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"Niedrige Geburtenraten und steigende Lebenserwartungen führen zu Prognosen eines demographischen Wandels, der sich bereits 2010 abzeichnet und in den Jahren 2020-2060 zuspitzen wird. Welche Konsequenzen dieser demographische Wandel für Jugendliche haben könnte ist weitgehend ein Forschungsdesiderat. Dieser Beitrag diskutiert die möglichen Folgen demographischer Entwicklungen für die Sozialisationsprozesse und Lebenslagen von Jugendlichen in der Bundesrepublik Deutschland. Ein besonderer Schwerpunkt wird dabei auf die Sozialisationskontexte Familie und Schule gelegt sowie auf die Herausarbeitung möglicher Chancen, die der demographische Wandel für Jugendliche mit sich bringt." (Autorenreferat)

Theoretischer Hintergrund: Obwohl Prüfungsangst ein häufiges Anliegen in psychologischen Beratungsstellen ist, wissen wir wenig über ihren zeitlichen Verlauf sowie über Risikofaktoren für hohe Prüfungsangst kurz vor den Prüfungen. Fragestellung: Diese Studie untersucht, ob sich die Intensität von Prüfungsangst während eines Semesters verändert und wie Personen mit hoher Belastung kurz vor der Prüfung früh identifiziert werden können. Methodik: Zu Beginn und kurz vor den Prüfungen des Wintersemesters 2014/15 wurden Prüfungsangst, Depressivität und Prokrastination bei 427 Studierenden (88.3 % Erstsemester; 68.4 % weiblich; Altersdurchschnitt 20.0 Jahre) erfasst. Ergebnisse: Die Analyse auf Einzelfallebene zeigte, dass sich die Prüfungsangst bei den meisten Studierenden nicht signifikant veränderte. Bei der Vorhersage der Prüfungsangst zum Semesterende stellten Prüfungsangst und Depressivität zu Semesterbeginn signifikante Prädiktoren dar. Diese wurden anhand von 80 % der Gesamtstichprobe ermittelt und an den anderen 20 % validiert. Schlussfolgerungen: Erhöhte Prüfungsangst und Depressivität zu Semesterbeginn können die frühe Identifikation von Studierenden mit bedeutsamer Prüfungsangst kurz vor Prüfungen ermöglichen.

Macroeconomic expectation data are of great interest to different agents due to their importance as central input factors in various applications. To name but a few, politicians, capital market participants, as well as academics, incorporate these forecast data into their decision processes. Consequently, a sound understanding of the quality properties of macroeconomic forecast data, their quality determinants, as well as potential ways to improve macroeconomic predictions is desirable. This thesis consists of three essays on the quality of analysts' forecasts. The first essay deals with macroeconomic forecast quality on the consensus level, while the second one investigates individual analysts' predictions and their quality determinants. In the third essay a bottom-up approach is introduced to derive macroeconomic forecasts from analysts' predictions at the microeconomic level. It is generally assumed that macroeconomic consensus forecasts provide a reasonable approximation of market participants' expectations regarding upcoming macroeconomic releases. Research areas in which these expectation data are a central input to isolate the unanticipated news component of a given announcement include studies analyzing the price impact of macroeconomic news in bond markets (e.g., Balduzzi et al., 2001; Gilbert et al., 2010), stock markets (e.g., Boyd et al., 2005; Cenesizoglu, 2011) as well as in foreign exchange markets (e.g., Andersen et al., 2003; Evans and Lyons, 2008). Furthermore, these forecast data are used to study market co-movement (e.g., Albuquerque and Vega, 2009), market volatility (e.g., Beber and Brandt, 2008; Brenner et al., 2009), changes in market liquidity (e.g., Brandt and Kavajecz, 2004; Pasquariello and Vega, 2007, 2009) as well as bond and equity risk premiums (e.g., Savor and Wilson, 2012; Dicke and Hess, 2012). It appears reasonable to assume that macroeconomic consensus forecasts represent market participants' expectations properly. So far available studies on forecast rationality at the consensus level largely test for general quality properties. They commonly find no evidence of systematic or persistent inefficiencies. In contrast to these previous studies, Campbell and Sharpe (2009) test for a specific behavioral inefficiency, the anchoring bias, first documented by Tversky and Kahneman (1974) in psychological experiments. Transferred to the context of macroeconomic forecasts, anchoring means that analysts put too much importance on last months' data and therefore underweight meanwhile released relevant information. This behavior implies a false incorporation of all available information into their forecasts. Consequently, a correction, i.e., the efficient use of the entire available information set would yield forecast improvements. Our analysis reveals a counter-intuitive result: We find strong statistical significance for anchoring in most macroeconomic forecast series, but applying a look-ahead bias free estimation and adjustment procedure leads to no systematic forecast improvements. Therefore, our results question the economical significance of the anchoring bias. To provide an explanation for the disconnection of statistical and economical significance, we decompose the anchoring bias test statistic and find that the test is biased itself. While the test assumes a univariate information environment, it neglects the possibility that analysts may provide superior forecasts by using a more comprehensive information set than just the univariate time series