Graph and download economic data for Income Before Taxes: Public Assistance, Supplemental Security Income, SNAP by Race: White and All Other Races, Not Including Black or African American (CXUWELFARELB0903M) from 2003 to 2023 about supplements, assistance, social assistance, public, SNAP, food stamps, tax, white, food, income, and USA.

In 2022, 39.8 percent of Snap's workforce in the United States was Asian, whilst 4.3 percent self-identified were Black. Overall, around one percent of U.S. employees were Indigenous, and 2.6 percent were Middle Eastern, North African, or Arab. Snap Inc. owns mobile photo, video, and messaging app Snapchat.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Title SNAP Households by Household Types and Demographics 2016-2020 ACS - SNAP_HH_2020

Summary SNAP Households by type and demographics from 2016-2020 5-year period in NM Census tracts

Notes

Source US CENSUS TABLE FOOD STAMPS/SUPPLEMENTAL NUTRITION ASSISTANCE PROGRAM (SNAP) S2201 2020 ACS 5-YEAR ESTIMATE

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=8c3e62b5050f4bcc8853ecf0130f976d

Alias Definition

ID id

GeoName Geographic Area Name

ETH_1 Estimate Total Households

ETH_2 Estimate Total Households With one or more people in the household 60 years and over

ETH_3 Estimate Total Households No people in the household 60 years and over

ETH_4 Estimate Total Households Married-couple family

ETH_5 Estimate Total Households Other family:

ETH_6 Estimate Total Households Other family: Male householder, no spouse present

ETH_7 Estimate Total Households Other family: Female householder, no spouse present

ETH_8 Estimate Total Households Nonfamily households

ETH_9 Estimate Total Households With children under 18 years

ETH_10 Estimate Total Households With children under 18 years Married-couple family

ETH_11 Estimate Total Households With children under 18 years Other family:

ETH_12 Estimate Total Households With children under 18 years Other family: Male householder, no spouse present

ETH_13 Estimate Total Households With children under 18 years Other family: Female householder, no spouse present

ETH_14 Estimate Total Households With children under 18 years Nonfamily households

ETH_15 Estimate Total Households No children under 18 years

ETH_16 Estimate Total Households No children under 18 years Married-couple family

ETH_17 Estimate Total Households No children under 18 years Other family:

ETH_18 Estimate Total Households No children under 18 years Other family: Male householder, no spouse present

ETH_19 Estimate Total Households No children under 18 years Other family: Female householder, no spouse present

ETH_20 Estimate Total Households No children under 18 years Nonfamily households

ETH_POV_1 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

ETH_POV_2 Estimate Total Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

ETH_DIS_1 Estimate Total Households DISABILITY STATUS With one or more people with a disability

ETH_DIS_2 Estimate Total Households DISABILITY STATUS With no persons with a disability

ETH_RHO_1 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

ETH_RHO_2 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

ETH_RHO_3 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

ETH_RHO_4 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

ETH_RHO_5 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

ETH_RHO_6 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

ETH_RHO_7 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

ETH_RHO_8 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

ETH_RHO_9 Estimate Total Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

ETH_INC_1 Estimate Total Households HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2020 INFLATION-ADJUSTED DOLLARS) Median income (dollars)

ETH_WS_1 Estimate Total WORK STATUS Families

ETH_WS_2 Estimate Total WORK STATUS Families No workers in past 12 months

ETH_WS_3 Estimate Total WORK STATUS Families 1 worker in past 12 months

ETH_WS_4 Estimate Total WORK STATUS Families 2 or more workers in past 12 months

EPH_2 Estimate Percent Households With one or more people in the household 60 years and over

EPH_3 Estimate Percent Households No people in the household 60 years and over

EPH_4 Estimate Percent Households Married-couple family

EPH_5 Estimate Percent Households Other family:

EPH_6 Estimate Percent Households Other family: Male householder, no spouse present

EPH_7 Estimate Percent Households Other family: Female householder, no spouse present

EPH_8 Estimate Percent Households Nonfamily households

EPH_9 Estimate Percent Households With children under 18 years

EPH_10 Estimate Percent Households With children under 18 years Married-couple family

EPH_11 Estimate Percent Households With children under 18 years Other family:

EPH_12 Estimate Percent Households With children under 18 years Other family: Male householder, no spouse present

EPH_13 Estimate Percent Households With children under 18 years Other family: Female householder, no spouse present

EPH_14 Estimate Percent Households With children under 18 years Nonfamily households

EPH_15 Estimate Percent Households No children under 18 years

EPH_16 Estimate Percent Households No children under 18 years Married-couple family

EPH_17 Estimate Percent Households No children under 18 years Other family:

EPH_18 Estimate Percent Households No children under 18 years Other family: Male householder, no spouse present

EPH_19 Estimate Percent Households No children under 18 years Other family: Female householder, no spouse present

