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Why #DataMustFall

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ZimBoth the Independent Communications Authority of South Africa (Icasa) and the Competition Commission are concerned about South Africa’s high data costs. It is about time. Of the 48 African countries ranked by ResearchICTAfrica.net for the first quarter of 2017, South Africa was the 22nd most expensive in which to buy 1GB data. All of South Africa’s main competitors on the continent, including Egypt (1st), Ghana (4th), Nigeria (8th) and Kenya (15th) ranked higher. Our poorest neighbour – Mozambique – ranked second, with US$ 2.27 for 1GB in contrast to our US$7.49.

Consumers have known this for some time. Last year, radio personality Thabo “Tbo Touch” Molefe started a Twitter campaign – #DataMustFall – that went viral. He was subsequently invited to address the parliamentary Portfolio Committee on Telecommunications and Postal Services about the high cost of broadband in South Africa. Said Molefe at the time: “The power of data gives access to education, mentorship, skills training, financial assistance, job searching and recruit.”

Molefe is correct. There is now ample evidence globally to show that internet access at affordable prices is correlated to better job market opportunities. This is especially true in South Africa, where the employment rate is seven percentage points higher in areas connected to the internet than those with no connection. The problem is that economists have struggled to show that this relationship is causal: areas with internet connectivity usually have all the other amenities that are associated with better job market prospects. It then becomes an empirical question of how to separate the effect of internet connectivity from things like education, infrastructure and wealth that also affect job market prospects?

A new NBER Working Paper by Jonas Hjort of Columbia University and Jonas Poulson of Harvard University offers an answer. The two authors exploit the gradual arrival of 10 submarine Internet cables from Europe in cities on Africa’s coast in the late 2000s and early 2010s to identify whether the higher speeds and cheaper data costs created new jobs. First, they show that the arrival of the cables did, in fact, increase average internet speeds and the expansion of the network. They then compare the changes in employment patterns in cities and towns with a bigger versus a smaller increase in access to fast Internet. “In each of three different datasets that together cover 12 African countries with a combined population of roughly half a billion people, we find a significant relative increase—of 4.2 to 10 percent—in the employment rate in connected areas when fast Internet becomes available.” Just as Molefe said: faster and cheaper internet creates jobs!

As with any economic change, there are both winners and losers. Hjort and Poulson show that the faster, cheaper internet reduces employment in unskilled jobs, but “enables a bigger increase in employment in higher-skill occupations”. In other words, just as automation does in the developed world, faster internet in Africa results in a change in the type of skills required. One might expect the consequence to be deeper levels of inequality. Not true, says the authors, especially in South Africa. Faster, cheaper internet enables South African workers of low and intermediate educational attainment “to shift into higher-skill jobs to a greater extent than highly educated workers”. The net effect is that fast internet lowers employment inequality across the educational attainment range in South Africa.

So what types of jobs were created by the arrival of the submarine cables? The authors find that “new and new types” of jobs were created via the “extensive margin” (meaning: new users) and “intensive margin” (meaning: different use of the internet by existing users). Using detailed firm level data, they show that, in South Africa, new firms are established, notably in sectors that benefit from ICT. In Ethiopia, by contrast, existing firms improve their productivity. In other African countries like Ghana, Kenya and Nigeria, firms with access to the faster, cheaper internet export much more, perhaps, the authors suggest, because “website communication with clients become easier”.

Technology is not just a threat to job creation – it is also an opportunity. But as the #DataMustFall movement has shown, fast internet access remains a mirage for most South Africans. That is hopefully changing. Non-profits, like Project Isizwe, want to facilitate the roll-out of free WiFi in public spaces in low income communities, as it is already doing in Tshwane. Similar initiatives are following in South Africa’s other metros. Both Google and Facebook are designing new technologies that could revolutionise connectivity for in rural areas.

Consumers are rightfully angry about the high cost of data in South Africa. Yet it is local entrepreneurs and their employees that should be most upset. As Hjort and Paulson show, cheap data will create more firms and more, better-paying jobs. “Employment responses of the magnitude we document indicate that building fast Internet infrastructure may be among the currently feasible policy options with the greatest employment-creating potential in Africa.”

Fast and cheap internet is probably the simplest way to alleviate South Africa’s high unemployment conundrum. Policy-makers should take note.

*An edited version of this first appeared in Finweek magazine of 24 August 2017.

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Written by Johan Fourie

August 30, 2017 at 10:56

What explains the rise of populism?

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Donald Trump

Consider the following thought experiment: Sibusiso and Thulani each own a firm that competes with the other. In each of the following scenarios, Sibusiso’s firm outcompetes Thulani’s. Which of the four do you consider unfair competition?

