Archive for the ‘Research’ Category
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.
Story-telling is as old as civilization. Around the fire, in religious texts, and in children’s books, stories give us identity, teach us right from wrong, and inculcate us with the norms and values that help us make sense of the world around us.
Economists are beginning to understand that stories also shape our behaviour, and therefore our economic outcomes. In a new NBER paper, financial economist Robert Shiller, the 2013 Nobel-prize winner, calls for the study of what he calls ‘economic narratives’. He argues that the way we talk about certain events, the stories that were told during the Great Depression (of the 1930s) or the Great Recession (of 2007) or even the stories we tell of Trump’s economic policies today, affected (or will affect) the outcomes of these events. Business cycles, he explains, cannot only be explained by the rationality of numbers. The stories we tell, and how these stories spread, matter too.
Economic stories or narratives are simplified ways to help us understand the world. They can take many forms: from newspaper articles and books, to memes, anecdotes, and even jokes. They often appeal to us not because they account for all facts, but because they explain the world in a way that strengthens our existing biases and beliefs. And their success is unpredictable: consider how difficult it is to identify the next ‘hit’ on YouTube or cultural trend to go ‘viral’.
Shiller uses, well, a story to explain the impact of stories. One evening in 1974, at the Two Continents in Washington DC, economist Arthur Laffer had dinner with White House influentials Dick Cheney and Donald Rumsfield. They discussed tax policy, and Laffer took a napkin and drew an inverted-u graph. On the left side, tax rates were 0%, which means tax income was also zero. On the right side, tax rates were 100%, which meant that no-one would work and tax income would also be zero. The point of the curve was to show that there is an optimal tax rate where tax income cannot increase further, whether you increase or decrease tax rates.
This meeting in 1974 would not have been remembered, was it not for the story-telling powers of Jude Wanniski, who wrote a colourful article in National Affairs about the dinner four years later. The story went viral (see image), and had a massive impact on Ronald Reagan’s election as US president in 1980 and his commitment to cutting taxes. (He argued that cutting taxes could increase tax revenue because America was on the wrong side of the Laffer curve). This story was so powerful that a napkin with a Laffer curve is today displayed in the National Museum of American History.
Shiller is, of course, not the first to argue that stories matter. A few years ago, Barry Eichengreen, professor of Economics at UC Berkeley, explained in his presidential speech to the Economic History Association that, while scientists use deductive or inductive reasoning in their research, policy-makers often rely on analogical reasoning. He knows this from experience: when the severity of the Great Recession became known in 2007, policy-makers realised they had to act fast. Had they followed a deductive approach, they would have had to agree on the theoretical reasons for the crisis. Eichengreen argues that this was almost impossible given the deep divides in the field of macroeconomics. Had they followed an inductive approach, they would have had to rely on statistical evidence, much of which was not available immediately.
So instead they turned to an event that they had studied: the Great Depression of the 1930s. Ben Bernanke, who was a student of the Great Depression, used analogical reasoning to ensure that the same mistakes were not repeated. Expansionary monetary and fiscal policy followed. The analogy with the Great Depression also made it easier to communicate their policy response to the broader public. Instead of trying to explain theory or statistics, they could construct a narrative that helped people understand why quantitative easing or fiscal stimulus was necessary.
If stories matter in shaping our response to economic events or in persuading us of the validity of some economic policies, what should economists do about it? Shiller suggests that we should incorporate textual analysis into our research: “There should be more serious efforts at collecting further time series data on narratives, going beyond the passive collection of others’ words, towards experiments that reveal meaning and psychological significance.” But this is difficult: “The meanings of words depend on context and change through time. The real meaning of a story, which accounts for its virality, may also change through time and is hard to track in the long run.” New techniques in data science may help.
Eichengreen proposes more emphasis on the study of history. Consider the case of a bank failure in South Africa today. What will we use as policy response: theory, statistics, or earlier bank failures, like Saambou and African Bank? Probably the latter. The problem, Eichengreen warns, is that there is not a single version of history. We all have our ideological glasses through which we look at the past. This is especially true when the facts of what had happened during these past failures are not widely known. The recent Bankorp saga comes to mind.
Because ‘historical narratives are contested’, Eichengreen suggests, we should see ‘more explicit attention to the question of how such narratives are formed’. In other words, if we want to improve our understanding of the world and our ability to predict the future, it’s time economists learn how people tell stories, and how these stories persuade us to behave differently.
