Percentage of food secure and food insecure households for different samples.

Eradicating hunger is a complex and multifaceted challenge, requiring evidence bases that can inform wide scale action, but that are also participatory and grounded to have local relevance and effectiveness. The Rural Household Multi-Indicator Surveys (RHoMIS) provides a broad assessment of household capabilities and food security outcomes, while ethnographic approaches evidence how individuals' perceptions, experiences and local socio-political context shape food security experiences and intervention outcomes. However, integrating these research approaches presents methodological and ontological challenges. We combine a quantitative approach with life history interviews to understand the drivers, experiences and outcomes of food insecurity in Guatemala's dry corridor region. We also reflect on the effectiveness and challenges of integrating the two methods for purposes of selective sampling, triangulating evidence, and producing a cohesive analyses of food insecurity in the region. Variables with a statistically significant association with severe food insecurity in the region are: coffee cultivation (when market participation is low), dependence on agricultural labor income, and poverty level. Drivers of food insecurity experiences most commonly identified by participants are: consecutive drought; ill health and displacement of income for medicine; social marginalization; high start-up costs in production; absence or separation of a household head; and a lack of income and education opportunity. Ethnographic approaches identify a broader range of drivers contributing to food insecurity experiences, and add explanatory power to a statistical model of severe food insecurity. This integrated analysis provides a holistic picture of food insecurity in Guatemala's dry corridor region.

Abstract In Brazil, female-headed households disproportionately experience food insecurity. However, empirical and theoretical evidence shows that women are better than men at allocating intra-household resources to achieve well-being. In observation of this paradox, the present work studies the process of poverty feminization, and presents a collective decision model to understand the vulnerability situation of women. Specifically, we aimed to observe how time use and food insecurity correlate. We estimated an ordered probit model with Brazilian National Household Sample Survey data. Our studies found that women manage a double burden of both paid and unpaid jobs. This increases their risk of food insecurity, confirming the importance of time allocation in household well-being. Conversely, this effect is inverted when household tasks are shared with another member, specifically the spouse. Single mother households still face several challenges, which require specific policies and studies.

Abstract

Scientific abstract Background: Acute malnutrition in infants and children less than 5 years is persistent in the arid and semi-arid lands (ASALs) of East Africa and the Sahel region despite years of investment. In the ASALs of Kenya, the situation is exacerbated by deep-rooted poverty and unequal access to basic services, sustained community conflicts, migration, poor seasonal rainfall/drought and other shocks. Nutrition specific and nutrition sensitive national and county level programs have either not been developed or not implemented effectively.

Objectives: To understand and map immediate, underlying, basic and systemic drivers of acute malnutrition for the development of overarching as well as micro-solutions for the sustainable reduction of persistent acute malnutrition and inform pilot studies and Phase 2 (second phase of USAID Nawiri project implementation) activities in Turkana and Samburu counties.

Methods: This study is a longitudinal mixed-methods observational study of children less than 3 years and their mothers and/or caregivers in Samburu and Turkana counties. Both quantitative and qualitative methods were utilized in the data collection processes. Data collection commenced in January 2021. Data analysis, learning and adapting was also ongoing so that results could inform pilots, theory of change review and Phase 2 activities throughout the study.

Study outcomes: To develop new interventions, and to adapt and contextualize existing interventions to prevent global acute malnutrition (GAM); strengthen social and behavior change (SBC) strategies around maternal, infant and young child nutrition (MIYCN), water and sanitation (WASH), community health systems, gender dynamics, livelihoods and resilience, and to inform improvements of the current nutrition surveillance system.

Study duration: 24 months. Summary budget: Total budget is KSH 140,400,000.00. Lay summary: The nutritional status of mothers and young children in Kenya's ASALs are strongly affected by deep-rooted poverty and unequal access to basic services, sustained community conflict, migration, poor seasonal rainfall/drought and other shocks. Inadequate women empowerment and limited control over household resources, high workload, domestic violence, insufficient household food security, inadequate social support, inadequate health services and an unhealthy environment, as well as inadequate dietary intake and high disease burden, are among other factors that contribute to poor maternal infant and young child feeding practice in these areas. Consequently, more than one in ten reproductive age women and 2-3 in ten young children in Turkana and in Samburu are undernourished. As such, this study aims to provide evidence for the appropriate policy and program design to improve the nutritional status of children and their mothers living in the two counties.

Geographic coverage

ASAL Counties coverage ( Turkana and Samburu )

Analysis unit

The unit of analysis is the sampled households in Turkana and Samburu Counties

Universe

The survey covered households with children under 3 years and their mothers/caregivers

Sampling procedure

SAMBURU

The study sample was population-based, with stratification by sub-counties grouped into three survey zones (Central, North, and East) reflecting administrative sub-counties used in the Samburu Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys. The study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 699 households. The qualitative data yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report.

The baseline data collection was carried out in June and July 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Wave 2 data collection was carried out in November-December 2021, Wave 3 in March-April 2022, Wave 4 in September-October 2022, Wave 5 in March-April 2023 and Wave 6 data collection in August-September 2023.

