1996 was a big year for minivans. While modern minivans had been around for about a decade by that point, 1996 marked a turning point. That year Dodge introduced what is referred to as the “third generation” of its Caravan, and it won Motor Trend’s car of the year award. That’s the first, and only time, that a minivan ever won this award. If you drive a minivan today or see one on the road, you are seeing the look, style, and features that were first introduced in 1996 (interestingly, that year also seems to have marked the peak in sales for the Chrysler family of minivans).
If you wanted to buy the cheapest possible Dodge Caravan in 1996, you would have paid about $18,500. You could always pay more for more features, as with any car, but if you wanted this “car of the year,” and you wanted it new and cheap, that was what you paid.
Dodge continued to produce the Caravan for the US market until 2020, when it was discontinued in favor of other nameplates (though it still lived on in Canada). In 2020, the base model Caravan was about $29,000 (and now only available in the “Grand” version, an upgrade in 1996).
Sometimes you read an academic article and the author fills in the data gaps with interpolation. That is, they assume some functional form of the data and then replace the missing values with the estimated ones. Often, lacking an informed opinion about functional form, authors will just linearly interpolate between the closest known values. Sometimes this method is OK. But sometimes we can do better.
Historical census data provides a good example because the frequency was only every ten years. Say that we want to know more about child migration patterns between 1850 and 1860. What happened in the intervening years? Who knows. Let’s look at the data.
Using data on individuals who have been linked across censuses allows us to fill in the gaps a little bit. For simplicity, let’s just look at whether a child migrant lived in an urban location and whether they lived on a farm. That means that there are 4 possible ways to describe their residence. Below is a summary of where children migrants lived at the age of zero in 1850 and where the same children lived a decade later at the age of ten in 1860 given that they moved counties.
When I’m the mean time did these children move from one place and to the other? We don’t know exactly. The popular answer is to say that they moved uniformly throughout the decade. That’s ‘fine’. But it assumes that the rate at which people departed places was rising and the rate at which they arrived places was falling. Maybe that’s true, but we don’t really know. Below-left is a graph that shows the linear interpolation.
The nice thing about linear interpolation is that everyone is accounted for at each point in time. The total number of people don’t rise or fall in the intervening interpolation period. But if we were to assume that children departed/arrived at each type of place at a constant rate (maybe a more reasonable assumption), then suddenly we lose track of people. That is, the sum of people dips below 100% as people depart faster than they arrive.
I’ve seen plenty of investigations of “Long Covid” based on surveys (ask people about their symptoms) or labs (x-ray the lungs, test the blood). But I just ran across a paper that uses insurance claims data instead, to test what happens to people’s use of medical care and their health spending in the months following a Covid diagnosis. The authors create some nice graphics showing that Long Covid is real and significant, in the sense that on average people use more health care for at least 6 months post-Covid compared to their pre-Covid baseline:
The graph is a bit odd in that its scales health spending relative to the month after people are diagnosed with Covid. Their spending that month is obviously high, so every other month winds up being negative, meaning just that they spent less than the month they had Covid. But the key is, how much less? At baseline 6 months prior it was over $1000/month less. The second month after the Covid diagnosis it was about $800 less- a big drop from the Covid month but still spending $200+/month more than baseline. Each month afterwards the “recovery” continues but even by month 6 its not quite back to baseline. I’m not posting it because it looks the same, but Figure 4 of the paper shows the same pattern for usage of health care services. By these measures, Long Covid is both statistically and economically significant and it can last at least 6 months, though worried people should know that it tends to get better each month.
I was somewhat surprised at the size of this “post Covid” effect, but much more surprised at the size of the “pre Covid” or “early Covid” effect- the run-up in spending in the months before a Covid diagnosis. For the month immediately before, the authors have a good explanation, the same one I had thought of- people are often sick with Covid a couple days before they get tested and diagnosed:
There is a lead-up of healthcare utilization to the diagnosis date as illustrated by the relatively high utilization levels 30–1 days before diagnosis. This may be attributed to healthcare visits only days prior to the lab-confirmed infection to assess symptoms before the manifestation or clinical detection of COVID-19.
But what about the second month prior to diagnosis? People are spending almost $150/month more than at the 6-month-prior baseline and it is clearly statistically significant (confidence intervals of months t-6 and t-2 don’t overlap). The authors appear not to discuss this at all in the paper, but to me ignoring this lead-up is burying the lede. What is going on here that looks like “Early Covid”?
