Working Papers

George Kudrna, Philip O'Keefe and John Piggott

Abstract: This paper reviews the current state of knowledge about pension policy and pension policy formulation in emerging economies undergoing demographic transition, and, with this background, indicated possible directions for future policy development. The countries we consider are primarily located in East and Southeast Asia, a region which is home to more than 30% of the world's population, and are characterised by increasing life expectancy, falling and /or low fertility ratios, immature social protection policy structures, high rates of informal employment, and in many cases, high rates of co-residency.

These features point to the relevance of strands of research which do not normally sit together in thinking about the evidence base for pension policy formulation and its impacts. They include fiscal implications; impacts on economic growth and intergenerational affordability; the relationship between alternative pension models and labour market (in)formality; the role of public benefits in the context of multi-generation households and intergenerational transfers; and the limitations of pension administration for older people who have worked in the informal sector for most or all of their lives.

The paper documents what we know about these various aspects of the issue and identifies knowledge gaps. On the basis of the evidence we do have, we indicate policy reform directions, in particular regarding development of social pensions directed to older people who have worked in the informal sector.

Ou Yang, Jongsay Yong, Yuting Zhang and Anthony Scott

Abstract: We quantify competition in Australia’s residential aged care sector and study how competition is associated with the quality of care and prices in the sector. Competition is defined three ways: the number of competitors within 10 km radius of the facility; the distance (in km) to the third closest competing facility; and Herfindahl-Hirschman index based on market share of facilities within 10 km. We further examine whether quality and price differ by ownership types (government owned, for profit and not for profit), after controlling for competition. We find that more competition is not associated with better quality or lower prices. Government-owned facilities, in comparison to for-profit and not-for-profit facilities, are found to provide higher quality in some domains but not in others yet tend to charge lower prices than other ownership types. The results indicate the possibility of market failures in aged care. Two key sources of market failures, the lack of public reporting of quality of care and price transparency, should be addressed as policy priorities before competition can work in residential aged care markets.

Keywords: Nursing home completion; Aged care quality; Aged care prices; Australia.

Salvatory R. Kessy, Michael Sherris, Andrés M. Villegas and Jonathan Ziveyi

Abstract: We present a stacked regression ensemble method that optimally combines dierent mortality models to reduce the mean squared errors of mortality rate forecasts and mitigate model selection risk. Stacked regression uses a supervised machine learning algorithm to approximate the horizon-specific weights by minimizing the cross-validation criterion for each forecasting horizon. The horizon-specific weights facilitate the development of a mortality model combination customized to each horizon. Unlike other model combination methods, stacked regression simultaneously solves model selection and estimates model combinations to improve model forecasts. Our numerical illustrations based on 44 populations from the Human Mortality Database demonstrate that stacking mortality models increases predictive accuracy. Using one-year-ahead to 15-year-ahead out-of-sample mean squared errors, we find that stacked regression improves mortality forecast accuracy by 13% - 49% and 19% - 90% over the individual mortality models for males and females, respectively. Therefore, combining the mortality rate forecasts provides lower out-of-sample point forecast errors than selecting the single best individual mortality method. Stacked regression ensemble also achieves better predictive accuracy than other model combination methods, namely Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. Our results support the stacked regression ensemble approach over individual mortality models and other model combination methods in forecasting mortality rates. We also provide a user-friendly open-source R package, CoMoMo, that combines multiple mortality rate forecasts using dierent model combination techniques.

Keywords: Stacked regression, ensemble learning, cross-validation, model uncertainty, model combination, age-period-cohort model, mortality forecasting.