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 Suffolk County, MA was 16.50% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Suffolk County, MA reached a record high of 21.20 in January of 2014 and a record low of 16.50 in January of 2023. 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 Suffolk County, MA - last updated from the United States Federal Reserve on July 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 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 Nantucket County, MA was 3.00% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Nantucket County, MA reached a record high of 11.60 in January of 2015 and a record low of 3.00 in January of 2023. 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 Nantucket County, MA - last updated from the United States Federal Reserve on July of 2025.

Percent of Population Below the Poverty Level (5-year estimate) in Essex County, MA was 9.40% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Essex County, MA reached a record high of 11.40 in January of 2015 and a record low of 9.40 in January of 2023. 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 Essex County, MA - last updated from the United States Federal Reserve on July 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.

The Evaluation of Child Care Subsidy Strategies is a multi-site, multi-year effort to determine whether and how different child care subsidy policies and procedures and quality improvement efforts help low-income parents obtain and hold onto jobs and improve outcomes for children. Funding from the Child Care and Development Fund (CCDF) administered by the Child Care Bureau are divided into two purposes. The vast majority are aimed at assisting children of low-income working parents whose eligibility is determined by states within broad federal guidelines, while a much smaller portion (4 percent) work with state matching funds to improve the quality of child care for all children. For this study series, four experiments were conducted, two test alternative subsidy policies for low-income families and two test approaches to the use of set-aside funds for improving child care quality for all children. The four study sites and focus of evaluation include: (1) the effectiveness of three language and literacy curricula on teaching practices and children's language and literacy outcomes (Miami Dade County, Florida); (2) the impact of alternative eligibility and re-determination child care subsidy policies on parental employment outcomes (Illinois); (3) the impact of alternative child care co-payment structures on use of child care subsidies and employment outcomes (Washington) and (4) the effectiveness of training on Learning Games curriculum in changing care-giving practices in family child care homes and children's developmental outcomes (Massachusetts).

The Washington evaluation was designed to test the impact of changing parental copayment levels on various child care and economic outcomes (such as type of care used, earnings, employment, etc.). The copayment amount refers to the amount that families who are receiving child care subsidies contribute to the cost of child care, while the copayment schedule refers to the amount or the rate at which the copayment changes as income increases or decreases. In all states, the copayment amount is larger for families with higher incomes. In Washington in 2005, a three-person family receiving child care subsidies paid 3 percent of the cost of child care if their income was 33 percent of the federal poverty threshold, but 16 percent of the cost of care if their income was 200 percent of the threshold. In the Washington child care subsidy program, families were divided into three income tiers. Families in Tier 1 had incomes at or below 82 percent of the federal poverty threshold, families in Tier 2 had incomes between 83 and 137.5 percent of the threshold, and families in Tier 3 had incomes between 137.5 and 200 percent of the threshold. Under the standard copayment schedule used by Washington in 2005, child care subsidy recipients in Tier 1 paid $15 per month, while recipients in Tier 2 paid $50 per month. Families in Tier 3 faced a sliding copayment schedule, with the copayment increasing by 44 cents for each additional dollar of income beyond 137.5 percent of the poverty threshold. In the evaluation, study participants were randomly assigned to one of two groups: (1) a control group assigned to the standard copayment schedule, and (2) a program group assigned to an alternative copayment schedule, which had copayment amounts that were equal to or lower than standard copayment schedule amounts.

Percent of Population Below the Poverty Level (5-year estimate) in Barnstable County, MA was 7.40% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Barnstable County, MA reached a record high of 9.70 in January of 2014 and a record low of 6.40 in January of 2019. 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 Barnstable County, MA - last updated from the United States Federal Reserve on July of 2025.

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.

Percent of Population Below the Poverty Level (5-year estimate) in Franklin County, MA was 12.20% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Franklin County, MA reached a record high of 12.20 in January of 2023 and a record low of 9.70 in January of 2019. 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 Franklin County, MA - last updated from the United States Federal Reserve on July of 2025.

Distribution and Incidence of catastrophic OOP by wealth quintile.

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.

