I’ve written about IPUMS before. It’s great. Among individual details are their occupations and industry of their occupation. That’s convenient because we can observe how technology spread across America by observing employment in those industries. We can also identify whether demographic subgroups differed or not by occupation. There’s plenty of ways to slice the data: sex, race, age, nativity, etc.
But what do we know about historical occupations and what they entailed? At first blush, we just have our intuition. But it turns out that we have more. There is a super boring 1949 report published by the Department of Labor called the “Dictionary of Occupational Titles”. The title says it all. But, the DOL published another report in 1956 that’s conceptually more interesting called “Estimates of Worker Trait Requirements for 4,000 Jobs as Defined in the Dictionary of Occupational Titles: An Alphabetical Index”. The report lists thousands of occupations and identifies typical worker aptitudes, worker temperaments, worker interests, worker physical capacities, and working conditions. Below is a sample of the how the table is organized:
Inflation continues to remain stubbornly high in the US. While Core CPI is down to 3.6%, the lowest it has been in 3 years, this is still well above the Fed’s 2% target (the Fed’s preferred Core PCE is a bit lower at 2.8%). But consumers are tired of the cumulative inflation, which, depending on your preferred gauge of inflation, is somewhere around 20% in the past 4 years. Consumers want to know: will prices ever go down again?
The answer is: Yes, and some prices already have declined!
For example, you can look at broad categories of consumer purchases, such as durable goods, which are down almost 5 percent since the peak in August 2022. Durable goods include items such as used cars (down 17.3 percent since February 2022), furniture (down 6 percent since August 2022), and appliances (down 7.2 percent since March 2023).
We can even jump into the nondurables category and look at specific items, such as groceries which seem to be on everyone’s mind. Here’s a list of items and the price decrease since their peak (I ignore a few items where it is only a purely seasonal cycle that made them cheaper in April 2024):
Spaghetti and macaroni: -4.3% (Feb 2023)
Bacon: -12.8% (Oct 2022)
Chicken legs: -10.6% (Aug 2023)
Chicken breasts: -14.4% (Sept 2022)
Eggs: -40.6% (Jan 2023)
Milk: -8.3% (Nov 2022)
Cheddar cheese: -9.4% (Sep 2022)
Bananas: -2.6% (Sept 2022)
Oranges: -14.7% (Sept 2022)
Lemons: -12.3% (May 2022)
Strawberries: -12.9% in the past year (and down 34.6% since seasonal peak in Dec 2022)
Ground coffee: -6.2% (Dec 2022)
It’s true that this is a cherry-picked list: lots of items are at all-time highs! My goal here is to show that, Yes!, some prices will fall. Others may too in the near future. And while it’s also true that most prices are still well above 2019 levels, that’s not universally true. The April 2024 prices of lemons, strawberries, and tomatoes are roughly equal to their April 2019 prices.
And it’s not just food. Natural gas this January was 20% cheaper than January 2023. Regular unleaded gasoline is down 11.6% from 2 years ago (and down 25% from the peak in Summer 2022, but we’ll wait to see what this summer looks like). Even some services, such as airline fares, are down 6.7% from 2 years ago (and down 16% from June 2022).
Some of these price decreases could be due to factors specific to the production and supply of those goods, but another factor is monetary policy. Broad measures of the money supply such as M2 show a decline of about 4 percent in the past 2 years. That hasn’t yet produced overall deflation, but it has probably contributed to the decline in the goods and services mentioned.
Looking at price changes can only tell us so much though, especially focusing on individual item prices. The big picture is that over the past 4 years, wages have increased more than prices overall across most of the income distribution (only the highest quintile lost out on the race between wages and prices). Falling prices would certainly help this trend continue, but most consumers have more buying power than they did in 2019, even if they don’t feel like they do.
Higher homeowner’s insurance premiums have been in the news. But are we just hearing about the extreme cases? This post is inspired by the FRED Blog post about property and casualty (P&C) insurance premium producer price indices. I dive a little deeper.
I’m in the process of writing a review of Jon Haidt’s book The Anxious Generation. I wrote some preliminary thoughts a few weeks ago, but I’m diving a lot deeper now, so watch for that review soon. But one of the main startling pieces of data in the book is the dramatic rise in suicides among young girls. Haidt isn’t the first to point this out, but in large part his book is an attempt to explain this rise (as well as the rise among boys and slightly older girls).
This got me thinking a bit more broadly about not just suicides, but all causes of mortality among young Americans. So in the style of my 2022 post about the leading causes of death among men ages 18-39, let’s look at the historical trends for deaths among girls 10-14 in the US.
Data comes from CDC WONDER. The top dark line shows total deaths, and the scale for total deaths is the right-axis. Notice that for total deaths, there is a U-shaped pattern. From 1999 to about 2012, deaths for girls aged 10-14 are falling. Then, the bottom out and start to rise again. While the end point in 2022 is lower than 1999 (by about 9 percent), there is a 22 percent increase from 2010 to 2022.
What’s driving those trends? A fall in motor vehicle accidents (blue line, the leading cause of death in both 1999 and 2022) is driving the decline. This category fell 41 percent over the entire time period: a big drop for the leading cause of death!
