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The formula for success

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The world of work is changing. Skills that were valuable only a decade or two ago seem less valuable nowadays, displaced by newer trends and technologies. Every few weeks a news article report on the threat of automation, and how it will destroy millions of jobs, including yours. A YouTube video of a robot jumping over obstacles confirms that the inevitable is only a few months away.

The answer, many seem to think, is to become like computers. Students, in South Africa and elsewhere, are encouraged to pursue STEM – science, technology, engineering and mathematics – careers. Humanities degrees or those in the social sciences, in contrast, are often considered less prestigious. We know that the best-paying jobs are still those where a decent amount of math is required. In South Africa, this is of particular concern, as the gender and racial composition of those enrolling for STEM degrees differ significantly from those enrolling for degrees in the humanities, exacerbating inequality.


But the story is more complicated than that. A new paper by David Deming, published in the Quarterly Journal of Economics (QJE), suggests that, at least in the United States, STEM graduates are not doing very well. In fact, between 2000 and 2012, STEM jobs shrank by 0.1 percentage points, after growing by 1.3 percentage points in the previous two decades. In contrast, all other ‘cognitive occupations’, jobs like managers, teachers, nurses, physicians, lawyers and economists , grew by 2.9 percentage points between 2000 and 2012, faster than the 2 percentage points in the previous decade. The figure below demonstrates this clearly: only 36% of STEM occupational categories had positive growth, compared to 85% of other professional occupations.

The reason for this, Deming argues, is that managers, teachers and physicians all require significant interpersonal skills. And because of the technological change – the same technological change we all fear! – these social skills are increasingly in demand. Deming explains: ‘The skills and tasks that cannot be substituted away by automation are generally complemented by it, and social interaction has, at least so far, proven difficult to automate. Our ability to read and react to others is based on tacit knowledge, and computers are still very poor substitutes for tasks where programmers don’t know “the rules”.’

Using a wide variety of sources, Deming finds that jobs that require high levels of both cognitive and social skills have fared particularly well, while high math, low social skill jobs (including many STEM occupations) have fared especially poorly. ‘Social skills were a much stronger predictor of employment and wages for young adults age 25 to 33 in the mid-2000s, compared to the 1980s and the 1990s.’ There is a revolution in the job market underway that no-one is talking about.


Where do these social skills come from? Deming speculates that much of it is formed during early childhood development, although this is difficult to prove. The latest research that have followed children from a young age into adulthood have found strong correlations between kids’ socioemotional skills in kindergarten and adult outcomes such as employment and earnings.

The demand for social skills may also have implications for gender imbalances in the workplace. Women often choose careers that require more social interaction. If these jobs are in greater demand, with faster earnings increases, the gender gap may close quicker.

The optimism about the closing of the gender gap, though, is pegged by another study published in the same journal, by authors Matthew Wiswall and Basit Zafar. They test a group of New York University students about their preferences for flexible hours when they enter the job market, and find that students are willing to give up 2.8% of their annual earnings for a job with a centage point lower probability of job dismissal. They also find that students are willing to give up 5.1% of their salary to have a job that offers the option of working part-time hours. But there is a large gender difference: female students have a much higher average preference for flexible hours – 7.3% – compared to men – 1.1%. What the authors then do is to track the students after they graduate, and record their actual earnings four years later. Wiswall and Zafar find that those students with a high preference for work flexibility do actually end up in occupations with greater flexibility. But because women already prefer more flexible work even before they’ve started working, they also tend to earn less once they’ve entered the labour market, choosing jobs with lower pay and greater flexibility. At least a quarter of the gender gap in the labour market, the authors argue, can be explained by just these differences in preference.

These studies show how our social skills and preferences affect our labour market outcomes. Policies that aim to address labour market distortions, like race or gender gaps, should know that the roots of the problem may lie in preferences and skills moulded in the early years of development.

That is not to say that nothing can be done: for those going to university this year, perhaps one thing to keep in mind is that your future earnings depend not only on attending math class, but also on developing your social skills. Here’s one formula to remember: Math + beer = success.

>>> An edited version of this article originally appeared in the 1 February 2018 edition of finweek.


Written by Johan Fourie

March 13, 2018 at 08:00

Patience is not only a virtue

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Cologne Cathedral. Painting by Josef Langl

Patience is not only a virtue. It might also be the root of economic prosperity.

