I’ll be giving a presentation today at 12pm ET over Zoom for the Ostrom Workshop Colloquium Series at Indiana University. It is my understanding that it is open to the public. The format is different than your typical economics seminar. I will give an introduction and brief summary of the paper for 20 minutes followed by questions.

You can find the full working paper here.

Since Marshall Sahlins in the 1970s (and thanks to James Suzman’s Work ), a weird idea has worked its way into popular imagination: people of the past did not work much. Well, more precisely, the idea is that for most of human history our ancestors worked far less and thought very differently about work than we do now. That is based on a weird starting point and a misunderstanding of how “work” works.

The starting point is the pre-neolithic era when the vast majority of time was spent hunting and gathering. In that setting, the effort to acquire calories was modest largely because food was abundant relative to a tiny human population. Some early estimates suggest that, because of that relative endowment, people worked maybe less than 20 hours per week hunting and gathering. Some say even less. That is probably correct and also wrong.

Notice that I italicized hunting and gathering above suggesting that the time commitment of these two tasks was quite small. However, this is not the sum of all work people did then. One has to understand that nomadic groups were nomad in part because the largest share of their calories was also quite mobile. This meant moving around significantly to track food under a key constraint — that calories from gathering be available.

This meant that people moved from “oasis” to “oasis” or from “patch” to “patch”. Between each patch/oasis, there was a lot of time spent “in transit” (let’s call this d for dead time). That time is technically not work for hunting or gathering — but it is work. Not counting it is a mistake.

To see how it matters, consider the graph below which depicts a forager who moves between oases/patches where food is available. As they stay in a oasis, the yield of food y is marginally decreasing so that at one point he may have an incentive to move on. When he moves on, he incurs the cost d which is dead time while moving. Suppose also that a single oasis/patch per year (which encompasses multiple time periods) is insufficient to survive the year. Thus, multiple patches must be exploited. Supposing that all oases are equally distant, of equal quality and that there are many oases in total, how can we picture the decision to move to another? If you want to maximize your food intake over a long period of time, you have to go to multiple oases in a year. This is where we introduce the dashed blue line which is the total yield from all oases/patches divided by time. Notice that it starts at origin so that we are capturing the cost of d.

These two lines tell us that you stay at a single oasis until its marginal return is inferior to the average yield over all oases/patches. Why does this matter? Well, imagine the implications if each patch is less productive? You have to move more to reach a certain target and incur d more frequently. That effectively means that you have to exploit a greater territory to meet a certain target of food (e.g. survival).

The estimate of time spent hunting and gathering are essentially the time within patches rather than the time spent for all patches. Thus, there is a massive underestimate. The yield on a single oasis/patch was so low in the pre-neolithic that moving was something that clans did often. In the late Ice Age, family groups apparently moved every 3-6 days. Modern nomads in certain regions move some 400 km per year. At 5km/h, this is 80 hours of work per year. However, that 5km/h is too high as there were children to carry which slow things down. At 3km/h, we are talking 133 hours per year (or roughly 2.6 extra hours per week). This is just dead time but it is work. As such, more exhaustive worktime estimates suggest values of 35 to 43 hours per week. Most western countries are below this level. Moreover, it is worth considering that work started at young ages and there was no retirement. With shorter lives and earlier work-entry, a smaller fraction of awake life-time was spent in leisurely pursuits. Ergo, it is insanely likely that no society today exhibits more “life-time” work than the prehistoric humans.

Finally, it is worth pointing out the very obvious. The introduction of agriculture, by removing the need to move around and also reducing variability in calories (i.e. fewer chances of catastrophes), essentially increased the benefit of working (i.e. making leisure relatively costlier). It is unsurprising then that the introduction of agriculture led to some increases in labor supply. However, that being said, it is clearly false than we work more today than our prehistoric ancestors did. There is no way around it.

Difficulties in the global supply chain are a recurrent news item since the beginning of fall. The result has been that many pundits or politicians have argued for new policies that spout platitudes such as the need to “rethink trade“. For my part, all I could think of was the O-Ring theory of development developed by Michael Kremer.

The name for that theory is taken from the 1986 Challenger disaster, in which the failure of one small, inexpensive part caused the shuttle to explode upon take-off. Generally, the theory is applied to questions of development and speaks to high complementarities between inputs. Suppose the economy is divided into multiple sectors that exchange intermediaries goods between them (i.e. all firms are dependent on each other). Each of these goods can be labelled as n and producing these goods require skills q. However, each sector buys multiple different n as intermediary goods. For example, this would mean that sector “Vincent” buys goods from sectors “Joy”, “Jeremy” and “James” to produce the “Vincent” goods.

Imagine now that q is the percentage chance that n is produced with sufficient quality so that it bears its full market value (in which case, 1-q is the probability that n is produced so poorly that it gets a zero-price). This means that, to produce its goods, sector “Vincent” needs sectors “Joy”, “Jeremy” and “James” to produce high-quality goods. If one of the intermediary goods “Vincent” buys from the other is inefficient, all of Vincent’s production is worthless. Hence, the analogy to the O-Ring of the Challenger disaster.

So what’s the link with the supply chain failures you ask? Well, its pretty straightforward: the O-Ring theory implies that the impact of a bottleneck has a multiplicative effect on other productions. Now, everyone may be excused for thinking that I simply explained in a complex way something that is simple (i.e. dont half-ass things). However, this way of formulating is very helpful because of q.

If q is the probability of a badly-performed task, what determines q? Some could say its the pandemic, but that would be incorrect. An article in Nature shows that COVID-19 has yielded widely disparate effects on supply chains in different countries. If it was global, it should be roughly similar everywhere. Ergo, some local factors must be in play. Local factors of relevance would be laws on shipping such as the Jones Act in the United States or the public ownership of ports in many western countries. By preventing cabotage and limiting foreign ships, such as in the Jones Act, there is little excess capacity in the American shipping industry available when demand shocks occur. By being more bureaucratically rigid, ports may be unable to adapt to unforeseen events (which is why there are papers in transportation economics that show that privatizing ports tends to increase productivity and reduce shipping costs notably by speeding turnarounds).

Each of these local factors have to do with local policies that reduce q and tend to increase the likelihood of failures (i.e. bottlenecks) which then reverberate on total output (beyond the narrow supply chain sector). From this, I get to a simple: complications that we attribute to the COVID crisis are more likely the results of local factors.

That’s the column this week. No ad hoc economic theory, no deep insight into the profession. No silly sports talk. Just a recommendation to watch a beautiful looking and sounding movie. Allow yourself to get invested in the world they are building. Reward their willingness to be sufficiently faithful to a masterpiece while also having the maturity to know that much of the intrigue, as designed in the book, wouldn’t translate to the screen.

They’re building a world where a single commodity is so valuable, and it’s supply so inelastic, that it serves as the fulcrum for an entire galaxy. Worries about peak oil feel like fretting about a possible shortage of student selfies when compared to the economics of spice. The political economy is coming, don’t you worry. For the moment though, just take in a cool movie.

Go watch Dune.

For those who didn’t know, as part of the American Rescue Plan, there were some changes made to the Child Tax Credit (CTC) for the tax year 2021.

• First, the credit was expanded from $2k to $3,600 per child for children under 6 years of age (to $3k otherwise). It’s also fully refundable.
• Second, half of the credit is being disbursed to tax-filers early: over the latter 6 months of 2021.

What does this mean?

For context, I have 3 children all under that age of 6. My total 2021 CTC is $10,800. Half of that is being distributed as monthly checks from July through December. For me, that’s a check for $900 per month that I had not anticipated. While it is true that I will see less of a remaining credit when I file my taxes by April of 2022, I strongly suspect that most similar households are somewhat short-sighted about these funds.

Depending on the number of children in a household, the monthly check from the IRS can be quite significant. From the parent point of view, there has been a lump-sum transfer. There is no endogenous response to obtain more children – there’s no time for that. The transfer also occurs regardless of any activities, economic or otherwise. In essence, tax-filers with children have experienced a positive income shock.

The big question is: What is the effect on employment?  

In one sense, the effect is ambiguous and depends on preferences: People can now afford more leisure and more consumption. How they engage in more of each is a matter of preference. But, given that both are goods, both will increase by some amount.

That’s my simple model. I hereby make multiple predictions:

1. Parents with children will have consumed more in the 3^rd and 4^th quarters of 2021.  
2. Parents with children will have lower employment growth for those quarters.
3. The effects will be stronger for parents with children under 6 years of age.
4. The employment rebound in the 1^st quarter of 2022 will be stronger for these groups (and stronger than forecasted overall).
5. Finally, while I’m feeling silly enough to make predictions publicly, I predict slower growth in consumer durable expenditures in 2022 Q1.

I looked at the BLS for data to corroborate my predictions. Excitingly, the Current Population Survey (CPS) does slice the data by sex, age, and age of own children (conveniently by younger than 6 and 6-17 years of age). This is where I post the great visual to demonstrate the veracity of my claims, right?

WOMP WOMP.

The relevant data is currently only available annually as recent as 2020…