In 2023, approximately 10.4 percent of Massachusetts' population lived below the poverty line. This accounts for persons or families whose collective income in the preceding 12 months was below the national poverty level of the United States.

Percent of Population Below the Poverty Level (5-year estimate) in Middlesex County, MA was 7.50% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Middlesex County, MA reached a record high of 8.40 in January of 2014 and a record low of 7.20 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Middlesex County, MA - last updated from the United States Federal Reserve on August of 2025.

Percent of Population Below the Poverty Level (5-year estimate) in Hampden County, MA was 15.70% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Hampden County, MA reached a record high of 17.70 in January of 2013 and a record low of 15.70 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Hampden County, MA - last updated from the United States Federal Reserve on August of 2025.

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.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Percent of Population Below the Poverty Level (5-year estimate) in Worcester County, MA was 10.30% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Worcester County, MA reached a record high of 11.80 in January of 2015 and a record low of 9.70 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Worcester County, MA - last updated from the United States Federal Reserve on August of 2025.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

In 2023, the poverty rate of the United States was around **** percent. Louisiana was the state with the highest poverty rate, at **** percent. Poverty rates in the United States are higher than in many parts of the world, and minority groups are much more likely to be living in poverty when compared to white people.

Percent of Population Below the Poverty Level (5-year estimate) in Bristol County, MA was 11.60% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Bristol County, MA reached a record high of 12.70 in January of 2014 and a record low of 10.70 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Bristol County, MA - last updated from the United States Federal Reserve on July of 2025.

BackgroundEconomic and supply chain shocks resulting from the COVID-19 pandemic in 2020 led to substantial increases in the numbers of individuals experiencing food-related hardship in the US, with programs aimed at addressing food insecurity like the Supplemental Nutrition Assistance Program (SNAP) and food pantries seeing significant upticks in utilization. While these programs have improved food access overall, the extent to which diet quality changed, and whether they helped mitigate diet quality disruptions, is not well understood.ObjectiveTo evaluate food insecurity, food pantry and/or SNAP participation associations with both diet quality as well as perceived disruptions in diet during the COVID-19 pandemic among Massachusetts adults with lower incomes.MethodsWe analyzed complete-case data from 1,256 individuals with complete data from a cross-sectional online survey of adults (ages 18 years and above) living in Massachusetts who responded to “The MA Statewide Food Access Survey” between October 2020 through January 2021. Study recruitment and survey administration were performed by The Greater Boston Food Bank. We excluded respondents who reported participation in assistance programs but were ineligible (n = 168), those who provided straightlined responses to the food frequency questionnaire component of the survey (n = 34), those with incomes above 300% of the federal poverty level (n = 1,427), those who completed the survey in 2021 (n = 8), and those who reported improved food insecurity (n = 55). Current dietary intake was assessed via food frequency questionnaire. Using Bayesian regression models, we examined associations between pandemic food insecurity, perceived disruption in diet, diet quality, and intakes of individual foods among those who completed a survey in 2020. We assessed interactions by pantry and SNAP participation to determine whether participation moderated these relationships.ResultsIndividuals experiencing food insecurity reported greater disruption in diet during the pandemic and reduced consumption of healthy/unhealthy foods. Pantry participation attenuated significant associations between food insecurity and lower consumption of unhealthy (b = −1.13 [95% CI −1.97 to −0.31]) and healthy foods (b = −1.07 [−1.82 to −0.34]) to null (unhealthy foods: −0.70 [−2.24 to 0.84]; healthy foods: 0.30 [−1.17 to 1.74]), whereas SNAP participation attenuated associations for healthy foods alone (from −1.07 [−1.82 to −0.34] to −0.75 [−1.83 to 0.32]). Results were robust to choice of prior as well as to alternative modeling specifications.ConclusionAmong adults with lower incomes, those experiencing food insecurity consumed less food, regardless of healthfulness, compared to individuals not experiencing food insecurity. Participation in safety-net programs, including SNAP and pantry participation, buffered this phenomenon. Continued support of SNAP and the food bank network and a focus on access to affordable healthy foods may simultaneously alleviate hunger while improving nutrition security.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..This table provides geographical mobility for persons relative to their previous place of residence. The characteristics crossed by geographical mobility reflect the current survey year. The estimates do not include people who moved to Puerto Rico, other U.S. Island Areas, or Foreign Countries..The number of people moving out of Alaska to a different state has been overestimated in previous years due to collection issues. See Errata Notes for details..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Tables for Geographical Mobility by Residence 1 Year Ago in the United States are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities; Combined New England City and Town Areas; New England City and Town Areas, and their associated Divisions and Principal Cities..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Percent of Population Below the Poverty Level (5-year estimate) in Berkshire County, MA was 11.00% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Berkshire County, MA reached a record high of 13.00 in January of 2015 and a record low of 9.70 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Berkshire County, MA - last updated from the United States Federal Reserve on August of 2025.

