Read the latest (14 March 2014) document from National Treasury on tax free savings vehicles for South Africa. I think it’s a fantastic idea – both from a policy perspective with carefully designed incentives to promote long-term savings and from a personal perspective. I’m definitely going to use one for my own savings. However, one paragraph stuck out as a pretty clear message from National Treasury on their views of life insurers – and views on current product offerings rather than any historical sins:
Products must permit flexible contributions and may not bind individuals into any future contribution schedules. Many insurance investment policies would currently not match these criteria. Government is not open to providing a tax incentive for products that have high charges and may have an adverse impact on household welfare at the point at which the household is increasingly vulnerable. In this regard some savings products, for example endowment policies and any similar investments that include excessively high penalties in the case of early termination of the policy, pose a policy challenge from a market conduct perspective and will not be allowed in these accounts.
As discussed, National Treasury will engage with the FSB and industry in determining a reasonable approach to charges and early termination.
Wow. I know there are many bad insurance products around and probably some still being sold. I also know of many insurance executives who strive for value for money and are reinventing products and distribution channels to this end.
KPMG has been awarded the FSB SAM Economic Impact Assessment project. This is a perfect opportunity to combine our insurance, actuarial, capital market and economics skills to deliver on a critical project for the insurance industry and FSB.
We still need to fine-tune scope with the FSB and the Economic Impact Task Team, but we’ll be covering some of these issues:
Expected impact on capital requirements, capital ratios and free capital for insurerrs
Resultant scenarios around capital raising, consolidation and what this means for new entrants
The once-off and BAU expenses of SAM compliance, what this does to returns to policyholders and shareholders.
Impact on capital markets (especially equity investments, government bonds, swaps, corporate paper, sources of capital issued by insurers) and interaction with banks in this space.
Impacts on reinsurers, then extending to interactions with other service providers and competing industries
Likely responses and actions by insures in response to this changed environment
Potential broader economic impacts on employment and economic activity arising from these changes to an important part of the financial services industry.
There’s fairly obviously a fair amount of subjectivity in all of this and we don’t expect to have everyone happy with the conclusions, but we are going to perform a rigorous analysis of the possibilities and will be engaging with a wide range of stakeholders in forming our views.
Statistics and sampling are fundamental to almost all of our understanding of the world. The world is too big to measure directly. Measuring representative samples is a way to understand the entire picture.
Popular and academic literature are both full of examples of poor sample selection resulting in flawed conclusions about the population. Some of the most famous examples relied on sampling from telephone books (in the days when phone books still mattered and only relatively wealthy people had telephones) resulting in skewed samples.
This post is not about bias in sample selection but rather the simpler matter of sample sizes.
Population size is usually irrelevant to sample size
I’ve read too often the quote: “Your sample was only 60 people from a population of 100,000. That’s not statistically relevant.” Which is of course plain wrong and frustratingly wide-spread.
Required Sample Size is dictated by:
How accurate one needs the estimate to be
The standard deviation of the population
The homogeneity of the population
Only in exceptional circumstances does population size matter at all. To demonstrate this, consider the graph of the standard error of the mean estimate as the sample size increases for a population of 1,000 with a standard deviation of the members of the population of 25.
Standard Error as Sample Size increases for population of 1,000
The standard error drops very quickly at first, then decreases very gradually thereafter even for a large sample of 100. Let’s see how this compares to a larger population of 10,000. Continue reading →
I blog from time to time about education in South African and its frightening link to unemployment and all the societal ills that go along with that. I also point out that as a nation we spend a fair amount of money on education with very poor results.
Teachers in our public school system took an average of19 days of sick leave per year. I also blog about the dangers of averages. For every teacher that doesn’t take sick leave (and I’m sure there are many) there are teachers taking more than 19 days of sick leave per year.
What’s interesting here is that not only is this an astonishingly high number, it’s also clearer more than the 10 days per year on average on a rolling 3 year basis that is allowed under the Basic Conditions of Employment Act. Let’s also not forget that while teachers should probably be paid more in an ideal world, they do also get vastly more annual leave than most already.
I’d also like a four day week every second week thank you.
How exactly are these teachers allowed to take so much sick leave? Well unfortunately the answer is the same as why our education system is in such a sorry state. Poorly trained, poorly motivated teachers without a culture of pride in their work, overly strong unions and no political will to do anything about it.
The result reflects a significant decrease in the rate of marriage. Of course this still isn’t the whole story. It should be fairly obvious to everyone that the rate of marriage is not constant across age – and that was a big part of my earlier posts on marriage. So as the population pyramid of South Africa changes, we would expect a difference in the overall rate of marriage even if the rates per age themselves didn’t change.
The analysis we really need is a “hazard rate” type analysis fitting marriage rates per age (and probably by race group given the significant differences by race) and then seeing whether these rate are changing.
The linked story also points to a 42% decline in customary marriages and a much smaller 4% decrease in civil unions. This then probably reflects a separate trend fundamentally away from more traditional approaches to more “modern” (no judgement attached!) approaches. If one considers the marriage rates in Northern Europe are massively lower than in more developing markets, I’d put money on this trend continuing in South Africa for a long while and with at least as big an impact.
A client recently mentioned that they were concerned about the implication that the adoption of Solvency Assessment and Management (SAM) would have on insurance accounting under current IFRS4.
The apparent concern was that measurement of policyholder liabilities for IFRS reporting would change to follow SAM automatically.
Let me start out by saying this is categorically not the case. The adoption of SAM should not change IFRS measurement of insurance liabilities. In this post I’ll cover some of the technical details and common misconceptions of IFRS4 to demonstrate why this conclusion is so clear. Continue reading →
Economists (and actuaries) like to measure things.
The easier to measure and the more reliable the measure, the more we like to measure it. This is not unlike the drunk looking for his keys under the street lamp because that’s where the light is even if it isn’t where he dropped the keys.
Sometimes the most important things to measure are very difficult to measure reliably. Happiness is one of these things. Economists have been trying to measure this for decades with interesting, counter-intuitive and sometimes contradictory results.