My guess is that people were getting sick with other conditions, and something about those illnesses (weakened immune system, more time in hospitals near Covid patients) made them more likely to catch Covid. But I’d love to hear actual evidence about this or other theories. The authors, or someone else using the same data, could test whether the types of health care people are using more of 2 months pre-diagnosis are different from the ones they use more of 2 months post-diagnosis. Doctors could weigh in on the immunological plausibility of the “weakened immune system” idea. Researchers could test whether they see similar pre-trends / “Early Covid” in other claims/utilization data; probably they have but if these pre-trends hold up they seem worthy of a full paper.
Income varies a lot across the US. So does the cost of living. Does it mostly wash out when you adjust incomes for the costs of living? No, not even close. Apples-to-apples comparisons are always hard, but it’s still worth making comparisons.
Let’s use some data that Ryan Radia put together that I really like, for several reasons. He uses the 100 largest MSAs — these comprise about 2/3 of the US population. He uses median income, so outliers shouldn’t effect the income data. He uses median family income, since the more common median household income is, in my opinion, very difficult to interpret (5 college students living together are a household, and so is one elderly person living alone). And Ryan also limits it to non-elderly, married couples, and then separates the data by the employment status of each member of the couple.
As an illustration, let’s use the data for married couples with only one spouse working full-time (I have played around with the data for other working statuses, and the results are similar). Before adjusting for the cost of living, here are the top MSAs with the highest median incomes:
Demography is cool generally, but life tables are really cool in their elegance. Don’t know what a life table is? Let me ‘splain.
A life table uses data from private or public death registers, or even genealogical records, to identify a variety of survival and death estimates. Briefly, the tables include for each age:
Probability of death in the next year
Probability of surviving to the age
The life expectancy
There is more in the tables, but these are the big items that people often want to know. All of the various table columns can be calculated from survival rates. The US government and the UN each has created many such tables for a variety of time, locations, and development details. For example, the earliest and most dependable one is from 1901 and includes separate tables by race, sex, migrant status, urbanity, and even for some specific states.
I’m looking for a new car now and would like to know what the safest reasonable option is. There are lots of ways to get some information about this, but none are very good.
The government provides safety ratings based on crash tests they perform. This is better than nothing but the crash tests only test certain things and don’t necessarily tell you how a car performs in the real world. They also have a habit of just giving their top rating (5 stars) to tons of vehicles so it doesn’t help you pick between them, and they only compare cars to other cars in the same “class”, ignoring that some classes are safer than others. On top of all the problems with the ratings themselves, they also don’t provide any lists of their ratings, instead making you search one car at a time.
Several other sites improve on the government ratings by using real-world data on how often cars actually crash (much of which comes from the government, which as usual is great at collecting data but not so great at presenting it in helpful user-friendly ways). The Auto Professor grades cars using real-world data but otherwise has the same problems as the government (NHTSA) site. Cars get letter grades rather than a rank or meaningful number, so it’s not actually clear which car is best, or how much better the good cars are than the average or bad cars. You can search the grades for one car at a time but they don’t just list the safest cars anywhere, including on their page labelled “safest cars list“.
The Insurance Institute for Highway Safety uses real world data and provides actual numbers of fatality rates for different vehicles. This is great because you don’t have the problem of “dozens of cars all have 5-star / A, which is best?” or the problem of “how much better is 5 star than 4 star, or A than B?”. But they don’t include data from the 2 most recent years, and they only post their ratings for a handful of cars. Not only do they not present a complete list, they seem to have no search function whatsoever for their real-world data (they do for their NHSTA-style crash test data). Some 3rd party sites seem to have posted more complete versions of their data, but it still doesn’t show data for most car models.
The least-terrible car safety site I have found is Real Safe Cars. The good: they use real-world safety data, they apply reasonable-sounding corrections and controls do it, they present meaningful quantitative measures like “vehicle lifetime fatality chance” and “vehicle lifetime injury chance”, and they present the data using both a search function and lists of “safest vehicles”. For 2020 you can see that the #1 car, the 2020 Audi e-tron Sportback, has a vehicle lifetime fatality chance of 0.0158%. Compare this to the #100 car, which is about average overall- the 2020 Acura TLX has a vehicle lifetime fatality chance of 0.0435% (almost 3x the safest). The site makes it hard to find the very worst car but near the bottom is the 2020 Hyundai Accent, which “has a vehicle lifetime fatality chance of 0.0744%”.