itself. Our empirical as well as our simulation results strongly support this explanation for a broad range of macroeconomic series. Our analysis contributes to different strands of literature. First, our results directly add to the scarce literature analyzing the efficiency of macroeconomic survey forecasts by showing that informational advantages of analysts, i.e., the incorporation of related macroeconomic data, enable them to outperform mechanically generated time series forecasts. Furthermore, our results provide motivation for other research areas, such as studies analyzing equity analysts' outputs, to control for a larger information set, for instance by including earnings information of related companies or information about overall business conditions. Second, our findings strongly support the assumption that macroeconomic survey forecasts represent a reasonable proxy measure for the anticipated information component in macroeconomic releases and consequently justify their use in the above mentioned research areas. Furthermore, our results highlight the danger to test for cognitive biases in a time series context which were previously only tested in controlled experiments. Especially when experiments are conducted in a highly regulated informational setting, i.e., when information given to test participants has to be strictly controlled for, as in anchoring bias experiments, it is questionable whether a direct transfer in a time series setting is possible at all. Future studies analyzing cognitive biases in time series frameworks have to consider carefully whether informational constraints might drive the results and lead to false conclusions. The first essay provides strong evidence for the quality of macroeconomic forecasts at the consensus level, the second essay deals with individual macroeconomic forecasts and analyzes why certain analysts provide better forecasts then others. In particular, we focus on the association between the idiosyncratic predictability of a given macroeconomic indicator and the relation between analyst characteristics and macroeconomic forecast accuracy. Obviously, there might be quality differences on the individual analyst level, i.e., there are more and less precise macroeconomic analysts. Exploiting these quality differences is a desirable task, because academics would obtain better proxy measures for market participants' expectations, and for investors an information advantage should translate into higher profits. We argue that if an indicator's idiosyncratic predictability is low, i.e., the series is almost not predictable, for instance due to information constrains and very volatile processes, then analysts' forecast performance is rather random than systematic because skills cannot take effect. In contrast, if a macroeconomic indicator has a high idiosyncratic predictability, then analysts with certain characteristics benefit from their abilities and skills, and generate more precise forecasts than less skilled analysts. Accordingly, for the unpredictable indicators the relation between analyst characteristics and forecast accuracy should be less pronounced than for the predictable ones. Consequently, we hypothesize that the idiosyncratic predictability of a certain macroeconomic indicator has to be taken into account whenever the relation between analyst characteristics and forecast accuracy is analyzed. So far there is only contradictory evidence concerning differences in individual forecast quality of macroeconomic analysts. While some studies provide evidence for different forecast quality among individual macroeconomic analysts (e.g. Zarnowitz, 1984; McNees, 1987; Zarnowitz and Braun, 1993; Kolb and Stekler, 1996; Brown et al., 2008) other articles come to the opposite conclusion (e.g. Stekler, 1987; Ashiya, 2006). Despite this disagreement, the relation between macroeconomic forecast accuracy differences and analyst characteristics has not been analyzed so far, although the extensive strand of literature analyzing the association of equity analyst characteristics and earnings per share forecast accuracy (e.g. Clement, 1999; Clement and Tse, 2005; Brown and Mohammad, 2010) provides a sound framework for an analysis. Most importantly, we find that model performance heavily depends on the idiosyncratic predictability of macroeconomic indicators. With decreasing idiosyncratic predictability the relevance of analyst characteristics for forecast accuracy diminishes for some characteristics and disappears for others. In terms of economic significance we find substantial differences between macroeconomic indicators with high and low idiosyncratic predictability. Consequently, our results show that the idiosyncratic predictability of a given forecast target has to be taken into account when the association between analyst characteristics and forecast accuracy is analyzed. Our findings have implications for different research areas. Most importantly we directly add to the literature analyzing individual macroeconomic analysts' forecast performance. We provide evidence that the idiosyncratic predictability of an indicator has to be taken into account if the relation between analyst characteristics and forecast accuracy is analyzed. Differentiation among analysts is only very limited if the figure to be forecasted is virtually unpredictable, because analysts do not benefit from their abilities and experiences. Systematic forecast accuracy differences arise if the forecast target is predictable at all and