In a post from July 2021, I discussed housing affordability and “zoning taxes” — in other words, how land use restrictions such as zoning were driving up the cost of housing in some US cities. San Francisco, Los Angeles, Seattle, and New York stood out as the clear outliers, with “zoning taxes” adding several multiples of median household income to housing costs.
The paper I was summarizing used data from 2013-2018, and it’s a very well done paper. But so much has changed in the US housing market since that time. In my post, I pointed to a map from 2017 showing that a large swatch of the interior country still had affordable housing — loosely defined as median home prices being no more than 3 times median income.
To see how much has changed so quickly, consider these two maps for 2017 and 2022 generated from this interactive tool from the Joint Center for Housing Studies.
My paper “Missouri’s Medicaid Contraction and Consumer Financial Outcomes” is now out at the American Journal of Health Economics. It is coauthored by Nate Blascak and Slava Mikhed, researchers at the Federal Reserve Bank of Philadelphia. They noticed that Missouri had done a cut in 2005 that removed about 100,000 people from Medicaid and reduced covered services for the remaining enrollees. Economists have mostly studied Medicaid expansions, which have been more common than cuts; those studying Medicaid cuts have focused on Tennessee’s 2005 dis-enrollments, so we were interested to see if things went differently in Missouri.
In short, we find that after Medicaid is cut, people do more out-of-pocket spending on health care, leading to increases in both credit card borrowing and debt in third-party collections. Our back-of-the-envelope calculations suggest that debt in collections increased by $494 per Medicaid-eligible Missourian, which is actually smaller than has been estimated for the Tennessee cut, and smaller than most estimates of the debt reduction following Medicaid expansions.
We bring some great data to bear on this; I used the restricted version of the Medical Expenditure Panel Survey to estimate what happened to health spending in Missouri compared to neighboring states, and my coauthors used Equifax data on credit outcomes that lets them compare even finer geographies:
The paper is a clear case of modern econometrics at work, in that it is almost painfully thorough. Counting the appendix, the version currently up at AJHE shows 130 pages with 29 tables and 11 figures (many of which are actually made up of 6 sub-figures each). We put a lot of thought into questioning the assumptions behind our difference-in-difference estimation, and into figuring out how best to bootstrap our standard errors given the small number of clusters. Sometimes this feels like overkill but hopefully it means the final results are really solid.
For those who want to read more and can’t access the journal version, an earlier ungated version is here.
Disclaimer: The results and conclusions in this paper are those of the authors and do not indicate concurrence by the Agency for Healthcare Research and Quality or the US Department of Health and Human Services. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
Regular readers know that I’ve written numerous times about the wealth levels of younger generations, such as this post from last month. Judged by average (and usually median too) wealth, younger generations are doing as well and often better than past generations. This is not too surprising, if you generally think that subsequent generations are better off than their parents, but many people today seem to think that progress has stopped. The data suggest it hasn’t stopped!
Now there’s a great new paper by Kevin Corinth and Jeff Larrimore which looks at not wealth but income levels by generation. The look at income in a variety of different ways, including both market income and post-tax/transfer income. But the result is pretty consistent: each generation has higher incomes (inflation adjusted) than the previous generation. Here’s a typical chart from the paper:
I recently learned about an interesting statistic for social scientists. It’s called the “Dissimilarity Index”. It allows you to compare the categorical distribution of two sets.
Many of us already know how to compare two distributions that have only 2 possible values. It’s easy because if you know the proportion of a group who are in category 1, then you know that 1-p will be in category 2. We can conveniently denote these with values of zero and one, and then conduct standard t-tests or z-tests to discover whether they are statistically different. But what about distributions across more than two possible categories?
Data on Gross State Product prior to 2017 has disappeared from the main page of the Bureau of Economic Analysis. It is also gone from some third party hosts like FRED. It turns out BEA is in the middle of revising how they calculate state GDP; they have the new version done back to 2017, and took down the older inconsistent estimates until they can recalculate them. After that, they tell me they will repost pre-2017 state Gross Domestic Product:
In the mean time, they offer some messy and seemingly incomplete versions of pre-2017 GDP here, and you can find 1980-2021 state GDP (along with many other nice variables) in a nice panel from the University of Kentucky Center for Poverty Research’s National Welfare Data.
You can find more details on the actual changes BEA is making to how they calculate GDP here. Most changes seem relatively minor for states, but might have more impact on the measured relative size of industries. For instance, “equity REITs will be reclassified from the funds, trusts, and other financial vehicles industry to the real estate industry, while mortgage REITs will remain classified as funds, trusts, and other financial vehicles”.
