Alex Usher sobre Estado innovador, Mazucatto y otros: 4 piezas
Marzo 21, 2023

Can Canada Out-think the Underpants Gnome?

I recently read a fascinating book called “How to Make an Entrepreneurial State: Why Innovation  Needs Bureaucracy “ by Rainer Kattel, Wolfgang Drechsler and Erkki Karo, all of whom are influenced by Marianna Mazucatto, whose work I have discussed here and here.  It’s fascinating for two reasons: first, that the book says next to nothing about how making the state more entrepreneurial or why innovation needs bureaucracy, but it is a very inclusive history of the types innovation policy structures of nation-states around the world.  Possibly one of the greatest good book/terrible title combos of all times.

I will spare you a general critique of the book – Caleb Watney has already done that in Foreign Affairs, and it’s a must-read – and focus on how the book relates to the Canadian context.  And, reader, you will probably not be shocked to learn that the Canadian context for discussion of innovation agencies is deeply impoverished.  I count three areas where we can learn some tricks from abroad.

First, the book helpfully discusses the difference between making policy choices and implementing policy and why it is important not to assume success simply because a choice has been made and money spent.  In Canada, this is very poorly understood, and deliberately so.  Most of Canadian governance is about evading responsibility in the event of non-achievement of results.  This is why we are so bad at collecting data, and averse to doing things like actually announcing policy targets as opposed to simply announcing the initiatives themselves.  This is not specific to the Canadian discourse on innovation, but it is a specific way in which Canadian discourse on innovation is impoverished.

Second, the book helpfully distinguishes between the functions of innovation agencies/policies, of which there are broadly three.  There are agencies which fund innovation, there are agencies which shape markets (for instance, by focusing on procurement and competition policies), and there are agencies which try to work on big-picture system transformation.   In Canada we typically only talk about the first of these three.  The idea of market-shaping is largely anathema to us because a) we’re pretty conservative when it comes to this kind of intervention and b) even when it comes to using governmental power to shape markets through things like procurement, our governments are so deathly afraid of risk that they never prioritize innovation. And when it comes to system transformation, forget it.  So, what happens in Canada are big fights about the way that a single policy level is used to effect change without ever really questioning which other levers we could be pulling.

Third, we are not even particularly interested in the purpose of the agencies we create, whether it be diffusion-oriented (how do we get large numbers of firms to take up new technologies) or mission-oriented (how do we solve problem X).  In fact, most of our innovation agencies are really neither because they are framed in terms of how to distribute money to various stakeholders.  Probably the ones that come closest to having a mission, per se, are Brain Canada and Genome Canada.  The famous CARPA proposal could have had a mission, in the way that DARPA’s mission is about US military supremacy, ARPA-E is about energy, etc. But the backers of this idea pointedly declined to give it a mission, because their assumption was that throwing money around in a diffuse manner would somehow generate product innovation equally well no matter which area was chosen.  This comes from a general confusion in Canada between something being “mission-oriented” (solving a coherent set of problems) and it being a “moonshot” (i.e. something huge and out-of-this-world).  The Apollo project was mission-oriented, but challenges don’t have to be gargantuan in order to qualify as a mission.

Of late, we do seem to care a little bit about the style of agencies we create, whether they are agile, like DARPA, or stable like NRC or the granting councils, or some combination of the too.  But historically, we’ve been pretty consistently Weberian on this: set up stable bureaucracies to hand out money to large, stable agents like universities.  The idea of small, agile organizations sheltered from political interference doing something other than hand out flipping great wodges of cash (what Dan Breznitz calls “peripheral agencies”) — while staying under the radar – is not really in our national DNA.

Put all this together and the following results: compared to many countries around the world, the Canadian policy discourse around innovation looks two- or even one-dimensional.  We don’t talk about outcomes at all, and very little about agency.  Missions?  Not really.  System transformations?  Fuhgeddaboudit, especially if we are talking about social or governmental transformation related to provision of education or social services or even something as obvious as road safety.  Links to other related areas, like competition policy or procurement, do occasionally show up in rhetoric. But in practice?  Not so much.

