Three cultures of governance

From Lessons from the Covid War:

The Covid war is a story of how our wondrous scientific knowledge has run far, far ahead of the organized human ability to apply that knowledge in practice…

…Real strategy is a notion of how someone plans concretely to connect ends with means. It is a theory of the ‘how.’ It is realized in blueprints of design, and by organizing, funding, training, and equipping many people to play their part in this choreography, people who sometimes must do very difficult things.

There are three main cultures in governance. One is a culture of programs and process. People get authority and money for a program. They administer processes. Many programs are controlled more by congressional committees than by their nominal agency heads. The programs are created to dispense money and they do that, following the given process.

Another is a culture of research and investigation, sometimes to offer advice or inform regulation. It is the dominant culture of high science, the realm of basic research and some ethical reflection. Its strengths were apparent in this crisis in, for example, the understanding of molecular biology and the NIH’s support for messenger RNA technology. The CDC nurtures cadres of disease detectives. It is a culture that can become insular, if the researchers mainly just judge each other, and judge only by their own cultural standards for methods, insight, and value.

A third is a culture of operations, to do things, to produce results in the field. It is a culture that can be resilient and adaptable, since the operators have to adjust to the real conditions they encounter in the field. It too can become insular and clannish in other ways. It is the dominant culture in most private firms, especially those that make products or deliver services. It is the dominant culture in a large part of the governance most Americans actually interact with every day, little of which is in the federal government.

The challenge in the Covid war, as in any great emergency, is to meld all these cultures in practice. it is very difficult to do this. What the Covid war exposed, what every recent crisis has exposed — even in Iraq and Afghanistan — is the erosion of operational capabilities in much of American civilian governance.

p. 14-19

On mergers

Over the last six years I have edited and written dozens of articles about industry concentration, declining competition, “superstar firms”, and antitrust policy. For a long while, I was trying to catalog all that I’d been reading on this topic here (I’ve since given up tracking).

The confusing thing about this topic is that over the last few decades, digital technology has transformed competition at the same time that there has been less antitrust enforcement and a more laissez-faire approach to big business. The former has both increased the advantage of being a big company and reallocated market share toward companies that are good at software, making them bigger. The latter has made it easier for big firms to merge, given firms more power over workers, and coincided with a rise in corporate lobbying that allowed firms to rig the rules in their favor.

It’s tricky to tease apart these two trends, and it frankly annoys me that the conversation about them remains somewhat two-tracked. A lot of antitrust people resist the idea that tech may be driving industry concentration for less nefarious reasons; a lot of people studying firm strategy and the role of tech don’t focus so much on rent-seeking.

Even with these two things tangled up, it’s clear at this point that competition is declining in the US economy. And in my view there’s one fairly straightforward policy lever that could go a long way toward fixing that:

It should be much harder for large firms to merge and acquire.

I have written and edited pieces about this before, and today I have a piece out in ProMarket, UChicago’s online publication covering competition, with my colleague and editor Brooke Fox. We recap some data and some theory on why so many mergers are harmful to competition and conclude that there ought to be a stronger presumption that mergers are anticompetitive.

This is not the same thing as being anti-bigness. Big firms have advantages. But we should make it much tougher for them to acquire their way to dominance.

Neoliberalism, again

There’s a new New York Times Opinion essay about the US turn away from neoliberalism. Twitter thread versions of the piece here and here.

I don’t really have any big point here except maybe a frustration about all the arguments that get tangled up in a topic like this. Here’s a sampling of the ideas in the piece: The claim that we’re turning away from the assumption that “What was good for markets was good for America.” Assuming this means financial markets, it’s definitely not true that what’s good for financial markets is always or even usually good for America. So, yay, sounds positive.

Here’s another one: “The ‘new consensus’ has meant enormous state investment, directed toward industrial revival all around the postindustrial world.” I like it!

But then: “Five years ago, sanctimonious neoliberals mocked Donald Trump’s zero-sum view of the world as a kind of Dunning-Kruger geopolitics. But today, you hear few invocations among politicians or diplomats or bureaucrats of any truly universal positive-sum model of free markets or economic growth.” I mean… most trade pretty clearly is positive sum. Is anyone really claiming most international trade is zero sum? Anyone? Bueller?

