American Time Use Survey: Daily Activities

Americans' Daily Activities: Education, Employment, Gender, and Leisure Time

By Throwback Thursday [source]

About this dataset

The American Time Use Survey dataset provides comprehensive information on how individuals in America allocate their time throughout the day. It includes various aspects of daily activities such as education level, age, employment status, gender, number of children, weekly earnings and hours worked. The dataset also includes data on specific activities individuals engage in like sleeping, grooming, housework, food and drink preparation, caring for children, playing with children, job searching, shopping and eating and drinking. Additionally it captures time spent on leisure activities like socializing and relaxing as well as engaging in specific hobbies such as watching television or golfing. The dataset also records the amount of time spent volunteering or running for exercise purposes.

Each entry is organized based on categorical variables such as education level (ranging from lower levels to higher degrees), age (capturing different age brackets), employment status (including employed full-time or part-time), gender (male or female) and the number of children an individual has. Furthermore it provides information regarding an individual's weekly earnings and hours worked.

This extensive dataset aims to provide insights into how Americans prioritize their time across various aspects of their lives. Whether it be focusing on work-related tasks or indulging in recreational activities,it offers a comprehensive look at the allocation of time among different demographic groups within American society.

This dataset can be used for understanding trends in daily activity patterns across demographics groups over multiple years without directly referencing specific dates

How to use the dataset

How to use this dataset: American Time Use Survey - Daily Activities

Welcome to the American Time Use Survey dataset! This dataset provides valuable information on how Americans spend their time on a daily basis. Here's a guide on how to effectively utilize this dataset for your analysis:

• Familiarize yourself with the columns:

  • Education Level: The level of education attained by the individual.
  • Age: The age of the individual.
  • Age Range: The age range the individual falls into.
  • Employment Status: The employment status of the individual.
  • Gender: The gender of the individual.
  • Children: The number of children that an individual has.
  • Weekly Earnings: The amount of money earned by an individual on a weekly basis.
  • Year: The year in which the data was collected.
  • Weekly Hours Worked: The number of hours worked by an individual on a weekly basis.

• Identify variables related to daily activities: This dataset provides information about various daily activities undertaken by individuals. Some important variables related to daily activities include:

  • Sleeping
  • Grooming
  • Housework
  • Food & Drink Prep
  • Caring for Children
  • Playing with Children
  • Job Searching …and many more!

• Analyze time spent on different activities: This dataset includes numerical values representing time spent in minutes for specific activities such as sleeping, grooming, housework, food and drink preparation, etc. You can use this data to analyze and compare how different groups of individuals allocate their time throughout the day.

• Explore demographic factors: In addition to daily activities, this dataset also includes columns such as education level, age range, employment status, gender, and number of children. You can cross-reference these demographic factors with activity data to gain insights into how different population subgroups spend their time differently.

• Identify trends and patterns: You can use this dataset to identify trends and patterns in how Americans allocate their time over the years. By analyzing data from different years, you may discover changes in certain activities and how they relate to demographic factors or societal shifts.

• Visualize the data: Creating visualizations such as bar graphs, line plots, or pie charts can provide a clear representation of how time is allocated for different activities among various groups of individuals. Visualizations help in understanding the distribution of time spent on different activities and identifying any significant differences or similarities across demographics.

Remember that each column represents a specific variable, whi...

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more. How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources. How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.

The Quarterly Food-Away-From-Home Prices (QFAFHP) data set provides quarterly prices (not including taxes) for food away from home (FAFH) and alcohol, both at home and away from home. Food away from home is an integral component of the typical American diet and food budget; it also plays a key role in the nutrition and health of Americans. Data on variation in food prices over time and across regions allow researchers to estimate how price changes affect the demand for different products—such as through changes in quantities purchased or expenditures—and, to examine how changes in demand, in turn, affect nutritional and health outcomes.

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Part 2 of the dataset is complete (for now!) There you'll find data specific to the Supplemental Nutrition Assistance (SNAP) Program. The US SNAP program provides food benefits to low-income families to supplement their grocery budget.

Link: US Public Food Assistance 2 - SNAP Please click on the ▲ if you find it useful -- it has almost 500 downloads!

Context

This dataset, Part 1, addresses another US program, the Special Supplemental Nutrition Program for Women, Infants, and Children Program, or simply WIC. The program allocates Federal and State funds to help low-income women and children up to age five who are at nutritional risk. Funds are used to provide supplemental foods, baby formula, health care, and nutrition education.

Content

Files may include participation data and spending for state programs, and poverty data for each state. Data for WIC covers fiscal years 2013-2016, which is actually October 2012 through September 2016.

Motivation

My original purpose here is two-fold:

• Explore various aspects of US Public Assistance. Show trends over recent years and better understand differences across state agencies. Although the federal government sponsors the program and provides funding, program are administered at the state level and can widely vary. Indian nations (native Americans) also administer their own programs.

• Share with the Kaggle Community the joy - and pain - of working with government data. Data is often spread across numerous agency sites and comes in a variety of formats. Often the data is provided in Excel, with the files consisting of multiple tabs. Also, files are formatted as reports and contain aggregated data (sums, averages, etc.) along with base data.

As of March 2nd, I am expanding the purpose to support the M5 Forecasting Challenges here on Kaggle. Store sales are partly driven by participation in Public Assistance programs. Participants typically receive the items free of charge. The store then recovers the sale price from the state agencies administering the program.

Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

U.S. Pandemic Mental Health Care

Impact on Households in Previous 4 Weeks

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

About this dataset

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

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How to use the dataset

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

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

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

Research Ideas

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

Acknowledgements

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

License

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

Columns

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

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).

