Ten interesting things that we read this week

3)    The magical thinking that misleads managers [Source: Financial Times]
Using the recent incidence of the UK’s biggest water companies admitting to using dowsing rods to help locate pipes and leaks, the author of this piece, Andrew Hill, discusses few examples of modern leadership’s superstition, credulity and blind faith.

Numerology: In China, mumbo-jumbo about feng shui and ominous or propitious flotation dates, trading symbols and stock codes often influences how supposedly sophisticated companies arrange their affairs. Elements of Alibaba’s 2014 listing appeared to revolve around the “lucky” number eight, for example. But before western chief executives scoff, they should consider how much they are still in thrall to the cult described in Alex Berenson’s 2003 book “The Number” — the quarterly earnings consensus they conspire with analysts and investors to hit, or better still, to beat. Regular evidence — recently, for instance, from Campbell Soup (a miss), and Home Depot (a “beat”) — suggests the cult is thriving. Indeed, the availability and crunchability of Big Data have broadened disciples of the number. They now include company bosses who worship near-term, data-driven answers, rather than holding out for better, if messier, longer-term solutions that take account of human intuition.

Leaps of faith: Any chief executive who has ever announced a corporate vision without a clear idea of the kinds of steps needed to achieve the goal is at least partly guilty of magical thinking. Richard Rumelt wrote in “Good Strategy/Bad Strategy” about the dangerous delusion that aiming for success can lead to success: “I would not care to fly in an aircraft designed by people who focused only on an image of a flying aeroplane and never considered modes of failure.”

Throw a coin and make a wish: Modern companies still close their eyes to evidence suggesting bonuses are at best a blunt incentive, and chuck cash at staff in the hope that it will help them reach their heart’s desire. At least wishing wells swallow the donation with no adverse consequence, other than the loss of your penny. Unfettered bonus culture, as the worst excesses of the financial crisis suggest, can backfire in unexpected ways.

Chants and mantras: Slavishly applied governance codes and regulations help box-ticking compliance staff and board members sleep easy by absolving them of the need to make difficult judgments. Meaningless mission statements give executives a mantra to recite as cover for not actually putting their values into practice.

Human sacrifice: Restructurings and lay-offs are the modern ritual for appeasing the gods (but without the benefits of bringing the community together for a bit of a celebration).

Hero worship: For all the modish talk of flat hierarchies and distributed leadership, chief executives still become the central figures in a myth that is largely of their own creation. The most dangerous part of this self-delusion is that they believe success was achieved entirely through their “skill, preparation and tenacity”, as described by Jim Collins and Morten Hansen in ‘Great by Choice’. The researchers found successful leaders could generate a greater “return on luck” by being more disciplined at exploiting opportunities and riding bad luck to make themselves stronger. But they pointed out there was a fine line between the best leaders and those who put their organisations at risk through an exaggerated and dangerous belief in their own powers. Such leaders had a tendency to make these sorts of assertions: “Luck played no role in my success - I’m just really good.” Here is where humble deference to unpredictable and poorly understood outside forces would be healthy.

4)    The challenges of a disembodied economy [Source: Financial Times]
 Unlike in the past, what’s new about today’s economy is that many of our best ideas remain disembodied. The idea is indeed valuable, but it does not take physical form. This changes almost everything. As captured in the book ‘Capitalism without Capital: The Rise of the Intangible Economy’, by Jonathan Haskel of Imperial College and Stian Westlake of Nesta, Apple, the world’s most valuable company, owns virtually no physical assets. It is its intangible assets — integration of design and software into a brand — that create value. In the US and UK, investment in intangible assets now exceeds that in tangible assets. This is also true in Sweden, but not in Germany, Italy or Spain. This matters because the properties of intangibles are fundamentally different from those of tangibles. Understanding those differences may explain some of the peculiar features of the modern economy, including rising inequality and slowing productivity.

The authors explain the features of intangible assets by referring to the “four Ss”: scalability; sunkenness; spillovers; and synergies. “Scalability” means that an intangible good can be enjoyed by one person without depriving another of its benefits. You cannot eat the same sandwich as I can. But an intangible asset can be used over and over again. In an economy where scalability — frequently turbo-charged by network effects — is important, some businesses will quickly become huge. These winners may also enjoy huge incumbency advantages. “Sunkenness” refers to the fact that intangible assets tend to have little or no market value, unlike, say, land or a factory. They have value as part of their owner’s business, but not to anybody else. This means that investment in intangible assets is risky. “Spillovers” means that a large part of the benefits of an investment may accrue to others. Even with protection of intellectual property, much of the benefit of investment in an idea is likely to accrue to people other than the discoverers. The presence of such spillovers weakens the incentive to invest. The answer is intellectual property rights, but these are inherently arbitrary and economically costly. Finally, intangibles exhibit synergies. Synergies encourage inter-firm co-operation (or outright mergers), while spillovers are likely to discourage it.