It’s hard to add all these ideas up and take stock of where people stand. Did we change our views on trade policy? Or did we know all along that trade was usually net positive but had all sorts of possible ill effects that had to be mitigated (and that we weren’t mitigating them)? Do we actually think ‘Made in America’ clauses are a good idea? Or are we holding our nose because they’re popular? When topics like this get rolled up into these big new paradigm pieces, it can be tough to keep track. (I still like big new paradigm pieces.)

Anyway, hence the post clipping together my writing and blogging on a few of the sub-themes that come up here…

Center-left neoliberalism: Kinda good!

First off, neoliberalism can mean a bunch of different things. It can mean ridiculous laissez-faire conservatism which I have zero interest in defending. But to the extent that one wants to take on the Obama administration center-left liberalism version of it… that version I think holds up fairly well? I wrote about this here, tracing the Obama economic policy and grading it on two specific policy areas. (I will also say, though, that to the extent neoliberalism is about fealty to markets, that’s not the right perspective. The whole game here is about reinventing the mixed economy.)

Free trade: Also kinda good?

One big frustration I have here is that it’s hard to tell which parts of the previous economic consensus on trade were actually wrong. Some areas of the US were hurt more by the China shock than policymakers suspected — though not necessarily more than economic models predicted. But as a general rule trade seems fairly positive if the right supporting policies are in place. My mind can definitely be changed, but I wish discussions of this topic were clearer.

Industrial policy: Also also good!

Governments should be more active in their economies, taking careful, evidence-based actions to boost productivity, steer the economy in positive ways, address climate change, etc. etc. I’ve been saying this for a long while. More recently I commissioned a whole series on industrial policy; this piece lays out the case nicely.

Political economy: ¯_(ツ)_/¯

I’ve long thought the best critique of center-left liberalism starts with political economy. Maybe a lot of these policies that seem good, and actually even maybe are good, don’t lend themselves to building and maintaining good political coalitions. Or maybe they seem good or start out good, but then get distorted and captured and so end up worse than the wonks predict. In the last year I’ve written two essays about this.

One was about the need to consider political economy and the idea that real-world policies are always second-best at best. The other was a review of Mancur Olson’s ‘The Rise and Decline of Nations’ which was republished for its 40th anniversary. I did a link roundup on other influences in this vein here.

Alright, that’s it for now. Onward, into the new, good-ish, deeply confusing economic era.

Political economy

I have a new essay on economic policy and political economy that I want to quote in a second. The piece is an attempt to write something in response to several questions that keep coming up for me. And those questions began for me, in part, from a few different sources that didn’t make it into the piece. So let me start with those.

Here’s Joseph Stiglitz in an essay for the volume “New Perspectives on Regulation” published in 2009:

Previously the presumption that markets were efficient was widespread, with the corollary that only under exceptional circumstances (such as monopoly and massive pollution) were there failures that warranted intervention. Now, among mainstream economists, there is no presumption that markets are efficient.

This has certainly been my experience talking to lots of mainstream economists over the years. There is absolutely no presumption that markets are perfectly competitive when left to their own devices.

Here is a 2019 piece from political scientist Henry Farrell on the excellent blog Crooked Timber, writing about left-leaning neoliberalism:

Two questions follow (for me, anyway). One is for the neoliberal leftists, as a part of a broader left coalition. When and to what extent will their preferred approach to delivering policy clash with, or undermine, the necessary conditions for achieving collective action and making the policy sustainable? If they are pushing for market means towards social democratic ends, that is fine and good – markets can indeed sometimes be the best way to deliver those ends, and few of us would want to be completely without them (including Marxists like Sam Gindin. But one key lesson of the last couple of decades is that market provision of benefits makes it harder to build and sustain coalitions – private gain and public solidarity are at best uncomfortable bedfellows. Figuring out the political tradeoffs – when market means are worthwhile even when they make collective action tougher, or where non-market means might be better for sustainability reasons, even when markets are more efficient – is going to be hard, and we need to start building shared language and concepts to make it easier to resolve the inevitable disputes.

It is not a coincidence that Farrell was also the person who sent me back to Mancur Olson.

Then there’s this line from Brad Delong in Concrete Economics: He describes the New Deal, admiringly, as “pragmatic experimentalism.”

Last but not least is the book Democracy for Realists which argues, more or less, that democracy is under-theorized.

Two questions from all the scattered links above:

  1. What does good policymaking look like when you grant that market failures are ubiquitous?
  2. What does good policymaking look like once you take political economy seriously?

