I’m struggling to find much inspiration sketching my little ad hoc economic models of daily life with the backdrop of Ukrainians struggling to survive in the face of an invading army. Perspective is a hell of a drug. This struggle has brought to the front of my mind Maslow’s Hierarchy of needs, which lays a psychological layering onto the economic prioritization of needs (food and shelter first, social needs second, “self-actualization” last). It’s the kind of model that gets used and abused because it adds a veneer of psychological depth to absurd reductionist theorizing. Don’t take my petty academic denigration too seriously, though. Just because I think it’s not particularly useful doesn’t mean it’s wrong.
Similarly, I find consternation over hedonic treadmills unnecessary because whenever your result is that utility is declining as resource constraints are loosening, the likely explanation is that you aren’t observing utility correctly. Specifically, there are dimensions to utility you aren’t observing, be it temporal (i.e. the distribution of future possibe utilities), network (i.e. sympathetic utilities of children, spouses, friends, etc), or most likely that you are in fact not observing utility but rather one of many inputs into total utility i.e. there’s more to utility than just “happiness”.
But maybe you’re not interested in how to optimally model the pursuit of happiness under the dual constraints of finite resources and the human condition. Maybe you’re just worried about managing your life under the limitations of your own flawed humanity. Maybe you’re worried about getting stuck on a hedonic treadmill, the carrot of self-actualization dangling forever just out of reach. Now I’m not a licensed therapist or trained psychologist, but I am an economist who has to constantly struggle against my own technical limitations. What that means is that I have a lot of experience solving problems beyond my own mathematical limitations, not through technical elegance but by simply hacking the problem until the problem solves itself.
You know. Cheating.
If you’re on a hedonic treadmill, all that really means is that you’ve defined your units wrong. It’s only a treadmill measured in feet. If you define happiness not as feet advanced but as having a positive first derivative in microns per microsecond, you can establish the model such that you’ll be long dead before you reach the dipping edge on the horizon. Happiness isn’t a destination or a journey. It’s a positive first derivative or, barring that, a sufficiently positive second deriviative. If that’s out of reach, f*** it, there’s a third one you can push into the positive.
Framed this way, Zeno’s paradox is no longer a curse, it’s a blessing. To always be advancing half-way to your goal for all eternity is to live in eternal bliss. To self-actualize. Whether you get there is outside the model. It’s irrelevant.
Next week, I am teaching collusive agreements in my price theory class. I decided to take a different approach to the discussion than the one usually found in textbook. The approach consists in showing how economic thought on a topic has evolved over time. For collusion, I decided to discuss George Stigler’s 1964 article on the theory of oligopoly published in the Journal of Political Economy.
Simply put, Stigler proposes a simple approach for stating how collusive agreements can break apart by asking how much extra sales a firm can obtain by cutting its prices without being detected by other firms. Stigler argued that detection got easier as the number of buyers increased or as concentration increased. He also argued that detection became harder if buyers do not repeat purchases and if there is growth in the market through the addition of new customers as firms are not able to detect whether the growth of other firms is due to new customers or because old customers are purchasing its wares. Detection also became harder with a greater number of sellers but he also argued that this was of equal (or maybe lesser) importance than low repeat-sales rates or the arrival of new customers into the market.
This is pretty standard price theory and it is well executed. After postulating the theory, Stigler throws the empirical kitchen sink to see if, broadly speaking, his point is confirmed. One interesting regression is from table 5 in the article (which is illustrated below). That regression estimated rates for a line of advertising in newspapers market (i.e., cities) conditional on circulation in 1939 (its a cross-section of 53 markets). The regression itself is uninteresting to Stigler as he wants to consider the residuals. Why? Because he could classify the residuals by the structure of the market (with only one newspapers or with two newspapers. The idea is that more newspapers should be marked with lower rates as collusive agreements tend to be harder to enforce. Stigler thought this confirmed his idea that “that the number of buyers, the proportion of new buyers, and the relative sizes of firms are as important as the number of rivals” (p. 56).
