## Fakultät Sozial- und Wirtschaftswissenschaften: Abschlussarbeiten

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- Fakultät Sozial- und Wirtschaftswissenschaften: Abschlussarbeiten (220)
- Lehrstuhl für Betriebswirtschaftslehre, insbesondere Finanzwirtschaft (13)
- Lehrstuhl für Betriebswirtschaftslehre, insbesondere Personalmanagement und Organisational Behaviour (13)
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- Lehrstuhl für Betriebswirtschaftslehre, insbesondere Internationales Management mit Schwerpunkt Europäisches Management (2)
- Lehrstuhl für Politikwissenschaft, insbesondere Politische Soziologie (1)

The exponential random graph model (ERGM) is a class of stochastic models for network data widely applied in statistical social network analysis. The ERGM can be used to model a wide range of social processes. However, it is generally difficult to estimate due to its intractable normalizing constant. Markov chain Monte Carlo maximum likelihood (MCMC-ML) ERGM estimation is available but tends to be numerically unstable due to model degeneracy of particular specifications. Bayesian ERGM estimation is robust to model degeneracy and is a practical alternative to the MCMC-ML approach.
Bayesian model selection is based on the Bayes factor which is the ratio of marginal likelihoods of concurring models. The research aim of this thesis is to estimate the marginal likelihood of the ERGM class using path sampling which is also called thermodynamic integration. Power posterior sampling is a discretized version of thermodynamic integration using a fixed path of tempering steps to transition from the prior distribution to the posterior distribution of interest. In this thesis, power posterior sampling is used both to integrate over the parameter space of the ERGM posterior distribution of interest and to yield an estimate of the respective intractable ERGM normalizing constant. Existing approaches of estimating the ERGM marginal likelihood rely on a non-parametric density approximation or a Laplace approximation. The proposed power posterior exchange algorithm with explicit evaluation of the likelihood (PPEA-EEL) does not require such approximations and yields a valid estimate of the ERGM marginal likelihood.
As the PPEA-EEL is a computationally expensive approach involving many MCMC samples, new graphical methods to evaluate power posterior samples are developed.
In this thesis a brief introduction to random graphs and network dependencies is given. The ERGM class is discussed and various dependency assumptions are illustrated. MCMC-ML ERGM estimation is applied to policy networks in Ghana, Senegal and Uganda. Bayesian ERGM estimation and Bayesian model selection are discussed. An overview is given on methods of estimating the marginal likelihood originating from importance sampling, namely bridge sampling, path sampling and power posterior sampling. The PPEA-EEL is applied to social network data and the numerical stability of the approach is evaluated.

How do institutional factors and their interaction with individual resources influence the length of mothers’ interruption durations after childbirth and return-to-work behaviour? This thesis answers this question from a family sociology and labour market perspective, and concentrates on how institutional factors explain varying durations of mothers’ employment interruptions and the differences in their return-to-work behaviour after childbirth. In this cumulative thesis, I analyse three institutional factors, the introduction of a paid leave entitlement, the expansion of childcare availabilities, and the specific hours of employment across different occupations. After an introduction in Chapter 1, the first two parts (Chapter 2 and 3) are concerned with the influence of two policy reforms (aiming at easing the conflict between family and career) on mothers’ return-to-work behaviour, whereas the third part (Chapter 4) seizes on the occupational opportunity structure and its impact on mothers’ return-to-work behaviour.
More specifically, Chapter 1 outlines the overarching framework, based on life course research, and discusses how the chapters relate to the existing literature of life course research. Chapter 2 studies the effect of a first-time roll-out of a paid maternity entitlement in Australia on mothers’ return-to-work behaviour and how the reform effect differs by educational groups. The results suggest that the introduction of a statutory paid leave entitlement has stimulated a change in re-entry behaviour to work, although its impact varies across educational groups.
Chapter 3, in co-operation with Gundula Zoch, examines how increased availability of low-cost, state-subsidised childcare for under-three-year-olds in Germany is associated with shorter employment interruptions amongst West and East German mothers. The results indicate that increased childcare availability for under-three-year-olds reduces the length of mothers’ employment interruptions, particularly for West German mothers.
Chapter 4, together with Sandra Buchholz, investigates whether occupation-specific hours of employment (not just the number of hours worked, but also the level of flexibility of when they are worked) affect mothers’ interruption duration and their return-to-work behaviour after childbirth. The results show that occupation-specific employment hours, even after controlling for individual characteristics, are significantly associated with the length of mothers’ employment interruptions. The effect of occupation-specific employment hours for the interruption duration depends on the mother’s level of education and as the results suggest they have a larger impact on the interruption duration of lower educated mothers.
The thesis contributes to the literature on how institutions shape individual life courses. It shows, in particular, that institutions do not have the same effect on all mothers but influence the lives of individuals in stratified ways and can contribute to growing inequalities of labour market opportunities for mothers with differing resources.

