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Chapter 2: Using data from the German Socio-Economic Panel, this study examines the relation-ship between immigrant residential segregation and immigrants" satisfaction with the neighbor-hood. The estimates show that immigrants living in segregated areas are less satisfied with the neighborhood. This is consistent with the hypothesis that housing discrimination rather than self-selection plays an important role in immigrant residential segregation. Our result holds true even when controlling for other influences such as household income and quality of the dwelling. It also holds true in fixed effects estimates that account for unobserved time-invariant influences. Chapter 3: Using survey data from the German Socio-Economic Panel, this study shows that immigrants living in segregated residential areas are more likely to report discrimination because of their ethnic background. This applies to both segregated areas where most neighbors are immigrants from the same country of origin as the surveyed person and segregated areas where most neighbors are immigrants from other countries of origin. The results suggest that housing discrimination rather than self-selection plays an important role in immigrant residential segregation. Chapter 4: Using data from the German Socio-Economic Panel (SOEP) and administrative data from 1996 to 2009, I investigate the question whether or not right-wing extremism of German residents is affected by the ethnic concentration of foreigners living in the same residential area. My results show a positive but insignificant relationship between ethnic concentration at the county level and the probability of extreme right-wing voting behavior for West Germany. However, due to potential endogeneity issues, I additionally instrument the share of foreigners in a county with the share of foreigners in each federal state (following an approach of Dustmann/Preston 2001). I find evidence for the interethnic contact theory, predicting a negative relationship between foreign-ers" share and right-wing voting. Moreover, I analyze the moderating role of education and the influence of cultural traits on this relationship. Chapter 5: Using data from the Socio-Economic Panel from 1998 to 2009 and administrative data on regional ethnic diversity, I show that ethnic diversity inhibits significantly people- political interest and participation in political organizations in West Germany. People seem to isolate themselves from political participation if exposed to more ethnic diversity which is particularly relevant with respect to the ongoing integration process of the European Union and the increasing transfer of legislative power from the national to European level. The results are robust if an instrumental variable strategy suggested by Dustmann and Preston (2001) is used to take into account that ethnic diversity measured on a local spatial level could be endogenous due to residential sorting. Interestingly, participation in non-political organizations is positively affected by ethnic diversity if selection bias is corrected for.
The outbreak of the COVID-19 pandemic has also led to many conspiracy theories. While the origin of the pandemic in China led some, including former US president Donald Trump, to dub the pathogen “Chinese virus” and to support anti-Chinese conspiracy narratives, it caused Chinese state officials to openly support anti-US conspiracy theories about the “true” origin of the virus. In this article, we study whether nationalism, or more precisely uncritical patriotism, is related to belief in conspiracy theories among normal people. We hypothesize based on group identity theory and motivated reasoning that for the particular case of conspiracy theories related to the origin of COVID-19, such a relation should be stronger for Chinese than for Germans. To test this hypothesis, we use survey data from Germany and China, including data from the Chinese community in Germany. We also look at relations to other factors, in particular media consumption and xenophobia.
