This study analyses empirically what patent characteristics matter for collateralization. In accordance with the finance literature, loan collateral is determined by the liquidation value of the asset which in turn depends on the three factors "physical attributes of the asset", "number of alternative users" and "financial strength of alternative users". Hence, the study is focusing on patent characteristics influencing the three factors of the liquidation value. To control for firm effects of the patent pledging firms, a treatment group of pledged patents and a comparison group of unpledged patents have been matched based on firm characteristics of the patent owner. The subsequent empirical analysis revealed that patent characteristics related to the physical attributes of patents enhancing their redeployability matter for collateralization. Patent characteristics related to the market liquidity measuring the financial strength of alternative users, are insignificant. Furthermore, the study confirms the additional function of patents as source of finance by offering them for loan collateral. Especially small and young firms, scare of tangible assets pledge patents for receiving debt finance.

Markets increase social welfare by matching willing buyers and sellers. It is important to understand whether markets are fulfilling their societal purpose and are operating efficiently. The prevalence of spatial arbitrage in markets is an important indicator of market efficiency. The two essays in my dissertation study spatial arbitrage and the behaviors of arbitrageurs Electronic commerce can improve market efficiency by helping buyers and sellers find and transact with each other across geographic distance. In the first essay, we study the effect of two distinct forms of electronic commerce on market efficiency, which we measure via the prevalence of spatial arbitrage. Spatial arbitrage is a more precise measure than price dispersion, which is typically used, because it accounts for the transaction costs of trading across distance and for unobserved product heterogeneity. Studying two forms of electronic commerce allows us to examine how the theoretical mechanisms of expanded reach and transaction immediacy affect market efficiency. We find that electronic commerce reduces the number of arbitrage opportunities but improves arbitrageur’s ability to identify and exploit those that remain. Overall, our results provide a novel and nuanced understanding of how electronic commerce improves market efficiency. Studying arbitrageur strategies will help us understand how arbitrageur behaviors impact markets by increasing/reducing spatial arbitrage. In the second essay, we study specialization strategies of arbitrageurs. Arbitrageurs specialize on asset type and sourcing locations. We investigate the role of specialization and find that specialization affects both arbitrage profits and arbitrage intensity. Subsequently, we find that specialization strategies evolve over time and different groups of arbitrageurs adapt differently based on behavioral biases and environmental factors. Overall, our findings support the predictions of the adaptive markets hypothesis and help us understand antecedents such as capital, arbitrage intensity, etc. which affect the evolution of arbitrageur strategy.

Within the DIECOFIS framework, ec3, the Division of Business Statistics from the Vienna University of Economics and Business Administration and ISTAT worked together to find methods to create a comprehensive database of enterprise data required for taxation microsimulations via integration of existing disparate enterprise data sources. This paper provides an overview of the broad spectrum of investigated methodology (including exact and statistical matching as well as imputation) and related statistical quality indicators, and emphasises the relevance of data integration, especially for official statistics, as a means of using available information more efficiently and improving the quality of a statistical agency's products. Finally, an outlook on an empirical study comparing different exact matching procedures in the maintenance of Statistics Austria's Business Register is presented.

As teacher turnover and the demands for accountability and student achievement persist, the need to hire and retain quality teachers becomes increasingly vital. The purpose of this study was to determine if there was a relationship between participation in Cross Career Learning Communities (CCLC), a type of Professional Learning Community (PLC), and teacher retention in participating Network for Enhancing Teacher-Quality (NET-Q) schools in a southeastern state. One-to-one exact matching was used to match 251 teachers in CCLC groups to 251 control teachers on eight variables including both system and individual level variables. Results showed a ten percent significant difference between the retention percentages within the state public school systems favoring CCLC teachers, χ2(1) = 21.17, N = 502, p < .05, with a medium effect size of h = .4. For teachers participating in CCLCs, a secondary research question asked if there were any differences in teacher retention in schools that had mandatory participation versus those that had voluntary participation. No significant difference was found between participation types and teacher retention. Furthermore, no significant difference was found in the attrition rates between the novice and the veteran teachers participating in the CCLCs. Based on a question from the NET-Q survey, the percentage of teachers indicating that their participation in CCLCs positively influenced their decision to continue in teaching, estimated at 31%, was statistically significant. This study extends the research on one particular type of PLC to teacher retention. The findings of this study may aid school leaders in better understanding how they can address and impact teacher retention in teaching and in their school buildings. Suggestions for future research and implications for policies addressing teacher support and retention are discussed.