TURKANA

The study sample was population-based, with stratification by sub-counties grouped into four survey zones (Central, North, West, and South) reflecting administrative sub-counties used in the Turkana SMART Surveys.
The study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 1,211 households. The qualitative data yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report.

The baseline data collection was carried out in May and June 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Anthropometric data were collected from all under-5 children in the sampled households. Wave 2 data collection was carried out in October-November 2021, Wave 3 in March-April 2022, Wave 4 in September-October 2022, Wave 5 in March-April 2023 and Wave 6 data collection in August-September 2023.

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

In Wave 6, one questionnaire with three different sections (Household section, Mother/caregiver section and Child section was administered in each sampled household to the Mother/caregiver

The household section collected various information on Household democraphics, Household Food insecurity coping strategies, water,hygiene and sanitation(WASH), Household shocks experienced, Social safety nets and economic safety guards, Household food insecurity experience scale(FIES), Interventions and services received by households,

The mother/caregiver section included,Mothers/caregivers information,Pregnancy and antenatal care, Family planning, Women's minimum dietary diversity, Gender, women empowerment, violence and community conflict, Psychological wellbeing.

The child section includes Infant and young child feeding practices, Supplementation and consuption of iron-rich or iron-fortified foods, Caregiving practices, Food safety, hygiene and sanitation practices, Child immunization, health and health-seeking practices, Acute Malnutrition screening, Anthropometric measurements.

Cleaning operations

Data quality monitoring processes and checks were implemented throughout the data collection process, during the time of developing the data collection tools (through built-in quality control in the tablet-based platform), during training of fieldworkers, in real time during data collection (routine monitoring by the research team and periodic cross-checks against the protocols), and during the data cleaning process. During fieldwork, data quality was enhanced through regular spot checks and sit-ins by supervisors to verify the authenticity of data collected. Data were then reviewed and certified by the field coordinator before they were transferred to the server.

The quantitative data were collected using SurveyCTO, a survey platform for electronic data collection that has in-built skips and quality checks. Using this software increased efficiency and reduced the time needed for cleaning the data. In addition, the platform supported offline data capturing for regions with slow or no internet connectivity and data transmission when the internet became available. Fieldwork was conducted by trained fieldworkers using digital tablets with the questionnaire loaded in SurveyCTO. The questionnaire included the following modules: (1) identification and tracking, (2) demographics and household composition, (3) anthropometry of children <5 years and mothers, (4) socioeconomics, (5) household food security, (6) WASH, (7) health-seeking behavior, (8) MIYCN, (9) shock experience/exposure, and (10) shock preparedness and response. Data were uploaded from the tablets onto a secure African Population and Health Research Center (APHRC) server after each day of data collection. Data were synchronized automatically to a server when the tablet was in a location with network coverage. The uploaded data were then checked for quality daily by a data manager and a team dedicated to coordinate field procedures and at the APHRC head office in Nairobi.

Response rate

Turkana: 96.3% Samburu: 92.8%

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during the implementation of this longitudinal study to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically. If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the study sample is the result of a multi-stage stratified design and consequently needs to use more complex formulae. The Stata complex samples module was used to calculate sampling errors.

Abstract Background The burden of food insecurity remains a public health challenge even in high income countries, such as Australia, and especially among culturally and linguistically diverse (CALD) communities. While research has been undertaken among several migrant communities in Australia, there is a knowledge gap about food security within some ethnic minorities such as migrants from the Middle East and North Africa (MENA). This study aims to determine the prevalence and correlates of food insecurity among Libyan migrant families in Australia. Methods A cross-sectional design utilising an online survey and convenience sampling was used to recruit 271 participants, each representing a family, who had migrated from Libya to Australia. Food security was measured using the single-item measure taken from the Australian Health Survey (AHS) and the 18-item measure from the United States Department of Agriculture Household Food Security Survey Module (USDA HFSSM). Multivariable logistic regression was used to identify independent correlates associated with food insecurity. Results Using the single-item measure, the prevalence of food insecurity was 13.7% whereas when the 18-item questionnaire was used, more than three out of five families (72.3%) reported being food insecure. In the multivariable logistic regression analysis for the single-item measure, those living alone or with others reported higher odds of being food insecure (AOR = 2.55, 95% CI 1.05, 6.21) compared to those living with their spouse, whereas higher annual income (≥AUD 40,000) was associated with lower odds of food insecurity (AOR = 0.30, 95% CI 0.11, 0.84). Higher annual income was also associated with lower odds of food insecurity (AOR = 0.49, 95% CI 0.25, 0.94) on the 18-item measure. On both single and 18-item measures, larger family size (AOR = 1.27, 95% CI 1.07, 1.49 and AOR = 1.21, 95% CI 1.01, 1.47 respectively) was associated with increased odds of food insecurity. Conclusion This study provides evidence that food insecurity amongst Libyan migrants in Australia is a widespread problem and is associated with a number of sociodemographic and socio-economic factors. The findings of this study serve to contribute to the depth and breadth of food security research among vulnerable communities, in this instance Libyan migrant families.