Abstract

To understand the socio-economic impact of COVID-19 and associated government measures, the first round of the COVID-19 National Panel Phone Survey 2020 was collected by the National Institute of Statistics of Djibouti (INSD) between July 7-22, 2020. Various channels of impact are explored such as job loss, availability and price changes of basic food items, and ability to access healthcare and education.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

The survey covers households that reported telephone numbers, are included in the social registry data collected by the Ministry of Social Affairs and Solidarity (MASS) and have been interviewed after 2017. Refugees are excluded from this first round.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE As a recently conducted representative household survey with telephone numbers was not available, data from the national social registry collected by the Ministry of Social Affairs (MASS) was used as the sampling frame. The social registry is an official database of households in Djibouti that may benefit from public transfers and be particular targets of poverty alleviation efforts. The sample consists of households drawn randomly from the social registry data restricted to urban households having at least one phone number and interviewed after July 1, 2017.

The sample design is a one-stage probability sample selected from the sampling frame and stratified along two dimensions: the survey domain (three categories) and the poverty status (binary). This yields six independent strata. Within each stratum, households are selected with the same ex-ante probability, but this differs across strata. Initially 1,590 households are drawn. Given a non-response rate averaging 30 percent, a replacement sample of 750 households was selected. About 589 of these replacement households have been contacted to reach the overall goal of 1,486 completed interviews.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is adapted from the template questionnaire prepared by the Poverty and Equity GP to measure the impact of COVID-19 on household welfare. It was designed in French and dispensed in local languages (Afar, Arabic, Somali, French or other). The questionnaire includes the following sections:

• Household Roster
• Knowledge and Behavior Regarding the COVID-19
• Employment
• Household's Income
• Needs
• Access
• Safety Nets

Cleaning operations

The CsPro CATI data entry application helped to enforce skip and range patterns during data collection. Standard consistency checks (like age differences between parents and children and unicity of household heads) were carried out at the time of the data collection. Because the entry application was strictly system-controlled, complete cases including missing items were avoided. The various checks resulted in a limited need for secondary data editing, which eventually entailed two main steps from the WB team. First, duplicated names of household members, who were otherwise distinct, were corrected by adding a suffix “bis” to the names. Second, after analysis of text responses mentioned in the residual “other” categories, a few items codes were adjusted (not exceeding 10 in any category).

Response rate

The response rate stood at 71.4 percent nationally with 1,486 interviewed households. Slight differences were observed across location with districts 1, 2 and 3 of Djibouti city more likely to respond than other locations (72.9 percent versus 70.9 and 70.4 percent respectively in Balbala and other urban areas).

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Tables for Workplace Geography are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities; Combined New England City and Town Areas; New England City and Town Areas, and their associated Divisions and Principal Cities. Tables B08601, B08602, B08603, and B08604 are also available for Place parts and County Subdivision parts for the 5-year ACS datasets..These tabulations are produced to provide estimates of workers at the location of their workplace. Estimates of counts of workers at the workplace may differ from those of other programs because of variations in definitions, coverage, methods of collection, reference periods, and estimation procedures. The ACS is a household survey which provides data that pertains to individuals, families, and households..Workers include members of the Armed Forces and civilians who were at work last week..2019 ACS data products include updates to several categories of the existing means of transportation question. For more information, see: Change to Means of Transportation..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Percent of Population Below the Poverty Level (5-year estimate) in Norfolk County, MA was 6.60% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Norfolk County, MA reached a record high of 6.70 in January of 2016 and a record low of 6.00 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Norfolk County, MA - last updated from the United States Federal Reserve on August of 2025.