On 15 August 1248, the Archbishop of Cologne laid the foundation stone of a cathedral that would take 632 years to complete. One of the remarkable things about the cathedral, apart from the mesmerizing Gothic architecture of its twin spires that stand 157m tall, is that it was largely funded by civil society. How is it possible, one wonders, that a community can fund the construction of a building that they, nor their children, nor even their children’s children, would never see completed?

Patience is a virtue, says a fifth-century poem, but economists are increasingly confident that is a key building block of economic prosperity too. Two concepts are of relevance. The first is to what degree do you consider the future in your decision-making – your time preference. The second concept is the period of time that is relevant for you current decision-making – your time horizon. Patient people tend to have a high time preference and a long time horizon. And as more and more experimental evidence now show, so do successful people.

The study of patience was made famous by the Stanford marshmallow experiment. In the late 1960s, Walter Mischel offered children, in a controlled experimental environment, a choice between a small reward like a marshmallow immediately or a larger award (two marshmallows) if they waited 15 minutes. Some children immediately grabbed the marshmallow and swallowed it down. Others waited a while, and then had a bite. But several waited diligently until the fifteen minutes had passed for their second marshmallow. To their surprise, the researchers found in follow-up studies that children who were able to wait for the higher pay-off were also more likely to have better school outcomes, BMI and other life measures.

The question, of course, is whether patience is the result of genetic inheritance or environmental influence. In a 2007 Journal of Public Economics paper, Eric Bettinger and Robert Slonim found that parent’s patience are uncorrelated with their children’s patience, questioning the belief that it’s only nature at work. They also found, however, that mathematics scores and whether a child has attended a private school was also uncorrelated, suggesting that nurture’s influence is also limited.

The one result that most studies find is that girls tend to be more patient than boys. This has important implications for motiving children. Girls are more likely, for example, to respond to student performance incentives. A study that paid Israeli students conditional on their performance on university exams had a greater effect on girls. Knowing what determines patience can go a long way in helping kids perform better at school, and achieve better life outcomes.

While most research focus on individual-level outcomes, the question is whether patience also matters at the societal level? Global surveys now ask questions that allow us to deduce some measure of time preference or time horizon. The results suggest that while these generally correlate positively with GDP per capita, it is not always the case. Citizens of Botswana and Kenya, for example, are more patient, on average, than those of Japan and France. (South Africa, by the way, is very close to the world average.)

Patience does not only vary across space, but also across time. At a conference at Stellenbosch University during November last year, Jan Luiten van Zanden and Gerarda Westerhuis of Utrecht University presented a paper on how time preference has changed across the last few centuries. They argue that, during the Middle Ages in Europe, the ‘future’ became more important, in other words, people’s time preference increased. The consequence was that saving and investment increase, and that people started to accumulate capital with long time horizons. The construction of the Cologne Cathedral is one example of this. Why that is remains somewhat of a mystery, but there are clues in the changing (religious) beliefs and institutions of the time. Consider, for example, the emergence of corporations, initially religious institutions and guilds but later companies, notably the limited liability company, which would transcend the life of shareholders. The rise of big business would follow in the late nineteenth and early twentieth century, where salaried managers now focused on long-term value creation.

Van Zanden and Westerhuis argue, however, that this trend has reversed in the last few decades. Since the 1970s, ‘short-terminism’ is on the increase, reflected in the focus of short-term value for shareholders (and performance-related pay for managers). One of the factors that might explain this is the shift from modernism to post-modernism. Modernists believed that the future could be changed, for example, through strategic planning in a business environment or (in the case of the Soviet Union) even an economy. The rise of post-modernist beliefs – the belief that reality cannot be known and the future cannot be predicted or changed – has shifted our long-term gaze to the present. Instant gratification, exacerbated by social media, is now the order of the day.

There are valid reasons to question these preliminary findings. Companies like Alphabet (Google’s parent company), Apple and Facebook seem to be able to invest in new technologies where the pay-offs are only likely to be in the medium- to long-run. But it is difficult to imagine that we invest in something that we won’t see the end of: perhaps that is why tackling climate change is so difficult!

Time preference and time horizon remains vastly understudied topics. We know that time preference (roughly proxied for by people’s patience) matters at the individual level; more patient people are more ‘successful’ later in life. What we don’t know is why they are patient, and how to improve our impatient natures.

