Number of City supported fresh food access points in census tracts with higher* than average food insecurity rates.

*Higher than the national average food insecurity rate of 16.7%

Glossary of food security terms.

Goal 2End hunger, achieve food security and improved nutrition and promote sustainable agricultureTarget 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year roundIndicator 2.1.1: Prevalence of undernourishmentSN_ITK_DEFC: Prevalence of undernourishment (%)SN_ITK_DEFCN: Number of undernourish people (millions)Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)AG_PRD_FIESMS: Prevalence of moderate or severe food insecurity in the adult population (%)AG_PRD_FIESMSN: Total population in moderate or severe food insecurity (thousands of people)AG_PRD_FIESS: Prevalence of severe food insecurity in the adult population (%)AG_PRD_FIESSN: Total population in severe food insecurity (thousands of people)Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older personsIndicator 2.2.1: Prevalence of stunting (height for age SH_STA_STNT: Proportion of children moderately or severely stunted (%)SH_STA_STNTN: Children moderately or severely stunted (thousands)+2 or SH_STA_WAST: Proportion of children moderately or severely wasted (%)SH_STA_WASTN: Children moderately or severely wasted (thousands)SN_STA_OVWGT: Proportion of children moderately or severely overweight (%)SN_STA_OVWGTN: Children moderately or severely overweight (thousands)Indicator 2.2.3: Prevalence of anaemia in women aged 15 to 49 years, by pregnancy status (percentage)SH_STA_ANEM: Proportion of women aged 15-49 years with anaemia (%)SH_STA_ANEM_PREG: Proportion of women aged 15-49 years with anaemia, pregnant (%)SH_STA_ANEM_NPRG: Proportion of women aged 15-49 years with anaemia, non-pregnant (%)Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employmentIndicator 2.3.1: Volume of production per labour unit by classes of farming/pastoral/forestry enterprise sizePD_AGR_SSFP: Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)PD_AGR_LSFP: Productivity of large-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous statusSI_AGR_SSFP: Average income of small-scale food producers, PPP (constant 2011 international $)SI_AGR_LSFP: Average income of large-scale food producers, PPP (constant 2011 international $)Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil qualityIndicator 2.4.1: Proportion of agricultural area under productive and sustainable agricultureTarget 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreedIndicator 2.5.1: Number of plant and animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilitiesER_GRF_ANIMRCNTN: Number of local breeds for which sufficient genetic resources are stored for reconstitutionER_GRF_PLNTSTOR: Plant breeds for which sufficient genetic resources are stored (number)Indicator 2.5.2: Proportion of local breeds classified as being at risk of extinctionER_RSK_LBREDS: Proportion of local breeds classified as being at risk as a share of local breeds with known level of extinction risk (%)Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countriesIndicator 2.a.1: The agriculture orientation index for government expendituresAG_PRD_ORTIND: Agriculture orientation index for government expendituresAG_PRD_AGVAS: Agriculture value added share of GDP (%)AG_XPD_AGSGB: Agriculture share of Government Expenditure (%)Indicator 2.a.2: Total official flows (official development assistance plus other official flows) to the agriculture sectorDC_TOF_AGRL: Total official flows (disbursements) for agriculture, by recipient countries (millions of constant 2018 United States dollars)Target 2.b: Correct and prevent trade restrictions and distortions in world agricultural markets, including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in accordance with the mandate of the Doha Development RoundIndicator 2.b.1: Agricultural export subsidiesAG_PRD_XSUBDY: Agricultural export subsidies (millions of current United States dollars)Target 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatilityIndicator 2.c.1: Indicator of food price anomaliesAG_FPA_COMM: Indicator of Food Price Anomalies (IFPA), by type of productAG_FPA_CFPI: Consumer Food Price IndexAG_FPA_HMFP: Proportion of countries recording abnormally high or moderately high food prices, according to the Indicator of Food Price Anomalies (%)

