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Price indices play a vital role in economic measurement as they reflect price levels
and measure price fluctuations. Price level measures are used with macroeconomic
indicators to express them in real terms. These measures are also used to index wages,
rents, and pensions. Furthermore, they are used as a reference for monetary policy
conducted by central banks. Therefore, the provision of accurate price indices is one
of the most important goals of National Statistical Institutes (NSIs), and numerous
studies have been devoted to this goal.
This cumulative dissertation also contributes to this goal. It contains four chapters,
each of which represents a separate research. The first two studies are devoted to
the treatment of seasonal products by using different price index methods. The first
research is co-authored with Ken van Loon. The third research is dedicated to finding
the most accurate method to make price predictions for missing products. The fourth
research is focused on the treatment of products by using different price index methods
when products’ quality characteristics are available.
Measuring the economic activity of a country requires high-quality data of businesses. In the case of Germany, this is not only required at national level, but also at federal state level and for different economic sectors. Important sources for high-quality business data are the business register and, among others, also 14 business surveys which are conducted by the Federal Statistical Office of Germany. However, the quality requirements of the Federal Statistical Office are in contrast to the interests of the businesses themselves. For them, answering to a survey's questionnaire is an additional cost factor, also known as response burden. A high response burden should be avoided, since it can have a negative impact on the quality of the businesses' responses to the surveys. Therefore, sample coordination can be used as a method to control the distribution of response burden while securing high-quality data.
When applying already existing business survey coordination systems, developed by different statistical institutes, legal and administrative standards of German official statistics have to be taken into account. These standards consider different sampling fractions, rotation fractions, periodicity, and stratification of the aforementioned 14 business surveys. Therefore, the aim of this doctoral thesis is to check the existing business survey coordination systems for their applicability in the context of German official statistics and, if necessary, to modify them accordingly. These modifications include the introduction of individual burden indicators which aim to take the individual perception of response burden into account.
For this purpose, several synthetic data sets have been created to test the application of the modified versions of the different business survey coordination systems through Monte Carlo simulation studies. These data sets include a large panel data set, reflecting the landscape of businesses in Rhineland-Palatinate and three smaller, synthetic data sets. The latter have been created with the help of the R package BuSuCo which has been developed within the scope of this thesis. The above mentioned simulation studies are evaluated based on different measures for estimation quality as well as for the concentration and distribution of response burden.
Bilevel problems are optimization problems for which parts of the variables
are constrained to be an optimal solution to another nested optimization
problem. This structure renders bilevel problems particularly well-suited for
modeling hierarchical decision-making processes. They are widely applicable
in areas such as energy markets, transportation systems, security planning,
and pricing. However, the hierarchical nature of these problems also makes
them inherently challenging to solve, both in theory and in practice.
In this thesis, we study different nonlinear problem settings for the
nested optimization problem. First, we focus on nonlinear but convex bilevel
problems with purely integer variables. We propose a solution algorithm that
uses a branch-and-cut framework with tailored cutting planes. We prove
correctness and finite termination of the method under suitable assumptions
and put it into context of existing literature. Moreover, we provide an
extensive numerical study to showcase the applicability of our method and
we compare it to the state-of-the-art approach for a less general setting on
suitable instances from the literature. Furthermore, we discuss challenges that
arise when we try to generalize our approach to the mixed-integer setting.
Next, we study mixed-integer bilevel problems for which the nested
problem has a nonconvex and quadratic objective function, linear constraints,
and continuous variables. We state and prove a complexity-theoretical hardness result for this
problem class and develop a lower and upper bounding scheme to solve
these problems. We prove correctness and finite termination of the proposed
method under suitable assumptions and test its applicability in a numerical
study.
Finally, we consider bilevel problems with continuous variables, where
the nested problem has a convex-quadratic objective function and linear
constraints. We reformulate them as single-level optimization problems using
necessary and sufficient optimality conditions for the nested problem. Then,
we explore the family of so-called P-split reformulations for this single-level
problem and test their applicability in a preliminary numerical study.
