This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Inlet, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 20,755, while the mean income for the highest quintile (20% of households with the highest income) is 186,714. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 350,363, which is 187.65% higher compared to the highest quintile, and 1688.09% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Inlet town median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle Paxton Township, Pennsylvania, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 26,364, while the mean income for the highest quintile (20% of households with the highest income) is 299,859. This indicates that the top earners earn 11 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 587,303, which is 195.86% higher compared to the highest quintile, and 2227.67% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Paxton township median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Middle River, MN, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 12,132, while the mean income for the highest quintile (20% of households with the highest income) is 141,420. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 186,294, which is 131.73% higher compared to the highest quintile, and 1535.56% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 River median household income. You can refer the same here

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.

United States US: Income Share Held by Highest 20% data was reported at 46.900 % in 2016. This records an increase from the previous number of 46.400 % for 2013. United States US: Income Share Held by Highest 20% data is updated yearly, averaging 46.000 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 46.900 % in 2016 and a record low of 41.200 % in 1979. United States US: 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 United States – Table US.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.

Context

This dataset was uploaded as supplemental data for the 2019 Kaggle ML & DS Survey. It allows classification of countries into income groups - low, lower-middle, upper-middle and high - by gross national income (GNI) per capita, in U.S. dollars,.

For details of this calculation see here and here.

Content

The csv file consists of 218 countries listed by name and country code and their corresponding income group and lending category.

Acknowledgements

Thanks to the World Bank for providing the data at "https://datahelpdesk.worldbank.org/knowledgebase/articles/906519">https://datahelpdesk.worldbank.org/knowledgebase/articles/906519

Inspiration

This dataset allows any other data containing country names or codes to be supplemented with income group data.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

This article investigates the dynamic impact of green energy consumption (GE), financial inclusion (FI), and military spending (MS) on environmental sustainability (ES) by utilizing a sample of 121 countries from 2003 to 2022. The dataset is divided into high-income, upper-middle income and low and lower-middle-income countries. We employed a two-step system GMM approach, which was further robust through panel Quantile and Driscoll-Kraay (D-K) regressions. The findings divulged that green energy resources benefit ES at global and all income levels because of having a significant negative impact of 5.9% on ecological footprints. At the same time, FI and MS significantly enhance ecological footprints by 7% and 6.9%, respectively, proving these factors detrimental to ES. Moreover, conflicts (CON), terrorism (TM), institutional quality (IQ), and socioeconomic conditions (SEC) also have a significantly positive association with global ecological footprints and most of the income level groups. Dissimilarly, financial inclusion and armed conflicts have a non-significant influence on ecological footprints in low-income and high-income countries, respectively. Furthermore, institutional quality enhances ES in upper-middle and low and lower-middle-income countries by negatively affecting ecological footprints. At the same time, terrorism significantly reduces ecological footprints in high-income countries. This research also provides the imperative policy inferences to accomplish various SDGs.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

This study delves into the global evolution of 43 Sustainable Development Goals (SDG) indicators, spanning 7 major health themes across 185 countries to evaluate the potential progress loss due to the COVID-19 pandemic. Both the cross-country and temporal variability of the dataset are employed to estimate an empirical model based on an extended version of the Preston curve, which links well-being to income levels and other key socioeconomic health determinants. The approach reveals significant global evolution trends operating in each SDG indicator assessed. We extrapolate the model yearly between 2020 and 2030 using the IMF’s pre-COVID-19 economic growth projections to show how each country in the dataset are expected to evolve in these health topics throughout the decade, assuming no other external shocks. The results of this baseline scenario are contrasted with a post-COVID-19 scenario, where most of the pandemic costs were already known. The study reveals that economic growth losses are, on average, estimated as 42% and 28% for low- and lower middle-income countries, and of 15% and 7% in high- and upper middle-income countries, respectively, according to the IMF’s projections. These disproportional figures are shown to exacerbate global health inequalities revealed by the curves. The expected progress loss in infectious diseases in low-income countries, for instance, is an average of 34%, against a mean of 6% in high-income countries. The theme of Infectious diseases is followed by injuries and violence; maternal and reproductive health; health systems coverage; and neonatal and infant health as those with worse performance. Low-income countries can expect an average progress loss of 16% across all health indicators assessed, whereas in high-income countries the estimated loss is as low as 3%. The disparity across countries is even more pronounced, with cases where the estimated progress loss is as high as nine times worse than the average loss of 8%. Conversely, countries with greater fiscal capacity are likely to fare much better under the circumstances, despite their worse death count, in many cases. Overall, these findings support the critical importance of integrating the fight against inequalities into the global development agendas.

