This dataset provides values for CONSUMER SPENDING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

In 2024, consumers in the United States expected to spend over one thousand U.S. dollars on holiday gifts on average. This is the first time the projected spending estimate reached that one thousand-dollar-mark. Holiday shopping The Christmas, or holiday season, is the single most critical sales period of the year for many retailers: this period includes days, such as Black Friday and Cyber Monday, and an increasing amount of Americans also shop online during this busy time. An incredible shopping hubbub is produced during this period, with a staggering 95 percent of U.S. consumers having said they intended to buy something during the Christmas season in 2024. Gift cards and vouchers Christmas is a public holiday in the United States and is celebrated on December 25th each year. It is known as a big economic stimulus for many people to purchase Christmas gifts for their beloved family and friends. After Christmas and New Year’s Eve, retail sales often peak again in January as many people redeem their received Christmas gift cards and vouchers. In fact, over half of U.S. consumers planned to buy gift cards or gift certificates for others. It is a popular gifting option, with many Americans indicating that it can be very convenient.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Indexes of real expenditure per capita in the United States relative to those in Canada for categories of gross domestic income (GDI), Canada=100, on an International Comparison Project Classification (ICP) basis.

Retail Sales in the United States increased 0.20 percent in February of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Name: Characterization of investments profiles on the energy transition for european citizens Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. License: cc-BY-SA Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943. Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein. Collection Date: 22/07/2022 Publication Date: 15/10/2023 DOI: 10.5281/zenodo.4455198 Other repositories: Author: University of Deusto Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions. Description: The dataset contains a ODS spreadsheet file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset Section 1 - Scenarios for energy transition. ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario ID191, ID192.. Amount of money people would spend based on an assumed case. ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you. [ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary. [ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary. [ID141, ID5, ID147]. Used for statements that best represent survey responder Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. ID100 Risk profile ID101 Added value ID102 Self-Satisfaction ID103 Technical Fit ID104 Own competence ID105 Knowledge ID106 cost-Efficiency ID107 Safety ID108 Trust ID109 Autarky ID110 Legal ID111 climate protection ID112 Wellbeing ID113 Coziness ID114 Rights and Duties ID115 Peer-Pressure ID116 Socialising ID117 Support ID118 Agreement ID119 Brag ID120 Fun ID121 Novelty ID122 Trends ID123 Authority ID124 Own Significance ID125 Poseur ID2 Frugality ID3 Environmental concerns ID31 Adherence ID52 Commitment ID97 Profits ID99 Credit Score Section 3 - “Socio-economic” description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked ID164 Understanding of questions ID300 Country of residence ID137 Age ID178 Highest level of education ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section) Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) Appliances ID42 Affordable cost of a Regular refrigerator ID45 Energy efficient refrigerator costs ID50 Willingness to purchase an energy efficient refrigerator ID65 Why no ID66 affordable cost of an energy efficient option ID67 Years to amortize an efficient option Insulation ID47 Affordable cost of updating to a state of the art insulation on the facade ID56 Willingness for paying/invest ID74 Why no? ID20 affordable cost of an energy efficient option ID34 Years to amortize an energy efficient option Energy Generation ID68 Affordable cost of a solar photovoltaic system ID76 Willingness for paying/invest ID84 Why no? ID132 Affordable cost of a photovoltaic system ID138 Years that amortize a photovoltaic system Energy Storage ID142 Affordable cost of an energy storage system ID146 Willingness for paying/invest ID181 Why no? ID182 Affordable cost of an energy storage system ID183 Years that amortize an energy storage systems Heating ID140 Affordable cost of a gas boiler ID209 Affordable cost of an energy efficient heating system ID217 Willingness for paying/invest ID238 Why no? ID239 Affordable cost of a energy efficient option ID241 Years that amortize a heat pumps Mobility ID41 Average kilometers traveled a typical day ID51 Usual travel option ID264 Affordable cost of a diesel or gasoline mid-range brand new car ID265 Affordable cost of a mid-range brand new electric car ID281 Willingness to buy an electric car ID289 Why no? ID290 Affordable price of an electric car ID291 Years that amortize an electric car Section 5 - Household characterization ID127 Selecting an asked value ID189 Type of living area ID202 Gender identity ID1 Those living in the house ID32 Number of inhabitants ID220 Average neat yearly income ID229 Average monthly saving ID240 Type of housing ID249 Owner / co-owner ID255 Usable area of the property (m²) ID263 Insulation level ID270 Climate zone ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist” ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists” ID88 Level of self-awareness about energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist” ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists” ID190 feedback about survey 5 star: ⭐⭐⭐ Preprocessing steps: anonymization, data fusion, imputation of gaps. Reuse: NA Update policy: No more updates are planned Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing

