Pumpkin Spice: 15th Century Edition

It’s pumpkin spice season. That means that not only can you get pumpkin spice lattes, but also pumpkin spice Oreos, pumpkin spice Cheerios, and even pumpkin spice oil changes.

The most important thing to know about “pumpkin spice” things is that they don’t actually taste like pumpkin. They taste like the spices that you use to flavor pumpkin pie. (Notable exception: Peter Suderman’s excellent pumpkin spice cocktail syrup, which does contain pumpkin puree.)

Last week economic historian Anton Howes posted a picture of the spice shelf at his grocery store and guessed that this would have been worth millions of dollars in 1600.

Some of the comments pushed back a little. OK, probably not millions but certainly a lot. Howes was alluding to the well-known fact that spices used to be expensive. Very expensive. Spices, along with precious metals, were one of the primary reasons for the global exploration, trade, and colonialism for centuries. Finding and controlling spices was a huge source of wealth.

But how much more expensive were spices in the past? One comment on Howes’ tweet points to an excellent essay by the late economic historian John Munro on the history of spices. And importantly, Munro gives us a nice comparison of the prices of spices in 15th century Europe, including a comparison to typical wages.

As I looked at the list of spices in Munro’s essay, I noticed: these are the pumpkin spices! Cloves, cinnamon, ginger, and mace (from the nutmeg seed, though not exactly the same as nutmeg). He’s even included sugar. That’s all we need to make a pumpkin spice syrup!

Last week in my Thanksgiving prices post I cautioned against looking at any one price or set of prices in isolation. You can’t tell a lot about standards of living by looking at just a few prices, you need to look at all prices. So let me just reiterate here that the following comparison is not a broad claim about living standards, just a fun exercise.

That being said, let’s see how much the prices of spices have fallen.

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This Is Not the Most Expensive Thanksgiving Ever

“Thanksgiving 2021 could be the most expensive meal in the history of the holiday.”

That’s the first sentence of a recent New York Times story. The Times and the New York Post rarely agree on editorial matters, but on this topic the Post ran a very similar story the same week. You can find many such headlines.

But is it true? In short: no. I’ll explain why, but my larger goal is to get you to think more clearly about inflation.

How should we measure the cost of a Thanksgiving meal? A widely used measure comes from the Farm Bureau, which shows that the cost of a traditional turkey-centric meal costs about 14% more than last year. In dollar terms it is $53.31 for a turkey, a pumpkin, cranberries, sweet potatoes, stuffing, etc. That’s more that it has ever been, in dollar terms. Farm Bureau has been tracking the cost of this same meal since 1986.

So in one sense, it seems like the headline claim is true. Most expensive Thanksgiving ever!

But we need to think deeper. A nominal price doesn’t actually tell us much. If a long-lost cousin from the Republic of Horpedahl told you it costs 1 million Jeremys to buy a Thanksgiving dinner, what would your reaction be? The first and best reaction is: how much do people earn in the Republic of Horpedahl?

We should ask the same question in the United States today: how do incomes today compare to incomes in the past? Which measure of income you use is important, but if we use median usual weekly earnings of full-time workers, we can make a simple comparison of how much of your weekly earnings would be needed to buy a traditional Thanksgiving meal. This chart shows exactly that. In 2021 that meal will be the second lowest it has ever been as a percent of median earnings — higher than last year, but tied with 2019 for the second lowest. And much less than in the late 1980s and early 1990s (I use third quarter data for each year, the most recent available).

Adjusting for income is the best way to look at this question. It’s not perfect — part of this depends on what income measure you use — but it’s much better than the alternative. The worst approach is to just look at nominal prices. This tells you virtually nothing.

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Compulsory Schooling by Gender & Age

This weekend I’ll be at the Southern Economic Association Conference in Houston Texas. I’m organizing and chairing a session called Education Policy Impacts by Sex (you should come by and see me if you will be there too!).

Personally, I will be presenting on the impact of compulsory school attendance laws on attendance. Today I just want to share and discuss a single graph that’s not my presentation.

