Romer and Nordhaus

There are many tie-ins between the two: both are “doers,” there are similarities in how they made their case within and beyond their field, and both worked on externalities — with Nordhaus most famous for his work on negative environmental externalities and Romer most famous for his description of positive externalities in the form of ideas. (Nordhaus, it must be noted, has done seminal work on positive externalities, too.)

To me, though, the most obvious connection between them is with an eye toward the future: among the biggest challenges society faces is encouraging innovation and economic growth without destroying the environment, most notably through climate change. Romer and Nordhaus have done more than almost anyone to address that challenge.

In honor of their work, here are a few things related to it:

The chart is from Our World in Data, but based on Nordhaus’ work. On the environmental side, his book Climate Casino is worth a read.

As for Romer, here’s his plain English overview of economic growth. Here’s a video by Marginal Revolution University on his work. And the excellent book Knowledge and the Wealth of Nations, by David Warsh, tracks Romer’s essential contribution to economics, with lots of great historical context.

‘The most common and durable source of factions’

Of the many causes of faction, there is one that James Madison called out in particular:

So strong is this propensity of mankind to fall into mutual animosities, that where no substantial occasion presents itself, the most frivolous and fanciful distinctions have been sufficient to kindle their unfriendly passions, and excite their most violent conflicts. But the most common and durable source of factions has been the various and unequal distribution of property.

Emphasis mine.

I was reminded of that line by James MacGregor Burns’ Fire and Light.

Notes on internet organization and production

This post has been updated as I’ve thought more about these distinctions.

My point in writing this post is both to note that there are internet-based models of organization and production other than the much-discussed “platforms,” and to distinguish a few different kinds of platforms. All of this is based just on my own reading and thinking. I’m sure others have better, more formal, and more considered definitions.

Different kinds of platforms

Participatory platforms: They offer users the ability to create something, to communicate, or similar — often in a fairly open-ended way. And they place few if any limits on those users’ activity or on who can join. Think Twitter or Tumblr. The scale and variety of users’ activity and the connections between users create something the platform owners could never have created on their own (for good or ill).

APIs: Application programming interfaces let other programmers communicate with your system, to access data or functions, or to do a variety of other things. They therefore let others build things using your product, data, or service. One version I’m counting within my loose version of APIs is operating systems which offer APIs so that people can build software “on top of” the OS. So think of Android and Windows as falling within this category, but also Facebook or Twitter at some points letting others build apps on top of their platforms or access pieces of their data. This is what people are getting at when they use the metaphor of a “platform” to generalize the idea of letting others “build on top” of whatever you offer.

Two-sided markets: These platforms match buyers and sellers. Think eBay, Uber, Airbnb.

Big companies like Google, Facebook, or Amazon fit multiple categories; they are platforms in multiple senses. Other platforms are platforms only in a single sense.

Non-platform models

Sometimes, reading the business press, you’d think platforms were the only way the internet changed organization. But there are other models made possible by digital technology and the internet:

Peer production: Users come together to collaborate, typically towards a project that will be a commons — like Wikipedia or Linux. The collaboration is complex and may require elaborate rules and norms to govern interactions.

Crowdsourcing: Users offer distinct inputs — votes, predictions, code — that are collected by a central entity. Sometimes they’re aggregated (as in prediction tournaments) and sometimes the top entrant is rewarded (as in contests on Kaggle or TopCoder).

Data loops: A product gathers data which is used to improve the product, creating a positive feedback cycle. Netflix’s recommendation algorithm is one example, and here is a piece on this model.

Aggregators: They sit on top of a mountain of content and help their users find things. Participatory platforms often have to build aggregators — like the Facebook news feed — but aggregators needn’t be participatory platforms. Google is an aggregator, but unlike Facebook the content it navigates is not it’s own.

(I’ve only tried here to include models where the nature of organization and production is changed. Of course there are lots of ways to use the internet and digital technology to make existing production more efficient.)

The problem with thinking of platforms as the end-all be-all of the internet is — other than the confusion that comes from the multiple types — that it distracts from the real concept at the internet’s heart: networks. These are all networked models of organization and production, made possible by ubiquitous connections and the constant flow of information between them.

