If I Had 2 Million Dollars

In July of 1992, the Barenaked Ladies released their debut studio album Gordon, which included one of their most popular songs: “If I Had $1000000.” Considering all the inflation we’ve had recently, you know that $1 million doesn’t buy as much as it did in 1992, but how much less? As measured by the Consumer Price Index in the US, prices have roughly doubled since 1992, meaning you would need about $2 million to buy the same amount of stuff as in 1992.

(Note: the Barenaked Ladies are Canadian, and prices in Canada haven’t quite doubled since 1992, but this song was included on early demo tapes in 1988 and 1989 released in Canada, and prices have roughly doubled there since then.)

So the value of a dollar that you held since 1992 has lost roughly half of its purchasing power. That’s bad. But how bad is it? What’s the normal US experience for how long it takes for prices to double?

It turns out that even with the recent huge run-up in inflation, we just lived through the lowest period of inflation for anyone alive today.

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Counting the missing poor in pre-industrial societies

There is a new paper available at Cliometrica. It is co-authored by Mathieu Lefebvre, Pierre Pestieau and Gregory Ponthiere and it deals with how the poor were counted in the past. More precisely, if the poor had “a survival disadvantage” they would die. As the authors make clear “poor individuals, facing worse survival conditions than non-poor ones, are under-represented in the studied populations, which
pushes poverty measures downwards.” However, any good economist would agree that people who died in a year X (say 1688) ought to have their living standards considered before they died in that same year (Amartya Sen made the same point about missing women). If not, you will undercount the poor and misestimate their actual material misery.

So what do Lefebvre et al. do deal with this? They adapt what looks like a population transition matrix (which is generally used to study in-,out-migration alongside natural changes in population — see example 10.15 in this favorite mathematical economics textbook of mine) to correctly estimate what the poor population would have been in a given years. Obviously, some assumptions have to be used regarding fertility and mortality differentials with the rich — but ranges can allow for differing estimates to get a “rough idea” of the problem’s size. What is particularly neat — and something I had never thought of — is that the author recognize that “it is not necessarily the case that a higher evolutionary advantage for the non-poor over the poor pushes measured poverty down”. Indeed, they point out that “when downward social mobility is high”, poverty measures can be artificially increased upward by “a stronger evolutionary advantage for the non-poor”. Indeed, if the rich can become poor, then the bias could work in the opposite direction (overstating rather than understating poverty). This is further added to their “transition matrix” (I do not have a better term and I am using the term I use in classes).

What is their results? Under assumptions of low downward mobility, pre-industrial poverty in England is understated by 10 to 50 percentage points (that is huge — as it means that 75% of England at worse was poor circa 1688 — I am very skeptical about this proportion at the high-end but I can buy a 35-40% figure without a sweat). What is interesting though is that they find that higher downward mobility would bring down the proportion by 5 percentage points. The authors do not speculate much as to how likely was downward mobility but I am going to assume that it was low and their results would be more relevant if the methodology was applied to 19th century America (which was highly mobile up and down — a fact that many fail to appreciate).

This Time was Way Different

The financial crisis recession that started in late 2007 was very different from the 2020 pandemic recession. Even now, 15 years later, we don’t all agree on the causes of the 2007 recession. Maybe it was due to the housing crisis, maybe due to the policy of allowing NGDP to fall, or maybe due to financial contagion. I watched Vernon Smith give a lecture in 2012 in which he explained that it was a housing crisis. Scott Sumner believes that a housing sectoral decline would have occurred, and that the economy-wide deep recession and subsequent slow recovery was caused by poor monetary policy.

Everyone agrees, however, that the 2007 recession was fundamentally different from the 2020 recession. The latter, many believe, reflected a supply shock or a technology shock. Performing social activities, including work, in close proximity to others became much less safe. As a result, we traded off productivity for safety.

The policy responses to each of the two were also different. In 2020, monetary policy was far more targeted in its interventions and the fiscal stimulus was much bigger. I’ll save the policy response differences for another post. In this post, I want to display a few graphs that broadly reflect the speed and magnitude of the recoveries. Because the recessions had different causes, I use broad measures that are applicable to both.

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Mises’s Bureaucracy, a Recap

My favorite two economists are Ludwig Von Mises and Milton Friedman. They might consider one another from very different schools of thought, though there is reason to think that they are not so different. As an undergraduate student, I liked them both, but I became more empirics-minded in graduate school and as a young assistant professor.