Abstract

According to the Food and Agricultural Organization (FAO) 123 million Chinese remained undernourished in 2003-2005. That represents 14% of the global total. UNICEF states that 7.2 million of the world's stunted children are located in China. In absolute terms, China continues to rank in the top countries carrying the global burden of under-nutrition. China must-and still can reduce under-nutrition, thus contributing even further to the global attainment of MDG1. In this context that the United Nations Joint Programme, in partnership with the Chinese government, has conducted this study. The key objective is to improve evidence of household food security through a baseline study in six pilot counties in rural China. The results will be used to guide policy and programmes aimed at reducing household food insecurity in the most vulnerable populations in China. The study is not meant to be an exhaustive analysis of the food security situation in the country, but to provide a demonstrative example of food assessment tools that may be replicated or scaled up to other places.

Geographic coverage

Six rural counties

Analysis unit

• Household
• Village

Universe

The survey covered household heads and women between 15-49 years resident of that household. A household is defined as a group of people currently living and eating together "under the same roof" (or in same compound if the household has 2 structures).

Kind of data

Sample survey data [ssd]

Sampling procedure

The required sample size for the survey was calculated using standard sample size calculations with each county representing a stratum. After the sample size was calculated, a two-stage clustering approach was applied. The first stage is the selection of villages using the probability proportional to size (PPS) method to create a self-weighted sample in which larger population clusters (villages) have a greater chance of selection, proportional to their size. Following the selection of the villages, 12 households within the village were selected using simple random selection.

Sampling deviation

Floods and landslides prevented the team from visiting two of the selected villages, one in Wuding and one in Panxian, so they substituted them with replacement villages.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household questionnaire was administered to all households in the survey and included modules on demography, education, migration and remittances, housing and facilities, household assets, agricultural, income activities, expenditure, food sources and consumption, shocks and coping strategies.

The objective of the village questionnaire was to gather contextual information on the six counties for descriptive purposes. In each village visited, a focus group discussion took place on topics including: population of the village, migrants, access to social services such as education and health, infrastructure, access to markets, difficulties facing the village, information on local agricultural practices.

The questionnaires were developed by WFP and Chinese Academy of Agricultural Sciences (CAAS) with inputs from partnering agencies. They were originally formulated in English and then translated into Mandarin. They were pilot tested in the field and corrected as needed. The final interviews were administered in Mandarin with translation provided in the local language when needed.

All questionnaires and modules are provided as external resources.

Cleaning operations

After data collection, data entry was carried out by CAAS staff in Beijing using EpiData software. The datasets were then exported into SPSS for analysis. Data cleaning was an iterative process throughout the data entry and analysis phases.

Descriptive analysis, correlation analysis, principle component analysis, cluster analysis and various other forms of analyses were conducted using SPSS.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

There is considerable global evidence on the effectiveness of cash transfers in improving health and nutrition outcomes; however, the evidence from South Asia, particularly India, is limited. In the context of India where more than a third of children are undernourished, and where there is considerable under-utilization of health and nutrition interventions, it is opportune to investigate the impact of cash transfer programs on the use of interventions. We study one conditional cash transfer program, Mamata scheme, implemented in the state of Odisha, in India that targeted pregnant and lactating women. Using survey data on 1161 households from three districts in the state of Odisha, we examine the effect of the scheme on eight outcomes: 1) pregnancy registration; 2) receipt of antenatal services; 3) receipt of iron and folic acid (IFA) tablets; 4) exposure to counseling during pregnancy; 5) exposure to postnatal counseling; 6) exclusive breastfeeding; 7) full immunization; and 8) household food security. We conduct regression analyses and correct for endogeneity using nearest-neighbor matching and inverse-probability weighting models. We find that the receipt of payments from the Mamata scheme is associated with a 5 percentage point (pp) increase in the likelihood of receiving antenatal services, a 10 pp increase in the likelihood of receiving IFA tablets, and a decline of 0.84 on the Household Food Insecurity Access Scale. These results provide the first quantitative estimates of effects associated with the Mamata scheme, which can inform the design of government policies related to conditional cash transfers.

The Trussell Trust has commissioned 'Hunger in the UK', a multi-year large-scale quantitative and qualitative research project to help support their strategic vision of ending the need for food banks. The Trussell Trust has appointed Ipsos Mori to deliver this research. The project focuses on three elements, each intended to build on existing evidence from research that the Trussell Trust had previously commissioned:

1. Exploring the life experiences and socio-demographics of people referred to food banks in the Trussell Trust network through quantitative research. This study includes a survey of people referred to food banks in the Trussell Trust network. The survey collected a broad range of demographic and socioeconomic status information at both the individual and household level.

2. A survey of the general population of the United Kingdom to establish benchmarks of, and track over time, the level of destitution, food-aid use, and food insecurity amongst this population. This survey mirrors the survey of people referred to food banks, thereby allowing for a comparative analysis of both populations.

3. Qualitative research with people experiencing food insecurity and destitution to understand their lived experience and enrich understanding of the drivers of food bank use and the impact on individuals and families.*

*Currently, this study includes only the survey data from elements 1. and 2. of the project.