Entrepreneurship is recognized as an important discipline to achieve sustainable development and to address sustainability goals without losing sight of economic aspects. However, entrepreneurship rates are rather low in many industrialized countries with high income levels. Research clearly shows that there is a gap in the entrepreneurial process between intentions and subsequent actions. This means that not everyone with entrepreneurial ambitions also follows through and implements actions. This gap also exists for aspects of sustainability. As a result, there is a need to better understand the traditional and sustainability-focused entrepreneurial process in order to increase corresponding actions. This dissertation offers such a comprehensive perspective and sheds light on individual and contextual predictors for traditional and sustainability-focused behavior of entrepreneurs and self-employed across four studies.
The first three studies focus on individual predictors. By providing a systematic literature review with 107 articles, Chapter 2 highlights the ambivalent role of religion for the entrepreneurial process. Relying on the theory of planned behavior (TPB) as theoretical basis, religion can have positive effects on entrepreneurial attitudes and behavioral control, but also negative consequences for other aspects of behavioral control and subjective norms due to religious restrictions.
The quantitative empirical study in Chapter 3 similarly relies on the TPB and sheds light on individual perceptual factors influencing the sustainability-related intention-action gap in entrepreneurship. Using data from the 2021 Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) including 22,008 early-stage entrepreneurs from 44 countries worldwide, the results support our theoretical reasoning that sustainability-focused intentions are positively related to social entrepreneurial actions. In addition, it is demonstrated that positive perceptual moderators such as self-efficacy and knowing other entrepreneurs as role models strengthen this relationship while a negative perception such as fear of failure restricts social actions in early-stage entrepreneurship.
The next quantitative empirical study in Chapter 4 examines the behavioral consequences of well-being at a sample of 6,955 German self-employed during COVID-19. This chapter builds on two complementary behavioral perspectives to predict how reductions in financial and non-financial well-being relate to investments in venture development. In this regard, reductions in financial well-being are positively related to time investments, supporting the performance feedback perspective in terms of higher search efforts under negative performance. In contrast, reductions in non-financial well-being are negatively related to time and monetary investments, yielding support for the broadening-and-build perspective indicating that negative psychological experiences narrow the thought-action repertoire and hinder resource deployment. The insights across these first three studies about individual predictors indicate that many different, subjective beliefs, perceptions and emotional states can influence the entrepreneurial process making entrepreneurship and self-employment highly individualized disciplines.
The last quantitative empirical study provides an explorative view on a large number of contextual predictors for social and ecological considerations in entrepreneurial actions. Combining GEM data from 2021 on country level with further information from the World Bank and the OECD, a machine learning approach is employed on a sample of 84 countries worldwide. The results suggest that governmental and regulatory as well as cultural factors are relevant to predict social and ecological considerations. Moreover, market-related aspects are shown to be relevant predictors, especially socio-economic factors for social considerations and economic factors for ecological considerations. Overall, the four studies in this dissertation highlight the complexity of the entrepreneurial process being determined by many different individual and contextual factors. Due to the multitude of potential predictors, this dissertation can only give an initial overview of a selection of factors with many more aspects and interdependencies still to be examined by future research.
Zirkularität und zirkulare Geschäftsmodelle in der Holzindustrie: eine empirische Untersuchung
(2025)
Der ökologische Zustand der Erde befindet sich infolge von Umweltverschmutzung, Abfallaufkommen und CO₂-bedingtem Klimawandel in einem kritischen Zustand. Mit rund 40 % trägt der Bau- und Gebäudesektor erheblich zu den globalen Treibhausgasemissionen bei. Holz gilt als klimafreundliche Alternative zu Beton und Stahl, bedarf jedoch ebenfalls einer nachhaltigen Nutzung. Die Kreislaufwirtschaft bietet mit der Wiederverwendung ein zukunftsweisendes Konzept: So sind etwa 45% des beim Rückbau von Gebäuden anfallenden Holzes potenziell als Rohstoff nutzbar. Dadurch werden alternative Rohstoffquellen erschlossen und das Abfallaufkommen reduziert.
