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).

This deposit contains three do files which were constructed as part of the project “Intergenerational income mobility: Gender, Partnerships and Poverty in the UK”, UKRI grant number ES/P007899/1. The aim of the do files is to convert partnership, fertility, and labour market activity information provided with the age 55 wave of the National Child Development Study into monthly panel format. There are separate do files to do this for each of the three aspects.This important new work looks to fill an 'evidence deficit' within the literature on intergenerational economic mobility by investigating intergenerational income mobility for two groups who are often overlooked in existing research: women and the poorest in society. To do this, the research will make two methodological advancements to previous work: First, moving to focus on the family unit in the second generation and total family resources rather than individual labour market earnings and second, looking across adulthood to observe partnership, fertility and poverty dynamics rather than a point-in-time static view of these important factors. Specifically it will ask four research questions: 1) What is the relationship between family incomes of parents in childhood and family incomes of daughters throughout adulthood? The majority of previous studies of intergenerational income mobility have focused on the relationship between parents' income in childhood and sons' prime-age labour market earnings. Women have therefore been consistently disregarded due to difficulties observing prime-age labour market earnings for women. This is because women often exit the labour market for fertility reasons, and the timing of this exit and the duration of the spell out of the labour market are related to both parental childhood income and current labour market earnings. This means that previous studies that have focused on employed women only are not representative of the entire population of women. By combining our two advancements, considering total family income and looking across adulthood for women, we can minimise these issues. The life course approach enables us to observe average resources across a long window of time, dealing with issues of temporary labour market withdrawal, while the use of total family income gives the most complete picture of resources available to the family unit including partner's earnings and income from other sources, including benefits. 2) What role do partnerships and assortative mating play in this process across the life course? The shift to focusing on the whole family unit emphasises the importance of partnerships including when they occur and breakdown and who people partner with in terms of education and current labour market earnings. Previous research on intergenerational income mobility in the UK has suggested an important role for who people partner with but has been limited to only focusing on those in partnerships. This work will advance our understanding of partnership dynamics by looking across adulthood at both those in partnerships and at the importance of family breakdown and lone parenthood in this relationship. 3) What is the extent of intergenerational poverty in the UK, and does this persist through adulthood? The previous focus on individuals' labour market earnings has often neglected to consider intergenerational income mobility for the poorest in society: those without labour market earnings for lengthy periods of time who rely on other income from transfers and benefits. The shift in focus to total family resources and the life course approach will allow us to assess whether those who grew up in poor households are more likely to experience persistent poverty themselves in adulthood. 4) What is the role of early skills, education and labour market experiences, including job tenure and progression, in driving these newly estimated relationships? Finally our proposed work will consider the potential mechanisms for these new estimates of intergenerational income mobility for women and the poorest in society for the first time and expand our understanding of potential mechanisms for men. While our previous work showed the importance of early skills and education in transmitting inequality across generations for males, this new work will also consider the role of labour market experiences including job tenure and promotions as part of the process. The NCDS covers all children in England, Scotland and Wales born in one week in 1958. The archived materials are do files that alter the format of existing NCDS datasets to create derived datasets. Original data can be accessed via Related Resources.

