The New York State Nursing Home Quality Initiative (NHQI) is an annual evaluation and ranking of eligible Medicaid-certified nursing homes in New York State. Nursing homes are evaluated on their performance in three components: Quality, Compliance, and Efficiency. Nursing homes are awarded points for their performance in each measure and ranked into overall quintiles, the first quintile containing the best performing homes. Refer to the Measures document to learn more about the specific measures in the NHQI, and the data sources and time frames used. Changes in measure specifications and the deletion or addition of measures will limit the ability to trend this data over time. The quality measures are based on past data and may not accurately reflect a nursing home’s most current quality performance. Refer to the Overview document for more information on the limitations of this dataset. The information in this dataset is intended to be used in conjunction with other sources for assessing quality of care in nursing homes, including in-person visits to a nursing home. For more information, go to the "About" tab.

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

Mortality from all circulatory diseases, directly age-standardised rate, persons, under 75 years, 2004-08 (pooled) per 100,000 European Standard population by local authority by local deprivation quintile. Local deprivation quintiles are calculated by ranking small areas (Lower Super Output Areas (LSOAs)) within each Local Authority based on their Index of Multiple Deprivation 2007 (IMD 2007) deprivation score, and then grouping the LSOAs in each Local Authority into five groups (quintiles) with approximately equal numbers of LSOAs in each. The upper local deprivation quintile (Quintile 1) corresponds with the 20% most deprived small areas within that Local Authority. The mortality rates have been directly age-standardised using the European Standard Population in order to make allowances for differences in the age structure of populations. There are inequalities in health. For example, people living in more deprived areas tend to have shorter life expectancy, and higher prevalence and mortality rates of circulatory disease. Circulatory disease accounts for nearly 40% of all deaths among persons in England every year1. Reducing inequalities in premature mortality from all cancers is a national priority, as set out in the Department of Health’s Vital Signs Operating Framework 2008/09-2010/112 and the PSA Delivery Agreement 183. However, existing indicators for premature circulatory disease mortality do not take deprivation into account. This indicator has been produced in order to quantify inequalities in circulatory disease mortality by deprivation. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01369

*Results adjusted for age, gender and study year.

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SVI MetadataWhen considering natural hazards, vulnerability generally refers to susceptibility or potential for experiencing the harmful impacts of a hazard event. The foundation of vulnerability analysis, a hazards assessment, generally focuses on a community’s exposure to hazard agents such as floods, surge, wave action, or winds.Social vulnerability (SV) is defined as ‘‘the characteristics of a person or group in terms of their capacity to anticipate, cope with, resist and recover from the impacts of a natural hazard.’’ Overall Social Vulnerability is determined by combining childcare, eldercare, transportation, shelter, and civic capacity needs factors. Factor county quintile is determined from statewide percentile score.Field DescriptionsPOPe – estimate of populationHHe – estimate of householdsVEHe - estimate of vehiclesSVI - Composite Social Vulnerability IndexCHILDCARE - Childcare needsELDERCARE - Eldercare needsTRANSNEED - Transportation needsSHELTNEED - Shelter needsCIVICCAP - Civic capacity needsCHILDCARECHILD – Children under 5 years populationSPHWC – Single parent householdsELDERCAREELDERHH – Elder householdsELDERHHPV – Elder households in povertyTRANSNEEDPTD – Public transportation dependent householdsHUNOVEL – Housing units with no vehicleSHELTNEEDVACHU – Vacant housing unitsRENTER – Renter householdsNONWHITE – Nonwhite populationGQ – Population in group quartersYEAR20 – Housing units older than 20 yearsMOBILE - Mobile home housing unitsPOPV - Population in povertyCIVICCAP HUNOTEL – Housing units with no telephoneNOHS – No high school diploma populationUNEMP – Unemployed civilian workforce population 16 years plusSPENW – Speak English poorly or not at all populationOTHER FACTORS (Not included in SVI calculation)RENTBURDN – Rent burdened households (more than 30% of monthly income spent on rent)NOINTNET – No broadband Internet access householdsMEDHVAL – Median home valueAll factor fields use following naming codes:e - estimate from ACS 5-year averagesi - index calculated as percent of table universez - statewide standard scorep - statewide percentile scoreq - county quintilec – quintile by USDA commerce zoneExample index field names:CHILDe – estimate of CHILD populationCHILDi – index calculated from proportion of CHILD in total populationCHILDz – z-score of CHILDCHILDp – statewide percentile rankCHILDq – county quintileSVI Calculation Steps 1. ACS data downloaded (R script) a. 5-year estimate data for 1st order indices, b. Tables and fields identified and selected by county, tract, and block group, c. Fields for each first order estimate (e)and table universe assembled to CSV file, 2. CSV imported to SQL Server database (SQL query), a. Index (i) calculated as percentage), z-score (z), statewide percentile rank (p) calculated, b. Quintiles (q) calculated by ranking percentiles within county, c. For some years, quintiles (c) by commerce zone (USDA) also calculated, d. 2nd order indices calculated as mean percentile rank and grouped by county quintile, e. 3rd order index calculated as mean of 2nd order indices and grouped by county quintile.

