Working Paper: NBER ID: w2470
Authors: John Rust
Abstract: This paper formulates a model of retirement behavior based on the solution to a stochastic dynamic programming problem. The workers objective is to maximize expected discounted utility over his remaining lifetime. At each time period the worker chooses how much to consume and whether to work full-time, part-time, or exit the labor force. The model accounts for the sequential nature f the retirement decision problem, and the role of expectations of uncertain future variables such as the worker's future lifespan, health status, marital and family status, employment status, as well as earnings from employment, assets, and social security retirement, disability and medicare payments. This paper applies a "nested fixed point" algorithm that converts the dynamic programming problem into the problem of repeatedly recomputing the fixed point to a contraction mapping operator as a subroutine of a standard nonlinear maximum likelihood program. The goal of the paper is to demonstrate that a fairly complex and realistic formulation of the retirement problem can be estimated using this algorithm and a current generation supercomputer, the Cray-2.
Keywords: retirement behavior; dynamic programming; social security
JEL Codes: J26; C61; D91
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| health status (I12) | retirement decisions (J26) |
| employment status (J63) | retirement decisions (J26) |
| expectations about future earnings and benefits (J32) | retirement decisions (J26) |
| poor health (I14) | retirement decisions (J26) |
| social security benefits (H55) | retirement timing (J26) |
| policy changes (J18) | reoptimization behavior (C61) |