EPH_20 Estimate Percent Households No children under 18 years Nonfamily households

EPH_POV_1 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS Below poverty level

EPH_POV_2 Estimate Percent Households POVERTY STATUS IN THE PAST 12 MONTHS At or above poverty level

EPH_DIS_1 Estimate Percent Households DISABILITY STATUS With one or more people with a disability

EPH_DIS_2 Estimate Percent Households DISABILITY STATUS With no persons with a disability

EPH_RHO_1 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone

EPH_RHO_2 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Black or African American alone

EPH_RHO_3 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER American Indian and Alaska Native alone

EPH_RHO_4 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Asian alone

EPH_RHO_5 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Native Hawaiian and Other Pacific Islander alone

EPH_RHO_6 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Some other race alone

EPH_RHO_7 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Two or more races

EPH_RHO_8 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER Hispanic or Latino origin (of any race)

EPH_RHO_9 Estimate Percent Households RACE AND HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER White alone, not Hispanic or Latino

EPH_WS_2 Estimate Percent WORK STATUS Families No workers in past 12 months

EPH_WS_3 Estimate Percent WORK STATUS Families 1 worker in past 12 months

EPH_WS_4 Estimate Percent WORK STATUS Families 2 or more workers in past 12 months

SNAP_1 Estimate Households receiving food stamps/SNAP Households

SNAP_2 Estimate Households receiving food stamps/SNAP Households With one or more people in the household 60 years and over

SNAP_3 Estimate Households receiving food stamps/SNAP Households No people in the household 60 years and over

SNAP_4 Estimate Households receiving food stamps/SNAP Households Married-couple family

SNAP_5 Estimate Households receiving food stamps/SNAP Households Other family:

SNAP_6 Estimate Households receiving food stamps/SNAP Households Other family: Male householder, no spouse present

SNAP_7 Estimate Households receiving food stamps/SNAP Households Other family: Female householder, no spouse present

SNAP_8 Estimate Households receiving food stamps/SNAP Households Nonfamily households

SNAP_9 Estimate Households receiving food stamps/SNAP Households With children under 18 years

SNAP_10 Estimate Households receiving food stamps/SNAP Households With children under 18 years Married-couple family

SNAP_11 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family:

SNAP_12 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Male householder, no spouse present

SNAP_13 Estimate Households receiving food stamps/SNAP Households With children under 18 years Other family: Female householder, no spouse present

SNAP_14 Estimate Households receiving food stamps/SNAP Households With children under 18 years Nonfamily households

SNAP_15 Estimate Households receiving food stamps/SNAP Households No children under 18 years

SNAP_16 Estimate Households receiving food stamps/SNAP Households No children under 18 years Married-couple family

SNAP_17 Estimate Households receiving food stamps/SNAP Households No children under 18 years Other family:

SNAP_18 Estimate Households

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Graph and download economic data for Income Before Taxes: Public Assistance, Supplemental Security Income, SNAP by Hispanic or Latino Origin: Not Hispanic or Latino: White and All Other Races, Not Including Black or African American (CXUWELFARELB1004M) from 2003 to 2023 about supplements, assistance, social assistance, public, SNAP, food stamps, tax, white, food, latino, hispanic, income, and USA.

In 2022, 22.1 percent of Snap Inc. employees in the United States in leadership roles were Asian, and 3.3 percent were Black. Overall, 4.4 percent were Hispanic or Latinx, and just one percent were Indigenous. Snap Inc. owns mobile photo, video, and messaging app Snapchat.

The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload. The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual. This dataset includes separate SNAP QC files for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months. Resources in this dataset:Resource Title: Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database (Period 2). File Name: qc_pub_fy2020_per2.csvResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.Resource Title: Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database (Period 1). File Name: qc_pub_fy2020_per1.csvResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.Resource Title: Technical Documentation for the Fiscal Year 2020 Supplemental Nutrition Assistance Program Quality Control Database and the QC Minimodel. File Name: FY2020TechDoc.pdfResource Description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA), providing millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2020, SNAP served an average of 39.9 million people monthly and paid out $74.2 billion in benefits, which includes the cost of emergency allotments to supplement SNAP benefits due to the COVID-19 public health emergency. In response to legislative adjustments to program rules and changes in economic and demographic trends, the characteristics of SNAP participants and households and the size of the SNAP caseload change over time. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) database. This database is an edited version of the raw data file of monthly case reviews conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for each State’s SNAP caseload.

The COVID-19 public health emergency resulted in an incomplete FY 2020 sample in the raw data file. FNS granted States temporary waivers on conducting QC reviews starting in March 2020. Very few States collected QC data from March 2020 through May 2020. Most States opted to conduct QC reviews from June 2020 through September 2020, although FNS was unable to provide its usual level of oversight of the sampling procedures. Furthermore, monthly State samples for this time period were often smaller than usual.