I could argue a lot of things in the national psyche are responsible for this (mainly: Canadians aren’t that interested in innovation because they prefer comfort to ambition)  but to drop the psycho-analyzing for a second: at a policy-structural level, Canadian governments have assumed that innovation policy = growth policy.  Not only that – they also have come to believe that industrial policy, competition policy and (under strong pressure from universities, who know a golden goose when they see one) science policy are also equivalent to innovation policy.  It’s all one giant mess, largely because Canadian government innovation initiatives, regardless of who is in power, never really have a logic structure more complicated than that of the Underpants Gnomes on South Park.

Step 1: Give government cheques to people doing gee-whizzy things.

Step 2: ?

Step 3: Growth!

Anyways, this book is really good if you want to understand how other governments make sense of Step 2, how they actually work to think through the how government initiatives affect real-world outcomes beyond the simple act of handing out cheques.  I hope a lot of people read it, because until Canadian policy makers can prove themselves more intellectually capable than the underpants gnomes, things aren’t going to get any better.

Fortunately, we can test the Underpants Gnomes v. Government of Canada policymaking by examining the recently-created Canada Innovation Corporation.  More on that tomorrow.




The State is not Entrepreneurial

If you’re interested in innovation policy, and haven’t spent time under a rock for the last couple of years, you’ve probably heard of Mariana Mazzucato.  She’s the professor economics at the University of Sussex who wrote The Entrepreneurial State, which is rapidly becoming the source of an enormous number of errors as far as science and economic policy are concerned.

Mazzucato’s work got a fair bit of publicity when it was released for pointing out that a lot of private sector tech is an outgrowth of public sector-sponsored research.  She has a nice chapter, for instance, outlining how various components of the iPhone – the touchscreen, the GPS, the clickwheels, the batteries… hell, the internet itself – are based on research done by the US government.  This is absolutely bleeding obvious if you’re in science policy, but apparently people out there need to be reminded once in awhile, so Mazzucato found an audience.

Where Mazzucato goes wrong, however, is when she begins to draw inferences; for instance, she suggests that because the state funds “risky” research (i.e. research that no one else wold fund), it’s role in R&D is that of a “risk-taking” entity.  She also argues that since the state takes a leading position in the scientific development of some industries (e.g. biotech), it is therefore an “entrepreneurial” entity.  From this, Mazzucato concludes that the state deserves a share of whatever profits private companies make when they use technology developed with public science.

There are two problems here.  The first is that Mazzucato is rather foolishly conflating risk and uncertainty (risk is tangible and calculable, uncertainty is not).  Governments are not a risk-takers in any meaningful sense: they are not in any danger of folding if investments come to naught, because they can use taxing power (or in extremis, the ability to print money) to stay afloat.  What they do via funding of basic research is to reduce uncertainty: to shed light on areas that were previously unknowable.  Individual companies do very little of this, not just because it’s difficult and expensive (if a company is big enough, that’s not a problem – see Bell Labs or indeed some of the quite amazing stuff Google is doing these days), but because the spillover from such research might allow competitors to reap much of its value (a point Kenneth Arrow made over fifty years ago).

The second issue is that nearly all of the examples Mazzucato offers of public research leading to technological innovation and profit are American, and a fairly high percentage of these examples were funded by the Defense Advanced Research Projects Agency (DARPA).  To put it mildly, these examples are sui generis.  It’s not at all clear that what works in terms of government investment in the US, with its massive defense infrastructure, huge pools of venture capital, and deep wells of entrepreneurial talent, hold very many lessons for countries like Canada, which are not similarly endowed.  Yet Mazzucato more or less acts as if her recommendations are universal.

The book’s recommendations amount to: government should own a share of young innovative companies by gaining shares in return for use of publicly-funded knowledge.  But this is pretty tricky: first, there are very few cases where you can draw a straight line from a specific piece of publicly-funded IP to a specific product, and even where you can, there’s no guarantee that the piece of IP was publicly-funded by your local government (Canadian start-ups benefit from knowledge that has been created through public subsidies in many different countries, not just Canada).  And while there’s a case for greater government investment in emerging companies (economist Dani Rodrik makes it here for instance), the case is not in any way predicated on government investments in R&D.  In Canada, the CPP could adopt such a policy right now if it wanted – there’s no reason why it needs to be linked to anything Industry Canada is doing in science funding.  To the contrary, as Stian Westlake points out, countries that have been most successful in converting public science investments into private hi-tech businesses eschew the idea of equity in return for scientific subsidies.