As teacher turnover and the demands for accountability and student achievement persist, the need to hire and retain quality teachers becomes increasingly vital. The purpose of this study was to determine if there was a relationship between participation in Cross Career Learning Communities (CCLC), a type of Professional Learning Community (PLC), and teacher retention in participating Network for Enhancing Teacher-Quality (NET-Q) schools in a southeastern state. One-to-one exact matching was used to match 251 teachers in CCLC groups to 251 control teachers on eight variables including both system and individual level variables. Results showed a ten percent significant difference between the retention percentages within the state public school systems favoring CCLC teachers, χ2(1) = 21.17, N = 502, p < .05, with a medium effect size of h = .4. For teachers participating in CCLCs, a secondary research question asked if there were any differences in teacher retention in schools that had mandatory participation versus those that had voluntary participation. No significant difference was found between participation types and teacher retention. Furthermore, no significant difference was found in the attrition rates between the novice and the veteran teachers participating in the CCLCs. Based on a question from the NET-Q survey, the percentage of teachers indicating that their participation in CCLCs positively influenced their decision to continue in teaching, estimated at 31%, was statistically significant. This study extends the research on one particular type of PLC to teacher retention. The findings of this study may aid school leaders in better understanding how they can address and impact teacher retention in teaching and in their school buildings. Suggestions for future research and implications for policies addressing teacher support and retention are discussed.

String matching algorithms are the algorithms used to search through different types of text in search of a certain pattern. Many of these algorithms achieve their impressive performance by analysing the pattern and saving that information. That information is then continuously used during the searching phase to know what parts of the text can be skipped. One such algorithm is the Berry-Ravindran. The Berry-Ravindran checks the two characters past the current try for a match and sees if those characters exist in the pattern. This thesis compares the Berry-Ravindran algorithm to new versions of itself that check three and four characters instead of two, along with the Boyer-Moore algorithm. Checking more characters improves the amount of the text that can be skipped by reducing the number of attempts needed but exponentially increases the pre-processing time. The improved performance in attempts does not necessarily mean a faster run-time because of the increased pre-processing time. The variable impacting the pre-processing time the biggest is the size of the alphabet that the text uses. This is researched by testing these algorithms with patterns ranging from 4 to 100 characters long on two different data sets. Protein data which has an alphabet size of 27 and DNA data which has an alphabet size of 4.

Rainfed agriculture is inherently risky, with climate change expected to intensify its variability. In the West African Sahel, where agriculture is crucial not only for subsistence but for national and household incomes through cotton production, the need to safeguard farmers' livelihoods against risk is essential. Formal crop insurance providers in such contexts cannot easily rely on traditional models, where indemnifications are based on realized losses, and have instead proposed a stream of index-based insurance products which indemnify clients based on a predefined, and yet objective parameter (the index). One promising product for Burkinabe cotton farmers is, the Double-Trigger Index-Based Insurance (2TIC), whose two-tier triggering mechanism has the potential of reducing moral hazard and minimizing basis risk. This dissertation uses three essays to consider a farmer-centric approach to assessing the implications of this double trigger mechanism for index-based insurance. The first essay explores cotton farmers' judgments of fairness vis-à-vis the 2TIC indemnification system by using Principal Component Analysis (PCA) and Logistic Regression Analyses, and examines if and how these judgments affect decisions to subscribe. The second essay assesses the impact of 2TIC on farmers' cotton-derived net income by employing Coarsened Exact Matching (CEM). The third essay compares the actuarially fair premium of the 2TIC with the commercial premium paid by cotton farmers, by using statistical approaches. The study provides important evidence-based insights into how 2TIC can be improved and promoted by incorporating farmers' needs and perspectives.

