This dataset contains information on the number of deaths and age-adjusted death rates for the five leading causes of death in 1900, 1950, and 2000. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

This dataset displays the number of television receivers by country for the time period covering 1990 through 1997. Covered throughout this dataset is 150+ countries, This dataset was gathered from the United Nations Statistics Division. http://unstats.un.org/unsd/databases.htm Access Date: October 31, 2007

Context

To better understand the facts around school shootings a few years ago I went looking for data and found it difficult to come by - often embedded in other datasets or fragmented and unusable. I decided to create my own compilation based on a mashup of the Pah/Amaral/Hagan research on school shootings with the Wikipedia article from 1990 to present.

Content

pah_wikp file: A list of all school shooting incidents from 1990 to present.

Fields:

• Date: date of incident
• City: location of incident
• State: location of incident
• Area Type: urban or suburban (only in Pah dataset)
• School: C = college, HS = high school, MS = middle school, ES = elementary school, - = unknown
• Fatalities: # killed
• Wounded: # wounded (only in Wikipedia dataset)
• Dupe: whether this incident appears in both datasets. Note: only the "Pah" version of the incident is marked.
• Source: Pah or Wikp
• Desc: text description of incident (only in Wikipedia dataset)

cps file: US census data on school populations. Fields should be fairly self explanatory.

Acknowledgements

Thanks to the authors referenced above as well as the Wikipedia contributors!

Inspiration

• Why are school shootings (and death counts) increasing over time?
• How does the risk of being killed in a school shooting compare with other risks?
• Are some schools / cities / states at higher risk?
• Is there a correlation between countermeasures and a decrease in fatalities?
• What else correlates with school shooting risks? In addition to firearms and the people who wield them, is there any clear causality?

github version

Note: the master version of these datasets can be found at https://github.com/ecodan/school-shooting-data.git and is open for community contributions. I'll try to keep this data up-to-date with the github version on at least a monthly basis.

This data collection consists of three data files, which can be used to determine infant mortality rates. The first file provides linked records of live births and deaths of children born in the United States in 1990 (residents and nonresidents). This file is referred to as the "Numerator" file. The second file consists of live births in the United States in 1990 and is referred to as the "Denominator-Plus" file. Variables include year of birth, state and county of birth, characteristics of the infant (age, sex, race, birth weight, gestation), characteristics of the mother (origin, race, age, education, marital status, state of birth), characteristics of the father (origin, race, age, education), pregnancy items (prenatal care, live births), and medical data. Beginning in 1989, a number of items were added to the U.S. Standard Certificate of Birth. These changes and/or additions led to the redesign of the linked file record layout for this series and to other changes in the linked file. In addition, variables from the numerator file have been added to the denominator file to facilitate processing, and this file is now called the "Denominator-Plus" file. The additional variables include age at death, underlying cause of death, autopsy, and place of accident. Other new variables added are infant death identification number, exact age at death, day of birth and death, and month of birth and death. The third file, the "Unlinked" file, consists of infant death records that could not be linked to their corresponding birth records. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06630.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES
1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United....

6. National Center for Health Statistics. County geography changes: 1990–2012. Available from: http://www.cdc.gov/nchs/data/nvss/bridged_race/County_Geography_Changes.pdf.

7. National Center for Health Statistics. County geography changes: 1990–2015. Available from: https://www.cdc.gov/nchs/nvss/bridged_race/county_geography-_changes2015....

The data is based on Economic Research Service (ERN) of USDA's dataset that shows where the creative people are in the U.S. Its an interpretation of Richard Florida's thesis that much of urban development is determined by people who work in the so called ideas and knowledge industry. The workers who are in ideas and knowledge industry are attracted to areas that offer jobs in these industries and also because of desirable traits such as quality of life indicators. For details see http://www.ers.usda.gov/data/creativeclasscodes/ and http://www.ers.usda.gov/Data/CreativeClassCodes/methods.htm

