The 1970s Crime Wave

Tyler and I wrap up our series of podcasts on the 1970s with The 1970s Crime Wave. Here’s one bit:

TABARROK: …people think that mass incarceration is a peculiarly American phenomena, or that it came out of nowhere, or was due solely to racism. Michelle Alexander’s, The New Jim Crow, takes his view. In fact, the United States was not a mass incarceration society in the 1960s.

It became one in the 1980s and 1990s due to the crime wave of the 1970s. It was not simply due to racism. It is true Blacks do commit more crimes relative to their population than whites, but Blacks are also overrepresented as victims. The simple fact of the matter is that Black victims of crime, the majority group, demanded more incarceration of Black criminals. In 1973, the NAACP demanded that the government lengthen minimum prison terms for muggers, pushers, and first-degree murders.

The Black newspaper, the Amsterdam News, advocated mandatory life sentences for “the non-addict drug pusher of hard drugs.” The Black columnist, Carl Rowan, wrote that “locking up thugs is not vindictive.” Eric Holder, under Obama, he was the secretary of—

COWEN: Of something.

TABARROK: Yes, of something. He called for stop and frisk. Eric Holder called for stop and frisk. Back then, the criminal justice system was also called racist, but the racism that people were pointing to was that Black criminals were let back on the streets to terrorize Black victims, and that Black criminals were given sentences which were too light. That was the criticism back then. It was Black and white victims together who drove the punishment of criminals. I think this actually tells you about two falsehoods. First, the primary driver of mass imprisonment was not racism. It was violent crime.

Second, this also puts the lie, sometimes you hear from conservatives, to this idea that Black leaders don’t care about Black-on-Black crime. That’s a lie. Many Black leaders have been, and were, and are tough on crime. Now, it’s true, as crime began to fall in the 1990s, many Blacks and whites began to have misgivings about mass incarceration. Crime was a huge problem in the 1970s and 1980s, and it hit the United States like a brick. It seemed to come out of nowhere. You can’t blame people for seeking solutions, even if the solutions come with their own problems.

A lot of amazing stuff in this episode. Here’s our Marginal Revolution Podcast 1970s trilogy

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Info Finance

Excellent post by Vitalik on prediction markets and the broader category of what he calls info finance:

Now, we get to the important part: predicting the election is just the first app. The broader concept is that you can use finance as a way to align incentives in order to provide viewers with valuable information.

…Similar to the concept of correct-by-construction in software engineering, info finance is a discipline where you (i) start from a fact that you want to know, and then (ii) deliberately design a market to optimally elicit that information from market participants.

Info finance as a three-sided market: bettors make predictions, readers read predictions. The market outputs predictions about the future as a public good (because that’s what it was designed to do).

One example of this is prediction markets: you want to know a specific fact that will take place in the future, and so you set up a market for people to bet on that fact. Another example is decision markets: you want to know whether decision A or decision B will produce a better outcome according to some metric M. To achieve this, you set up conditional markets: you ask people to bet on (i) which decision will be chosen, (ii) value of M if decision A is chosen, otherwise zero, (iii) value of M if decision B is chosen, otherwise zero. Given these three variables, you can figure out if the market thinks decision A or decision B is more bullish for the value of M.

Importantly, Vitalik notes that AI agents can make decision and prediction markets more liquid at much lower cost.

One technology that I expect will turbocharge info finance in the next decade is AI (whether LLMs or some future technology). This is because many of the most interesting applications of info finance are on “micro” questions: millions of mini-markets for decisions that individually have relatively low consequence. In practice, markets with low volume often do not work effectively: it does not make sense for a sophisticated participant to spend the time to make a detailed analysis just for the sake of a few hundred dollars of profit, and many have even argued that without subsidies such markets won’t work at all because on all but the most large and sensational questions, there are not enough naive traders for sophisticated traders to take profit from. AI changes that equation completely, and means that we could potentially get reasonably high-quality info elicited even on markets with $10 of volume. Even if subsidies are required, the size of the subsidy per question becomes extremely affordable.

