Ethical failures: Where they come from and how to address them

A review of

Gentilin, Dennis. The Origins of Ethical Failures. Lessons for Leaders. A Gower Book. Routledge (2016). ISBN: 978-1-138-69051-6

Ethical failures were in the press big-time in 2017. Prominently, creeps like Harvey Weinstein, James Toback, Bill Cosby, Larry Nassar, etc. were accused of sexual transgressions of various sorts (and in some cases admitted them to varying degrees). The sheer number of accusations leaves little doubt that, in their substance, they are correct. One thing that was truly shocking, on top of the specifics of many of the allegations, was that some of these transgressions went on for literally decades, that many people seem to have known about them for years (if not decades), and that the perpetrators did get away with them for an unconscionably long time. It is clear that organizational failures must have played a major role. This was implicitly acknowledged in the name of  The Royal Commision (RC) into Institutional Responses to Child Sexual Abuse, established under the Gillard government in 2013 and which reported all 17 volumes of its findings on December 15, 2017. The RC also laid out recommendations.

It did not really come as a surprise that once again massive organizational failure, in particular of the Catholic Church, was identified as a major finding. It did not come as a surprise because for years there had been a never-ending stream of trials, not just in Australia, suggesting just that, and providing plenty of evidence that the Catholic Church – in its (continued) belief that it is a law and world unto itself — had engaged for decades in what might generously be called economy with the truth.

Two weeks earlier, after another year of numerous reports of questionable practices, and record profits of the four major banks, the Turnbull government saw itself forced — by its own backbenchers, no less — to announce that it would establish a RC into misconduct in the banking industry. It was a step that Labor and the Greens had urged for more than a year. (The recent draft report of the Productivity Commission has made clear that some such RC is indeed overdue.) The Turnbull government’s acceptance of something that it could not prevent, and its subsequent attempts to undermine the effectiveness of the RC by simultaneously widening its scope and imposing an essentially unrealistic timeline, demonstrates, at the minimum, the kind of myopic opportunism that Australian politics seems drenched in.

Having graduated in 2001, Gentilin became a member of the FX trading desk of the National Australian Bank (NAB), one of the four major banks.  In 2004 that trading desk became involved in a trading scandal that rocked NAB and led, within a couple of weeks, to the resignation of both its chairman and CEO, the reconfiguration of the board of directors, and significant financial and reputational losses. Gentilin was the young trader who blew the whistle. Contrary to many other whistleblowers (who are typically harrassed out of the organizations on which they blew the whistle), he stayed with NAB for more than a decade – as head of the institutional sales team and a member of the corporate strategy team — before he resigned in January 2016 to found Human Systems Advisory, a name meant to be programmatic. The foreword of his book was written by the current chairman of NAB who states: “There are no simple answers in this book. But there are answers. And there are important truths, supported by deep and rigorous analysis. These should be of interest to all corporate leaders, in both executive and non-executive roles.” (p. xvi).   One such truth, says the chairman – apparently quoting Gentilin – is that “leaders must strive to articulate a meaningful social purpose for their organizations that is underpinned by a virtuous set of values.” That’s quite a mouthful, and the impending Royal Commission on the banking system suggests strongly that the major banks (that tried at first to fight off the RC until they realized that fight had been lost) have continuing trouble to understand that particular message, as does the recent draft of the related Productivity Commission report.

Below, I am interested in both the depth and rigor of the analysis and the truths that Gentilin establishes.  I am also interested in the implementability of the measures that he proposes.

In his Introduction, Gentilin states that he draws his evidence from “behavioural business ethics” which he defines as the intersection of business ethics and psychology (p. 5). While he is credited on his website with a degree in psychology, Gentilin makes clear that he wrote this book as a “practitioner” rather than “an academic, a philosopher or an ethicist” (p. 4). He does so in four chapters that explore “The Power of Context”,  “Group Dynamics”,  “Our Flawed Humanity”, and “What We Fail to See”.  A conclusion follows.

Gentilin relies heavily on summaries of articles from psychology that explore human nature and the circumstances under which nice behaviour might turn into, well, not so nice behaviour of different shades. While there is brief perfunctionary nod (p. 3) to the replicability crisis that has afflicted psychology, throughout the book there is little discussion of relevant laboratory design and implementation issues such as incentivisation, experimenter expectancy effects, external validity, and so on (Hertwig & Ortmann 2001; Ortmann 2005). Never mind the fact that much of the evidence on unethical behaviour paraded in this book has been produced with deceptive practices, arguably an unethical practice itself (Ortmann & Hertwig 2002; Hertwig & Ortmann 2008). There is no discussion of statistical issues such (lack of) power computations, p-hacking, publication biases, and what not here either.

