Agent-Based Modeling to Analyze the Effect of the 2009 Government Stimulus Package on the Labor Market

The unemployment rate can more than double during a recession, and to combat the negative effects of a recession the government stimulates the economy by using various combinations of three primary stimulus methods. These methods-tax cuts, government-funded projects and increasing the duration of unemployment insurance--are not equally effective. In this project I used an agent-based model to analyze the effectiveness of the different components of the stimulus package in improving the labor market. To reflect the U.S. market and the 2008 recession, I adjusted an agent-based model of a simple labor market using NetLogo multi-agent programmable modeling environment. Using this modified model, I ran experiments on individual aspects of the stimulus methods and combinations thereof. The results showed that decreasing tax rate can decrease unemployment, as it makes work more attractive to workers and makes it easier to match workers and employers. Similarly, increasing government funding of projects increases job vacancies, thus decreasing unemployment, because government-funded projects increase demand and create employment opportunities. On the other hand, increasing the duration of unemployment insurance has a detrimental effect on labor market recovery, as it decreases the workers’ willingness to agree to employment. Also, based on the results, we can conclude that agent-based modeling is an effective method for stimulus package analysis. During recessions, such analysis can help maximize the positive effect of government stimulus by balancing various components of the package.