see below). The raw data that contains household/item level data that was used to calculate the consumption aggregate has been included in the distribution file.There are 3,614 households and 26,579 individuals in the data.

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

The BFA EHCVM is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative, designed to improve the understanding of household and individual welfare, livelihoods, and smallholder agriculture in Africa. In Burkina Faso (West Africa), the EHCVM aimed to improve the quality, timeliness, and relevance of household-level agricultural statistics with an emphasis on sustainability, capacity building, and improving data collection methods. Two panel surveys were conducted with a nationally and regionally representative sample of households in urban and rural areas of Burkina Faso. The first (2018-2019) covered 7,010 households, and the second (2021-2022) followed up with 3,227 households from the first survey. Additional data collection included the EMC-18 experiment and longitudinal COVID-19 High-Frequency Phone Surveys with households who participated in the first panel survey.

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Coverage. ESS2 and ESS3 covered all regional states including the capital, Addis Ababa. The majority of the sample comprises rural areas as it was carried over from ESS1. The ESS2 and ESS3 were implemented in 433 enumeration areas (EAs) out of which 290 were rural, 43 were small town EAs from ESS1, and 100 were EAs from major urban areas.

Analysis unit

Households Communities

Universe

ESS uses a nationally representative sample of over 5,000 households living in rural and urban areas. The urban areas include both small and large towns.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a two-stage probability sample. The first stage of sampling entailed selecting primary sampling units, or CSA enumeration areas (EAs). A total of 433 EAs were selected based on probability proportional to size of the total EAs in each region. For the rural sample, 290 EAs were selected from the AgSS EAs. A total of 43 and 100 EAs were selected for small town and urban areas, respectively. In order to ensure sufficient sample size in the most populous regions (Amhara, Oromiya, SNNP, and Tigray) and Addis Ababa, quotas were set for the number of EAs in each region. The sample is not representative for each of the small regions including Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie regions. However, estimates can be produced for a combination of all smaller regions as one “other region” category. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey consisted of five questionnaires. These questionnaires are similar with the questionnaires used during in the ESS1 and ESS2 with revisions based both on the results of the ESS2 and also on identified areas of need for new data (see Section 7 of the Basic Information Document provided under the Related Materials tab). The household questionnaire was administered to all households in the sample. The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.3 The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on basic demographics; education; health (including anthropometric measurement for children); labor and time use; saving; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; credit; and other sources of household income (Table 2.1). Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information (Table 2.2).

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products (Table 2.3). The livestock module implemented in ESS3 is significantly difference from the module implemented in ESS1 and ESS2.

Cleaning operations

The interviews were carried out using pen-and-paper (PAPI) as well as computer-assisted personal interviewing (CAPI) method. A concurrent data entry arrangement was implemented for PAPI. In this arrangement, the enumerators did not wait until all the interviews were completed. Rather, once the enumerators completed approximately 3-4 questionnaires, supervisors collected these interviews from enumerators and brought them to the branch offices for data entry. This process took place as enumerators continued administering interviews with other households. Then questionnaires were keyed at the branch offices as soon as they were completed using the CSPro data entry application software. The data from the completed questionnaires were then checked for any interview or data entry errors using a STATA program. Data entry errors were flagged for the data entry clerks and the interview errors were then sent to back to the field for correction and feedback to the ongoing interviews. Several rounds of this process were undertaken until the final data files were produced. Additional cleaning was carried out, as needed, by checking the hard copies. In ESS3, CAPI (with a Survey Solutions platform) was used to collect the community data in large town areas.

Response rate

During wave 3, 1255 households were re-interviewed yielding a response rate of 85 percent. Attrition in urban areas is 15% due to consent refusal and inability to trace the whereabouts of sample households.

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study – Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship – following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters.

The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

Data Cleaning 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 in the field by the field-based field teams utilizing error messages generated by the Survey Solutions 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 supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field, and this resulted from the additional error reports generated in Stata, which were in turn sent to the field teams via email or DropBox. The field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call-backs while the team was still operating in the EA when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

The data cleaning process was done in several stages over the course of the fieldwork and through preliminary analyses. The first stage was during the interview itself. Because CAPI software was used, as enumerators asked the questions and recorded information, error messages were provided immediately when the information recorded did not match previously defined rules for that variable. For example, if the education level for a 12 year old respondent was given as post graduate. The second stage occurred during the review of the questionnaire by the Field Supervisor. The Survey Solutions software allows errors to remain in the data if the enumerator does not make a correction. The enumerator can write a comment to explain why the data appears to be incorrect. For example, if the previously mentioned 12 year old was, in fact, a genius who had completed graduate studies. The next stage occurred when the data were transferred to headquarters where the NSO staff would again review the data for errors and verify the comments from the