This deposit contains three do files which were constructed as part of the project “Intergenerational income mobility: Gender, Partnerships and Poverty in the UK”, UKRI grant number ES/P007899/1. The aim of the do files is to convert partnership, fertility, and labour market activity information provided with the age 46 wave of the British Cohort Study (BCS70) into monthly panel format. There are separate do files to do this for each of the three aspects.This important new work looks to fill an 'evidence deficit' within the literature on intergenerational economic mobility by investigating intergenerational income mobility for two groups who are often overlooked in existing research: women and the poorest in society. To do this, the research will make two methodological advancements to previous work: First, moving to focus on the family unit in the second generation and total family resources rather than individual labour market earnings and second, looking across adulthood to observe partnership, fertility and poverty dynamics rather than a point-in-time static view of these important factors. Specifically it will ask four research questions: 1) What is the relationship between family incomes of parents in childhood and family incomes of daughters throughout adulthood? The majority of previous studies of intergenerational income mobility have focused on the relationship between parents' income in childhood and sons' prime-age labour market earnings. Women have therefore been consistently disregarded due to difficulties observing prime-age labour market earnings for women. This is because women often exit the labour market for fertility reasons, and the timing of this exit and the duration of the spell out of the labour market are related to both parental childhood income and current labour market earnings. This means that previous studies that have focused on employed women only are not representative of the entire population of women. By combining our two advancements, considering total family income and looking across adulthood for women, we can minimise these issues. The life course approach enables us to observe average resources across a long window of time, dealing with issues of temporary labour market withdrawal, while the use of total family income gives the most complete picture of resources available to the family unit including partner's earnings and income from other sources, including benefits. 2) What role do partnerships and assortative mating play in this process across the life course? The shift to focusing on the whole family unit emphasises the importance of partnerships including when they occur and breakdown and who people partner with in terms of education and current labour market earnings. Previous research on intergenerational income mobility in the UK has suggested an important role for who people partner with but has been limited to only focusing on those in partnerships. This work will advance our understanding of partnership dynamics by looking across adulthood at both those in partnerships and at the importance of family breakdown and lone parenthood in this relationship. 3) What is the extent of intergenerational poverty in the UK, and does this persist through adulthood? The previous focus on individuals' labour market earnings has often neglected to consider intergenerational income mobility for the poorest in society: those without labour market earnings for lengthy periods of time who rely on other income from transfers and benefits. The shift in focus to total family resources and the life course approach will allow us to assess whether those who grew up in poor households are more likely to experience persistent poverty themselves in adulthood. 4) What is the role of early skills, education and labour market experiences, including job tenure and progression, in driving these newly estimated relationships? Finally our proposed work will consider the potential mechanisms for these new estimates of intergenerational income mobility for women and the poorest in society for the first time and expand our understanding of potential mechanisms for men. While our previous work showed the importance of early skills and education in transmitting inequality across generations for males, this new work will also consider the role of labour market experiences including job tenure and promotions as part of the process. The BCS70 study covers all children in England, Scotland and Wales born in one week in 1970. The archived materials are do files that alter the format of existing BCS70 datasets to create derived datasets. Original data can be accessed via Related Resources.

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.

Analysis of ‘Poverty rate - ACS 2015-2019 - Tempe Tracts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/65c43ceb-ca8c-49b7-a222-df271a777135 on 11 February 2022.

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

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter.

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This layer shows poverty status by age group. This layer is Census data from Esri's Living Atlas and is clipped to only show Tempe census tracts.

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).

Data is from US Census American Community Survey (ACS) 5-year estimates.

Vintage: 2015-2019

ACS 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

Date of Census update: December 10, 2020

National Figures: data.census.gov

Additional 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)

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

Previous works have estimated the level of chronic poverty suffered by children using a count index, that is, the number of times a child was observed to be poor over a specified period of time. In addressing the question of which child suffers greater chronic poverty, this study looks beyond a count-based approach by paying attention to poverty measurement approaches that account for the timing, spacing and severity of poverty spells. This study is the first to document the poverty experiences of children in a developed nation using these intertemporal lifetime poverty measures. Using the Panel Study of Income Dynamics longitudinal dataset of the United States, I demonstrate that the count index does not account for all aspects of chronic poverty. Specifically, the evidence suggests that spending fewer periods in poverty is not always an indication of less chronic poverty suffered if the depth and distribution of poverty are ignored. I compare chronic poverty experiences between groups of children based on race, age of mother at birth, region, type of household, parental educational attainment and experiences of parental marital dissolution. Not surprisingly, non-whites suffer more chronic poverty than whites. This study shows that this difference is significantly increased when the timing and spacing of poverty spells are accounted for.