  • Sibusiso works hard, saves and invests his profits, and invents new techniques and products, while Thulani’s products change little and he loses market share.
  • Sibusiso finds a higher quality input supplier in the US, which makes his products better and he therefore takes market share from Thulani.
  • Sibusiso outsources some of his services to Bangladesh, where workers work 12-hour shifts under hazardous conditions, earning very low wages.
  • Sibusiso brings Bangladeshi workers into South Africa under temporary contracts, and puts them to work at lower than minimum wages.

From an economic perspective, each of these scenarios have a similar result: there are winners as well as losers as they expand the economy. But people generally react very differently to them. Most people are happy with scenario 1 and 2: even if someone loses (Thulani and his employees), this comes through what is perceived as fair competition from Sibusiso. It is scenario 3 and 4 that creates problems: when Sibusiso ‘breaks’ local laws (even though it may be perfectly legal in the foreign country), his competitive advantage, and by implication international trade, is viewed as unfair.

In a provocative new NBER Working Paper, Harvard University economist Dani Rodrik use this example to argue that too-rapid globalisation – the increasing use of scenarios 3 and 4, of outsourcing production to the developing world or of employing immigrants – is the underlying cause for the rise of populism across the developed world. The ‘losers’ from globalisation feel that foreigners – abroad or as immigrants in their own countries – have taken unfair advantage of then, stealing their jobs. They have chosen the politics of populism as a way to ‘punish’ this rapidly globalising world.

Economists know that free trade creates both winners and losers, and that the winners almost always gain more than what the losers lose. If the winners could perfectly compensate the losers, everyone would be better off from a free-trading world.

But Rodrik argues that such compensation is not always easy, and rarely happens. Aside from Europe, where an extensive social safety net was institutionalized to support ‘losers’, most countries failed to find a way to sufficiently compensate those that suffered the consequences of open borders. Make no mistake: open borders resulted in massive global gains, notably for the poor of China and India. But in each country, as trade theory predicts, there were losers. In Rodrik’s words: “People thought they were losing ground not because they had taken an unkind draw from the lottery of market competition, but because the rules were unfair and others – financiers, large corporations, foreigners – were taking advantage of a rigged playing field.”

There are many new studies to back up this claim. In a 2016 paper, David Autor and his co-authors show, for example, that the trade shock of China joining the World Trade Organisation aggravated political polarisation in the United States: districts affected by the shock moved further to the right or left politically, depending which way they were leaning in the first place. Analysing the Brexit vote, Italo Colantone and Piero Stanig show that regions with larger import penetration from China had a higher Leave vote share. They repeat the study for fifteen European countries, showing that China’s entry into the WTO had similar political consequences across Europe. In a 2017 working paper, Luigi Guiso and his co-authors use European survey data to draw even more precise conclusions: the more individuals are exposed to competition from imports and immigrants (the higher their economic insecurity), the more they vote for populist parties.

To summarise: because there were uncompensated losers from global free trade, argues Rodrik, there were political consequences. Rodrik then constructs a model to explain this populist rise on both the left and the right. According to the model, there are three different groups in society: the elite, the majority, and the minority. Says Rodrik: “The elite are separated from the rest of society by their wealth. The minority is separated by particular identity markers (ethnicity, religion, immigrant status). Hence there are two cleavages: an ethno-national/cultural cleavage and an income/social class cleavage. An important implication of this reasoning is that even when the underlying shock is fundamentally economic the political manifestations can be cultural and nativist. What may look like a racist or xenophobic backlash may have its roots in economic anxieties and dislocations.”

Populists who emphasize the identity cleavage target foreigners or minorities, and this produces right-wing populism. Those who emphasize the income cleavage target the wealthy and large corporations, producing left-wing populism. The large numbers of immigrants into Western Europe has resulted in the rise of right-wing populists, for example, while Latin America, because of large disparities between rich and poor, has seen more left-wing populism. The United States, argues Rodrik, falls somewhere in the middle – with Donald Trump on the right and Bernie Sanders on the left.

These findings have important implications for South Africa too. South Africa joined the WTO in 1995 and liberalised our complicated tariff schedule, opening our borders to foreign competition. There were many winners from cheaper imports, notably consumers, but some firms and industries struggled, leading to job losses, often concentrated in certain regions. And although South Africa rolled out an impressively comprehensive social safety net for a middle-income country, they could not compensate all the losers, especially as the global financial crisis hit in 2007 and unemployment began to worsen. It is not entirely coincidental that the first large-scale xenophobic attacks on foreigners happened in 2008 (what Rodrik would call right-wing populism) and that the ANC shifted left with the election of Jacob Zuma as South African president in 2009.

Even if globalisation creates more winners than losers, the losers, like Thulani and his employees, may feel that the system is rigged, and retaliate by voting for more populist parties. As South Africa stumbles into another recession, this may have profound consequences for the ANC’s December elective conference – and the national election in 2019.

*An edited version of this first appeared in Finweek magazine of 10 August 2017.

Written by Johan Fourie

August 14, 2017 at 16:47

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.

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