Prior to my research, there was already a canon of existing literature on compulsory attendance legislation (CSL) and I’ve previously written on this blog about it (attendance, CSL, and differences by sex). However, the literature had some limitations. Authors examined smaller samples, ignored gender, or ignored different effects by age.

I examine full-count IPUMS data from the 1850-1910 US censuses of whites in order to investigate the so-far-omitted margins mentioned above. Here are some conclusions:

Prior to CSL:

  • Males and females attended school at similar rates until the age of 14.
  • After 14, women stopped attending school as much as men.
    • By the age of 18, the attendance gender gap was 10 percentage points.

After CSL

  • Male and female attendance increased from the ages of 6 to 14
  • Women began attending school more than prior to CSL until about age 18.
  • After the age of 18, women experienced no greater attendance than previously.
  • But, both sexes attended school less than prior to CSL for ages 5 and younger.
  • Men began attending school less after the age of 17.
  • CSL increased lifetime attendance for both males and females

Overall, examining the impact of CSL across many ages allows us to see when and not just whether people attended more school. Previous authors would say something like “CSL increased total years of school by about 5% on average”. For men, almost all of those gains were between the ages of 6 & 16. But women experienced greater attendance from ages 6 to 18.

Additionally, examining the data by age reveals that there was some intertemporal substitution. Once it became legally mandatory for children to attend school between the ages of 6 & 14, parents began sending their younger children to school at lower rates. Indeed – why invest in education for two or three early years of life if you’ll just have to send your children to school for another eight years anyway. Older boys dropped out of school at higher rates after CSL too. Essentially, the above figure became compressed horizontally. People ‘put in their time’, but then reduced investments at non-mandatory ages.

This reveals a shortcoming of the current literature, which focuses mostly on 14 year olds. By focusing on a popular age of attendance that was also compulsory, previous authors have missed the compensating fall in attendance at other ages. Granted, the life-time effect is still positive – but it’s attenuated by a richer picture. The picture reveals that individuals were not attending school by accident. Students or their parents had in mind an amount of educational investment for which they were aiming. When children were forced to attend school at particular ages, the attendance for other ages declined.

Inflation: Get Concrete, Get Specific

The recent debate over US inflation seems to be full of mood affiliation on both sides, where people start with a mood (“panic” or “don’t worry”) and then look for facts to fit the mood.

My natural temperament is “don’t worry” and that is what I’ve generally thought about inflation, but the latest number of 6.2% inflation over the last year is a bit concerning, and makes me glad the the Fed has announced they plan to taper off of new asset purchases. But overall I think people are still talking past each other, and I wish more people would answer these questions:

What will CPI inflation be over the next 12 months?

What specifically should the Fed do differently, if anything? How quickly should they taper and raise rates?

If you are currently thinking “panic” or “don’t worry”, what data could come in that would change your mind?

I’ll start with my answers, informed more by my gut than by quantitative models: my guess for inflation over the next year is 4-5%, the Fed has things about right but I’d say “tighten faster” rather than “tighten slower” if I had to pick. I expect inflation to slow noticeably in the spring as the economy transitions from the unusual boom in demand for goods back to demand for services after Christmas and the Delta wave, as more people get back to work and supply bottlenecks have time to work themselves out. I would start to get more seriously concerned if we see no slowing by June, or if market-based measures of inflation or NGDP projections start to move substantially (2pp) higher.

To the extent that I’ve been on the wrong side of this, I blame the cognitive bias I seem to fall prey to most often- mistaking reversed stupidity for intelligence. Just because lots of people make obviously incorrect predictions of hyperinflation doesn’t mean that inflation will be low.

No, 6.2% inflation per year is not in the same universe as hyperinflation (50% inflation per month)

*The usual disclaimer applies- my affiliation with the Fed gives me zero insider information about or influence over monetary policy and I don’t speak for them.

Data continues to improve sports performance

Joy: As a Data Analytics teacher, I often think about the applications of machine intelligence to work processes. Samford undergraduate Copeland Petitfils has written the following blog, which is a reminder to me that there are still many potential areas for growth.

