Refine
Year of publication
Document Type
- Doctoral Thesis (86) (remove)
Language
- English (86) (remove)
Keywords
- entrepreneurship (5)
- Arbeitslosigkeit (3)
- Arbeitsmarktpolitik (3)
- Entrepreneurship (3)
- Arbeitssuchverhalten (2)
- Blockchain (2)
- Deep Learning (2)
- Digitale Transformation (2)
- Digitalisierung (2)
- Gründungsförderung (2)
Institute
- Wirtschaftswissenschaften (86) (remove)
Entrepreneurship is known to be a main driver of economic growth. Hence, governments have an interest in supporting and promoting entrepreneurial activities. Start-up subsidies, which have been analyzed extensively, only aim at mitigating the lack of financial capital. However, some entrepreneurs also lack in human, social, and managerial capital. One way to address these shortcomings is by subsidizing coaching programs for entrepreneurs. However, theoretical and empirical evidence about business coaching and programs subsidizing coaching is scarce. This dissertation gives an extensive overview of coaching and is the first empirical study for Germany analyzing the effects of coaching programs on its participants. In the theoretical part of the dissertation the process of a business start-up is described and it is discussed how and in which stage of the company’s evolvement coaching can influence entrepreneurial success. The concept of coaching is compared to other non-monetary types of support as training, mentoring, consulting, and counseling. Furthermore, national and international support programs are described. Most programs have either no or small positive effects. However, there is little quantitative evidence in the international literature. In the empirical part of the dissertation the effectiveness of coaching is shown by evaluating two German coaching programs, which support entrepreneurs via publicly subsidized coaching sessions. One of the programs aims at entrepreneurs who have been employed before becoming self-employed, whereas the other program is targeted at former unemployed entrepreneurs. The analysis is based on the evaluation of a quantitative and a qualitative dataset. The qualitative data are gathered by intensive one-on-one interviews with coaches and entrepreneurs. These data give a detailed insight about the coaching topics, duration, process, effectiveness, and the thoughts of coaches and entrepreneurs. The quantitative data include information about 2,936 German-based entrepreneurs. Using propensity score matching, the success of participants of the two coaching programs is compared with adequate groups of non-participants. In contrast to many other studies also personality traits are observed and controlled for in the matching process. The results show that only the program for former unemployed entrepreneurs has small positive effects. Participants have a larger survival probability in self-employment and a larger probability to hire employees than matched non-participants. In contrast, the program for former employed individuals has negative effects. Compared to individuals who did not participate in the coaching program, participants have a lower probability to stay in self-employment, lower earned net income, lower number of employees and lower life satisfaction. There are several reasons for these differing results of the two programs. First, former unemployed individuals have more basic coaching needs than former employed individuals. Coaches can satisfy these basic coaching needs, whereas former employed individuals have more complex business problems, which are not very easy to be solved by a coaching intervention. Second, the analysis reveals that former employed individuals are very successful in general. It is easier to increase the success of former unemployed individuals as they have a lower base level of success than former employed individuals. An effect heterogeneity analysis shows that coaching effectiveness differs by region. Coaching for previously unemployed entrepreneurs is especially useful in regions with bad labor market conditions. In summary, in line with previous literature, it is found that coaching has little effects on the success of entrepreneurs. The previous employment status, the characteristics of the entrepreneur and the regional labor market conditions play a crucial role in the effectiveness of coaching. In conclusion, coaching needs to be well tailored to the individual and applied thoroughly. Therefore, governments should design and provide coaching programs only after due consideration.
The economic impact analysis contained in this book shows how irrigation farming is particularly susceptible when applying certain water management policies in the Australian Murray-Darling Basin, one of the world largest river basins and Australia’s most fertile region. By comparing different pricing and non-pricing water management policies with the help of the Water Integrated Market Model, it is found that the impact of water demand reducing policies is most severe on crops that need to be intensively irrigated and are at the same time less water productive. A combination of increasingly frequent and severe droughts and the application of policies that decrease agricultural water demand, in the same region, will create a situation in which the highly water dependent crops rice and cotton cannot be cultivated at all.