*An edited version of this first appeared in Finweek magazine of 23 February.
In his 1936 book that would give theoretical support to Franklin D. Roosevelt’s New Deal, the British economist John Maynard Keynes wrote the following: “The ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. Indeed the world is ruled by little else.”
There is little doubt that the ideas of economists – their models or theories or ideologies – have had a profound influence on our daily lives. To give just one example: when South Africa’s Monetary Policy Committee decides whether to lower or raise the interest rate, they do so based on a model that predicts the likely effects of the policy change.
But there is not one universal model. Macroeconomists, depending on their school of thought, have different beliefs about how the real world works. Some, like those that propound Real Business Cycle (RBC) theory, argue that business cycle fluctuations are the result of exogenous changes to an economy, and because wages and prices are flexible, there is little that governments can do through fiscal and monetary policy in a downswing. Others, like New Keynesians, see a greater role for government intervention because of things like sticky wages and prices.
Though these ideological disagreements in macroeconomics are nothing new, one would expect that the competition between these ideas would ultimately lead to a better way to explain what is happening in the world. This is, however, not the case, and one of the main reasons the World Bank’s new chief economist, Paul Romer, formerly professor at Stanford and New York University, recently wrote a stinging critique of macroeconomics. “I have observed more than three decades of intellectual regress”, he begins, explaining that macroeconomics has gone down a wrong path and have now reached a dead-end.
The basic premise is this: macroeconomists’ increasingly sophisticated models have ignored real-world evidence. Simon Wren-Lewis of Oxford University explains: “If we look at the rise of Real Business Cycle (RBC) research a few decades ago, that was only made possible because economists chose to ignore evidence about the nature of unemployment in recessions.”
Why would scholars willingly choose to ignore real-world evidence? “The obvious explanation is ideological” says Wren-Lewis. “I cannot prove it was ideological, but it is difficult to understand why – in an area which … suffers from a lack of data – you would choose to develop theories that ignore some of the evidence you have.” Ironically, this shift came at a time when the rest of the profession, fields like labour economics, development economics and international trade, shifted the other way: from theory to empirics.
To be fair, RBC models have become less popular, replaced by ‘New Keynesian’ models. These ones make more realistic assumptions of the real world, like adding sticky wages, but still share many of the same concerns. Because these new generation models, known as Dynamic Stochastic General Equilibrium models, are ‘built from the ground up’ (in other words, built on microeconomic foundations) they are far more appealing theoretically (and technically more difficult to construct) than the structural models of the previous generation, models which only considered the interlinkages of different macroeconomic variables. But in practice they turned out to be less than satisfactory: DSGE models require the modeller to make many assumptions about how individual agents behave, and it turns out that this behaviour is difficult to measure, making the assumptions rather subjective. The result is that, though theoretically plausible, the outcome is often far removed from the real-world evidence. And, most importantly, forecasts – that thing that most people think is an economist’s only job – are often far less accurate with DSGE models than with multiple-equation structural models. That is why most reserve banks, including our own, still mostly rely on structural models to help predict the future outcomes of their policy decisions.
So why continue with DSGE models? This is a consequence of the way scholarship works. In a blog post in response to Romer, Narayana Kocherlakota of the University of Rochester writes: “We tend to view research as being the process of posing a question and delivering a pretty precise answer to that question. In this process, machines that can be used by many scholars to generate answers to wide ranges of questions are highly prized. The King-Plosser-Rebelo real business cycle model was one such machine. The New Keynesian three-equation model is another. And people who have the mathematical and computational skills to make machines even more powerful, so that they can answer even more questions, are naturally highly valued.”
Yet if these ‘answers’ increasingly abstract away from reality, we are not much better off than before. Keynes, who developed a new theory when the existing paradigm could not explain the real world anymore, knew this all too well: “Practical men, who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist.”
For academic macroeconomics to regain its influence, it needs to be, as Kocherlakota writes, “a lot more flexible in our thinking about models and theory, so that they can be firmly grounded in an improved empirical understanding”. This won’t be easy. As Locherlakota says, it will be “hugely messy work”. Will this generation’s John Maynard Keynes please stand up?
*An edited version of this first appeared in Finweek magazine of 9 February.
Can African countries sustain the relatively high growth rates they attained since 2000? At the start of 2017, putting aside the newsworthy political shifts and the fear of many that the developing world has entered a ‘secular stagnation’, this remains the most vexing question for those of us on the African continent.
