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This dataset provides values for GDP GROWTH RATE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.
The statistic shows 130 chemical companies with the highest research and development spending in 2009, by country. In the U.S., 40 chemical companies spent a total of 7,252 million euros on research and development in 2009.
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Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach, coupled with Monte Carlo methods, to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 countries where reliable data are available. We find a positive relationship between the testing rate per 1,000 people and the implied true detection rate of COVID-19, and a negative relationship between the proportion who test positive and the implied true detection rate. Our estimates suggest that the true number of people infected across our sample of 15 developed countries is 18.2 (5-95% CI: 11.9-39.0) times greater than the reported number of cases. In individual countries, the true number of cases exceeds the reported figure by factors that range from 1.7 (5-95% CI: 1.1-3.6) for Australia to 35.6 (5-95% CI: 23.2-76.3) for Belgium.
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Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.
The United States is the leading country worldwide in terms of spending on research and development (R&D), with R&D expenditure exceeding *** billion purchasing power parity (PPP) U.S. dollars. China is invested about *** billion U.S. dollars into R&D. Health and technology Overall, health and technology dominate R&D spending globally. In 2022, health constituted nearly ** percent of all R&D spending, while hardware producers accounted for over ***percent and software producers accounted for over ***percent. Tech companies such as Meta, Amazon, and Alphabet contribute massively to tech spending, while spending continues to grow in areas such as medical technology and pharmaceuticals. Other sources of R&D spending Other sources of R&D spending include the automotive industry, chemicals, and manufacturing. Notably, within the automotive industry, the EU leads in spending, contributing nearly ** billion euros to the *** billion euros spent on automotive R&D globally. By company, Volkswagen spent the most at **** billion U.S. dollars, while in the United States, Ford spent the most on R&D at *** billion U.S. dollars.
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This dataset provides values for SOCIAL SECURITY RATE FOR COMPANIES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.
This repository contains datasets relating to coronavirus in Sierra Leone, as well as on demographic and other information from the 2015 Population and Household Census (PHC). It also includes mapping shapefiles by district, so that you can map the district-level coronavirus statistics.
See here for a full description of how the data files have been created from the source data, including the R code.
Last updated: 10 June 2020.
The novel 2019 coronavirus (covid-19) arrived late to West Africa and Sierra Leone in particular. This dataset provides the number of reported cases on a district-by-district basis for Sierra Leone, as well as various additional statistics at the country level. In addition, I provide district-by-district data on demographics and households' main sources of information, both from the 2015 census. For convenience, I also provide shapefiles for mapping the 14 districts of Sierra Leone.
The dataset consists of four main files, which are in the output
folder. See the column descriptions below for further details.
Coronavirus confirmed cases by district (sl_districts_coronavirus.csv
). I found the original data by looking in the static/js/data
folder in the source code for covid19.mic.gov.sl, last accessed 10 June 2020. The file contains the cumulative number of confirmed coronavirus cases in the 14 districts of Sierra Leone as a time series. I have used the R tidyverse to reshape the data and ensure naming is consistent with the other data files.
Demographic statistics by district (sl_districts_demographics.csv
). Data from the 2015 Population and Housing Census (PHC), sourced from Open Data Sierra Leone. The dataset covers the 14 districts of Sierra Leone, which increased to 16 in 2017. Last accessed 10 June 2020.
Main Sources of Information by district (sl_districts_info_sources.csv
). Data from the 2015 Population and Housing Census (PHC), sourced from Open Data Sierra Leone. The dataset presents the main sources of information, such as television or radio, for households in the 14 districts of Sierra Leone. Last accessed 2 June 2020. I note that I have made one correction to the source data (see R code with correction here).
Country-wide coronavirus statistics for Sierra Leone (sl_national_coronavirus.csv
). The original data also comes from covid19.mic.gov.sl, last accessed 10 June 2020. The file contains numerous statistics as time series, listed in the Column Description section below. I note that there are various potential issues in the file which I leave the user to decide how to deal with (duplicate datetimes, inconsistent statistics).
Additionally I include a set of five files with district-by-district mapping (shapefiles) and other data, unchanged from their original source. Each file is labelled in the following way: sl_districts_mapping.*
. These files come from Direct Relief Open Data on ArcGIS Hub. The data also include district-level data on maternal child health attributes, which was the original context of the mapping data.
Coronavirus confirmed cases by district sl_districts_coronavirus.csv
:
date
: Date of reportingdistrict
: District of Sierra Leone (based on pre-2017 administrative boundaries)confirmed_cases
: Cumulative number of confirmed coronavirus cases; NA if no data reporteddecrease
: Dummy variable indicating whether the number of reported cases has been revised down. NA if no reported cases on that date; 1 if there is a decrease from the last reported cases; 0 otherwiseDemographic statistics by district sl_districts_demographics.csv
:
district
: District of Sierra Leone (based on pre-2017 administrative boundaries)d_code
: District coded_id
: District idtotal_pop
: Total population in districtpop_share
: District's share of total country populationt_male
: Total male populationt_female
: Total female populations_ratio
: (*) Sex ratio at birth (number of males for every 100 females, under the age of 1)t_urban
: Total urban populationt_rural
: Total rural populationprop_urban
: Proportion urbant_h_pop
: Sum of h_male
and h_female
h_male
: (?)h_female
: (?)t_i_pop
: Sum of i_male
and i_female
i_male
: (?)i_female
: (?)working_pop
: Working populationdepend_pop
: Dependent population...
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Historical chart and dataset showing Russia death rate by year from 1950 to 2025.
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Historical chart and dataset showing South Korea literacy rate by year from 2008 to 2018.
