The worst insurance policy in the world

Aviva in France is still dealing with having written the worst insurance policy in the world. From the sounds of things, they weren’t alone in this foible. It’s also hard to say as an outsider what the right or reasonable resolution to their current problem is, but here is the policy that they wrote.

  • Buy a policy
  • Choose what funds you want to invest in
  • Unit prices calculated each Friday
  • Allow policyholders to switch funds on old prices until the next week
  • Hope like hell policyholders don’t switch out of poorly performing funds into well performing funds with perfect information based on backwards, stale prices.

Inconceivable – and since I don’t know more than I read on this blog post, maybe the reality and liability is really quite different.

See the post from FT Alphaville on the man who could own Aviva France.

Economic growth during and after Apartheid and the real problem with 1%

I read a letter from Pali Lehohla on news24 this weekend. Lehohla, the head of StatsSA, disagreed with a report by DaMina Advisors on economic growth in South Africa during and post the apartheid era.

To paraphrase Lehohla, he disagreed with their methodology, their data and their values and ethics:

First, I need to engage the author on methods. Second, I address the facts. Third, I focus on the morality of political systems and, finally, I question the integrity of the luminaries of DaMina and ask them to come clean.

This wasn’t data I had looked at before, but some of Lehohla’s criticisms seemed valid. Using nominal GDP growth data is close to meaningless over periods of different inflation.

Second, the methodology of interpreting economic growth should use real growth instead of nominal growth because this carries with it differing inflation rates. This is to standardise the rates across high and low inflation periods.

I haven’t confirmed the DaMina calculations, but the labels in their table do say “current USD prices” which suggests they have used nominal data. It’s little wonder any period including the 1970s looks great from a nominal growth perspective with nominal USD GDP growth in 1973 and 1974 being 34% and 23%, compared to real growth of 2.2% and 3.8%. The high inflation of the 1970s arising from oil shocks and breakdown of the gold standard distorts this analysis completely.

Lehohla’s other complaint is also important, but less straightforward to my mind –

The methods that underpin any comparison for a given country cannot be based on a currency other than that of the country concerned. The reason is that exchange-rate fluctuations exaggerate the changes beyond what they actually are.

Two problems here – one is that purchasing power adjusted GDP indices are not typically available going far back in history. The other is that if one is using real GDP, the worst of the problems of currency fluctuations are already ironed out. (The worst, certainly not all and it would still be a factor that should be analysed rather than completely overlooked.)

I was disappointed that neither piece mentioned anything at all about real GDP per capita. Does it really matter how much more we produce as a country if the income per person is declining? Income inequality aside, important as it is, more GDP per capita means more earning power per person, more income per person, more things per person. It is a far more useful measure of prosperity for a country, and particularly for comparing economic growth across countries with different population growth rates.

My own analysis, based on World Bank data (available from 1960 to 2013)

real GDP growth (annual %) real GDP per capita growth (annual %)
1961-1969 6.1% 3.5%
1970-1979 3.2% 1.0%
1980-1989 2.2% -0.3%
1990-1999 1.4% -0.8%
2000-2009 3.6% 2.0%
2010-2013 2.7% 1.1%
1961-1990 3.6% 1.2%
1971-1990 2.4% 0.1%
1991-2010 2.6% 1.3%
1991-2013 2.6% 0.8%

 

I’ve put these numbers out without much analysis. However, it’s pretty clear that on the most sensible measure (real GDP per capita) over the periods the DaMina study considered, post-apartheid growth has been better than during the 1971-1990 period of Apartheid.

The conclusion is reversed if one includes the 1960s Apartheid economy and the latest data to 2013, the picture is reversed on both measures.

This, above all else, should talk to the dangers of selecting data to suit the outcome.

This analysis doesn’t talk to the impact of the gold standard, the low cost of gold mining closer to the surface than it is now, the technological catch-up South Africa should have benefited from more in the past, the impact of international sanctions and expenditure on the old SADF and who knows what else. There are much big monsters lurking there that I am not equipped to begin to analyse.

My overall conclusion? The Apartheid days were not “economically better” even without ignoring the millions of lives damaged. Unfortunately, our economic growth has for decades been too low to progress our economy to provide a better life for all.

Here is the problem:

1961-2013 1961-2013
Real GDP growth Real per capita GDP growth
South Africa 3.2% 1.0%
Kenya 4.6% 1.3%
Brazil 4.3% 2.3%
USA 3.1% 2.0%

Despite the theory of “Convergence“, the US has had double South Africa’s per capita GDP growth for over five decades.  Real GDP per capita increased by 72% in South Africa over the entire period from 1960 to 2013, which sounds impressive until you realise that the US managed 189%. That is more than 2.5x our growth Brazil has done even better at 237%. “Even Kenya” outperformed us over this period.

1% per annum real per capita GDP growth is just not good enough.