Generally, multiple imputation is the recommended method for handling item nonresponse in surveys. Usually it is applied as chained equations approach based on parametric models. As Burgette & Reiter (2010) have shown classification and regression trees (CART) are a good alternative replacing the parametric models as conditional models especially when complex models occur, interactions and nonlinear models have to be handled and the amount of variables is very large. In large-scale panel studies many types of data sets with special data situations have to be handled. Based on the study of Burgette & Reiter (2010), this thesis intends to further assess the suitability of CART in combination with multiple imputation and data augmentation on some of these special situations.
Unit nonresponse, panel attrition in particular, is a problem with high impact on survey quality in social sciences. The first application aims at imputing missing values by CART to generate a proper data base for the decision whether weighting has to be considered. This decision was based on auxiliary information about respondents and nonrespondents. Both, auxiliary information and the participation status as response indicator, contained missing values that had to be imputed. The described situation originated in a school survey. The schools were asked to transmit auxiliary information about their students without knowing if they participated in the survey or not. In the end both information, auxiliary information and the participation status, should have been combined by their identification number by the survey research institute. Some data were collected and transmitted correctly, some were not. Due to those errors four data situations were distinguished and handled in different ways. 1) Complete cases, that is no missing values neither for the participation status, nor the auxiliary information. That means that the information whether the student participated were available and the auxiliary information were completely observed and correctly merged. 2) The participation status was missing, but the auxiliary information were complete. That happened when the school transmitted the auxiliary data of a student completely, but the combination with the survey participation information failed. 3) The participation status was available, but there were missings in the auxiliary information and 4) there were missings in participation status as
well as in the auxiliary information.
The procedure to handle the complete data situation 1) was a standard probit analysis. A Probit Forecast Draw was applied in situations 2) and 4) which was based on a Metropolis-Hasting algorithm that used the available information of the maximum number of participants conditional on an auxiliary variable. In practice, the amount of male and female students that participated in the survey was known. This number was used as a maximum when the auxiliary information were combined with a probable participation status. All missings in auxiliary information, that was situations 3) and 4), were augmented by CART. That means that the imputation values were drawn via Bayesian Bootstrap from final nodes of the classification and regression trees. Both, the imputation and the probit model with the response indicator as the dependent variable resulted in a data augmentation approach. All steps were chained to use as much information as possible for the analysis.
The application shows that CART can flexibly be combined with data augmentation resulting in a Markov chain Monte Carlo method or more precisely a Gibbs sampler. The results of the analysis of the (meta-)data showed a selectivity due to nonparticipation which could be explained by the variable sex. Female students tended to participate more likely than male students. The results based on the usage of CART differed clearly from those of the complete cases analysis ignoring the second level random effect as well as from those outcomes of the complete cases analysis including the second level random effect.
Surveys based on flexible filtering offer the opportunity to adjust the questionnaire to the respondents' situation. Hence, data quality can be increased and response burden can be decreased. Therefore, filters are often implemented in large-scale surveys resulting in a complex data structure, that has to be considered when imputing. The second study of this thesis shows how a data set containing many filters and a high filter-depth that limits the admissible range of values for multiple imputation can be handled by using CART. To get more into detail, a very large and complex data set contained variables that were used for the analysis of household net income. The variables were distributed over modules. Modules are blocks of questions referring to certain topics which are partially steered by filters. Additionally, within those modules the survey was steered by filter questions. As a consequence the number of respondents on each variable differed. It can be assumed that due to the structure of the survey missing values were mainly produced by filters or caused by the respondent intentionally and only a minor part were missing e.g. by interviewers overseeing them.