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 2 only. Round 1 is available under SN 5307, Round 3 under SN 6853, Round 4 under SN 7931 and Round 5 under SN 8357.Latest edition:For the fourth edition (August 2022), the Peruvian Younger cohort data file (pechildlevel5yrold) has been updated to include the mother's health variables. Main Topics: This dataset comprises the data from the 5-year-olds' and 12-year-olds' household surveys and the 12-year-olds' child survey carried out in 2006. For each of the four countries the dataset contains files at the community, household and child level for both ages. The household/child level data file for the 12-year-olds' survey also includes data from the child questionnaire. In addition there are several files at lower levels (i.e. where there are several records per household). These include the household roster and activity schedules for livelihoods. The Peru community level data includes an additional file with community data covering new communities for children who have migrated. Topics covered in the dataset include: community characteristics (environmental, social and economic); parental background; household education; livelihoods and asset framework; household food and non-food consumption and expenditure; social capital, economic changes and recent life history; socio-economic status; child care, education and activities; child health; anthropometry; caregivers' perceptions and attitudes; school and activities, child time use; social networks, social skills and social support; feelings and attitudes; parents' and household issues; child development; perception of the future, environment and household wealth. Also included are calculated indices such as a wealth index, various social capital scores, and mental health scores, which are all detailed in the documentation. The SPSS syntax code and/or Stata 'do' files that show methods of calculation for the composite indices are also included in the dataset. Purposive selection/case studies Face-to-face interview 2006 ACCESS TO INFORMATION ACCESS TO PUBLIC SE... ACCIDENTS AGE AGRICULTURAL EQUIPMENT AGRICULTURE ALIMONY ANIMAL HUSBANDRY ANTHROPOMETRIC DATA ARABLE FARMING ASPIRATION ATTITUDES AUTHORITY BEREAVEMENT BIRTH WEIGHT BREAST FEEDING BUILDING MAINTENANCE 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 COUGHING CREDIT CRIME VICTIMS CROP YIELDS CROPS CULTURAL GOODS Children Compulsory and pre ... DAY NURSERIES DEBTS DECISION MAKING DIARRHOEA DISABILITIES DISASTERS DISEASES DOMESTIC APPLIANCES EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL FEES ELECTRIC POWER EMOTIONAL STATES EMPLOYEES ETHNIC GROUPS Economic conditions... Education Equality Ethiopia FAMILIES FAMILY LIFE FAMILY MEMBERS FAMILY PLANNING FARM VEHICLES FATHER S EDUCATIONA... FATHERS FERTILIZERS FINANCIAL DIFFICULTIES FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FOOD AID FOOD AND NUTRITION FOOD SHORTAGES FOSSIL FUELS FURNITURE Family life and mar... GENDER GIFTS GROUPS Gender and gender r... General health and ... HANDICRAFTS HEALTH HEIGHT PHYSIOLOGY HOME OWNERSHIP HOUSEHOLD BUDGETS HOUSEHOLD INCOME HOUSEHOLDS HOUSING CONSTRUCTION HOUSING IMPROVEMENT Health behaviour ILL HEALTH IMMUNIZATION IMPRISONMENT INCOME INDUSTRIES INFANTS INFORMAL CARE INFORMATION SOURCES INJURIES India LABOUR DISPUTES LAND OWNERSHIP LANGUAGE SKILLS LAVATORIES LEARNING LIFE EVENTS LITERACY LIVESTOCK MARITAL STATUS MARRIAGE DISSOLUTION MEALS MEMBERSHIP MEN MOTHER TONGUE MOTHERS MOTOR VEHICLES Minorities NUMERACY ORGANIZATIONS PARENTS PAYMENTS PERSONAL FINANCE MA... POVERTY PRE PRIMARY EDUCATION PREGNANCY PREMATURE BIRTHS PRIVATE VOLUNTARY O... PURCHASING Peru QUALITY OF LIFE RELIGIOUS AFFILIATION RESIDENTIAL MOBILITY RESPONSIBILITY ROOMS RURAL AREAS SCHOOLCHILDREN SCHOOLS SELLING SHOPS SIBLINGS SMALL BUSINESSES SOCIAL CAPITAL SOCIAL CLASS SOCIAL NETWORKS SOCIAL SECURITY BEN... SOCIAL SUPPORT SPOUSES STANDARD OF LIVING STRUCTURAL ELEMENTS... STUDENT ATTITUDE STUDENT BEHAVIOUR STUDENT TRANSPORTATION SYMPTOMS Social and occupati... Social behaviour an... Social conditions a... Specific social ser... TELEPHONES THEFT TIME BUDGETS TRADE UNION MEMBERSHIP TRUANCY TRUST Time use UNITS OF MEASUREMENT URBAN AREAS VOTING BEHAVIOUR Vietnam WATER POLLUTION WEIGHT PHYSIOLOGY WOMEN YOUTH Youth inequality and soci...

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 July of 2025.

Determinants of catastrophic health expenditure for malaria (CHE = 1, Non CHE = 0).

