Fences, Schools, Dryer Lint, & Shower Levers

In game theory, coordination games reflects the benefits of everyone settling on the same rules. Settling on the same rules can avoid a conflict and destructive competition. For example, some rules may be arbitrary, such as on which side of the road we’ll all drive. It doesn’t much matter whether a country’s vehicles drive along the right or left side of the street. As long as everyone is in the same lane, we overwhelmingly benefit from our coordination. The matrix below describes the game.

The above game reflects that whether we agree to drive on the left or on the right is trivial and that the important detail is that we agree on what the rule is. Rules like this are arbitrary. No amount of cost benefit analysis changes the answer. Other coordination rules are seemingly arbitrary, but do have different welfare implications. For example, according to English common law, a farmer was entitled to prohibit a herdsman’s flock from trampling his crops even if the farmland had no fence. Herdsmen were responsible for corralling their flocks or paying damages if they grazed on the farm. With lots of nearby farms, total welfare was higher with a rule of cultivation rights rather than grazing rights.

But the property rights could have been assigned to the herdsman instead. The law could have said that the sheep were free to graze with impunity and that the onus was on the farmer to build fences in order to keep the sheep at bay. In a world where there are a lot of farmers who are very nearby to one another, a small flock of sheep can do a lot of damage. And so, the cost-benefit analysis prescribes that herdsmen bear the cost of restricting the flock rather than the farmer. The matrix that describes this circumstance is below.

The above matrix reflects that agreeing on any rule is better than no rule at all. And, the rule that is selected has societal welfare implications. Choosing the ‘wrong’ rule means that we could get stuck in a rut of lower payoffs because coordinating a change in the rules is hard.

Schools

Another way in which the specific rule can be important is by whether it instantiates or works contrary to pre-existing incentives. Before compulsory schooling laws were passed, US states already had very high school attendance rates. Most parents sent their kids to school because it was a good investment. The ages at which children should be required to attend is largely, though not entirely, arbitrary. And wouldn’t you know it, most states applied their compulsory schooling legislation to the age groups for which the vast majority of children were already attending school. Enforcing a law against the natural incentives of human capital investment would have been more costly. The particular ages of compulsory schooling had different welfare implications due to the differing costs of enforcement.

Dryer Lint

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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.

Is the Labor Market Back?

Last month I asked if travel was back. Air travel has recovered a lot from the depths of the pandemic, but it was still only about 80-85% of pre-pandemic levels.

Labor markets also plummeted during the worst of the pandemic, and have slowly (and sometimes quickly) clawing their way back. But are we back to pre-pandemic levels?

The national unemployment rate is now under 4%, a level which is rarely reached even in the best of times. But there is considerable variation across states.

The latest BLS release of state unemployment data shows that some states are at their historic lows, with one state standing out: Nebraska currently has the lowest unemployment rate a state has ever recorded at 1.7% in December 2021 (the data go back to 1976). Utah is also just below 2% in December — at 1.9% it’s the 2nd lowest in history (after Nebraska, of course).

Of course, all is not well everywhere. California and Nevada have the highest unemployment rates, at around 6.5%. This is well above their pre-pandemic levels of about 4%, and also well above what you would expect during normal times, other than during and immediately following at recession.

So is the labor market back in Nebraska, Utah, and other similar states? Not so fast.

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Pledging for effective altruism

I attended an administrative board meeting for a large local nonprofit organization this week. The report from the finance committee included a comment that our “giving” is up while “pledging” is trending down. People are giving money when they feel like it or when they have extra money.*

However, the finance committee wishes that more people would pledge their giving at the beginning of the year, so that the organization can plan ahead. They are trying to make an operating budget and want to make promises to the staff. It’s nerve-wracking to plunge into the year with no idea how the whims of thousands of people will affect the final revenue a year from now.

I don’t have any sources for this, outside of the representative’s report this week. They said that nonprofits all over the country are seeing a decline in pledges and an increase in (impulse) giving.

I am looped into niche online chatter about Effective Altruism. “You should give money for malaria instead of re-painting a lobby in America.” Fair enough. Most Americans don’t subscribe, and I’m not trying to make a case for the malaria pills right now.

What about giving to the same causes you already give to, in a new way? Make a pledge. If you lose your job or cannot pay, then there is no consequence. It’s not a legal contract. It’s just an indication of your intentions that helps leaders plan.

Millennials just recently outnumbered Boomers as the nation’s largest living adult generation. Trends in anything adults do are likely to be “generational shifts” for the next few years. I suggest to my fellow Millennials that your money can be spent more effectively by the nonprofit sector if you commit proactively instead of reacting to crises. See if the groups you give to allow for pledging.

