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 map was updated in April of 2022. To see the archived version of this map, click here: https://nmcdc.maps.arcgis.com/home/item.html?id=2e4c4c4cafcc49db80837f32912e66a5#overviewThis map displays data from the Selected Economic Indicators (DP03) dataset from the 2020 American Community Survey 5-Yr Estimates, U.S. Census Bureau. Data is shown at the level of Census Tract and County levels. Small Areas are not on this map at this time (aggregation of Census Tracts developed by the New Mexico Department of Health). Measuring poverty is a topic of much current discussion. See the following links: A Different Way to Measure Poverty - https://www.sanders.senate.gov/imo/media/image/census.jpg"Few topics in American society have more myths and stereotypes surrounding them than poverty, misconceptions that distort both our politics and our domestic policy making."They include the notion that poverty affects a relatively small number of Americans, that the poor are impoverished for years at a time, that most of those in poverty live in inner cities, that too much welfare assistance is provided and that poverty is ultimately a result of not working hard enough. Although pervasive, each assumption is flat-out wrong." -Mark Rank, Professor of Social Welfare at Washington University: https://opinionator.blogs.nytimes.com/2013/11/02/poverty-in-america-is-mainstream/

This map displays data from the Selected Economic Indicators (DP03) dataset from the 2010 American Community Survey 5-Yr Estimates, U.S. Census Bureau. Data is shown at the level of Census Tract, County, and Small Area (aggregation of Census Tracts developed by the New Mexico Department of Health). Measuring poverty is a topic of much current discussion. See the following links: A Different Way to Measure Poverty - http://www.sanders.senate.gov/imo/media/image/census.jpg"Few topics in American society have more myths and stereotypes surrounding them than poverty, misconceptions that distort both our politics and our domestic policy making."They include the notion that poverty affects a relatively small number of Americans, that the poor are impoverished for years at a time, that most of those in poverty live in inner cities, that too much welfare assistance is provided and that poverty is ultimately a result of not working hard enough. Although pervasive, each assumption is flat-out wrong." -Mark Rank, Professor of Social Welfare at Washington University: http://opinionator.blogs.nytimes.com/2013/11/02/poverty-in-america-is-mainstream/

This map displays data from the Selected Economic Indicators (DP03) dataset from the 2010 American Community Survey 5-Yr Estimates, U.S. Census Bureau. Data is shown at the level of Census Tract, County, and Small Area (aggregation of Census Tracts developed by the New Mexico Department of Health). Measuring poverty is a topic of much current discussion. See the following links: A Different Way to Measure Poverty - http://www.sanders.senate.gov/imo/media/image/census.jpg"Few topics in American society have more myths and stereotypes surrounding them than poverty, misconceptions that distort both our politics and our domestic policy making."They include the notion that poverty affects a relatively small number of Americans, that the poor are impoverished for years at a time, that most of those in poverty live in inner cities, that too much welfare assistance is provided and that poverty is ultimately a result of not working hard enough. Although pervasive, each assumption is flat-out wrong." -Mark Rank, Professor of Social Welfare at Washington University: http://opinionator.blogs.nytimes.com/2013/11/02/poverty-in-america-is-mainstream/

Table from the American Community Survey (ACS) 5-year series on poverty and employment status related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B23025 Employment Status for the Population 16 years and over, B23024 Poverty Status by Disability Status by Employment Status for the Population 20 to 64 years, B17010 Poverty Status of Families by Family Type by Presence of Related Children under 18 years, C17002 Ratio of Income to Poverty Level in the Past 12 Months. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B23025, B23024, B17010, C17002Data downloaded from: Census Bureau's Explore Census Data The 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. 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: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 2020 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.

Context

What is most important for how healthy, wealthy, and educated you are is not who you are, but where you are. Your knowledge and how hard you work matter too, but much less than the one factor that is entirely outside anyone’s control: whether you happen to be born into a productive, industrialized economy or not.

Global income inequality is vast. The chart – which shows the world population’s daily incomes adjusted for the price differences across countries – shows this.

The huge majority of the world is very poor. The poorer half of the world, almost 4 billion people, live on less than $6.70 a day.

If you live on $30 a day you are part of the richest 15% of the world ($30 a day roughly corresponds to the poverty lines set in high-income countries).

Content

Data comes from https://ourworldindata.org/global-economic-inequality-introduction

Acknowledgements

https://images.theconversation.com/files/183744/original/file-20170829-10454-jcn2n4.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1200&h=1200.0&fit=crop" alt="">

Inspiration

Compare, Analyze inequality per continent, per period...

This report and related data update district-level poverty maps for Laos using the small area estimation (SAE) technique and the most recent Lao Expenditure and Consumption Survey 2018–2019 (LECS 6) and the Population and Housing Census 2015 (PHC 4). On the one hand, LECS collects detailed information on household expenditures required for estimating monetary poverty but limits poverty estimation below the provincial level. On the other hand, PHC collects data from every household but does not include household expenditures as this data is generally too costly and time-consuming to include. The SAE technique combines two sources of data and produces monetary poverty indicators at the district level. This report presents the SAE results as well as poverty estimates and poverty maps at the district level. The three key findings are: (i) there is a large variation in poverty rates across districts within the same province; (ii) poverty is high in districts located in mountainous areas bordering Vietnam and low in districts located on the Mekong River plain and areas bordering China; and (iii) districts with the highest number of poor people are mainly located in Savannakhet, Oudomxay, and Saravan.

