Hohenheim discussion papers in business, economics and social sciences

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/15826

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A data–cleaning augmented Kalman filter for robust estimation of state space models
(2015) Marczak, Martyna; Proietti, Tommaso; Grassi, Stefano
This article presents a robust augmented Kalman filter that extends the data– cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one–step–ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M–type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the com- parative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.
Are sociocultural factors important for studying a science university major?
(2015) Grossmann, Volker; Osikominu, Aderonke; Osterfeld, Marius
This paper examines the role of the sociocultural background of students for choosing STEM fields in university. We combine rich survey data on university graduates in Switzerland with municipality level information from the census as well as nationwide elections and referenda to characterize a students home environment with respect to religious and political attitudes towards gender equality and science-related issues. Our empirical estimates are based on a structural Roy model which accounts for differences in costs (relative distance to the next technical university) and earnings across majors as well as for selection bias. Our findings suggest that male students from conservative municipalities are more likely to study a STEM field, whereas the sociocultural background plays little role for the major choice of females.
Clustering surgical procedures for master surgical scheduling
(2017) Kressner, Alexander; Schimmelpfeng, Katja
The sound management of operating rooms is a very important task in each hospital. To use this crucial resource efficiently, cyclic master surgery schedules are often developed. To derive sensible schedules, high-quality input data are necessary. In this paper, we focus on the (elective) surgical procedures’ stochastic durations to determine reasonable, cyclically scheduled surgical clusters. Therefore, we adapt the approach of van Oostrum et al (2008), which was specifically designed for clustering surgical procedures for master surgical scheduling, and present a two-stage solution approach that consists of a new construction heuristic and an improvement heuristic. We conducted a numerical study based on real-world data from a German hospital. The results reveal clusters with considerably reduced variability compared to those of van Oostrum et al(2008).
Detailed RIF decomposition with selection : the gender pay gap in Italy
(2017) Töpfer, Marina
In this paper, we estimate the gender pay gap along the wage distribution using a detailed decomposition approach based on unconditional quantile regressions. Non-randomness of the sample leads to biased and inconsistent estimates of the wage equation as well as of the components of the wage gap. Therefore, the method is extended to account for sample selection problems. The decomposition is conducted by using Italian microdata. Accounting for labor market selection may be particularly relevant for Italy given a comparably low female labor market participation rate. The results suggest not only differences in the income gap along the wage distribution (in particular glass ceiling), but also differences in the contribution of selection effects to the pay gap at different quantiles.
Food insecurity among older Europeans : evidence from the survey of health, ageing, and retirement in Europe
(2016) Sousa-Poza, Alfonso; Nie, Peng
Using data from the fifth wave of the Survey of Health, Ageing and Retirement in Europe, this study investigates the association between food insecurity (FI) and several demographic, socioeconomic, and health-related characteristics in a sample of European residents aged 50 and over. Our initial analysis reveals that in 2013, the proportions of 50+ individuals reporting an inability to afford meat/fish/poultry or fruit/vegetables more than 3 times per week were 11.1% and 12.6%, respectively. It also indicates that not only income but also functional impairment and chronic disease are significantly associated with an increased probability of food insecurity. In a subsequent nonlinear decompositional analysis of the food unaffordability gap between European countries with high versus low FI prevalence, our rich set of covariates explains 36–39% of intercountry differences, with household income, being employed, and having functional impairment and/or chronic disease as the most important contributors.
Livestock asset dynamics among pastoralists in Northern Kenya
(2017) Sousa-Poza, Alfonso; Mburu, Samuel; Kaiser, Micha
Understanding household-level asset dynamics has important implications for designing relevant poverty reduction policies. To advance this understanding, we develop a microeconomic model to analyze the impact of a shock (for example a drought) on the behavioral decisions of pastoralists in Northern Kenya. Using household panel data this study then explores the livestock asset dynamics using both non-parametric and semi-parametric techniques to establish the shape of the asset accumulation path and to determine whether multiple equilibria exist. More specifically, using tropical livestock units as a measure of livestock accumulation over time, we show not only that these assets converge to a single equilibrium but that forage availability and herd diversity play a major role in such livestock accumulation.
Perceived wages and the gender gap in STEM fields
(2018) Pfeifer, Gregor; Osikominu, Aderonke
We estimate gender differences in elicited wage expectations among German Uni- versity students applying for STEM and non-STEM fields. Descriptively, women expect to earn less than men and also have lower expectations about wages of average graduates across different fields. Using a two-step estimation procedure accounting for self-selection, we find that the gender gap in own expected wages can be explained to the extent of 54-69% by wage expectations for average graduates across different fields. However, gender differences in the wage expectations for average graduates across different fields do not contribute to explaining the gender gap in the choice of STEM majors.
Preschool child care and child well-being in Germany : does the migrant experience differ?