The research aims to contribute to the Trussell Trust’s goal of ending the need for food banks across the UK by providing evidence on the drivers of food insecurity and the need to receive support from a food bank. It allows exploration of the groups of people who are more likely to need support, how these experiences differ across the countries of the United Kingdom and what factors may allow people to escape food insecurity.

Further information may be found on The Trussell Trust's Hunger in the UK webpage.

Abstract

Efforts to improve Madagascar’s food security and nutrition over the last two years have been thwarted by political instability which has disrupted data collection. With up-to-date critical information needed to contribute to evidence based decision making, UNICEF and WFP agreed to carry out a joint Comprehensive Food and Nutrition Security and Vulnerability Analysis (CFSVA+N) in 2010. The survey’s primary objectives are to: • Provide an accurate and detailed assessment of the current food and nutrition security situation • Assess the causes and risk factors for food and nutrition insecurity • Identify potential ways to mitigate food and nutrition insecurity • Reveal pockets of vulnerability where special assistance may be required.

Geographic coverage

Rural areas of Madagascar

Analysis unit

• Household
• Community

Universe

The survey covered household heads and women of reproductive age (15-49 years) in each sampled household. Household is defined as one (or more) people living under the same roof or different roofs, but sharing the main meals and respecting the authority of the same person (the head of the household).

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to have a representative sample by region and livelihood zone, a two-stage cluster sampling was applied.

First stage, the number of clusters per region was calculated based on the required sample size to determine malnutrition and food security by region (details on sample size calculation are reported below). A minimum of 25 clusters per region was set. Since the Fokontany is the smallest administrative unit with population data available, this was established as the administrative unit from which clusters were selected. A list of all Fokontany and their estimated population was obtained from INSTAT.Urban communities as defined by the latest DHS-IV were not included in the sampling frame. Using this list and the livelihood zones, 176 segments were defined corresponding to both the regions and the livelihood zones. The number of clusters in each of the 176 segments was determined based on the population per segment weighted from the total regional population. For each segment, the Fokontany and their population were introduced into ENA and the required number of cluster per segment was selected using "probability proportional to population size" (PPS) sampling technique. This process was repeated for each of the 176 segments. In total, 606 clusters were selected across the 22 regions and distributed in the 8 Livelihood zones.

Second stage: the secondary sample unit was the household as defined by INSTAT. Within the selected clusters, households were selected from an exhaustive of households using systematic random sampling.

For the anthropometric data, the households' number (as defined by the sample size calculation) was selected using a calculated interval sampling (i.e. from a Fokontany with 200 households, 20 households were selected using a sample interval of 10). The households for the food security survey were sampled from the larger list of households already selected for the anthropometric survey. This was done using an independently calculated interval sampling (i.e. from the 20 households selected for anthropometric data, 5 households were selected for food security and health data using a sample interval of 4).

Sampling deviation

Thirty-three clusters out of the 606 selected were not visited due to bad weather or insecurity.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household questionnaires, community questionnaires and nutrition questionnaires (which included anthropometric measurements) were used to collect the data.

Background: There have been claims amongst nutrition stakeholders in Tanzania that the food basket regions are leading in stunting prevalence. However, we could not find evidence that combines food production and stunting levels to substantiate this claim. Therefore, this study aims to compare data of stunting, food production and consumption at administrative regions in Tanzania Mainland. Methods: The study used an ecological study design to show the relationship between stunting, poverty, and food production and consumption across administrative regions in Tanzania. The study used data from three national wide surveys: 2017/2018 Household Budget Survey (HBS), Tanzania National Nutrition Survey (TNNS) 2018 and Agriculture Statistics for Food Security report 2018/2019. Results: The study showed that there is a positive relationship between the prevalence of stunting and food production (r=0.43, p=0.03) while there is a negative relationship between stunting and the level of both the average monthly household consumption expenditure (r = -0.48, p = 0.01) and average monthly household food consumption expenditure (r = -0.509, p = 0.01). It was further found that some regions which have higher levels of stunting such as Njombe have the lowest level of basic need poverty. Conclusion: The study found a positive relationship between food production and the prevalence of stunting using data across regions in Mainland Tanzania. This is an indication that regional food security may not entail nutrition security hence a call for more advocacy on nutrition-sensitive agriculture. This is an ecological study design using secondary data compiled from three sources: Tanzania Household Budget Survey (HBS) 2017/2018, Tanzania National Nutrition Survey (TNNS 2018) and Agriculture Statistics for Food Security report 2018/2019. Both HBS and TNNS are national-wise cross-sectional data organized by the Ministry of Finance and Planning and Ministry of Health respectively while Agriculture Statistics for Food Security report 2018/2019 was prepared by the Ministry of Agriculture. The data used Poverty, Average Food Consumption in TZS, Average Non Food Consumption in TZS and Average Monthly Consumption in TZS Data from HBS 2017/18, Total Food Production, Non-Cereal Food Production, Cereal Food Production from Agriculture Statistics for Food Security report 2018/2019 and Stunting Data from TNNS 2018. Excel and Stata were the software used to show the relationship between this variables.