BackgroundAddressing Citizen’s perspectives on homelessness is crucial for the design of effective and durable policy responses, and available research in Europe is not yet substantive. We aim to explore citizens’ opinions about homelessness and to explain the differences in attitudes within the general population of eight European countries: France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden.MethodsA nationally representative telephone survey of European citizens was conducted in 2017. Three domains were investigated: Knowledge, Attitudes, and Practices about homelessness. Based on a multiple correspondence analysis (MCA), a generalized linear model for clustered and weighted samples was used to probe the associations between groups with opposing attitudes.ResultsResponse rates ranged from 30.4% to 33.5% (N = 5,295). Most respondents (57%) had poor knowledge about homelessness. Respondents who thought the government spent too much on homelessness, people who are homeless should be responsible for housing, people remain homeless by choice, or homelessness keeps capabilities/empowerment intact (regarding meals, family contact, and access to work) clustered together (negative attitudes, 30%). Respondents who were willing to pay taxes, welcomed a shelter, or acknowledged people who are homeless may lack some capabilities (i.e. agreed on discrimination in hiring) made another cluster (positive attitudes, 58%). Respondents living in semi-urban or urban areas (ORs 1.33 and 1.34) and those engaged in practices to support people who are homeless (ORs > 1.4; p<0.005) were more likely to report positive attitudes, whereas those from France and Poland (p<0.001) were less likely to report positive attitudes.ConclusionThe majority of European citizens hold positive attitudes towards people who are homeless, however there remain significant differences between and within countries. Although it is clear that there is strong support for increased government action and more effective solutions for Europe’s growing homelessness crisis, there also remain public opinion barriers rooted in enduring negative perceptions.

Abstract

In 2005, BPS do the Social Economic Colletion (PSE05), which aims to get the data in the form of micro poverty households directory that deserves a direct cash assistance (BLT) in 2005-2006. Given the data turns PSE05 considered better results compared to a database available in local government. Nevertheless, it is recognized that the data from PSE05 not perfect. The study of 56 universities found the data from PSE05 still contains 8 percent and 22.36 percent error inclusion exclusion error (Coordinating Minister for People's Welfare 2005). In addition to BLT program, data PSE05 also used in targeting households on several national programs, such as the Health Insurance of the Poor (HIP) and cheap rice program for the poor (Raskin). These programs succeeded in reducing poverty levels, poor households proved as much 17.8 percent in 2006 down to 15.4 percent in 2008. But be aware that the collection PSE05 an activity that is great for BPS, BPS so many other activities are pending at this time. After PSE05 activities, in 2007 the BPS also conducted the data collection for the household conditional direct cash assistance program (Family Hope Program / PKH) in 2007. The collection of data to support this program called Basic Health Care Survey and Education 2007 (SPDKP07). Results from SPDKP07 considered much better than the data from PSE05 because only less inclusion and exclusion errors of his. This is because SPDKP07 implemented only in 953 districts / cities were selected and a much larger budget.

Geographic coverage

Coverage provincial representative to the level of the village / district.

Analysis unit

The unit of analysis is the individual in the household, from each selected household collected information about the general state of each member of the household including name, relationship to head of household, sex, and age.

Universe

This survey covers all household members.

Kind of data

Sample survey data

Sampling procedure

In measuring poverty, BPS uses the concept of the ability to meet basic needs (basic needs). For macro data and information poverty, the data source is the National Socio-Economic Survey (NSES) BPS conducted every year. For micro poverty data, in 2005 the BPS has conducted Social Economic Colletion (PSE05), which aims to get a database of poor households who deserve direct cash assistance (BLT) in 2005-2006. In addition to BLT program, data PSE05 also used in targeting households on several national programs, such as the Health Insurance of the Poor (HIP) and cheap rice program for the poor (Raskin). After PSE05 activities, in 2007 the BPS also organize poverty micro data collection for household database program recipients of Direct Conditional Cash Transfer (Family Hope Program) in 2007 and 2008 through a survey of Primary Health Care and Education 2007 (SPDKP07).

Mode of data collection

Face-to-face [f2f]

Life situation, wishes, needs and interests of children. Feelings of justice and fears.

I. Children´s questionnaire:

Topics: 1. Colloquial language at home; evaluation of the time available from mother and father or of their new life partners for the child; satisfaction with the care of parents; praying at home; regular attendance at church; number of books in the household; own or shared children´s room; media equipment of the children´s room; experiencing restrictions or poverty (scale); satisfaction with the freedoms granted by parents; sense of justice (rich parents should pay more for the journey of a care group than parents of poorer children, some families have little, some very much money, adults decide on the construction of children´s playgrounds, foreign children may only speak German during breaks); sense of justice in the family, at school, with friends, in Germany and all over the world; fair treatment in Germany with the following groups: children or young people, old people, foreigners, disabled people, poor people; frequency of perceived disadvantages due to age, gender, appearance, poverty of the parental home, foreign origin of a parent.

1. School/ Institutions: school class attended; satisfaction with school; self-assessment of school performance; attending a half-day or full-day school; preference for half-day school; forms of co-determination at school (classroom design, choice of bank neighbour, arrangement of tables, school excursion goals, project topics, design of class rules and school festivals); regular use of after-school care (lunchtime care at the school, after-school care centre, other facility or group for afternoon care); satisfaction with afternoon care; private tutoring; targeted secondary school; targeted school leaving certificate.

2. Leisure time, media use and friendships: frequency of selected leisure activities; satisfaction with leisure time; reading frequency; television consumption per day; frequency of computer games; computer play time per day; own mobile phone; Internet access; regular Internet use; number of hours per week on the Internet; preferred activities on the Internet; number of friends; number of really good friends; easy or difficult to make friends; frequency of contact with friends at school, at lunchtime, outdoors, at home, with friends at their home, at the club and online; satisfaction with the circle of friends; feeling comfortable in the neighbourhood (only a few public transport, scolding neighbours, enough play friends in the neighbourhood, too much traffic in the street, fear of aggressive young people and adults from the neighbourhood, playground or free meadow within walking distance); satisfaction with one´s own body weight.

3. Attitudes and participation in everyday life: parents´ permission to make own decisions in various areas (e.g. what friends and clothes, pocket money, leisure activities, etc.); co-determination in the family with regard to leisure activities; importance of one´s own opinion among selected persons; frequency of fears in selected areas (bad marks, unemployment of parents, being threatened or beaten, environmental pollution, more poor people, outbreak of war, migration of foreigners to Germany); political interest; politicians think about the well-being of children; life satisfaction.

Demography: sex; age; household size; relationship to persons living in the household (household composition); siblings; number of younger and older brothers and sisters; country of birth of parents (migration background).