The year 2023 was a pretty good one for the economy, whether judged by the labor market or economic growth. Despite this good economic growth, total receipts of the federal government were down about 7 percent from 2022 (note: I’m using calendar years, rather than fiscal years). Here’s a chart (note: in NOMINAL dollars) of total federal revenue since 2009:
I want to stress that these are nominal dollars (there, I’ve said it three times, hopefully there is no confusion). Nominal dollars are usually not the best way to look at historical data, but for purposes of looking at recent government budgets, sometimes it is. Especially when revenue is declining: if I adjusted this for inflation, the decline in 2023 would be even larger!
You’ll notice also that the decline in 2023 is even larger than the decline in 2020, the height of the pandemic when many people were out of work due to government regulations and changes in consumer behavior. The 2023 decline is big!
So, what the heck in going on with federal revenue in 2023?
When you look across countries, it appears that the first $1000 per person per year spent on health buys a lot; spending beyond that buys a little, and eventually nothing. The US spends the most in the world on health care, but doesn’t appear to get much for it. A classic story of diminishing returns:
This might tempt you to go full Robin Hanson and say the US should spend dramatically less on health care. But when you look at the same measures across US states, it seems like health care spending helps after all:
Source: My calculations from 2019 IHME Life Expectancy and 2019 KFF Health Spending Per Capita
Last week though, I showed how health spending across states looks a lot different if we measure it as a share of GDP instead of in dollars per capita. When measured this way, the correlation of health spending and life expectancy turns sharply negative:
Source: My calculations from 2019 IHME life expectancy, Gross State Product, and NHEA provider spending
Does this mean states should be drastically cutting health care spending? Not necessarily; as we saw before, states spending more dollars per person on health is associated with longer lives. States having a high share of health spending does seem to be bad, but this is more because it means the rest of their economy is too small, rather than health care being too big. Having a larger GDP per capita doesn’t just mean people are materially better off, it also predicts longer life expectancy:
Source: My calculations from 2019 IHME life expectancy and 2019 Gross State Product
As you can see, higher GDP per capita predicts longer lives even more strongly than higher health spending per capita. Here’s what happens when we put them into a horse race in the same regression:
The effect of health spending goes negative and insignificant, while GDP per capita remains positive and strongly significant. The coefficient looks small because it is measured in dollars, but what it means is that a $10,000 increase in GDP per capita in a state is associated with 1.13 years more life expectancy.
My guess is that the correlation of GDP and life expectancy across states is real but mostly not caused by GDP itself; rather, various 3rd factors cause both. I think the lack of effect of health spending across states is real, between diminishing returns to spending and the fact that health is mostly not about health care. Perhaps Robin Hanson is right after all to suggest cutting medicine in half.
I’ve written numerous times about generational wealth on this blog. My biggest post was one comparing different generations using the Fed’s Distributional Financial Accounts back in September 2021. I’ve posted several updates to that post as new the quarterly data was released, but this post contains a major update. I’ll explain in great detail below about the updates, but first let me present the latest version of the chart (through 2023q3):
Regular readers will notice a few differences compared with past charts. The big one is that young people have a lot more wealth than it appeared in past versions of this chart! You’ll also notice that I have relabeled this line “Millennials & Gen Z (18+)” and shifted that line over to the left a few years to account for the fact that this isn’t just the wealth of Millennials, and therefore the median age of this group is lower than in my past charts. The two dollar figures I highlighted are at the median age of 30 for these age cohorts (unfortunately we don’t have data for Boomers at that age).
State health care spending usually gets reported in terms of dollars per capita, leading to maps like this that show Alaska as the highest-spending state and Utah as the lowest:
But states differ greatly in how rich they are and how much they have to spend. I wanted to know the states where health care takes up the largest and smallest share of the economy, so I got the data:
Health Care Spending as Share of State Gross Domestic Product in 2019:
You can see that health spending as a share of GDP looks pretty different from health spending in raw dollars. We’ve gone from a high-spending North and low-spending South to more of a mix. Health spending is now highest in West Virginia, where it makes up more than a fourth of the economy; and lowest in Washington State and Washington D.C., where it makes up less than one ninth of the economy.
The biggest change when considering things this way is in Washington D.C., which has the highest spending in $ terms but the lowest as a share of GDP because it has an enormous GDP per capita. Many other states that spend a lot in $ also fall a lot in the rankings due to high GDP per capita, including Alaska, New York, and Massachusetts. The states that rise the most in this ranking are poor states like Arkansas, Alabama, and Mississippi. Mississippi rises the most, gaining 37 spots in the rankings of highest-spending states when we go from $ per capita to share of GDP.
I share the data here so you can do your own comparisons:
As we enter election season, I can sympathize with those that want to ignore it as much as possible. But if you do want to follow it closely, here is my advice: talk is cheap, so follow the money.
And by money, I am not referring to campaign contributions. I mean prediction markets, where people are putting their money where their mouth is, rather than just making predictions based on their own intuition (or their own “model,” which is just a fancy intuition).
There are a number of betting markets online today, but a good aggregator of them is Election Betting Odds.
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