United States US: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data was reported at 9.100 % in 2022. This records an increase from the previous number of 8.600 % for 2021. United States US: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data is updated yearly, averaging 8.750 % from Dec 2015 (Median) to 2022, with 8 observations. The data reached an all-time high of 10.500 % in 2015 and a record low of 8.000 % in 2020. United States US: Prevalence of Moderate or Severe Food Insecurity in the Population: % of population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.;Food and Agriculture Organization of the United Nations (FAO);;

The great majority of households in the United States were food-secure in 2024. Over **** percent of households in the country were food-insecure comprised of both adults and children.

This statistic shows the number of food secure and food insecure individuals in the United States in 2022, by status. At that time, there were some **** million people living in food secure households in the United States.

This statistic shows the shares of food-secure and food-insecure individuals in the United States in 2023 by race and ethnicity. At that time, 89.6 percent of people living in the United States lived in food-secure households. However, figures revealed disparities by race and ethnicity. For example, during that year, the shares of Black non-Hispanic and Hispanic individuals living in low food security households were three times higher than those of White non-Hispanics. Furthermore, seven percent of Black non-Hispanic individuals lived in homes with very low food security, the highest share across all races and ethnicities.

This statistic shows the distribution of food secure and food insecure individuals in the United States from 2020 and 2023, by status. In 2023, **** percent of people in the U.S. were living in food secure households.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). The Food and Agriculture Organization of the United Nations (FAO) conducted the fourth round of the Data in Emergencies household survey (DIEM-Monitoring) in Chad between 16 December 2022 and 10 January 2023 to assess agricultural livelihoods and food security. Data was collected through face-to-face surveys in the provinces of Kanem, Lac, Moyen-Chari, Logone Occidental, Moyen-Kebbi Est and Wadi Fira. A total of 5310 households were interviewed. Data collection took place after the rainy season, during the harvest period. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey for Phase 4 was developed in partnership with INSEED to achieve representation at the administrative level 2, drawing upon the 2009 General Census of Population and Housing (RGPH 2) and incorporating a 3.5% estimated annual growth rate. Selection criteria, aligned with FAO standards and in collaboration with SISAAP, prioritized vulnerability as identified in the Harmonized Framework outcome analysis, particularly for communities in levels 3 and 4 within Sahelian and Sudanian zones, and factored in the FAO's operational presence. This selection also considered regions significantly affected by the floods in 2022. The methodology employed a two-stage probability sampling, designating villages as the primary sampling units and households as the secondary units.

The methodology stipulated a cluster size of 12, necessitating a minimum of 22 village clusters, resulting in a sample size of 264 per stratum. Consequently, the survey encompassed 5,808 households across 22 departments, ensuring representativeness at the admin 2 level for the designated provinces. For more details on the sampling procedure, consult the methodology document attached in the documentations tab.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

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

Cleaning operations

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

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). The FAO launched a household survey in South Sudan through the DIEM-Monitoring System to monitor agricultural livelihoods and food security. Data were conducted through face-to-face surveys in all ten states of South Sudan: Central Equatoria, Eastern Equatoria, Jonglei, Lakes, Northern Bahr el Ghazal, Unity, Upper Nile, Warrap, Western Bahr el Ghazal and Western Equatoria. A total of 3 090 households were surveyed between 27 May and 29 July 2022. All ten states surpassed the targeted sample size of 270 households per state. However, 17 households were dropped based on data quality issues.

Geographic coverage

National Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 3 090 households were surveyed. All ten states surpassed the targeted sample size of 270 households per state. However, 17 households were dropped based on data quality issues. The sampling frame was based on the 27th round of the Food Security and Nutrition Monitoring Report of South Sudan in 2021. The lowest administrative unit of measurement in South Sudan is a boma, followed by a payam, a county and then a state, before reaching the national level. The data were collected at boma level and were aggregated in order to be representative at state level. Two-stage sampling was applied – cluster sampling to generate the list of clusters, followed by simple random sampling to ensure that all households in the targeted cluster had an equal chance of being selected. At the second stage, households were selected using simple random sampling.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

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

The fourth round of the Myanmar Household Welfare Survey (MHWS)–a nationwide phone panel consisting of 12,924 households–was implemented between October 12, 2022 and December 30, 2022. The objective of the survey was to collect data on a wide range of household and individual welfare indicators–including wealth, livelihoods, unemployment, food insecurity, diet quality, health shocks, and coping strategies–in a country exceptionally hard hit by conflict, severe economic collapse, and several damaging waves of COVID-19. The respondents interviewed in the MHWS were purposely selected from a large phone database aimed at being representative at the region/state level and urban/rural level in Myanmar. A novel sampling strategy in combination with the development of household and population weights allows for estimates that are nationally, regionally, and urban/rural representative.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). In Nigeria, the FAO launched the second-round survey which was conducted through face-to-face interviews from 26 June to 8 July 2022 and reached 1703 households. Data collection took place at the beginning of the planting season across five states: Adamawa, Borno, Katsina, Yobe and Zamfara. Some Enumeration Areas (EAs), mostly in Borno, were replaced following the original sampling design due to insecurity. The EAs were replaced by randomly selecting other areas that were considered accessible in consultation with local government authorities. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was conducted through face-to-face interviews from 26 June to 8 July 2022 and reached 1703 households. Data collection took place at the beginning of the planting season across five states: Adamawa, Borno, Katsina, Yobe and Zamfara. Some Enumeration Areas (EAs), mostly in Borno, were replaced following the original sampling design due to insecurity. The EAs were replaced by randomly selecting other areas that were considered accessible in consultation with local government authorities. For more details on the sampling procedure, consult the methodology document attached in the documentations tab.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

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

The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA). SNAP provides millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2022, SNAP served an average of 41.2 million people monthly and paid out $114 billion in benefits, including emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency.The characteristics of SNAP participants and households and the size of the SNAP caseload change over time in response to changes in program rules as well as economic and demographic trends. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) Database. This database is an edited version of the raw data file of monthly case reviews that are conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for their SNAP caseloads. These data cover FY 2022.

From 2017 to 2021, the share of households living under the poverty line in Venezuela has been surpassing 90 percent. In addition, more than six out of every ten households (67.97 percent) lived in extreme poverty in 2021. The overall household poverty rate in Venezuela has registered a steady growth from 2014 to 2019, after having remained relatively stable, below 40 percent, since 2005. Although poverty is widespread among the population as a whole, some groups are more vulnerable than others. That is the case of younger generations and particularly children: 98.03 percent of Venezuelans aged 15 or younger lived in poverty in 2021. An economy in disarray Venezuela, the country with the largest oil reserves in the world and whose economy has been largely dependent on oil revenues for decades, was once one of the most prosperous countries in Latin America. Today, hyperinflation and an astronomic public debt are only some of the many pressing concerns that affect the domestic economy. The socio-economic consequences of the crisis As a result of the economic recession, more than half of the population in every state in Venezuela lives in extreme poverty. This issue is particularly noteworthy in the states of Amazonas, Monagas, and Falcón, where the extreme poverty rate hovers over 80 percent. Such alarming levels of poverty, together with persistent food shortages, provoked a rapid increase in undernourishment, which was estimated at 17.9 percent between 2020 and 2022. The combination of humanitarian crisis, political turmoil and economic havoc led to the Venezuelan refugee and migrant crisis. As of 2020, more than five million Venezuelans had fled their home country, with neighboring Colombia being the main country of destination.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

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

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet