Author: Zachary Bartsch

Interpreting New DIDs

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If you didn’t know already, the past five years has been a whirl-wind of new methods in the staggered Differences-in-differences (DID) literature – a popular method to try to tease out causal effects statistically. This post restates practical advice from Jonathan Roth.

The prior standard was to use Two-Way-Fixed-Effects (TWFE). This controlled for a lot of unobserved variation over individuals or groups and time. The fancier TWFE methods were interacted with the time relative to treatment. That allowed event studies and dynamic effects.

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Human Capital is Technologically Contingent

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The seminal paper in the theory of human capital by Paul Romer. In it, he recognizes different types of human capital such as physical skills, educational skills, work experience, etc. Subsequent macro papers in the literature often just clumped together some measures of human capital as if it was a single substance. There were a lot of cross-country RGDP per capita comparison papers that included determinants like ‘years of schooling’, ‘IQ’, and the like.

But more recent papers have been more detailed. For example, the average biological difference between men and women concerning brawn has been shown to be a determinant of occupational choice. If we believe that comparative advantage is true, then occupational sorting by human capital is the theoretical outcome. That’s exactly what we see in the data.

Similarly, my own forthcoming paper on the 19th century US deaf population illustrates that people who had less sensitive or absent ability to hear engaged in fewer management and commercial occupations, or were less commonly in industries that required strong verbal skills (on average).

Clearly, there are different types of human capital and they matter differently for different jobs. Technology also changes what skills are necessary to boot. This post shares some thoughts about how to think about human capital and technology. The easiest way to illustrate the points is with a simplified example.

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The Consumingest States of 2023

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This post is quick and simple. We all know that states have different land areas and different populations. We also know that different states produce different amounts of output. We have a pretty good sense for which are the ‘big’ states since these things often go hand-in-hand. But what about household spending on consumption? It’s easy to imagine that some states produce plenty but then invest the proceeds. So, which states consume the most relative to their income?

The map above illustrates which states consume more of their income. There’s not much correlation geographically. But, among the ‘big’ states (Texas, California, New York, Illinois), the consumption per GDP is below the average of 67%. Can we make sense of this? As it turns, out more productive states also tend to have a higher per capita output. So, those higher GDP states also have richer populations on average. And, sensibly, those richer populations have lower marginal propensities to consume. They save more. But this is just spit-balling.

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More Immigrants, More Safety

Zachary Bartsch4 Comments

The headlines often read with the criminal threats that illegal/undocumented immigrants pose to the US native population. The story usually includes a heart wrenching and tragic story about a native minor who was harmed by an immigrant and a politician to help propose a solution. There’s also usually a number cited for how many such crimes happened in the most recent year with data. Stories like this are designed to provoke feelings – not to provoke thinkings.

First, the tragic story is probably not representative. Even if it is, the citation of a raw count of crimes is not communicative in a helpful way.  Sometimes politicians will say something like “one victim of a crime by an illegal immigrant is too many”.  But that seems like a silly argument to make *if* immigrants reduce the probability of being a victim of a crime.

I argue that (1) immigrants who commit crimes at a lower probability than the native population cause the native population to be safer and, counterintuitively, (2) immigrants who commit crimes at a *higher* probability than the native population cause the native population to be safer.

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Persistent Beliefs

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The things that happen between people’s ears are difficult to study. Similarly, the actions that we take and the symbolic gestures that we communicate to the people around us are also difficult to study. We often and easily perceive the social signals of otherwise mundane activities, but they are nearly impossible to quantify systematically beyond 1st person accounts. And that’s me being generous. Part of the reason that these things are hard to study is that communication requires both a transmitter and a receiver. One person transmits a message and another person receives it. Sometimes, they’re on slightly or very different wavelengths and the message gets garbled or sent inadvertently and then conflict ensues.

Having common beliefs and understandings about the world help us to communicate more effectively. Those beliefs also tend to be relevant about the material world too. A small example is sunscreen. Because a parent rightly believes that sunscreen will protect their child from short-run pain and long-run sickness, they might lather it on. But, due to their belief, they also signal their love, compassion, and stewardship for their child. A spouse or another adult failing to apply sunscreen to a child signals the lack thereof and conflict can ensue even when the long-term impact of one-time and brief sun exposure is almost zero.

People cry both sad and happy tears because of how they interpret the actions of others – often apart from the other external effects. Therefore, beliefs imbue with costs and benefits even the behaviors that have seemingly immaterial consequences otherwise. We can argue all day about beliefs. And while beliefs might change with temporary changes in the technology, society, and the environment, core beliefs need to be durable over time. Therefore, if this economist were to recommend beliefs, then I would focus on the prerequisite of persistence before even trying to find a locally optimal set.

Here are three inexhaustive criteria for a durable beliefs:

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Services, and Goods, and Software (Oh My!)

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When I was in high school I remember talking about video game consumption. Yes, an Xbox was more than two hundred dollars, but one could enjoy the next hour of that video game play at a cost of almost zero. Video games lowered the marginal cost and increased the marginal utility of what is measured as leisure. Similarly, the 20th century was the time of mass production. Labor-saving devices and a deluge of goods pervaded. Remember servants? That’s a pre-20th century technology. Domestic work in another person’s house was very popular in the 1800s. Less so as the 20th century progressed. Now we devices that save on both labor and physical resources. Software helps us surpass the historical limits of moving physical objects in the real world.


