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While growth in India stayed relatively stable over the last decade, Brazil fell into deep recession and a fundamental political and economic crisis. Why did these two countries, despite their similarities, diverge so massively within only 10 years? Through a paired comparison, this article probes two alternative approaches to capitalist diversity to explain the divergence among two rising economic powers and ‘state capitalisms’. It finds that through the lens of a firm-centred supply-side approach, one largely sees institutional stability in both economies, while a focus on the demand side and respective growth models makes visible fundamental destabilization in Brazil. The fragility of domestic demand, the vulnerability of global economic integration and the erosion of key social coalitions, we contend, are key to unpack the divergence between Brazil and India. This study thereby not only sheds a new light on emerging market capitalism but also discusses further possibilities for the analysis of state capitalism within comparative political economy.
The World Health Organization declared the emergence of the novel coronavirus (SARS-CoV-2) in January 2020. To trace infection chains, Germany launched its smartphone contact tracing app, the “Corona-Warn-App” (CWA), in June 2020. In order to be successful as a tool for fighting the pandemic, a high adoption rate is required in the population. We analyse the respective factors influencing app adoption based on the health belief model (HBM) with a cross-sectional online study including 1752 participants from Germany. The study was conducted with a certified panel provider from the end of December 2020 to January 2021. This model is primarily known from evaluations of medical treatments, such as breast cancer screenings, but it was rarely applied in prior work for a health-related information system such as the CWA. Our results indicate that intrinsic and extrinsic motivation to use the CWA are the strongest drivers of app use. In contrast, technical barriers, privacy concerns and lower income are the main inhibitors. Our findings contribute to the literature on the adoption of contact tracing apps by questioning actual users and non-users of the CWA, and we provide valuable insights for policymakers regarding influences of adoption and potential user groups of disease prevention technologies in times of pandemics.
Background: The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times.
Objective: In this study, we investigate the influence of (non-)users' social environments on the usage of the CWA during two time periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.
Methods: We conducted a longitudinal survey study in Germany with 833 participants in two waves to investigate how participants perceive their peer-groups opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with Generalized Estimating Equations (GEE). And with two-related-samples tests to test for differences between users of the CWA and non-users and between the two points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates). Results: Participants perceive that peer-groups have a positive opinion towards using the CWA, with more positive opinions by the media, family doctors, politicians and virologists/RKI and a lower, only slightly negative opinion originating from social
media. Users of the CWA perceive their peer groups’ opinions about using the app as more positive than non-users do. Furthermore, the perceived positive opinion of the media and politicians is significantly lower in wave 2 compared to wave 1. The perceived opinion of friends and family as well as their perceived influence towards using the CWA is significantly higher in the latter period compared to wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) has the highest positive effect on using the CWA. We only find one decreasing effect of the influence of politicians.
Conclusions: Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of Virologists / Robert Koch Institute and family/friends exerts the strongest effect on participants decision to use the CWA while politicians had a slightly negative influence. Our results indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption which is backed up by political decision-makers subject-matter experts.
Background: The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times.
Objective: This study aims to investigate the influence of (non)users’ social environments on the usage of the CWA during 2 periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.
Methods: We conducted a longitudinal survey study in Germany with 833 participants in 2 waves to investigate how participants perceive their peer groups’ opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with generalized estimating equations. Further, 2 related sample tests were performed to test for differences between users of the CWA and nonusers and between the 2 points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates).
Results: Participants perceived that peer groups have a positive opinion toward using the CWA, with more positive opinions by the media, family doctors, politicians, and virologists/Robert Koch Institute and a lower, only slightly negative opinion originating from social media. Users of the CWA perceived their peer groups’ opinions about using the app as more positive than nonusers do. Furthermore, the perceived positive opinion of the media (P=.001) and politicians (P<.001) was significantly lower in wave 2 compared with that in wave 1. The perceived opinion of friends and family (P<.001) as well as their perceived influence (P=.02) among nonusers toward using the CWA was significantly higher in the latter period compared with that in wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) had the highest positive effect on using the CWA (B=0.363, P<.001). We only found 1 decreasing effect of the influence of politicians (B=–0.098, P=.04).