China’s Libertarian Medical City

You’ve likely heard of Prospera, the private city in Honduras established under the ZEDE (Zone for Employment and Economic Development) law, which has drawn global investment for medical innovation. The current Honduran government is trying to break its contracts and evict Prospera from Honduras. The libertarian concept of an autonomous medical hub, free to attract top talent, pharmaceuticals, medical devices, ideas, and technology from around the world is, however, gaining traction elsewhere—most notably and perhaps surprisngly in the Boao Hope Lecheng Medical Tourism Pilot Zone in Hainan, China.

Boao Hope City is a special medical zone supported by the local and national governments. Treatments in Boao Hope City do not have to be approved by the Chinese medical authorities as Boao Hope City is following the peer approval model I have long argued for:

Daxue: Medical institutions within the zone can import and use pharmaceuticals and medical devices already available in other countries as clinically urgent items before obtaining approval in China. This allows domestic patients to access innovative treatments without the need to travel abroad…. The medical products to be used in the pilot zone must possess a CE mark, an FDA license, or PMDA approval, which respectively indicate that they have been approved in the European Union, the US, and Japan for their safe and effective use.

Moreover, evidence on the new drugs and devices used within the zone can be used to support approval from the Chinese FDA–this seems to work similar to Bartley Madden’s dual track procedure.

Daxue: Since 2020, the National Medical Products Administration has introduced regulations on real-world evidence (RWE), with the pilot zone being the exclusive RWE pilot in China. This means that clinical data from licensed items used within the zone can be transformed into RWE for registration and approval in China. Consequently, medical institutions in the zone possess added leverage in negotiations with international pharmaceutical and medical device manufacturers seeking to enter the Chinese market.

… This process significantly reduces the time required for approval to just a few months, saving businesses three to five years compared to traditional registration methods. As of March 2024, 30 medical devices and drugs have been through this process, among which 13 have obtained approval for being sold in China.

The zone also uses peer-approval for imports of health food, has eliminated tariffs on imported drugs and devices and waived visa requirements for many medical tourists

To be sure, it’s difficult to find information about Boao Hope medical zone beyond some news reports and press releases so take everything with a grain of salt. Nevertheless, the free city model is catching on. There are already 29 hospitals in the zone including international hospitals and hundreds of thousands of medical tourists a year. The medical zone is part of a larger free port project.

Prospera is ideally placed for a medical zone for North and South America. The Honduran government should look to China’s Boao Hope Medical Zone to see what Prospera could achieve for Honduras with support instead of oppositon.

Hat tip: MvH.

Prediction Markets for the Win

The prediction markets predicted the election outcome more accurately and more quickly than polls or other forecasting methods, just as expected from decades of research. In this election, however, many people discounted the prediction markets because of large trades on Polymarket. Paul Krugman, for example, wrote:

Never mind the prediction markets, which are thin and easily manipulated.

None of that was true but perhaps that was par for the course. Even some prediction market experts, however, began to wobble under the influence of “whale” manipulation theories. But this story was always shaky. What was the supposed logic?

Few directly articulated the theory—perhaps because it sounds absurd when spelled out. The idea seems to be that whales shifted market odds from 50:50 to 40:60, hoping this would drive more people to vote for Trump. Really? Were voters in Pennsylvania watching Polymarket to decide who to vote for? In a decision market, manipulation might be desirable to a whale (albeit unlikely to succeed), but in prediction markets, this scenario seems dubious: a) people would need to know about these markets, b) they’d need to care about probability shifts on these markets (as opposed to voting say the way their family and neighbors were voting), and c) this would have to be an effective way to spend money to influence votes compared to the myriad other ways of influencing voting. Each step seems dubious.

