Consumer Spending in the United States increased to 16291.80 USD Billion in the first quarter of 2025 from 16273.20 USD Billion in the fourth quarter of 2024. 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.

Government spending in the United States was last recorded at 39.7 percent of GDP in 2024 . This dataset provides - United States Government Spending To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar 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...

Personal Spending in the United States decreased 0.10 percent in May 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.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

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.

Government Spending in the United States decreased to 3990.60 USD Billion in the first quarter of 2025 from 3996.30 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Government Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table shows the net transactions for the current month, and the current and prior fiscal year-to-date, as well as account balances for the beginning of the current fiscal year and current accounting month and the close of the current accounting month. This activity is related to the means used to finance the budget deficit or to dispose of a budget surplus. An asset account would represent an asset to the United States Government, for example United States Treasury Operating Cash. A liability account would represent a liability to the United States Government, for example Borrowing from the Public. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This table shows the gross outlays, applicable receipts and net outlays for the current month, current fiscal year-to-date and prior fiscal year-to-date by various agency programs accounted for in the budget of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This dataset provides detailed information on U.S. federal spending for the cost of zoonotic disease monitoring and surveillance programs. Data are gathered from the USAspending.gov database, an open data source that allows to track how federal money is allocated and spent across various countries. We retrieved information at country-level over the period 2008-2023 for more than 130 countries.

Our aim was to estimate the total yearly spending from US-based organisations on zoonotic disease surveillance worldwide. Hence, we focussed on programs related to zoonotic disease monitoring and surveillance funded by the following agencies: Centers for Disease Control and Prevention (CDC), U.S. Agency for International Development (USAID) and Bureau of Global Health, Security and Diplomacy.

The dataset consists on:

(i) country code;

(ii) country name;

(iii) fiscal year;

(iv) Recipient ($), which is the total amount of funds allocated to a given country in a specific year, referring to the legal business addresses of organizations that are awarded with federal contracts, grants or financial assistance;

(v) Performance ($), which is the total amount of funds associated with the country, specifying the local where most of the work takes place, e.g. for a manufacturing contract this indicates the main place where items are produced;

(vi) Spending ($), which is the sum of recipient and performance funds for a country for each available year.

This dataset can serve as a valuable resource not only for researchers, but also for decision-makers as it supports the analysis of federal spending patterns. Despite the data account for U.S. spending for (excluding extranational spending of other big investors, such as the European Union), they are produced based on a consistent approach providing comparable information among years and countries. The usefulness and applicability of this dataset is indeed manifold: it can be used for examining the trends in funding distribution and outcomes, assessing the effectiveness of spending policies, and analyzing the outcome of investment programs over time. Likewise, analysis of trends in spending can be crucial to improve public services, such as healthcare, by identifying gaps and weaknesses, which may lead to the identification of more targeted actions for reducing the risk of zoonotic disease. Moreover, these data can be exploited to forecast economic trends and identify areas which are underfunded and might require future investment.

This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for functions of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

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.

This summary table shows the on-budget and off-budget receipts and outlays, the on-budget and off-budget surplus/deficit, and the means of financing the budget surplus/deficit. The table also shows the budgeted amounts estimated in the President's Budget for the current fiscal year and next fiscal year for each item on the table. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

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 management costs are allocated to the different parts of Public Works (e.g. Building Repair, Urban Forestry, Construction Management).

Revenue Transfer Adjustments: -Revenue Transfers move revenue from one fund to another as required by governmental accounting standards. -Transfer Adjustments for Revenue Transfers work much like those for Expenditure Recovery. -They are recorded with a Revenue Transfer Out and a Revenue Transfer In Character (in the Type hierarchy) instead of using Services of Other Departments and Expenditure Recovery. -They can be within the same department or they can be between different departments. -The Transfer Adjustment is shown in the fund with the Revenue Transfer Out as shown in the following example of a transfer from Fund A to Fund B.

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).

Disposable Personal Income in the United States decreased to 22454.56 USD Billion in May from 22579.58 USD Billion in April of 2025. This dataset provides - United States Disposable Personal Income - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

SafeGraph is just a data company. That's all we do.SafeGraph Places for ArcGIS is a subset of SafeGraph Places. SafeGraph Places is a points-of-interest (POI) dataset with business listing, building footprint, visitor insights, & foot-traffic data for every place people spend money in the U.S.The complete SafeGraph Places dataset has ~ 5.4 million points-of-interest in the USA and is updated monthly (to reflect store openings & closings).Here, for free on this listing, SafeGraph offers a subset of attributes from SafeGraph Places: POI business listing information and POI locations (building centroids).Columns in this dataset:safegraph_place_idparent_safegraph_place_idlocation_namesafegraph_brand_idsbrandstop_categorystreet_addresscitystatezip_codeNAICS codeGeometry Point data. Latitude and longitude of building centroid.For data definitions and complete documentation visit SafeGraph Developer and Data Scientist Docs.For statistics on the dataset, see SafeGraph Places Summary Statistics.Data is available as a hosted Feature Service to easily integrate with all ESRI products in the ArcGIS ecosystem.Want More? Want this POI data for use outside of ArcGIS Online? Want POI data for Canada? Want POI building footprints (Geometry)?Want more detailed category information (Core Places)?Want phone numbers or operating hours (Core Places)?Want POI visitor insights & foot-traffic data (Places Patterns)?To see more, preview & download all SafeGraph Places, Patterns, & Geometry data from SafeGraph’s Data Bar.Or drop us a line! Your data needs are our data delights. Contact: support-esri@safegraph.comView Terms of Use

This table shows the receipts and outlays of the United States Government by month for the current fiscal year, up to and including the current accounting month. The table also shows the total receipts and outlays for the current fiscal year-to-date and the comparable prior fiscal year-to-date. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This summary table shows the total amount of receipts and outlays and the amount of the budget surplus/deficit by month for the current and prior fiscal years. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.