Foreign land ownership

Foreign person? Foreign company? Foreign trust? Local company owned by foreigners? Local company owned partly by foreigners? Foreign company owned by locals? Local company owned by locals with debt finance from foreigners?  Local bank with foreign shareholders and repossessed properties? Local insurance company issuing policies to foreigners? BRICS bank? Foreigner married in community of property to local? Local living permanently overseas?

You don’t even need to look at this proposal being counterproductive, populist silliness.

Summary of November links

These are some of the stories I’ve followed or commented on in November:

  • Pro-cyclical fiscal policy from Nigeria. Knee-jerk reactions are usually not the right answer ow.ly/EQawj
  • eVoting seems error-prone and fraud-suspect around the world. Good luck Namibia… ow.ly/EUMFW
  • IASB: re-deliberations will extend into 2015. Performance measurement, participating contracts still unresolved ow.ly/EQ4ZB
  • Namibia has the second highest house-price inflation in the world of 29% every year – second to Dubai ow.ly/EQ9Qj
  • Please stop using SA85-90 “combined” as your base mortality table ow.ly/EyG29
  • 2014 mid-year EV analysis ow.ly/EDT9x
  • MG digs dirt, eventually, on 2002 Zimbabwe elections. Asks some thought provoking questions about election monitoring ow.ly/ECSEn
  • Consolidation in Kenyan life insurance sector. More needed, more to come surely. ow.ly/EDUsF
  • Doing business on our continent isn’t always easy. ow.ly/ECehr
  • Selective lapse impacting mortality? ow.ly/EAn6m
  • Why model structure matters, not just passing the calibration tests ow.ly/EyFG6
  • The inevitable growth of solar energy. Declining prices help emerging markets with plenty of sunshine. #Africa computerworld.com/article/284887…
  • The Beveridge curve and long term unemployment. ow.ly/E1aS0

 

SA85-90 “combined” and more actuarial sloppiness

I know of far too many actuaries who think that the “average” SA85/90 table is an appropriate base for their insured lives mortality assumption.

It’s not.

It’s also a good example of “actuarial sloppiness”.

To be specific, it is equally inappropriate if your current experience is a reasonable fit for the combined SA85/90 table.

SA85/90 was graduated based on South African insured lives data from 1985 to 1990. This period is important because it’s generally felt to be the last period in South Africa where HIV/AIDS would not have had a significant impact on mortality. (Estimates differ, but 1985 is often taken as the starting point for the HIV epidemic in South Africa and even though there might have been some deaths within the first five years, it is inconceivable to have affected a significant portion of the population.)

SA85/90 came in two version, “light” and “heavy”. Somewhat disappointingly, no distinction was made between males and females. Light mortality reflected the typical, historical, insured life characteristics which was pretty much white males. If I recall correctly, “Coloured” and “Indian” males were also combined into the light table. “Heavy” mortality reflected the growing black policyholder base in South Africa.

For all the awkwardness of this racial classification, the light and heavy tables reflect the dramatically different mortality in South Africa based on wealth, education, nutrition and access to healthcare. Combining the results into a single table wasn’t reliable since there were significant differences in mortality AND expected changes in the proportions of the heavy and light populations in the insured populations into the future.

A combined table was still created at the time. I suspect Rob Dorrington may have some regrets at having created this in the first place or at least in not having included a clearer health warning directly in the table name. The combined table reflects the weighted experience of light and heavy based on the relative sizes of the light and heavy sub-populations during the 1985 to 1990 period. I think a safer name would have been “SA85/90 arbitrary point in time combined table not to be used in practice”.

There is no particular reason to believe that the sub-population that you are modelling reflects these same weights. Even for the South African population as a whole these weights are no longer representative. The groups, at least in the superficial sense we view any particular citizen as coming from distinctly one group, will fairly obviously have experienced different mortality but will also have experience different fertility and immigration rates.

Our actuarial pursuit of separating groups of people into smaller, homogenous groups should also indicate that in most cases the sub-population you are modelling will more closely reflect one or the other of these groups rather than both of them.

But even if, just for the sake of argument, your sub-population of interest does reflect the same mix at each and every age as baked into the combined SA85/90 table, then it would still be entirely inappropriate to use the table for all but the crudest of tasks. After all, there a reason for our penchant for homogenous groups. If you model your sub-population for any length of time, the mix will surely change as those exposed to higher mortality die at a faster rate than those with low mortality.

The first order impact would be that you would be modelling higher mortality over time than truly expected. Due to the relative mortality between the two populations differing by age, the actual outcome will be somewhat more complex than that and more difficult to estimate in advance. This is particularly important with insurance products where the timing of death is critically important to profitability.

So, just because you can get a reasonable fit to your experience of an age- or percentage-adjusted SA85/90 combined table does not mean you have an appropriate basis for modelling future mortality. It may not vastly different from a more robust approach, but it’s just sloppy.