Alternatively, maybe whales were simply wasting money, “memeing” away millions of dollars? Is that something that whales do? The memeing theory is more plausible with many small traders, not a few whales. Or maybe the whales aimed to spark excitement among the minnows, hoping to build momentum before cashing out. However, exciting small traders to inflate prices and then exiting is risky; the same power that whales have to drive up prices can drive prices down just as quickly, making a profitable exit challenging. In short, while not impossible, the idea of whale-driven manipulation in prediction markets was far-fetched.

In fact, we now know that the biggest whale was moving the markets towards accuracy (against his own interest by the way). In an excellent WSJ article we learn:

The mystery trader known as the “Trump whale” is set to reap almost $50 million in profit after running the table on a series of bold bets tied to the presidential election.

Not only did he see Donald Trump winning the presidency, he wagered that Trump would win the popular vote—an outcome that many political observers saw as unlikely. “Théo,” as the trader called himself, also bet that Trump would win the “blue wall” swing states of Pennsylvania, Michigan and Wisconsin.

Now, Théo is set for a huge payday. He made his wagers on Polymarket, a crypto-based betting platform, using four anonymous accounts. Although he has declined to share his identity, he has been communicating with a Wall Street Journal reporter since an article on Oct. 18 drew attention to his bets.

In dozens of emails, Théo said his wager was essentially a bet against the accuracy of polling data. Describing himself as a wealthy Frenchman who had previously worked as a trader for several banks, he told the Journal that he began applying his mathematical know-how to analyze U.S. polls over the summer. 

Here’s the most remarkable bit. Theo commissioned his own polls using a different methodology!

Polls failed to account for the “shy Trump voter effect,” Théo said. Either Trump backers were reluctant to tell pollsters that they supported the former president, or they didn’t want to participate in polls, Théo wrote.

To solve this problem, Théo argued that pollsters should use what are known as neighbor polls that ask respondents which candidates they expect their neighbors to support. The idea is that people might not want to reveal their own preferences, but will indirectly reveal them when asked to guess who their neighbors plan to vote for.

…In an email, he told the Journal that he had commissioned his own surveys to measure the neighbor effect, using a major pollster whom he declined to name. The results, he wrote, “were mind blowing to the favor of Trump!”

Théo declined to share those surveys, saying his agreement with the pollster required him to keep the results private. But he argued that U.S. pollsters should use the neighbor method in future surveys to avoid another embarrassing miss.

Thus, a big win for prediction markets, for Polymarket and for GMU’s Robin Hanson, the father of prediction markets, whose work directly influenced the creation of Polymarket.

What is the Best-Case Scenario for a Trump Presidency?

The economy is strong and Trump has a significant opportunity to simply take credit for that if he avoids major disruptions. While he must fulfill some of his campaign promises, people voted for Trump not for his policies per se. Trump has leeway. No one will accuse him of flip-flopping. While these are not my first-best policies, Trump won against astounding media and elite opposition and an attempted assassination. The people have spoken, so here’s a best-case outline for following through on Trump’s policies without cratering the economy:

