In the 11 months since that April first draft, there has been an explosion of new academic studies on remote work. The following figure charts the annual number of economics working papers in the REPEC depository that include the phrase “remote work” in the title or abstract.

What can we learn from all this new work? After diving into this new literature, here are six lessons I am taking away.

(Aside: if you want to dive in yourself, I recommend starting with Barrero, Bloom and Davis 2020 and Microsoft’s The New Future of Work. Both contain recent, complementary, literature reviews. You’ll see them quoted more than any other study below)

1. Pre-covid studies of remote work have held up

Prior to covid-19, there were a handful of economic studies on remote work. These studies’ conclusions have held up quite well.

First, before covid-19, quasiexperimental and actually experimental research on a life sciences company, call center operators, and patent examiners all found remote work to be more productive than being in the office. Other approaches found more mixed results: firms that switched to offering remote work capabilities experienced small negative productivity effects on average, but with lots of variation across types of firms. Some firms saw positive effects, many saw no change, and some saw adverse effects.

A variety of evidence from 2020 validates these findings. Barrero, Bloom and Davis (2020) report on the results of a large survey (10,000+ US respondents) in August through November that asks about self-reported efficiency of working from home. Fully 41% of respondents think they are more efficient working from home, and 44% think they are about the same. These kinds of results are broadly consistent with many other surveys that find the majority of workers found the same or higher productivity during the forced transition to remote work.

These self-reported assessments are consistent with the smaller number of studies with objective measures of productivity as well. Emmanuel and Harrington (2020) finds calls answered per hour rose by 8–10% when call center operators switched to remote work due to covid-19, relative to workers who were already working remotely and thus unaffected by the change. Notably, this is quite close to Bloom et al. (2015)’s findings that productivity in call centers rose by 13% when workers switched to working from home. At Microsoft, the number of weekly code pull requests shows no significant change around weeks 13–18, when lockdowns were rolling out across the world, as compared to the previous year. Neither did Microsoft observe a decline in other quantitative measures of software engineer productivity (though 38% of software engineers felt less productive).

A second finding from the pre-covid literature was that — on average — workers enjoyed the opportunity to work remotely. Mas and Pallais (2017) ran an experiment where they offered various versions of call center jobs to applicants — some with the option to work remotely, and some not — and also varied the wages for these hypothetical jobs. On average, they found workers were willing to accept an 8% lower wage for the opportunity to work remotely. The large survey by Barrero, Bloom, and Davis largely confirms this finding too. On average, respondents value the opportunity to continue working from home after the pandemic at about 7.6% of wages.

On the other hand, Bloom et al. (2015) found that, after their 9-month experiment with working from home 4-days per week, half of participants chose to go back to their office and only 35% of staff who initially expressed a desire to work remotely ended up taking up the opportunity. Interviews suggested remote workers felt lonely being out of the office for so long. This matches the kinds of concerns that are raised in internal studies of Microsoft employees working remotely during covid-19: one study found “missing social interactions” was one of the most frequent challenges reported.

Moreover, while workers value the option to work remotely, the majority of them still want to go into the office some of the time. The following figure breaks down remote workers preferred number of days to be working remotely: nearly 50% prefer a hybrid option, compared to 27% who want to be fully remote, and 23% fully in the office.

Source: Barrero, Bloom, Davis 2020

2. Pre-covid beliefs about remote work were wrong but hard to correct

That said, if remote work is so productive, it is something of a puzzle why it was so rarely used prior to covid-19. Research on the covid-19 pivot to remote work provides some answers. Most importantly, perceptions about the efficacy of remote work were way off. Ozimek (2020) reports on an April survey of hiring managers that finds many more were underestimating the potential or remote work than were overestimating it.

Barrero, Bloom and Davis (2020)’s survey of workers later in the pandemic also finds consistent misperceptions about how well remote work would work: 19% said working from home was “hugely better” than expected, and 61% said it was better. Only 12.7% thought working from home was worse than they had expected.

So one reason remote work was not more common was that views on it were simply incorrect. But why did these beliefs persist?

First, correcting these beliefs by experimenting with remote work was not easy. Barrerro, Bloom, and Davis (2020) estimate the costs of adapting to remote work during the pandemic added up to about 14 hours of training per person, and $660 in new equipment investments per person. Moreover, a consistent theme across many internal studies of remote work at Microsoft was that experienced remote workers adapted with less disruption during the pandemic than those who were new to working remotely. Together, this means it is both expensive for a company to “try out” remote work, and that the full benefits of doing so take awhile to be clear.

Second, a study by Emmanuel and Harrington shows how, prior to covid-19, in at least one context less productive workers gravitated to remote work. This created a perception that remote work was inherently less productive, when the reverse was actually true. As discussed above, when covid-19 forced everyone in a call center to work remotely, Emmanuel and Harrington find the productivity of workers increased. They also show that, prior to covid-19, when the company offered on-site workers the option to work remotely, those who made the switch became more productive than they had been at the office. But, because the least productive call center workers were the ones who seized the opportunity to work remotely, on average remote workers were less productive than on site ones prior to covid-19. It’s just that remote workers would have been even less productive if they had been on site as well.

If these findings generalize beyond call centers, then it’s easy to tell a story where this kind of dynamic can be self-perpetuating. Suppose everyone believes remote work is unproductive and also believes this view is widely shared. In such a context, workers who want to climb the career ladder will tend to avoid remote work, to avoid the stigma of being lower productivity. This will ensure remote work is disproportionately performed by more complacent workers. This, in turn, might ensure that remote workers are — on average — less productive and reinforce the belief.

However, it seems covid-19 has broken this belief. As noted above, it’s no longer the case that most people believe remote work is less productive (surveys suggest nearly 90% think it’s as productive or more productive). Moreover, Barrerro, Bloom, and Davis also show workers believe the improved perception of remote work is now widely shared.

3. Concerns about social interaction and serendipity remain

While productivity has been, on average, unchanged or even improved by the shift to remote work, there are some signs that maintaining productivity and innovation in the long run faces additional challenges. One of the critiques of fully remote work has always been that it deprives workers of the social environment of the office which allows for clearer communication, the growth and maintenance of social networks, and serendipity.

Several studies have documented how firms have adapted to maintain social ties in the absence of physical colocation. DeFilipis et al. (2020) document a shift towards more frequent, shorter virtual meetings with more people in the 8 weeks following the onset of lockdowns, perhaps as a substitute for hallway conversations and popping into offices for a quick chat. Espin and Rojas (2021) document a similar shift in the use of remote meeting technologies following covid-19 lockdowns (and also document a marked decrease in the audiovisual quality of these meetings as they are conducted via residential internet connections). Oz and Crooks (2020) provide evidence via a small sample study that workers substitute instant messaging for face-to-face contact.

Source: DeFilipis et al. (2020)

These alternatives for maintaining contact seem to have been effective at preserving pre-existing relationships among coworkers. An internal study by Microsoft tracking email, instant message, and phone calls of 92,000 employees both before and after lockdown found no decline in daily contact, and an increase in the size of the network of people contacted.

But all these forms of communication are purposeful; they require one party to purposefully arrange a contact with another. That makes them less useful for new workers who have not yet built a network of coworkers. A study of new employees at Microsoft Azure found less social connection (and lower productivity) than is typical of new hires in the office. More generally, all surveyed workers worried they were losing connection with “weak ties.”

Microsoft internal surveys also found remote collaboration technology was less suited for some kinds of work. Software engineers missed gathering around a whiteboard (a complaint echoed by an earlier study of distributed teams at Google), and found it harder to do big picture/creative work while fully remote. Others complained it was harder to plan, brainstorm, and change directions quickly in virtual meetings than had been the case on site.

4. Remote work will improve over time

At the same time, we also have plenty of indications that remote work will improve. The fact that remote work appears to be likely to persist (see next section) has created a large market opportunity for improved remote work technology. Bloom, Davis, and Zhestkova (2021) document that the share of US patent applications that include words associated with remote work expanded dramatically at the beginning of 2020, as the scope of the covid-19 crisis became clear.

Moreover, internal studies at Microsoft consistently find that problems associated with remote work are less common among experienced remote workers. The same studies also identify various processes and policies that mitigate remote work challenges. For example, at Microsoft a more formal system of check-ins and 1:1’s with a manager helped reduce problems associated with onboarding, and explicit efforts to build community and social ties was beneficial for new hires. With better technology coming, workers gaining experience, and best practices being discovered and diffused, the experience of full time remote work is likely to improve.

Perhaps most importantly, many challenges associated with remote work appear likely to subside once the pandemic ends. Social isolation due to reduced contact with work colleagues is not being fully offset by out-of-office socializing, given widespread social distancing. Moreover, the clear preference for hybrid work over fully in the office or fully remote (though sizable support for the latter also exists) may allow workers to strengthen weak social ties and use in-person meetings strategically to perform tasks less well suited for remote work (like gathering around a white board or settling on the direction of a new project).

5. Remote work will be more common after covid-19 subsides

When the covid-19 pandemic eventually subsides, it appears quite likely that remote work will become significantly more common. In an April survey of 1,500 hiring managers in Ozimek (2020), 62% of respondents planned to make their workforce more remote than before, as a result of covid-19, with 26% planning to make their workforce significantly more remote. A May 2020 survey of 1700 small business leaders found 41% of respondents planned to let more than 40% of their remote workers stay remote when covid-19 ends; 17% of small business leaders plan to let nearly all their remote workers stay remote.

Lastly, in Barrero, Bloom, and Davis (2020)’s large survey of US workers, they ask workers how much their employers plan to let them work remotely when covid-19 ends. They use the responses to estimate 22% of all workdays will be remote after covid-19 ends, compared to just 5% of workdays before. Note that this estimate is conservative, because it assumes all employees who have not heard from employers about the post-covid remote work policy will be 100% on site. Workers would prefer to work remotely on 44% of work days.

Survey responses that indicate wider use of remote work are also consistent with changes in property prices (though it is tricky to disentangle the effect of remote work from the effect of covid-19 here). Gupta et al. (2021) document that prices for new residential property has grown more quickly farther away from the city center than it has in the city center, during 2020. Indeed, in places like New York and San Francisco, there have actually been price declines in city centers in addition to price rises in the suburbs.

This reflects increased demand for living farther away from city centers. This data is adjusted for the quality of housing, so this does not simply reflect a desire for more space during social distancing. Moreover, Gupta et al. (2020) find this effect of rising suburban prices relative to city center prices is stronger in cities with a greater share of jobs that can be performed remotely. Interpreting suburban prices as a forecast of the desirability of living far from city centers for many years, this indicates a strong belief that the downsides of living far from the office are likely to be lower in the future than they have been in the past.

Gupta et al. (2020) is about relocating from the city center to the suburbs. In contrast, Brueckner, Khan, and Lin (2021) show (among other things) that you can see the same kind of thing happening across cities.

Cities with highly productive firms pay high wages, but in the era before widespread remote work, these workers needed to reside within commuting distance of the office, resulting in high housing prices. Brueckner, Khan, and Lin (2021) write a simple model to show that, given the opportunity to work from anywhere and keep their high productivity job, workers in these highly productive cities will tend to relocate to areas with lower house prices. This will tend to reduce housing prices in these high productivity cities where jobs can be done remotely (and raise prices in the cities being relocated to). They show that this is indeed what has happened since the onset of mass remote work. Using a sample of 792 different US counties, they show counties with high productivity workers and a high share of jobs that can be done remotely experienced slower growth in housing prices than otherwise similar counties.

6. The new equilibrium will have winners and losers, but will probably raise overall welfare

If remote work is here to stay, is that a good thing? With this major transition underway, it’s too early for the data to say. To think through the possible ramifications, we have to turn to models.

In 2020, economists have begun tweaking models of the spatial economy to incorporate new assumptions about the viability of remote work. Delventhal, Kwon, and Parkhomenko (2021) develops a model of the LA economy where workers vary in their ability to work remotely, and choose where to live and work (in LA). Another paper by Delventhal and Parkhomenko uses a similar model to understand the entire US economy, allowing workers to live and work in entirely different cities depending on whether or not their job can be performed remotely. Both models tie together a bunch of phenomena affected by decisions of where to live and work: housing prices, commuting times, quality of local amenities, and so on. Both then use actual data about all these things to calibrate their model, and then they see what happens when they change the model’s assumptions about the share of people who can work remotely.

Some of the predicted impacts are what you would expect. Consistent with the last section, on average, greater ability to work remotely leads people to move away from city centers to the suburbs, and away from larger cities to smaller ones in the interior. But some large cities with exceptional amenities (such as San Diego) see an increase in residents. Other superstar cities such as New York and Washington DC see the number of jobs associated with them increase, as more workers set up residence outside the expensive city and commute in occasionally, and because the price of downtown commercial real estate falls.

But their model also has a lot of fascinating second-order effects.

For example, in LA the rise in remote work leads to faster commutes for everyone who still needs to go in to the office. Less traffic on the roads means commuters can also afford to live further from the city center, since they can expect faster travel times during their commutes. There is also less pollution, due to less total commuting. Both effects benefit workers who do not work remotely.

More ambitiously though, these models take a shot at understanding the potential impact of remote work on agglomeration spillovers. Agglomeration spillovers is a name economists give to the productivity enhancing power of population density. What happens to these spillovers when a large share of the work force is not on site? Do they evaporate? If so, does that offset the benefits described above?

