The main goal of this project is to evaluate the effect of Chile’s pension system rules and regulations on individuals’ contribution patterns. The few empirical studies on the Chilean Pension System have been limited to the use of aggregate and macro data. This project’s contribution is to analyze pension contribution patterns under the Chilean AFP system using micro data and state-of-the-art modeling methods. Previous researchers employing dynamic models to analyze retirement decisions have used US data which may not reflect behavior of workers covered under an individual accounts defined contribution retirement system. Accordingly, we will use the 2002 and 2004 rounds of the Chilean Encuesta de Prevision Social or Social Protection Survey (EPS), containing socio-demographic data, labor market data, and information regarding the pension system at the individual level, and link it to administrative records on monthly individual account movements and monthly wages. These linkages will allow the longitudinal analysis of contribution decisions and labor force participation decisions. We will then use regression analysis under different specifications to identify the variables that affect contribution patterns. In addition we will work to develop a dynamic behavioral model of individual decision-making about labor force participation and monthly contributions taking into account the fact that individuals working in different labor sectors face different contribution rules. Finally we propose to develop ways to estimate the parameters of this behavioral model using the simulated maximum likelihood method. Our particular interest will be to use the results to assess the possible impact on contribution patterns of changes in pension system returns and fees, as well as requirements for receiving the minimum pension.