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The Macroeconomic Dimensions of Credit: A Comprehensive Analysis of Finance, Inequality and Growth
(2024)
Schumpeter's monetary growth theory is particularly influential for the modern understanding of the macroeconomic role of banks and credit. Based on this theory, this dissertation examines the macroeconomic role of the financial system, especially credit, in (1) generating economic growth, (2) directing economic resources and (3) distributing wealth.
Chapter 3 first shows empirically that 1) there is a positive correlation between the growth of credit and economic growth, even for developed countries, 2) no empirical correlation between household saving and economic growth can be established, and 3) there are both positive, negative and insignificant effects of credit on economic growth at country-specific level. Thus, there is broad empirical support for Schumpeter's monetary hypotheses.
A particularly interesting application of Schumpeter's growth theory can be seen in China. The results of the empirical study suggest that there is generally a positive correlation between credit and economic growth in China, that is, however, not linear in terms of regions, time and size of the financial system. Furthermore, the results in Chapter 4 suggest that credit-financed industrial policy in China may have contributed to more investment and GDP growth, although there are non-linearities between individual industries and types of companies.
Finally, Chapter 5 raises the question of the role of the financial system in the distribution of wealth. While credit to households and companies, together with indicators of working and saving behavior and the age structure of the population, are the most important determinants of wealth inequality, there are also various non-linearities in the relationship between credit and wealth inequality, including in relation to the level of development of financial systems and home ownership ratios.
In a world of constant change, uncertainty has become a daily challenge for businesses. Rapidly shifting market conditions highlight the need for flexible responses to unforeseen events. Operations Management (OM) is crucial for optimizing business processes, including site planning, production control, and inventory management. Traditionally, companies have relied on theoretical models from microeconomics, game theory, optimization, and simulation. However, advancements in machine learning and mathematical optimization have led to a new research field: data-driven OM.
Data-driven OM uses real data, especially time series data, to create more realistic models that better capture decision-making complexities. Despite the promise of this new research area, a significant challenge remains: the availability of extensive historical training data. Synthetic data, which mimics real data, has been used to address this issue in other machine learning applications.
Therefore, this dissertation explores how synthetic data can be leveraged to improve decisions for data-driven inventory management, focusing on the single-period newsvendor problem, a classic stochastic optimization problem in inventory management.
The first article, "A Meta Analysis of Data-Driven Newsvendor Approaches", presents a standardized evaluation framework for data-driven prescriptive approaches, tested through a numerical study. Findings suggest model performance is not robust, emphasizing the need for a standardized evaluation process.
The second article, "Application of Generative Adversarial Networks in Inventory Management", examines using synthetic data generated by Generative Adversarial Networks (GANs) for the newsvendor problem. This study shows GANs can model complex demand relationships, offering a promising alternative to traditional methods.
The third article, "Combining Synthetic Data and Transfer Learning for Deep Reinforcement Learning in Inventory Management", proposes a method using Deep Reinforcement Learning (DRL) with synthetic and real data through transfer learning. This approach trains a generative model to learn demand distributions, generates synthetic data, and fine-tunes a DRL agent on a smaller real dataset. This method outperforms traditional approaches in controlled and practical settings, though further research is needed to generalize these findings.
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 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.
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.
Diese Arbeit präsentiert ein stochastisches Überlappungsmodell von
Generationen mit endogenen Gesundheitsinvestitionen und endogenem
Mortalitätsrisiko. Dieses Modell ermöglicht es, makroökonomische und
Auswirkungen von Gesundheitsreformen in Deutschland zu quantifizieren.
Zusätzlich werden Wohlfahrtsaspekte solcher Reformen beleuchtet. Zu Beginn
der Arbeit wird ein Ausgangsgleichgewicht dargestellt, welches die Situation in
Deutschland im Jahr 2020 abbildet. Hierbei sind Individuen entweder gesetzlich
oder privat krankenversichert. Die Versicherungen unterscheiden sich hinsichtlich
der Finanzierung sowie der Behandlungskosten und -qualität. Die Arbeit
untersucht den Übergang zu einem einheitlichen System, welches entweder
umlagefinanziert ist oder mit dem Kapitaldeckungsverfahren arbeitet. Die
Simulationsergebnisse deuten darauf hin, dass die gesetzliche
Krankenversicherung und somit einkommensabhängige Beiträge mit besseren
Versicherungseigenschaften verbunden sind, die die Verzerrungen bei der
Arbeitsangebotsmenge kompensieren können, jedoch auf Kosten eines höheren
moralischen Risikos gehen. Prämienmodelle hingegen führen zu einem höheren
Arbeitsangebot und besserem Vorsorge-Verhalten in Form von Ersparnissen oder
Gesundheitsinvestitionen. Ich stellen auch fest, dass obligatorische Selbstbehalte
das aggregierte Wohlergehen in Deutschland verringern würden, obwohl sie das
moralische Risiko reduzieren und private Gesundheitsinvestitionen erhöhen.
