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Posts Tagged ‘jobs

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

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.

The future of work: don’t fear the robots, embrace them

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One of the things of being an economist teaching at a university is that parents inevitably think you have a lot of insight about the future of the job market. What is the ‘safest’ programme, parents typically ask, that will guarantee Ryan or Samantha a well-paying job at the end of three years? Translated: How do I maximize the return on my investment?

As with any investment, there are risks. Not all university students graduate; a recent study on higher education pass-through rates – by Stellenbosch University’s Research in Social and Economic Policy (ReSEP)-unit – shows that less than 40% of South African students attain their degree within four years of starting (remember, most degrees are three-year programmes). Only 58% of students complete their degree within 6 years. (The numbers are particularly low at UNISA, a distance-learning university, where only 28% of students complete their degree within six years.) There is a good chance Ryan never completes his degree in the first place, leaving only debt, psychological scars and forgone income in the labour market behind. The researchers also find that, while matric marks are strongly correlated with access to university, they matter less for university success. Samantha may have been a bright spark in school, but that is no guarantee that she will be successful at university.

But what worries most parents about their investment is not so much the internal factors that lead to success (like getting Ryan to attend class, one of the most important determinants of success), but the external threats that may affect his chances of finding a job. The biggest culprit nowadays: robots.

The threat of robots is everywhere, it seems. Autonomous vehicles will soon substitute the most ubiquitous job of the twentieth century – taxi and truck drivers. Blue-collar jobs are first in the firing line, from farm labourers replaced by GPS-coordinated harvesters to postal workers replaced by, well, e-mail. But white collar work – which is often the domain of university graduates – will be soon to follow: lawyers, accountants, and middle-management, to name a few that have been singled out. Basically any job with repetitive tasks run the risk of robotification.

Parents are eager to know which job types are most likely to succumb to the robot overlords. If lawyers are of no use in the future, why study law? This is, of course, a reasonable concern. Several of the standard activities undertaken by lawyers are repetitive, easily-automatable. And artificial intelligence challenges even non-repetitive work: it allows software to search through large volumes of legal texts at a fraction of the time a paralegal would during the ‘discovery’ phase of a case. Not so fast, says Tim Bessen, an economist at the Boston University School of Law. He shows that, in the period that this software has spread through the US, the number of paralegals have increased by 1.1% per year. Because the costs of undertaking these ‘discovery’ services have fallen dramatically as a result of the new technology, the frequency of such services have increased even more, requiring more paralegals, not fewer.

It is not only that robots substitute existing repetitive work, it is that they can do it so much better. Although robots and their algorithms are not entirely objective – because algorithms adjust to human behaviour, they can often reinforce our prejudices – their biases tend to be more transparent and corrigible. A new NBER study shows just how robots could transform one of the oldest human professions – the judge – and in so doing realise huge societal benefits. The five authors, three computer scientists and two economists, want to know the following: can US judges’ decisions be improved by using a machine learning algorithm?

Every year, more than 10 million Americans are arrested. Soon after arrest, a judge must decide where defendants will await trail – at home or in jail. By law, judges should base their decision on the probability of the defendant fleeing or committing another murder. Whether the defendant is guilty or not should not enter this decision.

To investigate whether judges make fair decisions, the authors train a face recognition algorithm on a dataset of 758 027 defendants in New York City. They have detailed information about these defendants: whether they were released, whether they committed new crimes, etc. They then construct an algorithm to process the same information a judge would have at their disposal, and the algorithm then provides a prediction of the crime risk associated with each defendant.

Comparing their results to those of the judges, they find that an algorithm can have large welfare gains: a ‘policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates’. All categories of crime, including violent crimes, decline. The percentage of African-Americans and Hispanics in jail also fall significantly.

Will robots replace judges? Probably not – but the quality of judges’ decisions can be improved significantly by using robots. This will be true in most other skilled professions too, from law to management to academic economists like me.

Matriculants on the cusp of their careers (and their anxious investor-parents) have no reason to fear the coming of the robots. If Ryan and Samantha, regardless of their field-of-study, see them as complements – by learning their language, and how to collaborate with them – the benefits, for themselves and society-at-large, will be greater than the costs.

*An edited version of this first appeared in Finweek magazine of 4 May.

