If you were university educated in South Africa, you are likely to be in the market at various times in your life for a large LED backlit LCD panel with a high refresh rate and more HDMI inputs than you will ever need. You will also quite likely have a market-oriented, Anglo-Saxon view of government’s role in industrial policy and international trade. Thus you would probably say “yes, import cheap TVs from China so I can buy a cheap TV and not pay for inefficient local firms to manufacturer expensive, inferior TVs.”

If you are a TV snob, you will still want free imports of Chinese TVs to keep the prices down of competing, but fancier Sony and LG models from Japan and Korea.

If you are a little cynical, you might say South Africa could never have the manufacturing capability and scale to produce all the components and assemble them into a modern LCD TV. That’s not actually the debate I ant to pursue now, so in that case let’s say the alternative would be to locally assemble sets made with significant local components, even if the LCD panel itself were imported. Of course, the reason South Africa doesn’t have the scale to produce the panels themselves at the moment is a function of industrial policy decisions decades go. There is no absolute reason we couldn’t have that capability. But, that debate is related but separate post.

If you never completed Grade 8 (that I still think of s Standard 6) then you probably would rather have a job than a Chinese TV and even a single HDMI input or output is one too many. Also, you don’t care about free international trade leading to optimal allocation of economic resources or the World Trade Association for that matter.

But you wouldn’t necessarily be wrong.

Reason #1
A large-screen LCD TV is not a factor of production of any industry that I’m aware of. Restricting cheap imports won’t increase the costs of a domestic industry an therefore won’t reduce the competitiveness of a local company or industry.

So far so good.

Reason #2
The Chinese will still buy or raw natural resources even if we don’t buy their TVs. We aren’t a sufficiently big market for them in the first place. We don’t reduce our exports but we do reduce our imports. This grows the local economy.

This does assume that over time, the higher prices of Imported TVs will discourage their purchase for other goods or encourage purchase of South African made TVs. It also assumes,NAND this is where things get more complex quickly, that our currency won’t strengthen to reflect the reduced imports. A strengthened currency would make all our exports less competitive and imports more competitive, leading to higher imports and lower exports across all industries and consumption.

Reason #3
Te burden if higher prices will fall on the wealthy, whereas the boom of greater employment will fall on the less wealthy, even increasing total demand for labour.

A critical question is what sort of labour.

If it is skilled labour, then our right skilled labour market will have wage pressure without increased employment and there will be a transfer of wealth from television set consumers to skilled labour and an increase in the cost of production of many other industries. All in all not a great outcome. We’ve damaged several industries and are reliant on the relative consumption habits of skilled labour versus television set consumers to see what this means for domestic versus import consumption.

If the demand is for unskilled labour, or separate but well-timed government education and training policies provide unskilled labour with the necessary skills, the result is far more appealing. Wages won’t increase as unemployed resources are put to work. So, no damage to other industries. The productive capacity of unemployed persons is put to work in an economically productive way. The country is producing more things domestically so all the additional money spent on more expensive TVs is employed back into the economy. The recently newly employed are likely to focus their purchases on domestically produced items (especially since TVs are now made locally)

Assuming the caloric requirements of these newly employed persons are not much different from during their unemployed years, the opportunity cost for the economy as a whole of paying them to make TVs less efficiently than the Chinese is zero. There is noting better they could have done with their time.

The arguments of comparative advantage totally break down when there is significant unemployment in one country. Free trade is centred on the idea that countries should focus on producing goods where they have a comparative advantage. Quotas and tariffs distort the true comparative advantage leading to suboptimal solutions.

There are other ways of achieving a similar result. Taxing TV set consumption to pay for skills training and subsidised public transport might lower some of the restrictions on supply and demand imbalances in the labour force, but without the focus on growing a particular industrial sector that will give rise to a cluster of related industries, developed scale to lower costs, eventually gain efficiencies and export to African markets with lower cost than the Chinese.

Why should the Chinese target attractive industries for growth through strong industrial policy and South Africa doesn’t? Why are the Chinese so uniquely well placed to target LG and Samsung?

