Author: Zachary Bartsch

Ricardian Equivalence: Reasonable Assumption #2

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There are several requirements for Ricardian Equivalence:

  1. Individuals or their families act as infinitely lived agents.
  2. All governments and agents can borrow and lend at a single rate.
  3. The path of government expenditures is independent of financing choices

Assumption 2) appears patently absurd on its face. I certainly cannot borrow at the same interest rate that the US Treasury can. QED. Do not pass go, do not collect $200. The yield on 1-year US treasuries is 3.58%. I can’t borrow at that rate… Or can I?

Let’s do some casuistry.

What is a loan?

It’s a contract that:

  • Provides the borrower with access to spending
  • with or without collateral
  • with a promise to repay the lender at defined times, usually with interest.

So, when you borrow $5 from a friend and pay it back on the same day, it’s a loan. The contract is verbal, there is no collateral, the repayment time is ‘soon’ with flexibility, and the interest rate is zero.

A mortgage is a collateralized loan. You borrow from a bank, make monthly payments for the term of the loan, and accrue interest on the principal. The contract is written, the house or a portion of its value is the collateral, and the interest rate is positive.

What about a Pawnshop loan? Most of us are probably unfamiliar with these. In this circumstance, a person has valuable non-assets that and the pawnshop has money.  They engage in a contractual asset swap. The borrower lends the non-money asset to the pawnshop as collateral and borrows money from the pawnshop. The pawnshop borrows the non-money asset and lends the money to the borrower. The borrower can use the money as they please, but the pawnshop can not use the non-money asset – they can simply hold it. They collect interest in order to cover their opportunity costs.

One outcome is that the borrower repays the loan and interest by the maturity date and reclaims their non-money asset. Another outcome is that the borrower retains the option to default without any further obligation. But they lose the right to reclaim their property according to the repayment terms. If the borrower exercises the option to default, then the pawnshop acquires full rights to the non-money asset. The pawnshop often resells the asset at a profit. The profit is relatively reliable because the illiquidity of the non-money asset allows the pawnshop to lend much less than its retail value. That illiquidity is also why the borrower is willing to accept the terms.

If we accept that the pawnshop contract is a loan, which is just a collateralized loan with a mostly standard default option, then get ready for this.

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Regulatory Burden By Presidential Administration

Zachary Bartsch1 Comment

During president Trump’s first term in office, he made a bunch of waves (as he’s wont to do). His more educated supporters said that he engaged in substantial deregulation of telecommunications, which got a lot of press. There was a quiet contingent of educated voters who were relatively silently supportive on Trump’s regulatory policy, even if his character was indefensible or his other policy was less desirable.

But was Trump a great deregulator? Or was it one of those cases when we say that he regulated *less* than his fellow executives? The George Washington University Regulatory Studies Center can help shed some light with their data. Specifically, they have calculated the number of ‘economically significant’ regulations passed during each month of each president going back through Ronald Reagan’s term. What counts as ‘economically significant’? The definition has changed over time. But, generally, ‘economically significant’ regulations:

  1. “Have an annual [adverse] effect on the economy of $100 million or more
  2. Or, adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or tribal governments or communities.”

The only exception to this is between April 6, 2023 and January 20, 2025 when the threshold was raised to $200 million.

The Data

The graph below-left shows the number of economically significant regulations for each president since the start of his term, through July of 2025. It’s reproduced from the link above except that I appended Trump’s second term onto his first term. What does the graph tell us? There doesn’t seem to be much of a difference between republicans and democrats. Rather, it seems that, generally, the number of economically significant regulations increases over time. Importantly, the below lines are cumulative by president. So each year’s regulations each cost $100m annually and that’s on top of the existing ones already in place. So, regulatory costs generally rise, with the caveat that we don’t see the relief provided by small or rescinded regulations (for that matter, we don’t see small regulatory burdens here either). Something else that the below graph tells us is that presidents tend to accelerate their economically significant regulations prior to leaving office. Reagan was the only exception to this pattern and he *slowed* the number of regulations as the end of his term approached.