Lastly, I’d like to brag about my group for pivoting this January to provide for new Afghan refugees in Birmingham. Having extra money on hand from record 2021 revenue helped make that possible.

… and finally, pledging could be a good topic for economists to look at.

*New papers on giving after windfall income bumps are here (published) and here (working).

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?

What we pay for the thing that some workers do that most people do not

In middle school, I broke my leg in a soccer tournament game. I needed to go to the hospital and get extra support for the next month. Some of the workers who helped me were not highly paid, but my value of their services was very high.

Why bring this up? There has been conversation about the label “low skill” work this week. Brian Albrecht summarized the debate. Brian tangentially mentioned the “diamond-water paradox,” but I think it is worth talking more about that. Economists have a few models and stories that change the way you think about the world.

When I teach Labor Economics, we read an excerpt from Average is Over and then I explain the diamond-water paradox in class. I ask the students why diamonds cost more than water, even though water is more important. The answer can help us understand how wages get set for human workers (I say “human” because by that time we are deep in the topic of robot workers as substitutes).

I tell my students that some of the low-pay work performed by humans is extremely important. I’m still looking for the perfect illustration here. The one I use goes something like this, which is related to my broken leg anecdote… imagine if you tripped on train tracks and couldn’t get yourself out of the way of an oncoming train. How much would you pay a human to haul you to safety? Almost any human could perform the task. That service would be as valuable as a glass of water if you are about to die from thirst, which is to say that your value for it is almost infinite.

The key to understanding the market price of cleaners as opposed to the high wages for repairing Facebook code is marginal thinking. There is a lot of water, so the next glass is going to be cheap.

In writing Average is Over, Tyler Cowen is trying to understand why wages for the-less-highly-paid-skills have stagnated recently, while wages for the-highly-paid-skills are increasing along with GDP. He brings computers and technology into the conversation, as one culprit for recent changes. There is a limited supply of humans who can show up to a tech job and contribute reliably. “Programmers” are not the only highly paid class of workers, but it’s easy to see that the supply of people who are proficient with Python is limited.

I see two opposing forces in the tech world, which I have been following for a few years. First, we have boot camps, code clubs and all kinds of resources to both equip and encourage people to go into tech. I volunteer to advise a club that provides resources for female college students taking a technical route. On the other hand, lots of people who do get a foot into the door of a tech company become upset and quit.

Here is a quitter (a twitter quitter?):

You can read about this specific situation at this woman’s website. It seems like she made the right choice for herself. She is actually on a mission to change tech for women. I’ll reproduce the text here, in case someone can’t see the tweet: “first day at my new job! i am now a ceramicist because it lets me have no commute, make my own hours, decide the value of my work, and bring people joy. make no mistake, i wanted to code, but tech fulfilled none of that. so i hand off the baton. please fix tech while i make pots!”

The point is that she is one of many people who have dropped out of the tech workforce. Those employees who remain are pushed up toward the “diamond market price” and away from the “water market price”. Here is a blog about “burnout” survey data from 2018.

Populations in rich countries are not growing and labor force participation is down. Could the market wage for lower-skill-requirement jobs in the US rise dramatically in the next century, or at least keep pace with the wage increases that were recently enjoyed by those-with-the-capabilities-that-are-highly-valued? Marginal utility still apply, but prices will change if supply shifts.

See my old blog about Andrew Weaver who is researching skills that are in demand.

Optimal Policy & Technological Contingency

A person’s optimal choice depends on what they know. To consume more ice cream? Or to consume more alcohol? It depends on what we know about the expected utility across time. If a person thinks that alcohol has few calories, then it is understandable that they would choose to drink rather than eat. The person might be totally wrong, but they are acting optimally contingent on their knowledge about the world. (FWIW, 4oz of ethanol has 262 calories and 4oz of typical ice cream has 228 calories.)

The case is analogous for good government policy. The best policy is contingent on accessing the distribution of knowledge that’s inside of multiple people’s heads. It’s not sensible to assert that a policy is suboptimal if the optimal policy requires knowledge that neither a single individual nor all people together have. Even if the sum of all knowledge does exist, it may not be possible to access it.

Economists like to tell their undergraduate classes that it doesn’t matter who you tax. But that’s contingent on 1) identical compliance costs among buyers and sellers and 2) identical relevant information. If a tax comes as a surprise to the buyer or the seller, then it absolutely matters who is taxed.