Description: The dataset represents a significant effort to compile and clean a comprehensive set of seasonal yield data for sub-saharan West Africa (Benin, Burkina Faso, Mali, Niger). This dataset, overing more than 22,000 survey answers scattered across more than 2500 unique locations of smallholder producers’ households groups, is instrumental for researchers and policymakers working in agricultural planning and food security in the region. It integrates data from two sources, the LSMS-ISA program (link to the World Bank's site), and the RHoMIS dataset (link to RHoMIS files, RHoMIS' DOI).

The construction of the dataset involved meticulous processes, including converting production into standardized unit, yield calculation for each dataset, standardization of column names, assembly of data, extensive data cleaning, and making it a hopefully robust and reliable resource for understanding spatial yield distribution in the region.

Data Sources: The dataset comprises seven spatialized yield data sources, six of which are from the LSMS-ISA program (Mali 2014, Mali 2017, Mali 2018, Benin 2018, Burkina Faso 2018, Niger 2018) and one from the RHoMIS study (only Mali 2017 and Burkina Faso 2018 data selected).

Dataset Preparation Methods: The preparation involved integration of machine-readable files, data cleaning and finalization using Python/Jupyter Notebook. This process should ensure the accuracy and consistency of the dataset. Yield have been calculated with declared production quantities and GPS-measured plot areas. Each yield value corresponds to a single plot.

Discussion: This dataset, with its extensive data compilation, presents an invaluable resource for agricultural productivity-related studies in West Africa. However, users must navigate its complexities, including potential biases due to survey and due to UML units, and data inconsistencies. The dataset's comprehensive nature requires careful handling and validation in research applications.

Authors Contributions:

Data treatment: Eliott Baboz, Jérémy Lavarenne.

Documentation: Jérémy Lavarenne.

Funding: This project was funded by the INTEN-SAHEL TOSCA project (Centre national d’études spatiales). "123456789" was chosen randomly and is not the actual award number because there is none, but it was mandatory to put one here on Zenodo.

Changelog:

v1.0.0 : initial submission

These are datasets produced by geographically combining Living-Standards Measurement Study - Integrated Studies on Agriculture (LSMS-ISA) data spanning 3 waves, from 2009 to 2012, and refugee data provided by the UNHCR at the settlement level. The Stata code for running the analyses is also provided.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. 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.

Response

Abstract

The General Household Survey Panel, Farm Area Measurement Validation Study (GHSP- FAMVS), 2013 was conducted on a subsample of the GHS-Panel survey, it focused on the land area measurement component. The survey was motivated by observed differences between farmer estimates of plot area and GPS measurement in Nigeria and other countries with LSMS-ISA surveys. The GHSP-FAMVS, set out to validate GPS measurement and farmer self-reported estimates against the compass and rope measurement, commonly accepted as the gold standard method. The LSMS-ISA, an agriculture-focused project of the LSMS program, and the institutional collaborations on which it is built, provides an ideal platform to support methodological research. The broader LSMS-ISA research agenda is composed of seven primary components: 1. Land area measurement 2. Soil fertility 3. Water resources 4. Labor inputs 5. Skill measurement 6. Production of continuous and extended-harvest crops 7. Computer-assisted personal interviewing for agricultural data

Four states were purposefully selected based on safety and past performance in area measurement (Benue, Osun, Oyo, and Kogi). The total number of plots measured and included in the validation study were 495, coming from a total of 202 households. The GHSP-FAMVS was carried out in 2013 by the Nigeria National Bureau of Statistics (NBS) in collaboration with The World Bank Living Standards Measurement Study (LSMS) team. Fieldwork began in March 2013 and lasted for approximately 3 weeks.

Geographic coverage

Four states: Benue, Oyo, Osun, Kogi

Analysis unit

-Agricultural plots -Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The plot size plays a significant role in the accuracy of plot area measurement using the various methods, the validation sample was stratified on four plot size strata to ensure we could test the various methods on larger plots, which are much more rare. Four states were purposefully selected based on safety and past performance in area measurement (Benue, Osun, Oyo, and Kogi). Using the second wave of the GHS panel as the sample frame and the GPS measurement of the plot taken in the post-planting visit, every plot was assigned to some plot-size strata (strata 1: <=1000 sq. meters; strata 2: 1000-2500 sq. meters; strata 3: 2500-5000sq. meters; strata 4: >5000 sq. meters). One hundred plots were then randomly selected from each stratum. This process yielded the selection of 400 plots (211 households). However, in order to maximize the sample at minimal added cost, we included all plots from the selected households, not only the plots that were selected in the first step (totaling 518 plots).

