Between 1850 and 1910, most US censuses asked whether an individual was deaf. There were four alternative descriptions among the combinations of deafness and dumbness. Seems straightforward enough. The problem is that these aren’t discrete categories, they’re continuous. That is, one’s ability to hear can be zero, very good, bad, or just middling. What constitutes the threshold for deafness? In practice, it was the discretion of the enumerator. Understandably, there was a lot of variation in judgement from one enumerator to another. A lot of older people were categorized as deaf, even if they had some hearing loss.
Recently I was watching a lecture by historian Marcus Witcher which addressed the treatment of African Americans in the Jim Crow era. Witcher mentioned the “pig laws,” which were severe legal punishments given to Blacks in the South for what used to be petty crimes. Such as stealing a pig. He mentioned that the fines could be anywhere from $100 to $500, and then he asked me directly: how much is $100 adjusted for inflation today?
My initial, immediate answer was about $3,000. That turns out to be almost exactly correct for around 1880. But the more I thought about it, the more I realized that this wasn’t a satisfactory answer. We were trying to put $100 from a distant past year in context to understand how much of a burden this was for African Americans at the time. Does knowing that adjusted for inflation it’s about $3,000 give us much context?
It covers the years 1990 to 2019 for every US state, and has life expectancy at birth, age 25, and age 65. It includes breakdowns by sex and by race and ethnicity, though the race and ethnicity breakdowns aren’t available for every state and year.
This is one of those things that you’d think would be easy to find elsewhere, but isn’t. The CDC’s National Center for Health Statistics publishes state life expectancy data, but only makes it easily available back to 2018. The United States Mortality DataBase has state life expectancy data back to 1959, but makes it quite hard to use: it requires creating an account, uses opaque variable names, and puts the data for each state into a different spreadsheet, requiring users who want a state panel to merge 50 sheets. It also bans re-sharing the data, which is why the dataset I present here is based on IHME’s data instead.
The IHME data is much more user-friendly than the CDC or USMDB, but still has major issues. By including lots of extraneous information and arranging the data in an odd way, it has over 600,000 rows of data; covering 50 states over 30 years should only take about 1,500 rows, which is what I’ve cleaned and rearranged it to. IHME also never actually gives the most basic variable: life expectancy at birth by state. They only ever give separate life expectancies for men and women. I created overall life expectancy by state by averaging life expectancy for men and women. This gives people any easy number to use, but a simple average is not the ideal way to do this, since state populations aren’t exactly 50/50, particularly for 65 year olds. If you’re doing serious work on 65yo life expectancy you probably want to find a better way to do this, or just use the separate male/female variables. You might also consider sticking with the original IHME data (if its important to have population and all cause mortality by age, which I deleted as extraneous) or the United States Mortality DataBase (if you want pre-1990 data).
Overall though, my state life expectancy panel should provide a quick and easy option that works well for most people.
Here’s an example of what can be done with the data:
If states are on the red line, their life expectancy didn’t change from 1990 to 2019. If a state were below the red line, it would mean their life expectancy fell, which done did (some state names spill over the line, but the true data point is at the start of the name). The higher above the line a state is, the more the life expectancy increased from 1990 to 2019. So Oklahoma, Mississippi, West Virginia, Kentucky and North Dakota barely improved, gaining less than 1.5 years. On the other extreme Alaska, California, New York improved by more than 5 years; the biggest improvement was in DC, which gained a whopping 9.1 years of life expectancy over 30 years. My initial thought was that this was mainly driven by the changing racial composition of DC, but in fact it appears that the gains were broad based: black life expectancy rose from 65 to 72, while white life expectancy rose from 77 to 87.
That’s the title of a blockbuster new paper by Shikhar Singla. The headline finding is that increased regulatory costs are responsible for over 30% of the increase in market power in the US since the 1990’s. That’s a big deal, but not what I found most interesting.
One big advance is simply the data on regulation. If you want to measure the effect of regulation on different industries, you need to come up with a way to measure how regulated they are. The crude, simple old approach is to count how many pages of regulation apply to a broad industry. The big advance of Mercatus’ RegData was to use machine learning to identify which specific industry is being discussed near “restrictive words” in the Code of Federal Regulation that indicate a regulatory restriction is being imposed. But not all regulatory words (even restrictive ones) are created equal; some impose very costly restrictions, most impose less costly restrictions, and some are even deregulatory. Singla’s solution is to take the government’s estimates of regulatory costs and apply machine learning there:
This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period.
