Since 2013, the Federal Reserve Board has conducted the Survey of Household Economics and Decision-making (SHED), which measures the economic well-being of U.S. households and identifies potential risks to their finances. The survey includes modules on a range of topics of current relevance to financial well-being including credit access and behaviors, savings, retirement, economic fragility, and education and student loans.

“Sustainable consumption” defines a comprehensive measure of household economic well-being that integrates income, assets, debt, transfers, and rates of return to estimate a feasible lifetime consumption path. We find that sustainable consumption anchors actual spending, with deviations in one period adjusting back toward the sustainable level in subsequent periods. After the Great Recession, sustainable consumption fell more than actual consumption, in part due to lower real asset returns. Decomposing sustainable consumption into its components reveals primary support from taxable income, but its share has declined while Social Security’s has grown. Substantial differences are also evident across race-ethnicity and educational levels.

he Survey of Consumer Finances (SCF) is a periodic survey sponsored by the U.S. Federal Reserve, conducted every three years. Its primary purpose is to gather comprehensive information on the financial status of households in the United States. Covering diverse topics such as income, assets, liabilities, and demographic characteristics, the SCF aims to present a detailed and accurate portrayal of the economic well-being of American families. This valuable dataset serves as a crucial resource for researchers, policymakers, and the public, enabling in-depth analysis of economic dynamics, wealth distribution, and income inequality trends over time. Through a combination of detailed interviews and supplementary surveys, the SCF plays a significant role in enhancing our understanding of the economic landscape within U.S. households.

The Survey of Consumer Finances (SCF) dataset, provided by the Federal Reserve, offers comprehensive insights into the financial condition of U.S. households. This dataset is invaluable for researchers, policymakers, and analysts interested in understanding consumer behavior, wealth distribution, and economic trends in the United States.

The SCF dataset includes detailed information on household income, assets, liabilities, and various demographic characteristics. It is collected every three years and serves as a crucial resource for analyzing the financial well-being of American families.

Key Features: Income Data: Information on various sources of income, including wages, investments, and government assistance. Asset Ownership: Detailed accounts of household assets, such as real estate, retirement accounts, stocks, and other investments. Liabilities:Comprehensive details on household debts, including mortgages, credit card debts, and student loans. Demographics: Data covering age, education, race, and family structure, allowing for nuanced analysis of financial trends across different segments of the population.

Use Cases: Economic research and analysis, Policy formulation and assessment, Understanding wealth inequality, Consumer behavior studies

Citing the Dataset:

When using this dataset in your research, please ensure to cite the Federal Reserve Board and the SCF as the original source.

Note: The dataset is intended for educational and research purposes. Users are encouraged to adhere to ethical guidelines when analyzing and interpreting the data.

This paper examines the association between the Great Recession and real assets among families with young children. Real assets such as homes and cars are key indicators of economic well-being that may be especially valuable to low-income families. Using longitudinal data from the Fragile Families and Child Wellbeing Study (N = 4,898), we investigate the association between the city unemployment rate and home and car ownership and how the relationship varies by family structure (married, cohabiting, and single parents) and by race/ethnicity (White, Black, and Hispanic mothers). Using mother fixed-effects models, we find that a one percentage point increase in the unemployment rate is associated with a -0.5 percentage point decline in the probability of home ownership and a -0.7 percentage point decline in the probability of car ownership. We also find that the recession was associated with lower levels of home ownership for cohabiting families and for Hispanic families, as well as lower car ownership among single mothers and among Black mothers, whereas no change was observed among married families or White households. Considering that homes and cars are the most important assets among middle and low-income households in the U.S., these results suggest that the rise in the unemployment rate during the Great Recession may have increased household asset inequality across family structures and race/ethnicities, limiting economic mobility, and exacerbating the cycle of poverty.