The cost of providing safe, reliable water services in the United States is increasing for utilities and their customers, raising questions about the scale and scope of water affordability challenges. How we measure and understand water affordability is debated. Here, we developed an open and repeatable approach that calculates five affordability metrics, including a new metric that combines affordability prevalence and burden along a continuum. We calculated these metrics for multiple volumes of water usage (from 0 to 16,000 gallons per month) using rate data available in 2020 at the scale of census block groups and service areas. We applied this approach to 1,791 utilities in four states (California, Pennsylvania, North Carolina, and Texas), which cumulatively serve 72 million persons. We found 77% of utilities had more than 20% of their population below 200% of the federal poverty level, suggesting widespread poverty contributes to affordability challenges for many utilities. Minimum wage earners spend more than a day of labor per month to pay water bills for relatively low usage (4,000 gallons per month) in 67% of utilities, but upwards of 3 days of labor at higher volumes (12,000 gallons per month) in 29% of utilities. Depending on how much water a household uses, our results suggest a tenth to a third of households are working more than a day each month to afford their water bills. We developed an interactive data visualization tool to bring greater transparency to water affordability by allowing users to explore affordability at the block group and utility scale at different volumes of usage. The underlying data in the visualization tool can be expanded and updated over time, further increasing the transparency and understanding of water affordability in the U.S. ... [Read More]