Additionally coded: respondent-ID; year of the survey; willingness of the respondent to cooperate; survey in the presence of third parties; degree of relationship to persons present; intervention of persons in the course of the interview.

II Parent Questionnaire:

The parents were asked for themselves and their partner: mother or father of the child; family situation; age; highest school leaving certificate; employment situation; professional position; unemployment; desire for more or less work (only employed persons); desire for gainful employment (not employed persons); country of birth; nationality; religion; nationality of the child (German, non-German, dual nationality); type of school attended by the child; association membership of the child; child has attended a kindergarten; age of the child at the time of first attendance of a kindergarten; type of house; residential status; adequate household income to ma...

Poverty and social exclusion, social services, climate change, and the national economic situation and statistics.

Topics: 1. Poverty and social exclusion: own life satisfaction (scale); satisfaction with family life, health, job, and satisfaction with standard of living (scale); personal definition of poverty; incidence of poverty in the own country; estimated proportion of the poor in the total population; poor persons in the own residential area; estimated increase of poverty: in the residential area, in the own country, in the EU, and in the world; reasons for poverty in general; social and individual reasons for poverty; population group with the highest risk of poverty; things that are necessary to being able to afford to have a minimum acceptable standard of living (heating facility, adequate housing, a place to live with enough space and privacy, diversified meals, repairing or replacing a refrigerator or a washing machine, annual family holidays, medical care, dental care, access to banking services as well as to public transport, access to modern means of communication, to leisure and cultural activities, electricity, and running water); perceived deprivation through poverty in the own country regarding: access to decent housing, education, medical care, regular meals, bank services, modern means of communication, finding a job, starting up a business of one’s own, maintaining a network of friends and acquaintances; assessment of the financial situation of future generations and current generations compared to parent and grandparent generations; attitude towards poverty: necessity for the government to take action, too large income differences, national government should ensure the fair redistribution of wealth, higher taxes for the rich, economic growth reduces poverty automatically, poverty will always exist, income inequality is necessary for economic development; perceived tensions between population groups: rich and poor, management and workers, young and old, ethnic groups; general trust in people, in the national parliament, and the national government (scale); trust in institutions regarding poverty reduction: EU, national government, local authorities, NGOs, religious institutions, private companies, citizens; reasons for poverty in the own country: globalisation, low economic growth, pursuit of profit, global financial system, politics, immigration, inadequate national social protection system; primarily responsible body for poverty reduction; importance of the EU in the fight against poverty; prioritized policies of the national government to combat poverty; assessment of the effectiveness of public policies to reduce poverty; opinion on the amount of financial support for the poor; preference for governmental or private provision of jobs; attitude towards tuition fees; increase of taxes to support social spending; individual or governmental responsibility (welfare state) to ensure provision; attitude towards a minimum wage; optimism about the future; perceived own social exclusion; perceived difficulties to access to financial services: bank account, bank card, credit card, consumer loans, and mortgage; personal risk of over-indebtedness; attitude towards loans: interest free loans for the poor, stronger verification of borrowers by the credit institutions, easier access to start-up loans for the unemployed, free financial advice for the poor, possibility to open a basic bank account for everyone; affordable housing in the residential area; extent of homelessness in the residential area, and recent change; adequacy of the expenditures for the homeless by the national government, and the local authorities; assumed reasons for homelessness: unemployment, no affordable housing, destruction of the living space by a natural disaster, debt, illness, drug or alcohol addiction, family breakdown, loss of a close relative, mental health problems, lack of access to social services and support facilities, lack of identity papers, free choice of this life; probability to become homeless oneself; own support of homeless people: monetary donations to charities, volunteer work in a charity, help find access in emergency shelters and with job search, direct donations of clothes to homeless people, buying newspapers sold by homeless people, food donations; sufficient household income, or difficulties to make ends meet; ability to afford the heating costs, a week’s holiday once a year, and a meal with meat ever...

