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).

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.

We study the role of different labor market integration policies on economic performance and convergence of two distinct regions in an agent-based model. Due to a complementarity between the cost saving nature of capital goods with a higher quality and specific skills needed to fully exploit the technologically advanced capital stock, distinct labor market policies result in non-trivially different outcomes. We show that various labor market integration policies yield via differing regional worker flows to distinct regional distributions of specific skills. Through this mechanism relative regional prices are affected determining the shares that the regions can capture from overall consumption good demand. There occurs to be a trade-off between aggregate output and convergence of regions with closed labor markets resulting in relatively high convergence but low output, and more integrated labor markets yielding higher output but lower convergence. Furthermore, results differ substantially in several respects as distinct labor market opening policies are applied.

This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.

Here is the full list of agent types: † Firms (consumption goods producers) † Households (workers and consumers) † Investment goods producers † Malls (retail outlets selling consumption goods) † Banks (providing credit and taking savings and investments) † Clearing house (managing the market and buying and selling of equity-shares, bonds, etc.) † Government (setting fiscal, labor, and other policy and collecting taxes, offering subsidies, etc.) † Central Bank (managing money supply, interest rates, etc.) † Eurostat (collecting and reporting economic statistics)Each agent type is active in different markets, and these markets interact with each other. There are 159 functions using 55 message types (see Figure 4). Figure 4. Some agent hierarchies. 178 M. Holcombe et al. Complex Systems, 22