There are separate SNAP QC databases for FY 2020. The first covers the “pre-pandemic” period of October 2019 through February 2020. The second covers the “waiver” period of June 2020 through September 2020 for the 47 States and territories that provided sufficient data for at least one of those months.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

The Current Population Survey Food Security Supplement (CPS-FSS) is a nationally representative dataset created by the U.S. Census Bureau under sponsorship by the U.S. Department of Agriculture (USDA) Economic Research Service (ERS). It has been collected annually since 1995 as a supplement to the monthly CPS, which surveys approximately 50,000 households on labor force and socioeconomic topics. The dataset measures food security, including indicators like food expenditure, access to food, quality, safety, and participation in federal food assistance programs. It uses a standardized 18-item module to assess household-level food insecurity severity. Key features include state-level estimates (since 1998) and alignment with USDA’s annual reports on food security. Its primary purpose is to monitor hunger and food insecurity trends in the U.S., informing policy decisions and programs like SNAP (Supplemental Nutrition Assistance Program). Researchers and policymakers use it to analyze disparities by demographic factors (e.g., income, race, geography) and evaluate the impact of economic or public health crises (e.g., the COVID-19 pandemic). Unique aspects include its longitudinal consistency, integration with labor force data, and public accessibility via platforms like the USDA ERS and Census Bureau, enabling robust analyses of food insecurity’s social and economic determinants. (Source: USDA ERS, Census Bureau, and academic analyses cited in search results.)

The American Time Use Survey (ATUS) collects information on how people living in the United States spend their time. Data collected in this study measured the amount of time that people spent doing various activities in 2008, such as paid work, child care, religious activities, volunteering, and socializing. Respondents were interviewed once about how they spent their time on the previous day including where they were and whom they were with. Part 1, Respondent and Activity Summary File, contains demographic information about respondents and a summary of the total amount of time they spent doing each activity that day. Part 2, Roster File, contains information about household members and non-household children under the age of 18. Part 3, Activity File, includes additional information on activities in which respondents participated, including the location of each activity and the total time spent on secondary child care. Part 4, Who File, includes data on who was present during each activity. Part 5, ATUS-CPS 2008 File, contains demographic and occupational data on respondents and members of their household collected during their participation in the Current Population Survey (CPS). Parts 6-9 contain supplemental data files that can be used for further analysis of the data. Part 6, Case History File, contains information about the interview process. Part 7, Call History File, gives information about each call attempt. Part 8, Trips File, provides information about the number, duration, and purpose of overnight trips away from home for two or more nights in a row in a given reference month. Part 9, ATUS 2008 Replicate Weights File, contains base weights, replicate base weights, and replicate final weights for each case that was selected to be interviewed for the ATUS. The Eating and Health (EH) Module collected data to analyze (1) the relationships among time use patterns and eating patterns, nutrition, and obesity, and (2) food and nutrition assistance programs, and grocery shopping and meal preparation. The Eating and Health Module contained four files, parts 10-13. Part 10, EH Respondent File, contains information about (1) EH respondents, including variables about grocery shopping and meal preparation, food stamp participation, general health, height, and weight, and (2) household income. Part 11, EH Activity File, contains information on respondents' secondary eating and secondary drinking of beverages. Part 12, EH Child File, contains information on children (under age 19) in respondent households who ate a breakfast or lunch in the previous week that was prepared and served at a school, day care, Head Start center, or summer day program. Part 13, EH Replicate Weights File, contains the 160 replicate final weights that can be used to calculate standard errors and variances for EH Module estimates. Note that the EH Replicate Weights file contains records only for those cases that completed EH Module interviews. Demographic variables include sex, age, race, ethnicity, marital status, education level, income, employment status, occupation, citizenship status, country of origin, labor union membership of household members, and household composition.

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010

Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

As of February 2025, one in five global Snapchat users were men between the ages of 18 and 24 years, whilst women of the same age made up 17.2 percent of all users, making this age group the photo app's main target market. Additionally, almost 20 percent of users were between the ages of 13 and 17 years. Overall, people over 35 years are substantially less likely to use Snapchat. Snapchat’s global users Snapchat has experienced significant increases in global audience size in recent years. In 2022, Snapchat had over *** million users, an increase of over *** percent when compared with the number of users in 2018. By 2027, it is estimated that *** million people will be using the app. As of January 2024, India was home to almost *** million Snapchat users, whilst the United States and Pakistan had *** million and ** million users, respectively. Snapchat+ subscription Snapchat released a paid version of the service in June 2022, offering pre-release and experimental features to those willing to pay **** U.S. dollars per month. As of the fourth quarter of 2023, around ***** million users were subscribing to Snapchat+. Snapchat is not the only social platform to have introduced a subscription option. Meta Verified, X/Twitter Blue, and Reddit also present advantages to users in exchange for a monthly charge.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.