Attitudes of young people towards science. Topics: interest in each of the following topics: sports, politics, science and technology, economics, culture and entertainment; interest in each of the following subjects: information and communication technologies, earth and environment, universe, medical discoveries, new inventions and technologies; attitude towards selected statements on science and technology: science brings more benefits than harm, help eliminate hunger and poverty around the world, technology creates more jobs than it eliminates, science is too much influenced by profit, make lives healthier and more comfortable; attitude towards the following statements on the purpose of scientific research: should above all serve the development of knowledge, should above all serve economic development, should above all serve businesses and enterprises; awareness about innovations in the following areas of research: genetically modified food, nanotechnology, nuclear energy, mobile phones, human embryo research, brain research, computer and video surveillance techniques; attitude towards risks and advantages of the aforementioned research areas; most effective measures in tackling green-house effect and global warming; expected development in the following areas in the next twenty years in the own country: food quality, quality of air in cities, health, water quality, communication between people; assessment of the health risks of: air pollution caused by cars, pesticides used in plant production, genetically modified foods, fertilizers in underground water, vicinity of nuclear power plants, use of mobile phones, vicinity of high tension power lines, vicinity of chemical plants, new epidemics; preferred authorities to have biggest influence on decisions with regard to financing research: scientific community, government, citizens, private enterprises, research organisations, European Union, media; attitude towards the following statements on scientists: devoted to the good of humanity, dangerous power due to their knowledge; considerations to take up studies in the following fields: natural sciences, mathematics, engineering, biology or medicine, social sciences or humanities, economics; reasons for not taking up studies in the aforementioned fields; preferred kind of scientific profession: researcher in public sector, teacher, researcher in private sector, engineer, technician, health professional; attitude towards selected statements: young people’s interest in science is essential for future prosperity, girls and young women should be encouraged to take up careers in science, science classes at school are not appealing, national government should spend more money on scientific research, EU should spend more money on scientific research, need for better cooperation between member states and EU. Demography: sex; age; highest completed level of full time education; full time student; occupation of main income earner in the household; professional position of main income earner in the household; type of community. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; region; weighting factor. Interesse junger Menschen an Wissenschaft und Technologie. Themen: Interesse an Nachrichten über: Sport, Politik, Wissenschaft und Technologie, Wirtschaft, Kultur und Unterhaltung; Interesse an den Themen: Informations- und Kommunikationstechnologien, Erde und Umwelt, Universum, menschlicher Körper und Medizin, Erfindungen und Technologien; Einstellung zu Wissenschaft und Technologie (Skala): Wissenschaft als Nutzen oder Schaden, Verringerung der Armut, Schaffung von Arbeitsplätzen, Wissenschaft durch Profit beeinflusst, Lebenserleichterung; Zweck von Wissenschaft: Wissensgenerierung, wirtschaftliche Entwicklung, Nutzen für Unternehmen; Kenntnis von Innovationen im Bereich: genetisch veränderten Lebensmitteln, Nanotechnologie, Mobiltelefonie, Atomenergie, Embryonenforschung, Gehirnforschung, Überwachungstechniken sowie Einschätzung der Risiken dieser Forschungsfelder für die Gesellschaft; Lösung des Klimawandels durch Technik, Lebensweise oder Gesetze; Verbesserung der Situation im eigenen Land bei: Lebensmittelqualität sowie der Stadtluft und der Wasserqualität, Gesundheit der Bevölkerung, Kommunikation zwischen Menschen; Einschätzung des Risikos für die Menschheit durch: Luftverschmutzung, Pestizide, genetisch veränderte Lebensmittel, Verschmutzung des Grundwassers durch Düngen, Atomkraft, Mobiltelefone, Hochspannungsleitungen, Chemiewerke, Epidemien; präferierte gesellschaftliche Gruppe mit dem größten Einfluss auf Entscheidungen zur Forschungsfinanzierung; Meinung über Wissenschaftler: hingebungsvolle Menschen, die für das Wohl der Menschheit arbeiten, Gefahr der Wissensmacht; Interesse an einem Studium; Berufsziel; Gründe gegen ein Studium; Meinung zur Bedeutung der Wissenschaft für die Gesellschaft (Skala): entscheidend für zukünftigen Wohlstand, Ermutigung von jungen Leuten, ein wissenschaftliches Studium oder Berufe in der Wissenschaft zu ergreifen, Unattraktivität des Wissenschaftsunterrichts in der Schule, mehr Forschungsförderung durch die eigene Regierung sowie durch die EU, Forderung nach besserer Koordination der Forschung zwischen Mitgliedsstaaten der EU. Demographie: Geschlecht; Alter; höchster Bildungsabschluss; Vollzeitstudent; Beruf des Haupteinkommensbeziehers im Haushalt; berufliche Stellung des Haupteinkommensbeziehers im Haushalt; Urbanisierungsgrad. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Region; Gewichtungsfaktor.