According to the Global Hunger Index 2024, which was adopted by the International Food Policy Research Institute, Somalia was the most affected by hunger and malnutrition, with an index of 44.1. Yemen and Chad followed behind. The World Hunger Index combines three indicators: undernourishment, child underweight, and child mortality. Sub-Saharan Africa most affected The index is dominated by countries in Sub-Saharan Africa. In the region, more than one fifth of the population is undernourished . In terms of individuals, however, South Asia has the highest number of undernourished people. Globally, there are 735 million people that are considered undernourished or starving. A lack of food is increasing in over 20 countries worldwide. Undernourishment worldwide The term malnutrition includes both undernutrition and overnutrition. Undernutrition occurs when an individual cannot maintain normal bodily functions such as growth, recovering from disease, and both learning and physical work. Some conditions such as diarrhea, malaria, and HIV/AIDS can all have a negative impact on undernutrition. Rural and agricultural communities can be especially susceptible to hunger during certain seasons. The annual hunger gap occurs when a family’s food supply may run out before the next season’s harvest is available and can result in malnutrition. Nevertheless, the prevalence of people worldwide that are undernourished has decreased over the last decades, from 18.7 percent in 1990-92 to 9.2 percent in 2022, but it has slightly increased since the outbreak of COVID-19. According to the Global Hunger Index, the reduction of global hunger has stagnated over the past decade.

Environmental risk factors mapped to the dimensions of food security.

This layer contains information about the recurrence of food insecurity conditions observed and used for the Integrated Context Analysis (ICA) run in Jordan in 2019. Data source: Department of Statistics (DoS), 2010-2014. The key indicator used for the analysis was the Food Consumption Score (FCS), which aggregates household-level data on the diversity and frequency of food groups consumed over the previous seven days, then weighted according to the relative nutritional value of the consumed food groups. Given the values of food insecurity across the country (with a national, multi-year average of 4.6%), a threshold equal to 5% has been chosen to allow a better separation of severely affected areas from better-off geographical areas.

Demographic risk factors mapped to the dimensions of food security.

As of 2024, there were 975 food banks in Germany. This was an increase compared to the previous year at 964. It was also the highest number of food banks since 1993, when the German Tafel scheme was set up. Food bank usage ‘Tafel’ in Germany is an organization that it similar to the concept of food banks in the United States. These food banks operate at a regional level and provide food that would otherwise be destroyed to those in need either for free or at a heavily discounted price. In 2022, around two million people were using food banks in Germany, this was the highest figure since 2014. This new peak is likely due to the large increase in food prices over the past two years. Both 2022 and 2023 saw a year-on-year increase of over 12 percent. It is not just Germany that is facing higher food prices. Countries across the world have been experiencing a rise in the price of groceries. Over 10 percent of people living in Spain, Great Britain, Germany, France, and Italy said that it was usually difficult for them to afford food items at the end of 2022. In France and Italy there were noticeably higher rates. Poverty When it came to the average financial wealth of adults in Europe, Switzerland, Iceland, and Denmark topped the list. Germany ranked 13th on the list, with average wealth of adults at 113,00 U.S. dollars. This average, however, does not represent the entire population, and there are people in Germany, as in every country, who struggle to finance day-to-day life. In 2022, there were around 16.7 percent of people at risk of living in poverty. This was a slight decrease compared to the previous year, but still significantly higher than in previous years. In certain cities the risk of living in poverty was even higher than the national average. The city of Duisburg, which is located in western Germany, had an at risk of living in poverty rate of over 30 percent. In Bremen, a city close to Hamburg, the share of those facing financial difficulties was almost 30 percent.

This dataset contains a series of indicators related to nutritional facts for Kiribati, Solomon Islands and Vanuatu based on Household Income and Expenditure Surveys (HIES). Indicators included are the following: Average edible quantity, Average Dietary Energy Consumption, Average expenditures, Percentage of HH who consumed at least one product of the group, Average quantity as acquired, Percentage of households who consumed more than the average number of products consumed in the group, Percentage of households who consumed less than the average number of products consumed in the group, Average number of products consumed by household by food group. The table provides a breakdown by type of food (21 FAO groups), geography (1 sub-national level), sex, age and urbanization, poverty status (2 categories) and food security status (2 categories). This dataset has been compiled as a result of a collaborative project on food security between the Pacific Community (SPC) and the Food and Agriculture Organization of the United Nations (FAO).

Find more Pacific data on PDH.stat.

Health-related risk factors mapped to the dimensions of food security.

Abstract

The primary objective of the survey was to obtain a better understanding of food insecurity and vulnerability among rural households at provincial levels throughout the country in a post-emergency setting, in particular answering the questions: 1. Who are the hungry poor? 2. How many are they? 3. Where do they live? 4. What are the underlying causes of food insecurity? 5. What is the role of food assistance, if any?

In addition to the survey findings, the report also summarizes the current available information obtained from secondary data sources – focusing on the area of the survey. However there is little recent information available and most is at national or regional level, thus making provincial comparisons/inferences difficult.