Translation (Dau Hkawng) The story I am going to tell is about a poor man and a rich man. Once upon a time, there was a rich man in a small town. The rich man was wealthy, but he was very proud and selfish and he never helped anyone and lived without loving others. But the poor man was a man who loved others very much and was very helpful. He also helped a lot when others were injured. One day, the rich man was traveling, he met with the poor man while he visited a village. The rich man knew and saw how the poor man did to help others. Then, the rich man asked to the poor man, "Why are you helping people in need?" The poor man replied to the rich man, "I was so gratified to help those desperate persons." So, the rich man said to the poor man, "I did not want to help the poor people at all." "If I have difficulties, they will not help me back," continued the rich man. "You should not say like that! "if you help others, you will have someone to help you when you need help," the poor man replied. "I do not understand! Don't say anything to me! Yelled the rich man. Then, the rich man was wondering, "Why this poor man wanted so much to help others." No matter how hard he tried, he could not figure it out. The poor man kept on helping others, and the day came that the others were helping back to him. Then, the rich man considered by himself, "the poor man was right." And he decided that what he needed to do as he learned how to do it right. He met the poor man again while helping people, and the poor man asked, "Why are you helping people to those who were in need?" So, "because I became to want to help people as you were helping people", replied the rich man. At the same time, "let me help people together with you," the rich man asked the poor man. "Well! If you have yearned to help people, fine and good," replied the poor man. From then on, the poor man and the rich man worked together to help the needy. And also, they helped each other and gradually became close friends. Transcription (Lu Awng) Moi shawng e aw ngai tsun na maumwi gaw matsan hte sahte la wa ai lam re nga ai. Moi shawng e da mare langai mi hta sahte langai mi nga ai da. Dai sahte wa gaw masha ni hpe ma nchye garum ai, tsawra myit ma n rawng ai sha i shi gaw grai gawng ba ai, ngai gumhpraw lu ai, ngai lauban re nga na shi tingyeng myit tinang na tinang dai hku nga ai da. Matsan ai wa gaw i masha ni hpe ma grai chye tsawra ai da, re na masha ni hpe ma grai garum ai da, masha ni dai hku kala n ba ni hkrum nga yang mung shi masha ni hpe grai chye garum la ai da. Lani mi na aten hta da lauban wa gaw i shi dai hku hkrunlam hkawm ai she mare hkan e wam hkawm ai shaloi ndai matsan wa hte hkrum ai da. Matsan wa gaw i ding re matsan yen ni hpe garum taw ai lauban wa mu ai da. Mu na nang hpa majaw i matsan mayen ni hpe garum taw ai rai ngu tsun ai da. Ngai gaw i nding re matsan mayen ni hpe garum ai hpe sha ngai myit shadik la lu ai re ngu dai hku tsun ai da. Dai shaloi lauban wa ngai gaw dingre matsan mayen ni hpe kachyi pi nkam garum ai ngu da. Shanhte mung ngai dingre byin wa yan shanhte ngai hpe garum na nre ngu dai hku tsun ai da. Da hku tsun ai shaloi dai matsan wa gaw nang dai hku hkum myit le masha ni i, tinang hta masha ni hpe garum yang lani mi ten hta tinang bai yak hkak hkrum wa yang i masha ni tinang hpe bai garum na nga le ngu na dai hku tsun, lauban wa gaw ngai hpe hpa hkum sa tsun ngai hpa nchye na ai ngu dai hku tsun ai da, lauban wa gaw dai shaloi dai hku sha nga re shaloi shi gaw i tinang hkrai myit ai, ndai wa hpa na masha ni hpe dai hku garum ai ta ngu dai hku tsun ai shaloi dai lauban wa gaw i gara hku tim (ahpye) nlu shapraw ai da, matsan wa gaw lani hte lani dai hku garum garum re na masha ni hte ma shi hpe garum ai ten bai du wa ai da. Dai shaloi lauban wa gaw shaloi she ndai wa tsun ai kaja wa teng ai she re nga ngu, ngai ma masha ni hpe garum na re ngu na dai hku tsun na myit shadang na nan shi garum wa ai ten hta lani mi matsan wa bai mu ai da, nang hpa majaw i nang bai garum ai rai ngu matsan wa gaw lauban wa hpe bai san ai da, dai shaloi lauban wa gaw nang pyi naw masha ni hpe garum ai zawn ngai ma garum mayu ai ngu dai hku tsun ai da. Dai shaloi lauban wa gaw ngai hpe ma i nang hte rau masha ni hpe garum na ahkaw ahkang jaw rit ngu na matsan wa i, nang hte rau garum na ah hkang jaw rit ngu na matsan wa kaw a hkaw ahkang hpyi ai shaloi gaw nang she garum na nga yang gaw mai ai ngu na dai matsan wa gaw i dai kaw na shan 2 gaw i rau pawng na matsan mayen ni hpe i ding re shat ni nlu sha, hka ni nlu lu ding re matsan mayen ni hpe i garum shingtau na i shan 2 gau ngwi gau ngwi na manaw manang kaja tai wa ai da. . Language as given: Jinghpaw

Our ability to recognise and identify or categorise stimuli underlies almost all of our interaction with the world. We identify and categorise items many times each day. For example, we can recognise hundreds or thousands of different faces, and seem able to do so almost effortlessly. The research will investigate some of the cognitive processes that underlie this ability. Previous research has revealed that despite our ability to deal with stimuli that differ from one another on lots of different attributes (eg, faces), we are very bad at identifying stimuli that differ from one another on only a single attribute. We can only accurately identify each stimulus in a set if the set contains fewer than approximately seven members. These sorts of tasks are called absolute identification tasks. For example, we can only identify about five or six stimuli if the stimuli differ only in how bright they are. Further, this limit seems to be common to all of our sensory modalities. We can only identify up to about five or six tones that differ from one another in how loud they are, or drinks that differ from one another in how sweet they are, or electric shocks that differ from one another only in how intense they are, or smells that differ from one another in how strong they are. The fact that this result holds across such a wide variety of stimuli suggests that there is some fundamental cognitive limit in this unidimensional identification ability. However, a full account of why we should be so bad at this has yet to be developed, despite at least fifty years of work in the area. The research will deliver a new, unified account of people's ability to represent and process simple perceptual attributes (eg, brightness, loudness, sweetness, etc). The existing models of this ability all assume that people identify a stimulus by comparing it to long-term internal representations of the magnitudes (ie, loudnesses, brightnesses, sweetnesses, etc) of previously encountered stimuli. Ultimately, a single model will be selected that incorporates the strengths of the current models within a single framework. The new unified model can then be used by psychologists as a building block in models of more complicated cognitive tasks.

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Bangladesh has continued to improve access to education and educational attainment. Gains have been equitable, reducing disparities by gender, wealth, and geography. Yet progress is still needed at higher education levels, and there are still persistent gaps between the poor and rich and across districts. Gains are partly the result of Government of Bangladesh (GOB) efforts to improve education outcomes, but also reflect increased private spending by households. GOB education spending is still low compared to other countries in the region and presents large variation across the territory, which is not correlated with education outcomes and internal efficiency indicators. Only when public spending translates into lower student-to-teacher ratios do outcomes seem to improve, but those ratios remain inadequate compared to other countries and unevenly distributed across districts. Focusing on higher quality spending rather than increasing overall budgets will be a priority for further progress. Stipend programs help with the progressivity of the system at the primary level. However, at the secondary level, there is still significant room to improve the progressivity of these benefits. Finally, addressing norms and expectations around the benefits of schooling can be an important avenue to increase school attendance. About four in ten secondary school-age children out of school report lack of interest or being too old to go back as their main reasons for not attending school; three in ten females cite family chores and marriage as reasons for not attending.

