Johan Fourie's blog

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How do we build a prosperous, decolonized South Africa?

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boys-plowing

I recently attended an academic conference at the University of the Free State on the topic ‘Decolonizing Africa’. Much of the debate was, understandably, about the past: about the lingering effects of the (Atlantic) slave trade, European colonization that included the imposition of largely artificial borders, and the post-colonial failures of independent Africa. But at the final keynote, delivered by Prof Alois Mlambo of the University of Pretoria, the discussion turned to the future. How do we build a prosperous, decolonized South Africa?

One unescapably emotive topic is land reform. The expropriation and dispossession of land in South Africa is the root, many agreed, of the severe levels of inequality that plague the region. But how to correct this past injustice was not so easy; in the audience, too, were several Zimbabwean scholars quite critical of that country’s land reform programme. Over lunch, one Zimbabwean student told me the tragic story of his grandfather, a former farm worker on a white farm turned successful tobacco farmer after land reform, only to lose his land because he was considered ‘too successful’ by the ruling ZANU-PF party. The farm is now dormant.

Getting land reform right is fraught with difficulty. Not everyone that suffered land expropriation wants to return to farming – by far the largest number of recipients of successful land claims in South Africa choose the cash instead of the land. (This is often ignored by politicians and commentators when simply taking the hectares transferred as measure of land reform success.)  And even when recipients choose to return to the land, they often struggle to support themselves because of the small size of land allocated, or a lack of capital investment, or a lack of technical or management skills. There are also political consequences: because land recipients, like those in Zimbabwe, often do not receive title deed to the land they are given, they become ensnared by the political party that gave them the land. Why do people still vote for ZANU-PF despite the state of the economy? Because they worry a vote for the opposition means that they might lose their land. Most worryingly, it is often the original farm workers who lose the most, like the Zimbabwean student’s grandfather.

This is not to say that some form of wealth redistribution is not imperative. But whereas land (and the minerals it contained) was clearly the most productive resource when it was expropriated in the nineteenth century (which is the reason it was expropriated), a valid question is whether it still is the most productive. Of course, people value land not only for its economic uses: there are a myriad of historic, cultural and religious reasons why the land of your ancestors are treasured. But as a redistributive policy aimed at creating a more equitable society, is land reform the best way to create prosperity for those who suffered historical injustice?

Think of the fastest growing companies globally: which of them still rely predominantly on land ownership? AirBnB is a great example: it is the world’s largest accommodation service, without owning any property! For AirBnB and the myriad other unicorns that have created incredible wealth for their founders and shareholders, it is not land or physical property that creates wealth, but science and technology. (Even farmers know this: that is why they are investing in science to improve their crops and in technology to mechanize production.)

In the twenty-first century, land is what you buy with your wealth, and not the reason for your wealth. A quip about Stellenbosch wine farmers summarize this well: How do you make R1 million farming in Stellenbosch? You spend R2 million.

Prof Mlambo remarked that India and China, both with a history of colonisation, is not growing at above 5% because they have redistributed land. They have prospered because they embraced science and technology. Consider this: in the 2015/2016 academic year, 328,547 Chinese students studied in the United States; only 1,813 South African students did. (If you account for population size, 7 times more Chinese than South Africans students study in the US.) Take South Korea, a country with roughly the same population size as South Africa: 61,007 South Koreans traveled to study in the US in 2015/2016, 33 times more than South Africa.

So how would a redistribution policy look that takes science and technology seriously? I don’t have the answers, but here are some suggestions. Most of us would agree that education is key, but the South African education system has not made much progress in the last decade and it is unlikely to do so in the next. Redistribution must start at the first year of life. Publicly funded but privately run nurseries will remove the gap between the rich and poor that has already emerged when kids arrive at school. For primary and secondary education, a voucher system that incentivize private schools for the poor is an option. At tertiary level, we need more and better-funded universities, notably in science and technology. (It would help to send more of our smartest students abroad to study at the frontiers of science – they will return with new ideas and networks to propel our industries forward.) Visas for and recruitment of skilled immigrants can boost research and entrepreneurship. Improve free wifi access and invest in renewable energies. The private sector, because that is where most innovation occur, can be incentivized through appropriate legislation to offer shares to workers – or to those living in communities where they operate. There are a myriad of innovative possibilities.