Below-right is the same data, but the x-axis is months until leaving office. Every president since Bush-41 has accelerated their burdensome regulations during their final months in office. The timing of the acceleration corresponds to how close the preceding election was and whether the incumbent president lost. Whereas all presidents regulate more in their last 2-3 months in office, the presidents who were less likely to win re-election started regulating more starting around eight months prior to leaving office. Of course, they wouldn’t say that they expected to lose, but they sure regulated like there was no tomorrow.

What about Trump? Trump’s fewer regulations is caused by his single term. He definitely still added to the regulatory burden (among economically significant regulations, anyway). While Trump started with the fewest additional regulations since Reagan, and Biden started with the most ever initial regulations, together they earn the top prizes for most regulations added in their first term.

What if we append these regulations from end-to-end? That’s what the below chart does. We do have to be careful because the series is a measure of gross economically significant regulations and not net economically significant regulations. So, it’s possible that some rescissions dampened the below values, but this is the data that I have for the moment. While each presidential administrations increases regulation more than the prior, the good news is that the rate of change is not exponential. The line of best fit is quadratic. We’re experiencing growing regulations, but at least it’s not compound growth.

The Cost

We can estimate the costs of these economically significant regulations. It’s a rough cut, and definitely a lower bound since rescission is rare and $100 million is itself a lower bound, but we can multiply the number of regulations by $100m to get minimum annual cost. Like I said, the Biden criterion from April 2023 through January 20, 2025 changed, so those regulations get counted as $200 million instead. The change in definition means that the regulation counts underestimate the late-term Biden regulations relative to the other presidencies.

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Vaccine Variety

Zachary Bartsch4 Comments

The flu and covid-19 vaccines don’t work super well. Both vaccines permit infection and transmission at quite high rates. The benefit from these vaccines come largely from reductions in mortality or severe symptoms conditional on infection. The covid-19 vaccine is itself especially risky or ineffective depending on the age and health of the individual. Plenty of people eschew vaccines.

I live in Collier County, Florida where there have been 61 confirmed cases of measles so far this year. I have since learned that Measles is EXTREMELY contagious. It floats around the air and on items and just sort of hangs out and waits for a place to replicate. I’ve also learned that symptoms include a fever, eye irritation, possible brain swelling, severe dehydration, and a characteristic rash. The severe dehydration easily puts people in the hospital, the eye irritation can lead to permanent vision loss, and the brain swelling can be acute, or a symptom delayed by 5-6 years, which can also be fatal. I’ve also learned that having the vaccine, which is usually administered in two doses, provides about 97% immunity. The vaccine works so well, that the department of health recommends no behavioral change among the vaccinated population when there is a measles outbreak. Barring unique circumstances, measles immunity can persist for a lifetime.

Unfortunately, a large segment of the anti-vaccine mood affiliation retains the salience of the covid-19 vaccine characteristics. Other vaccines and diseases in the typical pediatric schedule are not similar. Most of these prevent infection >90% of the time (TDAP is low at 73%), prevent transmission, reduce mortality when there are breakthrough infections, are effective for years or decades, and are extremely safe for all age groups.

The risks of disease versus the corresponding vaccine are orders of magnitude away from each other. The tables below summarize the data (with sources). I did not double check the source on every single figure. If you glance below, then you’ll see why: Even if the numbers are closer by 10 or 100 times, vaccines still look really good.

First, mortality: The data is divided by disease and age group, and provides mortality rates for both the disease and for the vaccine. The numbers are proportions, conditional on infection or vaccination. There are a lot of zeros in the vaccine mortality rates and certainly more than for the diseases. For example, a measles infection is 10,000 more lethal than the MMR vaccine which prevents it. In fact, all of those zeros in the vaccine rates reflect mortality that is so uncommon, that the estimated one out of every 10 million is just rounded up because researchers don’t think that the risk is zero.