Poverty is not a simple matter. He is not only related to income. Poverty is associated as well to the lack of fundamental rights to develop and maintain a more dignified life. One of the basic rights of poor people in inherent rights is to have the human value, to be audible voice. Even when defining the "poor", they must be given space to define their poverty with their own perspective and mind. On the other hand, some of the various poverty reduction programs that have been done in Indonesia, were not exactly targeted, so that often raise conflicts among people, and between communities with the government. Incomplete data and wrong targeting people suspected as some of the causes of these problems. So they who should be the target and get the help do not receive it, and vice versa, they who had not been feasible receive the donation. Targeting becomes priority for programs of social assistance for poor families. In order to provide better targeting results, it needs to search a better indicator or the effective method to improve the identification of the target households who are feasible for various assistance programs that will be implemented in the future period. This activity is called then the Determination of the Household Welfare Ranking 2008 (P2K08). This method namely the Determination of the households Welfare Ranking 2008 (P2K08) from the most insecure to the most secure combines participatory approaches and statistical test. District, city and village and village elected in the application of this method are determined randomly.

A household survey of a randomized control trial in rural Bangladesh conducted in 2014 which collected data on the long-run outcomes of the Targeting the Ultra Poor program conducted by the NGO BRAC. This survey is the 7 year follow-up. The research examines a new set of interventions, pioneered by the world's largest NGO BRAC in Bangladesh, which simultaneously tackle the capital and skills constraint in an attempt to encourage occupational change amongst the world's poorest women. We use randomised control trials of this type of program in Bangladesh to look at whether providing capital and skills can encourage basic entrepreneurship. The issue at hand is whether one can create successful female entrepreneurs - who acquire skills and make use of productive capital - out of poor women who started out with neither. Key to this question is whether asset and skill transfers can induce the poor to alter their occupational choices and permanently exit poverty, as opposed to simply enabling them to increase their consumption in the short term. These questions are highly salient as the world is littered with examples of anti-poverty programs, which despite their best intentions, fail to have any appreciable impact on their intended beneficiaries.The world's poorest people typically lack both capital and skills. They tend to work as in occupations such as agricultural labor or subsistence cultivation which are often insecure and seasonal in nature and which do not require capital or skills. The non-poor, in contrast, tend to be engaged in secure wage employment or to operate their own businesses. Consequently, most anti-poverty programs attempt to target the poor to help them overcome either a lack of capital and or skills. Notable policy interventions along these lines include microfinance programs on the capital side, or vocational training and adult education on the skills side. Yet it is uncertain whether many of these programs are, in fact, able to transform the occupational choices of the poor, and thereby enable them to permanently exit poverty. Occupational change is central to development and growth, but it is the result of a complex set of interactions between individuals, markets and the state, and it is therefore difficult to credibly link occupational change to a lack of capital and skills. The proposed research thus speaks directly to the first overarching question posed in the ESRC-DFID Joint Fund for Poverty Alleviation Research 2012-13 on finding effective means to allow the poorest to exit and stay out of poverty. It also addresses directly the crosscutting issues on structural inequalities (particularly as regards gender) and measurement and metrics (particularly as regards measuring empowerment and the social dynamics and general equilibrium effects induced by the program). The interventions we examine are fundamentally about empowering the poorest women within rural communities both socially and economically so that they can exit and stay out of poverty. The proposed research evaluates an on-going large randomized evaluation of the ultra poor program which is being carried out jointly by the Principal Investigator, Professor Robin Burgess, and the world's largest NGO, BRAC (Bandiera et al, 2012). The data was collected via household surveys conducted in-home by trained enumerators in rural Bangladesh. Answers were written on paper and digitized after survey completion.

