Institut für Volkswirtschaftslehre

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Governance of responsible research and innovation: A social welfare, psychologically grounded multicriteria decision analysis approach
(2025) Paredes-Frigolett, Harold; Pyka, Andreas; Bevilacqua Leoneti, Alexandre; Nachar-Calderón, Pablo
Our article deals with the governance of responsible research and innovation (RRI) and aims to set out a first psychologically grounded decision-theoretic method for the governance of RRI. We approach the governance of RRI as a multicriteria group decision analysis problem of delivering social welfare in an innovation ecosystem. Following such a methodological approach, we develop a psychologically grounded multicriteria group decision analysis method that integrates in its value function the main psychological effects captured in the value function of prospect theory as the main theory of individual decision-making under risk. The method first applies a psychologically motivated multicriteria decision analysis function that measures the welfare delivered to all stakeholders involved in a research and innovation consortium. The method then applies a social welfare function on the welfare measurements of stakeholders to propose a social welfare solution that emerges as an RRI-compliant solution for the consortium. The results are a first psychologically grounded multicriteria group decision analysis method and its first application to the governance of RRI. The implications of our results are theoretical but also practical, as our method contributes not only to the established field of multicriteria decision analysis by setting out new method but also to the field of RRI by delivering a psychologically grounded decision-theoretic method for the governance of RRI.
Comparing cars with apples? Identifying the appropriate benchmark countries for relative ecological pollution rankings and international learning
(2021) Hartmann, Dominik; Ferraz, Diogo; Bezerra, Mayra; Pyka, Andreas; Pinheiro, Flávio L.
One of the most difficult tasks that economies face is how to generate economic growth without causing environmental damage. Research in economic complexity has provided new methods to reveal structural constraints and opportunities for green economic diversification and sophistication, as well as the effects of economic complexity on environmental pollution indicators. However, no research so far has compared the ecological efficiency of countries with similar productive structures and levels of economic complexity, and used this information to identify the best learning partners. This matters, because there are substantial differences in the environmental damage caused by the same product in different countries, and green diversification needs to be complemented by substantial efficiency improvements of existing products. In this article, we use data on 774 different types of exports, CO2 emissions, and the ecological footprint of 99 countries to create first a relative ecological pollution ranking (REPR). Then, we use methods from network science to reveal a benchmark network of the best learning partners based on country pairs with a large extent of export similarity, yet significant differences in pollution values. This is important because it helps to reveal adequate benchmark countries for efficiency improvements and sustainable production, considering that countries may specialize in substantially different types of economic activities. Finally, the article i) illustrates large efficiency improvements within current global output levels, ii) helps to identify countries that can best learn from each other, and iii) improves the information base in international negotiations for the sake of a cleaner global production system.
Sustainable human development at the municipal level: A data envelopment analysis index
(2022) Lima, Pedro A. B.; Paião Júnior, Gilberto D.; Santos, Thalita L.; Furlan, Marcelo; Battistelle, Rosane A. G.; Silva, Gustavo H. R.; Ferraz, Diogo; Mariano, Enzo B.
The development of indexes for human development and environmental sustainability issues are an emerging topic in the current literature. However, the literature has put less emphasis on municipal indexes, which is the focus of this research. In this paper, we considered municipal environmental management as the adoption of environmental activities and the development of infrastructural and technical capacities in municipalities. This article aims to create a sustainable human development index with municipal data from the state of São Paulo in Brazil. Using information from the Municipal Human Development Index (IDHm) and the GreenBlue Municipal Program (PMVA), we applied the data envelopment analysis (DEA) technique to connect human development and environmental sustainability in 645 Brazilian municipalities. Our findings show that regions with higher human development present better DEA scores on the Sustainable Human Development Index. In contrast, regions with a low or a middle level of human development do not present significant change considering both dimensions. Moreover, our findings reveal that PMVA certification has a different and statistically significant impact on the DEA score considering certified, qualified, or not qualified regions. We found similar results for urbanized and service-oriented municipalities. Our indicator is an essential and straightforward tool for regional policymakers, helping to allocate resources and to find human development and environmental sustainability benchmarks among developing regions.