United States US: Number of People Spending More Than 10% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data was reported at 15,100,000.000 Person in 2013. This records a decrease from the previous number of 15,700,000.000 Person for 2012. United States US: Number of People Spending More Than 10% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data is updated yearly, averaging 16,450,000.000 Person from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 21,800,000.000 Person in 1998 and a record low of 13,900,000.000 Person in 2008. United States US: Number of People Spending More Than 10% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure 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: Poverty. Number of people spending more than 10% of household consumption or income on out-of-pocket health care expenditure; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Sum;

U.S. Pandemic Mental Health Care

Impact on Households in Previous 4 Weeks

By US Open Data Portal, data.gov [source]

About this dataset

This U.S. Household Pandemic Impacts dataset assesses the mental health care that households in America have been receiving over the past four weeks during the Covid-19 pandemic. Produced by a collaboration between the U.S. Census Bureau, and five other federal agencies, this survey was designed to measure both social and economic impacts of Covid-19 on American households, such as employment status, consumer spending trends, food security levels and housing disruptions among other important factors. The data collected was based on an internet questionnaire which was conducted through emails and text messages sent to randomly selected housing units from across America linked with email addresses or cell phone numbers from the Census Bureau Master Address File Data; all estimates comply with NCHS Data Presentation Standards for Proportions. Be sure to check out more about how U.S Government Works for further details!

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How to use the dataset

This dataset can be useful to examine the impact of the Covid-19 pandemic on access to and utilization of mental health care by U.S. households in the last 4 weeks.

By studying this dataset, you can gain insight into how people’s mental health has been affected by the pandemic and identify trends based on population subgroups, states, phases of the survey and more.

Instructions for Use: - To get started, open up ‘csv-1’ found in this dataset. This file contains information on access to and utilization of mental health care by U.S households in the last 4 weeks, broken down into 14 different columns (e.g., Indicator, Group, State).
- Familiarize yourself with each column label (e.g., Time Period Start Date), data type (e

Research Ideas

• Analyzing the impact of pandemic-induced stress on different demographic groups, such as age and race/ethnicity.
• Comparing the mental health care services received in different states over time.
• Investigating the correlation between socio-economic status and access to mental health care services during Covid-19 pandemic

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: csv-1.csv | Column name | Description | |:---------------------------|:-------------------------------------------------------------------| | Indicator | The type of indicator being measured. (String) | | Group | The group (by age, gender or race) being measured. (String) | | State | The state where the data was collected. (String) | | Subgroup | A narrower level categorization within Group. (String) | | Phase | Phase number reflective of survey iteration. (Integer) | | Time Period | A label indicating duration captured by survey period. (String) | | Time Period Label | A label indicating duration captured by survey period. (String) | | Time Period Start Date | Beginning date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Time Period End Date | End date for surveyed period. (DateFormat ‘YYYY-MM-DD’) | | Value | The value of the indicator being measured. (Float) | | LowCI | The lower confidence interval of the value. (Float) | | HighCI | The higher confidence interval of the value. (Float) | | Quartile Range | The quartile range of the value. (String) | | Suppression Flag | A f...

Income, consumption and wealth (ICW) statistics are experimental statistics computed by Eurostat through the statistical matching of three data sources: the EU Statistics on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS) and the Household Finance and Consumption Survey (HFCS). These statistics enable us to observe at the same time the income that households receive, their expenditures and their accumulated wealth.

The annual collection of EU-SILC was launched in 2003 and is governed by Regulation 1700/2019 (previously: Regulation 1177/2003) of the European Parliament and of the Council. The EU-SILC collects cross-sectional and longitudinal information on income. HBS is a survey conducted every 5 years on the basis of an agreement between Eurostat, the Member States and EFTA countries. Data are collected using national questionnaires and, in most cases, expenditure diaries that respondents are asked to keep over a certain period of time. HFCS collects information on assets, liabilities, and to a limited extent income and consumption, of households. The survey is run by National Central Banks and coordinated by the European Central Bank.

This page focuses on the main issues of importance for the use and interpretation of ICW statistics. Information on the primary data sources can be found on the respective EU-SILC and HBS metadata pages and following the links provided in the sections 'related metadata' and 'annexes' below.