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE NATIONAL INSTITUTE OF STATISTICS - TUNISIA (INS)

The National Survey on Household Budget, Consumption, and Standard of Living is a quinquennial survey. The 2010 survey is the ninth of its kind that was carried out by the National Institute of Statistics (INS) in Tunisia. The eight previous surveys were conducted in 1968, 1975, 1980, 1985, 1990, 1995, 2000 and 2005, concurrently with the preparatory work for the Tunisian development plans.

The survey aims at providing detailed information on the procurement of goods and services for consumption. Its data was collected from direct observation of household consumption to allow for having the necessary elements to assess the situation & changes in the living standards & conditions of the households.

The National Survey on Household Budget, Consumption, and Standard of Living consists of three fundamental parts; the budget survey, the nutrition survey and the access to community services survey. Thus, it tackles three areas of study: 1- Households expenses and acquisitions during the survey period. 2 - Food consumption and nutritional status of households. 3 - Household access to health and education community services.

The main objectives of the "budget survey" are: a- Estimate the levels of expenditure on the household level: The total expenditure of the household is not only an indicator on household income, but it is also a quantitative assessment of the standard of living index. b- Evaluate the income distribution: Due to the absence of data on income distribution, the mass distribution of expenditure between the different categories of the population constitutes a first sketch for the income distribution in the country. c- Assess the structure of expenditure: Detailed information collected on expenditures per product are used to establish the structures of the household expenditure, as well as the budget coefficients according to different levels of classifications of goods and services. These coefficients are particularly useful in the revision and development of the Consumer Prices Index (CPI) weights. d- Predict the demand of households: The household behavior, assessed in terms of product demand, is synthesized by the coefficients of income elasticity, which, according to the model of consumption retained and under the assumptions of the growth of income and population, allows predicting future household demand. e- Analyze the importance of consumer subsidies: analysis of the consumption of subsidized goods by expenditure deciles allows identifying the impact of direct consumer subsidies. It also allows evaluating the effectiveness of public policies grants.

The main objectives of "the nutrition survey" are: a- Provide estimates of food consumption by product for different groups of households according to their demographic and socio-economic characteristics. b- Estimate food consumption of each product by collecting data on the quantities consumed of each product by source, whether purchased or own produced. c- Identify the nutritional status of the population according to its demographic, geographic and socio-economic level. The comparison between the standards needs of nutrients to those acquired by the household enables assessing of the nutritional status and thus deficits in different nutrients such as calories, protein, vitamins, calcium, ... can also be captured. d- Estimate the calorie intake and energy needs of the Tunisian population: This estimate is indispensible in the calculation of the food component of the poverty line and, in consequence, the threshold of global poverty.

The main objective of "the access to community services survey" is to provide an overview on the state of morbidity of the Tunisian population, from one hand, and on the households' access to various health and education public services on other hand.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of all urban, small and medium towns and rural areas.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method

The National Survey on Household Budget, Consumption and Standard of Living, 2010 has focused initially on a sample of 13,392 households drawn using a two stages stratified random sampling in each governorate. The sampling frame follows that of the General Census of Population and Housing in 2004 which was updated during the implementation of the National Population and Employment Survey in 2009.

Stratification criteria: The sampling frame is stratified by two geographical criteria: namely the governorate and the living area. The latter is stratified as follows: large cities, medium and small cities, and non-communal areas.

These stratification criteria (governorate, living area and size of city) represent variables that differentiate between surveyed households' lifestyles. Thus, the 3 strata types used are as follows:

Stratum of large cities (stratum 1): This stratum is formed of large urban centers corresponding to municipalities with more than 100.000 inhabitants and neighboring municipalities.

Stratum of medium and small cities (stratum 2): This stratum includes all medium and small sized cities other than those classified in the stratum of large cities.

Stratum of non-communal areas (stratum 3): It includes agglomerations in rural areas that are classified as major agglomerations in the General Census of Population and Housing 2004 and the National Population and Employment Survey in 2009. In addition to other areas that are located outside the territory of main municipalities and cities. 

Households in these areas reside in scattered dwellings or are grouped in small agglomerations.

Survey type

The sampling frame is divided on the level of each governorate according to strata previously defined. On the stratum level, a two-stage random sampling is planned for the selection of the survey sample of households. This process allows to breakdown the sample into clusters of 12 households relatively little distant from each other, thereby facilitating the conduct of the survey at the time of the information collection in the field.

