Pension Fund Death Spiral

In a very simplified model (Pensions Dynamics, PPT), professor of investment strategy, Alan White, concludes that defined benefit pension plans probably cannot succeed on the long term.

Death Spiral
White shows that every pension fund with a non risk-free asset approach, will eventually encounter a “Death Spiral” which will lead to the collapse of the fund. The only solutions are:

• Raising contribution rates
• Lowering promised pension benefits.

Assumptions
All conclusions are based on the next summarized main assumptions:

• Compensation growth: 2% per year
• Pension contribution: 15% of yearly compensation
• Yearly retirement income objective: 70% of his final salary
• Risk-free rate of interest is 3%;Risk premium on the risky assets: 3%
• Annual volatility of the risky assets: 15%
• Time horizon: 100-year
• Risky Assets investment part : 60% of the portfolio
• Corresponding final pay pension defined as 20 year annuity
• Required minimum average Pension Fund asset value in steady state
  - at 3% return: €/$ 47,200
  - at 6% return: €/$ 23,600

Frequency Distribution Outcome
One of the most striking outcomes of this study is the fact that as we look farther in to the future of the simulated pension fund, the amplitude of the frequency distribution of asset values appears to be dropping to zero. The chance that (average) asset values will be between $10,000 and $100,000 gets smaller and smaller.

The reason for this is that the probability of very high asset values and the probability of entering a collapsed state (the collapsed funds are not shown in the next figure) both increase as we expand out time horizon. As a result the probability that assets remain in the intermediate interval, is reduced.

Another interesting facts are:

• Asset values appear to become more sustainable as the part 'risky assets' increases
• Collapse rates for growing pension funds are, (almost) independently of the asset mix, negligible.
• Collapse rates for more mature (steady state) pension funds are substantial and increase to deadly percentages as the time horizon increases from 50 to 100 years.

Remarks
Although Whites model is perhaps oversimplified and can be easily criticized, it clearly shows the essential principles of running a pension fund.

In a commentary, Rob Bauer (ABP, University of Maastricht) argues White's conclusions. Nevertheless, interesting stuff, that stimulates actuarial insight.

Links
Interesting corresponding links:

• Longer Horizons Can Handle Riskier Holdings
• Stochastic Interest rate models (2008)
• The calculus of retirement income

Influenced Decisions

As sincere actuaries, we all think our decisions are made in a pure professional and rational manner. Upon our turn, the board we advise, takes decisions based on our 'objective' unbiased advices.

Too bad, nothing is less is true! Decisions are strongly influenced by the way we present our proposals.


Influenced Decisions
In a splendid TED Video Presentation called 'Are we in control of our own decisions' (half an our fun and learning!) , Dan Ariely, an Israeli professor of behavioral economics and head of the eRationality research group at the MIT Media Lab, shows the astonishing effect of how decisions can be fundamentally changed by adding dummies in proposals:

First experiment
Ariely tested the next ad on the website of the Economist.com on a group of 100 MIT students:

As expected, most students wanted the combo deal (84%). Students can read, so nobody wanted the middle option.

But now, if you have an option nobody wants, you can take it off. Right? So Ariely tested another version of this ad on another group of students, eliminating the middle option. This is what happened:

Now the most popular option (84%) suddenly became the least popular (32%). And the least popular (16%) became the most popular (68%) option.

What happened was that the 'useless' option in the middle, was useless in the sense that nobody wanted it. But it wasn't useless in the sense that it helped people figure out what they wanted. In fact, relative to the option in the middle, which was get only the print for $125, the print and web for $125 looked like a fantastic deal. And as a consequence, people chose it.

The general idea here is that we actually don't know our preferences that well. And because we don't know our preferences that well we're susceptible to all of these influences from the external forces.

Second experiment
People believe that when they see somebody, they immediately know whether they like that person or not. Ariely decided to put this statement to the test.

He showed his students a picture of Tom and a picture of Jerry (real people in practice). Then he asked "Who do you want to date? Tom or Jerry?" But for half the people he added a slightly less attractive (photoshopped) version of Jerry. For the other half of the students he added a slightly less attractive (ugly) version of Tom.

Now the question was, will ugly Jerry and ugly Tom help their respective, more attractive brothers?

The answer was absolutely YES. When ugly Jerry was around, Jerry was popular. When ugly Tom was around, Tom was popular.


Conclusions: The Dummy Effect
What can we conclude from these two experiments?

When a board has to take a decision between two main proposals, their decision might be positively influenced by adding a third 'slightly less attractive version' (the dummy) of the proposal you - as an actuary - value as most favorable.

The danger that you - unaware of this dummy-effect - add slightly other proposals is substantial, as - in searching for the best decision - you'll be naturally inclined to add a few solutions nearby the optimal solution.

From now on...
Now that you've become aware of this dummy-effect, your next board proposals will be 'cleaner' than before and 'undummied'. Also you'll have a more enriched look at third party (or employee) proposals that are on your or on your boards table. From now on your board advise will not only focus on the technical or actuarial matters, but also include a professional opinion about the way a proposal is structured and presented.

Good luck in developing proposals.....

Links
- Book Predictably Irrational by Dan Ariely
- MIT Center for future banking

Happy Life Expectancy

As we know, Life Expectation can be measured in many ways. The three most common methods are:

• LE = Life Expectation (standard), the average number of years that a newborn can expect to live.
• HALE = Health Adjusted Life Expectation, the average number of years that a newborn can expect to live in "full health"
• HLE = Healthy Life Expectation, the average number of years that a newborn can expect to live in "full perceived health"

As comparisons between LE an HALE show, 'living longer' doesn't necessarily mean 'living longer in good health'. However, it has become clear that a strong Healthy Working Life Expectancy at age 50 or higher is the best guarantee that people will be able to work longer as they live longer.

One step further. Living in "good (perceived) health" doesn't automatically mean that people are living a happy life.

Happiness is one of the most important lifestyle statistics. Optimizing the number of 'happy years' in our life is therefore an important issue.

Happy Life Expectancy
Here is where Prof.dr. Ruut Veenhoven (Publications), comes in.

Veenhoven defines a different HLE as:

Happy Life Expectancy

In formula:

HLE = LE x Happiness-score/10

The Happiness-score (H) is the average happiness as expressed on a 0-10 scale.

Let's compare the HALE an HLE (Happy Life Expectancy) scores with each other for different (top-30 ranked) countries:

A full list and data is available at the World Database of Happiness.

It's clear that in most top-30 countries we spend about 90% of our life in healthy conditions and only about 70-80% in happy conditions. There room for improvement here! I'll leave the other conclusions up to yourself....

Let's conclude with two other correlated interesting findings:

1. Happy Life Expectancy Determination
What public policies are most conducive to happiness? This requires a view on the determinants of happiness in nations:

It turns out that six societal qualities (wealth, security, freedom, inequality, brotherhood and justice) explain 83% of the differences in Average happiness, 71% of the differences in Inequality of happiness and no less than 87% of the differences in Happy Life Years.
Enough for an interesting discussion between actuaries and politicians, I would say....

2. Wealth and happiness correlation
As expected wealth (expressed in GDP per capita) and happiness (e.g. highly satisfaction) are strongly correlated in clear distinguished regions.
Also the 'mean life satisfaction' turns out to be correlated to different age-groups and countries:


These graphics are food for thought on the relationship between mortality and wealth. More about that soon......