{"title":"Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland","authors":"Tobias Schoch, André Müller","doi":"10.1007/s11943-020-00275-8","DOIUrl":"10.1007/s11943-020-00275-8","url":null,"abstract":"<div><p> The credibility of microsimulation modeling with the research community and policymakers depends on high-quality baseline surveys. Quality problems with the baseline survey tend to impair the quality of microsimulation built on top of the survey data. We address two potential issues that both relate to skewed and heavy-tailed distributions.</p><p>First, we find that ultra-high-income households are under-represented in the baseline household survey. Moreover, the sample estimate of average income underestimates the known population average. Although the Deville–Särndal calibration method corrects the under-representation, it cannot achieve alignment of estimated average income in the right tail of the distribution with known population values without distorting the empirical income distribution. To overcome the problem, we introduce a Pareto tail model. With the help of the tail model, we can adjust the sample income distribution in the tail to meet the alignment targets. Our method can be a useful tool for microsimulation modelers working with survey income data.</p><p>The second contribution refers to the treatment of an outlier-prone variable that has been added to the survey by record linkage (our empirical example is health care cost). The nature of the baseline survey is not affected by record linkage, that is, the baseline survey still covers only a small part of the population. Hence, the sampling weights are relatively large. An outlying observation together with a high sampling weight can heavily influence or even ruin an estimate of a population characteristic. Thus, we argue that it is beneficial—in terms of mean square error—to use robust estimation and alignment methods, because robust methods are less affected by the presence of outliers.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"14 3-4","pages":"267 - 304"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11943-020-00275-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50437269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Die Lohnungleichheit von Vollzeitbeschäftigten in Deutschland: Rückblick und Überblick","authors":"Bernd Fitzenberger, Arnim Seidlitz","doi":"10.1007/s11943-020-00273-w","DOIUrl":"10.1007/s11943-020-00273-w","url":null,"abstract":"<div><h2>Zusammenfassung</h2><div><p> In Westdeutschland stieg zwischen 1980 und 2010 die Lohnungleichheit von Vollzeitbeschäftigten deutlich an. Der Anstieg beschränkte sich zunächst auf den oberen Bereich der Lohnverteilung und setzte sich ab Mitte der 1990er Jahre sowohl im oberen als auch im unteren Bereich der Lohnverteilung fort. Im Zeitraum 1995 bis 2010 ging die Entwicklung mit starken Reallohnverlusten im unteren Bereich der Lohnverteilung einher. Nach 2010 stiegen die Reallöhne über die gesamte Lohnverteilung deutlich an, aber die Lohnungleichheit für Vollzeitbeschäftigte verblieb auf hohem Niveau trotz eines leichten Rückgangs am untersten Ende der Verteilung ab 2015. Dieser Beitrag dokumentiert und interpretiert die Entwicklung der Lohnungleichheit und geht auf mögliche Datenprobleme ein.</p></div></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"14 2","pages":"125 - 143"},"PeriodicalIF":0.0,"publicationDate":"2020-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11943-020-00273-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50486817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Datenerhebung bei Mietspiegeln: Überblick und Einordnung aus Sicht der Statistik","authors":"Göran Kauermann, Michael Windmann, Ralf Münnich","doi":"10.1007/s11943-020-00272-x","DOIUrl":"10.1007/s11943-020-00272-x","url":null,"abstract":"<div><h2>Zusammenfassung</h2><div><p>Der Artikel diskutiert die verschiedenen Methoden bei der Datenerhebung von Mietspiegeln. Es werden Vor- und Nachteile der in der Praxis zu findenden Methoden diskutiert und aus dem statistischen Blickwinkel beleuchtet. Dabei gehen wir den drei Fragen nach: Wer wird befragt? Wie wird befragt? Wie erfolgt die Stichprobenziehung? Neben statistischen Aspekten werden die Mietspiegel der 30 größten Städte als Beispiel herangezogen, um aufzuzeigen, dass die angewandte Methodik in der Praxis sehr heterogen ist.</p></div></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"14 2","pages":"145 - 162"},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11943-020-00272-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50480601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sabine Zinn, Ariane Würbach, Hans Walter Steinhauer, Angelina Hammon
{"title":"Attrition and selectivity of the NEPS starting cohorts: an overview of the past 8 years","authors":"Sabine Zinn, Ariane Würbach, Hans Walter Steinhauer, Angelina Hammon","doi":"10.1007/s11943-020-00268-7","DOIUrl":"10.1007/s11943-020-00268-7","url":null,"abstract":"<div><p>This article documents the number of target persons participating in the panel surveys of the National Educational Panel Study (NEPS) as well as the number of respondents who temporarily dropout and of those leaving the panel (attrition). NEPS comprises panel surveys with six mutually exclusive starting cohorts covering the complete life span. Sample sizes, numbers of participants and temporary as well as final dropouts and participation rates are reported in detail for each wave and for subsamples, if applicable. Sample particularities, such as the conversion of temporary dropouts into final ones, are elaborated on. All figures presented are derived from the corresponding Scientific Use Files (SUFs) published by February 1, 2018. Selectivity due to attrition (i.e., final dropouts) is studied. For this purpose, we examine how attrition distorts the NEPS samples with respect to relevant design variables (such as stratification criteria) and panel member characteristics (like sex and birth year). In detail, we study the panel status of each panel member, that is being part of the panel or having dropped out finally, along all of the panel waves with respect to starting cohort and population specific characteristics. We conclude this article with some recommendations for dealing with the detected selection bias in statistical analyses.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"14 2","pages":"163 - 206"},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11943-020-00268-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50468129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Interview mit Wolfgang Schmid","authors":"Walter Krämer","doi":"10.1007/s11943-019-00267-3","DOIUrl":"10.1007/s11943-019-00267-3","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"14 1","pages":"103 - 111"},"PeriodicalIF":0.0,"publicationDate":"2019-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11943-019-00267-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50517289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joachim Engel, Rolf Biehler, Daniel Frischemeier, Susanne Podworny, Achim Schiller, Laura Martignon
{"title":"Erratum to: Zivilstatistik: Konzept einer neuen Perspektive auf Data Literacy und Statistical Literacy","authors":"Joachim Engel, Rolf Biehler, Daniel Frischemeier, Susanne Podworny, Achim Schiller, Laura Martignon","doi":"10.1007/s11943-019-00266-4","DOIUrl":"10.1007/s11943-019-00266-4","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"14 1","pages":"113 - 114"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11943-019-00266-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50495650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}