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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.

The standard property rights approach is focused on ex ante investment incentives, while there are no transaction costs that might restrain ex post negotiations. We explore the implications of such transaction costs. Prominent conclusions of the property rights theory may be overturned: A party may have stronger investment incentives when a non investing party is the owner, and joint ownership can be the uniquely optimal ownership structure. Intuitively, an ownership structure that is unattractive in the standard model may now be desirable, because it implies large gains from trade, such that the parties are more inclined to incur the transaction costs.