Das erste Kapitel "ECOWAS" capability and potential to overcome constraints to growth and poverty reduction of its member states" diskutiert die Analyse wirtschaftlicher und sozialer Barrieren für ökonomisches Wachstum " eine der Hauptelemente für Entwicklungs- und Armutsreduktionsstrategien in Entwicklungsländern. Die Form der länderspezifischen Analyse von Wachstumsbarrieren wurde nach dem Scheitern der auf alle Länder generalisierten Entwicklungsstrategie des Washington Consensus insbesondere durch den Ansatz der "Growth Diagnostics" der Harvard Professoren Hausman, Rodrik und Velasco eingeführt. Es zeigt sich jedoch, dass bisher der Fokus rein auf den länderspezifischen Analysen bzw. Strategieentwicklungen liegt. Diese Arbeit erweiterte die Diskussion auf die regionale Ebene, indem es beispielhaft an der Economic Community of West African States (ECOWAS) die länderspezifischen Wachstumsbarrieren mit den regionalen Wachstumsbarrieren vergleicht. Dies erfolgt mittels einer Darstellung der in Studien und Strategien bereits identifizierten, länderspezifischen Wachstumsbarrieren in den jeweiligen Ländern sowie mit der Auswertung der regionalen Strategien der ECOWAS. Dazu wird ermittelt, inwieweit auf der regionalen Ebene auch messbare Ergebnisse bei der Bekämpfung von Wachstumsbarrieren erzielt werden. Es zeigt sich, dass ,trotz der wirtschaftlichen und sozialen Diversität der Region, die ECOWAS den Großteil der in den Ländern identifizierten Wachstumsbarrieren ebenfalls auflistet und darüber hinaus sogar mit messbaren Ergebnissen dazu beiträgt, Veränderungen des Status Quo zu erreichen. Die Erweiterung des Ansatzes der Growth Diagnostics auf die regionale Ebene sowie die Erweiterung um das vergleichende Element von länderspezifischen und regionalen Wachstumsbarrieren zeigen sich als praktikabler Weg, Entwicklungsstrategien auf regionaler Ebene zu prüfen und subsidiär weiterzuentwickeln. Das zweite Kapitel "Simplifying evaluation of potential causalities in development projects using Qualitative Comparative Analysis (QCA)" diskutiert die Methode der qualitativen komperativen Analyse (QCA) als Evaluierungsmethodik für Projekte der Entwicklungszusammenarbeit. Hierbei stehen die adäquate Messung sowie die verständliche Darstellung der Wirkung von Entwicklungszusammenarbeit im Vordergrund. Dies ist ein Beitrag zu der intensiv geführten Diskussion, wie Wirkung von Hilfe in Entwicklungsländern gemessen und daraus für weitere Projekte gelernt werden kann. Mit der beispielhaften Anwendung der QCA auf einen Datensatz der deutschen Entwicklungszusammenarbeit im Senegal wird erstmalig diese Methode für die Entwicklungszusammenarbeit in der Praxis angewandt. Der Fokus liegt dabei auf der Überprüfung von bestimmten Programmtheorien, d.h. der Annahme bestimmter Zusammenhänge zwischen eingesetzten Mitteln, äußeren Umständen und den Projektergebnissen bei der Implementierung von Projekten. Während solche Programmtheorien in dem Großteil der Projektskizzen der deutschen Entwicklungszusammenarbeit enthalten sind, werden die wenigsten dieser Programmtheorien geprüft. Diese Arbeit zeigt QCA als eine effiziente Methode für diese Überprüfung. Eine eindeutige Bestätigung oder Falsifizierung dieser Theorien ist mittels dieser Methodik möglich. Dazu können die Ergebnisse bei den beiden einfacheren Formen der QCA, der crisp-set sowie der multi-value QCA, leicht nachvollziehbar vermittelt werden. Des Weiteren zeigt die Arbeit, dass QCA ebenfalls die Weiterentwicklung einer Programmtheorie ermöglicht, allerdings ist diese Weiterentwicklung nur begrenzt effizient und stark von den vorliegenden Daten sowie der Datenstruktur abhängig. Die Arbeit zeigt somit das Potential der QCA insbesondere für den Test von Programmtheorien auf und stellt die praktische Anwendung für mögliche Replizierung beispielhaft dar. Das dritte und letzte Kapitel der Doktorarbeit "The regional trade dynamics of Turkey: a panel data gravity model" analysiert den türkischen Handel, um die Veränderungen der letzten Jahrzehnte aufzuzeigen und daran zu diskutieren, inwieweit sich die Türkei als aufstrebendes Schwellenland von den bestehenden Handelsstrukturen loslöst. Diese Arbeit ist ein Beitrag zur Diskussion der sich Verschiebenden Machtkonstellationen durch das wirtschaftliche Aufholen der Schwellenländer. Bei der Türkei ist diese Diskussion zusätzlich interessant, da die Frage, ob die Türkei sich von der westlichen Welt, Nordamerika und Europa, abwendet, berücksichtigt wird. Mittels Dummy-Variablen für verschiedene Regionen in einem Gravitätsmodell werden die türkischen Handelsdaten zuerst insgesamt und nach Sektoren analysiert und die Veränderungen über verschieden Perioden des türkischen Außenhandels betrachtet. Es zeigt sich, dass in den türkischen Handelsbeziehungen eine Regionalisierung und eine Diversifizierung der Handelspartner stattfinden. Allerdings geht dies nicht mit einer Abkehr von westlichen Handelspartnern einher.
