Consumer Spending in the United States increased to 16445.70 USD Billion in the second quarter of 2025 from 16345.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Context

USASpending.gov is the government's official tool for tracking spending, it shows where money goes and who benefits from federal funds.

The Federal Funding Accountability and Transparency Act of 2006 required that federal contract, grant, loan awards over $25k be searchable online to give the American public access to government spending. The data that is collected in USAspending.gov is derived from data gathered at more than a hundred agencies, as well as other government systems. Federal agencies submit contracts, grants, loans and other awards information to be uploaded on USAspending.gov at least twice a month.

Content

The United States spends a lot of money on contracts every year but where does it all go? This data set has information about how much different agencies have spent on awards for the fiscal year 2021. More data can be downloaded, for other years, on USAspending.gov.

Contracts are published to the GSA's Federal Procurement Data System within five days of being awarded, with contract reporting automatically getting posted on USAspending.gov by 9 AM the next day and going live at 8:00 am EST two mornings later

Learn more about the contents here: https://www.usaspending.gov/data-dictionary

The Bureau of the Fiscal Service, United States Department of the Treasury, is dedicated to making government spending data available to everyone.

Data Description

This data starts off separated into smaller files that need to be joined.

Data Overview

The federal government buys a lot of things, like office furniture and aircraft. It also buys services, like telephone and Internet access. The Federal Government and its sub-agencies use contracts to buy these things. They use Product and Service Codes (PSC) to classify the items and services they purchase.

An obligation is a promise to spend money. An outlay is when the government spends money. When the government enters into a contract or grant, it promises to spend all of the money. This is so it can pay people who do what they agreed to do. When the government actually pays someone, then it counts as an outlay.

Data Items that Help get Started

There are many different variables in this database, which are spread across multiple files. The most important ones to start learning are:

1. The contractor who won the award - recipient_name
2. The agency issuing the award - awarding_agency_name
3. The product or service code (PSC) - product_or_service_code
4. The industry classification code (NAICS) of the vendor - naics_code
5. How much was obligated - total_dollars_obligated or total_obligated_amount
6. The contract modification number - modification_number
7. The description of the award - award_description
8. The date of award - action_date or award_base_action_date

Data Dictionary and Analyst Guide

To learn more about the data, you can reference the data dictionary. The data dictionary includes information on outlays, which are not included in the data provided here. https://www.usaspending.gov/data-dictionary

Please see the analysts guide for more information: https://datalab.usaspending.gov/analyst-guide/

License

The U.S. Department of the Treasury, Bureau of the Fiscal Service is committed to providing open data to enable effective tracking of federal spending. The data is available to copy, adapt, redistribute, or otherwise use for non-commercial or for commercial purposes, subject to the Limitation on Permissible Use of Dun & Bradstreet, Inc. Data noted on the homepage. https://www.usaspending.gov/db_info

Acknowledgements

USAspending.gov collects data from all over the government to provide information to the public. Special thanks for the Data Transparency Team within the Office of the Chief Data Officer at the Bureau of Fiscal Services.

Inspiration

Can we find any patterns to help the public? How about predicting future spending needs or opportunities? Test out your ideas here!

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

From the website: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DHMZOW

The world has become much more peaceful, and yet, even after adjusting for inflation, global military spending is now three times greater than at the height of the Cold War. These developments have motivated a renewed interest from both policy makers and scholars about the drivers of military spending and the implications that follow. Existing findings on the relationship between threat and arming and arms races and war hinge on the completeness and accuracy of existing military spending data. Moreover, data on military spending is used to measure important concepts from international relations such as the distribution of power, balancing, the severity of states’ military burdens, and arms races. Everything we know about which states are most powerful, whether nations are balancing, and whether military burdens and arms races are growing more or less severe rests on the accuracy of existing military spending estimates.

Data is plural description: Global military spending. How much money has each country spent, each year, on its military? Different datasets have different answers, cover different timeframes, and use different methodologies. Miriam Barnum et al.’s Global Military Spending Dataset attempts to bring them together. By uniting “76 variables from 9 dataset collection projects,” the authors write, “we provide the most comprehensive and complete set of published datasets on military spending ever assembled.” Each of the variables represents one source/methodology, and each observation is a country-year. “Disagreement on the actual expenditure value for a given country-year is common, even between datasets produced by the same project,” they find. Previously: The Stockholm International Peace Research Institute’s Military Expenditure Database (DIP 2017.03.29), one of the sources.