Abstract copyright UK Data Service and data collection copyright owner.The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.Further information about the survey, including publications, can be downloaded from the Young Lives website. This study includes data and documentation for Round 4 only. Round 1 is available under SN 5307, Round 2 under SN 6852, Round 3 under SN 6853 and Round 5 under SN 8357.Latest edition:For the third edition (August 2022), the Peruvian younger cohort household level data file (pe_r4_ychh_youngerhousehold) has been updated to include the mother's health variables. In addition, an error in the Vietnamese younger cohort reading comprehension variables (vn_r4_yccog_youngerchildtest) has been corrected. Main Topics: Older Cohort Household Questionnaire (age 19): includes sections on: Parental background; Household and child education; Livelihoods and asset framework; Household food and non-food consumption and expenditure; Social capital; Economic changes and recent life history; Socio-economic status.Older Cohort Child Questionnaire (age 19): includes sections on: Parents and Caregiver update; Mobility; Subjective well-being; Education; Employment, earnings, and time-use; Feelings and attitudes; Household decision-making; Marital and living arrangements; Fertility; Anthropometry; Health and nutrition.Older Cohort Cognitive Tests (age 19): includes Mathematics test; Reading comprehension test.Older Cohort Self-Administered Questionnaire (age 19): includes sections on: Relationship with parents, Smoking, Violence, Alcohol, Sexual behaviour (administered in Peru only).Younger Cohort Household Questionnaire (age 12): includes sections: on Parental background; Household and child education; Livelihoods and asset framework; Household food and non-food consumption and expenditure; Social capital; Economic changes and recent life history; Socio-economic status, Health; Anthropometry (for the study child and a sibling); Caregiver perceptions and attitudes.Younger Cohort Child Questionnaire (age 12): includes sections on Schooling; Time-use; Health; Social networks; Feelings and attitudes.Younger Cohort Cognitive Tests (age 12): include Peabody Picture Vocabulary Test (administered to the study child and a sibling); Mathematics test; Reading comprehension test. In Ethiopia only an additional English and Amharic reading test.Community Questionnaire: (administered in the main communities where Young Lives children live) includes sections on: General characteristics of the locality; Social environment; Access to services; Economy; Local prices; Social protection; Educational services; Health services; Migration. Mini-community questionnaire: (administered in communities into which one or study children moved) includes sections on: General characteristics of the locality; Social environment; Access to Services; Economy; Local prices. Purposive selection/case studies Face-to-face interview Self-administered questionnaire 2013 2014 ACCESS TO INFORMATION ACCESS TO PUBLIC SE... ACCIDENTS ADULT EDUCATION AGE AGRICULTURAL EQUIPMENT AGRICULTURE ALIMONY ANIMAL HUSBANDRY ANTHROPOMETRIC DATA ARABLE FARMING ASPIRATION ATTITUDES AUTHORITY Agriculture and rur... BEREAVEMENT BIRTH WEIGHT BREAST FEEDING BUILDING MAINTENANCE BULLYING CARE OF DEPENDANTS CASTE CHILD CARE CHILD DEVELOPMENT CHILD LABOUR CHILD WORKERS CHILDBIRTH CHILDREN CHRONIC ILLNESS COMMUNITIES COMMUNITY ACTION COMMUNITY BEHAVIOUR COMMUNITY PARTICIPA... CONSCRIPTION CONSUMER GOODS COST OF LIVING COSTS CREDIT CROP YIELDS CROPS CULTURAL GOODS DAY NURSERIES DEBILITATIVE ILLNESS DEBTS DEVELOPMENT PROGRAMMES DISABILITIES DISASTERS DOMESTIC APPLIANCES DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL FEES EDUCATIONAL TESTS ELECTRIC POWER EMOTIONAL STATES EMPLOYEES ETHNIC GROUPS Education Ethiopia FAMILIES FAMILY LIFE FAMILY MEMBERS FARM VEHICLES FATHERS FERTILIZERS FINANCIAL DIFFICULTIES FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FOOD AID FOOD AND NUTRITION FOOD SHORTAGES FOSSIL FUELS FRIENDS FURNITURE Family life and mar... GENDER GIFTS GROUPS General health and ... HANDICRAFTS HEALTH HEATING SYSTEMS HEIGHT PHYSIOLOGY HOME OWNERSHIP HOMEWORK HOUSEHOLD BUDGETS HOUSEHOLD INCOME HOUSEHOLDS HOUSING CONSTRUCTION HOUSING IMPROVEMENT Housing IMMUNIZATION IMPRISONMENT INCOME INDUSTRIES INFANTS INFORMAL CARE INJURIES INTERNET ACCESS India KITCHENS LAND OWNERSHIP LAND TENURE LANGUAGE SKILLS LANGUAGES USED AT HOME LAVATORIES LEARNING LIFE EVENTS LIFE SATISFACTION LITERACY LIVESTOCK LIVING CONDITIONS Labour and employment MARITAL STATUS MARRIAGE DISSOLUTION MEALS MEDICAL CARE MEMBERSHIP MOBILE PHONES MORTGAGES MOTHER TONGUE MOTHERS MOTOR VEHICLES NUMERACY ORGANIZATIONS PARENTS PAYMENTS PERSONAL FINANCE MA... POPULATION MIGRATION POVERTY PRE PRIMARY EDUCATION PREGNANCY PREMATURE BIRTHS PRIVATE VOLUNTARY O... PUBLIC WORKS PURCHASING Peru QUALITY OF LIFE RESIDENTIAL MOBILITY RESPONSIBILITY ROOMS RURAL AREAS SATISFACTION SCHOOL PUNISHMENTS SCHOOLCHILDREN SCHOOLS SELLING SEXUAL AWARENESS SIBLINGS SINGLE SEX SCHOOLS SLEEP SOCIAL CAPITAL SOCIAL CLASS SOCIAL NETWORKS SOCIAL SECURITY BEN... SOCIAL SKILLS SOCIAL SUPPORT SOCIO ECONOMIC STATUS SPOUSES STANDARD OF LIVING STRUCTURAL ELEMENTS... STUDENT ATTITUDE STUDENT BEHAVIOUR STUDENT TRANSPORTATION Specific social ser... TELEPHONES TEMPORARY EMPLOYMENT THEFT TIME BUDGETS TRADE UNION MEMBERSHIP TRANSPORT FARES TRAVELLING TIME TRUANCY TRUST TUTORING Time use UNITS OF MEASUREMENT URBAN AREAS VOTING BEHAVIOUR Vietnam WATER POLLUTION WEIGHT PHYSIOLOGY WOMEN YOUTH Youth

Abstract

To understand the socio-economic impact of COVID-19 and associated government measures over the long term, the third round of the COVID-19 National Panel Phone Survey 2020 was collected by the National Institute of Statistics of Djibouti (INSD) between December 20, 2020 and February 2, 2021. Various channels of impact are explored such as job loss, availability and price changes of basic food items, ability to access healthcare, and food insecurity. New sections on the attitudes toward a potential vaccine and shock coping strategies have also been added (compared to the second round).