Written by Johan Fourie

May 26, 2017 at 09:33

Again: What to study in South Africa

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Big shots: Joseph Stiglitz, Jeffrey Sachs and Edward Glaeser, amongst others, were at the ASSA meetings in San Francisco

Big shots: Joseph Stiglitz, Jeffrey Sachs and Edward Glaeser, amongst others, were at the ASSA meetings in San Francisco

My most popular post on this blog – by far! – remains the Why and what to study in South Africa entry I wrote in May 2013. My advice was pretty simple: if you can do math, study a degree where you will develop your math skills further. Math and statistics, combined with economics, computer science and/or engineering sciences, will make you an incredibly desirable employee: both in South Africa and abroad.

I was reminded of this advice when I attended the world’s largest gathering of economists last week in San Francisco. The ASSA meetings spanned three days, had more than 500 sessions with more than 12 000 participants. I presented a paper with Dieter von Fintel (on persistence and reversal of fortune) in a session on apartheid – with excellent papers by Johannes Norling (on fertility), Dan de Kadt and Melissa Sands (on voting), and Martine Mariotti and Taryn Dinkelman (on remittances and migration). And there were many other excellent sessions: notable ones I attended was a session on long-run inequality (with a very entertaining Philippe Aghion), a session on writing books (see photo), and a session on early childhood development (where Melissa Kearney presented a paper I reported on here).

But what reminded me about my math advice was a discussion during one session about the need to diversify academia. One commentator mentioned that the reason for the slow diversification of economics faculty is the high level of mathematics required to do a PhD in Economics in the US. (The slow transformation was quite apparent at the conference: the vast majority of attendees were white males.) Much like in South Africa, black students in the US would often opt out of math courses because of poor grades or a bad experience at school. They are thus more likely to end up in the humanities and less likely to study more ‘mathy’ degrees, like economics.

Yet, there is an increasing realisation that the current state of affairs – the white, male bias – is neither fair nor sustainable. Harvard’s chair of the Economics department, David Laibson, confirmed this: he was quite explicit that Harvard will focus on hiring more diverse staff during his tenure. This is likely to increase the demand for female and black economists (and engineers, scientists, actuaries, statisticians) significantly in the foreseeable future. But to suspect that the market will automatically adjust – that the higher demand will induce more black students to study economics – is unlikely. That is why there are several programmes in the US to inform high school students of the possibilities that economics can offer, showing them the wide applicability of economics in their daily lives. (Economists, for example, study how Discovery Vitality can get their members to live healthier lives, they study how to make things like Uber and Airbnb more efficient, they study what’s wrong with the school system and how to improve it, they study how firms compete and grow, they study the minimum wage and its impact and, yes, they also study financial markets and the banking system. Just watch this video).

Economics departments in South Africa are certainly not doing enough to promote the field to young scholars. Prospective students have a very narrow view of what an economist does, if they have a view at all. I know I never thought much about Economics before I arrived in my Economics 1 class. But the truth is that there is a massive demand for good economists, both in South Africa and, as I witnessed for myself in San Francisco, abroad. South Africa’s services industry needs far more graduates with strong mathematical or statistical backgrounds; the industries of the future will require the analyses and interpretation of (big) data, skills for which economists are well-equipped.

So, what should you study? This is an incredibly tough decision to make at a young age, and it almost certainly will have a big impact on the quality of your life. But here goes: if you have the ability, you can narrow the risk that your choice will turn out to be a bad one by developing your math and stats capabilities. And if you really want to enjoy what you’re doing (yes, I’m biased), combine it with Economics.

Written by Johan Fourie

January 13, 2016 at 19:00

Fort Hare deserves a better future

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Two former graduates of the University of Fort Hare: Oliver Tambo and Nelson Mandela

Two former graduates of the University of Fort Hare: Oliver Tambo and Nelson Mandela

Imagine a university that trained most of the leaders of the largest political party of a country. A university which educated many past and existing leaders of several other countries. A university which trained thousands of doctors, lawyers and other civil servants. A university which educated a Nobel Peace Prize winner.

This university would be the flagship of any country’s education system, yet in South Africa it is not. Fort Hare, despite its illustrious history, is not ranked in the top 10 universities in South Africa. It barely makes it into the top 100 in Africa.

And, unfortunately, UFH seems poised to remain there.  The university has a R100-million deficit. It has reportedly used National Student Financial Aid Scheme (NSFAS) money – intended to subsidise students from poor backgrounds – to pay staff salaries. And only last Friday it emerged that the university’s registrar, Prof Mike Somniso‚ was recorded saying to a colleague that he will unleash the ANC’s uMkhonto weSizwe military veterans on DASO, the Democratic Alliance’s Student Organisations that, surprisingly, won the Student Representative Council elections last year. Let’s think carefully about that: a university registrar calling for violence against students.