In the first stage, a sample of 1,116 primary units is drawn in proportion to the number of households identified in the 2009National Population and Employment Survey. Taking into consideration that the primary units correspond to the districts that have been defined in the General Census of Population and Housing in 2004, which are geographic areas comprising on average 70 households.

In the second stage, from each primary unit (or cluster), twelve households are drawn through a simple random sampling technique. A substitutive sample of 12 additional households is further drawn from each primary unit. Those additional households constituting a substitutive list are used to cover for unidentified households at the time of the survey, given the mobility of households and the period between the date on which the sample is drawn and the date on which the survey is conducted.

Sample size

The size of the sample drawn in the first stage is 1,116 primary sampling units (PSU) corresponding to 13,392 households. The samples in the second stage are 12 households per primary unit. To optimize the use of logistic and material resources available, a sample of at least 36 PSU was selected from the less populated governorates, 3 PSU per month (the survey is conducted over a 12 months period). This represents the monthly work of the survey team (3 interviews and 1 supervisor to whom a car is assigned). Moreover, as the number of households varies from one governorate to another, it was agreed to adopt different rate of sampling from one governorate to another.

The following table shows the regional distribution of the sample and the corresponding sampling rates.

Regional Distribution of the Survey Sample

Abstract (en): In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a quarterly survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the second round of the survey (second quarter 2013) include self-reported health status, type of and satisfaction with current health insurance coverage, access to and use of health care, health care affordability, whether the respondent considered purchasing or tried to purchase health insurance coverage directly from an insurance company, whether the respondent considered obtaining coverage through Medicaid or other government sponsored assistance plan based on income or disability, sources of information about health insurance, and the importance of various criteria in choosing a health insurance plan. Additional information collected by the survey includes age, education, race, Hispanic origin, gender, income, household size, housing type, marital status, employment status, number of employees at place of work, United States citizenship, smoking, internet access, home ownership, body mass index, sexual orientation, and whether the respondent reported an ambulatory care sensitive condition or a mental or behavioral condition. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The HRMS response rate is roughly five percent each quarter. Datasets:DS0: Study-Level FilesDS1: Public-use DataDS2: Restricted-use Data Household population aged 18-64. Each quarterly HRMS sample is drawn from the KnowledgePanel, a probability-based, nationally representative Internet panel maintained by GfK Custom Research. Beginning with the second quarter of 2013, the HRMS includes oversamples of adults with family incomes at or below 138 percent of the federal poverty level and adults from selected state groups based on (1) the potential for gains in insurance coverage in the state under the ACA as estimated by the Urban Institute's microsimulation model and (2) states of specific interest to the HRMS funders. Additional funders have supported oversamples of adults from individual states or subgroups of interest (including children). However, ICPSR received data only for the adults in the general national sample and the income and state group oversamples. 2019-07-10 Variable Q7_F was removed from public dataset. An updated codebook excluding this variable was provided for public use. Current release will feature DS1 as public-use data only and DS2 as restricted-use data. Previous release included both public and restricted versions of DS1. Study title updated to include geographic information.2017-06-20 The principal investigators added a new weight variable to the data file and the technical documentation was updated accordingly.2015-03-23 The principal investigators deleted the multiple imputation variables _1_famsize, _2_famsize, _3_famsize, _4_famsize and _5_famsize. ICPSR revised the codebook accordingly and added to the collection a plain text version of the data with a Stata setup and record layout file. Funding institution(s): Ford Foundation. Urban Institute. Robert Wood Johnson Foundation (71390). web-based survey

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).