If Zimbabwe has taught us anything, it is that politics may triumph over economic logic. Land reform in Zimbabwe was not an economic strategy in as much as it was a strategy to keep the ruling party in power. It has had severe economic consequences, as anyone visiting Zimbabwe today can attest. The real radical economic transformations of our age – just in my lifetime, the Chinese has managed to reduce the share of people living in absolute poverty from 88% to less than 2% – have not come from redistributing an unproductive twenty-first century resource. It has instead been the result of investments in science and technology. Any attempt to redistribute with the purpose of building a more prosperous society should take this as the point of departure.

*An edited version of this first appeared in Finweek magazine of 29 June 2017.

How social status drives our consumption – and inequality

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louis-vuitton

A couple of years ago I attended a focus group for Finweek. The magazine was rebranding and it had invited a diversity of people to comment on the content it should offer. The conversation turned to investment options for young professionals: should young people invest their monthly savings in a new property, or stocks, or something else? The facilitator asked the thoughts of a young woman that had been quiet for most of the meeting. Her answer, and its consequences for many young South Africans like her, stunned me: I invest in expensive clothes, because I have to signal to a potential husband that I am wealthy. In other words: I buy brand names, because I want to improve my social status.

Economists have known since Adam Smith already that people buy luxury goods not only for the value they derive from consuming it, but because these goods offer something else: social status. Conspicuous consumption, as economist Thorstein Veblen coined our affinity for status goods, has helped explain economic phenomenon like our excessive expenditure on weddings or the difference between black and white incomes in America.

However, so far economists have struggled to differentiate between our affinity for nice things (in economics jargon: our unobserved consumption utility) and our affinity for the status that those nice things signal. In other words, I might buy a Ferrari not only because I really like fast and furious cars (consumption utility), but also because I want to signal to the everyone else that I am rich (status).

A team of five economists, in a new NBER Working Paper, has now found a way to test the importance of social status. They worked with a large Indonesian bank that distribute credit cards to clients. (Indonesia is a great place for a test like this, because it is in developing economies, as Veblen theorized, where you are most likely to see conspicuous consumption. Also, Indonesia has 74 million middle-class consumers, expected to double by 2020.) They used platinum credit cards, which come with a number of benefits like a higher credit limit and discounts on luxury purchases and is typically sold to high-income individuals, in their experiment.

How do they show that social status matter? They randomly offered a fancy-looking platinum and standard-looking credit card to their customers at the same price and with the same benefits. If customers only cared about the utility of the new card (like the benefits on offer), there should be no difference in the take-up of the fancy-looking or standard-looking card. And yet, there is a 7 percentage point difference: 21% purchased the fancier card versus only 14% for the standard card. The mere fact that the fancy-looking card was associated with a higher status meant that people purchased it.

Perhaps it is not that surprising that people purchase something because it conveys an additional status element, but what is surprising about the experiment is that poorer individuals bought more of the fancy-looking card. The rich, in contrast, showed no difference in demand for the fancy or standard card. The authors ascribe this finding to the fact that “richer individuals already have ways to signal their income, while the platinum credit cards are a more powerful signaling tool for those with comparatively lower incomes”. This also explains the behaviour of the young woman in our focus group; she was more limited in her ability to show social status and thus had to resort to clothing.

In a second experiment, the authors then look at how the customers use their cards. Consistent with their theory, they find that the customers that bought the fancy-looking card (remember: it had the same privileges as the standard-looking card) used the card more often in social settings, such as spending in restaurants, bars and clubs, where the card is more visible to others. Here, too, there is somewhat of a surprise: the use of this card comes at a cost, because in 48% of the cases the customers have another card that would have given them discounts on those purchases. In other words, they chose to ignore the discount just so that they can use the fancy-looking card that gives them social status! If this is true for credit cards where there is a limited audience (only your buddies who joined you for dinner can see you paying with a fancy-looking card), imagine what people are willing to forego for luxury products with a larger audience, like clothes and cars.

The authors conduct several other experiments, all of which support the authors’ theory that social status matter in explaining our consumption behaviour. We do not only buy luxury goods because they provide us with utility; we buy them because they signal something about our social status. And because poorer individuals tend to have fewer ways of signaling social status than richer ones, they are the most eager to grasp at opportunities for showcasing their status. (That is why direct marketing is never aimed at the wealthiest individuals!)

Such findings have implications for the distribution of wealth. The choice for a young person between investing your meager savings in stocks or a new car may not only depend on the financial returns they can get, but also the psychological returns they might get from purchasing a luxury good. If poorer individuals tend to buy more luxury goods to earn social status, like the young woman in the Finweek focus group, while the rich invest in assets that yield positive financial returns (because they already have assets that give them social status), the only logical conclusion is a widening wealth gap. There is little any policy, like a purported wealth tax, can do to prevent that instinctive human yearning for status.

