Consumer Spending in the United States increased to 16350.20 USD Billion in the second quarter of 2025 from 16291.80 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.

Personal Spending in the United States increased 0.50 percent in July of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 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 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 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.

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

dataset contains detailed financial and demographic data for 20,000 individuals, focusing on income, expenses, and potential savings across various categories. The data aims to provide insights into personal financial management and spending patterns.

• Income & Demographics:
  • Income: Monthly income in currency units.
  • Age: Age of the individual.
  • Dependents: Number of dependents supported by the individual.
  • Occupation: Type of employment or job role.
  • City_Tier: A categorical variable representing the living area tier (e.g., Tier 1, Tier 2).
• Monthly Expenses:
  • Categories like Rent, Loan_Repayment, Insurance, Groceries, Transport, Eating_Out, Entertainment, Utilities, Healthcare, Education, and Miscellaneous record various monthly expenses.
• Financial Goals & Savings:
  • Desired_Savings_Percentage and Desired_Savings: Targets for monthly savings.
  • Disposable_Income: Income remaining after all expenses are accounted for.
• Potential Savings:
  • Includes estimates of potential savings across different spending areas such as Groceries, Transport, Eating_Out, Entertainment, Utilities, Healthcare, Education, and Miscellaneous.

Consumer Spending in Serbia increased to 1188601.30 RSD Million in the second quarter of 2025 from 1019000.10 RSD Million in the first quarter of 2025. This dataset provides - Serbia Consumer Spending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Spending in El Salvador increased to 28158.34 USD Million in 2024 from 26802.89 USD Million in 2023. This dataset provides - El Salvador 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.

The Government of Canada Spend Data by Department dataset is a summary of procurement expenditure data by department. It is derived from expenditure data submitted annually from over 55 departments and agencies, and is grouped by commodity broken down by family, group, category and subcategory. The data covers about 95 per cent of government’s total expenditure on goods and services. This dataset details the Spend Data from April 2010 through to March 2016, and was compiled through a collaboration of efforts from participating departments and agencies; however since 2017, Public Services and Procurement Canada no longer receives the information centrally, and is therefore unable to continue the publication of this dataset. >Note: the framework used by the Government of Canada to classify commodities has evolved over time. When comparing the breakout of expenditures as reported in this dataset vs those in the earlier Archived, Government of Canada Spend Data dataset, the totals reported across the commodity framework will at times be different for FY2010/2011, FY2011/2012, FY12/13 and FY2013/2014. This is because the data specific to those fiscal years was re-calculated in FY2014/2015 for the release of this newer dataset (in January 2015), using the commodity reporting structure and organisation naming conventions that were current at that time. The overall totals reported for each fiscal year and each department would have remained the same across both reporting frameworks.

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. The CE program is comprised of two separate components, each with its own questionnaire and independent sample: (1) the quarterly Interview Survey, and (2) the Diary Survey. This data collection contains the quarterly Interview Survey data, which was designed to collect data on major items of expense which respondents could be expected to recall for 3 months or longer. Items include relatively large expenditures, such as those for property, automobiles, and major durable goods, and those that occurred on a regular basis, such as rent or utilities. The Interview Survey does not collect data on expenses for housekeeping supplies, personal care products, and nonprescription drugs, which contribute about 5 to 15 percent of total expenditures. The 2013 Interview Survey contains eight groups of Interview data files (FMLI, MEMI, MTBI, ITBI, ITII, NTAXI, FPAR, and MCHI), forty-three Detailed Expenditure (EXPN) files, and processing files. The FMLI, MEMI, MTBI, ITBI, ITII, and NTAXI files are organized by the calendar quarter of the year in which the data were collected. There are five quarterly datasets for each of these files, running from the first quarter of 2013 through the first quarter of 2014 (with NTAXI files starting the second quarter of 2013). The FMLI file contains consumer unit (CU) characteristics, income, and summary level expenditures; the MEMI file contains member characteristics and income data; the MTBI file contains expenditures organized on a monthly basis at the Universal Classification Code (UCC) level; the ITBI file contains income data converted to a monthly time frame and assigned to UCCs; and the ITII file contains the five imputation variants of the income data converted to a monthly time frame and assigned to UCCs. The NTAXI file contains federal and state tax information for each tax unit within the CU. The FPAR and MCHI datasets are grouped as 2-year datasets (2012 and 2013), plus the first quarter of 2014, and contain paradata about the Interview survey. The FPAR file contains CU level data about the Interview survey, including timing and record use. The MCHI file contains data about each interview contact attempt, including reasons for refusal and times of contact. Both FPAR and MCHI files contain five quarters of data. The EXPN files contain expenditure data and ancillary descriptive information, often not available on the FMLI or MTBI files, in a format similar to the Interview questionnaire. In addition to the extra information available on the EXPN files, users can identify distinct spending categories easily and reduce processing time due to the organization of the files by type of expenditure. Each of the 43 EXPN files contains five quarters of data, directly derived from their respective questionnaire sections. The processing files enhance computer processing and tabulation of data, and provide descriptive information on item codes. There are two types of processing files: (1) aggregation scheme files used in the published consumer expenditure survey interview tables and integrated tables (ISTUB and INTSTUB), and (2) a vehicle make file (CAPIVEHI). The processing files are further explained in the Interview Survey Users' Guide, Section III.H.9. "Processing Files." In addition to the primary users' guide, the Users' Guide to Income Imputation provides information on how to appropriately use the imputed income data. Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over. The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on "Other" in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Detailed breakdown of average weekly household expenditure on goods and services in the UK. Data are shown by place of purchase, income group (deciles) and age of household reference person.