Geographic coverage

Rural households in southeastern provinces of Angola.

Analysis unit

• Household
• Anthropometric
• Community

Universe

The survey covered all household heads and women (with anthropometric measurements taken on both women 14-49 years of age and children 6 and 59 months old) 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

The survey was designed to draw representative samples of rural households at a provincial level. From each of the 6 provinces a two-stage probability sampling method was used to select villages and households with in each village. The sample size per province was calculated to provide estimates of food insecurity and vulnerability with 90% confidence. In total, 1,716 households in 143 rural communities (villages) were surveyed across the south-eastern area of Angola.

Sampling deviation

Due to access constraints in Kuando Kubango province, more than 75% of the selected villages were not reached. Most of the communities surveyed are near the border with Namibia or near the provincial capital where commercial factors play a more important role in the livelihoods of the population. Thus, the situation found for the households in that sample is probably better than the average livelihood security found throughout Kuando Kubango province.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire was used to collect quantitative information on household demography, housing conditions, assets, income sources and expenditures, food consumption, food sufficiency, risk, shocks and coping strategies, mothers and young children health and nutrition.

A community survey questionnaire was used to collect information at community level, such as access to school, health and market infrastructures, and external interventions.

All questionnaires and modules are provided as external resources.

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.

The Nigerian states of Sokoto and Taraba had the largest percentage of people living below the poverty line as of 2019. The lowest poverty rates were recorded in the South and South-Western states. In Lagos, this figure equaled 4.5 percent, the lowest rate in Nigeria.

A large population in poverty

In Nigeria, an individual is considered poor when they have an availability of less than 137.4 thousand Nigerian Naira (roughly 334 U.S. dollars) per year. Similarly, a person having under 87.8 thousand Naira (about 213 U.S. dollars) in a year available for food was living below the poverty line according to Nigerian national standards. In total, 40.1 percent of the population in Nigeria lived in poverty.

Food insecurity on the rise

On average, 21.4 percent of the population in Nigeria experienced hunger between 2018 and 2020. People in severe food insecurity would go for entire days without food due to lack of money or other resources. Over the last years, the prevalence with severe food among Nigerians has been increasing, as the demand for food is rising together with a fast-growing population.

Abstract

Multiple factors contributed to high and volatile food prices in Bangladesh during the 2007/2008 period. A “perfect storm” of international, regional, and national conditions delivered a powerful economic shock to the country's food security. Rising global food and fuel prices, regional trade barriers for food exports from South and Southeast Asia, and efforts to ensure macro-economic stability within Bangladesh, all played important roles as the shock of high food prices reverberated throughout the economy.

These important information and knowledge gaps were the major impetus for UNICEF, WFP and the Institute of Public Health Nutrition to jointly undertake a national Household Food Security and Nutrition Assessment of the Situation. The assessment aimed to analyse the current impact of the food price hikes on food security and nutrition and health status through the capture of changes in household food and nutrition security over time in order to suggest response options and recommendations for the short and medium term. More specific objectives pertained to understanding in greater detail, aspects of food security and nutrition, including food markets, household food access and food utilization, nutrition and health, and water and sanitation. The food security component and market analysis were led by WFP and the nutritional component by UNICEF and IPHN.

Geographic coverage

National coverage

Analysis unit

• Household
• Nutrition
• Trader

Universe

The survey covered household heads, women between 15-49 years plus their pre-school children (0-59 months) resident of that household. A household was defined as persons routinely sharing food from the same cooking pot and living in the same compound or physical location or dependent family member living home or abroad.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling size estimates were made to ensure that key indicators would be statistically representative at the national, urban, rural, and divisional levels. Sample sizes were calculated with a 0.05 statistical significance (95% confidence interval) for the key indicators. Based on previous surveys, assumptions were made that each household would have an average of one child aged 6 to 59 months of age, a household size of six members and one mother. Prevalence estimates were based on the BDHS 2007 survey, which estimated acute malnutrition at 16 %, stunting at 50%, and an underweight prevalence of 48%.

A two-stage cluster sampling was used, the sample size was increased by a factor that would allow for the design effect; thus, design effects of 1.5% for acute malnutrition and 2% for stunting and underweight were used, and the 5% desired precision was based on the estimated prevalence of the BDHS 2007.