While looking at Stigler’s regression, I thought that there might an interesting economic history paper to write. Notice that the source of the data used is cited below the table. Retracing that source and checking if (because there are clearly volumes of the Market and Newspaper Statistics) a panel can be constructed could allow for something interesting to be done. Indeed, a panel allows to directly test for the new customers’ hypothesis by adding a population growth variable. This advantage compounds that of increasing the number of observations. Both of those advantages could allow to test the relative importance of the mechanisms highlighted by Stigler.
A paper of this kind, I believe, would be immensely interesting. It is always worth engaging with important theoretical articles ontheir own terms. As Stigler set this test as one of his illustration, a paper that extends his test would engage Stigler on his own term and could provide a usefully contained discussion of the evolution of the theory of oligopoly. I honestly could see this published in journals like History of Political Economy or Journal of the History of Economic Thought or journals of economic history such as Cliometrica, European Review of Economic History or Explorations in Economic History.
In one of those truth-can-be-stranger-fiction events, two weeks ago Elon Musk tweeted this challenge to Vladimir Putin: “I hereby challenge Vladimir Putin to single combat. Stakes are Ukraine,” adding in Russian, “Do you accept this fight?”
I am not aware of this challenge affecting the course of Russia’s war on Ukraine, but Musk has made a significant contribution in another area. Modern warfare is all about rapid, voluminous information gathering, processing, and dissemination. The internet has become the backbone of much communication. In areas like Ukraine with less-developed cable and fiber infrastructure, internet access is commonly via cellular service.
Ukraine’s cellular service was significantly degraded by the first week of the invasion by loss of territory and widespread bombing of infrastructure. What could be done? It turns out that Elon Musk’s Starlink swarm of low-orbit satellites is designed to provide internet service for areas of the globe that are underserved by standard methods like cable and cellular. Ukraine’s Vice Prime Minister and Minister of Digital Transformation, Mykhailo Fedorov, tweeted to Musk, “While you try to colonize Mars – Russia try to occupy Ukraine! While your rockets successfully land from space – Russian rockets attack Ukrainian civil people! We ask you to provide Ukraine with Starlink stations and to address sane Russians to stand.”
Musk responded within days by launching and/or repositioning satellites and providing thousands of ground-based Starlink terminals, providing much-needed communications for the beleaguered Ukrainians. Starlink is now the most-downloaded app in Ukraine, and is used to direct Ukrainian attacks on Russian tanks. Such is the power of private enterprise. One wonders if the U.S. governmental agencies would have been able to provide such service so quickly.
As reported by The Wire, the Russians have complained that Musk’s actions constitute interference: “When Russia implements its highest national interests on the territory of Ukraine, Elon Musk appears with his Starlink, which was previously declared purely civilian.” Musk’s ironic reply: ““Ukraine civilian Internet was experiencing strange outages – bad weather perhaps? – so SpaceX is helping fix it.”
A reader (though perhaps not yet a loyal one) wrote me:
“I don’t know if you take reader requests – but on the Nurse/Teacher/Kitchen Staff post from a little while ago – I am curious what the economic data might say about career switchability. I.e. sure, a teacher or nurse may feel trapped, but how free does everyone else actually feel? I’m assuming it’s hard to get data on this (what counts as an actual career change?) – but I (as someone scanning a list of blog titles and clicking on the one titled “It’s a Trap!”) would be interested in your perspective on this from an economics angle.”
I’m not quite sure how to go about answering this question directly, but I’ll venture a couple things. Some lazy searching on google scholar turned up a paper from 1988 that itself rediscovered a survey by the San Diego Teachers Association from 1964(!) that found “A feeling of being trapped in the profession” to be the #1 cause of burnout reported by teachers. A couple thoughts!
First, 1964! Second, while the reasons for feeling trapped in the teaching profession in 1964 were no doubt different than they are today (*cough* extreme institutionalized sexism *cough*), but we need to consider that the profession of teaching at the primary and secondary levels isn’t one that creates a lot of opportunities for adding to your human capital, which can lead to feeling, correctly and incorrectly, of the job market passing you buy.