An artificial neural network (ANN) method was developed for solving boundary value problems (BVPs) on an arbitrary irregular domain in such a manner that all Dirichlet and/or Neuman boundary conditions (BCs) are automatically satisfied. Exact satisfaction of BCs is not available with traditional numerical solution techniques such as the finite element method (FEM). The ANN is trained by reducing error in the given differential equation (DE) at certain points within the domain. Selection of these points is significantly simpler than the often difficult definition of meshes for the FEM. The approximate solution is continuous and differentiable, and can be evaluated at any location in the domain independent of the set of points used for training. The continuous solution eliminates interpolation required of discrete solutions produced by the FEM. Reducing error in the DE at a particular location in the domain does not necessarily imply improvement in the approximate solution there. A theorem was developed, proving that the solution will improve whenever error in the DE is reduced at all locations in the domain during training. The actual training of ANNs reasonably approximates the assumptions required by the proof. This dissertation offers a significant contribution to the field by developing a method for solving BVPs where all BCs are automatically satisfied. It had already been established in the literature that such automatic BC satisfaction is beneficial when solving problems on rectangular domains, but this dissertation presents the first method applying the technique to irregular domain shapes. This was accomplished by developing an innovative length factor. Length factors ensure BC satisfaction extrapolate the values at Dirichlet boundaries into the domain, providing a solid starting point for ANN training to begin. The resulting method has been successful at solving even nonlinear and non-homogenous BVPs to accuracy sufficient for typical engineering applications.

In der Dissertation werden die Effekte von Offshoring auf dem Arbeitsmarkt eines Hochlohnlandes untersucht. Sie beinhaltet 3 voneinander unabhängige Studien am Beispiel Deutschland. Im 1. Kapitel werden die Lohneffekte von Offshoring untersucht. Dabei wird Arbeit nach der Komplexität seines Aufgabenspektrums unterschieden und Offshoring je nach Lohnniveau des Ziellandes eingeteilt. Die Ergebnisse zeigen, dass Offshoring nach Westeuropa zu relativen Lohngewinnen für weniger komplexe Jobs in Deutschland führt, während der Lohn komplexer Jobs negativ beeinflusst wird. Offshoring nach Osteuropa hat entgegengesetzte Lohneffekte. Zudem zeichnet sich ab, dass Offshoring nach Westeuropa mit einer arbeits- und nach Osteuropa mit einer kapitalintensiveren Produktion einhergeht. Das 2. Kapitel untersucht ausländischen Direktinvestitionen (FDI) deutscher multinationale Unternehmen (MNE) in Tschechien. Es wird analysiert wie sich die Beschäftigung verändert, wenn MNE Zugang zu “Niedriglohnarbeit” erhalten. Bei Verwendung des Coarsened Exact Matching und eines Event-Study-Ansatzes ergibt sich, dass das inländische Beschäftigungswachstum von MNE im Vergleich zu nicht-MNE abnimmt. Das betrifft im verarbeitenden Gewerbe vor allem Beschäftigte mit niedrigem oder mittlerem Bildungsabschluss und im Dienstleistungssektor Beschäftigte mit mittlerem oder hohem Bildungsniveau. Das 3. Kapitel basiert auf dem gleichen Daten, um die Auswirkungen von FDI auf die Nachfrage von bestimmten Tätigkeiten zu schätzen. Eine neue Methode schätzt Propensity Scores für FDI-Entscheidungen mithilfe von Lasso-Logit-Regressionen. Dabei wird gezeigt, dass Unternehmen mit viel juristischen oder organisatorischen Aufgaben eher zu FDI neigen. Nach einem Matching-Verfahren, werden in einem Diff-in-Diff-Ansatz die heimischen Nachfrageverschiebungen bestimmter Aufgaben untersucht, nachdem FDI getätigt wurde. MNE erhöhen typische Aktivitäten eines Unternehmenssitz, wie managen, analysieren oder verhandeln. Im verarbeitenden Gewerbe reduzieren sie zudem typische Aufgaben der Produktion wie das Überwachen von Maschinen, Herstellen oder Messen. Im Servicesektor werden hingegen typische Servicetätigkeiten reduziert, wie das Beraten/Informieren, Reparieren sowie medizinische Tätigkeiten. / This dissertation comprises 3 chapters that each contain an independent study on the labor market effects of offshoring from a high-wage source country, namely Germany. Chapter 1 includes an estimation of the onshore wage effects of offshoring to either low-wage Eastern Europe or high-wage Western Europe. Using a Mincer-type wage equation, the study shows that offshoring has substantially different wage effects with respect to the destination region of the offshoring activity and with respect to the complexity of task profiles of the affected jobs. While offshoring to the West puts pressure on the wages of complex jobs and increases the wages of simple jobs, offshoring to the East entails the opposite effect. Chapter 2 explores the onshore employment effects of German firms that conduct foreign direct investment (FDI) in the Czech Republic, a country with substantially lower average wages. Applying coarsened exact matching and an event-study design, the results exhibit that the onshore employment growth of multinational enterprises (MNEs) decreases relative to that of non-MNEs and that the worst affected are those with low or medium educational attainment in the manufacturing sector and with medium or high educational attainment in the service sector. The study in Chapter 3 uses the same dataset and adds detailed task information to the workers' occupations (such as managing, producing, or legal tasks). It is therefore able to provide insights into the changed task demand of German MNEs after their FDI in the Czech Republic. Methodologically, an enhanced matching procedure exploits lasso logit regressions to estimate the firms' propensity of FDI. It thereby shows that high task intensities of managing, administration, and labor legislation play a major role in firms engaging in international expansions in the near future. After matching, a difference-in-differences approach reveals the onshore demand changes of specific tasks after the FDI. Relative to non-MNEs, MNEs increase their intensities of typical headquarter activities such as managing, analyzing, and negotiating. In manufacturing MNEs, the estimates further reveal a reduction in typical production tasks such as monitoring, producing, and measuring, while service MNEs reduce typical service tasks such as informing, medical tasks, and repairing.

