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Mobile technologies and digital platforms have expanded rapidly across sub-Saharan Africa, creating opportunities to strengthen state capacity, broaden financial inclusion, and build agricultural resilience. This dissertation examines their development impacts across three settings. Chapter 1 combines novel administrative tax records with high resolution data on mobile network rollout in Uganda to estimate the effects of mobile internet access on firm tax behavior and public revenue. Exploiting plausibly exogenous rollout timing, we find that improved access increases firm formalization and expands the tax base, strengthening revenue collection. Chapter 2 presents a randomized controlled trial in Niger—the world’s most financially excluded country—to identify barriers to adoption of a mobile money platform. Information provision raises awareness but not use, consistent with information being necessary but insufficient for diffusion. By contrast, a modest financial incentive significantly increases both adoption and usage. Chapter 3 uses a household panel collected before and after a severe drought in northern Ghana and exploits variation in rainfall in a differences-in-differences framework to estimate effects on production, income sources and adaptation plans. We find that increasing drought severity lowers soybean yields and revenues and decreases reliance on own-business income and remittances, reflecting broader livelihood impacts. Farmers who use mobile phones to access agricultural information make different input choices and adaptation plans. Taken together, these findings highlight the promise of mobile technologies and digital platforms for development. Their effectiveness, however, depends on complementary infrastructure and local capacity, underscoring the need for scalable, context-specific strategies to harness these tools for inclusive growth and resilience.
Siehe https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-20704 für um Abbildung A2.4 ergänzte Fassung.
See https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-20704 for version including Figure A2.4.
Mobile technologies and digital platforms have expanded rapidly across sub-Saharan Africa, creating opportunities to strengthen state capacity, broaden financial inclusion, and build agricultural resilience. This dissertation examines their development impacts across three settings. Chapter 1 combines novel administrative tax records with high resolution data on mobile network rollout in Uganda to estimate the effects of mobile internet access on firm tax behavior and public revenue. Exploiting plausibly exogenous rollout timing, we find that improved access increases firm formalization and expands the tax base, strengthening revenue collection. Chapter 2 presents a randomized controlled trial in Niger—the world’s most financially excluded country—to identify barriers to adoption of a mobile money platform. Information provision raises awareness but not use, consistent with information being necessary but insufficient for diffusion. By contrast, a modest financial incentive significantly increases both adoption and usage. Chapter 3 uses a household panel collected before and after a severe drought in northern Ghana and exploits variation in rainfall in a differences-in-differences framework to estimate effects on production, income sources and adaptation plans. We find that increasing drought severity lowers soybean yields and revenues and decreases reliance on own-business income and remittances, reflecting broader livelihood impacts. Farmers who use mobile phones to access agricultural information make different input choices and adaptation plans. Taken together, these findings highlight the promise of mobile technologies and digital platforms for development. Their effectiveness, however, depends on complementary infrastructure and local capacity, underscoring the need for scalable, context-specific strategies to harness these tools for inclusive growth and resilience.
This dissertation exploits quasi-experimental methods and rich microdata to identify causal effects of public interventions that speak directly to the goals outlined in the 2030 Agenda for Sustainable Development.
Specifically, the first chapter analyzes the effect of public child care provision on mothers’ career trajectories, focusing on the timing of labor market re-entry and the quality of occupational outcomes. It thereby contributes to the Sustainable Development Goals of "Gender Equality", "Reducing Inequalities", and "Decent Work and Economic Growth".
The second chapter investigates the impact of all-day school programs on juvenile property, violent, and drug-related crime. By providing evidence on how school schedules can be structured to promote safe learning environments, it contributes in particular to the goal of "Quality Education". In addition, it also contributes to broader objectives related to "Good Health and Well-Being" as well as "Peace, Justice and Strong Institutions".
The third chapter examines public attitudes toward climate change and carbon pricing in Germany and analyzes whether different types of information shift people’s policy views. By exploring measures to reduce resistance to effective but politically unpopular environmental policies, this chapter contributes to the goal of "Climate Action".
Tech titans and crypto giants : mutual returns predictability and trading strategy implications
(2024)
This study examines the directional return predictability between the technology sector of U.S. stock market and three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin). Using daily data from August 7, 2015, to February 8, 2024, and the cross-quantilogram approach in both static and dynamic settings, the results reveal significant positive predictability in the stock market–cryptocurrency nexus. The technology sector, semiconductors subsector, and Nvidia Corporation exert predictive power over cryptocurrency returns and vice versa across several quantiles and lags. When controlling for the impact of other financial variables, namely, U.S. dollar and U.S. treasury markets, the return predictability holds, especially for the two largest cryptocurrencies, Bitcoin and Ethereum, which reflects their importance and tighter connections with the U.S. technology sector. A trading strategy based on the results of the cross-quantilograms outperforms a benchmark strategy (i.e., always long position in either stocks or cryptocurrency), which underlines the practical implications of our main findings, particularly in terms of the significant return interactions between U.S. technology/semiconductors stocks and large cryptocurrencies.