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Truth: The Strength and Weakness of AI Coding

Zachary Bartsch1 Comment

There was a seismic shift in the AI world recently. In case you didn’t know, a Claude Code update was released just before the Christmas break. It could code awesomely and had a bigger context window, which is sort of like memory and attention span. Scott Cunningham wrote a series of posts demonstrating the power of Claude Code in ways that made economists take notice. Then, ChatGPT Codex was updated and released in January as if to say ‘we are still on the frontier’. The battle between Claude Code and Codex is active as we speak.

The differentiation is becoming clearer, depending on who you talk to. Claude Code feels architectural. It designs a project or system and thrives when you hand it the blueprint and say “Design this properly.” It’s your amazingly productive partner. Codex feels like it’s for the specialist. You tell it exactly what you want. No fluff. No ornamental abstraction unless you request it.

Codex flourishes with prompts like “Refactor this function to eliminate recursion”, or “ Take this response data and apply the Bayesian Dawid-Skene method”. It does exactly that. It assumes competence on your part and does not attempt to decorate the output. It assumes that you know what you’re doing. It’s like your RA that can do amazing things if you tell it what task you want completed. Having said all of this, I’ve heard the inverse evaluations too. It probably matters a lot what the programmer brings to the table.

Both Claude Code and Codex are remarkably adept at catching code and syntax errors. That is not mysterious. Code is valid or invalid. The AI writes something, and the environment immediately reveals whether it conforms to the rules. Truth is embedded in the logical structure. When a single error appears, correction is often trivial.

When multiple errors appear, the problem becomes combinatorial. Fix A? Fix B? Change the type? Modify the loop? There are potentially infinite branching possibilities. Even then, the space is constrained. The code must run, or time out. That constraint disciplines the search. The reason these models code so well is that the code itself is the truth. So long as the logic isn’t violated, the axioms lead to the result. The AI anchors on the code to be internally consistent. The model can triangulate because the target is stable and verifiable.

AI struggles when the anchor disappears

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Against Eugenics, on its Own Terms

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Once upon a time, eugenics was all the rage. It was nascent during the reconstruction era and persisted into the 20th century. It grew out of biological evolutionary theory and emphasized reproductive fitness. In brief, the theory asserted that there are differences in individual fitness and that the more fit living things will survive better and reproduce, eventually becoming a greater part of the population. The ability to compile and evaluate statistics about various human measurements made inferences hard to resist. Of course, researchers were plagued by small sample size, omitted variable bias, and social biases of the day (for example, phrenology inferred fitness characteristics from skull shape).

People employing eugenic thinking, overwhelmingly, supported theories that their own type of person was among the more fit. Eugenicists didn’t promote theories of their own un-fitness. In the progressive era of the early 20th century, eugenics met the prevailing attitude that government could be employed to resolve social and economic ills. This era is when the income tax emerged, prohibition was enacted, the Federal Reserve was formed, and various labor regulations were enacted.

The result was that policy sometimes pursued greater ‘fitness’ among its populations. Rather than systematically encouraging the supposedly more fit with economic incentives, most policy was geared toward reducing the reproductive success of supposedly less fit people. These included forced sterilization, institutionalization, and economic exclusion. Besides rejecting basics individual human dignity, the harm was all the more tragic given that fitness was often poorly specified. That is, policy criteria weren’t dependably related to fitness. Fatal conceit, indeed!

One of my favorite ways to argue is to grant premises and then change details on the margin to see whether the conclusion changes. Let’s do that. Let’s grant that there are innate differences between people that are related to biological success. Since survivability is related to resource acquisition, let’s grant also that economic success overlaps at least somewhat.  Taking that as granted, does pursuit of the historical eugenic policy still follow?

It does not.

There are two mistakes that eugenicists and various sorts of racists and xenophobes made. They assert or imply 1) that fitness characteristics are stable and systematically identifiable, and 2) that policy needed to intentionally select for the fitness characteristics.

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Drawbacks of Long Term Thinking

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This post is just some thoughts about perspective. I apologize for any lack of organization.