In Chapter 1 of the dissertation, the role of social networks is analyzed as an important determinant in the search behavior of the unemployed. Based on the hypothesis that the unemployed generate information on vacancies through their social network, search theory predicts that individuals with large social networks should experience an increased productivity of informal search, and reduce their search in formal channels. Due to the higher productivity of search, unemployed with a larger network are also expected to have a higher reservation wage than unemployed with a small network. The model-theoretic predictions are tested and confirmed empirically. It is found that the search behavior of unemployed is significantly affected by the presence of social contacts, with larger networks implying a stronger substitution away from formal search channels towards informal channels. The substitution is particularly pronounced for passive formal search methods, i.e., search methods that generate rather non-specific types of job offer information at low relative cost. We also find small but significant positive effects of an increase of the network size on the reservation wage. These results have important implications on the analysis of the job search monitoring or counseling measures that are usually targeted at formal search only. Chapter 2 of the dissertation addresses the labor market effects of vacancy information during the early stages of unemployment. The outcomes considered are the speed of exit from unemployment, the effects on the quality of employment and the short-and medium-term effects on active labor market program (ALMP) participation. It is found that vacancy information significantly increases the speed of entry into employment; at the same time the probability to participate in ALMP is significantly reduced. Whereas the long-term reduction in the ALMP arises in consequence of the earlier exit from unemployment, we also observe a short-run decrease for some labor market groups which suggest that caseworker use high and low intensity activation measures interchangeably which is clearly questionable from an efficiency point of view. For unemployed who find a job through vacancy information we observe a small negative effect on the weekly number of hours worked. In Chapter 3, the long-term effects of participation in ALMP are assessed for unemployed youth under 25 years of age. Complementary to the analysis in Chapter 2, the effects of participation in time- and cost-intensive measures of active labor market policies are examined. In particular we study the effects of job creation schemes, wage subsidies, short-and long-term training measures and measures to promote the participation in vocational training. The outcome variables of interest are the probability to be in regular employment, and participation in further education during the 60 months following program entry. The analysis shows that all programs, except job creation schemes have positive and long-term effects on the employment probability of youth. In the short-run only short-term training measures generate positive effects, as long-term training programs and wage subsidies exhibit significant locking-in'' effects. Measures to promote vocational training are found to increase the probability of attending education and training significantly, whereas all other programs have either no or a negative effect on training participation. Effect heterogeneity with respect to the pre-treatment level education shows that young people with higher pre-treatment educational levels benefit more from participation most programs. However, for longer-term wage subsidies we also find strong positive effects for young people with low initial education levels. The relative benefit of training measures is higher in West than in East Germany. In the evaluation studies of Chapters 2 and 3 semi-parametric balancing methods of Propensity Score Matching (PSM) and Inverse Probability Weighting (IPW) are used to eliminate the effects of counfounding factors that influence both the treatment participation as well as the outcome variable of interest, and to establish a causal relation between program participation and outcome differences. While PSM and IPW are intuitive and methodologically attractive as they do not require parametric assumptions, the practical implementation may become quite challenging due to their sensitivity to various data features. Given the importance of these methods in the evaluation literature, and the vast number of recent methodological contributions in this field, Chapter 4 aims to reduce the knowledge gap between the methodological and applied literature by summarizing new findings of the empirical and statistical literature and practical guidelines for future applied research. In contrast to previous publications this study does not only focus on the estimation of causal effects, but stresses that the balancing challenge can and should be discussed independent of question of causal identification of treatment effects on most empirical applications. Following a brief outline of the practical implementation steps required for PSM and IPW, these steps are presented in detail chronologically, outlining practical advice for each step. Subsequently, the topics of effect estimation, inference, sensitivity analysis and the combination with parametric estimation methods are discussed. Finally, new extensions of the methodology and avenues for future research are presented.
African states are often called corrupt indicating that the political system in Africa differs from the one prevalent in the economically advanced democracies. This however does not give us any insight into what makes corruption the ruling norm of African statehood. Thus we must turn to the overly neglected theoretical work on the political economy of Africa in order to determine how the poverty of governance in Africa is firmly anchored both in Africa’s domestic socioeconomic reality, as well as in the region’s role in the international economic order. Instead of focusing on increased monitoring, enforcement and formal democratic procedures, this book integrates economic analysis with political theory in order to arrive at a better understanding of the political-economic roots of corruption in Sub-Saharan Africa.