A more recent paper from 2002 notes that “The lack of anything resembling a genuine career ladder contributes to the feeling of many teachers that they are trapped in a career that has become not only joyless but futureless.” As someone who’s been there myself, I can tell you there grows quickly in the mind a specific anxiety that that to stay a teacher too long is to risk being left on a career ladder with no rungs. If there was ever a reason to have the now clichéd “quarter-life crisis”, that’s it.
While teachers may leave the profession early for fear of being trapped by atrophied human capital, nurses appear to be more a story of over-specialized human capital. A relatively simple analysis found that nurses with more education and experience were more likely to stay within the professions. Nothing terribly shocking (or causally identified) there, but other work has found within-profession concerns of overspecialization as well: one paper found that emergency department nurses were especially concerned about becoming trapped ED-only nurses, particularly those in more rural hospitals, losing access to more lucrative urban jobs that require more advanced care-giving and physician support related skills.
This is a great time to remember that causal identification is important, but it isn’t everything. Sometimes its really useful to create a super-charged summary statistic and look for patterns, like the above.
To get back to the readers question about extending beyond teachers and nurses, I think the key to understanding the transition costs of a career is to appreciate the two channels for becoming trapped:
Human capital atrophy
Human capital overspecialization
Atrophy speaks to a lack of options because of an absolute disadvantage, while overspecialization is because of an intense comparative advantage. The first is, in most ways, far scarier because you have limited options save to stay in a career where years tenure is your only real advantage. The second, on the other hand, is really only problematic if you have a strong preference against the field of your specialization or you fear the risk of obsolescence. That doesn’t mean you shouldn’t take overspecialization fears seriously. We’ve all seen a againg musician who can still fill an audience but looks like they’d rather get a root canal than spend another evening on stage. They’re not there because they want to, they’re because they’re second best option can’t cover their mortgage.
Do I have an career advice for maximizing career advancement and adaptability ?
Do I ever! Get an advanced degree in economics from a respectable school. Or, barring that, a school entirely absent in respect or prestige. More specifically (and more seriously), my advice is this: major in tools, minor in substance.
Substance can be acquired piecemeal, in a disjointed sequence with random and sometimes large intermittent breaks. Acquiring tools, on the other hand, is far more dependent on uninterrupted periods of intense learning and application. You can read about the Ottoman empire over coffee breaks and bus rides. Learning Python, R, real analysis, econometrics, virology, chemical spectroscopy, or evolutionary game theory are all far more easily learned if you can dedicate months or years to them in large uninterrupted bursts of focus.
Further, tools tend to exist in their own phylogenetic hierarchy. Once you’ve acquired a tool, it is often an order of magnitude easier to acquire a new, closely related tool. It might have taken 2 years to get really good at C++ or Java, but because of that you can learn Python in a couple weeks of fooling around on a side project. Those first tools are the most important ones you will ever acquire, but they are also the hardest.
A secondary bit of advice: major in something that people know is always at least a little hard. I try not to overrate the “signalling theory of education” but there remains the hard to deny reality that education does have some signaling value. One of the signals is “I’m smart”, but as a signal I think it’s highly overrated. A more important signal is “I’m willing to learn things that are hard”. Most careers within persistance advancement and robust demand require the continuing acquisition of new skills and adaptation to new circumstances. You want very badly to signal, early and often, that you are someone who is willing to put in the effort to adapt and remain productive.
Despite that some members within my vocation may suggests, however, the answer to every problem is not in fact more school. Which leads me to my final, most important, but probably most trite piece of advice:
Quit.
No, seriously, quit. If you can pay your bills and you want out, get out. If you can’t, start laying the groundwork for your exit. Yesterday would have been better, but today is a close second. There’s no room for sunk cost thinking in careers. You only booked two commercials in 7 years in LA? Move to Kansas City and learn to code. You want out of the service industry? Jump start your BS in chemistry two classes a semester. You hate nursing? Start applying for admin positions in your hospital, apply for reimbursement for a 2 year executive MS in IT management through your hospital. You hate your PhD program and realize there’s no market for your degree outside of academia? Start writing ad and social media copy for local restaurants trying to get off the ground.
