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:
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). 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.
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.
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 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.
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.”
“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.
The key point made in the book is that entrepreneurship is not – and should not – the responsibility of the private sector. Indeed, it cannot be the responsibility of the private sector.
Early on in the book, she makes the strongest case there is to be made for her thesis, by arguing that the United States of America has known this, and practiced this, for years on end. The rest of the world, she says, would do well to emulate the USA:
If the rest of the world wants to emulate the US model they should do as the United States actually did, not as it says it did: more State not less.
LOCATION: 372 (Note that the location refers throughout to the Kindle version)
I want to focus on three key points in this essay: horizons, incentives and spillovers. Let’s tackle each in turn.
Moonshots is a word that has become increasingly popular over the last two decades, and it refers to projects or even ideas that have a relatively low chance of succeeding. The payoff, if these ideas succeed, is so large that that it may compensate for the relatively low probability of this actually happening. That, of course, is exactly what expectations are all about.
But for a firm, particularly one that may not have the luxury of time and money on its side, placing bets on projects that may not work out – and indeed most of them will not – is a rather risky thing to do. Money is an obvious constraint, but a less obvious one is time.
Firms just do not have the luxury of waiting while a project turns out to be successful… eventually. These kind of moonshots, then, are perhaps best handled, for this specific reason, by the state.
In fact, the point is even more nuanced, because a firm is much more likely to (if at all) invest in a moonshot project based on a specifically desired outcome. The word project itself is an indication of this fact – this is not “blue sky research” that we are talking about.
But blue sky research is important!
A core difference between the US and Europe is the degree to which public R&D spending is for ‘general advancement’ rather than mission-oriented. Market failure theories of R&D are more useful to understand general ‘advancement of knowledge’–type R&D than that which is ‘mission oriented’ (Mazzucato 2015). Mission-oriented R&D investment targets a government agency programme or goal that may be found, for example, in defence, space, agriculture, health, energy or industrial-technology programmes (Mazzucato and Penna 2015).
Governments need to focus, for the sake of their own economies, their domestic firms and their long term growth, on focusing on moonshot projects, precisely because firms are reluctant to do so. The state needs, in other words, to take risks that private firms will not.
Saying this is easy, but how to go about doing this?
That is, if governments need to tackle long-term low-probability-of-success and uncertain-outcome initiatives that are important, but unlikely to be taken up by the private sector, the question that then arises is: how?
Mazzucato offers two points in this regard that I found interesting:
Block (2008, 188) identifies the four key characteristics of the DARPA model:
1. A series of relatively small offices, often staffed with leading scientists and engineers, are given considerable budget autonomy to support promising ideas. These offices are proactive rather than reactive and work to set an agenda for researchers in the field. The goal is to create a scientific community with a presence in universities, the public sector and corporations that focuses on specific technological challenges that have to be overcome.
2. Funding is provided to a mix of university-based researchers, start-up firms, established firms and industry consortia.
3. There is no dividing line between ‘basic research’ and ‘applied research’, since the two are deeply intertwined. Moreover, the DARPA personnel are encouraged to cut off funding to groups that are not making progress and reallocate resources to other groups that have more promise.
4. Since the goal is to produce usable technological advances, the agency’s mandate extends to helping firms get products to the stage of commercial viability. The agency can provide firms with assistance that goes well beyond research funding. Part of the agency’s task is to use its oversight role to link ideas, resources and people in constructive ways across the different research and development sites.
In effect, she is suggesting that government alone cannot do this, it needs to be a “scientific community” that is decentralized, has autonomy, sets the agenda, and applies Darwinian principles (see point 3). Hmm, sounds familiar. Different context, but a similar lesson!
And elsewhere in the book, her example of how Japan did this in the 1970’s is instructive:
The general point can be illustrated by contrasting the experience of Japan in the 1970s and 1980s with that of the Soviet Union (Freeman 1995). The rise of Japan is explained as new knowledge flowing through a more horizontal economic structure consisting of the Ministry of International Trade and Industry (MITI), academia and business R&D. In the 1970s Japan was spending 2.5 percent of its GDP on R&D while the Soviet Union was spending more than 4 per cent. Yet Japan eventually grew much faster than the Soviet Union because R&D funding was spread across a wider variety of economic sectors, not just those focused on the military and space as was the case in the Soviet Union. In Japan, there was a strong integration between R&D, production and technology import activities at the enterprise level, whereas in the Soviet Union there was separation.
Equally important were the lessons learned by Japanese people that went abroad to study Western technologies for their companies, and relationships between those companies and US firms. These companies benefited from the lessons of the US (hidden) ‘Developmental State’, and then transferred that knowledge to Japanese companies which developed internal routines that could produce Western technologies and eventually surpass them.