Surveys are commonly tailored to produce estimates of aggregate statistics with a desired level of precision. This may lead to very small sample sizes for subpopulations of interest, defined geographically or by content, which are not incorporated into the survey design. We refer to subpopulations where the sample size is too small to provide direct estimates with adequate precision as small areas or small domains. Despite the small sample sizes, reliable small area estimates are needed for economic and political decision making. Hence, model-based estimation techniques are used which increase the effective sample size by borrowing strength from other areas to provide accurate information for small areas. The paragraph above introduced small area estimation as a field of survey statistics where two conflicting philosophies of statistical inference meet: the design-based and the model-based approach. While the first approach is well suited for the precise estimation of aggregate statistics, the latter approach furnishes reliable small area estimates. In most applications, estimates for both large and small domains based on the same sample are needed. This poses a challenge to the survey planner, as the sampling design has to reflect different and potentially conflicting requirements simultaneously. In order to enable efficient design-based estimates for large domains, the sampling design should incorporate information related to the variables of interest. This may be achieved using stratification or sampling with unequal probabilities. Many model-based small area techniques require an ignorable sampling design such that after conditioning on the covariates the variable of interest does not contain further information about the sample membership. If this condition is not fulfilled, biased model-based estimates may result, as the model which holds for the sample is different from the one valid for the population. Hence, an optimisation of the sampling design without investigating the implications for model-based approaches will not be sufficient. Analogously, disregarding the design altogether and focussing only on the model is prone to failure as well. Instead, a profound knowledge of the interplay between the sample design and statistical modelling is a prerequisite for implementing an effective small area estimation strategy. In this work, we concentrate on two approaches to address this conflict. Our first approach takes the sampling design as given and can be used after the sample has been collected. It amounts to incorporate the survey design into the small area model to avoid biases stemming from informative sampling. Thus, once a model is validated for the sample, we know that it holds for the population as well. We derive such a procedure under a lognormal mixed model, which is a popular choice when the support of the dependent variable is limited to positive values. Besides, we propose a three pillar strategy to select the additional variable accounting for the design, based on a graphical examination of the relationship, a comparison of the predictive accuracy of the choices and a check regarding the normality assumptions.rnrnOur second approach to deal with the conflict is based on the notion that the design should allow applying a wide variety of analyses using the sample data. Thus, if the use of model-based estimation strategies can be anticipated before the sample is drawn, this should be reflected in the design. The same applies for the estimation of national statistics using design-based approaches. Therefore, we propose to construct the design such that the sampling mechanism is non-informative but allows for precise design-based estimates at an aggregate level.
The Belt and Road Initiative (BRI) has had a significant impact on China in political, economic, and cultural terms. This study focuses on the cultural domain, especially on scholarship students from the countries that signed bilateral cooperation agreements with China under the BRI. Using an integrated approach combining the difference-in-differences method and the gravity model, we explore the correlation between the BRI and the increasing number of international scholarship students funded by the Chinese government, as well as the determinants of students' decision to study in China. The panel data from 2010 to 2018 show that the launch of BRI has had a positive impact on the number of scholarship students from BRI countries. The number of scholarship recipients from non-BRI countries also increased, but at a much slower rate than those from BRI countries. The sole exception is the United States, which has trended downward for both state-funded and self-funded students.
The equity premium (Mehra and Prescott, 1985) is still a puzzle in the sense that there are still no convincing explanations for the size of the equity premium. In this dissertation, we study this long-standing puzzle and several possible behavioral explanations. First, we apply the IRR methodology proposed by Fama and French (1999) to achieve large firm level data on the equity premia for N = 28,256 companies in 54 countries around the world. Second, by using preferences data from the INTRA study (Rieger et. al., 2014), we could test the relevant risk factors together with time cognition to explain the equity premium. We document the failure of the Myopic Loss Aversion hypothesis by Benartzi and Thaler (1995) but provides rigorous empirical evidence to support the behavioral theory of ambiguity aversion to account for the equity premium. The observations shed some light on the new approach of integrating risk and ambiguity (together with time preferences) into a more general model of uncertainty, in which both risk premium and ambiguity premium play roles in asset pricing models.
Non-probability sampling is a topic of growing relevance, especially due to its occurrence in the context of new emerging data sources like web surveys and Big Data.
This thesis addresses statistical challenges arising from non-probability samples, where unknown or uncontrolled sampling mechanisms raise concerns in terms of data quality and representativity.
