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Models of labor demand usually use cost or production functions to derive profit-maximizing firm performance. These models often rely on the assumption of symmetrical behavior,i.e., the response to a positive or negative wage shock of the same relative size is identical to theshock, and the estimated labor demand elasticities are the same for increasing and decreasingemployment. However, behavioral economics models like loss aversion and endowment effectsquestion the assumption of symmetry in labor demand. In addition, the influence of a labor shortageshould be reflected in the investigations. Estimations of Fractional Panel Probit models for threedifferent skill levels are applied to evaluate these findings with a large panel of Germanestablishments. The results indicate asymmetrical structures for long-run own-wage elasticities andfor some cross-wage elasticities, putting some doubt on the assumption of strict rationality in labordemand and indicating the influence of labor shortages.
The Role of Fintech in Promoting Financial Inclusion in Developing Countries: THE CASE OF MEXICO
(2019)
Financial inclusion is globally on the rise since 2011; however, still, nearly 1.7 billion adults worldwide do not have a bank account at a formal institution (Demirguc- Kunt et al, 2018). Supply, demand and societal factors may lead to barriers to financial inclusion, and accelerate voluntarily and involuntarily exclusions in the developing countries (World Bank, 2008; Beck and De La Torre, 2006; Beck el at, 2008). With the mobile phone technology and advancements in the innovations, the number of participants has increased with the entrance of new challenger i.e. Fintech startups, in the financial services market. The literature reveals that Fintech has a great
potential to broaden access to financial services through lowering costs, reducing information asymmetries, enabling more transparency, increasing competitiveness, etc. This thesis analyzes the role of Fintech in promoting financial inclusion with the case of Mexico, approaching the lack of financial inclusion issue from the perspective of the household. To approach this issue, one of the objectives of this thesis is to investigate the barriers refraining households from accessing and using financial services.
In Mexico, more than half of the adult population do not have a bank account at a formal institution including mobile money account (Demirguc-Kunt et al, 2018). The G20’s financial inclusion indicators revealed that account ownership as well as saving and borrowing at financial institutions has decreased since 2014. The main barriers are the difficulties to use financial services, financial illiteracy and insufficient financial infrastructure leads to barriers to
financial inclusion in Mexico. Ideally, Fintech has a great potential to reduce the barriers to financial inclusion and promote access to and use of financial services for the people who are excluded by traditional financial institutions due to prohibitive prices, lack of documentation, etc. However, Fintech alone is not sufficient to transmit its full potential benefits. Even though Fintech ecosystem is growing rapidly in Mexico, and seen as a potential solution for lack of financial inclusion issue in Mexico, this study reveals that Fintech ecosystem couldn’t achieve its potential in Mexico so far.
This Master’s thesis examines the impact of heterogeneity on the assessment of systemic risk in the context of the German banking sector. Precisely, it is questioned whether currently employed, official systemic risk indicators are able to account for the German banking sector’s heterogeneity and to signal systemic risk reliably regardless of different bank types’ individual characteristics. For the assessment, a two-step procedure is employed. First, currently employed, official risk indicators are applied to bank-type-specific data for six different bank types from 1990 until 2018 and benchmarked against crises that occurred during the assessment period. Second, the implications of sectoral characteristics on systemic risk are assessed. The findings suggest that indicators are indeed able to account for the German banking sector’s heterogeneity, issuing different signals for various bank types. Moreover, the indicators allow for the identification of individual bank types’ behavior and their role in the accumulation of systemic risk. Yet, they are only partially able to signal crises correctly and behave more like thermometers than barometers of risk. Lastly, structural features of the German banking sector amplify the risk of individual institutions and thus their contribution to systemic risk at large.