Trotz dieses Potenzials liegt der Zirkularitätsgrad der Weltwirtschaft derzeit nur bei 7,2 %. Vor diesem Hintergrund untersucht die Dissertation, welche Wettbewerbsstrategien und welche organisationalen Fähigkeiten die Entwicklung zirkulärer Geschäftsmodelle fördern. Der Fokus liegt auf der Holzindustrie der DACH-Region, die historisch durch forstwirtschaftliche Nachhaltigkeit geprägt ist, jedoch bislang überwiegend linearen Strukturen folgt.
Die Arbeit kombiniert theoretische Fundierung, eine vierjährige Literaturrecherche, Experteninterviews sowie im Zentrum eine quantitative Unternehmensbefragung (n = 200). Daraus wurde eine aktivitätsorientierte Skala zur Bewertung der Zirkularität eines Geschäftsmodells entwickelt. Analysiert wurden drei Perspektiven: Fähigkeiten, Strategien und Stakeholder.
Im Kontext der Fähigkeitsperspektive wurde ermittelt, dass die dynamischen Fähigkeiten positive Implikationen auf die Umsetzung von Zirkularität haben. Im Forschungsfeld der Strategieperspektive wurde deutlich, dass die Innovationsführerschaft positive Effekte auf die Umsetzung der Kreislaufwirtschaft besitzt. Zudem weisen sowohl die Innovationsführerschaft als auch die Qualitätsführerschaft einen positiven indirekten Effekt über die dynamischen Fähigkeiten auf die Entwicklung zirkulärer Geschäftsmodelle auf. Im Rahmen der Stakeholderperspektive wurde eruiert, dass der Stakeholder-Druck im Zusammenwirken mit einem grünen Unternehmensimage eine Katalysator-Wirkung besitzt. Der Einfluss der Interessengruppen führt dazu, dass die Unternehmen ein grünes Image in eine substanzielle Umsetzungsphase überführen. Darüber hinaus wurde ersichtlich, dass der Stakeholder-Druck als zentraler Veränderungsfaktor wirkt. Während die direkten Auswirkungen der dynamischen Fähigkeiten durch den Druck zurückgehen, nehmen die indirekten Effekte auf die Erreichung von Zirkularität zu. Abschließend werden Handlungsempfehlungen für Unternehmen sowie wissenschaftliche Implikationen und zukünftige Forschungsmöglichkeiten abgeleitet.
Case-Based Reasoning (CBR) is a symbolic Artificial Intelligence (AI) approach that has been successfully applied across various domains, including medical diagnosis, product configuration, and customer support, to solve problems based on experiential knowledge and analogy. A key aspect of CBR is its problem-solving procedure, where new solutions are created by referencing similar experiences, which makes CBR explainable and effective even with small amounts of data. However, one of the most significant challenges in CBR lies in defining and computing meaningful similarities between new and past problems, which heavily relies on domain-specific knowledge. This knowledge, typically only available through human experts, must be manually acquired, leading to what is commonly known as the knowledge-acquisition bottleneck.
One way to mitigate the knowledge-acquisition bottleneck is through a hybrid approach that combines the symbolic reasoning strengths of CBR with the learning capabilities of Deep Learning (DL), a sub-symbolic AI method. DL, which utilizes deep neural networks, has gained immense popularity due to its ability to automatically learn from raw data to solve complex AI problems such as object detection, question answering, and machine translation. While DL minimizes manual knowledge acquisition by automatically training models from data, it comes with its own limitations, such as requiring large datasets, and being difficult to explain, often functioning as a "black box". By bringing together the symbolic nature of CBR and the data-driven learning abilities of DL, a neuro-symbolic, hybrid AI approach can potentially overcome the limitations of both methods, resulting in systems that are both explainable and capable of learning from data.
