This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.

The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.

The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.

Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.

*According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Poverty Status by Town reports the number and percentage of people and children living in poverty, by race/ethnicity and age range.

In 2024, approximately 10.6 percent of the population was living below the national poverty line in the United States. This reflected a 0.5 percentage point decrease from the previous year. Most recently, poverty levels in the country peaked in 2010 at just over 15 percent. Poverty in the U.S. States The number of people living in poverty in the U.S. as well as poverty rates, vary greatly from state to state. With their large populations, California and Texas led that charts in terms of the size of their impoverished residents. On the other hand, Louisiana had the highest rates of poverty, standing at 20 percent in 2024. The state with the lowest poverty rate was New Hampshire at 5.9 percent. Vulnerable populations The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the highest levels of poverty in 2024, with about 19 percent earning an income below the official threshold. In comparison, only about 7.5 percent of the White (non-Hispanic) and Asian populations were living below the poverty line. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2024. Child poverty peaked in 1993 with 22.7 percent of children living in poverty. Despite fluctuations, in 2024, poverty among minors reached its lowest level in decades, falling to 14.3 percent.

This table contains data on the percentage of the total population living below 200% of the Federal Poverty Level (FPL), and the percentage of children living below 200% FPL for California, its regions, counties, cities, towns, public use microdata areas, and census tracts. Data for time periods 2011-2015 (overall poverty) and 2012-2016 (child poverty) and with race/ethnicity stratification is included in the table. The poverty rate table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Poverty is an important social determinant of health (see http://www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=39) that can impact people’s access to basic necessities (housing, food, education, jobs, and transportation), and is associated with higher incidence and prevalence of illness, and with reduced access to quality health care. More information on the data table and a data dictionary can be found in the About/Attachments section.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17. Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.

References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 11/2017

This dataset provides information on poverty-level wages in the United States from 1973 to 2022.

It includes data on both annual and hourly poverty-level wages, as well as wage shares for different income brackets.

The dataset is based on the Economic Policy Institute’s State of Working America Data Library, which offers comprehensive economic data for analyzing trends and patterns in the labor market.

Intresting Task Ideas:

1. Analyze the trend of annual poverty-level wages over the years.
2. Compare the hourly poverty-level wages between men and women.
3. Investigate the share of workers earning below poverty-level wages based on race.
4. Explore the distribution of wages across different poverty wage ranges.
5. Examine the income disparities across different income brackets (0-75%, 75-100%, etc.) for men and women.
6. Determine the proportion of workers earning above the poverty level (300%+) over time.
7. Calculate the percentage change in poverty-level wages from one year to another.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

USA Wage Comparison for College vs. High School

Productivity and Hourly Compensation

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USA Unemployment Rates by Demographics & Race

Gender Wage Gap in the USA

Photo by Jon Tyson on Unsplash

This layer shows poverty status by age group. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percentage of the population whose income falls below the Federal poverty line. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): B17020 (Not all lines of these ACS tables are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: data.census.govAdditional Census data notes and data processing notes are available at the Esri Living Atlas Layer:https://tempegov.maps.arcgis.com/home/item.html?id=0e468b75bca545ee8dc4b039cbb5aff6 (Esri's Living Atlas always shows latest data)

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Goal and Objective : Primary objective is to study variability in the poverty rate in the US counties by means of one or more of independent or control variable and provide best suitable model to quantify relationships in determining target value Our goal is to design various models to take into consideration the effect of various factors like employment, population and education to predict the poverty rate in all US Counties We further wish to analyze the status of a county based on whether it is metropolitan or not

List of datasets:

Socioeconomic indicators like poverty rates, population change, unemployment rates, and education levels vary geographically across U.S. States and counties 1. Unemployment 2. PovertyEstimates 3.Population Estimates 4. Education

All the four individual datasets have common unique id FIPS Code defined as State-County FIPS Code. It is unique for each county falling under the states. In our dataset, we are covering all 52 USA states including federal district DC and Puerto Rico.

Data Modelling :

Target Variable: Metro_2015 – This binary variable shows status of County as Metro or Non-Metro A decision tree model designed using Metro_2015 as target variable will efficiently determine the classification of the population into Metro and Non-metro counties. Dataset will be partitioned into training and validation datasets before implementing decision tree rules. The attributes that will be considered in selecting best model will be fit statistics, misclassification rate, and average square error.

Clustering can be performed to create the collection of objects similar to each other which will give insight into data distribution. Variables will be standardized before performing clustering to avoid noisy data and outliers. Euclidean distance will be the measure to determine stability and separation.

Recommendation :

The regression equation determines % Poverty rate in a particular county based on significant factors. This model can be This model can be used by education boards to increase or decrease the funds spent on the education system in different counties in order to lower the poverty rate. Census board can use this model in identifying poverty line index based on a population estimate an average household income. By estimating the poverty rate and considering factors like unemployment and education, an analysis can be done to set up employment opportunities in targeted counties.

