Impact of environmental regulation on economic activity-Austria

Zusammenfassung Die Umweltbelastung und Maßnahmen zur Verbesserung der Situation sind ein zentrales Thema der öffentlichen Debatte und Politik. Die vorliegende Arbeit erweitert Ansätze aus der Input-Output-Analyse (im Rahmen eines allgemeinen Gleichgewichtsmodelles) um Umweltbilanzen und Entsorgungskosten. Dies ist ein Ansatz, die wirtschaftlichen Auswirkungen der aktuellen umweltpolitischen Zielvorstellungen in Österreich zu quantifizieren. Dabei zeigt sich, daß die Auswirkungen auf die gesamte Volkswirtschaft überraschend gering bleiben (selbst bei radikaler Schadstoffreduktion), aber sektoral unterschiedliche Belastungen zu erwarten sind. Dies könnte bereits bestehende Strukturprobleme verschärfen.

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I am grateful for the assistance of Florian Gollob; he complied the voluminous data. The cooperation with Roland Pumberger was essential in order to complete this task. I also like to thank Bernhard Böhm, Josef Christl, and Elisabeth Szirucsek for their support. The suggestions and comments of the editor and two anonymous referees were very helpful in revising an earlier draft of this paper. Of course, I am responsible for any remaining error.     

Impact of regulation on demand side conservation programs

This paper reviews critically the economics of utility demand-side conservation programs under different regulatory regimes introducing a service-oriented framework in order to derive simultaneously the demand for electricity and for efficiency. This framework establishes a relation between engineering efficiency improvements, the rebound effect (i.e., more efficient appliances tend to increase the service demand), and actual conservation. Price-cap regulation, which is consistent with least cost planning (LCP), leads to the necessary condition for (profitable) DSM that the price cap does not cover the marginal costs of supplying electricity. The difference between the marginal costs and the price cap determines the upper bound on the costs of a negawatt. This necessary condition for LCP cannot be met within the traditional model of rate-of-return regulation so that other incentives (shared saving) induce the utility to undertake DSM. A profit-maximizing, regulated utility subverts the DSM expenditures to inflate the rate base yet minimizing the impact on revenues. Therefore, a rate-of-return regulated utility might favor inefficient conservation programs, which helps to explain that the costs of actual conservation exceed the a priori expectations by far. Finally, DSM as a permanent option will increase free riding substantially, due to strategic consumer reactions.I acknowledge discussions with Reinhard Haas and suggestions from the editor of the journal, Professor Michael A. Crew. I am particularly grateful for the extensive, constructive and, in the end, very helpful comments from an anonymous referee.     

Changing of the guards: New coaches in Austria’s premier football league

Professional team sports provide an ideal real world experiment to explore the effect of top executive replacement due to the availability of excellent performance data. This paper investigates how replacing the coach affects the performance of football teams. Analyzing almost 2,000 matches of the Austrian premier football league by ordinal (probit) regression techniques we find that a new coach tends to enhance team performance in home matches but to weaken it in away matches. However, neither of these effects turns out to be statistically significant. Nevertheless, the much discussed coach effect—ousting an underperforming coach in order to improve team performance at least temporarily—may make sense despite the costs involved by providing stronger ex-ante incentives for effort.

Interpretable machine learning for real estate market analysis

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.