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This paper examines the potential reinforcement of motivated beliefs when individuals with identical biases communicate. We propose a controlled online experiment that allows to manipulate belief biases and the communication environment. We find that communication, even among like-minded individuals, diminishes motivated beliefs if it takes place in an environment without previously declared external opinions. In the presence of external plural opinions, however, communication does not reduce but rather aggravates motivated beliefs. Our results indicate a potential drawback of the plurality of opinions - it may create communication environments wherein motivated beliefs not only persist but also become contagious within social networks.
Expanding on a general equilibrium model of offshoring, we analyze the effects of a unilateral emissions tax increase on the environment, income, and inequality. Heterogeneous firms allocate labor across production tasks and emissions abatement, while only the most productive can benefit from lower labor and/or emissions costs abroad and offshore. We find a non-monotonic effect on global emissions, which decline if the initial difference in emissions taxes is small. For a sufficiently large difference, global emissions rise, implying emissions leakage of more than 100%. The underlying driver is a global technique effect: While the emissions intensity of incumbent non-offshoring firms declines, the cleanest firms start offshoring. Moreover, offshoring firms become dirtier, induced by a reduction in the foreign effective emissions tax in general equilibrium. Implementing a BCA prevents emissions leakage, reduces income inequality in the reforming country, but raises inequality across countries.
We propose that false beliefs about own current economic status are an important factor for explaining populist attitudes. Eliciting subjects’ receptiveness to rightwing populism and their perceived relative income positions in a representative survey of German households, we find that people with pessimistic beliefs about their income position are more attuned to populist statements. Key to understanding the misperception-populism relationship are strong gender differences in the mechanism: men are much more likely to channel their discontent into affection for populist ideas. A simple information provision does neither sustainably reduce misperception nor curb populism.
The necessary adjustments to prominent measures of the neutral rate of interest following the COVID pandemic sparked a wide-ranging debate on the measurement and usefulness of r-star. Due to high uncertainty about relevant determinants, trend patterns and the correct estimation method, we propose in this paper a simple alternative approach derived from a standard macro model. Starting from a loss function, neutral periods can be determined in which a neutral real interest rate is observable. Using these values, a medium-term trend for a neutral interest rate can be determined. An application to the USA shows that our simple calculation of a neutral interest rate delivers comparable results to existing studies. A Taylor rule based on our neutral interest rate also does a fairly good job of explaining US monetary policy over the past 60 years.
This study describes the Chinese growth model over the past 40 years. We show that China's growth model, with its dominant role of the banking system and "the banker", is a perfect illustration of the necessity and power of Schumpeter's "monetary analysis". This approach has allowed us to elaborate theoretically and empirically the uniqueness of the Chinese model. In our empirical analysis, we use a new dataset of Chinese provincial data to analyze the impact of the financial system, especially banks, on Chinese economic development. We also empirically assess the role of the financial system in Chinese industrial policy and provide case studies of the effects of industrial policy in specific sectors. Finally, we also discuss macroeconomic dimensions of the Chinese growth process and lessons that can be drawn from the Chinese experience for other countries.
International trade is highly imbalanced both in terms of values and in terms of embodied carbon emissions. We show that the persistent current value trade imbalance patterns contribute to a higher level of global emissions compared to a world of balanced international trade. Specifically, we build a Ricardian quantitative trade model including sectoral input-output linkages, trade imbalances, fossil fuel extraction, and carbon emissions from fossil fuel combustion and use this framework to simulate counterfactual changes to countries' trade balances. For individual countries, the emission effects of removing their trade imbalances depend on the carbon intensities of their production and consumption patterns, as well as on their fossil resource abundance. Eliminating the Russian trade surplus and the US trade deficit would lead to the largest environmental benefits in terms of lower global emissions. Globally, the simultaneous removal of all trade imbalances would lower world carbon emissions by 0.9 percent or 295 million tons of carbon dioxide.
We study nominal exchange rate dynamics in the aftermath of U.S. monetary policy announcements. Using high-frequency interest rate and stock price movements around FOMC announcements, we distinguish between pure monetary policy shocks and information shocks, which are associated with new information contained in the announcements. Contractionary pure policy shocks give rise to a strong, but transitory, appreciation on impact. Information shocks also appreciate the exchange rate, but the effect builds up only slowly over time and is highly persistent. Thus, we conclude that although the short-run effects on the exchange rate are primarily due to pure policy shocks, the medium-run response is driven by information effects.