Effects of inbound tourism on the ecological footprint. An application of an innovative dynamic panel threshold model
(2022) Li, Xiaojuan; Meo, Muhammad Saeed; Aziz, Noshaba; Arain, Hira; Ferraz, Diogo
This study uses a new and innovative dynamic panel threshold technique to examine the relationship between inbound tourism and ecological footprint (EF). This method was applied to the 10 most popular destinations spanning 1995–2021. These findings demonstrate that inbound tourism and EF have a threshold effect. To be specific, we find that only a certain threshold of tourism is beneficial to the environment; beyond that point, increasing tourism is likely to cause EF. Additionally, economic growth, infrastructure investment, and energy all benefited the EF. But water availability negatively affects EF. The findings of this study may have important policy implications for policymakers.
Navigating the biocosmos: Cornerstones of a bioeconomic utopia
(2023) Onyeali, Wolfgang; Schlaile, Michael P.; Winkler, Bastian
One important insight from complexity science is that the future is open, and that this openness is an opportunity for us to participate in its shaping. The bioeconomy has been part of this process of “future-making”. But instead of a fertile ecosystem of imagined futures, a dry monoculture of ideas seems to dominate the landscape, promising salvation through technology. With this article, weintend to contribute to regenerating the ecological foundations of the bioeconomy. What would it entail if we were to merge with the biosphere instead of machines? To lay the cornerstones of a bioeconomic utopia, we explore the basic principles of self-organization that underlie biological, ecological, social, and psychological processes alike. All these are self-assembling and self-regulating elastic structures that exist at the edge of chaos and order. We then revisit the Promethean problem that lies at the foundation of bioeconomic thought and discuss how, during industrialization, the principles of spontaneous self-organization were replaced by the linear processes of the assembly line. We ultimately propose a bioeconomy based on human needs with the household as the basic unit: the biocosmos. The biocosmos is an agroecological habitat system of irreducible complexity, a newhumanniche embedded into the local ecosystem.
Modelling and diagnostics of spatially autocorrelated counts
(2022) Jung, Robert C.; Glaser, Stephanie
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.
Editorial: Financial and trade globalization, greener technologies and energy transition
(2023) Mariano, Enzo Barberio; Ferraz, Diogo; Radulescu, Magdalena; Shahzadi, Irum
Curtailment of civil liberties and subjective life satisfaction
(2021) Windsteiger, Lisa; Ahlheim, Michael; Konrad, Kai A.
This analysis focuses on the lockdown measures in the context of the Covid-19 crisis in Spring 2020 in Germany. In a randomized survey experiment, respondents were asked to evaluate their current life satisfaction after being provided with varying degrees of information about the lethality of Covid-19. We use reactance as a measure of the intensity of a preference for freedom to explain the variation in the observed subjective life satisfaction loss. Our results suggest that it is not high reactance alone that is associated with large losses of life satisfaction due to the curtailment of liberties. The satisfaction loss occurs in particular in combination with receiving information about the (previously overestimated) lethality of Covid-19.
Bioeconomy innovation networks in urban regions: The case of Stuttgart
(2023) Stöber, Lea F.; Boesino, Marius; Pyka, Andreas; Schuenemann, Franziska
For a successful transformation towards a sustainable bioeconomy, cooperative knowledge creation leading to innovations through research at the company and academic level are important. Urban regions are the centre of economic and research activities. The example of the region of Stuttgart, which aims to complement its mature industrial structure with new opportunities related to the knowledge-based bioeconomy, is an interesting case for the application of social network analysis to shed light on the dynamics of innovation networks to support the transformation of urban regions. As with smaller spatial levels of observation connectivity in network decreases, we find a scale-free network structure for the supra-regional network and a star-like network structure for the regional network, with two universities and one transfer-oriented research institutes at the core. While research collaborations beyond regional borders and across different industries foster knowledge co-creation, the central actors can be recognized as gatekeepers who dominantly influence knowledge flows. To potentially strengthen the resilience of the network, policy and industry associations serving as network facilitators can foster collaboration between periphery actors. The case of the Stuttgart region impressively illustrates the opportunities of the knowledge-based bioeconomy for urban regions and the complementary role traditional manufacturing sectors can take in the transformation towards higher degrees of sustainability.