The lists could be better; the only list that includes all vehicle classes is restricted to only 2020 makes. Meanwhile when you search a car it ranks it only relative to cars in the same year, though you can make comparisons across years yourself using the quantitative “fatality chance” and “injury chance” measures. I’m not totally convinced of the ratings themselves, given how well many smaller sedans do. Their front page explains how taller cars are generally safer, but also lists the Mini Cooper as the #18 safest car of 2020 across all classes. But Real Safe Cars seems like the current best site to me (maybe I’m biased since one of its creators is an economics professor).
I hope these sites will address some of the weaknesses I identified here, though I’m not optimistic about most of them, because other than Real Safe Cars the “bad” decisions seem to be clearly driven by incentives like keeping car companies happy or SEO.
I also think there’s still room for another effort by economists or other quantitatively-skilled people to make another site. The underlying crash data is public and the statistical problems are not especially hard; I think a single economist could run the numbers in about the time it takes to write a typical economics paper (weeks to months for a 1st draft), and a decent website could be built off that quickly as well. You could probably make a decent amount of money off the site, though perhaps not if you do the right thing and publicly post all the data and code. Posting the data would make it easy for others to copy you and make their own sites. You could fight that with copyright, but given the huge public good aspect here and the lives at stake it might make more sense to get grant funding up front and then make the data and code totally public. A sane world would have done this already; NHTSA’s annual budget is over $1 billion, with $35 million of that going to research and analysis. I think any decent funder should be able to do at least as well as the sites above with under $200k, or anyone with good data chops could do it out of the goodness of their heart in a few months. I don’t have a few months right now but perhaps one of you could take this up or start applying for grants to do it.
For everyone who just wants to know about which cars are safe, for now I think Real Safe Cars is the best bet, though I’d also like to hear if you think I missed anything.
The answer to that question is, of course, “no.” No one number can alone tell us the whole story, whether we are talking about the economy, health, education, population, or any other social statistic. But when you look at other measures of the health of the labor market, you usually find that they tell a similar story to the unemployment rate.
My goal in this post is to dive a little deeper into the data on the labor market, but really the goal is broader: to give you a little insight about how to interpret data. Some rules of thumb, perhaps. But really there is One Big Rule: numbers need context. A number on its own doesn’t tell us much of anything. How does it compare to the past? How does it compare to other places?
With the unemployment rate at historic lows for both the US and many states, I’ve started to see many people saying that, not only doesn’t the unemployment rate give us the full story, but many other indicators point in the opposite direction. Is this true? Let’s dig into the data. Here’s one example of someone saying this for Arkansas. I’ll focus on Arkansas, since that’s where I live and I pay attention to the economic data here pretty closely, but I’ll also refer to national data where appropriate.
While we have stepped back from the meme stock craziness of 2021, US stocks remain quite expensive by historical standards, with our Cyclically Adjusted Price to Earnings (CAPE) ratio at almost twice its long-run average:
Even at a high price, US stocks could still be worth it, and I certainly hold plenty. But I also think it it a good time to consider the alternatives. US Treasury bond yields are the highest they’ve been since 2007. But there are also many countries where stocks are dramatically cheaper than the US- and not just high-risk basket-cases, but stable “investable” countries.
There are several reasonable ways to measure what counts as “expensive” for stocks in addition to the CAPE ratio I mention above. The Idea Farm averages out four such measures to determine how expensive different “investable” (large, stable) country stock markets are. Here is their latest update:
You can see that US stocks are expensive not only relative to our own history, but also relative to other countries, lagging only India and Denmark. That means that much of the world looks like a relative bargain, with the cheapest countries being Colombia, Poland, Chile, Czech Republic, and Brazil.
Of course, sometimes stocks, just like regular goods and services, are cheap for a reason: they just aren’t that good. They might be cheap because investors expect slow growth, or a recession, or political risk. But if you don’t share these expectations about a cheap stock (or country), that’s when to really take a look. I certainly did well buying Poland after I saw they were the cheapest in last year’s global valuation update and thought there was no good reason for them to stay that cheap.
I like that the chart above provides a simple ranking of investable markets. But if you wish it included more valuation measures, or small frontier markets, you can find that from Aswath Damodaran here. Some day I hope to provide a data-based, rather than vibes-based, analysis of which countries are “cheap/expensive for a reason” vs “cheap/expensive for no good reason”, featuring measures like industry composition, population growth, predictors of economic growth, and economic freedom. For now you just get my uninformed impression that Poland and Colombia seem like fine countries to me.
Disclosure: I’m long stocks or indices in several countries mentioned, including EPOL, FRDM, PBR.A, CIB, and SMIN. Not investment advice.