Abstract

The Sierra Leone Core Welfare Indicators Questionnaire (CWIQ) survey provides information for management of the Sierra Leone economy and society. It embodies the results of a household survey designed to produce indicators for social welfare in a cheaper and more regular way to provide instruments for continuous monitoring of the poverty alleviation programme. The CWIQ survey produces information for measuring key changes in social indicators for different population groups in particular Indicators of access, use and satisfaction with social services. The overall objective of the Sierra Leone CWIQ survey, 2007 was to provide timely information for monitoring the implementation of the Sierra Leone Poverty Reduction Strategy and to begin a process of capacity building for the design, implementation, processing and analysis of household surveys within Statistics Sierra Leone (SSL) to strengthen the Poverty Reduction Strategy Monitoring and Evaluation System. This report presents the major findings of the CWIQ survey carried out from 5 April-10 May 2007 by SSL. A sample size of 7,800 households, covering rural and urban areas, in all nineteen Local Councils of the four administrative regions of the country was selected from a total of 520 Enumeration Areas. Detailed information was collected on most aspects of poverty such as demographic characteristics, education, health, employment, household assets, household amenities, poverty predictors, children under five, maternal child health and agriculture. The major findings of the survey are summarized in the order of the relevant chapters of the report.

Geographic coverage

The CWIQ Survey covered all four Sierra Leone administrative regions and nineteen Local Councils. Five hundred and twenty (520) Enumeration Areas (E.A.s) covering rural and urban areas in each of the Local Councils were sampled. Fifteen households were sampled in each EA and resulted in an overall sample of 7,800 households.

Analysis unit

The survey design was based on a stratified two-stage sample design using existing SSL sampling frame (2004 Population and Housing Census). E.A.'s served as primary sampling units while households served as secondary sampling units. The survey design enabled reporting of results at Local Council, Regional and National levels.

Universe

The survey covered sampled households and all household members country-wide.

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 7,797 households were enumerated from a sample of 7,800 households, in 520 Enumeration Areas, giving the survey coverage rate of 98.4 percent (Table 1.2 of Appendix 1).For each enumeration area a reserve list of three household was selected for replacement due to refusals, respondents not at home, households not located, moved away among others. Only three Local Councils Kenema District, Kenema Town and Kambia District had 100% completed of the original households in the sample. The rest of the Local Council areas had some household replaced due to refusals or not found. The Southern Region had the highest level of replacement households of 3.3% and the Eastern Region had the lowest level of replacement of 0.1%. Nationally, 4,905 households were covered in the rural areas while 2,892 households were covered in the urban areas.

Sampling deviation

For each enumeration area a reserve list of three household was selected for replacement due to refusals, respondents not at home, households not located, moved away among others.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instruments included the modified generic scannable CWIQ questionnaire; the interviewer's manual and supervisor's manual. CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 IMPORTANT Create a reference number by combining the household and questionnaire numbers. Write this number NOW on the top of all pages. Page 1 of 14 Q.1 INTERVIEWER'S NAME A.1 CLUSTER A.2 HOUSEHOLD A.3 SEQ. A.4 INTERVIEWER A.5 DATE A.6 TIME A.7 RESPONDENT Q.2 NAME OF HEAD OF HOUSEHOLD Q.3 PROVINCE/REGION Q.4 DISTRICT Statistics Sierra Leone A.J. Momoh Street Freetown, Sierra Leone Comments A - INTERVIEW INFORMATION C W I Q Core Welfare Indicators Questionnaire I Q.5 LOCAL COUNCIL Q.6 CHIEFDOM/WARD Quest. N o. Hour Min. AM PM Member N o. A.8 RESULT Complete with selected household Complete with replacement - refusal Complete with replacement - not found Incomplete 1 2 3 4 A.9 INTERVIEW END Hour Min. AM PM PRINTING AND SHADING INSTRUCTIONS For optimum accuracy, please print carefully and avoid contact with the edges of the box. The following will serve as an example: Tel: 022-223287 Q.7 SECTION Q.8 VILLAGE/LOCALITY Day Mon th Y ear Reference Number 8862244644 CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 IF RESPONSE IS NO OR DON'T KNOW GO TO NEXT PERSON MEMBER NUMBER 1 2 3 4 5 6 7 8 9 10 Head B Page 2 of 14 - LIST OF HOUSEHOLD MEMBERS WRITE DOWN THE NAMES OF ALL PERSONS WHO NORMALLY LIVE AND EAT TOGETHER IN THIS HOUSEHOLD, STARTING WITH THE HEAD. B.1 Is [NAME] male or female? Male Female B.2 How long has [NAME] been away in the last 12 months? Never Less than 6 months 6 months or more B.3 What is [NAME]'s relationship to the head of household? Head Spouse Child Parent Other relative Not related B.4 How old was [NAME] at last birthday? (RECORD AGE IN COMPLETED YEARS.) B.9 Is [NAME]'s mother living in the household? Yes No What is [NAME]'s marital status? Never married Married(monogamous) Married(polygamous) Divorced Separated Widowed Is [NAME]'s father alive? Yes No Don't know B.7 Is [NAME]'s father living in the household? Yes No B.8 Is [NAME]'s mother alive? Yes No Don't know M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N IF RESPONSE IS NO OR DON'T KNOW GO TO B.8 M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N M F 1 2 3 1 2 3 4 5 6 1 2 3 4 5 6 Y N X Y N Y N X Y N B.5 IF PERSON IS UNDER AGE 10 GO TO B.6 B.6 IF PERSON IS AGED 18 OR ABOVE GO TO NEXT PERSON Reference Number 7226244647 CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 GO TO NEXT PERSON GO TO NEXT PERSON MEMBER NUMBER 1 2 3 4 5 6 7 8 9 10 Page 3 of 14 C - EDUCATION Can [NAME] read and write in any language? Yes No C.2 Has [NAME] ever attended school? Yes No Why has [NAME] not started school? (YOU MUST MARK AT LEAST ONE ANSWER) a Too young b Too far away c Too expensive d Is working (home or job) e Useless/uninteresting f Illness g Other C.3 What is the highest grade [NAME] completed? C.4 Did [NAME] attend school last year? Yes No C.5 Is [NAME] currently in school? Yes No C.6 What is the current grade [NAME] is attending? C.7 Who runs the school [NAME] is attending? Government Religious organization Private Community Other C.8 Did [NAME] have any problems with school? (YOU MUST MARK AT LEAST ONE ANSWER) a No problem (satisfied) b Lack of books/supplies c Poor teaching d Not enough teachers e Teachers often absent f Lack of space g Facilities in bad condition h High fees i Other problem C.9 Why is [NAME] not currently in school? (YOU MUST MARK AT LEAST ONE ANSWER) a Completed school b Too far away c Too expensive d Is working (home or job) e Illness f Drug related problem g Pregnancy h Got married i Useless/uninteresting j Failed exam k Awaiting admission l Dismissed m Other C3 - Highest grade completed 00 None 01 Pre-school 11 P1 21 JSS1 31 University 12 P2 22 JSS2 41 Vocational 13 P3 23 JSS3 42 Teacher training 14 P4 24 SSS1 43 Technical 15 P5 25 SSS2 16 P6 26 SSS3 C6 - Current grade attending 01 Pre-school 11 P1 21 JSS1 31 University 12 P2 22 JSS2 41 Vocational 13 P3 23 JSS3 42 Teacher training 14 P4 24 SSS1 43 Technical 15 P5 25 SSS2 16 P6 26 SSS3 Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y N Y N Y N 1 2 3 4 5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y IF C5 RESPONSE IS NO GO TO C.9 IF C2 IS NO AND [NAME]IS BELOW 19 YEARS GO TO C.10; ELSE GO TO NEXT PERSON C.10 ASK C10 IF PERSON IS BELOW 19 YEARS C.1 ASK C.1 IF PERSON IS AGE 15 OR ABOVE OTHERWISE GO TO C.2 Reference Number 2017244640 CWIQ2007(24464): Sierra Leone 2007 Main Survey 19-03-2007 MEMBER NUMBER 1 2 3 4 5 6 7 8 9 10 Page 4 of 14 D - HEALTH D.1 Is [NAME] physically or mentally handicapped or disabled? Include person only if handicap prevents him or her from maintaining a significant activity or schooling. What sort of sickness/injury did [NAME] suffer? (YOU MUST MARK AT LEAST ONE ANSWER) D.5 What kind of health provider did [NAME] see? D.6 How did [NAME] pay for the consultation? a No need b Too expensive c Too far d Lack of confidence e Other D.8 Why did [NAME]

