Liberal Democracies and Authoritarian Regimes: The Case for Law Enforcement. (Part 4 of 12)

Different degrees of law enforcement

Law enforcement systems range from ideal types of pure blind and automatic rule enforcement to pure discretion. The ideal of automatic law enforcement denies the reality of errors, the fragmentation of knowledge of special circumstances of time and place, and information costs. Meanwhile, complete discretion is the very negation of the law as abstract and general normative statements. However, defining both poles, the first factually unrealizable and the second contradictory in itself, allows us to identify the trend that characterizes the various legal systems given.

Likewise, information costs and discretion are variables that determine the degree of law enforcement. Both the criminal sanctions and the sentences to compensate damages depend to a large extent on questions of proof and evidence about the facts contained in the norm as a condition for the application of the legal solution envisaged. Likewise, the law itself imposes limits and criteria for collecting and assessing evidence, such as due process guarantees, which include the right not to testify against oneself and the inviolability of the person. Therefore, when a rule provides, for example, a fine of $1,000 – for the offender, the deterrent of said consequent depends on the degree of probability that the legal system will identify the infraction, the person responsible for the infraction and be able to prove said fact before the courts in a process supervised by the offender, who may present his defense and offer his own evidence.

Continuing with this example, if the probability of being fined is 80%, then the fine represented by the eventual offender is reduced to $ 800. Suppose then, that a driver needs to get to work on time so that the day is not deducted, which would mean a loss of $900. Then, the person in our example will maximize his choice if he violates any traffic rule, assuming the risk of losing $800 – in order to avoid the risk of losing $900. Of course, if it is discovered, your gross loss will be $1,000, but your net loss will have been reduced to $100, while if it is not discovered, your gross result will be $0, but your Net result will amount to $900, since thanks to his decision to assume the risk of being fined, he avoided losing the payment for the day of work. Therefore, given the incentive system given to the maximizing agent in our example, the most rational thing for him is to assume the risk of transgressing the norm.

This elementary example suggests several conclusions. The first one is that it should not be ruled out that society itself maximizes the utility of its resources by admitting a certain range of transgressions. However, these cases are not extra-systemic, but are justified or exempted from liability, as the case may be, within the legal system. Running a red light in order to urgently take a badly injured person to the hospital is a cause of justification. Doing it on a completely deserted street in order not to be late for work could be accepted as an acquittal. In these cases we are also faced with a certain degree of judicial discretion, in order to weigh the legal meaning of certain facts and circumstances as justifying or mitigating responsibility. But another issue related to this is to recognize that the agent himself has a higher level of information regarding his own circumstances than that of any other external observer, which allows him to make better decisions attentive to his level of immediacy with the facts. Finally, society itself also organizes itself spontaneously around a certain margin of extra-systemic regulatory breaches: in the example mentioned, society as a whole will maximize the utility of its resources if the offender arrives early at work, at the risk of paying a fee. penalty fee; while the traffic fines will have as their real destination those drivers who are not pressured by such an urgency, in which case it is more socially beneficial that they comply with the traffic regulations.

The latter brings us to another question, of singular relevance, which consists in defining the distinction between a liberal legal system and a police one. Legal systems that recognize the value of human dignity and are organized around a principle of autonomy of the will give each individual the power to decide whether to transgress certain norms at the price of assuming their consequences. Instead, police systems seek to prevent each individual from making such a decision, for the sake of certain collective values, such as security or mere compliance with the orders issued by the public powers. Of course, even in liberal legal systems, values ​​such as the protection of human life and public safety entail certain mechanisms and norms for crime prevention, but always considering that these mean an injury to individual freedoms, not an absolute public authority.

Finally, although without definitively exhausting this debate, one characteristic of particular systemic relevance deserves to be mentioned, on which it will have to be discussed in greater depth: the relationship between the decision to increase the degree of application of the norm or to increase the threat of punishment, in order to achieve a certain degree of compliance by citizens.

[Editor’s note: this is Part 4 in a 12-part essay; you can read Part 3 here or read the essay in its entirety here.]

Confessions of a Fragilista: Talebian Redundancies and Insurance

I’ve been on a Taleb streak this year (here, here and here). Nassim Nicholas Taleb, that is, the options trader-turned-mathematician-turned public intellectual (and I even managed to get myself on his infamous blocklist after arguing back at him). Many years ago, I read Fooled by Randomness but for some reason it didn’t resonate with me and I wasn’t seeing the brilliance.

Last spring, upon reading former poker champion Annie Duke’s Thinking in Bets and physicist Leonard Mlodinow’s The Drunkard’s Walk, I plunged into Taleb land again, voraciously consuming Fooled, The Black Swan and Skin in the Game, followed by Antifragile just a few months ago.

Taleb is a strange creature; vastly productive and incredibly successful, everything he touches does not quite become gold, but surely stirs up controversy. What he’s managed to do in his popular writing (collected in the Incerto series) is to tie almost every aspect of human life into his One Big Idea (think Isaiah Berlin’s hedgehog): the role of randomness, risk and uncertainty in everyday life.

One theme that comes up again and again is the idea of redundancies: having several different and overlapping systems – back-ups to back-ups – that minimize the chance of fatally bad outcomes. The failures of one of those systems will not result in the extremely bad event you’re trying to avoid.

Focusing primarily on survivability – “absorbing barriers” – through the handed-down wisdom of the Ancients and the Classic, the take-away lesson for Taleb in almost all areas of life is overlapping redundancies. Reality is complicated, and the distribution from which events are drawn is not a well-behaved Gaussian normal distribution, but one of thick tails. How thick nobody knows, but wisdom in the presence of absorbing barriers suggest that taking extreme caution is a prudent long-term strategy.

