I read a great essay recently that does a fantastic job of explaining why we should be pushing to use much more nuclear energy than we do at present:
Nuclear energy has been quietly producing carbon-free energy for decades, but most don’t know that it accounts for 20% of the US’s electricity and over half of its carbon-free electricity. It’s been the underdog energy source—rarely celebrated, or worse, villainized, and deeply underinvested in. The war in Ukraine and subsequent global energy crisis, alongside longstanding concern around climate change, has policymakers grappling with how to ensure energy is reliable, abundant, and carbon-free. Nuclear energy is the only energy source that solves for all three. So why aren’t we building more?
Oh, and by the way, do take a look at the artist who made the picture at the start of the essay!
I hope you read the whole thing, and I hope that you, like me, are also a fan of using much more nuclear energy in the years to come. If you aren’t, maybe this essay will convince you to at least read more about the issue.
But this essay is also a great way to brush up on your knowledge of the principles of economics!
How safe (or dangerous, if you prefer to be clear about framing effects) is nuclear fuel? Well, shouldn’t one always be asking relative to what? And if you do ask that question, take a look at this chart for an answer!
Do read the whole thing, please. There’s lots of nice little nuggets in the essay that make it a very enjoyable read, including an xkcd cartoon, great resources that you can add to your bookmarks folder and lots of statistics and links to very interesting reads (Noah’s post on construction productivity is a personal favorite).
But most importantly, if you are a student of economics, get into the habit of reading stuff and deploying your knowledge of the principles of economics. It makes the read more interesting, more thought-provoking and best of all, more understandable.
A somewhat less sexy, but more accurate title would have been ” Economics Isn’t Just About Money”.
But the decision to jettison the word “just” is deliberate, and not just for the sake of a headline that makes you want to click through. It is, instead, to emphasize the point that economics is about so much more than just about making money.
I have some close friends to thank for inspiring this post, with whom I had a conversation about tomorrow’s blogpost. They told me that they had been under the impression that economics is about money, and to my surprise, that seems to be an idea that most people I have spoken to are comfortable with.
But these people I have spoken with, and whoever has taught them economics, have less than half the right answer. Economics isn’t about money alone.
No matter what you do in life, you have a range of choices to choose from. Should I watch Netflix for an hour or study for an hour? Should I read a couple of pages from a book, or should I quickly scroll through Twitter? Should I enroll in an engineering course, or should I pursue law instead? Should I start with the salad at a buffet, or should I start with desserts instead?
Life is all about choices, every single second of your life. Economics helps you be clear about your choices, and also helps you potentially expand your choice set. One option regarding the last question in the paragraph above, is to say neither, and fast instead. Be aware of your entire choice set, and only then set about choosing one.
Horizons is about thinking about the long term, rather than the short term. My favorite example in introductory economics is to ask my students if I should have a second gulab jamun for desserts after lunch today. I tell them that present day Ashish will definitely say yes, and seventy year old Ashish (assuming I live for that long) will definitely say no. Because the consequences of choices I make today truly matter in the long run, bur are underestimated in the short run.
Incentives are about what motivate you to do (or not do) things. Economics teaches you how to use your own incentives, and those of others, to Get Things Done. My favorite example comes from Tyler Cowen, who helps us understand how to use incentives to not be bored in a museum. Ask yourself, he suggests, which painting would you choose to steal from each room, to install in your own home – and you cannot choose more than one per room. Your incentives have flipped – now it’s not about “seeing” each and every painting having paid the price of admission, but instead about asking yourself which painting will look best in your home.
And costs are about the realization that nothing in life comes for free. No matter what you are doing, you could always be doing something else. Instead of having read this far (thank you!), you could have given up halfway through and watched funny cat videos instead. Opportunity costs are everywhere, and whatever your choice, it ain’t for free.
The point that unites each of the examples above is that none of them are about money! They are economics-y concepts: choices, horizons, incentives and costs. But what to have in a buffet, whether to have a second gulab jamun, deciding which painting to steal and watching cat videos are not about money.