That’s what my new essay for the Stigler Center/Promarket is about. The title is “Biden’s Second-Best Economic Agenda.” Here are a couple bits of it:

In the wake of the Trump presidency and the pandemic, the Biden administration is keenly aware that policies that seem optimal when considered on their own might in practice not be optimal at all. Concerns about efficiency have taken a backseat while concerns over political economy have grown…

If the case for some of these policies is not efficiency or innovation but political economy, it’s reasonable to ask for a more-detailed justification on those grounds. If we’re going to rely less on economic theory, let’s at least have more political science in its place…

Without a better theory of political economy concerns, the theory of the second-best can become a shield for defending almost any policy…

Still, the fact remains that the Biden administration was able to accomplish through industrial policy what the Obama administration couldn’t get done with a carbon price… Last year, the US finally passed a climate bill on the scale of the climate challenge. The second-best approach sure looks better than doing nothing.

More on why data helps

I’ve written before about the puzzle of why data helps. Why do even really basic, descriptive analytics seem anecdotally to be so useful?

First, just one more time evidence that data sure seems to help. From a paper in Nature about forecasting social change:

We compared forecasting approaches relying on (1) no data modelling (but possible consideration of theories), (2) pure data modelling (but no consideration of subject matter theories) and (3) hybrid approaches. Roughly half of the teams relied on data-based modelling as a basis for their forecasts, whereas the other half of the teams in each tournament relied only on their intuitions or theoretical considerations… Forecasts that considered historical data as part of the forecast modelling were more accurate than models that did not… There were no domains where data-free models were more accurate than data-inclusive models.

(Amazingly the data-only models did even better than hybrid models.)

By contrast:

The results from two forecasting tournaments conducted during the first year of the COVID-19 pandemic show that for most domains, social scientists’ predictions were no better than those from a sample of the (non-specialist) general public.

The only time the experts did better than the public? When they used data:

Which strategies and team characteristics were associated with more effective forecasts? One defining feature of more effective forecasters was that they relied on prior data rather than theory alone. This observation fits with prior studies on the performance of algorithmic versus intuitive human judgements21. Social scientists who relied on prior data also performed better than lay crowds and were overrepresented among the winning teams

Why does data work? Why does quantifying seem to be so useful?

Here’s a totally separate study at Voxeu that compares stories to data and illustrates a key driver of the systematic biases that drive human judgment awry: memory.

To examine the belief impact of stories versus statistics, we conducted controlled online experiments. The key idea of these experiments is to compare the immediate belief impact of stories and statistics to the belief impact after some delay, to isolate the role of memory. Participants in our experiment were informed that hypothetical products received a number of reviews. The task of participants was to guess whether a randomly selected review is positive. Before stating their guess, participants either received news in the form of a statistic, a story, or no information. We conceptualise statistics as abstract summaries of multiple data points (multiple reviews). Stories, by contrast, contain one datapoint (one review), but in addition provide contextual qualitative information about the review. Each participant saw three different product scenarios across which they were presented with one story, one statistic, and once no information. Crucial to our experimental design was that we elicited beliefs from participants twice, once immediately after they received the information and once following a delay of one day. This allows us to track the belief impact of stories versus statistics over time…

…both stories and statistics have an immediate effect on beliefs. On average, subjects immediately adjust their beliefs by about 20 percentage points for statistics, and by about 18 percentage points for stories. This pattern, however, looks markedly different after a one-day delay. While there remains a substantial belief impact for stories (about 12 percentage points), the belief impact of statistics drops to about five percentage points. In other words, we document a pronounced story-statistic gap in the evolution of beliefs. While the impact of statistics on beliefs decays rapidly, stories have a more persistent effect on beliefs. Using recall data, we confirm that the reason for this dynamic pattern is that stories are more easily retrieved than statistics.

As I wrote in my summary of behavioral economics, “We rely heavily on the information we can easily recall.” Memory gives us a biased view based on the stories we can most easily recall. But what comes easily to mind may have little to do with what’s actually going on: we’re misled by what’s most unusual or extreme or striking. Data works because it focuses us on what usually happens, not what is most memorable, and so has a de-biasing effect.

The amazing thing is that our judgment is so poor that a lot of the time we can’t do any better than just totally deferring to a super basic, content-free extrapolation from that data. Quantification has its own problems, of course. It helps not because it’s so great but because of how limited human reason can be.

Forecasting on INFER

Last year I had the opportunity to be a “Pro” forecaster on INFER, the crowd forecasting tournament run by Cultivate Labs and the University of Maryland (formerly Foretell of Georgetown’s CSET). Basically you get a small stipend to participate each month. It was fun and I recommend it!