There are a hundred problems with this scenario, including possibly the choice of TV as the product, but there is more to this discussion than the Washington Consensus would have you believe. Disagree, please comment below!

A MODEL ISN’T REALITY

Any simplified model I can imagine will also therefore not match reality exactly. The closer the model gets to the real world in more scenarios, the better it is.

Not all model parameters are created equal

Part of the approach to getting a model to match reality as closely as possible is calibration. Models will typically have a range of parameters. Some will be well-established and can be set confidently without much debate. Others will have a range of reasonable or possible values based on empirical research or theory. Yet others will be relatively arbitrary or unobservable.

We don’t have to guess these values, even for the unobservable parameters. Through the process of calibration, the outputs of our model can be matched as closely as possible to actual historical values by changing the input parameters. The more certain we are of the parameters a priori the less we vary the parameters to calibrate the model. The parameters with most uncertainty are free to move as much as possible to fit the desired outputs.

During this process, the more structure or relationships that can be specified the better. The danger is that with relatively few data points (typically) and relatively many parameters (again typically) there will be multiple parameter sets that fit the data with possibly only very limited difference in “goodness of fit” for the results. The more information we add to the calibration process (additional raw data, more narrowly constrained parameters based on other research, tighter relationships between parameters) the more likely we are to derive a useful, sensible model that not only fits out calibration data well but also will be useful for predictions of the future or different decisions.

How not to calibrate a model

Scientific American has a naive article outlining “why economic models are always wrong”. I have two major problems with the story:

1. All models are wrong. Some are useful (George Box). “wrongness” isn’t a problem with a model, but lack of usefulness is. The headline demonstrates a starting point poorly informed about the point of economic models.
2. The calibration approach criticised in the article is an extremely poor way to calibrate a model. No serious researcher thinks that is the right way to calibrate a model. So the article merely creates a straw man and then demonstrates how easy it is to knock the argument over.

Calibration and back-testing on separate data sets

The right way to calibrate a model is to separate the data-set into at least two independent subsets. Firstly, the “training set” or portion from which we will calibrate our parameters to get them to match as closely as possible the data. Again, this should make use of all information available and may give rise to several competing models that appear to fit the data similarly well.

The next step is crucial. We back-test the derived models against the second subset of data. This data comes from the same reality (perhaps a different time period) as used to calibrate the model, but the model won’t trivially match the data because none of that data was used to calibrate the model in the first place.

The importance of back-testing

Back-testing is critically important in the model building process, but back-testing against the same data used to calibrate the model is worth than useless (since it takes time and effort and an create a false sense of accuracy or reliability in the model.) Separating the data into two or more subsets is absolutely required, although it has the unfortunate side-effect of reducing the size of the data-set available for calibration.

Yes, it really matters.

A common example of the dangers of bad models fitting data well is with Economic Scenario Generators. These simulate economic scenarios to be used in valuing complex financial securities. If a model is properly calibrated, it will recreate the observable market prices of a wide range of instruments. However, the model could be a black-box neural network, a carefully constructed theoretical model with plausible relationships and constraints, or the proverbial ten thousand (possibly inebriated) monkeys. If all three models are perfectly calibrated to observable market prices, is any of the models inferior to any of the others?

Clearly the answer is yes, but only when it comes to extrapolation. I have far more confidence in the model’s ability to create “market consistent” valuations for instruments that do not have observable prices in the market if I understand how the mechanics of the model make sense on a level other than pure calibration.

Trivial 3 point example

Perfect fit of two models to 3 data points

The example above shows a perfect of a quadratic and cubic model to 3 data points. From this graph, both models appear exactly the same.

However, if we use the model to extrapolate to future time periods, the results are very different. Without additional data to back-test the results on, it’s not possible to tell whether either or any of these models is appropriate, but clearly both can’t be correct.