more skilled analysts have the opportunity to differentiate themselves form less skilled ones based on superior skills. Since there are differences in the predictability of company earnings our framework is transferable. Analogous to our findings for macroeconomic analysts, we expect that idiosyncratic predictability plays an equally important role analyzing the association between equity analysts' characteristics and their earnings per share forecast performance, i.e., for company earnings with higher idiosyncratic predictability we expect higher heterogeneity in forecast accuracy which can be explained by analyst characteristics. The first two essays provide evidence that macroeconomic predictions are in general of high quality as they incorporate rationally information from various sources. Besides the previously analyzed macroeconomic forecasts, agents such as politicians and employers, also heavily rely on other information, for example, on coincident and leading macroeconomic indicators. Determining the current state of the economy and obtaining sound projections about future overall macroeconomic developments plays an important role in their decision processes. Coincident and leading macroeconomic indicators incorporate a large set of macroeconomic variables as well as stock and bond market measures, e.g., returns and interest rate spreads. However, there is no evidence about how expectations at the microeconomic level relate to expectations at the macroeconomic level. Consequently, an aggregate of microeconomic expectation data, i.e., individual company expectations, are not included in coincident and leading macroeconomic indicators so far. To overcome this shortcoming we introduce a bottom-up approach that aggregates individual company expectations to derive macroeconomic content. Since the development of the entire economy is closely related to the development of its individual parts, among them individual companies, aggregated company information must contain macroeconomic information. Unfortunately, there is no database containing managements' expectations, however, we use equity analysts' outputs as proxy measure. Equity analysts' information sets comprise public macroeconomic-, industry- and company-specific content as well as non-public company-specific information (Grossman and Stiglitz, 1980) and is therefore arguably the best available proxy for managements' expectations. Regarding the choice of the best analyst's output we use recommendation changes instead of earnings per share (EPS) changes, because recommendations comprise more information. Besides the one year earnings estimate, recommendations also contain a series of future earnings expectations as well as interest rate and risk premium expectations. We show that aggregated recommendation changes as proxy measure for changing company outlooks have predictive power for overall economic developments. Our results provide evidence that aggregated recommendation changes, which approximate changing expectations about individual companies' economic prospects, have predictive power for future macroeconomic developments of about one year. Controlling for other well established macroeconomic predictors our results remain robust indicating that our measure contains additional independent information. Consequently, it seems promising to include our new predictor into the set of macroeconomic predictors in future applications. Additionally, we find that EPS changes have no predictive power lending support to our assumption that more forward looking information, as included in recommendation changes, is required if one attempts to forecast future macroeconomic developments. Furthermore, our findings provide the missing link between previous studies showing that aggregated analyst outputs have predictive power for overall stock market developments (Howe et al., 2009) and those showing that the stock market leads the real economy (Stock and Watson, 1998). Our results support the notion that changes in expectations about future company performance rationally determine asset values in advance of overall economic activity changes providing the explanation why stock markets lead the real economy. Overall, the three essays in this thesis advance different strands of literature. We show that macroeconomic consensus forecasts are a reliable proxy measure for market participants' expectations. Furthermore, our results provide strong evidence that it is dangerous to transfer psychological experiments into time series frameworks without appropriately controlling the informational environment. Additionally, we show that the idiosyncratic predictability of a given forecast objective, i.e. whether a forecast task is satisfyingly feasible at all, has to be taken into account whenever the association between analyst characteristics and forecast accuracy is analyzed. Macroeconomic analysts do only benefit from their superior skills compared to their competitors if the macroeconomic series is idiosyncratically predictable. For unpredictable series, forecast accuracy is rather random than systematic, because superior skills do not systematically translate in better forecasts. Finally, we show that the aggregation of forecasts on the microeconomic level, i.e., company expectations, is a promising approach to extract macroeconomic information. Overall, we conclude that macroeconomic analysts are very efficient information processors and play an important role as intermediaries in financial markets.

