DOCSLIB.ORG

Dokumentasjonsnotat – Bydeler I

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Dokumentasjonsnotat – Bydeler I Prepared by Norwegian Social Science Data Services Harald Hårfagres gate 29 5007 Bergen Norway Phone: +47 55 58 21 17 Fax: + 47 55 58 96 50 Internet: www.nsd.uib.no Email: [email protected] Published in December 2007. The report and appendices are available for download at http://ess.nsd.uib.no. i Contents Preface............................................................................................................................................................v Introduction ..................................................................................................................................................1 1. Background and objectives............................................................................................................1 2. Availability of contextual data.......................................................................................................2 3. Coverage of contextual data..........................................................................................................4 4. Quality and comparability..............................................................................................................5 5. Documentation ...............................................................................................................................9 6. Room for improvements .............................................................................................................10 7. Outline of the report ....................................................................................................................12 Part 1: Comparability and quality of contextual data ................................................... 15 Chapter 1: Regional-level data..................................................................................................................16 Chapter 2: GDP statistics..........................................................................................................................27 Chapter 3: Unemployment statistics........................................................................................................42 Chapter 4: Education statistics.................................................................................................................59 Chapter 5: Political indicators...................................................................................................................65 Part 2: Data sources......................................................................................................... 100 List of data sources ..................................................................................................................................104 1. ACLP Political and Economic Database..........................................................................................106 2. Centripetal Democratic Governance ................................................................................................110 3. CIRI Human Rights Project...............................................................................................................113 4. CivicActive............................................................................................................................................116 5. Comparative Parties Dataset ..............................................................................................................120 6. Comparative Political Datasets ..........................................................................................................123 7. Comparative Welfare Entitlements Dataset.....................................................................................132 8. Comparative Welfare States Dataset.................................................................................................135 9. Constituency-Level Elections Dataset ..............................................................................................138 10. Cross-National Time-Series Data Archive.....................................................................................141 11. Database of Political Institutions.....................................................................................................144 12. Democratic Electoral Systems Around the World .......................................................................147 13. Election Resources on the Internet.................................................................................................150 ii 14. Environmental Performance Measurement Project .....................................................................153 15. Eurostat...............................................................................................................................................158 16. Fractionalisation Data .......................................................................................................................165 17. Freedom House..................................................................................................................................168 18. Groningen Growth and Development Centre..............................................................................175 19. International Labour Organisation..................................................................................................179 20. International Monetary Fund...........................................................................................................185 21. Inter-Parliamentary Union................................................................................................................190 22. Judicial Checks and Balances ...........................................................................................................192 23. Lijphart Elections Archive................................................................................................................195 24. Migration DRC...................................................................................................................................198 25. Minorities at Risk Project..................................................................................................................201 26. OECD .................................................................................................................................................206 27. Party Policy in Modern Democracies .............................................................................................218 28. Penn World Table..............................................................................................................................221 29. Political Constraint Index Dataset...................................................................................................225 30. Political Terror Scale .........................................................................................................................228 31. Political Transformation in Post-Communist Europe .................................................................232 32. Polity Project ......................................................................................................................................235 33. Polyarchy and Contestation Scales ..................................................................................................239 34. Polyarchy Dataset (Vanhanen).........................................................................................................242 35. Psephos (Adam Carr’s Election Archive).......................................................................................246 36. Quality of Government (La Porta et al.) ........................................................................................249 37. Quality of Government Datasets (QoG Institute) .......................................................................252 38. Reporters sans frontières ..................................................................................................................256 39. Terrorism in Western Europe: Events Data..................................................................................259 40. Transparency International...............................................................................................................262 41. UNESCO............................................................................................................................................267 42. UNICEF..............................................................................................................................................274 43. Union Centralisation Among Advanced Industrial Societies......................................................280 44. United Nations Economic Commission for Europe ...................................................................283 45. United Nations Statistical Division .................................................................................................288 46. World Bank.........................................................................................................................................293 47. World Christian Database.................................................................................................................298 iii 48. World Health Organisation ..............................................................................................................302 49. World Income Inequality Database ................................................................................................308
Load more

Recommended publications
  • This Project Has Received Funding from the European Union's Horizon
    Deliverable Number: D6.6 Deliverable Title: Report on legal and ethical framework and strategies related to access, use, re-use, dissemination and preservation of administrative data Work Package: 6: New forms of data: legal, ethical and quality matters Deliverable type: Report Dissemination status: Public Submitted by: NIDI Authors: George Groenewold, Susana Cabaco, Linn-Merethe Rød, Tom Emery Date submitted: 23/08/19 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654221. www.seriss.eu @SERISS_EU SERISS (Synergies for Europe’s Research Infrastructures in the Social Sciences) aims to exploit synergies, foster collaboration and develop shared standards between Europe’s social science infrastructures in order to better equip these infrastructures to play a major role in addressing Europe’s grand societal challenges and ensure that European policymaking is built on a solid base of the highest-quality socio-economic evidence. The four year project (2015-19) is a collaboration between the three leading European Research Infrastructures in the social sciences – the European Social Survey (ESS ERIC), the Survey for Health Aging and Retirement in Europe (SHARE ERIC) and the Consortium of European Social Science Data Archives (CESSDA AS) – and organisations representing the Generations and Gender Programme (GGP), European Values Study (EVS) and the WageIndicator Survey. Work focuses on three key areas: Addressing key challenges for cross-national data collection, breaking down barriers between social science infrastructures and embracing the future of the social sciences. Please cite this deliverable as: Groenewold, G., Cabaco, S., Rød, L.M., Emery, T., (2019) Report on legal and ethical framework and strategies related to access, use, re-use, dissemination and preservation of administrative data.
