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
January 2023

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
1980-2000

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2007-2021
Form C17002

CONTACT INFORMATION
vitalsigns.info@mtc.ca.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. 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 non-cash benefits (such as public housing, Medicaid and food stamps).

For the national poverty level definitions by year, see: US Census Bureau Poverty Thresholds - https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

For an explanation on how the Census Bureau measures poverty, see: How the Census Bureau Measures Poverty - https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html.

American Community Survey (ACS) 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

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.

Key findings in the Struggling to Get By report show that one in three California households (31%) do not have sufficient income to meet their basic costs of living. This is nearly three times the number officially considered poor according to the Federal Poverty Level.Families with inadequate incomes are found throughout California, but are most concentrated in the northern coastal region, the Central Valley, and in the southern metropolitan areas.The costs for the same family composition in different geographic regions of California also vary widely. In expensive regions such as the San Francisco Bay Region and the Southern California coastal region, the Real Cost Budget, a monthly budget calculation of what is needed to meet basic needs, can range from 32% to 48% more (depending on family type) than in less expensive counties such as Kern, Tulare, and Kings counties. Nevertheless, incomes in the higher cost regions are also higher, relatively and absolutely, so that the proportions below the Real Cost Measure are generally lower in high-cost than low-cost regions.

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.

In 2023, there were a total of seven million families living below the poverty line in the United States. Poverty is the state of one who lacks a certain amount of material possessions or money. Absolute poverty or destitution is inability to afford basic human needs, which commonly includes clean and fresh water, nutrition, health care, education, clothing, and shelter.

Open access publication rates have been steadily increasing over time. In spite of this growth, academics in low income settings struggle to gain access to the full canon of research literature. While the vast majority of open access repositories and funding organizations with open access policies are based in high income countries, the geographic patterns of open access publication itself are not well characterized. In this study, we developed a computational approach to better understand the topical and geographical landscape of open access publications in the biomedical research literature. Surprisingly, we found a strong negative correlation between country per capita income and the percentage of open access publication. Open access publication rates were particularly high in sub-Saharan Africa, but vastly lower in the Middle East and North Africa, South Asia, and East Asia and the Pacific. These effects persisted when considering papers only bearing authors from within each region and income group. However, papers resulting from international collaborations did have a higher percentage of OA than single-country papers, and inter-regional collaboration increased OA publication for all world regions. There was no clear relationship between the number of open access policies in a region and the percentage of open access publications in that region. To understand the distribution of open access across topics of biomedical research, we examined keywords that were most enriched and depleted in open access papers. Keywords related to genomics, computational biology, animal models, and infectious disease were enriched in open access publications, while keywords related to the environment, nursing, and surgery were depleted in open access publications. This work identifies geographic regions and fields of research that could be priority areas for open access advocacy. The finding that open access publication rates are highest in sub-Saharan Africa and low income countries suggests that factors other than open access policy strongly influence authors’ decisions to make their work openly accessible. The high proportion of OA resulting from international collaborations indicates yet another benefit of collaborative research. Certain applied fields of medical research, notably nursing, surgery, and environmental fields, appear to have a greater proportion of fee-for-access publications, which presumably creates barriers that prevent researchers and practitioners in low income settings from accessing the literature in those fields.