(2017) Kaiser, Micha; Bauer, Jan M.
Because the value of preschool child care is under intensive debate among both policy - makers and society in general,this paper analyzes the relation between preschool care and the well-being of children and adolescents in Germany. It also examines differences in outcomes based on child socioeconomic background by focusing on the heterogeneous effects for migrant children. Our findings, based on data from the German Health Interview and Examination Survey of Children and Adolescents, suggest that children who have experienced child care have a slightly lower well-being overall. For migrant children, however, the outcomes indicate a positive relation.
The return of happiness : resilience in times of pandemic
(2022) Ahlheim, Michael; Kim, In Woo; Vuong, Duy Thanh
Many papers have been written about peoples loss of life satisfaction during the first wave of the COVID-19 pandemic in 2020, but not much has been said about their resilience after the first shock had passed. Were people able to return, at least in part, to their original level of life satisfaction? This amounts to the question to which degree people had shown psychological resilience during the first wave of the COVID-19 crisis. In this context, it is also of interest which internal and external factors supported a persons tendency to prove resilient during the crisis. Based on an online survey conducted in August / September 2020 in Germany we try to answer these questions. We find that after a loss of average life satisfaction during the first three months after the outbreak of the pandemic in Germany many peoples life satisfaction increased again. Roughly 60% of the respondents proved resilient in the sense that eight months after the outbreak of the pandemic they had regained the same or an even higher level of life satisfaction as compared to the situation before the COVID-19 crisis. Our results show that besides socioeconomic characteristics like age and income and certain character traits, peoples personal experience during the crisis and their approval or disapproval of government policy during the crisis had an important influence on their chance to prove resilient. Therefore, a consistent and competent crisis communication building up trust in governments crisis management capacity is essential for peoples resilience in a crisis.
Unlocking the power of generative AI models and systems such asGPT-4 and ChatGPT for higher education
(2023) Vandrik, Steffen; Urbach, Nils; Gimpel, Henner; Hall, Kristina; Decker, Stefan; Eymann, Torsten; Lämmermann, Luis; Mädche, Alexander; Röglinger, Maximilian; Ruiner, Caroline; Schoch, Manfred; Schoop, Mareike
Generative AI technologies, such as large language models, have the potential to revolutionize much of our higher education teaching and learning. ChatGPT is an impressive, easy-to-use, publicly accessible system demonstrating the power of large language models such as GPT-4. Other compa- rable generative models are available for text processing, images, audio, video, and other outputs – and we expect a massive further performance increase, integration in larger software systems, and diffusion in the coming years. This technological development triggers substantial uncertainty and change in university-level teaching and learning. Students ask questions like: How can ChatGPT or other artificial intelligence tools support me? Am I allowed to use ChatGPT for a seminar or final paper, or is that cheating? How exactly do I use ChatGPT best? Are there other ways to access models such as GPT-4? Given that such tools are here to stay, what skills should I acquire, and what is obsolete? Lecturers ask similar questions from a different perspective: What skills should I teach? How can I test students’ competencies rather than their ability to prompt generative AI models? How can I use ChatGPT and other systems based on generative AI to increase my efficiency or even improve my students’ learning experience and outcomes? Even if the current discussion revolves around ChatGPT and GPT-4, these are only the forerunners of what we can expect from future generative AI-based models and tools. So even if you think ChatGPT is not yet technically mature, it is worth looking into its impact on higher education. This is where this whitepaper comes in. It looks at ChatGPT as a contemporary example of a conversational user interface that leverages large language models. The whitepaper looks at ChatGPT from the perspective of students and lecturers. It focuses on everyday areas of higher education: teaching courses, learning for an exam, crafting seminar papers and theses, and assessing students’ learning outcomes and performance. For this purpose, we consider the chances and concrete application possibilities, the limits and risks of ChatGPT, and the underlying large language models. This serves two purposes: • First, we aim to provide concrete examples and guidance for individual students and lecturers to find their way of dealing with ChatGPT and similar tools. • Second, this whitepaper shall inform the more extensive organizational sensemaking processes on embracing and enclosing large language models or related tools in higher education. We wrote this whitepaper based on our experience in information systems, computer science, management, and sociology. We have hands-on experience in using generative AI tools. As professors, postdocs, doctoral candidates, and students, we constantly innovate our teaching and learning. Fully embracing the chances and challenges of generative AI requires adding further perspectives from scholars in various other disciplines (focusing on didactics of higher education and legal aspects), university administrations, and broader student groups. Overall, we have a positive picture of generative AI models and tools such as GPT-4 and ChatGPT. As always, there is light and dark, and change is difficult. However, if we issue clear guidelines on the part of the universities, faculties, and individual lecturers, and if lecturers and students use such systems efficiently and responsibly, our higher education system may improve. We see a greatchance for that if we embrace and manage the change appropriately.

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Browsing Hohenheim discussion papers in business, economics and social sciences by Classification "300"