There’s something that I think about a lot and I’ve been thinking about it for 20 years. It’s simple and not comprehensive, but I still think that it makes sense.

  • Labor is highly regulated and costly.
  • Physical capital is less regulated than labor.
  • Software and writing more generally is less regulated than physical capital.


I think that just about anyone would agree with the above. Labor is regulated by health and safety standards, “human resource” concerns, legal compliance and preemption, environmental impact, and transportation infrastructure, etc. It’s expensive to employ someone, and it’s especially expensive to have them employ their physical labor.

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Recession Prospecting & Fed Tea Leaves

Zachary Bartsch4 Comments

Will a recession happen? It’s famously hard/impossible to predict. Personally, I have a relatively monetarist take. I consider the goals of the Federal reserve, what tools they have, and how they make their decisions. I also think about the very recent trend in the macroeconomy and how it’s situated relative to history. Right now, the yield curve has been inverted for quite some time and the Sahm rule has been satisfied, both are historical indicators of recession.

Recessions are determined by the NBER’s Business Cycle Dating Committee. They always make their determination in hindsight and almost never in real time. They look at a variety of indicators and judge whether each declines, for how long, how deeply, and the breadth of decline across the economy. So plenty of ‘bad’ things can happen without triggering a recession designation.

In my expert opinion, recessions can largely be prevented by maintaining expected and steady growth in NGDP. This won’t solve real sectoral problems, but it will help to prevent contagion and spirals.  The Fed can control NGDP to a great degree. In doing so, they can affect unemployment and growth in the short run, and inflation in the medium to long run.

One drawback of the NGDP series is that it’s infrequent, published only quarterly. It’s hard to know whether a dip is momentary, a false signal that will later be updated, or whether there is a recession coming. So, what should one examine? One could examine leading indicators or the various high-frequency indicators of economic activity. But those are a little too much like tarot cards and fortune telling for my taste.

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IPUMS Data Intensive Workshop & Conference

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I just returned from the Full Count IPUMS data workshop at the Data-Intensive Research Conference that was hosted by the Network on Data Intensive Research on Aging and IPUMS. The theme of this conference was “Linking Records”.

It was the best workshop and conference that I’ve ever attended. I’d attended the conference remotely in the past. But attending the workshop was exceptional. Myself and about 20 other people were flown to the Minneapolis Population Center and put up in a hotel during our stay (that made the conference a low-stress affair). The whole workshop was well organized, the speakers built on one another’s content, and there was a hands-on lab for us to complete. I felt my human capital growing by the hour.  

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You, Parent, Should have a Robot Vacuum

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Do you have a robot vacuum? The first model was introduced in 2002 for $199. I don’t know how good that first model was, but I remember seeing plenty of ads for them by 2010 or so. My family was the cost-cutting kind of family that didn’t buy such things. I wondered how well they actually performed ‘in real life’. Given that they were on the shelves for $400-$1,200 dollars, I had the impression that there was a lot of quality difference among them. I didn’t need one, given that I rented or had a small floor area to clean, and I sure didn’t want to spend money on one that didn’t actually clean the floors. I lacked domain-specific knowledge. So I didn’t bother with them.

Fast forward to 2024: I’ve got four kids, a larger floor area, and less time. My wife and I agreed early in our marriage that we would be a ‘no shoes in the house’ kind of family.  That said, we have different views when it comes to floor cleanliness. Mine is: if the floors are dirty, then let’s wait until the source of crumbs is gone, and then clean them when they will remain clean. In practice, this means sweeping or vacuuming after the kids go to bed, and then steam mopping (we have tile) after parties (not before). My wife, in contrast, feels the crumbs on her feet now and wants it to stop ASAP. Not to mention that it makes her stressed about non-floor clutter or chaos too.

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MSNE Echoes PSNE

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Let’s talk game theory. I’ve written in the past about Pure Strategy Nash Equilibria (PSNE). They identify possible equilibrium strategies, even if players are unlikely to adopt those strategies in real life. Students don’t like the implausibility of many PSNE strategies, and they sometimes struggle to limit their conclusions to the premises that yield PSNE. Students have a similar dissonance to Mixed Strategy Nash Equilibria (MSNE).

What is MSNE? A set of MSNE strategies allow a player to choose some strategies probabilistically – with probabilities that are less than 100%. That’s the feature of MSNE that distinguishes it from PSNE. In PSNE, a strategy is chosen with 0% or 100% probability.

Here’s an example to illustrate. Imagine that you are shopping at the grocery store with your shopping cart. You’re at one end of the aisle and another shopper is at the other end and your heading straight toward one another at a snail’s pace. Ideally, you’d not hit each other or awkwardly arrive in each other’s path. For simplicity, let’s say that each of you can walk on the right or the left side of the aisle only.* Below is a simultaneous normal form game with arbitrary payoffs.

There are two PSNE in the above game: each person walks on their right or their left side of the aisle. If you and the other person are both walking on your respective rights or lefts, then neither of you has an incentive to deviate. The alternative is that you are heading straight for one another and one of you must veer from their path or play an awkwardly low stakes game of chicken.

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