Ultimately, I decided not to keep going as a Pro in 2023. I started Nonrival in August, which I’ll blog about one of these days, and since then my forecasting time has been focused on that project.

I still plan to collaborate with the INFER team in some other capacities (more on that soon too perhaps) but I won’t be paid to make forecasts there.

I’ve “exited” the three questions that I’d forecasted on and weren’t yet resolved, but I’ll still be scored eventually for the period of time I was active on them — so this assessment isn’t quite a true scoring of my time there, but it’s close. How’d I do?

  • In the 2022 season, 344 users made at least 5 forecasts (that resolved), and by default that’s the cutoff INFER uses on its leaderboard so I’ll use it here, too.
  • I finished 76th, with 8 questions resolved and scored. That puts me at the 78th percentile.
  • On the “all-time” leaderboard for INFER (which for me counts my two questions forecast in 2021) I’m 71st of 620, which puts me at the 89th percentile.
  • Lifetime on INFER, I’m better-than-median on 9 out of 10 questions (7 out of 8 for 2022 season), with my one blunder being a forecast of Intel’s earnings where I seemingly underrated the chance of an out-of-sample result.

Overall, my MO seems to be consistently being just a tiny bit better than the crowd. Not bad! But that leaves plenty of room for improvement. Some of it is I think I could do better by simply spending more time and updating more regularly on news and shifts by other forecasters.

But there’s also a “tenaciousness” that Tetlock describes when talking about the superforecasters that includes a willingness or even an eagerness to sift through details when necessary until you find what you need. I saw some of that with teammates during my year as a Pro. And that’s something I’ve not had the time or maybe the inclination for. I think I’ve done a pretty consistent job of avoiding the basic mistakes that lead to poor forecasts: I look for quantitative data, seek out multiple perspectives, I often blend my own judgment with others’, etc. But if I want to get to the next level I need to immerse myself more in the details of a topic, at least some of the time.

Past forecasting record posts here and here.

Code is not law

I’m fond of Lessig’s saying that “code is law” and I often mention it on the blog. But there’s a deeply distorted version of this idea cropping up in crypto lately and it’s worth distinguishing it from the original meme.

Lessig’s idea was that human behavior was affected by four types of “governance” including markets, laws, norms, and what he called “architecture.” Architecture (if I’m remembering the book correctly) encompassed stuff we built in the physical world that affected human behavior. If I build a speed bump, you might drive differently; if I build a skyscraper, it might affect your view or change your walk to work or what-have-you. The things we build impose certain constraints on us — they shift how we behave.

Lessig then argued that in the digital world, code was the architecture. You could make some things possible or impossible, easy or hard, through the way the software was built. Code became a form of digital governance, alongside markets, laws, and norms.

Compare that to the crypto-maximalist version of “code is law,” which holds that anything the code allows is fair game. Here, via Matt Levine, is the defense provided by a trader who allegedly rigged a crypto market in a way that clearly would not be allowed in any normal financial market:

I believe all of our actions were legal open market actions, using the protocol as designed, even if the development team did not fully anticipate all the consequences of setting parameters the way they are.

You see the logic here: If you wanted what I did to be illegal, why did you write the code to make it possible? This is code-is-law maximalism.

There’s a less maximalist, dorm-room version of this that you sometimes see in crypto that maybe deserves some consideration. This version doesn’t argue that anything the code allows is OK. But it does say we should rely more on code for our regulation. It wants code to play a bigger role in setting the rules, bringing us closer to a world where anything the code allows is OK — even if we’re not there yet and even if we never get all the way there. I’m OK with a bit of utopianism and so I don’t mind entertaining this as a thought experiment. But so far crypto has mostly served to show why anything the code allows is OK is not OK.

To see just how damaging this maximalism is, compare it to a totally different case:

The U.S. Supreme Court on Monday let Meta Platforms Inc’s (META.O) WhatsApp pursue a lawsuit accusing Israel’s NSO Group of exploiting a bug in the WhatsApp messaging app to install spy software allowing the surveillance of 1,400 people, including journalists, human rights activists and dissidents.

If you exploit a bug to do bad things, you can’t just hide behind anything the code allows is OK. In this case, we’re talking about the murky world of international affairs where law is less effective. No one thinks this is a good thing: the world of international espionage is much closer than other spheres to anything the code allows is OK and no person in their right mind would want to run the rest of an economy that way. Code is law maximalism forfeits three-fourths of the original code-as-law formulation: Governing human behavior is hard, and we need all the tools we can find. As much as code increasingly does govern our behavior, laws, and incentives, and norms are all still essential.

