It may be time to pull out the old Milo Optimisation Post. It was trivial 5 years ago and it’s still intended as funny rather than serious, but the sad, sad thing is that the lesson still hasn’t been learnt in our airports and probably a range of other entities.
The security check point is a bottleneck. More specifically, the number of scanners open at certain rush times isn’t enough to cope with the arrival rate of passengers at the check point.
The bottleneck is the small number of open scanners. The bottleneck could be fixed and throughout increased by having more scanners. No amount of hurrying up the process to inspect tickets will change the bottleneck. No amount of directing people to stand in queues at each scanner will change the rate at which people go through the scanners (as long as there is always a queue at each of them). None of this changes the throughput of the sytem.
But it does mean that there are extra people employed to inspect tickets and extra handlers directing people to queues – these employees could presumably operate an extra scanner and massively increase throughput. This might in turn create a bottleneck somewhere else, but if that happens it shows that throughput has already been increased. It also presents an opportunity for the next stage of optimisation.
Should South Africa import Chinese television sets? Your answer to this question depends probably on your education.
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. Continue reading
Any model is a simplification of reality. If it isn’t, then it isn’t a model as rather is the reality.
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: Continue reading
Stand aside Freakonomics, here is a very real, very current, very practical application of economics, incentives and game theory to a universal city development problem. Provide fewer parking spaces to help manage the car and parking problem in congested downtown areas.
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.
South Africa’s unemployment is a different creature from that in the US and in the developed world’s papers at the moment. We don’t have a cyclical lack of demand (although demand isn’t as robust as I’d like). We have massive, unmanaged structural unemployment in large sectors of the economy.
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? Continue reading
Found these views on teamwork interesting, and largely in line with my own experiences. In particular, I’ve gone from thinking videoconferencing is all we need to the realisation that in-person interaction is important. I’d add to the point about stable, long-standing teams performing better by suggesting this is at least partly through better understanding each other’s strengths and weaknesses.
There is much to recommend in purchasing an annuity at retirement to manage the risks and uncertainty of longevity. It’s well known though that surprisingly few people who have the option to purchase an annuity do so.
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.