The dataset comprises a panel of 1,824 agricultural households located across 75 communities in the departments of Huehuetenango, Quiche, and San Marcos in Guatemala that were both interviewed in person in November-December 2019, for the baseline survey, and over the phone in a follow-up survey in May-June 2020 to assess the impacts of COVID-19 on individual and social preferences. This is only subset of the data which is constructed from these two survey and consist limited information household socioeconomic characteristics, dwelling characteristics, income, asset ownership, agricultural activities, changes in food consumption, food insecure experiences, and self-reported preferences. The full dataset is planned for release in near future after completion of few more rounds of follow-up survey.

Abstract

The recent global economic slowdown, caused by the COVID-19 pandemic, created an urgent need for timely data to monitor the socioeconomic impacts of the pandemic. Tanzania is among other countries in the world which are affected by the recent global economic slowdown, caused by the COVID-19 pandemic. Therefore, there is an urgent need for timely data to monitor and mitigate the socio-economic impacts of the crisis in the country. Responding to this need, the National Bureau of Statistics (NBS) and the Office of the Chief Government Statistician (OCGS), Zanzibar in collaboration with the World Bank and Research on Poverty Alleviation (REPOA) implemented a rapid household telephone survey called the Tanzania High-Frequency Welfare Monitoring Survey (HFWMS).

Thus, the main objective of the survey is to obtain timely data that is critical for evidence-based decision making aimed at mitigating the socio-economic impact of the downturn caused by COVID-19 pandemic by filling critical gaps of information that can be used by the government and stakeholders to help design policies to mitigate the negative impacts on its population.

Geographic coverage

National

Analysis unit

Households Individuals

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

Phase one of the Tanzania High Frequency Welfare Monitoring Panel Survey (THFWMPS I) draws its sample from various previous face-to-face surveys, including the Mainland Household Budget Survey (HBS) 2017/18, the Zanzibar HBS 2019/20, and the National Panel Survey (NPS) 2014. The inclusion of telephone numbers from most participants of these surveys provides the foundation for the survey sample.

The target for monthly sample completion is approximately 3,000 households. The NPS serves as the primary sample frame, supplemented by the Mainland and Zanzibar HBS. For THFWMPS Phase II, the sample frame comprises respondents from Phase I who did not explicitly refuse to participate (2,200 households), alongside additional households from the 2021 Booster sample of NPS Wave 5 (NPS 5) households with available phone numbers.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Each survey round consists of one questionnaire - a Household Questionnaire administered to all households in the sample.

Baseline The questionnaire gathers information on demographics; employment; education; access to basic services; food security; TASAF; and mental health. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment: Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Education: School attendance, type of school attended, learning activities of children at home, return to school, contact with children’s teachers during school closure.
• Access to Basic Services:Household’s access to staple food (maize grain, cassava, rice, and maize flour), medical treatment, and reasons for not being able to access the services.
• Food Security: Household’s food security status during the last 30 days.
• TASAF: Households access to the TASAF money, use of the money received, challenges encountered in accessing the funds.
• Mental Health: Information on 8 items pertaining to measuring mental health.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 2 The questionnaire gathers information on demographics; employment; non-farm enterprise; tourism; education; access to health services; and TASAF. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment: Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Tourism: Employment of household members in tourism sector, and who benefits from tourism.
• Education (selected members aged 4-18 years): School attendance, reason for not attending, grade attending, type of school, absence and reason for being absent.
• Access to Health Services: Women’s access to pre-natal/post-natal care, household’s access to preventative care and medical treatment, and reasons for not being able to access the services.
• TASAF: Households access to the TASAF money, use of the money received, challenges encountered in accessing the funds.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview

Round 3 The questionnaire gathers information on demographics; employment (respondent and other household members); non-farm enterprise; credit; women savings; and shocks and coping. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Employment (other members): Status in employment (current and 2020), consistency of work in 2020, why currently not working, job search, change in jobs, actual job.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Credit: Household’s debts status since the beginning of the coronavirus crisis; use of loan, ability to repay loan when their scheduled payment is due.
• Women Savings: Women having bank accounts to financial institutions and changes in their savings since the start of the pandemic.
• Shocks and Coping: Shocks that affected household since the baseline interview and their coping strategies.
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 4 The questionnaire gathers information on demographics; employment; non-farm enterprise; digital technology; and income changes. The contents of questionnaire are outlined below:

• Cover: Household identifiers and enumerator identifiers.
• Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to.
• Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left.
• Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual.
• Non-farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business.
• Income changes: Household’s sources of livelihood.
• Digital Technology: Household ownership of phones, computers and digital devices, access to internet and challenges in accessing internet
• Recontact: Data on how the household can be recontacted in the future.
• Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 5 The questionnaire gathers information on demographics;

Abstract

This report presents the final results from the last seven scientific rounds of the Tanzania High Frequency Welfare Monitoring Phone Survey (THFWMPS) which was conducted by the National Bureau of Statistics (NBS) and Office of Chief Government Statistician (OCGS) Zanzibar, in collaboration with World Bank (WB) and the Research on Poverty Alleviation (REPOA). The key findings from these high frequency survey rounds are intended to be used to monitor and mitigate the negative impacts of the emerging crisis such as pandemics on the economic and population wellbeing of the country.