BackgroundLife expectancy at birth in the United States will likely surpass 80 years in the coming decade. Yet recent studies suggest that longevity gains are unevenly shared across age and socioeconomic groups. First, mortality in midlife has risen among non-Hispanic whites. Second, low-educated whites have suffered stalls (men) or declines (women) in adult life expectancy, which is significantly lower than among their college-educated counterparts. Estimating the number of life years lost or gained by age and cause of death, broken down by educational attainment, is crucial in identifying vulnerable populations.Methods and FindingsUsing U.S. vital statistics data from 1990 to 2010, this study decomposes the change in life expectancy at age 25 by age and cause of death across educational attainment groups, broken down by race and gender. The findings reveal that mortality in midlife increased for white women (and to a lesser extent men) with 12 or fewer years of schooling, accounting for most of the stalls or declines in adult life expectancy observed in those groups. Among blacks, mortality declined in nearly all age and educational attainment groups. Although an educational gradient was found across multiple causes of death, between 60 and 80 percent of the gap in adult life expectancy was explained by cardiovascular diseases, smoking-related diseases, and external causes of death. Furthermore, the number of life years lost to smoking-related, external, and other causes of death increased among low- and high school-educated whites, explaining recent stalls or declines in longevity.ConclusionsLarge segments of the American population—particularly low- and high school-educated whites under age 55—are diverging from their college-educated counterparts and losing additional years of life to smoking-related diseases and external causes of death. If this trend continues, old-age mortality may also increase for these birth cohorts in the coming decades.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

This data set illustrates the total forest area, the average annual percent, the net change, and the percent change of forests across the globe. http://earthtrends.wri.org/searchable_db/index.php?step=countries&ccID%5B%5D=0&allcountries=checkbox&theme=9&variable_ID=296&action=select_years http://earthtrends.wri.org/searchable_db/index.php?step=countries&ccID%5B%5D=0&allcountries=checkbox&theme=9&variable_ID=299&action=select_years http://earthtrends.wri.org/searchable_db/index.php?step=countries&ccID%5B%5D=0&allcountries=checkbox&theme=9&variable_ID=297&action=select_years http://earthtrends.wri.org/searchable_db/index.php?step=countries&ccID%5B%5D=0&allcountries=checkbox&theme=9&variable_ID=298&action=select_years September 26, 2007

ObjectiveTo examine changes in cause-specific Years of Life Lost (YLL) by age, race, and sex group in the USA from 1990 to 2014.Methods60 million death reports from the National Center for Health Statistics (NCHS) were categorized by age group, sex, race, and cause of death. YLL were calculated using age-specific life expectancies. Age groups were: infants <1, children 1–19, adults 20–64, and older adults 65+.ResultsBlacks have historically experienced more years of life lost than whites or other racial groups in the USA. In the year 1990 the YLL per 100,000 population was 21,103 for blacks, 14,160 for whites, and 7,417 for others. Between 1990 and 2014 overall YLL in the USA improved by 10%, but with marked variations in the rate of change across age, race, and sex groups. Blacks (all ages, both sexes) showed substantial improvement with a 28% reduction in YLL, compared to whites (all ages, both sexes) who showed a 4% reduction. Among blacks, improvements were seen in all age groups: reductions of 43%, 48%, 28%, and 25% among infants, children, adults, and older adults, respectively. Among whites, reductions of 33%, 44%, and 18% were seen in infants, children, and older adults, but there was a 6% increase in YLL among white adults. YLL increased by 18% in white adult females and declined 1% in white adult males. American Indian/Alaska Native women also had worsening in YLL, with an 8% increase. Asian Pacific Islanders consistently had the lowest YLL across all ages. Whites had a higher proportion of YLL due to overdose; blacks had a higher proportion due to homicide at younger ages and to heart disease at older ages.ConclusionsRace-based disparities in YLL in the USA since 1990 have narrowed considerably, largely as a result of improvements among blacks compared to whites. Adult white and American Indian / Alaskan Native females have experienced worsening YLL, while white males have experienced essentially no change. If recent trajectories continue, adult black/white disparities in YLL will continue to narrow.

This data collection provides annual data on prisoners under a sentence of death and on those whose offense sentences were commuted or vacated during the period 1973-1990. Information is supplied for basic sociodemographic characteristics such as age, sex, race, ethnicity, marital status at time of imprisonment, level of education, and state of incarceration. Criminal history data include prior felony convictions for criminal homicide and legal status at the time of the capital offense. Additional information is available for those inmates removed from death row by yearend 1990 and for those inmates who were executed.

Chronic kidney disease (CKD) caused heavy burden globally. This study aimed to investigate the patterns and temporal variations in the burden of CKD in China, Japan, the United Kingdom (U.K.), and the United States (U.S.) from 1990 to 2019, and decompose the difference in CKD disease burden between 1990 and 2019 into demographic factors. From 1990 to 2019, although the age-standardized rate (ASR) of incidence remained stable in the four countries, and the ASR of mortality and disability-adjusted life years (DALY) have declined in four countries (except for the increase in U.S.), the number of CKD incidence, death, and DALY increased significantly. The average disease burden per case in U.S. has increased between 1990 and 2019, with an increasing proportion of death-related disease burden. For the CKD due to diabetes and hypertension, whose incidences accounted for < 25% of the total CKD, while it accounts for more than 70% of the deaths (except in U.K. with 54.14% in women and 51.75% in men). CKD due to diabetes and hypertension should be the focus of CKD prevention and control. Considering the high treatment costs of CKD and ESRD, it is urgent and necessary to transform CKD treatment into primary and secondary prevention.