In industrialized economies such as the European countries unemployment rates are very responsive to the business cycle and significant shares stay unemployed for more than one year. To fight cyclical and long-term unemployment countries spend significant shares of their budget on Active Labor Market Policies (ALMP). To improve the allocation and design of ALMP it is essential for policy makers to have reliable evidence on the effectiveness of such programs available. Although the number of studies has been increased during the last decades, policy makers still lack evidence on innovative programs and for specific subgroups of the labor market. Using Germany as a case study, the dissertation aims at contributing in this way by providing new evidence on start-up subsidies, marginal employment and programs for youth unemployed. The idea behind start-up subsidies is to encourage unemployed individuals to exit unemployment by starting their own business. Those programs have compared to traditional programs of ALMP the advantage that not only the participant escapes unemployment but also might generate additional jobs for other individuals. Considering two distinct start-up subsidy programs, the dissertation adds three substantial aspects to the literature: First, the programs are effective in improving the employment and income situation of participants compared to non-participants in the long-run. Second, the analysis on effect heterogeneity reveals that the programs are particularly effective for disadvantaged groups in the labor market like low educated or low qualified individuals, and in regions with unfavorable economic conditions. Third, the analysis considers the effectiveness of start-up programs for women. Due to higher preferences for flexible working hours and limited part-time jobs, unemployed women often face more difficulties to integrate in dependent employment. It can be shown that start-up subsidy programs are very promising as unemployed women become self-employed which gives them more flexibility to reconcile work and family. Overall, the results suggest that the promotion of self-employment among the unemployed is a sensible strategy to fight unemployment by abolishing labor market barriers for disadvantaged groups and sustainably integrating those into the labor market. The next chapter of the dissertation considers the impact of marginal employment on labor market outcomes of the unemployed. Unemployed individuals in Germany are allowed to earn additional income during unemployment without suffering a reduction in their unemployment benefits. Those additional earnings are usually earned by taking up so-called marginal employment that is employment below a certain income level subject to reduced payroll taxes (also known as “mini-job”). The dissertation provides an empirical evaluation of the impact of marginal employment on unemployment duration and subsequent job quality. The results suggest that being marginal employed during unemployment has no significant effect on unemployment duration but extends employment duration. Moreover, it can be shown that taking up marginal employment is particularly effective for long-term unemployed, leading to higher job-finding probabilities and stronger job stability. It seems that mini-jobs can be an effective instrument to help long-term unemployed individuals to find (stable) jobs which is particularly interesting given the persistently high shares of long-term unemployed in European countries. Finally, the dissertation provides an empirical evaluation of the effectiveness of ALMP programs to improve labor market prospects of unemployed youth. Youth are generally considered a population at risk as they have lower search skills and little work experience compared to adults. This results in above-average turnover rates between jobs and unemployment for youth which is particularly sensitive to economic fluctuations. Therefore, countries spend significant resources on ALMP programs to fight youth unemployment. However, so far only little is known about the effectiveness of ALMP for unemployed youth and with respect to Germany no comprehensive quantitative analysis exists at all. Considering seven different ALMP programs, the results show an overall positive picture with respect to post-treatment employment probabilities for all measures under scrutiny except for job creation schemes. With respect to effect heterogeneity, it can be shown that almost all programs particularly improve the labor market prospects of youths with high levels of pretreatment schooling. Furthermore, youths who are assigned to the most successful employment measures have much better characteristics in terms of their pre-treatment employment chances compared to non-participants. Therefore, the program assignment process seems to favor individuals for whom the measures are most beneficial, indicating a lack of ALMP alternatives that could benefit low-educated youths.