Of course, in the short run, redundancy amounts to “wasted” resources. In chapter 4 of Fooled, Taleb relates a story from his option trading days where a client angrily calling him up about tail-risk insurance he had sold them. The catastrophic event from which the insurance protected had not taken place, and so the client felt cheated. This behavior, Taleb maintains quite correctly, is idiotic. After all, if an insurance company’s clients consist of only soon-to-be claimants, the company won’t exist for long (or it prices insurance at prohibitively high rates, undermining the business model).

Same thing applies for one of his verbose rants about airline “efficiency,” a rather absurd episode of illustrating “asymmetry” – the idea that downside risks are larger than upside gains. Consider a plane departing JFK for London, a trip scheduled to take 7h trip. Some things can happen to make the trip quicker (speedy departure, weather conditions, landing slot available etc), but only marginally; it would, for instance, not be possible to arrive in London after only an hour. In contrast, the asymmetry arises as there are many things that can delay the trip from mere minutes to infinity – again, weather events, mechanical failures, tech or communication problems.

So, when airlines striving to make their services more efficient by minimizing turnaround time – Southwest’s legendary claim to fame – they hit Taleb’s antifragile asymmetry; getting rid of redundant time on the ground, makes the process of on-loading and off-loading passengers fragile. Any little mistake can cause serious delays, delays that accumulate and domino their way through crowded airport networks.

Embracing redundancies would mean having more time in-between flights, with extra planes and extra mechanics and spare parts available at many airports. Clearly, airlines’ already brittle business model would crumble in a heartbeat.

The flipside efficiency is Taleb’s redundancy. Without optimization, we constantly use more than we need, effectively operating as a tax on all activity. Taleb would of course quibble with that, pointing out that the probability distribution of what “we need” must include Black Swan events that standard optimization arguments overlook.

That’s fine if one places as high a value on risks that Taleb does, and indeed they’re voluntarily paid for. If customers wanted to pay triple the money for airfares in order to avoid this or that delay, there is a market for that – it just seems few people value that price over the damage from (low-probability) delays.

Another example is earthquake-proving buildings that Nate Silver discussed in his The Signal and the Noise regarding the Gutenberg-Ritcher law (the reliably inverse relationship between frequency and magnitude of earthquakes). Constructing buildings that can withstand a high-magnitude earthquake, say a one-in-three-hundred-year event is something rich Californians or Japanese can afford – much-less so a poor country like the Philippines. Yes, Taleb correctly argues, the poor country pays its earthquake expenses in heightened risk of devastating damage.

Large redundancies, back-ups to back-ups, are great if you a) can afford them, and b) are risk-averse enough. Judging by his writing, Taleb is – ironically – far out along the right-tail of risk aversion; for most other people, we have more urgent needs to look after. That means occasionally “blowing up” and suffer hours and hours of airline delays or collapsing buildings after an earthquake.

Taleb rarely considers the trade-offs, and the different subjective value scales (or discount rates!) that differ between people. While Taleb may cherish his redundancies, most of us would rather eliminate them for asymmetrically small gains.

Insurance is a relative assessment of price and risks. Keeping a reserve of redundancies are subjective choices, not an objective necessities.

Do risk preferences account for 1.4 percentage points of the gender pay gap?

A few days ago, this study of gender pay differences for Uber drivers came out. The key finding, that women earned 7% less than men, was stunning because Uber uses a gender-blind algorithm. The figure below was the most interesting one from the study as it summarized the differences in pay quite well.

DataUber

To explain this, the authors highlight a few explanations borne out by the data: men drive faster allowing them to have more clients; men have spent more time working for Uber and have more experience that may be unobserved; choices of where and when to drive matters. It is this latter point that I find fascinating because it speaks to an issue that I keep underlining regarding pay gaps when I teach.

For reasons that may be sociological or biological (I am agnostic on that), men tend to occupy jobs that have high rates of occupational mortality (see notably this British study on the topic) in the forms of accidents (think construction, firemen) or diseases (think miners and trashmen). They also tend to take the jobs in further removed areas in order to gain access to a distance premium (which is a form of risk in the sense that it affects  family life etc.). The premiums to taking risky jobs are well documented (see notably the work of Kip Viscusi who measured the wage premium accruing to workers who were employed in bars where smoking was permitted). If these premiums are non-negligible but tend to be preferred by men (who are willing to incur the risk to be injured or fall sick), then risk preferences matter to the gender wage gap.

However, there are hard to properly measure in order to assess the share of the wage gap truly explained by discrimination. Here with the case of Uber, we can get an idea of the amplitude of the differences. Male Uber drivers prefer riskier hours (more risks of having an inebriated and potentially aggressive client), riskier places (high traffic with more risks of accidents) and riskier behavior (driving faster to get more clients per hour).  The return to taking these risks is greater earnings. According to the study, 20% of the gap stems from this series of choices or roughly 1.4 percentage points.

I think that this is significantly large to warrant further consideration in the future in the debate. More often than not, the emphasis is on education, experience, marital status, and industry codes (NAICS code) to explain wage differences. The use of industry codes has never convinced me. There is wide variance within industries regarding work accidents and diseases. The NAICS codes industries by wide sectors and then by sub-sectors of activities (see for example the six-digits codes to agriculture, forestry, fishing and hunting here). This does not allow to take account of the risks associated with a job. There are a few study that try to account for this problem, but there are … well … few in numbers. And rarely are they considered in public discussions.

Here, the Uber case shows the necessity to bring back this subtopic in order to properly explain the wage gap.