The role of consumers in business model innovations for a sustainable circular bioeconomy
(2023) Lang, Stephanie; Minnucci, Giulia; Mueller, Matthias; Schlaile, Michael P.
Over the last decade, various governments and supranational bodies have promoted the development of a circular bioeconomy (CBE) as a response to sustainability challenges. The transition towards a CBE requires the collaboration of different actors in the innovation (eco)system. With this conceptual paper, we apply a circular business model lens to address the research question: “What are the archetypical roles of consumers in business model innovations for a sustainable CBE?” We use a combination of complementary theories from the circular economy and bioeconomy literature, evolutionary innovation economics, sustainability transitions research, the business model literature, and the work on active consumers. Considering consumers’ agency as a continuum between the manufacturer-active paradigm and the consumer-active paradigm, we propose: (i) consumers in the manufacturer-active paradigm can actively influence circular business models with their purchase decision; (ii) consumers can act as lobbyists and influencers for circular business model innovation; (iii) in their different roles as customer, user, repairer, and reseller, consumers can incentivize organizations to adapt their business models to their needs; (iv) consumers can become key partners in the process of defining the normative orientation of the innovation paradigm for a CBE; (v) consumers can actively co-create value by means of co-ownership (e.g., through platform cooperatives).
Responsibly shaping technology innovation for the energy transition: an RRI indicator system as a tool
(2023) Buchmann, Tobias; Wolf, Patrick; Müller, Matthias; Dreyer, Marion; Dratsdrummer, Frank; Witzel, Bianca
Efforts to reduce global greenhouse gas emissions have had limited success. For many, the hopes rest on new energy innovations to advance the energy transition process. In this paper, we develop a Responsible Research and Innovation (RRI) base indicator system to steer the design of innovations in the field of energy transition innovations and, thus, improve social acceptance of these innovations. We propose a guideline for its application to assist R&D performing organizations and funding organizations in the design, selection, and communication of research proposals. The indicator system is intended to promote early integration of environmental and social aspects, support the formation of teams aware of the different responsibility aspects of innovation, and monitor progress in regard to relevant RRI dimensions.
Editorial: Responsible research and innovation as a toolkit: Indicators, application, and context
(2023) Buchmann, Tobias; Dreyer, Marion; Müller, Matthias; Pyka, Andreas
Saving the Vietnamese Mekong River Delta - People's attitudes, opinions and willingness to help
(2023) Ahlheim, Michael; Vuong, Duy Thanh
The unique nature and environment of the Vietnamese Mekong Delta as well as its agricultural production and its traditional lifestyle are endangered by a rising sea level and increasing salinization of the ground and surface water. This paper aims at the assessment of Vietnamese people's information on and attitudes towards these problems as well as their respective convictions and beliefs. Imbedded in an online survey with 2000 completed interviews we also conducted a Contingent Valuation study with which we want to assess people's willingness to contribute personally and financially to saving the Mekong Delta as an indication of the benefits they would expect from such a project. We interviewed three different groups of respondents, one of which lives directly in the Mekong Delta, a second lives outside the Delta, but close to it, that is in Ho Chi Minh City, and the third group lives far away from the Delta in Hanoi. With these three subsamples of respondents we wanted to capture not only the use benefits but also the nonuse benefits accruing from such a project. In the course of the interviews, we found that the Mekong Delta is of great interest and importance to all interviewees, no matter in which part of Vietnam they live. They were mostly well informed on the problems there and had strong opinions on the causes of these problems as well as on suitable strategies to fight them. In our Contingent Valuation study, we assessed the willingness of people at the different study sites to contribute financially to a hypothetical project for the preservation of the Mekong Delta and the socio‐economic, attitudinal and psychological determinants of this willingness. Besides these empirical findings, we also obtained valuable insights regarding various methodological aspects of Contingent Valuation studies.
