This dataset provides values for GDP PER CAPITA PPP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Abstract copyright UK Data Service and data collection copyright owner. The OECD's quarterly national accounts (QNA) dataset presents data collected from all the OECD member countries and some other major economies on the basis of a standardised questionnaire. It contains a wide selection of generally seasonally adjusted quarterly series most widely used for economic analysis. It contains a wide selection of generally seasonally adjusted quarterly series most widely used for economic analysis from 1947 or whenever available:- GDP expenditure and output approaches (current prices and volume estimates);- GDP income approach (current prices);- Gross fixed capital formation (current prices and volume estimates) broken down separately by type of asset or product and by institutional sector;- Disposable income and Real disposable income components;- Saving and net lending (current prices);- Population and Employment (in persons);- Employment by industry (in persons and hours worked);- Compensation of employees (current prices);- Household final consumption expenditure by durability (current prices and volume estimates).Please note that OECD reference year changed from 2010 to 2015 on Tuesday 3rd of December, 2019. These data were first provided by the UK Data Service in October 2006. Main Topics: The database covers:Gross Domestic Productlendingsavingincomehousehold final consumption expendituredetailed accounts for population and employmentexchange rates and purchasing power paritiestotal employment, self-employment, and employment by industry sectorGross Domestic Product by type of expenditure and by industrygross fixed capital formation by product and by institutional sectorcomponents of disposable income. 1947 2021 Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Rep... Chad Channel Islands Chile China Colombia Comoros Congo Costa Rica Croatia Cuba Curacao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic EMPLOYMENT EXCHANGE RATES EXPENDITURE Economic conditions... Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Europe European Union Coun... Faroe Islands Finland France GROSS DOMESTIC PRODUCT Gabon Gambia Georgia Germany October 1990 Ghana Gibraltar Greece Grenada Guatemala Guinea Guinea Bissau Honduras Hong Kong Hungary INCOME INDUSTRIES Iceland India Indonesia Iran Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kosovo Kuwait Kyrgyzstan Latvia Lebanon Lesotho Liberia Lithuania Luxembourg Macao Macedonia Madagascar Malawi Malaysia Mali Malta Mauritania Mauritius Mexico Moldova Montenegro Morocco Mozambique Multi nation NATIONAL ACCOUNTING NATIONAL INCOME Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Norway Romania Russia Rwanda Saint Lucia Saint Martin Saint Vincent Saotome Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea Spain Sri Lanka Sudan Surinam Swaziland Switzerland Tajikistan Tanzania Thailand Togo Trinidad and Tobago Turkey Turkmenistan Uganda Ukraine United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Virgin Islands USA Zambia Zimbabwe

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

• Eurostat [3]
• Directorate-General for Mobility and Transport (DG MOVE). European Union [4]
• The World Bank [5]
• World Health Organization (WHO) [6]
• European Transport Safety Council (ETSC) [7]
• European Road Safety Observatory (ERSO) [8]
• European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]
• EU BestPoint-Project [10]
• Ministerstvo dopravy, República Checa [11]
• Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]
• Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]
• National Statistics Office, Malta [14]
• Ministério da Economia e Transição Digital, Portugal [15]
• Ministerio de Fomento, España [16]
• Trafikverket, Suecia [17]
• Ministère de l’environnement de l’énergie et de la mer, Francia [18]
• Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]
• Statistisk sentralbyrå, Noruega [26-29]
• Instituto Nacional de Estatística, Portugal [30]
• Infraestruturas de Portugal S.A., Portugal [31–35]
• Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

| Code | Variable and unit | | fatal_pc_km | Fatalities per billion passenger-km | | fatal_mIn | Fatalities per million inhabitants | | accid_adj_pc_km | Accidents per billion passenger-km | | p_km | Billions of passenger-km | | croad_inv_km | Investment in roads construction per kilometer, €/km (2015 constant prices) | | croad_maint_km | Expenditure on roads maintenance per kilometer €/km (2015 constant prices) | | prop_motorwa | Proportion of motorways over the total road network (%) | | populat | Population, in millions of inhabitants | | unemploy | Unemployment rate (%) | | petro_car | Consumption of gasolina and petrol derivatives (tons), per tourism | | alcohol | Alcohol consumption, in liters per capita (age > 15) | | mot_index | Motorization index, in cars per 1,000 inhabitants | | den_populat | Population density, inhabitants/km2 | | cgdp | Gross Domestic Product (GDP), in € (2015 constant prices) | | cgdp_cap | GDP per capita, in € (2015 constant prices) | | precipit | Average depth of rain water during a year (mm) | | prop_elder | Proportion of people over 65 years (%) | | dps | Demerit Point System, dummy variable (0: no; 1: yes) | | freight | Freight transport, in billions of ton-km |

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946.

12. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947.

13. Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371.

14. Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371.

15. Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021).

16. Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain;

17. Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6.

18. Ministère de l’Équipement, du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005;

19. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anno 2000; Istituto Poligrafico e Zecca dello Stato: Roma, Italy, 2001;