Severe housing deprivation rate is defined as the percentage of population living in the dwelling which is considered as overcrowded, while also exhibiting at least one of the housing deprivation measures.Housing deprivation is a measure of poor amenities and is calculated by referring to those households with a leaking roof, no bath/shower and no indoor toilet, or a dwelling considered too dark.

Translation (Dau Hkawng) The mule and the man. This story was about a man who lived in a village long ago, and he was poverty-stricken. He was poverty-stricken, extremely poor, but he never collects or solicits other people's goods and property for free. He lived like a very righteous man. He was poor but honest and lived in poverty, and one night he had a dream. In the dream, a young man of his age spoke to him. "Oh my dear friend, I want to come and stay with you," "Please accept me and welcome me," said the young man. So, "I'm just the poverty man and I had no one to stay with me. Why do you want to stay? “I will not be able to welcome you well either,” replied the poor man to the young man. At that time, he could not see the young man in the dream. The young man disappeared from his dream. The next night, the young man returned and said the same thing he had said the night before. In the dream, the poor man repeated the story of how poor he was the night before. After saying that, the young man returned to his dream on the third night and said the same thing the night before. The young man repeated that "You must be warmly welcome with good hospitality to me." So, he woke up from the dream, and the next day, he found a white mule lying cold in the rain on his way home from work. So, he took pity on the white mule, took it home, and set fire and helped him warm. When the white mule got well, he fed the corn it was stored by him for planting. Then, "I picked up a white mule and fed at my home, "Please let them know if the owner comes," the poor young man advertised to the people of the village. The Merchants often passed by with their mules through the village, so the poor young man thought that one of them left a mule. Despite the announcement, the owner of the mule did not appear. He fed the mule until two and three years. After catered for the mule for two or three years, he continued to mow the grass, tied it with long ropes, and fed it. When the owner of the white mule did not appear up, the poor man owned the mule. Then one night, in a dream, he dreamed again of the young man who had been in the dream before meeting the mule. The young man came to the poor man's dream and said, "Oh my friend, do you still not know me? "Don't you know me? We were friends back then. You helped me a lot when I was in so much trouble, so now I come to you to help you again," and "now I am an adult, I can carry my merchandise. Let me carry goods and merchandise, and make money to buy goods." continued the young man in the dream. So, the poor man replied, "I do not even have the money to buy any merchandise to let you carry." When the poor man said that, the young man in the dream replied, "Hire me to the merchants who are passing by from this village. Then you can get money to invest and use it to buy and sell goods," replied the young man in the dream. The young man said that and disappeared from the dream. He would never see him again. As the young man in the dream said, he hired his mule to the merchants passing through the village. After hiring for a year, he made a lot of money. He took back his white mule because he had a lot of money. He used his money to buy and sell goods and merchandise on his mule. He trades with mules, bought more mules, and became very rich with many servants. He became wealthy and he was able to work with his white mule for another two or three years and became very rich again. Then the young man in the dream came to his dream again. “Oh my friend, now you are very affluent already,” said the young man in the dream. "You are so affluent that you have nothing you need." "That's enough of me to come and help you, "I'm going to get it back," continued the young man in the dream. So the wealthy man said to the young man in the dream, "You do not have to go anywhere, do nothing, do not carry anything, and do not go anywhere, just stay here." "That's not going to happen, "even I won't go anywhere, I have to go back," continued the young man in the dream. The young man then disappeared from the dream, and in the upcoming early morning, they found his white mule dead. "It was my friend," said the wealthy man. He was also so thankful, and he was crying with great sadness and great sorrow. With that, he buried his white mule with funeral service like a human being. From then on, the man was once very poverty, but with the help of this dead white mule, he became a very wealthy man. Transcription (Lu Awng) Lawze langai mi hte la langai mi a lam. Ndai mung moi moi shawng de e mare langai mi kaw e la langai mi gaw grai matsan ai la langai mi nga ai da. Grai matsan ai, grai matsan ai, retim shi gaw masha ni rai ni hpe retim mung majoi mi hta la na, hta lang na hpyi la na ngu n nga ai da. Grai myit ding