When I was in 1st grade in North Carolina, my class went on a field trip to a Christmas tree farm. We learned a bunch about maintaining the farm and we got to choose a pumpkin to take home. At the end of our visit we took turns perusing the gift shop. My mother had generously given me a dollar to spend  and I was eager to spend it (I rarely had money to spend). Unfortunately, even in the early mid-90s, most of the things in the shop cost more than $1. So, I settled on purchasing some beef jerky that cost 99 cents.

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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.

Latest Inflation Data: Hot Dogs and Cheese On Sale!

The latest CPI inflation data was released this morning. Mostly the new data just confirms what we’ve seen the past few months: consumer price inflation is at the highest levels in decades, and it is now very broad based.

To see how broad based the inflation is, we can look at any of the “special aggregates” that the BLS produces. CPI less food. CPI less shelter. CPI less food, shelter, energy, used cars and trucks (what a mouthful!). All of these are up substantially over the past year. The lowest number you can get is that last aggregate I listed, which excludes almost 60% of consumer spending, and even it is up 4.7% over the past year — the largest increase since 1991 for that particular special index.

Or, you can just look at food. We all have probably observed that meat prices are way up recently — about 15% over the past year. But it’s not just meat. It’s fruit, vegetables, grains, dairy… the whole darn food pyramid. In fact, there are only two food categories (hot dogs and cheese) and two drinks (tea and wine) that are actually down since December 2020.

I’ve covered the symbolic importance of hot dog prices before, but the fact that only four food or drink categories had price decreases are indications that food-price inflation is extremely broad-based.

So what’s causing the inflation?

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Remittances Eye-tracking Experiment: Meet the authors and paper

I am pleased to have been asked to discuss a paper in an ASHE (American Society of Hispanic Economists) session at the 2022 AEA meeting. Our session is “Hispanics and Finance” on Sunday January 9 at 12:15pm Eastern Time.

The paper is “Neuroeconomics for Development: Eye-Tracking to Understand Migrant Remittances”. Here is a bit about each author. Meeting in person is a benefit that I miss this time, since the meeting is virtual.

Eduardo Nakasone of Michigan State University has several papers on information and communication technologies and agricultural markets. I pondered this sentence from one of his abstracts, “Under certain situations, ICTs can improve rural households’ agricultural production, farm profitability, job opportunities, adoption of healthier practices, and risk management. All these effects have the potential to increase wellbeing and food security in rural areas of developing countries. Several challenges to effectively scaling up the use of ICTs for development remain, however.” His prior work on ICTs is relevant to the paper at hand, which is about how migrants utilize information about remittance tools.

Máximo Torero is the Chief Economist of the Food and Agriculture Organization (FAO). He has worked on development and poverty in many capacities including at the World Bank.

Angelino Viceisza, an associate professor at Spelman College, is doing interesting work at the intersection of Development and Experimental Economics. Here is his 2022 paper (Happy New Year!) published in the Journal of Development Economics.  

I am discussing their paper on how migrants choose financial services. The pre-analysis plan is public. Remittance sending is important for migrants and for the entire world economy. The authors remind us that a significant chunk of what migrants earn is “lost” to service fees. The authors are examining how migrants incorporate new information about competitive alternative services.

Some neat aspects of their work:

  • Their subject pool is migrants who send remittances, recruited in the DC area.
  • Like most experiments I am used to, the stakes are real and significant.
  • Not only can they observe which service is selected, but by using eye-tracking they can get a sense of what information was salient or persuasive.

It is potentially a big deal for migrants to compare services more rigorously and switch providers more readily. The internet, as least in theory, makes it easy to find information on transaction fees. Policy makers have even proposed subsidizing websites that compare the fees of money transfer operators (MTOs). The authors are trying to understand how such a website might impact behavior. A basic question is: does information in this format affect behavior? A small change in behavior could have a huge impact on the world economy and recipient countries. Imagine if a country currently receiving a billion dollars in remittances had 1% more next year because migrants switched to a more efficient service. Might it be cheaper to nudge people toward low-fee services than to send foreign aid?

Their experiment will reveal whether people make switches based on new information, and it also helps us start to understand which attributes of MTOs migrants consider. Their design includes a treatment manipulation that sometimes emphasizes either transfer speed or user reviews.

If you have read this far hoping for a summary of their results, I will disappoint. Their paper is not public yet and data is still being analyzed. I can say that migrant subjects do sometimes switch their choice of MTO, based on information, in some circumstances. They are more likely to make a switch when the induced stakes are higher. If you tune into the session tomorrow, you will get to hear a summary of preliminary results by the author (not free to public, requires conference registration).