Various methods to quantify and reduce the potential selectivity and biases of non-probability samples in estimation and inference are discussed. The thesis introduces new forms of prediction and weighting methods, namely
a) semi-parametric artificial neural networks (ANNs) that integrate B-spline layers with optimal knot positioning in the general structure and fitting procedure of artificial neural networks, and
b) calibrated semi-parametric ANNs that determine weights for non-probability samples by integrating an ANN as response model with calibration constraints for totals, covariances and correlations.
Custom-made computational implementations are developed for fitting (calibrated) semi-parametric ANNs by means of stochastic gradient descent, BFGS and sequential quadratic programming algorithms.
The performance of all the discussed methods is evaluated and compared for a bandwidth of non-probability sampling scenarios in a Monte Carlo simulation study as well as an application to a real non-probability sample, the WageIndicator web survey.
Potentials and limitations of the different methods for dealing with the challenges of non-probability sampling under various circumstances are highlighted. It is shown that the best strategy for using non-probability samples heavily depends on the particular selection mechanism, research interest and available auxiliary information.
Nevertheless, the findings show that existing as well as newly proposed methods can be used to ease or even fully counterbalance the issues of non-probability samples and highlight the conditions under which this is possible.
The demand for reliable statistics has been growing over the past decades, because more and more political and economic decisions are based on statistics, e.g. regional planning, allocation of funds or business decisions. Therefore, it has become increasingly important to develop and to obtain precise regional indicators as well as disaggregated values in order to compare regions or specific groups. In general, surveys provide the information for these indicators only for larger areas like countries or administrative divisions. However, in practice, it is more interesting to obtain indicators for specific subdivisions like on NUTS 2 or NUTS 3 levels. The Nomenclature of Units for Territorial Statistics (NUTS) is a hierarchical system of the European Union used in statistics to refer to subdivisions of countries. In many cases, the sample information on such detailed levels is not available. Thus, there are projects such as the European Census, which have the goal to provide precise numbers on NUTS 3 or even community level. The European Census is conducted amongst others in Germany and Switzerland in 2011. Most of the participating countries use sample and register information in a combined form for the estimation process. The classical estimation methods of small areas or subgroups, such as the Horvitz-Thompson (HT) estimator or the generalized regression (GREG) estimator, suffer from small area-specific sample sizes which cause high variances of the estimates. The application of small area methods, for instance the empirical best linear unbiased predictor (EBLUP), reduces the variance of the estimates by including auxiliary information to increase the effective sample size. These estimation methods lead to higher accuracy of the variables of interest. Small area estimation is also used in the context of business data. For example during the estimation of the revenues of specific subgroups like on NACE 3 or NACE 4 levels, small sample sizes can occur. The Nomenclature statistique des activités économiques dans la Communauté européenne (NACE) is a system of the European Union which defines an industry standard classification. Besides small sample sizes, business data have further special characteristics. The main challenge is that business data have skewed distributions with a few large companies and many small businesses. For instance, in the automotive industry in Germany, there are many small suppliers but only few large original equipment manufacturers (OEM). Altogether, highly influential units and outliers can be observed in business statistics. These extreme values in connection with small sample sizes cause severe problems when standard small area models are applied. These models are generally based on the normality assumption, which does not hold in the case of outliers. One way to solve these peculiarities is to apply outlier robust small area methods. The availability of adequate covariates is important for the accuracy of the above described small area methods. However, in business data, the auxiliary variables are hardly available on population level. One of several reasons for that is the fact that in Germany a lot of enterprises are not reflected in business registers due to truncation limits. Furthermore, only listed enterprises or companies which trespass specific thresholds are obligated to publish their results. This limits the number of potential auxiliary variables for the estimation. Even though there are issues with available covariates, business data often include spatial dependencies which can be used to enhance small area methods. Next to spatial information based on geographic characteristics, group-specific similarities like related industries based on NACE codes can be used. For instance, enterprises from the same NACE 2 level, e.g. sector 47 retail trade, behave more similar than two companies from different NACE 2 levels, e.g. sector 05 mining of coal and sector 64 financial services. This spatial correlation can be incorporated by extending the general linear mixed model trough the integration of spatially correlated random effects. In business data, outliers as well as geographic or content-wise spatial dependencies between areas or domains are closely linked. The coincidence of these two factors and the resulting consequences have not been fully covered in the relevant literature. The only approach that combines robust small area methods with spatial dependencies is the M-quantile geographically weighted regression model. In the context of EBLUP-based small area models, the combination of robust and spatial methods has not been considered yet. Therefore, this thesis provides a theoretical approach to this scientific and practical problem and shows its relevance in an empirical study.