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

The Affordable Connectivity Program (ACP) is a federal program to help people struggling to afford internet service during the COVID-19 pandemic. The benefit will connect eligible people to jobs, healthcare services, virtual classrooms, and more. The Affordable Connectivity Program federal poverty level eligibility methodology was developed by Rural Local Initiative Support Corporation (LISC).Data Dictionary:Households: total estimated households in King County provided by the Census American Community Survey (ACS) B11016 tableAverage Household Size: average estimated household size in King County provided by the Census American Community Survey (ACS) B25010 tableHousehold Income: estimated household income in King County provided by the Census American Community Survey (ACS) B19001 tableHousehold Income Limit: calculated value that uses average household size and household income developed by Rural Local Initiative Support Corporation (LISC) based on ACP income eligibility guidance ($17,180 + $9,440 * (Average Household Size - 1))Income Eligible Households: total number households in King County at or under the Household Income Limit provided by the Census American Community Survey (ACS) B19001 tablePercent of Income Eligible Households: calculated percentage of eligible households in King County at or under the ACP Household Income LimitHouseholds Enrolled in ACP: total number of households enrolled in ACP reported by the ACPPercent of Households Enrolled in ACP: calculated percentage of households enrolled in ACP reported by the ACPIncome Eligible Households not enrolled in ACP: number of income eligible households not enrolled in ACPPercent of Income Eligible Households not enrolled in ACP: calculated percentage of income eligible households not enrolled in ACPZip Code Rank: indicates the rank by Percent of Income Eligible Households not enrolled in ACP to prioritize outreachClaimed Devices: number of devices claimed by Households using ACP discountInternet Subscription in Household: estimated number of households with an internet subscription in King County provided by the Census American Community Survey (ACS) B28011 tablePercent of Households with an Internet Subscription: calculated calculated percentage of estimated households with an internet subscription divided by total estimated number of households in King County provided by the Census American Community Survey (ACS) B28011 tableDesktop or laptop in Household: estimated number of households with a desktop or laptop in King County provided by the Census American Community Survey (ACS) B28001 tablePercent of Households with a desktop or laptop in Household: calculated percentage of estimated households with a desktop or laptop divided by total estimated number of households in King County provided by the Census American Community Survey (ACS) B28001 tableSmartphone in Household: estimated number of households with a smartphone and no other computing device in King County provided by the Census American Community Survey (ACS) B28001 tablePercent of Households with a smartphone in Household: calculated percentage of estimated households with a smartphone and no other computing device divided by total estimated number of households in King County provided by the Census American Community Survey (ACS) B28001 tableTablet in Household: estimated number of households with a tablet or other portable wireless computer and no other computing device in King County provided by the Census American Community Survey (ACS) B28001 tablePercent of Households with a tablet in Household: calculated percentage of estimated with a tablet or other portable wireless computer and no other computing device divided by total estimated number of households in King County provided by the Census American Community Survey (ACS) B28001 tableHouseholds with Public Assistance Income: estimated number of households with public assistance income in King County provided by the Census American Community Survey (ACS) B19057 tablePercent of Households with Public Assistance Income: calculated percentage of estimated households with public assistance income divided by total estimated number of households in King County provided by the Census American Community Survey (ACS) B19057 tableHouseholds who received Food Stamps/SNAP: estimated number of households who received Food Stamps/SNAP in King County provided by the Census American Community Survey (ACS) B22010 tablePercent of Households who received Food Stamps/SNAP: calculated percentage of estimated households who received Food Stamps/SNAP divided by total estimated number of households in King County provided by the Census American Community Survey (ACS) B22010 tablePreferred City: city associated with a zip codeData Sources: Affordable Connectivity Program (ACP) Enrollments and Claims Tracker - Universal Service Administrative Company (usac.org)Census American Community Survey (ACS): 2017-2021 5-Year EstimatesThis data is reported at the Zip Code for all enrolled households in King County.

Heidrick & Struggles International reported $14.83M in Net Income for its fiscal quarter ending in September of 2024. Data for Heidrick & Struggles International | HSII - Net Income including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Percentage of OA papers by country income brackets.

When Canadian resident were interviewed in May of 2021, almost two-thirds of respondents without disabilities (62 percent) reported living at least comfortably and being able to afford what they wanted. This portion was only 39 percent among people with disabilities. In addition, Canadians with disabilities who indicated that they struggled to get by represented 20 percent of people with disabilities, compared to five percent of those without disabilities.

Average Hourly Earnings in the United States increased 0.20 percent in June of 2025 over the previous month. This dataset provides the latest reported value for - United States Average Hourly Earnings - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Heidrick & Struggles eps - earnings per share from 2010 to 2025. Eps - earnings per share can be defined as a company's net earnings or losses attributable to common shareholders per diluted share base, which includes all convertible securities and debt, options and warrants.

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.

Percentage of OA papers by world region.

The average marginal effect of the probit estimator on the probability that a household suffers from mental health deterioration: the non-poor versus poor households.

This study examines the impact of government support on mental health in Vietnam using Vietnam’s Households Living Standard Surveys in 2018 and 2020 and a probit estimator. Characteristics of the households and the households’ heads are also examined. We find that government support tends to worsen mental health in Vietnam, implying the current Government support is insufficient to improve mental health in households, particularly during stressful times during the COVID-19 pandemic. Female-headed households appear to experience a more significant mental health deterioration compared to their counterparts, whereas households living in urban areas are mentally struggling compared to those living in rural areas. Our results also indicate that mental health deterioration exhibits an inverted U-shaped relationship with age, implying mental health appears to be a significant issue for young individuals in Vietnam. Household incomes and assets act as a buffer against mental health deterioration. These findings support the view that mental health deterioration appears to emerge from financial distress. Households suffer mental health deterioration if their financial circumstances are not improved and support from the government is insufficient.