Worst of all – though this is not entirely Mazzucato’s fault – her argument is being picked up and distorted by the usual suspects on the left.  These distortions are usually variations on: “Someone said the state is entrepreneurial?  That means the state must know how to run businesses!  Let’s get the state more involved in the direction of the economy/shaping how technology is used!”  This way disaster lies.

So, Mazzucato did everyone a service by forcefully reminding people about the importance of publicly-funded R&D to any innovation system.  But her policy prescriptions are much less impressive.  Treat with care.

Missions and Moonshots

There is a crowd of policy entrepreneurs in Canada – mostly but not entirely Liberal, mostly but not entirely based in Ottawa – who have really cottoned on to the whole notion of innovation.  Like many of us who have despaired over successive governments’ lack of cluefulness on this issue, they are dissatisfied with the status quo.  Unfortunately, these people are currently marching with wholly unjustified confidence towards policies that are largely buzzword-driven.

It’s not just this ludicrous notion of a “Canadian DARPA”: now the policy-nerd airwaves are full of talk about “Moonshots” and “mission-based innovation”.  For convenience, Ottawa’s Public Policy Forum and Toronto’s Brookfield Institute have managed to combine these terms into a single argot-tastic project known as Canada’s Moonshot: Charting A Mission-Oriented Innovation Strategy.

Let me explain why this is nonsense on stilts.

First of all, what is a “moonshot” in innovation?  In contemporary Canadian innovation buzzword bingo, it seems to mean “do something big”.  What, exactly, we are meant to go big about is unclear, but whatever we are doing now isn’t cutting it, so why not go big (“we must do something, this is something, etc.”).  And after all, isn’t there a big literature on moonshots which suggest that they often work out well, have lots of spin off effects, etc?

The problem here is that this argument relies on people failing to understand that what the literature describes as a moonshot and what the buzzword bingo types claim as moonshots are two different things.  A moonshot, by definition, is something that a country does in response to a truly existential threat.  The Manhattan Project was a Moonshot to end the bloodiest war the world has ever known.  The Apollo project was a literal moonshot pushed by the fact that the Americans thought they were about to lose global technical pre-eminence, particularly in rocketry.  Give or take some multilateralism and the participation of the private sector, COVID vaccines might be considered a moonshot too.

But Moonshots are a by-product of existential threats (a point made in detail and extremely ably by Mark Zachary Taylor in his book The Politics of Innovation, which I reviewed back here).  Countries don’t do moonshots because they wake up one morning and say “hey, let’s do big thing”, they do it because they are deeply terrified of what will happen if they don’t invest heavily in this one complex task.  Canada, though, does not face any existential threats.  The best the moonshot types can come up with is weak sauce like “saving health care” (a case for raising taxes, maybe, but not for an innovation program) or worse, something like “wouldn’t it be great if we were #1 in AgTech” (DARPA for Tractors).  So, a moonshot isn’t a realistic option.  In fact, if we could shoot the word moonshot into the Sun, that would probably improve innovation discourse in Canada enormously.

But what about “mission-oriented innovation strategies”?  Well this is a phrase coined by policy entrepreneur Mariana Mazzucato, who is most famous for her book The Entrepreneurial State in which she re-discovered Kenneth Arrow’s ideas about the public role in subsidizing research and then proceeded to mangle the concept by claiming that the subsidization of basic research meant that the state was actually an entrepreneur (big hint here: if you have the power of taxation, you are not meaningfully at financial risk of…well, almost anything, and so cannot possibly be an entrepreneur).  “Mission-oriented” innovation is her new shtick.

But the thing is that if you read what Mazzucato means by this term (and I think the best summary is probably this article), it’s pretty clear that what she is articulating is less an innovation process than a theory of how governments should be organized internally (fewer silos, more freedom to innovate) and how it should interact with the private sector (co-structuring markets and then getting out of the way.  Read this document, which Mazzucato helped produce for the Inter-American Development Bank, where she enumerates a number of “mission-driven innovation policies” in Latin America, and where she singles out policies like reducing diabetes in Mexico, or improving education in the slums of Medellin.