Salience bias and overwork
(2022)
In this study, we enrich a standard principal–agent model with hidden action by introducing salience-biased perception on the agent's side. The agent's misguided focus on salient payoffs, which leads the agent's and the principal's probability assessments to diverge, has two effects: First, the agent focuses too much on obtaining a bonus, which facilitates incentive provision. Second, the principal may exploit the diverging probability assessments to relax participation. We show that salience bias can reverse the nature of the inefficiency arising from moral hazard; i.e., the principal does not necessarily provide insufficient incentives that result in inefficiently low effort but instead may well provide excessive incentives that result in inefficiently high effort.
This thesis is about composite-based structural equation modeling. Structural equation modeling in general can be used to model both theoretical concepts and their relations to one another. In traditional factor-based structural equation modeling, these theoretical concepts are modeled as common factors, i.e., as latent variables which explain the covariance structure of their observed variables. In contrast, in composite-based structural equation modeling, the theoretical concepts can be modeled both as common factors and as composites, i.e., as linear combinations of observed variables that convey all the information between their observed variables and all other variables in the model. This thesis presents some methodological advancements in the field of composite-based structural equation modeling. In all, this thesis is made up of seven chapters. Chapter 1 provides an overview of the underlying model, as well as explicating the meaning of the term composite-based structural equation modeling. Chapter 2 gives guidelines on how to perform Monte Carlo simulations in the statistic software R using the package “cSEM” with various estimators in the context of composite-based structural equation modeling. These guidelines are illustrated by an example simulation study that investigates the finite sample behavior of partial least squares path modeling (PLS-PM) and consistent partial least squares (PLSc) estimates, particularly regarding the consequences of sample correlations between measurement errors on statistical inference. The third Chapter presents estimators of composite-based structural equation modeling that are robust in responding to outlier distortion. For this purpose, estimators of composite-based structural equation modeling, PLS-PM and PLSc, are adapted. Unlike the original estimators, these adjustments can avoid distortion that could arise from random outliers in samples, as is demonstrated through a simulation study. Chapter 4 presents an approach to performing predictions based on models estimated with ordinal partial least squares and ordinal consistent partial least squares. Here, the observed variables lie on an ordinal categorical scale which is explicitly taken into account in both estimation and prediction. The prediction performance is evaluated by means of a simulation study. In addition, the chapter gives guidelines on how to perform such predictions using the R package “cSEM”. This is demonstrated by means of an empirical example. Chapter 5 introduces confirmatory composite analysis (CCA) for research in “Human Development”. Using CCA, composite models can be estimated and assessed. This chapter uses the Henseler-Ogasawara specification for composite models, allowing, for example, the maximum likelihood method to be used for parameter estimation. Since the maximum likelihood estimator based on the Henseler-Ogasawara specification has limitations, Chapter 6 presents another specification of the composite model by means of which composite models can be estimated with the maximum likelihood method. The results of this maximum likelihood estimator are compared with those of PLS-PM, thus showing that this maximum likelihood estimator gives valid results even in finite samples. The last chapter, Chapter 7, gives an overview of the development and different strands of composite-based structural equation modeling. Additionally, here I examine the contribution the previous chapters make to the wider distribution of composite-based structural equation modeling.
Within three self-contained studies, this dissertation studies the impact and interactions between different macroeconomic policy measures in the context of financial markets empirically and quantitatively. The first study of this dissertation sheds light on the financial market effects of unconventional central bank asset purchase programs in the Eurozone, in particular sovereign bond asset purchase programs. The second study quantifies the direct implications of unconventional monetary policy on decisions by German public debt management regarding the maturity structure of gross issuance. The third study provides novel evidence on the role of private credit markets in the propagation of public spending toward private consumption in the U.S. economy. Across these three studies a set of different time-series econometric methods is applied including error correction models and event study frameworks to analyze contemporaneous interactions in financial and macroeconomic data in the context of unconventional monetary policy, as well as vector auto regressions (VARs) and local projections to trace the dynamic consequences of macroeconomic policies over time.