  1. Trade Policy: Moderate tariff increases on China. No Chinese electric cars for us. But drop the “tariffs on everything” language. He can always say his rhetoric was a threat to get other countries to lower their tariffs. Let’s instead talk tough against our enemies but shift toward “friend-shoring”, maintaining or even lowering tariffs with allied nations, such as Canada, Europe, and possibly India, as part of a broader strategy to contain China’s influence.
  2. Border Control: Trump must strengthen the border. But let’s limit deportations to individuals who arrived in the past four years. Control the border, throw some illegals out but minimize human misery by not deporting long-term residents and their US-citizen families. Declare a win while avoiding economic disruption and strengthening the police state.
  3. Vaccine and Health Policy: Appoint Robert F. Kennedy Jr. to head a committee on vaccine policy and, after several years of investigation, write a report. Take medical freedom more seriously.
  4. Crypto Regulation: Appoint Hester M. Peirce to head the SEC. Stabilize the regulatory environment for cryptocurrency. Simplify tax rules for crypto. Support digital dollar growth and treat stablecoins as what they are, namely, the US dollar dominating world electronic payments.
  5. Space and Innovation: U.S. Space Force! Commit to Mars exploration and position the U.S. as a leader in space innovation. Get advice from Elon.
  6. US AI. Immediately approve Meta for its nuclear-AI program. Swat the bees. Approve Amazon as well. Tell the FERC that their job is to increase the supply of energy. Keep the Chip Act but make it clear that the goal is to dominate the space not make jobs or social policy. We are the world leaders in AI. Let’s keep it that way.
  7. Kill Bureaucracy: Let Elon Musk take the chainsaw to a few bureaucracies like Javier Milei. Afuera! Afuera! Afuera! Streamline bureaucratic processes, cut red tape and invigorate tech and infrastructure initiatives.
  8. Respect Meritocracy: End race and gender based discrimination in government programs.
  9. Expand Housing Supply: Build baby build! Trump is a natural to lead this. Trump the developer! Incentivize states and localities to streamline zoning laws and reduce restrictions that hamper new housing developments. Increase housing supply.

Each of these policies is consistent with Trump’s priorities and rhetoric and has broad appeal for voters who value economic opportunity, accountability, and national resilience. The economy is strong. Trump has the wind at his back. If he is sensible, all of this would make for a successful presidency. If Trump wants the judgment of history, the path is open should he choose to walk it.

Increasing the Supply of Very High-IQ Workers

I have argued that there are on the order of just 164 thousand very high-IQ workers in the United States. How do we get more? Ian Calaway on the job market from Stanford has an interesting paper arguing that early math mentors can be a force multiplier for students with superior math abilities. Calaway estimates that having a math mentor at a school, someone who runs a math club and organizes entry into top math competitions, increases the number of students earning PhDs and pursing careers as scientists and professors. Not every school has such a math mentor but Calaway estimates (after taking into account underlying abilities, he’s not naive) that over 27 years, math mentors identified 9,092 American Math Competitions students (the cream of the crop) but there were 11,168 missing students of very high ability.

These 11,168 additional students represent the missing exceptional math talents who would have participated in the AMC and been identified as exceptional if they had access to a mentor…these mentors would have increased the number of these students attending selective universities (3,017 students), majoring in STEM (3,465 students), earning PhDs (1,652 students), and pursuing careers as scientists and professors (1,850 students) during this twenty-seven year period.

11,168 missing students of very high ability over 27 years may not sound like much but we are talking about the very top talent level. A footnote illustrates:

Sergey Brin (Google), Mark Zuckerberg (Meta), Peter Thiel (PayPal), and Sam Altman (OpenAI) were all top AMC scorers (Committee on the American Mathematics
Competitions, 1980–2023)

High-IQ individuals don’t simply vanish without mentorship; they likely still have decent careers. However, even if you are skeptical about the social value of earning a PhD, the number of mentored individuals who go on to start firms or earn patents appears substantial. Just as athletic talent can wither without guidance, it seems that intellectual talent may also be underutilized without proper mentorship, with many high-IQ individuals failing to reach their full potential.

The Immigration Rap Battle

From the team that brought you Hayek v. Keynes we have the immigration rap battle featuring “George Borjas,” “Garett Jones” and “Stephen Miller” on team build the wall and “Bryan Caplan” and “Alex Nowrasteh” on open the border. I wouldn’t say the actors (AI?), look very much like their real world counterparts but much respect to the author of the rap lyrics who has brilliantly captured the essence of the ideas economically and thematically.

Sugar Babies

Science: We examined the impact of sugar exposure within 1000 days since conception on diabetes and hypertension, leveraging quasi-experimental variation from the end of the United Kingdom’s sugar rationing in September 1953. Rationing restricted sugar intake to levels within current dietary guidelines, yet consumption nearly doubled immediately post-rationing. Using an event study design with UK Biobank data comparing adults conceived just before or after rationing ended, we found that early-life rationing reduced diabetes and hypertension risk by about 35% and 20%, respectively, and delayed disease onset by 4 and 2 years. Protection was evident with in-utero exposure and increased with postnatal sugar restriction, especially after six months when solid foods likely began. In-utero sugar rationing alone accounted for about one third of the risk reduction.