Here is Max du Preez on Facebook about the recording:

So how come this is not a scandal in South Africa? A senior administrator at a university planning violent attacks on student leaders to make it impossible for groups other than the ANC to operate on campus? Where is the reaction of the minister of Higher Education – this was revealed on Friday morning already. Have we written off Fort Hare as an academic institution? Isn’t it perhaps time to launch an ‪#‎OpenFortHare‬ campaign?

It is difficult not to become cynical about the attempts on other South African campuses to reform higher education when Fort Hare, a beacon of hope for many black scholars in South Africa and elsewhere in Africa during apartheid’s darkest days, is withering away. Just imagine, some would say, what the response would have been had a UCT or Stellenbosch or Wits registrar called for violence against students!

Instead, we find a deafening silence. No resignation. No national twitter campaign ostracizing the individual or institution. No call to appear before Parliament’s Higher Education Portfolio Committee. (To be sure, UFH was due to appear on the 23rd of September to explain the charges of fraud, but the meeting was postponed indefinitely.)

Those of us who care deeply about the state of higher education in South Africa are left bewildered. What will it take to transform Fort Hare (and many of the other formerly black universities) into a national asset that can deliver minds that can contribute to a more prosperous South Africa? Funding? Management? Student activism? I don’t know, but the many brilliant minds that go there – I know, one of my own PhD students is a former graduate – deserve better.

I don’t want to belittle the legitimate demands for transformation at South Africa’s top universities. But the number of classrooms and lecturers at these universities are simply too few to provide a quality education to all who want it. If we want to improve South Africa, we – the government, yes, but also civil society like the campus movements pushing for change – need to shine a light on all places that can provide quality education for thousands of students who won’t find places at (or cannot afford) the top universities. That includes Fort Hare.

This is currently not happening, which means that the financial mismanagement and the utterances of a registrar is not delivering on Fort Hare’s vision of In lumine tuo videbimus lumen (In Thy Light We See Light), a vision that had inspired the likes of Oliver Tambo, Nelson Mandela, Govan Mbeki, Robert Sobukwe and Mangosuthu Buthelezi.

We need to #LightUpFortHare. Their future students (and the legends of the past) deserve nothing less.

Written by Johan Fourie

September 30, 2015 at 07:33

What can you do with only matric?

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Of the 1 252 071 South African students who entered Grade 1 in 2003, only 150 752 (or 12%) matriculated with access to a Bachelors degree at university. That single statistic encapsulates the sad reality of the South African education system. Even worse, a large proportion of the 12% won’t ever make it to university, either because they have alternative plans or, more likely, because they cannot afford it. Those who make a success of their university education will go on to find well-paying jobs; those without access (or who fail) will have to compete with the 88% remaining 18-year old’s for a job in a country with a broad unemployment rate of close to 40%. The severe income inequality in South Africa today is perpetuated by the inequality of our education system.

In addition, fewer unskilled and semi-skilled jobs are being created. Mechanisation and computerisation mean that robots are increasingly doing the jobs of unskilled workers; walk into a motor vehicle assembly plant, or visit a maize farm, or go to a supermarket in a developed country and you can easily see how robots and machines are replacing human labour. I even get phone calls from electronic telemarketers (surely, those can’t be successful?). So, given the large supply of unskilled labour in South Africa and the dearth of demand for such workers, what can those matriculants without access to university do?

A lot. Although there are many jobs that are becoming redundant because machines can perform them better, technological innovation can also be complimentary to unskilled labour, i.e. robots can also create jobs for poor people. In contrast to the first phase of industrialisation – when poor, unskilled (blue collar) workers worked on farms or in mines and (manu)factories and rich, skilled (white collar) workers had cosy desk jobs in the services industries like banking and insurance – the trend is reversing: the highest paying jobs are now building and programming the robots who do all the farming and mining and manufacturing, while poorer, less-qualified workers work in the services industries. Yes, some service industries, like lawyers and accountants and dentists, are still incredibly well paid, but other service industries that provide work for unskilled labourers are also flourishing.

