Main Street Entrepreneurship is Back

Silicon Valley venture-backed tech startups have had a wildly successful twenty years, coming to dominate the markets. But tech remains a relatively small sector in terms of the total number of businesses and employees, and by many measures entrepreneurship and small business have been in relative decline in the US during the 2000’s.

Source: Business Employment Dynamics data compiled by Kauffman Foundation https://indicators.kauffman.org/reports/2021-early-stage-entrepreneurship-national

Covid accelerated many pre-existing trends, like the shift to remote work. But it reversed other trends, and seems to have led to a revival in entrepreneurship broadly.

Source: Current Population Survey Data complied by Kauffman Foundation https://indicators.kauffman.org/reports/2021-early-stage-entrepreneurship-national

A new report from the Kauffman Foundation, “2021 NATIONAL REPORT ON EARLY-STAGE ENTREPRENEURSHIP IN THE UNITED STATES“, illustrates this reversal, showing that the rate of new entrepreneurs is the highest its been since at least 1996.

This wasn’t all good at first- in 2020, the share of “necessity entrepreneurs” also reached record highs. These are people who start a business because they can’t get the job they want, not because they expect their business to be wildly successful. But in 2021, the rate of new entrepreneurs remained high while the share of “necessity entrepreneurs” and “opportunity entrepreneurs” returned to their normal balance.

Another good sign is that the share of businesses surviving at least a year is also at record levels:

More cracks in the Great Stagnation.

FTX Future Fund

Crypto is a lot of things- a store of value, a means of payment, a building block for other tools on the web. But while much of its value as a tool is yet to be realized, one big effect we see already is that it has made a lot of nerds very rich very young, even by the standards of tech and finance generally. These newly minted millionaires and billionaires have started giving their money away in very different ways than the traditional older philanthropists.

The latest, and I believe biggest example is the FTX Future Fund. It plans to give away at least $100 million this year, funded primarily by 30-year-old Sam Bankman-Fried, the CEO of crypto exchange FTX. I recommend that everyone read their full list of the 35 types of projects that they’d like to fund, but I’ll highlight a few you wouldn’t see from older foundations:

Demonstrate the ability to rapidly scale food production in the case of nuclear winter

Biorisk and Recovery from Catastrophe

In addition to quickly killing hundreds of millions of people, a nuclear war could cause nuclear winter and stunt agricultural production due to blocking sunlight for years. We’re interested in funding demonstration projects that are part of an end-to-end operational plan for scaling backup food production and feed the world in the event of such a catastrophe. Thanks to Dave Denkenberger and ALLFED for inspiring this idea

Prediction markets

Epistemic Institutions

We’re excited about new prediction market platforms that can acquire regulatory approval and widespread usage. We’re especially keen if these platforms include key questions relevant to our priority areas, such as questions about the future trajectory of AI development.

Critiquing our approach

Research That Can Help Us Improve

We’d love to fund research that changes our worldview—for example, by highlighting a billion-dollar cause area we are missing—or significantly narrows down our range of uncertainty. We’d also be excited to fund research that tries to identify mistakes in our reasoning or approach, or in the reasoning or approach of effective altruism or longtermism more generally.

They also seem to be borrowing some of Tyler Cowen’s approach to Fast Grants and Emergent Ventures- the application is relatively short and simple, and they promise response times that will be measured in weeks, rather than the months or years typical of large funders.

But they expect applicants to be fast too- this fund was just announced a few days ago, and applications are due March 21st. Economists will be natural fits for some of their project ideas, since their areas of interest include “economic growth” and “epistemic institutions”. I’ll be applying with my book project on why US health care spending is so high. But they are clearly casting a wide net to find the best ideas, so I encourage everyone to check it out and consider applying.