City Budget Summary and Recommendations

City Budget Summary and Recommendations for FY2022

By data.world's Admin [source]

About this dataset

This dataset provides information on the city budget for the fiscal year 2022. It includes various financial data such as actuals for the years 2019 and 2020, budget for 2021, and a recommended budget for 2022. The dataset includes categorical variables such as Fund, Acct-Type, Acct-Div, Acct-Dept, Summ-Acct, and GL-Account which specify different aspects of the accounts in the city's finance system. These variables help categorize and classify accounts based on their type, department, division, summary code, general ledger account code, and fund affiliation. Additionally, there are numeric variables such as actuals for 2019 and 2020 to represent the financial values associated with each account in those years. The dataset also provides budgeted amounts for the years 2021 and forecasted budget amounts for 2023. This information is crucial for analyzing the city's financial situation over time and making informed decisions regarding resource allocation and expenditure planning

How to use the dataset

Introduction:

• Fund: The Fund column categorizes each account into specific funding sources or funding types. It provides insights into where the money comes from or which program it is allocated towards.

• Acct-Type: The Acct-Type column classifies each account based on its type, such as revenue, expense, assets, liabilities, etc. This classification helps in understanding different financial aspects of various accounts.

• Acct-Div: The Acct-Div column identifies the division to which each account belongs within the city's organization structure. It provides a hierarchical view of how accounts are divided based on their functions or responsibilities.

• Acct-Dept: The Acct-Dept column specifies the department responsible for managing each account within the city administration. This information helps identify which departments are allocated budgeted amounts and track their finances separately.

• Summ-Acct: The Summ-Acct column represents a summary account code that groups multiple related detailed accounts together into broader categories for simplification and analysis purposes.

• GL-Account: The GL-Accountcolumn refers to general ledger codes assigned to individual accounts in order to track transactions accurately across various financial statements.

• Actuals (2019-Actual & 2020-Actual): These columns present actual financial data for 2019 and 2020 respectively—summarizing income/expenditure details that were recorded during those years.

• Budget (2021-Budget): The 2021-Budget column displays the budgeted amount for the year 2021. It represents the city's projected income and expenses for that specific period.

• Recommendations (2022-Recommend): The 2022-Recommend column suggests a recommended budget amount for the upcoming fiscal year, which helps in planning and decision-making processes.

