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.

Household Saving Rate in the United States increased to 4.60 percent in January from 3.50 percent in December of 2024. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset demonstrates the affordability of the average Austin Water residential customer’s annual combined water and wastewater bill as a percentage of median household income. Austin Water utilized CensusReporter.org for 2019 and 2020 MHI data. The American Community Survey is the source for Census Reporter. Data sources: Austin Water Rates and Charges Team and American Community Survey (ACS) reported by the U.S. Census Bureau, DataUSA, and CensusReporter.org. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percent-of-median-household-income-spent-on-the-av/w8c4-v9a2

Survey of Household Spending (SHS), average household spending by age of reference person.

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

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.

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.

Analysis of ‘Parking Statistics in North America’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/terenceshin/searching-for-parking-statistics-in-north-america on 28 January 2022.

--- Dataset description provided by original source is as follows ---

ABOUT

This dataset identifies areas within a city where drivers are experiencing difficulty searching for parking. Cities can use this data to identify problem areas, adjust signage, and more. Only cities with a population of more than 100,000 are included.

Data

Some variables to highlight:

• AvgTimeToPark: The average time taken to search for parking (in minutes)
• AvgTimeToParkRatio: The ratio between the average time taken to search for parking and of those not searching for parking in the current geohash
• TotalSearching: The number of drivers searching for parking
• PercentSearching: The percentage of drivers that were searching for parking
• AvgUniqueGeohashes: The average number of unique geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• AvgTotalGeohashes: The average number of all geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking
• CirclingDistribution: JSON object representing the neighbouring geohashes at the 7 character level whereby vehicles searching for parking tend to spend their time. Each geohash will have the average percentage of time spent in that geohash prior to parking.
• HourlyDistribution: JSON object representing the average prevalence of searching for parking by hour of day (% distribution based on number of vehicles experiencing parking problems)
• SearchingByHour: JSON object representing the average percentage of vehicles searching for parking within the hour
• PercentCar: Percentage of vehicles with parking issues that were cars
• PercentMPV: Percentage of vehicles with parking issues that were multi purpose vehicles
• PercentLDT: Percentage of vehicles with parking issues that were light duty trucks
• PercentMDT: Percentage of vehicles with parking issues that were medium duty trucks
• PercentHDT: Percentage of vehicles with parking issues that were heavy duty trucks
• PercentOther: Percentage of vehicles with parking issues that were unknown classification

Content

This dataset is aggregated over the previous 6 months and is updated monthly. This data is publicly available from Geotab (geotab.com).

Inspiration

As some inspiration, here are some questions:

• Which cities are the hardest to find parking?
• By joining population data externally, can you determine a relationship between a region's population and the time that it takes to find parking?
• Similarly, by finding external data, is there a correlation between GDP and parking times? What about average household income?

--- Original source retains full ownership of the source dataset ---

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

This product provides information on the annual weekly average cost ($) per month and annual average of Nutritious Food Basket items for a Family of four in Edmonton. Total average weekly nutritious food basket cost is included.

VITAL SIGNS INDICATOR
Commute Time (T3)

FULL MEASURE NAME
Commute time by residential location

LAST UPDATED
January 2023

DESCRIPTION
Commute time refers to the average number of minutes a commuter spends traveling to work on a typical day. The dataset includes metropolitan area, county, city, and census tract tables by place of residence.

DATA SOURCE
U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation.htm

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2006-2021
Form C08136
Form C08536
Form B08301
Form B08301
Form B08301

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
For the decennial Census datasets, breakdown of commute times was unavailable by mode; only overall data could be provided on a historical basis.

For the American Community Survey (ACS) datasets, 1-year rolling average data was used for all metros, region and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Similarly, modal data is not available for every Bay Area city or census tract, even when the 5-year data is used for those localized geographies.

Regional commute times were calculated by summing aggregate county travel times and dividing by the relevant population; similarly, modal commute times were calculated using aggregate times and dividing by the number of communities choosing that mode for the given geography.

Census tract data is not available for tracts with insufficient numbers of residents. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary metropolitan statistical areas (MSAs) for the nine other major metropolitan areas.

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.

The ACCRA Cost of Living Index (COLI) is a measure of living cost differences among urban areas compiled by the Council for Community and Economic Research. Conducted quarterly, the index compares the price of goods and services among approximately 300 communities in the United States and Canada. This Microsoft Excel file contains the average prices of goods and services published in the ACCRA Cost of Living Index since 1990.