20. Ministero delle Infrastrutture e dei Trasporti Conto nazionale dei trasporti 1999. 2000.

21. Generale, D.; Informativi, S. delle Infrastrutture e dei Trasporti Anno 2004.

22. Ministero delle Infrastrutture e dei Trasporti *Conto Nazionale delle Infrastrutture e dei

This dataset provides values for GOVERNMENT DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The research project is a subproject of the research association “Strengthening of integration potentials within a modern society” (Scientific head: Prof. Dr. Wilhelm Heitmeyer, Bielefeld) which contains 17 subprojects and is supported by the ministry of education and research. In almost all the economically highly developed countries violent crime increased significantly in the second part of the last century - in contrast to the long term trend of decline of individual (non-governmental) violence since the beginning of modern times. The authors develop an explanatory approach for these facts which is inspired mainly by Norbert Elias´s civilization theory and Emil Durkheim´s theory on society. Detailed time series on the development of different forms of violent crime are presented and set in relation with certain aspects of economic and social structural changes in three countries and also refer to the changes in integration of modern societies. The analysis deals especially with effectivity and legitimacy of the governmental monopoly of violence, the public beneficial security and power system, forms of building social capital, economic and social inequality, precarity of employment, different aspects of increasing economization of society, changes in family structures and usage of mass media and modern communication technologies. Register of tables in HISTAT: A: Crime statistics A.01 Frequency of types of crimes in different countries (1953-2000) A.02 Suspects by crimes of 100.000 inhabitants of Germany, England and Sweden (1955-1998) A.03 Murders, manslaughter and intentional injuries by other persons by sex of 100.000 persons after the statistics of causes of death (1953-2000) A.04 Clearance rate by types of crimes in Germany, England and Sweden (1953-1997) A.05 Prisoners of 100.000 inhabitants of Germany, Great Britain and Sweden (1950-2000) B: Key indicators for economic development in Germany, Great Britain, Sweden and the USA B1: Data on the overall economic framework B1.01 Percent changes in the real GDP per capita in purchasing power parities (1956-1987) B1.02 Percent changes in GDP per capita in prices from 2000 (1955-1998) B1.03 GDP of Germany, Sweden and the United Kingdom in purchasing power parities in percent og the US GDP (1950-1992) B1.04 Labor productivity index for different countries, base: USA 1996 = 100 (1950-1999) B1.05 GDP per hour of labor in different countries in EKS-$ from 1999 (1950-2003) B1.06 Foreign trade - exports and imports in percent of the GDP of different countries (1949-2003) B1.07 GDP, wages and Unit-Labor-Cost in different countries (1960-2003) B2: Unemployment B2.01 Standardized unemployment rate in different countries with regard to the entire working population (1960-2003) B2.02 Share of long-term unemployed of the total number of unemployed in different countries in percent (1992-2004) B2.03 Youth unemployment in different countries in percent (1970-2004) B2.04 Unemployment rate in percent by sex in different countries (1963-2000) B3: Employment B3.01 Employment rate in percent in different countries (1960-2000) B3.02 Share of fixed-term employees and persons in dependent employment in percent in different countries (1983-2004) B3.03 Share of part-time employees by sex compared to the entire working population in different countries (1973-2000) B3.04 Share of un-voluntarily part-time employees by sex in different countries (1983-2003) B3.05 Share of contract workers in different countries in percent of the entire working population (1975-2002) B3.06 Share of self-employed persons in different countries in percent of the entire working population (1970-2004) B3.07 Shift worker rate in different countries in percent (1992-2005) B3.08 Yearly working hours per employee in different countries (1950-2004) B3.09 Employment by sectors in different countries (1950-2003) B3.10 Share of employees in public civil services in percent of the population between 15 and 64 years in different countries (1960-1999) B3.11 Female population, female employees and female workers in percent of the population between 16 and 64 years in different countries (1960-2000) B3.12 Employees, self-employed persons in percent of the entire working population in different countries (1960-2000) B4: Taxes and duties B4.01 Taxes and social security contributions in percent of the GDP (1965-2002) B4.02 Social expenditure in percent of the GDP (1965-2002) B4.03 Social expenditure in percent of the GDP (1960-2000) B4.04 Public expenditure in percent of the GDP in different countries (1960-2003) B4.05 Education expenditure in percent of GDP (1950-2001) B5: Debt B5.01 Insolvencies in Germany and England (1960-2004) B5.02 Insolvencies with regard to total population in different countries (1950-2002) B5.03 Consumer credits in different countries (1960-2002) C: Income distribution in Germany, Great Britain and Sweden C.01 Income inequality in different countries Einkommensungleicheit in verschiedenen Ländern (1949-2000) C.02 Income inequality after different indices and calculations in different countries (1969-2000) C.03 Redistribution: Decline in Gini-Index through transfers and taxes in percent in different countries (1969-2000) C.04 Redistribution: Decline in Gini-Index through transfers and taxes in percent with a population structure as in the United Kingdom in 1969 in different countries (1969-2000) C.05 Redistribution efficiency: Decline in Gini-/ Atkinson-Index through transfers and the share of social expenditure of the GDP in different countries (1969-2000) C.06 Index for concentration of transfers in different countries (1981-2000) C.07 Distribution of wealth in West-Germany (1953-1998) C.08 Distribution of wealth in the United Kingdom (1950-2000) C.09 Distribution of wealth in Sweden (1951-1999) C.10 Relative income poverty in different countries (1969-2000) C.11 Reduction of poverty in different countries (1969-2000) C.12 Neocorporalism index in different countries (1960-1994) D: Perception of safety D.01 Satisfaction with democracy in different countries (1976-2004) D.02 Revenues and employees in the private security sector in different countries (1950-2001) D.03 Decommodification-Score in different countries (1971-2002) E: Demographics E.01 Birth rates: Birth per 1000 women between 15 and 49 years in different countries (1951-2001) E.02 Fertility rate in different countries (1950-2004) E.03 Marriages per 100.000 persons in different countries (1950-2003) E.04 Share of foreigners of the entire population in different countries (1951-2002) E.05 Internal migration in different countries (1952-2001)

This dataset provides values for PRIVATE DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Australia GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 63,150.000 USD in 2023. This records an increase from the previous number of 60,710.000 USD for 2022. Australia GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 18,750.000 USD from Dec 1962 (Median) to 2023, with 62 observations. The data reached an all-time high of 66,080.000 USD in 2013 and a record low of 1,870.000 USD in 1962. Australia GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;