You could put a monetary value to all of them using subjective valuations, of course, but some things shouldn’t have numbers attached to them. Not because they’re not important, but because they’re fundamentally unquantifiable. What price (and I’m not joking here) can you possibly put on a parent choosing to read a story to a child? Economists have an answer to this question, of course, but it isn’t one that I am entirely comfortable with, especially if it involves a definitive number.
And that’s what I mean when I say that economics is about so much more than just money.
That still does not answer the question of what economics is about – I have written about it earlier, and will defend my answer in tomorrow’s post.
I’ve lost count of the number of times I’ve rewatched parts of The Last Dance, the documentary on Michael Jordan, and now, in the 40th year of my life, I’ve slowly started to develop more than a passing interest in basketball.
This video, about the 3 point line in basketball, might not resonate much if you haven’t seen a single game of basketball, but I would argue it is worth thinking about how your sport has changed over time, and how players are responding to these changed (non-monetary) incentives.
The Higg Index is an apparel and footwear industry self-assessment standard for assessing environmental and social sustainability throughout the supply chain. Launched in 2012, it was developed by the Sustainable Apparel Coalition, a nonprofit organization founded by a group of fashion companies, the United States government Environmental Protection Agency, and other nonprofit entities.
I had no clue that such a thing existed, but it would seem that a lot of apparel stores use this index as a way to advertise the fact that the products that they’re selling have been produced in a sustainable manner.
The Higg Index is spread across three categories: product tools, facility tools and brand and retail tool.
I came across the Higg Index in a New York Times article that warns us about depending too much on an index of this sort:
An explosion in the use of inexpensive, petroleum-based materials has transformed the fashion industry, aided by the successful rebranding of synthetic materials like plastic leather (once less flatteringly referred to as “pleather”) into hip alternatives like “vegan leather,” a marketing masterstroke meant to suggest environmental virtue. Underlying that effort has been an influential rating system assessing the environmental impact of all sorts of fabrics and materials. Named the Higg Index, the ratings system was introduced in 2011 by some of the world’s largest fashion brands and retailers, led by Walmart and Patagonia, to measure and ultimately help shrink the brands’ environmental footprints by cutting down on the water used to produce the clothes and shoes they sell, for example, or by reining in their use of harmful chemicals. But the Higg Index also strongly favors synthetic materials made from fossil fuels over natural ones like cotton, wool or leather. Now, those ratings are coming under fire from independent experts as well as representatives from natural-fiber industries who say the Higg Index is being used to portray the increasing use of synthetics use as environmentally desirable despite questions over synthetics’ environmental toll.
I don’t know enough about the Higg Index to able to tell you about whether it ‘makes sense’ or not, but this is a good way to start to think about incentives.
When you meet an index such as this one, some simple questions are worth asking:
How long has this index been around?
Who created it?
Who funds it?
Who uses it?
What did it replace, and why?
Are there other indices that do a similar job?
Try and answer these questions for the Higg Index, for example. The NYTimes article carries a slightly sceptical tone about the Higg Index (but is, ultimately, a balanced take) – once you finish answering these questions, try giving it a read, and then reach your own conclusions about its reliability.
And as usual, the most important lesson of them all: all the other indices that you may have come across, apply the same set of questions!
The average person will not have heard of Dipali Biswas or Nirmalendu Mukherjee and may not be aware of the case decided by the Supreme Court on October 5, 2021. The case was decided by a division bench, consisting of Hemant Gupta and V Ramasubramanian and the judgment was authored by Justice V Ramasubramanian. Justice Ramasubramanian observed (not part of the judgment), “Not to be put off by repeated failures, the appellants herein, like the tireless Vikramaditya, who made repeated attempts to capture Betal, started the present round and hopefully the final round.” Other than smiling about a case that took 50 years to be resolved and making wisecracks about “tareekh pe tareekh”, shouldn’t we be concerned about rules and procedures (all in the name of natural justice) that permit a travesty of justice?
I know (alas) next to nothing about the law, but there were two excerpts in this article that I wanted to highlight as a student of statistics and economics. We’ll go with statistics first.
