Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Newport News city, VA (S1701ACS051700) from 2012 to 2023 about Newport News City, VA; Virginia Beach; VA; poverty; percent; 5-year; population; and USA.

Newport News City, VA - Percent of Population Below the Poverty Level (5-year estimate) in Newport News city, VA was 15.10% in January of 2023, according to the United States Federal Reserve. Historically, Newport News City, VA - Percent of Population Below the Poverty Level (5-year estimate) in Newport News city, VA reached a record high of 16.40 in January of 2017 and a record low of 14.50 in January of 2012. Trading Economics provides the current actual value, an historical data chart and related indicators for Newport News City, VA - Percent of Population Below the Poverty Level (5-year estimate) in Newport News city, VA - last updated from the United States Federal Reserve on November of 2025.

According to a study conducted in 2024 using the most recently available data, the average poverty rate in news deserts in the United States (counties without access to or with very limited access to local news) was around five percent higher than the country average, at ** percent. Citizens living in counties without newspapers were also earning a lower median annual income than the general population average, with the figure estimated at less than ****** U.S. dollars compared to more than **** thousand U.S. dollars for the U.S. as a whole.

News that Cleveland’s poverty rate is the worst in the nation—and rising—has elevated the community’s concern about conditions in the city. But a closer look at the way poverty rates are calculated suggests that all the possible causes of Cleveland’s ranking have not been fully understood.

Table from the American Community Survey (ACS) B17004 of poverty status in the past 12 months of individuals by sex by work experience. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.

In 2022, approximately 4.7 percent of the Mexican population were living on less than 3.20 U.S. dollars per day, a considerable decrease in comparison to the previous year. Furthermore, unemployment rate in this Latin American country during this period was at 3.2 percent. Poverty is considerably higher in the South In 2022, the three states with the highest poverty rate in the Aztec country were Chiapas, Guerrero, and Oaxaca, all in the southern region. In contrast, the top eight federal entities with the lowest were all in the North. The clear division is further accentuated by the Northern Border Free Zone, which encompasses 43 municipalities in the Mexico-U.S. border with higher minimum wages and lower taxes. Poverty in states such as Chiapas reaches over 67 percent, which means two out of three residents are under the poverty line and almost one out of three under extreme poverty conditions.
A country troubled by inequality Poverty and inequality are no news in Mexico. In the most recent data, around 80 percent of the total wealth of the country was concentrated in the top 10 percent of the population. Moreover, the bottom 50 percent had a negative share, meaning that half of the Mexican population had more debts than assets. But inequality does not only encompass wealth distribution, but Mexico also has a problem regarding gender inequality. The government has failed to achieve many of its goals to reduce the gap between genders.

Table from the American Community Survey (ACS) B17001 poverty status of the population. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually.

Table from the American Community Survey (ACS) 5-year series on poverty and employment status related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B23025 Employment Status for the Population 16 years and over, B23024 Poverty Status by Disability Status by Employment Status for the Population 20 to 64 years, B17010 Poverty Status of Families by Family Type by Presence of Related Children under 18 years, C17002 Ratio of Income to Poverty Level in the Past 12 Months. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Table of INEBase At-risk-of-poverty rate, by Autonomous Community. Annual. Autonomous Communities and Cities. Living Conditions Survey (LCS)

The subject in this study is the linguistic-discursive representation of poverty by online media. The theoretical and methodological support used is that of Critical Discourse Analysis (FAIRCLOUGH, 2001) and the transitivity system of the Systemic Functional Grammar (HALLIDAY; MATTHIESSEN, 2004). The corpus comprises ten stories published between May and November 2011 in online editions of renowned Brazilian newspapers: Zero Hora, Correio do Povo, O Globo, O Estado de São Paulo and Folha de São Paulo. Two news were selected from each newspaper and submitted to a qualitative, interpretative analysis. Initially, how each newspaper represents poverty was identified, and then it was observed how online media represents poverty. We found three representations for poverty: as an object susceptible to human action, as an entity that acts on individuals, and as a situation faced by many people.

Content

Police Killings

This directory contains the data behind the story Where Police Have Killed Americans In 2015.

We linked entries from the Guardian's database on police killings to census data from the American Community Survey. The Guardian data was downloaded on June 2, 2015. More information about its database is available here.

Census data was calculated at the tract level from the 2015 5-year American Community Survey using the tables S0601 (demographics), S1901 (tract-level income and poverty), S1701 (employment and education) and DP03 (county-level income). Census tracts were determined by geocoding addresses to latitude/longitude using the Bing Maps and Google Maps APIs and then overlaying points onto 2014 census tracts. GEOIDs are census-standard and should be easily joinable to other ACS tables -- let us know if you find anything interesting.

Field descriptions:

Note regarding income calculations:

All income fields are in inflation-adjusted 2013 dollars.

comp_income is simply tract-level median household income as a share of county-level median household income.

county_bucket provides where the tract's median household income falls in the distribution (by quintile) of all tracts in the county. (1 indicates a tract falls in the poorest 20% of tracts within the county.) Distribution is not weighted by population.

nat_bucket is the same but for all U.S. counties.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

In this essay, we ask whether the distributions of life expectancy and mortality have become generally more unequal, as many seem to believe, and we report some good news. Focusing on groups of counties ranked by their poverty rates, we show that gains in life expectancy at birth have actually been relatively equally distributed between rich and poor areas. Analysts who have concluded that inequality in life expectancy is increasing have generally focused on life expectancy at age 40 to 50. This observation suggests that it is important to examine trends in mortality for younger and older ages separately. Turning to an analysis of age-specific mortality rates, we show that among adults age 50 and over, mortality has declined more quickly in richer areas than in poorer ones, resulting in increased inequality in mortality. This finding is consistent with previous research on the subject. However, among children, mortality has been falling more quickly in poorer areas with the result that inequality in mortality has fallen substantially over time. We also show that there have been stunning declines in mortality rates for African Americans between 1990 and 2010, especially for black men. Finally we offer some hypotheses about causes for the results we see, including a discussion of differential smoking patterns by age and socioeconomic status.