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 1 only. Round 2 is available under SN 6852, Round 3 under SN 6853, Round 4 under SN 7931 and Round 5 under SN 8357.Latest edition:For the seventh edition (August 2022), the Peruvian younger cohort data file (pechildlevel1yrold) has been updated to include the care-giver psycho social well-being variables. The erroneous variable SHIGH has been removed from the Peruvian data for both cohorts (pechildlevel1yrold and pechildlevel8yrold). Main Topics: This dataset comprises the baseline household surveys for the main sample of 1-year-old and 8-year-old children. For each country, files are included at the community, household and child level for both ages. The household/child level data for the 8-year old children also include information from the child questionnaire. In addition, several files are included at lower levels (i.e. where there are several records per household). These include the household roster and activity schedules for livelihoods. Topics covered include: community characteristics (environmental, social and economic); household composition; child health; caregiver background; livelihoods; economic changes; socio-economic status; social capital and anthropometry. In addition, the information gathered for younger children also includes details from the caregiver on pregnancy, delivery, breastfeeding, mental health, and child care. Topics specific to the older 8-year-olds survey include child's schooling and work; child mental health (not available for Peru or Ethiopia), and child development. Also included are calculated indices such as a wealth index, various social capital scores, and mental health scores, which are all detailed in the documentation. The SPSS syntax code and/or Stata 'do' files that show methods of calculation for the composite indices are also included in the dataset. Standard Measures: Child development for the 8-year-olds was measured through use of: Ravens, J.C. (1988) Raven's Coloured Progressive Matrices, Oxford: Harcourt Assessment. Purposive selection/case studies Face-to-face interview 2002 ACCESS TO INFORMATION ACCESS TO PUBLIC SE... ACCIDENTS AGE AGRICULTURAL EQUIPMENT AGRICULTURE ALIMONY ANIMAL HUSBANDRY ANTENATAL CARE ANTHROPOMETRIC DATA ARABLE FARMING ASPIRATION ATTITUDES AUTHORITY BANKS BEREAVEMENT BIRTH WEIGHT BREAST FEEDING BUILDING MAINTENANCE CAESARIAN SECTIONS CARE OF DEPENDANTS CASTE CHILD CARE CHILD DEVELOPMENT CHILD LABOUR CHILD WORKERS CHILDBIRTH CHILDREN CHRONIC ILLNESS CITIZENSHIP COMMUNITIES COMMUNITY ACTION COMMUNITY BEHAVIOUR COMMUNITY PARTICIPA... CONSCRIPTION CONSUMER GOODS COST OF LIVING COSTS COUGHING CREDIT CRIME VICTIMS CROP YIELDS CROPS CULTURAL GOODS Children Compulsory and pre ... DAY NURSERIES DEBTS DECISION MAKING DEPRESSION DIARRHOEA DISABILITIES DISASTERS DISEASES DOMESTIC APPLIANCES EDUCATIONAL BACKGROUND EDUCATIONAL CHOICE EDUCATIONAL FEES ELECTRIC POWER EMOTIONAL STATES EMPLOYEES ETHNIC GROUPS Economic conditions... Education Equality Ethiopia FAMILIES FAMILY LIFE FAMILY MEMBERS FAMILY PLANNING FARM VEHICLES FATHERS FERTILIZERS FINANCIAL DIFFICULTIES FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FOOD AID FOOD AND NUTRITION FOOD SHORTAGES FOSSIL FUELS FURNITURE Family life and mar... GENDER GIFTS GROUPS Gender and gender r... General health and ... HANDICRAFTS HEALTH HEIGHT PHYSIOLOGY HOME OWNERSHIP HOUSEHOLD BUDGETS HOUSEHOLD INCOME HOUSEHOLDS HOUSING CONSTRUCTION HOUSING IMPROVEMENT Health behaviour ILL HEALTH IMMUNIZATION IMPRISONMENT INCOME INDUSTRIES INFANTS INFORMAL CARE INFORMATION SOURCES INJURIES India LABOUR DISPUTES LAND OWNERSHIP LANGUAGE SKILLS LAVATORIES LEARNING LIFE EVENTS LITERACY LIVESTOCK MARITAL STATUS MARRIAGE DISSOLUTION MEALS MEMBERSHIP MEN MOTHER S EDUCATIONA... MOTHERS MOTOR VEHICLES Minorities NUMERACY ORGANIZATIONS PARENTS PAYMENTS PERSONAL FINANCE MA... POLICE SERVICES POVERTY PRE PRIMARY EDUCATION PREGNANCY PREMATURE BIRTHS PRIVATE VOLUNTARY O... PURCHASING Peru QUALITY OF LIFE REFUSE RELIGIOUS AFFILIATION RESIDENTIAL MOBILITY RESPONSIBILITY ROOMS RURAL AREAS SCHOOLCHILDREN SCHOOLS SELLING SHOPS SIBLINGS SMALL BUSINESSES SOCIAL CAPITAL SOCIAL CLASS SOCIAL NETWORKS SOCIAL SECURITY BEN... SOCIAL SUPPORT SPOUSES STANDARD OF LIVING STRUCTURAL ELEMENTS... STUDENT ATTITUDE STUDENT BEHAVIOUR STUDENT TRANSPORTATION SYMPTOMS Social and occupati... Social behaviour an... Social conditions a... Specific social ser... TELEPHONES THEFT TIME BUDGETS TRADE UNION MEMBERSHIP TRUANCY TRUST Time use UNITS OF MEASUREMENT URBAN AREAS Vietnam WATER POLLUTION WEIGHT PHYSIOLOGY YOUTH Youth inequality and soci...