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Vietnam, Yemen, Zambia, Zimbabwe, WORLD

Follow data.kapsarc.org for timely data to advance energy economics research.

Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

Content

The World Bank classifies the world's economies into four income groups — high, upper-middle, lower-middle, and low. We base this assignment on Gross National Income (GNI) per capita (current US$) calculated using the Atlas method. The classification is updated each year on July 1st.

The classification of countries is determined by two factors:

A country’s GNI per capita, which can change with economic growth, inflation, exchange rates, and population. Revisions to national accounts methods and data can also influence GNI per capita.
Classification threshold: The thresholds are adjusted for inflation annually using the SDR deflator.

Check this link for more: https://blogs.worldbank.org/opendata/new-country-classifications-income-level-2019-2020

Source

https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the layer's data dictionaryNote: This layer is symbolized to display the percentile distribution of the Limited Resources Sub-Index. However, it includes all data for each indicator and sub-index within the citywide census tracts TEPI.What is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -1.180 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -1.180 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -1.180 % in 2021 and a record low of -1.180 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Research Hypothesis

The research investigates the relationship between economic growth and income inequality, drawing on Kuznets' theory of an inverted U-shaped relationship. The central hypotheses are:

H0: Income inequality is not affected by GDP growth, indicating no relationship between economic growth and income inequality.
H1: GDP growth influences income inequality, which may increase or decrease depending on societal and economic contexts.
H2: GDP growth positively affects income inequality, widening income disparities.
H3: GDP growth negatively affects income inequality, reducing disparities and promoting equitable distribution.
H4: In lower-middle-income countries, GDP growth reduces income inequality.

Description of Data

The study utilizes data from the World Bank for 39 countries spanning the years 2004 to 2019. The dataset includes:

Gross Domestic Product (GDP): Measured in constant local currency units (LOGGDP), used as a proxy for economic growth.
Gini Index: A standardized measure of income inequality, ranging from 0 (perfect equality) to 100 (maximum inequality).
Income Categories: Countries are grouped into high, upper-middle, and lower-middle income categories based on the World Bank’s GNI per capita classification.

Methodology and Data Gathering

Selection Criteria: Countries were selected to represent diverse income groups, ensuring a balanced and comprehensive analysis of varying economic contexts.
Data Source: All data were sourced from the World Bank’s publicly available databases.
Data Analysis:
  Correlation analysis to explore the general relationship between GDP and inequality.
  Linear regression models to identify causal relationships across income categories.
  Group-specific analysis to investigate how GDP impacts inequality within high-, upper-middle-, and lower-middle-income countries.

Notable Findings

Overall Trends:
  Across all countries, a positive correlation was observed between GDP and the Gini index, indicating that GDP growth is generally associated with increasing income inequality.
  The regression model (GINI = 23.931 + 0.937 × LOGGDP) confirmed a statistically significant relationship, with an F-value (p < 0.05) supporting the model’s validity.

Income Group Analysis:
  High-Income Countries: No statistically significant relationship between GDP growth and inequality.
  Upper-Middle-Income Countries: A weak relationship was observed, but it lacked statistical significance.
  Lower-Middle-Income Countries: A significant negative relationship was identified (β = -22.291, p < 0.001), suggesting that in these countries, GDP growth reduces income inequality.