Conclusions: Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of virologists/Robert Koch Institute and family/friends exerts the strongest effect on participants’ decisions to use the CWA while politicians had a slightly negative influence. Our results also indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption that is backed up by political decision makers and subject matter experts.
The rise of shale gas and tight oil development has triggered a major debate about hydraulic fracturing (HF). In an effort to bring light to HF practices and their potential risks to water quality, many U.S. states have mandated disclosure for HF wells and the fluids used. We employ this setting to study whether targeting corporate activities that have dispersed externalities with transparency reduces their environmental impact. Examining salt concentrations that are considered signatures for HF impact, we find significant and lasting improvements in surface water quality between 9-14% after the mandates. Most of the improvement comes from the intensive margin. We document that operators pollute less per unit of production, cause fewer spills of HF fluids and wastewater and use fewer hazardous chemicals. Turning to how transparency regulation works, we show that it increases public pressure and enables social movements, which facilitates internalization.
We examine the impact of increasing competition among the fastest traders by analyzing a new low-latency microwave network connecting exchanges trading the same stocks. Using a difference-in-differences approach comparing German stocks with similar French stocks, we find improved market integration, faster incorporation of stock-specific information, and an increased contribution to price discovery by the smaller exchange. Liquidity worsens for large caps due to increased sniping but improves for mid caps due to fast liquidity provision. Trading volume on the smaller exchange declines across all stocks. We thus uncover nuanced effects of fast trader participation that depend on their prior involvement.
This paper investigates the implications of monetary policy rules during the surge and subsequent decline of inflation in the euro area and compares them to the interest rate decisions of the European Central Bank (ECB). It focuses on versions of the Taylor (1993) and Orphanides and Wieland (OW) (2013) rules. Rules that respond to recent outcomes of HICP Core or domestic inflation data called for raising interest rates in 2021 and well ahead of the rate increases implemented by the ECB. Thus, such simple outcome-based policy rules deserve more attention in the ECB’s monetary policy strategy. Interestingly, the rules support the recent shift of the ECB to policy easing. Yet, they add a note of caution by suggesting that policy rates should not decline as fast as apparently anticipated by traded derivative-based interest rate forecasts.
We show that exposure to anti-capitalist ideology can exert a lasting influence on attitudes towards capital markets and stock-market participation. Utilizing novel survey, bank, and broker data, we document that, decades after Germany's reunification, East Germans invest significantly less in stocks and hold more negative views on capital markets. Effects vary by personal experience under communism. Results are strongest for individuals remembering life in the German Democratic Republic positively, e. g., because of local Olympic champions or living in a "showcase city". Results reverse for those with negative experiences like religious oppression, environmental pollution, or lack of Western TV entertainment.
We examine the effect of personal, two-way communication on the payment behavior of delinquent borrowers. Borrowers who speak with a randomly assigned bank agent are significantly more likely to successfully resolve the delinquency relative to borrowers who do not speak with a bank agent. Call characteristics related to the human touch of the call, such as the likeability of the agent’s voice, significantly affect payment behavior. Borrowers who speak with a bank agent are also significantly less likely to become delinquent again. Our findings highlight the value of a human element in interactions between financial institutions and their customers.
This study compares the performance of various machine learning methods in predicting the outcomes of mergers and acquisitions (M&A), with application in merger arbitrage. Merger arbitrage capitalizes on price inefficiencies around merger announcements, empirically offering consistent, near-market-neutral returns with Sharpe ratios around 1.20 and a beta of 0.14. Leveraging logistic regression, random forest, gradient boosting machine, and neural network, I analyse 21,020 M&A deals with up to 522 predictors from 1999 to 2023. I examine two datasets: one with all features available prior to deal resolution, serving as an upper bound for predictability, and another with only features available on the announcement. Among the applied methods, XGBoost outperforms in predicting deal closure probabilities, with pseudo-out-of-sample receiver operating characteristic area under the curve (ROC-AUC) scores of 0.99 and 0.81 for the full-feature and announcement-date-only sets, respectively.