There is nothing wrong in principle with what she is advocating, but it absolutely not innovation policy in the way we define this term in Canada.  And more to the point it is the antithesis of a “moonshot” approach.  One is about pouring money monomaniacally towards a particular technological objective while the other is largely about harnessing a wide variety of public and private actors to contribute to solutions on broader societal challenges.  More broadly, one is about technology and the other is not.

It takes a deeply impoverished national discourse on innovation to think these two ideas belong in the same room, let alone the same sentence.  And yet here we are.  Welcome to Canada.

Look, innovation policy has some basic tennets.  We have here in Canada the world’s greatest theoretician of innovation, namely the University of Toronto’s Dan Breznitz.  He has written several books – most recently Innovation in Real Places: Strategies for Prosperity in an Unforgiving World– which lay out a few very simple principles for inclusive growth.  First,  expand flows of knowledge, demand and inputs between the local and global level.  Second, increase the supply of public and semi-public goods which fuel innovation (mainly: high-quality education/training facilities and community facilities), and third, build local ecosystems that reinforce firm-level benefits of the first two fundamentals.

That’s it, that’s all.  It’s not hard to understand.  The question is why we don’t simply do it and stick to it instead of screwing around with ideas like DARPA for Tractors.

Or to put it another way: policy entrepreneurs are going to policy entrepreneur, but why is anyone in Ottawa bothering to listen to them?

But Seriously, How Do We Make an Entrepreneurial State?

How to Make an Entrepreneurial State:
Why Innovation Needs Bureaucracy
by Rainer Kattel, Wolfgang Drechsler, and Erkki Karo
Yale University Press, 2022, 288 pages

Gone are the debates about limited versus big government. The motivating question across most of the political spectrum today is how to build an effective government. Or perhaps less ambitiously, how can we build a government that doesn’t immediately fail when it tries something new?

Liberals are frustrated that the United States can’t build public trans­portation or deploy clean energy fast enough. Conservatives are increasingly interested in industrial policy to counter a rising China. Congress just passed massive science funding, infrastructure, and climate packages and is banking on the administrative state to effectively imple­ment them. A high-inflation macroenvironment has heightened the awareness of supply-side bottlenecks across the economy and turned dredging and port automation into headline grabbers. Public intellec­tuals like Ezra Klein and Derek Thompson are writing about the importance of a liberalism that builds and the need for an “abundance agenda.” Nearly everyone is disappointed with how our public health agencies handled the Covid-19 pandemic (even as they disagree about what went wrong).

These conversations are by no means limited to the United States, and the appropriately titled How to Make an Entrepreneurial State, by a trio of European academics—Rainer Kattel, Wolfgang Drechsler, and Erkki Karo—is the latest attempt to provide a handbook with some answers. Building on the work of Mariana Mazzucato’s The Entrepreneurial State from 2013, the book is a fiery defense of the essential role the public sector will need to play in supporting innovation to solve the great challenges of our age, and seeks to inform practical policy.

The book makes the important observation that state support for innovation is well and good, but without a clear plan for improving state capacity, such efforts often prove to be wasteful and ineffective. As it notes, “In innovation policy and beyond, we have decades of lessons and empirical proof that just focusing on and overemphasizing public sector agility, entrepreneurship and innovation does not work.”

The book’s lofty ambitions make its eventual shortcomings more disappointing, however. How to Make an Entrepreneurial State gets mired in high-level abstractions and historical case studies, and ultimately neglects practical questions. A better title for the book might be “Why to Make an Entrepreneurial State”—the “How” is scarcely to be found.

The book is at its strongest when describing the evolution and transformation of various “innovation bureaucracies” across time and around the world. While digging into historical examples like the original Advanced Research Projects Agency, the Japanese Ministry of International Trade and Industry, and the Swedish innovation agency Vinnova, the authors surface certain themes that repeat across case studies: the need for flexible hiring rules, the importance of attracting a nation’s best and brightest into public service, and the significance of overarching “missions” to focus the public sector.

A unifying theme discussed throughout the book is the idea of “agile stability” for state bureaucracies: the tension between ensuring stability in the core functions that they provide to the public while also maintaining the flexibility to evolve and add new capabilities over time. Nevertheless, the book is frustrating in its lack of practical detail as to how agencies can embed agility into their functions or how to structurally enable bureaucratic actors to take risks. But the fundamental question is an essential one for our current moment, and the book can help point us in the right direction.

