In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between 125,000 and 500,000 Indian rupees a year. On the other hand, about three percent of households that same year, accounted for the rich, earning over 3 million rupees annually. The middle class more than doubled that year compared to 14 percent in financial year 2005.

Middle-class income group and the COVID-19 pandemic

During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant.

Economic inequality

While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last three decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Middle Taylor township. The dataset can be utilized to gain insights into gender-based income distribution within the Middle Taylor township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Middle Taylor township, among individuals aged 15 years and older with income, there were 286 men and 315 women in the workforce. Among them, 160 men were engaged in full-time, year-round employment, while 97 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.50% fell within the income range of under $24,999, while 18.56% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 14.37% of men in full-time roles earned incomes exceeding $100,000, while 10.31% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Middle Taylor township median household income by race. You can refer the same here

Income statistics by economic family type and income source, annual.

California State Income Limits reflect updated median income and household income levels for acutely low-, extremely low-, very low-, low- and moderate-income households for California’s 58 counties (required by Health and Safety Code Section 50093). These income limits apply to State and local affordable housing programs statutorily linked to HUD income limits and differ from income limits applicable to other specific federal, State, or local programs.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

Analysis of ‘ Decomposing World Income Distribution Database’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/search/dataset/0041692/ on 21 November 2021.

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

Using national income and expenditure distribution data from 119 countries, the authors decompose total income inequality between the individuals in the world, by continent and by "region" (countries grouped by income level). They use a Gini decomposition that allows for an exact breakdown (without a residual term) of the overall Gini by recipients. Looking first at income inequality in income between countries is more important than inequality within countries. Africa, Latin America, and Western Europe and North America are quite homogeneous continent, with small differences between countries (so that most of the inequality on these continents is explained by inequality within countries). Next the authors divide the world into three groups: the rich G7 countries (and those with similar income levels), the less developed countries (those with per capita income less than or equal to Brazil's), and the middle-income countries (those with per capita income between Brazil's and Italy's). They find little overlap between such groups - very few people in developing countries have incomes in the range of those in the rich countries.

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

This data file includes the Inequality and Poverty Key Figures (as of March 2022), constructed for all Luxembourg Income Study (LIS) Study datasets in all waves. It includes multiple national-level measures: • on inequality measures: Gini, Atkinson coefficients, and percentile ratios • on relative poverty rates for various demographic groups • median and mean of disposable household income

This project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations.

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2023 about households, median, income, and USA.

Graph and download economic data for Real Median Household Income in New York (MEHOINUSNYA672N) from 1984 to 2023 about NY, households, median, income, real, and USA.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Middle Smithfield township. The dataset can be utilized to gain insights into gender-based income distribution within the Middle Smithfield township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Middle Smithfield township, among individuals aged 15 years and older with income, there were 6,047 men and 5,807 women in the workforce. Among them, 3,288 men were engaged in full-time, year-round employment, while 2,590 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 9.82% fell within the income range of under $24,999, while 11.20% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 20.01% of men in full-time roles earned incomes exceeding $100,000, while 20.46% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Middle Smithfield township median household income by race. You can refer the same here

Families of tax filers; Distribution of total income by census family type and age of older partner, parent or individual (final T1 Family File; T1FF).

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure. In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression. The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists. The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population. The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways. First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data. Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes. Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work. Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes. Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status. Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

ObjectiveThis study aims to assess the acceptability of a novel technology, MAchine Learning Application (MALA), among the mothers of newborns who required resuscitation.SettingThis study took place at Bharatpur Hospital, which is the second-largest public referral hospital with 13 000 deliveries per year in Nepal.DesignThis is a cross-sectional survey.Data collection and analysisData collection took place from January 21 to February 13, 2022. Self-administered questionnaires on acceptability (ranged 1–5 scale) were collected from participating mothers. The acceptability of the MALA system, which included video and audio recordings of the newborn resuscitation, was examined among mothers according to their age, parity, education level and technology use status using a stratified analysis.ResultsThe median age of 21 mothers who completed the survey was 25 years (range 18–37). Among them, 11 mothers (52.4%) completed their bachelor’s or master’s level of education, 13 (61.9%) delivered first child, 14 (66.7%) owned a computer and 16 (76.2%) carried a smartphone. Overall acceptability was high that all participating mothers positively perceived the novel technology with video and audio recordings of the infant’s care during resuscitation. There was no statistical difference in mothers’ acceptability of MALA system, when stratified by mothers’ age, parity, or technology usage (p>0.05). When the acceptability of the technology was stratified by mothers’ education level (up to higher secondary level vs. bachelor’s level or higher), mothers with Bachelor’s degree or higher more strongly felt that they were comfortable with the infant’s care being video recorded (p = 0.026) and someone using a tablet when observing the infant’s care (p = 0.046). Compared with those without a computer (n = 7), mothers who had a computer at home (n = 14) more strongly agreed that they were comfortable with someone observing the resuscitation activity of their newborns (71.4% vs. 14.3%) (p = 0.024).ConclusionThe novel technology using video and audio recordings for newborn resuscitation was accepted by mothers in this study. Its application has the potential to improve resuscitation quality in low-and-middle income settings, given proper informed consent and data protection measures are in place.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