Experimental ICW statistics cover six topics: household economic resources, affordability of essential services, saving rates, poverty, household characteristics and taxation. Each topic contains several indicators with a number of different breakdowns, mainly by income quantile, by the age group of the household reference person, by household type, by the educational attainment level of the reference person, by the activity status of the reference person and by the degree of urbanization of the household. The indicators provide information on the joint distribution of income, consumption and wealth and the links between these three economic dimensions. They help to describe households' economic vulnerability and material well-being. They also help to explain the dynamics of wealth inequalities.

All indicators are to be understood to describe households, not persons. Breakdowns by age group, educational attainment level and activity status refer to the household reference person, which is the person with the highest income. The only exception are the tables icw_pov_01, icw_pov_10, icw_pov_11 and icw_pov_12 for which the income, consumption and wealth of households have been equivalised such that equal shares were attributed to each household member. Values in tables icw_aff are calculated for households reporting non-zero values only.

Note on table icw _res_01 and icw_res_02: The indicator “Households” [HH] in icw_res_01 shows the share of households in the selection, which hold the corresponding shares of total disposable income [INC_DISP], consumption expenditure [EXPN_CONS] and net wealth [WLTH_NET] of the entire population. In theory, turning two of the three dimensions [quant_inc, quant_expn, quant_wlth] to TOTAL and the third one to any quintile, should result into a share of 20% of households. Nevertheless, this share is often below or above 20% of the total population of households in the country. The reason for this is that our figures are based on sample surveys. This means that the share of households corresponds indeed to 20% of households in the sample, however when we multiply each household of the sample with its sampling weight, the resulting shares of households in the total population differ from the 20%. If, for example, we disregard the income and wealth of households in our sample, the first consumption quintile contains the 20% of households with lowest consumption in the sample. However, multiplying this selection of households with their corresponding sampling weights may result into a different share of the total population. The “Households” [HH] indicator indicates the real share of households in the population that make up the theoretical quintile.

Graph and download economic data for Real Personal Consumption Expenditures: Services: Household Consumption Expenditures (for Services): Health Care (DHLCRL1Q225SBEA) from Q2 1959 to Q2 2025 about health, PCE, consumption expenditures, consumption, households, personal, services, real, GDP, rate, and USA.

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

United States US: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data was reported at 2,469,000.000 Person in 2013. This records a decrease from the previous number of 2,689,000.000 Person for 2012. United States US: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data is updated yearly, averaging 2,639,500.000 Person from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 3,041,000.000 Person in 2000 and a record low of 2,201,000.000 Person in 2008. United States US: Number of People Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Number of people spending more than 25% of household consumption or income on out-of-pocket health care expenditure; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Sum;

Graph and download economic data for Real Personal Consumption Expenditures: Services: Household Consumption Expenditures (for Services): Health Care (DHLCRL1A225NBEA) from 1930 to 2024 about health, PCE, consumption expenditures, consumption, households, personal, services, real, GDP, rate, and USA.

Abstract

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS3 drew its sample from IFLS1, IFLS2, IFLS2+. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly costeffectiveness reasons, 14 of the then existing 27 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi).

Household Survey:

Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA.This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

IFLS3 Re-Contact Protocols The sampling approach in IFLS3 was to re-contact all original IFLS1 households having living members the last time they had been contacted, plus split-off households from both IFLS2 and IFLS2+, so-called target households (8,347 households-as shown in Table 2.1*) Main field work for IFLS3 went on from June through November, 2000. A total of 10,574 households were contacted in 2000; meaning that they were interviewed, had all members died since the last time they were contacted, or had joined another IFLS household which had been previously interviewed (Table 2.1*). Of these, 7,928 were IFLS3 target households and 2,646 were new split-off households. A 95.0% re-contact rate was thus achieved of all IFLS3 "target" households. The re-contacted households included 6,800 original 1993 households, or 95.3% of those. Of IFLS1 households, somewhat lower re-contact rates were achieved in Jakarta, 84.5%, and North Sumatra,

In this project, we construct an extended well-being variable in the US from 1999 to 2019, transforming household wealth into an income flow to approximate total consumption possibilities. Subsequently, we calculate the individual propensity to suffer well-being losses in the short term to assess economic insecurity. The dataset presents all variables necessary to construct the extended well-being variable, as well as the economic insecurity measure.