在觀察性研究或非隨機試驗研究中，欲探討因果效應時，研究者需要重新對觀察性研究進行設計，設計目的在於重新建立一個隨機指派受試者的機制，使其得以近似一個隨機試驗研究，這樣的研究一般稱為「類隨機試驗研究」(quasi-randomized-experiments)。 傾向分數分析即為一種設計觀察性研究的方法，在不牽涉到反應變數結果之下進行設計。本文於一個病例對照研究(case-control study)中使用傾向分數進行配對接著再進一步估計處理效果，傾向分數配對是可降低觀察性研究中的選擇性偏誤的方法，透過配對可減少實驗組與對照組間的系統性差異，使研究群體在所觀察到的控制變數分配達到相似，進而得到處理效果(treatment effect)的不偏估計，為近年廣受流行病學、經濟學以及社會學領域使用的方法之一。傾向分數本身為一個條件機率，定義為研究受試者在其所觀察到的控制變數之下，接受某處理或被指派至某特定群體的機率，估計傾向分數最常見的方法為羅吉斯迴歸。 此外，自1970年代起，配對方法(matching method)開始被使用來選取合適的實驗組與對照組並進行兩群體的比較，其中「確切配對」屬於最常使用的配對方法，過去文獻中經常可見各種配對方法的結合使用，因此，本文電腦模擬研究部份，欲比較四種情境之下「傾向分數配對」與「確切配對」結合使用的效果，分別以偏誤降低比例、信賴區間覆蓋率、均方誤衡量兩種配對方法結合使用的適合情境。結果顯示若對「與處理指派中度相關的變數」且「與反應變數高度相關的變數」，其效果最為明顯。根據結果，我們總結認為「確切配對與傾向分數配對合併使用」確實會有較好的表現，但表現的好壞也取決於確切配對的變數。實證研究部份，探討家庭結構對青少年偏差行為之影響，欲了解來自非完整家庭之青少年是否較來自完整家庭之青少年更有容易出現偏差行為。 / In observational or nonrandomized studies, treatments are not randomly assigned so that baseline differences between treated and control groups are typically observed. Without properly executed, the differences would bias the treatment effect estimates. There has been a long history of using matching to eliminate confounder bias, and inferences are made based on the matched observations. The theoretical basis for matching has been developed since 1970, and among those matching methods commonly in use, the exact matching is probably the most popular one. On the other hand, introduced by Rosenbuam and Rubin in 1983, propensity scores, the conditional probability of being exposed or treated given the observed covariates, has been a welcome alternative used to adjust for baseline differences between study groups of late. Instead of matching a treated with an untreated subject by their covariates, subjects in both treated and control groups are matched by their propensity scores. In this study, we explore the benefits of using propensity score matching together with the exact matching for adjusting for baseline differences through Monte Carlo simulations. An empirical study is also be provided for illustration.