Claiming that “explanations of unethical conduct rarely give proper consideration to the system within which people operate … (and) tend to focus on identifying ‘bad apples’ or ‘rogues’” (p. 7), in Chapter 1, Gentilin explores how the environment can impact human (mis)behaviour and, on balance, concludes that “the ‘barrel’ within which the ‘bad apples’ operate must be given as much (if not more) attention as the ‘bad apples’ themselves.” (p. 8). Before he reviews the lessons to be learned from the Stanford Prison Experiment, Gentilin reviews literature on social norms and how they affect behaviour.  The well-known Cialdini et al. littering and Mazar et al. (dis)honesty studies are paraded, as is an interesting lab study by MacNeil & Sherif (1976) in which the authors demonstrate generational transfer of (questionable) practices, and a related field study by Pierce & Snyder (2008). Distinguishing between descriptive (“derived from what is”) and injunctive (“derived from what ought to be”) norms, Gentilin documents cases where unethical descriptive norms tear to smithereens injunctive ones. He relates this to his reading of what led to the FX trading scandal at the NAB: “young people in particular are vulnerable and endorsing immoral social norms … In the FX trading scandal that engulfed the NAB, immoral social norms emerged that promoted excessive risk taking and misstating the true value of the currency options portfolio.” (pp. 18 – 19). This is hardly surprising, and indeed Gentilin mentions the LIBOR rate-fixing scandal and the professional cycling drug-taking as other high-visibility events. He could have also mentioned the lending practices of major US banks before the housing and mortgage crises (e.g., Gjerstad & Smith 2014), the despicable transgressions at Abu Ghraib, or zillions of other real-world examples.  After having reviewed the Stanford Prison experiment in some detail, Gentilin identifies two important take-home lessons from it: first, a specific context “can cause people of sound character to behave in totally uncharacteristic and inappropriate ways.” (p. 24) and, second, the emergence of such contexts is possible only when leaders allow it. Drawing on more experimental evidence (such as Bandura’s children imitating adults’ behaviour experiments), he suggests the obvious parallel for what happened at NAB: “Just as the adults were the role models in Bandura’s experiments, leaders that control the bases of power are the role models in large organizations. For these leaders there will inevitably appear some key moments where, through their actions, choices and decisions, they will send powerful messages that shape the ethical climate for their organizations and types of social norms that emerge.  … how a leader responds in these ‘defining moments’ shapes the ‘character of their companies’.” (p. 30). Only leaders who are veritable role models will be able to prevent formal mechanism being eroded by informal mechanisms that hammer away at them. Again, Gentilin suggests that such failure of leadership is what happened at NAB and at the Barclays Bank during the LIBOR rate-fixing schedule, and for that matter in the phone-hacking scandal that led to the demise of News of the World. Gentilin concludes the chapter with a list of “ten questions for senior leaders within any organization” (pp. 37 – 38). Presumably, these questions are unlikely to be answered in an honest manner where it matters. It is the evidence accumulated in this chapter but also elsewhere (Dana et al. 2007 comes to mind, or Miller & Ross 1976) that suggests that much.

Gentilin starts off Chapter 2 with a Nietzsche quotation that sets the stage: “Madness is the exception in individuals but the rule in groups.” (p. 45). The basic point made is that group membership can reinforce – cue social media echo chambers – the drifting away from injunctive norms to descriptive ones. Writes he: “In my experience at the NAB, dysfunctional group dynamics in the currency options business played a significant role in promoting the emergence and maintenance of immoral social norms and unethical behaviour [such as flagrant and persistent limit breaches or excessive risk taking, AO]”.  To buttress the case, Gentilin presents Milgram’s 1974 obedience studies, as well as Gina Perry’s recent critique of them (Perry 2012) which, in light of considerable supporting evidence of the original studies (e.g., Haslam et al. 2014), he dismissesin their substance. He then highlights what we learn from Milgram’s inclusion of a variation that drew on the group paradigm.  That motivates a discussion of the conformity experiments through which Asch (1956) tried to identify the conditions under which participants would contradict a majority.  In this context, Gentilin also briefly discusses a between-subjects study by Woodzicka & LeFrance (2001) who had a male interviewer ask female applicants inappropriate questions. The basic result was that 6 out of 10 subjects claimed they would object (hypothetically) but none in the control group refused the answer in a “real-life” scenario.  That seems the kind of pattern that allowed the Weinsteins of this world to get their way for too long. Only in the case of Weinstein and similar assholes (here used in the technical sense of Sutton 2007), the stakes were arguably considerably higher. People’s lack of willingness to stand up and be counted is, unfortunately, so widespread that it is well documented and it is a recurrent theme of great movies such as Hidden Figures.  Gentilin makes clear that, based on his experience at NAB, “facing in the fork in the road in a hypothetical scenario is vastly different from facing it in reality.” (p. 67) He also states, “I am personally sceptical of other research into whistleblowing that focuses on ascertaining the types of personality or dispositional characteristics that may predict whether an observer of wrongdoing will take action and report it. …This line of enquiry fails to properly consider the power of the situation.” (p. 67). Gentilin concludes the chapter with another list of “ten questions for senior leaders (and followers) within any organization” (pp. 73). I doubt that these questions will be answered in an honest manner where it matters, for essentially the exact reason that Gentilin has identified in the chapter.