So, how does one actually make an entrepreneurial state? There is no single correct model, and in fact, an essential element of entrepreneurship is the ability to correct course and revise plans in real time to accomplish overarching goals. With that in mind, some recent attempts at rebuilding state capacity and fostering agile stability within the U.S. federal government can shed some light on the “how” of making an entrepreneurial state.

Creative Destruction within and across Agencies

The authors often contrast two views of innovation, one stemming from Max Weber, a German sociologist and theorist of administrative bureau­cracy, and the other from Joseph Schumpeter, the Austrian-born econo­mist who coined the term “creative destruction.”

The authors make a distinction between Weber Type I and Type II organizations. A Type I organization is characterized by stability and process orientation in bureaucratic administration, whereas a Type II organization is typically smaller, more nimble, and willing to experiment with new models of service delivery or organization. The authors discuss organizations switching between the two models either at an individual level or at an ecosystem level, as nimble new agencies are started to meet new challenges (Type II) but eventually solidify into a “normal” Type I structure. The authors then theorize a new, blended model—classified as a Weber Type III organization—which is able to host both Type I and Type II capabilities under a single umbrella. This typifies the agile stability they consistently praise. (This kind of innova­tion jargon is unfortunately littered throughout the book, and I will hereafter attempt to avoid it.)

Schumpeter, meanwhile, is used somewhat as a foil, and his views on innovation through competition are dismissed as out of reach for public agencies, which need to maintain a higher degree of stability than the private sector. The authors write: “As Schumpeter argues, fear of death by competition is what drives businesses to innovate and explore new pastures. Companies sense opportunities and threats, seize them—or not—and reconfigure what they do. The public sector is not primarily defined by competition, even if in some cases we pretend otherwise and create rankings of schools, doctors, and so on, or compete with other countries, war-like or in peace.” But this may be giving short shrift to the suitability of creative destruction as a model.

At first glance, it is true that the concept of creative destruction has some obvious barriers to application in the public sector. For a variety of practical and political reasons, it would be difficult to set up a direct competitor to the Federal Aviation Administration, for instance, and then ask airlines to decide which agency they would rather be governed by. But that doesn’t rule out the idea of trying to foment the gale of creative destruction within public agencies, as opposed to across them.

To illustrate how this could work, we might look at (what I hesitantly call) “The Netflix Model of Institutional Competition.” For the first decade of its existence, Netflix was essentially a DVD-by-mail company. Not content with this business model, and intrigued by the early success of YouTube, leadership at the company started investigating the possibility of streaming video directly over the internet, and launched a streaming product in 2007. To do this, they set up a new division that was somewhat isolated from the traditional processes and metrics used to govern the rest of the firm. According to one history of the company, the DVD executives were even kicked out of certain management meetings to make sure the streaming division had room to grow.

This separate streaming division eventually grew to become the modern Netflix. Netflix competed with itself internally across product verticals rather than let an outside competitor beat it to the new market. But for this new team to have success, they needed to be able to pull from a different talent pool, be judged by a different set of metrics, and productively interface with the rest of the company without kickstarting a turf war. In other words, they needed a high degree of institutional autonomy.

A similar scenario might be playing out right now with the launch of two brand new science funding offices: the Advanced Research Projects Agency for Health (arpa-h) at the National Institutes of Health (NIH) and the Technology, Innovation, and Partnerships (TIP) directorate at the National Science Foundation (NSF). Arpa-h, which is modeled after darpa—the innovative defense research agency which helped push forward the internet, GPS, and mRNA vaccines—is tasked with seeking out the set of high-risk, high-reward medical breakthroughs that the NIH is often criticized for missing. Likewise, the new TIP directorate’s mission is to develop a new portfolio of applied technology investments, regional innovation efforts, and novel funding mechanisms.