Pregnant women might want to ration their sugar intake, as well as alcohol, during pregnancy.

Hat tip: Kevin Lewis.

Principles of Economics Textbooks and the Market for Ice Cream

Rey Hernández-Julián and Frank Limehouse writing in the Journal of Economics Teaching write that very few principles of economics textbooks deal with modern information and digital tech industries:

The main takeaways of our review are highlighted by two stand-alone textboxes found in Mankiw’s (2023) textbook. This textbook has been regarded as one of the most dominant players in the principles of economics textbook market for over 20 years. In the introductory chapter of the 10th Edition (2023), “Ten Principles of Economics” there is a stand-alone textbox with the Netflix logo with the following caption: “Many movie streaming services set the marginal cost of a movie equal to zero”. However, there is no further explanation of this statement in the chapter and no presentation of the concept of zero marginal cost pricing in the remainder of the entire textbook. In Chapter 2 (“Thinking Like an Economist”), there is an In the News article from the New York Times, “Why Tech Companies Hire Economists”, but very little coverage in the text on how to apply microeconomic concepts to the tech industry. These two discussions of the tech industry in Mankiw’s text exemplify many of our findings from other texts….updated examples from the modern economy seem to be afterthoughts and detached from the central discussion of the text.

…There are some notable exceptions. The most significant coverage of these questions is in Chapter 16 of Cowen and Tabarrok’s Modern Principles of Microeconomics, 5th edition (2021). In this chapter, the authors discuss platform service providers, such as Facebook, Amazon, Google, Visa, and Uber, and the role they play in competing “for the market,” instead of “in the market.” They also discuss why the prevailing product is not necessarily the best one, how music is a network good, and why these platform services may give away goods for ‘free’.

I would also point out that our example of a constant-cost industry (flat long-run supply curve) is domain name registration! As we write in Modern Principles:

Now consider what happens when the demand for domain names increases. In 2005, there were more than 60 million domain names. Just one year later, as the Internet exploded in popularity, there were more than 100 million domain names. If the demand for oil nearly doubled, the price of oil would rise dramatically, but despite nearly doubling in size, the price of registering a domain name did not increase…the expansion of old firms and the entry of new firms quickly pushed the price back down to average cost.

In short, it’s called Modern Principles for a reason! Tyler and I are committed to keeping up with the times and not just adding the occasional box and resting on our laurels.

See Hernández-Julián and Limehouse for some further examples of how to introduce modern industries into principles of economics.

The MR Podcast–Oil Shocks, Price Controls and War

Our second podcast on the 1970s titled Oil Shocks, Price Controls and War is now available! Here’s one bit:

Tabarrok: …Sheikh Ahmed Yamani, in a famous statement, he was the oil minister for the Kingdom of Saudi Arabia, he’s a leader of OPEC, he says on October 16th, this is 10 days after the war begins, “This is a moment for which I have been waiting for a long time. The moment has come. We are masters of our own commodity.” They raise the price of oil. Oil production falls by about 9 percent to 10 percent. That doesn’t seem on the surface to be a huge amount, but it reveals something which people had not been prepared for, and that was the inelasticity of oil demand.

I would put it this way. I think this is the key idea here. Almost accidentally, the exporting countries had discovered that the demand for oil was more inelastic than anyone had ever realized. The main lesson they drew before 1973, the oil exporting countries thought that the only way to increase revenues was to produce more. After 1973, they learned that an even better way to increase revenues was to produce less.