Lessons from a Failed Merger

The two largest hospital systems in Rhode Island, Lifespan and Care New England, wanted to merge. I wrote previously that:

Basic economics tells us that if a company with 50% market share buys a company with 25% market share in the same industry, they have strong market power and are likely to use this monopoly position to raise prices…. I think the Federal Trade Commission will almost certainly challenge the merger, and that they will likely succeed in doing so

It turns out I was right about the FTC challenge, but wrong that it would be necessary. The same day that the FTC challenged the merger, Rhode Island Attorney General Neronha blocked it. The law in Rhode Island is such that he doesn’t need to convince a judge like the FTC would; the merger was done unless the parties tried to appeal. But today they gave up and officially terminated the merger.

I was surprised by the AG’s move because the merging parties have so much political clout in the state, and many politicians like Senator (and former RI AG) Whitehouse had expressed support for the merger. I expected that even if state leaders didn’t like the merger, they would approve it with the expectation that the FTC would step in and be the bad guy for them. So AG Neronha blocking the merger was a pleasant surprise.

I also said previously that the FTC might challenge the merger for creating a monopsony (predominant employer of health care workers) as well as a monopoly (predominant provider of hospital services). This turned out to be one vote short of true. The FTC voted 4-0 to challenge the merger, but released two concurring statements explaining why. The two Democratic commissioners wanted to challenge the merger on both monopoly and monopsony grounds, while the two Republican commissioners thought it would only be a monopoly. This didn’t matter for this case, since they all thought it would be a monopoly, and since the AG blocked it. It was also odd that the Democratic FTC commissioners were more worried about labor than the actual unions involved. But it may be a sign of more monopsony challenges to come, particularly once the vacant spot gets filled and a 3rd Democrat is breaking the ties.

This was the first big political / economic issue I’ve got involved in since moving to Rhode Island, and I have to admit I was worried about making enemies. But despite speaking against the merger at the same forum as its most powerful proponents, speaking to several journalists, and at the AG’s public forum, I didn’t hear a single angry response; if anything I made friends.

One final surprise in all this is that the two hospitals systems are reported to have spent $28 million pursuing the merger. Apparently money can’t buy everything. But what a lot to spend on something that so many of us thought was clearly destined to fail.

Economics of the Russia-Ukraine Conflict

Russia launched a full invasion of Ukraine last night. Most of the discussion I’ve seen has naturally focused on the fighting itself- what is happening, what is likely to happen, how did it come to this.

Since there are plenty of better sources to follow about that, I’ll simply offer a few observations on the economics of the conflict:

  1. Russia is not only more than 3 times as populous as Ukraine, it also more than twice as well off on a per-capita basis. This means its overall economy is more than 6 times the size of Ukraine’s. This gap has been growing since the fall of the Soviet Union, as Russia’s per-capita GDP growth has been much stronger, while its population has shrunk much less than Ukraine’s. Putting this together, Ukraine’s measured real GDP is actually smaller than it was in 1990, while Russia’s is larger.

2. Russia’s much larger economy allows it to spend much more on its military. Russia spends $60 billion per year, the 4th most of any country (after the US, China and India). Ukraine spends only $6 billion per year on its military. So Russia is starting with a big economic advantage here, though Ukraine has some of its own advantages, like fighting on their own ground and receiving more foreign support.

3. War is bad for business. Russian stocks are down 33% in a day, their biggest-ever loss; Ukraine shut down trading entirely, and their bonds are being hit even worse than Russia’s. Regardless of which side “wins” the fight for territory, both countries will be economically worse off for years as a result of the war.

4. Russia, though, expected that the war would lead to sanctions from the West that would harm their economy, and prepared for this by building up hundreds of billions of dollars worth of foreign reserves over many years.

5. US markets are down only slightly, much less than they would be if traders thought the US would get involved directly in the fighting. But this slight overall decline conceals huge swings. Companies that do business in Ukraine or Russia are big losers. But those that compete with Russian exports see their value rising given the expected sanctions. Because Russia’s biggest exports are oil and natural gas, the value of US-based oil & gas companies is rising, while alternatives like solar are also up substantially.

