Editor’s Letter

This issue of the JOR is more varied in its subject coverage than usual. It includes articles by several advisory board members who have been major contributors: David Blanchett and his Morningstar colleague Paul Kaplan; Alicia Munnell, head of the Center for Retirement Research at Boston College and her former colleague Tony Webb; my friend and fellow Washingtonian, John Turner; and Javier Estrada of IESE’s business school in Barcelona contributes his latest in a series of articles on the proper treatment of the risk to a retiree of running out of money. However, we also include papers by newcomers, one of which leads off the batting order.

The first article, “Predicting Longevity: An Analysis of Potential Alternatives to Life Expectancy Reports” by newcomers Jiahua Xu and Adrian Hoesch, proposes a novel way of reducing the uncertainty that insurers confront in offering life insurance that could be applicable to life annuities—an important development, given the potential role that a more vibrant annuity market could play in enhancing retirement security. Increases in life expectancy (LE), even if they were completely predictable, reduce the sustained income that a given capital sum can produce. However, increasing LE has also entailed increasing variability around the mean, which increases insurers’ risk, whether they are offering life insurance products or annuities.

The authors argue that big data can be the source of information on a person’s health that is not currently being used by underwriters. However, they are also mindful that big data, like small data, can be flawed. Their own proposal involves the use of this important new informational source combined with a medical test that is intended to gauge more accurately any gap between an insurance applicant’s biological and chronological ages and to correct for mistakes arising with the use of big data. The article’s discussion of the relevant science is fascinating, and this brief discussion does not do it justice.

Although the article does not address the issue of data integrity, the recent evidence of large-scale misuse of personal data by major players would require that safeguards against such abuse in other applications would have to be taken very seriously.

The next two articles both deal with a more conventional but very important issue: how to get a better handle on the risks inherent in target-date funds, and how to ensure that they are not excessively “one size fits all.” Target-date fund (TDF) holders who had the misfortune of having to include the impact of the Great Recession in their retirement plans found out the hard way that TDFs are not risk free. Even more seriously, the losses suffered by TDFs for those nearing retirement could vary enormously even if they appeared to have similar overall risk profiles.

The article by David Blanchett and Paul Kaplan, “Beyond the Glide Path: The Drivers of Target-Date Fund Returns,” does exactly what its title suggests: it goes beyond aggregate asset allocation to show how superficially similar TDFs might nonetheless behave quite differently in different states of the world. In addition to aggregate asset allocation (e.g., the percentage of a portfolio in equities and bonds) they consider the risk created by the composition of the portfolio of plan asset classes as well as other influences, like managerial style. Overall asset allocation remains important, but a proper assessment of the risk of TDF will require a good look at these other factors as well.

The article by Jill Fisch and John Turner, “Making a Complex Investment Problem Less Difficult: Robo Target-Date Funds,” is a nice complement to the Blanchett–Kaplan study. It addresses what the authors see as some limitations in the design of TDFs. They note, for example, that new participants of a similar age will generally be defaulted into the same plan—for example, a thirtyish participant might end up in a 2050 plan, 2050 being the year planned for retirement. The share of equities in such a plan might be high, inappropriately so if the participant is risk averse, even if she has 30 years to go until retirement. When it comes to risk taking, one size definitely does not fit everyone. Novice investors may not be aware that they could opt for a lower-risk plan with a closer retirement date. The paper sets out three proposals to deal with this issue: (1) allow participants the choice between a conservative, a balanced, and a more aggressive fund; (2) involve robo-advisers to guide participants in making informed decisions based on their personal circumstances; and (3) provide participants with the relevant financial education, on the spot, as they consider their decision. One issue the paper did not have the space to address was the quality of the questionnaire that robo-advisors would rely on. Will they all be well designed?