It’s an open question. Agglomeration spillovers are typically understood as stemming from many distinct microfoundations. To take one example, as noted above, there is some evidence that it’s harder to communicate some kinds of ideas remotely, and harder to build and maintain social networks. At the level of a single organization, these might have difficult to detect but deleterious effects on things like overall strategy, corporate culture, and innovation. These might, in turn have knock-on effects to other firms in the same region (since they may be expected to share workers and borrow ideas from each other). Weaker social networks might also hamper the efficient matching of the right worker to the right job. On the other hand, maybe these concerns are overblown; maybe we’ll get good at communicating online and socialize via twitter and occasional in-person meetings.

In Delventhal, Kwon, and Parkhomenko (2021), remote workers do not generate agglomeration spillovers, and so the increase in remote work reduces firm productivity. But it turns out this is just barely offset by suburban firms relocating their offices away to cheaper city centers. In their model, city centers tend to be inherently more productive and also to benefit from density of the remaining on-site workers. On the whole, workers are slightly better off and earn higher wages after the transition to remote work!

Delventhal and Parkhomenko’s model of the entire US economy is less optimistic, but considers a broader range of outcomes. In their most pessimistic scenario, workers only generate agglomeration spillovers when they are physically on site. In this setting, an increase in remote work slightly increases total welfare overall, but almost all of the gains flow to 100% remote workers, who benefit from being able to reside wherever they like. Most hybrid workers and 100% on site workers are worse off overall due to the decrease in productivity (and hence, wages) that happens in response to the unwinding of agglomeration spillovers. But Delventhal and Parkhomenko also consider the possibility that remote workers might generate some fraction of the agglomeration spillovers generated by on-site workers. In their model if remote workers generate about 60% as many spillovers as fully on site workers do, then this is enough to ensure that even fully on-site workers benefit from the increased use of remote work.

It’s still early days, and I have no doubt there is much more work to come on this. But my takeaway from these first passes at quantitatively thinking through the economy-wide impacts of remote work is that on the whole it’s a good thing.

If you think this is interesting, I have a free newsletter about recent social science research on innovation. Check it out here.

Fully Grown: Why a Stagnant Economy is a Sign of Success by Dietrich Vollrath is a deep dive into economic statistics that implicitly argues there’s not much evidence for a “technological stagnation” over the last several decades. The first part of this review covers Vollrath’s argument about technological stagnation, while the second focuses on whether the slowdown is itself desirable.

The Slowdown

The puzzle Vollrath sets himself to explain is why GDP per capita growth in the US averaged 2.25% for the latter half of the 20th century, but only 1.00% in the first 20 years of the 21st century. This is not just due to the negative pull of the great recession. Growth was weaker before and after the recession too.

(Note that, unlike some other stagnationists, Vollrath is focused on the relatively recent growth slowdown; this isn’t a book about the difference between growth in the 50 years before and after 1970)

One explanation for this is that something has gone wrong with technological progress. There are a variety of takes on the question of technological stagnation, but one throughline is that advances in digital technology have not been enough to offset stagnating innovation in the physical world. We’re enjoying innovation in bits, not atoms — “we wanted flying cars and got 140 characters.”

The trouble with this hypothesis is that it’s hard to test. Even if you limit yourself to testing for technological slowdowns in a single industry, it can be hard to know what to measure. For instance, is stagnation in the speed of air travel evidence of an innovation deficit? Or are improvements in fuel efficiency, noise pollution, and safety evidence of an innovation surplus? Or take agriculture: do you look at crop yields? Or crop resilience? Or reductions in the use of other inputs? (I wrote a whole thread last Thanksgiving about how hard it is just to measure the impact of genetic engineering on corn).

The problem is even harder when you want to talk about “technology” writ large. How do you create an index of technology that encompasses advances in everything from airplanes to agriculture? Well, one way to go is to use economic proxies.

The most basic measure of “technology” is simply GDP per capita. All else equal, better technology leads to more economic output. But everyone knows this is imperfect, since GDP per capita can be affected by inputs — more capital, more education, etc — in addition to technology. Better to use total factor productivity (TFP), which is a measure of how much “extra” GDP you get after attempting to account for all those inputs. Since TFP represents a measure of how efficiently inputs are translated into outputs, it is frequently used as a proxy for overall technology.

Now: everyone knows that if you want to use GDP per capita or TFP as a proxy for overall technology, you have to include the caveat that “these measures are not perfect.” But then, most people go on to use them because… what else are you gonna do?

One of Vollrath’s contributions is to underscore just imperfect these measures are as proxies for technological change. Vollrath shows it can easily be the case that technological progress did not slow at all, and that the slowdown in real GDP per capita growth and TFP growth is entirely driven by non-technological factors.

What Caused the Slowdown?

Vollrath’s preferred decomposition of the causes of the 1.25% annual slowdown in real GDP per capita growth is:

• 0.80pp — Declining growth in human capital
• 0.20pp — The shift of spending from goods to services
• 0.15pp — Declining reallocation of workers and firms
• 0.10pp — Declining geographic mobility

The first striking thing about this is that changes in human capital alone accounts for two-thirds of the slowdown. The decline in the growth of human capital stems from four main factors: older workers, fewer workers, fewer hours worked, and stabilizing educational attainment. Of these the biggest shift is the decline in the number of workers per capita, which stems from the choice rich societies made to have fewer children.

This leaves about 0.45pp left over for explanations related to the capital stock and TFP. Indeed, you can see in the graph above that GDP per capita growth drops noticeably in 2007, but TFP growth only drops a bit. Thus, right off the bat, Vollrath argues a slowdown in technological progress explains at most part of one-third of the growth slowdown.

But Vollrath goes farther and argues the drop in TFP growth likely has non-technological explanations.

Shifting Preferences

TFP growth for the entire economy is the weighted average of TFP growth across the different sectors that make up our economy. The weights are determined by spending shares across sectors: if we spend 60% our income on goods and 40% our income on services, then economy-wide TFP growth is 60% TFP growth of goods and 40% TFP growth of services. The trouble is, these shares change over time. It could be that within each sector, TFP growth has remained unchanged, but we shift our spending towards sectors with slower TFP growth. This would pull down our measure of economy-wide TFP growth, even though no sector is slowing down.

Now, that’s not exactly what happened. Various sectors have seen a slowdown in TFP growth. But it turns out a large chunk of the TFP slowdown can be accounted for by the shift in our spending from goods to services. TFP growth in goods is typically faster than for services, and so putting more weight on the TFP growth of services has lowered economy-wide TFP growth.

Why is TFP growth in services slower? Well, recall that TFP is a measure of how much extra output you can get from a given set of inputs (labor and capital). For goods, we don’t really care about what the inputs were; all we want is the output (a new TV, iPhone, coat, whatever).

But many services involve the purchase of attention from someone: childcare workers, doctors, restaurant servers, and so on. To the extent you are paying for attention from someone, it’s very hard to improve TFP. For these kind of services, TFP growth would entail finding a way to get more minutes of attention out of the same number of workers. Since we all have the same 24 hours in a day, it’s not clear where the extra attention can come from. More on this next week.

The Rest of the TFP slowdown

Human capital and the shift to services accounts for about 80% of the slowdown. The remaining 20% is driven by reduced entry and exit of firms and reduced job changes. Since more productive firms tend to supplant lower productivity ones, and since workers tend to move to higher wages (and therefore, higher productivity, if standard economics is mostly right), when firm and job turnover slows, this can reduce TFP growth.

The slowdown in entry of higher productivity firms might be one manifestation of a technological slowdown. Maybe, for instance, a reduction in cost-reducing and quality-enhancing innovations is reducing the supply of new business models? But there are also other possible causes. It might be that lax antitrust policy has led to an increase in market power. Others argue the slowdown in firm entry is another consequence of our decision to have less children back in the 1970s.

The decline in geographic mobility could also have worrying and benign causes. It may be that workers want to move to higher productivity regions, but are locked out by the rapidly rising price of housing. This, in turn, might be due to policies in major cities that restrict the housing supply. But, it could also be that people don’t actually like to move and the high rates of mobility in the late twentieth century are the real aberration.

The Slowdown We Wanted?

Once you account for all the above factors, there’s not much need to bring in the hypothesis of a technological slowdown to account for the data. Next, I want to turn to the argument in the book’s subtitle — that the actual causes of the growth slowdown are mostly desirable.

This is a nuanced point. It’s not that slower economic growth is good in and of itself. Instead, it’s more that the things that have caused the growth slowdown are not the result of something breaking that we now need to fix. Essentially, we’ve traded slower growth for something else valuable. That doesn’t mean we can’t offset the slowdown with policies that boost growth through other channels. But any such policies were always worth doing anyway.

It’s an interesting reframing. I normally see the slowdown presented as an emerging problem that needs to be solved or else we should be worried about the future. Vollrath spins this around and says, more or less, that maybe we don’t have so much to worry about in the future because we’ve solved some problems in the past. I thought this was an interesting perspective to take, so this week I’ll try to give you the same experience by presenting the slowdown in the best light I can.

The Slowdown We Chose

First, the biggest chunk of the growth slowdown is directly derived from our own choices. We wanted smaller families and that’s what we’re getting — annual births per 1,000 people has fallen from about 30 to 15 between 1960 and 2020. We wanted to work less, and between 1965 and 2017 weekly hours worked declined from 39 to 34. We decided we wanted more education, and so we got the majority of the working age population a high school education. Women wanted to work, and when social, political, cultural, and technological factors changed to make it easier to enter the workforce, they did. Now they’re here and we no longer have a very large group of people denied the opportunity to work.

Taken together, these all slowed the growth of the national stock of human capital and this directly accounts for 0.80pp of the 1.25% slowdown in real GDP per capita growth. But this may understate the full impact of our choices. Vollrath attributes a further 0.15pp of the real GDP per capita slowdown to a reduction in firm entry and exit. Why has there been a reduction in new firm entry? Hopenhayn, Neira, and Singhania (2018) argue this too is ultimately caused by slowing population growth. Slower population growth reduces the supply of labor, which makes it easier for the existing set of firms to absorb labor through expansion. There is less free labor around for new firms to take advantage of, which reduces the entry of these new firms. If that’s the case, then the full impact of slower human capital formation might be closer to 0.95pp.

Why might that be good? Mainly because decisions around work, education, and family are some of the most important ones people make and economists trust people to make the choices that are best for themselves. Prosperity, political changes, and technology have drastically reduced the constraints on these choices over the 20th century and it appears those constraints were binding, since people are changing their behavior when able to do so.

As noted last week, Vollrath attributes another 0.1pp of the slowdown to a reduction in geographic mobility. Whether this is good or bad hinges on why people are less likely to move. One explanation is that housing policy has locked people out of cities they want to move to. Another is that Americans have become complacent. But a more optimistic possibility is that people are forming deeper attachments to where they live and just don’t want to move.

Patrick Coate and Kyle Mangum have a recent working paper arguing that the decline in US mobility is largely a result of people forming deeper ties to the previously new cities of the West and Southwest (e.g., Phoenix, LA, Las Vegas). For example, in 1960, only 20% of the US-born population of Los Angeles was born in California. Lacking deep ties to the city, the other 80% was much more likely to move around the country. In contrast, by 2010, 71% of the US-born population of Los Angeles was born in California. With deeper connection to the region, they have less desire to move. Coate and Magnum argue the increasingly deep roots of the inhabitants of “new” cities out west accounts for about 2/3 of the decline in geographic mobility. The aging population (a consequence of lower fertility) accounts for much of the rest.

All told, maybe 1.05pp of the 1.25pp slowdown in real GDP per capita growth stems from people optimally choosing what’s best for them. We wouldn’t serve them well by reversing their choices and getting back high growth. Other policies boost growth and leave their choices be are still worth doing — but that was always true.

The Decline of Stuff

The last chunk of the slowdown in TFP growth can be attributed to the shift in spending away from goods and towards services. Services tend to have slower productivity growth, so this has pulled down economy-wide TFP growth. So it is a bad thing that we’ve shifted our spending towards slow-productivity growth sectors?

Maybe not. You can think of it as a sign of victory. We’ve gotten so good at producing stuff at high quality and low prices that we don’t really need more of it. I mean, yes, we would usually take more if we could get it — we’re not enlightened. But in economist speak, the marginal utility we get from more stuff has gotten really small. Nice job scientists and engineers! We’re now using the savings from lower prices and higher quality to consume more of the things that still have very high marginal utility, namely services.

(By the way, innovation in services can be great. Speaking for myself, I would rather have 140 characters than a flying car.)

The shift from stuff to services should also make environmentalists worried about “infinite growth on a finite planet” happy. As Andrew McAfee highlights in his recent book More from Less, the shift away from spending on goods has also manifested as an absolute reduction in our use of physical resources. In the figure below I plot US consumption of energy, US carbon emissions, consumption of steel, concrete, and timber over 1900–2017 (McAfee highlights many additional reductions in resource use). In all cases, consumption increased fairly steadily before it stopped or reversed in the 21st century, which is also when we’ve experienced our growth slowdown.

Another argument against de-emphasizing innovation in “stuff” is that it’s short-sighted. For example, Dan Wang argues US outsourcing of manufacturing has been a mistake. According to this argument, much of manufacturing knowledge is tacit and acquired via learning-by-doing. In the long-run, we will lose the ability to innovate in the production of physical goods because it’s not possible to outsource production and retain manufacturing research and design capabilities. The countries we outsourced too (for example, China), will skill up and become future leaders in the production of physical stuff.

I think this argument is likely true (although it’s important to note the US still does a lot of the most complex manufacturing here). But what if it’s actually a good decision to cede innovation in the goods sector to other countries, because the future of what people want is not goods but services? Maybe the USA is actually putting itself in a position to lead the future by specializing in services innovation?