The second application shows that the described procedure is able to consider the complex data structure as the draws from CART are flexibly limited due to the changing filter structure which is generated by imputed filter steering values as well. Regarding the amount of 213 chosen variables for the household net income imputation, CART in contrast to other approaches obviously leads to time savings as no model specification is needed for each variable that has to be imputed. Still, there is a need to get some feedback concerning the suitability of CART-based imputation. Therefore, as third application of this thesis, a simulation study was conducted to show the performance of CART in a combination with multiple imputation by chained equations (MICE) on cross-sectional data. Additionally, it was checked whether a change of settings improves the performance for the given data. There were three different data generating functions of Y .
The first was a typical linear model with a normally distributed error term. The second included a chi-squared error term. The third included a non-linear (logarithmic) term. The rate of missing values was set to 60% steered by a missing at random mechanism. Regression parameters, mean, quantiles and correlations were calculated and combined. The quality of the estimation for before deletion, complete cases and the imputed data was measured by coverage, i.e. the proportion of 95%-confidence intervals for the estimated parameters that contain the true value. Additionally, bias and mean squared error were calculated.
Then, the settings were changed for the first type of data set, that was the ordinary linear model. First, the initialization was changed to a tree-based initialization instead of draws from the unconditional empirical distribution. Second, the iterations of the tree-based MI approach were increased from 20 to 50. Third, the number of imputed data sets that were combined for the confidence intervals was doubled from 15 to 30. CART-based MICE showed a good performance (88.8% to 91.8%) for all three data sets. Additionally, it was not worthwhile changing the settings of CART for the partitioning of the simulated data. Moreover, the third application shows some insights about the performance and the settings of CART-based MICE. There were many default settings and peculiarities that had to be considered when using CART-based MICE. The results suggest that the default settings and the performance of CART in general lead to sufficient results when conducted on cross-sectional data. Respective the settings, changing the initialization from tree-based draws to draws from the unconditional empirical distribution is recommendable for typical survey data, that is data with missing values in large parts of the data.
The fourth application gives some insights into the performance of CART-based MICE on panel data. Therefore, the first simulated data set was extended to panel data containing information from two waves. Four data situations were distinguished, that was three random effects models with different combinations of time-variant and time-invariant variables and a fixed effects model. The last was defined by an intercept that is correlated to a regressor, the missingness steering variable X1. CART-based MICE showed a good performance (89.0% to 91.4%) for all four data sets. CART chose the variables from the correct wave for each of the four data situations and waves. That means that only first wave information was used for the imputation of the first wave variable Yt=1, respectively only second wave information was used for the second wave variable Yt=2. This is crucial as the data generation for each of both waves was conducted as either independent of the other wave or the variables were time-variant for all four data situations.
This thesis demonstrates that CART can be used as a highly flexible imputation component which can be recommended with constraints for large-scale panel studies. Missing values in cross-sectional data as well as panel data can both be handled with CART-based MICE. Of course, the accuracy depends on the availability of explanatory power and correlations for both, cross-sectional and panel data. The combination of CART with data augmentation and the extension concerning the filtering of the data are both feasible and promising. In addition, further research about the performance of CART is highly recommended, for example by extending the current simulation study by changes of the variables over time based on past values of the same variable, more waves or different data generation processes.