In Chapter 3, Gentilin – notwithstanding his, in my considered opinion, sensible stand on the relative importance of context and dispositional characteristics – dives into “our flawed humanity”. Programmatically, he starts with an epigraph featuring a quotation from Kant, “Out of the crooked timber of humanity, no straight thing has ever been made.” (p. 80).  Gentilin then tries to answer questions such as “Are Humans Self-Interested?”, cursorily sampling evidence from experimental economics, neuroscience, and evolutionary biology. Predictably he concludes that this research shows that “human nature (is) far different from the one suggested by the axiom of self-interest” (p. 86), though he qualifies the statement with the caveat that we are not always altruistic and cooperative.  This alleged “paradigm shift” (p. 87) is, unfortunately, the major bone of contention between those marketing Behavioural Economics (and often shamelessly benefitting from it) and those doing Experimental Economics, and I believe that the social-preferences literature that has created it has as much merits as the IN oxytocin, ego depletion, and power poses research now, for all I can see, thoroughly debunked. Better not plan your life, or organization, on such flimsy evidence. From an evidence point of view, and also a theory point of view (e.g., the important insights stemming from repeated game situations), this chapter is the weakest.  Gentilin’s sampling of the evidence strikes me as scattershot and unsystematic. After discussions of issues such as power and its corrupting influence and fear and awareness of our own mortality that feeds into it, Gentilin concludes the chapter with a list of “eleven questions for senior leaders within any organization” (pp. 118)  I fear, these questions, again, are unlikely to be answered in an honest manner where it matters.

In Chapter 4, Gentilin starts with a quotation from Kahneman’s best-seller Thinking Fast and Slow: “We can be blind to the obvious, and we can also be blind to our blindness.”  This double-whammy – a variant of the Dunning – Krueger effect — is why questions to senior leaders are unlikely to be answered honestly and self-critically.  After a brief mention of another persistent bone of contention – the System 1 / System 2 delineation  – and our alleged propensity to rely too much on automatic system 1 which makes us, presumably, liable to various biases (in this chapter loss aversion, framing, overconfidence, moral disengagement, euphemistic labelling), Gentilin lays out the slippery-slope argument that in his view was at the heart of the events that led to the NAB trading scandal: “The FX trading incident at the NAB classically illustrated the slippery slope in action. Not only did ethical standards erode over time, but the seriousness of the ethical transgressions accelerated … “ (p. 130). Laboratory evidence is provided to  make that point (e.g., the interesting Gino & Bazerman 2008 study) along with field evidence from the NAB case (pp. 131). An intervention discussed here is to give people more time and essentially get them to break out of their System 1 mode: “There are now numerous studies that illustrate how providing a person with more time whenever they are confronted with an ethical dilemma tends to lead to a more virtuous decision being made.” (pp. 146-7). I have serious doubt about the relevance of, say, the Good-Samaritian study mentioned here for real-world decision making and suspect that a theoretical grounding in organizational economics and repeated game theory would really help to address the challenges that organizations and their leaders face.

Gentilin concludes his book with a plea for more (business ethics) education, a call for the installation of Chief Ethics Officers, and more Lessons for Leaders. He wants business schools to challenge their students intellectually, emotionally, and spiritually. That sounds like something straight out of a high-gloss advertisement such schools produce. The reality, however, of Australian business schools (and undoubtedly business schools everywhere) is that they are rarely intellectually demanding. Their inability to challenge their students emotionally and spiritually is shown effectively by their treatment of casuals and staff. What business schools typically do not have are, in particular, truly independent ethics officers, and HR departments, that could hold the feet of currently widely unaccountable senior leadership to the fire. So, while the idea of a Chief Ethics Officer, who has “a genuine ‘seat at the table’” (p. 161), and is independent, able to freely raise matters of concern, and able to freely “speak truth to power” (p. 161), is conceptually on the money, realistically it is very unlikely to be implemented any time soon, as are truly independent HR departments. As to Lessons for Leaders, Gentilin wants them to be virtuous in the sense of having some community-oriented values.  There is a lot of wishful thinking on display here (e.g., that others are willing to take the same risks that he took in 2004) but I think, after everything we learned through the flurry of recent examples mentioned at the beginning of this review, there is not much reason for hope. Even something that should have been uncontroversial, such as the Royal Commission on banking, and the way it came about, demonstrates that common ground is hard to find and cannot be relied on. I fear much harder thinking will be needed to address ethical failures and I fear some strategies will be of the innovative kind provided by the #MeToo campaign that not only has brought down some true monsters but is likely to have changed power and gender relations in the working world irreversibly.

In summary then, Gentilin tackles arguably the most important issue of our times – ethical failures within organizations and for that matter ethical failures more generally. His book is strongest where he illustrates the emergence of his insights with examples from his own NAB 2004 experience. His illustration of various arguments he makes with evidence from behavioural business ethics is wanting. As pointed out above, to his credit Gentilin himself – although unaware of important methodological debates among psychologists as well as between psychologists and economists – grasps intuitively the lack of external validity of some of the evidence that he presents and it is clear that his NAB 2004 experience has been a good guide to identify which laboratory evidence has some external validity, and which does not. I think the book could be considerably improved with a more even-handed and complete assessment of the evidence from psychology and other social sciences (and here in particular economics) as well as an additional focus on incentive-compatible organizational design.  To rely on business ethics education in business schools (whether in Australia or elsewhere) or a sense of community oriented-ness of business leaders is just not going to cut the mustard, as the widely perceived need for the Royal Commission in the banking system demonstrates.

Having recently interacted with NAB, once again, with mortgage related issues, I have no doubt that NAB culture is pervaded with everything but a meaningful social purpose that is underpinned by a virtuous set of values (e.g., the loan officer I dealt with did everything to prevent me from comparison shopping, and essentially gave me misleading information about the rates that I would be getting), and I have little doubt that the same applies to each of the other three major banks. There is a reason why the major banks in Australia have had outsized profits and some of the highest returns on equity in the world. The recent draft of the related Productivity Commission report spells them out.