Although TFP growth in services has traditionally been slow, that doesn’t mean innovation doesn’t happen. Artificial intelligence, for example, seems poised to take over many of the jobs that currently require human attention. And even if it’s very hard to squeeze more attention out of workers (given, well, the nature of time) that doesn’t mean we can’t innovate on the quality of that attention. I think of how my daycare still requires a similar number of workers, but now they send photos, videos, and all sorts of information about how my kids spend their days to me via an app.

Lastly, it’s important to note all of this is just a matter of degree. It’s not that we don’t want to have children anymore — we just want fewer. It’s not that we don’t want to move for work anymore — just not as much. It’s not that growth in producing goods has ground to a halt — it just accounts for less of the economy. If the human race survives, we’ll still get to whatever science fiction future is waiting for us. It’ll just take longer. But maybe that’s ok because we’ll be happier on the way.

So that’s my attempt at the best case scenario for the growth slowdown. Vollrath’s take is more nuanced than what’s presented here (he’s not actually sure all of these changes stem from good causes) and mine is too. But I also think what I’ve written above is part of the story, even if it might not be the whole story.

To an important degree, “innovation” is a process of combining pre-existing ideas and technologies in novel ways. Twenty years ago, the late Martin Weitzman spelled out what this model of innovation means for long-run economic growth ( here and here for summaries). But a number of recent papers have deepened these ideas.

When to make a module?

The fundamental assumption in Weitzman (1998) is that new ideas are made by combining two previously existing ideas and putting in a bit of research effort. So, given n ideas, the number of possible innovations (i.e., the number of unique pairs) is n( n- 1)/2. Only some of these will turn out to be useful.

What drives growth in the long run is that, sometimes, one of these ideas is so good that it becomes a new self-contained idea. When this happens, it raises the number of ideas you can build with to n + 1 and the number of possible innovations by n (since you can pair the new idea with all n of the old ones).

A recent working paper by Fink and Teimouri takes a closer look at this process of combining idea-components into self-contained ideas. When does it make sense to permanently fuse several components together into a brand new component, and when is it better to simply leave the components separated?

I think the intuition is clearest if think in terms of computer code. Suppose I am working in a language where I can add up number N1 and N2 with the command SUM(N1,N2) and divide N1 by N2 with the command DIV(N1,N2). I can find the average of these two numbers with the code DIV(SUM(N1,N2),2). If I need to find the average frequently, I can define this as a new command AVE(N1,N2) = DIV(SUM(N1,N2),2).

The key idea is that if I’m teaching a new person this language, I can tell them how to take the average of two numbers by teaching them two things — the commands for SUM and DIV — or by teaching them one thing — the command AVE. If they rarely need to add or divide except to find averages, I can more efficiently give them the tools to start coding by simply teaching them AVE. In fact, if they are really time-constrained and only have the time to learn three commands, I can potentially expand the set of things they do by having them learn AVE and two other commands, instead of SUM, DIV and one other command. The trade-off is if they face a problem where they need to add or divide but not take the average, they’ll be out of luck.

Now suppose I need to computes the reciprocal of the average, which can be computed as DIV(2,SUM(N1,N2)). I could define a new command R_AVE(N1,N2) = DIV(2,SUM(N1,N2)), but since it’s not very common to require the reciprocal of the average, it’s probably not a very useful command to define. If a student only has capacity to learn three commands, I would almost certainly be better off teaching them DIV, SUM and a third command, instead of R_AVE and two commands.

More generally, FT assume innovators acquire n components and can create anything built from those components. Only some such combinations are useful though. Importantly, when components are fused together, the resulting component only counts as one of the innovator’s n components (as in the above example, where learning the AVE or R_AVE command is just as easy as learning SUM or DIV). In this framework, it makes sense to fuse components when doing so expands the set of useful innovations you can make when restricted to n components.

It ends up being best to fuse together components that are frequently used together (e.g., creating AVE from SUM and DIV). Fusing together components rarely used together is inefficient (i.e., R_AVE), since it results in a waste of the innovator’s n components.

What’s cool about this paper is they actually investigate this with data. They look at the number of recipes that can be made out of 381 ingredients and the number of combination therapies that can be made by combining 901 different drugs. They then ask, for example, if you had a set of n ingredients how many actually existing recipes could you make? How does this change if a random set of k ingredients are combined into one ingredient (e.g., in a pre-mixed spice pack, or pre-made dough, etc.), freeing up space for k — 1 other ingredients?

Two results are interesting. First, most of the time, combining multiple components into one does not expand the set of possible innovations. For example, there are over 65 million different ways to combine 2–5 ingredients. Of those, if you are restricted to a set of n items in your kitchen, giving one of these slots to a combined item only expands the set of possible recipes for 9,839 of those combinations. That is, for recipes, only 0.02% of possible combinations of ingredients were more useful together than decomposed into their constituents. Similar results follow for drug combinations (though less extreme).

Second, restricting attention to these useful combinations, the vast majority are themselves viable recipes or drug combinations! Of the 9,839 combinations of ingredients that expand the set of recipes a kitchen can cook, 96.88% were combinations that themselves complete a recipe. Again, similarly for drug combinations.

What’s the take-away? One interpretation is that innovators are relatively short-sighted. Successful combinations of components are originally built to be stand-alone technologies. Only later do people realize this technology can itself be usefully combined with other technological components. It is not typically the case that innovators foresee a set of components will enable many other technologies if combined, but do not on their own do anything useful. (At least for these little datasets)

This is in the spirit of Weitzman’s model, where innovators combine ideas to invent technologies that are useful in the production of economic goods, not to enable follow-on innovation that builds on their idea. It fits well a view of technology where we stumble onto unexpected new possibilities after creating new technologies not originally created with the intention of opening up new domains.

The Industrial Revolution

One of the implications of Weitzman (1990) is that the growth of ideas is initially constrained by the number of possible ideas. During this period, every possible combination is investigated and innovation is very slow. However, once enough components are added to the stock of ideas, the number of possibilities grows explosively. In other words, innovation has a long period of near-stagnation, followed by faster-than-exponential growth.

Weitzman alludes in passing to the fact that this seems to fit the history of innovation quite well. A working paper by Koppl, Devereaux, Herriot, and Kauffman expands on this notion as a potential explanation for the industrial revolution. Both KDHK and Weitzman adopt the model that combinations of pre-existing technologies sometimes yield new technologies. For Weitzman, this is a purposeful pairing of two components. For KDHK, this is modeled as a random evolutionary process, where there is some probability any pair of components results in a new component, a lower probability that triple-combinations result in a new component, a still lower probability that quadruple-combinations result in a new component, and so on. KDHK show this simple process generates the same slow-then-fast growth of technology.

Now; their point is not so much that industrial revolution is now a solved question. They merely show a process where people occasionally combine random sets of created artifacts around them and notice when they yield useful inventions inevitably transitions from slow to very rapid growth. It’s not that this explains why the industrial revolution happened in Great Britain in the 1700–1800s; instead, they argue an industrial revolution was inevitable somewhere at some time, given these dynamics. Also interesting to me is the idea that this might have happened regardless of the institutional environment innovators were working in. If random tinkering is allowed to happen, with or without a profit motive, then you can get a phase-change in the technological trajectory of a society as the set of combinatorial possibilities grows.

Long-run Growth and AI

A final point of Weitzman (1998) is that, eventually, the space of possible ideas expands far more rapidly than the economy’s GDP. From this point on, the growth of technology is constrained not by the set of possible ideas, but by the growth of R&D resources. What ultimately happens to the rate of economic growth depends on two factors: the cost of R&D and the extent to which knowledge can be substituted for physical capital. In a world where the cost of R&D falls aymptotically to zero, growth goes to infinity if there is sufficient ability to replace the function of machinery and other capital with better ideas about producing goods.

Why would we assume R&D would ever fall to zero? Well, one possibility is that technological progress yields technologies that increase the productivity of R&D. One such technology is artificial intelligence.

The impact of AI on the rate of growth is what motivates Agrawal, McHale, and Oettl (2018). They build a new model of economic growth by combining a combinatorial model of innovation (inspired by Weitzman) with a now standard model of endogenous growth by Charles Jones. Their contribution is to more rigorously found the innovation side of the economy in a way that they can push and prod different parameters to think through the impact on economic growth.

What happens, for example, if researchers can access a greater share of human knowledge via improved search and the internet? In their model, growth is enhanced in the short and long-run.

A more interesting example is what if AI allows researchers to more fully explore the space of possible ideas? Consider the cooking ingredients example from FT above. If there were only 10 possible kitchen ingredients, a dedicated cook could work their way through all 1,024 possible combinations. However, there were over 65 million different ways to combine up to 5 ingredients, given 381 possible kitchen ingredients. By FT’s criteria, only 0.02% of these were “useful.” No human cook could ever try all these combinations out, but maybe an AI (with a model of human taste) could.

As you would expect, an AI that expands your ability to sort through the space of combinatorial possibilities increases the rate of growth in the short-run. But perhaps surprisingly, in the long run it does not.

This result comes from the idea that combinatorial spaces quickly grow to a size that is inconceivably big, even for a super-powerful AI. If you consider all possible combinations of 381 ingredients — not just those with 5 or fewer components — there are more possibilities than there are atoms in the universe. That means, in the long run, artificial intelligence is no better than human intelligence, since both explore a negligible share of the space of possibilities. (That doesn’t mean AI doesn’t improve long-run levels of GDP though, since they do raise the growth rate on your way to the long-run)

What if an AI could actually keep pace with the combinatorial explosion? In that case, you get a singularity — growth increases without bound.

Combining Insights

To sum up; Weitzman was among the first to write up a model of the economy with innovation as combination at it’s heart. In my experience though, it was the kind of paper that was cited a lot for it’s conceptual contribution, but rarely did people build on it’s model of the innovation process explicitly. In fact, since it did not become a workhorse model in economics, the paper is probably not as well known by people working in the area as it could be. (I know I encountered it after I had already been working on combinatorial innovation, and at least one of these papers does not cite it either). A pleasant surprise then to encounter three separate papers in the last few years that deepen and extend these ideas.

Originally published at https://mattsclancy.substack.com.

Growth when Innovation is Combinatorial was originally published in The Startup on Medium, where people are continuing the conversation by highlighting and responding to this story.

I read 50/53 books this year, depending on whether you count the Murderbot novellas as 1 or 4 books. Last year I started grouping books by “conceptual non-fiction” and “narrative fiction and non-fiction,” since Karl Ove Knausgaard’s books present as non-fiction, but narrate the events of his own life, so it felt weird to compare him to other non-fiction books. This year, things got even trickier, since my favorite book was probably Strangers Drowning by Larissa MacFarquhar, which is a mix of narrative and conceptual non-fiction. I ended up filing it under a third category. Without further ado…

Conceptual Non-Fiction

1. Why Buddhism is True by Robert Wright: A wonderful marriage of modern cognitive science and (non-supernatural) Buddhism. Nearly convinced me to go Buddhist!
2. Good Reasons for Bad Feelings by Randolph M. Nesse: Evolution provides a wonderful organizing schema for thinking about the origin of “bad” emotions, mental disorders, and more.
3. A Culture of Growth by Joel Mokyr: I hope to write a lot more about this book someday, but in short, I think it’s the most right theory of why the Industrial Revolution happened when and where it did.
4. The Years That Matter Most by Paul Tough: An expansive portrait of how American higher education is and is not a good engine of social mobility that argues we can do better, even if there is no silver bullet
5. How to Change Your Mind by Michael Pollan: A great companion to my #1 and #2 choices that is every bit as good as people say
6. Because Internet by Gretchen McCulloch: We’re getting better at communicating and interacting with each other over this new internet thing
7. Open Borders by Bryan Caplan and Zach Wienersmith: Somehow this year I ended up reading 3 Bryan Caplan books — he has a knack for finding thought-provoking positions on important issues. Efficiently delivered in a compelling and creative way, this one’s my favorite.
8. The Myth of the Rational Voter by Bryan Caplan: The trouble with Democracy is the voters are people, and people are the worst.
9. Building the Intentional University by Stephen M. Kosslyn, Ben Nelson, and Bob Kerrey: A top-to-bottom rethinking of higher education based on evidence (such as it is).
10. The Case Against Education by Bryan Caplan: High school and college are not nearly as efficient at building human capital as we think they are.
11. Reinventing Discovery by Michael Nielsen: An optimistic take on how the internet can be used to rethink the infrastructure of scientific enterprise.
12. The Mind Illuminated by Culadasa: An interesting complement to my #1, from the perspective of someone whose done it — but my feelings are complicated after it emerged Culadasa was cheating on his wife without her consent.
13. Our Mathematical Universe by Max Tegmark: A nice overview of contemporary physics, a physics career, and Tegmark’s ideas about why there is something rather than nothing.
14. Lost in Math by Sabine Hossenfelder: In the absence of new data, foundational theoretical physics has gravitated towards aesthetically based criteria for evaluating theories. This may not be the best strategy for finding truth if reality feels no need to be pretty.
15. Creative Selection by Ken Kocienda: A set of great first person case studies on how innovation happens in the context of Apple, with thoughtful analysis.
16. The New Geography of Jobs by Enrico Moretti: A classic popular summary of the new urban economics literature which emphasizes the importance of agglomeration effects.
17. The Fate of Rome by Kyle Harper: A great example of fruitful interdisciplinary work — Rome’s decline was hastened by climatic changes and disease outbreaks.
18. The Box by Marc Levinson: The history of the shipping container illustrates tons of cool ideas about the economics of innovation. Should be right up my alley, but I found it a bit dry at times.
19. Jump-Starting America by Jonathan Gruber and Simon Johnson: Good overview of important research on economics of innovation, and naturally I like that one of the places they think could be a new research hub is the Des Moines-Ames corridor.
20. Make it Stick by Peter C. Brown: A good summary of the literature on learning, though I worry a lot of the lit won’t survive the replication crisis.
21. The Rise of Universities by Charles Homer Hoskins: A very short history about… the rise of universities (in the middle ages).
22. Life Finds a Way by Andreas Wagner: What can evolution teach us about innovation and creativity in general. Wagner is really good on evolution, but a lot of the other stuff was a bit surface level.
23. Greek and Roman Education by R. Barrow: The deep roots of formal education in Europe.
24. TED Talks by Chris J. Andrews: My takeaways — be comfortable, use throughlines, and only your speech from memory if you can do it really, really, well.
25. More from Less by Andrew McAfee: The central idea that US consumption of natural resources is falling, is actually a pretty small part of the book and I already kind of knew the rest.