Round 6 to 12 comprises findings from the following key areas; Demographic Characteristics, Employment Status and Reasons for Not Working, Economic Sentiments, Natural Disasters and Climate Events, Access to Essential Goods and Services, Types of Shocks Experienced by Households ( Environmental Shocks and Agricultural Shocks), Transportation Usage for Different Locations in Tanzania (Market Transportation, Workplace and School Transportation and Transport use for health facilities), Household Subjective Welfare Situation , Crop Production and Livestock.

The objective of Round 12 is divided into two aspects: testing the installed call center gadgets and conducting the Round 12 phone survey. The installed gadgets at the call center were tested to gain insight into how well the center functions and to identify areas for improvement, whether in customer experience, agent performance, or technical infrastructure. The objective of the Round 12 phone survey was to gather timely data to fill information gaps and support evidence-based decision-making for welfare monitoring and understanding the impacts of crises, such as extreme weather events, epidemics, pandemics and any other crises occurred.

Geographic coverage

National

Analysis unit

Household Individuals

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

Phase one of the Tanzania High Frequency Welfare Monitoring Panel Survey (THFWMPS I) draws its sample from various previous face-to-face surveys, including the Mainland Household Budget Survey (HBS) 2017/18, the Zanzibar HBS 2019/20, and the National Panel Survey (NPS) 2014. The inclusion of telephone numbers from most participants of these surveys provides the foundation for the survey sample.

The target for monthly sample completion is approximately 3,000 households. The NPS serves as the primary sample frame, supplemented by the Mainland and Zanzibar HBS. For THFWMPS Phase II, the sample frame comprises respondents from Phase I who did not explicitly refuse to participate (2,200 households), alongside additional households from the 2021 Booster sample of NPS Wave 5 (NPS 5) households with available phone numbers.

The Survey Round twelfth conducted from October - November 2024 includes a total of 2,489 households, contributing to the continued monitoring welfare within Tanzanian households

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Round 6 questionnaire The questionnaire gathers information on demographics; employment; non-farm enterprise; COVID-19 Vaccine; access to health services; and youth contact details. The contents of questionnaire are outlined below:

Cover: Household identifiers and enumerator identifiers Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to. Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left. Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual. Economic Sentiments: How household feels about past and future household economic situation, past and future country economic situation, past and future consumer prices, major household purchases, extreme weather shocks to household’s financial status in the future. Food Prices: Availability of specific food items in the country, current price of the item, as well as price of the same item 30 days prior. Fuel Prices: Household has ever bought petrol/diesel, last time household purchased petrol, difficulties encountered when purchasing petrol. Recontact: Data on how the household can be recontacted in the future, including phone number, time of day they can be reached in the future. Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 7 questionnaire The questionnaire gathers information on demographics; employment; economic sentiments; access to essential goods and services; food prices; energy prices; transportation prices; food insecurity; dietary diversity, and subjective welfare. The contents of questionnaire are outlined below:

Cover: Household identifiers and enumerator identifiers Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to. Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left. Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual. Economic Sentiments: Household interpretations of past and future household economic situation, past and future country economic situation, past and future consumer prices, major household purchases, and extreme weather shocks to household’s financial status in the future. Access to Goods and Services: Household’s access to staple foods (maize grain, cassava, rice, and maize flour), essential goods (medicine, soap, fuel/gasoline, and fertilizers) and reasons for not being able to access the goods and services. Food Prices: Availability of specific food items in the country, current price of the item, as well as price of the same item 30 days prior. Energy Prices: Household purchases of energy/fuel (petrol, diesel, LPG, kerosene), last purchase of energy/fuel, number of liters purchased, total amount paid, and changes in the price in the last month. Transportation Prices: Mode of transportation for selected destinations, amount paid in total, as well as changes in the price in last month. Subjective Welfare: How the household feels about their food consumption, housing, clothing, health care, and the level of current household income over the past one month. Food Insecurity: Household’s food security status during the last 30 days. Dietary Diversity: Household’s consumption of a variety of food groups over the last 7 days, as well as how the food was acquired. Recontact Information: Data on how the household can be recontacted in the future, including phone number, time of day they can be reached in the future. Interview Results: Result of interview including observation notes by enumerator regarding the interview, respondent and language of interview.

Round 8 questionnaire The questionnaire gathers information on demographics; employment; economic sentiments; access to essential goods and services; food prices; energy prices; transportation prices; food insecurity; dietary diversity, and subjective welfare. The contents of questionnaire are outlined below:

Cover: Household identifiers and enumerator identifiers Interview Information: Details of call attempts, result and respondent of call attempt, interview consent, date and time of call back, phone numbers called, the information of the person that the listed phone number belongs to. Basic Information: Roster of members of the household, relationship to the household head, gender, age, relationship to head, reason for joining the household if new, and reason for leaving the household if left. Employment (respondent): Status and information of income-generating activities (wage work, family business and farming), reason for stopped working, and reason for not able to perform activities as usual. Economic Sentiments: Household interpretations of past and future household economic situation, past and future country economic situation, past and future consumer prices, major household purchases, and extreme weather shocks to household’s financial status in the future. Non-Farm Enterprise: Status and information of non-farm income-generating activities, reason for stopped operating, reason for not able to perform activities as usual, and reason for reduced revenue from family business Access to Goods and Services: Household’s access to staple foods (maize grain, cassava, rice, and maize flour), essential goods (medicine, soap, fuel/gasoline, and fertilizers) and reasons for not being able to access the goods and services. Food Prices: Availability of specific food items in the country, current price of the item, as well as price of the same item 30 days prior. Energy Prices: Household purchases of energy/fuel (petrol, diesel, LPG, kerosene), last purchase of energy/fuel, number of liters purchased, total amount paid, and changes in the price in the last