Since “Moneyball”, we have seen the growth of analytics throughout sports. However, many teams have stuck to the same old way of playing baseball, like the Braves. This past May, the Braves took a new innovative approach and saw room for growth on their defensive side.

The general manager, Alex Anthopoulos, implemented a radical strategy and improved the defense by using shifts with data analytics. While “Moneyball” looked at the statistics of acquiring cheaper players who had good batting averages and improved the offensive side, the Braves looked at improving the defensive side and the way they shift between pitches to improve their chances of getting a ground ball out. A defensive shift in baseball refers to the infield changing positions from normal to a certain area of the infield based on the pitches and using stat cast to predict where the batter is most likely to hit the ball depending on the type of pitches. Shifting can increase the probability for players to get ground balls out rather than hits.

Statistically, the Braves ranked at the bottom of defensive shifts in the MLB, and Anthopoulos, the general manager, saw this as an opportunity to improve. The Braves started the 2021 season with no shifting at all to shifting on 50.6% of pitches by the end of the year, which was the highest in baseball this year only behind the Dodgers. The shifting ultimately allows the Braves to improve in converting ground balls to out rather than turning into hits. At the start of the season, the Braves converted under 75% of ground balls into outs which ranked middle of the pack in defense. However, since implementing the shift the number jumped to 77%, which was the second-best in baseball. Although these jumps in percentages seem small, they allowed the Braves to field 25 more ground balls into outs rather than hits.

The data analytics the Braves used allowed the players to be put in a better position to succeed, and as the season progressed, they started to get better and better at it. These decisions turned around the Braves’ season, and now they are on their way to the World Series for the first time since 1999 after beating the Dodgers in the NL Championship.

Coda by Joy: That said, guess who failed at data driven decision making? Zillow!

In a statement Tuesday, Chief Executive Rich Barton said Zillow had failed to predict the pace of home-price appreciation accurately, marking an end to a venture the company once said could generate $20 billion a year. Instead, the company said it now plans to cut 25% of its workforce… “We’ve determined the unpredictability in forecasting home prices far exceeds what we anticipated and continuing to scale Zillow Offers would result in too much earnings and balance-sheet volatility,” Mr. Barton said.

Inflation is Here. Why? What Can We Do?

The latest CPI release today shows that real inflation is here. Headline inflation for consumer prices is up 6.2% compared to a year ago and a almost full percentage point in just the past month (seasonally adjusted, so compared to the normal monthly increase).

Back in June, we could reasonably say that 45% of the increase that month (and 27% over the prior year) had been due to just the price of new and used cars, in the past month only 17% can be attributed to vehicle prices. That’s still a lot, considering cars are only about 8 percent of the overall CPI, but inflation is clearly showing up in other areas.

Gasoline prices (also car related!) are always volatile, but they are up sharply in the past year. The over 50% increase for regular unleaded gasoline translates to $1.22 more per gallon than a year ago (and $1.50 more gallon than Spring 2020), which is the largest nominal price increase consumers have seen in a 12-month period (the data stretches back to 1977).

But gasoline is only about 4 percent of consumer spending. What if we look more broadly? Even excluding energy prices, inflation is 4.7% over the past year, the highest increase since 1991.

The natural related questions are Why? And what can we do about it?

The Why question is tricky. The Federal Reserve is very interested in whether the increase in prices is caused by monetary policy. It very much guides their action. Consumers don’t really care that much. They just want the pain to stop. Unfortunately, though, part of the pain may be induced by consumers themselves: spending on goods is extremely high right now, with the year so far 18% above the comparable period in 2019. Higher spending will increase prices in any environment, but the strain it is putting on supply chains only exacerbates the problem. This is not to “blame the victim,” but rather to understand what is going on.