Similarly, we know that some societies, at certain times in history, had a longer time horizon. Those societies were then able to invest and accumulate, improving the prosperity of the generations to follow.

Consider this: children born in 2018 are likely to live to the year 2100. Are our political and business leaders factoring the year 2100 into their long-term strategies? Unlikely. Perhaps we need a bit more of the long-term horizon the inhabitants of Cologne had when they decided to build a cathedral 632 years before it was completed.

>>> An edited version of this article originally appeared in the 14 December 2017 edition of finweek.

The compelling case for technological optimism

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In September last year, I visited the Computer History Museum in Mountain View, California. The museum is dedicated to preserving and presenting all aspects of the computer revolution, from its roots in the twentieth century to self-driving cars today. What is remarkable is to observe, while walking through the more than 90000 objects on display, the profound change in technology over the last three decades. The mobile computing display, I thought, summarised this change best, showing the first laptop computers of the 1980s (see image above) to a modern-day iPhone. But what also became clear from the exhibitions was that those ‘in the know’ at the start of the revolution were right about the transformational impact of computers, but almost certainly wrong about the way it would affect us.

We are now at the cusp of another revolution. Artificial intelligence, led by remarkable innovations in machine learning technology, is making rapid progress. It is already all around us. The image-recognition software of Facebook, the voice recognition of Apple’s Siri and, probably most ambitiously, the self-driving ability of Tesla’s electric cars all rely on machine learning. And computer scientists are finding more applications every day, from financial markets – Michael Jordaan recently announced a machine learning unit trust – to court judgements – a team of economists and computer scientists have shown that the quality of New York verdicts can be significantly improved with machine learning technology.  Ask any technology optimist, and they will tell you the next few years will see the release of new applications that we currently cannot even imagine.

But there is a paradox. Just as machine learning technology is taking off, a new NBER Working Paper by three economists, Erik Brynjolfsson, Chad Syverson and Daniel Rock affiliated to MIT and Chicago, show something peculiar: a decline in labour productivity over the last decade. Across both the developed and developing world, growth in labour productivity, meaning the amount of output per worker, is falling. Whereas one would expect that rapid improvements in technology would boost total factor productivity, boosting investment and raising the ability of workers to build more stuff faster, we observe slower growth, and in some countries even stagnation.


This has led some to be more pessimistic about the prospects of artificial intelligence, and in technological innovation more generally. Robert Gordon, in his ‘The Rise and Fall of American Growth’, argue that, despite an upward shift in productivity between 1995 and 2004, American productivity is on a long-run decline. Other notable economists, including Nicholas Bloom and William Nordhaus, are somewhat pessimistic about the ability of long-run productivity growth to return to earlier levels. Even the Congressional Budget Office in the US has reduced its 110-year labour productivity forecast, from 1.8 to 1.5%. On 10 years, that is equivalent to a decline of $600 billion in 2017.

How is it possible, to paraphrase Robert Solow in 1987, that we see machine learning applications everywhere but in the productivity statistics? The simplest explanation, of course, is that our optimism is misplaced. Has Siri or Facebook’s image recognition software really made us that more productive? Some technologies never live up to the hype. Peter Thiel famously quipped: ‘We wanted flying cars, instead we got 140 characters’.

Brynjolfsson and co-authors, though, make a compelling case for technological optimism, offering three reasons for why ‘even a modest number of currently existing technologies could combine to substantially raise productivity growth and societal welfare’. One reason for the apparent paradox, the authors argue, is the mismeasurement of output and productivity. The slowdown in productivity of productivity in the last decade may simply be an illusion, as most new technologies – think of Google Maps’ accuracy in estimating our arrival time – involve no monetary cost. Even though these ‘free’ technologies significantly improve our living standards, they are not picked up by traditional estimates of GDP and productivity. A second reason is that the benefits of the AI revolution are concentrated, with little improvement in productivity for the median worker. Google (now Alphabet), Apple, and Facebook have seen their market share increase rapidly in comparison to other large industries. Where AI was adopted outside ICT, these were often in zero-sum industries, like finance or advertising. A third, and perhaps most likely, reason is that it takes a considerable time to be able to sufficiently harness new technologies. This is especially true, the authors argue, ‘for those major new technologies that ultimately have an important effect on aggregate statistics and welfare’, also known as general purpose technologies (GPT).