This report provides a systematic review and empirical evidence related to the experiences of middle-income countries and economies participating in the Programme for International Student Assessment (PISA), 2000 to 2015. PISA is a triennial survey that aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. To date, students representing more than 70 countries and economies have participated in the assessment, including 44 middle-income countries, many of which are developing countries receiving foreign aid. This report provides answers to six important questions about these middle-income countries and their experiences of participating in PISA: What is the extent of developing country participation in PISA and other international learning assessments? Why do these countries join PISA? What are the financial, technical, and cultural challenges for their participation in PISA? What impact has participation had on their national assessment capacity? How have PISA results influenced their national policy discussions? And what does PISA data tell us about education in these countries and the policies and practices that influence student performance? The findings of this report are being used by the OECD to support its efforts to make PISA more relevant to a wider range of countries, and by the World Bank as part of its on-going dialogue with its client countries regarding participation in international large-scale assessments.

This paper uses a large-scale nationally representative dataset to examine the nonlinear effect of income on mental health. To investigate their causal relationship, the exogenous impact of automation on income is utilized as the instrument variable (IV). In addition, to explore their nonlinear relationship, both income and its quadratic term are included in regressions. It is found that the impact of income on mental health is U-shaped rather than linear. The turning point (7.698) of this nonlinear relation is near the midpoint of the income interval ([0, 16.113]). This suggests that depression declines as income increases at the lower-income level. However, beyond middle income, further increases in income take pronounced mental health costs, leading to a positive relationship between the two factors. We further exclude the possibility of more complex nonlinear relationships by testing higher order terms of income. In addition, robustness checks, using other instrument variables and mental health indicators, different IV models and placebo analysis, all support above conclusions. Heterogeneity analysis demonstrates that males, older workers, ethnic minorities and those with lower health and socioeconomic status experience higher levels of depression. Highly educated and urban residents suffer from greater mental disorders after the turning point. Religious believers and Communist Party of China members are mentally healthier at lower income levels, meaning that religious and political beliefs moderate the relationship between income and mental health.

Sri Lanka LK: Income Share Held by Highest 20% data was reported at 47.600 % in 2016. This records an increase from the previous number of 47.000 % for 2012. Sri Lanka LK: Income Share Held by Highest 20% data is updated yearly, averaging 45.850 % from Dec 1985 (Median) to 2016, with 8 observations. The data reached an all-time high of 48.600 % in 2002 and a record low of 41.100 % in 1985. Sri Lanka LK: Income Share Held by Highest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Low income cut-offs (LICOs) before and after tax by community size and family size, in current dollars, annual.

Understanding the costs of health interventions is critical for generating budgets, planning and managing programs, and conducting economic evaluations to use when allocating scarce resources. Here, we utilize techniques from the hedonic pricing literature to estimate the characteristics of the costs of social and behavior change communication (SBCC) interventions, which aim to improve health-seeking behaviors and important intermediate determinants to behavior change. SBCC encompasses a wide range of interventions including mass media (e.g., radio, television), mid media (e.g., community announcements, live dramas), digital media (e.g., short message service/phone reminders, social media), interpersonal communication (e.g., individual or group counseling), and provider-based SBCC interventions focused on improving provider attitudes and provider-client communication. While studies have reported on the costs of specific SBCC interventions in low- and middle-income countries, little has been done to examine SBCC costs across multiple studies and interventions. We use compiled data across multiple SBCC intervention types, health areas, and low- and middle-income countries to explore the characteristics of the costs of SBCC interventions. Despite the wide variation seen in the unit cost data, we can explain between 63 and 97 percent of total variance and identify a statistically significant set of characteristics (e.g., health area) for media and interpersonal communication interventions. Intervention intensity is an important determinant for both media and interpersonal communication, with costs increasing as intervention intensity increases; other important characteristics for media interventions include intervention subtype, target population group, and country income as measured by per capita Gross National Income. Important characteristics for interpersonal communication interventions include health area, intervention subtype, target population group and geographic scope.