Why do some people become entrepreneurs while others stay in paid employment? Searching for a distinctive set of entrepreneurial skills that matches the profile of the entrepreneurial task, Lazear introduced a theoretical model featuring skill variety for entrepreneurs. He argues that because entrepreneurs perform many different tasks, they should be multi-skilled in various areas. First, this dissertation provides the reader with an overview of previous relevant research results on skill variety with regard to entrepreneurship. The majority of the studies discussed focus on the effects of skill variety. Most studies come to the conclusion that skill variety mainly affects the decision to become self-employed. Skill variety also favors entrepreneurial intentions. Less clear are the results with regard to the influence of skill variety on the entrepreneurial success. Measured on the basis of income and survival of the company, a negative or U-shaped correlation is shown. Within the empirical part of this dissertation three research goals are tackled. First, this dissertation investigates whether a variety of early interests and activities in adolescence predicts subsequent variety in skills and knowledge. Second, the determinants of skill variety and variety of early interests and activities are investigated. Third, skill variety is tested as a mediator of the gender gap in entrepreneurial intentions. This dissertation employs structural equation modeling (SEM) using longitudinal data collected over ten years from Finnish secondary school students aged 16 to 26. As indicator for skill variety the number of functional areas in which the participant had prior educational or work experience is used. The results of the study suggest that a variety of early interests and activities lead to skill variety, which in turn leads to entrepreneurial intentions. Furthermore, the study shows that an early variety is predicted by openness and an entrepreneurial personality profile. Skill variety is also encouraged by an entrepreneurial personality profile. From a gender perspective, there is indeed a gap in entrepreneurial intentions. While a positive correlation has been found between the early variety of subjects and being female, there are negative correlations between the other two variables, education and work related Skill variety, and being female. The negative effect of work-related skill variety is the strongest. The results of this dissertation are relevant for research, politics, educational institutions and special entrepreneurship education programs. The results are also important for self-employed parents that plan the succession of the family business. Educational programs promoting entrepreneurship can be optimized on the basis of the results of this dissertation by making the transmission of a variety of skills a central goal. A focus on teenagers could also increase the success as well as a preselection based on the personality profile of the participants. Regarding the gender gap, state policies should aim to provide women with more incentives to acquire skill variety. For this purpose, education programs can be tailored specifically to women and self-employment can be presented as an attractive alternative to dependent employment.
International private equity development is highly volatile with increasing global diversification. This thesis examines the transaction patterns of cross-border private equity investment with a particular focus on the affinity of country pairs. Analysis is based on a comprehensive dataset of 99 countries over 25 years. A three-dimensional gravity model analysis covering source and host country over time exposes the effects of the country determinants: economic mass, economic distance, banking system, corporate endowment, as well as legal, political, and institutional system on the transactions. A new method is developed to examine countries in their dual roles as investor and target. This approach verifies their global importance as source and host, and also makes possible an analysis of overall private equity investment. For private equity-specific multi-investor deals, a scheme is designed to measure cross-border activity with more precision by participation, proportional deal participation, and deal flow. The analysis identifies intense level of affinity between country pairs and reveals that no single country is ideal for private equity activity. Instead, the findings show that the specific push and pull factors within each country constellation define the optimal country as trading partner. The results verify a correlation between cross-border deals and economic masses and reduced economic distance of countries. Geographic distance and cultural similarities, such as language and legal system, intensify the likelihood of initiating transactions. International trade-oriented countries with a high level of development lower the entrance barriers and increase the chances of deal success. A well-funded financial system for the investor and an efficient and competitive banking system of target countries enhance the probability of investment between countries. Also relevant for the likelihood of starting cross-border deals are low corporate tax burdens and advanced scientific competitiveness, and a well-developed stock market in the investor country. Fundamental to frequency and likelihood of success are well-established, high standards of a country- social, political, and legal systems with widespread confidence in the rules of society. In particular, the reliability of contract enforcement, with proven quality of regulations that promote private sector development, proves to be crucial for deal success.