Whenever I start to teach a new course, I always tell my students that there are two kinds of errors I can make. I can either make sure that I complete the syllabus, irrespective of whether everybody has understood it or not. Or I can make sure that everybody has understood whatever I have taught, irrespective of whether the syllabus is completed or not. Speed versus thoroughness, if you will – and both cannot be optimized for at the same time. If you’re wondering, I prefer to err on the side of making sure everybody has understood, even if it comes at the cost of an incomplete syllabus.
This is, of course, closely related to formulating the null hypothesis and asking which type of error one would rather avoid. And the reason I bring it up, is because of this exceprt:
Innumerable judgments have quoted the maxim, “justice hurried is justice buried”. By the same token, justice tarried is also justice buried and inordinate delays mean the legal system doesn’t provide adequate deterrence to mala fide action. In my view, for most civil cases, the moment issues are framed, one can predict the outcome within a range, with a reasonable degree of certainty. (Obviously, I don’t mean constitutional cases before the Supreme Court.) With no disrespect to the legal system, I think AI (artificial intelligence) is capable of delivering judgments in such cases, freeing court time for non-trivial cases.
“Justice hurried is justice buried” and “Justice tarried is justice buried” are both problems, and optimizing for one means not optimizing for the other. What Bibek Debroy is saying here is that what we have ended up choosing to optimize for the former. We make sure that every case has the opportunity to be heard at great length, and with sufficient maneuvering room for both parties.
And that’s great, but the opportunity cost is the fact that sometimes judgments can take over fifty years (and counting!).
And what is Bibek Debroy’s solution? When he suggests that AI is capable of delivering judgments in such cases, he is not saying that the AI will give a perfect judgment every time. He is not even saying that one should use AI (I think the point is rhetorical, although of course I could be wrong). He is saying that the gains in efficiency are worth the occasional case being incorrectly judged. In other words, he is optimizing for justice tarried is also justice buried – he would rather avoid the error of taking up too much time for each case, and would (presumably) be fine paying the price of having the occasional case being misjudged.
It is up to you to agree or disagree with him, or with me when it comes to how I conduct classes. But all of us should be cognizant of the opportunity costs when we decide which error we’d rather avoid!
And economics second:
Litigants and lawyers (at least on one side of a civil case) have no incentive to finish a case fast (Does the judiciary have it?).
This is more of a question (or rumination) on my part – what are the incentives of the judiciary? I can imagine scenarios in which those “on one side of a civil case” can use both official rules and underhanded stratagems to delay the eventual judgment. And since there is no incentivization in terms of speedier resolutions, are we just left with a system that is geared towards moving along ponderously forever more?
And if so, how might this be changed for the better? This is, and I’m not joking, (more than) a trillion dollar question.
And finally, as a bonus, culture:
This isn’t a loophole in the CPC. Injunctions were only to be granted upon security for costs. That’s also the rule in the UK and injunctions are rare. It’s the people, not the CPC.
My friend Murali Neelakantan makes the point here that isn’t really about incentive design at all, that the problem is more rooted in how we, the people of India, use and abuse the provisions of the CPC.
That takes me into even deeper and ever more unfamiliar waters, so I shall think more about this before trying to write about it!
What a fascinating question to be asked, and I have had a lot of fun thinking about it. Here are my notes:
An absence of cynicism is certainly not ideal, and although the idea is very tempting to me, neither should one be exclusively cynical.
When I say that the idea is very tempting, I am not joking. Here is the definition of cynicism, taken from Google: “an inclination to believe that people are motivated purely by self-interest; scepticism.” People respond to their incentives, in other words. That’s one of the building blocks of economic theory!
But this is one of those cases where I think we economists would do well to think a little bit about philosophical questions, before embarking on economic theory. What are, and what should be, a person’s incentives? These are two very different questions, and economics spends far too much time on the first, and not enough on the latter.
So here’s a first pass answer: given a person’s incentives, one should be a cynic. For example, politicians maximize votes. They don’t do what’s best for folks in the long run. Managers maximize short run profits. And so on.
But one shouldn’t be a cynic, at all, about working towards changing incentives. Giving up on expecting politicians to do the “right” thing, given the status quo, is fine. Giving up on trying to come up with a system that incentivizes politicians better than the status quo wouldn’t be fine, as far as I am concerned.