Both arpa-h and the TIP directorate have been granted a degree of institutional autonomy by Congress and have a clear mandate to push the envelope on funding research, identifying talent, and working with the private sector in ways that the parent agencies have been unable or unwilling to pursue. If arpa-h and TIP succeed in pushing forward the technological frontier, and in piloting new ways to identify promising scientific investments, their parent agencies will inevitably feel pressure to begin scoping similar mechanisms or to step up their performance in other ways. Oftentimes, having a shining example of what good agency behavior makes possible is more productive for spurring reform than ten examples of agency failure. For instance, if arpa-h is now able to successfully fund a younger cohort of scientists with positive impact, it might highlight the fact that NIH could be doing more to enable talent­ed early-career researchers.

While it’s unlikely that arpa-h and TIP will fully replace their host organizations in the same way that streaming replaced DVDs at Netflix, providing a bit of internal competitive pressure to our premier science funding agencies would be a welcome step toward a more entrepreneurial state. And this model of creating new agency divisions with a high degree of autonomy, which can eventually augment the agencies hosting them, could be more generally applicable.

Experimentation as a Discrete Institutional Goal

If we want federal agencies to be able to embody agile stability or, more simply, to be able to learn how to improve their operating procedures over time, then we need to think carefully about the bureaucratic incentives to experiment. And by “experiment,” I don’t simply mean trying out a new system or process in a one-off fashion. I mean the scientific sense of experiment—systematically testing a hypothesis using careful design, data collection, and evaluation with an eye toward the likely (or, in the case of a randomized trial, the actual) counterfactual impact.

It may seem obvious in the abstract, but it is often difficult for federal agencies to prioritize a softer target like “run more experiments” unless there are specific individuals who see experimentation as a discrete goal tied to their career advancement. When an agency’s performance is judged primarily on short-term outcomes—how many vaccines were shipped, were the grants paid out, and so on—it’s quite easy for longer-term process improvements to fall by the wayside.

One way to incentivize such improvements is to build new offices or directorates within an organization (readers may be sensing a theme here) that have an explicit mandate, dedicated funding, and enough autonomy to run experiments on different aspects of the agency’s core mission. An actually existing example of this kind of experimentation center within the federal government is the Center for Medicare and Medicaid Innovation (CMMI), which is a division of the Centers for Medicare and Medicaid Services and works in precisely this way to pilot new health care delivery and payment models. For example, CMMI is the division tasked with actually testing a new payment model for hip replacement surgery and seeing if it delivers better or cheaper results than the existing models. CMMI can run the big randomized control trial between hip replacement payment models A and B, and if B turns out to cut costs by 40 percent while making no difference for patient outcomes, then Medicaid and Medicare can safely scale those interventions across their large patient populations. CMMI effectively does all the hard work of de-risking new payment and delivery models for CMS. And best of all, fewer people complain when an experiment fails to pay off because the whole point of experimentation (and therefore of CMMI) is to find out which new ways of doing things produce better outcomes and which ones do not.

An experimentation center at the National Institutes of Health, for example, could test out new funding mechanisms, like giving reviewers a “golden ticket” that they could use to ensure a particularly exciting pro­posal would receive funding. An experimentation center at the Department of Labor could pilot new models for retraining workers and provide an evidence base for future expansions. An experimentation center at the Department of Transportation could test out new schemes for delivering transit grants that drive cost reductions in public works. Thoughtful experimental design can help us to understand the underlying causal impact of possible reforms, and can also provide a way for bureaucracies to communicate the rationale for big policy changes in a manner that minimizes the appearance of arbitrary, unfair, or unaccountable decision-making.

In Revolt of the Public (2018), Martin Gurri discusses the phenomenon by which the legibility of the internet age has made the flaws of elites and public institutions more visible than they used to be (even if they are happening at the same rate). This pushes government agencies toward a kind of banal proceduralism, which often leaves the public unsatisfied, but is itself a reaction to public outcry over perceived unfairness. Consider, for instance, whether the “I’m using my discretion to give all my smart scientist friends grant funding” approach taken by Vannevar Bush, when he was running the Office of Scientific Research and Development (the precursor to the National Science Foundation) during World War II, would be applauded now.

Indeed, much of the institutional conservatism we see today stems from a reasonable desire to appear accountable and fair when deviating from an existing set of procedures, but which in practice creates a strong presumption for sticking with the status quo even when alternatives are desperately needed. Hence the importance of providing a scientific evi­dence base through experi­mentation to justify reforms (that often involve upsetting an established interest group) in a value-neutral language that public actors are well versed in and comfortable with. But to ensure that serious efforts at experimentation (and credible evaluation) are undertaken within a bureaucracy requires making it a discrete institutional goal.