6. There is still some hope for Ukraine to expel Russian troops, but its not looking good, and even a victory would involve huge costs. This leaves people all over the world wondering, how did it come to this? How might future conflicts like this be avoided? There is of course a lot to say about military preparedness, nuclear umbrellas, and ways the West can impose costs on Russia as a deterrent. But what stands out to me is that a stagnant economy and shrinking population make a country weak and vulnerable. Ukraine has a worse economic freedom score than Russia; this combined with its relative lack of natural resources explains much of the stagnation. Political elites often focus on grabbing a large share of the pie, rather than growing the pie and risk empowering domestic opponents. But we’re now seeing how stagnation carries its own risks. A growing economy, and especially growing energy sources that don’t depend on hostile nations, is the path to independence and survival.

Health Insurance Benefit Mandates and Health Care Affordability

My article on benefit mandates was published today at the Journal of Risk and Financial Management. It begins:

Every US state requires private health insurers to cover certain conditions, treatments, and providers. These benefit mandates were rare as recently as the 1960s, but the average state now has more than forty. These mandates are intended to promote the affordability of necessary health care. This study aims to determine the extent to which benefit mandates succeed at this goal

I began my research career by writing about these mandates, and my goal with this article was to tie up that whole chapter. I realized that all my articles on benefit mandates, as well as most of what other economists write about them, simply try to measure their costs- how much they raise health insurance premiums, raise employee contributions to premiums, lower wages, lower employment, or harm smaller businesses. Its good to know their costs, but to really evaluate a policy we should learn about its benefits too so that we can compare costs and benefits.

One key benefit that had yet to be measured was how much a typical mandate lowers out-of-pocket health care costs. In this article, I estimate that the average benefit mandate lowers costs by 0.8%-1%. I argue that combining this with a measure of how mandates affect total health spending by households could provide a sufficient statistic for the net benefits of mandates for households. I’m not totally confident this works in theory though, and it has a big challenge in practice- one of my empirical strategies finds that mandates reduce total spending, but the other finds they don’t. So I think the main contribution of the article ends up being the first estimate of how the average state health insurance benefit mandate affects out-of-pocket costs.

I’m currently planning to move on from writing about mandates- other topics are catching my eye, state policymakers don’t seem to particularly care what the research says about mandates, and changes in how economists use difference-in-difference methods are making it harder to publish articles like this that study continuous treatments. But I think there are still big opportunities here for anyone who wants to take up the torch. First, the ACA Essential Health Benefits provision changed the game for state mandates in a way that I have yet to see the empirical literature grapple with. Second, there are more than a hundred separate types of state benefit mandates; in most of my articles I aggregate them but they should really be studied separately. A handful have been, such as mandates for autism treatments, infertility treatments, and telemedicine. But the vast majority appear to be completely unstudied.

P.S. Writing this article gave me two wildly varying opinions of our federal bureaucracy. I tried to get both data and funding from the Agency for Healthcare Research and Quality for this article. The data side worked well- they were surprisingly fast, efficient and reasonable about the process of accessing restricted data. On the other hand, I applied for funding from AHRQ in March 2019 and still have yet to officially hear back about it (it is “pending council review” in NIH Commons). This sort of thing is why nimble organizations like Fast Grants can do so much good despite having much smaller budgets.

P.P.S. This article is part of a special issue on Health Economics and Insurance that is still accepting submissions. I’m the guest editor and would handle your submission, though my own got handled by other editors and put though multiple rounds of revisions.

What Proportion of Journalists Live in NYC?

Michael’s post this week on the dangers of high-status, low wage jobs opened by citing this tweet:

Michael presents a fascinating model that applies well beyond journalism. But when his post went viral, some commenters asked how accurate Josh Barro’s claim about half of young journalists living in Brooklyn was. Clearly Michael’s post doesn’t depend on it being true, and I’m not even sure Barro meant it literally, but it got me wondering- just what proportion of all young journalists do live in NYC?