What can be done? That’s an even harder question. It’s convenient to blame the President for things like gas prices. And certainly many voters and pundits will blame him. This charge is not completely without basis, as there are certainly things at the margin a president could do to ease gas prices in the short run (allow more drilling, gas tax hiatus), but we shouldn’t oversell this. And in other areas too, perhaps there are changes that could be made at the margin. But given the massive increases in consumer spending (at least for now), any changes won’t put a dent in the overall inflation rate.

But what about at the individual level? Milton Friedman was asked this question in 1980. That year inflation was 13.5%, the highest since World War II. Friedman’s answer: high living. He said there is no asset which you can expect to protect you against inflation, so you should spend what you have now on something nice. Buy a nice house, a nice suit, a picture to hang on the wall. This is what economists sometimes call “the flight to real values,” or as Phil Donahue put it “convert your money into material things.” While this advice may make sense at the individual level, it doesn’t have great implications for the current supply chain issues.

Friedman did have clear advice for the nation: the Federal Reserve should stop increasing the money supply. Whether that advice will work in the current environment, or whether it will stall the economic recovery, is the hard question the Fed is wrestling with at this very moment.

COVID and The Young

The CDC just approved vaccines from Americans aged 5-11. That’s great news! But today, I want to talk about another age group: mine.

A few months ago I wrote a post summarizing data for COVID-19 deaths among people in their 30s and 40s. While we have primarily thought of COVID as a disease impacting the elderly (and indeed in the aggregate, it is), there have been major health consequences for those under 65 too. Including major health consequences for the age group 30-49 (which I believe is the age range of all our bloggers here at EWED).

I wanted to update that data because a few new things have come to light. First, I highly recommend reading a recent paper by my friend Julian Reif and co-authors. They estimate the number of Years of Life Lost and Quality-Adjusted Years of Life Lost for different age groups from COVID-19. Their data runs through mid-March 2021, so before vaccines probably had much of a chance to impact the aggregate death numbers (though vaccines were being rolled out at the time).

Here’s their main result: while most of the deaths from COVID were among those aged 65 and older (80% through March 2021), most of the life lost in terms of years was for Americans under 65 (54% of QALYs). And even for very young adults, the risk in terms of years of life lost was not minimal. A comparison from the paper: “Adults aged 85 years or older faced 70 times more excess risk for death than those aged 25 to 34 years but only 3.9 times more individualized loss of QALYs per capita.” Compared to the 35-44 age group, the relevant factor is 2.8 times more individualized loss for the 85+ group.

It’s a great paper, but it only goes through March. What has happened since March 2021? While 80% of the COVID deaths up through March 2021 were among the elderly (65 and older), since April 2021 only 60% of the COVID deaths have been among the elderly. Part of this is because deaths are down among the elderly, but it’s also because deaths are up for the non-elderly. The table is my attempt to show this effect, looking at the period from March-September in both 2020 and 2021 (data is current as of October 27, so the September 2021 data is still not complete, but instructive).

For the oldest Americans, COVID deaths fell by 50%. That’s great! But for younger Americans, COVID deaths roughly doubled. Not good!

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Growing/Shrinking Jobs of Next Decade: Good Times Ahead for Nurse Practitioners, Wind Turbine Technicians, and Animal Trainers

The good folks at Visual Capitalist have put together a big juicy infographic depicting employment trends over the next decade, based on projections from the Bureau of Labor Statistics. The vertical axis is % decadal growth for each category, the horizontal axis is 2020 median annual wage for that category, and the size of the bubble indicates the absolute numbers of change. The color of each bubble is keyed to “Occupational Group”, i.e.,  “Health related”, “Computer and mathematical”, etc.

Below I snipped part of the infographic which shows occupations which will be growing. The horizontal positioning (median annual wage) runs from $20,000 on the left to $120,000 on the far right; nurse practitioners fall in the $105,000-120,000 range. The fastest growing, percentagewise, are wind turbine service technicians (68%), followed by nurse practitioners and solar installers tied at about 52%. The biggest absolute numbers of job growth are in “Home health and personal care aides”, to tend aging baby boomers.