Here’s another:

COWEN: Since the 1980s, economists, for a number of reasons, have underrated real shocks as a source of business cycles and downturns. You have the Keynesians who didn’t want to talk about it, and then you had the Monetarists, Milton Friedman, who wanted to promote their own recipe, and people just stopped talking about it. Even 2008, which clearly had a lot to do with a major negative shock to aggregate demand, but the price of oil is quite high at the time when that’s breaking, and it was a major factor behind the downturn.

TABARROK: Absolutely.

COWEN: No one wants to talk about that.

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Iranian Kidney Donors

Iran is one of the few countries in the world to have eliminated the shortage of kidneys. A useful new paper looks at what donors are like,

First some background:

The adoption of a regulated market mechanism for kidney procurement in Iran started in 1988 in the absence of sufficient posthumous donations (Ghods and Savaj, 2006). The mechanism allows living unrelated Iranian individuals to donate kidneys to Iranian patients with end stage renal disease (ESRD) for financial gains. The program was successful in eliminating the renal transplant waiting list within a decade of its implementation (Mahdavi-Mazdeh, 2012). In addition, the Organ Transplant Act legalized brain-stem death donations in 2000. Both ESRD patients and potential kidney donors are referred to and registered with The Association for Supporting Renal Patients, a non-profit organization (NGO) which conducts a primary medical evaluation and facilitates the market exchange. Upon successful completion of the test, a formal consent is acquired and the potential donor and the recipient are introduced to each other. At this stage both the patient and the donor are referred to a nephrologist for further evaluation, cross-match, and angiography. If the patient-donor pair is compatible, in the next step the pair negotiate the terms and conditions of the exchange. All terms within the price-cap are guaranteed and enforceable by the NGO. The price-cap is frequently adjusted for inflation and during the course of our study was set at 180 million Iranian Rial (US$4700 in August 2017). However, the negotiation is private and the pair can agree any terms they wish. The donor also receives a “gift of altruism” and 1 year of insurance from the government through the Charity Foundation for Special Diseases. Transplant surgery is carried out free of charge in public university hospitals. The Iranian Ministry of Health and Medical Education introduced further procedural changes in July 2019. In particular, they established a center for organ transplant and procurement at the ministry which acts as the matching centre and provides oversight and overall control of the process.

Are the donors irrational, risk-loving, impatient? No, they are normal people making the best of sometimes limited opportunities:

The overall picture is of individuals who were in financial need, often unemployed but with a family to support and where alternatives sources of financial support were grim. However, despite their financial position, these individuals were typically patient and not especially prone to risk-taking. They were no less rational than the average, but those who ended up completing the process might be characterized as more altruistic than those who did not….More broadly our findings indicate that even in situations of extreme poverty we should not assume lower levels of rationality will be pervasive.

Given that donation saves lives and that kidney donation is not especially risky (much less risky than driving a motorcycle, for example) the tradeoff seems positive and well within ordinary bounds.

Turning to the US, here is Sally Satel on a proposed tax credit for kidney donation:

What if we could solve the organ donor shortage with a simple tax credit? That is the idea behind the End Kidney Deaths Act (EKDA) (HR 9275).

The bill, advanced by the Coalition to Modify NOTA (NOTA stands for the National Organ Transplant Act passed in 1984) would provide a $50,000 refundable tax credit—$10,000 per year for five years—to any living donor who gave a kidney to the next person on the waiting list. The tax credit would be a 10-year pilot program.

The credit would save 10,000 to perhaps as many as 100,000 lives over ten years.

FYI, I am a supporter of Modify NOTA (along with Al Roth, Steve Levitt, and Mario Macis, to name just a few of the economists, joined by surgeons, nephrologists and others).

Hat tip: Kevin Lewis.

Acemoglu, Johnson and Robinson Win Nobel Prize for Institutions and Prosperity

The Nobel prize goes to Daron Acemoglu, Simon Johnson and James Robinson for their work on institutions, prosperity, and economic growth. Here is a key piece summarizing their work: Institutions as a Fundamental Cause of Long-Run Growth.