Taxes and innovation

A new paper finds that tax rates (corporate and individual) have large effects on innovation:

Our main findings can therefore be summarized as follows. Taxation – in the form of both personal income taxes and corporate income taxes – matters for innovation along the intensive and extensive margins, and both at the micro and macro levels. Taxes affect the amount of innovation, the quality of innovation, and the location of inventive activity. The effects are economically large especially at the macro state-level, where cross-state spillovers and extensive margin location and entry decisions compound the micro, individual-level elasticities. Not all the effects of taxes at the macro-level are accounted for by cross-state business stealing or spillovers. Corporate inventors are most sensitive to taxation; and positive agglomeration effects play an important role, perhaps in offering a type of compensating differential for taxation.

This goes against my prior, at least to some degree. I would have said before that incentives do matter for innovation — and that that includes taxes — but that the effect of taxes on innovation may not be that large. Here’s what I wrote a few years ago:

Of course benefits are only one side of the ledger. Taxes are just as often held up as a threat to entrepreneurship and a dynamic economy. A lower capital gains tax rate does seem to be associated with a greater supply of entrepreneurs. But keeping the capital gains rate low to help startups is incredibly inefficient, since only a small portion of realized capital gains are from entrepreneurial activity. As Harvard Business School professors Paul Gompers and Josh Lerner write, “policies that increase the relative attractiveness of becoming an entrepreneur and promote technology innovation probably would have more of an effect on venture capital investments than an across the board cut in the capital gains tax rate.”

Instead of preserving low tax rates, entrepreneur-friendly tax reform would encourage startup investment by shifting the tax code away from its current bias for debt over equity, and could preserve or expand key tax credits like the exemption for long-term investment in small businesses.

Here are my notes on taxes and economic growth in general.

I remain interested in how efficient low corporate, income, and capital gains taxes are or aren’t for incentivizing innovation, and how the tax code and other incentives might provide more tailored options. But taxes do seem to have a meaningful effect.

What tech is for

There is a topic I occasionally try to write about, but which always seems to turn out poorly. It goes like this:

The development of technology is, broadly speaking, an important driver of progress in the world. That development depends on social context, including culture, incentives, and rules which can further technological development or impede it. Getting the culture, incentives, and rules right requires mutual understanding, cooperation, and trust. And so it is worrisome when the tech sector and the rest of society seem so distant from one another, and so at odds. In particular, it’s worrisome when most of society believes the tech world has little to offer them.

Now, a few loosely related digressions…

What is the central idea behind Silicon Valley? Here’s how The Economist describes it today:

The Valley is not just a place. It is also an idea. Ever since Bill Hewlett and David Packard set up in a garage nearly 80 years ago, it has been a byword for innovation and ingenuity. It has been at the centre of several cycles of Schumpeterian destruction and regeneration, in silicon chips, personal computers, software and internet services. Some of its inventions have been ludicrous: internet-connected teapots, or an app that sold people coins to use at laundromats. But others are world-beaters: microprocessor chips, databases and smartphones all trace their lineage to the Valley.

Next, a bit of a detour, to the founding of the venture capital industry, on the east coast:

The first modern venture capital firm was formed in 1946, when MIT president Karl Compton, Massachusetts Investors Trust chairman Merrill Griswold, Federal Reserve Bank of Boston president Ralph Flanders, and Harvard Business School professor General Georges F. Doriot started American Research and Development (ARD) [Lample, 1989]. The goal of the company was to finance commercial applications of technologies that were developed during World War II.

Doriot was the heart and soul of ARD and is justifiably called the “father of venture capital.” Doriot’s focus was on adding value to companies, not just supplying money. Companies funded by ARD were considered to be “members of the family” [Sexton and Kasarda, 1991.] ARD’s staff under Doriot’s direction began providing industry expertise and management experience to the companies they backed in order to increase their chances of ultimate success.

Doriot served as ARD’s president until it was acquired by Textron in 1972. During the course of his tenure at ARD, Doriot’s vision was not one of “making money” but rather financing “noble” ideas. The first investment made by ARD in 1947 was in High Voltage Engineering Company. The firm, founded by several MIT professors, was established to develop X-ray technology in the treatment of cancer. ARD invested in the company for reasons noted by Compton’s comment to Doriot:

They [High Voltage Engineering Company] probably won’t ever make any money, but the ethics of the thing and the human qualities of treating cancer with X-rays are so outstanding that I’m sure it should be in your [Doriot’s] portfolio. [Lample, 1989]

When High Voltage went public in 1955, the original $200,000 investment was worth $1.8 million.