man ai hku na nga ai la langai mi nga ai da. Ndai wa gaw dai hku na shi matsan tim matsan ai hku na nga rai nga she, lani mi na ten, lana mi ten hta gaw shi yup mang mu ai da. Ndai yup mang hta gaw shi hte e maren sha shi hte e ram ai asak bung ai la langai mi la ramma langai mi gaw shi hpe sa tsun ai da. E manang wa e ngai nang kaw e sa manam na yaw, ngai hpe e a tsawm sha re na hkalum la ra ai re lu ngu na tsun ai da. Dai shi gaw e ngai gaw grai matsan ai wa she re gaw, ngai kaw sa manam mayu ai masha gaw kadai pyi nnga ai, hpa rai na nang dai hku sa tsun ai re ta? ngai gaw nang hpe mung grai re na hkap hkalum la lu na nre wa mi ngu na dai hku tsun ai da. Shaloi jang yup mang de e sa tsun ai la shabrang hpe bai nmu mat sai da. Shaloi jang she hpang shana bai dai hku sha bai la shabrang dai sha bai sa tsun ai da, yup mang de dai hku sha bai sa tsun ai majaw ndai la ndai mung dai hku ngai gaw grai matsan ai re ngu na shawng shana na hku sha bai tsun dat ai da. Dai hku tsun dat re yang she shi gaw hpang shana 3 na ngu na hta e dai hku sha bai sa tsun ai da. Ngai hpe e atsawm sha hkalum la ra ai yaw ngu na dai hku bai tsun ai da. Dai hku na bai hprang mat re shaloi gaw hpang shani re jang e she shi gaw le shi bungli sa na wa ai lam kaw wa she lawze ahpraw langai re lawze kasha langai mi she marang mung htu re kaw grai kashung gari nna dai hku na hpum taw nga ai hpe mu ai da. Shaloi she shi gaw ndai lawze hpe grai matsan dum ai majaw dai lawze hpe nta de woi wa na nta nhku e wan wut shakra di na alum ala re na shi gaw wa woi nga da ai da. Re na shi gaw ndai lawze wa akaja sha re jang shi na hkainu ni bai, hkai na ngu hkainu li bai dai ni bai sharun jaw re na shi gaw lawze ndai hpe bau da ai da. Re jang she mare masha ni hpe mung tsun da ai da, ngai lawze ahpraw langai mi hpe mu hta da ai yaw, bau da ai yaw, ndai na madu na madu pru jang shanhte san hkrup jang tsun dan marit ngai bai jaw na re ngu na tsun ai da. mare hku gaw hpaga la ni ma lai lai re da, lawze hte e rai htaw ai ni ma lai lai re da, shi gaw dai ni na lawze ngam kau da ai re sam ai ngu na dai hku na shadu ai da. Shingrai na tsun dan re yang she madu mung n pru kraw re da, shi gaw 2ning 3 ning daram na wa sai da, ndai lawze hpe bau ai gaw 2, 3ning na wa sai, namdan na jaw, na dai hku na oh shinggan kaw e sumri galu law na dai hku na shi hpe gyit dun na u shat ni shayaw di na dai hku na sha bau taw ai da. Dai shaloi gaw madu mung npru re jang e gaw shi na rai mat sai hku re nga dai majaw, lana mi gaw shi yup mang bai mu ai da, mi dai lawze hpe nmu la shi ai shaloi na la shabrang wa bai sa ai da. Sa rai na she e manang wa e ya mung nang ngai hpe nchye shi ai i ngu da. Ngai hpe n nchye ai di, moi an rau ganawn ai manang re le, ngai grai matsan ai ten hta e nang ngai hpe e grai garum la ai wa re nga ndai, dai majaw ya ngai nang kaw sa na nang hpe garum na matu nang kaw sa ai re, ya gaw ngai kaba sai gaw ngai lit gun dang sai, ngai hpe lit shagun rit, lit shagun na gumhpraw tam nu ngu tsun ai da. Re jang she ndai la shabrang wa gaw aw ngai nang hpe lit shagun na matu ngai dai kaw mari htaw na gumhrpaw nlu ai gaw, rai mari htaw na gumhpraw nlu ai ngu na yup mang de dai hku tsun ai shaloi gaw, ndai shabrang wa gaw bai tsun ai da. Nang ngai hpe e ndai hku lai lai re hpaga la ni kaw e shap sha u ngu da, 1ning mi shap sha u, shaloi jang e nang gumhpraw lu sa na re, dai hte e nang gun rai ni hpe lu htaw sa na re nga ai ngu tsun ai da. Shing ngu na shabrang dai gaw bai wa mat sai da. Bai nmu mat sai da, kaja wa nan shabrang dai tsun da ai hku na she shi mung dai hku na gun rai htaw lai lai re hpaga la ni kaw shi na lawze hpe shap ya ai da. 1ning mi shap ya jang gaw shi gumhpraw grai lu mat ai da. Gumhpraw grai lu mat re jang gaw ndai shabrang wa hpe ndai gumhpraw grai lu mat sai re majaw gaw lawze hpe mung bai lu, dai gumhpraw mung lu re dai hpe she shi gaw gun rai ni mari nna she shi gaw ndai lawze hta htaw nna dut kahkrang re taw sai da, lawze kaga mung bai lu mari jat, shagun ma hte hpa hte dusat yamnga kaga ni mung grai lu sai da, shi gaw grai lu su mat ai da. Grai lu su mat ai, ndai gumra ndai lawze ndai hte e bungli galaw ai gaw 2, 3 ning bai lu galaw ai hpang e ndai la ndai wa grai re na lu su mat wa ai. Shaloi gaw da ndai la shabrang mi sha yup mang kaw sa sa re la shabrang ndai wa gaw shi kaw bai sa ai da. E manang wa e ya gaw nang grai re na lu nit dai. Hpa mung ra nrawng hka rai nit dai, dai majaw ngai nang hpe e sa garum ai ndai hte rai sai, ngai wa na ni ai ngu na tsun ai da. Shing re jang gaw ndai la wa mung e nang gara de mung hkum sa, nang bungli mung hpa n galaw tim nra ai, nang hpa n htaw tim nra sai, nang gara de mung nsa sai sha ningkaw sha nga taw u ngu na tsun ai da. Shaloi jang nmai ai ngai gara de nsa yang mung nmai ai ngu na ngai wa sana ngu na tsun da ai da. Shing rai na la dai bai yup mang kaw na bai mye re na bai mat mat ai shaloi gaw hpang jahpawt yu yang wa she ndai shi na lawze ahpraw gaw si taw mat ai da. Ndai gaw nye na manang rai sai ngu na shi mung grai chyeju dum ai hte hkrap ai hte myit n pyaw ai hte yawn ai hte re na i, lawze ndai hpe mung shinggyin masha zawn zawn di na lup makoi rai na galaw ya ai da. Dai kaw na