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This data set included population and expenditure breakdowns by LGA,\r demographic statistics, labor statistics and Socio Economis Indexes for Areas\r (SEIFA) LGA score and ranking per LGA.\r \r Detailed descriptions of this data set include: \r \- LGA name \r \- LGA code \r \- Region \r \- Total Net Expenditure \r \- SEIFA DIS RANK State \r \- SEIFA DIS RANK Country \r \- SEIFA DIS RANK Metro \r \- SEIFA ADV DIS Score \r \- SEIFA ADV DIS RANK State \r \- SEIFA ADV DIS RANK Country \r \- SEIFA ADV DIS RANK Metro \r \- Adult population \r \- Adult population per venue \r \- EGM numbers per 1000 adults \r \- Expenditure per adult \r \- Workforce \r \- Unemployment \r \- Unemployment rate\r \r
Over the last two observations, the number of users is forecast to significantly increase in all regions. From the selected regions, the ranking by number of users in the e-commerce market is forecast to be lead by Indonesia with 99.1 million users. In contrast, the ranking is trailed by Singapore with 4.9 million users, recording a difference of 94.2 million users to Indonesia. Find further statistics on other topics such as a comparison of countries or regions regarding the penetration rate. The Statista Market Insights cover a broad range of additional markets.
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We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.
This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:
Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.
Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.
For detailed information about the scenarios and parameters:
Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201
All underlying data (UN World Population Prospects 2022) are openly available at:
https://population.un.org/wpp/Download/Archive
Code
1_Data.R:
2_Scenarios.R:
3_Robustness_checks.R:
Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.
Datasets
The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.
Acknowledgements
Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.
Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.
The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a regular basis with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also scraped from worldometer.
COVID-19 cases
covid19_world.csv
. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University.
covid19_asia.csv
, covid19_africa.csv
, covid19_europe.csv
, covid19_northamerica.csv
, covid19.southamerica.csv
, covid19_oceania.csv
, and covid19_others.csv
. These contain the cumulative number of COVID-19 cases organized by the continent.
Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)
COVID-19 tests
covid19_tests.csv
. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.
Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people
2020 world population
world_population(2020).csv
. It contains the 2020 world population as reported by woldometer.
Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage
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In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010.\r \r The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars.\r \r Data are recorded on an end-quarter basis.\r \r There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only locational data are reported in this statistical table. \r \r Data are shown for a range of countries and regions. Similar data for a selected group of countries are also available in B12.2.\r \r Country and regional groupings are based on the classification used in the IBS.\r \r Some liabilities are reported at market value, but contractual or nominal values are used where market values are not appropriate.\r \r This statistical table contains seven data worksheets. Six present data for countries within each specified region, while the 'Summary' worksheet shows total international liabilities of Australian-located banks (and RFCs between March 2003 and September 2010) for each region, and Australia. In each of these worksheets, the data in the last column measures total international liabilities for the region. Total international liabilities for each country add to total international liabilities for the region. However, in some quarters, this cannot be directly verified because data for individual countries and regions have blank entries in order to avoid disclosing confidential bank exposures.\r \r In the 'Summary' worksheet, the positions by region are summed to produce a ‘Total non-residents’ figure that represents reporting entities’ total positions with offshore counterparties in all currencies. The positions shown for 'Australia' are positions with residents in foreign currency.\r
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We estimate time-varying national natural real rates of interest (r?) for the four largest economies of the euro area over 1999-2016. We further derive the associated national real interest rate gaps, which gauge the perceived monetary policy stance in each country. We find that the average r? have been lower after 2008. Furthermore, national r? were significantly negative in southern countries during the sovereign crisis. As their effective real rates soared, national rate gaps across the euro area diverged. However, a common policy stance has been restored since 2014 as the European Central Bank's unconventional programs gathered pace.
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Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 4.040 % pa in 2016. This records an increase from the previous number of 3.730 % pa for 2015. Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 3.100 % pa from Dec 1995 (Median) to 2016, with 16 observations. The data reached an all-time high of 8.660 % pa in 2012 and a record low of -6.090 % pa in 1995. Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;
In 2022, Israel invested *** percent of the country's gross domestic product (GDP) into research and development, the highest worldwide. In South Korea, the expenditure on R&D reached over **** percent of its GDP.
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Means of prevalence (%) of obesity (>30kg/m2) for males and females in countries with Ibs values above and below median are shown.
Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance. Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071. Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/. Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067. Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322. Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013. Please see README.txt for further information HMD international_updated Jan 2019.xlsx Comprises 20 worksheets, of which 14 contain data. These data are arranged by country and by year. Missing data codes: "" The tab 'contents and sources' provides descriptions of the data source and contents of each sheet. HMD Scotland time trend analysis.xlsx Comprises 5 worksheets, including a combination of data and charts. The sheet 'contents' describes the data source and contents of other sheets. The variables include year, life expectancy, and various measures of change in life expectancy Missing data codes: "" Segmented regression chart.xlsx Comprises 2 worksheets, 'Data' and 'Chart'. Variables within the 'data' worksheet include: Year 4 quarter rolling period ending Female observed mortality rate Female predicted by one-break model Female predicted by two-break model Male observed mortality rate Male predicted by one-break model Male predicted by two-break model Chart breakpoint indicator Missing data codes: (blank space) Summary findings from segmented regression.xlsx Excel workbook containing table 1 of paper 'summary of results of segmented regression by population group and model/test'
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IntroductionMany national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level.MethodsRandom-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970–2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes.FindingsAll variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level.ConclusionsThe method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.
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This dataset provides values for GDP GROWTH RATE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.