Yearning for learning

I’ve found many ways of the years to continue my education – formal and informal. From time to time I feel my self stagnate a little and a change of approach can reinvigorate the process.

For the last few months, podcasts have been the answer. I’m fortunate enough not to have a very long commute typically. I still get 20 minutes each way listening to some amazing podcasts, and longer when I have the opportunity.

Here are three of my favourites:

Sam covers a range of technological, philosophical, neuroscience, political and AI related topics. Generally a super calm, balanced host, he does have occasion to show emotion from time to time, most notably in relation to Scott Adams, the Dilbert creator.

This one is TED, but better.  More curated, more condensed but also supplemented with additional discussions with the speakers, and in a beautiful audio-only format. It turns out that visuals of earnest speaking individuals swaying across the floor doesn’t add as much as you might think to the content.

Best of all, listen to all the podcasts, don’t be tempted to select only the topics you find interesting. Allow yourself to explore unexpected new areas.

Including this is a bit cheeky since I’ve only listened to two episodes. Most recently, on flood insurance (in the wake of Hurricane Harvey at al in the US). This will likely inspire a separate blogpost soon.

I’d like to say that I’ll add to this list over time, but time constraints mean I barely keep up with just these.  I may look to change them up over time, but for now I’m really enjoying the boundary stretching intellectual stimulation of this selection.

CT Water: News come 4 October?

Not much of value in this shoddily wording reporting on CT’s water shortage, except that we will hear an official update on the water disaster plan on 4 October.

This is a topic of direct personal and business relevance, but also of a technical forecasting and measurement perspective. Very little I’ve seen so far gives my confidence in the forecasting, which is either because of poor forecasting or from very limited communication.

I don’t know which bothers me more.

Who owns Defined Benefit holes?

In 2014, John Oliphant was fired from the Government Employees’ Pension Fund (GEPF) where he was principal executive officer.  There were allegations, some details not disputed, that had Mr Oliphant accused of exceeding his powers and not following due process which amounted to, at the maximum, some hundreds of thousands of Rands.

That outcome, with those modest numbers, especially with the murk that surrounded the allegations, contrasts markedly with the hundreds of millions of Rands known to be deeply mired in scandal at the moment without consequence.

The GEPF and the Public Investment Corporation (the PIC, which investments money on behalf of many state entities) are proximate with huge amounts of money. It could be expected to be an attractive target for those with sticky fingers and connections. So when stories emerge of PIC directing GEPF investments towards SOEs or other private investments for merits other than the investment returns for the fund, nobody is surprised but plenty are concerned.

Most of the stories in the press on this matter are misinformed.  The claims that pensioners’ money is at risk are misleading at best. One exception is the Daily Maverick article by Dirk De Vos. In this article, De Vos explains why tax payers rather than (only) government pensioners will bear the burden of failed investments for the GEPF. Continue reading “Who owns Defined Benefit holes?”

31, 151 and what comes next

This is my first new post in over two years. There are many reasons for that, and I may get into that in a future post.  As to why I’m restarting – a conversation with an old friend last night combined with a lunch discussion with an actuarial student a couple of weeks ago has inspired me to attempt to, temporarily at least, restart my blog.

I’m going further than just restarting, I’m committing to a new blog post each day for October. Now the reasons for having stopped blogging haven’t suddenly changed, so it’s likely that some of these posts will be short. (And similarly, some of them long.) Since the decision was made last night, I also haven’t though through anything like a full plan for the month.  I invite you along to see how it goes.

I’m probably not alone in being slightly more jaded, slightly less optimistic than I was two years ago. A summary of the two years might make its way into another post, more to help me collect my thoughts than anything else.

Cape Town is experiencing an intense, multi-year drought and there is a real possibility of the city running out of water before next winter. I will definitely be blogging more about the vacuum of credible communication and forecasting on this front in a later post. For now, a single-purpose website http://www.howmanydaysofwaterdoescapetownhaveleft.co.za/  is currently proclaims (they update weekly, I think, based on updated weekly reports of dam levels) that we have 151 days of water left and will run out of useable water on 1 March 2018.

For now, the claims of cholera in Puerto Rico have not been proven, but it does feel like it’s only a matter of time. Anyone fretting over drinking water in Cape Town should probably bump diseases such as cholera up their list.

The official position of the City of Cape Town is still “we won’t run out of water”, but there are reasons to doubt this and be concerned. I’m keen to work out objectively what the level of risk is. To that end, it would have been useful to be able to dissect the http://www.howmanydaysofwaterdoescapetownhaveleft.co.za/  methodology to understand how credible their forecast is. This is the entire disclosure of their methodology:

Using our recent consumption as a model for future usage, we’re predicting that dam levels will reach 10% on the 1st of March, 2018.

I’m not losing sleep over their forecast. So for now, sleep.

Modest data

I’m as excited as the next guy about the possibilities of “Big Data!” but possibly more excited about the opportunities presented by plain old “Modest Data”. I believe there is plenty of scope for useful analysis on fairly moderate data sets with the right approach and tools.

I’d go as far as to say that many of the “Big Data!” stories and analysis currently performed is really plain old statistical analysis with a few new touches from the ever-expanding list of R libraries.

For example, it seems that papers with shorter titles get more citations by other researchers.  Although the research considered 140,000 papers, there is nothing especially “Big Data!” about the analysis. The paper and authors suggest several possible causes related to the quality of the journal, period of time etc. Disappointingly, they don’t seem to have modelled these possible effects directly to understand whether there is any residual effect.

There is scope for great analysis without “Big Data!” and plenty of scope of poor analysis with all the data in the world.

South Africa ranks 2nd in financial inclusion study

The Brookings Financial and Digital Inclusion Project measures South Africa one place behind Kenya in terms of financial inclusion.

I’m still working my way through the full report, but Kenya’s score is a significant jump above South Africa and the closely contested positions below it. Is Kenya genuinely making such inroads or is this a function of the measures used?

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 FT on the man who could sink Aiva

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.