This isn’t me trying to admonish you with “by-your-bootstraps” ra-ra BS. This is me saying that the time you’ve put in shouldn’t matter if you want something else. But maybe you don’t want something else. That’s fine too! Just don’t tell me you’re trapped then, just say that you’re bored and you need a new hobby. And then sell your hobby on Etsy. And then market your hobby through google. And then write a book and tell Martha Stewart about it. That’d be pretty cool.
But then again, it’s easy to give advice. Do your best. Feed your kids. Keep trying. It’ll be fine.
There is a new paper available at Cliometrica. It is co-authored by Mathieu Lefebvre, Pierre Pestieau and Gregory Ponthiere and it deals with how the poor were counted in the past. More precisely, if the poor had “a survival disadvantage” they would die. As the authors make clear “poor individuals, facing worse survival conditions than non-poor ones, are under-represented in the studied populations, which pushes poverty measures downwards.” However, any good economist would agree that people who died in a year X (say 1688) ought to have their living standards considered before they died in that same year (Amartya Sen made the same point about missing women). If not, you will undercount the poor and misestimate their actual material misery.
So what do Lefebvre et al. do deal with this? They adapt what looks like a population transition matrix (which is generally used to study in-,out-migration alongside natural changes in population — see example 10.15 in this favorite mathematical economics textbook of mine) to correctly estimate what the poor population would have been in a given years. Obviously, some assumptions have to be used regarding fertility and mortality differentials with the rich — but ranges can allow for differing estimates to get a “rough idea” of the problem’s size. What is particularly neat — and something I had never thought of — is that the author recognize that “it is not necessarily the case that a higher evolutionary advantage for the non-poor over the poor pushes measured poverty down”. Indeed, they point out that “when downward social mobility is high”, poverty measures can be artificially increased upward by “a stronger evolutionary advantage for the non-poor”. Indeed, if the rich can become poor, then the bias could work in the opposite direction (overstating rather than understating poverty). This is further added to their “transition matrix” (I do not have a better term and I am using the term I use in classes).
What is their results? Under assumptions of low downward mobility, pre-industrial poverty in England is understated by 10 to 50 percentage points (that is huge — as it means that 75% of England at worse was poor circa 1688 — I am very skeptical about this proportion at the high-end but I can buy a 35-40% figure without a sweat). What is interesting though is that they find that higher downward mobility would bring down the proportion by 5 percentage points. The authors do not speculate much as to how likely was downward mobility but I am going to assume that it was low and their results would be more relevant if the methodology was applied to 19th century America (which was highly mobile up and down — a fact that many fail to appreciate).
By the time most students exit undergrad, they get acquainted with the Aggregate Supply – Aggregate Demand model. I think that this model is so important that my Principles of Macro class spends twice the amount of time on it as on any other topic. The model is nice because it uses the familiar tools of Supply & Demand and throws a macro twist on them. Below is a graph of the short-run AS-AD model.
Quick primer: The AD curve increases to the right and decreases to the left. The Federal Reserve and Federal government can both affect AD by increasing or decreasing total spending in the economy. Economists differ on the circumstances in which one authority is more relevant than another.
The AS curve reflects inflation expectations, short-run productivity (intercept), and nominal rigidity (slope). If inflation expectations rise, then the AS curve shifts up vertically. If there is transitory decline in productivity, then it shifts up vertically and left horizontally.