Taken together, these features explain two other core features of the intangible economy: uncertainty and contestedness. The market economy ceases to function in the familiar ways. Does the rise of intangibles explain what has come to be called “secular stagnation”? Partly. This is not, it turns out, so much because growth has been underestimated. But a big issue is the growing gap in performance between leading companies and the laggards. The failure of the latter to benefit from investment in intangibles, by themselves or by others, may partly explain their weak growth in productivity. Again, the rise in intangibles may also partly explain the increase in inequality. One way it can do so is that workers in the most successful businesses tend to share in their employers’ success. Furthermore, people with relevant skills do outstandingly well. In addition and intriguingly, intangible-intensive businesses tend to cluster in thriving cities. This not only concentrates opportunity, but raises property values, spreading wealth to those who own these properties. Finally, intangible assets are mobile, which makes them hard to tax.

This transformation of the economy demands a rethink of public policy. Here are five challenges. First, frameworks for protection of intellectual property are more important. But this definitely does not mean these protections must be still friendlier to the owners of such property. Intellectual property monopolies may indeed be necessary, but, like all monopolies, they can be costly. Second, since synergies are so important, policymakers need to consider how to encourage them, including via policies on telecommunications and urban development. Third, financing intangibles is hard. For traditional collateral-backed bank lending, it is almost impossible. The financial system will need to change. Fourth, the difficulty of appropriating gains from investment in intangibles might create chronic under-investment in a market economy. Government will have to play an important role in sharing the risks. Finally, governments must also consider how to tackle the inequalities created by intangibles, one of which is the rise of super-dominant companies.

5)    Gay men used to earn less than straight men, now they earn more [Source: HBR]
Acceptance of LGBTQ people in all spheres of society – work, family, and community – has grown at a remarkable pace in the United States. A recent Pew Research Foundation study reported that 92% of all LGBTQ adults felt that society is more accepting of them than a decade ago, and 87% of adults report personally knowing someone who is gay or lesbian (up from 61% in 1993). Whether these massive changes have translated into improvements in workplace outcomes for the average gay man or lesbian, however, is not so obvious. There is, for example, no federal nondiscrimination protection on the basis of sexual orientation or gender identity. A natural question, then, is: have the shifts in approval of LGBTQ individuals corresponded to equivalent improvements in their paychecks?

Economists and management scholars have been crunching the numbers on this question for over 20 years, and until very recently, nearly all the studies have found an identical result: if you compare the earnings of two men with similar education profiles, years of experience, skills, and job responsibilities, gay men consistently earns less than straight men (between 5% and 10% less). The stability of this finding has been remarkable: it has been replicated across numerous datasets in several different countries. That is until now. A recent paper, analyzed data from a major federal survey in the United States that had not previously been used in this literature – presumably because it only recently began to ask about sexual orientation – and found that the gay male earnings penalty had disappeared. And not only had it disappeared, it had turned into a 10% premium, meaning that gay men in recent years earned substantially more than straight men with similar education, experience, and job profiles.  

One interpretation of the literature’s near-universal prior finding of a gay male earnings penalty was that it was a consequence of labor market discrimination against gay men. If that’s the case, then, naturally, improved attitudes toward LGBTQ people would reduce this penalty. Moreover, a few patterns in the literature support this possibility, including the fact that two recent well-controlled field experiments failed to find meaningful differences in employment outcomes for fake candidates whose profiles were manipulated to be either gay or straight. And yet there are also patterns that make the explanation imperfect. While improving attitudes explain the gradual disappearance of an earnings penalty, it does not seem well-suited for explaining the emergence of an earnings premium.

