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Hans Schneeweiß is- one of the best-known German econometricians and statisticians. He was born in Glatz, Silesia, on March 13, 1933. Hans SchneeweiB studied mathematics and physics and received his Ph. D. degree from the Johann-Wolfgang-Goethe University, Frankfurt, in 1960. He was member of the academic staff of the Faculty of Law and Economics of the Saar University between 1959 and 1965. Following his Habilitation in 1964, he was appointed to the chair of Statistics and Econometrics at the same university. As a visiting professor he worked at the Institute for Aca­ demic Studies in Vienna in 1967 and at the Department of Statistics of the University of Waterloo, Canada, in 1970/71. He has been a full professor of Econometrics and Statistics at the Ludwig-Maximilians-University in Mu­ nich since 1973. His extensive research activities abroad included important projects in Waterloo, Vienna, Dundee (Scotland), Sidney, China, Kiev. During the more than 40 years of his academic work he has published outstanding, original articles on econometrics and statistics. To give an ex­ ample, it is his research on decision theory which has marked developments in this field. His book Entscheidungskriterien bei Risiko, published in 1967, is an excellent starting for anyone looking for an introduction to the complex issues involved.

Microaggregation is a popular statistical disclosure control technique for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. However, while reducing the disclosure risk of data files, the technique also affects the results of statistical analyses. The paper deals with the impact of microaggregation on a multiple linear regression in continuous variables. We show that parameter estimates are biased if the dependent variable is used to form the groups. Using this result, we develop a consistent estimator that removes the aggregation bias, and derive its asymptotic covariance matrix.

Das Buch entwickelt ausgehend von einem interdisziplinären Lehrprojekt neue Verfahren zur Analyse von Wählerwanderungen und erprobt diese praktisch. Dazu wurde eine Nachwahlbefragung bei der Landtagswahl und der Bundestagswahl 2013 in München durchgeführt und ausgewertet. Es wird eine ausführliche Analyse der Wählerwanderung im Vergleich zu den vorherigen Wahlen und zwischen den beiden Wahlen, die innerhalb einer Woche stattfanden, inklusive der Gründe für Parteienwechsel präsentiert. Weiter werden methodische Neuentwicklungen, sogenannte Hybridmodelle zur Analyse der Kombination von Befragungsdaten und offiziellen Wahlergebnissen der einzelnen Stimmbezirke aufgezeigt. Diese Form der Analyse hat ein hohes Potential für die valide Schätzung von Wählerwanderung. Der Inhalt Ausgangspunkt und Motivation der Studie • München im Vergleich zum Bundes- und Landestrend • Wählerwanderung • Studiendesign • Befragungsgüte • Wechselgründe • Hochrechnungen • Ökologische Inferenz • Hybride Modelle Die Zielgruppen Politikwissenschaftler/innen • Wirtschaftswissenschaftler/innen • Mitarbeiter/innen von Wahlforschungsinstituten Die Herausgeber/innen André Klima ist wissenschaftlicher Mitarbeiter im Statistischen Beratungslabor der Ludwig-Maximilians-Universität München. Prof. Dr. Helmut Küchenhoff ist Leiter des Statistischen Beratungslabors der Ludwig-Maximilians-Universität München. Mirjam Selzer ist Junior-Projektleiterin in der Rechtsforschung und im Datenschutz. Prof. Dr. Paul W. Thurner hat den Lehrstuhl für Empirische Politikforschung und Policy Analysis an der Ludwig-Maximilians-Universität München inne