Income, consumption and wealth (ICW) statistics are experimental statistics computed by Eurostat through the statistical matching of three data sources: the EU Statistics on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS) and the Household Finance and Consumption Survey (HFCS). These statistics enable us to observe at the same time the income that households receive, their expenditures and their accumulated wealth.

The annual collection of EU-SILC was launched in 2003 and is governed by Regulation 1700/2019 (previously: Regulation 1177/2003) of the European Parliament and of the Council. The EU-SILC collects cross-sectional and longitudinal information on income. HBS is a survey conducted every 5 years on the basis of an agreement between Eurostat, the Member States and EFTA countries. Data are collected using national questionnaires and, in most cases, expenditure diaries that respondents are asked to keep over a certain period of time. HFCS collects information on assets, liabilities, and to a limited extent income and consumption, of households. The survey is run by National Central Banks and coordinated by the European Central Bank.

This page focuses on the main issues of importance for the use and interpretation of ICW statistics. Information on the primary data sources can be found on the respective EU-SILC and HBS metadata pages and following the links provided in the sections 'related metadata' and 'annexes' below.

Experimental ICW statistics cover six topics: household economic resources, affordability of essential services, saving rates, poverty, household characteristics and taxation. Each topic contains several indicators with a number of different breakdowns, mainly by income quantile, by the age group of the household reference person, by household type, by the educational attainment level of the reference person, by the activity status of the reference person and by the degree of urbanization of the household. The indicators provide information on the joint distribution of income, consumption and wealth and the links between these three economic dimensions. They help to describe households' economic vulnerability and material well-being. They also help to explain the dynamics of wealth inequalities.

All indicators are to be understood to describe households, not persons. Breakdowns by age group, educational attainment level and activity status refer to the household reference person, which is the person with the highest income. The only exception are the tables icw_pov_01, icw_pov_10, icw_pov_11 and icw_pov_12 for which the income, consumption and wealth of households have been equivalised such that equal shares were attributed to each household member. Values in tables icw_aff are calculated for households reporting non-zero values only.

Note on table icw _res_01 and icw_res_02: The indicator “Households” [HH] in icw_res_01 shows the share of households in the selection, which hold the corresponding shares of total disposable income [INC_DISP], consumption expenditure [EXPN_CONS] and net wealth [WLTH_NET] of the entire population. In theory, turning two of the three dimensions [quant_inc, quant_expn, quant_wlth] to TOTAL and the third one to any quintile, should result into a share of 20% of households. Nevertheless, this share is often below or above 20% of the total population of households in the country. The reason for this is that our figures are based on sample surveys. This means that the share of households corresponds indeed to 20% of households in the sample, however when we multiply each household of the sample with its sampling weight, the resulting shares of households in the total population differ from the 20%. If, for example, we disregard the income and wealth of households in our sample, the first consumption quintile contains the 20% of households with lowest consumption in the sample. However, multiplying this selection of households with their corresponding sampling weights may result into a different share of the total population. The “Households” [HH] indicator indicates the real share of households in the population that make up the theoretical quintile.

IntroductionThe COVID-19 pandemic may constitute a traumatic event for families with young children due to its acute onset, the unpredictable and ubiquitous nature, and the highly distressing disruptions it caused in family lives. Despite the prevalent challenges such as material hardships, child care disruptions, and social isolation, some families evinced remarkable resilience in the face of this potentially traumatic event. This study examined domains of changes perceived by parents of young children that were consistent with the post-traumatic growth (PTG) model as factors that facilitate family resilience processes.MethodsThis study drew data from the RAPID project, a large ongoing national study that used frequent online surveys to examine the pandemic impact on U.S. households with young children. A subsample of 669 families was leveraged for the current investigation, including 8.07% Black, 9.57% Latino(a), 74.44% non-Latino(a) White families, and 7.92% households of other racial/ethnic backgrounds. In this subsample, 26.36% were below 200% federal poverty level.ResultsApproximately half of the parents reported moderate-to-large degrees of changes during the pandemic, and the most prevalent domain of change was appreciation of life, followed by personal strengths, new possibilities, improved relationships, and spiritual growth. Black and Latino(a) parents reported more changes in all five domains than White parents and more spiritual growth than parents of the other racial/ethnic groups. Moreover, parent-reported improved relationships were found to indirectly reduce young children’s overall fussiness/defiance and fear/anxiety symptoms through reducing parents’ emotional distress. Perceived changes in the new possibilities, personal strengths, and appreciation of life domains were found to serve as protective factors that buffered the indirect impacts of material hardship mean levels on child behavioral symptoms via mitigating parents’ emotional distress.DiscussionThese findings shed light on resilience processes of a family system in a large-scale, disruptive, and stressful socio-historical event such as the COVID-19 pandemic. The five PTG domains could inform therapeutic and intervention practices in the face of future similar events. Importantly, these findings and the evinced family resilience should not negate the urgent needs of policy and program efforts to address material hardships, financial instabilities, and race/ethnicity-based structural inequalities for families of young children.