You may have heard that there is a new viral song which deals with a few economic issues. Noah Smith has a good analysis of “Rich Men North of Richmond,” which he mostly finds to be incorrect in its analysis (for example, of welfare policy). But Smith does say that the song has a point: manufacturing wages haven’t performed well in recent years. Not only has pay for factory workers “[lagged] the national average in recent years,” for those workers in Virginia, it’s lower in real terms than in 2010.
Well that all doesn’t sound good! Smith is only going back to about 2000 with the data he shows. What if we took a longer run perspective? What if we took a really long-run perspecitive?
Here’s wages for blue-collar factor workers that goes back to 1939 in the US:
The wage data (for manufacturing production workers) is from BLS and the PCE price index is from the BEA. What do you notice as you look at the data?
First, it is true that the last 20 years or so hasn’t been great. Only about 8% cumulative growth since 2002. That’s not great!
But as you look back further, you’ll notice that gains are substantial. Compared to what some might consider the “golden age” of manufacturing wages, the early 1950s, real wages have roughly doubled. It’s true, the growth rate from 1939-1973 is much, much better than the following 50 years. Wouldn’t it be nice if that growth rate had continued! But no doubt you’ve seen many memes saying something like “in the 1950s you could support a family on one high-school graduate income, but not today!” This data suggests that view of the 1950s is a little distorted by nostalgia.
One final thing to note: we might think that one big change in recent decades is that a lot more compensation goes to benefits, rather than wages. There’s actually a total compensation series for blue-collar workers going all the way back to 1790:
The total compensation data, as well as the CPI data that I used to inflation-adjust the figures (to 2022 dollars), comes from the fantastic resource Measuring Worth. This is a total compensation measurement, so it includes benefits, but the source data tells us that up until the late 1930s, it’s really just a wage measure. So potentially we could splice this together with the above chart, to get a “wage only” series covering the entire history of the US.
However, when we look at total compensation, we still see the post-1970s stagnation. Real compensation is roughly the same as about 1977. Yikes! Note here that we’re using the CPI, since the PCE index only goes back to 1929, and the CPI tends to overstate inflation (yes, that’s right, sorry CPI truthers). Still, it’s not the most optimistic picture.
Or isn’t it? With all of the automation and global competition in manufacturing coming on board in the past 50 years, perhaps our baseline is that things could have been much worse. In any case, if we look at total compensation, it’s currently about double what it was in the post-WW2 era. That’s even with the dip in 2022 due to high CPI inflation.
Wages and compensation of blue-collar productions workers have indeed been growing slowly for the past few decades. That much is true. On the other hand, they are still among the highest they have ever been in history, over 50 times (not 50%, 50 times!) higher than at the birth of this nation. This ranks them as probably the highest wages anywhere in world history for an occupation that doesn’t require an advanced degree. That history is worth knowing.
Inpatient costs were 27% higher (95% CI 0.252, 0.285), but length of stay was 12% shorter (95% CI −0.131, −0.100), in Comprehensive Cancer Centers relative to community hospitals.
In other words, these cutting-edge hospitals that tend to treat complex cases are more expensive, as you would expect; but despite getting tough cases they actually manage a shorter average length of stay. We can’t nail down the mechanism for this but our guess is that they simply provide higher-quality care and make fewer errors, which lets people get well faster.
The NCI Cancer Centers Program was created as part of the National Cancer Act of 1971 and is one of the anchors of the nation’s cancer research effort. Through this program, NCI recognizes centers around the country that meet rigorous standards for transdisciplinary, state-of-the-art research focused on developing new and better approaches to preventing, diagnosing, and treating cancer.
Our paper focuses on New York state because of their excellent data, the New York State Statewide Planning and Research Cooperative System Hospital Inpatient Discharges dataset, which lets us track essentially all hospital patients in the state:
We use data on patient demographics, total treatment costs, and lengths of stay for patients discharged from New York hospitals with cancer-related diagnoses between 2017 and 2019.
You know I’m all about sharing data; you can find our data and code for the paper on my OSF page here.
My coauthor on this paper is Ryan Fodero, who wrote the initial draft of this paper in my Economics Senior Capstone class last Fall. He is deservedly first author- he had the idea, found the data, and wrote the first draft; I just offered comments, cleaned things up for publication, and dealt with the journal. I’ve published with undergraduates several times before but this is the first time I’ve seen one of my undergrads hit anything close to a top field journal. You can find a profile of Ryan here; I suspect it won’t be the last you hear of him.
Economist Writing Every Day 