So, bottom-line: the state has to get in this business, but it can’t “go” it alone. There needs to be a community of academicians, researchers, firms, scholars – and as the example of Japan shows, this community needs fostering, and horizontal collaboration.
Or, if you prefer to put it simply, this is going to be hard.
Academia suffers from the same problem that government bureaucracy does in India: the incentives are all wrong. Both are about risk minimization.
A professor in a college has no incentive to try and do something new, something risky, something innovative. Why, if you think about it, should she? Your best case scenario is that it works, but you get no upside for it: remember, wages aren’t a function of what you do, they are a function of how long you have been in the system. Your worst case scenario is that what you tried to do blows up in your face. So why take the risk?
And it is the same, of course, with a government bureaucrat. And that makes the conclusion of the previous section even more problematic, for where, exactly, are you going to unearth government bureaucrats willing and able to make this happen?
I’m all for the state being more entrepreneurial. I buy into the idea. But I worry, especially in a country like India, about the feasibility of it, for hey, incentives matter!
In a blogpost I had written earlier this year about the budget, I had touched upon this point:
Here is Ninan’s solution:
“Is there a solution? Yes, railway engineers of old like the metro builder E Sreedharan, builders of government companies like D V Kapur and V Krishnamurthy, and agricultural scientists like M S Swaminathan have shown how they made a difference when given a free hand. Vineet Nayyar as head of Gas Authority of India was able to build a massive gas pipeline within cost and deadline in the 1980s. The officers who are in charge of Swachh Bharat and Ayushman Bharat, and the one who has cleaned up Indore, are others who, while they may not match China’s speed, can deliver. Perhaps all we have to do is to spot more like them and give them a free hand.”
But as any experienced HR professional will tell you, spotting them is very difficult, even in the corporate world. And as any corporate CEO will tell you, giving these talented folks a free hand is even more difficult. And as any student of government bureaucracy will tell you, achieving the intersection set of these two things in a governmental setup is all but impossible.
And so what we need to study and copy from China is not so much anything else, but lessons in achieving, and sustaining, excellence in government bureaucracy. Or, if you prefer, how to improve state capacity.
In short, quality of government, not size of government, is what matters for freedom and prosperity.
That point resonates even more in this context: fostering an ecosystem led by the government is dead in the water without either the proper incentives, or at least bureaucrats who are able to work through poorly designed incentives. It is a hard problem, state led entrepreneurship, and made harder by the problem of incentives.
Or externalities, if you prefer. It doesn’t matter how hard the problem is, the payoffs are worth it!
Ruttan (2006) argues that large-scale and long-term government investment has been the engine behind almost every GPT (general purpose technology) in the last century. He analysed the development of six different technology complexes (the US ‘mass production’ system, aviation technologies, space technologies, information technology, Internet technologies and nuclear power) and concluded that government investments have been important in bringing these new technologies into being.
(Note: emphasis added)
If those GPT’s are the outcome of general, as opposed to specific, R&D, sign me up. They are magnificent positive externalities. Indeed, elsewhere in the book, Mazzucato points to how almost everything produced by Apple today simply could not have been produced without an entrepreneurial state:
The final point that I’ll make relates to how Mazzucato proposes “capturing” some of these externalities:
Where an applied technological breakthrough is directly financed by the government , the government should in return be able to extract a royalty from its application . Returns from the royalties , earned across sectors and technologies , should be paid into a national ‘ innovation fund ’ which the government can use to fund future innovations . Granting a return to the State should not prohibit the dissemination of new technology throughout the economy , or disincentivize innovators from taking on their share of the risk . Instead it makes the policy of spending taxpayers ’ money to catalyse radical innovations more sustainable , by enabling part of the financial gains from so doing to be recycled directly back into the programme over time .
Mazzucato does present alternative schemes to the one shown above, but this is the one that strikes me as being the one with the most promise, if administered well, with appropriate risk-mitigation built in. But again, saying that is much easier than actually getting it done.
But all the being said, one simple fact is inescapable: India needs to be thinking about how to get something like this off the ground, and ASAP.
For that reason alone, more of us should be reading this book.
But beware! Incentives aren’t easy to design!
“Studies show that offering incentives for losing weight, quitting smoking, using seat belts, or (in the case of children) acting generously is not only less effective than other strategies but often proves worse than doing nothing at all. Incentives, a version of what psychologists call extrinsic motivators, do not alter the attitudes that underlie our behaviors. They do not create an enduring commitment to any value or action. Rather, incentives merely—and temporarily—change what we do.”
A Forbes article that tells you how might mitigate some of the problems with incentive design.