Background of the study: Research on destination choice using aggregated data found people increasingly travel longer distance as new technological developments in transportation occurred (Castro et al. 2020), with economic prosperity in the source market (Sun and Lin 2019) and due to innovations in communication technologies (Yang et al. 2018) that facilitated access to information about destinations at long distance. In addition to these macro-level developments, destination choice, and hence travel distance changes throughout someone's life cycle due to changing personal circumstances and increasing age (Bernini and Cracolici 2015) and between generations (Lohmann and Danielsson 2001). Hence, longitudinal changes in travel behavior are triggered simultaneously and interactively by age- (i.e., internal), period- (i.e., external), and cohort-effects (i.e., generational) (Oppermann 1995); calling for advanced statistical approaches to separate them. Only a few studies in tourism research have examined alterations in travel behavior based on all three temporal dimensions so far (e.g., Oppermann 1995). Purpose of the study: The purpose of this study is to explain how and why people's destination choice changes over time. This study aims to estimate the impact of internal (e.g., life-cycle stage, aging, generational membership) and external (e.g., economic development, societal change, technological advancements, political events) temporal factors on individuals' destination choice using the example of travel distances. Methodology: We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971-2018. The data used in this study were collected in the Reiseanalyse, an annual representative survey of approximately 7,500 German residents (~330,000 respondents and ~227,000 trips in total). To separate the temporal factors we apply statistical age-period-cohort (APC) analysis methods to tourism research and estimate internal temporal developments regarding the individual tourist or external changes in the circumstance of holiday trips. We use generalized additive regression models as a state-of-the-art tool to circumvent the identification problem of APC analyses. We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Results: The pure APC model (i.e., age, period and cohort as only temporal factors) shows that travel distances vary across all observed temporal dimensions. While short-haul trips are mainly associated with age differences (i.e., increase with age), long-distance travel changes mostly over the period (i.e., increase over time). The impact of generational membership was less pronounced regarding travel distances. The observed tendencies may imply that choosing short-haul destinations depends on personal characteristics and age-related travel constraints such as physical or family restrictions (You and O'leary 2000). Contrarily, long-distance travel might be more constrained by macro-level factors such as developments in transport technology attributed to reduced costs for long-haul travel or economic growth leading to an increase in disposable income, which can be used for more expensive long-distance travel (Sun and Lin 2019). The covariate APC model (i.e., inclusion of additional internal factors shaping travel behavior) reveals how trip duration, household size and income can also affect travel distances in addition to age-, period- and cohort-effects. For example, assuming trips of equal length, the chance for holiday trips over 6,000 km increases more steeply both over time and across generations underlining the higher affordability and easier accessibility of long-haul trips in recent years and for younger cohorts. External factors of destination choice (e.g., economic climate, technological developments) are indirectly included in the period effect, assuming that individual travelers are affected similarly by societal changes and socialization processes of new technology. Conclusions: Often it is the interplay between internal and external factors, related to the tourist and the destination, that shapes travel decision-making and consequently tourism demand. For instance, the individual motivation to travel and the price level at and transport costs to a destination commonly influence tourists' destination choices (Nicolau and Mάs 2006). Our methodological framework enables to simultaneously incorporate variables on the individual (e.g., income of the traveler) and macro-level (e.g., general economic indices), which leads to more precise estimates of spatio-temporal travel changes. Research implications and limitations: The developed age-period-cohort analysis framework can be easily adapted to investigate other temporal changes in tourism behavior (e.g., transport choice for life-cycle environmental footprint analysis) or the impact of external factors on temporal changes in tourism demand (e.g., comparative analysis of natural and human-induced hazards). Understanding which and how internal and external factors cause changes in travel behavior may lead to better predictions of future tourism demand, supporting touristic stakeholders in tourism planning and management. References: Bernini, C., & Cracolici, M.F. (2015). Demographic change, tourism expenditure and life cycle behaviour. Tourism Management 47 191-205. Castro, R., Lohmann, G., Spasojevic, B., Fraga, C., & Allis, T. (2020). The future past of aircraft technology and its impact on stopover destinations. In: Yeoman, I., & McMahon-Beattie, U. (eds.) The future past of tourism, The future of tourism. Bristol: Channel View Publications 93-104. Lohmann, M., & Danielsson, J. (2001). Predicting travel patterns of senior citizens: How the past may provide a key to the future. Journal of Vacation Marketing 7 (4) 357-366. Nicolau, J.L., & Más, F.J. (2006). The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations. Tourism Management 27 (5) 982-996. Oppermann, M. (1995). Travel life cycle. Annals of Tourism Research 22 (3) 535-552. Sun, Y.Y., & Lin, P.C. (2019). How far will we travel? a global distance pattern of international travel from both demand and supply perspectives. Tourism Economics 25 (8) 1200-1223. Yang, Y., Liu, H., Li, X., & Harrill, R. (2018). A shrinking world for tourists? Examining the changing role of distance factors in understanding destination choices. Journal of Business Research 92 350-359.

AbstractWhich issue-related motives underlie voters' decision to switch parties at the polls? Do switchers stick to the newly chosen party, or do they oscillate in a short-term way at intermediate elections? Relying on the behavioral theory of elections, we assumed aspiration-based voting of boundedly rational voters. We elicited issue-related switch and stay motives in an open-ended survey question format to identify the individual dominant aspirational frame. We traced the respondents' voting trajectories over three consecutive elections, including two state (2013 and 2018) elections in Bavaria (Germany) and one German federal election (2017). We focused on one of the most polarizing and salient issues in these elections, namely immigration. The case of reference is the 2018 Bavarian state election. Here, the incumbent majoritarian center-right party Christian Social Union tried to deter the entry of the right-wing populist party Alternative for Germany by adapting to it on the immigration issue in tone and position. The selected case allows assessment of the impact of issue-based adaptive behavior of the incumbent party at the level of the voters' switch or stay choices. We estimated the direction and number of voter flows for two interelection sequences of different lengths between different types of polls (federal and state). Our transition estimates are based on the hybrid multinomial Dirichlet model, a new technique integrating individual-level survey data and official aggregate data. Our estimates uncover substantial behavioral differences in the immigration issue public.

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020, with particularly high case fatality ratio (CFR). To estimate seroprevalence, underreported infections, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020, we conducted a population-based study including home visits for the elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests. Latent class analysis yielded 8.6% standardized county-wide seroprevalence, a factor of underreported infections of 5.0, and 2.5% overall IFR. Seroprevalence was two-fold higher among medical workers and one third among current smokers with similar proportions of registered infections. While seroprevalence did not show an age-trend, the factor of underreported infections was 12.2 in the young versus 1.7 for ≥85-year-old. Age-specific IFRs were <0.5% below 60 years of age, 1.0% for age 60–69, and 13.2% for age 70+. Senior care homes accounted for 45% of COVID-19-related deaths, reflected by an IFR of 7.5% among individuals aged 70+ and an overall IFR of 1.4% when excluding senior care home residents from our computation. Our data underscore senior care home infections as key determinant of IFR additionally to age, insufficient targeted testing in the young, and the need for further investigations on behavioral or molecular causes of the fewer infections among current smokers.