The Search and Matching Model: Challenges and Solutions

This dissertation focuses on explaining the cyclicality of unemployment, job vacancies, job creation and market tightness in the US economy. The framework used to model unemployment and job creation throughout this work, is the search and matching model, created by Mortensen and Pissarides (1994). This dissertation proposes three different mechanisms to improve the performance of a dynamic stochastic general equilibrium model (DSGE) with search unemployment, to align the model's predictions with the quarterly US data from 1955--2005.;The first chapter proposes a New Keynesian model with search and matching frictions in the labor market that can account for the cyclicality and persistence of vacancies, unemployment, job creation, inflation and the real wage, after a monetary shock. Motivated by evidence from psychology, unemployment is modeled as a social norm. The norm is the belief that individuals should exert effort to earn their living and free riders are a burden to society. Households pressure the unemployed to find jobs: the less unemployed workers there are, the more supporters the norm has and therefore the greater the pressure and psychological cost experienced by each unemployed searcher. By altering the value of being unemployed, this procyclical psychological cost hinders the wage from crowding out vacancy creation after a monetary shock. Thus, the model is able to capture the high volatility of vacancies and unemployment observed in the data, accounting for the Shimer puzzle. The paper also departs from the literature by introducing price rigidity in the labor market, inducing additional inertia and persistence in the response of inflation and the real wage after a monetary shock. The model's responses after a monetary shock are in line with the responses obtained from a VAR on US data.;In the second chapter I attempt to solve the amplification puzzle, the inability of the standard search and matching model to account for the volatility in vacancies and unemployment, by exploring the connection between R&D and employment. R&D affects product creation and product creation affects employment. An improvement in technology benefits the economy in two ways. Same products can be produced more efficiently and also new products are created. Empirical evidence suggests that the increase in production for already existing goods does not imply increases in employment, while new products are associated with increases in employment. The search and matching model implies that changes in technology do not imply large changes in employment for already existing goods which is in line with what the evidence suggest. However, when the search and matching model applies for sectors that innovate and produce new products, changes in employment significantly increase. Therefore, in this model I assume all agents need to innovate first before they create a job opening, because firms that invent new products are the ones that contribute more to the volatility of employment according to the evidence. Since ideas are cheaper to implement after a technological expansion, the cost of vacancies becomes countercyclical which boosts job creation and vacancies. The model can amplify the volatilities of vacancies, unemployment and market tightness approximately by up to 300 percent.;The third chapter investigates the macroeconomic implications from introducing perpetual learning in a simple search and matching model. When the agents with rational expectations are replaced with agents that are boundedly rational, the volatilities of vacancies, unemployment and market tightness are increased significantly. Job creation is connected to the present discounted value of future cash flows, which means that if agents do not form rational expectations, their forecasts of future cash flows are subject to periods of either excess optimism or excess pessimism. Those extra distortions of the agents' forecasts amplify the volatility of job creation. Therefore, the amplification puzzle arising from the search and matching model might be due to the strong assumption that agents are rational; thus, when agents need to form multi-period forecasts using past data as in Preston (2005) and Eusepi and Preston (2010), the search and matching model's amplification potential is enhanced. The model can replicate moments from quarterly US data from 1955Q1 to 2007Q4. However the more amplification added to the model through higher gain parameter, the further the correlations generated by the model are from the ones obtained from US data. That is because higher amplification induces vacancies to fluctuate around the rational expectations equilibrium less smoothly. Moreover, higher amplification through learning worsens the prediction of the model for the slope of the Beveridge curve.