Health Outcomes and Socioeconomic Factors

A Study of US County Data

By Data Exercises [source]

About this dataset

This dataset contains a wealth of health-related information and socio-economic data aggregated from multiple sources such as the American Community Survey, clinicaltrials.gov, and cancer.gov, covering a variety of US counties. Your task is to use this collection of data to build an Ordinary Least Squares (OLS) regression model that predicts the target death rate in each county. The model should incorporate variables related to population size, health insurance coverage, educational attainment levels, median incomes and poverty rates. Additionally you will need to assess linearity between your model parameters; measure serial independence among errors; test for heteroskedasticity; evaluate normality in the residual distribution; identify any outliers or missing values and determine how categories variables are handled; compare models through implementation with k=10 cross validation within linear regressions as well as assessing multicollinearity among model parameters. Examine your results by utilizing statistical agreements such as R-squared values and Root Mean Square Error (RMSE) while also interpreting implications uncovered by your analysis based on health outcomes compared to correlates among demographics surrounding those effected most closely by land structure along geographic boundaries throughout the United States

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How to use the dataset

This dataset provides data on health outcomes, demographics, and socio-economic factors for various US counties from 2010-2016. It can be used to uncover trends in health outcomes and socioeconomic factors across different counties in the US over a six year period.

The dataset contains a variety of information including statefips (a two digit code that identifies the state), countyfips (a three digit code that identifies the county), avg household size, avg annual count of cancer cases, average deaths per year, target death rate, median household income, population estimate for 2015, poverty percent study per capita binned income as well as demographic information such as median age of male and female population percent married households adults with no high school diploma adults with high school diploma percentage with some college education bachelor's degree holders among adults over 25 years old employed persons 16 and over unemployed persons 16 and over private coverage available private coverage available alone temporary private coverage available public coverage available public coverage available alone percentages of white black Asian other race married households and birth rate.

Using this dataset you can build a multivariate ordinary least squares regression model to predict “target_deathrate”. You will also need to implement k-fold (k=10) cross validation to best select your model parameters. Model diagnostics should be performed in order to assess linearity serial independence heteroskedasticity normality multicollinearity etc., while outliers missing values or categorical variables will also have an effect your model selection process. Finally it is important to interpret the resulting models within their context based upon all given factors associated with it such as outliers missing values demographic changes etc., before arriving at a meaningful conclusion which may explain trends in health outcomes and socioeconomic factors found within this dataset

Research Ideas

• Analysis of factors influencing target deathrates in different US counties.
• Prediction of the effects of varying poverty levels on health outcomes in different US counties.
• In-depth analysis of how various socio-economic factors (e.g., median income, educational attainment, etc.) contribute to overall public health outcomes in US counties

Acknowledgements

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

License

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

The database (version August 2022) is built upon the released Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021). In this database, we assemble a new panel dataset that provides (headcount) poverty rates using the daily poverty lines of US $1.90, $3.20, and $5.50 (based on the revised 2011 Purchasing Power Parity (PPP) dollars). This database is generated using household income and consumption surveys from the World Bank’s Global Monitoring Database (GMD), which underlie country official poverty statistics, and offers the most detailed subnational poverty data on a global scale to date. The Global Subnational Atlas of Poverty (GSAP) is produced by the World Bank’s Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) at the World Bank. The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports. Reference: World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington. https://datacatalog.worldbank.org/search/dataset/0042041

Adjusted Income and Poverty in the US

Changes in People and Health Insurance

By U.S. Census Bureau [source]

About this dataset

The U.S. Bureau of the Census' Current Population Survey, Annual Social and Economic Supplements, presents an insightful look into American society at any given time period. Through it's annual data, one can understand the makeup of a nation across a multitude of parameters--including income level distribution measures, poverty status characteristics and health insurance coverage broken down by age, race/ethnicity and gender.

This chart series is like a snapshot into America's past--allowing us to monitor both current progress made in regards to economic stability while also reflecting on the growth (or lack thereof) achieved over different decades in terms of racial discrepancies in poverty levels as well as an individual's ability present etc to maintain financial health. The series looks at data collected from 1959-2015; providing information on number/percentage all noninstitutionalized population (15+ years old) who are below or above poverty thresholds as well as median earnings for male/female earners adjusted for real inflation values (based on current dollars). Insights such as these enable us to gain key information about how economic disparities have fared during each year throughout this half century time span and how policy changes have impacted the overall wellbeing on a national level since then

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How to use the dataset

Introduction

This dataset contains information on the equivalence-adjusted income and poverty in the US from 1967 to 2015. It includes information on the population without health insurance coverage by state, total workers and full-time, year-round workers by sex and female-to-male earnings ratio, selected measures of equivalence-adjusted income dispersion, people in poverty by selected characteristics, and measures of income inequality. This guide will explain how to use this dataset effectively for analysis.

Data Overview

The datasets contain both summary statistics and detailed breakdowns for different categories throughout the years 1967 to 2015. In Table A1 you can find data on population without health insurance coverage by state during that time period. Table A4 contains total numbers of workers as well as real median earning details organized by sex and male/female earning ratios over time period in question. The tables A3 through 5 include more specific details related to measurements of Equivalence Adjusted Income Dispersion such as Gini Coefficient values.. Both table 2 & 3 provides detail breakdowns relating to Income distribution measurements between 2014 & 2015 along with other related statistical figures regarding individuals below poverty line during this time period based upon age , race , Hispanic Origin factors.