*An edited version of this first appeared in Finweek magazine of 15 June 2017.

Written by Johan Fourie

July 12, 2017 at 11:02

How our emotional intelligence makes us productive

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Emotional-Intelligence-1

Economists spend a lot of time investigating the factors that make people more productive. This is because more productive people – producing more, with less – is the reason we can today afford a much higher standard of living than our ancestors – in Africa, India or Europe – two centuries ago.

Many things improve our productivity. Technological improvements like a computer can allow us to use the power of machines to substitute manual labour. Education allow us to build faster and stronger computers. Both technology and education are key if we are to continue building and sharing a prosperous future.

But it is not only technology and education that improve our living standards. There are formal and informal institutions – things like the criminal-justice system, property right regimes and the political system – that create the incentives for us to invest in technology and education. And there are the even less tangible things, like the way we make decisions (often referred to as ‘culture’), or our personalities. Economists are only now beginning to explore the roots of these ‘soft’ determinants.

Psychologists have known for long that our personality affect the way we make decisions. One example: Whether we apply for that senior position may depend on whether we exhibit the leadership qualities that is required to lead a large team. But what determines whether we have those leadership abilities? Is it nature or nurture?

One option is to look at siblings. If genetic traits (nature) were the only source of leadership qualities, then almost all the variation we find in society would be between families. In other words, there should be little variation between brothers, for example, as they have a lot of genetic overlap.

This is not the case, however, at least according to a recent NBER Working Paper written by three economists, Sandra Black, Björn Öckert and Erik Gröngqvist. Almost a third of total variation in personality traits, they note, are within the family. So, if it is not only nature that determine much of your personality, where do these within-family differences come from?

One possibility, they argue, is birth-order. Using a very rich Swedish dataset, the authors find that first-born children are ‘advantaged’ when measured on their ‘emotional stability, persistence, social outgoingness, willingness to assume responsibility and ability to take initiative’. Note: these are non-cognitive abilities, i.e. there is little difference in terms of a first-born and a third-born’s innate ability to do math, for example. It is on the softer abilities, instead, that first-borns clearly outperform their lower-ranked siblings: third-born children, for example, have non-cognitive abilities that are 0.2 standard deviations below first-born children.

These non-cognitive abilities matter. Controlling for many things, they show that first-born children are almost 30% more likely to be Top Managers compared to third-borns. This is because managerial positions, they argue, tend to require all Big Five domains of personality: openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability.

But why does birth-order matter? The authors argue for largely three possible reasons. First, biology. Successive children may have less of the stereotypical male behavioural traits due to the mother’s immunization to the H-Y antigen. But this seems unlikely to explain most of the variation, as the authors also find that birth order patterns vary depending on the sex composition of the older children: third-born sons perform worse on non-cognitive tests when their older siblings are male compared to when they are female.

This suggests that it has something to do with how parents allocate their time and resources, especially in the early years. ‘First-born children have the full attention of parents, but as families grow the family environment is diluted and parental resources become scarcer’, the authors argue. Parents may also have incentives for more strict parenting practices towards the first born to ensure a reputation for “toughness” necessary to induce effort among later born children.

Thirdly, children may also act strategically in competing for parental resources. Siblings compete for possession of property and access to the mother. Older siblings, research shows, tend to take a more dominant role in conflict and have more elaborate conflict strategies. To minimise conflict, parents tend to invest more in the dominant, older sibling.

Using a novel approach, the authors can identify which of these effects is largest. They find that biological factors only explain a small part, and may actually benefit later-born children. It is however in the behaviour of parents that there are distinct differences between first- and later-born children: they find that later-born children spend substantially less time on homework and more time watching TV. Parents are also less likely to discuss school work with later-born children, suggesting that it is the parents that lower their investment which explains the large gap in non-cognitive skills.

What the authors do not do is to link their results with the general improvement in living standards over the last two centuries. We are becoming ‘better angels of our nature’ because we grow up in smaller families with more parental attention and resources, improving our non-cognitive abilities.

It is not only the vast improvement in technology and education that has made us more productive, but also because we have become more conscientious, agreeable, responsible and willing to take the initiative. We are rich, in part, because we are more emotionally intelligent.

*An edited version of this first appeared in Finweek magazine of 1 June 2017.