Learning by Doing and the Operational Mindset

One of the ongoing public reckonings relevant to the present discussion is why, exactly, the Centers for Disease Control and Prevention (CDC) failed so extensively at controlling and preventing disease during the Covid-19 pandemic. At nearly every turn, the CDC slow-walked the response and avoided taking proactive steps on issues like testing, while simultaneously boxing out other societal actors who were trying to fill the void.

The director of the agency, Rochelle Walensky, has acknowledged as much and specifically cited the tendency of the agency to act like an academic institution rather than an emergency responder. As Walensky said, the CDC was too focused on producing “data for publication” rather than “data for action.”

This is a startling contrast with the origins of the CDC in the 1940s as a quasi arm of the U.S. military, originally named the “Office of Malaria Control in War Areas,” which had a heavy operational focus in deploying medical countermeasures in the field. Over time, as malaria and other diseases were pushed back, the agency morphed into a more scholarly institution that attracted academics aiming to publish papers more than logistics and supply-chain professionals.

That’s not to say that scholarly work on disease spread is unhelpful. But an agency whose mission is fundamentally operational in nature—to take real action in the world to prevent the spread of disease—needs to find ways to maintain an operational culture over time. It perhaps should not be surprising that an agency run like a university would struggle when asked to turn into an emergency management operation overnight. To maintain this operational focus even when there’s not a pandemic to fight, the CDC should be practicing and operating systems in the real world—for instance by expanding a pathogen surveillance network in wastewater systems and airports, and by doing more work with international partners to proactively monitor and contain the spread of viruses around the world.

Indeed, the importance of maintaining an operational focus is more generally applicable for maintaining (or rebuilding) state capacity. There is a large literature in economics on the importance of learning by doing, i.e., the ability of firms to discover new efficiency improvements by repeating processes again and again. This repetition builds up tacit knowledge, which is embedded both in individual workers and in the larger organizational infrastructure. But this knowledge will erode if the communities of practice within which this process knowledge is mas­tered begin to atrophy. These are two sides of the same coin: practice generates learning by doing, but if we stop practicing, then our ability to do the activity at all can fade.

The state, too, can benefit from learning-by-doing loops through a focus on operational excellence. Sometimes that may require instantiating practices in the real world instead of in academic papers, as with the CDC. In other cases, it may require a shift in agency procedure and prioritization towards usability.

A basic truth of public service is that the government will do things that it has an office named for. Consider, for instance, the Census Bureau. The Census Bureau has an office of privacy; in fact, the bureau’s privacy procedures have recently become controversial because they’ve gone a step too far and made census data tangibly less useful in an effort to protect against hypothetical edge cases.

On the other hand, the Census Bureau has no office of usability. Compare this to an archetypal software company which prioritizes usability: half of this company might be focused on A/B testing small tweaks to the user interface to make its software slightly more intuitive or helpful. Yet emphasis on usability is largely missing from the public sector. A first step would be to have dedicated public offices within agencies that have a mandate to increase the usability of key services or products.

Not all agencies have an operational mission, of course. But for the ones that do, a focus on building and maintaining a community of practice by focusing on operational excellence should be a key strategy.

Government as Market Maker

One analytical framework introduced by How to Make an Entrepreneurial State formalizes the different kinds of roles that innovation agencies can play within a larger ecosystem. Specifically, there are creators (like the National Labs), doers (like darpa), funders (like NSF), intermediaries (like NIST), and rulers (like OSTP) that overlap with each other.

Moreover, a single organization can evolve over time from inhabiting one or multiple of these roles into others. Although not discussed in this sense in the book, NASA is a compelling example of an innovation agency that shifted from exclusively being a “doer”—directly manning and operating space exploration missions like Apollo—to a more com­plementary role as the market maker (“funder”) and shaper (“ruler”) of the commercial space sector today.