 

I appreciate Dennis Gentilin’s comments on a draft of this review.

 

How to tax the platform economy?

In the engine room of nation states, ie the tax departments, the coming battle with platform providers is taking shape. Uber, airbnb, facebook, linkedin, ebay, jobseek, and a myriad of specialised platform providers facilitate micro-trades that are largely untaxed by the authorities. In stead, the platform providers themselves take a cut, partially via advertising and partially via a direct fee for their services. They have taken over an activity that has mainly been provided by governments in the past: places to trade. The town square, the stock exchange, public infrastructure, and the unemployment office are relics of a past where governments were market providers that facilitated trades. Now, it is largely private companies with tax-avoidance structures that have taken on this role on the internet. That role is set to expand hugely.

This is a crucial battle that, so far, the tax authorities are losing because they have not yet grasped the magnitude of the shift. They lack the key new power that they must attain: the power to deny the operation of a platform provider in their country.

At the moment, tax authorities around the world, lead by the Scandinavians whose tax needs are high, are going the usual ‘reporting route’. They are trying to get Uber, Airbnb, and all the other ones to report the trades and the value of the trades that they have facilitated. Understandably, these companies are refusing to play ball because they of course are taxing the same trades themselves in a different way. They are competing with national tax authorities and hence their business model depends on tax evasion, so of course they refuse to help their competitors. Their lawyers make millions from refusing to play ball. The horror example for these companies is the 2015 data on Uber that had to be released to the Dutch tax authorities and that was subsequently shared with Denmark which promptly went after the drivers for added tax payments. This reflected the circumstance that the administration of Uber was in the Netherlands at that time, which allowed the Dutch to force Uber to hand over some of their data, a mistake Uber wont make again. The others too will have learned a salutary lesson from that episode.

Frustrated, the tax authorities are turning to pretty hopeless measures, such as new international treaties on the reporting of micro-trades by private entities. In a race to the bottom between countries trying to attract large companies, that is just a hopeless avenue where the authorities will always be many steps behind the tax-advisers of the big trading platforms.

What are the next moves we might then see when the tax authorities get up to speed? I think two developments are likely: full internet observation by national agencies and government-lead internet firms.

Full internet observation follows the model of China, which now has the capacity to track most of the internet activity of most of the population. That allows it to observe the trades facilitated on internet platforms, which in turn can be used for tax purposes. Those observations can be used to directly go after individual traders or can be used to go after the platform providers, simply by making their activities illegal if the platforms do not assist in tax observations. Adopting the China route would spell the end of internet privacy, but it probably works. And tax is such a key part of the nation state that it in the end trumps privacy concerns.

The second possibility is for the government to re-enter the market for platforms and set up its own internet firms for micro-trades and social media. It can simply copy the best examples on the internet for how to set these things up. The transition will come with losses, but authorities can appeal to national pride to get support from their populations and companies cannot compete with that. For micro-trades within a country or tax region (the US and, in the future, the EU) that should work. For international trades, one should expect more difficulties because government-backed firms from different countries might then directly compete with each other, which in turn might lead to competency battles and new dispute resolution mechanisms.

Lemonade and the question of (laboratory) evidence

Lemonade Inc., the New York based fintech startup that sells home and renters insurance has been in the news recently. It has raised tens of millions in venture capital  and also considerable interest in the top echelons of corporate Australia. I know because I was asked to reflect on it as part of a workshop on behavioral economics/behavioral science that I conducted a couple of months ago. I have to admit that I did not know about Lemonade before that request.

Turns out that Lemonade uses “Behavioral Science (and Technology) To Onboard Customers and Keep Them Honest”, so the title of a piece in Fast Company earlier this year. Lemonade bets that insights from Behavioral Economics (BE) will give it the edge over incumbent competitors. It bets specifically that the BE insights of Dan Ariely (he of Predictably Irrational and TED talk fame, and now Lemonade’s CBE = Chief Behavioral Officer) will provide that edge, important components being “trusting our customers” and “giving back” to charity all unused excess funds. On top of these components, or maybe undergirding it, is the promise that Lemonade commits to spending at most 20 percent of its income on administration and marketing, which presumably prevents it from profit maximizing at the expense of its customers. Lemonade also promises that it will process claims fast and relatively un-bureaucratically, at least by the standard of an industry that has a reputation for delaying tactics and for its persistent attempts to evade having to pay up. Examples of speedy processing are featured prominently on Lemonade’s website.

And not only that: A couple of months ago, Lemonade launched its Zero Everything policy which gets rid of deductibles and rate hikes after claims and is supposed to pay for itself through elimination of the paperwork that comes with relatively small claims.

BE principles are also appealed to when customers that make claims are asked to submit a brief video outlining their claim and to provide at the same time a honesty pledge which supposedly induces more honesty.

In sum then, Lemonade builds its business allegedly on the trust(worthiness) of its customers, and of itself, and also honesty on the part of both parties.