United States HHNO: saar: FIN: NAFA: IPS: Retiree Health Care Funds data was reported at 16.614 USD bn in Mar 2018. This records a decrease from the previous number of 17.027 USD bn for Dec 2017. United States HHNO: saar: FIN: NAFA: IPS: Retiree Health Care Funds data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 53.017 USD bn in Sep 2011 and a record low of -16.984 USD bn in Jun 2011. United States HHNO: saar: FIN: NAFA: IPS: Retiree Health Care Funds data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB075: Integrated Macroeconomic Accounts: Households and Nonprofit Institution Serving Households.

Abstract (en): This is a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to help link individuals to the core files. Topical module data for the 1992 Panel cover the following topics: Topical Module 1 -- welfare and other aid recipiency and employment, Topical Module 2 -- work disability, education and training, marital status, migration, and fertility histories, Topical Module 3 -- extended measures of well-being, including consumer durables, living conditions, and basic needs, Topical Module 4 -- assets and liabilities, retirement expectations and pension plan coverage, real estate, property, and vehicles, Topical Module 5 -- school enrollment and financing, Topical Module 6 -- work schedules, child care, support for nonhousehold members, functional limitations and disabilities, utilization of health care services, and home-based self-employment and size of firm, Topical Module 7 -- selected financial assets, medical expenses and work disability, real estate, shelter costs, dependent care, and vehicles, Topical Module 8 -- school enrollment and financing, Topical Module 9 -- work schedule, child care, child support agreements, child support, support for nonhousehold members, functional limitations and disability, utilization of health care, functional limitations and disability of children, health status and utilization of health care services, and utilization of health care services for children. Parts 26 and 27 are the Wave 5 and Wave 8 Topical Module Microdata Research Files obtained from the Census Bureau. These two topical module files include data on annual income, retirement accounts and taxes, and school enrollment and financing. These topical module files have not been edited nor imputed, although they have been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. Resident population of the United States, excluding persons living in institutions and military barracks. A multistage, stratified sampling design was used. One-fourth of the sample households were interviewed each month, and households were reinterviewed at four-month intervals. All persons at least 15 years old who were present as household members at the time of the first interview were included for the entire study, except those who joined the military, were institutionalized for the entire study period, or moved from the United States. Original household members who moved during the study period were followed to their new residences and interviewed there. New persons moving into households of members of the original sample also were included in the survey, but were not followed if they left the household of an original sample person. 2002-11-08 Part 26, Wave 5 Topical Module Microdata Research File, and Part 27, Wave 8 Topical Module Research File, have been added to the collection with corresponding PDF documentation. These topical module files have not been edited nor imputed, although they have been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities.1998-08-24 Part 17, Wave 5 Topical Module Microdata File, and Part 25, Wave 9 Topical Module Microdata File, have been added to the collection with corresponding PDF documentation. Beginning with the 1990 Panel, the file structure of SIPP was changed. The un...

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);;

Graph and download economic data for Receipts from sales of goods and services by nonprofit institutions serving households: Health (DHLRRC1A027NBEA) from 1959 to 2024 about receipts, nonprofit organizations, health, sales, households, goods, services, GDP, and USA.