But that necessarily implies that one should be a very good (and eternal) student of getting the “right” incentives in place.
And being cynical about that would be really and truly depressing 🙂
I and a colleague conducted a small behavioral economics and experimental economics workshop for our students at the Gokhale Institute. It was a very small, very basic workshop, but one of the things that came up was the reproducibility problem, or as Wikipedia puts it, the replication crisis.
The replication crisis (also called the replicability crisis and the reproducibility crisis) is an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate or reproduce. The replication crisis most severely affects the social sciences and medicine. The phrase was coined in the early 2010s as part of a growing awareness of the problem. The replication crisis represents an important body of research in the field of metascience.
A 2016 poll of 1,500 scientists reported that 70% of them had failed to reproduce at least one other scientist’s experiment (50% had failed to reproduce one of their own experiments).[9] In 2009, 2% of scientists admitted to falsifying studies at least once and 14% admitted to personally knowing someone who did. Misconducts were reported more frequently by medical researchers than others.
The basic idea behind replicability is very simple: you should be able to take the data and the code from the paper you are reading/reviewing, and replicate the results obtained. You don’t have to agree with the choice of method, or with the results or with anything – you should be able to replicate the results, that’s all.
One basic standard of economic research is surely that someone else should be able to reproduce what you have done. They don’t have to agree with what you’ve done. They may think your data is terrible and your methodology is worse. But as a minimal standard, they should be able to reproduce your result, so that the follow-up research can then be in a position to think about what might have been done differently or better. This standard may seem obvious, but during the last 30 years or so, the methods for reproducibility have been transformed.
Now (to me, at any rate) this is interesting enough in and of itself, but at the risk of becoming a little meta, reading the rest of Tim Taylor’s post is worth it because it raises so many interesting issues.
The first is a link to a lovely overview of the problem by Lars Vilhuber, published in the Harvard Data Science Review. It is relatively simple to read, and is recommended reading. For example, Vilhuber draws a careful distinction between replicability and reproducibility, and is full of interesting nuggets of information. I’ll list out the major ones (major to me) here. Note that I have simply copy-pasted from the link:
Publication of research articles specifically in economics can be traced back at least to the 1844 publication of the Zeitschrift für die Gesamte Staatswissenschaft (Stigler et al., 1995).
As the first editor of Econometrica, Ragnar Frisch noted, “the original data will, as a rule, be published, unless their volume is excessive […] to stimulate criticism, control, and further studies” (Frisch, 1933)
…only 17.4% of articles in Econometrica in 1989–1990 had empirical content (Stigler et al., 1995)
As Dewald et al. (1986) note: “Many authors cited only general sources such as Survey of Current Business, Federal Reserve Bulletin, or International Financial Statistics, but did not identify the specific issues, tables, and pages from which the data had been extracted.”
Among reproducibility supplements posted alongside articles in the AEA’s journals between 2010 and 2019, Stata is the most popular (72.96% of all supplements), followed by Matlab (22.45%; Vilhuber et al., 2020) (Note: Do check figure 2 at the link. Fascinating stuff.)
It was concluded that “there is no tradition of replication in economics” (McCullough et al., 2006).
The extent of the use of replication exercises in economics classes is anecdotally high, but I am not aware of any study or survey demonstrating this.
The most famous example in economics is, of course, the exchange between Reinhart and Rogoff, and graduate student Thomas Herndon, together with professors Pollin and Ash (Herndon et al., 2014; Reinhart & Rogoff, 2010). (Note to students: this is a fascinating tale. Read up about it!)
There is much more at the link of course, but Tim Taylor’s post does a good job of extracting the key points. I’m noting them here in bullet point fashion, but you really should read the entire thing.
Economic data – our understanding of the phrase needs to change, because a lot of it is in fact not publicly available today.
“Vilhuber writes: “In 1960, 76% of empirical AER [American Economic Review- articles used public-use data. By 2010, 60% used administrative data, presumably none of which is public use …””
Restricted Access Data Environments is a new thing that I discovered while writing this blogpost. “…where accredited researchers can get access to detailed data, but in ways that protect individual privacy. For example, there are now 30 Federal Statistical Data Research Centers around the country, mostly located close to big universities.” We could do with something like this in India. Actually, we would be a lot happier with just dbie working the way it was supposed to, but that’s for another day.