The dataset, provided both in comma-separated values (.csv) and the more informative Stata (.dta) format, contains place/year demographic data on more than 300 rural Alaska communities annually for 1990 to 2022 -- about 10,000 place/years. For each of the available place/years, the data include population estimates from the Alaska Department of Labor and Workforce Development or (in Census years) from the US Census. For a subset consisting of 104 northern or western Alaska (Arctic/subarctic) towns and villages, the dataset also contains yearly estimates of natural increase (births minus deaths) and net migration (population minus last year's population plus natural increase). Natural increase was calculated from birth and death counts provided confidentially to researchers by the Alaska Health Analytics and Vital Records Section (HAVRS). By agreement with HAVRS, the community-level birth and death counts are not available for publication. Population, natural increase, and net migration estimates reflect mid-year values, or change over the past fiscal rather than calendar year. For example, the natural increase value for a community in 2020 is based on births and deaths of residents from July 1, 2019 to June 31, 2020. We emphasize that all values here are best estimates, based on records of the Alaska government organizations. The dataset contains 19 variables: placename Place name (string) placenum Place name (numeric) placefips Place FIPS code year Year borough Borough name boroughfips Borough FIPS code latitude Latitude (decimal, - denotes S) longitude Longitude (decimal, - denotes W) town Village {0:pop2020<2,000} or town {1:pop2020>2,000} village104 104 selected Arctic/rural communities {0,1} arctic43 43 Arctic communities {0,1}, Hamilton et al. 2016 north37 37 Northern Alaska communities {0,1), Hamilton et al. 2016 pop Population (2022 data) cpopP Change in population, percent natinc Natural increase: births-deaths natincP Natural increase, percent netmig Net migration estimate netmigP Net migration, percent nipop Population without migration Three of these variables flag particular subsets of communities. The first two subsets (43 or 37 places) were analyzed in earlier publications, so the flags might be useful for replications or comparisons. The third subset (104 places) is a newer, expanded group of Arctic/subarctic towns and villages for which natural increase and net migration estimates are now available. The flag variables are: If arctic43 = 1 Subset consisting of 43 Arctic towns and villages, previously studied in three published articles: 1. Hamilton, L.C. & A.M. Mitiguy. 2009. “Visualizing population dynamics of Alaska’s Arctic communities.” Arctic 62(4):393–398. https://doi.org/10.14430/arctic170 2. Hamilton, L.C., D.M. White, R.B. Lammers & G. Myerchin. 2012. “Population, climate and electricity use in the Arctic: Integrated analysis of Alaska community data.” Population and Environment 33(4):269–283. https://doi.org/10.1007/s11111-011-0145-1 3. Hamilton, L.C., K. Saito, P.A. Loring, R.B. Lammers & H.P. Huntington. 2016. “Climigration? Population and climate change in Arctic Alaska.” Population and Environment 38(2):115–133. https://doi.org/10.1007/s11111-016-0259-6 If north37 = 1 Subset consisting of 37 northern Alaska towns and villages, previously analyzed for comparison with Nunavut and Greenland in a paper on demographics of the Inuit Arctic: 4. Hamilton, L.C., J. Wirsing & K. Saito. 2018. “Demographic variation and change in the Inuit Arctic.” Environmental Research Letters 13:11507. https://doi.org/10.1088/1748-9326/aae7ef If village104 = 1 Expanded group consisting of 104 communities, including all those in the arctic43 and north37 subsets. This group includes most rural Arctic/subarctic communities that had reasonably complete, continuous data, and 2018 populations of at least 100 people. These data were developed by updating older work and drawing in 61 additional towns or villages, as part of the NSF-supported Arctic Village Dynamics project (OPP-1822424).

