Policy and Complex Systems
The policymaker agent defines active labor market policies ( ALMPs ).
During the simulation periods , the firms are hit by shocks and dismiss their employees that are “ too costly .” The worker ’ s “ cost ” depends on his productivity
P i and his nationality ( wage inequality , as described in Browne & Misra , 2003 ). Unemployed workers have to invest in their human capital , using their wealth ( which at the beginning is set equal to initialwealth ), to qualify for vacancies opened in different sectors . The policymaker finances the unemployed workers ’ human capital investment .
At present , the model does not consider an explicit relationship structure between agents but deals with the complex issue of aggregated phenomena in a regional labor market ; characterized by an environment where individual agents ( firms and worker agents ), their decisions at the micro-level and the policies defined at a macro-level interact in a systemic way . We are therefore addressing some subjects like connecting the economic and cellular spatial simulation models .
Specifically , this study seeks to identify traceable connections between micro- and macro-economic scales exploring a virtual regional labor market based on the real labor market in Southern Switzerland ( Ticino and some districts of the Grisons ). The particular geographical position of the Italian-speaking part of Switzerland was an incentive to build strong relationships between the cantons and the neighboring Italian provinces ; as an example , one in four workers is a transborder commuter .
The model is programmed into NetLogo , a program specifically designed to implement agent-based modeling , which has a programming language that is flexible and easy to learn . b . Design Concepts
When a simulation starts firms and workers are randomly allocated to sectors . If a firm is employing workers , it checks if they are “ too costly ”; if so they are fired . To do so firms check productivity and nationality ( if the option foreign is selected at the beginning of the simulation ). Moreover , a “ layoff ” threshold ( strict ) controls the rigidity of the general labor market : the higher the threshold , the more is difficult for firms to fire workers .
Unemployed agents decide whether to apply for a skill upgrading . They choose to undergo a skill upgrading in the sector j only if the following three conditions are satisfied :
( a ) the vacancies in sector j are greater than a threshold ( thrsflex2 for the secondary sector and thrsflex3 for the tertiary sector ); ( b ) their wealth is greater than 0 ( the upgrade costs 1 unit of wealth ); ( c ) the cost of the human capital investment is less than a defined payoff ( thrsprev ), according to a particular investment strategy .
The first condition limit tries to capture the “ flexibility ” constraint pursued by modern labor market policies ; the latter is a wealth constraint .
The investment strategy is defined according to a learning model , which takes into account the job history of the worker , and measures the average payoffs the agent has gained in the previous periods . The best strategy is the one that has the sum of smallest difference between the current upgraded and the predicted upgraded for each of the preceding periods . The parameters number-strategies and memorysize , respectively , define the maximum
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