The San Francisco Controller's Office maintains a database of budgetary data that appears in summarized form in each Annual Appropriation Ordinance (AAO). This data is presented on the Budget report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. New data is added on an annual basis when the AAO is published for each new fiscal year. Data is available from fiscal year 2010 forward. The City and County of San Francisco's budget is a two-year plan for how the City government will spend money with available resources. In the budget process, a budget is proposed by the Mayor, and then modified and approved by the Board of Supervisors as the Appropriation Ordinance. Each year, the City will update the Budget for the upcoming fiscal year and also set a budget for the subsequent fiscal year, which will be updated and approved in the following year. Enterprise departments do not submit a budget for the second year of the two year budget; rather, estimates of enterprise department budgets in the second year of the budget are incorporated into high-level spending and revenue figures. This dataset and the Appropriation Ordinance departmental views answer the question "How much does each department spend?". To show how much is spent by departments from the General Fund we make the following adjustments to the regular revenues and fund balance & reserves: + Transfers from one department to another (leaving out transfers within the same department) + Recoveries from one department to another (leaving out recoveries within the same department) - GF spent in other funds (this is deducted from GF Sources and added to the other fund's Sources) This is the gross total. By removing the transfers and recoveries that go from one department to the another we see the same net total that is in the Appropriation Ordinance Consolidated Schedule of Sources and Uses. Note that the amount added for transfers into the General Fund that move from one department to another is different than the amount deducted to eliminate the double counting caused by transfers. Transfer Adjustments: To meet accounting needs, money can be moved from one fund or department to another. For example, Public Works provides building maintenance services for the Fire Department for which the Fire Department pays Public Works. To solve this double counting problem, this dataset shows a reduction of $100,000 called Transfer Adjustments (Citywide) to the budgeted spending & revenue for the department providing the service. This lets the dataset display both the gross total of activity for both departments and the net total use of City and County revenues. In the example above, the money is moving both between departments, from Fire to Public Works, and between funds, from General Fund Operating to General Fund Works Orders/Overhead. Transfer Adjustments (Citywide): -Transfer Adjustments (Citywide) are used when money is moved from one department to another. These are deducted from the gross total to create the net total. -Transfer Adjustments are included in the gross total when they are within the same department. A separate sub-object is used to distinguish departmental Transfer Adjustments from Transfer Adjustments (Citywide). -Transfer Adjustments (Citywide) may differ from transfer adjustment lines in other public reports as a result of different approaches used to report transfers; however, the net total will remain the same across this dataset, the Mayor's Budget Book, and the Appropriations Ordinance, with limited exceptions due to error corrections and different methodologies used to present net totals. For more information, contact us. An example of a Transfer Adjustment within a department would be Public Works overhead allocations. Overhead costs cannot easily be isolated to a direct service or unit and so are allocated across those units using accepted accounting methods. Central m

The National Archives is the UK government's official archive. We are guardians of some of the most iconic national documents dating back over 1,000 years. We give detailed guidance to government departments and the public sector on information management and advise others about the care of historical archives. Our role is to collect and secure the future of the record, both digital and physical, to preserve it for generations to come, and to make it as accessible and available as possible. Find out how we spend our money, where we receive funding, what income we generate, and how this compares with last year's spending and income with the diagrams and data