This paper develops the empirical and theoretical case that differences in economic institutions are the fundamental cause of differences in economic development. We first document the empirical importance of institutions by focusing on two “quasi-natural experiments” in history, the division of Korea into two parts with very different economic institutions and the colonization of much of the world by European powers starting in the fifteenth century. We then develop the basic outline of a framework for thinking about why economic institutions differ across countries. Economic institutions determine the incentives of and the constraints on economic actors, and shape economic outcomes. As such, they are social decisions, chosen for their consequences. Because different groups and individuals typically benefit from different economic institutions, there is generally a conflict over these social choices, ultimately resolved in favor of groups with greater political power. The distribution of political power in society is in turn determined by political institutions and the distribution of resources. Political institutions allocate de jure political power, while groups with greater economic might typically possess greater de facto political power…Economic institutions encouraging economic growth emerge when political institutions allocate power to groups with interests in broad-based property rights enforcement, when they create effective constraints on power-holders, and when there are relatively few rents to be captured by power-holders.

See this great MRU video on Institutions for a quick overview! Here from an interview with Acemoglu, is a slightly more pointed perspective. Politics keeps people poor:

Why is it that certain different types of institutions stick?….it wouldn’t make sense, in terms of economic growth, to have a set of institutions that ban private property or create private property that is highly insecure, where I can encroach on your rights. But politically, it might make a lot of sense.

If I have the political power, and I’m afraid of you becoming rich and challenging me politically, then it makes a lot of sense for me to create a set of institutions that don’t give you secure property rights. If I’m afraid of you starting new businesses and attracting my workers away from me, it makes a lot of sense for me to regulate you in such a way that it totally kills your ability to grow or undertake innovations.

So, if I am really afraid of losing political power to you, that really brings me to the politics of institutions, where the logic is not so much the economic consequences, but the political consequences. This means that, say, when considering some reform, what most politicians and powerful elites in society really care about is not whether this reform will make the population at large better off, but whether it will make it easier or harder for them to cling to power.

Those are the sort of issues that become first-order if you want to understand how these things work.

One interesting aspect of this year’s Nobel is that almost all of AJRs Nobel work is accessible to the public because it has come primarily through popular books rather than papers. The Economic Origins of Dictatorship and Democracy, Why Nations Fail, and the The Narrow Corridor all by Acemoglu and Robinson and Power and Progress by Acemoglu and Johnson are all very readable books aimed squarely at the general public. The books are in many ways deeper and more subtle than the academic work which might have triggered the broader ideas (such as the famous Settler Mortality paper). Many of the key papers such as Reversal of Fortune are also very readable.

This is not to say that the authors have not also made many technical contributions to economics, most especially Acemoglu. I think of Daron Acemoglu (GS) as the Wilt Chamberlin of economics, an absolute monster of productivity who racks up the papers and the citations at nearly unprecedented rates. According to Google Scholar he has 247,440 citations and an H-index of 175, which means 175 papers each with more than 175 citations. Pause on that for a moment. Daron got his PhD in 1992 so that’s over 5 papers per year which would be tremendous by itself–but we are talking 5 path-breaking, highly-cited papers per year plus many others! (Of course, most written with excellent co-authors). In addition, he’s the author of a massive textbook on economic growthMore than any other economist Daron has pushed the cutting-edge of technical economics and has also written books of deep scholarship still accessible to the public. In his overview of Daron’s work for the John Bates Clark medal Robert Shimer wrote “he can write faster than I can digest his research.” I believe that is true for the profession as a whole. We are all catching-up to Daron Acemoglu.

Indeed, in reading a book like Why Nations Fail and papers like The Network Origins of Aggregate Fluctuations (one of my favorite Acemoglu papers) and The Uniqueness of Solutions for Nonlinear and Mixed Complementarity Problems it’s difficult to believe they are co-authored by the same person. Acemoglu is as comfortable talking history, politics, and political economy as he is talking about the economics of recessions and abstruse mathematics.