Replication data for: "Testing Rank Similarity". Visit https://dataone.org/datasets/sha256%3Aff104c4404b2216f536f7f87ea0721d40af1b91e6fa35ed53e64b9dccf12428a for complete metadata about this dataset.

Data on teenage conceptions at ward level has been analysed by quintiles and are presented as maps to illustrate the variation whilst avoiding the risk of disclosing information on individuals. Under 18 conception rates at ward level were produced by aggregating the number of conceptions to all girls aged under 18 over three year periods (2000-2002 and 2001-2003) and calculating the rate as the number of conceptions per 1,000 women aged 15-17 resident in the area using the mid year ward population estimates. Quintiles were then produced by ranking ward level under 18 conceptions rates from the lowest to highest at National level and then allocating wards to one of five equal groups based on the total number of wards. Quintile 1 therefore includes wards with the lowest rates, whilst quintile 5 includes wards with the highest rates in England and Wales. Source: Office for National Statistics (ONS) Publisher: Neighbourhood Statistics Geographies: Ward, Local Authority District (LAD) Geographic coverage: England and Wales Time coverage: 2000-2002, 2001-2003 Type of data: Administrative data

Quantile rank analysis of miRNA expression data.

The Centers for Disease Control Social Vulnerability Index shows which communities are especially at risk during public health emergencies because of factors like socioeconomic status, household composition, racial composition of neighborhoods, or housing type and transportation. The CDC SVI uses 15 U.S. census variables to identify communities that may need support before, during, or after disasters. Learn more here. The condition is the overall ranking of four social theme rankings where lower values indicate high vulnerability and high values indicate low vulnerability. Quintiles for this condition were determined for all the Census tracts in King County. Quintile 1 is the most vulnerable residents, Quintile 5 is the least vulnerable residents. Data is released every 2 years following the American Community Survey release in December of the year following the Survey. The most recent data for 2018 was downloaded from the ATSDR website.

We estimate intergenerational income mobility in Italy using administrative data from tax returns. Our estimates of mobility in Italy are higher than prior work using survey data and other indirect methods. The rank-rank slope of parent-child income in Italy is 0.22, compared to 0.18 in Denmark and 0.34 in the United States. The probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 0.11. Upward mobility is higher for sons and first-born children. We uncover substantial geographical variation: upward mobility rates are much higher in Northern Italy, where provinces have higher measured school quality, more stable families, and more favorable labor market conditions.

This work supports the Wild Coast Project. This is a FEATURE LAYER for the accompanying Web Map.Rankings: All final data layers were ranked by quintiles. Quintiles represent a percentile ranking with 5classes. This allows the user to see an easily interpretable rank-score for each hexagon.Input layers include: 1) Human Disturbance Index2) Physical Intactness Index3) Habitat and Species Diversity Index4) Ecological Intactness Index. Additional Layers for Visualization Include: 1) Conservation Management Status2) Marine Protected AreasLayer Description: 1) The Human Disturbance Index based on three indices relevant to coastal access and density of build features including roads and buildings, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank- score for the index. A value of 1 is low human disturbance where a value of 5 is high human disturbance.2) Physical Intactness Index is based on three indices articulating the built environment with a focus on the coast and coastal structures including counts of piers, jetty’s, harbors and percent of armored shoreline, and the intensity of the surrounding built environment through the built environment intensity index from CCA 2018, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank- score for the index. A value of 1 is high physical intactness where a value of 5 is low physical intactness.3) Habitat and Species Diversity Index is solely based on the rarity -weighted index which is a combined spatial index of 40 habitats and 159 imperiled species that characterizes the relative biodiversity and conservation value across the landscape in terms of value of habitat type and imperiled species presence. This metric was derived at the 1km scale for the California Coastal Assessment. We used this same index and aggregated it to the scale of the ACE datasets at 2.5 sq. mile. Quintiles were again taken from the distribution generated from the model to represent a rank-score for the index. A value of 1 is low species and habitat diversity where a value of 5 is high species and habitat diversity.4) Ecological Intactness Index is based on four indicators of connectivity, natural landscape blocks, and counts of shorebird and marine mammal colonies and haul out areas, summed in an additive model framework. Quintiles were again taken from the distribution generated from the model to represent a rank-score for the index. A value of 1 is low ecological intactness where a value of 5 is high ecological intactness.All data was compiled from the following paper: Reynolds, M., Gleason, M. G., Heady, W., Easterday, K., & Morrison, S. A. The Importance of Identifying and Protecting Coastal Wildness. Frontiers in Conservation Science, 4, 1224618.