This layer shows poverty status by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. 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. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The study aimed to gain insight into the ways that narratives of self-responsibility were taken up and embodied - or alternatively, resisted - within economically disadvantaged communities; the ways that these narratives and associated welfare reforms impacted on mental distress; and the way that these narratives interconnected with the medicalisation and pathologisation of poverty-related distress. (1) The study involved sixteen focus groups with ninety-seven participants (aged 18-65) from economically disadvantaged communities to establish the source and impact of narratives of self-responsibility within people’s everyday lives (36 men, 61 women). (2)Fifty-seven low-income residents (aged 18-65) who had experienced poverty-related mental distress were also interviewed (26 men, 31 women) to understand the cause(s) of their distress, and their responses to this. Participants who were receiving mental health treatment at the time of the study, and participants who wanted more time to discuss their experiences were interviewed on two occasions (total interviews n=80), enabling us to track responses over time and facilitating the triangulation of data. All lived on low-incomes. Whilst not specifically asked to define their status in terms of class, people commonly defined themselves through characteristics or inferences usually associated with being ‘working class.’ (3) Interviews with General Practitioners (n=10) working in low income areas were undertaken to understand their experiences and the challenges they faced supporting mental health amongst patients experiencing poverty, and their perceptions of current treatment options. The provision of effective treatment and support for mental distress is a stated aim of the Department of Health and civil society organisations e.g. Mind. Yet despite a stated need to tackle health inequalities, current strategies e.g. Closing the Gap: Priorities for Essential Change in Mental Health (DoH 2014), frame mental distress as a psychological problem that lies within the individual concerned. This not only suggests that distress can be 'corrected' through medical treatment, but also masks the factors that often underlie the root causes of suffering e.g. poor living conditions, unemployment. At the same time, policies in place to restrict welfare support, and popular media e.g. Benefits Street, draw on moralising narratives that promote the idea that people are responsible for their own actions and circumstances. This research aims to explore how these moralising narratives impact on the ways that people in low-income communities perceive and respond to mental distress caused by material deprivation and social disadvantage, and to examine the impacts of this on their wellbeing. This was achieved through in-depth research in two low-income communities in the South West, which sought to understand: i) the way that moral narratives are defined and used or resisted in people's everyday lives in relation to mental distress; ii) the influence of moral narratives on people's decisions to seek medical support for distress; iii) how moral narratives manifest within GP consultations and influence treatment decisions and patient wellbeing; iv) which responses to mental distress have the potential to effectively support vulnerable populations, and to inform ethical debates on the medicalisation of distress in a way that benefits patients, and assists practitioners and policy makers seeking to support low-income communities. The DeStress Project was a two and half-year research project with two very low-income urban communities (one post-industrial, one coastal with a seasonal employment structure) in the UK’s south-west region. Ethics permission was obtained from the NHS Cambridgeshire and Hertfordshire Research Ethics Committee. The study aimed to gain insight into the ways that narratives of self-responsibility were taken up and embodied - or alternatively, resisted - within economically disadvantaged communities; the ways that these narratives and associated welfare reforms impacted on mental distress; and the way that these narratives interconnected with the medicalisation and pathologisation of poverty-related distress. (1) The study involved sixteen focus groups with ninety-seven participants (aged 18-65) from economically disadvantaged communities to establish the source and impact of narratives of self-responsibility within people’s everyday lives (36 men, 61 women). (2) Fifty-seven low-income residents (aged 18-65) who had experienced poverty-related mental distress were also interviewed (26 men, 31 women) to understand the cause(s) of their distress, and their responses to this. Of these participants, eighty one per cent had been prescribed antidepressants, whilst a further seven per cent had refused the prescription offered. The remaining thirteen per cent had been advised to self-refer to talking therapy, or had chosen to avoid interaction with health services. Potential participants were alerted to the study by community and health practitioners, social media and word-of-mouth and recruited through community groups and GP surgeries. Participants who were receiving mental health treatment at the time of the study, and participants who wanted more time to discuss their experiences were interviewed on two occasions (total interviews n=80), enabling us to track responses over time and facilitating the triangulation of data. In almost all cases, study participants had lived in an economically disadvantaged area throughout their lives, though older participants in one area had also lived there at a time when it was more prosperous. All lived on low-incomes. Whilst not specifically asked to define their status in terms of class, people commonly defined themselves through characteristics or inferences usually associated with being ‘working class.’ (3) Interviews with General Practitioners (n=10) working in low income areas were undertaken to understand their experiences and the challenges they faced supporting mental health amongst patients experiencing poverty, and their perceptions of current treatment options. Informal discussions with key service providers from health, education and social sectors were also undertaken to gain insight into their experiences of working with people living with the stresses of poverty. Sixteen focus groups with a total of ninety-seven participants, aged 18-65, from the two study sites (36 men and 61 women), with the gender ratio reflecting reported rates of common mental disorders in England (NHS Digital 2016) . Participants were recruited via community groups and settings, word of mouth and advertising on posters and social media. Participants were asked about the main health issues and stresses faced by local residents, how people respond to those stresses and their impact on wellbeing. In addition, eighty interviews were undertaken with fifty-seven residents (aged 18-65) who had experienced poverty-related distress (26 men, 31 women) to gain a more in-depth understanding of the source(s) of this distress, and their responses to it. Interviewees were recruited via the focus groups and word of mouth but also via GP surgeries to capture a broad range of views and experiences (including those who may be socially isolated). In the majority of cases, people had sought medical support for their distress, although two had chosen not to. Participants who were engaged in the health system for their distress at the time of the study, and participants who wanted more time to discuss their experiences were interviewed on two occasions, enabling us to capture any changes over time and understand the ongoing dynamic interaction between mental ill-health and welfare reform. The interviews and focus groups generated a rich body of narrative data that gives prominence to the voices and experiences of people living in low-income communities. This data has been supplemented with interviews with General Practitioners (n=10) to understand the challenges they face supporting people experiencing poverty-related distress.