Javier Estrada has written a number of the papers for the JOR on the best way of gauging the impact of a particular investment strategy on retirement security. A standard way of doing this is to calculate the probability of a shortfall in retirement income before some stipulated retirement period (like 30 years) has elapsed. One difficulty with this approach is that one strategy might entail a greater probability of a shortfall of some length than another, whereas the strategy that falls short less often might fall more severely short. The likelihood of a shortfall might be less, but the likelihood of a severe income shortage should a shortfall occur might be greater. In “Replacing the Failure Rate: A Downside Risk Perspective,” Estrada proposes a measure that unlike a measure of risk based on variations, be they positive or negative, would place more weight on negative deviations. This is what he terms the “downside risk-adjusted measure.” This is measuring risk with the semideviation and not the standard deviation, and places more weight on unhappy outcomes.

One of the key decisions the country’s many state and local defined benefit plans need to make is whether or not to outsource the investment function, in part or in whole. In “Six Key Influences on the Efficiency of Insourcing in State and Local Plans,” Michael Urban argues that under the right conditions, insourcing can lead to sound investment decisions and be cost-reducing. He identifies, as the paper’s title implies, six key influences on the decision whether to insource or not to insource. These include (1) the state of a plan’s cash flow, (2) the nature and significance of economies of scale, (3) the way assets are allocated among different asset classes (which is related to the scale issue), (4) the manner in which a plan’s administrative expenditure is financed, (5) its geographical location, and (6) the role of fiduciary oversight. A plan with poor cash-flow will have more difficulty in investing in an internal investment management program than one in better financial shape. Economies of scale are obviously important: large plans can spread the cost of specialized investment functions more easily than small plans. That said, smaller plans can reduce the cost (to them) of insourcing by choosing less expensive investment strategies—index funds over actively managed funds, for example. A curious feature of many state and local plans is that the expenditures they incur on internal capacities are billed to the legislature, whereas fees paid to external managers can be charged to plan assets. This arrangement can definitely put a damper on the development of in-house investment management. Location apparently matters: plans with administrative headquarters near an active local or regional financial hub or located in close proximity to the big financial centers are more likely to outsource. However, large plans some distance away from a major financial center may still be in a position to negotiate fee reductions with an external management team. Finally, Urban contends that the role given to the board to provide effective oversight to an insourced investment program is critical. I have only sketched some of the many arguments advanced in this very interesting and institutionally oriented study.

We often hear the mantra about living healthier longer, and it may seem natural that one way to avoid a straitened retirement is simply to work longer. It is true that many of us are living longer with fewer health problems. However, many are not. Even if back-breaking labor is largely a thing of the past, many jobs can impose significant physical and mental demands that become harder to deal with as we age. Life expectancy has increased disproportionately among the more affluent, and the gap in life expectancies between rich and poor has widened. In their article “To What Extent Does Socioeconomic Status Lead People to Retire Too Soon?” Alicia Munnell, Anthony Webb, and Anqi Chen work with data on planned retirement ages and calculate how long a typical household might have to work beyond that age to maintain their pre-retirement standard of living. They find that this retirement gap is larger for less-well-off households. In any case, surveys conducted by the Society of Actuaries and others also find that many people are forced to retire sooner than they had planned because of unexpected illness or job loss. If working longer to bridge the gap is not feasible for everyone, then simply working additional years will not be a panacea. Other policies to alleviate distress in retirement might have to be considered.

The seventh and last article in this issue is a big-picture piece by a noted pension expert from New Zealand, Michael Littlewood, author of “Governments Have It Wrong on Pensions—Personal Lessons from a Consulting Career.” His diagnosis and proposed course of treatment for the issues facing countries around the world calls for more government intervention than many of the JOR’s readers and contributors might like. Nonetheless, I recommend this forthright article to our readership.

This issue ends with my review of a wonderful “little book” by the dean of our advisory board, John C. Bogle. Please take a look.

This issue completes the JOR’s first five years of publication, and I have decided that for both personal and professional reasons it is time for me to step aside, in order to concentrate more on my own writing. I have learned an enormous amount as editor and rubbed shoulders with some brilliant people. The experience has definitely improved my own game, and I hope that my editorial efforts may have helped the games of my colleagues as well. I am profoundly grateful to my board colleagues and other contributors for their support and to our readership for their interest.

TOPICS: Retirement, big data/machine learning, legal/regulatory/public policy

George A. (Sandy) Mackenzie

Editor

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