Narrative

1. Middlemarch by George Eliot: A whole world of psychologically realized people (and features the ultimate nightmare of an academic; wasting your life on a project that ends up wrong and unpublished).
2. The Unwomanly Face of War by Svetlana Alexievich: The human experience of World War II, from the perspective of Soviet women, who served throughout the ranks. It’s real war, which means it’s sad, not fun.
3. The House of Mirth by Edith Wharton: The second time I’ve read the story of Lily Bart’s long and sad decline. Will read again.
4. Summer by Karl Ove Knausgaard: Much like the Buddhists described in my #1 non-fiction, Knausgaard has shed the assumptions that color and organize our perception of the world, so that he renders mundane life new and vivid.
5. Jane Eyre by Charlotte Bronte: Incredible characters; St. John could be someone straight out of my #2. The audiobook is read incredibly well by Thandie Newton.
6. The Children of Hurin by J.R.R. Tolkein: Sad and hypnotic work with powerful and iconic passages.
7. The Left Hand of Darkness by Ursula K. Le Guin: A book that jolted me out of my routine (also features one of my favorite genres — arctic journey!).
8. Silas Marner by George Eliot: What if a Hallmark movie was written by one of the greatest writers in the English language?
9. The Ocean at the End of the Lane by Neil Gaiman: A lovely modern fairy tale.
10. The Name of the Rose by Umberto Eco: Sherlock Holmes and Watson reimagined as monks just before the renaissance.
11. A Confederacy of Dunces: A portrait of the kind of guy who today would haunt reddit and be a big fan of Mencius Moldbug.
12. Kindred by Octavia E. Butler: Excellent. Uses the sci-fi time travel premise to unpack the psychology of American slavery in a way that would be very difficult to achieve otherwise.
13. The Dark Forest by Liu Cixin: A mix of amazing sci-fi and cringingly bad stuff, but ultimately you’ll remember the high points more than the low.
14. The Last Viking by Stephen R. Bown: An excellent companion to “The Worst Journey in the World,” (which I read last year) as it shows what might have been and makes the mistakes of the Scott expedition clear.
15. Jane, Unlimited by Kristin Cashore: I was not loving this book until it opened up after the first third and kept getting wilder and wilder.
16. The Murderbot Diaries (#1-#4) by Martha Wells: Actually 4 novellas, but they tell the fun thriller-ey story of murderbot’s self-actualization.
17. Annihilation by Jeff VanderMeer: Kind of cosmic horror that’s afraid of biology instead of physics.
18. Daytripper by Gabriel Ba and Fabio Moon: The images do a good job of evoking day-to-day life.
19. Under Red Skies by Karoline Kan: Interesting memoir of a country undergoing enormous change (China) — how representative are these views though? Who knows?
20. Blacksad (#1-#3) by Juan Diaz Canales: Zootopia for adults.
21. The Private Eye by Brian K. Vaughan: I don’t believe the main theme about the baleful power of the internet, but it’s great to look at.
22. The Department of Sensitive Crimes by Alexander McCall Smith: Not bad, but not for me.
23. Fall, or Dodge in Hell: Amazing first 200 pages, but I was pretty unhappy with it after that.
24. The Name of the Wind by Patrick Rothfuss: Would have quit if this wasn’t a book we were reading for book club.

A bit of both

1. Strangers Drowning by Larissa MacFarquhar: What happens if you take seriously the notion that if you can save a life without losing your own, you have a duty to do that? One of my favorite books of the decade.

Originally published at http://matt-clancy.com on January 6, 2020.

A few weeks ago, I ran across the following argument from Scott Sumner’s blog:

The film industry is in long-term decline, which happens to all art forms after they express their most potent ideas. Painting peaked in the 1500s and 1600s. Pop music in the 1960s and 1970s. So on film I’m a pessimist.

Sumner isn’t the only one who seems to think film has peaked. The BFI Sight and Sound top movies poll gives only 4 out of 100 spots to movies made since 1990(!).

This is a bit puzzling for two reasons. First, technology in general continues to improve year after year. Movies are sort of a technology, so why don’t they also get better? After all, surely film-makers are learning all the time from what worked in the past. And they have much improved technical tools. And there’s just a lot more movies being made by a lot more people around the world. Why doesn’t that translate to steady improvement in film?

Second, some movie genres don’t seem to have peaked. In particular, one of my favorite genres — horror — appears to be in the midst of a golden age. If horror movies are getting better, why not the rest?

The problem, of course, is that producing great art is about both craft and originality. Craft may indeed be improving all the time, but it gets harder to be original with every passing year. The need to be original is a handicap not faced by technology in general, and likely accounts for the difference between film and technology.

But it would still be interesting to see if the craft of film-making is improving. How do you measure the advance of film making craft though? Well, with horror, I think it’s pretty obvious. A well executed movie should be scary. There are lots of techniques related to framing, sound design, and editing that can put an audience on edge or have them jump in fright. If these techniques have to be discovered, then later film-makers have advantages over earlier ones, since they can copy them.

So I decided to see if there’s any evidence horror movies are getting scarier. (Of course this is not a decisive proof about anything, but it was a fun evening project).

Ideally, I would have some human subjects watch horror movies across different eras while being hooked up to instruments measuring their physiological responses: heart rate, sweat, goosebumps, etc. As far as I can tell, no one has done this. And since I doubt the NSF will go for it if I submit a proposal, I’m taking a simpler approach.

I spent my lunch hour searching the internet for lists of the “scariest movies of all time.” I restricted my attention to lists published in the last two years (so I can capture the new golden age of horror), and put together by staff at publications. So no fan lists. I found 8 such lists from Harper’s Bazaar, Esquire, Reader’s Digest, NME, Complex, Newsday, Consequence of Sound, and Hollywood Reporter. These lists ranked (or simply listed) between 10 and 100 movies each, for a total of 302 rankings.

There are 144 unique movies included in these 8 lists. These are all movies that someone thought were scary enough to merit inclusion on a list of top scariest movies of all time. In the figure up top, I plot the number of such movies released in the previous 10 years, from 1929 to 2017. This is the line in black.

By this metric, there was relatively steady progress in making scary movies up through the 1980s, followed by a major retrenchment in the 1990s. While progress has not been steady (at all), we are indeed in a new golden age of scary movies. The previous peak of 1982 (35 scariest movies released in previous 10 years) was passed in 2010 (37 scariest movies released in previous 10 years) and shows no sign of abating.

By another measure though, horror has indeed peaked. To try and identify the top 25 scariest films, I gave each film a vote for each list it appeared in, where it was ranked in the top 25. There were two lists that named 32 and 35 films, but did not rank them, so in these cases I assumed each film in the list had a 25/32 and 25/35 probability of being in the top 25. I gave them votes weighted accordingly. I took the 25 films with the most votes to assemble a list of the top 25 scariest movies of all time.

I then repeated the same exercise, plotting the number of top 25 films released in the previous decade. That’s the red line above.

Here we see that the first golden age of horror has not been surpassed. Twelve of the films most likely to appear in the top 25 were released between 1973 and 1982. That’s nearly half in one decade! While there has been a minor resurgence in the 2000s, we are far below the peak.

So which is it? Are movies getting scarier or not? I actually think these figures support the general idea that the craft of making horror movies has risen. To see why, let’s take a look at these top 25 horror movies.

Psycho (1960)
Night of the Living Dead (1968)
Rosemary’s Baby (1968)
The Exorcist (1973)
Black Christmas (1974)
The Texas Chainsaw Massacre (1974)
Jaws (1975)
Carrie (1976)
The Omen (1976)
Halloween (1978)
Alien (1979)
The Shining (1980)
The Evil Dead (1981)
Poltergeist (1982)
The Thing (1982)
A Nightmare on Elm Street (1984)
The Fly (1986)
The Silence of the Lambs (1991)
Ringu (1998)
Audition (1999)
The Blair Witch Project (1999)
The Descent (2005)
Paranormal Activity (2007)
The Strangers (2008)
Hereditary (2018)

These are pretty scary movies! But they’re not just well crafted movies. Many of them are also original or ground-breaking in an important way. Even though these lists are supposedly about what is scary, not what constitutes great art, I don’t think the presence of so much originality is necessarily (or only) because “great” movies are sneaking onto a list of movies that are just supposed to be scary. Instead, I think it’s because we are scared by the unknown, the unfamiliar, and the shocking. The scariest stuff tends to have something original that keeps us on the edge of our seats.

It may be that film-makers today have an edge over their peers in the past, when it comes to making a pretty scary movie. The craft has improved, and it’s never been more likely that a movie released in the recent past is very scary. That’s the black line above. But to make the scariest movies of all time, good craft isn’t enough. You need an original and frightening idea, something the audience hasn’t grown used to. And as time goes on, the threshold for originality keeps getting raised. Hence, the red line.

Bonus:

As we approach the end of the 2010s, my list of the greatest (not scariest) horror movies released during the golden age of the last 10 years:

1. It Follows
2. Midsommar
3. Under the Skin
4. Hereditary
5. The Wailing
6. The Invitation
7. The Witch
8. Cabin in the Woods
9. Get Out
10. The Babadook

Happy Halloween!

Originally published at http://matt-clancy.com on October 30, 2019.

Caveat: these readings are selected to illustrate a big concept in the economics of innovation and to lead to a good discussion. They aren’t necessarily the “best” or “most convincing” paper on the topic and of course this list is far from exhaustive. Needless to say, I don’t necessarily endorse all the arguments made either.

Preliminaries

Why study innovation?

• “We Need a New Science of Progress” (2019) by Patrick Collison and Tyler Cowen
• Marginal Revolution University: introduction to the Solow Model.

Where do ideas come from?

Is necessity the mother of invention?

• “Chapter 3: Lost European Explorers” (2016) in The Secret of Our Success by Joseph Henrich.

If people randomly come up with new ideas, does more people = more ideas?

• Research Digest: “Population Growth and Technological Change: One Million B.C. to 1990” (1998) by Michael Kremer

Do new ideas come from new knowledge?

• Summary of the Report: “Science: The Endless Frontier” (1945) by Vannevar Bush.
• “The Dual Frontier: Patented Inventions and Prior Scientific Advance” (2017) by Mohammed Ahmadpoor and Benjamin Jones

Do better ideas come from experience and learning?

• Excerpt: “Towards an Understanding of Learning by Doing: Evidence from an Automobile Assembly Plant” (2013) by Steven D. Levitt, John A. List, and Chad Syverson.

Is innovation just another form of evolution?

• Excerpt: Life Finds a Way (2019) by Andreas Wagner
• pgs 70–76: “Chapter 4: What are big brains for? Or, how evolution stole our guts” (2016) in The Secret of Our Success by Joseph Henrich

Is innovation about combining different things in a new way?

• Excerpt: The Nature of Technology (2011) by W. Brian Arthur
• Video: “Inventing by Combining Pre-Existing Technologies: Evidence from Patents” (2017) by Matt Clancy
• Research Digest: “Recombinant Growth” (1998) by Martin Weitzman

Putting it all together:

• “Invention and Cumulative Culture Through Tweaks and Leaps in Online Programming Contests” (2018) by Elena Miu, Ned Gulley, Kevin Laland, and Luke Rendell.
• Excerpt (inventing a smartphone keyboard): Creative Selection: Inside Apple’s Design Process During the Golden Age of Steve Jobs (2018) by Ken Kocienda

What background factors matter for innovation?

Why do big cities generate more ideas?

• “Why Big Cities Promote Less Innovation Than They Once Did” (2015) by Eric Jaffe (write-up of “Cities and Ideas” by Packalen and Bhattacharya)
• “The Geography of Innovation” (2017) by Richard Florida (write-up of “The Geography of Unconventional Innovation” by Berkes and Gaetani)

How is innovation affected by institutions?

• Podcast: “How Violence Limits Economic Activity” (2019) on Planet Money (discussion of “Violence and Economic Activity: Evidence from African American Patents, 1870–1940” (2014) by Lisa Cook)

Why do so many people accept things as they are?

• “Is Innovation in Human Nature?” (2016) by Anton Howes
• Non-technical summary: “Who Becomes an Inventor in America? The Importance of Exposure to Innovation” (2018) by Alex Bell, Raj Chetty, Xavier Jaravel, Nevianna Petkova, and John Van Reenen
• 118–120. “Chapter 7: On the Origins of Faith” (2016) in The Secret of Our Success by Joseph Henrich.

How can we incentivize innovation?

What are spillovers and what challenges do they present to businesses doing R&D?

• Research Digest: “Identifying Technology Spillovers and Product Market Rivalry” (2013) by Nicholas Bloom, Mark Schankerman, and John Van Reenen.

What if we just innovate without using the market at all?

• Excerpt: Free Innovation (2016) by Eric von Hippel

Which is better: secrecy or intellectual property rights?