Data that is given by creating a sub-sample, data that is ephemeral (try researching Instagram stories, for example) and data that you need to pay for are all challenging, and relatively recent, developments.
I worked for four years in the analytics industry, so believe me when I say this. Data cleaning is a huge issue.
Tim Taylor writes five paragraphs after this one, but this is a glorious para, worth quoting in full: “As a final thought, I’ll point out that academic researchers have mixed incentives when it comes to data. They always want access to new data, because new data is often a reliable pathway to published papers that can build a reputation and a paycheck. They often want access to the data used by rival researchers, to understand and to critique their results. But making access available to details of their own data doesn’t necessarily help them much.”
If there are those amongst you who are considering getting into academia, and are wondering what field to specialize in, reproducibility and replicability are fields worth investigating, precisely because they are relatively underrated today, and are only going to get more important tomorrow.
Devon Zuegel (@devonzuegel on Twitter, and definitely worth following) was less than happy with Airbnb recently:
1/5
Hey @airbnb, a recent stay really hurt my trust in your platform, and your support team's answers to my questions about what the heck happened have been disappointingly evasive.
This paper relates quality and uncertainty. The existence of goods of many grades poses interesting and important problems for the theory of markets.
Akerlof, G. (1970). The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3), 488-500
It’s a paper that every undergraduate student ought to read. Not just economics undergraduate student, mind you, but every undergraduate student. Because it helps you get an understanding of many modern businesses today.
But first, a relatively simple explanation of the core idea of the paper:
Suppose buyers cannot distinguish between a high-quality car (a “peach”) and a “lemon”. Then they are only willing to pay a fixed price for a car that averages the value of a “peach” and “lemon” together (pavg). But sellers know whether they hold a peach or a lemon. Given the fixed price at which buyers will buy, sellers will sell only when they hold “lemons” (since plemon < pavg) and they will leave the market when they hold “peaches” (since ppeach > pavg). Eventually, as enough sellers of “peaches” leave the market, the average willingness-to-pay of buyers will decrease (since the average quality of cars on the market decreased), leading to even more sellers of high-quality cars to leave the market through a positive feedback loop.
Thus the uninformed buyer’s price creates an adverse selection problem that drives the high-quality cars from the market. Adverse selection is a market mechanism that can lead to a market collapse.
Akerlof’s paper shows how prices can determine the quality of goods traded on the market. Low prices drive away sellers of high-quality goods, leaving only lemons behind. In 2001, Akerlof, along with Michael Spence, and Joseph Stiglitz, jointly received the Nobel Memorial Prize in Economic Sciences, for their research on issues related to asymmetric information.
Now, one way to understand the value of many businesses today is to realize that they’re solving asymmetry of information problems. Or at least, that’s how I think of it when I end up looking up the rating for a restaurant on Zomato in a unfamiliar part of town. I don’t know enough about this part of town, and I certainly don’t know this restaurant. Should I walk in for a meal or not?
I could always check if the people already inside are smiling or not, of course, but let’s face it, most of us will simply Zomato our way through this problem. Zomato is reducing the asymmetry of information problem. Successfully or not is a matter of opinion and perhaps controversy. But my argument here is that this is a potentially useful way of thinking about the problem: how to decide where to eat?
How to decide whom to trust? Look ’em up on Facebook, or Twitter, or Instagram, or wherever it is that people look up people these days.
How to decide which product to buy on Amazon? Check out the user ratings. In fact, sort by average user ratings! Yes, Amazon does provide this option.
How to decide which book to read? Goodreads.
How to… you get the drift, right. Part of the reason these firms are so highly valued by the public is because they solve the asymmetry of information problem.
And so does Airbnb. Or does it?
And that brings us back to Devon Zuegel’s tweet.
Every review left on Airbnb informs potential users about the quality of a stay at a particular host’s place. The more information they are able to glean from reviews left by previous users, the more they are likely to definitively transact…or not. That is, potential users will either stay at a particular place, or will definitely not.