As I progressed through graduate school and conducted empirical research, my opinions and policy prescriptions changed and were refined from what they once were. In graduate school, I didn’t study Austrian Economics, though it was certainly in the water at George Mason University. Recently, as an assistant professor with a few years under my belt, I picked up Bureaucracy (1944) and read it as a matter of leisure.

One word:

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Car Prices and Quality

Inflation is on everyone’s mind. Everybody freaks out. You cannot do anything about it. As such, lets talk about something mildly related: how price indexes (those that we use to talk about inflation) deal with quality changes.

One big problem when we try to measure the cost of living is that the price information we collect does not reflect the same thing we consume. I know that sentence seems weird. After all, 1$ for a pound of bread is 1$ for a pound a bread. And if prices go up 10%, then the price per pound of bread is 1.10$!

If you think that, you’re wrong. Think about the following example from my native province of Quebec. In the 1990s, Quebec deregulated opening hours for grocery stores. The result was … higher prices at large superstores. Why? Before the reform, stores had shorter hours especially on sundays. This meant that stores were competing with each other on a smaller quality dimension which meant more price-based competition. With deregulation, some consumers were willing to pay slightly higher prices to shop at ungodly hours. What were these consumers consuming? Were they consuming only the breadloafs they bought or were they consuming those loafs and the flexible schedule of the grocery stores? The answer is the latter! Ergo, the change from 1$ per pound to 1.10$ per pound does not mean that the price of bread alone increased — it may have even fallen all else being equal!

So how do you adjust for that? There are many papers on how to do hedonic adjustments (hedonic is the fancy words we use to say “quality-adjusted”) and they are all a pain to read unless you are very familiar with real analysis, set theory and advanced calculus (and even there, its still a pain). Fortunately, I recently found a neat little application from an old econometrics graduate text from the 1960s (see image below) that allows me to teach this to my students (and now, you too!) in an easy-to-get format.

A neat book

The book has a neat chapter by one of the most famous econometricians of the 20th century, Zvi Griliches, titled “Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Change”. In the chapter, Griliches points out that from 1954 to 1960, car prices went up some 20% — well above the overall price index. From 1937 to 1950, prices for cars went up in line with inflation. Taken together, these two facts suggest that the real price of cars stayed constant from 1937 to 1950 and increased to 1960. But that suggestion is wrong Griliches points out because of our aforementioned quality issues. Up until 1960, there were considerable improvement in vehicle quality: better gears, better brakes, more horsepower, safer settings, automatic transmission, hardtops, switching to V-8 engines rather than 6 cylinders engines etc.

How do you account for these quality changes? Griliches simply went about consulting guide books for autobuyers. He collected price data for the cars as well the details regarding quality. And he used this very simple specification where the log of the nominal price is set as a dependent variable.

Griliches’ specification

The vector X is all the quality dimensions he could find (horsepower, shipping weight, length, V-8 engine, hardtop, automatic transmission, power steering, power brakes, compact car). All of these dimensions were statistically significant determinants of the price of cars (with the exception of V-8 engines which was not significant). Then, Griliches assumed that all quality dimensions were “unchanged” from 1954 to 1960 in order to see how prices would have evolved without any changes in quality. The result is the figure below. The blue line depicts the actual prices he collected where you can see the 20% increase to 1960 (which is a 30%+ increase to 1959). The orange line depicts the price holding quality constant. That orange line is unambiguous: quality-constant car prices didn’t change much during the 1950s. Adjusting for inflation during the period suggests a drop in 10% in the real price of a quality-constant car.


Isn’t that a fascinating way to understand what we are actually measuring when we collect prices to talk about inflation? I find this to be an utterly fascinating example (and a useful teaching tool). Okay, I am done, you can go back to freaking out about inflation and how bad the Fed, Bank of Canada, ECB are.

What to Read: Claudia Goldin’s Career and Family

A better battery is an excellent gift, but for the gift that never needs recharging, a book is always a great idea. So this week Joy asked us to recommend a book. Again, this would be great as a gift or for yourself!

My recommendation is a very new book: Claudia Goldin’s Career and Family, which just came out this month. Confession: the book is so new, that I’ve only read about half of it so far! But this book is, as they say, self-recommending.

Goldin has spent almost her entire academic career studying the history of women’s participation in the US labor force. I think it’s fair to say that there is no person living today that knows more about the subject, possibly no one ever. This book is her attempt to sum up much of her research into a cohesive narrative about the changes in women’s labor force participation throughout the 20th century.