Deutsch: Auf die inkludierende Wirkung (neuer) Partizipationsformen, wie beispielsweise direktdemokratische Verfahren, wurden zeitweise große Hoffnungen gesetzt. Diese Hoffnungen haben sich jedoch als wenig realistisch erwiesen. Direktdemokratische Verfahren werden, hierzulande spätestens seit dem Hamburger Schulentscheid, häufig als Ungleichheit fördernd bewertet - Direktdemokratie würde Gleichheit eher verhindern und Ungleichheiten verschärfen. Doch tragen direktdemokratische Verfahren tatsächlich zur Stabilisierung oder sogar zu einer Vertiefung von Ungleichheiten bei? Das Projekt untersucht basierend auf partizipations-, ungleichheits- und institutionentheoretischen Zugängen quantitativ-vergleichend die Effekte unterschiedlicher direktdemokratischer Verfahrenstypen auf sozio-ökonomische, rechtliche und politische Ungleichheiten. Wir gehen davon aus, dass sich direkte Demokratie auf alle Ungleichheitsdimensionen auswirkt. Zudem vermuten wir unterschiedliche Wirkungen unterschiedlicher direktdemokratischer Verfahren sowohl auf Ungleichheit als auch auf die Konfiguration des politischen Felds. Widersprüchliche Ergebnisse der bisherigen Forschung, die sich hauptsächlich auf die Schweiz und die USA beziehen, hoffen wir durch eine breitere Datenbasis aufklären zu können. Zur Analyse dieser Zusammenhänge untersuchen wir konkrete Policy-Entscheidungen in einer Vielzahl an (Glied-)Staaten quantitativ-statistisch und vergleichend. Dabei konzentrieren wir uns auf die Zeitspanne 1990-2015, da in vielen Staaten direktdemokratische Verfahren im Laufe der 1990er Jahre eingeführt wurden. Bei der Datenerhebung werden alle direktdemokratischen Verfahren in Demokratien auf nationaler Ebene berücksichtigt. Das Projekt verfolgt in erster Linie eine wissenschaftliche Zielsetzung. Allerdings verspricht es auch Antworten auf eine Reihe aktueller, gesellschaftspolitischer Fragen. Denn derzeit werden die Chancen und Risiken direktdemokratischer Verfahren weltweit intensiv diskutiert. Umso wichtiger ist es, keine voreiligen Schlüsse aus einzelnen Verfahren oder aus Forschungen zu einzelnen Ländern zu ziehen, sondern die Ergebnisse umfassender Datensätze – auch jenseits der Schweiz und den USA – abzuwarten. Mit unserem Projekt leisten wir daher einen zentralen Beitrag für die gesellschaftspolitische Diskussion. English: (New) modes of participation, such as direct democratic votes, for a long time have been presented as a "cure" for rising inequalities in western societies. In practice these aspirations have proven themselves to be rather unrealistic. At least since the “Hamburger Schulentscheid” direct democratic votes have often been estimated to rather increase inequality. It is presumed that direct democracy hinders equality and exacerbates inequality. But do direct democratic votes really reinforce inequalities? This question is addressed not only within the public discourse but also in political science. While some authors proclaim the positive effect of referenda, others point to the dangers of direct democracy such as the possible discrimination of minorities. The current state of the art displays three research gaps that have not yet been addressed: first, the majority of academic work is limited to the comparative analysis on the subnational level (mostly Switzerland and the US). Second, predominantly one dimension of inequality, namely socio-economic inequality, is regarded. The other dimensions (political and legal) are mostly excluded from analysis. Third, often only the existence of direct democratic options has been inspected. What is missing is an in-depth review and analysis of the actual outputs of referenda. The project aims at closing these research gaps by drawing on datasets of national referenda in democracies worldwide from 1990-2015. Based on theories of participation, inequality and institutions, the research project quantitative-comparatively examines the outputs of different direct democratic votes on socio-economic, legal, and political inequalities. We assume that direct democracy has an impact on all of these dimensions of inequality. Additionally, we expect different effects of different direct democratic instruments. We hope to add to the somewhat contradictory results of previous research, primarily based on Switzerland and the US, by deploying a more extensive database. Thereby this project contributes to a discourse in society. We focus on the timespan between 1990 and 2015, because in many states direct democratic options were introduced during the nineties. First and foremost this project pursues an academic benefit. Additionally, it presents itself to be a promissory source for answers to current, socio-political issues. Currently opportunities and risks of direct democratic options are part of a worldwide discussion. Here, it is crucial not to jump to quick conclusions, but to make statements based on more extensive databases like those set up in our project.