It is not a question with an easy answer. The stellar economic performance of several African countries has created an ‘Africa rising’ narrative where further progress – and catch-up to the developed world – seems inevitable. A more pessimistic counternarrative argues that this growth, from a low base, is largely the result of favourable commodity prices and Chinese investment. Both narratives had, unfortunately, made little use of either economic theory or history.
Enter Dani Rodrik, professor of International Political Economy at the John F. Kennedy School of Government at Harvard University, who tackles this question in a new paper in the Journal of African Economies. He first shows that many African economies have indeed improved since 2000, but that many, including Senegal, the DRC, the Ivory Coast and Zambia, remain on levels below those immediately following colonialism (around 1960). The second fact he establishes is that the rapid growth of the last dozen years has not lead to a large structural transformation of the economy. Whereas rapid growth in south-east Asian economies during the late twentieth century resulted in the growth of manufacturing, a more productive activity than subsistence farming, high growth rates in Africa have not had any effect on the relative size of manufacturing. In fact, in many countries, the size of the manufacturing sector has actually declined since 1975.
Rodrik attributes these changes not so much to factors unique to Africa – like a poor business climate or weak institutions or bad geography – but to a global trend of deindustrialisation. Even Vietnam, a country which has recently experienced rapid growth, has not seen much growth in manufacturing. And Latin American countries, which have decidedly better institutions than three decades ago, have also not seen much growth in manufacturing. Technological change – the move to automation, for example – is one likely reason.
So despite high growth rates, African countries have not industrialised – and, in fact, may have even begun to deindustrialise. This is why Rodrik is pessimistic about Africa’s future growth prospects. He nevertheless concludes by considering potential scenarios in which Africa can indeed sustain high growth, and identifies four possibilities: 1) To revive manufacturing and industrialise, 2) To generate agricultural-led growth, 3) To generate service-led growth and 4) To generate natural resource-led growth.
Let’s start with agriculture. Although many African countries have a lot of potential to expand their agricultural sectors, productivity in the agricultural sector remains low. Many farmers are subsistence producers, with low economies of scale. Such a scenario will require a reversal in the current trend away from agriculture. A recent study by Diao, Harttgen and McMillan show clearly how the share of agriculture is falling, particularly as women older than 25 are moving to the cities and into manufacturing and services. This trend seems irreversible, even if changes to technology (like seed varieties or market access opportunities) or institutions (like private property) are made, which means that an agricultural-led high growth scenario seems highly unlikely.
A natural resource-led strategy also seems unlikely for most African countries. Yes, most countries on the continent are well-endowed with resources, but the problems of the Natural Resource Curse and Dutch Disease are well known. It may be an option for some small economies, like Botswana has shown, but one has to question to what extent it can be sustainable beyond a certain level of income.
A third option is to reverse the trend of deindustrialisation. Because a growing manufacturing base seems to be, at least if we consider past examples of industrialisation, the only way to increase labour productivity over a sustained period of time, this is the option preferred by many development agencies. Yet there are many obstacles in the way of a thriving manufacturing sector, including poor infrastructure (transport and power in particular), red tape and corruption, low levels of human capital, and political and legal risk. But as explained earlier, Rodrik believes that even if these (very difficult) barriers can be overcome, it is not clear that manufacturing will return. The Fourth Industrial Revolution may completely alter the nature of manufacturing away from absorbing unskilled labour to capital and knowledge-intensive production. As I’ve said before, it is dangerous to follow a twentieth-century blueprint when production technologies are so different.
That leaves us with one scenario: services-led growth. Services have traditionally not acted as an ‘escalator sector’ as Rodrik explains. The problem is that services typically require high-skilled labourers, one thing that is in short supply in a developing economy. Rodrik does acknowledge, though, that the past will not necessarily look like the future. “Perhaps Africa will be the breeding ground of new technologies that will revolutionise services for broad masses, and do so in a way that creates high-wage jobs for all. Perhaps; but it is too early to be confident about the likelihood of this scenario.”