New Zealand has one of the highest house price-to-income ratios in the world; nonetheless, since the first quarter of 2022, the country's house price-to-income ratio started to trend downward. In the third quarter of 2024, the ratio was 118, a slight decrease from the same quarter of the previous year. This ratio was calculated by dividing nominal house prices by nominal disposable income per head and is considered a measure of affordability. Homeownership dream New Zealand has been in what is widely considered a housing bubble. The disproportionately large increases in residential house prices have placed the dream of owning their own home out of reach for many in the country. In 2024, around 28 percent of residential properties were sold for over a million New Zealand dollars. The majority of mortgage lending in the country went to owner-occupiers where the property was not their first home, with first-home buyers often struggling to secure a loan. In general, only New Zealand residents and citizens can buy homes in the country to live in, with new regulations tightening investment activity in that market. Rent affordability Due to New Zealand's high property prices, many individuals and families are stuck renting for prolonged periods. However, with rent prices increasing across the country and the share of monthly income spent on rent trending upwards in tandem with a highly competitive rental market, renting is becoming a less appealing prospect for many. The Auckland and Bay of Plenty regions had the highest weekly rent prices across the country as of December 2024, with the Southland region recording the lowest rent prices per week.

Alabama faces major challenges that affect Black, rural, and low-income communities. This story map explains these problems and their impacts.Alabama's Political LandscapeBlack voters in Alabama are underrepresented because of unfair voting districts. These districts are drawn to weaken their political power. Despite making up a large part of the population, Black communities have limited representation in government.The Effect of Climate ChangeClimate change is causing more flooding in Alabama. In rural areas, old septic tanks fail during heavy rains, polluting the water and creating health problems. Many communities do not have the resources to fix these issues or prepare for future problems.Digital DivideMany people in Alabama cannot access the internet because broadband is too expensive or unavailable. This is especially true in rural areas. Without the internet, families struggle to find jobs, access education, or get basic information. Public libraries are often the only option, but they cannot meet the growing need.Environmental JusticePollution and environmental hazards in Alabama often affect Black and low-income communities the most. Factories and landfills are built near their homes, making the air and water unsafe. Flooding from poor drainage systems damages homes and neighborhoods, with little help for recovery.Communities in Alabama are working hard to solve these problems and create a better future for everyone.

After analyzing the map, there are some spatial concerns that need to be addressed. Most of the city has a trend of low median household income, and the least poor being in the central part of the city. The neighborhood of Jefferson in the eastern part of Denver is evidence of a food desert, as well as in the Lakeside suburb and Westwood (“Planting Seeds in Food Deserts: Neighborhood Gardens, Produce in Corner Stores” 2014) . One thing that really stood out to me was the lack of grocery stores in the eastern part of Denver, just bordering Aurora. This area is a food desert, and the average income of the area is below average. The wealthier neighborhoods are granted more sufficient access just near this part of Denver. We also see quality access to food towards the center of the city in the areas with lower income. The further we move outwards, the more and more the grocery stores start spreading out from a cluster form. From analyzing the map, it seems like living on the outskirts of Denver is where the lower income households will struggle with access to food

There are some very wealthy neighborhoods, specifically along Colorado Boulevard, where there are a lot of high quality grocery stores. This extensive street only has grocery stores located in the wealthy part of it. If we look north, towards the interstate, we see absolutely none located along Colorado Blvd. It is clear that the grocery stores were placed in the central part of Colorado Blvd, as opposed to the northern and southern parts where the average income is much lower. I believe this to be concrete evidence of a biased towards socioeconomic status.sources: “Planting Seeds in Food Deserts: Neighborhood Gardens, Produce in Corner Stores.” 2014. Accessed April 30. http://www.denverpost.com/news/ci_14906833.

“USDA Economic Research Service - Food Access Research Atlas.” 2014. Accessed April 30. http://www.ers.usda.gov/data-products/food-access-research-atlas#.U2A99le0RPW.