Extrapolation of the two models shows divergent results

Bit by bit, for the past 40 years, the city of Copenhagen has done something revolutionary: The Danish capital has reduced its parking supply. Cutting the total number of parking spaces by a small percentage each year stands in stark contrast to the more common pattern of cities adding more and more parking to accommodate private cars.

It’s a longish article, but definitely worth reading. Hat-tip to Samora Adams of @CapeChat fame for the link.

I say “in large sectors of the economy” because it isn’t true to say that we have universal unemployment. In fact, a feature of structural unemployment is that it usually is not uniform throughout the economy (like cyclical unemployment often is).  I don’t know any actuaries or engineers who are unemployed for more than a brief period between jobs, and usually the jobs start and end back to back. There will be other examples too.

Unemployment is driven by education

Interesting that 75% of our unemployed are “unskilled”. (I heard this on the radio, so I don’t know that  the number is correct or it’s source, but it does map to my previous analysis based on census showing unemployment by education level attained.

• The unemployment rate for those with less than “matric with university exemption” is between 30% and 40%.
• Matric with university exemption unemployment is 23%
• The unemployment rate for this with better than “matric with university exemption” is on average below 10%.

Supply and Demand for Labour

Economic growth isn’t the only solution to unemployment; in fact it’s not even necessarily a solution.  Prior periods of strong economic growth added jobs only very slowly. We have massive, structural unemployment in this country. We are making some of the right noises with our government’s new jobs plan and jobs fund.

Education in South Africa is not performing as needed

However, given the obvious relationship to education, why don’t we take the problems of our education system seriously?

• Standards have dropped (although enrollment has increased, which is also critically important for improving average education attainment)
• While enrollment has increased, the percentage of GDP spent on education has dropped from 6.4% in 1994/1995 to 5.3% in 2006/2007. This is partly explained by an increase in government spending in total related to social programmes. The real expenditure per learner has increased by approximately 5% per year from 2000 to 2007. We still spend a high proportion of our budget on education, but with poor outcomes.
• We don’t seem to participate in as many international standardised tests as before (reflecting a lack of appreciation for wanting to know what our standards are actually like)
• Standards are too low so that passing isn’t sufficient for further education / job placement
• “Literacy” measures have improved, although this is measured as having completed 7 grades of education. Why is it so hard to appreciate the need to  use measures (or at least also use measures) independent of the system to measure the success of the system?
• The Department of Educations 2009 Macro report uses only 1999 and 2003 data from TIMSS.  It shows our results are very low, but have not decreased by much. We didn’t participate in 2007 (probably because it’s embarrassing coming bottom of the class), which is a shame. 2003 data is really not appropriate to be using in 2011 to evaluate performance of our education system. (It looks like we are at least signed up to participate in 2011, which is great news. We’ll have to wait till December 2012 to get the results though.)
• The Quality Improvement, Development, Support and Upliftment Programme has budget allocated per province to spend on improving education.  While the Free State managed to spend 99% of its budget and the Western Cape 96%, the Eastern Cape spent only 51%, Limpopo 36% and KZN an impressive 11%. This is also home to possibly the worst, most impossible to read, mixed-numbers, mixed-decimal point table I have ever seen.

What does this table even mean? Except that provinces are generally not even spending the budget allocated to improve underperforming schools. How is it even possible that the Eastern Cape has so little budget allocated to this problem when they are one of the biggest culprits when it comes to poor school infrastructure and poor education results?

International indicators of performance

TIMSS 1999 Rock-bottom in Maths and Science. 6 provinces results went backwards in Maths and Science. The DoE’s report indicates that our syllabus has the least overlap with the TIMSS assessment framework – this offered as an excuse for poor performance (which it is) but without even considering that maybe that is a problem in itself. Should our education system really be going about things in a different way to established international norms? Maybe, but I think that needs to be proved and the default position should be international standards unless otherwise proved.

PIRLS 2006 Rock-bottom in reading, although this is against primarily developed countries (and only above Kuwait and Qatar in terms of gender inequality of reading education) (Chapter 1 and full report) (Curiously, other measures including our own Department of Education and SACMEQ studies seem to show girls outperforming boys, rather than the other way around.)