I don’t see an alternative, though. Yes, some countries, like Mozambique or Tanzania, will be able to expand their agricultural sectors – but higher productivity will probably mean larger farms with fewer workers. A few small countries will be able to benefit from natural resources – from diamonds to rare minerals like tantalum (used in cellphones and laptops); oil-producing countries will struggle, though, as the cost of renewable energies keeps falling. And some coastal countries may even develop their manufacturing sectors, like Ethiopia and South Africa. But for most of Africa, services offer the only reprieve from low productivity, low-wage jobs. From semi-skilled jobs like call-centres and virtual au pairs (apparently the next big thing) to professional services like accountants and designers and programmers, digital technologies must help leapfrog the barriers of poor infrastructure, bad geography and weak institutions. If it cannot, Dani Rodrik’s pessimistic vision of Africa’s future is likely to come true.
*An edited version of this first appeared in Finweek magazine of 26 January.
Humans know how to adapt. We have populated the planet not because we found an agreeable environment everywhere, but because we were able to adapt to the diverse and often hostile environments we moved into. And so it is today. To survive and thrive, we need to adapt to the global forces of our times, from climate change to automation.
Those with the freedom and ability to adapt to these global forces will benefit most. Take automation. Artificial intelligence and robotics now allow most tasks that manual labourers perform to be done without human intervention. One of the most exciting technologies revealed at the end of 2016, from my perspective at least, is an automated washing and ironing machine. Dirty clothes go in on one side and the fully-ironed clothes, folded by tiny robotic hands inside the machine, come out on the other side. Finally those dreary Sunday afternoon ironing exercises will be a thing of the past! Collectively this technology will save millions of productive human hours, particularly for women who in almost every society are still responsible for most home labour.
And yet, this wonderful new technology won’t be welcomed by everyone. South Africans employ more than 1 million domestic workers (or more than 8% of the work force), most of whom are women from poor households. If the cost of this new machine falls considerably in the next decade (and minimum wages continue to rise), we might soon see a significant decline in demand for ironing services. Because poor South Africans do not have the freedom to adapt to these new technologies, unemployment and inequality will likely increase.
There are many other examples. Tesla and other car companies are working on self-driving cars (no need for taxi drivers) and, which is likely to have an even bigger effect, self-driving buses. Truck driving is America’s sixth most common occupation. Or consider McDonald’s most recent innovation: self-ordering counters. No need to employ more expensive and unreliable staff. How long until everything in a McDonald’s restaurant is automated, from food preparation to servicing and cleaning? Amazon has recently revealed its plan to open 2000 automated grocery stores across the US. And then there are the many disruptive digital technologies, which The Economist editor Ryan Avent writes about in his latest book The Wealth of Humans.
The political consequences of these supersonic changes are unknown. As Avent notes, we are the first generation to live through an industrial revolution. There is little in history that tells us how society will react to such rapid changes. He predicts social unrest, unless government or civil society can reform social welfare programs on a massive scale. We have already seen this in South Africa and elsewhere: the democratic process, for many, is too slow and cumbersome. Service delivery protests, the #MustFall-movement and the global shift towards a more nativist conservatism suggest that the voices of those at the bottom of the income distribution will be heard outside the ballot box.
More creative solutions to support those left behind by the benefits of technological innovation and globalisation must be found. One idea is to institute a basic income grant that would give every person in South Africa a monthly stipend. This is no novel idea – Thomas Paine proposed a similar idea in 1797 – but economists are increasingly willing to put the idea in practice: Utrecht, a beautiful Dutch city south of Amsterdam, will next year give several hundred of its inhabitants an annual monthly stipend of 960 euro.
The concern is that people opt out of productive labour if they receive money for free. The consensus, though, is that this is unlikely: the aspirational drive of humans to move up the income ladder will push them to work hard regardless. What a basic income grant does is to make sure the ladder is solidly grounded.
But even a basic income grant won’t do enough. The rapid change will bring about psychological and sociological consequences that are hard to predict. Which social policies to implement, from early-childhood development to adult retraining programmes, in order to combat the technological disruption will be important research questions in the next few years. Creative use of technology, ironically, might be one solution.
Donald Rumsfeld famously quipped that there are known knows (the things we know we know), unknown knows (the things we know we don’t know), and unknown unknowns (the things we don’t know we don’t know). The future used to be mostly unknown knows. With some degree of likelihood, we could analyse the past and make conjectures, following somewhat linear trends, about what the future might hold. Change was incremental; we had time to adapt.
The period of rapid change we have seen since the dawn of the Internet is only likely to accelerate. As a species, we have never been required to adapt this fast, and not everyone in society will have the freedom and ability to do so. This will lead to social conflict. To minimise the consequences of this social conflict, the greatest challenge of the next decade is to enable as many as possible to adapt to the inevitable unknown unknowns of our rapidly-changing world.