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need. The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were: Wealth creation in terms of income and expenditure dynamics and asset endowments; Demographic dynamics as these relate to household composition and migration; Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments;
Access to cash transfers and social services

Wave 1 of the survey, conducted in 2008, collected the detailed information for the national sample. In 2010/2011 Wave 2 of NIDS re-interviewed these people, gathering information on developments in their lives since they were interviewed first in 2008. As such, the comparison of Wave 1 and Wave 2 information provides a detailed picture of how South Africans have fared over two years of very difficult socio-economic circumstances.

Completed and non-response interviews in the NIDS Data: The NIDS datasets contain both completed and non-response interviews (e.g. Refusals). It is recommended that researchers limit their research to completed interviews to avoid item non-response from non-response interviews. The completed interviews can be identified by making use of the wx'_y'_outcome variables, where x' represents the wave andy' represents the relevant data file/outcome type indicator. These outcome variables can be found in each of the following data files, Adult, Child, Proxy, HHQuestionnaire and Link File. The only exception to this is Wave 1 where no outcome variable exists. This is because at a household level, all of the interviews are completed. However this does not apply at an individual level where non-response interviews can be identified by making use of the "Reason for refusal" variables, namely w1_a_refexpl or w1_c_refexpl in the Adult and Child data files respectively.

Geographic coverage

The NIDS data is nationally representative. The survey began in 2008 with a nationally representative sample of over 28,000 individuals in 7,300 households across the country. The survey is repeated every two years with these same household members, who are called Continuing Sample Members (CSMs). The survey is designed to follow people who are CSMs, wherever they may be in SA at the time of interview. The NIDS data is therefore, by design, not representative provincially or at a lower level of geography (e.g. District Council).

Analysis unit

Households and individuals

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

As in Wave 1 four types of questionnaires were administered in Wave 2:

Household questionnaire: One household questionnaire was completed per household by the oldest woman in the household or another person knowledgeable about household affairs and particularly household spending. Household questionnaires took approximately 45 minutes in non-agricultural households and 70 minutes in agricultural households to complete. Individual Adult questionnaire: The Adult questionnaire was applied to all present Continuing Sample Members and other household member's resident in their households that are aged 15 years or over. This questionnaire took an average of 45 minutes per adult to complete. Individual Proxy Questionnaire: Should an individual qualifying for an Adult questionnaire not be present then a Proxy Questionnaire (a much reduced Adult Questionnaire using third party referencing in the questioning) was taken on their behalf with a present resident adult. On average a Proxy questionnaire took 20 minutes. Proxy Questionnaires were also asked for CSMs who had moved out of scope (out of South Africa or to a non-accessible institution such as prison), except if the whole household moved out of scope, and could therefore not be tracked or interviewed directly. Child questionnaire: This questionnaire collected information about all Continuing Sample Members and residents in their household younger than 15. Information about the child was gathered from the care-giver of the child. The questionnaire focused on the child's educational history, education, anthropometrics and access to grants. This questionnaire took an average of 20 minutes per child to complete.

Phase Two of Wave 2: In June 2011 NIDS commissioned a Phase Two of Wave 2 as a Non-Response Follow-Up from Phase 1 of Wave 2. Household included in this subsample where those that refused and those that could not be located or tracked in Phase 1. Out of a total of 1064 households attempted, an additional 389 households were successfully interviewed in Phase Two.

Questionnaire Differences between W2 Phase 1 & W2 Phase2 There are two important methodological differences between Phase 1 and Phase 2: 1. Not all sections of the original Wave 2 questionnaires were asked. This reduced respondent burden and the time required for fieldworker training. Questions NOT asked in Phase 2 are indicated with the non-response code “-2”. Core modules such as household composition and income were still asked. Consult the Wave 2 Phase 2 questionnaires for more details of these differences. 2. Movers out of Phase 2 dwelling units were not tracked further. Address information was collected for this sub-sample and they will be tracked as part of the Wave 3 fieldwork exercise. These individuals are classified as “Not tracked” in the Wave 2 dataset.

A 2022 survey determined that most diners, 42 percent, spend on average 11 to 20 U.S. dollars on dining out in the United States. Meanwhile eight percent of survey respondents spent 50 U.S. dollars or more.

The United States recorded a Government Debt to GDP of 122.30 percent of the country's Gross Domestic Product in 2023. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This statistic shows a ranking of the estimated per capita consumer spending on healthcare in 2020 in Latin America and the Caribbean, differentiated by country. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 06. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.The shown forecast is adjusted for the expected impact of the COVID-19 pandemic on the local economy. The impact has been estimated by considering both direct (e.g. because of restrictions on personal movement) and indirect (e.g. because of weakened purchasing power) effects. The impact assessment is subject to periodic review as more data becomes available.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This statistic shows a ranking of the estimated current healthcare spending in 2020 in Latin America and the Caribbean, differentiated by country. The spending refers to current spending of both governments and consumers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.