The government’s estimates of the costs are of course imperfect, but almost certainly add information over a word-count based approach. Both approaches agree that regulation has increased dramatically over time. How does this affect businesses? Here’s what’s highlighted in the abstract:
We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in con- centration is associated with lower elasticity of entry with respect to Tobin’s Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms
More from the paper:
an average small firm faces an average of $9,093 per employee in our sample period compared to $5,246 for a large firm
a 100% increase in regulatory costs leads to a 1.2%, 1.4% and 1.9% increase in the number of establishments, employees and wages, respectively, for large firms, whereas it leads to 1.4%, 1.5% and 1.6% decrease in the number of establishments, employees and wages, respectively for small firms when compared within the state-industry-time groups. Results on employees and wages provide evidence that an increase in regulatory costs creates a competitive advantage for large firms. Large firms get larger and small firms get smaller.
The fact that large firms benefit while small firms are harmed is what drives the increase in concentration and market power.
What I like and dislike most about this paper is the same thing: its a much better version of what Diana Thomas and I tried to do in our 2017 Journal of Regulatory Economics paper. We used RegData restriction counts to measure how regulation affected the number of establishments and employees by industry, and how this differed by firm size. I wish I had thought of using published regulatory cost measures like Singla does, but realistically even if I had the idea I wouldn’t have had the machine learning chops to execute it. The push to quantify what “micro” estimates mean for economy-wide measures is also excellent. I hope and expect to see this published soon in a top-5 economics journal.
Much ink has been spilled making cross-country comparisons since the start of the COVID-19 pandemic. I have made a few of these, such as a comparison of GDP declines and COVID death rates among about three dozen countries in late 2021. I also made a similar comparison of G-7 countries in early 2022. But all such comparisons are tricky to interpret if we want to know why these differences exist between countries, which surely ultimately we would like to know. I tried to stress in those blog posts that I was just trying to visualize the effects, not make any claims about causation.
Here’s one more chart which I think is a very useful visualization, and it may give us some hint at causation. The following scatterplot shows COVID vaccination rates and excess mortality for a selection of European countries (more detail below on these measures and the countries selected):
The selection of countries is based on data availability. For vaccination rates, I chose to use the rate for ages 60-69 at the end of 2021. Ages 60-69 is somewhat arbitrary, but I wanted a rate for an elderly age group that was somewhat widely available. There is no standard source for an international organization that published these age-specific vaccine rates (that I’m aware of), but Our World in Data has done an excellent job of compiling comparable data that is available.
Note: I’m using the data on at least one dose of the vaccine. OWID also has it available by full vaccine series, and by booster, but first dose seemed like a reasonable approach to me. Also, I could have used different age groups, such as 70-79 or 80+, but once you get to those age groups the data gets weird because you have a lot of countries over 100%, probably due to both challenging denominator calculations and just general challenges with collecting data on vaccination rates. By using 60-69, only one country in my sample (Portugal) is over 100%, and I just code them as 100%. Using the end of 2021, rather than the most current data, is a bit arbitrary too, but I wanted to capture how well early vaccination efforts went, though ultimately it probably wouldn’t have mattered much.
Also: dropping the outliers of Bulgaria and Romania doesn’t change things much. The second-degree best fit polynomial still has an R2 over 0.60 (for those unfamiliar with these statistics, that means about 60% of the variation is “explained” in a correlational sense).
The excess mortality measure I use comes from the following chart. In fact, this entire post is inspired by the fact that this chart and others similar to it have been shared frequently on social media.
The chart comes from a Tweet thread by Paul Collyer. The whole thread is worth reading, but this chart is the key and summary of the thread. What he has done is shown the average and range of a variety of ways of calculating excess mortality. Read his thread for all the details, but the basic issues are what baseline to use (2015-2019 or 2017-2019? A case can be made for both), how to do the age-standardized mortality, and other issues. I won’t make a claim as to which method is best, but averaging across them seems like a fine approach to me.
For the y-axis in my chart, I just used the average for each country from Collyer’s chart. There are 34 countries in his chart, but in the OWID age-specific vaccination rates, only 22 countries were available the overlapped with his group. Unfortunately, this means we drop major countries like Italy, Spain, the UK, and Germany, but you work with the data you have.
For many sharing this and similar chart (such as charts with just one of those methods), the surprising (or not surprising) result to them is that Sweden comes out with almost the lowest excess mortality rate. Some approaches even put Sweden as the very lowest. Sweden!