Here are Previous MR posts on Daron Acemoglu including this post on democracy where I find the effect of democracy on growth to be ho-hum. Here is Maxwell Tabarrok on Acemoglu on AI. Here is Conversations with Tyler with Acemoglu and a separate conversation with Simon Johnson.

As noted, one of my favorite Acemoglu papers (with Carvalho, Ozdaglar, and Tahbaz-Salehi) is The Network Origins of Aggregate Fluctuations. Conventional economics models the aggregate economy as if it were a single large firm. In fact, the economy is a network. An auto plants needs steel and oil to operate so fluctuations in the steel and oil industry will influence production in the auto industry. For a long time, the network nature of production has been ignored. In part because there are some situations in which a network can be modeled as if it were a single firm and in part because it’s just much easier to do the math that way. Acemoglu et al. show that aggregate fluctuations can be generated by sector fluctuations and that organization of the network cannot be ignored. This is a modern approach to real business cycles. See also my post on Gabaix and granular fluctuations).

In recent work, Acemoglu and Restrepo have created a new way of modeling production functions which divides work into tasks, some of which are better performed by capital and others by labor. Technological change is not simply about increasing the productivity of labor or capital (modeled in standard economics as making one laborer today worth two of yesterday’s) but about changing which tasks can best be done by capital and which by labor. As a task moves from labor to capital the demand for labor falls but productivity increases which generates demand for other kinds of labor. In addition, as capital replaces labor in some tasks entirely new tasks may be created for which labor has a comparative advantage. A number of interesting points come out of this including the idea that what we have to fear most is not super-robots but mediocre-robots. A super-robot replaces labor but has an immense productivity advantage which generates wealth and demand for labor elsewhere. A mediocre-robot replaces the same labor but doesn’t have a huge productivity advantage. In an empirical breakdown, Acemoglu and Restrepo suggest that what has happened in the 1990s and especially since 2000 is mediocre-robots. As a result, there has been a decline in labor on net. Thus, Acemoglu is more negative than many economists on automation, at least as it has occurred recently. Acemoglu and Restrepo is some of the best recent work going beyond the old tired debates to reformulate how we think of production and to use that reformulation to tie those reformulations to what is actually happening in the economy.

Solow thought of technical change as exogenous which is still the first-pass approach to thinking about technical change. Acemoglu in contrast focuses on price and market size. In particular, the larger the market the greater the incentive to invest in R&D to serve that market (see also my TED talk). Thus, technical change will tend to be cumulative. A sector with a productivity improvement will grow which can make that sector even more remunerative for further technical advances (depending on elasticities). This matters a lot for environmental change because it suggests that a relative small intervention today–including subsidizing research on clean technologies–can have a huge payoff in the future because by directing technical change in the right direction you make it easier to switch later on. (from this interview)

But let’s think of the logic of directed technical change with cumulative research. The less we do on green technology today, the less knowledge is accumulated in the green sector, so the bigger is the gap between fossil-fuel-based technology and energy, and the cleaner energy, so the harder it will be in the future to close that gap. With more proactive, decisive action today, we already start closing the gap, and we’re making it easier to deal with the problem in the future.

Simon Johnson has also written important books on banking and finance including White House Burning: Our National Debt and Why It Matters to You and that was before the big run up in American debt! James Robinson has written widely on African development and colonialism and African development more generally.

Overall, I’d say that this is an award for political science and for popular economics in the very best sense of economics that matters. Go buy their books and read them!

The Missing Dockworkers

One of the most amazing facts to come out of the dockworker strike (now resolved, it seems):

WSJ: Start with the astounding fact that there were 50,000 or so ILA strikers but only 25,000 or so port jobs. That’s right, only about half of the union’s members are obliged to show up to work each day. The rest sit at home collecting “container royalties” negotiated in previous ILA contracts intended to protect against job losses that result from innovation.

Hat tip: Scott Lincicome.