But the rise in suicides (thick red line) starting in 2013 is the clear driver of the reversal of the overall trend. Suicides for this demographic in 2022 were 268 percent higher than 1999, and 116 percent higher than 2010. Haidt and others are right to investigate the causes of this trend (I’m not convinced they have the complete answer, but more on that in my forthcoming book review).
There has been no clear trend in cancer deaths over this time period, and the combination of all the three of these trends means that roughly equal number of girls ages 10-14 die from car accidents, suicide, and cancer.
What can we learn from this data? First, we should acknowledge just how rare death is for girls ages 10-14. At 14.8 deaths per 100,000 population, it is the lowest 5-year age-gender cohort, other than the ages just below it (ages 5-9, for both boys and girls). But just because it is small doesn’t mean we should ignore it. The big increase, especially in suicides, in the past decade is worrying and could be indicative of broader worrying social trends (and suicides have risen for almost every age group too, see my linked post above).
If a concern, though, is that we are over-protecting our kids and this is leading them to retreat into a world of social media, we might want to see if there are any benefits of this overprotection in addition to the costs. The decline in motor vehicle accidents is one candidate. Is this decline just a result of the overall increase in car safety? Or is there something specific going on that is leading to fewer deaths among young teens and pre-teens?
As we know from other data, a lot fewer young people are getting driver’s licenses these days, especially compared to 1999 (and engaging in fewer risky behaviors across the board). Of course, 10-14 year-olds themselves usually weren’t the ones getting licenses — they are too young in most states — but their 15 and 16 year-old siblings might be the ones driving them around. Is fewer teens driving around their pre-teen siblings a cause of the decline in motor vehicle deaths? We can’t tell from this data, but it is worth investigating further (note: best I can tell, only about 23 percent of the decline is from fewer pedestrian deaths, though in the long-run this is a bigger factor).
Social tradeoffs are hard. If there really is a tradeoff between fewer car accident deaths and more suicides, how should we think about that tradeoff? Or is the tradeoff illusory, and we could actually have fewer deaths of both kinds? I don’t think I know the answer, but I do think that many others are being way too confident that they have the answer based on what data we have so far.
One final note on suicides. For all suicides in the US, the most common method is suicide by firearm: about 55% of suicides in the US were committed with guns in 2022, with suffocations a distant second at about 25%. For girls ages 10-14, this is not the case, with suffocation being by far the leading method: 62% versus just 17% with firearms. I only mention this because some might think the increasing availability of firearms is the reason for the rise in suicides. It could be true overall, but it’s not the case for young girls.
Health spending keeps rising, and hospitals keep consolidating, so the largest health systems in the US keep growing bigger. But getting exact data on how big is surprisingly difficult. So I appreciate that someone else did the work, in this case Blake Madden of Hospitalogy. Here are his top 10:
See his post for the full list of the largest 113 health systems, and details and caveats on the methodology. I have found that Hospitalogy generally has good coverage of the business of health care, and that following Blake on Twitter is a good way to keep up with it.
If we have learned anything in the past 2 years, it’s that people don’t like inflation. Well, you probably already knew that. But I guess we learned that they really, really don’t like inflation. Polls of various sorts still indicate that Americans are upset about inflation, even though the worst of it was happening in June 2022, almost 2 full years ago.
But how much inflation do Americans want? The answer: almost 0%. In fact, the median preference is exactly 0% according to a new working paper titled simply “Inflation Preferences.” The mean preference was 0.2%.
But this paper does more than just survey people on their preferences. It also presents to them several “narratives” about inflation, and to see whether people who have considered those narratives have different preferences. Given my many blog posts about the relationship between wages and inflation (or rather, the race between them), this narrative was interesting to me:
T4 (Wage inflation) When prices increase over time (inflation), worker’s wages may not immediately adjust in proportion. Inflation, therefore, affects the amount of goods and services that workers can buy with their wages. By keeping inflation low, workers can buy a similar amount of goods and services over time.
People who had considered that narrative (wages increases trail price increases) tended to prefer even lower inflation rates, by about 0.7 percentage points. Again, perhaps this is obvious, but it is important to understand how different individuals think about inflation (it was the only one of five narratives that had a statistically significant negative impact on inflation preferences).
Finally, as one final interesting tidbit, survey respondents that were also Economics Majors in college reported higher inflation preferences, by about 1 percentage point.
What with all the talk about semi-conductor production and rare-earth mineral extraction, I think that it’s worth examining what the USA produces in terms of what we get out of the ground. This includes mining, quarrying, oil and natural gas extraction, and some support activities (I’ll jump more into the weeds in the future). I’ll broadly call them the ‘extractive’ sectors. How important are these industries? In 2021 extractive production was worth $520 billion. That was roughly 2% of all GDP. Below is the break down by type of extraction.
Examining the graph of total extraction output below tells a story. The US increased production of extracted material substantially between the Great Depression and 1970. That’s near the time that the clean water and clean air acts were passed. But the change in the output growth rate is so stark, that I suspect that those were not the only causes of change (reasonable people can differ). For the next 40 years, there was a malaise in output. This was the period during which it was popular to talk about our natural resource insecurity. As in, if we were to be engaged in a large war, then would we be able to access the necessary materials for wartime production?