• Forecast (2023-Forecast): The 2023-Forecast column showcases a projected or forecasted budget amount for the year 2023. This estimation helps anticipate future financial requirements and plan accordingly

Research Ideas

• Financial Analysis: This dataset can be used for financial analysis by comparing the actual financial data for different years (2019 and 2020) with the budgeted amounts for 2021 and recommended amounts for 2022. It can help identify any discrepancies or trends in spending or revenue across different funds, departments, and divisions.
• Budget Planning: The dataset provides information on the budgeted amounts for 2021 and recommended amounts for 2022 and 2023. It can be used to assist in budget planning by analyzing previous actuals, forecasts, and recommendations to determine appropriate funding allocations for different accounts, departments, and divisions.
• Performance Evaluation: By comparing the actual financial data with the budgeted amount and forecasted amount, this dataset can help evaluate the performance of different accounts, departments, or divisions within an organization. It can provide insights into areas where there may have been overspending or underspending compared to initial budgets or forecasts. This information can be valuable in making strategic decisions to improve future financial performance

Acknowledgements

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

License

**License: [Open Database License (ODbL) v1.0](https://opendatacommons.org/licenses/odbl/...

Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

This dataset is about countries per year in the Americas. It has 2,240 rows. It features 4 columns: country, military expenditure, and individuals using the Internet.

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.

Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

Atlas AI’s Spending layer estimates Consumption Expenditure, a measure of how much households spend on the goods and services they consume. The term Consumption Expenditure is redundant because consumption indicates the volume of goods and services while expenditure attaches a monetary ($) value to that consumption. A simpler way to capture this idea is to say “spending”.

The Spending concept is one good way to measure economic well-being. It reflects what people need to spend money on for basic needs and services, as well as what they choose to spend money on for discretionary activities and goods. To make comparisons and calculations easier, we normalize this as household spending per person, per day.

This data is available in a single version in raster format and is updated annually for several geographic regions. Yearly and regional coverage for the current version is:

• v2022: 43 countries in Sub-Saharan Africa from 2003–2021 at 1km resolution

• v2022 (beta): Six countries in South Asia from 2015–2021 at 1km resolution

In addition to spending estimates, this dataset includes estimates of the population living below three poverty thresholds (determined by mean daily household spending at the pixel level, where the pixel value is the mean of the log-normally distributed Spending values of all households in that pixel).

The three poverty thresholds are:

• $1.90/day (Sub-Saharan Africa only)

• $3.20/day

• $5.50/day

For details on methods, input data sources, validation, and background citations, please consult: https://docs.atlasai.co/economic%20well-being/spending/

This data is offered under a restricted license. Please see resource constraints section below for terms and conditions.

Supplemental Information:

Unit of measure: PPP dollars per day (Spending) or people (Poverty Thresholds)

Flags (including missing value):

• No-data value: 1.79769e+308 (Spending) or 3.40282e+38 (Poverty Thresholds)

Citation:

Publications, books, articles, blogs, conference papers, reports or other derivative works employing data obtained from Atlas AI should cite the source and the dataset(s) as indicated below:

Atlas AI (year dataset accessed) "Dataset name (dataset version)”

e.g. Atlas AI (2021) "Spending, v.2021”

Contact points:

Resource Contact: Vivek Sakhrani

Metadata Contact: FAO-Data

Resource constraints:

The use of this dataset is restricted. The Atlas AI dataset is distributed to authorized users within the Food and Agriculture Organization of the UN. FAO grants a license to download, use and print the materials contained in the Atlas AI dataset solely for non-commercial purposes and in accordance with the conditions specified in the data license agreement. Sharing the dataset in raw or aggregated form without express prior consent from AtlasAI is not allowed.

For any further information, please contact FAO-Data at fao-data@fao.org.

Online resources:

Dataset documentation, default symbology, and code samples

This dataset provides a comprehensive view of key macroeconomic indicators across various entities (countries or regions) over time. It includes annual data for the following variables:

Entity: The name of the country or region for which the data is recorded. Code: A standardized three-letter country or region code, facilitating easier identification and merging with other datasets. Year: The calendar year for which the economic indicators are reported. GDP per capita: The gross domestic product (GDP) divided by the midyear population. It represents the average economic output per person and is a common measure of living standards and economic development. Value of global merchandise exports as a share of GDP: This indicates the proportion of a country's total economic output that is represented by the value of its exported goods. It highlights the importance of international trade in the economy. Government expenditure (% of GDP): The total spending by the government as a percentage of the country's GDP. This reflects the size and scope of government involvement in the economy. Trade as a Share of GDP: The sum of a country's total exports and imports of goods and services, expressed as a percentage of its GDP. This metric indicates the overall openness of an economy to international trade. ****Inflation, consumer prices (annual %)****: The percentage change in the average prices of goods and services typically purchased by households over a one-year period. It measures the rate at which the cost of living is changing.