Since Airbnb gets a cut from each transaction, but not from each no-stay, they have an incentive to put up only positive reviews. And that is the problem that we have to think about when we read Devon Zuegel’s tweets. Is Airbnb incentivized to leave only positive reviews up? Short answer: yes. Therefore, will they leave only positive reviews up? I’d say it’s a question of horizons, but it is also a question of the calculus.
Airbnb will not last for very long if they pull down every single negative review, because that will destroy trust.
But:
every now and then…
particularly for really highly rated hosts…
especially during a pandemic…
will the odd negative review…
have a higher chance of being pulled down?
Nothing in life is ever black and white, and the truth lies somewhere in the middle. So no, Airbnb will not pull down every single negative review, but we also shouldn’t assume that it will leave every single negative review up.
More information in the hands of the consumer is a wonderful thing, and it does reduce the asymmetry of information. But who is providing the information to the consumers, and what are their incentives? What if the providers of the good/service are the ones that are making information available to the eventual consumers? Will that need to be regulated, and if so, how?
Zomato, LinkedIn, Uber, Airbnb – it’s a great time to be alive, because these firms, and many others like them, have provided for many services that would simply have not been possible otherwise. They have successfully reduced the asymmetry of information problem. But it’s not the end of the asymmetry of information problem, not just yet.
And you should now be asking, “what does that mean?”
The latest post on her Substack (god, I can’t afford to subscribe to all the substacks I want to!) is a wonderful essay on how she learnt about the pandemic last year, and how she learnt about how to learn – but I’ll get to that in a bit.
Zeynep Tufekci (Turkish: Zeynep Tüfekçi; [zejˈnep tyˈfektʃi]; ZAY-nep tuu-FEK-chee) is a Turkish sociologist and writer. Her work focuses on the social implications of new technologies, such as artificial intelligence and big data, as well as societal challenges such as the pandemic using complex and systems-based thinking. She has been described as “having a habit on being right on the big things” by The New York Times and as one of the most prominent academic voices on social media by The Chronicle of Higher Education.
I learnt about her for the first time when I cam across a review of her book, Twitter and Tear Gas over on Aadisht’s blog. I haven’t read it yet, but I still remember this from his review, because it resonated a fair bit:
A point this book makes often is that digital tools mean that networked protests are enabled, and that protests can spring up much quicker than they used to. But prior protests used to be much more organised, because the threshold to start a protest used to be so high that it would take a long time and lots of organisation to hit it – and that meant that there would be an organisation capable of pushing for change after the protests. The digitally fuelled protests haven’t quite figured out what change to ask for, and how to push it, yet.
But Zeynep’s writing reached another level altogether (both in terms of relevance and in terms of impact) during the pandemic. This, for example in the NYT (note the date!), or this from The Atlantic.
But her latest post, on the 31st of January, is worth pondering at great length. And that’s because while it speaks about the pandemic, and how she learnt about how serious it is going to be, it also contains lessons that are applicable everywhere else in life.
Please – pretty please! – read the whole post, but here are my key takeaways:
China’s attempts at downplaying human-to-human transmission and the WHO’s complicity in it are of course wrong, but this is also a good lesson in understanding why exponentials are worth learning about – if nothing else, at least because manufactured lies cannot stand up to the steep part of an exponential curve. And no matter your opinion about whether or not we underestimated the current pandemic and its impact, you should ask where else this lesson can be applied:
Let’s call this the Principle of “You Can’t Finesse the Steep Part of an Exponential,” after a Dylan H. Morris quote included in a previous article of mine trying to warn about the more transmissible variants.
Principles of economics: incentives matter. Up until the point in time when Wuhan was locked down, China’s incentive was to try and suppress news about the upcoming pandemic. Wuhan being locked down was drastic action, yes, but it was also a signal. And the signal was that from here on in, China’s incentive was to warn the rest of the world about how severe and catastrophic (both in terms of health outcomes as well as economic outcomes) this virus was going to be.
Why did the incentive flip? Because the costs of downplaying the virus (in terms of being blamed for the origin, the suppression and therefore the inevitable spread) now outweighed the benefits.