Abstract

This dataset presents findings from the 2023 Standardized Expanded Nutrition Survey (SENS) conducted at the Alemwach refugee site in Ethiopia’s Amhara region. Established in 2020, Alemwach hosts Eritrean refugees relocated from northern Tigray due to conflict. The survey, led by UNHCR and RRS in collaboration with government and partner organizations, aimed to assess the health, nutrition, food security, and WASH conditions of the refugee population. Using the SENS Version 3 (2018) and SMART methodology, data were collected through face-to-face interviews with households selected via simple random sampling. As of December 1, 2023, Alemwach hosted 21,557 individuals, including 1,940 children under five (9% of the population). The anonymized dataset supports evidence-based planning and targeted interventions to address the essential needs of refugees residing in Alemwach.

Geographic coverage

Alemwach Refugee Camp.

Analysis unit

Household

Universe

Refugees and asylum seekers residing in Alemwach Refugee Camp.

Kind of data

Sample survey data [ssd]

Sampling procedure

A simple random sampling strategy was applied to select households within the Alemwach refugee site. The sample size was calculated using UNHCR SENS Version 3 (2018) guidelines, taking into account estimated prevalence rates, desired precision, and design effect. The survey team conducted household listing prior to selection to ensure a complete sampling frame.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire followed the Standardized Expanded Nutrition Survey (SENS) Version 3 (2018) tools, covering modules on household demographics, child anthropometry, health, food security, and water, sanitation and hygiene (WASH).

Feed the Future initiative in Malawi is committed to improving food security and nutrition in farming households while reducing rural poverty through an agriculture-led, integrated economic growth, nutrition, and natural resource management strategy. The goal of the impact evaluation is to determine whether integrating nutrition interventions alongside agricultural value chain interventions will contribute to a greater reduction in malnutrition among children under 3 years of age, compared to nutrition improvements anticipated from stand-alone value chain activities. The main objectives are to enable USAID missions to meet the performance monitoring requirements of Feed the Future and maximize the use and benefits of data collected; provide high-quality empirical evidence to inform program design and investment decisions that will promote sustainable food security; ensure timely availability of highquality data for use in monitoring performance and evaluating impacts of the Feed the Future initiative; and facilitate accountability and learning about which Feed the Future interventions work best, under what conditions, and at what cost.

Abstract

The main purpose of a Household Income and Expenditure Survey (HIES) survey was to present high quality and representative national household data on income and expenditure in order to update Consumer Price Index (CPI), improve statistics on National Accounts and measure poverty within the country. These statistics are a requirement for evidence based policy-making in reducing poverty within the country and monitor progress in the national strategic plan in place.

Geographic coverage

Urban (Funafuti) and rural areas (outer islands).

Analysis unit

Household and Individual.

Universe

Private households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Tuvalu 2022 HIES consists in the random selection of the appropriate numbers of households (within each strata urban and rural) in order to be able to disaggregate HIES results at the strata level (in addition to National level). The urban strata of Tuvalu is made of the island of Funafuti (as a whole) and the rest of the country (all outer islands) compose the rural strata. The statistical unit used to run this sampling analysis is the household. The sample procedure is based on the following steps: - Assessment of the accuracy of the previous 2015 HIES in terms of per capita total expenditure (variable of interest) and check whether the sample size at that time were appropriate and correctly distributed among both stratas, - Update this assessment process by using the most recent population count to get the new sample size and distribution, - Proceed to the random selection of households using this most recent population count. The sampling frame (most recent household listing and population count) used to update and select is the 2021 Tuvalu Household Listing conducted by the Central Statistics Division of Tuvalu. At the National level, the 2015 Tuvalu HIES reported a good accuracy of the per capita total expenditure (less than 5%) but the disaggregation results by strata showed a lower quality of the result in Tuvalu urban. The Tuvalu 2021 household listing provides the most recent distribution of the households across all the islands of Tuvalu. This step consists in updating the accuracy of the previous 2015 HIES by using this recent household count and get the appropriate RSE by changing the sample size. For budget constraint, the total sample size cannot get increased, as the funding situation does not allow higher sample size. It means that the only parameter that can be modified is the distribution of the sample across the strata. Sample size by stratum: -Urban: 350 (out of 1,010 urban households as per the 2021 listing) -Rural: 310 (out of 835 rural households as per the 2021 listing) -National: 660 (out of 1,845 total households as per the 2021 listing)

2015 per capita mean total expenditure (AUD): -Urban: 3,190 -Rural: 2,780 -National: 3,000

Relative Standard Error (RSE): -Urban: 5.1% -Rural: 4.1% -National: 3.3%

It results from this new sample design a new distribution that shows an increase in Funafuti urban, mainly due to: - The low quality of the survey results from the 2015 HIES, - The number of households that have increased by more than 15% between 2015 and 2020 in Tuvalu urban area.