This performance measure had changed to reflect a different approach to tracking how the City is determining the success of its Capital Improvement Program (CIP). Historically, the measure tracked spending on CIP construction projects each year. However, as economic conditions may change significantly from the time the CIP program is developed to when project spending actually occurs, the capacity to complete all programs as envisioned may be impacted. As a result, new criteria and data has been developed to track how reliably projects are completed starting after a project’s design phase and when construction has begun, which is more under the City’s control.The City’s Capital Improvement Program (CIP) strives to optimize the use of limited resources, both financial and operational, to best meet the capital needs of the City. One key factor in assessing the City’s fiscal capacity to fund projects is the ability to determine the amount of budgeted funds that will be spent or encumbered in the fiscal year. Sound Capital planning includes accurate project cost estimates and timing the funding of projects to coincide with the organization’s capacity to complete the project. These practices limit the amount of funds tied up but not used in a fiscal year, which could have been used for other needs.This page provides data for the CIP Funds Spent or Encumbered performance measure.Provides quarterly updates on the progress on the percent of the annual CIP budget that has been spent or encumbered. Data includes the budget, expenditures and encumbrances.The performance measure dashboard is available at 5.09 CIP Funds Spent or Encumbered.Additional InformationSource: PeopleSoft FinancialsContact: Benicia BensonContact E-Mail: Benicia_Benson@tempe.govData Source Type: ExcelPreparation Method: Data is downloaded from PS Financials via a query. Budgeted amounts for contingency are deducted to derive the measurable budget amount.Publish Frequency: QuarterlyPublish Method: ManualData Dictionary

Consumer Spending in Portugal increased to 38675.30 EUR Million in the third quarter of 2025 from 38223 EUR Million in the second quarter of 2025. This dataset provides - Portugal Consumer Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.

Taiwan number dataset will help you generate sales leads. First of all, people can start text with product info and descriptions and send buyers through this dataset. In fact, driving a telemarketing campaign is required at present. Moreover, you can literally call and message with the help of this Taiwan number dataset. Also, the Taiwan number dataset is crucial to let your audience know of the features and uses of your product. Above all, by doing this people can easily increase their marketing area. Even, they can create a bond with tier client and gain their trust with this mobile cell phone number list. Taiwan phone data has the potential to get valuable customers. A businessman will be able to earn more money without spending too much on ads. The SMS marketing plan is the best option, that possible to run promotions cheaply here. So, take the contact number directory at an affordable cost and try it for your help. Taiwan phone data will sustain your telemarketing with useful details. On the other hand, if anyone needs to reach someone as soon as possible, then the phone number is the best choice. Besides, you can directly send messages to their inbox through these datasets. Therefore, the numbers on our Taiwan phone data will aid your marketing efforts greatly. Overall, you can use List To Data for your product publicity so that you can find curious buyers among them. Taiwan phone number list is a top-notch mobile database. Likewise, the List To Data website is obstinate about giving our clients the best service for their money. Mainly, we have organized a 24/7 active support group to ensure that. You can ask them anything about this package, or even bring 95% real samples of the lead from them. Both your branding and sales will be enhanced with this Taiwan phone number list. Hence, make a good conclusion for your business and collect this lead right now. Further, the Taiwan phone number list will let you continue to promote any products all across the country. The user count of these platforms is so big that even that provides you with such a big customer base. Clearly, this will surely raise the possibility of finding interested customers for your benefit.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4560787%2F1bf7d8acca3f6ca6adbae87c95df1f33%2F1_MIXrCZ0QAVp6qoElgWea-A.jpg?generation=1697784111548502&alt=media" alt="">

Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

A. SUMMARY This dataset contains data from financial statements of state committees that (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC during the 90 days before an election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period, an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the committees that file with the California Secretary of State. The data comes from a nightly export of the Secretary of State's database. A second dataset includes Non-Primarily Formed committees that file with the San Francisco Ethics Commission. C. UPDATE PROCESS This dataset is rewritten nightly based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F496, or F497P2 represent expenditures or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are also in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee's amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the California Fair Political Practices Commission. Depending on timing, transactions which have occurred may not be listed as they might not have been reported yet. E. RELATED DATASETS <a href=

The government is committed to setting new standards for transparency so the public can more easily see how and where taxpayers’ money is being spent and hold politicians, government departments and public bodies to account.

All central government departments must publish details of their spending over £25,000 and publish monthly information.

From January 2016 both administration and programme spend are now collected under the same code so appear as a single spreadsheet. See https://data.gov.uk/dataset/uk-trade-and-investment-spend

As part of the Department's aim to make more of its statistics available we will be publishing schools' expenditure for 2009-10 from the Consistent Financial Reporting exercise. This is in support of the Coalition Government's data transparency agenda, making data more accessible to the general public. The information to be published provides details on how schools spend their money and will help schools and others compare the extent to which they deliver value for money. It will also equip parents with the information they need to ask questions of schools. The data will be published online (via the Department for Education website) in downloadable tables and will also include headline Key Stage 2 and Key Stage 4 performance indicators.