From the 518 selected plots, 23 plots were unable to be measured (5 due to land disputes, 4 due to respondent refusal, 14 for other reasons). Therefore, the total number of plots measured and included in the farm area measurement validation study is 495, coming from a total of 202 households.

Stratification by plot size in the validation sample results in the unequal probability of plot selection within households from the GHS-Wave 2 sample. Household-level sampling weights were calculated for the validation sample to make them representative of the same household population sampled in Wave 2. Refer to Annex I of the Basic Information Document for details on the construction of the sampling weights.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey consists of a single questionnaire instrument which includes in-field GPS measurement details, compass and rope measurement details, and the farmer self-reported area estimate.

Data are derived from LSMS-ISA surveys: Malawi Integrated Household Survey 2016–2017 and Integrated Household Panel Survey 2016; Tanzania National Panel Survey 2012–2013; and Nigeria General Household Survey Panel 2015–2016. These data are publicly available on http://surveys.worldbank.org/lsms/programs/integrated-surveys-agriculture-ISA. (DTA)

This dataset supports the manuscript “Climate change incidence, risk perception, and food security among smallholders in Tigray, Ethiopia†. The dataset contains three folders and a file from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on climate change conducted in August 2019 in Tigray; and (3) 4km x 4km monthly grided Climate data (Rainfall, Max & min temperature). The files include socioeconomic data and household features, beliefs and opinions on climate change, and climatological data (monthly rainfall, maximum and minimum temperatures). The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and represents the region. It can be useful for studies on climate change risk perception and adaptation, environmental protection, and..., I collected socioeconomic data from the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for the Tigray region. I also conducted a follow-up survey on the beliefs and opinions of respondents on climate change in August 2019 in Tigray. I also collected climatological data (Rainfall, Max & min temperature) from the Ethiopian National Meteorological Services Agency (NMA) for the years 1983 - 2015. I processed the socioeconomic data using user-written codes in STATA v.17, the climatological data using R. I performed a Fixed effects analysis of climate change risk perception and random effects Ordered Logit analysis of food insecurity determinants using the socioeconomic data and climate change trend analysis using climatological data., , # Climate change incidence, risk perception, and food security nexus

https://doi.org/10.5061/dryad.76hdr7t3d

The dataset contains three folders and one file from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) 4km x 4km monthly grided Climate data (Rainfall, Max & min temperature).

Description of the data and file structure

This dataset underpins the research presented in the manuscript titled “Assessing Climate Change Impacts on Risk Perception and Food Security Among Small-Holders in Tigray, Ethiopia: A Panel Data Analysis with Trend and Variability Tests,†currently under review by Hindawi. The dataset is composed of two core data...

Abstract

Feed the Future is a global hunger and food security initiative which aims to refocus attention on addressing the root causes of global food insecurity, including agricultural development and nutrition. Led by the United States Agency for International Development (USAID), this initiative reflects a coordinated focus on building productive, resilient agricultural systems throughout 19 countries in need, including Tanzania. Emphasis is placed on smallholder farmers and women in particular who are making progress towards the development of sustainable agriculture sectors. As food security plays a critical role in poverty reduction, Feed the Future has been made a primary development assistance tool in the reduction of poverty. In Tanzania, Feed the Future efforts are focused on improving agricultural productivity and market access, increasing trade, and improving the nutritional status of children through promotion of fortified foods and behaviour change. For maximum impact, the Feed the Future initiative has targeted its investments in six regions in the country considered to be the Zone of Influence (ZOI): Dodoma, Manyara, Morogoro, Mbeya, Iringa, and all three areas of Unguja in Zanzibar. The Feed the Future Interim Supplemental Survey (FTFISS) was developed to measure and elaborate on consumption habits in Tanzania, and to provide a more comprehensive view of the food security situation in the country. Additionally, this project provides a valuable opportunity to expand upon food security information gathered in the Tanzania National Panel Survey (NPS), as questionnaire themes in the FTFISS were modelled to reflect those topics considered central to the comprehension of food security. To further enhance value of this expansion, only NPS households residing in the ZOI regions targeted by the Feed the Future initiative were chosen to participate in the FTFISS project. NPS households in these six regions were tracked and re-interviewed following conclusion of the 2014/2015 NPS. The 2014/2015 NPS was the fourth round in a series of nationally representative household panel surveys that collect information on a wide range of topics including agricultural production, non-farm income generating activities, consumption expenditures, and a wealth of other socio-economic characteristics. All four rounds of the NPS were implemented by the Tanzania National Bureau of Statistics (NBS) with assistance provided by the World Bank through the Living Standards Measurement Study - Integrated Surveys on Agriculture [LSMS-ISA ] program.