The focus of this thesis is on integrating DL into the core task of similarity assessment within CBR, specifically in the domain of process management. Processes are fundamental to numerous industries and sectors, with process management techniques, particularly Business Process Management (BPM), being widely applied to optimize organizational workflows. Process-Oriented Case-Based Reasoning (POCBR) extends traditional CBR to handle procedural data, enabling applications such as adaptive manufacturing, where past processes are analyzed to find alternative solutions when problems arise. However, applying CBR to process management introduces additional complexity, as procedural cases are typically represented as semantically annotated graphs, increasing the knowledge-acquisition effort for both case modeling and similarity assessment.
The key contributions of this thesis are as follows: It presents a method for preparing procedural cases, represented as semantic graphs, to be used as input for neural networks. Handling such complex, structured data represents a significant challenge, particularly given the scarcity of available process data in most organizations. To overcome the issue of data scarcity, the thesis proposes data augmentation techniques to artificially expand the process datasets, enabling more effective training of DL models. Moreover, it explores several deep learning architectures and training setups for learning similarity measures between procedural cases in POCBR applications. This includes the use of experience-based Hyperparameter Optimization (HPO) methods to fine-tune the deep learning models.
Additionally, the thesis addresses the computational challenges posed by graph-based similarity assessments in CBR. The traditional method of determining similarity through subgraph isomorphism checks, which compare nodes and edges across graphs, is computationally expensive. To alleviate this issue, the hybrid approach seeks to use DL models to approximate these similarity calculations more efficiently, thus reducing the computational complexity involved in graph matching.
The experimental evaluations of the corresponding contributions provide consistent results that indicate the benefits of using DL-based similarity measures and case retrieval methods in POCBR applications. The comparison with existing methods, e.g., based on subgraph isomorphism, shows several advantages but also some disadvantages of the compared methods. In summary, the methods and contributions outlined in this work enable more efficient and robust applications of hybrid CBR and DL in process management applications.
The gender wage gap in labor market outcomes has been intensively investigated for decades, yet it remains a relevant and innovative research topic in labor economics. Chapter 2 of this dissertation explores the pressing issue of gender wage disparity in Ethiopia. By applying various empirical methodologies and measures of occupational segregation, this chapter aims to analyze the role of female occupational segregation in explaining the gender wage gap across the pay distribution. The findings reveal a significant difference in monthly wages, with women consistently earning lower wages across the wage distribution.
Importantly, the result indicates a negative association between female occupational segregation and the average earnings of both men and women. Furthermore, the estimation result shows that female occupational segregation partially explains the gender wage gap at the bottom of the wage distribution. I find that the magnitude of the gender wage gap in the private sector is higher than in the public sector.
In Chapter 3, the Ethiopian Demography and Health Survey data are leveraged to explore the causal relationship between female labor force participation and domestic violence. Domestic violence against women is a pervasive public health concern, particularly in Africa, including Ethiopia, where a significant proportion of women endure various forms of domestic violence perpetrated by intimate partners. Economic empowerment of women through increased participation in the labor market can be one of the mechanisms for mitigating the risk of domestic violence.
This study seeks to provide empirical evidence supporting this hypothesis. Using the employment rate of women at the community level as an instrumental variable, the finding suggests that employment significantly reduces the risk of domestic violence against women. More precisely, the result shows that women’s employment status significantly reduces domestic violence by about 15 percentage points. This finding is robust for different dimensions of domestic violence, such as physical, sexual, and emotional violence.