Government Spending in Brazil increased to 59493.03 BRL Million in the second quarter of 2025 from 57326.04 BRL Million in the first quarter of 2025. This dataset provides - Brazil Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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. 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.

This database summarises central government expenditures on housing in Hungary, from the official Budget Acts and Closing Accounts Acts and other sources (e.g. Hungarian Central Statistical Office).

The aim of the database was to determine how much money the government has spent on housing since 1990 and how much of this expenditure was socially targeted, i.e. how much was used to reduce housing poverty. The database organises publicly available central budget data into a machine-readable database. Thus, it is also making the government’s management of public funds more transparent.

The database was created for Habitat for Humanity Hungary’s Annual Report on Housing Poverty 2021 (Hungarian / English), updated for the 2024 report. Further methodological details are included in the 2021 annual report.

File list

description.rtf - Detailed description of the database (in Hungarian and English).

lakaskiadasok_values_long.csv - Values of the detailed budget expenditures. Data for each year are in the rows of the table. Budget expenditures were grouped. Data sources are also included in this table. Expenditures are presented in both current and 2024 prices.

lakaskiadasok_values_wide.csv - Values of the detailed budget expenditures. Data for each year are in the columns of the table. Budget expenditures were grouped. Data sources are also included in this table. Expenditures are presented in both current and 2024 prices.

lakaskiadasok_variables.csv - Description of the variables of the detailed budget expenditures, both in English and in Hungarian.

lakastamogatasok_values.csv - Values of the database on the ‘Housing subsidies’ budget line and the data source of each cell. Expenditures are presented in both current and 2024 prices.

lakastamogatasok_variables.csv - Description of the variables on the expenditures of the ‘Housing subsidies’ budget line, both in English and Hungarian.

price_deflator.csv - Value of the consumer price index used for the conversion to 2024 prices (2024=100).

The dataset and its description is fully bilingual (Hungarian and English).

This dataset contains budget and spending data for City Initiatives that use American Rescue Plan Act of 2021 (ARPA) or Coronavirus Response and Relief Supplemental Appropriations Act of 2021 (CRRSAA) federal funds. Each row is a different "Initiative Detailed", which is IBO's understanding of the purpose of the funding. IBO developed seven categories of initiative to standardize the comparison of budgeted and spent amounts across agencies: "Initiative Category". The IBO definitions of these categories are below, in order of prioritization (e.g. if a budget code fits the definition of 2. COVID Response – Public Programs and 4. Programmatic Support, it is listed under 2). If readers require more detailed information on spending, the underlying data with the previous initiative names is available for download. Covid Response – City Operations: Spending to keep city agencies operating during the Covid pandemic, such as city employee leave for quarantining and vaccinations, air purifiers, personal protective equipment (PPE) for city employees, etc. Covid Response – Public Programs: Programs created to protect people in New York City from Covid-19. Direct Human Services: Public services provided to meet the financial, physical, or mental needs of New York City residents. This includes ongoing services for housing, food, addiction treatment, childcare, education, anti-poverty, etc. These services are either provided by the government or a nonprofit. Programmatic Support: Funds used on temporary governmental programs. Note: this includes youth training and summer work programs because they are optional and extra-curricular, while public education and adult job training programs are direct human services. Government Operations: Federal funding supplemented lost revenue during the pandemic-related recession. These funds are for Other than Personal Spending, those administrative costs or public services provided in perpetuity (as opposed to services defined as Programmatic Support). Personnel/Staffing: Federal funding supplemented lost revenue during the pandemic-related recession. These funds are for salaries and wages paid to city employees, often called “Personal Services.” Salaries and wages related to Covid response, temporary programs, and direct human services are excluded from this category. Hiring & Attrition Management: Administrative costs related to managing the inflow and outflow of city employees.

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.

Disposable Personal Income in the United States increased to 23033.50 USD Billion in August from 22947.50 USD Billion in July of 2025. This dataset provides - United States Disposable Personal Income - actual values, historical data, forecast, chart, statistics, economic calendar and news.