*An edited version of this first appeared in Finweek magazine of 29 December.
**As this is my first post of the year, I would like to wish all readers a productive and memorable 2017. Let’s hope this will be a good one.
One of the baffling things in explaining the Industrial Revolution is that education, that pillar most economists believe to be critical for economic growth, seems to have played a relatively minor role. Universal public education was a consequence rather than a cause of the Industrial Revolution. Eighteenth-century England did not first have a skilled population before they had an economic transformation; the uncomfortable truth is that it was the other way round.
This uncomfortable truth does not suggest that formal education was completely unimportant. It suggests, instead, that much of what caused the Industrial Revolution was the scientific knowledge obtained by an elite group of highly skilled artisans, inventors and entrepreneurs. It was not the average level of education of every Brit that mattered. Most of the breakthrough technologies of the era – the Spinning Jenny, the steam engine – came instead from upper-tail tinkerers who had hoped to make a profit from their innovations.
A wonderful new research paper by economists Mara Squicciarini and Nico Voigtländer in the Quarterly Journal of Economics confirm this. They use the subscriber list to the mid-eighteenth century French magazine Encyclopédie to show that knowledge elites mattered in explaining the first Industrial Revolution: in those French towns and cities where subscriber density to the magazine was high, cities grew much faster in the following century, even when controlling for a variety of other things, like wealth and general levels of literacy. Their explanation? Knowledge elites (engineers, scientists, inventors) raise the productivity at the local level through their piecemeal innovations, with large positive spill-overs for everyone around them.
Fast-forward to the twenty-first century. High-skilled workers are the stars of today’s knowledge economy. Their innovations and scientific discoveries spur productivity gains and economic growth. Think, for example, of the immense contributions of Sergey Brin’s Google, or Elon Musk’s Tesla, or even Jan Koum’s WhatsApp. It is for this reason that the mobility of such highly talented individuals has become such an important topic – consider that all three individuals mentioned above are immigrants to the United States. There is little doubt that the most prosperous economies of the future will be the ones to attract the most skilled talent.
Which is why understanding the push-and-pull factors of current global talent flows are so important, and the subject of an important new article in the Journal of Economic Perspectives. The four authors begin with the facts. High-skilled elites are more mobile: between 1990 and 2010, the number of migrants with a tertiary degree increased by 130%; those with only primary education increased by only 40%. More of these high-skilled migrants depart from a broader range of countries and head to a narrower range. While OECD countries constitute less than a fifth of the world’s population, they host two-thirds of high-skilled migrants. 70% of these are located in only four countries: the United States, the United Kingdom, Canada and Australia.
The United States, unsurprisingly, dominates all rankings. Since the 1980s, of all the Nobel Prizes awarded for Physics, Chemistry, Medicine and Economics, academics associated with American institutions have won over 65%, yet only 46% of this group was born in the United States.
One fascinating and underappreciated fact of global migrant flows is the role of highly educated women. Between 1990 and 2010, high-skilled women immigrants to OECD countries increased from 5.7 to 14.4 million; in fact, by 2010, the stock of highly skilled women migrants exceeded male migrants! As the authors note, ‘Africa and Asia experienced the largest growth of high-skilled female emigration, indicating the potential role of gender inequalities and labour market challenges in origin countries as push factors.’
And what about South Africa? The authors calculate the emigration rates of high-skilled individuals by country for 2010, and plot these on a graph. South Africa is a clear outlier: emigration of high-skilled individuals is the sixth highest of the countries included, and by far the highest for countries with more than 10 million people. This is worrisome. True, some of this emigration is made up by high-skilled immigrants from our African neighbours, like Zambia and Zimbabwe, who also have high emigration rates. But the fact remains: our economic outlook will remain precarious if we continue to shed high-skilled individuals at these exorbitant rates.
Is there something to do? The authors mention various push and pull factors that affect the decision to migrate, from gatekeepers that pull the best talent by giving citizenship based on a points system to repressive political systems that suppress freedom of speech and scientific discovery and push the best and brightest to emigrate. If South Africa is to prosper, high-skilled individuals should be recruited and retained – not pushed to find opportunities elsewhere. Protests at universities do not help; providing residency to graduates, as the South African government has proposed, will.
In the knowledge economy, knowledge elites are the bedrock of success. If we are to learn from history, cultivating them should be our number one priority.
*An edited version of this first appeared in Finweek magazine of 3 November.