Let’s start with the (laboratory) evidence for trust(worthiness). On its web page, Lemonade illustrates the advantages of trust(worthiness) with one of the workhorses of experimental economics, the trust, or investment, game. According to the web page, a person that invests (the trustor) will see her investment to a trustee of $100 quadruple and then see the trustee return half of that $400 to herself (the trustor), for an impressive ROI of one hundred percent. Trust pays off, we learn: “We are more trusting and reciprocating than what standard economic theory predicts.”

Ignoring the stab at economic theory (which shows little more than a lack of elementary knowledge of modern economic theory), there are at least three problems with the Lemonade narrative. First, it is not clear at all why this particular game, in this particular parameterization, captures the customer – insurance company situation. Second, I am not aware of anyone ever having experimentally tested this game with that specific parametrization (specifically, a multiplication factor of 4), and I am not aware — the multiplication factors typically used being 3 or 2 — of responders returning more than what was invested. In fact, the results of my own work (which are very much in line with the literature in this area) suggest that trustors invest about half of what they were given and trustees return slightly less than what was invested. It is noteworthy that there is much heterogeneous behavior to be found in these experiments, with many of those that trust (“invest”) being brutally exploited.

  “Everyone has a price, the important thing is to find out what it is.” (P. Escobar)

Which brings us to the question of honesty. There is indeed some evidence that the way in which people are being prompted makes a difference and, more generally, that context matters (see Various, JEBO 2016). Friesen & Gangadharan  (Economics Letters 2012) use an individual performance task (“matrix task”) after which they ask their subjects to self-report the number of successes that participants had. While very few of their participants – only one out of 12 — are dishonest to the maximal extent, about one out of 3 are to different degrees, with men (in particular those of Aussie and NZ provenance) being more dishonest, and more frequently so, than female participants. Rosenbaum, Billinger, & Stieglitz  (Journal of Economic Psychology 2014) review experimental evidence of (dis)honesty 63 experiments from economics and psychology (including Friesen and Gangadharan EL 2012) and find the robust presence of unconditional cheaters and non-cheaters with the honesty of the remaining individuals being particularly susceptible to monitoring and intrinsic lying costs. Most of these experiments involve fairly low stakes, so those intrinsic lying costs are unlikely to be much of a constraint when stakes increase. The fraction of unconditional non-cheaters is almost certain to shrink towards the Escobar limit when stakes increase.

Interestingly, notwithstanding its public declarations in the good of people, Lemonade tells itself that, while trust is good, control is better.  It runs its claimants, on top of the honesty pledges, through 18 different fraud detection algorithms before it pays up. On top of this, Lemonade engages in blatant cream-skimming. For example, it did not quote half of their customers that wanted to insure their homes. And it reports that the customers that are joining, or allowed to join, are younger, educated, tech-savvy, above-average earners, and female. So much for trust, trustworthiness, and all that BE marketing horsemanure. Pretty cold-blooded standard economic theory if you ask me. Note that this screening takes care of a key problem with their advertised approach: the likely adverse selection of bad types that mere trusting would invite, a very likely whammy on top of the moral hazard problem that every insurer faces.

So is Lemonade a viable business model?

Time will tell.

In the State of New York, Lemonade claims to have overtaken Allstate, GEICO, Liberty Mutual, State Farm, etc. in what is probably the single most critical market (renters and home insurance) share metric of all: NY renters buying new insurance policies since 1 Jan 2017.

Lemonade, we are told, is growing “exponentially” = “new bookings have doubled every ten weeks since launch, and show no sign of letting up.” According to its most recent Thanksgiving Transparency ‘17 report, Lemonade has now branched out into, and is selling in, Illinois, California and Nevada, Texas, New Jersey and Rhode Island, and has been licensed in 15 other states.

Of course, collecting insurance premia is one thing. Paying insurance claims and balancing the books is another thing altogether and the verdict on that one will be out for a while.

If Lemonade succeeds – and we all should hope it does –, it will do so because it engages in cream-skimming, targeting of low-risk market segments, and massive control and surveillance of its clientele. It will not do so because of its invocation of the feel-good alleged BE findings so prominently displayed on its web page.

 

 

 

 

 

 

 

 

Why Blockchain has no economic future

When Bitcoin went public in 2009 it introduced to the world of finance and economics the technology of blockchain. Even the many who thought Bitcoin would never make it as a major currency were intrigued by the BlockChain technology and a large set of new companies have tried to figure out how to offer new services based on blockchain technology. It is still fair to say that very few economists and social scientists understand blockchain, and governments are even further behind.

I will argue that blockchain has no economic future in the regular economy. I will give you the bottom-line, then describe blockchain, discuss its key supposed advantages, and then take it apart as a viable technology by giving you a much more efficient alternative to the same market demand opportunities.

The bottom line for those not interested in the intricacies of blockchains and public trust

The essence of my argument is that a large country can organise a much more trustworthy information system than a distributed network using blockchain can, and at lower costs, meaning that any large economic role for blockchain is easily displaced by a cheaper and even larger national institution.

So in the 19th century, large private companies circulated their own money, in competition with towns and princedoms. In that competition, national governments won, as they will again now.