Her 2006 AEA Ely Lecture, “The Quiet Revolution,” was an earlier attempt to explain these long changes, and it is highly readable still today. Her 2014 AEA Presidential Address, “A Grand Gender Convergence,” is also excellent (watch the video of it too!). But this book brings all the ideas together into a complete narrative, tracking five cohorts of women and their experience in the labor force from 1900 to 2000. The last of these five cohorts matches the title of her book, the generation of women that entered the labor force since 1980 and now have a reasonable chance of achieving both an career and a family, rather than having to chose between the two.

This does not mean, and certainly Goldin would not say, that the journey is over and all is well for women today. Goldin focuses primarily on college graduates in this story, since they are the group most well-positioned to achieve the goal of having a career and a family. Obviously there are still challenges, and Goldin spends some time discussing one that the COVID pandemic revealed but was always there: the challenge of finding affordable childcare.

If you want a taste of the book, you can read or watch her 2020 Feldstein Lecture, “Journey Across a Century of Women.” But really the story is so complex that it does take a book to explain it all.

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Have you heard about Human Capital?

While writing a paper recently, I was reminded of the importance of economic modelling.

Macroeconomic models are fun to rag on – everybody does it. But all economic models help us to express our understanding of the world clearly and help us to be specific when the temptation to hand-wave is strong. After all, a model is just a fancy way of saying “a system of logic”.

The paper linked above is several revisions in. What you don’t see are the mistakes that my co-author and I made along the way and the vagueness that we had to resolve. An earlier version of the paper simply stated that deaf people were endowed with less human capital than people who could hear. So far so good. But then we said that it was ambiguous who, the deaf or the hearing, would ultimately have more human capital after making additional human capital investments.

But this is not the case!

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Steve Horwitz on “The Graduate Student Disease”

On Sunday the world lost a great teacher, economist, and all-around fantastic person in Steve Horwitz. If you don’t know about Steve, I recommend reading the tributes from Pete Boettke and Art Carden.

Pete and Art speak to Steve’s overall legacy and greatness. But I will tell you about a very specific piece of advice that Steve gave me about teaching undergrads.

Steve called it “the graduate student disease.” By this he meant the tendency of newly minted PhD economists to teach undergraduate courses as if they were mini versions of graduate courses. Steve insisted this was the wrong approach.

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Wait for the Lower Cost Version of Policy

I’ve written previously about initial US state compulsory schooling laws in regard to literacy and in school attendance rates. I ended with a political economy hypothesis. Here’s the logic:

  1. Legislators like lower costs, all else constant (more funding is available for other priorities).
  2. Enforcing truancy and educating an illiterate populous is costly.
  3. Therefore, state legislatures that passed compulsory attendance legislation will already have had relatively high rates of school attendance and literacy.

That’s it. Standard political economy incentives. But is it true? Well, we can’t tell what’s going on in politician heads today, much less 150 years ago. Though, we can observe evidence that might corroborate the story. In plain terms, consistent evidence for the hypothesis would be that school attendance and literacy rates were rising prior to compulsory schooling legislation. The figures below show attendance and literacy rates for children ages 10 to 18.

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Working Hard for the Money

40 hours. That’s what we think of as a typical workweek. 8 hours per day. 5 days per week. Perhaps the widespread practice of working from home during the pandemic (as well as the abnormal schedule changes for those unable to work from home), has led some to rethink the nature of the workweek. But the truth is that the workweek has always been evolving.

Take this chart, for example. It comes from Our World in Data (be sure to read their excellent related essay as well), and the historical data comes from a paper by Huberman and Minns. I’ve singled out 4 countries, but you can add others at the OWiD link.

The historical declines are dramatic. This is especially true in Sweden. The average Swedish worker labored for over 3,400 hours per year in 1870. Today, that’s down to 1,600 hours. In other words, the typical Swede works less than half as many hours as her historical counterpart. Wow! The decline for the US is not quite as dramatic, but still astonishing: a US worker today labors for only about 57% of the hours of his 1870 predecessor.

It’s tempting to focus on the differences across countries today: the average worker in the US works about 250 hours more than the average French worker. That’s 6 weeks of vacation! And as recently as 1980, the US and France were roughly equal on this measure. We might also wonder why these historical changes happened. For a very brief introduction to the research, I recommend the last section of this essay by Robert Whaples.

But still, the historical declines are dramatic, even if we in the US haven’t seen much improvement in the past generation (and those poor Swedes, working 100 hours per year more than 40 years ago).

I think another natural question to ask is whether GDP data is distorted, at least as a measure of well being, given these differences in working hours. The answer is partially. Let’s look at the data!

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