Geographic coverage

Regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

NPS households in the regions of Dodoma, Manyara, Morogoro, Mbeya, Iringa, and all three areas of Unguja in Zanzibar were included in the FTFISS sample. These regions were selected as they mirror the six ZOI regions targeted by the Feed the Future initiative. For purposes of the FTFISS, a household is defined as people who live together, share the same meal, and contribute to the household income and also basic needs. In other words, residents of a household share the same centre of production and consume from that centre. Even those persons who are not blood relations (such as servants, lodgers, or agricultural laborers) are members of the household if they have stayed in the household at least 3 months of the past 6 months. In cases where the household had no female members, the household was not considered eligible for the FTFISS and was not interviewed to completion. The resulting sample size of eligible households for the first FTFISS was 727 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The FTFISS contains a robust quality assurance and data management system, assuring proper data collection as well as respondent eligibility. Great effort was placed on the development and utilization of this system with technical assistance from the World Bank. Data files were sent to the study headquarters, and regular checks were performed to ensure the fieldwork was proceeding according to the schedule and that quality standards were met.

Sampling error estimates

As with the NPS, STATA was utilized to perform more complex, aggregated checks post-collection, as the final stage of data processing for FTFISS data. Adjustments of the data post-entry were conducted under the principle of absolute certainty where adjustments must be evidence based and correction values true beyond a reasonable doubt. As such, the resulting final datasets may still contain some inconsistencies and outliers. Handling of these values is thus left entirely to the data user.

LSMS-ISA 2010-11, LSMS-ISA 2013-14, and LSMS-ISA 2015-16 of Uganda

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

The IHS1 was conducted in Malawi from November 1997 through October 1998 and provided for a broad set of applications on policy issues regarding households' behaviour and welfare, distribution of income, employment, health and education. The Second Integrated Household Survey was implemented with technical assistance from the World Bank in order to compare the current situation with the situation in 1997-98, and to collect more detailed information in specific areas. The IHS2 fieldwork took placed from March 2004 through February 2005. The Third Integrated Household Survey (IHS3) expanded on the agricultural content of the IHS2 and was implemented from March 2010 to March 2011 under the umbrella of the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative, whose primary objective is to provide financial and technical support to governments in sub-Saharan Africa in the design and implementation of nationally-representative multi-topic panel household surveys with a strong focus on agriculture. The Fourth Integrated Household Survey 2016-2017 (IHS4) which was implemented in the period of April 2016-April 2017 covering 780 EAs throughout Malawi. As part of this project NSO also implemented the Integrated Household Panel Survey 2016 as a follow up to the IHPS 2013.

Geographic coverage

National

Analysis unit

Households

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS4 sampling frame is based on the listing information and cartography from the 2008 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. This is the first round of the survey to include the island district of Likoma in the sampling frame. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS4 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS4. Note: Detailed sample design information is presented in the "Fourth Integrated Household Survey 2016-2017, Basic Information Document" document.

Sampling deviation

The total sample size for the IHS4 was 12,480 households sampled from a total of 779 EAs5. At the end of the survey, a total of 12,447 households were interviewed. The survey allowed replacement of households. Of the 12,447 interviewed households, 557 were replacements (4.5 percent).

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS4 was implemented using the World Bank's Survey Solutions CAPI software. To carry out IHS4, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8-inch GPS-enabled Samsung Galaxy Tab S2 tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar - checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

DATA MANAGEMENT The IHS4 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS4 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in IHS3 and IHPS. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS4 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA CLEANING The data cleaning process was done in several stages over the course of field work and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field based field teams utilizing errors generated with the Survey Solutions application. For questions that flagged an error, 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. Supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some Supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the Supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field and this resulted from the additional error reports generated in STATA and sent to teams via email. Field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call backs while the team was still operating in the enumeration area when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

Additional cleaning was performed after interviews were “Approved” where appropriate to resolve systematic errors and organize data modules for consistency and efficient use. Case by case cleaning was also performed during the preliminary analysis specifically pertaining to out of range and outlier variables. All cleaning activities were conducted in collaboration with the WB staff providing technical assistance to the NSO in the design and implementation of the IHS4.