There are two reasons why it takes long for GPTs to be seen in the statistics. It takes time to build up the stock necessary to have an impact on the aggregate statistics. While mobile phones are everywhere, the applications that benefit from machine learning are still only a small part of our daily lives. Second, it takes time to identify the complementary technologies and make these investments. ‘While the fundamental importance of the core invention and its potential for society might be clearly recognizable at the outset, the myriad necessary co-inventions, obstacles and adjustments needed along the way await discovery over time, and the required path may be lengthy and arduous. Never mistake a clear view for a short distance.’

As Brynjolfsson and friends argue, even if we do not see AI technology in the productivity statistics yet, it is too early to be pessimistic. The high valuations of AI companies suggest that investors believe there is real value in those companies, and it is likely that the effects on living standards may be even larger than the benefits that investors hope to capture.

Machine learning technology, in particular, will shape our lives in many ways. But much like those looking towards the future in the early 1990s and wondering how computers may affect our lives, we have little idea of the applications and complementary innovations that will determine the Googles and Facebooks of the next decade. Let the Machine (Learning) Age begin!

An edited version of this article originally appeared in the 30 November 2017 edition of finweek.


Onwards and upwards

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I turn 35 today. I’ve heard a theory that increments of seven are momentous birthdays. I can see that. 14. 21. 28. And now, 35. I don’t feel much older, except for the fact that my body still hurts after Saturday’s half-marathon. Recovery just takes a little longer.

It has been a busy month, as the low frequency of my blog posts will illustrate. LEAP hosted the 7th African Economic History Network meetings, from 25 to 27 October, with more than 80 participants. The LEAP page on Facebook has all the photos from the conference. Emmanuel Akyeampong delivered the second LEAP Lecture (which should be available as a working paper soon) to start the conference, and Trudi Makhaya the final keynote. As I noted in my welcoming address, the remarkable thing about the AEH network is the young average age of participants. I also thought the quality of the presentations were excellent: an increase not only of the quantity but also the quality of research.

LEAP hosted another workshop on Monday and Tuesday. The workshop, sponsored by Utrecht University’s Centre for Global Economic History, was on ‘Time preference and time horizon’. I delivered the final keynote. My argument was that, just as our future orientation affects our current decisions, so do our perspectives on the past. We live in the Age of Nostalgia, as some recent scholars have argued, constructing an often romanticized version of history, and such (biased) views can have political and economic consequences. Think Trump and Brexit.

Welcoming people to Stellenbosch is always nice. I enjoyed many fruitful discussions over good wine with great friends. Now, though, it is time for a return to research before I accompany a group of Stellenbosch students on our annual trip to Leuven – more on that later – and then present at Utrecht, Gothenberg, Lund and Bologna.

It’s raining in Stellenbosch; a surprising, unexpected rain. That can only be a good sign of things to come.


Written by Johan Fourie

November 9, 2017 at 07:40

Policy uncertainty is killing investment in what matters

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Much has already been said about South Africa’s inefficient public sector. Not only has the public sector wage bill escalated beyond the realms of the sustainable, but this has come at almost zero public sector productivity growth. In other words, we are paying more for government to do less. Add to that the poor performances of state-owned enterprises like Eskom, the SABC and most notoriously, South African Airways, and it seems that there is little more that the South African government can do to hurt the prospects for economic growth.

But there is. A new NBER working paper, published by Jose Maria Barrero, Nicholas Bloom and Ian Wright, uses new data on about 4000 US firms to investigate the sources of uncertainty in the US economy. They first distinguish between short-term and long-term uncertainty, identifying the factors that cause each type of uncertainty. They then ask how each type of uncertainty affect firms’ behaviour.

Short-term uncertainty, they find, is caused by oil price volatility. In contrast, economic variables like the oil price has less of an effect on long-term uncertainty where political risk, like policy uncertainty, has a much larger effect. The important result is that short-term and long-term uncertainty have different consequences for firm behaviour. Short-term uncertainty affects employment; long-term uncertainty affects investment in research and development.

If we assume this is true for South Africa too, how would it play out? Volatility of several macroeconomic variables, like the oil price and exchange rate, cause higher short-term uncertainty. This would likely make firms unwilling to hire new workers, or make managers unwilling to offer higher wages. These are the consequences economic commentators typically cite when referring to an unstable macroeconomic environment.