Abstract copyright UK Data Service and data collection copyright owner.BackgroundThe British Social Attitudes (BSA) survey series began in 1983. The series is designed to produce annual measures of attitudinal movements to complement large-scale government surveys that deal largely with facts and behaviour patterns, and the data on party political attitudes produced by opinion polls. One of the BSA's main purposes is to allow the monitoring of patterns of continuity and change, and the examination of the relative rates at which attitudes, in respect of a range of social issues, change over time. Some questions are asked regularly, others less often. Funding for BSA comes from a number of sources (including government departments, the Economic and Social Research Council and other research foundations), but the final responsibility for the coverage and wording of the annual questionnaires rests with NatCen Social Research (formerly Social and Community Planning Research). The BSA has been conducted every year since 1983, except in 1988 and 1992 when core funding was devoted to the British Election Study (BES).Further information about the series and links to publications may be found on the NatCen Social Research British Social Attitudes webpage. Main Topics:Each year, the BSA interview questionnaire contains a number of 'core' questions, which are repeated in most years. In addition, a wide range of background and classificatory questions is included. The remainder of the questionnaire is devoted to a series of questions (modules) on a range of social, economic, political and moral issues - some are asked regularly, others less often. Cross-indexes of those questions asked more than once appear in the reports. Multi-stage stratified random sample See documentation for each BSA year for full details. 2006 ACADEMIC ACHIEVEMENT ADULTS AGE AID AIR TRANSPORT AIR TRAVEL ANTISOCIAL BEHAVIOUR APPLICATION FOR EMP... ATTITUDES AUTONOMY AT WORK BICYCLES BRITISH POLITICAL P... BROADBAND BROKEN FAMILIES BUSES CARE OF THE DISABLED CARE OF THE ELDERLY CAREER DEVELOPMENT CARS CENTRAL GOVERNMENT CHILD BENEFITS CHILD CARE CHILD DAY CARE CHILD SAFETY CHILD SUPPORT PAYMENTS CHILDREN CITIZEN PARTICIPATION CIVIL AND POLITICAL... CLASS CONFLICT CLIMATE CHANGE COHABITATION COMMUNITIES COMMUTING COMPUTER LITERACY COMPUTERS CONDITIONS OF EMPLO... CONSERVATIVE PARTY ... CONSTITUTIONAL CHANGE CORRUPTION COUNTERTERRORISM CRIMINAL DAMAGE DEATH PENALTY DEBILITATIVE ILLNESS DECENTRALIZED GOVER... DECISION MAKING DEFENCE DEGREES DEMOCRACY DENTISTS DIGITAL GAMES DISABILITIES DISABLED PERSONS DIVORCE DOCTOR PATIENT RELA... DOMESTIC RESPONSIBI... DRIVING ECONOMIC ACTIVITY ECONOMIC CONDITIONS EDUCATION EDUCATIONAL BACKGROUND EDUCATIONAL EXPENDI... EDUCATIONAL RESOURCES ELDERLY ELECTRONIC MAIL EMPLOYEES EMPLOYERS EMPLOYMENT EMPLOYMENT HISTORY EQUALITY BEFORE THE... ETHNIC GROUPS EUROPEAN UNION EXAMINATIONS FAMILIES FAMILY BENEFITS FAMILY MEMBERS FAMILY ROLES FATHERS FLEXIBLE