Response rate

99.7 percent

Abstract

The Ethiopian Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study- Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic panel household level data with a special focus on improving agriculture statistics and the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

The specific objectives of the ESS are:

• Development of an innovative model for collecting agricultural data in conjunction with household data;
• Strengthening the capacity to generate a sustainable system for producing accurate and timely information on agricultural households in Ethiopia;
• Development of a model of inter-institutional collaboration between the CSA and relevant federal and local government agencies as well as national and international research and development partners; and
• Comprehensive analysis of household income, well-being, and socio-economic characteristics of households in rural areas and small towns.

The ESS contains several innovative features:

• Integration of household welfare data with agricultural data;
• Creation of a panel data set that can be used to study welfare dynamics, the role of agriculture in development and the changes over time in health, education and labor activities, inter alia;.
• Collection of information on the network of buyers and sellers of goods with which the household interacts;
• Expanding the use of GPS units for measuring agricultural land areas;
• Involvement of multiple actors in government, academia and the donor community in the development of the survey and its contents as well as its implementation and analysis;
• Creation of publicly available micro data sets for researchers and policy makers;

Geographic coverage

The Ethiopia Socioeconomic Survey 2013/2014 (ESS2) covered all regional states including the capital, Addis Ababa. The majority of the sample comprises rural areas as it was carried over from ESS1. The ESS2 was implemented in 433 enumeration areas (EAs) out of which 290 were rural, 43 were small town EAs from ESS1, and 100 were new EAs from major urban areas.

Analysis unit

Households, individuals and communities.

Kind of data

Sample survey data [ssd]

Sampling procedure

ESS is designed to collect panel data in rural and urban areas on a range of household and community level characteristics linked to agricultural activities. The first wave was implemented in 2011-12 and the second wave is implemented in 2013-14. The first wave, ERSS, covered only rural and small town areas. The second wave, ESS, added samples from large town areas. The second wave is nationally representative. The existing panel data (2011/12-2013/14) is only for rural and small towns. Large towns were added during the second wave and, so far, there is only one round. The planned follow-up ESS surveys will continue to be nationally representative. The ESS sample size provides estimates at the national level for rural and small town households. At the regional level, it provides estimates for five regions including Addis Ababa, Amhara, Oromiya, SNNP, and Tigray.

The sample is a two-stage probability sample. The first stage of sampling entailed selecting primary sampling units, which are a sample of the CSA enumeration areas (EAs). A total of 433 EAs were selected based on probability proportional to size of the total EAs in each region. For the rural sample, 290 EAs were selected from the AgSS EAs. For small town EAs, a total of 43 EAs and for large towns 100 EAs were selected. In order to ensure sufficient sample in the most populous regions (Amhara, Oromiya, SNNP, and Tigray) and Addis Ababa, quotas were set for the number of EAs in each region. The sample is not representative for each of the small regions including Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie regions. However, estimates can be produced for a combination of all smaller regions as one "other region" category.

During the second wave 100 urban EAs were added. The addition also included one more region to the sample, Addis Ababa. In each EA 15 households were selected. The addition of urban EAs increased the sample size from 333 to 433 EAs or from about 3,969 to 5,469 households.

The second stage of sampling was the selection of households to be interviewed in each EA. For rural EAs, a total of 12 households are sampled in each EA. Of these, 10 households were randomly selected from the sample of 30 AgSS households. The AgSS households are households which are involved in farming or livestock activities. Another 2 households were randomly selected from all other non-agricultural households in the selected rural EA (those not involved in agriculture or livestock). In some EAs, there is only one or no such households, in which case, less than two non-agricultural households were surveyed and more agricultural households were interviewed instead so that the total number of households per EA remains the same.

In the small town EAs, 12 households are selected randomly from the listing of each EA, with no stratification as to whether the household is engaged in agriculture/livestock. The same procedure is followed in the large town EAs. However, 15 households were selected in each large town EA.

Households were not selected using replacement. Thus, the final number of household interviewed was slightly less than the 5,469 as planned in the design. A total of 3,776 panel households and 1,486 new households (total 5,262 households) were interviewed with a response rate of 96.2 percent.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey was collected using five questionnaires: household, community, post-planting agriculture, post-harvest agriculture and livestock questionnaires.