Consider cellphone repair shops in townships. A decade ago, only fixed-line pay phones were available in poor areas, and they were serviced by technicians of Telkom. Now, with a little bit of ingenuity and experience, anyone can be a cellphone (or laptop) repair man (or woman). Smart phones are not only connecting companies with clients, but also with a work force they would never have had access to. As The Economist writes this week, the future of work will increasingly be outsourced. That is true both for skilled occupations, like lawyers and HR and management consultants, but also for unskilled labourers. Consider Uber, a car service which was founded in San Francisco in 2009 and which already operates in 53 countries including South Africa. Technology allows anyone with a decent car to act like a taxi service, creating jobs for people that only need a drivers license. It will certainly injure the existing taxi services. But it is generating far more new jobs than it is destroying, simply because far more people will use the new (cheaper and more efficient) service. (Unfortunately, government regulations are very slow to adapt to new technologies, and it is incredibly disappointing that Uber cars are now being pulled off Cape Town roads simply because government officials are unwilling, or unable, to understand the immense benefits of the new service, killing jobs for those who need it most.) Or consider Handy, a company where you can find someone to clean your house, or do small plumbing jobs, or paint, or fix the paintings to the wall. Technology (such as smart phone apps) now allow the providers of such services to be matched to the suppliers of such services at very low cost, creating jobs for the unskilled.

What can be done to encourage more of this behaviour? Governments could ease regulation to make such exchanges legal and less complicated. Entrepreneurs should build apps that allow people to match their needs (dog sitters, electricians, massage therapists, tattooists, midwives, house cleaners, snake catchers, whatever) to those who can provide it. What we need is a Gumtree for the service industry, with an interface like Uber.

But kids leaving school can help themselves too. They can start by acquiring basic skills that will be needed in a future where robots are our friends. A drivers license can still get you a job (especially working for yourself through Uber), but perhaps Google’s self-driving car will make that obsolete in ten years’ time. So here is my advice: think about what services cannot be done by machines. Sport coaches. Au pair services. Beauticians. Chefs. Wedding planners. Gardeners. Music teachers. Barbers. Paramedics. The best thing is that none of these require a university education. And these jobs will be in-demand for a long time to come; in fact, chances are you are more likely to find a job qualified in one of these professions than if you were to leave university with only a Bachelors degree. Often they will require extremely hard work and long hours, but in most cases you will be able to work for yourself, which means you determine the lifestyle you want.

Robots are not the evil things that will destroy the jobs of the poor. They may destroy some jobs, yes, but they will create far more jobs in other places; in fact, they may be the saving grace for our faltering education system. To identify the opportunities new technologies offer, matriculants without a university access will have to innovate, experiment, be entrepreneurial and dedicated. They will also have to learn to work with robots, not against them.

*If you want more advice on what to study, click here. If you want more advice on what to do when you get to university, this might help.

Written by Johan Fourie

January 11, 2015 at 07:08

Why my job is safe (and, if you read this, yours probably too)

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Another labour-saving technology: goodbye chauffeurs and taxi-drivers, hello millions of hours of additional work time for commuters. Picture taken by my wife Helanya as we drove through Silicon Valley, November 2013. My biggest fear was crashing into it.

Another labour-saving technology by Google: goodbye chauffeurs and taxi-drivers, hello millions of hours of additional work time for commuters. Picture taken by my wife, Helanya, as we drove through Silicon Valley, November 2013. My biggest fear was crashing into it.

I teach economics at Stellenbosch. I don’t consider myself the best teacher there’s ever been (this is backed by empirical evidence on rating scores and faculty rewards), but also probably not the worst either (purely anecdotal and experiential evidence). Some of my teaching is at the postgraduate level – Economic History – but most of it is undergraduate, from a textbook, and mostly replicable by any university that offer second-year Macroeconomics.

And, increasingly, also by online universities that now offer MOOCs: ‘massive open online courses’. Instead of listening to me explain the IS-LM model, students now have the opportunity to sign up for online lectures from the best macroeconomists out there, and listen to these when they want, where they want. MOOCs have been billed as the ‘end of universities as we know it’. See here, here and here. Instead of every university offering mediocre, entry-level courses, why not have one superprofessor at the best university in the world broadcast his lectures to a global audience? Broader access, high quality and for a much lower price.