South Africa’s SACMEQ scores didn’t  change significantly from 200 to 2007 – whereas only one peer country had a reading score decline and 2 had mathematics scores decline. and 14 countries’ scores increased across maths and science. The percentage of students not sharing a textbook declined from 45.5% in 2000 to 45.0% in 2007. We are not making progress.

The link between enrollment and attainment levels is clear, but more clearly than that South Africa is massively underperforming.Curiously, the DoE’s report seems to suggest that a reason for our low attainment is high enrollment, whereas the graph above shows that high enrollment is associated with high attainment for those enrolled. My guess at the cause of this strong correlation is that “countries that take education seriously focus on both enrollment and attainment”.

Some South African problems with matric pass rate measurement

The pass rate of matric exams is a function of the quality of education (higher means higher pass rate), standards (higher means lower pass rate) and selection of learners who write the exam (restricting it to only relatively strong students artificially increases the pass rate). We should not celebrate an increase in the pass rate unless we have shown that standards of the exam have not been decreased and that learners weren’t discouraged from writing the exam. I would suggest that the standard of the test is more likely to vary from one year to the next rather than miraculous changes in education quality levels for learners who have had 12 years (or more…) of education to get there.

For example, this graph worries me no end.

As the number of candidates writing the exam goes up, so the pass rate comes down. This reflects progress in keeping learners in school on the one hand, but also likely over-eager promotion of students at lower grades. As the number of candidates reduces (as steps are taken to ensure non-trivial promotion) the pass rate trends back up again. From this graph it's hard to know whether the pass rate tells us anything at all.

I’m not going to show the real/nominal expenditure per capita because the glaring errors in the “2006 real expenditure per capita column” suggests to me that the creator of the table needs to go back to school.

Conclusions?

This post has been more of a collection of observations and thoughts and extracts from other research. In summary:

1. Education is key to unemployment and growth. Unless we recognise that education is the most important long-term solution to structural unemployment we can’t take the problem seriously. Skilled citizens by and large are employed. Unskilled citizens are unemployed at 30% to 40% levels.
2. Government spends significant budget on education, but with poor outcomes. Some budget available is not being spent at all.
3. Measures of literacy and matric pass rates are fundamentally flawed. We need to place increased focus on outcomes compared to international standards if our measures are to be useful.
4. We need to participate more, and more regularly, in international comparatives, no matter how embarrassing the current results are.
5. Current performance against international (and other African country) performance is poor and not improving.
6. The quality of research and ability to recognise the problems in our education system by the Department of Education itself needs to be improved.

Richard Thaler presents some of the common perception problems with annuities in this article in the NY Times. The basic message is still as it has been for decades. Individuals are reluctant to pay a large portion (often the majority) of their life savings to an insurer with the risk that they will die in a few years and “not have got their money back”. The peace of mind that should come to the policyholder turns into a matter of stress.

The bequeath motive is strong – and amplified by a lack of understanding of exactly how long we’re likely to live in retirement these days and how much money will be required. Those to whom many plan to bequeath may ultimately become the source of support when the income draw-down products are depleted with no longevity guarantee to boost the funds available.

It’s a good explanation although he doesn’t break much new ground. He also doesn’t talk about the concerns some potential policyholders have, in some countries at least, of whether the insurance company who sells the annuity will definitely be around over the next 40 years come what may.  This is more common in developing markets with weaker regulation (probably a good reason to have concerns) and less history of annuities (a cultural bias that will probably disappear over time).

Mr Thaler doesn’t propose any solutions for the insurers in boosting sales – a common “fix” is to combine a traditional pay-until-death annuity with a guaranteed minimum period or a death benefit (either for a limited term or at any point).  These adjustments reduce the “risk” of “making the wrong decision but purchasing an annuity but only living for a short period”.

There’s no free lunch.  In the same way that cash-back bonuses on short-term insurance products actually increase the average cost of insurance and reduce the risk-transfer from insured to insurer, these guarantee periods increase the cost of annuities.