The household selection process is based on a simple random procedure within each stratum: - The 350 households in Funafuti are selected using the same probability of selection across all villages of the islands - The 310 household in rural Tuvalu are distributed proportionally to the size of each rural island of Tuvalu. This proportional allocation of the sample across rural Tuvalu islands generates the best accuracy at the strata level.

Distribution of sample accross strata: Urban: Funafuti 350 Rural: Nanumea 42
Nanumaga 37 Niutao 46
Nui 39
Vaitupu 75
Nukufetau 45
Nukulaelae 23
Niukalita 4

Non-response is a problem in surveys, and it is crucial that the field teams interview the selected households (the location on the map and the name of the household head are used to help to determine the selected households). During the first visit, interviewers must do their best to convince the household head to participate in the survey (and get his/her approval to proceed to interview). It may happen in the field that the first visit results in: I. A refusal: the household head does not show any interest in the survey and is reluctant to participate, II. The house is empty (household members away at the time of the visit).

(I) Refusal: if the interviewer cannot convince the household head to participate, he has to liaise with the survey management, and the supervisor will help in the discussion to convince the household head to respond. In this case, it is important to mention that all responses are kept confidential and insist on the importance of it for the benefit of Tuvalu population. (II) Empty house: the interviewer must investigate (checking with neighbours) whether or not the house is still inhabited by the family: o If it is not the case, the dwelling is then vacant, and the replacement procedure must be activated. o If the dwelling is still occupied, interviewer must come back later the same day or the day after at different time

Only in extreme cases of persistent refusal or empty house (household members away during the time of the collection) the replacement procedure must be activated. The replacement procedure consists in changing the selected household to the closest neighbour who is available.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2022 Tuvalu Household Income and Expenditure Survey (HIES) questionnaire was developed in English language and it follows the Pacific Standard HIES questionnaire structure. It is administered on CAPI using Survey Solution, and the diary is no longer part of the form. All transactions (food, non food, home production and gifts) are collected through different recall sections during the same visit. The traditional 14 days diary is no longer recommended in the region. This new method of implementing the HIES present some interesting and valuable advantages such as: cost saving, data quality, time reduction for data processing and reporting. The 2022 HIES of Tuvalu was directly integrated to a census through a Long Form Census (LFC). The LFC was an experiment led by the World Bank and the Pacific Community to try and group a census and a HIES collection. All households were normally enumerated during the 2022 Census and households selected to participate to the HIES were then asked the HIES questions.

Below is a list of all modules in this questionnaire: -Household ID -Demographic characteristics -Education -Health -Functional difficulties -Communication -Alcohol -Other individual expenses -Labour force -Fisheries -Handicraft and home-processed food -Dwelling characteristics -Assets -Home maintenance -Vehicles -International trips -Domestic trips -Household services -Financial support -Other household expenditure -Ceremonies -Remittances -Food insecurity -Financial inclusion -Livestock & aquaculture -Agriculture parcel -Agriculture vegetables -Agriculture rootcrops -Agriculture fruits

The survey questionnaire can be found in this documentation.

Cleaning operations

Data was edited, cleaned and imputed using the software Stata.

Response rate

There was a total of 662 households from the original selection of the sample. 592 of them were contacted 528 accepted the interviews. The number of valid households is 464, or 70% of households before replacement. After replacement, 54 households were considered valid making the final completion rate at 78% (73% in urban and 85% in rural area).

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

The purpose of this study is to evaluate the impact that the Feed the Future Nutrition-led Agriculture Project for Food Security in Senegal (known as "Yaajeende") has had on reducing malnutrition and poverty in its intervention area. This report details the findings, conclusions and recommendations of a mixed-methods quasi-experimental final impact evaluation (FIE) of the United States Agency for International Development’s (USAID) $50 million, seven-year Feed the Future Nutrition-Led Agriculture Project for Food Security in Senegal, known as “Yaajeende.” The National Cooperative Business Association’s Cooperative League of the USA (NCBA CLUSA) implemented the project. To combat poverty and child malnutrition, Yaajeende sought to accelerate the participation of the very poor in rural economic growth and improve the four dimensions of food security: availability, access, utilization and stability. Yaajeende worked in 790 villages across 49 municipalities (“communes” in French) and nine departments in the Matam, Tambacounda, Kédougou and Kolda regions. The project’s implementation period was November 1, 2010, to September 30, 2017. The FIE aims to provide USAID with an evidence base on the impacts of the nutrition-led agriculture (NLA) approach that the project utilized on its key objectives, including reduced poverty and malnutrition. The findings are expected to provide accountability and learning value to USAID, including both the Senegal Mission and USAID/Feed the Future. Additional stakeholders include the Government of Senegal, implementing partners and other agencies, donors and practitioners active in nutrition, health, agriculture and integrated sectors.

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).