    [Show full text]
  • European Attitudes to Climate Change and Energy: Topline Results from Round 8 of the European Social Survey
    European Attitudes to Climate Change and Energy: Topline Results from Round 8 of the European Social Survey ESS Topline Issue Results Series9 2 European Attitudes to Climate Change and Energy This latest issue in our Topline Results we hope that this latest data will influence series examines public attitudes academic, public and policy debate in this towards climate change and energy for area. the first time in the ESS. The module We include two different topics in was selected for inclusion due to its each round of the survey to expand the academic excellence as well as the relevance of our data into new areas increasing relevance of this issue. For and to allow repetition if the case can be example the Paris Agreement made made to examine the same area again. by 195 United Nations Framework Everyone at the ESS is delighted with the Convention on Climate Change work of the Questionnaire Design Team (UNFCCC) countries in 2016 who led on the design of this module, underlines the salience of the topic. and who have written this excellent With many parts of Europe and the publication. world recording rising temperatures and Rory Fitzgerald experiencing more extreme weather, ESS ERIC Director the subject is a key grand challenge. City, University of London By assessing public opinion on climate change and the related issue of energy The authors of this issue: • Wouter Poortinga, Professor of Environmental Psychology, Cardiff University • Stephen Fisher, Associate Professor in Political Sociology, Trinity College, University of Oxford • Gisela
    [Show full text]
  • Department of Geography
    Department of Geography UNIVERSITY OF FLORIDA, SPRING 2019 GEO 4167c section #09A6 / GEO 6161 section # 09A9 (3.0 credit hours) Course# 15235/15271 Intermediate Quantitative Methods Instructor: Timothy J. Fik, Ph.D. (Associate Professor) Prerequisite: GEO 3162 / GEO 6160 or equivalent Lecture Time/Location: Tuesdays, Periods 3-5: 9:35AM-12:35PM / Turlington 3012 Instructor’s Office: 3137 Turlington Hall Instructor’s e-mail address: [email protected] Formal Office Hours Tuesdays -- 1:00PM – 4:30PM Thursdays -- 1:30PM – 3:00PM; and 4:00PM – 4:30PM Course Materials (Power-point presentations in pdf format) will be uploaded to the on-line course Lecture folder on Canvas. Course Overview GEO 4167x/GEO 6161 surveys various statistical modeling techniques that are widely used in the social, behavioral, and environmental sciences. Lectures will focus on several important topics… including common indices of spatial association and dependence, linear and non-linear model development, model diagnostics, and remedial measures. The lectures will largely be devoted to the topic of Regression Analysis/Econometrics (and the General Linear Model). Applications will involve regression models using cross-sectional, quantitative, qualitative, categorical, time-series, and/or spatial data. Selected topics include, yet are not limited to, the following: Classic Least Squares Regression plus Extensions of the General Linear Model (GLM) Matrix Algebra approach to Regression and the GLM Join-Count Statistics (Dacey’s Contiguity Tests) Spatial Autocorrelation / Regression
    [Show full text]
  • Assessment of Socio-Demographic Sample Composition in ESS Round 61
    Assessment of socio-demographic sample composition in ESS Round 61 Achim Koch GESIS – Leibniz Institute for the Social Sciences, Mannheim/Germany, June 2016 Contents 1. Introduction 2 2. Assessing socio-demographic sample composition with external benchmark data 3 3. The European Union Labour Force Survey 3 4. Data and variables 6 5. Description of ESS-LFS differences 8 6. A summary measure of ESS-LFS differences 17 7. Comparison of results for ESS 6 with results for ESS 5 19 8. Correlates of ESS-LFS differences 23 9. Summary and conclusions 27 References 1 The CST of the ESS requests that the following citation for this document should be used: Koch, A. (2016). Assessment of socio-demographic sample composition in ESS Round 6. Mannheim: European Social Survey, GESIS. 1. Introduction The European Social Survey (ESS) is an academically driven cross-national survey that has been conducted every two years across Europe since 2002. The ESS aims to produce high- quality data on social structure, attitudes, values and behaviour patterns in Europe. Much emphasis is placed on the standardisation of survey methods and procedures across countries and over time. Each country implementing the ESS has to follow detailed requirements that are laid down in the “Specifications for participating countries”. These standards cover the whole survey life cycle. They refer to sampling, questionnaire translation, data collection and data preparation and delivery. As regards sampling, for instance, the ESS requires that only strict probability samples should be used; quota sampling and substitution are not allowed. Each country is required to achieve an effective sample size of 1,500 completed interviews, taking into account potential design effects due to the clustering of the sample and/or the variation in inclusion probabilities.
