Contributions in Merlin

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We investigate how differential exposures by German banks to the US real estate market affect domestic lending in Germany when home prices started to decline in the US. We find that banks with an exposure to the US real estate sector and to conduits shift their domestic lending to industry–region combinations with lower insolvency ratios following a decrease in US home prices. These banks also contract their lending to German firms more than banks that do not have such exposure. We mainly document that possible losses abroad shift bank lending at home where the size of the effect depends on the type and the degree of exposure the bank has.

This paper brings together the academic literature on individual and institutional investors in order to understand the nature of difficulties faced by them and set the background for the Special Issue. This introductory article and the papers in the Special Issue contribute to the debate on how to support individuals in their savings commitments and investment decision-making and whether and how institutional investors have fulfilled their role in supporting the development of the funded pension industry. There are three main conclusions: (i) individual investors are not ready for the role that has been assigned to them in the pension industry, (ii) institutional investors are a long way short of establishing healthy relational contracts and trustworthy relationships with their clients, and (iii) more effective regulation may be needed.

This paper shows that rising real estate prices reduce industry productivity, because they lead to a reallocation of capital and labor towards inefficient firms. I establish that the rise in real estate value during the US housing boom relaxes firms’ financial constraints. Companies borrow additional funds to invest, hire labor, and increase output. However, firms holding real estate are significantly less productive than non-holders. Rising real estate prices thus reallocate capital and labor towards inefficient firms. This has significant negative consequences for aggregate industry productivity. I find that industries with stronger growth in real estate value see a significant reduction in total factor productivity growth. A 10 % increase in real estate value lowers TFP growth by 0.62 %. The negative effect is driven by misallocation. To shed light on the role of financial sector, I show that banks with superior information about borrowers are better at identifying productive borrowers and supply less credit to unproductive firms when collateral values rise. My results provide direct evidence that financial frictions drive misallocation and suggest a channel for reallocation’s falling contribution to growth in recent years.

Obtaining a good dataset to conduct empirical studies on the engineering of Android apps is an open challenge. To start tackling this challenge, we present AndroidTimeMachine, the first, self-contained, publicly available dataset weaving spread-out data sources about real-world, open-source Android apps. Encoded as a graph-based database, AndroidTimeMachine concerns 8,431 real open-source Android apps and contains: (i) metadata about the apps' GitHub projects, (ii) Git repositories with full commit history and (iii) metadata extracted from the Google Play store, such as app ratings and permissions.

I provide a new way to model bounded rationality and show the existence of recur-sive equilibria with bounded rational agents. The existence proof applies to dynamic stochastic general equilibrium models with infinitely lived heterogeneous agents and incomplete markets. In this type of models, recursive methods are widely used to compute equilibria, yet recursive equilibria do not exist generically with rational agents. I change the rational expectation assumption and model bounded rationality as fol-lows. Different from a rational agent, a bounded rational agent does not know the true Markov transition of the state space of the economy. In order to make decisions, the bounded rational agent would try to compute a stationary distribution of the state space using a numerical method and then use the Markov transition associated with it to maximize utility. For a certain distribution of the current period, given other agents’ strategies, the agent would get its next-period transition: the distribution of the state space in the next period that results from the competitive equilibrium in the next period. However, if a distribution stays “closer” to its next-period transition than the minimum error the numerical method can observe, the agent would consider it as computational stationary. In equilibrium, each agent maximizes utility with a computational stationary distribution and markets clear. I use the Kantorovich-Rubinshtein norm to characterize the distance between distributions of the state space. With this set up, usual convergence criteria used in the literature can be incorporated and thus many computed equilibria in the literature using recursive methods can be categorized as bounded rational recursive equilibria in the sense of this paper.

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To gain a deeper empirical understanding of how developers work on Android apps, we investigate self-reported activities of Android developers and to what extent these activities can be classified with machine learning techniques. To this aim, we firstly create a taxonomy of self-reported activities coming from the manual analysis of 5,000 commit messages from 8,280 Android apps. Then, we study the frequency of each category of self-reported activities identified in the taxonomy, and investigate the feasibility of an automated classification approach. Our findings can inform be used by both practitioners and researchers to take informed decisions or support other software engineering activities.

Automated testing is considered an essential process for ensuring software quality. However, writing and maintaining high-quality test code is challenging and frequently considered of secondary importance. For production code, many open source and industrial software projects employ code review, a well-established software quality practice, but the question remains whether and how code review is also used for ensuring the quality of test code. The aim of this research is to answer this question and to increase our understanding of what developers think and do when it comes to reviewing test code. We conducted both quantitative and qualitative methods to analyze more than 300,000 code reviews, and interviewed 12 developers about how they review test files. This work resulted in an overview of current code reviewing practices, a set of identified obstacles limiting the review of test code, and a set of issues that developers would like to see improved in code review tools. The study reveals that reviewing test files is very different from reviewing production files, and that the navigation within the review itself is one of the main issues developers currently face. Based on our findings, we propose a series of recommendations and suggestions for the design of tools and future research.