From the color coding, we can see at a glance that job growth is mainly in the Health Related and Computer and Mathematical categories, with a smattering of “Other”, including Animal Trainers (for dog obedience schools ??) and Crematory Operators, as those baby boomers age all the way out.

Some of the losing professions are shown below. Most of these are in the “Office and Admin Support” (purple) category and Production workers (including nuclear power reactor operators). Some “Other” categories will get hit hard, such as parking officers and door-to-door salesmen.

Most of these shrinking jobs are lower paid, while many of the growing jobs are better paid. Bottom line: advise your kids to consider careers like data security/analysis, or a health care specialty, including management.

Racial Gaps and Data Gaps

Are there racial gaps in the distribution of the COVID-19 vaccine? This is an important and interesting question in its own right. But I’ll talk about this question today because it’s an interesting example of how confusing and sometimes misleading data can be.

How do we answer this question? One is by surveying people. There are a number of surveys that ask this question, but a recent one by the Kaiser Family Foundation finds that among adults 70% of Blacks and 71% of Whites report being vaccinated. And given the sampling error possible with surveys, we would say that these are virtually identical. No racial gap! (Note: there was a racial gap when they did the same survey back in April, with 66% of Whites and 59% of Blacks vaccinated.)

But, surveys are just a sample, and perhaps people are lying. Maybe we shouldn’t trust surveys! And shouldn’t there be hard data on vaccines? Indeed, the CDC does publish data on vaccinations by race. That data shows a fairly large gap: 42.3% of Whites and only 36.6% of Blacks vaccinated. This is for at least one dose, and the percentages are of the total population (which is why it’s lower than the survey data). So maybe there is a racial gap after all!

But wait, if you look closely at the footnotes (always read the footnotes!), you’ll see something curious: the CDC admits that the race data are only available for 65.8% of the data. We don’t have the race information for over one-third of those in this data. Yikes! And given the exist disparities we know about in terms of income and access to healthcare, we might suspect that the errors are not randomly distributed. In other words, if there is probably good reason to suspect that Blacks are disproportionately reflected in the “unknown” category. But we just don’t know.

So what can we do? Since this data comes from US states, we can look at the individual state data and see if perhaps some of it is better (fewer unknowns). What does that data show us?

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Clemens and Strain on Large and Small Minimum Wage Changes

In my Labor Economics class, I do a lecture on empirical work and the minimum wage, starting with Card & Kreuger (1993). I’m going to quickly tack on the new working paper by Clemens & Strain “The Heterogeneous Effects of Large and Small Minimum Wage Changes: Evidence over the Short and Medium Run Using a Pre-Analysis Plan”.

The results, as summarized in the second half of their abstract are:

relatively large minimum wage increases reduced employment rates among low-skilled individuals by just over 2.5 percentage points. Our estimates of the effects of relatively small minimum wage increases vary across data sets and specifications but are, on average, both economically and statistically indistinguishable from zero. We estimate that medium-run effects exceed short-run effects and that the elasticity of employment with respect to the minimum wage is substantially more negative for large minimum wage increases than for small increases.

The variation in the data comes from choices by states to raise the minimum wage.

A number of states legislated and began to enact minimum wage changes that varied substantially in their magnitude. … The past decade thus provided a suitable opportunity to study the medium-run effects of both moderate minimum wage changes and historically large minimum wage changes.

We divide states into four groups designed to track several plausibly relevant differences in their minimum wage regimes. The first group consists of states that enacted no minimum wage changes between January 2013 and the later years of our sample. The second group consists of states that enacted minimum wage changes due to prior legislation that calls for indexing the minimum wage for inflation. The third and fourth groups consist of states that have enacted minimum wage changes through relatively recent legislation. We divide the latter set of states into two groups based on the size of their minimum wage changes and based on how early in our sample they passed the underlying legislation.

The “large” increase group includes states that enacted considerable change. New York and California “have legislated pathways to a $15 minimum wage, the full increase to which firms are responding exceed 60 log points in total.” Data comes from the American Community Survey (ACS) and the Current Population Survey (CPS).

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