Interpretation and Use of Data: The findings can be interpreted in light of Kuznets' hypothesis, which posits that inequality first rises and then falls as economies develop.

About

The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).

This open data repository contains four datasets:

(1) Synthetic Agents,

(2) Activity Plans of the Agents,

(3) Travel Trajectories of the Agents, and

(4) Road Network (EPSG: 3006)

(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)

The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.

Methodology

This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim).

The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.

The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘Synthetic Agents’ contains all synthetic agents regardless of their ‘feasibility’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included.

The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling.

Data Description

(1) Synthetic Agents

This dataset contains all agents in Sweden and their socioeconomic characteristics.

The attribute ‘feasibility’ has two categories: feasible agents (73%), and infeasible agents (27%). Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.

File name: 1_syn_pop_all.parquet

Column

Description

Data type

Unit

PId

Agent ID

Integer

-

Deso Zone code of Demographic statistical areas (DeSO)1

String

kommun

Municipality code

Integer

marital

Marital Status (single/ couple/ child)

String

sex

Gender (0 = Male, 1 = Female)

Integer

age

Age

Integer

HId

A unique identifier for households

Integer

HHtype

Type of households (single/ couple/ other)

String

HHsize

Number of people living in the households

Integer

num_babies

Number of children less than six years old in the household

Integer

employment Employment Status (0 = Not Employed, 1 = Employed)

Integer

studenthood Studenthood Status (0 = Not Student, 1 = Student)

Integer

income_class Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)

Integer

num_cars Number of cars owned by an individual

Integer

HHcars Number of cars in the household

Integer

feasibility

Status of the individual (1=feasible, 0=infeasible)

Integer

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Activity Plans of the Agents

The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday.

File name: 2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

Column

Description

Data type

Unit

act_purpose

Activity purpose (work/ home/ school/ other)

String

-

PId

Agent ID

Integer

-

act_end

End time of activity (0:00:00 – 23:59:59)

String

hour:minute:seco

nd

act_id

Activity index of each agent

Integer

-

mode

Transport mode to reach the activity location

String

-

POINT_X

Coordinate X of activity location (SWEREF99TM)

Float

metre

POINT_Y

Coordinate Y of activity location (SWEREF99TM)

Float

metre

dep_time

Departure time (0:00:00 – 23:59:59)

String

hour:minute:seco

nd

score

Utility score of the simulation day as obtained from MATSim

Float

-

trav_time

Travel time to reach the activity location

String

hour:minute:seco

nd

trav_time_min

Travel time in decimal minute

Float

minute

act_time

Activity duration in decimal minute

Float

minute

distance

Travel distance between the origin and the destination

Float

km

speed

Travel speed to reach the activity location

Float

km/h

(3) Travel Trajectories of the Agents

This dataset contains the driving trajectories of all the agents on the road network, and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.

File name: 3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

Column

Description

Data type

Unit

time

Time in second in a simulation day (0-86399)

Integer

second

type

Event type defined by MATSim simulation*

String

person

Agent ID

Integer

link

Nearest road link consistent with the road network

String

vehicle

Vehicle ID identical to person

Integer

from_node

Start node of the link

Integer

to_node

End node of the link

Integer

• One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart)

(4) Road Network

This dataset contains the road network.

File name: 4_network.shp

Column

Description

Data type

Unit

length

The length of road link

Float

metre

freespeed

Free speed

Float

km/h

capacity

Number of vehicles

Integer

permlanes

Number of lanes

Integer

oneway

Whether the segment is one-way (0=no, 1=yes)

Integer

modes

Transport mode

String

from_node

Start node of the link

Integer

to_node

End node of the link

Integer

geometry

LINESTRING (SWEREF99TM)

geometry

metre

Additional Notes

This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.

Contributions

YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.

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.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.