WORKING TIME FRAUD FREEDOM OF SPEECH FRIENDSHIP FULL TIME EMPLOYMENT GENDER GENERAL PRACTITIONERS GOVERNMENT POLICY GRANDCHILDREN GRANDPARENTS HAPPINESS HEALTH HEALTH SERVICES HIGHER EDUCATION HOLIDAYS HOME BASED WORK HOME OWNERSHIP HOSPITAL OUTPATIENT... HOSPITAL SERVICES HOSPITAL WAITING LISTS HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLD INCOME HOUSEHOLDS HOUSING HOUSING TENURE HUMAN RIGHTS IMMIGRATION INCOME INCOME DISTRIBUTION INFIDELITY INFORMATION SOURCES INTERNET ACCESS INTERNET USE INTERPERSONAL CONFLICT JOB CHANGING JOB HUNTING JOB SATISFACTION JOB SECURITY JUVENILE DELINQUENCY LABOUR PARTY GREAT ... LABOUR RELATIONS LANDLORDS LEISURE TIME LIBERAL DEMOCRATS G... LIVING CONDITIONS MARITAL STATUS MARRIAGE MEDICAL CARE MEMBERS OF PARLIAMENT MENTAL HEALTH ATTIT... MIDDLE CLASS MOBILE PHONES MONARCHY MORAL BEHAVIOUR MORAL VALUES MOTHER S EMPLOYMENT... MOTHER S PLACE OF B... MOTHERS MOTOR VEHICLES NATIONAL ECONOMY NATIONAL IDENTITY NATIONAL PRIDE NATIONALIZATION NEWSPAPER READERSHIP NEWSPAPERS NUMERACY OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS ONE PARENT FAMILIES ONLINE BANKING ONLINE SHOPPING PARENT CHILD RELATI... PARENT RESPONSIBILITY PART TIME EMPLOYMENT PARTNERSHIPS PERSONAL PATIENTS PEDESTRIANS PERSONAL IDENTIFICA... PHYSICAL DISABILITIES POLICE SERVICES POLITICAL ALLEGIANCE POLITICAL ATTITUDES POLITICAL EXTREMISM POLITICAL INFLUENCE POLITICAL INTEREST POLITICAL OPPOSITION POLITICAL PARTICIPA... POLITICAL SYSTEMS POLITICIANS POPULATION DENSITY POVERTY PRIMARY EDUCATION PRIMARY SCHOOL TEAC... PRIMARY SCHOOLS PRISON SYSTEM PRIVATE EDUCATION PRIVATE SECTOR PROMOTION JOB PROPORTIONAL REPRES... PSYCHOLOGICAL EFFECTS PUBLIC EXPENDITURE PUBLIC INFORMATION PUBLIC SECTOR PUBLIC TRANSPORT QUALIFICATIONS QUALITY OF LIFE RAILWAY TRAVEL REGIONAL GOVERNMENT RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RETIREMENT RETIREMENT AGE ROAD SAFETY ROAD TAX ROAD TOLL CHARGES ROAD TRAFFIC ROAD TRAFFIC POLLUTION ROADS RURAL AREAS SAME SEX RELATIONSHIPS SATISFACTION SECONDARY EDUCATION SECONDARY SCHOOLS SELF EMPLOYED SELF GOVERNMENT SINGLE EUROPEAN CUR... SMOKING RESTRICTIONS SOCIAL ATTITUDES SOCIAL CAPITAL SOCIAL CLASS SOCIAL HOUSING SOCIAL ISSUES SOCIAL PROTEST SOCIAL SECURITY BEN... SOCIAL WELFARE EXPE... SOCIAL WELFARE PHIL... SOCIO CULTURAL CLUBS SOCIO ECONOMIC STATUS SPEED LIMITS SPOUSE S ECONOMIC A... SPOUSE S EMPLOYMENT SPOUSE S OCCUPATION SPOUSES STANDARD OF LIVING STATE HEALTH SERVICES STATE RESPONSIBILITY STEPCHILDREN STRESS PSYCHOLOGICAL SUPERVISORY STATUS Social behaviour an... Social conditions a... TAX RELIEF TAXATION TELEPHONES TELEWORK TERMINATION OF SERVICE TERRORISM TRADE UNION MEMBERSHIP TRADE UNIONS TRAFFIC CALMING MEA... TRAFFIC OFFENCES TRAVEL TRUST TRUST IN GOVERNMENT UNEMPLOYMENT BENEFITS URBAN AREAS VOCATIONAL EDUCATION VOTING BEHAVIOUR WAGES WEBSITES WOMEN S ROLE WORK ATTITUDE WORKERS PARTICIPATION WORKING CLASS WORKING CONDITIONS WORKING MOTHERS WORKING WOMEN WORKPLACE YOUTH YOUTH ORGANIZATIONS