Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms

TL;DR

This paper demonstrates that deep reinforced learning algorithms, specifically ACKTR, can effectively approximate solutions to complex discrete-choice life cycle models, aiding analysis of social security reforms where traditional methods are infeasible.

Contribution

It introduces the use of deep reinforced learning, particularly ACKTR, as a viable alternative to dynamic programming for solving large-scale life cycle models in social security analysis.

Findings

ACKTR yields near-equivalent results to dynamic programming.

ACKTR provides a good approximation but with less spiked employment profiles.

Reinforced learning algorithms can effectively analyze social security reforms.

Abstract

Discrete-choice life cycle models of labor supply can be used to estimate how social security reforms influence employment rate. In a life cycle model, optimal employment choices during the life course of an individual must be solved. Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model. Solving a complex life cycle model requires the use of approximate methods, such as reinforced learning algorithms. We compare how well a deep reinforced learning algorithm ACKTR and dynamic programming solve a relatively simple life cycle model. To analyze results, we use a selection of statistics and also compare the resulting optimal employment choices at various states. The statistics demonstrate that ACKTR yields almost as good results as dynamic programming. Qualitatively, dynamic programming yields more spiked aggregate employment profiles than ACKTR. The results obtained with ACKTR provide a good, yet not perfect, approximation to the results of dynamic programming. In addition to the baseline case, we analyze two social security reforms: (1) an increase of retirement age, and (2) universal basic income. Our results suggest that reinforced learning algorithms can be of significant value in developing social security reforms.