Sample clusters were used as the first-stage sample, and 361 EAs were selected with probability proportional to the EA size. Some of the selected EAs were of a large size. Therefore, EAs having more than 300 households were further segmented and only one segment was selected for the assessment, with probability proportional to the segment size. Thus, a cluster was either an EA or a segment of an EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

MACROECONOMIC PERFORMANCE ANALYSES: Events and developments pertaining to public food stocks, imports, procurement (both domestic and international), and production were examined. Regional trade, including barriers to trade, price trends, and macroeconomic stability. MARKET ANALYSES: An analysis of market performance was conducted using a combination of primary data from the trader's survey and secondary price data. The analysis of the traders survey data focused on numerous topics including the availability of food on local markets, food flows (including volumes and quantities sold), prices (both actual and expected trends), and perceived reasons for price increases. Other topics analyse were profit margins, access to credit, constraints to trade, and the capacity of food markets to respond to increased demand. HOUSEHOLD FOOD SECURITY: The analysis of the household food security survey data focused on numerous topics including changes in livelihoods and income sources, the effects of inflation on income, changes in wages, salaries and purchasing power, and changes in the “net seller or net buyer status” of agricultural households. The impact of higher food prices on food and non-food expenditures was also examine, as was the impact of other “shocks” and the seasonality and timing of such “shocks”. Extensive analysis was undertaken on household coping strategies and food consumption, using a food consumption score. The score was based on both dietary diversity and the frequency of various foods consumed. NUTRITION and HEALTH STATUS of WOMEN: Mid-upper arm circumferences were taken from the mothers of children aged from birth to 59 months of age or from pregnant women to measure their nutritional status. Information was also collected regarding micronutrient supplementation with Vitamin A post-partum and iron and folate supplementation during pregnancy. Vitamin A capsules and iron and folate tables were shown to the women in order to avoid any misunderstanding. INFANT and YOUNG CHILD FEEDING PRACTICES: Enumerators asked questions regarding infant and young-child feeding practices to all mothers with a child aged from birth to 23 months in the surveyed household. The indicators were related to breastfeeding practices and the introduction of complementary food in time, quantity, and quality (diet diversity). Exclusive breastfeeding, continued breastfeeding at one year and two years, proportion of infants 6 to 8 months of age who received solid, semi-solid, or soft foods, minimum meal frequency, minimum diet diversity and minimum acceptable diet. HEALTH of CHILDREN: Caregivers were asked if the child had been ill during the two weeks prior to the assessment, what illness the child presented with, and if the child had been taken to a health facility. The coverage of Vitamin A supplementation in children from 9 to 59 months was also assessed. HEALTH of GENERAL POPULATION: Households were asked if any household member had been ill in the two weeks prior to the assessment, the main cause of illness, and if treatment had been sought outside the house. MORTALITY: Mortality was assessed using the retrospective household census method. Respondents were asked the following information: number of deaths in the family in the six months prior to the assessment, and how many were children under five years of age; and presumed cause of death. WATER and SANITATION: Access to safe water sources, types of toilet facilities, treatment of drinking water, use of toilet facility and sharing latrine at household level.

All interviews were conducted in Bangla or in a local dialect and data was recorded onto paper questionnaire

All questionnaires and modules are provided as external resources.

Cleaning operations

Following the field data collection period from November 2008 to January 2009, Mitra and Associates carried out data entry in February 2009.

Response rate

In Barisal and Sylhet response rates were 76.5% and 74% respectively.

Food and Agriculture Organization of the United Nations (2017). Food and Agriculture Organization Statistics: Food Security - Indicators from Household Surveys | Survey: Malawi - 2004-2005 | Breakdown: Age of household head: 60 yrs or more | Gender: Female-headed household | Indicator: Carbohydrates consumption | Measure: Mean, 2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 067-001-070. Dataset: Presents statistics for food security indicators by sociodemographic and socioeconomic characteristics of households. For definitions of each indicator, see the technical documentation. The time-series and cross-sectional data provided here are from the FAOSTAT database of the Food and Agriculture Organization of the United Nations. Statistics include measures related to the food supply; forestry; agricultural production, prices, and investment; and trade and use of resources, such as fertilizers, land, and pesticides. As available, data are provided for approximately 245 countries and 35 regional areas from 1961 through the present. The data are typically supplied by governments to FAO Statistics through national publications and FAO questionnaires. Official data have sometimes been supplemented with data from unofficial sources and from other national or international agencies or organizations. In particular, for the European Union member countries, with the exception of Spain, data obtained from EUROSTAT have been used. Category: Agriculture and Food, International Relations and Trade Source: Food and Agriculture Organization of the United Nations Established in 1945 as a specialized agency of the United Nations, the Food and Agricultural Organization’s mandate is to raise levels of nutrition, improve agricultural productivity, better the lives of rural populations, and contribute to the growth of the world economy. Staff experts in seven FAO departments serve as a knowledge network to collect, analyze, and disseminate data, sharing policy expertise with member countries and implementing projects and programs throughout the world aimed at achieving rural development and hunger alleviation goals. The Statistics Division of the Food and Agricultural Organization collates and disseminates food and agricultural statistics globally. http://www.fao.org/ Subject: Food Supply, Sociodemographic Characteristics, Social Development, Food Security