Data Cleaning/Preparation Specifics

This dataset follows a similar notation used throughout each table so it shouldn't be difficult understand what is being represented .However representing individual components like Gini Coefficient (TableA3) or Female ratio Vs Male earnings remains abstract in comparison especially when attempting visualization techniques (Charting). In order for users not familiar with certain terms like “Equivalence -Adjusted Income Dispersion” it would need explaining thoroughly or these terms should at least be highlighted & avoid confusing readers . Level out Missing Data that is within range statistically makes sense according “Census Technical Docs” . For example missing value data pertaining Individual Poverty estimates have based upon qualification requirements where numbers are rounded up after exchange calculations ( See official Raw Data column Notes available under Sources ).

Visualization Strategies

For effective visualization there needs be understanding between what counts supplied are actually representing For example: Column such as Difference Between Female & Male Earnings shown TableA4 helps gauge pay gap but difference between % Measures significantly important when charting any changes overtime diagrams or identifying movements visually from various bar /line graphs dealing this type data set . Other numerical aspects such Gender Ratio

Research Ideas

• Tracking changes in poverty levels over time by state and ethnicity
• Examining the impact of government programs like the EITC and CTC on pov...

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.

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/7lCxY0628A2QHXhMTo3ccQ This indicator is defined as the share of persons with an equivalised disposable income below the risk-of-poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers).

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.

Poverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.

FOCUSONLONDON2011:POVERTY:THEHIDDENCITY One of the defining features of London is that it is a city of contrasts. Although it is considered one of the richest cities in the world, over a million Londoners are living in relative poverty, even before the additional costs of living in the capital are considered. This edition of Focus on London, authored by Rachel Leeser, presents a detailed analysis of poverty in London that reveals the scale and distribution of poverty in the capital. CHARTS: The motion chart shows the relationship between child poverty and worklessness at borough level, and shows how these two measures have changed since 2006. It reveals a significant reduction in workless households in Hackney (down 12 per cent), and to a lesser extent in Brent (down 7 per cent). The bar chart shows child poverty rates and the change in child poverty since 2006. It reveals that while Tower Hamlets has the highest rate of child poverty, it also has one of the fastest falling rates (down 12 per cent), though Haringey had the biggest fall (15 per cent). DATA: All the data contained within the Poverty: The Hidden City report as well as the data used to create the charts and maps can be accessed in the spreadsheet. FACTS: Some interesting facts from the data… ● Highest proportion of children in workless households, by borough, 2010 Westminster – 35.6% Barking and Dagenham – 33.6% Lewisham – 33.1% Newham – 31.4% Islington – 30.6% -31. Barnet – 9.1% -32. Richmond upon Thames – 7.0% ● Changes in proportions of workless households, 2006-09, by borough Hackney – down 12.3% Brent – down 7.3% Tower Hamlets – down 4.8% Lambeth – down 4.2% Hillingdon – down 4.1% -31. Enfield – up 5.8% -32. Bexley – up 7.3% ● Highest reduction in rates of child poverty 2006-09, by borough: Haringey – down 15.0% Newham – down 12.9% Hackney – down 12.8% Tower Hamlets – down 12.1% Southwark – down 11.5% -31. Bexley – up 6.0% -32. Havering – up 10.3%

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

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.

Context

This dataset was uploaded to support the Data Science For Good Kiva crowdfunding challenge. In particular, in uploading this dataset, I intend to assist with mapping subnational locations in the Kiva dataset to more accurate geocodes.

Content

This dataset contains poverty data at the administrative unit level 1, based on national poverty line(s). Administrative unit level 1 refers to the highest subnational unit level (examples include ‘state’, ‘governorate’, ‘province’). This dataset also provides data and methodology for distinguishing between poverty rates in urban and rural regions.

This dataset includes one main .csv file: Subnational-PovertyData.csv, which includes a set of poverty indicators at the national and subnational level between the years 1996-2013. Many countries are missing data for multiple years, and no country has data for the years 1997-1999.

It also includes three metadata .csv files: 1. Subnational-PovertyCountry.csv, which describes the country codes and subregions. 2.Subnational-PovertySeries.csv, which describes the three series indicators for national, urban, and rural poverty headcount ratios. This metadata file also including limitations, statistical methodologies, and development relevance for these metrics. 3. Subnational-Povertyfootnote.csv, which describes the years and sources for all of the country-series combinations.

Acknowledgements

This dataset is provided openly by the World Bank. Individual sources for the different data series are available in Subnational-Povertyfootnote.csv.

This dataset is classified as Public under the Access to Information Classification Policy. Users inside and outside the World Bank can access this dataset. It is licensed under CC-BY 4.0.

Metadata

Type: Time Series Topics: Economic Growth Poverty Economy Coverage: IBRD Languages Supported: English Number of Economies: 60 Geographical Coverage: World Access Options: Download, Query Tool Temporal Coverage: 1996 - 2013 Last Updated: April 27, 2015