This study, commonly known as the Longitudinal Study of Aging (LSOA), was conducted by the National Center for Health Statistics (NCHS) in collaboration with the National Institute on Aging (NIA) and designed to (1) provide mortality rates by demographic, social, economic, and health characteristics that are not available from the vital statistics system, (2) measure change in the functional status and living arrangements of older people, and (3) provide measures of health care use. It was also designed to describe the continuum from functionally independent living in the community through dependence, possible institutionalization, and finally death. The LSOA is an extension of the National Health Interview Survey (NHIS) of 1984, following its sample of 16,148 noninstitutionalized elderly people (55 years and over) living in the United States, with a special focus on those who were 70 years and over in 1984. This release of the LSOA contains data on those respondents who had been 70 years and older at the time of their 1984 interviews. The data include 1986, 1988, and 1990 reinterviews, National Death Index matches from 1984-1989, and 1987 interviews with contact persons named by decedents, as well as selected variables from the 1984 NHIS core questionnaire and its two supplements, Health Insurance and the Supplement on Aging (SOA). Two Medicare files are also included: Part 2, Medicare Hospital Records, and Part 3, Other Medicare Use Records (which covers home health care, hospice, and outpatient use). Links also are provided to allow merging of additional variables from the NATIONAL HEALTH INTERVIEW SURVEY, 1984 (ICPSR 8659). (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08719.v7. We highly recommend using the ICPSR version as they have this dataset available in multiple data formats.

US: Improved Sanitation Facilities: % of Population with Access data was reported at 100.000 % in 2015. This stayed constant from the previous number of 100.000 % for 2014. US: Improved Sanitation Facilities: % of Population with Access data is updated yearly, averaging 99.800 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 100.000 % in 2015 and a record low of 99.500 % in 1991. US: Improved Sanitation Facilities: % of Population with Access data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Access to improved sanitation facilities refers to the percentage of the population using improved sanitation facilities. Improved sanitation facilities are likely to ensure hygienic separation of human excreta from human contact. They include flush/pour flush (to piped sewer system, septic tank, pit latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and composting toilet.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation (http://www.wssinfo.org/).; Weighted average;

This dataset explores Full-time-equivalent (FTE) fall enrollment in degree-granting institutions, by control and state - Selected years, 1980 through 2005 NOTE: Data through 1990 are for institutions of higher education, while later data are for degree-granting institutions. Degree-granting institutions grant associate's or higher degrees and participate in Title IV federal financial aid programs. The degree-granting classification is very similar to the earlier higher education classification, but it includes more 2-year colleges and excludes a few higher education institutions that did not grant degrees. (See Guide to Sources for details.) SOURCE: U.S. Department of Education, National Center for Education Statistics, Higher Education General Information Survey (HEGIS), "Fall Enrollment in Colleges and Universities" 1980 survey; and 1990 through 2005 Integrated Postsecondary Education Data System, "Fall Enrollment Survey" (IPEDS-EF:90), and Spring 2001 through Spring 2006. (This table was prepared August 2006.) http://nces.ed.gov/programs/digest/d06/tables/dt06_206.asp Accessed on 12 November 2007

This data set illustrates the protected and total are of global non tropical forests. They are further defined as "Tropical forests included all forests located between the Tropics of Cancer and Capricorn. All other forests were put into the non-tropical categories. Montane forests within the tropics that were classified in the source maps as "temperate" were registered in the "tropical forests" categories in this study" (Earth Trends). http://earthtrends.wri.org/searchable_db/index.php?step=countries&ccID%5B%5D=0&allcountries=checkbox&theme=9&variable_ID=321&action=select_years http://earthtrends.wri.org/searchable_db/index.php?step=countries&ccID%5B%5D=0&allcountries=checkbox&theme=9&variable_ID=320&action=select_years September 25, 2007

This dataset tracks the average applied tariff rates in both industrial and developing countries. Data is averaged for the years 1981-2005. Figures for 2005 have been estimated. Notes: All tariff rates are based on unweighted averages for all goods in ad valorem rates, or applied rates, or MFN rates whichever data is available in a longer period. Tariff data is primarily based on UNCTAD TRAINS database and then used WTO IDB data for gap filling if possible. Data in 1980s is taken from other source.** Tariff data in these countries came from IMF Global Monitoring Tariff file in 2004 which might include other duties or charges. Country codes are based on the classifications by income in WDI 2006, where 1 = low income, 2 = middle income, 3 = high incone non-OECDs, and 4 = high income OECD countries. Sources: UNCTAD TRAINS database (through WITS); WTO IDB database (through WITS); WTO IDB CD ROMs, various years and Trade Policy Review -- Country Reports in various issues, 1990-2005; UNCTAD Handbook of Trade Control Measures of Developing Countries -- Supplement 1987 and Directory of Import Regimes 1994; World Bank Trade Policy Reform in Developing Countries since 1985, WB Discussion Paper #267, 1994 and World Development Indicators, 1998-2006; The Uruguay Round: Statistics on Tariffs Concessions Given and Received, 1996; OECD Indicators of Tariff and Non-Tariff Trade Barriers, 1996 and 2000; and IMF Global Monitoring Tariff data file 2004. Data source: http://go.worldbank.org/LGOXFTV550 Access Date: October 17, 2007