By examining the employment outcomes of immigrants in the labor market, Chapter 4 extends the dissertation's inquiry to the dynamics of immigrant economic integration into the destination country. Drawing on data from the German Socio-Economic Panel, the chapter scrutinizes the employment gap between native-born individuals and two distinct groups of first-generation immigrants: refugees and other migrants. Through rigorous analysis, Chapter 4 aims to identify the factors contributing to disparities in employment outcomes among these groups. In this chapter, I aim to disentangle the heterogeneity characteristic of refugees and other immigrants in the labor market, thereby contributing to a deeper understanding of immigrant labor market integration in Germany.
The results show that refugees and other migrants are less likely to find employment than comparable natives. The refugee-native employment gap is much wider than other migrant-native employment gap. Moreover, the findings vary by gender and migration categories. While other migrant men do not differ from native men in the probability of being employed, refugee women are the most disadvantaged group compared to other migrant women and native women in the probability of being employed. The study suggests that German language proficiency and permanent resident permits partially explain the lower employment probability of refugees in the German labor market.
Chapter 5 (co-authored with Uwe Jirjahn) utilizes the same dataset to explore the immigrant-native trade union membership gap, focusing on the role of integration in the workplace and into society. The integration of immigrants into society and the workplace is vital not only to improve migrant's performance in the labor market but also to actively participate in institutions such as trade unions. In this study, we argue that the incomplete integration of immigrants into the workplace and society implies that immigrants are less likely to be union members than natives. Our findings show that first-generation immigrants are less likely to be trade union members than natives. Notably, the analysis shows that the immigrant-native gap in union membership depends on immigrants’ integration into the workplace and society. The gap is smaller for immigrants working in firms with a works council and having social contacts with Germans. Moreover, the results reveal that the immigrant-native union membership gap is decreasing in the year since arrival in Germany.
Die Masterarbeit untersucht den Zusammenhang zwischen Libertarismus und Rechtsextremismus, wobei der Fokus auf der Entwicklung der libertären Szene in Deutschland liegt. Zunächst wird ein ausführlicher theoretischer Teil präsentiert, in dem gezeigt wird, dass zwischen einer radikal wirtschaftsliberalen und einer rechtsextremen Weltauffassung partiell gemeinsame Elemente bestehen. Insbesondere werden ein spezifischer Antiegalitarismus, eine Naturalisierung gesellschaftlicher Sachverhalte sowie eine gemeinsame Feindbildkonstruktion als verbindende Merkmale identifiziert, die beide Ideologien, die auf Ungleichwertigkeitsvorstellungen basieren, prägen. Im Anschluss folgt eine empirische Analyse des libertären Magazins eigentümlich frei, das eine zentrale Rolle in der deutschsprachigen libertären Bewegung spielt. Der soziologische Neo-Institutionalismus dient als theoretische Perspektive, um den institutionellen Wandel innerhalb der libertären Szene zu erfassen und zu analysieren. Die empirische Untersuchung bestätigt die theoretischen Annahmen und zeigt, dass sich im libertären Diskurs eine zunehmende Annäherung an rechtsextreme Ideologien vollzieht. Fünf Phasen des institutionellen Wandels werden identifiziert, die mit einer verstärkten Vernetzung der libertären Bewegung mit dem rechtsextremen Spektrum und der Veränderung von Diskursen einhergehen. Die Arbeit kommt zu dem Schluss, dass die libertäre Szene um eigentlich frei dem rechtsextremen Spektrum zuzuordnen ist. Die Untersuchung schlägt vor, den Libertarismus im Rahmen dieser Entwicklung als „Paläolibertarismus“ zu bezeichnen, was auf eine ideologische Nähe zur Alt-Right-Bewegung hinweist. Zentrale Merkmale dieser Ideologie sind neben einer radikal wirtschaftsliberalen Ausrichtung auch die Forderung nach einer Privatisierung gesellschaftlicher Institutionen und die Etablierung von sozialen Autoritäten wie Familie und Kirche zum Schutz des Individuums vor staatlicher Einflussnahme.