He was saying that he doesn’t see the point in investing in debt instruments.  He explained that the return is low and the risk high since if the company gets into trouble, you’ll likely only get a few cents on the dollar back.

Well, he’s wrong.

Risk and asset-liability matching

Fixed Interest investments are often the only investment that makes sense when you need to match or hedge fixed liabilities.  Naively consdering expected return only and not asset-liability risks  gives naive results.

Credit risk premia more than compensate for default experience over time

It’s worth exploring risk a little further. The caller stated that if the company gets into trouble, it’s likely the bondholders will also be hurt, and will likely only get a few cents on the dollar. Well he’s wrong here too.

The historical default frequency for investment great bonds (BBB and above) has been hardly more than a few single digit percent.  The Loss Given Default (how much an investor will typically lose if the bond issuer does default) is anywhere from 35% to 80%, depending on the seniority of the instrument, which estimate you trust, how it is measured and when the estimate was made. It’s because there are so few investment grade defaults that the data is so sparse and the estimates so wide. However, it’s clear that the likely return won’t be “a few cents on the dollar”.

I’m going to hunt round for some references here so you’re not just trusting my word.

Illiquidity premia = higher returns for some

Given the illiquidity of many corporate bonds, the expected returns are even higher if you as an investors are not considered with easy liquidation of your investment. This is a “pure risk premium” that you will earn over time without expected loss.  You could purchase extremely high quality, well-collateralised debt and earn a good return above risk-free as long as you have the patience and resources to hold it for long periods or until maturity.

Have they considered that this attracts people to drive by it slowly rather than attract people driving past it to drive slowly?  Instant congestion. Economics is all about incentives and often about unintended consequences.

Yes, I know they probably weren’t serious about this idea.

Typically viewed as risky investments, the junk bonds boom of the 80s showed that there is more to junk than just a risky investment.

Locally, our pension funds and other retirement savings money should be more heavily invested in junk bonds. I’m surprised more people aren’t talking about this. It might be due to the limited availability of junk in the SA market. On the other hand, if demand picked up, I’m sure we could see more original-issue junk bonds as yields drop and become more attractive financing vehicles.

There are always marks for considering tax

Why should pension funds be invested in junk? Tax. Approved retirement savings vehicles in South Africa don’t pay income tax. Thus, the value of securities that would attract significant tax is higher for these investors than for the market as a whole. If risk and return are balanced for the market as a whole, the extra return available to retirement vehicles through not paying tax is a bonus over and above that appropriate for the risk.

Conversely, pension funds should stay far away from tax-efficient instruments such as preference shares. The prices of these instruments have already been bid up by tax-paying investors.

Investment attractiveness from an untaxed investors perspective

Property isn’t bad from a tax perspective, since it provides taxable rental income, inflation-like growth in rental streams and an additional return given the large investment size and illiquidity of the investment.

Equities are awful for a retirement investors from a tax perspective. Tax exempt dividends and lower-taxed capital gains make up the return. Yes, this is still a source of good long-term returns and a way to earn the Equity Risk Premium over time. But more of the excess returns required by pension funds should be generated by junk bonds.

Junk Bonds provide a high return (all of it taxable in the hands of a tax-paying investor) from credit risk and illiquidity risk.

Some junk is pink, but it’s not all rosy

Pension funds should have less equity, less government bonds, more more more junk bonds. We need to work to find more and better ways of building this market so pension funds can make use of it. (Regulation 28 limits the extent of foreign exposure, another possible way to increase junk bonds investments). The added diversification of adding a different type of risk category to the investment portfolio means we will likely be able to maintain the overall level of risk (or even decrease it) while boosting returns due to the tax premium.

I’m not discounting the default risk inherent in junk bonds, and that this isn’t ideal for a pension fund. It’s a real pity pension funds can’t short the equity of the underlying companies (in moderation) to provide some protection against deterioration of illiquid junk bonds they start migrating down the credit ratings from BB towards ultimate default.