Proactive business sustainability
constructing a framework for enhancing the sustainability commitment of automotive companies
(2023) Ben Messaoud, Rachid; Pyka, Andreas
This dissertation scrutinizes the dilemma between corporate sustainability pledges and the tangible impact of prevailing global economic, ecological, and societal challenges. Despite a growing commitment to sustainability, there is a glaring discrepancy between intentions and actions. Thus, the need for proactive sustainability practices has never been more crucial. This research proposes a solution to this issue through the Proactive Business Sustainability (PBS) model. The PBS model, informed by a thorough review of the pertinent literature, expert interviews, and two meticulous case studies, provides a practical and actionable blueprint for businesses to adopt. Incorporating a dual-action scheme, encompassing managerial and organization-wide measures, the model advocates that businesses can generate significant sustainability solutions within their core activities, integrating sustainability seamlessly into their business models and profit generation mechanisms. The outcome of this research is an in-depth guide for the PBS model implementation, effectively bridging the identified sustainability gap. This guide furnishes a comprehensive methodology, thereby ensuring that sustainability transcends corporate rhetoric to become a core tenet that positively influences the world's economic, ecological, and societal challenges. The model underlines the critical role of commitment from all organizational tiers, emphasizing that the success of the PBS model is contingent on collective, cohesive action.
Monetärer Keynesianismus
Versuch einer Rekonstruktion von Hajo Rieses "Theorie der Geldwirtschaft"
(2024) Spahn, Peter
Hajo Riese (FU Berlin) was a pioneer of the "Berlin School of Monetary Keynesianism", particularly in the 1980s and 1990s. His research work was based on the roots of monetary theory in the work of J. M. Keynes, with a particular focus on the theory of capital and interest rates. While in Keynes liquidity preference remained an element of money demand, for Riese it formed the central variable of a theory of credit supply. The credit contract is not based on goods or goods equivalents, but on the nominal category of money, because this is the sole medium for the fulfilment of contracts. In addition to the interest rate, the rate of return on real capital is also determined by the liquidity premium. The central bank has to take into account the regulatory-theoretical significance of monetary stability, which runs counter to the "easy money policy" usually demanded by Keynesians.
Screen for collusive behavior
a machine learning approach
(2024) Bantle, Melissa
The paper uses a machine learning technique to build up a screen for collusive behavior. Such tools can be applied by competition authorities but also by companies to screen the behavior of their suppliers. The method is applied to the German retail gasoline market to detect anomalous behavior in the price setting of the filling stations. Therefore, the algorithm identifies anomalies in the data-generating process. The results show that various anomalies can be detected with this method. These anomalies in the price setting behavior are then discussed with respect to their implications for the competitiveness of the market.