The author says that there's no foolproof explanation for why the gay male earnings penalty disappeared and turned into a premium. It does suggest several avenues for future study though. First, there are increasingly more large federal surveys with information on sexual orientation and workplace outcomes, as well as education, experience, and job characteristics. Scholars should see if the gay male earnings premium identified in recent studies replicates in other recently fielded surveys. Second, because it is clear that workplace dynamics associated with sexual orientation are different for sexual minority men than for sexual minority women (the author says that there has been consistent evidence of a gay male earnings penalty and a lesbian earnings premium for most of the past two decades of study), more research is needed to understand the nature of workplace attitudes regarding sexual orientation and how these might differ between gay men and lesbians. It could be, for example, that historically strong associations between gay men and the HIV epidemic contributed heavily to negative attitudes toward gay men specifically and that reductions in these views benefited gay men relative to straight men but not lesbians relative to straight women.

Finally, it is possible that the changing nature of family lives is strongly linked to the changing nature of workplace chances for the LGBTQ community. Sexual minority women enter into and formalize their same-sex relationships at a higher rate than sexual minority men. But fundamental changes in family opportunities and responsibilities brought about by recent nationwide same-sex marriage may be exerting very different influences in gay male households than in lesbian households, and this changing nature of household specialization – theorized by Nobel Prize-winning economist Gary Becker – could be producing some of the patterns being documented. A gay male couple who gets married may have one partner select out of the workforce to focus on caregiving responsibilities; this might make the other partner more productive at work, resulting in relative improvements in gay men’s earnings relative to those of straight men. If the relatively lower earning partner systematically selects out of the labor market, this productivity effect would be compounded by a compositional change in the sample of relatively higher earning gay men we observe working. And if the effect of relationship recognition has less effect on women in same-sex couples – perhaps because they were more likely to be functioning as a household unit in the absence of formal recognition – then this could explain the large difference we see in relative male earnings compared to prior studies and the lack of difference we see in relative female earnings compared with prior work.

6)    Veteran investors in vanguard of big bet on technology shares [Source: Financial Times]
When technology shares tumbled from in 2000, Stanley Druckenmiller, then second-in-command to George Soros at his famed Quantum Fund, suffered the fate of having held on too long. Nearly two decades on Mr Druckenmiller, who now runs his own family office but remains a closely-followed investor, holds vast amounts of his wealth in technology shares that have risen massively in value over the past year. US regulatory filings released this month showed that in the third quarter Mr Druckenmiller’s Duquesne Family Office held 41 per cent of its long US equity holdings in just five technology shares — Microsoft, Facebook, Amazon, Alibaba and Alphabet, formerly known as Google. Yet Mr Druckenmiller’s portfolio construction has in fact been a relatively contrarian bet in spite of the huge gains for technology shares. His enthusiasm contrasts with that of rank-and-file professional fund managers who have by and large remained deeply sceptical of the ferocious rally in US and Asian technology giants.

But Mr Druckenmiller is not the only contrarian. The portfolios of other highly respected veteran investors also have significant holdings in high-flying technology shares. Julian Robertson, the octogenarian patriarch of the Tiger group of hedge funds who famously suffered before the dotcom crash for eschewing tech shares, holds just under a quarter of his portfolio’s long US equity exposure in Facebook, Alphabet, Microsoft and Alibaba. Even Warren Buffett’s Berkshire Hathaway, which also sat out the dotcom bubble, has seen its investment in Apple pay off handsomely since building it up over the past year. These big bets by some of the world’s well-respected investors using large amounts of their own wealth have been vastly rewarded in a year when Facebook shares have risen by almost 60 per cent, and Alibaba has more than doubled in value. But reticence elsewhere remains. The most recent Bank of America Merrill Lynch fund manager survey, which polls investors who manage $533bn in money for clients, saw 34 per cent of those questioned claim that “long Nasdaq” was the world’s most “crowded trade”.

However, in spite of their surging market capitalisations, certain technology stocks have not until recently been particularly well-owned by institutional investors. Amazon, for example, was only the 18th most widely held US stock by all equity strategies in the second quarter of 2016. By the end of the first half of this year it had shot up to ninth place, but still ranked behind companies including United Health Group and Cisco. Facebook meanwhile moved up from 10th most owned to sixth place over the same period. While the largest technology companies, Apple and Microsoft, are the most owned shares, current position concentration among investors as a whole does not match perfectly with the excess levels of profitability being achieved by this breakout group of shares in the US. One way of measuring this is by looking at these companies’ return on invested capital, which measures their profitability based on every dollar of debt or equity capital invested in their businesses. Certain analysts argue this represents a far cleaner portrayal of corporate profitability than standard accounting-based earnings numbers. On this basis, some of the largest tech shares remain significantly ahead of “old economy” peers.