This thesis investigates the impacts of large-scale asset purchases (LSAPs), which are an unconventional monetary policy (UMP) used by the Fed in the response to the 2008 global financial crisis and recession, on gender and racial wealth inequality in the US. After demonstrating that monetary policies have gendered and racial impacts and that none of these studies have yet considered UMPs, the thesis will then explain theoretically what the transmission channels of LSAPs to the wealth distribution are. Empirical studies show that LSAPs created a wealth effect, through increasing the price of some asset owned by households, primarily stocks prices, and to a lesser
extent house prices. Current literature on the impact of LSAPs in the US is still in dissension over whether it increased net wealth inequality or not. However, there is ample evidence from the current gender and racial economic literature that the wealth distribution in the US is significantly unequal, and a hole in the literature on the impact of LSAPs on the highly gendered and racial US wealth distribution. The thesis then begins to fill in some of these gaps in the literature by investigating what has happened to the financial and non-financial wealth of households disaggregated by gender and race in the period of the LSAPs, and whether the LSAPs did contribute to or reinforce these wealth inequalities. Due to limitations in the data collection the thesis is not able to conclude that there was a net negative gender wealth inequality effect. Nevertheless, there is strong empirical evidence that the LSAPs did increase racial wealth
inequality, due to white households disproportionately owning stocks and having higher rates on homeownership.
This thesis aims at analysing the potential benefits and obstacles of an Employer-of-Last-resort(ELR) policy in the case of Germany. Three main conclusions can be drawn from the analysis. First, as a bottom-up approach an ELR policy can tackle the issue of unemployment on the macroeconomic, socioeconomic and individual level in a unique way and promotes social inclusion of the unemployed. An ELR addresses non-pecuniary costs of unemployment and has the potential to tackle further socioeconomic problems. Second, this work points out that an ELR’s impact on inflation depends on excess production capacities of economic sectors as well as wage bargaining structures. In this regard, trade unions, government and employer representatives must
cooperate comprehensively in order to adapt to a potential raise of workers’ class consciousness. Third, the institutional setup of the European union does not allow for the application of functional
finance and the necessary fiscal spending for financing an ELR. Hence, Germany would need to abolish or redefine the debt break strategy, e.g. in accordance to the so-called Golden Rule, in order to implement a comprehensive ELR policy.
This paper aims to introduce a new topic for the economic policies associated with financial instability, both theoretically and through data analysis, discussing the roots of instability and the policy recommendations in the literature of Minsky and examining the role of National Development Banks (NDBs) in the economy. The purpose of the work is to acknowledge NDBs as Thwarting Institutions in a Minskyian sense. These special financial institutions have two sides of effects in the economy, one side related to their financial nature, through their capacity to provide finance to key sectors of the economy, and the other related to the real effects the financing of development projects implicate. Due to these influences, National Development Banks can be considered strong pillars to strengthen the economy, providing additional mechanisms for economic policies for stabilisation and recovery purposes, which is the definition of Thwarting Institution. The study case of the Brazilian National Bank for Economic and Social Development (BNDES) provided a detailed data analysis of the general performance of the bank between 2000-2018, its importance for the economy and especially the countercyclical role of the bank after the 2008s’ financial crisis. The last section will provide the analysis of financial stability in Brazil, showing important variables both from the financial and real side of the economy, in accordance with the Minskyian theory.
European Neighboorhood Policy as a power instrument of the Euopean Union: the case Azerbaijan
(2019)
The Economic Partnership Agreements between ACP (African, Caribbean, and Pacific) countries and the EU has been ongoing since 2002 until current times. Outcome of the negotiations has been varied with some Regional Economic Communities (RECs) quickly concluding a comprehensive agreement whereas other RECs have not and negotiations have prolonged beyond expected deadline date.
This paper looks at the relevant research question of what causes the variations witnessed about outcome of the negotiations of the EPAs. The explanatory power of the Best Alternative to Negotiated Agreement – BATNA - as an independent variable derived from the Negotiation Analysis framework is chosen to explain this situation as the traditional Eurocentric theories of the EU as power have proven limited in explaining the varied conclusions of the respective EPAs.
Findings derived was that, outcomes of the negotiated EPAs were principally function by the availability or otherwise of a BATNA as perceived by the parties involved.
Today’s labour market is exposed to rapidly changing influences. Digitalization and globalization play a major role in this development. Since, generation Y and Z grew up with these changes, they are used to live in fast-moving times and are mostly able to adapt and learn quickly. According to demographic changes both generations are becoming more and more important for organizations as employers. To ensure the future success of a business, employer branding and recruitment need to consider the upcoming generation’s needs to gain valuable talents. This paper examines generation Y and Z and the job and workplace conditions they prefer. The study identified trends of both generations as well as some discrepancies between them. The results give insights about the attraction, engagement, and retention of employees from generation Y and Z.