For a first pass, we can look at the BLS Occupational Employment and Wage Statistics for the category “News Analysts, Reporters, and Journalists“. Their latest data (May 2020) shows 41,580 workers employed in those occupations nationwide. It also shows that the NYC metro has by far the most journalists with 5,940, more than twice as many as the second place metro (DC). This implies that 14.2% of all journalists in America live in the NYC metro. Since only 6% of all Americans live in the NYC metro, journalists are clearly clustering there, though clearly well over half of journalists still live elsewhere.

But, this doesn’t quite get at Barro’s claim, which is about journalists under 40 concentrating in Brooklyn. I don’t know of any data source that would let me really test the Brooklyn part, but I can get at the under-40 claim using Microdata from the American Community Survey, which also zooms us in a bit closer to Brooklyn since it tells us who is in NYC proper (not just the metro area).

The 2019 ACS shows that 10% of all “news analysts, reporters, and journalists” are in NYC proper, rising to 14% if I only consider journalists under 40 years old. This is quite concentrated (only 2.5% of all Americans live in NYC proper), but still a lot less that half of all journalists.

As Michael suggested, the vast majority of young NYC journalists are white (77%) with a college degree (91%), though English was only their second most common major after Communications.

The data confirm the last part of Michael’s post quite well- as journalists get older they are likely to move out of NYC and switch to more lucrative fields like PR. Only 5% of all “public relations specialists” are in NYC.

SCORE Replications- Final Call

The Systematizing Confidence in Open Research and Evidence (SCORE) project is an attempt to replicate hundreds of social science papers, and to search for patterns that predict what types of papers are more likely to replicate. You can read all about it at their site, and get a sense of its bigger picture importance in this great post by someone who participated in their prediction markets.

I’ve been involved in the project replicating and reviewing papers, and I plan to do a long post about what it taught me later this year. For now I just wanted to highlight that you can still join the project now, but the chance is ending soon, probably at the end of the month. I think its a great opportunity to advance science, work with the Center for Open Science on a DARPA-funded project, and get paid:

We are recruiting researchers and data analysts across the social-behavioral sciences for replication projects that use existing data that was not part of the original study. For these projects, you will select an original finding to replicate, receive or find alternative data to test the original claim, plan the preregistration of your analysis, receive peer review of your plan, and report your findings in a structured format. You will receive $3,000-$7,800 for each replication study, and you will also be eligible for co-authorship on the report of all replication studies.

South Carolina Certificate of Need Repeal

The South Carolina Senate just voted 35-6 to repeal its Certificate of Need laws, which required hospitals and many other health care providers to get the permission of a state board before opening or expanding. The bill still needs to make it through the house, and these sorts of legislative fights often turn into a years-long slog, but the vote count in the senate makes me wonder if it might simply pass this year. That would make South Carolina the first state in the Southeast to fully repeal their CON laws, although Florida dramatically shrunk their CON requirements in 2019.

Source: Mercatus Center at George Mason University

This seems like good news; here at EWED we’re previously written about some of the costs of CON. I’ve written several academic papers measuring the effects of CON, finding for instance that it leads to higher health care spending. I aimed to summarize the academic literature on CON in an accessible way in this article focused on CON in North Carolina.

CON makes for strange bedfellows. Generally the main supporter of CON is the state hospital association, while the laws are opposed by economists, libertarians, Federal antitrust regulators, doctors trying to grow their practices, and most normal people who actually know they exist. CON has persisted in most states because the hospitals are especially powerful in state politics and because CON is a bigger issue for them than for most groups that oppose it. But whenever the issue becomes salient, the widespread desire for change has a real chance to overcome one special interest group fighting for the status quo. Covid may have provided that spark, as people saw full hospitals and wondered why state governments were making it harder to add hospital beds.

Has the Economic Theory Job Market Returned to Equilibrium?

When I was on the job market in 2014, everyone thought that it was terrible to be a theorist. The profession has moved dramatically toward empirical work, so all the hiring was there. But lots of new PhDs were still doing theory, so the supply of theorists exceeded demand and they had a hard time finding jobs.