Three essays on the labor market effects of technological change and unemployment benefits
(2023) Brall, Franziska; Beißinger, Thomas
The dissertation essentially contributes to the discourse on how technological change and a reduction in unemployment benefits affect the labor market. The thesis incorporates an empirical analysis of the influence of automation technologies on wage inequality in Germany. Additionally, the dissertation introduces a novel general equilibrium model to analyze the impact of technological change on the wage setting behavior of labor unions and reevaluate the labor market effects of a cut in unemployment benefits. The first essay contributes to the existing literature in examining the relative importance of automation technologies on wage inequality in the German manufacturing sector between 1996 and 2017. The analysis introduces a novel measure of automation threat, combining occupation- and requirement-specific scores of automation risk with sector-specific robot densities. Using the RIF-based Oaxaca–Blinder decomposition method, the analysis demonstrates that automation threat significantly contributes to wage inequality, in addition to the commonly used demographic factors. On the one hand, there is an observable trend towards occupations with medium automation threat, accompanied by decreasing shares of occupations with high and low automation threat. Due to the fact that within-group wage inequality is the lowest in the group with the highest automation threat, those compositional changes contribute to increasing wage inequality. On the other hand, an increasing wage dispersion between occupations with low automation threat (containing especially non-routine tasks) and occupations with high automation threat (containing especially routine tasks) contributes to rising wage inequality. This is in line with the predictions of routine-biased technical change, where technology particularly substitutes routine tasks. The second essay develops a novel modeling framework for the analysis of skill-biased technical change (SBTC), combining the task approach, wage setting by labor unions, as well as search and matching frictions. The important insight from this analysis is that changes in the firm’s assignment of tasks to low- and high-skilled workers have an impact on the wage setting power of labor unions. The effect of such a change in the task allocation on the labor demand elasticity, and consequently on the labor union’s wage markup, is ambiguous. This has consequences for the effects of SBTC. Unlike the conventional result that SBTC has a positive impact on employment and wages of low-skilled workers, the task-based matching model presents the possibility that low-skilled workers may instead experience either higher unemployment or lower real wages. The model is calibrated to German and French data for the periods 1995-2005 and 2010-2017 to illustrate that the impact of SBTC may even change its sign over time. The results depend on the shape of the task productivity schedule, which reflects the substitutability of high-and low-skilled workers. The third essay revisits the labor market effects of a reduction in unemployment benefits using a modified version of the previously developed task-based matching model. The analysis demonstrates that a cut in low-skilled unemployment benefits triggers a reallocation of tasks towards low-skilled workers. This leads to additional effects on labor market outcomes that are disregarded in the prevailing literature. To highlight the importance of endogenous task allocation, the task-based matching model with exogenous and constant task allocation is considered. Both model variants are calibrated to analyze the effects of the Hartz IV reform in Germany, which involved a substantial cut in unemployment benefits. The calibration reveals a remarkable decrease in the low-skilled unemployment rate by 4 percentage points resulting from Hartz IV. In the case of exogenous and constant task allocation, the decline is limited to 3.4 percentage points, but there are stronger effects on low- and high skilled wages, causing wage inequality to rise more sharply. The results emphasize the importance of considering endogenous task allocation in the evaluation of labor market reforms.
Three essays on wage inequality in Germany
the impact of automation, migration and the minimum wage
(2023) Schmid, Ramona Elisabeth; Beißinger, Thomas
Economic inequality has increased in the majority of countries worldwide over the last three decades and is highly present in public discussion, political debate and scientific research. Due to the large number and complexity of driving forces behind changes in wage inequality, this cumulative dissertation focuses on three challenges of the German labour market. The first paper addresses the question to which extent automation and robotization impact wage inequality in the manufacturing sector in Germany between 1996 and 2017. Applying decomposition analyses along the entire wage distribution, driving factors behind changes in wage inequality are identified. On the basis of administrative data and a new introduced measure of automation threat, which combines occupation- and requirement-specific scores of automation risk with yearly sector-specific robot densities, the study provides new evidence to existing literature. Besides the traditional factors education and age, the detailed decomposition analysis provides evidence that automation threat contributes significantly to rising wage inequality. On the one hand, changes in the composition of the workforce that is exposed to automation and robotization led to significant increases in wage inequality in the German manufacturing sector during the last two decades. On the other hand, evidence of a growing wage dispersion between occupations with low automation threat (especially associated with non-routine tasks) and occupations with high automation threat (especially associated with routine tasks) is revealed. This trend contributes