Convex Duality in Consumption-Portfolio Choice Problems with Epstein-Zin Recursive Preferences
(2025)
This thesis deals with consumption-investment allocation problems with Epstein-Zin recursive utility, building upon the dualization procedure introduced by [Matoussi and Xing, 2018]. While their work exclusively focuses on truly recursive utility, we extend their procedure to include time-additive utility using results from general convex analysis. The dual problem is expressed in terms of a backward stochastic differential equation (BSDE), for which existence and uniqueness results are established. In this regard, we close a gap left open in previous works, by extending results restricted to specific subsets of parameters to cover all parameter constellations within our duality setting.
Using duality theory, we analyze the utility loss of an investor with recursive preferences, that is, her difference in utility between acting suboptimally in a given market, compared to her best possible (optimal) consumption-investment behaviour. In particular, we derive universal power utility bounds, presenting a novel and tractable approximation of the investors’ optimal utility and her welfare loss associated to specific investment-consumption choices. To address quantitative shortcomings of those power utility bounds, we additionally introduce one-sided variational bounds that offer a more effective approximation for recursive utilities. The theoretical value of our power utility bounds is demonstrated through their application in a new existence and uniqueness result for the BSDE characterizing the dual problem.
Moreover, we propose two approximation approaches for consumption-investment optimization problems with Epstein-Zin recursive preferences. The first approach directly formalizes the classical concept of least favorable completion, providing an analytic approximation fully characterized by a system of ordinary differential equations. In the special case of power utility, this approach can be interpreted as a variation of the well-known Campbell-Shiller approximation, improving some of its qualitative shortcomings with respect to state dependence of the resulting approximate strategies. The second approach introduces a PDE-iteration scheme, by reinterpreting artificial completion as a dynamic game, where the investor and a dual opponent interact until reaching an equilibrium that corresponds to an approximate solution of the investors optimization problem. Despite the need for additional approximations within each iteration, this scheme is shown to be quantitatively and qualitatively accurate. Moreover, it is capable of approximating high dimensional optimization problems, essentially avoiding the curse of dimensionality and providing analytical results.
This dissertation examines the relevance of regimes for stock markets. In three research articles, we cover the identification and predictability of regimes and their relationships to macroeconomic and financial variables in the United States.
The initial two chapters contribute to the debate on the predictability of stock markets. While various approaches can demonstrate in-sample predictability, their predictive power diminishes substantially in out-of-sample studies. Parameter instability and model uncertainty are the primary challenges. However, certain methods have demonstrated efficacy in addressing these issues. In Chapter 1 and 2, we present frameworks that combine these methods meaningfully. Chapter 3 focuses on the role of regimes in explaining macro-financial relationships and examines the state-dependent effects of macroeconomic expectations on cross-sectional stock returns. Although it is common to capture the variation in stock returns using factor models, their macroeconomic risk sources are unclear. According to macro-financial asset pricing, expectations about state variables may be viable candidates to explain these sources. We examine their usefulness in explaining factor premia and assess their suitability for pricing stock portfolios.
In summary, this dissertation improves our understanding of stock market regimes in three ways. First, we show that it is worthwhile to exploit the regime dependence of stock markets. Markov-switching models and their extensions are valuable tools for filtering the stock market dynamics and identifying and predicting regimes in real-time. Moreover, accounting for regime-dependent relationships helps to examine the dynamic impact of macroeconomic shocks on stock returns. Second, we emphasize the usefulness of macro-financial variables for the stock market. Regime identification and forecasting benefit from their inclusion. This is particularly true in periods of high uncertainty when information processing in financial markets is less efficient. Finally, we recommend to address parameter instability, estimation risk, and model uncertainty in empirical models. Because it is difficult to find a single approach that meets all of these challenges simultaneously, it is advisable to combine appropriate methods in a meaningful way. The framework should be as complex as necessary but as parsimonious as possible to mitigate additional estimation risk. This is especially recommended when working with financial market data with a typically low signal-to-noise ratio.