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

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 survey questionnaire and independent sample), the Diary Survey and the quarterly Interview Survey (ICPSR 36237). This data collection contains the Diary Survey component, which was designed to obtain data on frequently purchased smaller items, including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. Each consumer unit (CU) recorded its expenditures in a diary for two consecutive 1-week periods. Although the diary was designed to collect information on expenditures that could not be easily recalled over time, respondents were asked to report all expenses (except overnight travel) that the CU incurred during the survey week.

The 2013 Diary Survey release contains five sets of data files (FMLD, MEMD, EXPD, DTBD, DTID), and one processing file (DSTUB). The FMLD, MEMD, EXPD, DTBD, and DTID files are organized by the quarter of the calendar year in which the data were collected. There are four quarterly datasets for each of these files.

The FMLD files contain CU characteristics, income, and summary level expenditures; the MEMD files contain member characteristics and income data; the EXPD files contain detailed weekly expenditures at the Universal Classification Code (UCC) level; the DTBD files contain the CU's reported annual income values or the mean of the five imputed income values in the multiple imputation method; and the DTID files contain the five imputed income values. Please note that the summary level expenditure and income information on the FMLD files permit the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

The DSTUB file provides the aggregation scheme used in the published consumer expenditure tables. The DSTUB file is further explained in Section III.F.6. 'Processing Files' of the Diary Survey Users' Guide. A second documentation guide, the 'Users' Guide to Income Imputation,' includes 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 estimate. The BLS unpublished integrated CE data tables are provided as an easy-to-use tool for obtaining spending estimates. However, users are cautioned to read the BLS explanatory letter accompanying the tables. The letter explains that estimates of average expenditures on detailed spending items (such as leisure and art-related categories) may be unreliable due to so few reports of expenditures for those items.

The Great American Coffee Taste Test

In October 2023, "world champion barista" James Hoffmann and coffee company Cometeer held the "Great American Coffee Taste Test" on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Data blogger Robert McKeon Aloe analyzed the data the following month.

Do you think participants in this survey are representative of Americans in general?

Data Dictionary

coffee_survey.csv

variable	class	description
submission_id	character	Submission ID
age	character	What is your age?
cups	character	How many cups of coffee do you typically drink per day?
where_drink	character	Where do you typically drink coffee?
brew	character	How do you brew coffee at home?
brew_other	character	How else do you brew coffee at home?
purchase	character	On the go, where do you typically purchase coffee?
purchase_other	character	Where else do you purchase coffee?
favorite	character	What is your favorite coffee drink?
favorite_specify	character	Please specify what your favorite coffee drink is
additions	character	Do you usually add anything to your coffee?
additions_other	character	What else do you add to your coffee?
dairy	character	What kind of dairy do you add?
sweetener	character	What kind of sugar or sweetener do you add?
style	character	Before today's tasting, which of the following best described what kind of coffee you like?
strength	character	How strong do you like your coffee?
roast_level	character	What roast level of coffee do you prefer?
caffeine	character	How much caffeine do you like in your coffee?
expertise	numeric	Lastly, how would you rate your own coffee expertise?
coffee_a_bitterness	numeric	Coffee A - Bitterness
coffee_a_acidity	numeric	Coffee A - Acidity
coffee_a_personal_preference	numeric	Coffee A - Personal Preference
coffee_a_notes	character	Coffee A - Notes
coffee_b_bitterness	numeric	Coffee B - Bitterness
coffee_b_acidity	numeric	Coffee B - Acidity
coffee_b_personal_preference	numeric	Coffee B - Personal Preference
coffee_b_notes	character	Coffee B - Notes
coffee_c_bitterness	numeric	Coffee C - Bitterness
coffee_c_acidity	numeric	Coffee C - Acidity
coffee_c_personal_preference	numeric	Coffee C - Personal Preference
coffee_c_notes	character	Coffee C - Notes
coffee_d_bitterness	numeric	Coffee D - Bitterness
coffee_d_acidity	numeric	Coffee D - Acidity
coffee_d_personal_preference	numeric	Coffee D - Personal Preference
coffee_d_notes	character	Coffee D - Notes
prefer_abc	character	Between Coffee A, Coffee B, and Coffee C which did you prefer?
prefer_ad	character	Between Coffee A and Coffee D, which did you prefer?
prefer_overall	character	Lastly, what was your favorite overall coffee?
wfh	character	Do you work from home or in person?
total_spend	character	In total, much money do you typically spend on coffee in a month?
why_drink	character	Why do you drink coffee?
why_drink_other	character	Other reason for drinking coffee
taste	character	Do you like the taste of coffee?
know_source	character	Do you know where your coffee comes from?
most_paid	character	What is the most you've ever paid for a cup of coffee?
most_willing	character	What is the most you'd ever be willing to pay for a cup of coffee?
value_cafe	character	Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
spent_equipment	character	Approximately how much have you spent on coffee equipment in the past 5 years?
value_equipment	character	Do you feel like you’re getting good value for your mo...

This article explores the methods of a prior larger research project to understand flows in the US energy economy, quantifying energy use across American history (1800–2020). As a case study, it uses a subset of this data—agricultural energy use—to examine the methods, sources, and problems around estimating the production and consumption of energy at a national level. By combining statistical data with primary sources (like government and private studies on livestock feed demands), we produce a database that sums all energy used both on-field and in the processing and production of food more generally—and offer several counterintuitive conclusions. Per-capita agricultural energy use actually fell between 1800 and 2020. During this time period, the overall per-capita energy expenditure on food (in processing and cooking) remained fairly steady. We conclude the article by noting various uses for the data in reframing long-term agricultural trends and their environmental impacts. Energy flows are a fundamental component of social metabolism research. What this paper adds to this work is an unusual American case, one in which per capita on-field energy use declined.