    [Show full text]
  • The Evolution of Econometric Software Design: a Developer's View
    Journal of Economic and Social Measurement 29 (2004) 205–259 205 IOS Press The evolution of econometric software design: A developer’s view Houston H. Stokes Department of Economics, College of Business Administration, University of Illinois at Chicago, 601 South Morgan Street, Room 2103, Chicago, IL 60607-7121, USA E-mail: [email protected] In the last 30 years, changes in operating systems, computer hardware, compiler technology and the needs of research in applied econometrics have all influenced econometric software development and the environment of statistical computing. The evolution of various representative software systems, including B34S developed by the author, are used to illustrate differences in software design and the interrelation of a number of factors that influenced these choices. A list of desired econometric software features, software design goals and econometric programming language characteristics are suggested. It is stressed that there is no one “ideal” software system that will work effectively in all situations. System integration of statistical software provides a means by which capability can be leveraged. 1. Introduction 1.1. Overview The development of modern econometric software has been influenced by the changing needs of applied econometric research, the expanding capability of com- puter hardware (CPU speed, disk storage and memory), changes in the design and capability of compilers, and the availability of high-quality subroutine libraries. Soft- ware design in turn has itself impacted applied econometric research, which has seen its horizons expand rapidly in the last 30 years as new techniques of analysis became computationally possible. How some of these interrelationships have evolved over time is illustrated by a discussion of the evolution of the design and capability of the B34S Software system [55] which is contrasted to a selection of other software systems.
    [Show full text]
  • International Journal of Forecasting Guidelines for IJF Software Reviewers
    International Journal of Forecasting Guidelines for IJF Software Reviewers It is desirable that there be some small degree of uniformity amongst the software reviews in this journal, so that regular readers of the journal can have some idea of what to expect when they read a software review. In particular, I wish to standardize the second section (after the introduction) of the review, and the penultimate section (before the conclusions). As stand-alone sections, they will not materially affect the reviewers abillity to craft the review as he/she sees fit, while still providing consistency between reviews. This applies mostly to single-product reviews, but some of the ideas presented herein can be successfully adapted to a multi-product review. The second section, Overview, is an overview of the package, and should include several things. · Contact information for the developer, including website address. · Platforms on which the package runs, and corresponding prices, if available. · Ancillary programs included with the package, if any. · The final part of this section should address Berk's (1987) list of criteria for evaluating statistical software. Relevant items from this list should be mentioned, as in my review of RATS (McCullough, 1997, pp.182- 183). · My use of Berk was extremely terse, and should be considered a lower bound. Feel free to amplify considerably, if the review warrants it. In fact, Berk's criteria, if considered in sufficient detail, could be the outline for a review itself. The penultimate section, Numerical Details, directly addresses numerical accuracy and reliality, if these topics are not addressed elsewhere in the review.