The household questionnaire collected information on basic demographics; education; health (including anthropometric measurement for children); labor and time use; partial food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; credit; and other sources of household income. The household questionnaire, when relevant, is comparable to the Welfare Monitoring Survey (WMS).

The community questionnaire gathered information on access to infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Post-planting and post-harvest agriculture questionnaires were completed in those households with at least one member of the household engaged in crop farming using owned or rented land The post-planting and post-harvest agriculture questionnaires focused on farming activities and solicit information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization.

The livestock questionnaire interviews were implemented in households where at least one member was engaged in livestock rearing. The livestock questionnaire collected information on animal holdings and costs; and production, cost and sales of livestock byproducts.

Cleaning operations

The interviews were carried out using paper and pen interviewing method. However, a concurrent data entry arrangement was introduced in this wave. In this arrangement, the enumerators did not wait until all the interviews were completed. Rather, once the enumerators completed some 3 to 4 questionnaires, the supervisors collected those completed interviews from the enumerators and brought them to the branch offices for data entry, while the enumerators are still conducting interviews with other households. Then questionnaires are keyed at the branch offices as soon as they are completed using CSPro data entry application software. The data from the completed questionnaires are then checked for any interview or data entry errors using a stata program. Data entry errors are checked with the data entry clerks and the interview errors are then sent to back to the field for correction and feedback to the ongoing interviews. Several rounds of this process were undertaken until the final data files are produced. In addition, after the fieldwork was completed the paper questionnaires were sent to the CSA headquarters in Addis Ababa for further checking. Additional cleaning was carried out, as needed, by checking the hard copies.

Response rate

Response rate was 96.2 percent.

The ERSS/LSMS-ISA datasets are available in the public domain: Ethiopia Socioeconomic Survey (ESS) 2011-2012, Wave 1, http://dx.doi.org/10.48529/80xt-9m68; Ethiopia Socioeconomic Survey (ESS) 2013-2014, Wave 2, http://dx.doi.org/10.48529/mccp-y123; Ethiopia Socioeconomic Survey (ESS) 2015-2016, Wave 3, http://dx.doi.org/10.48529/ampf-7988. The data was accessed and filtered using the following criteria: saq01==1 & rural!=3, corresponding to selected households in the Tigray region engaged in farming activities in rural areas and small towns. Moreover, anyone can access LanSat imageries at https://earthexplorer.usgs.gov/

Abstract

Uganda has experienced strong economic growth over the past two decades, and has made great strides towards improving the quality of life and access to services. In order to continue to promote pro-poor economic growth, the Government of Uganda (GoU) developed the National Development Plan (NDP) and a Joint Budget Support strategy as part of the implementation of the National Development Strategy (NDS). The GoU recognizes the need for adequate data collection to effectively monitor outcomes of the NDS. Towards this end, the Uganda Bureau of Statistics (UBOS) is implementing the Uganda National Panel Survey (UNPS) program, with financial and technical support from the Government of Netherlands, and the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project. The main objective of the UNPS program is to implement four waves of a multi-topic panel household survey over a five-year period, starting in 2009/10, and with a strong focus on smallholder agriculture. Given the frequency with which the data will be collected, the UNPS program will provide an opportunity to inform policymaking in advance of the Budget, through descriptive reports in time for the initial work on sector budget framework papers

Geographic coverage

The Entire Country

Analysis unit

Individual Household Community

Universe

The survey covered all household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

The UNPS initial sample was a subset of about 3,220 households, selected from the 7,426 households visited by the UNHS-06.

This initial sample will be visited for two consecutive years (2009/10 and 20010/11,) after which parts of the sample will start to be replaced by new households extracted from the updated sample frames developed by UBOS from the 2012 census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The UNPS fits within the Long Term Census and Household Survey Program, such that the questionnaire and the timing of data collection will be coordinated with the surveys and census implemented by the UBOS. To suit its multiple objectives, the UNPS is comprised of a set of survey instruments, namely:

• Household Questionnaire,
• Woman Questionnaire,
• Agriculture Questionnaire, administered to the subset of UNPS households engaged in agricultural activities, including fishing and livestock,
• Community Questionnaire, and
• Market Questionnaire

Cleaning operations

Ultra Mobile Personal Computers (UMPSs) were used during data Processing. 49 UMPSs were used for electronic data capture and these UMPSs were running a CWEST (Capture with Enhanced Survey Technology) application. Data Analysis was done using Stata 11.0

Dataset for Temporary Migration and Climate Variation in Eastern Africa ( LSMS ISA, Climate MERRA)