While there are differing viewpoints on the valuations of these tech companies, the bigger point is that the bifurcation or polarisation across the market between unloved sectors such as retail and tech is as big as one can remember in a long time.” There are anecdotal signs that a growing number of conventional fund managers are now throwing in the towel and following the veterans into holding larger amounts of these seemingly unstoppable technology shares. The question for those who are choosing to do this in 2018 will be whether they now risk arriving at the party too late.

7)    Startups that seek to ‘disrupt’ get more funding than those that seek to ‘build’ [Source: HBR]
Since its HBR debut in 1995, the concept of disruptive innovation—the process by which a smaller company with limited resources is able to launch a product or service that displaces established competitors—has been extensively incorporated into startup vernacular. Entrepreneurs often use a version of the phrase when launching products, raising funds, unveiling strategies, hiring teams, and engaging partners. Yet we do not know much about how entrepreneurs are integrating the concept into their identities and what consequences this has for their startups. Research has previously shown that “entrepreneurial identity,” or how one defines and identifies with his or her entrepreneurial role, affects a startup’s ability to amass key resources. The research in this piece aimed to understand correlation between this entrepreneurial identity and their ability to attract and retain two types of critical resources: financial and human capital.

It turns out that the phrases entrepreneurs use to describe themselves and to position their startups on sites like LinkedIn function as a useful window into their entrepreneurial identities. When examining the LinkedIn profiles for the presence of the root “disrupt_,” the authors noticed something interesting: those entrepreneurs who did not mention disruption tended to instead embrace the language of building by favoring the root “build_,” with minimal overlap between the two groups. The entrepreneurs in these categories did not markedly differ in terms of age, gender, or years of experience, but disrupters were significantly more likely than builders to be serial entrepreneurs. These two distinct entrepreneurial archetypes were associated with divergent outcomes for their respective startups in terms of the ability to attract and retain resources. Although the authors’ data set revealed that builder-led startups were nearly ten times more common than disrupter-led ones, “disrupter” startups received 1.7 times more funding, on average, than “builder” startups.

In order to further understand how disrupters and builders differ when it comes to attracting resources, the authors conducted an online experiment on 100 Amazon Mechanical Turk participants (81.5% with previous startup and/or investing experience). They had them read a company description that featured either disrupter or builder language, holding all other company information constant. They asked these individuals how much hypothetical funding they would invest in each startup and found that they allocated nearly twice as much funding to the disrupt condition ($58,018) as they did to the build condition ($29,545). They also asked participants to imagine themselves as prospective new hires and to evaluate how the company makes them feel. They learned that the description of the disrupter startup made them feel significantly more excited, energized, independent, and inspired than the builder startup. Perhaps by enticing others with their exciting ideas, those who value disrupting things can attract certain resources more effectively than those who value building things. But it appears they are unable to retain those resources as readily.

These two startup styles also differed in employee tenure rates. Controlling for business category, founding date, team size, and operating status, average employee tenure at builders’ startups was 8 months longer than average tenure at disrupters’ startups, which can make a world of difference when it comes to young companies. While investors allocate significantly more money to disrupters, that capital is potentially being deployed less efficiently due to heightened costs associated with recruiting, onboarding, training and severance. Taken together, the results uncovered two distinct types of people who are attracted to startups—those who value breaking vs. building—and different consequences for their respective startups. Disrupters’ flashy ideas may energize and inspire others, but that might not be enough to keep them. Disrupters may also move on to the next disruptive idea once the one they are working on reaches a point of stability, given they display a higher incidence of serial entrepreneurship than builders. Conversely, those who value building something may experience more difficulty in attracting capital (both financial and human), but they tend to stick with the startup for the longer term and seem to influence others to do so as well.

8)   Should we trust our fellow app users more than politicians  [Financial Times]
Smartphones are embedded in our lives now, and the use of websites and apps such as Amazon, Airbnb and Uber are, for many of us, habits that have become ingrained. But reflect for a moment on the cultural patterns that have been created by these apps and you can see that something rather peculiar is happening. These days we are constantly being told that public trust in institutions is fast eroding, if not collapsing. Since the 2008 financial crisis, for example, trust in banks has slumped, while respect for other types of business leadership has declined, along with confidence in government. A survey of 28 countries by the public relations group Edelman suggests that only 29 per cent of people trust ¬government leaders and only 37 per cent trust CEOs.  Even non-governmental institutions are suffering: globally, only 53 per cent trust NGOs, according to Edelman.