On falsification

From Richard McElreath’s textbook Statistical Rethinking, via Data Elixir:

…The above is a kind of folk Popperism, an informal philosophy of science common among scientists but not among philosophers of science. Science is not describe by the falsification standard, and Popper recognized that. In fact, deductive falsification is impossible in nearly every scientific context. In this section, I review two reasons for this impossibility: 1) Hypotheses are not models… 2) Measurement matters…

…For both of these reasons, deductive falsification never works. The scientific method cannot be reduced to a statistical procedure, and so our statistical methods should not pretend. Statistical evidence is part of the hot mess that is science, with all of its combat and egotism and mutual coercion. If you believe, as I do, that science does often work, then learning that it doesn’t work via falsification shouldn’t change your mind. But it might help you do better science. It might open your eyes to many legitimately useful functions of statistical golems…

…So if attempting to mimic falsification is not a generally useful approach to statistical methods, what are we to do? We are to model. Models can be made into testing procedures–all statistical tests are also models–but they can also be used to design, forecast, and argue…

Related: Strevens’ iron rule of explanation.

Notes on trade and globalization

I’ve been trying to revisit the arguments and evidence for global trade and trade liberalization recently. I want to post a few links so I don’t lose track of them.

Overall, I came away suspecting that the pro-trade side, which I’ve been sympathetic toward, is a bit overconfident relative to the evidence. But also that the anti-trade or trade-skeptical side has even less evidence to back it up. More liberal trade policies do seem, on net, economically positive — but with a lot of uncertainty around just how positive and when they might be less and more so. And, all things considered, trade seems slightly second-tier, behind say technology and good policy and political institutions and public health as a driver of prosperity. Plus, as left-leaning trade proponents have said forever, it’s incumbent on policymakers to put in place the complementary policies that make trade as positive for people as it can be.

That’s the TLDR. Here are some notes:

What are the arguments for trade?

I’m sticking just to the economic arguments here. Raghuram Rajan has a one paragraph summary in a recent Foreign Affairs piece that’s worth quoting (speaking in reverse about the losses from deglobalization):

“Deglobalization has many costs, some of which are already evident. They include the higher cost of goods and services as production no longer takes place in the most efficient locations, the loss of scale economies as production becomes fragmented, the increase in the power of domestic oligopolies as global competition is restrained, the decline of learning by doing as multinational corporations no longer spread best practices, and the rise in inflationary pressures as local supply-demand imbalances are no longer tempered by a global market.”

This paper runs through a very similar list: There is the traditional argument of comparative advantage, but also returns to scale, increased competition, more learning and therefore innovation, and more product variety.

How much does the US gain from trade?

There’s really not a satisfying answer. This paper tries to provide one but it’s powered by some very heroic theoretical assumptions about willingness to pay and elasticity. Basically, gains from trade are driven by how much you trade, and how easily you could find substitutes if that trade stopped. Fair enough, but a pretty simplified story. Nonetheless, the upshot: “Our analysis points towards welfare gains from trade ranging from 2 to 8 percent of GDP.” That’s meaningful! But it’s not everything, and it’s coming from the fairly trade-friendly assumptions of mainstream economics.

What about micro evidence?

If the estimate above didn’t do much to convince me, what about more traditional microeconomic evidence? For this, I read Our World In Data’s briefing on the subject which summarizes several papers but I did not read the papers myself. With that said, this type of evidence I find more convincing: It’s looking directly at the data on trade and trying to use econometrics to find plausibly causal relationships. Their upshot:

 “On the whole, the available evidence suggests trade liberalization does improve economic efficiency. This evidence comes from different political and economic contexts, and includes both micro and macro measures of efficiency.”

What about the China shock?

A lot of attention has been paid to a series of papers by David Autor and colleagues on the “China Shock” — basically the rapid increase of trade between the US and China. Those papers find concentrated job losses in a number of regions of the US. The Our World In Data briefing summarizes those papers, too.