Mobile retention has recently become a focused issue for app-based companies due to its importance and contribution in achieving revenue goals. A crucial aspect of mobile retention is to monitor and control the churn rate of app users. This thesis therefore investigates how churn analysis can enable the improvement of mobile retention campaigns and give recommendations for better integration of churn analysis in planning retention activities. Six expert interviews conducted at app-based companies in multiple industries have provided precious insights, especially the usage of churn analysis in practical business situations. The findings were interesting: at most companies, churn analysis is not a new concept, yet has not been implemented properly due to lack of resources and customer data. However, it is believed by experts that the planning of mobile retention strategy should follow the suggestions from churn analysis since certain positive effects have been recorded. With these solid findings, a framework was presented to help improve the performance of mobile retention with churn analysis.
The role of branding in a marketing strategy against counterfeiting for high-fashion companies
(2019)
In order to contribute to our planet’s sustainability, a sustainable diet is inevitable. It turns out to be a challenge for consumers to identify the food products’ degree of sustainability. In this regard, quality labels can be a help. However, the latter contributes, inter alia, to consumer confusion. The latter occurs when the cognitive processing of product information is being disrupted, which can lead to postponing or abandoning the purchase decision. The present work determines the most relevant aspects of sustainability in the food context on the German market from a consumer’s perspective. Furthermore, it is being figured out, which requirements a quality label needs to meet when aiming to reduce consumer confusion.
This thesis analyzes whether financial news articles can predict the stock prices of companies and the S&P 500 Index. The selected companies for the analysis are Facebook Inc., Apple Inc., Microsoft Corp., Google (Alphabet Inc.), and Amazon.com Inc. This thesis evaluates the predictive power of financial news by comparing the sentiment of financial news articles published on a business day to
the corresponding closing value of stock prices and the S&P 500 Index. For the analysis, financial news from various resources is used for a time period between January 2018 and May 2018. The sentiment of the news articles is analyzed using a lexicon-based approach. Then, regression models are used to predict the stock market. The models used for forecasting the stock prices and the S&P 500 Index
are Auto-Regressive Integrated Moving Average (ARIMA), Support Vector
Regression (SVR), and Linear Regression (LR). The results of the research in overall indicate that ARIMA performs better for predicting the stock prices and the S&P 500 Index.
In this research paper, the main focus was to concerns the role of Business Intelligence (BI) in decision-making processes in organizations. The biggest goal of this thesis is to explore how decision-makers use and deploy this BI output to structure collective vision and achieve organizational choices. Business intelligence
can be described not as a process or a product or even a framework, but as a new strategy in organizational architecture based on velocity in data analysis in order to make correct strategic choices in company with maximum performance in a minimum quantity of time. After a study evaluating a conceptual model of the impact of business intelligence on strategic choices was constructed together with interviews among decision-makers. In order to evaluate the model, few questions
were raised and later solved during the studies. The research concentrated on knowing how the information supplied by the business intelligence system were used by the staff in the organization. The project focused on conducting in-depth interviews in a diverse organization with managers with six different backgrounds.
Each of the interviewers make decisions based on the output of BI.
In recent years, music genre classification has been studied widely within the musical information retrieval community to detect music genre (e.g., pop, rap) automatically. The existing methods, reported in the literature, usually extract features from the melodic content or lyrics of the song and address this classification as a multi-class problem. This thesis presents a comprehensive investigation of the prediction of the music genre solely from an examination of the lyrical content of the songs. The lyrics were thoroughly analyzed to obtain the features as inputs to the various machine learning algorithms and the features were represented using tf-idf values. In order to perform the algorithms, a dataset with a total of 12,000 songs for 12 genres was created by crawling the music websites.
Furthermore, this study considers the genre classification as a multi-label task in which a song can belong to more than one genre as it is encountered. Therefore, multi-label approaches were examined in-depth and applied along with the popular classifiers. The experiments in this thesis show that binary relevance method conducted with logistic regression model outperforms among all for the lyric-based multi-label genre classification.