But not all types of faith have eroded. One area of our life where trust is high — and rising — is in relation to what sociologists describe as “distributed” trust; processes that require people to trust in their peer group or community, rather than put blind faith in a big institution or somebody of a supposedly superior status. The numbers from the Edelman survey reflect this: globally, some 60 per cent of respondents said they trusted their peers as a source of advice — far higher percentages than those cited for government officials or corporate executives. You can see the rise of distributed trust in the popularity of services such as Airbnb and Uber. These work on the basis of community control: people enter a stranger’s car or home partly because there is group oversight, peer ratings and an exchange of personal pictures. Bitcoin is similar in some ways, since its value rests on faith in computer code that has been created not in a central bank but by peers — as have the reviews on sites such as Amazon or Expedia.

It’s tempting to see this as just one more aspect of the digital economy. But another way to understand this cultural pattern is to use the intellectual framework offered by the business consultant and social analyst Rachel Botsman in her fascinating recent book, Who Can You Trust? Botsman argues that the rise of sites such as Airbnb means there are now essentially three patterns of trust in the modern world. The first of these - the trust that is forged on the basis of face-to-face contact between individuals is as old as humankind. This (relatively) egalitarian, or “horizontal”, pattern has predominated in most communities, during most periods of history, gluing together villages, clans and families. The second type of trust is vertical rather than horizontal, and emerges when a society has institutions such as a royal family, a government or a church that hold it together. This type of trust enables huge groups to collaborate even when they do not know each other. For Botsman, what makes the digital distributed economy fascinating is that it presents a third category of social glue. This new type of trust relies on peer-to-peer contact, in a fairly egalitarian manner, and on group oversight, which is powerful, since a breach of trust can be punished by creating a sense of shame or exclusion.

A cynic might point out that cyber-distributed trust only “works” if there is also some implicit institutional back-up: if something goes wrong with Uber, Airbnb or Expedia, we tend to assume that regulators or the police will step in. We also trust that the cyber ¬technology will work. If that is ever called into question, the pattern of distributed trust might crumble. So far, this has not happened, despite all the recent cyber-attacks, the growing political controversy around digital privacy, and the failure of social media sites such as Facebook to prevent fake news. Maybe that will change in 2018. But unless it does, our economy will be partly driven by rising levels of distributed trust or, as Botsman says, “people trusting other people through technology”. It is a powerful sign of the unpredictable cultural contradiction created by the internet.

9)   Watch out for effects of Tax reform on Tax migration, the fiscal conditions of affected states and polarity in US [LinkedIn]
Ray Dalio in this piece talks about how while wealth disparity in the US is well documented, we haven’t talked enough about the tax migration that is taking place because of growing differences in state and local tax rates. This tax migration issue is especially important to focus on now because of the expected elimination (under the new tax legislation) of the deductibility of state and local taxes (SALT) against federal income taxes. The dynamic Ray talks about is the inevitable and self-reinforcing process in which those high SALT locations that: a) have big disparities in income and fiscal shortfalls; and b) can neither cut their financial supports to the “have-nots” (because their conditions are already unacceptably low) nor raise taxes on the “haves” (because they will move due to tax rates) suffer from tax migration.

The dynamic works as follows. As state and local tax rates and debts rise because there are shortfalls that can’t be narrowed, it is financially smart for high income taxpayers to escape these taxes and debt burdens by moving to lower tax and less indebted locations, so they do. As they do, property values decline, further raising the costs of staying in the high SALT location. In other words, the financial cost of being in one of those high tax locations equals the tax rate difference plus the property value decline, which can be substantial. Also, the reduced population of higher income and higher spending folks leads to reduced spending in these locations, which further depresses the high SALT economies. Because both the remaining high income and low income folks are increasingly stressed and tend to blame the other, tensions rise, which makes these environments even more inhospitable, which further contributes to high income earners’ emigration. Realizing this, other locations increasingly appeal to the “haves” by offering tax incentives and creating environments in which they are more comfortable living with other “haves.”