But employment and wages are not the only variables affected by uncertainty. One of the key indicators of a thriving economy is businesses’ willingness to invest in research and development. Take R&D as a percentage of GDP, shown in the Figure below. There is large variation in the share that countries spend on research and development: Israel and South Korea, for example, spend more than 4% of their GDP on R&D. South Africa spend less than 0.8%. (This figure almost reached 0.9% in the 2006-2008 period, a period not surprisingly correlated with high growth rates.)


There is a strong positive correlation between countries that grow fast and those that invest in research and development. South Africa, unfortunately, significantly lags those countries at the technological frontier. It is important, though, to understand why this is the case. It is not only government that invests in R&D; in fact, more than half of all R&D investment in South Africa comes from the private sector.

So what will encourage businesses to invest more in R&D? Well, according to Barrero, Bloom and Wright, political risk and policy uncertainty is the biggest determinant of private sector investment in R&D. In a political environment with little policy coherence, business are unlikely to make investments where the returns can only be realized in the long-run. Even if the possible returns are substantial, a rational investment response to a murky policy environment would be to sit back and see what happens. Lower investment in R&D means falling further behind international competitors.

There are some in the South African government who realise this. Minister of Science and Technology, Naledi Pandor, has committed to doubling R&D expenditure as a percentage of GDP by 2020. This is commendable, but in the current budgetary environment, unlikely to get the support from the Minister of Finance. Other initiatives to get the private sector investing in R&D, like a refundable tax credit that will benefit small businesses, have not been implemented.

These problems are not unique to South Africa. As the authors argue: ‘Our findings are significant in the wake of recent events like Britain’s vote to leave the European Union and Donald Trump’s assumption of the US Presidency, which have generated considerable uncertainty over future economic policy around the world. As we have shown, such policy uncertainty is particularly linked with long-run uncertainty and in turn with low rates of investment and R&D that can have significant consequences for the global economic outlook in years to come.’

R&D is the bedrock of future prosperity. Political risk that leads to policy uncertainty hurts not only economic growth and employment creation, but also deters firms from investing in the one thing that can create prosperity for all. If the ruling party is serious about its slogan, it better start by enacting more coherent economic policy.

*An edited version of this first appeared in Finweek magazine of 21 September 2017.


Here we go again

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Gigaba (1 of 1)

Late last night, South African president Jacob Zuma fired Pravin Gordhan and Mcebisi Jonas as Minister and Deputy Minister of Finance, and appointed Malusi Gigaba (pictured) and Sifiso Buthelezi in their place. With this move, he has gained the keys to Treasury. Aside from Finance ministry, Zuma appointed 18 new ministers and deputy ministers, including Fikile Mbalula, the former Minister of Sport, as Minister of Police. Bathabile Dlamini, Minister of Social Development, whose incompetence was recently exposed when her actions risked the well-being of 17 million South Africans, remains in her portfolio.

It all sounds so familiar. In December 2015, Zuma fired then Minister of Finance Nhlanhla Nene and replaced him with Desmond van Rooyen. After the rand plummeted more than 5%, Zuma was forced to reverse his decision and appoint Pravin Gordhan in the position three days later.

I wrote a post immediately after the appointment of Van Rooyen. Most of the points I raised there are now valid again. Zuma has captured Treasury – with a Zuma-loyalist in charge, he can now sign off on projects that benefit him and his backers, the Guptas.

The question, again, is what to do. And again I have to say, I don’t know. I see calls on social media for mass action, but I am not too sure Zuma and his cronies would pay much attention. Blog posts, I fear, will also not have much of an impact. What I will do, however, is to encourage Treasury employees, many who are brilliant economists and also good friends, to remain in office, despite the obvious challenges that they will face with a Zuma-loyalist at the helm. How long, though, can one remain honourable and incorruptible in an environment where you might become complicit in whatever shady nuclear or other deals Zuma has up his sleeve?

What this reminds me of is a tweet by veteran Zimbabwean businessman Trevor Ncube:

If something doesn’t happen soon to reverse this process of decline – and this can only happen when Zuma is gone, although that will only be a start – we risk destroying the progress we’ve made since 1994. The irresponsible actions of last night will hurt the economy badly, from a weakening currency (which has already fallen by more than 3%) to almost definite downgrade, which means more money spent on paying loans than building roads, houses and clinics. And if Zuma’s pet projects, like a nuclear deal with Russia, is signed, the cost for South African taxpayers – and the opportunity costs for South Africa’s poor – will be horrific.