Just one more detour…

I recently finished AMC’s Halt and Catch Fire, which was the subject of one of the first pieces I wrote on this topic, back in 2014. Here’s what I wrote, comparing the show, which begins in the 80’s, to HBO’s satirical Silicon Valley, set in the present:

Why the contrast? Why do we increasingly glorify the tech industry’s past while mocking or dismissing its present? One answer is that history has a way of filtering out the also-rans and focusing on the greats. That may be part of the explanation, but there’s more to it.

Despite the current pace of technological change, it’s hard to shake the feeling that today’s new products and services are somehow smaller than the innovation we saw 20 or 30 years ago. The companies have fewer employees and rarely push the boundaries of basic or even applied science. The ideas that get hatched and funded are apps masquerading as platforms, platforms masquerading as breakthroughs.

Whatever we think of today’s tech companies and however much we believe the industry needs to be reined in or reformed, we must continue to incentivize and celebrate the development of new technologies — something that historically the U.S. and, most of all, Silicon Valley has excelled at. That isn’t a little thing, or an afterthought. It’s arguably one of the most important ingredients for economic success. Doing this doesn’t mean just blindly lionizing entrepreneurs, or obsessing over every VC fundraising. It doesn’t even have to revolve so heavily around VC-backed startups; that’s been a very successful model, but one among many. What it means is focusing on the job the tech companies are supposed to be doing: turning cutting-edge technologies into useful products and services that make society better off. That’s what Halt and Catch Fire was about, and that’s what ARD apparently was about. That’s the history of the modern tech industry. It ought to be its future, too.

Getting that right isn’t trivial, and right now the U.S. is largely just coasting off path dependence. We were good at innovation before, so odds are we’ll keep being pretty good at it. Over time, though, culture, incentives, and rules change or decay. We need to think about what we want from technology and actively create the sort of society that can deliver it. That requires some level of trust between the people in tech and the rest of us.

Two stories about competition and market power

Here’s an interesting interview with Elizabeth Warren:

Foer: There are all these hints of Louis Brandeis in what you do. Brandeis had a vision of how the economy could be structured differently when the rules that he wanted were applied. He  favored the small shopkeeper. In your vision, who gets favored? Are there forces in the market that you feel like are being unfairly shackled that you want to see unleashed?

Warren: Yes. Perfect. Competition. I love competition. I want to see every start-up business, everybody who’s got a good idea, have a chance to get in the market and try. This is what’s so interesting to me. There are so many people right now who argue against these reforms and other reforms, who claim they are pro-business. They’re not. They’re pro-monopoly. They’re pro–concentration of power, which crushes competition.

This is where the political and the economic interact. Once a corporation climbs up the ladder so that it’s got hundreds of millions—no, so that it’s got billions of dollars in resources—today too many of them turn around and use those resources to influence government to cut off that ladder, so nobody else climbs it. To cut off that ladder so that the big guys don’t have to compete with the little guys anymore.

Vox’s Weeds podcast had a good episode on Warren’s corporate governance plan that included analysis of this side of Warren — the fact that she thinks about how markets should work more than many progressives, rather than focusing just on what government should be doing. Foer has also covered this before in The Atlantic. I quote the relevant passage here.

But I want to compare Warren’s quote to the conclusion of a review paper by MIT’s John Van Reenen, who summed up the evidence on market power recently at the central bankers’ Jackson Hole meeting. Van Reenen:

Increased concentration brings with it the concern of market power and indeed, some have argued that many of the economic ills we face today in terms of sluggish productivity and real wage growth are due to rising monopoly power. My view is that this conclusion is premature. Rising aggregate markups and concentration may also reflect changes in the nature of competition where superstar firms are rewarded with greater market share in “winner take most” markets. I have offered some evidence more in line with the nuanced superstar firm model than a general fall in competition due to anti-trust and regulation. But this is for sure not the final paper in this area, however, and there are substantial uncertainties.