Search Engine Opt imisation, also known as SEO, is one of the online marketing channels t hat, when it is s et u p s uitably, it could continue to pay dividends over time without investment. Recently, SEO teams of some companies keep investigating historical data to predict the future trends of revenue associated with the number of clicks, number of imp ressions, and number of sear ches which is prop osed to help with companies’ quality planning and campaign investment. Some of the challenges experienced by SEO analysts when attempting to forecast the revenue is that there is currently no way to standardize or forecast customers’ behaviour, which means the trends could be different every day, month, and year. In this research, SEO traffic data from one of the online travel a gencies are collected for the purpose of data exploration, analysis and forecasting which are expected to bring business values and give some be neficialinsights. Moreover, different time-series forecasting models are selected to conduct experiments seeking the best fit model for SEO data; Autoregressive Integrated Moving Average (ARIMA) model is initially performed, followed by Long-Short Term Memory (LSTM) of Recurrent Neural Networks (RNNs). As a result, it is proved that ARIMA is yet a classical statistics model but powerful enough for such small-size data, albeit the data is non-stationary and has too much white noise. Me anwhile, the LSTM is a deep learning tool which could deal with different types of data, but still need to be applied with a larger size of data to prove its competence.
Entrepreneurship and innovation are regarded as decisive elements for economic success and growth. Since no entrepreneur can be successful on his own, it is worth considering the entrepreneurial environment. In this so-called Entrepreneurial Ecosystem, all stakeholders interact with the founders who are the focus of attention. Since the concept in this form is relatively new, there are many different models and opinions. One goal of this work is therefore to create a uniform understanding of the Entrepreneurial Ecosystem and then apply it to the leading startup hubs in Germany and Israel. These two economies have very different backgrounds: Germany is considered as a strong industrial nation and Israel as “Startup Nation” (Senor und Singer 2009). Both nations have particular strengths and weaknesses that have grown out of their histories. Germany has special advantages as the more populous country and its central position within Europe, while Israel is a small country, surrounded by enemies and does not possess any natural resources. How one of the most innovative economies in the world could develop there despite this fact, or perhaps precisely because of it and how they could even overtake Germany in some highly technological fields, will be explained in the course of this thesis. However, the main research goal of this thesis was to find out which synergistic potentials result from this exciting combination for the Entrepreneurial Ecosystems of both countries. To create synergies, the Entrepreneurial Ecosystems of Germany and Israel should focus on their strengths and try to reduce their weaknesses through collaboration with the other country. Nine qualitative expert interviews were conducted to underline the theoretical foundation and to enable a stronger argumentation in the results. The research found, that the exchange of strengths should take place at three relevant levels: private business engagement, education and IP / technology transfer, and location marketing to attract foreign founders.
The purpose of this thesis is to enhance knowledge in the field of foreign assignments and the thereto relating cultural aspects. More in detail, this research will examine the question of what makes international assignments special, with a strong focus on cultural challenges and the subsequent adjustment process linked to coping strategies and methods. The author chose a qualitative research method to receive in-depth and rich data from the participants of the study and decided for semi-structured interviews.
The 25 interviews have been mostly done in a face-to-face manner and through the telephone, as the expatriates were located in different countries around the globe. Most of the interviews were done on-site in Kuala Lumpur, Malaysia and in Berlin, Germany during spring 2019. Based on 25 semi-structured interviews, findings uncovered a diverse range of challenges and coping strategies in work and non-work-related
contexts. Different perceptions of time, interpersonal interactions and efficiency were outstanding challenges, whereas the chosen coping strategy was related to the type of challenge someone faced. Active and passive coping was discovered, as well as strategies of avoiding.
Artificial Intelligence and Data Science are transforming the businesses of today, and it is contributing significantly in Human resource management as well. Data science has paved the way to workforce analytics or people analytics, and Artificial Intelligence is supporting the HR department in various human resource management processes. Through this study, the author is exploring the impact of Artificial Intelligence and Data Science in Human Resource Management by analysing the underlying opportunities, challenges, and threats. It also focuses on recommending the skills, competencies, and capabilities that will be required by the HR department in the future to implement AI and Data Science systems. The review of the literature and expert interviews form the base for the research findings and recommendations. Furthermore, the study covers insights about the application of AI and Data Science tools and techniques in processes such as recruitment and selection, onboarding, performance management, employee engagement, training and development, and strategic decision making. Finally, the thesis concludes by addressing the practical implications, limitations, and the scope for future research.