Schließlich ist der Übergang zu einer reinen privat Versicherung für
Übergangskohorten kostspielig, was auf eine Präferenz für kostengünstigere
umlagefinanzierte Prämien aufgrund von Effizienzüberlegungen hinweist.
Der demografische Wandel im Zusammenhang mit einer alternden Bevölkerung sorgt dafür, dass Regierungen weltweit zur Reformierung ihrer Rentensysteme gezwungen werden. Ein beliebtes Mittel hierbei ist die Anhebung der Regelaltersgrenze. Diese Maßnahme ist jedoch in der Bevölkerung unbeliebt, weshalb hier nach alternativen Wegen gesucht wird, um frühzeitig in den Ruhestand einzutreten. Eine Möglichkeit, solchen angepassten Altersrentenregelungen zu entgehen, ist der Bezug einer Erwerbsminderungsrente. Deutschland schuf hier neue Anreize, in die Erwerbsminderung einzutreten, indem es die erwarteten Rentenzahlbeträge anhob. Dies ist der Ausgangspunkt der vorliegenden Arbeit, in der das Renteneintrittsverhalten und die daraus resultierenden makroökonomischen Effekte von Rentenreformen unter Verwendung eines allgemeinen Gleichgewichtsmodells untersucht werden. In diesem können Haushalte sowohl über den Zeitpunkt als auch die Art ihres Renteneintritts entscheiden, wobei sie zwischen einer Erwerbsminderungs- und einer Altersrente wählen können. Bei der Bewertung der tatsächlich realisierten Rentenreformen von 2007 und 2018 wird ersichtlich, dass die Anhebung der Regelaltersgrenze zu positiven Effekten sowohl mit Blick auf die Tragfähigkeit des Rentensystems als auch die gesamtwirtschaftliche Wohlfahrt geführt hätte. Die Realisierung dieser Gewinne wird jedoch durch die 2018 realisierte Anhebung der Zurechnungszeiten beinahe komplett zunichte gemacht. Allein die fiskalischen Auswirkungen, bei denen von Verhaltensreaktionen von Seiten der Haushalte abgesehen wird, würden fiskalische Kosten erzeugen, die ungefähr ein Drittel der zuvor generierten positiven Effekte eliminieren. Können die Haushalte komplett frei über ihre Ruhestandsentscheidung verfügen, verschwinden die zuvor generierten Wohlfahrtsgewinne sogar beinahe vollständig, und das Rentensystem sowie die makroökonomischen Größen befinden sich auf einem Niveau, das vergleichbar mit dem des Ausgangsgleichgewichts ist. Alternative Rentenreformen, basierend auf der Gesetzeslage von 2018, verdeutlichen, dass effektive Rentenpolitik nur dann funktionieren kann, wenn Alters- und Erwerbsminderungsrente als Gesamtpaket betrachtet werden. Hierdurch werden Erkenntnisse für die Gestaltung zukünftiger Rentenreformen gewonnen und die Bedeutung eines ganzheitlichen Ansatzes betont, der die verschiedenen Aspekte des Rentensystems berücksichtigt.
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.
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.
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.
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.
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.
Over the last few decades, hours worked per capita have declined substantially in many OECD economies. Using the standard neoclassical growth model with endogenous work–leisure choice, we assess the role of trend growth slowdown in accounting for the decline in hours worked. In the model, a permanent reduction in technological growth decreases steady‐state hours worked by increasing the consumption–output ratio. Our empirical analysis exploits cross‐country variation in the timing and size of the decline in technological growth to show that technological growth has a highly significant positive effect on hours. A decline in the long‐run trend of technological growth by 1 percentage point is associated with a decline in trend hours worked in the range of 1–3%. This result is robust to controlling for taxes, which have been found in previous studies to be an important determinant of hours. Our empirical finding is quantitatively in line with the one implied by a calibrated version of the model, though evidence for the model’s implication that the effect on hours works via changes in the consumption–output ratio is rather mixed.