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen’s kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

Dataset of the results published in ChemElectroChem 2024, 11, e202300525 (doi.org/10.1002/celc.202300525).

The use of symmetrical cells is becoming popular for the search of new electroactive materials in redox flow batteries. Unfortunately, low-cost battery cyclers, commonly used for electrochemical battery testing, are not compatible with symmetrical cells since they usually cannot apply negative bias voltages needed for symmetrical cells. The insertion of a Ni-Cd battery in the voltage sensing path is a simple and effective methodology to overcome this limitation for certain battery cyclers. Herein, the validity of this useful method is evaluated for other battery cyclers, realizing that the strategy is not universal. A modified methodology is developed for a battery cycler in which the previous method is not valid. The new strategy is based on inserting a Ni-MH battery in the current path, and enables using a low-cost Neware CT-4008T-5V6A-S1 cycler for ferro- /ferricyanide symmetrical cells demonstrating proper operation for >19 days. This new method possesses advantages, e.g. direct reading of the cell voltage, and disadvantages, e.g. the Ni-MH battery is charged/discharged during operation, which are discussed. The four battery cyclers evaluated shows that, despite neither method is universal, both methods are complementary to each other. Thus, the decision of using either one method or the other must be reached on a case-by-case basis.

Have you ever considered where pockets of poverty exist and who is most affected? Unfortunately, global patterns indicate that children are most impacted by poverty. Across the globe, a staggering 333 million children live in conditions of extreme poverty. Why is poverty such a critical issue? Because it affects the overall well-being of a person. Those living in poverty often encounter barriers to basic necessities like food, shelter, and healthcare. Growing up without consistent nutrition, shelter, and safety can have long-lasting developmental impacts on children and can cause lifelong problems. For more, read: Child poverty | UNICEF

This round of Euro-Barometer surveys queried respondents on standard Euro-Barometer measures such as public awareness of and attitudes toward the Common Market and the European Community (EC), and also focused on poverty and social exclusion, examining the extent and immediacy of these problems for respondents. Items covered whether the respondent's family or friends were experiencing poverty or social exclusion, how often the respondent saw instances of poverty and social exclusion, and whether the respondent believed that people had a chance of rising out of these circumstances. Respondents were also asked about the main reasons for poverty and social exclusion, the best ways to combat these conditions, what the role of volunteer groups, unions, employers, and the European Community (EC) should be, and whether the fight against poverty and social exclusion should be a priority objective for the EC. Also included were questions that asked whether respondents had given or would give any time to help disadvantaged people and what types of activities they had performed or would be prepared to perform. Respondents were asked to compare the current general economic and employment situations in their countries, the financial situation of their households, and their job situations with those of 12 months ago and 12 months ahead. Respondents were also asked to rate various aspects of their everyday life, including housing, income, work, social entitlements, and health. On EC matters, respondents were asked how well-informed they felt about the EC, what sources of information about the EC they used, whether their country had benefited from being an EC member, and the extent of their personal interest in EC matters. Demographic and other background information was gathered on number of people residing in the home, size of locality, home ownership, trade union membership, region of residence, and occupation of the head of household, as well as the respondent's age, sex, marital status, education, occupation, work sector, religion, religiosity, subjective social class, left-right political self-placement, and opinion leadership. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR06360.v2. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.