This is of course part of what I think will be the key question for future generations: the extent which human labour can and should be replaced by robots. In Europe and the US, convenience store staff are being replaced by automated payment points. Instead of call centre operators, I now get calls from recorded human voices selling products (which, incidentally, is much easier to switch off). But let’s remember that technology can be both labour-substituting and complementary: while a harvesting machine will probably substitute a farmer’s need for many workers, a tractor makes a farmer more productive and its acquisition might even require the farmer to employ more people to expand production. To take an example closer to home: technological advancement (like more advanced computers and better statistical packages) augments my ability to do research; speak to any economist older than 50 and they will tell you how they used to use punch cards for regression analysis. Quaint, but extremely time-consuming.

So technological change creates winners and losers. Capital investment (accumulation as Adam Smith called it) make labour more productive, increasing productivity, wages, and therefore living standards. But technological improvement also changes the labour requirement: it often substitutes lower-skilled jobs for higher-skilled jobs. Here is the best example: Tesla’s new manufacturing plant really only requires people for the first 18 months of its operation (to programme the circuit). Thereafter, as soon as production begins, everything (apart from oversight) will be done by computers and robotic arms. Is this the future of car manufacturing? Will the workers in our vehicle manufacturing plants become obsolete? Probably.

This is of course of great concern if you live in a country with an unemployment rate of 40%, a country where every political party fighting in this year’s election hopes to win votes by ‘creating jobs’. What is the solution, then? To ban technological innovation and change? Of course not. To do so would lead to stagnation in productivity growth, meaning a decline in our ability to remain internationally competitive, meaning lower profits for firms, lower incomes and wages, and lower living standards. (Incidentally to the labour economists out there, won’t labour subsidies have the same effect?) But to stick our collective heads in the sand is also not a solution: workers that lose their jobs is not only a political dilemma, but a moral one too. And a difficult one. And then there’s still the additional (and bigger) problem of the 5 million South Africans that are actively looking for work, and another 4 million that would like to work but has stopped looking.

So who is likely to lose to technology? Is my job threatened by the arrival of these MOOCs? Probably not. I would argue that any service job that requires some socialising to be effective – and learning, especially in the social sciences, is a social activity – will be complementary to technology rather than being substituted by it. A course in pure mathematics or machine learning can perhaps be usefully taught online. But any course where context becomes important – where the past experience of the student interacts with the teaching material to create a unique product for all the other students – is unlikely to be substitutable by an online, one-way interaction. A new NBER Working Paper by Acemoglu, Laibson and List supports this view. They investigate the concern that internet-based educational resources (MOOCs) will be ‘disequalising’, creating superstar teachers and a winner-take-all education system that, for example, will put me out of a job. Instead, they find quite the opposite:

We contend that a major impact of web-based educational technologies will be the democratisation of education: educational resources will be more equally distributed, and lower-skilled teachers will benefit. At the root of our results is the observation that skilled lecturers can only exploit their comparative advantage if other teachers complement those lectures with face-to-face instruction. This complementarity will increase the quantity and quality of face-to-face teaching services, potentially increasing the marginal product and wages of lower-skill teachers.

And, I would argue, this is not only true for university professors, but for any service industry. Simple, replicable tasks will be outsourced to robots, but any task that requires context and interaction, and especially ones that require an emotive response, will require humans, and lots of them. The Economist listed a number of jobs that are likely to be replaced by robots. (I wrote about it here.) The top four jobs most unlikely to be replaced are recreational therapists, dentists, athletic trainers and clergy. Context, interaction, emotion. The four jobs most likely to be replaced are telemarketers, accountants and auditors, retail salespeople and technical writers. Simple, replicable tasks.

Technological advancement creates far more winners than losers. Users benefit (hello, smart phones). The inventors benefit (hello, Apple and Samsung). It creates thousands of new job opportunities (hello, app builders) and fantastic incomes (like the 55 people that just sold WhatsApp to Facebook for $19 billion. Even if each employee only had a 0.01% share in the business, that equates to R160 million each). We all become more productive because of it (you might be reading this post on your smart phone on your way to work). Yet the proprietors and employees of the previous technology suffer (Nokia, the South African Postal Service, or the messenger pigeon).

Not only is my job safe, but I will probably benefit from the MOOCs. This dynamic is also true in all other industries: Certainly, some jobs will be replaced by technology, but many others (even jobs that don’t yet exist, or that require only basic skills) will benefit from it. If our high level of unemployment is to fall, then we need to 1) focus our training on those skills that are complementary to the new technology, and 2) ensure that government policies do not obstruct the impact of technological change.

Written by Johan Fourie

March 5, 2014 at 10:18