My academic influences include North, Weingast, Coase, Hayek, the field of Public Choice, and others. I’m not an ‘adherent’ to any school of thought. Those guys just provided some insights that I find myself often using.

What lessons did they teach? Plenty. When I see the world of firms, governments, and other institutions, I maintain a sharp distinction between intention and outcome. Any given policy that’s enacted is probably not the welfare maximizing one, but rather must keep special interests relatively happy. So, the presence of special interests is a given and doesn’t get me riled up. When I see an imperfect policy outcome, I think about who had to be enticed to vote for it. We live in a world where ‘first bests’ aren’t usually on the table.

Historically, or in lower income countries, I think about violence. Their rules and laws are not operating in a vacuum of peaceful consent. There is always the threat of violence. Laws are enforced (or not) conditional on whether and what type of violence that may result. All of the ideal legislation is irrelevant if theft and fraud are the lay of the land.

I think about institutional evolution with both internal and external pressures. I’m a bit worried about the persistence of the US republic, or at least worried for its pro-growth policies. I’m not worried about China in the long run. I don’t think they have the institutions that get them to ‘high income’ status. I do think that they are a tactical concern in the short run and that the government does/will have access to great volumes of resources in the medium run. That’s a bit of a concern. But like I said, I’m not super worried in the long run.

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Which Economies Grow with Shrinking Populations?

Zachary Bartsch4 Comments

If you didn’t know, China has had negative population growth for the past 4 years. Japan has had negative population growth for the past 15 years. The public and economists both have some decent intuition that a falling population makes falling total output more likely. Economists, however, maintain that income per capita is not so certain to fall. After all, both the numerator and denominator of GDP per capita can fall such that the net effect on the entire ratio is a wash or even increase. In fact, aggregate real output can still continue to grow *if* labor productivity rises faster than the rate of employment decline.

But this is a big if. After all, some of the thrust of endogenous growth theory emphasizes that population growth corresponds to more human brains, which results in more innovation when those brains engage with economic problems. Therefore, in the long run, smaller populations innovate more slowly than larger populations. Furthermore, given that information can cross borders relatively easily no one on the globe is insulated from the effects of lower global population. Because information crosses borders relatively well, the brains-to-riches model doesn’t tell us who will innovate more or experience greater productivity growth.

What follows is not the only answer. There are certainly multiple. For example, recent Nobel Prize winner Joel Mokyr says that both basic science *and* knowledge about applications must grow together. That’s not the route that I’ll elaborate.

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Costly University Interviews can be Worthwhile

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I’m writing because I am catching up on the backlog of The Answer is Transaction Costs (TAITC), a podcast hosted by Michael Munger. Specifically, in an episode published August 27, 2024, a listener writes asking about what seems to be the extremely costly practice of interviewing college applicants prior to acceptance.

As it turns out, I work at a private university that enacted an interview policy in a quasi-random way and the university president gave me permission to share.

Initially, my university did not interview standard applicants. Our aid packages were poorly designed because applicants tend to look similar on paper. There was a pooling equilibrium at the application stage. As a result, we accepted a high proportion and offered some generous aid packages to students who were not good mission fits and we neglected some who were. Aid packages are scarce resources, and we didn’t have enough information to economize on them well.

The situation was impossible for the admissions team. The amount of aid that they could award was endogenous to the number of applicant deposits because student attendance drives revenue. But, the deposits were endogenous to the aid packages offered! There was a separating equilibrium where some good students attended along with some students who were a poor fit and were over-awarded aid. The latter attended one or two semesters before departing the university, harming retention and revenues. Great but under-awarded students tended not to attend our university. Student morale was also low due to poor fits and their friends leaving.

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Rising Chinese Zombie Firms

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Have you ever looked up and wondered where the time went? One moment you’re living your life, and the next moment you realize that you’ve just lost time that you’ll never get back? That’s what happened to Japan’s economy at the turn of the century in an episode that’s known as ‘the lost decades’. It was a period of slow or null economic growth. Economists differ with their explanations. One cause was the prevalence of ‘zombie firms’.