The reason that the tech community is investing in blockchain companies is partially because some are in love with the technicalities of blockchain, some hope to attract the same criminal and gullible element that Bitcoin has, some lack awareness of the evolution and reality of political systems, and some see a second-best opportunity not yet taken by others. But even in this brief period of missing-in-action governments, large companies will easily outperform blockchain communities on any mayor market. Except the criminal markets, which is hence the only real future of blockchain communities. Continue reading “Why Blockchain has no economic future”

Mobile termination: Zero is a good place to start

Last week I did an interview with Phil Dobbie for CommsDay on the ACCC’s approach to the setting of rates that carriers pay each other to terminate calls. I argued — as I did 15 years ago — that marginal cost rather than Total Service Long-Run Incremental Cost makes more sense and will generate more welfare without cutting investment incentives. It has the advantage of being a light-handed approach, computationally easy to calculate and good for consumers. And if you are concerned about Telstra not having incentives to build out mobile networks in regional areas remember they do get a virtual monopoly on customers in those areas so they are hardly suffering.

I’m about 8 minutes in.

At one point I talk about asymmetry in network size and whether that matters. As Philip Williams pointed out years ago that argument is a red herring. To see that, suppose that one network is 99% of the customers and another network has 1% of the customers. Suppose that customers call people independent of what network they are on. That means that the share of calls made to the customer on the small network is 1% while the probability that a customer on the small network calls someone on the large one is 99%. So the total amount of termination revenue earned by the small network is 1% times 99% times the termination charge while the termination revenue earned by the large network is 99% times 1% times the charge. It is easy to see that the termination revenue earned by each network is exactly the same. Thus, having a higher charge does not favour or discriminate against the larger network.

Vision 28

How would you measure the safety of private motor vehicle travel?

Let’s agree to focus on fatalities. Serious injuries are also important, but all the points I am going to make hold equally as well for injuries as for fatalities. Continue reading “Vision 28”

Are there unhelpful mathematical models of economic phenomena?

Take your bog-standard first-year economics story of why money (sea shells, coins, notes, bank statements) exist. Money, you will be told, is a means of exchange, a store of value, and a unit of accounting, thoughts going back to David Hume (18th century) and earlier.

When explaining the idea of exchange to students you say things like ‘you can’t exchange a hundredth of a sheep for a loaf of bread so you want something to represent the value of a hundredth of a sheep, and in any case it’s a long slog to the market carrying a sheep around’.

When explaining the idea of a store of value you say things like ‘You would like to be able to consume things when you are old without working when you are old. That means you need to save up wealth in the form of something that doesn’t perish. Sheep perish, gold does not’; and when explaining the unit of value idea you say things like ‘we all think of the value of things in terms of a numeraire, such as that milk costs 1 dollar per liter and flour 2 dollars a kilo. None of us think in terms of 1 liter of milk being worth half a kilo of flour. Given many different products, it is more convenient to think of the value of each of them in terms of something you can compare across these goods. Money performs that role and you will find that even when the unit of money changes (such as moves from the Deutschmark to the Euro) that people will continue to calculate everything back in terms of the old money for many years’.
Simple stories, no? And most students will ‘get the point’ of each of these three stories. They will see the difficulties of exchange with lumpy goods that cannot easily be stored and exchanged, and they will see the point of being able to save up for a later date and that requires some form of storable money.

Simple though these arguments are, you will be hard-pressed to find mathematical models of them that anyone would recognise as remotely capturing these verbal arguments. It tells you something about the limits of mathematical models to think through why recognisable models of money do not exist. So bear with me as I take you through the actual difficulties of modelling money and how those difficulties end up as unhelpful advise from theoretical economists to policy makers.Think of the actual difficulties involved in modelling the story of money as a medium of exchange. Before even thinking about money, you have to start from a model with exchange. This means you need to model the production of more than one good and you must build in a reason, like comparative advantage, why individuals do not simply produce all the goods they need by themselves. For realism you would want the goods to be lumpy, perishable, and to require long-term investments. After all, sheep herding and crop-growing do not happen overnight and neither sheep nor apples can meaningfully be stored for very long or exchanged in halves.
You immediately hit your first mathematical snag right there: if production is lumpy (you can’t produce half-apples), then you won’t get the simple outcome that someone will spend all his time on what he is best at. An individual could optimally spend his time by producing one sheep and two apples even though he has a comparative advantage in sheep, simply because he can’t make exactly two sheep. If you want lumpiness in your model, you thus would have to solve the problem of how a person would optimally allocate a fixed amount of time over lumpy investment projects. This is known in the Operations Research literature as the knap-sack problem  (in which you need to decide which lumpy goods to put into a knapsack of particular size) and it is known to be an ‘NP-hard’ problem. Simply put, you know of such problems that there is a single optimal solution but it may take a long time to actually find it. Solving just that knapsack problem for a single individual is already something that may take a computer years if you choose the bundle of potential goods to be large enough, and there will be cases in which you will find that even with comparative advantage the sheep herders may grow enough apples to not need exchange.
How do you solve that snag, which incidentally arises in all models of production? The reality is that you don’t because solving just that one leaves you with a model in which you can solve little else and in which you are not assured of any real impetus for exchange. Hence you ‘simplify reality’. You thus presume that there is no such thing as a lumpy good and that people spend their time producing a ‘continuous’ amount of goods, say, 3.271 sheep or 14.231 apples. Without lumpiness, people will specialise in making one thing and have a reason to trade. Note that you thus have already given up on describing the most intuitive reasons for having money around: you can no longer meaningfully talk about the difficulties of exchanging a hundredth of a sheep for half an apple since you now have presumed a world in which you produce sheep in hundredths and apples in halves.
Moving on, the next modelling problem you hit is that it must be the case that different individuals happen to want what the other produces, a ‘coincidence of wants’. Indeed, you want some kind of place (a market) where people come to exchange what they have produced. In model-land you must answer every counter-factual. You must thus have a reason why traders would use money instead of giving each other credit or just exchanging bundles of good (since goods are now not lumpy, you can just go to the market with your 2/3 sheep and exchange it in one big free-for-all for all the goods you need). Such thoughts may sound absurd to you, but working them through has occupied really good mathematicians for years. It is in fact nigh impossible to solve models in which people do not know exactly beforehand what will happen in a market.