• 1–9: “Patents and Innovation: Evidence from Economic History” (2013) by Petra Moser.
• Podcast: “When Patents Attack!” (2011) from This American Life
• Excerpt: “The Nature and Function of the Patent System” (1977) by Edmund Kitch

What about innovation prizes?

• “Incentivizing Innovation: Adding to the Tool Kit” (2010) by Michael Kremer and Heidi Williams

Can we use tax policy to steer innovation where we want it to go?

• Research Digest (coming): “Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry” (2016) by Philippe Aghion, Antoine Dechezplepretre, David Hemous, Ralf Martin, and John Van Reenen.

Can we boost innovation by increasing the supply of scientists?

• Research Digest (coming): “The Collapse of the Soviet Union and the Productivity of American Mathematicians” (2012) by George Borjas and Kirk Doran.
• “Does Government R&D Policy Mainly Benefit Scientists and Engineers?” (1998) by Austan Goolsbee.

How can we encourage scientists to take bigger risks?

• Research Digest (coming): “Incentives and Creativity: Evidence from the Life Sciences” (2011) by Pierre Azoulay, Joshua S. Graff Zivin, and Gustavo Manso.

Do we need to stop thinking of individual incentives to innovate?

• “The Increasing Dominance of Teams in Knowledge Production” (2007) by Stefan Wuchty, Benjamin F. Jones, and Brian Uzzi.

How should we organize to make innovation happen?

• Excerpt: “Funding Breakthrough Research: Promises and Challenges of the ‘ARPA model’ ” by Pierre Azoulay, Erica Fuchs, Anna Goldstein, and Michael Kierney
• “Google X and the Science of Creativity” by Derek Thompson (2017)

Putting it all together:

• “A Toolkit of Policies to Promote Innovation” (2019) by Nicholas Bloom, Jonathan Van Reenen, and Heidi Williams.

What’s the past and future of innovation?

How has the USA organized research in the past?

• “The Changing Structure of American Innovation: Some Cautionary Remarks for Economic Growth” (2019) by Ashish Arora, Sharon Belenzon, Andrea Patacconi, and Jungkyu Suh.

What have been the big inventions?

• “What’s Next for the Future of American Innovation?” (2018) by Andrew Van Dam (write-up of “Measuring Innovation over the Long-Run” by Brian Kelly, Dimitris Papanikolaou, Amit Seru, and Matt Taddy)

Is innovation getting harder?

• Research Digest (coming): “Are Ideas getting Harder to Find?” (2019) by Nicholas Bloom, Charles Jones, John Van Reenen, and Michael Webb.
• NBER Reporter Research Summary: “Slower U.S. Growth in the Long- and Medium-Run” (2015) by Robert Gordon
• Research Digest: “Age and Great Invention” (2010) by Benjamin Jones
• Research Digest: “The Burden of Knowledge and the ‘Death of the Renaissance Man’: Is Innovation Getting Harder?” (2009) by Benjamin Jones

Or is the singularity approaching?

• “The AI Revolution: The Road to Superintelligence: Parts 1 and 2” (2015) by Tim Urban.

My #1 advice to new economists is to get on twitter and plug into the #EconTwitter community. This document is a guide on how to do that. It’s a bit geared towards academics, since that’s what I know best, but #EconTwitter is a lot more than academics and I’ve tried to write so it’s useful to people outside academia too.

Update: Confusingly, there are now two EconTwitter’s to choose from. This article is geared towards the original EconTwitter, which lives on twitter.com. Let’s call this EconTwitter 1.0. Spurred on by some longstanding dissatisfaction with Twitter and galvanized by Elon Musk’s October 2022 takeover of twitter, many economists have migrated to an alternative open source platform called Mastodon. As a joke, this new offshoot also calls itself EconTwitter, but for clarity here, I’ll call it EconTwitter 2.0.

Things are changing fast, so this guide will probably be revised again within the year. EconTwitter 2.0 is growing much faster than EconTwitter 1.0 at the moment, but it may yet run out of steam. Both communities have many of the same norms described below, but also differ in interesting ways. I advise checking out both.

For some guides about getting started on EconTwitter 2.0, the simplest possible guide is here:

Paul Goldsmith-Pinkham on Twitter: "If you want to join the https://t.co/H3bOUnGijl server,1. go to https://t.co/Pwb5CQ7HWX2. create an account on the lefthand side3. confirm the email4. Done! / Twitter"

If you want to join the https://t.co/H3bOUnGijl server,1. go to https://t.co/Pwb5CQ7HWX2. create an account on the lefthand side3. confirm the email4. Done!

Also check out A Mastodon Crash Course for the Impatient Economist by Olivier Simard-Casanova.

Now on to the guide to EconTwitter 1.0.

What’s #EconTwitter?

Literally? Twitter is a website that lets users broadcast 280 characters of text to other users. These are called tweets, and they can also include photos and stuff. You can “follow” other users so that you automatically see their tweets. #EconTwitter is a subnetwork of twitter users who tend to be economists (academic, professional, aspiring), who tweet about economics, and who follow each other.

Metaphorically? Twitter is the hallway of a global economics department. Conversations break out spontaneously in the hall, you overhear it, and go over to listen or join. People post job and seminar announcements up on department corkboard. But it’s a lot more than academia, so maybe a better metaphor is a pub or coffeeshop where users hang out to talk about whatever interests them; but it’s a pub where there’s always an open chair for you.

If you want a concrete idea of what goes on in a typical week on #EconTwitter, I can’t recommend enough the free weekly email newsletter Best of EconTwitter, which curates, well, the best of #EconTwitter.

Why join #EconTwitter?

You’ll learn a ton. It’s a forum people use to share interesting research (their own and others), job notices, grant opportunities, calls for papers, conferences, and other opportunities. It’s a place people rigorously discuss methodological questions. It’s a place for experts to talk about current events. And it’s a place where the hidden curriculum of economics — the advice about how to thrive, not usually disseminated in articles — is freely discussed.

You can publicize your own work. It’s common for #EconTwitter people to announce publication of their papers and write short summaries. When other users find your work interesting, it gets “retweeted” (re-broadcast to the followers of your followers), expanding awareness of your work to an audience who might otherwise never encounter it.

You’ll be part of a community. Being an economist has its own set of unique frustrations, whether those relate to grad school, research, publishing, teaching, the academic hierarchy, public sector employment, private sector employment, outright discrimination; you name it. #EconTwitter if full of people who know what it’s like, who are friendly, and who support each other. And there’s nothing stopping connections that start on #EconTwitter from moving into the real world. The conference circuit, to take one example, is a great place to meet up in real life. You might even find a collaborator!

Why is there a “#”?

Twitter lets you attach keywords to your tweets by putting a hashtag (#) in front of them. In theory, you can find tweets about #EconTwitter by searching for it in twitter. It doesn’t actually work that well (people don’t usually attach the #EconTwitter keyword). But don’t worry, you’ve got this guide!

Setting up a Twitter Profile

Joining twitter is like joining any other social media website. It’s free.

One of your first choices is your choice of handle and name. Like an email address, your handle (or username) is unique to you. It’s preceded by an “@” sign. Mine is @mattsclancy. It’s public so choose one that you don’t mind associating with your professional identity. Your name is not unique and can be anything. Mine is just my real name: “Matt Clancy.” You can change your name with little consequence (and people sometimes do for whimsical reasons).

The norm on #EconTwitter is to use your real name, since you are usually intending to link the account to your professional identity as an economist. There are exceptions (@pseudoerasmus being the most prominent example to my mind), but they’re rare.

Next, pick a picture you like for your profile. Again, the norm is to use a real picture of yourself, but not nearly as universal as the norm of using your real name. Your picture will be small and attached to every tweet you make, so if you do your whole body, it will be hard to see your face.

You also have space to write a little bit about yourself and to link to a website. This is the main way people who don’t know you are going to find out about you, so tell them what you want them to know as succinctly as possible. Most people aren’t going to scroll through your tweets to learn who you are. Some archetypal examples:

“Economist studying [topic]. Asst. prof at [short name for university]. [one other thing]”

“[Country] treasury economist. Tweets about [list of areas of particular interest/expertise]”

“PhD Econ student at [university]. [Areas of interest]. [One more thing]”

“Researcher at [company]. [non-professional identity (e.g., “Dad”)]. [topics]”

Following People

Once you set up your account, the one thing that will most impact your twitter experience is who you follow. When you sign into twitter, three kinds of content will form the bulk of what you see:

1. The tweets of people you follow. This is the majority of the content you’ll see, so who you follow will significantly shape your perception of the site.
2. Tweets of people you do not follow, but which have been retweeted by people you do follow.
3. Tweets the twitter algorithm thinks you would like.

Getting Started

When you first join, twitter is going to recommend a bunch of popular people to follow after it asks you some general questions. You do you, but my advice is to ignore all of twitter’s suggestions for now. #EconTwitter is an unusually warm and welcoming part of twitter; the rest of the site is… not. It’s frequently called “this hell site” by its own users. Better to avoid all the toxicity and drama until you find your feet. Start with #EconTwitter.

You might think you should just follow economists you’ve heard of who happen to be on twitter. Your Paul Krugman’s, Ben Bernanke’s, the top people in your field, etc. There’s nothing wrong with that, but you may be surprised that this won’t get you very far into the #EconTwitter community. One of the most interesting things about #EconTwitter is that to the extent it has a hierarchy, it’s a hierarchy with very low correlation with the traditional econ hierarchy. The people everyone follows aren’t necessarily the same people that are on the short list for this year’s Nobel.

Who should you follow? This was asked in 2019 on #EconTwitter, so I stole the list of recommendations (which I definitely endorse) and made a public list. You can access it by clicking here, or by searching for @mattsclancy, accessing my profile, and then go to my lists. Click on the #EconTwitter Starter Set list to see a good group of people to get you started. This is very much a non-exhaustive list, but I don’t want to get into the game of picking and choosing who belongs on it, so I’m just going to leave it as it (unless anyone wants off). There is no easy way to “add all” with twitter, so you’ll have to follow each person by hovering over their name and clicking follow. I’ve also listed everyone on the list at the bottom of this post, in case the list ever goes down.

Naturally, you should also follow anyone you know in real life if they’re on twitter and you want to get in touch.

Some Additional Lists

• RePEc maintains many lists of twitter users who are also on RePEc, broken down by country, field, institution type, and other. Click on the “More Twitter Listings” button on this page to see these lists.
• James Tierney (@James_Tierney) maintains lists of economics educators and high school economics instructors.
• Anna Gifty Opoku-Agyeman (@itsafronomics)’s list #NextGenEcon is a list of “The next generation of economics, policy, and biz that you should be following”
• The @WelcomeEcon twitter account is an account I set up with the sole function of broadcasting tweets new users compose introducing themselves to the #EconTwitter community (see below). It follows everyone it retweets, so the users it follows is also a list of who is new on twitter. At the same time, everyone who follows the account is also someone who is interested enough in #EconTwitter that they want to hear who is new (or they’re a bot; it happens).

After You’re Set Up

Once you have a community started, the main way you’ll learn about new people is:

1. Retweets from people you follow
2. Replies to your tweets and the tweets of people you follow

Whenever you run across someone who seems interesting, follow them. You can unfollow at any time, so if someone is annoying you with tweets that aggravate you, just unfollow. They can figure out that you unfollowed if they notice your profile no longer says “following you” or if they see you are missing from their list of followers, but twitter won’t give them any kind of specific notification that you have unfollowed them.

Finally, twitter at it’s best is a serendipity machine that lets you encounter ideas you wouldn’t normally see. The best kinds of innovation are frequently born from seeing a connection between previously unrelated ideas. So follow widely, outside your field and indeed, outside economics, so long as the people you follow enhance rather than detract from your twitter experience.

Getting Followers

You can get plenty out of #EconTwitter just by following other people. You don’t have to interact. But if you want to participate in the discussions or toss your own thoughts out there, you’ll probably want some followers — otherwise, you’re just talking into a void.

What follows is some common ways to get followers. It’s worth knowing about them, but to be honest, I wouldn’t recommend being too calculating or strategic about using this information. People can usually tell when you’re only doing something to get a follower; best to be genuine! Still, on the margin…

The first thing you can do is send out an introductory tweet. Something like “Hello! I’m an economist working on [topic] at [org], excited to join #EconTwitter!” If you have some followers already, they might retweet you to their own followers, essentially introducing you to the community.

I have also set up a dedicated twitter account (@WelcomeEcon) whose sole function is to retweet people who are introducing themselves to the #EconTwitter community for the first time. To take advantage of it, just tack an @WelcomeEcon to the end of your first tweet, or reply to your own tweet with @WelcomeEcon. That will notify me about the tweet’s existence next time I check the @WelcomeEcon account, and at that point I will make sure the account retweets your first tweet. This will broadcast your introduction to everyone who follows the @WelcomeEcon account, and this is a group self-selected to be interested in seeing who is new on #EconTwitter. The @WelcomeEcon account will also follow you at that point. (Note, it just retweets your first tweet, introducing yourself to the community, and not any followup messages you would like to broadcast.)

After your first tweet, one way to get followers is simply to follow other people. Anytime someone follows me on twitter, I get a little notification. A new follow is interesting, so I’ll usually click over to see who they are. If they seem interesting, I’ll follow them back. I can always unfollow in the future if they turn out to be annoying (almost never happens). This is one reason it’s important to be clear about who you are in your profile, since that’s how people will learn who you are and decide if they want to follow back.

I also get notifications whenever someone “likes” one of my tweets or replies to it, so I’ll usually check out who is responding and follow if they seem interesting.