Written by Johan Fourie

June 23, 2017 at 07:49

We are shopping less, but buying more

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CheckersMini

One of the things I realised soon after marriage, is that my wife and I share different strategies when it comes to grocery shopping. I like to stock up, buying bulk on the cheap, while she prefers to visit the store more frequently, acquiring only what is necessary for the next few days. This of course means that we never run out of canned beans, but often out of milk.

Such choices are at the heart of economics. Understanding how, why and when a buyer chooses a product or service is often the difference between a thriving and failing business. That is why every successful firm, from banks to health insurance to mobile communications companies, spend considerable resources these days analysing ‘Big Data’ to understand and ‘nudge’ the behaviour of their customers.

Even general retail, a sector often caricatured as unaffected by technological change, now has to adjust to the new technological possibilities, like sensing technologies that track the movement of customers as they browse a store. Not only can technology help retailers to optimise store lay-out, but, with a little leap of the imagination, they can have advertising that can recommend new products when a new customer walks past based on the content of their previous purchases, of their existing basket or of the purchases of their friends that is connected to them on social media. (Imagine buying shampoo, and being prompted: Your friend, Herman, purchased Organics in this store five days ago.) And then there is a plethora of other technologies that are likely to revolutionise the shopper’s experience, from mobile payments (in South Africa: wiCode or SnapScan), to digital receipts (another South African upstart: Pocketslip), to online shopping.

There is no doubt that these new technologies will shape the way we make decisions about what, how and where to buy our groceries, but technology is not the only thing that affects our spending behaviour. A new NBER Working Paper by three authors affiliated to US universities, identifies an interesting trend in the US over the last four decades: the rise of spending inequality, or a widening gap between how much different households spend when they go shopping.

We usually measure inequality by comparing peoples’ incomes. But presumably we are also interested in how people spend their incomes: are there huge differences between how much some households spend vis-à-vis others, and do these differences change over time? In fact, it seems like this is indeed the case: the difference in household spending patterns in the US seem to be on the increase. Some families seem to be spending a lot more than others.

One suggestion for the rise in income inequality is the impact of technology. But this is where the authors find an interesting result: the reason for the rise in spending inequality, they argue, is not because of growing differences in consumption caused by greater levels of income inequality (i.e. the rich still consume more than the poor, but this gap is not increasing), but instead because Americans go shopping less frequently. They explain it as follows: if a household starts buying groceries once a month instead of once a week, their consumption may not change (they stockpile to smooth their consumption), but the measured spending inequality will change because some households in surveys will appear as if they spend a lot, while others will appear as if they spend nothing. This difference was less dramatic when households went shopping every week, and so it appears as if inequality is on the rise.

Using various datasets, the authors find two distinct trends to support this theory: first, the number of shopping trips that Americans make has been steadily falling since 1980. In contrast, the average expenditure per trip has been steadily rising. Americans are making fewer, but larger, shopping trips on average. Second, the quantity of goods Americans buy have been rising, while the amount of time spent shopping has declined. All of this, the authors conclude, points to higher levels of stockpiling by Americans.

What explains this changing behaviour? Surprisingly, it is not technology innovation, which is often considered the source of most disruption. Instead, the authors show, the increasing stockpiling is a result of the emergence of warehouse stores, like Costco, that sell larger quantities of goods at lower unit prices. “As these stores have expanded throughout the country since the 1980s, it has become easier for households to stock up in ways that were not feasible in the past, consistent with the decreased frequency of shopping that we observe.”

Technological improvements like mobile payments, digital receipts and online shopping is aimed at reducing transaction costs, making it easier and cheaper for consumers to do their grocery shopping. Such lower costs should result in a higher frequency of shopping. And yet, the trends, at least for the US, point in exactly the opposite direction: fewer visits to the supermarket, with consumers preferring to buy in bulk and on the cheap.

Perhaps South African consumers behave differently. Perhaps the digital revolution will reverse these trends quickly; once your fridge can order canned beans automatically from the local supermarket when supplies run low, we won’t need to buy in bulk. But any retailer worth their salt would do well to be aware that the promise of technology can often overshadow deeper forces pulling in the other direction. Technology reduces transaction costs, but the benefits of buying bulk seem to outweigh the costs. Now to convince my wife.

*An edited version of this first appeared in Finweek magazine of 18 May.

Written by Johan Fourie

June 13, 2017 at 05:48

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

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Mugshot

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

Can Twitter predict the markets?