Nominal rigidity refers to the total spending elasticity of the quantity produced. In laymen’s terms, nominal rigidity describes how production changes when there is a short-run increase in total spending. The figure above displays 3 possible SR-AS’s. AS0 reflects that firms will simply produce more when there is greater spending and they will not raise their prices. AS2 reflects that producers mostly raise prices and increase output only somewhat. AS1 is an intermediate case. One of the things that determines nominal rigidity is how accurate the inflation expectations are. The more accurate the inflation expectations, the more vertical the SR-AS curve appears.*
The AS-AD model has many of the typical S&D features. The initial equilibrium is the intersection between the original AS and AD curves. There is a price and quantity implication when one of the curves move. An increase in AD results in some combination of higher prices and greater output – depending on nominal rigidities. An increase in the SR-AS curve results in some combination of lower prices and higher output – depending on the slope of aggregate demand.
Of course, the real world is complicated – sometimes multiple shocks occur and multiple curves move simultaneously. If that is the case, then we can simply say which curve ‘moved more’. We should also expect that the long-run productive capacity of the economy increased over the past two years, say due to technological improvements, such that the new equilibrium output is several percentage points to the right. We can’t observe the AD and AS curves directly, but we can observe their results.
The big questions are:
What happened during and after the 2020 recession?
It’s spring break and that means catching up on both research and my social network. It also means college basketball. I remain firmly in the camp that college athletes should be paid for their incredibly high-value labor and, in turn, recapture a huge share of the surplus currently enjoyed by schools and coaches. What I am beginning to rethink, however, is the way that “professionalization” can and will play out.
This rethinking began with the the realization that my enjoyment of the product is largely insensitive to the presence of great players. The gap between NBA and NCAA basketball, in terms of quality of play, is so great that I simply don’t watch the sports in the same way. I consume the NBA the way I do Denis Villeneuve films: enjoying an artform in its closest approximation to perfection at the bleeding edge of innovation. NCAA basketball, in contrast, is a soap opera for genre aficionados. It’s Battlestar Galactica for sports fans.
There is a floating, ever-changing cast of characters supporting a handful of recurring leads. Clans and sub-clans. Rises and falls. Tragic failures and heroic redemption arcs. And, much like the latest show about wizards or post-apocaplyptic alien invasion survivors on the SciFy channel, the enjoyment of this product doesn’t require high level precision or execution. Quite frankly, the show is more enjoyable when the actors aren’t famous or especially elite; it keeps me squarely focused on the shlocky fun, rather than getting distracted by any urge to pick apart the film composition, story logic, or actor subtext. College basketball, in much the same way, keeps me squarely focused on the drama of gifted athletes doing their best to help their team achieve success in a limited window before moving on to the rest of their lives. Trying to get a little slice of glory now, while their knees will allow for greatness, before getting on with the endless particulars of adult life later.
Which brings me back to the eventual professionalization of college sports with athlete compensation. Schools will find themselves faced with a decision of whether they should spend money on the very best athletes or try to compete with less expensive players. Athletes will have to decide where the best opportunities to develop their professional game are, and how much of their human capital investment portfolio they want to dedicate to sports. What might the equilibrium look like?
We can coarsely reduce the pool of athlete’s into three categories: all-in on athletics, those looking to purely subsidize secondary education, and those aiming for a mix of both. Currently schools capture the most rents from the pure athletics all-ins, who dedicate nothing but the bare minimum to schooling while maximizing their athletic preparation. The all-ins will often be the best players, who get the most media attention and contribute the most to winning glory, attracting applications from young fans and donations from nostalgic alumni. You might expect that compensation would shift the most suprlus to them. We have to consider, however, the possibility that a proper market for elite college athletic labor would provide the prices needed to accelerate the formation of pre-professional academies and player futures contracts. The very best 18-year old basketball players may find it far more lucrative to take a $120K in income and full-time coaching today in exchange for 2% of future professional earnings.
At the same time, college basketball may similarly learn the true nature of their collective good: that it is, in fact, a zero-sum competition where the total amount of talent isn’t nearly as important for earnings as they think. While a small number of schools absorbing all of the top talent might be exciting for covers of no longer existent sports magazines, in reality 120 teams competing for a less skewed distribution of talent more predominantly interested in subsidizing the full cost of college (i.e. tuition, lost wages, etc) may actually make for more drama, which means more ratings, which means more money. Why try to compete with the academies for 1 year of the next Lebron when those same resources, will get you 5 good players for 4 years? Combined with the fact that this bundle of athletes will place greater value on (nearly) marginally costless scholarships, teams looking to compete in the long-term with a maximimally effcient allocation of resources could shift the competitive equiibrium could actually shift away from the top talent.