Prepare for a bumpy ride.


How to get good politicians

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South African President Jacob Zuma visit Berlin

Politicians can shape the fortunes of countries. Presidents, in particular, set the tone: balancing many stakeholder interests, their job is to create a unifying vision that should guide policy-making. Members of parliament act upon this vision, designing and implementing policies that affect the lives of millions of people. One would imagine, then, that those with the best aptitude for leadership get elected.

That is the theory. But in practice politics is a messy business. For many reasons, it is often not the smartest candidate who gets elected, or the most effective member who gets selected for higher honours. Some economic models even explain why it is not the most capable that move up: Someone without a proper education (but a charismatic personality) has a much higher chance to see greater returns in politics than in the private sector. (In technical terms, lower opportunity costs give the less able a comparative advantage at entering public life.) These selection effects are compounded by the free-rider problem in politics, where work effort is not directly correlated to political outcomes. In other words, according to this model, it is society’s ‘chancers’ that are more likely to end up in politics – and the hard-working, smart ones will tend to end up in the private sector.

Competency in public office is, of course, is not the only goal of a parliamentary system. Representation – having politicians that reflect the demographic and geographic make-up of society-at-large – is also a key concern. But competency and representation, at least theoretically, do not always correlate. Take the following example: a proportional representation system, like we have in South Africa, would require members of all districts to be represented. But what if one region – let’s call it Farmville – has few university-trained citizens, whereas another region – Science City – has many citizens with university degrees? A proportional representation system will necessitate some Farmville politicians also be elected to parliament, even though the Science City politicians will probably be best qualified for the job. In contrast, in a plurality rule system – where the candidate with the most votes gets the job – competency often trumps representation.

A new NBER Working paper – Who Becomes a Politician? – by five Swedish social scientists, casts doubt on this trade-off. Using an extraordinarily rich dataset on the social background and competence levels of Swedish politicians and the general public, they show that an ‘inclusive meritocracy’ is an achievable goal, i.e. a society where competency and representation correlate in public office. They find that Swedish politicians are, on average, significantly smarter and better leaders than the population they represent. This, they find, is not because Swedish politicians are only drawn from the elite of society; in fact, the representation of politicians in Swedish municipalities, as measured by parental income or occupational class, is remarkably even. They conclude that there is at best a weak trade-off between competency and representation, mostly because there is ‘strong positive selection of politicians of low (parental) socioeconomic status.

These results are valid for Sweden, of course, which is a country unlike South Africa. Yet there are lessons that we can learn. First, what seems to matter is a combination of ‘well-paid full-time positions and a strong intrinsic motivation to serve in uncompensated ones’. In other words, a political party in South Africa that rewards hard work for those who serve in uncompensated positions, are likely to see the best leaders rise to the top, where they should be rewarded with market-related salaries. Second, an electoral system which allows parties to ‘represent various segments of society’. Political competition is good. Third, the ‘availability of talent across social classes’. This, they argue, is perhaps unique to Sweden, known for its universal high-quality education.

This reminded me of our State of the Nation red carpet event, where the cameras fixated on the gowns and glamour of South Africa’s political elite. How do the levels of competency in our parliament, I wondered, compare to Sweden and other countries?

Let’s just look at the top of the pyramid. The president of Brazil, Michel Temer, completed a doctorate in public law in 1974. He has published four major books in constitutional law. The Chinese president, Xi Jinping, also has a PhD in Law, although his initial field of study was chemical engineering. Narendra Modi, prime minister of India, has a Master’s degree in Political Science. Former US president Barack Obama graduated with a Doctor of Jurisprudence-degree magna cum laude from Harvard University. Angela Merkel, chancellor of Germany, has a PhD in quantum chemistry. Most of these widely respected leaders gave up a top job in the private sector or academe to pursue a political career.

Politics is messy, but given the right conditions, it can still attract high-quality leaders. For that to happen, though, aspiring politicians must put in the hard yards, even if initially uncompensated, supported by a competitive political party system and broad access to quality education. South Africa, unfortunately, is still a long way from meeting these criteria.

*An edited version of this first appeared in Finweek magazine of 9 March.


Written by Johan Fourie

March 24, 2017 at 07:35