    [Show full text]
  • Support for Redistribution in an Age of Rising Inequality: New Stylized Facts and Some Tentative Explanations
    VIVEKINAN ASHOK Yale University ILYANA KUZIEMKO Princeton University EBONYA WASHINGTON Yale University Support for Redistribution in an Age of Rising Inequality: New Stylized Facts and Some Tentative Explanations ABSTRACT Despite the large increases in economic inequality since 1970, American survey respondents exhibit no increase in support for redistribution, contrary to the predictions from standard theories of redistributive preferences. We replicate these results but further demonstrate substantial heterogeneity by demographic group. In particular, the two groups that have most moved against income redistribution are the elderly and African Americans. We find little evidence that these subgroup trends are explained by relative economic gains or growing cultural conservatism, two common explanations. We further show that the trend among the elderly is uniquely American, at least relative to other developed countries with comparable survey data. While we are unable to provide definitive evidence on the cause of these two groups’ declining redistributive support, we provide additional correlations that may offer fruitful directions for future research on the topic. One story consistent with the data on elderly trends is that older Americans worry that redistribution will come at their expense, in particular through cuts to Medicare. We find that the elderly have grown increasingly opposed to government provision of health insurance and that controlling for this tendency explains about 40 percent of their declin- ing support for redistribution.
    [Show full text]
  • Estimating Regression Models for Categorical Dependent Variables Using SAS, Stata, LIMDEP, and SPSS*
    © 2003-2008, The Trustees of Indiana University Regression Models for Categorical Dependent Variables: 1 Estimating Regression Models for Categorical Dependent Variables Using SAS, Stata, LIMDEP, and SPSS* Hun Myoung Park (kucc625) This document summarizes regression models for categorical dependent variables and illustrates how to estimate individual models using SAS 9.1, Stata 10.0, LIMDEP 9.0, and SPSS 16.0. 1. Introduction 2. The Binary Logit Model 3. The Binary Probit Model 4. Bivariate Logit/Probit Models 5. Ordered Logit/Probit Models 6. The Multinomial Logit Model 7. The Conditional Logit Model 8. The Nested Logit Model 9. Conclusion 1. Introduction A categorical variable here refers to a variable that is binary, ordinal, or nominal. Event count data are discrete (categorical) but often treated as continuous variables. When a dependent variable is categorical, the ordinary least squares (OLS) method can no longer produce the best linear unbiased estimator (BLUE); that is, OLS is biased and inefficient. Consequently, researchers have developed various regression models for categorical dependent variables. The nonlinearity of categorical dependent variable models (CDVMs) makes it difficult to fit the models and interpret their results. 1.1 Regression Models for Categorical Dependent Variables In CDVMs, the left-hand side (LHS) variable or dependent variable is neither interval nor ratio, but rather categorical. The level of measurement and data generation process (DGP) of a dependent variable determines the proper type of CDVM. Binary responses (0 or 1) are modeled with binary logit and probit regressions, ordinal responses (1st, 2nd, 3rd, …) are formulated into (generalized) ordered logit/probit regressions, and nominal responses are analyzed by multinomial logit, conditional logit, or nested logit models depending on specific circumstances.
    [Show full text]
  • Investigating Data Management Practices in Australian Universities
    Investigating Data Management Practices in Australian Universities Margaret Henty, The Australian National University Belinda Weaver, The University of Queensland Stephanie Bradbury, Queensland University of Technology Simon Porter, The University of Melbourne http://www.apsr.edu.au/investigating_data_management July, 2008 ii Table of Contents Introduction ...................................................................................... 1 About the survey ................................................................................ 1 About this report ................................................................................ 1 The respondents................................................................................. 2 The survey results............................................................................... 2 Tables and Comments .......................................................................... 3 Digital data .................................................................................... 3 Non-digital data forms....................................................................... 3 Types of digital data ......................................................................... 4 Size of data collection ....................................................................... 5 Software used for analysis or manipulation .............................................. 6 Software storage and retention ............................................................ 7 Research Data Management Plans.........................................................
    [Show full text]
  • Round 9 ESS Sampling Guidelines
    European Social Survey Round 9 Sampling Guidelines: Principles and Implementation The ESS Sampling and Weighting Expert Panel, 26 January 2018 Contents Page Summary 2 1. The ESS Sample Design Process 3 1.1 Objectives 2 1.2 The Sample Design Process 2 1.3 The Sample Design Summary 4 2. Principles for Sampling in the ESS 5 2.1 Population Coverage 5 2.2 Probability Sampling 6 2.3 Statistical Precision 6 3. Tips for Good Sample Design 9 3.1 Sampling Frames 9 3.2 Multi-Stage Sampling 12 3.3 Stratification 13 3.4 Predicting deff p 14 3.5 Predicting deff c 17 4. Calculating the Required Sample Size 19 Annex: Sample Design Summary 20 Summary The document sets out the principles of ESS sampling and provides guidance on how to produce an effective design that is consistent with these principles. It also explains the procedure required to approve a sampling design to be used in the ESS. The document has been produced by the ESS Sampling and Weighting Expert Panel (SWEP), a group of experts appointed by the ESS Director to evaluate and help implement the sampling design in each of the ESS countries in close cooperation with National Coordinators (NCs). A core objective of the SWEP is to support NCs in implementing sample designs of the highest possible quality, and consistent with the ESS sampling principles. Changes to this Document These guidelines have been substantially restructured and rewritten since Round 8. The main changes are: • The inclusion of explicit tips on how best to handle key aspects of sample design (section 3), including a summary box of “key tips” at the end of each sub-section; • Worked examples of key calculations (deff p, deff c and gross sample size); • Separation of principles (section 2), sample design considerations (section 3), and a description of the process of developing a design and getting it approved (section 1); • Minor revisions to the “Sign-off Form”, which has been renamed the “Sample Design Summary” (Annex).