While NASA still directly operates a number of space missions, its arguably more impactful role today is as a facilitator of the nascent commercial space industry. SpaceX, in particular, has benefited tre­mendously from NASA’s clever use of a milestone-based payment model which let a scrappy start-up compete with incumbent space and aeronautics contractors in resupplying the International Space Station. In return, SpaceX has been able to massively drive down the costs of entering orbit by engineering a fully reusable orbital rocket that can be mass manufactured.

But all this was enabled by NASA’s willingness to reconceive of their core mission and intelligently leverage the flexible procurement author­ization they had, called “Other Transaction Authority.” One can imagine a counterfactual scenario in which NASA instead insisted on doubling down on the consistently over-budget (but directly administered) Space Shuttle program or, alternatively, accepted dependence on the Russian Soyuz spacecraft.

Moreover, NASA continues to flex its muscles in its new “market maker” role with an intriguing contract to purchase lunar rocks from four different companies, conditional on them getting to the moon first. The idea here is twofold: first, to help jump-start a market by providing financial incentives to get to the moon; second (and perhaps more importantly), to establish some degree of international precedent for extraterrestrial property rights.

The agency will continue to wield substantial leverage in setting priorities for space exploration and commercialization over the coming decades. NASA is acting as the vanguard not only for space exploration but also for innovation agencies considering a similar transition from direct doer to market maker. Knowing if, how, and when other agencies should make a similar transition will need to be a core part of the entrepreneurial-state playbook going forward.

Another innovation agency using market-shaping financial mechanisms to its advantage is the Biomedical Advanced Research and Development Authority (barda). Barda is tasked with harnessing the private sector to fund defenses against high-risk, low-probability hazards that aren’t addressed by commercial markets. Using a broad suite of innovative procurement models, such as prizes, milestone-based awards and payments, advanced market commitments, and funding for university-affiliated research centers, barda is trying to proactively bootstrap and shape a market to fight pandemics, and to provide a defense against other chemical, biological, radiological, and nuclear incidents.

Essential to the success of this kind of operation is an appropriate grasp of the wide variety of financing mechanisms that exist. When is an innovation prize a better fit for a technical problem than a loan guaran­tee, advance market commitment, or a milestone payment? Mastering the nitty-gritty of government procurement best practices is not exactly a sexy policy topic, but it will be essential for building an entrepreneurial state.

First Steps

How to Make an Entrepreneurial State asks the essential question for the twenty-first century. And the high-level frameworks and historical case studies it presents can help put us on the right track—even if this volume cannot function as a handbook.

The truth is that we don’t really know how to make an entrepreneurial state. There’s no single template that can be applied uniformly to rescue us from bureaucratic complacency. But the nature of entrepreneurship is to go out there and try new things. The strategies detailed above are in no way comprehensive, but they nonetheless have shown early signs of promise.

An underlying principle cutting across many of these strategies is a focus on creating new units within existing institutions that have a notable level of autonomy for the purpose of (a) providing internal competitive pressure, (b) de-risking and formalizing experimentation, (c) providing space for a community of practice and operational excellence, and/or (d) developing expertise in utilizing market-shaping mechanisms.

Often, a new office can achieve one or more of these goals at the same time. For instance, a new operational office in the CDC focused on proactive disease surveillance would certainly be attempting to achieve (a) and (c), but could perhaps involve (b) and (d) as well, depending on its structure.

It can be tempting for reformers faced with the obvious failure of our institutions to imagine the best path forward is a hard reset. But reforming our agencies won’t be as simple as turning them off and back on again.

Entrepreneurship often starts with small steps. The start-up that begins in the garage eventually grows up to take on the incumbent giant. Likewise, laying the seeds for effective institutional growth in small ways today can pay dividends down the road. And launching a new office or subagency is nearly always an easier political lift than creating a new agency from scratch.

This is certainly not to say that all issues of state capacity can be solved by creating new, autonomous offices, or that more ambitious reorganizations should never be considered. The use of market-shaping mechanisms, operational efficiencies, and experimentation should in no way be limited to particular offices and would ideally be embraced across all of government.

Our state capacity has been diminished over the years through the combination of conservative anti-statism and progressive pro­ceduralism. To rebuild it will require a proactive vision of what we want the state to do and a clear-eyed understanding of the mechanisms needed to incen­tivize risk-taking.

This article originally appeared in American Affairs Volume VI, Number 4 (Winter 2022): 3–13.


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