These are the replication files for Oraby (JCRE, 2022). The paper aims to replicate Svensson (American Economic Journal: Macroeconomics, 2015). Abstract: This paper replicates the main analysis of Svensson (2015) with some expansion to the original analysis, mainly for the United States. Overall, the replication exercise successfully confirms the conclusions of Svensson (2015). In both Sweden and the United States, empirical evidence supports the existence of a non-vertical long run Phillips curve. The slope of the long run Phillips curve recorded -0.75 in Sweden and -0.23 in the United States. While the average inflation rate in the United States was very close to its targeted level, the average inflation rate in Sweden was 0.6 percentage points below its targeted level over the sample period. The deviation of inflation rate from its targeted level in Sweden resulted in an unemployment cost equivalent to 0.8 percentage points over the sample period where the average unemployment rate recorded 7.4 percent compared with an estimated 6.6 percent had the average inflation rate been at its targeted level.

Summary

The State of Early Education and Care in Boston: Supply, Demand, Affordability, and Quality, is the first in what is planned as a recurrent landscape survey of early childhood, preschool and childcare programs in every neighborhood of Boston. It focuses on potential supply, demand and gaps in child-care seats (availability, quality and affordability). This report’s estimates set a baseline understanding to help focus and track investments and policy changes for early childhood in the city.

This publication is a culmination of efforts by a diverse data committee representing providers, parents, funding agencies, policymakers, advocates, and researchers. The report includes data from several sources, such as American Community Survey, Massachusetts Department of Early Education and Care, Massachusetts Department of Elementary & Secondary Education, Boston Public Health Commission, City of Boston, among others. For detailed information on methodology, findings and recommendations, please access the full report here

The first dataset contains all Census data used in the publication. Data is presented by neighborhoods:

• Population 0 – 5 years;
• Population 0 – 2 years;
• Population 3 – 5 years;
• Race/ethnicity for children 0 – 4 years (White, non-Hispanic; Black; Asian; Hispanic/Latinx);
• Family type (married couples, female householder, male householder);
• Poverty status;
• Family median income in the past 12 months;
• Average cost of care as a percentage of median family income (infant, preschool);
• Share of families that cannot afford care (infant, preschool)

The Boston Planning & Development Agency Research Division analyzed 2013-2017 American Community Survey data to estimate numbers by ZIP-Code. The Boston Opportunity Agenda combined that data by the approximate neighborhoods and estimated cost of care and affordability.

Additional notes:

• Record Type: Each record represents a ZIP-Code defined neighborhood. See list below for detailed information on Boston ZIP-Codes used to create each one of the 15 neighborhoods.
• Data Quality: Numbers presented here came from 2013-2017 American Community Survey data. Therefore, these are ESTIMATES and have margin of errors. The smaller the geographical unit, the greater the margin of error. The Boston Planning & Development Agency analyzed the data to estimate numbers by ZIP-Code.
• Race/Ethnicity: Non-White Hispanics may be double counted due to data limitations.
• Cost of Care: The average cost of care as a percentage of median family income was computed assuming the annual average cost of infant care was $19,877 and the average cost of preschool care was $ 13,771 (Childcare Aware of America, 2019). For each neighborhood we estimated the impact of child care (infant and preschool) on its median annual family income.
• Affordability: The Department of Health and Human Services (DHHS) sets a standard regarding the affordability of child care, where the annual cost of child care should not exceed 10 percent of household annual income. Using this 10% threshold, we estimated that to afford market rate infant care, a family’s annual income would have to be at least $198,770. The census income bracket closest to this income was a family income of $150,000– 199,999. To afford preschool care, a family's annual income should be at least $137,710. Thus, the census income bracket that encompass this income is $125,000 - 149,999. For both infant and preschool care, we underestimated the number of families that can afford care.

Eggs US decreased 2.89 USD/DOZEN or 49.69% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs US.

Major wage settlements, including number of agreements, number of employees, average duration of agreements, first year average percentage wage adjustment, and annual average percentage wage adjustment, by jurisdiction, industry, sector, and cost of living adjustment (COLA), annually, from 1977 to 2020.

Data on annual expenditure by educational institutions per student, in Canadian and American dollars, reference year 2020/2021. At the primary/secondary level, the amount spent on educational core services and ancillary services is also presented.

Consumer Confidence in the United States decreased to 57.90 points in March from 64.70 points in February of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.