    [Show full text]
  • Ess4 - 2008 Documentation Report
    ESS4 - 2008 DOCUMENTATION REPORT THE ESS DATA ARCHIVE Edition 5.5 Version Notes, ESS4 - 2008 Documentation Report ESS4 edition 5.5 (published 01.12.18): Applies to datafile ESS4 edition 4.5. Changes from edition 5.4: Czechia: Country name changed from Czech Republic to Czechia in accordance with change in ISO 3166 standard. 25 Version notes. Information updated for ESS4 ed. 4.5 data. 26 Completeness of collection stored. Information updated for ESS4 ed. 4.5 data. Israel: 46 Deviations amended. Deviation in F1-F4 (HHMMB, GNDR-GNDRN, YRBRN-YRBRNN, RSHIP2-RSHIPN) added. Appendix: Appendix A3 Variables and Questions and Appendix A4 Variable lists have been replaced with Appendix A3 Codebook. ESS4 edition 5.4 (published 01.12.16): Applies to datafile ESS4 edition 4.4. Changes from edition 5.3: 25 Version notes. Information updated for ESS4 ed.4.4 data. 26 Completeness of collection stored. Information updated for ESS4 ed.4.4 data. Slovenia: 46 Deviations. Amended. Deviation in B15 (WRKORG) added. Appendix: A2 Classifications and Coding standards amended for EISCED. A3 Variables and Questions amended for EISCED, WRKORG. Documents: Education Upgrade ESS1-4 amended for EISCED. ESS4 edition 5.3 (published 26.11.14): Applies to datafile ESS4 edition 4.3 Changes from edition 5.2: All links to the ESS Website have been updated. 21 Weighting: Information regarding post-stratification weights updated. 25 Version notes: Information updated for ESS4 ed.4.3 data. 26 Completeness of collection stored. Information updated for ESS4 ed.4.3 data. Lithuania: ESS4 - 2008 Documentation Report Edition 5.5 2 46 Deviations.
    [Show full text]
  • Package 'Ecdat'
    Package ‘Ecdat’ November 3, 2020 Version 0.3-9 Date 2020-11-02 Title Data Sets for Econometrics Author Yves Croissant <[email protected]> and Spencer Graves Maintainer Spencer Graves <[email protected]> Depends R (>= 3.5.0), Ecfun Suggests Description Data sets for econometrics, including political science. LazyData true License GPL (>= 2) Language en-us URL https://www.r-project.org NeedsCompilation no Repository CRAN Date/Publication 2020-11-03 06:40:34 UTC R topics documented: Accident . .4 AccountantsAuditorsPct . .5 Airline . .7 Airq.............................................8 bankingCrises . .9 Benefits . 10 Bids............................................. 11 breaches . 12 BudgetFood . 15 BudgetItaly . 16 BudgetUK . 17 Bwages . 18 1 2 R topics documented: Capm ............................................ 19 Car.............................................. 20 Caschool . 21 Catsup . 22 Cigar ............................................ 23 Cigarette . 24 Clothing . 25 Computers . 26 Consumption . 27 coolingFromNuclearWar . 27 CPSch3 . 28 Cracker . 29 CRANpackages . 30 Crime . 31 CRSPday . 33 CRSPmon . 34 Diamond . 35 DM ............................................. 36 Doctor . 37 DoctorAUS . 38 DoctorContacts . 39 Earnings . 40 Electricity . 41 Fair ............................................. 42 Fatality . 43 FinancialCrisisFiles . 44 Fishing . 45 Forward . 46 FriendFoe . 47 Garch . 48 Gasoline . 49 Griliches . 50 Grunfeld . 51 HC.............................................. 52 Heating . 53
    [Show full text]