Forecasting and simulation of the impact of public policies on industrial districts using an agent-based model
The research in the topic of industrial districts has been focused on the identification of which industries are forming industrial districts and on the causes behind the development of the clusters. As well as there are historical and efficiency reasons that are behind the current configuration of the industrial districts, up to now it seemed not crucial to clarify how different public policies affect the structure and relationships between the enterprises that are included in the clusters. With the use of an agent-based model we can analyze and forecast how each enterprise will change in stochastic terms. Moreover, it make feasible to predict changes in the size and structure of clusters and possible spillovers. ABMs are based on the assumption in which the economy fluctuates according to the behaviour of agents, which react in a proactive way. This difference makes ABMs an accurate tool for forecasting during crisis taking into account both changes in expectations and in policy instruments. In conventional models interactions are indirect, but agent-based modeling (ABM) allow simulating a plenty of shifts in agentsâ€™ behaviour through imitation or in their strategies according to the behaviour of the majority. These capabilities applied to firms permit to modify many not explicit assumptions incorporated into the majority of conventional models with the objective of predicting changes in the size and structure of industrial districts. Moreover, ABM allow making simulations changing parameters included in one or several public policies and obtaining the effects of these policies on clusters, accordingly to their own characteristics. The starting point is the building, trough statistical matching techniques making use of microdata sources, of a general database that replicates the attributes and location of all individuals and companies located in a specific spatial context. Then, behaviours are established for both companies and individuals who are interacting according to their preferences and endowments. In addition to these agents we include a raster of locations, built through downscaling techniques and display the current situation of different policies, in order to measure properly the changes introduced for making simulations. Finally, it would be possible to identify with high accuracy each cluster and its different characteristics. This permits to forecast and simulate the impact of changes in public policies on clusters structure and performance in stochastic terms thus enabling a better assessment of policy outcomes taking into account the robustness of the effect, related to the stochastic nature of the aggregated results. That is, ABM will allow us a better assessment of both policy outcomes and the certainty about the results. JEL: L52, R12, R58 Key words: Agent-based model, policy evaluation, industrial districts
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