Why is Sweden so important? Sweden has been probably the most debated country (especially by people not living in the country in question) in the COVID pandemic conversation. In short, Sweden took a less restrictive (some might say much less restrictive) approach to the pandemic. This debate was probably the most fevered in mid-to-late 2020, when some were even claiming that the pandemic was over in Sweden (it wasn’t). The extent to which Sweden took a radically different approach is somewhat overstated, especially in relation to other Nordic countries. And as is clear in both charts above, the Nordic countries all did relatively very well on excess mortality.
The bottom line from my first chart is that what really matters for a country’s overall excess mortality during the pandemic is how well they vaccinated their population. There seems to be a lot of interest on social media to rehash the debates about whether lockdowns (and lighter restrictions) or masks worked in 2020. But what really mattered was 2021, and vaccines were key. A scatterplot isn’t the last word on this (we should control for lots of other things), but it does suggest that a big part of the picture is vaccines (you can see this in scatterplots of US states too). It’s frustrating that many of those wanting to rehash the 2020 debates to “prove” masks don’t work, or whatever, either ignore vaccines or have bought into varying degrees of anti-vaccination theories. It’s completely possible that lockdowns don’t pass a cost/benefit test, but that vaccines also work very well (this has always been my position).
Why did Sweden have such great relative performance on excess mortality? Vaccines are almost certainly the most important factor among many that matter to a much smaller degree.
What About the US?
Note: for those wondering about the US, we don’t have the vaccination rate for ages 60-69 that I can find. Collyer also didn’t include the US in his analysis, it was only Europe. So, for both reasons, I didn’t include them in this post. The CDC does report first-dose vaccinations for ages 65+ in the US, though they top-code states at 95%. As of the end of 2021, here are the states that were below 95%: Mississippi, Louisiana, Tennessee, West Virginia, Indiana, Ohio, Wyoming, Georgia, Arkansas, Idaho, Alabama, Montana, Alaska, Missouri, Texas, Michigan, and Kentucky. These states generally have very high age-adjusted COVID death rates. Ideally we would use age-adjusted excess mortality for US states, but in the US that is horribly confounded by the rise in overdoses, homicides, car accidents, and other causes that are independent of vaccination rates (though they may be related to 2020 COVID policies — this is still a matter of huge debate).
We all like to think that we are individuals. We like to think that we grow and that our tastes develop and mature. We begin to appreciate different things in life, and among other behaviors, our spending habits change.
But what would you say if I told you that your maturing tastes didn’t cause your maturing consumption patterns? Indeed, what if it’s the other way around? Maybe, you’re just a bumbling ball bearing bouncing about and pinging off of various stimuli in a very predictable fashion. What if the prices that you face changed over the course of the past two decades, adjusting your optimal bundle of consumption, and then you contrived reasons for your new behavior in an elegant post-hoc fashion.
Have you *really* taken a liking to whole wheat bread and pasta over the past decade because your tastes have developed? Or maybe it’s because you found that scrumptious New York Times recipe that turned you away from potatoes and toward rice. Whether it’s a personal experience, a personal influence, or a personal development, we like to think about ourselves as complex organisms with a narrative that makes sense of the way in which we interact with the world.
On the other hand, we have price theory. Price theory still accepts that you are special and that you have preferences. Then, it asserts that your preferences remain fixed and that your changes in behavior are merely responses to changing costs and benefits that you perceive in the world. Maybe you’re not any more inclined to eat healthily than you were previously, but the price ratio of whole wheat bread to white bread is 10% less than it use to be. Maybe your east-Asian inspired recipe didn’t cause you to spurn potatoes, but instead the price ratio of rice to potatoes fell by 20%.
There are many papers with titles in the style of “The Economics of X” with X covering a wide variety of topics, some deadly serious (“Economics of Suicide“) and others more trivial or unintentionally hilarious (“The Economics of Sleep and Boredom” comes to mind). There is a related genre of papers on “The Political Economy of Y,” once again with papers that are both serious and occasionally silly (or sometimes deadly serious papers with silly-sounding titles, such as “The Political Economy of Coffee, Dictatorship, and Genocide“).
But perhaps the best paper of this sort is a 1974 article on the Journal of Political Economy by Alan Blinder, titled “The Economics of Brushing Teeth.” It is, as you might guess, a paper that is somewhat tongue-in-cheek (tongue-in-teeth?), but the paper carefully follows the formal style you would expect from a JPE paper in 1974. I recommend reading the paper in full, and I can assure you that it is not at all like pulling teeth. But if you prefer not to look a gift horse in the mouth, here are a few favorite parts.
The paper is, of course, full of tooth-related puns, even in the footnotes, such as this acknowledgment: “I wish to thank my dentist for filling in some important gaps in the analysis.”
