The report analyses the development of mortality assumptions to build mortality tables to better protect retirement income provision. It first provides an international overview of longevity trends and drivers over the last several decades, including the impact of the COVID-19 pandemic. It then explores considerations and traditional approaches for developing mortality tables, and details the standard mortality tables developed across OECD member countries. It concludes with guidelines to assist regulators and supervisors in assessing whether the mortality assumptions and tables used in the context of retirement income provision are appropriate.
The OECD will provide an overview of the publication, followed by a roundtable discussion with government and industry stakeholders. Topics discussed will include:
Recent mortality trends and drivers
How mortality trends/drivers can inform future expectations, and how to account for that in modelling
The challenge of accounting for COVID in setting mortality assumptions
Trade-offs for different modelling approaches
The usefulness of the guidelines included in the report in practice
How to better communicate around mortality assumptions to non-experts
Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the SOA’s Longevity Advisory Group (“Advisory Group”) developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement. The Mortality Improvement Model, MIM-2021-v2, Tools and User Guides, compose the consistent approach and are defined below.
A report describing MIM-2021-v2 which summarizes the evolution of MIM-2021-v2; provides an overview of MIM-2021-v2; presents considerations for applying mortality assumptions in the model; and outlines issues the Advisory Group is currently considering for future model enhancements.
A status report of the items listed in Section V of Developing a Consistent Framework for Mortality Improvement. This report advises practitioners about subsequent research and analysis conducted by the Advisory Group regarding these items.
An Excel-based tool, MIM-2021-v2 Application Tool, and user guide, MIM-2021-v2 Application Tool User Guide, for practitioners to construct sets of mortality improvement rates under this framework for specific applications.
An Excel-based tool, MIM-2021-v2 Data Analysis Tool, and user guide, MIM-2021-v2 Data Analysis Tool User Guide, for practitioners to analyze the historical data sets included in the MIM-2021-v2 Application Tool.
The Longevity Advisory Group is planning to update the framework annually as new data and enhancements become available. MIM-2021-v2 is the first revision since the initial release in April 2021. This version uses the same underpinning as the initial MIM-2021 release but has been refreshed to include another year of historical U.S. population mortality data as well as more user flexibility and functionality to replicate RPEC’s MP-2021 and O2-2021 scales.
The most likely scenario, says Lessler, is that children do get vaccinated and no super-spreading variant emerges. In that case, the combo model forecasts that new infections would slowly, but fairly continuously, drop from about 140,000 today now to about 9,000 a day by March.
Deaths from COVID-19 would fall from about 1,500 a day now to fewer than 100 a day by March 2022.
That’s around the level U.S. cases and deaths were in late March 2020 when the pandemic just started to flare up in the U.S. and better than things looked early this summer when many thought the pandemic was waning.
And this scenario projects that there will be no winter surge, though Lessler cautions that there is uncertainty in the models and a “moderate” surge is still theoretically possible.
There’s wide range of uncertainty in the models, he notes, and it’s plausible, though very unlikely, that cases could continue to rise to as many as 232,000 per day before starting to decline.
This paper investigates mortality inequality across U.S. states by modelling and forecasting mortality rates via a forecast reconciliation approach. Understanding the heterogeneity in state-level mortality experience is of fundamental importance, as it can assist decision-making for policy makers, health authorities, as well as local communities who are seeking to reduce inequalities and disparities in life expectancy. A key challenge of multi-population mortality modeling is high dimensionality, and the resulting complex dependence structures across sub-populations. Moreover, when projecting future mortality rates, it is important to ensure that the state-level forecasts are coherent with the national-level forecasts. We address these issues by first obtaining independent state-level forecasts based on classical stochastic mortality models, and then incorporating the dependence structure in the forecast reconciliation process. Both traditional bottom-up reconciliation and the cutting-edge trace minimization reconciliation methods are considered. Based on the U.S. total mortality data for the period 1969–2017, we project the 10-year-ahead mortality rates at both national-level and state-level up to 2027. We find that the geographical inequality in the longevity levels is likely to continue in the future, and the mortality improvement rates will tend to slow down in the coming decades.