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Twitter-PPC

Ask anyone about the pitfalls of Twitter, and they might point to recent gaffs by a prominent South African politician as evidence that the dangers outweigh its benefits. But such warnings have not stopped many others, most notably the president of the United States, from tweeting on a regular basis: Twitter’s user base creates more than 500 million tweets a day, and it has added about 2 million new users in the last quarter of 2016.

Presumably this wealth of information must have some value. Twitter, sadly for its shareholders, struggles to turn such growth into profit: in the last quarter of 2016, revenue growth was only 1%. But because it captures public sentiment at a very granular level, it has attracted the interest of both scientists and entrepreneurs hoping to turn this information into public or private benefit.

The use of social media for prediction is, of course, not a recent phenomenon. Google Flu Trends, founded in 2008, used Google’s search engine to track the spread of flu in 25 countries. But excitement about the project waned as it struggled to make accurate predictions. A 2014 Nature paper noted the value of social media ‘Big Data’, but warned that ‘we are far from a place where they can supplant more traditional methods or theories’.

Twitter, though, seems to attract increasing attention. A 2014 paper uses Twitter to predict crime. A 2015 paper show how psychological language on Twitter predicts heart disease mortality. Another 2015 paper show how Twitter sentiment predicts enrollment of Obamacare. A 2016 paper show how Twitter could be used to predict the 2015 UK general elections.

But it is, understandably, the financial markets that has attracted the most attention. A 2016 paper by Eli Bartov (NYU Stern School of Business), Lucile Faurel (Arizona State University) and Partha Mohanram (University of Toronto) shows how Twitter can predict firm-level earnings and stock returns. They use a dataset of nearly a million corporate tweets by 3662 firms between 2009 and 2012, all tweeted in the nine-trading-day period leading to firms’ quarterly earnings announcements. The authors find, unsurprisingly, that the tweets successfully predicts the company’s forthcoming quarterly earnings, but find, more surprisingly, that the tweets predict the ‘immediate abnormal stock price reaction to the quarterly earnings announcement’. These findings are more pronounced for firms in weaker information environments, such as ‘smaller firms with lower analyst following and lower institutional ownership’, and are not driven by concurrent information from sources other than Twitter, such as press articles or web portals.

It makes sense that corporate communication provides information, but can public sentiment on Twitter also inform market activity? A 2017 NBER Working Paper, by Vahid Gholampour (Bucknell University) and Eric van Wincoop (University of Virginia), answers this question by looking at the Euro/Dollar exchange rate. They start with all Twitter messages that mention EURUSD in their text and that were posted between October 9, 2013 and March 11, 2016. There were 268 770 of these messages, for an average of 578 per day. What they hope to do, is to identify whether informed opinions about future currency changes can actually predict actual currency changes, so they eliminate all tweets that do not express a sentiment about the future behaviour of the two currencies. This reduces the sample to 43 tweets per day, or 27 557 in total. They then classify each of these tweets as positive, neutral or negative using a detailed financial lexicon that they develop to translate verbal tweets into opinions, and create a Twitter Sentiment index for each day. They also split the sample in two: those opinions expressed by individuals with more than 500 followers, which they call the ‘informed opinion’, and those with fewer than 500 followers, which they call the ‘uninformed opinion’.

So what do they find? It turns out that the 633 days of data they have is too short to calculate the Sharpe ratio, a measure of the risk-adjusted return. The annualized Sharpe ratio based on daily returns is 1.09 for the informed group and -0.19 for the uninformed group. The Sharpe ratio of 1.09 for the informed group is impressive, but it has a large standard error of 0.6. The 95% confidence interval is therefore very wide, ranging from -0.09 to 2.27. They then construct a model with a precise information structure, estimate the parameters and then recalculate the Sharpe ratio to average at 1.68 with a 95% confidence interval between 1.59 and 1.78. Success: ‘the large Sharpe ratios that we have reported’, they conclude, ‘suggest that there are significant gains from trading strategies based on Twitter Sentiment’.

If all this sounds terribly complicated, that is exactly the point. Translating opinions into numbers is not an easy undertaking, and discerning the ‘informed’ opinions from the noise is even less so. But there is no doubt that Twitter does offer some useful, perhaps even lucrative, insights. Whoever can exploit that knowledge first, stand to benefit most.

*An edited version of this first appeared in Finweek magazine of 20 April.

Written by Johan Fourie

May 16, 2017 at 06:24

Do CEOs deserve their high salaries?

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Whitey Basson

Late last year, Bloomberg reported that South African Chief Executive Officers earn the 7th most of any country in the world – a whopping R102 million per person per annum. This was equivalent to 541 times the income of an average South Africa. The study, understandably, caused some outrage.