But researchers at CSIS recently published a literature review of the various papers on the China shock, and I commissioned a shorter writeup for HBR. Here is their ultimate conclusion after comparing Autor’s results with two other research groups looking at similar questions with slightly different datasets:

So, what does a broader review of the data from multiple studies show? Scholars generally find that prior to 2010, imports from China negatively affected manufacturing jobs in the U.S. However, there are mixed findings on the net effect on the economy, the final balance of jobs lost in manufacturing, and the growth in service sector jobs. There is also no evidence of trade with China having a significant negative effect on jobs after 2010 — the job loss in manufacturing documented in the early 2000s due to trade with China is not continuing today. There is one other result that all scholars seem to agree on: better-educated, more economically diverse regions of the United States were affected far less by the surge in imports from China.

So very real job losses in some regions, but no clear evidence of net job loss for the US much less a net loss to the economy overall.

The Autor China Shock papers did overturn conventional wisdom — just not about the aggregate effects of trade. As his co-author Gordon Hanson writes in a different Foreign Affairs piece:

Our findings went against cherished economic frameworks, which predict that workers in struggling communities migrate in search of employment elsewhere and that new industries expand into downtrodden areas to take advantage of an idle labor pool. Neither type of recovery materialized. Overall, relatively small percentages of people left their communities, and businesses didn’t expand enough to absorb workers who had earlier lost their jobs. Economists still can’t explain why workers did not abandon regions in decline, but relationships may play a role. Moving can mean separating from family members, who care for children, provide support when times are tough, and offer a comforting social network.

The shock to conventional wisdom was how long-lasting and geographically concentrated the costs of trade were.

The upshot

As I said, I take all of this to be sort of a mixed bag for the conventional wisdom on trade. On the one hand, it really seems like we don’t totally know exactly how and how much the US economy has been affected by trade. I doubt we understand the variety of circumstances under which those effects could be larger or smaller. Caution is therefore in order. And the estimates of trade’s benefits to the US, such as they are, are large but not staggering. They’re a big deal but they’re not the thing that explains our overall level of prosperity at least according to the estimate I cited.

On the other hand… The arguments that trade helps an economy grow do make a lot of sense and do have considerable evidence behind them. At least on the economic merits it’s hard for me to come away from this review feeling skeptical about trade, except in the broader “It’s hard to know stuff for sure” sense of generic humility. Even the China shock, as persistently bad as it seems to have been for some parts of the country, was a very mixed bag that helped lots of people and probably grew the US economy somewhat.

The case for trade therefore seems fairly solid, provided it’s kept in perspective and made with some humility. And — as we’ve known forever — the public policy that surrounds it really matters. There’s a lot the government can do to make things better or worse.

Technology adoption

Derek Thompson has a piece at The Atlantic on why America “doesn’t build what it invents.” It covers a lot of good ground. Here I just want to link to a few other things that I think speak to one piece of this topic.

Paul Romer testified in 2020 that the US was first a leader in technology (adoption) not science, then briefly led in both, and now leads just in science not in technology. (I wrote a two paragraph post.)

James Bessen at BU may be the most underrated chronicler of technology’s diffusion. His book “Learning By Doing” is all about that but his newer book “The New Goliaths” is a fascinating look at how today’s giant companies might prevent adoption of new technologies. Here’s an excerpt.

On the theme of complementary assets that you might need for technology to spread, here’s Raffaella Sadun writing about CEOs needing to have skills and knowledge specific to their firms. You might add this to Bessen’s story and think about a lack of managers with the right complementary knowledge to allow new firms to use technologies housed in the dominant firm.

And here’s a ProMarket piece on incentives to innovate as just one piece of innovation:

The creation of these mRNA vaccines tells two stories about encouraging innovation. The first story is familiar: how enormous incentives (which have made senior executives at Moderna, Pfizer, and BioNTech billionaires) can marshal capital and talent to achieve herculean feats (let’s call this story Innovation-as-Incentives). The second story is less discussed but just as critical: how innovation happens when public and private agents share knowledge and combine capabilities, supported throughout by government and non-profit institutions (let’s call this story Innovation-as-Capabilities).

Derek hits on a lot of crucial reasons why the US lags in technology adoption and they all make sense to me. In the links above I’ve been trying to expand on one of them, closest in Derek’s framework to the “incumbency bottleneck.”

It’s not just that we have too many large firms or not enough antitrust. It’s that we need to actively create an economy where knowledge spreads, and where innovation incentives combine with innovation capabilities. We need to make it possible and profitable to adopt a technology, start a new firm, tinker and improve something invented elsewhere. That’s an antitrust problem, a management problem, an industrial policy problem, a labor market problem, an education problem, and more.