But for the past 15 years we’ve experienced a boom with extracted output rising by 50%, an average growth rate of 2.7% per year. That’s practically break-neck speeds for an old industry at a time when the phrase ‘great stagnation’ was being thrown about more generally. By 2023, we were near all-time-high output levels (pre-pandemic was higher by a smidge).
For people concerned about resource security, the recent boom is good news. For people who associate digging with environmental degradation, greater extraction is viewed with less enthusiasm. Those emotions are especially high when it comes to fossil fuel production. Below is a graph that identifies the three major components of extraction indexed to the 2021 constant prices. By indexing to the relative outputs of a particular year, the below graph is a close-ish proxy to real output that is comparable in levels.
In January 2023 I had a post looking at the different ways that the Bureau of Labor Statistics measures employment. Those who follow the data closely probably know about the difference between the household and establishment surveys, which the monthly jobs report data is based on. But these are just surveys.
The more comprehensive data (close to the universe of workers, roughly 95%) is the Quarterly Census of Employment and Wages. While more comprehensive, this data comes out with a much longer lag, and is only released once per quarter. The QCEW is just the raw count of workers, which is useful in some ways, but we also know that there are normal seasonal fluctuations, which the QCEW doesn’t adjust for. Therefore, year-over-year changes in jobs are the best way to look at trends in this data. In September 2023 (latest month available), the US had 2.25 million more workers than in the previous September. For comparison, the establishment survey showed an increase of 3.13 million jobs that month, and the household survey showed a change of 2.66 million — suggesting they both might be overstating job growth.
Still with me? Here’s one more set of jobs data: the Business Employment Dynamics data. This dataset is built on the QCEW data, but allows more fine detailed insights into what types and sizes of firms are gaining or losing jobs. Like the QCEW, the most recent data is for the 3rd quarter of 2023 (just released today), but when looking at the aggregate data, it has one advantage over the QCEW: it is seasonally adjusted, so we can look at the most recent quarterly change (not really useful for not-seasonally-adjusted data). The BED data also looks only at private sector jobs, so it is looking at the health of the private labor market (and ignoring changes in government employment).
The latest BED data do show a possibly worrying trend: the 3rd quarter of 2023 showed a net loss of 192,000 private-sector jobs. That’s the first loss since the height of the pandemic, and ignoring the first half of 2020, the only quarterly decline since 2017. Here’s the chart (note: y-axis is truncated because the 2020q2 job loss is so large it makes the chart unreadable):
I should note that this data is subject to revisions, even though the QCEW is mostly complete. The second quarter of 2022 originally showed a decline, but that was later revised upwards as QCEW is updated and seasonal adjustment factors are updated. Still as, this data stands, it is a worrying jobs number that differs from the monthly surveys. For the change from 2023q2 to 2023q3, the establishment survey shows a gain of 640,000 jobs and the household survey also shows a gain of 546,000. Like the QCEW raw data, the BED seasonally adjusted data suggests that the monthly surveys may be overstating job growth.
Last month, Jeremy wrote about how long it takes for prices to double. He identified a few intervals of time that are sensible. But I want to pick up the ball and move it further down the field. Not only can we identify how long it took for prices to double in particular eras, we can also do it for *every month*. Below, is a graph that shows us how many years had passed since prices were half as high (PCE Chained Prices).
Expectedly, the minimum time to double consumer prices was in the early 80s, taking just under 9 years for price to double. The prior decade included the highest inflation rates in the past 70 years. Since that time, the number of years needed in order for prices to double steadily rose as the average inflation rate fell. That is, until after the pandemic stimuli which caused the time to plateau. But to be clear, that must mean that prices aren’t doubling any fast that they used to, despite what we’ve heard on the news.
Except… prices are in fact rising faster by 21st century standards. Indeed, measuring the time that it took prices to double covers up a lot of variation. After all, The PCEPI was 15.19 in 1959 and is 122.3 now. That’s only enough difference for three doublings. But as we lower the threshold for price changes, we can see more of the price level patterns. Below-left is the time that was necessary for prices to increase by 50% and below-right is the time that was necessary for prices to rise by 25%.
In these graphs we can see more of the action that happened post-Covid. The time needed for prices to rise by 50% has fallen by about five years since 2020. That’s a 20% shorter time necessary for a 50% increase in prices. The time needed for a 25% increase in prices is even more drastic. As of 2020, people were accustomed to experiencing upwards of 14 years before overall prices rose by 25%. That number fell below 8 years by 2024.
And finally, the most unnerving graph of all is below: the time that was needed for prices to rise by 10%.
While there are many factors to consider, ultimately whether living standards are rising is a race between prices and income. What does that race look like if we start the clock in December 2019, just before the pandemic?
Whether we use median weekly earnings (the purple line) or average hourly earnings for non-management workers (the blue line), they have clearly won the race with two commonly used price indexes (the CPI-U and the PCEPI). That’s good news, and probably not something you hear very often in the discourse about the economy (unless you spend a lot of time reading this blog).
Economist Writing Every Day 