The Consumer Expenditure Survey (CE) program consists of two surveys: the quarterly Interview survey and the annual Diary survey. Combined, these two surveys provide information on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. The survey data are collected for the U.S. Bureau of Labor Statistics (BLS) by the U.S. Census Bureau. The CE collects all on all spending components including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. The CE tables are an easy-to-use tool for obtaining arts-related spending estimates. They feature several arts-related spending categories, including the following items: Spending on Admissions Plays, theater, opera, and concerts Movies, parks, and museums Spending on Reading Newspapers and magazines Books Digital book readers Spending on Other Arts-Related Items Musical instruments Photographic equipment Audio-visual equipment Toys, games, arts and crafts The CE is important because it is the only Federal survey to provide information on the complete range of consumers' expenditures and incomes, as well as the characteristics of those consumers. It is used by economic policymakers examining the impact of policy changes on economic groups, by the Census Bureau as the source of thresholds for the Supplemental Poverty Measure, by businesses and academic researchers studying consumers' spending habits and trends, by other Federal agencies, and, perhaps most importantly, to regularly revise the Consumer Price Index market basket of goods and services and their relative importance. The most recent data tables are for 2022 and include: 1) Detailed tables with the most granular level of expenditure data available, along with variances and percent reporting for each expenditure item, for all consumer units (listed as "Other" in the Download menu); and 2) Tables with calendar year aggregate shares by demographic characteristics that provide annual aggregate expenditures and shares across demographic groups (listed as "Excel" in the Download menu). Also, see Featured CE Tables and Economic News Releases sections on the CE home page for current data tables and news release. The 1980 through 2022 CE public-use microdata, including Interview Survey data, Diary Survey data, and paradata (information about the data collection process), are available on the CE website.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

This dataset tracks electric school bus (ESB) adoption across the United States. It tracks the number of “committed” ESBs at the school district level, as well as details about individual buses, including the bus manufacturer and funding source(s). It also tracks when each ESB passed through the phases of the adoption process and the current phase of each bus. The dataset contains school district socio-economic characteristics, like poverty rates, racial composition and air pollution to enable wider analysis including whether the transition to ESBs is happening equitably. This dataset was developed as part of WRI’s Electric School Bus Initiative.

Methodology

The dataset is organized by both school district and individual ESB and tracks the number of “committed” ESBs. An ESB is considered “committed” starting from the point when a school district or fleet operator has been awarded funding to purchase it or has made formal agreement to purchase it from a manufacturer or dealer. We would not consider an ESB “committed” if a school district or other fleet operator only expressed interest in ESBs or stated that they plan to acquire ESBs, without awarded funding or an agreement with a third party. The dataset also tracks the progress of each individual ESB through the four phases of the adoption process: “awarded,” “ordered,” “delivered,” and “operating.” It also contains school district characteristics including poverty, racial composition, air pollution, and locale (urban, suburban, town, or rural), to enable wider analysis of the adoption of ESBs, including the extent to which the transition to ESBs is happening equitably.

ESB-related data were collected from a variety of publicly available sources, including news articles, school websites, industry publications like School Bus Fleet magazine, and social media posts. Other demographic and economic data come from reputable, public datasets including the Environmental Protection Agency (EPA), U.S. Census, and National Center for Education Statistics. This dataset will be updated quarterly over the life of WRI’s to include new ESB commitments and additional indicators.

Usage

This dataset is the result of new data collection by WRI’s Electric School Bus Initiative, and is sourced from hundreds of news articles, school district webpages, and other online sources. To the best of our knowledge, these data are up to date as of March 2022, but represent a snapshot in time, in a rapidly evolving space. We will update this dataset quarterly for the duration of WRI’s Electric School Bus Initiative.

District-level Data on Electric School Bus Adoption:

This category includes the base table of this dataset, which comes from the district directory of the National Center for Education Statistics (NCES) for the 2020–21 school year. The approximately 19,500 LEAs in the United States make up the rows of this dataset. There are nine types of LEAs, including several types of public education-related entities beyond what is typically referred to as a “school district,” such as a state-operated agency or a service agency. This ESB adoption dataset includes all LEA types because there may eventually be ESBs owned by any of these LEA types. The dataset also includes any other entities (without LEA IDs) that have obtained electric school buses (i.e., private schools and private fleet operators).

The data also describe the social, economic, and demographic characteristics of the school district. As described in “Indicator Selection Criteria,” we tried to include data that would provide an adequately holistic understanding of socioeconomic and environmental health condition disparities among school districts, in alignment with wider thinking on the topic and what is relevant to ESBs, without including so many indicators that they burden nontechnical users with researching and selecting indicators. This section includes data on each school district’s number of enrolled students, whether the district is controlled by an Indian Tribe or the Bureau of Indian Education (Bureau of Indian Education n.d.), median household income, percentage of households below the federal poverty level, the distribution of the population among race and ethnic categories, the number of school students with a disability, and whether the school district was qualified for ESB funding from the American Rescue Plan. Also included are the variables; percent low-income, percent non-white and/or Hispanic, average ozone concentration (parts per billion, ppb), and average concentration of fine particulate matter (PM2.5, measured in micrograms per cubic meter, μg/ m3).

Utilities:

This category includes information on the electric power utilities operating in each school district. The “Utility name” variables include the names of all utility companies that operate within the boundaries of the school