A final word of warning. Even if it was the case that the world is closer to the superstar firm model, this does not mean that anti-trust policy should be relaxed. Even if superstar firms attain their currently dominant positions on their merits of out-competing rivals, it does not mean that they will always use their power for the good of consumers. They may well try to entrench their position through lobbying, erecting entry barriers and buying up future rivals. As larger parts of the modern economy become winner take most/all, it is important that competition authorities develop better tools for understanding harm to innovation and future competition, rather than the traditional emphasis on the pricing decisions of current rivals.

In my view, as someone following this evidence closely, Van Reenen nails it. He takes seriously the idea that market power has risen as a result of rent-seeking and anticompetitive behavior. And he takes seriously the alternative that technology and globalization have changed competition in ways that made some firms bigger and more productive. He notes that while the latter may sound more optimistic than the former (and probably is less harmful), it’s hardly benign.

Returning to Warren… it’s tempting to frame her view as belonging to the Jefferson-Hamilton debate that’s been going on since America’s founding, in which one side prefers industry and is fine with bigness, while the other prioritizes the little guy. That may be one productive way to think about it.

But I prefer to think of it this way: two things have happened in the U.S. economy over the past 30 or so years. First, information technology has dramatically changed the nature of the economy, of firms, and of how they compete. Second, corporations have become more powerful for a whole variety of reasons, and along the way become better able to shape competition in their favor. In some ways these are separate trends; we could have had one without the other. But to an extent they’re related. As Van Reenen notes, concerns that grow large due to technology can then turn their power toward rigging the game, what Zingales calls the “Medici Cycle.” Just as important, firms that are large but aren’t great at technology and so are threatened by digital competitors may up their lobbying and other rent-seeking activities in order to “compete”. See: massive consolidation in the media business in response to Netflix. If you can’t beat ’em, the theory goes, get bigger.

It’s not just that Warren is stepping onto the scene and offering a Jeffersonian view. It’s that she realizes the rise of corporate power over decades has had pernicious effects in the form of rent-seeking and anticompetitive behavior. An agenda that seeks to limit that power would likely do tremendous good. However, it’s important that advocates of this view recognize that it’s only half the story. There’s another major reason why big firms have gotten bigger, why certain firms pay better than others, why workers have less bargaining power, etc. That’s the role of technology. It doesn’t invalidate the other theory. But it calls for different remedies, and so it’s important to simultaneously keep both accounts in mind.

Crowdsourced priors, aka prices

A crowd of experts can forecast future research results; by some measures a crowd of non-experts can, too. (At least, when the crowd results are properly aggregated.) I posted about that result a while back, now here’s new work on replication and prediction markets. Ed Yong at The Atlantic, via Tyler Cowen:

Consider the new results from the Social Sciences Replication Project, in which 24 researchers attempted to replicate social-science studies published between 2010 and 2015 in Nature and Science—the world’s top two scientific journals. The replicators ran much bigger versions of the original studies, recruiting around five times as many volunteers as before. They did all their work in the open, and ran their plans past the teams behind the original experiments. And ultimately, they could only reproduce the results of 13 out of 21 studies—62 percent.

As it turned out, that finding was entirely predictable. While the SSRP team was doing their experimental re-runs, they also ran a “prediction market”—a stock exchange in which volunteers could buy or sell “shares” in the 21 studies, based on how reproducible they seemed. They recruited 206 volunteers—a mix of psychologists and economists, students and professors, none of whom were involved in the SSRP itself. Each started with $100 and could earn more by correctly betting on studies that eventually panned out.

At the start of the market, shares for every study cost $0.50 each. As trading continued, those prices soared and dipped depending on the traders’ activities. And after two weeks, the final price reflected the traders’ collective view on the odds that each study would successfully replicate. So, for example, a stock price of $0.87 would mean a study had an 87 percent chance of replicating. Overall, the traders thought that studies in the market would replicate 63 percent of the time—a figure that was uncannily close to the actual 62-percent success rate.

The traders’ instincts were also unfailingly sound when it came to individual studies. Look at the graph below. The market assigned higher odds of success for the 13 studies that were successfully replicated than the eight that weren’t—compare the blue diamonds to the yellow diamonds.