Sports are fun when they are played at the highest level. They are also fun, however, when a little chaos is injected into the drama. It’s great when Steph Curry casually hits shots 40 feet from the basket, when Lebron James or Nikola Jokic make Matrix-esque passes through impossible angles. But it’s also great watching players struggle at the edge of far more human limitations to a find to win on the biggest stage of their lives while wearing the jersey of one of hundreds of colleges. The highest drama includes players making shots, but sometimes it needs players to dribble off their foot, too.
We don’t have to limit earnings to capture that glory. We don’t have to take money from young people whose particular talents put them in the sliver of the human population whose greatest earning potential might be age 20. We don’t need to appeal to platitudes or false nostalgia to explain why they’re being compensated with something better than money. We can just pay them. Some things will change, but I think you’ll be shocked to see how little the experience of college basketball will change. College sports will remain largely the same, but it will be a bit less shady, a bit less hypocritical. It will place greater value on, and care for, the players they have directly invested in.
Which, at least to me, would be a little more fun.
There is a new paper in Journal of Economic Perspectives. Its author, Dan Sichel, studies the price of nails since 1695 (image below). Most of you have already tuned off your attention by now. Please don’t do that: the price of nails is full of lessons about economic growth.
Indeed, Sichel is clear in the title in the subtitle about why we should care — nail prices offer “a window into economic change”. Why? Because we can use them to track the evolution of productivity over centuries.
Take a profit-maximizing firm and set up a constrained optimization problem like the one below. For simplicity, assume that there is only one input, labor. Assume also that a firm is in a relatively competitive market so as to remove the firm’s ability to affect prices so that, when you try to do your solutions, all the quantity-related variables will be subsumed into a n term that represent’s the firm share of the market which inches close to zero.
If you take your first order conditions and solve for A (the technological scalar). You will find this this identity
What does this mean? Ignore the n and consider only w and p. If wages go up, marginal costs also increase. From a profit-maximizing firm’s standpoint trying to produce a given quantity, if prices (i.e. marginal revenue) remained the same, there must have been an increase in total factor productivity (A). Express in log-form, this means that changes in total factor productivity are equal to αW – αP. This means that, if you have estimates of output and input prices, you can estimate total factor productivity with minimal data. This is what Sichel essentially does (and Douglas North did the same in 1968 when estimating shipping productivity). All that Sichel needs to do is rearrange the identity above to explain price changes. This is how he gets the table below.
The table above showcases the strength of Sichel’s application of a relatively simple tool. Consider for example the period from 1791 to 1820. Real nail prices declined about 0.4 percent a year even though the cost of all inputs increased noticeably. This means that total factor productivity played a powerful role in pushing prices down (he estimates that advances in multifactor productivity pulled down nail prices by an average of 1.5 percentage points per year). This is massive and suggestive of great efficiency gains in America’s nail industry! In fact, this efficiency increases continued and accelerated to 1860 (reinforcing the thesis of economic historians like Lindert and Williamson in Unequal Gainsthat American had caught up to Britain by the Civil War).
I know you probably think that the price of nails is boring, but this is a great paper to teach how profit-maximizing (and constrained optimization) logic can be used to deal with problems of data paucity to speak to important economic changes in the past.
The financial crisis recession that started in late 2007 was very different from the 2020 pandemic recession. Even now, 15 years later, we don’t all agree on the causes of the 2007 recession. Maybe it was due to the housing crisis, maybe due to the policy of allowing NGDP to fall, or maybe due to financial contagion. I watched Vernon Smith give a lecture in 2012 in which he explained that it was a housing crisis. Scott Sumner believes that a housing sectoral decline would have occurred, and that the economy-wide deep recession and subsequent slow recovery was caused by poor monetary policy.