to rising wage inequality as predicted by routine-biased technological change. The second research study presents new evidence on immigrant-native wage differentials in consideration of regional differences between metropolitan and non-metropolitan areas between 2000 and 2019 in Germany. Since gaps in remuneration provide information on the effectiveness of immigration and labour market policies as well as identify the degree of economic integration of foreign workers, the analysis is currently of great importance. Using administrative data, aggregate decomposition results support the hypothesis that the majority of wage differentials can be explained by differences in observed characteristics. However, overall wage differentials at the median exhibit an increasing trend, and on average higher gaps in remuneration are revealed in urban areas. Detailed decomposition analyses show that the effects of explanatory factors not only change over time but the sources of gaps also vary along the wage distribution. Decisive explanatory variables in this context are the practised profession, the economic sector affiliation and labour market experience. Distinguishing between metropolitan and non-metropolitan areas provides evidence that especially differences in educational attainment impact immigrant-native wage gaps in urban areas. The third paper evaluates the effects of the introduced national minimum wage in 2015 on the gender wage gap in Germany. Being confronted with a low-wage sector of considerable extent and comparably high wage differentials between men and women, this study on Germany provides necessary new insights in this area of research. On the basis of administrative data and counterfactual difference-in-differences analyses significant decreases of wage gaps between men and women that can be traced back to the introduced statutory wage floor are revealed. Especially at the lowest observed wage level and in the East of Germany the highest decreases are observable. The analysis, differentiated by educational level, age and occupational activity, provides detailed information on the effectiveness of the wage floor for different target groups. In particular, at lower wage levels for the least educated and middle aged workers the introduction of the minimum wage is the driving factor that significantly lowers group-specific gender wage gaps. Counterfactual decomposition analyses finally provide first evidence that in the West of Germany possible discrimination against women at the lowest wages is restricted by the wage floor.
Consumer prices
effects of learning algorithms and pandemic-related policy measures
(2023) Buchali, Katrin; Schwalbe, Ulrich
When it comes to product prices, two major topics have dominated the public debate in recent years: One is pricing with the help of artificial intelligence, and the other is the price level, which has risen more than usual with the onset of the COVID-19 pandemic. Higher prices create a loss of consumer surplus and possibly total welfare, which is the reason this topic has become ubiquitous in political discussions. This dissertation contributes to the debate by extending the existing literature on algorithmic pricing, which is said to facilitate personalized pricing, as well as collusive behavior and to enhance the general understanding of how government measures enforced during the COVID-19 pandemic contributed to (short-time) price developments. Thereby, the first part of the thesis addresses the concern that tacit collusion might occur if firms employ learning algorithms, as several simulation studies have demonstrated that algorithms using reinforcement learning are able to coordinate their pricing behavior and, as a result, achieve a collusive outcome without having been programmed for it. We discuss several conceptual challenges as well as challenges in the real-world application of algorithms and show by or own simulations that resulting market prices strongly depend on the type of algorithm or heuristic that is used by the firms to set prices. In the subsequent part of the thesis we examine how a self-learning pricing algorithm performs when faced with inequity-averse consumers. From our simulations we can conclude that consumers sense of fairness, which have prevented firms from engaging in price discrimination in the past years, can be incorporated into firms pricing decisions with the help of learning algorithms, making differential pricing strategies more feasible. The discussion surrounding the above-average price levels in many countries during the COVID-19 pandemic is extended in the third part of the thesis. We present empirical evidence for the impact of government-imposed restrictions and, as a consequence of their enforcement, reduced mobility on consumer prices during the COVID-19 pandemic. We show that the stringency of government measures had a positive and significant impact on consumer prices mainly in the food sector, which means that more stringent measures induced higher consumer prices in these categories.
Strategic choice of price-setting algorithms
(2023) Schwalbe, Ulrich; Muijs, Matthias; Grüb, Jens; Buchali, Katrin
Recent experimental simulations have shown that autonomous pricing algorithms are able to learn collusive behavior and thus charge supra-competitive prices without being explicitly programmed to do so. These simulations assume, however, that both firms employ the identical price-setting algorithm based on Q-learning. Thus, the question arises whether the underlying assumption that both firms employ a Q-learning algorithm can be supported as an equilibrium in a game where firms can chose between different pricing rules. Our simulations show that when both firms use a learning algorithm, the outcome is not an equilibrium when alternative price setting rules are available. In fact, simpler price setting rules as for example meeting competition clauses yield higher payoffs compared to Q-learning algorithms.

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