You see, as soon as you say that a person does not know beforehand what other people have produced and at what prices they might trade, you are in the world of limited information and in the world where it is possible that people make mistakes (go to the market empty handed, produce the wrong things, etc.). You are then in the business of having to specify how people form expectations about what others would do and what prices they would trade at.

You are then also in the business of working out whether there are perhaps multiple equilibria (i.e. different configurations of the whole economy) and the issue of how people who don’t know each other could actually coordinate on a particular configuration. You then for instance have to contend with the possibility that nobody shows up at the market because they expect nobody else to show up. You have to contend with the possibility that you get the wrong prices, under which there is no specialisation at all.

You have to contend with the problem that the only people to show up wouldn’t want to trade with each other because they have produced the same thing and you have to figure out how a group of people would actually arrive at a price (or prices). Each of these sub-problems is considered exceptionally hard by theorists: only under very specific mathematical assumptions can you be absolutely guaranteed that the problems above do not occur.
Hence, what do you do? Well, again, the reality is that you assume away all these problems. You simply make those assumptions that guarantee you that everyone who produces something is ‘magically’ matched up with someone else who has something they want to trade with. Also, you now presume the existence of some kind of all-powerful benevolent entity, say god. You need such an entity to do away with elements in your model you cannot model but need anyway, such as how prices arise before any exchange takes place (if prices change during exchange one gets into exceptionally complicated dynamics where you need to start talking about the expectations that people have of possible price paths). So you invent a god that takes care of such issues. God, in his first incarnation as a Walrasian auctioneer, announces the prices at which everyone is willing to trade, whereby everyone believes god and acts accordingly. God, now in his second role as a benevolent and completely trusted government, then also provides a means of exchange that is not perishable, i.e. money.

Usually, a third sleight of hand is needed to get a workable model and that is to have a situation in which there is no such thing as a mistake because there is no such thing as expectations that are incorrect. This of course basically presumes away the original problem you were starting out to model, but that is an almost inevitable casualty of the wish to have a tractable economic model.

What kind of models of money do we end up with? To my taste, the best that mathematicians have come up with is the story that some sheep producers have a craving for eating apples in the night, but they are themselves just innately incapable of producing apples and their sheep always die at the end of the day (i.e. they must be eaten before the end of the day. New ones are only born at the start of the next day). This means that the sheep herder must sell his sheep during the day to the apple maker whilst buying the apples during the night (apples also perish at the end of each half day so he can’t trade during the day). In a modelling sense, that ensures you the ‘coincidence of wants’ you need to have a role for exchange and ensures that sheep herders and apple farmers cannot just trade their produce. By assuming that they not trust each other, but that they do trust the provider of money, you ensure that they do not just trade promises but use money for their trades. Within this kind of basic set-up you can even introduce monetary policy in the form of allowing god to hand out more money to specific groups or to reduce the value of the money in circulation. Whole ‘policy edifices’ have been built upon the basic structure of sheep herders having cravings for apples in the night. For those who are interested, I am talking about the model by Lagos and Wright (2005) and the many extensions on their basic idea.
Now, anyone in his right mind would laugh out loud at the story above as it comes nowhere close to the historical stories told about why we have money and what its role is in the economy: big historical problems in the emergence of money concerned the fact that there was no trusted government, and the value of money had a lot to do with the actual costs of information and transportation, costs that the story talked about above had to assume away. Yet the story of apples and sheep above, believe it or not, is one of the dominant stories told in ‘micro-founded’ monetary economics. It is in that kind of model-economy that they talk about money, credit, banks, regulation, etc. If it weren’t for the fact that it is deemed cutting-edge research, you would have to cry.
I hope you will take my word for it that the problems of generating models in which money exists because of savings and as a numeraire good are equally hard to set up and hence such models don’t exist at all as far as I know.
The value of the actual models of money are mainly as proof of concept, i.e. that you can think of a micro-model in which money emerges and where you can base the emergence of money on at least one of the underlying micro-motivations you think are important for the existence of money (the advantage of having a more varied consumption bundle). It is not the model you would have wanted but at least you can have it in the back of your mind as an example of the micro-mechanisms that are relevant.
The problem with the monetary model talked about above is that it fits so poorly. It hardly fits the many historical examples we know of the emergence of money, nor does it capture the problems we face today when thinking about money markets (trust in the institutions, the incentive problems inside organisations, the investment problem). Hence it is singularly unsuitable to use as a mental laboratory for the policy problems of today, or even as a descriptive model of the actual roles of money in our economy.

The problem of poor fit carries over to unhelpful advise: despite the fact that it is such a poor fit to reality, it is the only ‘game in town’ when it comes to micro-models of money. A most unfortunate and destructive phenomenon then appears, which is that the only game in town becomes the truth to a whole set of people making their careers on the back of it.