This strategy won’t necessarily work of course. Some people have set up twitter to give them limited notifications (more on that below). Some people don’t have the time (or inclination) to look into who is liking or following them, especially if they are a big account that gets a lot of notifications. Some people don’t use twitter much. So don’t be offended if no one follows you back. It’s not personal!

After you get set up, the main way to get followers is to tweet interesting stuff. If your tweet gets retweeted by people who follow you, your tweet is exposed to a new audience of potential followers.

Another avenue is to reply to other people’s tweets. Your reply is viewable by other people reading the conversation. For example, suppose I tweet out a link to my latest publication. You and I follow each other, so you see my tweet. You reply to it (see how below) “Cool article. I’ve been thinking about this topic too.”

Anyone who follows me also sees my original tweet. They’ll also see a notification that there are replies to my tweet. If they click on my tweet, they will see all the replies, including yours. And maybe they are also interested in the same topic, so they decide to follow you.

Finally, if applicable, you can get yourself added to the RePEc lists of twitter users. Follow the instructions when you click “How to get listed” on this page.

How to Tweet

There are four main types of tweet.

1. The most common is a standard text tweet. You have 280 characters of text. Twitter uses a URL shortener that reduces the number of characters in any URL to 23 characters. You can add up to 4 pictures to a tweet, or 1 gif.
2. If you want to make a larger point, you can thread several tweets together. When you do this, your followers will typically see the first tweet in your thread, along with an indicator that there are more tweets in the thread. If they click on the first tweet, the thread is expanded and they see all your tweets in sequence. It’s kind of like reading a post sentence by sentence. Works surprisingly well!
   There are two ways to make threads. One option is to hit the little “plus” button in the corner when you compose your first tweet. You can do this as much as you like, drafting all the tweets in your thread at once and editing them before you hit “tweet all.” Alternatively, you can just compose your thread on the fly by hitting “reply” to the latest tweet in your thread. If you do this, followers will see each of your tweets as they come, instead of just the first tweet with an indicator that there is a thread below. But when you thread by replying to your own tweets, you can’t go back and edit the earlier tweets in the thread.
3. If you like what someone else said and want to share it with your own followers, you can retweet it by hitting the retweet button under the tweet. When you do this, you’ll have the choice to simply retweet or to quote retweet. If you simply retweet, your followers will see the tweet in its original state, with an indicator that it was retweeted by you. If you quote retweet, your followers will see the original tweet in a little box, embedded in your own tweet, where you have the standard 280 characters to make a comment on it.
4. You can also reply to other people’s tweets. Just click on the “reply” icon under the tweet. You’ll have the usual 280 characters to respond. Twitter will notify the person whose tweet you are replying to that you have replied, but it will also notify anyone “tagged” in that tweet. The etiquette of this is discussed a bit in the “best practices” section. In general, your followers will not see your reply unless they also follow the tweet you are replying to (but if they are really curious, they can find them by going to your profile and looking at your “tweets and replies”). People who follow the tweet you are replying to will not see your reply either, unless they choose to read the replies to that tweet (which is common).

There’s also an option to conduct a poll with your tweet. This lets you ask your followers a multiple-choice question with up to 4 answers. Respondents have between 5 minutes and 7 days to respond (your choice, but default is 24 hours). After that interval the results of the poll are visible to all, but individual responses are anonymous (except you can infer they come from the population of people who see your tweet).

You can also schedule your tweets. This can be useful if you think you have something to share that a lot of people will want to hear — maybe a new paper, or just a new idea — but most of your followers are not active at the moment. For example, if most of your followers are based in the USA and it’s the middle of the night, you might decide to write the tweet before you forget it, but schedule it to go out during waking hours.

It’s important to realize that twitter does not allow you to edit your tweets after they are posted. This can be really annoying if you make a typo on a tweet that becomes really popular, but it’s designed to prevent various abuses. Twitter (not #EconTwitter) is full of trolls who would likely abuse the ability to edit tweets, for example, by changing a benign message to an abusive one. Everyone who retweeted or liked the tweet would then be seen to be liking or retweeting an abusive message. If you really want to rephrase your tweet, you can always delete it and try again.

Lastly, it’s also possible to “like” tweets. Socially, this has roughly the same function as liking on any other social media. As noted above, users will usually be alerted that you like their tweet.

What to Tweet

Whatever you want! If you want to get an idea of what kinds of things people tweet on #EconTwitter, check out what the tweets from people in the #EconTwitter Starter Set.

Best Practices

Adhering to a couple best practices will also make your experience and the experience of everyone else better.

Tone

Probably the most important thing to realize is that #EconTwitter is not Econ Job Market Rumors (EJMR). It’s much friendlier and supportive. The community is quite diverse and everything you say is publicly viewable. Don’t be a jerk. Don’t punch down. Think carefully about how you phrase critical comments and tweets that can be misinterpreted. Bigoted comments will be called out.

Two things differentiate #EconTwitter from EJMR. First, everyone knows who you are. Yes, you can use a pseudonym, but it’s hard to get many followers that way. Anonymous trolls don’t get much traction. Second, everyone on twitter curates their own feed by choosing their followers. If they don’t like what you’re putting out, they can un-follow, mute, or block you (see below). Nobody has to follow anyone.

Pinned Tweets

Twitter lets you choose one of your tweets to be a pinned tweet. This tweet is always displayed at the top of your list of tweets, so it is a useful supplement to your profile. It can tell people about whatever it is you most want them to know about. It can also be a way to get traction on a tweet that didn’t get much attention the first time around, since everyone who checks out your profile will likely see the tweet. In academia, announcements of a recent publication are a popular kind of pinned tweet. To pin a tweet, click on the little icon at the top-right corner of your tweet.

Direct Messages

Twitter also lets you communicate privately with other users via direct messages (DMs). Normally you can only send direct messages to users who have followed you, though it’s possible to enable the ability to receive DMs from anyone (under settings; “privacy and safey”; receive direct messages from anyone). If you really want to talk privately with someone who isn’t following you, you can tag them in a tweet that requests they follow you.

Quote Tweeting vs. Replies

Retweeting with a comment (also called quote tweeting) and replying are both ways of responding to a tweet, but there are norms about how to use them. Quote tweeting posts your comment to all your followers, but not the followers of the person you are retweeting. Replying does not post your comment to your followers (unless they also follow the tweet you are responding to). This makes it tempting to respond with quote tweets by default. But actually, quote tweeting as a default is considered kind of rude.

There are a few reasons. First, it’s much harder to follow a conversation where everyone responds by quote tweeting. Replying keeps the conversation in one relatively easy-to-navigate place, while quote tweeting splinters it into many separate tweets that are hard to follow. Also, your quote tweet is posted to your followers, but not to the followers of the original tweet (unless they also follow you). So they can’t easily respond to you, and you end up hijacking the conversation.

Often this is perfectly appropriate. If you are not really looking to engage with the existing conversation and just want to make a quick comment, then quote retweeting is great. But if your goal is to actually have a conversation with people interested in the first tweet, replies are best. If you really want to bring your own followers into the conversation, you can reply and then retweet your own reply.

Tagging etiquette

When you “tag” someone in your tweet, that person will receive a notification that they have been tagged. You can tag someone by adding their username to your tweet (for example, I suggested new users introduce themselves and tag me by adding @mattsclancy so I would see the tweet and retweet it). These kinds of tags count towards your 280 character limit.

However, when you reply to a tweet, your reply automatically tags the person who posted the original tweet, and anyone tagged in that tweet. These tags do not count towards your 280 character limit.

Tagging creates two issues. First, if you are talking about someone else’s work, should you tag them? I don’t think there is unanimity of opinion on this, but my view is that when you are saying something nice about someone, it’s probably slightly better to tag them. Compliments about academic work tend to be rarer than criticisms (even friendly criticisms), so people appreciate hearing good things about their work. The issue is much stickier when you are being critical. Some people don’t like to be notified by twitter that someone out there on the internet is criticizing them. Others think being critical without tagging is tantamount to talking about someone behind their back. I don’t know what’s best here, but be aware of the issue.

The second issue with tagging is that when you are replying to tweets in a conversation, sometimes people will be automatically tagged who aren’t interested. For example, suppose economist A tweets a new article evaluating some health policy intervention. Economist B replies to the tweet saying they wished this article had used a DAG (directed acyclic graph) approach. Economist C responds to B’s comment asking where they can learn more about DAGs. Economist D responds to C with a list of textbooks. Economist E jumps in with some comments on which of these textbooks is their favorite. And so on. The thing with replies is that economist B’s reply tags A; C’s reply tags A and B; D’s reply tags A, B, and C; and E’s reply tags A, B, C, and D. If a long conversation then breaks out between D and E, every one of their back-and-forth will also tag A, B, and C. This can get annoying, especially if A doesn’t even care about DAGs.

To avoid this problem, you can untag people. When you are jumping into a conversation based on replies to a tweet, it can be a good idea to take a look at who is tagged and to untag anyone who doesn’t seem particularly engaged in your corner of the conversation.

Blocking and Muting

As noted above, if another user irritates you, there are a hierarchy of responses.

Suppose @troll is bothering @economist. First, @economist can unfollow @troll. They’ll no longer see @troll’s posts, but this does nothing to dissuade @troll from bothering @economist if they are persistent. @troll can still see all of @economist’s posts, and can reply and tag @economist, which brings up a notification for @economist each time.

The next step up the chain is to mute @troll. This blocks all communication from @troll to @economist. @economist will no longer see @troll’s tweets, even when @troll tags @economist or replies to their tweets. And @troll is not told they’ve been muted, though there are third-party apps they can use to figure this out.

The problem with muting is that a particularly nasty troll can still pollute your threads for other followers. By muting a troll, you don’t see what they’re doing, but all your followers do and @troll can still interact with them. This is why many people prefer to block irritating users. Blocking a person means they can no longer see your tweets, and therefore cannot reply to them either. This means your followers do not see @troll either, unless they follow @troll.

That said, really nasty trolls command followers that they can ask to harass you on their behalf. It can be exhausting to individually block all these followers, and twitter doesn’t have great tools yet for dealing with this.

For a variety of reasons, I haven’t had to deal with any kind of harassment: I’m not that popular on twitter, I’m not part of a demographic that people are bigoted against, and the stuff I study isn’t too controversial. But those who do have to deal with twitter harassment seem to recommend a policy of fast and easy blocking to keep the experience positive. Basically, it’s fine to block for the slightest infraction: e.g., a single rude comment. There are also lists of twitter users who are known trolls, and some people will just automatically block everyone who appears on these lists. You can always unblock later if you’ve made a mistake (this usually happens via an intermediary — the blocked party asks another user to ask you to unblock them).

Finally, one last trick: if you want someone to stop following you, you can block and then immediately unblock them. This drops them from your follower list (and does not alert them to this fact), though they can re-follow you if they want.

Fencing Off Your Account

Just as there are a hierarchy of ways to deal with unpleasant users, there is a hierarchy of privacy settings on twitter.

At the lowest, default level, anyone on the site can see and interact with your tweets. Note, however, that unless they follow you, or you are retweeted by someone they follow you, your tweets won’t typically show up on their timeline.

At a step up from that, you can control who is able to interact with your tweets by clicking on the default text “everyone can reply” when composing a tweet. This will open a set of options that increasingly restrict who is allowed to respond to that particular tweet:

• Everyone can reply: this is the default and allows anyone on twitter to reply to the tweet
• People you follow: this will lock your tweet so that only people you follow can reply to the tweet.
• Only people you mention: this will lock your tweet so that only people mentioned directly in the tweet itself (see tagging below) can reply.

Note that in all of the above options, the tweet will still be visible to everyone on twitter, and that everyone on twitter can retweet you, including retweeting and adding their own comments. You will be notified that you’ve been retweeted, but you won’t see any replies to quote retweets (unless you go looking for them).

More broadly, you can also limit your notifications in a similar fashion. By default, you are notified anytime someone likes your tweets, replies to you, retweets you, or follows you (though twitter has some kind of algorithm designed to weed out notifications from bots and other “low quality” interactions). But you can also selectively mute notifications. For example, you can mute all notifications from twitter users who have not supplied a phone number or email address, have not supplied a custom image, or have very new accounts, all of which filters out some kinds of anonymous troll accounts. You can also restrict notifications from people who follow you or who you follow. To change your default notifications, go to “settings and privacy”, then “notifications”, then “filter notifications”, then “muted notifications.”

Note again, this doesn’t prevent others from viewing or interacting with you. But since twitter won’t alert you to their attempts to interact, you won’t know it’s happening and presumably won’t feel bothered by it.

But if you also want to prevent unwanted people from seeing or attempting to interact with you in any way, as a last step, you can lock your account. If you lock your account, people will need your permission to follow you, and if they don’t get that permission, they will not be able to see your tweets at all (or interact with you in any way). An important caveat is that if you lock your account after it’s been public, everyone who has previously followed you (while your account was public) will still be following you after your account is locked. Thus, if you start to have problems with new followers, taking your account private (by locking it) won’t exclude those new followers who are causing problems, though it will prevent new people from following you. To eject those troublesome new followers, you’ll want to use blocking tools instead (described above). To lock your account, go to “settings and privacy”, then “your account”, then “account information.” At this point, you’ll need to re-enter your twitter password. Then, click on “protected tweets” and check the box to protect your tweets.

Focus?

To close, Gray Kimbrough (@graykimbrough) had this advice: “My biggest suggestion is to spend some time thinking about what you want your contribution to be. Many people here feel like they need to comment on every issue and event. Focusing on areas where you have value to add (your area of research? Baking?) is helpful, in my opinion.”