He says that such location cost arbitrage motivated migrations happen all the time. For example, New York City saw migration from the Upper East Side to Downtown and then to Brooklyn brought about by cost arbitrages. Every area in the world has this sort of cost and desirability motivated migration going on constantly. Cost differences drive migrations that change the characters and costs of neighborhoods and happen in self-reinforcing ways until the cost differences change to make the newly hot neighborhoods expensive and other areas relatively cheap, so the immigration shifts to emigration. With regards to the US, Dalio says that ending SALT deductibility will result in a sizable increase in the effective tax rate faced by high earners in high tax states (3-5% for most making over $500,000), notable outbound migration of high income filers (he estimates 1-2.5% will leave for most states), and a hit to state tax revenues (around 1%). In his opinion, these numbers understate the impacts, especially for the highest taxpayers (who pay the most taxes), because it is the nominal level of dollars of increased taxes that matters more than percentages, and the calculations don’t fully account for all the second- and third-order consequences previously mentioned.

Dalio indicates that states with both higher than average incomes and higher than average taxes (especially New York, Connecticut, New Jersey, and California) are most vulnerable by these measures. The vulnerability of a state to tax emigration is affected by tax revenue being concentrated in the hands of those high income earners who are most affected by the changes. That means that it would take only a tiny percentage of the population to move to have a devastating effect on the state’s finances. He points towards the emigration data to highlight that this hypothesis is in fact playing out. A look at the states where people have been leaving fastest from and going fastest to points toward states like New York, Connecticut, New Jersey, California, and Illinois being the most vulnerable, and states like Florida, Texas, Nevada, Washington, and Arizona benefiting the most from this shift.

10)   Can earthquakes be predicted? [bbvaopenmind.com]
It would seem that nowadays we are able to foresee with greater or lesser accuracy the different types of natural disasters that stalk us—except for one. Every time the earth trembles we are caught completely off guard, often suffering great devastation and loss of life. Why has 21st century science still not managed to accurately predict when a quake will strike? “We can see inside weather systems from below, from above and from inside them. We understand the laws of physics and the mathematical equations that control weather and climate,” Terry Tullis, former chairman of the National Earthquake Prediction Evaluation Council of the United States Geological Survey, tells OpenMind. “For earthquakes it is harder for many reasons. The Earth is opaque, so we can’t see inside it to figure out what is going on,” says Tullis. The seismologist notes that it is possible to observe certain geophysical parameters, but that earthquakes generally originate at considerable depths where it is impossible to place measuring instruments.

Predicting seismic movements is an age-old aspiration of human beings, even before the development of modern science. In ancient times it was believed that animals sensed earthquakes. As the physicist-chemist and writer Helmut Tributsch recounted in When the Snakes Awake (The MIT Press, 1984), many historical anecdotes speak of changes in the behaviour of animals before a tremor. The British biologist Rachel Grant too has published several studies documenting how different species in different places, including toads and cows in Italy or rodents and tapirs in the Andes, have modified their behaviour before an earthquake. With the collaboration of geophysicist Friedemann Freund from the NASA Ames Research Center and the SETI Institute, Grant has linked these changes with disturbances in the ionosphere due to electrical phenomena in the rocks under stress, which in turn can alter the chemistry of water.

Scientists thus far have made progress on assigning probabilistic forecasts for earthquakes – as in the case of modelling the string of aftershocks that occurs after a large earthquake. “The main conclusion is that we have models that can describe probabilistically with accuracy the evolution of complex seismic sequences with different bursts of seismicity,” summarizes the study’s director, Warner Marzocchi, of the Istituto Nazionale di Geofisica e Vulcanologia de Roma, for OpenMind. “We provide probabilities and not deterministic predictions,” he clarifies. The seismologist adds that these models are already being applied to other regions such as New Zealand or the US, so they will help to refine forecasting. “For now the largest weekly probabilities of large earthquakes are about a few percent; when we are able to move above 10% or even larger, it will be a great step forward, and it will facilitate the adoption of mitigation measures”.

The fact that the likelihood of a tremor for a period of days or weeks is still very small deters the taking of steps that might help to mitigate the effects of the quakes, beyond the permanent ones, such as the design of safer buildings. According to Tullis, the so-called earthquake “early warning systems” are still in fact “very-last-minute warning systems.” These are devices that detect the start of a tremor and calculate how long it will take the waves to reach certain places, so that the surrounding areas can be alerted. But Tullis warns that, in many cases, it is not even possible to know in the first moments how much the magnitude of an earthquake will grow. For example, in the case of the 2011 earthquake in Tohoku (Japan), which caused the accident at the Fukushima nuclear power plant, the movement grew disproportionately after the initial detection, so the warning issued underestimated the violence of the shocks.