Japan’s Economy

Japan had a current account surplus from 1980-2020, which means that they had more savings than they effectively utilized domestically. Metaphorically, they were so full of savings that they exhausted productive domestic investment opportunities and their savings spilled out into other counties in the form of foreign investments. This was driven by high household savings and slow growth in domestic investment demand. The result was the Japanese firms had easy access to credit. Maybe a little too easy…

Private corporate debt ballooned throughout the 1980s. That’s not intrinsically a problem. In the 1990s, households began saving somewhat less, and most firms began to drastically deleverage… But not all firms. The net effect of the mass deleveraging was that interest rates fell.  The firms that remained in debt were the ones that risked insolvency. Less productive firms had slim profits and their Earnings Before Interest, Taxes, Depreciation, and amortization (EBITDA) was slim. So slim, that they couldn’t pay their debts. Faced with the prospect of insolvency, firms did what was sensible. They refinanced at the lower interest rates. Firms went to their banks and to bond markets and rolled over their debt, which they couldn’t afford, and replaced it with debt that had a lower interest rate. This occurred across industries, but especially in non-tradable goods and services that were insulated from international competition. Crisis averted.

Except this process of refinancing, while avoiding acute defaults and a potential financial crises, ensured that the less productive firms would survive. Not exactly failing and not exactly thriving, they could sort of just hold on to something that looks like life. Well, high debt and low profits aren’t much of a life for a firm. It’s more like being undead – like a zombie. Between 1991 and 1996, the share of non-finance firm assets held by zombie firms ballooned from 3% to 16%. The run-up differed by industry: Manufacturing zombie assets rose from 2% to 12%, from 5% to 33% in real estate, and from 11% to 39% in services.  These zombie firms linger on, tying up valuable resources with low-productivity activities and drag on the economy.

China’s Economy

I’m not prone to China hysteria generally. However, I do have uncertainty about the plans and actions of the Chinese government because I don’t know that domestic economic welfare is its priority. That makes forecasting more political and less economic and outside my expertise. Regardless, the Chinese economy is a constraint on the government, whether they like it or not.  And there are some echoes of the Japanese economy’s lost decades.

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Tariffs Are Not Smart Industrial Policy

Zachary Bartsch2 Comments

Economists overwhelmingly see tariffs as clearly welfare-reducing. Tariffs on imports result in higher prices, fewer imports, less consumption, and more domestic production. In fact, it is the higher prices that solicit and make profitable the greater domestic production. We don’t get the greater domestic output at the pre-tariff price. We can show graphically that domestic welfare is harmed with either export or import tariffs. The basic economics are very clear.

However, the standard model of international trade makes a huge assumption: Peace. That is, the model assumes that there are secure property rights and no threats of violence. All transactions are consensual. This is where the political scientists, who often don’t understand the model in the first place, say ‘Ah ha!. Silly economists…’ They proceed to argue for tariffs on the grounds of national security and the need for emergency manufacturing capacity. But is an intellectual mistake.  

Just as economists have a good idea for how to increase welfare with exchange, we also have good ideas about how to achieve greater or fewer quantities transacted in particular markets. This is not a case of economists knowing the ideal answer that happens to be politically impossible.  Rather, if it pleases politicians, economists can provide a whole menu of methods to increase US manufacturing, vaccine manufacturing, weapons manufacturing… Heck, we can identify multiple ways to achieve more of just about any good or service. Let the politicians choose from the menu of alternatives.

The problem with tariffs is that they reduce consumer welfare a lot, given some amount of increased production in the protected industry. Importantly, this assumes that the tariffs aren’t hitting inputs to those industries and are only being applied to direct foreign competitors. The below argument is even stronger against imperfectly applied tariffs, like the US tariffs of 2025.

What’s the alternative?

The alternative is a more focused tack. If the government wants more missile or ship production, then what should it do? There’s plenty, but here’s a short list of more effective and less harmful alternatives to tariffs:

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