I apply these predictions to cash-only merger arbitrage from 2021 to 2023, using a classification method and testing a promising fair value investment criterion. I find that equal-weighted portfolios perform best, driven by diversification and small-size premia, achieving annualized alphas of 10 to 20% against the Fama-French five-factor model. XGBoost’s superior predictive power translates into the best merger arbitrage performance, delivering Sharpe ratios of up to 1.57 for long-only portfolios and 0.60 for zero-net-investment long-short strategies, with the latter maintaining market neutrality. I confirm these results during a second trading period from 2018 to 2020, revealing different market dynamics and similar or better model performance, with Sharpe ratios as high as 2.15.
These findings establish new benchmarks for M&A deal closure prediction, highlight the value of machine learning-driven strategies in enhancing merger arbitrage performance, and offer valuable insights for both researchers and practitioners.
Banking Union is crucial for European integration, ensuring financial stability in the single market for financial services. The Court of Justice of the European Union (CJEU) plays an essential role in interpreting and enforcing the legal framework of the Banking Union, especially regarding the Single Supervisory Mechanism (SSM) and the Single Resolution Mechanism (SRM). This in-depth analysis scrutinises the pertinent CJEU case law and highlights its implications for the Banking Union and the EU legal order.
This document was provided/prepared by the Economic Governance and EMU Scrutiny Unit at the request of the ECON Committee.
We provide empirical evidence that the pricing of green bonds tends to be highly sophisticated and based on a two-tiered approach. When buying a green bond, investors do not look only at the green label of the bond but also consider additional characteristics that involve the soundness of the underlying project and the environmental score of the issuer. By comparing the yields at issuance of green bonds to those of a matched control sample of conventional bonds, we identify a premium of 16 basis points for the green label alone. However, when the environmental score of the issuer is in the top tercile of the cross-sectional distribution, the greenium increases up to doubling. Green certification and periods of heightened climate uncertainty also significantly influence the size of the greenium. Additionally, we find that this pricing mechanism fully emerged only after the Paris Agreement came into force in late 2016.
SAFE Update September 2024
(2024)
This study investigates the socio-economic characteristics, behavioral preferences, and consumption of individuals who own crypto-assets. Our empirical analysis utilizes data from a German personal finance management app where users connect their bank accounts and depots. We conducted a survey and elicited behavioral factors for financial decision-making. By combining survey with account and security account data, we identify crypto investors’ preferences for financial decision-making and financial advice. Our results suggest that, in particular, students or self-employed, young, and male individuals who are risk-seeking and impatient are more likely to have invested in crypto-assets. Most crypto owners have less experience with financial advisory. They see it as too time-consuming and qualitatively poor, and instead, they prefer to decide on their own as they have self-reported high financial literacy. Investigating their consumption in more detail we conclude that crypto investors more often spend on travelling, electronics, and food delivery and less on health. Our findings suggest policymakers in identifying high-risk consumers and investors, and help financial institutions develop appropriate products.
We provide evidence on narratives about the macroeconomy - the stories people tell to explain macroeconomic phenomena - in the context of a historic surge in inflation. In surveys with more than 10,000 US households and 100 academic experts, we measure economic narratives in open-ended survey responses and represent them as Directed Acyclic Graphs. Households' narratives are strongly heterogeneous, coarser than experts' narratives, focus more on the supply side than on the demand side, and often feature politically loaded explanations. Households' narratives matter for their inflation expectation formation, which we demonstrate with descriptive survey data and a series of experiments. Informed by these findings, we incorporate narratives into an otherwise conventional New Keynesian model and demonstrate their importance for aggregate outcomes.
We examine the evolution of spatial house price dispersion during Germany's recent housing boom. Using a dataset of sales listings, we find that house price dispersion has significantly increased, which is driven entirely by rising price variation across postal codes. We show that both price divergence across labor market regions and widening spatial price variation within these regions are important factors for this trend. We propose and estimate a directed search model of the housing market to understand the driving forces of rising spatial price dispersion, highlighting the role of housing supply, housing demand and frictions in the matching process between buyers and sellers. While both shifts in housing supply and housing demand matter for overall price increases and for regional divergence, we find that variation in housing demand is the primary factor contributing to the widening spatial dispersion within labor market regions.