I think that advice is particularly useful for someone new to twitter. When you don’t know anyone, focusing on a particular area can help potential new followers figure out who you are and give them a reason to follow you, especially if the “one thing” is your strong suit. Speaking for myself, I get way more interest in my tweets on the economics of innovation (my area) than my attempts at jokes (even when I think they’re quite good).

Some people go as far as to set up separate twitter accounts that each focus on different topics (e.g., professional and personal, economics and politics). On the other hand, as Gray notes plenty (plenty) of people don’t follow anything like this kind of rule and it works for them.

Additional Resources

Justin Wolfers (@JustinWolfers) made a presentation about using twitter as an economist back in 2015.

Sarah Jacobson (@SarahJacobsonEc) has written a similar guide to using twitter professionally. See pages 21–24. An updated set of slides available here.

Anne Burton (@anne_m_burton) gives a grad student perspective in these slides.

The End

Hope you found this helpful! If you have any suggestions, questions or comments, you can email me. Better yet, contact me on twitter!

This has been edited on August 5, 2021. For the original 2019 version, see http://matt-clancy.com/a-beginners-guide-to-econtwitter-preliminary-version/

The best thing we could do for rural economies is to kill the link between physical proximity and agglomeration benefits by using information technology to push agglomeration benefits out of physical space and into the digital realm. My vision is a world where remote work is common (if not the norm), and where better online social networks and socializing options substitute for the invisible networks of social connection and information exchange which are currently based on physical proximity. If people everywhere can enjoy the benefits of agglomeration without the need to physically cluster together there will no longer be a need to flee the country for the city. If we succeed, not only will we bring rural economies into the virtuous cycle of agglomeration benefits, but we may even accelerate innovation and growth by connecting up the entire country into one digital city.

An Idea Ahead of it’s Time?

This is hardly a new idea. At the dawn of the internet era, it was conventional wisdom that the world was flat and the internet would spell the death of distance. It didn’t happen. Instead, the rising importance of knowledge work favored returns to agglomeration more than ever before, accelerating the clustering of knowledge workers into cities. So what went wrong?

I think we all made the classic mistake of underestimating how long it would take for us to figure out how to effectively use a new general purpose technology. I’m reminded of how it took decades for the benefits of electrification to become fully manifest. It wasn’t particularly effective to simply substitute electric motors for the old gigantic steam ones; new distributed production systems had to be invented and built. Why should it be any different for the internet?

It may yet turn out that the death of distance is right, but was just ahead of its time.

In this post, I’ll point to three benefits of physical agglomeration, and speculate on how people can use the internet to retain these benefits without the need for physical proximity (today or in the future). What’s changed is not so much technical advance (although that has happened as well), but social innovations in how we interact with each other online. It may yet turn out that the death of distance is right, but was just ahead of its time.

Making Remote Work Work

Most obviously, pushing agglomeration benefits out of meat space and online will require making remote work work. I have some experience with working remotely. At my previous job in DC I worked remotely two days a week. Most people I socialized with worked remotely at least some of the time too. After moving back to Iowa, I continued to work remotely both at home and out of a regional field office. In my current position, I collaborate with researchers who are geographically separated.

The biggest challenge to remote work is the perception that a remote work force is not as productive as a physically present one. This may because collaboration is hobbled when workers are not physically together or because individual workers shirk when working remotely. In my experience, remote workers are quite heterogeneous but these criticisms are true of some.

Let’s take collaboration and communication first. The trouble with remote work is the increase in transaction costs to transmit information. This isn’t a problem for important information because the value of the communication exceeds transaction costs. When something important needs to be communicated, a conference call, emailed slide deck, or memo will get the job done.

The problem is instead the heap of minor communications, too small to individually exceed transaction costs but cumulatively significant. It’s the absence of spontaneously popping by a co-worker’s cubicle to ask for a clarification, advice, or simply to chat. It’s the absence of body language cues in a teleconference call. I’ve even heard people say the lack of peripheral vision and pheromones impedes communication in meetings. Collectively, these gaps in information add up; problems/opportunities are not identified as early as they could; corporate culture is harder to sustain; trust between employees is harder to build.

But I think a lot of these problems are already being solved by a combination of technological and social innovations. Things like slack substitute for “popping in to ask for a clarification.” Better video technology adds back visual cues in a teleconference. But perhaps most important are cultural adaptations. It’s been interesting to see gifs and emojis emerge as an increasingly robust substitute for body language and vocal tone. Video chatting is something we’re all getting experience with now. And there is at least one highly selective university where all student collaboration happens online.

perhaps most important are cultural adaptations.

Similarly, I think we’ll find ways to solve the problem of worker shirking. In my experience, some workers do take advantage of reduced monitoring to shirk their duties, but this tends to be because old systems of monitoring effort (like walking around and keeping an eye on people) are ill suited to remote work. In principle, it’s actually easier to monitor the work of remote workers than physical ones, since you can make a comprehensive record of their digital work (for example, by continuously recording what’s on the screen). Now, that may not end up being necessary. Maybe giving managers the option to call up a live view of their worker’s screens will suffice to prevent shirking. Or maybe a working norm will evolve where you can see your whole team all the time, muted in a ribbon at the bottom of your screen. Or something else entirely.

Thick Labor Markets

A second benefit of urban economies are thick labor markets. If you want to make a movie, you can be confident there will be a plethora of people with just the skills you need, if you live in Los Angeles. If you want to make a new digital product, there will be no shortage of qualified engineers if you locate in the Bay area. If you need the capacity to rapidly scale up manufacturing, qualified workers and managers can be found in Shenzen.

But it’s not just the presence of these workers that matters. It’s also the network of informal social connections that helps match people to the right jobs. You can ask a trusted colleague for advice on a candidate or the firm they work at. Once we can make remote work work, the biggest problem will be building a comparable system for matching remote workers to employers.

In the long run, if we succeed in pushing a lot of work online, this won’t be an issue because the same informal networks that exist in cities will exist in online communities. To some extent then, we just need to be patient. If remote work works, then there will probably be cost advantages to using it (a lower cost of living in rural economies will mean remote workers can out-compete urban ones in terms of wages). Industries most suited to remote work will lead the way, and informal professional networks will follow.

But this process can be accelerated with online services that match employers with employees, like LinkedIn or Upwork. But to really substitute for thick labor markets, we need the digital equivalent of person-to-person networking events and other mixers. Offline, it may be enough to serve drinks, put on some kind of notional speaker, and then let people socialize. But new kinds of events will be needed online. The best candidates to my mind are online games and collaborative educational workshops.

Instead of an after work mixer at the bar, there could be an after work fortnite party (or some more professionally appropriate game). We’ll have to experiment to find what works best, but I suspect a game with low cognitive burden (so you can focus on socializing) and frequent shuffling of the players would be ideal.

Instead of an educational workshop, imagine an online workshop that uses active learning pedagogy, so participants have to interact to learn the material. As with remote work, a generational shift may make existing technology much more effective in the future. The combination of better technology and the ascent of “nerd culture” means more young people today have extensive experience socializing over large online multiplayer games. At the same time, the rise of online active learning education (though nascent) means the next generation may have experience doing school work in a collaborative online environment.

Knowledge Spillovers

Most subtle of all, but perhaps most important in the long run, are the agglomeration benefits related to innovation. Density appears to promote innovation. Why?

Part of it is thick networks. Most innovation happens in teams, and if you know more people, you can put together a better team. For ideas in a very preliminary stage, big networks make it easier to find someone trustworthy to talk things over with. The same kinds of digital activities that promote professional network development could also promote innovation supporting networks.

But cities are also probably better for innovation stemming from serendipity and unexpected connections. Websites like twitter are probably the best online substitute for the unexpected connections and encounters that occur in dense cities. Though there is a strain of toxicity running through much of the site, it also houses wonderfully supportive online communities gathered around common interests.

My experience is with #EconTwitter, a collection of professors, researchers, private sector economists, students and enthusiastic amateurs. The forum genuinely facilitates the open exchange of ideas and knowledge, including much of the “hidden curriculum” that is traditionally communicated in person-to-person contact, and is not typically codified in handbooks and academic articles. I’ve met people in real life that I first encountered on twitter, and even started a research collaboration with someone I’ve never met in the real world. And I’m sure many others have had similar experiences.

I have a hunch that one reason for the advantage of physical meeting of video conference (so far) has been that their high bandwidth nature makes it easier to build trust and openness. This, in turn, gets people to let down their guard and share some of their wilder ideas. Many of these are likely to be duds but wilder ideas are also more likely to lead to major innovations.

If online norms can evolve to make people comfortable taking a risk and sharing ideas that might be bad (e.g., maybe this whole post?), then it may turn out the internet is even better at promoting fruitful connections leading to innovation, since the audience for such ideas is so potentially large. The trouble is, with the internet (especially twitter), unlike at the bar, a screenshot can make a permanent record of any bad take. There may be some technical fixes, but again I think (hope?) in the end we’ll just develop new social norms to deal with it. Part of that may be thick skin, part of it may be liberal uses of block/mute features, and part of it may be an increased threshold before we get outraged about something. Indeed, I see all three of these being hashed out online even now.

Policy Implications

To some extent, all of this is just going to take time. We need to learn to communicate as well online as we do face-to-face, and how to run organizations online. We’re on our way, but it may take a generation raised in this environment. But there are some obvious things we can do to hasten its arrival.

First off, it’s crucial to promote rural broadband and other IT infrastructure, if this policy is to get off the ground. In addition to simply laying down the cable necessary to connect rural communities, this could include low-interest loans to individuals to purchase computer equipment they would need to work remotely. Alternatively, it may turn out to be better to build small communal co-working spaces with necessary equipment and an on-site IT technician.

Second, to promote the use of remote work, it may be desirable for states and small towns to offer wage subsidies and other incentives for distant firms to hire local remote workers. This would be a micro version of the much larger tax breaks that are used today to try and lure businesses to invest locally. The argument for subsidies for remote work from the perspective of an economist is that there is a positive externality from remote work. As more firms experiment with remote work, best practices for its conduct will emerge and this knowledge will diffuse, tipping other firms to increase their use of remote workers.

Third, local universities should offer online degrees tailored to the needs of remote workers. By being online, the courses would be accessible to people who would most hope to subsequently obtain remote work. The degrees would also provide an opportunity to develop the soft skills necessary to be a successful remote worker. They could even practice collaborative team-based pedagogy, to accelerate the development of norms for online collaboration and work.

Fourth, further research on what kinds of social networks facilitate the free exchange of information and formation of social ties is needed.

Fifth and finally, we can promote online gaming and other communal digital activities. Promotion could be cultural, in that we encourage kids to join e-gaming leagues in the same way we now encourage physical sports leagues. Or it could be financial, with subsidies to these industries until they reach a level of maturity where they are a common mode of meeting new people.

Fifth and finally, we can promote online gaming and other communal digital activities.

There are other aspects of agglomeration economies that also warrant discussion, but for which I have little to add. Capital and finance tend to be clustered in certain geographic regions. Transport infrastructure may be another avenue worth exploring, since it will probably be desirable to continue to maintain some physical interaction. And it’s certainly true that not all industries are amenable to remote work.

I hope we can figure out a way to make it work, because as other have noted there are few other options. Industries where rural areas have comparative advantage, such as agriculture, are rare and increasing productivity means they are shedding workers. And the industries of the future appear to be ones where agglomeration matters. It’s just too powerful a force to be ignored.

Originally published at matt-clancy.com on March 29, 2019.

Minerva is a new university best known for its global campus. Students spend their first year in San Francisco, and then each class moves en masse through Seoul, Hyderabad, Buenos Aires, Berlin, London, and Taipei over the remaining six semesters. During my undergraduate years, studying abroad was an important experience for me, so I’ve been interested in Minerva since I heard about it a few years ago. Earlier this year I read their semi-official book Building the Intentional University: Minerva and the Future of Higher Education (eds. Stephen M. Kosslyn and Ben Nelson). But after reading the book, I’ve come to think the global campus is a bit of a sideshow. Minerva’s really innovative idea is actually in the considerably less flashy domain of curriculum design.

In this post, I’ll briefly explain why I think their curriculum is so interesting and what problem it is trying to solve. But up front I want to make it clear that I don’t work for Minerva or even know anyone who does. I just read their book, some other books, and teach at a university myself.

Minerva’s curriculum is designed to foster certain habits of thought. These are habits we would associate with critical reasoning, problem solving, communication, leadership, and teamwork skills. So far, so bland. What curriculum isn’t designed to foster good habits of thought? But as we will see, most universities do a fairly poor job of teaching these skills. The difference may well be that where most universities are laissez-faire about how they teach good habits of thought, Minerva is intentional.

Before turning to the Minerva approach, it’s necessary to take a minute to establish the disappointing track record of the traditional approach, and to suggest some reasons why it has disappointed. This is all laid out quite well in Bryan Caplan’s The Case Against Education: Why the Education System is a Waste of Time and Money.

What Does Education Do?

Caplan’s book is about explaining why an extra year of education is correlated with a 10% increase in wages. The traditional explanation for this fact is that education builds valuable knowledge and skills, called “human capital”, which makes graduates more productive employees. Caplan’s central argument is that education is primarily (80%) about signaling, rather than the accumulation of useful skills and knowledge. By “signaling,” Caplan means that the primary purpose of education is to certify students as smart, diligent, focused, submissive, and conforming to social conventions. This basket of characteristics is highly desirable to employers and employees with these characteristics are compensated with higher wages. The key assertion is that education merely certifies these traits; it does not build them.

While he makes several arguments that signaling rather than human capital explains the educational wage premium, the most convincing to me is simply the observation that higher education does not appear to build many skills useful to employers.