The Centers for Disease Control Social Vulnerability Index shows which communities are especially at risk during public health emergencies because of factors like socioeconomic status, household composition, racial composition of neighborhoods, or housing type and transportation. The CDC SVI uses 15 U.S. census variables to identify communities that may need support before, during, or after disasters. Learn more here.The condition is the overall ranking of four social theme rankings where lower values indicate high vulnerability and high values indicate low vulnerability. Quintiles for this condition were determined for all the Census tracts in King County. Quintile 1 is the most vulnerable residents, Quintile 5 is the least vulnerable residents. Data is released every 2 years following the American Community Survey release in December of the year following the Survey. The most recent data for 2018 was downloaded from the ATSDR website. This data is also available from Esri's Living Atlas of the World. Source: Agency for Toxic Substances and Disease Registry

We present new evidence on trends in intergenerational mobility in the United States using administrative earnings records. We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971–1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child's probability of attending college and her parents' income rank. We also calculate transition probabilities, such as a child's chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery”–the parents to whom a child is born–are larger today than in the past.

File List

    Ambystoma.tigrinum.csv
    trends.rtf

Description
 trends.rtf is a text file with commands for implementing the quantile regression analyses on Ambystoma tigrinum trends in the text file Ambystoma.tigrinum.csv using the quantreg package in R.

    Ambystoma.tigrinum.csv
    Column 1: species - amphibian species,
    Column 2: year - year of count,
    Column 3: abundance - numbers per constant effort search,
    Column 4: lagabundance - abundance lagged 1 year.
    trends.rtf

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

The New York State Nursing Home Quality Initiative (NHQI) is an annual evaluation and ranking of eligible Medicaid-certified nursing homes in New York State. Nursing homes are evaluated on their performance in three components: Quality, Compliance, and Efficiency. Nursing homes are awarded points for their performance in each measure and ranked into overall quintiles, the first quintile containing the best performing homes. Refer to the Measures document to learn more about the specific measures in the NHQI, and the data sources and time frames used. Changes in measure specifications and the deletion or addition of measures will limit the ability to trend this data over time. The quality measures are based on past data and may not accurately reflect a nursing home’s most current quality performance. Refer to the Overview document for more information on the limitations of this dataset. The information in this dataset is intended to be used in conjunction with other sources for assessing quality of care in nursing homes, including in-person visits to a nursing home. For more information, go to the "About" tab.

In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and proposes a parametric estimation based on the minimum distance method. However, when the dimensionality of the covariates is large, the minimum distance estimation using this process is computationally demanding. To overcome this problem, we propose a two-step estimation method based on the quantile regression and minimum distance methods. We then show the uniform asymptotic properties of our estimator and the validity of the nonparametric bootstrap. The Monte Carlo studies indicate that our estimator performs well in finite samples. Finally, we present two empirical illustrations, to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development.

This release provides insights into self-reported health in England and Wales in 2021, broken down by age and sex. Key findings are presented at country, regional and local authority level. Additional analyses compare general health to the 2011 Census and examines the relationship between deprivation and health at a national decile (England) or quintile (Wales) level can be found here.

In 2021 and 2011, people were asked “How is your health in general?”. The response options were:

• Very good
• Good
• Fair
• Bad
• Very bad

Age specific percentage

Age-specific percentages are estimates of disability prevalence in each age group, and are used to allow comparisons between specified age groups. Further information is in the glossary.

Age-standardised percentage

Age-standardised percentages are estimates of disability prevalence in the population, across all age groups. They allow for comparison between populations over time and across geographies, as they account for differences in the population size and age structure. Further information is in the glossary.

Details on usage of Age-standardised percentage can be found here

Count

The count is the number of usual residents by general health status from very good to very bad, sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included..

General health

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

Index of Multiple Deprivation and Welsh Index of Multiple Deprivation

National deciles and quintiles of area deprivation are created through ranking small geographical populations known as Lower layer Super Output Areas (LSOAs), based on their deprivation score from most to least deprived. They are then grouped into 10 (deciles) or 5 (quintiles) divisions based on the subsequent ranking. We have used the 2019 IMD and WIMD because this is the most up-to-date version at the time of publishing.

Population

The population is the number of usual residents of each sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included.

Usual resident

For Census 2021, a usual resident of the UK is anyone who, on census day, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.