BackgroundIn Ethiopia, child malnutrition is a significant public health problem. To address the problem, Nutrition-Sensitive Agriculture (NSA) program was introduced. However, there is a paucity of evidence about the prevalence of child undernutrition in NSA-implemented districts. Therefore, this study aimed to assess the prevalence of undernutrition among children aged 6–59 months in NSA-implemented districts.MethodA community-based cross-sectional study was conducted by enrolling 422 children aged 6–59 months paired with their mothers. A systematic sampling technique was used to select respondents. Data were collected by Open Data Kit (ODK) data collection platform, and Stata version 16 was used for analysis. The multivariable logistic analysis model was fitted to assess the association between variables, and 95% CI was estimated to measure the strength of the association. The level of statistical significance was declared at a p-value of less than 0.05 in the multivariable model.ResultOverall, 406 respondents participated in the study, and a response rate of 96.2% was obtained. The prevalence of stunting, wasting, and underweight was 24.1% (95% CI: 19.9–28.4), 8.87% (95% CI: 6.3–12.1) and 19.95% (95% CI: 16.2–24.2), respectively. Household food insecurity was significantly associated with being underweight (AOR: 3.31, 95% CI (1.7–6.3). Child dietary diversity (AOR: 0.06, 95% CI: 0.01–0.48) and being a beneficiary of the NSA (AOR: 0.12, 95% CI: 0.02–0.96) program were associated with wasting. Lack of ANC visits and diarrhea in the past two weeks was associated with stunting and wasting, respectively.ConclusionThe prevalence of malnutrition was a moderate public health problem. Wasting was more prevalent than the recent national and Amhara region averages. However, the prevalence of stunting and underweight was lower than the national average and other studies conducted in Ethiopia. Healthcare providers should work to increase dietary diversity, ANC visits, and reduce diarrheal disease.

Abstract

The overall objective of this survey is to provide broad and up-to-date baseline information on food production and household food security for the implementation of the Sierra Leone PRSP. The principal aspects covered by the study are local farm production, trading of food in rural areas, access of rural households to food, utilisation of food at the household level including nutrition and health aspects, and vulnerability of the rural population to the various facets of food insecurity.

This research process was divided into three separate but complementary surveys that covered the same households in sampled districts: Farm Production Survey, Food Security and vulnerability survey and a Nutrition and health in women and young children. The objective of the combined surveys was to provide insight on a wide range of factors that influence the degree of food security or vulnerability to food insecurity for rural households and will provide guidance for the policies that should be implemented in order to achieve the overall targets set by the PRSP.

Geographic coverage

National

Analysis unit

• Household
• Community

Universe

The survey covered all household heads and women (with anthropometric measurements taken on both women 15-49 years of age and children 0-59 months old) in each sampled household.

A household is defined as a person or a group of persons related or unrelated, living together or not, making common cooking arrangements and under the authority of the same household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey used a two-stage cluster sampling strategy. Statistics Sierra Leone (Statistic SL) helped to design of the sample frame, based on recent pre-census data that provided information on settlement names, populations, household sizes. Statistics SL grouped communities, consisting either of one larger village or several smaller settlements located in close proximity, into Enumeration Areas (EAs) that could be treated as the basic clusters. Codes were available for all EAs and GPS coordinates for the sampled communities were to be recorded during the survey.

The aim of the sampling strategy was to obtain at representative results at the district level, now known as Local Council Areas. Population figures from the recent pre-census were available only at Chiefdom level, but not for individual EAs.