Everyone agrees, however, that the 2007 recession was fundamentally different from the 2020 recession. The latter, many believe, reflected a supply shock or a technology shock. Performing social activities, including work, in close proximity to others became much less safe. As a result, we traded off productivity for safety.
The policy responses to each of the two were also different. In 2020, monetary policy was far more targeted in its interventions and the fiscal stimulus was much bigger. I’ll save the policy response differences for another post. In this post, I want to display a few graphs that broadly reflect the speed and magnitude of the recoveries. Because the recessions had different causes, I use broad measures that are applicable to both.
The saying that “The first casualty of war is the truth” has been credited to anti-war Senator Hiram Warren Johnson in 1918 and also to the ancient Greek dramatist Aeschylus. We have seen this played out dramatically with Russia’s invasion of Ukraine. From the Ukrainian side have come the predictable overinflated estimates of the enemy’s losses, and perhaps understated reporting of their own casualties. Also, on the first day or two of the war there was a raunchy defiant response of Ukrainian defenders to a “Russian ship” that was demanding their surrender; as far as I know that exchange was for real, but the initial report by Ukraine that all the heroic defenders were killed was not true. Maybe I am biased here, but these sorts of excesses are stretching some core truth, not trampling over it roughshod.
On the Russian side, perhaps because there is no even vaguely legitimate justification for their invasion, the lies have been simply ludicrous. Apparently, the Russian troops have been told that they are going there to rescue Ukrainians from the current regime which is a bunch of “neo-Nazis”. If Putin’s thugs had a sense of humor or perspective, they might have discerned the irony of characterizing the Ukrainian regime as “neo-Nazi” when the president (Zelenskyy) is a Jew, whose grandfather’s brothers died in Nazi concentration camps.
And the Russian lies go beyond ludicrous, to revolting and inhuman. Russian Foreign Minister Sergey Lavrov has dismissed concerns about civilian casualties as “pathetic shrieks” from Russia’s enemies, and denied Ukraine had even been invaded.
The Associated Press snapped a picture in the besieged city of Mariupol a few days ago which went viral, showing a pregnant woman with a bleeding abdomen being carried out on a stretcher from a maternity hospital which the Russians had bombed. The local surgeon tried to save her and her baby, but neither one survived. The Russian side put out a string of bizarre and contradictory stories, claiming that they had bombed the hospital because it was a militia base (a neo-Nazi militia, of course) but also that no, they didn’t bomb it, the hospital had been evacuated and the explosions were staged by the Ukrainians, and the bloody woman in the photos was a made-up model. Ugh. I find it chilling to observe a regime in operation where there is absolutely no respect for what the truth actually is; rather, lies are manufactured to serve whatever purpose will suit the regime.
I know that some of that goes on even with Western democracies, but we are still usually ashamed of outright lying, and stand discredited when exposed. But with hardcore authoritarian regimes, there does not seem to be even this minimal respect for integrity.
Freedom of speech becomes even more critical as cynicism about truth becomes more widespread in the world, even in our own political discourse. Putin is trying to suppress the truth within Russia, now with very harsh penalties (fifteen years in prison) for those disseminating information contrary to the party line. All he needs to do is deem such talk as “treasonous”, and into the clink you go.
I do worry about similar trends towards censorship within the West. In our case, it is not so much governments (so far) doing the censorship, but Big Tech. If Google [search engine and YouTube] / Facebook/Twitter disapprove of your content, they can label it “hate speech” or whatever, and your voice disappears from public discourse. But what gives the high priests of big tech the authority and the powers of moral discernment to rule on what discourse is permissible? Also, the algorithms of social media sites usually direct you towards other sites that reinforce your own point of view, so you rarely get exposed to why the other side believes what it does. However annoying it may be to see various forms of nonsense circulating on-line, the time-tested democratic response is to allow (nearly) all points of view to be fairly stated, and to trust in the people to figure out where the truth lies. Otherwise, the truth can become a casualty of culture wars, as it is in shooting wars.
Economist Writing Every Day 