Das Kommunistische Manifest wird als eine weitsichtige Prognose kapitalistischer Globalisierung angesehen. Andererseits haben sich anti-kapitalistische Revolutionen ganz anders entwickelt als vom Manifest erwartet. Der Kapitalismus hat sehr viel flexibler als angenommen auf die Herausforderungen sowohl seiner eigenen Dynamik als auch der Russischen und Chinesischen Revolution reagiert. Der Autor vertritt die These, dass vor allem die Vernachlässigung der Wechselbeziehungen zwischen Zeit und Raum diesen Fehlprognosen zugrunde liegen. Nach einer Übersicht über Ansätze der Prognose des sozialen und politischen Wandels (einschließlich Futurologie und Utopien), diskutiert er die Rolle von Prognosen in der globalen Umweltpolitik, ausgehend von einer umfassenden Studie der US-Regierung (Global 2000). Angesichts fehlender transformativer Visionen, wendet er sich der kritischen Kapitalismusanalyse zu und fasst die Beziehungen zwischen der Vision des Kommunistischen Manifestes, dem Wandel revolutionärer Konzepte und ihres Scheiterns zusammen. In seiner Vorhersage nationaler Revolutionen unterschätzte Marx den Wandel globaler Raumstrukturen im Verlaufe kapitalistischer Entwicklung, die zu einer Vertiefung der Globalisierung und zum Entstehen einer Arena globaler Politik führten. Die Regulationstheorie hat die Abfolge spezifischer Phasen kapitalistischer Akkumulation analysiert. Diese Entwicklung wird jedoch in jüngster Zeit von einer wachsenden Resilienz der Nationalstaaten begleitet, wobei Profite aus der ungleichen Entwicklung eine Quelle der Finanzierung sozialer Kompromisse in den fortgeschrittensten und mächtigsten Ländern darstellen. Schließlich stellt eine massive internationale Migration die Prinzipien einer globalen Kapitalmobilität und einer nationalen Kontrolle der Mobilität von Arbeitskräften in Frage. Während die globale politische Fragmentierung in vielerlei Hinsicht im Konflikt mit Menschenrechtsnormen und dem Kampf gegen den Klimawandel steht, stärkt eben diese Fragmentierung die nationale Identifizierung vieler Bürger im Globalen Norden. Das Schlusskapitel diskutiert die Probleme von Prognosen über die Zukunft des Kapitalismus sowie konkrete Utopien einer postkapitalistischen Gesellschaft vor dem Hintergrund von Konflikten zwischen einem humanitären und ökologischen Globalismus und der Resilienz nationaler Egoismen im Globalen Norden.

