Means-Tested Public Pensions, Portfolio Choice and Decumulation in Retirement*

Abstract

Age Pension means-testing buffers retired households against shocks to wealth and may influence decumulation patterns and portfolio allocations. Simulations from a simple model of optimal consumption and allocation strategies for a means-tested retired household indicate that, relative to benchmark, eligible and neareligible households should optimally decumulate faster, and choose more risky portfolios, especially early in retirement. Empirical modelling of a Household, Income and Labour Dynamics in Australia panel of pensioner households confirms a riskier portfolio allocation by wealthier retired households. Poorer pensioner households decumulate at around 5 per cent p.a. on average; however, better-off households continue to add around 3 per cent p.a. to wealth, even when facing a steeper implicit tax rate on wealth.

Assessment of Policy Changes to Means-Tested Age Pension Using Expected Utility Model: Implication for Decisions in Retirement

“…The model in Ding (2014) does not constrain drawdown with minimum withdrawal, which would limit the author from finding a semi-closed form solution. Similarly, other authors that focus on means-tested pension also do not enforce minimum withdrawal rates, such as Hulley et al (2013), who use Constant Relative Risk Aversion (CRRA) utility to understand consumption and investment behaviour, or Iskhakov et al (2015), who investigate how annuity purchases change in relation to Age Pension. It should be noted that their assumptions do not include Allocated Pension accounts; thus, minimum withdrawal rates may not apply.…”

Optimal Time to Enter a Retirement Village

“…This trend is similar to that reported in the study by Ding et al [17], in which retirees are assumed to replicate a European put option for their wealth requirement. 4 Retirees are also found to use increasing risk exposure to hedge against risk in other studies, such as Hulley et al [47] and Thorp et al [48].…”

Non-Parametric Integral Estimation Using Data Clustering in Stochastic dynamic Programming: An Introduction Using Lifetime Financial Modelling

“…Further, the desire investigate more interesting and realistic problems has led to an expansion beyond those problems that can be solved analytically. Examples include the introduction of different investment products in the decision making process (Hanewald et al 2013;Horneff et al 2015), and the desire to use more complicated assumptions (Hulley et al 2013;Michaelides and Zhang 2015). Shapiro (2010) gives a detailed literature review on decision making in preparing for and during retirement.…”