In der vorliegenden Dissertation werden die Entscheidungsprozesse im strategischen Währungsmanagement internationaler Unternehmen betrachtet. Dabei liegt der Fokus auf der Auswahl, Anwendung und Evaluation von realwirtschaftlichen Hedgingstrategien (z. B. Natural Hedging). Im Rahmen einer vergleichenden Fallstudienanalyse werden Interview- und Unternehmensberichtsdaten erhoben und die empirischen Ergebnisse der Unternehmen aus der Automobil- mit denen aus der Airline-Branche verglichen. Auf dieser Basis werden wesentliche Gemeinsamkeiten und Unterschiede zwischen einer Produktions- und einer Dienstleistungsbranche in Bezug auf das strategische Währungsmanagement herausgearbeitet und unternehmenspolitische Implikationen formuliert.

Die Untersuchung widmet sich energiepolitischen Einstellungen im internationalen Vergleich am Beispiel der Sachfrage Kernenergie – sowohl in Abwesenheit von exogenen Schocks als auch im Kontext nuklearer Zwischenfälle. Da davon auszugehen ist, dass Bürger energiepolitischen Themen nicht zwangsläufig eine hohe Bedeutung beimessen, werden die Implikationen der relativen Sachfragensalienz, insbesondere mit Blick auf Politisierungsunterschiede, aus verschiedenen Perspektiven theoretisch diskutiert. Die empirische Analyse für „ruhige Phasen“ offenbart markante kontextspezifische Einflussmuster bei der individuellen Verknüpfung von Voreinstellungen und der Technologiebewertung. Theoretische Wirkungsmechanismen, die in der Literatur oftmals pauschal angenommen werden, treten empirisch vornehmlich in ökonomisch fortschrittlichen Staaten auf. Anhand des Fukushima-Unglücks zeigt die Untersuchung anschließend, dass Einstellungs- und politische Verhaltensreaktionen als das Resultat eines komplexen Wechselspiels aus Elitenbotschaften, individuellen Voreinstellungen und der langfristigen Salienzdynamik verstanden werden müssen. Auf Basis von drei Fallstudien wird deutlich, dass von einer Salienzsteigerung höchstens im unmittelbaren Kontext eines nuklearen Zwischenfalls auszugehen ist. Da kontextspezifische Politisierungsprozesse für diverse Sachfragen vorstellbar sind, insbesondere für vergleichsweise spezielle Fragen der politischen Auseinandersetzung, beinhaltet die Untersuchung Implikationen, die über das Fallbeispiel Kernenergie hinausweisen.

Das Europäische Emissionshandelssystem hat sich seit seiner Einführung 2005 grundlegend gewandelt. Die 2003 beschlossene Richtlinie zur Einführung eines europäischen Emissionshandelssystems kann dabei als kleinster gemeinsamer Nenner bewertet werden, denn wichtige Mitgliedstaaten stehen dem Konzept des Emissionshandels skeptisch gegenüber. So enthält das Handelssystem lasche Regelungen und belässt zentrale Befugnisse sowie große Handlungsspielräume bei den Mitgliedstaaten. Nur sechs Jahre später fordern die gleichen Mitgliedstaaten ein System mit strikten Regelungen und beschließen eine neue Richtlinie, die sie im künftigen Handelssystem vollständig entmachtet. Was hat zu dieser Entwicklung geführt und wie kann diese nachgezeichnet und erklärt werden?
Dieses Buch deckt die Integrationsdynamik der Institution Emissionshandel auf europäischer Ebene inkrementell auf. Hierzu wird ein theoretisches Modell entwickelt, das die einzelnen Schritte, die zur Revision des Handelssystems geführt haben, erklären kann. Dabei werden mit Hilfe von großen Energieunternehmen in Deutschland, Großbritannien und Frankreich die zentralen Akteure dieser Entwicklung identifiziert und ihr Zusammenspiel mit den politischen Akteuren offengelegt. Konkret wird dabei die Entwicklung zwischen den ersten gescheiterten Versuchen der EU Anfang der 1990er, ein Instrument zur Reduktion von Treibhausgasen einzuführen, bis zu den ersten beiden Jahren der dritten Handelsphase (2013/ 2014) berücksichtigt.

The African Peer Review Mechanism (APRM) is the peer review instrument adopted by the African Union as a mechanism to foster the adoption of policies to enhance the quality of governance among member States. Its review process relies on using self-reporting and monitoring tools to assess and encourage improvements in the policies of participating States. This dissertation explores the question of why, and under what conditions, member States acting within the African Peer Review Mechanism (APRM) decide to delegate decision competence to other bodies as part of the Review’s decision-making process. It investigates how, and with what consequences, this delegation creates consequent mechanisms of functional differentiation which determines the patterns of decision-making in the APRM review process and the contents of country review reports. The dissertation engages modern institutional theory in order to explain the emergence of the governance system of the APR process. The APR Forum (the assembly of Heads of State participating in the APRM) has acted over time to create an increasingly complex governance structure and to delegate decision-making competencies to subsidiary bodies in order to arrive at decisions that require implementation by participating member States under the review process. With insight taken from modern institutional theory, this dissertation argues that the organizational structure of the APRM process is functionally differentiated, and that the decision-making system embedded within the review process therefore becomes divided between a rule-making function and the concomitant application of those rules to the specific historical circumstances of APR country processes. In this regard, therefore, the APR Forum concentrates its functions on formulating general rules, while the APR Panel and subsidiary committees subsequently apply these rules to individual case specific review processes. This dissertation contends that such functional differentiation in the APRM promotes deliberations, at both the rule making stage and the norm application phase of the decision process, which consequently affect the decision process in ways that favour merit-based decisions-making and inhibit raw power politics. Based on previously overlooked sources, the dissertation further conducts an empirical analysis of the effects of functional differentiation in the examples of three APR country processes (i.e. South Africa, Ghana and Kenya), using qualitative expert interviews and process-tracing. It concludes that functional differentiation in the decision process of the APRM provides a powerful mechanism to promote merit-based decisions, even at the country level where the process is led by the country under review.