The dissertation examines the use of performance information by public managers. “Use” is conceptualized as purposeful utilization in order to steer, learn, and improve public services. The main research question is: Why do public managers use performance information? To answer this question, I systematically review the existing literature, identify research gaps and introduce the approach of my dissertation. The first part deals with manager-related variables that might affect performance information use but which have thus far been disregarded. The second part models performance data use by applying a theory from social psychology which is based on the assumption that this management behavior is conscious and reasoned. The third part examines the extent to which explanations of performance information use vary if we include others sources of “unsystematic” feedback in our analysis. The empirical results are based on survey data from 2011. I surveyed middle managers from eight selected divisions of all German cities with county status (n=954). To analyze the data, I used factor analysis, multiple regression analysis, and structural equation modeling. My research resulted in four major findings: 1) The use of performance information can be modeled as a reasoned behavior which is determined by the attitude of the managers and of their immediate peers. 2) Regular users of performance data surprisingly are not generally inclined to analyze abstract data but rather prefer gathering information through personal interaction. 3) Managers who take on ownership of performance information at an early stage in the measurement process are also more likely to use this data when it is reported to them. 4) Performance reports are only one source of information among many. Public managers prefer verbal feedback from insiders and feedback from external stakeholders over systematic performance reports. The dissertation explains these findings using a deductive approach and discusses their implications for theory and practice.
The present thesis introduces an iterative expert-based Bayesian approach for assessing greenhouse gas (GHG) emissions from the 2030 German new vehicle fleet and quantifying the impacts of their main drivers. A first set of expert interviews has been carried out in order to identify technologies which may help to lower car GHG emissions and to quantify their emission reduction potentials. Moreover, experts were asked for their probability assessments that the different technologies will be widely adopted, as well as for important prerequisites that could foster or hamper their adoption. Drawing on the results of these expert interviews, a Bayesian Belief Network has been built which explicitly models three vehicle types: Internal Combustion Engine Vehicles (which include mild and full Hybrid Electric Vehicles), Plug-In Hybrid Electric Vehicles, and Battery Electric Vehicles. The conditional dependencies of twelve central variables within the BBN - battery energy, fuel and electricity consumption, relative costs, and sales shares of the vehicle types - have been quantified by experts from German car manufacturers in a second series of interviews. For each of the seven second-round interviews, an expert's individually specified BBN results. The BBN have been run for different hypothetical 2030 scenarios which differ, e.g., in regard to battery development, regulation, and fuel and electricity GHG intensities. The present thesis delivers results both in regard to the subject of the investigation and in regard to its method. On the subject level, it has been found that the different experts expect 2030 German new car fleet emission to be at 50 to 65% of 2008 new fleet emissions under the baseline scenario. They can be further reduced to 40 to 50% of the emissions of the 2008 fleet though a combination of a higher share of renewables in the electricity mix, a larger share of biofuels in the fuel mix, and a stricter regulation of car CO$_2$ emissions in the European Union. Technically, 2030 German new car fleet GHG emissions can be reduced to a minimum of 18 to 44% of 2008 emissions, a development which can not be triggered by any combination of measures modeled in the BBN alone but needs further commitment. Out of a wealth of existing BBN, few have been specified by individual experts through elicitation, and to my knowledge, none of them has been employed for analyzing perspectives for the future. On the level of methods, this work shows that expert-based BBN are a valuable tool for making experts' expectations for the future explicit and amenable to the analysis of different hypothetical scenarios. BBN can also be employed for quantifying the impacts of main drivers. They have been demonstrated to be a valuable tool for iterative stakeholder-based science approaches.
Within our research group Bayesian Risk Solutions we have coined the idea of a Bayesian Risk Management (BRM). It claims (1) a more transparent and diligent data analysis as well as (2)an open-minded incorporation of human expertise in risk management. In this dissertation we formulize a framework for BRM based on the two pillars Hardcore-Bayesianism (HCB) and Softcore-Bayesianism (SCB) providing solutions for the claims above. For data analysis we favor Bayesian statistics with its Markov Chain Monte Carlo (MCMC) simulation algorithm. It provides a full illustration of data-induced uncertainty beyond classical point-estimates. We calibrate twelve different stochastic processes to four years of CO2 price data. Besides, we calculate derived risk measures (ex ante/ post value-at-risks, capital charges, option prices) and compare them to their classical counterparts. When statistics fails because of a lack of reliable data we propose our integrated Bayesian Risk Analysis (iBRA) concept. It is a basic guideline for an expertise-driven quantification of critical risks. We additionally review elicitation techniques and tools supporting experts to express their uncertainty. Unfortunately, Bayesian thinking is often blamed for its arbitrariness. Therefore, we introduce the idea of a Bayesian due diligence judging expert assessments according to their information content and their inter-subjectivity.