My school is hiring in Game Theory / Industrial Organization this year, and based on my previous experience I expected a flood of applications from theorists- but it never arrived. We got substantially fewer applications than when we hired in Applied Micro a couple years ago, and even in the applications we did get, lots were out-of-field or doing empirical IO. I think we will still be able to hire well, I’m certainly happy with the three candidates we are flying out, but there is a lot less depth than I expected. It seems that PhD students have got the message that the demand for theorists is low, and so not many choose theory anymore.

I haven’t been able to find great data to either confirm or rebut my impressions; the closest is the data from this 2019 report with a low response rate. There is no “theory” field in it but I think the closest proxies are “Math & Quantitative Methods” and “Microeconomics”, which collectively made up 20% of demand but only 14% of supply.

I’d be interested to hear what everyone else has seen recently- is doing economic theory once again a sane career move?

The Return of Independent Research

Universities have been around for about a thousand years, but for much of that time it was typical for cutting-edge research to happen outside of them. Copernicus wasn’t a professor, Darwin wasn’t a professor. Others like Isaac Newton, Robert Hooke, and Albert Einstein became professors only after completing some of their best work. Scientists didn’t need the resources of a university, they simply needed a means of support that gave them enough time to think. Many were independently wealthy (Robert Boyle, Antoine Lavoisier) or supported by the church (Gregor Mendel). Some worked “real jobs”, David Ricardo as a banker, Einstein famously as a patent clerk.

Over time academia grew and an increasing share of research was done by professors, with most of the rest happening inside the few non-academic institutions that paid people to do full time research: national labs, government agencies, and a few companies like Xerox Parc, Bell Labs and 3M. In many fields research came to require expensive equipment that was only available in the best-funded labs. “Researcher” became a job, and research conducted by those without that job became viewed with suspicion over the 20th century.

But the Internet Age is leading to the growth in opportunities outside academia, opportunities not just economic but intellectual. Anyone with a laptop and internet can access most of the key tools that professors use, often for free- scientific articles, seminars, supercomputers, data, data analysis. Particularly outside of the lab sciences, the only remaining barrier to independent research is again what it was before the 20th century- finding a means of support that gives you time to think. This will never be easy, but becoming a professor isn’t either, and a growing number of people are either becoming independently wealthy, able to support themselves with fewer work hours (even vs academics), or finding jobs that encourage part time research. If you work for the right company you might even get better data than the academics have.

Particularly in artificial intelligence and machine learning, the frontier seems to be outside academia, with many of the best professors getting offers from industry they can’t refuse.

Even in the lab sciences, money is increasingly pouring in for those who want to leave academia to run a start-up instead:

I think it’s great for science that these new opportunities are opening up. A natural advantage of independent research is that it allows people to work on topics or use methods they couldn’t in academia because they are seen as too high risk, too out there, make too many enemies, or otherwise fall into an academic “blind spot“.

I’m still happy to be in academia, and independent research clearly has its challenges too. But over my lifetime it seems like we have shifted from academia being the obvious best place to do research, to academia being one of several good options. Even as research has begun to move elsewhere though, universities still seem to be doing well at their original purpose of teaching students. Almost all of the people I’ve highlighted as great independent researchers were still trained at universities; most of the modern ones I linked to even have PhDs. There are always exceptions and the internet could still change this, but for now universities retain a near-monopoly on training good researchers even as the employment of good researchers becomes competitive.

As an academic I may not be the right person to write about all this, so I’ll leave you with the suggestion to listen to this podcast where Spencer Greenberg and Andy Matuschak discuss their world of “para-academic research”. Spencer is a great example of everything I’ve said- an Applied Math PhD who makes money in private sector finance/tech but has the time to publish great research, partly in math/CS where a university lab is unnecessary, but more interestingly in psychology where being a professor would actually slow him down- independent researchers don’t need to wait weeks for permission from an institutional review board every time they want to run a survey.