"Unter Klimatologen besteht weitgehend Einigkeit, dass Vorhersagen von Klimamodellen unausweichlich
unsicher sind. Unsicherheit rechtfertigt aber keineswegs politische Inaktivität. In diesem Aufsatz wird deshalb diskutiert, in wieweit epistemische Unsicherheiten für die praktische Entscheidungsfindung von Relevanz sind. Insbesondere soll gezeigt werden, dass das Vorsorgeprinzip nicht in der Lage ist, den spezifischen Unsicherheiten, wie sie in Klimaprognosen auftreten, gerecht zu werden. Nichtquantifizierte Unsicherheiten dürfen in Entscheidungsfindungsprozessen weder ignoriert werden, noch lassen sie sich durch Einführung subjektiver Wahrscheinlichkeiten auf quantifizierte Unsicherheiten reduzieren. Dies unterscheidet die ethischen Aspekte des Klimawandels wesentlich von anderen ethischen Problemen aus dem Bereich der Energieversorgung." (Autorenreferat)

"Diese Untersuchung befasst sich mit der Frage, ob sich Männer und Frauen hinsichtlich der Häufigkeit und der Art der Untreue unterscheiden und ob Liebesstile, Bindungsstile, Sexuelle Einstellungen und Geschlechtsrollenorientierung einen Einfluss auf das berichtete Untreueverhalten haben. Befragt wurden 96 Personen zwischen 19 und 35 Jahren, die zur Zeit der Untersuchung in einer Beziehung lebten. Die Ergebnisse zeigten, dass Männer und Frauen gleich häufig über Untreue berichteten und dass sie sich in der Form der Untreue (emotional vs. sexuell) nicht bedeutend unterschieden, wenn das Alter der Befragten kontrolliert wurde. Ein Alterseffekt bestand darin, dass ältere Personen häufiger Untreue zum Ausdruck brachten als jüngere. Die Prüfung der Hypothesen ergab, dass spielerische und romantische Liebe, permissive und instrumentelle sexuelle Einstellungen und ein vermeidender Bindungsstil mit Untreue zusammenhingen. Spielerische und romantische Liebe sowie sexuelle Einstellungen erwiesen sich als unabhängige Prädiktoren der Untreue." (Autorenreferat)

Abstract. Alcohol consumption among adolescents is higher in athletes, especially in team sports such as football, compared with nonathletes. This study investigated factors influencing alcohol consumption in adolescent football players in Germany. Structural equation modeling was used to understand how the different predictors work together, thereby improving alcohol prevention in the field of football. The hypothesized model was largely confirmed and the most significant predictive factor of alcohol consumption was the drinking behavior of friends. Alcohol expectancies and drinking refusal self-efficacy (DRSE) were also shown to impact alcohol use. Friend norms regarding alcohol use had little influence on alcohol consumption. There was no direct association between alcohol consumption and the motivational climate during football training (task and competitive orientation) and support provided by the coach. This highlights the importance of focusing on adjusting the perception of alcohol use in friends and alcohol use norms as well as social self-efficacy in resisting peer pressure to drink in alcohol prevention strategies in team sport.

Over the last few years, data has often been described as the oil of the 21st century, e.g., by Bhageshpur (2019). Just as access to oil dominated power and development in the last century, this claim implies that personal data is not only assumed to be similarly valuable, but also equally as influential in politics and society as oil once was. However, oil sources mainly diverge in their accessibility, quality, quantity, and cost of exploitation but, once extracted and refined theses sources may lead to roughly similar products. Data also differs in these four categories but, additionally, data sources typically lead to very specific insights. One single data source is often neither sufficient to answer important and complex scientific (or economic) questions nor to make any predictions with fine granularity and high precision. In such cases, combining data from different sources that provide additional aspects to the problem at hand is one promising approach to achieve these aims. In this dissertation, combining data sources is conducted for two purposes. Part I of this work focuses on combining data to achieve additional understanding. In the paper presented in Part I, the authors analyze reasons why students drop out of undergraduate courses in economics and business administration. From a university perspective, administrative data is readily available, e.g., which modules are completed in which semester, how many educational credit points are achieved by each student in each semester. Socioeconomic data at individual level, however, is usually unavailable to university administrations. In order to overcome this hurdle, the authors proposed and executed a novel prospective study design. A survey was conducted on students starting the second semester and the data was combined with administrative longitudinal data. Hence, the authors were able to analyze individual studying behavior conditioned on a large pool of socio-demographic variables. Among other results, the authors were able to show that college ...

Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-ofsample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.