These numbers have been disputed, mostly because they include CEOs who earn in foreign currency. A study by 21st Century Consultants found that the CEO salary of the median large cap South African firm in 2016 was less than R6 million, roughly 5% of the Bloomberg average. PwC found the median between R3.1 and R7.7 million.

But even at these lower levels, many ask whether CEOs deserve what they earn. Do the value that they add outweigh the millions spent on salaries and bonuses? This, of course, is an incredibly complex question. Economists have no laboratory where they can randomly assign CEOs their salaries, and see what the likely outcome might be. Instead, we have CEOs that respond to the firm’s internal and external demands in various ways, planning, strategizing, meeting and organizing. Which of these activities adds more value seems impossible to determine.

That is, until now. A new study by four economists – Oriana Bandiera (LSE), Stephen Hansen (Oxford), Andrea Prat (Columbia Business School) and Raffeala Sadun (Harvard Business School) – measure the behaviour of CEOs in Brazil, France, Germany, India, the UK and the US, and compare these measurements to their firm’s performance. They do this using a two-stage method: first, they collect the weekly diaries of 1114 CEOs in the six countries. These diaries include detailed information about the hourly activities of each CEO: with whom they met, the number and duration of plant/shop-floor visits, business lunches, how many people joined, and the functions of these participants (whether they were in finance or marketing, for example, or clients or suppliers).

Their finding is that CEO activities differ remarkably across firms. While CEOs spend most of their time in meetings, they ‘differ in the extent to which their focus is on firms’ employees vs outsiders, and within the former, whether they mostly interact with high-level executives vs. production employees’.

The authors then use a machine learning algorithm to create an index of CEO behaviour. At low values of the index, CEOs spend more time with production and in one-on-one meetings with employees and suppliers, and at high values CEOs spend more time with executives and in meetings with more participants.

The authors note that there is no theoretical reason for one type of behaviour to lead to better outcomes. That such different types of behaviour exist may just be a consequence of the fact that firms require different types of CEOs, i.e. some firms will do better with a low-index CEO while others would do better with a high-index CEO. When CEOs are perfectly matched – or ‘assigned’ – to the type of firm that suit their style, there should be no correlation between the index-value of a CEO and the firm’s performance. In other words, a low-index CEO matched to a firm that will benefit from a low-index CEO style would perform just as well as a high-index CEO matched to a firm that will benefit from this CEO style type.

The results, however, shows the opposite. High values on the CEO index are strongly correlated with higher firm productivity, a measure of firm performance. CEOs who spend most of their time in meetings with senior executives, engage in communication (phone calls, videoconferences, etc.), bring together inside and outside functions, and bring together more than one function of a kind are also more likely to lead more productive firms.

Their results also show that CEOs are often not matched to the right firm: “Our estimates indicate that, while low-index CEOs are optimal for some of the sample firms, their supply generally overstrips demand, such that 17% of the firms end up with the ‘wrong’ CEO.”

More importantly, it is in the two developing countries in their sample – Brazil and India – where this matching is especially bad: 36% of firms in those countries end up with the ‘wrong’ CEO compared to the only 5% in the four developed countries. “The productivity loss generated by the misallocation of CEOs to firms equals 13% of the labour productivity gap between high and low income countries”.

The authors do not speculate on why this difference exists. One likely reason is weaker competition for top jobs within a thinner talent pool owing to the unequal levels of education in these countries. Another may be that appointments happen for reasons other than merit.

What the study does show, though, is that the choice of CEO is critical for firm success. Appoint the wrong type of CEO, and productivity is likely to decline. Although some firms benefit from a CEO who frequently has one-on-one conversations and visits the production floor, most firms benefit from a CEO who spend their days leading large meetings with top executives from different fields.

That helps to explain the high salaries for CEOs in South Africa too. A mismatch between CEO and firm is costly and seems to happen quite frequently. The small talent pool means that most firms are willing to pay exceptional salaries to those rare individuals with a high CEO index-value. If they don’t, the firm is likely to suffer far more costly productivity losses.

It also points to the dangers of policies that hope to place an upper-bound on managerial remuneration. Lower levels of remuneration will likely lead to fewer CEOs with high index-value, and to higher levels of mismatch between CEOs and firms. That, as the authors show, will be devastating for firm-level productivity, and economic development. Beware the unintended consequences of policies made with good intentions.

*An edited version of this first appeared in Finweek magazine of 6 April.

Written by Johan Fourie

May 1, 2017 at 05:57