All the potential advantages of models become a disadvantage when a poorly-fitting model is taken too seriously. One potential advantage of models is that they can be the codification of previous knowledge and as such a good model is a quick way of conveying a lot of knowledge to the next generation who don’t have to learn what reasons went into the construction of the model in the first place.

This now becomes a disadvantage: the new generation that looks to write papers ‘on money’ need know nothing about the history of money or its uses today but only need know the dominant model, which turns into a disadvantage because that new generation will come up with twists and extensions of something that is innately unsuitable to answer any interesting question. Yet that new generation will be blissfully ignorant of the uselessness of what they are doing because they, unlike the originators of the first models on money, will lack the historical database in their heads of what actually goes on. They are simply proving their worth by being more acquainted with the mathematical ins and outs of these models than anyone else and that is what supplies them their daily dinner, not whether the model is useful to anyone else.

Another potential advantage of a good model is that you can make consistent statements instead of waffling on incoherently. One real advantage of model-land is that it is fairly easy to spot someone who is not capable of understanding models. This advantage also becomes a disadvantage in a model that fits poorly because you will see a great proliferation of consistent statements that are based on poor abstractions of real phenomena. You might term this the proliferation of ‘precisely wrong’ statements.

And it is a cop-out to say that these precisely wrong statements are not intended to be taken literally: despite being mere models, the adherents deliberately use words that convey its supposed usefulness, such as monetary policy, government, banks, etc. The pretense of usefulness pervades each paper and each grant proposal using these models. Worse still, that modelling community is a group with a big incentive to pretend that the assumptions made for convenience are ‘actually true’, i.e. it is a constituency of individuals with an incentive to presume there is no such thing as transaction costs or a trust problem when it comes to money. When such people become important they will poo poo those who make different assumptions and force them to first invest in their models. In short, a poor model that is taken seriously becomes a part of the problem.

Would you also have the same problems if monetary economics were mainly based on a set of historical case studies and an awareness of the problems faced today by economic actors? Unlikely, because you then at least have set up an ultimate goal of the discipline, which is to understand how the world came to be as it is and to help economic actors shape their world to their advantage, i.e. you are grounding your discipline in historical reality and real world problems. Having said this, one should not be blind to the disadvantage of a more verbal discipline though. The disadvantage is that when knowledge consists of a collection of examples and lessons, there is more room for the wafflers of this world to ply their trade, and there are millions of eager wafflers around.

Are there any good economic models you might ask? I believe there are and my prime example would be Industrial Organisation models of competition and market interaction. These are the Cournot models, Stackleberg models, models of complementary investments in vertical markets, oligopoly models, models of the internet as a platform, etc. The nice thing about these models is that the motivations they presume of their actors (pure greed) are pretty well spot-on and that it is not that hard in reality to see what kind of market interaction is happening, i.e. which of the I/O models to use.

Though it is hard to measure for a statistician, it is not so hard to spot as a human whether, say, the oil companies are engaging in collusion or not. It is not hard to spot a cartel, or the basic information structure of a market, nor is it hard to spot the structure of investment complementarities. In short, I/O models can do a remarkably good job of describing the particular aspects of reality one can optimally intervene in, which is of course why they are so central to the work of regulation authorities and why, for instance, auction design on the internet is done by mathematically schooled geeks. They need to know nothing of the history of auctions to nevertheless be damned good designers of auctions as long as they understand the models and have learned to spot the market patterns around them.

There are thus good models out there and the groups of disconnected geeks working on extending them are, often to their own surprise, doing something useful with their lives. We wouldn’t want to go back to waffling in those areas. The problem is thus not the existence of mathematical models per se, but rather that there are aspects of economic reality where the best we can do is a bad model.

Is money the only area where we can do no better than bad models that are worse than useless when they are taken seriously? Alas, no. What goes for money goes for many economic phenomena. To have an economic model where growth is driven by specialisation (which is what most historical economists believed was the engine of growth) has so far been beyond us, which is why we have ended up with these ridiculous representative agent models. What the pragmatists believe is true about specialisation can’t be modelled by the best minds in math econ land (this is not to say there are no models of specialisation, simply none that get close to illuminating the path-dependence, trust, and institutions that sustain it). Satisfactory ‘des-equilibrium’ models of recessions also simply don’t exist. Models of human behaviour drawing upon more than two of the known ‘irrationalities in our make-up’ are also too hard to solve. The list goes on and on: if one insists on consistent mathematical theorising from ‘micro-foundations’, nearly all of the big drivers of economic growth and economic institutions are beyond our ability to model even remotely realistically.

Mathematical models are hence in many areas a problem because they fit poorly but nevertheless live a life of their own, taking up valuable mental time of smart people, leading individuals to think about the wrong problems, leading people to think in terms of the wrong assumptions, motivating statisticians to measure the wrong things, and divorcing their discipline from reality.

Suppose you believe all this, but nevertheless want to make progress in disciplines by doing proper science, differentiating yourself from the wafflers. What is ‘proper science’ in an area where we cannot make much mathematical headway and hence where we can be reasonably certain that every grand story we tell (in maths or in words) has inconsistent parts to it? That’s the subject of a future blog….