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With free delivery of products virtually being a standard in E-commerce, product returns pose a major challenge for online retailers and society. For retailers, product returns involve significant transportation, labor, disposal, and administrative costs. From a societal perspective, product returns contribute to greenhouse gas emissions and packaging disposal and are often a waste of natural resources. Therefore, reducing product returns has become a key challenge. This paper develops and validates a novel smart green nudging approach to tackle the problem of product returns during customers’ online shopping processes. We combine a green nudge with a novel data enrichment strategy and a modern causal machine learning method. We first run a large-scale randomized field experiment in the online shop of a German fashion retailer to test the efficacy of a novel green nudge. Subsequently, we fuse the data from about 50,000 customers with publicly-available aggregate data to create what we call enriched digital footprints and train a causal machine learning system capable of optimizing the administration of the green nudge. We report two main findings: First, our field study shows that the large-scale deployment of a simple, low-cost green nudge can significantly reduce product returns while increasing retailer profits. Second, we show how a causal machine learning system trained on the enriched digital footprint can amplify the effectiveness of the green nudge by “smartly” administering it only to certain types of customers. Overall, this paper demonstrates how combining a low-cost marketing instrument, a privacy-preserving data enrichment strategy, and a causal machine learning method can create a win-win situation from both an environmental and economic perspective by simultaneously reducing product returns and increasing retailers’ profits.
Households regularly fail to make optimal financial decisions. But what are the underlying reasons for this? Using two conceptually distinct measures of time inconsistency based on bank account transaction data and behavioral measurement experiments, we show that the excessive use of bank account overdrafts is linked to time inconsistency. By contrast, there is no correlation between a survey-based measure of financial literacy and overdraft usage. Our results indicate that consumer education and information may not suffice to overcome mistakes in households’ financial decision-making. Rather, behaviorally motivated interventions targeting specific biases in decision-making should also be considered as effective policy tools.
We analyze efficient risk-sharing arrangements when the value from deviating is determined endogenously by another risk sharing arrangement. Coalitions form to insure against idiosyncratic income risk. Self-enforcing contracts for both the original coalition and any coalition formed (joined) after deviations rely on a belief in future cooperation which we term "trust". We treat the contracting conditions of original and deviation coalitions symmetrically and show that higher trust tightens incentive constraints since it facilitates the formation of deviating coalitions. As a consequence, although trust facilitates the initial formation of coalitions, the extent of risk sharing in successfully formed coalitions is declining in the extent of trust and efficient allocations might feature resource burning or utility burning: trust is indeed a double-edged sword.
We investigate the impact of uneven transparency regulation across countries and industries on the location of economic activity. Using two distinct sources of regulatory variation—the varying extent of financial-reporting requirements and the staggered introduction of electronic business registers in Europe—, we consistently document that direct exposure to transparency regulation is negatively associated with the focal industry’s economic activity in terms of inputs (e.g., employment) and outputs (e.g., production). By contrast, we find that indirect exposure to supplier and customer industries’ transparency regulation is positively associated with the focal industry’s economic activity. Our evidence suggests uneven transparency regulation can reallocate economic activity from regulated toward unregulated countries and industries, distorting the location of economic activity.
Using granular supervisory data from Germany, we investigate the impact of unconventional monetary policies via central banks’ purchase of corporate bonds. While this policy results in a loosening of credit market conditions as intended by policy makers, we document two unintended side effects. First, banks that are more exposed to borrowers benefiting from the bond purchases now lend more to high-risk firms with no access to bond markets. Since more loan write-offs arise from these firms and banks are not compensated for this risk by higher interest rates, we document a drop in bank profitability. Second, the policy impacts the allocation of loans among industries. Affected banks reallocate loans from investment grade firms active on bond markets to mainly real estate firms without investment grade rating. Overall, our findings suggest that central banks’ quantitative easing via the corporate bond markets has the potential to contribute to both banking sector instability and real estate bubbles.
If service providers can identify reasons users are in favor of or against a service, they have insightful information that can help them understand user behavior and what they need to do to change such behavior. This article argues that the novel text-mining technique referred to as information-seeking argument mining (IS-AM) can identify these reasons. The empirical study applies IS-AM to news articles and reviews about electric scooter-sharing systems (i.e., a service enabling the short-term rentals of electric motorized scooters). Its results point to IS-AM as a promising technique to improve service; the data enable the authors to identify 40 reasons to use or not use electric scooter-sharing systems, as well as their importance to users. Furthermore, the results show that news articles are better data sources than reviews because they are longer and contain more arguments and, thus, reasons.
Why bank money creation?
(2022)
We provide a rationale for bank money creation in our current monetary system by investigating its merits over a system with banks as intermediaries of loanable funds. The latter system could result when CBDCs are introduced. In the loanable funds system, households limit banks’ leverage ratios when providing deposits to make sure they have enough “skin in the game” to opt for loan monitoring. When there is unobservable heterogeneity among banks with regard to their (opportunity) costs from monitoring, aggregate lending to bank-dependent firms is inefficiently low. A monetary system with bank money creation alleviates this problem, as banks can initiate lending by creating bank deposits without relying on household funding. With a suitable regulatory leverage constraint, the gains from higher lending by banks with a high repayment pledgeability outweigh losses from banks which are less diligent in monitoring. Bank-risk assessments, combined with appropriate risk-sensitive capital requirements, can reduce or even eliminate such losses.
Identifying the cause of discrimination is crucial to design effective policies and to understand discrimination dynamics. Building on traditional models, this paper introduces a new explanation for discrimination: discrimination based on motivated reasoning. By systematically acquiring and processing information, individuals form motivated beliefs and consequentially discriminate based on these beliefs. Through a series of experiments, I show the existence of discrimination based on motivated reasoning and demonstrate important differences to statistical discrimination and taste-based discrimination. Finally, I demonstrate how this form of discrimination can be alleviated by limiting individuals’ scope to interpret information.