A broad and generalized selection of 2011-2015 US Census Bureau 2015 5-year American Community Survey poverty data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of populations living below the poverty level, as grouped by age, sex, education, workforce status, and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Accuracy measures on a new independent dataset with PCA.

A flexible income and fixed expenses : Economic stress among on call-employees The aim of the study was to investigate the economic situation of temporary employees. The study group consisted of 778 on call employees.The response rate was 56 percent. Twenty percent had experience from being treated in different economic affairs.

Over 50 percent of them answered that the form of employment was an impediment to get a loan and above 40 percent that it was an impediment to get a lease. The study group is strongly polarized as regards to economical matters. There is a clear connection between poverty and health. Those who often worried about economy and were poor had lower psychological well-being (GHQ-12), more complaints from stomach, back and neck, more headache, and were more tired and listless.

Purpose:

To investigate the economic situation of temporary employees.

Early chemical enrichment processes can be unveiled by the careful study of metal-poor stars. In our Local Group, we can obtain spectra of individual stars to measure their precise, but not always accurate, chemical abundances. Unfortunately, stellar abundances are typically estimated under the simplistic assumption of local thermodynamic equilibrium (LTE). This can systematically alter both the abundance patterns of individual stars and the global trends of chemical enrichment. The SAGA database compiles the largest catalogue of metal-poor stars in the Milky Way. For the first time, we provide the community with the SAGA catalogue fully corrected for non-LTE (NLTE) effects, using state-of-the-art publicly available grids. In addition, we present an easy-to-use online tool NLiTE that quickly provides NLTE corrections for large stellar samples. For further scientific exploration, NLiTE facilitates the comparison of different NLTE grids to investigate their intrinsic uncertainties. Finally, we compare the NLTE-SAGA catalogue with our cosmological galaxy formation and chemical evolution model, NEFERTITI. By accounting for NLTE effects, we can solve the long-standing discrepancy between models and observations in the abundance ratio of [C/Fe], which is the best tracer of the first stellar populations. At low [Fe/H]<-3.5, models were unable to reproduce the high measured [C/Fe] in LTE, which are lowered in NLTE, aligning with the simulations. Other elements are a mixed bag, where some show improved agreement with the models (e.g. Na) and other appear even worse (e.g. Co). Few elemental ratios do not change significantly (e.g. [Mg/Fe], [Ca/Fe]). Properly accounting for NLTE effects is fundamental for correctly interpreting the chemical abundances of metal-poor stars. Our new NLiTE tool, thus, enables a meaningful comparison of stellar samples with chemical and stellar evolution models as well as with low-metallicity gaseous environments at higher redshift. Cone search capability for table J/A+A/699/A32/table4 (Coordinates and stellar atmospheric parameters of all MP SAGA entries)

Much recent attention has been paid to the interaction between poverty and conflict in developing countries. However, it is surprising that neither the academic nor the international development community has as of yet, systematically examined the influence of international inequalities upon poverty and conflict. The project proposes that the prevalence of poverty and conflict is strongly conditioned by countries' position within the international economic system. The nature of a country's economic ties with the rest of the world - often deeply unequal - can create significant dependencies and / or incentives to challenge the status quo, resulting in poverty-provoked violence. The project uses network analysis and matching methods. The network analysis is used to map out key international economic networks (aid, trade, and FDI) and generate measures of countries' direct and indirect relations with other states plus their position within the overall structure. These network measures are then used in a statistical method of matching countries to infer whether dependent countries are more likely to succumb to poverty-provoked conflict. The findings from the project will identify the extent to which international inequality traps lead to poverty and conflict traps in developing countries, and help to draw out the policy implications of this.

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.