We study the effects of temperature on occupational health using administrative data on Swiss occupational accidents from 1996 to 2019. Our results imply that on hot days (T𝑚𝑎𝑥 ≥ 30 ◦C) the number of occupational accidents increases by 7.4% and on ice days (T𝑚𝑎𝑥 < 0 ◦C) by 6.3%, relative to mild days. We find that extreme temperatures cause an average of 2600 workplace accidents each year, costing CHF 91 million annually. We provide suggestive evidence for insufficient sleep on hot days as a mechanism. While extreme temperatures worsen occupational health, we observe limited labor supply adaption for most workers.
This study advances the field of Computationally Intensive Theory Development (CTD) by examining the capabilities of Explainable Artificial Intelligence (XAI), in particular SHapley Additive exPlanations (SHAP), for theory development, while providing guidelines for this process. We evaluate SHAP’s methodological abilities and develop a structured approach for using SHAP to harness insights from black-box predictive models. For this purpose, we leverage a dual-methodological approach. First, to assess SHAP’s capabilities in uncovering patterns that shape a phenomenon, we conduct a Monte-Carlo simulation study. Second, to illustrate and guide the theory development process with SHAP for CTD, we apply SHAP in a use-case using real-world data. Based on these analyses, we propose a stepwise uniform and replicable approach giving guidance that can benefit rigorous theory development and increase the traceability of the theorizing process. With our structured approach, we contribute to the use of XAI approaches in research and, by uncovering patterns in black-box prediction models, add to the ongoing search for next-generation theorizing methods in the field of Information Systems (IS).
There is empirical evidence that decision makers show negative behaviours towards algorithmic advice compared to human advice, termed as algorithm aversion. Taking a trust theoretical perspective, this study broadens the quite monolithic view on behaviour to its cognitive antecedent: cognitive trust, i.e. trusting beliefs and trusting intentions. We examine initial trust (cognitive trust and behaviour) as well as its development after performance feedback by conducting an online experiment that asked participants to forecast the expected demand for a product. Advice accuracy was manipulated by ± 5 % relative to the participant’s initial forecasting accuracy determined in a pre-test. Results show that initial behaviour towards algorithmic advice is not influenced by cognitive trust. Furthermore, the decision maker’s initial forecasting accuracy indicates a threshold between near-perfect and bad advice. When advice accuracy is at this threshold, we observe behavioural algorithm appreciation, particularly due to higher trusting integrity beliefs in algorithmic advice.
In robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model this uncertainty set has a decisive influence on the complexity of the corresponding robust problem. For this reason, budgeted uncertainty sets are often studied, as they enable us to decompose the robust problem into easier subproblems. We propose a variant of discrete budgeted uncertainty for cardinality-based constraints or objectives, where a weight vector is applied to the budget constraint. We show that while the adversarial problem can be solved in linear time, the robust problem becomes NP-hard and not approximable. We discuss different possibilities to model the robust problem and show experimentally that despite the hardness result, some models scale relatively well in the problem size.
A growing number of multinational companies (MNCs) report on their progress toward contributing to the Sustainable Development Goals (SDGs) in their annual reports, yet the amount and quality of the information they disclose varies significantly. The aim of this study is twofold: First, we investigate how transparent MNCs report on their SDG engagement and second, we study how the reported SDG engagement changed over time due to major shifts in sustainability reporting requirements. Using a dataset of the largest German MNCs, we analyze their disclosure of SDG contribution reporting practices. Our results show that, overall, between 2016 and 2021 all German MNCs increased their SDG reporting activity and that the MNCs adopted four different practices to report their SDG engagement. We also found that the largest MNCs were more transparent and systematic in their SDG reporting than smaller MNCs, whose reporting was more heterogeneous. Our study contributes to legitimacy research and the role of transparency in corporate reporting as well as the debates on the efficacy of sustainability reporting legislation in the context of SDG engagement. Our findings imply that regulations on environmental and social regulation increase the transparency of sustainability reporting.
Corporate Sustainability Performance (CSP) reporting is becoming increasingly important to investors who seek to identify and invest in companies that are managing their Environmental, Social and Governance (ESG) risks effectively. The European Union's Non‐Financial Reporting Directive (NFRD), which was implemented in 2017, mandates that certain large companies must disclose their sustainability performance. This study examines the impact of the EU NFRD on the firm value of listed European firms using a difference‐in‐differences regression model. We find that the mandatory disclosure of corporate sustainability performance does not significantly affect firm value at an aggregate level. However, the results suggest minor inter‐industry differences, which can be attributed to varying sustainability performance metrics across industries. These findings contribute not only to the nascent literature on mandatory sustainability disclosures but also to the deliberations of policymakers and regulators across the world who are devising and implementing mandatory corporate sustainability performance disclosure regulations.