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This document provides a description of the modeling assumptions and economic features of the Eurace@Unibi model. Furthermore, the document shows typical patterns of the output generated by this model and compares it to empirically observable stylized facts. The Eurace@Unibi model provides a representation of a closed macroeconomic model with spatial structure. The main objective is to provide a micro-founded macroeconomic model that can be used as a unified framework for policy analysis in different economic policy areas and for the examination of generic macroeconomic research questions. In spite of this general agenda the model has been constructed with certain specific research questions in mind and therefore certain parts of the model, e.g. the mechanisms driving technological change, have been worked out in more detail than others.The purpose of this document is to give an overview over the model itself and its features rather than discussing how insights into particular economic issues can be obtained using the Eurace@Unibi model. The model has been designed as a framework for economic analysis in various domains of economics. A number of economic issues have been examined using (prior versions of) the model (see Dawid et al. (2008) (2010)) and recent extensions of the model have substantially extended its applicability in various economic policy domains, however results of such policy analyses will be reported elsewhere. Whereas the overall modeling approach, the different modeling choices and the economic rationale behind these choices is discussed in some detail in this document, no detailed description of the implementation is given. Such a detailed documentation is provided in the accompanying document Dawid et al. (2011b).

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The aim of the paper is to assess whether there is free-riding in teams when team production is sequential and when there is competition between teams. This a common case, which, however, has not been considered in the literature so far. We develop a model where team members contributing earlier have an incentive to free-ride more even when there is competition between teams. These predictions are tested on more than 300.000 observations on swimmers' performance at competitions from all over the world. We find that swimmers in relays perform weaker as compared to their individual performance, and that earlier swimmers' performance in relays is weaker relative to later swimmers. Our results suggest that competition does not solve the free-riding problem in team production with sequential contributions.

We develop an agent-based macroeconomic model featuring a distinct geographical dimension and heterogeneous workers with respect to skill types. The model, which will become part of a larger simulation platform for European policymaking (EURACE), allows us to conduct ex-ante evaluations of a wide range of public policy measures and their interaction. In particular, we study the growth and labor market effects of various policy types that promote workers' general skill levels. Using a calibrated model it is examined in how far effects differ if spending is uniformly spread over all regions in the economy or focused in one particular region. We find that the geographic distribution of policy measures significantly affects the effects of the policy even if total spending is kept constant. Focussing training efforts in one region is the worst policy outcome while spreading funds equally across regions generates a larger output in the long-run but not in the short-run.

This paper provides a detailed description of the Eurace@Unibi model, which has been developed as a versatile tool for economic policy analysis. The model explicitly incorporates the decentralized interaction of heterogeneous agents across different sectors and regions. The modeling of individual behavior is based on heuristics with empirical microfoundations. Although Eurace@Unibi has been applied successfully to different policy domains, the complexity of the structure of the model, which is similar to other agent-based macroeconomic models, has given rise to concerns about the reproducibility and robustness of the obtained insights. This paper addresses these concerns by describing the exact details of all decision rules, interaction protocols and balance sheets used in the model. Furthermore, we discuss the use of a virtual appliance as a tool allowing third parties to reproduce and verify the simulation results. The paper provides a systematic and extensive sensitivity analysis of the simulation output with respect to a set of key parameters. Particular emphasis is put on the question which parameter constellations give rise to strong economic fluctuations and high frequencies of sudden downturns in economic activity.