Due to the lack of accurate population figures at EA level it was decided to apply the Probability Proportional to Size (PPS) method at Chiefdom level, meaning that the more populated Chiefdoms had a higher probability of selection. In each Local Council Area (LCA) approximately half of the Chiefdoms (on average 45%) were selected. The few larger urban-type settlements outside of Freetown were excluded from the selection process. In a second step, five EAs (communities) per Chiefdom were selected using simple random sampling techniques. The total number of EAs (or clusters in statistical terms) per Local Council Area was 25, with a total sample size of approximately 4500 households for food security and farm production, and 5600 for nutrition and health.

The sampling procedures used at EA (community) level are as follows: · Within the EA, household lists were created by the survey teams with assistance from the village leaders and then a sample of 12 households was selected using a random number draw. · As it can be assumed that a large proportion of the households were engaged in farming as primary or secondary occupation, and thus there was no need to differentiate between farming/non-farming families when selecting the households to be interviewed. If families without agriculture, livestock or fisheries activities were encountered, the farm production questionnaire was simply left blank (except for some general information).

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire: Demography; Housing and household facilities; Assets; Main sources of income; expenditure; Food consumption; shocks and coping strategies; land ownership and use; Household land ownership and us; Cropping system; Water and sanitation; Crops harvested last season; Food and cash crops sold; Livestock, Fisheries; Maternal health and nutrition; Child health and nutrition.

Community Questionnaire: Demography; Economy and infrastructure; education; health; agriculture; trading of food and cash crops; seasonal availability of main food crops and price trends.

The dataset details the average gross commodity prices paid to farmers for various commodities for the period 2013 to 2017. Tea prices increased by 23.9 per cent from KSh 24,732.35 per 100 kilogram in 2016 to KSh 30,652.18 per 100 kilogram in 2017. Coffee prices paid to farmers improved by 16.5 per cent from KSh 40,815.54 per 100 kilogram in 2016 to KSh 47,547.71 per 100 kilogram in 2017. Favourable prices were also realized for maize, sugarcane, milk, beef and pork.

Abstract

This is the fourth Labor Force Survey of Tonga. The first one was conducted in 1990. Earlier surveys were conducted in 1990, 1993/94, and 2003 and the results of those surveys were published by the Statistics Department.

The objective of the LFS survey is providing information on not only well-known employment and unemployment as well as providing comprehensive information on other standard indicators characterizing the country labour market. It covers those age 10 and over in the whole Kingdom. Information includes age, sex, activity, current and usual employment status, hours worked and wages and in addition included a seperate Food Insecurity Experiences Survey (FIES) questionniare module at the Household Level.

The conceptual framework used in this labour force survey in Tonga aligns closely with the standards and guidelines set out in Resolutions of International Conferences of Labour Statistician.

Geographic coverage

National coverage.

There are six statistical regions known as Division's in Tonga namely Tongatapu urban area, Tongatapu rural area, Vava'u, Ha'pai, Eua and the Niuas.Tongatapu Urban refers to the capital Nuku'alofa is the urban area while the other five divisions are rural areas. Each Division is subdivided into political districts, each district into villages and each village into census enumeration areas known as Census Blocks. The sample for the 2018 Labour Force Survey (LFS) was designed to cover at least 2500 employed population aged 10 years and over from all the regions. This was made mainly to have sufficient cases to provide information on the employed population.

Analysis unit

• Households (for food insecurity module questionnaire)
• Individuals.

Universe

Population living in private households in Tonga. The labour force questionnaire is directed to the population aged 10 and above. Disability short set of questions is directed to all individuals age 2 and above and the food insecurity experience scale is directed to the head of household.

Kind of data

Sample survey data [ssd]

Sampling procedure

2018 Tonga Labour force survey aimed at estimating all the main ILO indicators at the island group level (geographical stratas). The sampling strategy is based on a two stages stratified random survey.

1. Computation of the survey parameters: Total sample size per strata, number of households to interview in each Primary Sampling Unit (PSU = census block) and number of PSUs to select The stratification of the survey is the geographical breakdown by island group (6 stratas Tongatapu urban, Tongatapu rural, Vava'u, Ha'apai, 'Eua, Niuas)
2. The selection strategy is a 2 stages random survey where: Random selection of census blocks within each
3. Census blocks are randomly selected in first place, using probability proportional to size

4. 15 households per block are randomly selected using uniform probability

5. The sampling frame used to select PSUs (census blocks) and household is the 2016 Tonga population census.

The computation of sample size required the use of: - Tonga 2015 HIES dataset (labour force section) - Tonga 2016 population census (distribution of households across the stratas) The resource variable used to compute the sample size is the labour force participation rate from the 2015 HIES. The use of the 2015 labour force section of the Tonga HIES allows the computation of the design effect of the labour force participation rate within each strata. The design effect and sampling errors of the labour force participation rate estimated from the 2015 HIES in combination with the 2016 household population distribution allow to predict the minimum sample size required (per strata) to get a robust estimate from the 2018 LFS.