Note that a sample of 564 refugee households living in Djibouti has been collected during the same time frame and using the same questionnaire. This data set will be available for download separately on the microdata library.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covers national households that reported telephone numbers, are included in the social registry data collected by the Ministry of Social Affairs and Solidarity (MASS) and have been interviewed after 2017.

Kind of data

Sample survey data [ssd]

Sampling procedure

As a recently conducted representative household survey with telephone numbers was not available, data from the national social registry collected by the Ministry of Social Affairs (MASS) was used as the sampling frame of the national sample. The social registry is an official database of households in Djibouti that may benefit from public transfers and be particular targets of poverty alleviation efforts. The sample consists of households drawn randomly from the social registry data restricted to urban households having at least one phone number and interviewed after July 1, 2017. The sample design is a one-stage probability sample selected from the sampling frame and stratified along two dimensions: the survey domain (three categories) and the poverty status (binary). This yields six independent strata. Within each stratum, households are selected with the same ex-ante probability, but this differs across strata. With a non-response rate averaging 26 percent for the national households, the third wave consisted of 1,383 interviewed national households with complete information that were representative of the urban national population, out of which 990 households were also interviewed in the two first waves, 190 were added as replacement households in the second wave and re-interviewed in the third one, and 203 were added as replacement households in the third wave.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire of the third round is adapted from the questionnaire of the second round and in accordance with the template questionnaire prepared by the Poverty and Equity GP to measure the impact of COVID-19 on household welfare. It was designed in French and dispensed in local language (Afar, Arabic, Somali, French or other). The questionnaire includes the following sections: - Household roster - Employment - Household's income sources - Needs - Access to services - Safety nets - Food insecurity - Shock coping strategies - Vaccine attitudes

Cleaning operations

The CSPro CATI data entry application helped to enforce skip and range patterns during data collection. Standard consistency checks (like age differences between parents and children and unicity of household heads) were carried out at the time of the data collection. Because the entry application was strictly system-controlled, complete cases including missing items were avoided. The various checks resulted in a limited need for secondary data editing, which eventually entailed two main steps from the WB team. First, duplicated names of household members, who were otherwise distinct, were corrected by adding a suffix “bis” to the names. Second, after analysis of text responses mentioned in the residual “other” categories, a few items codes were adjusted (not exceeding 10 in any category).

Response rate

The response rate among the national sample was about 74.3 percent with 1,383 interviewed national households. Slight differences were observed across location, with districts 1, 2 and 3 of Djibouti city more likely to respond than other locations (response rate at 76.4 percent versus 75.9 and 70.5 percent, respectively in Balbala and the other urban areas).

Recent data from the United States (US) Energy Information Administration reveals that nearly one in three households in the US report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We produce a novel machine learning approach based on sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. Our analysis confirms that average household energy burdens increased, and the range of households suffering energy poverty broadened. We provide an optimized allocation structure to urge policy makers to revise the distribution of funds to better match assistance needs. Methods We use machine learning to determine how various demographic and physical characteristics are correlated with household energy burdens across the US. Energy burden estimates allow us to identify where energy poverty may be concentrated at the census-tract level. Our analysis extends and improves upon the Low-income Energy Affordability Data (LEAD) tool, developed by the US Department of Energy’s National Renewable Energy Laboratory to estimate energy expenditures and burdens in several ways (28). The LEAD tool is designed to help local and state governments with decisions for addressing energy poverty; however, it is static in time and uses self-reported energy expenditures given only for one month of the year, which is not reported publicly. The reliance on one month implies that the estimation of annual values is not guaranteed to account for the seasonal variation in energy costs throughout the months. The sampling done by the survey must sufficiently cover all months of the year, and this is not verifiable from the publicly available data. In addition, which month is used varies across respondents. Different from LEAD, we use household-level sociodemographic and geographic data, detailed in the following subsection, from the Energy Information Administration’s (EIA) Residential Energy Consumption Survey (RECS) to estimate the annual energy burden. This survey is completed every five years, enabling us to track changes in energy burden over time. To develop our projections at a census-tract level, we use an adaptive least absolute shrinkage and selection operator (LASSO) technique to select important variables from the RECS data to be applied to census-tract level information from the US Census Bureau’s American Community Survey (ACS).