This data was collected as a part of a project that set out to investigate the history of statutory poor relief in Ireland from the end of the Great Famine in 1850 to the establishment of an independent Irish state in 1922. Using qualitative and quantitative data, it explored the character, organisation and operation of the poor law in Ireland. The project combined macro and micro analysis to compile a picture of poor relief that moved from the national perspective, through the regional, to the local. Annual published returns of poor law statistics were used to identify national, regional and local trends in the provision and utilisation of relief, revealing the existence of what appear to be distinct welfare regimes with both regional and ideological characteristics. Case studies of twelve poor law unions throughout the country were then undertaken utilising the administrative records of the poor law boards in order to explore the influence of factors such as religion, politics and regional economics on the scope and character of relief practices. By analysing both general trends in relief policy and practice, and the micro-politics of relief, the project has produced new insights into the understanding and experience of poverty in post-Famine Ireland and the evolution of welfare systems. The data produced by the project can be grouped into two main types, national and local. There is one national database of poor law statistics covering the whole of Ireland. This contains relief and expenditure figures extracted from the annual published returns for all of the 163 poor law unions in Ireland for the period 1850-1914 (Full sets of figure were not produced after this date). The remaining data relates to the poor law unions selected as case studies. These were: Belfast, Ballycastle and Ballymoney in the north: Glenties, Westport and Tralee in the West; and North Dublin, Cork, Kinsale, Thurles, Mountmellick and Kilmallock in the south. The aim was to provide a representative cross section of unions, although the availability of source material also influenced the final selection. Where workhouse admissions registers were available (Belfast, Ballycastle, Ballymoney, Glenties, Cork, Kinsale, North Dublin and Thurles), these were used to create spread sheets. The information is recorded as it was entered in the registers. Whenever possible, the registers for census years were used, but this was not possible in all cases. Details of all admissions for the administrative Year (October to September) were entered unless the number of admissions was too great to include every admission in which case sampling was used. This applied to Belfast, where all admissions for particular months were entered, and to Cork and North Dublin 1901, where every 10th admission was entered. Spread sheets were also created from outdoor relief registers for Ballycastle and Ballymoney. Where admission registers were not available, weekly indoor and outdoor relief figures were extracted from the minute books at five or ten-year intervals creating spread sheets. Notes were taken from the minute books from census years of decisions taken and other relevant information. These are available as text files.

Description:

A good education offers individuals the opportunity to lead richer, more interesting lives. At a societal level, it creates opportunities for humanity to solve its pressing problems.

The world has gone through a dramatic transition over the last few centuries, from one where very few had any basic education to one where most people do. This is not only reflected in the inputs to education – enrollment and attendance – but also in outcomes, where literacy rates have greatly improved.

Getting children into school is also not enough. What they learn matters. There are large differences in educational outcomes: in low-income countries, most children cannot read by the end of primary school. These inequalities in education exacerbate poverty and existing inequalities in global incomes.

About Dataset: There are 4 dataset in this page: 1- share-of-the-world-population-with-at-least-basic-education:

Access to education is now seen as a fundamental right – in many cases, it’s the government’s duty to provide it.

But formal education is a very recent phenomenon. In the chart, we see the share of the adult population – those older than 15 – that has received some basic education and those who haven’t.

In the early 1800s, fewer than 1 in 5 adults had some basic education. Education was a luxury, in all places, it was only available to a small elite.

But you can see that this share has grown dramatically, such that this ratio is now reversed. Less than 1 in 5 adults has not received any formal education.

This is reflected in literacy data, too: 200 years ago, very few could read and write. Now most adults have basic literacy skills.

2- learning-adjusted-years-of-school-lays:

There are still significant inequalities in the amount of education children get across the world.

This can be measured as the total number of years that children spend in school. However, researchers can also adjust for the quality of education to estimate how many years of quality learning they receive. This is done using an indicator called “learning-adjusted years of schooling”.

On the map, you see vast differences across the world.

In many of the world’s poorest countries, children receive less than three years of learning-adjusted schooling. In most rich countries, this is more than 10 years.

Across most countries in South Asia and Sub-Saharan Africa – where the largest share of children live – the average years of quality schooling are less than 7.

3- number-of-out-of-school-children:

While most children worldwide get the opportunity to go to school, hundreds of millions still don’t.

In the chart, we see the number of children who aren’t in school across primary and secondary education.

This number was around 260 million in 2019.

Many children who attend primary school drop out and do not attend secondary school. That means many more children or adolescents are missing from secondary school than primary education.

4- gender-gap-education-levels:

Globally, until recently, boys were more likely to attend school than girls. The world has focused on closing this gap to ensure every child gets the opportunity to go to school.

Today, these gender gaps have largely disappeared. In the chart, we see the difference in the global enrollment rates for primary, secondary, and tertiary (post-secondary) education. The share of children who complete primary school is also shown.

We see these lines converging over time, and recently they met: rates between boys and girls are the same.

For tertiary education, young women are now more likely than young men to be enrolled.

Have a great analysis !

By Hannah Ritchie, Veronika Samborska, Natasha Ahuja, Esteban Ortiz-Ospina and Max Roser