I think of myself. I did a double-major in Physics and Religious Studies for my undergraduate degree and was a very strong student. For the most part I loved the experience. But when I look back at my transcripts, I count five classes for which I would struggle to remember more than a sentence-worth of content. I count four more classes that I don’t even remember taking. As for the rest, while I often found the material interesting, I think the professional applicability of the specific facts, models, and tools I learned is quite limited. Indeed, I spent two years as an economic analyst after graduating and never used any physics (actually, the religious studies mattered more!). This is not to say nothing was useful. But Caplan’s estimate that only 20% of what we learn in school directly contributes to work productivity seems on the face plausible.

And I don’t think my experiences are that unusual. Naturally, there are majors and courses that build a lot of skills and knowledge directly relevant to certain career paths. But as Caplan points out, even here the human capital story faces challenges, because students frequently fail to get jobs in the field closest to their major. Few science majors become scientists, few psychology majors becomes psychologists, few economics majors become economists and so on. And then there are all the other majors that do not even try to build skills for specific professions. This is not to say such degrees are a bad idea (wages aren’t everything!), only that the human capital story as an explanation for the positive education-wage correlation looks weaker when we look more closely at what is studied. (Caplan also argues education does a bad job of building wisdom and other non-pecuniary forms of human capital, but I’m not going to go into that).

Deeper Knowledge?

The natural retort to this line of argument is that the specifics of curriculum do not matter because education develops deeper capacities. What is “really” taught are habits of thought: how to construct and evaluate arguments, how to weigh evidence, how to communicate effectively, etc. Whether we study English, economics, engineering, or entomology isn’t particularly relevant. They just give us the raw material for us to practice and hone critical thinking and communication skills.

For Caplan, this is wishful thinking. There are a host of studies that look for domain general forms of learning, and the results tend to disappoint. Caplan describes a study measuring the quality of arguments and reasoning given by students at different points in their education. Fourth year undergraduates did no better than first year undergraduates, and fourth year graduate students did only marginally better than first year graduate students. Another study looks at how students apply statistical reasoning to real life examples, again finding the vast majority fail to do so.

Another way to think about this issue is through the lens of knowledge transfer. It may not matter that people retain specific facts and surface details as long as they do retain the deep structure of theories and ideas. They can then apply these deep structures to contexts with different surface details. Unfortunately, a body of work by educational psychologists finds that people do not naturally transfer models and ideas from one domain to another. Most of the things we learn get “stuck” in the context in which we learn them. If we don’t use those skills frequently, they fade away within a few years. This is the fate of most of what we learn in school.

The problem is this: students tend to learn only what will be on the test and domain general thinking is not on the test. Again taking myself as an example, I grade my students on how well they do in my class; I do not follow them out into the world (or even into other classes) and adjust their grade based on how well they apply the models I teach them in novel contexts. The end result is that knowledge remains bottled up in the context of the specific domain it was learned and unless a student builds explicitly on that domain, it is lost through lack of use.

Reorganizing Undergraduate College

Caplan’s solution to this problem is to scale back the signaling arms race by cutting education subsidies and to expand curriculum that explicitly teaches skills employers want — namely, vocational training. Minerva, in contrast, thinks it can actually teach those habits of thought that we want college to develop. But to do that, it recognizes that it needs to change the way college is traditionally organized. It does this in three main ways:

1. The curriculum explicitly teaches and assesses domain-general habits of thought
2. It teaches these habits of thought in varied contexts
3. It assess student mastery over all four years of education

Let’s take these in turn.

The curriculum explicitly teaches and assesses domain-general habits of thought

Today’s universities spend the majority of their time explicitly teaching and assessing students on their mastery of domain-specific content. We trust this effort will also lead to the development of domain-general critical thinking and communication skills as a useful by-product. For example, if you take an economics class, you are taught economics and assessed on your command of it. However, to the extent mastering economics requires deeper critical thinking skills, you will pick those up too.

Minerva flips this around, and explicitly teaches and assesses students on their mastery of 100 domain-general “habits of mind.” When I say “explicit” I mean it; the 100 habits of mind are enumerated and spelled out in appendix A of Building the Intentional University. Course content is selected to illustrate and practice the use of these principles.

What are these habits of mind? Examples include:

• Evaluate whether hypotheses lead to testable predictions.
• Identify and minimize bias that results from searching for or interpreting information to confirm preconceptions.
• Apply effective strategies to teach yourself specific types of material.
• Tailor oral and written work for the context and the audience.

Students are assessed on their successful use of these and the other 96 habits of thought both through coursework and during classtime. Classes are delivered online with students participating via webcam, and so there is a video record of all class discussions and activities. Classes are designed so that no more than 25% of class time is spent passively absorbing material, meaning students spend the majority of class time on activities in which they can practice or demonstrate habits of thought. Typically a few habits will be emphasized per week. After classes, professors go back through the footage and assess students’ use of habits of mind, offering feedback to students.

If you want to teach deep thinking habits, it’s probably best to explicitly teach them, rather than to trust they will be extracted from course content. In Caplan’s discussion of the literature on critical thinking skills, he describes a study where students were taught a solution concept either explicitly as an algebraic technique, or in the guise of a structurally equivalent physics technique. Students were then asked to solve a problem using the same solution technique, but in the guise opposite to what they learned (so algebra students solved a physics problem and physics students solved an algebra problem). Most of the students who studied the algebraic version of the technique used it on the physics problem, but few of the students who studied the physics technique used it on the algebra. It seems to be easier to transfer knowledge from the general to the specific than vice-versa.

It teaches habits of thought in varied contexts

While Minerva teaches and assesses domain-general “habits of mind,” some concrete context is still necessary. You can’t really teach someone how to “evaluate whether hypotheses lead to testable predictions” in some kind of abstract Platonic ideal. All these habits of thought are instead practiced in the context of more traditional course content. The problem is that anytime you introduce context, you run the risk of trapping the habit of mind in that specific context. As noted above, far transfer is hard. If you practice the “testability” habit of mind in a psychology class, it might never occur to you to use the same habit in a business context.

To address this problem, all Minerva students take the same courses in their first year. These courses give students a foundational liberal arts and sciences education. This naturally provides many different contexts for students to practice the same habits of thought, helping prevent these habits from getting trapped in the context where it was first taught. And in all of these courses, students are graded on their mastery of these 100 habits of thought.

Because of this scheme, far transfer itself becomes a skill you learn to cultivate and practice. You will learn a habit of thought in one class and be graded on your ability to use it in a different one. I can’t grade a student on her ability to apply the concepts I teach her in her other courses, but in Minerva something like this is the norm. This is only possible because the first year curriculum and assessment criteria are collaboratively developed by the Minerva faculty.

It assess student mastery over all four years of education

A final challenge to learning these habits of mind is we quickly forget what we don’t use. To avoid this fate, Minerva adopts an unusual grading system. As noted above, students are graded in their first year on their mastery of 100 habits of thought. But this grade is retroactively adjusted over the following three years, based on how well students continue to use habits of thought. Rather than learning something for a semester and then forgetting it, students must maintain (or try to improve) their first-year grades by continuing to demonstrate mastery over these habits. Again, something like this is only possible when the curriculum is collaboratively developed by the entire Minerva faculty. It only works because professors teaching later courses “buy-in” to it.

Can Existing Universities Adopt these Ideas?

To summarize, Minerva tries to teach habits of thought by explicitly teaching them, assessing them, and then forcing students to practice far transfer and retention by requiring the use of the habits across all classes for four years. I think this is all pretty cool, and if it works it’s exciting to think we have found a better way to teach domain-general thinking.

However, it works for Minerva because curriculum design has become a collective rather than an individual endeavor. It’s hard for existing institutions to completely adopt this format, since professors have so much autonomy in what and how they teach. But to close, I’ll toss out a few ideas for how a traditional university might experiment with some of the same concepts ideas.

One incremental step could be pairing up complementary classes, who then share assessment. For example, a writing class could pair up with a philosophy class where students have to write essays. Students would learn explicit habits of thought — in this case about writing — in one of the classes, and practice that knowledge in another. The essays would give students a specific context in which to practice their writing skills, and could be jointly graded by both instructors: the philosophy instructor grading for philosophy, the writing instructor for writing.

A step beyond this would be the creation of an explicit “habits of thought” class that is taught every semester. Like Minerva, it would explicitly teach and assess mastery of habits of thought. But the class could be structured so that students are assessed on how they use these habits in their other classes, which would be taught in traditional ways. For example, perhaps students write reflections at the end of each day on how they used the habit of thought in their other classes. Or perhaps students taking the habits of thought class are organized into small groups of students who are taking the same classes, and these could be a forum for discussing how the habits of thought were applicable (or not).

A further step would be the creation of a “habits of thought” minor with its own set of core classes organized much like Minerva’s first year. This minor would lock all students into a required set of classes in their first year, but these courses could be carefully honed so that students can slip into the second-year stream of traditional majors. For example, a student could become an economics student with a minor in “habits of thought” by taking a foundational year that covers a broad set of classes satisfying normal general education requirements. Ideally, these classes would also include enough of the material taught in introductory economics courses so that students could jump into an intermediate course in the following year. The minor could continue to meet throughout the next four years to keep the habits of thought in practice as well.

Originally published at matt-clancy.com on February 18, 2019.

Caveat: I liked all of these. I abandon books I don’t like.

Conceptual Non-Fiction

1. The Second World Wars by Victor Davis Hanson: Wonderfully organized opus on World War II as a contest of national productive and organizational capacity.
2. The Book of Why by Judea Pearl and Dana Mackenzie: Correlation can imply causation, (when you combine data with models).
3. The Enigma of Reason by Hugo Mercier and Dan Sperber: Reason as a social influence tool that, as a bonus, helps you figure out how the world works.
4. Old Masters and Young Geniuses by David Galenson: Aesthetic innovation comes from either evolutionary processes (tinker and evaluate) or reason (plan and execute).
5. The Son Also Rises by Gregory Clark: Social status is really sticky across generations.
6. The Measure of Reality by Alfred W. Crosby: Between 1250 and 1600 in Europe, numbers and measurement colonized new domains, potentially setting up our current paradigm of continuous technological progress.
7. On Writing by Stephen King: Moving story of King’s own entry into the writing life, and his intuitive story-first method of writing (he’s an evolutionary creator).
8. The Great Leveler by Walter Schiedel: Final two sentences sums it up; “All of us who prize greater economic equality would do well to remember that, with the rarest of exceptions, it was only ever brought forth in sorrow. Be careful what you wish for.”
9. How to Read a Book by Mortimer J. Adler and Charles Van Dorn: Very useful, but I need to read it again correctly.
10. Radical Markets by E. Glen Weyl and Eric Posner: A feast of ideas to wrestle with. (related post)
11. Cognitive Gadgets by Cecilia Heyes: Pushing cultural evolution even farther; the ways we think and learn are themselves cultural products.
12. Surfaces and Essences by Douglas R. Hofstadter and Emmanuel Sander: I wasn’t a fan of the presentation, but it’s an impressive feast of ideas to wrestle with. (short review)
13. The Allure of Battle by Cathal J. Nolan: Using the history of (mostly) Western war to argue warmakers endlessly underestimate the cost and duration of their wars (a good companion to my #1 pick). (related post)
14. Capitalism without Capital by Jonathan Haskel and Stian Westlake: Goes a long way towards explaining several contemporary economic puzzles.
15. Misbehavin’ by Richard Thaler: How to shift a paradigm.
16. The Disruption Dilemma by Joshua Gans: It’s more complicated than Christensen claims.
17. Free Innovation by Eric Von Hippel: Neat little book on innovation outside the market system (also, it practices what it preaches).
18. Improbable Destinies by Jonathan B. Losos: Evolution happens faster than you think.
19. Zero to One by Peter Thiel: Efficiently communicates a lot of original ideas.
20. The Hungry Brain by Stephan Guyenet: Good overview of the brain and will convince you that dieting is complicated.
21. The Lean Startup by Eric Ries: More like “The Lea(r)n Startup.”
22. Theory and Reality by Peter Godfrey-Smith: Very good overview of the basics of philosophy of science.
23. True Stories and Other Essay by Francis Spufford: Probably for Spufford fans only (I count myself one).

Fiction and Narrative Non-Fiction

1. Spring by Karl Ove Knausgaard: A left turn in the seasons quartet that completely recontextualizes Autumn and Winter.
2. The Dispossessed by Ursula K. LeGuin: A masterpiece of worldbuilding, exploring how an anarchist society would work.
3. The Worst Journey in the World by Apsley Cherry-Garrard: Who knew Earth could be so inhospitable to its children?
4. Winter by Karl Ove Knausgaard: See the world new again.
5. The Age of Innocence by Edith Wharton: The impossibility of shrugging off society’s constraints and retaining its gifts (a reread).
6. Educated by Tara Westover: A stew of ideas — gaslighting, abuse, cultural immigration, the limits of familial reconciliation, and the founding of religions.
7. Pet Sematary by Steven King: A heatseeking missile to the heart of this new parent.
8. The Fifth Season by N.K. Jemison: Exploring grief and prejudice in a geologically active fantasy world.
9. Bad Blood by John Carreyrou: Riveting account of Theranos’ rise and fall.
10. Golden Hill by Francis Spufford: The pre-revolutionary war New York is a wonderful setting.
11. The Player of Games by Iain M. Banks: Explores similar terrain as the Dispossessed.
12. White Darkness by David Grann: The strange siren call of the Antarctic.
13. The Everything Store by Brad Stone: Solid story of the rise of Amazon.
14. Louis Riel by Chester Brown: Nuanced story of a complicated revolutionary.