Total sample size: 2685 households Geographical stratification: 6 island groups Selection process: 2 stages random survey where census blocks are selected using Probability Proportional to Size (Primary Sampling Unit) in the first place and households are randomly selected within each selected blocks (15 households per block) Non response: a 10% increase of the sample happened in all stratas to account for non-response Sampling frame: the household listing from the 2016 population census was used as a sampling frame and the 2015 labour force section of the HIES was used to compute the sample size (using labour force participation rate.

Sampling deviation

No major deviation from the original sample has taken place.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2018 Tonga Labour Force Survey questionnaire included 15 sections:

IDENTIFICATION SECTION B: INDIVIDUAL CHARACTERISTICS SECTION C: EDUCATION (AGE 3+) SECTIONS B & C: EMPLOYMENT IDENTIFICATION AND TEMPORARY ABSENCE (AGE 10+) SECTION D: AGRICULTURE WORK AND MARKET DESTINATION SECTION E1: MAIN EMPLOYMENT CHARACTERISTICS SECTION E2: SECOND PAID JOB/ BUSINESS ACTIVITY CHARACTERISTICS SECTION F: INCOME FROM EMPLOYMENT SECTION G: WORKING TIME SECTION H: JOB SEARCH SECTION I: PREVIOUS WORK EXPERIENCE SECTION J: MAIN ACTIVITY SECTION K: OWN USE PRODUCTION WORK FOOD INSECURITY EXPERIENCES GPS + PHOTO

The questionniares were developed and administered in English and were translated into Tongan language. The questionnaire is provided as external resources.

The draft questionnaire was pre-tested during the supervisors training and during the enumerators training and it was finally tested during the pilot test. The pilot testing was undertaken on the 27th of May to the 1st of June 2018 in Tongatapu Urban and Rural areas. The questionnaire was revised rigorously in accordance to the feedback received from each test. At the same time, a field operations manual for supervisors and enumerators was prepared and modified accordingly for field operators to use as a reference during the field work.

Cleaning operations

The World Bank Survey Solutions software was used for Data Processing, STATA software was used for data cleaning, tabulation tabulation and analysis.

Editing and tabulation of the data will be undertaken in February/March 2019 in collaboration with SPC and ILO.

Response rate

A total, 2,685 households were selected for the sample. Of these existing households, 2,584 were successfully interviewed, giving a household response rate of 96.2%.

Response rates were higher in urban areas than in the rural area of Tongatapu.

-1 Tongatapu urban: 97.30%
-2 Tongatapu rural: 93.00%
-3 Vava'u: 100.00% -4 Ha'pai: 100.00% -5 Eua: 95.20% -6 Niuas: 80.00% -Total: 96.20%.

Sampling error estimates

Sampling errors were computed and are presented in the final report.

The sampling error were computed using the survey set package in Stata. The Finite Population Correction was included in the sample design (optional in svy set Stata command) as follow: - Fpc 1: total number of census blocks within the strata (variable toteas) - Fpc 2: Here is a list of some LF indicators presented with sampling error

-RSE: Labour force population: 2.2% Employment - population in employment: 2.2% Labour force participation rate (%): 1.7% Unemployment rate (%): 13.5% Composite rate of labour underutilization (%): 7.3% Youth unemployment rate (%): 18.2% Informal employment rate (%): 2.7% Average monthly wages - employees (TOP): 12%.

-95% Interval: Labour force population: 28,203 => 30,804 Employment - population in employment: 27,341 => 29,855 Labour force participation rate (%): 45.2% => 48.2% Unemployment rate (%): 2.2% => 3.9% Composite rate of labour underutilization (%): 16% => 21.4% Youth unemployment rate (%): 5.7% => 12.1% Informal employment rate (%): 44.3% => 49.4% Average monthly wages - employees (TOP): 1,174 => 1,904.

Socio demographic characteristics and COVID related factors by household food security status and household dietary diversity status of the participants.