There are also plenty of jokes about human capital theory, jokes that only an economist could love, such as: “The basic assumption is common to all human capital theory: that individuals seek to maximize their incomes. It follows immediately that each individual does whatever amount of toothbrushing will maximize his income.”
Another section manages to poke fun at both sociologists and economists. In reference to a fake paper (no, there is no Journal of Dental Sociology), Blinder chastises the fake sociologist for misattributing a change in brushing patterns (assistant professors brush more) to advancing hygiene standards over time. No! It must be about maximizing income: “To a human capital theorist, of course, this pattern is exactly what would be expected from the higher wages received in the higher professorial ranks, and from the fact that younger professors, looking for promotions, cannot afford to have bad breath.”
And what good is a paper without a formal model of teeth brushing? This is the kind of model that many young economists cut their teeth on in graduate school.
Have you heard the hubbub about eggs? People say that they’re expensive. My wife told me that if she’s going to pay an arm and a leg, then she may as well get the organic, pasture raised eggs. Absolutely. That’s what the substitution effect predicts. As the price ratio of low-quality to high-quality eggs rises, we’re incentivized to consume more of the high-quality version. It has to do with opportunity costs.
Consider a world in which the low-quality eggs cost $2 and the high-quality eggs cost $6 per dozen. Every high-quality egg costs 3 low-quality eggs. You might still choose the high-quality option, but you know that you’re giving up a lot by doing so. Consider the current world where low-quality eggs are priced on par with high-quality eggs. Now, the opportunity cost of consuming the fancy, pasture-raised eggs has fallen. When consuming one high-quality egg costs you one low-quality egg, it’s much easier to opt for the high-quality version. You’re not giving up as much when you purchase it.
For vegetarians, the recent price swing has probably been rough. Not eating meat, they’re facing the price squeeze more so than their omnivorous counterparts. Through the magic of math, median wages, and average retail prices, the figure below charts the affordability of eggs and dairy products.* The median person has been facing falling egg affordability for two decades. Indeed, it’s only been the past few years, punctuated by the Covid crisis, that consumers experienced more affordable eggs.
Dairy products, however, have become much more affordable. The median American can now afford 50% more of their namesake cheese. Further, we can afford 20-25% more whole milk and cheddar cheese. So, the vegetarians are not so poorly off after all.
The US government is great at collecting data, but not so good at sharing it in easy-to-use ways. When people try to access these datasets they either get discouraged and give up, or spend hours getting the data into a usable form. One of the crazy things about this is all the duplicated effort- hundreds of people might end up spending hours cleaning the data in mostly the same way. Ideally the government would just post a better version of the data on their official page. But barring that, researchers and other “data heroes” can provide a huge public service by publicly posting datasets that they have already cleaned up- and some have done so.
That’s what I said in December when I added a data page to my website that highlights some of these “most improved datasets”. Now I’m adding the Behavioral Risk Factor Surveillance Survey. The BRFSS has been collected by the Centers for Disease Control since the 1980s. It now surveys 400,000 Americans each year on health-related topics including alcohol and drug use, health status, chronic disease, health care use, height and weight, diet, and exercise, along with demographics and geography. It’s a great survey that is underused because the CDC only offers it in XPT and ASC formats. So I offer it in Stata DTA and Excel CSV formats here.
Let me know what dataset you’d like to see improved next.
The minimum wage is one of the most studied topics in economics, and also something that is frequently discussed on this blog from many different angles. For someone that isn’t an expert in this area, it can be hard to keep track of all the most recent, cutting-edge research on the topic.
Here’s a brand-new paper in the literature with an important finding: raising the minimum wage increases crime. Specifically, in “The Unintended Effects of Minimum Wage Increases on Crime” the authors find that 16-to-24-year-olds commit more property crimes after a minimum wage increase. For every 1% increase in the minimum wage, there is a 0.2% increase in property crime. That implies a doubling the minimum wage would increase property crimes for this age group by 20%. Here’s a figure from the paper showing this increase in crime:
What is the mechanism by which the rising minimum wage increases crime? Here the authors move into examining one of the central questions of the empirical minimum wage debate: the labor market. The authors do find evidence that employment decreases for this same age group following an increase in the minimum wage. Again, a figure from the paper:
The results in this paper add one more element to the cost-benefit calculus of the minimum wage. But I think the results are also interesting because they seem to point in the opposite direction of a paper co-authored by fellow EWED blogger Mike Makowsky. His paper “The Minimum Wage, EITC, and Criminal Recidivism” found that increasing the minimum wage made it less likely that former prisoners would commit another crime. I would be interested to hear Mike’s thoughts on this paper!
Economist Writing Every Day 