Put another way, if China (if not through its statements, then through its actions) is signaling that its message has flipped, well, things must be really bad.
When it comes to political leadership, ignore what they say, and study what they do.
Political leadership doesn’t just mean governments. This applies to every single political unit, from the United Nations down until your family. Actions, as they say, speak louder than words.
Outrage and counter-outrage on Twitter is words. Action is action, and a far more reliable signal.
And I learnt from this post about the criterion of embarrassment
The criterion of embarrassment is a type of critical analysis in which an account likely to be embarrassing to its author is presumed to be true as the author would have no reason to invent an account which might embarrass him.
If the guy giving you the bad news is embarrassing himself in the process, then the payoff from making the announcement must be more than the cost of being embarrassed.
If intellectual honesty is at a low premium today in society (and if you ask me, it has always been the case) then a leader being (or allowing others to be) honest isn’t about morality, it is about the cost calculus.
So, the thumb rule: if the leader of any kind of group fesses up, be very worried. Think of it this way: map out, consultant style, two axes about public announcements.
Is the announcement good news or bad news (that is, is the leadership that is making the announcement going to be benefit from it, or be embarrassed by it)?
The third is the second last sentence in Zeynep’s post: “Everything we needed to know to act was right there in front of us, but it required not just knowledge, but a theory of knowledge to turn it into actionable, timely information.”
And that, my friends, is the point of metaepistomology.
I’d gone to the RTO the other day for some work, and I suppose you know what comes next.
I wouldn’t say it is impossible to get work done without the help of an agent, but it is certainly true that it isn’t a breeze either. And if one teaches opportunity costs, it makes sense to take the “help” of an agent. Sure you can do it yourself, but it then becomes eye-wateringly expensive in terms of time. And therefore, money.
And while I waited in the numerous byzantine lines to get my work done, I reflected, like every good economist should, on what could be done to reform the system.
Just ban agents, my understandably irrational brain screamed as a first pass solution. Why doesn’t the bureaucracy come up with a better process map that just gets out of the way instead, Cold Calculating Rationality suggested.
Because they aren’t incentivized to, C.C.R went on to reason, proceeding to shut me out of the conversation altogether. Although I was, truth be told, a very interested bystander by now.
But why aren’t they incentivized to – isn’t that the next logical question to ask, mused C.C.R.
I mean, won’t it make their job easier if they make their processes easier?
Well, yes, but they earn the same either way, no? It’s not like payments are linked to productivity increases.
How would they earn more?
Maybe through a Coasean solution in which there’s connivance with the agents, and they get a cut? That is, make the process impossibly cumbersome, and continue to keep it cumbersome, no matter what any well meaning committee proposes. That then facilitates agents stepping in and “helping” blissfully ignorant citizens get their work done faster – for a fee, of course.
They take a cut of the fee – and hey, there you have it! Bureacracts have an incentive – but not to simplify the system! They have an incentive to continue to clog up the system.
C.C.R needed a break at this point in time, so it and I played a couple of rounds of Fruit Ninja on my phone.
But why, C.C.R asked – for it can take only so many minutes of mindless swiping – would anybody want to be an agent? I mean, there are surely better, more remunerative ways to earn a living.
C.C.R. and I stared at each other in part jubilation, and part horror.
“There aren’t better ways, no?!”, we said in unison.
“I mean, if markets are weakly efficient, nobody would willingly work as an agent, surely”, said C.C.R triumphantly.
“And so”, C.C.R went on to say in that insufferably smug way that is its wont, “if you really want to reform the system, you need to create better employment opportunities everywhere else. Reforming this particular system is just putting a band-aid on a cancer. Because yes middle-mean are bad, but nobody grows up dreaming of being a middleman. Of course the middlemen, and that entire nightmare of a system is going to be up in arms if you seek to eliminate it. The lack of alternative, viable careers: that’s the real problem.”
“So, just more pro-growth policies, you’re saying?”, poor old irrational me asked timidly.
“I wonder where else we can apply this line of thinking”, I was about to ask C.C.R… but then it was my turn at the window, and I was so happy that I was finally done with the whole thing that I stopped thinking about it altogether.
