Working Papers

Lingfeng Lyu, Yang Shen, Michael Sherris, Jonathan Ziveyi

Abstract

Demographic projections are widely used by policymakers, planners, and researchers. They provide essential information for decision-making across sectors such as healthcare, education, and infrastructure. This study explores the use of demographic projections in Australia, focusing on government practitioners while also including respondents from academia and the private sector. Through a 15-minute online survey of 62 participants, the research identified who used projections, their purposes, and the features users valued most. Key findings highlighted significant differences in needs between government and academic users. Government practitioners prioritised medium-term horizons (10–19 years), national and state-level data, local-level data (e.g., SA2 and LGA), and frequent updates. Similarly, academic users in the small sample reported using national and state-level data but placed less emphasis on small-area projections. There is a strong demand for projections that include uncertainty ranges, yet many users reported limited confidence in interpreting and applying these measures. Additionally, government users emphasised the need for scenario-based projections to account for dynamic factors such as migration policies or economic shifts. Respondents also identified challenges, including insufficient granularity, infrequent updates, and limited transparency around projection assumptions. These findings underscore the importance of aligning research with the needs of government practitioners and fostering collaboration between researchers and policymakers. By addressing these gaps, this work aims to strengthen the usability of demographic projections and encourage future partnerships to enhance evidence-based policy and planning.

Funding: This paper was supported by an ARC Linkage Project (grant number: LP210200733) and the Australian Research Council's Centre of Excellence in Population Ageing Research (grant number: CE1101029). The findings and views reported in this article, however, are those of the authors and should not be attributed to our government partners.

Appendix: A Survey of Demographic Projection Users

Yuxin Zhou, Len Patrick Garces, Yang Shen, Michael Sherris, and Jonathan Ziveyi

Abstract: Risk-sharing rules have been applied to mortality pooling products to ensure these products are actuarially fair and self-sustaining. However, most of the existing studies on the risk-sharing rules of mortality pooling products assume deterministic mortality rates, whereas the literature on mortality models provides empirical evidence suggesting that mortality rates are stochastic and correlated between cohorts. In this paper, we extend existing risk-sharing rules and introduce a new risk-sharing rule, named the joint expectation rule, to ensure the actuarial fairness of mortality pooling products while accounting for stochastic and correlated mortality rates. Moreover, we perform a systematic study of how the choice of risk-sharing rule, the volatility and correlation of mortality rates, pool size, account balance, and age affect the distribution of mortality credits. Then, we explore a dynamic pool that accommodates heterogeneous members and allows new entrants, and we track the income payments for different members over time. Furthermore, we compare different risk-sharing rules under the scenario of a systematic shock in mortality rates. We find that the account balance affects the distribution of mortality credits for the regression rule, while it has no effect under the proportional, joint expectation, and alive-only rules. We also find that a larger pool size increases the sensitivity to the deviation in total mortality credits for cohorts with mortality rates that are volatile and highly correlated with those of other cohorts, under the stochastic regression rule. Finally, we find that risk-sharing rules significantly influence the effect of mortality shocks on fund balances since, under different risk-sharing rules, fund balances have different sensitivities to deviations in mortality credits.

Keywords: Risk-sharing rule, mortality pooling product, stochastic mortality rate, correlated mortality rate