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.
This paper examines situations where two vertically integrated firms consider supplying an input to an independent downstream competitor via privately observed contracts. We identify equilibria where competition in the upstream market emerges—the downstream competitor gets supplied—as well as when the downstream firm does not receive the input and is excluded from the market. The likelihood of the outcome in which the downstream firm does not get supplied depends not only on demand parameters, but also on contractual flexibility and observability. We show that when contracts are unobservable, downstream entry will occur less often. Furthermore, our results suggest that permitting contracts that enable the contracting parties to coordinate their behavior in the downstream market may improve welfare by increasing the likelihood that the downstream firm is supplied.
The contribution of this dissertation is to empirically analyze the link between income distribution, sectoral financial balances, and the current account. Firstly, it examines the relationship between the personal and the functional income distribution which may have rather different implications for aggregate demand and the current account. Secondly, it analyzes the importance of different sectors of the economy for current account balances and tests whether households are able to fully pierce the institutional veils of the corporate and the government sector. Thirdly, it investigates how changes in the personal and the functional income distribution affect the saving and investment decisions of the household and the corporate sector, and hence the current account. Finally, it shows how different growth regimes are linked to different patterns of personal and functional income distribution, and how differences in wage bargaining institutions contribute to explaining these different patterns of income distribution.
Structural equation modeling (SEM) has been used and developed for decades across various domains and research fields such as, among others, psychology, sociology, and business research. Although no unique definition exists, SEM is best understood as the entirety of a set of related theories, mathematical models, methods, algorithms, and terminologies related to analyzing the relationships between theoretical entities -- so-called concepts --, their statistical representations -- referred to as constructs --, and observables -- usually called indicators, items or manifest variables.
This thesis is concerned with aspects of a particular strain of research within SEM -- namely, composite-based SEM. Composite-based SEM is defined as SEM involving linear compounds, i.e., linear combinations of observables when estimating parameters of interest.
The content of the thesis is based on a working paper (Chapter 2), a published refereed journal article (Chapter 3), a working paper that is, at the time of submission of this thesis, under review for publication (Chapter 4), and a steadily growing documentation that I am writing for the R package cSEM (Chapter 5). The cSEM package -- written by myself and my former colleague at the University of Wuerzburg, Florian Schuberth -- provides functions to estimate, analyze, assess, and test nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures.
In Chapter 1, I briefly discuss some of the key SEM terminology.
Chapter 2 is based on a working paper to be submitted to the Journal of Business Research titled “Assessing overall model fit of composite models in structural equation modeling”. The article is concerned with the topic of overall model fit assessment of the composite model. Three main contributions to the literature are made. First, we discuss the concept of model fit in SEM in general and composite-based SEM in particular. Second, we review common fit indices and explain if and how they can be applied to assess composite models. Third, we show that, if used for overall model fit assessment, the root mean square outer residual covariance (RMS_theta) is identical to another well-known index called the standardized root mean square residual (SRMR).
Chapter 3 is based on a journal article published in Internet Research called “Measurement error correlation within blocks of indicators in consistent partial least squares: Issues and remedies”. The article enhances consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. This is achieved by modifying the correction for attenuation as originally applied by PLSc to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. The paper is joint work with Florian Schuberth and Theo Dijkstra.
Chapter 4 is based on a journal article under review for publication in Industrial Management & Data Systems called “Estimating and testing second-order constructs using PLS-PM: the case of composites of composites”. The purpose of this article is threefold: (i) evaluate and compare common approaches to estimate models containing second-order constructs modeled as composites of composites, (ii) provide and statistically assess a two-step testing procedure to test the overall model fit of such models, and (iii) formulate recommendation for practitioners based on our findings. Moreover, a Monte Carlo simulation to compare the approaches in terms of Fisher consistency, estimated bias, and RMSE is conducted. The paper is joint work with Florian Schuberth and Jörg Henseler.
This thesis contributes to the understanding of the labor market effects of international trade, with a focus on the effects on wage and earnings inequality. The thesis draws on high-quality micro data and applies modern econometric techniques and theoretical concepts to improve our understanding of the distributional effects of international trade. The thesis focuses on the effects in Germany and the USA.