Most US cities make it hard for housing supply to meet demand because of rules that prevent large apartment buildings. Usually cities do this with zoning rules that limit the number of homes per parcel, often to as low as 1. New York City relies more on rules about Floor Area Ratio (the ratio of the floor area to the area of the parcel). But how binding are these rules? If we relaxed or repealed them, how much new construction would we see, and where would we see it?
I build a dynamic general equilibrium model of the supply and demand of floorspace in a city , which I estimate using a novel parcel-level panel dataset of land use and zoning in New York City. I validate the model using quasi-experimental variation from recent zoning reforms and use it to simulate the effects of zoning changes on construction and prices.
He finds that eliminating these rules in NYC would lead to a construction boom, with a 79% increase in the amount of floor space available by 2060. This would allow many more people to live in New York, with a 52% increase in population; but many of the benefits would go to existing NYC residents, with more floor space per person and modestly lower rents leading to higher wellbeing:
Where exactly would we see the building boom? Not Manhattan, but Brooklyn and Queens. The intuition is that zoning is most binding in places where housing prices are currently high but where the buildings are currently small; this is where there is the biggest incentive to tear down existing buildings and build taller if you are allowed to.
I have a good friend who is a professor in philosophy at another university. He was telling me about the struggle among his colleagues to determine the recipient of their annual department award. Every year the department chooses from among the graduating philosophy major students one to recognize for excellence. This year, they faced the challenge of incommensurables.
One student had a high GPA in the major, but had a severe case of senioritis and had phoned-in her senior courses. A second had a slightly worse GPA, but had face-planted the senior thesis. Still a 3rd student had merely a good GPA, but wrote an excellent publishable thesis.
The philosophy faculty could not agree. They each shared stories and arguments about the relative weights of the performance indicators and the relative value of the performances. I don’t know if you know any academics, but suffice it to say that they both A) tend not to be good administrators and B) tend not to be invited to productive meetings. I’m glad that I wasn’t in the room.
In fact, the faculty met twice! They were at an impasse. The department award winner is usually no contest. The person who excels in one area tends to also excel in the others. This year, the decision was so unclear and the faculty were so divided that they even seriously considered withholding the award entirely. None of the candidates was excellent on all counts.
Finally, trying to come to a decision – if not an agreement – they decided to adopt something that they’d heard good things about: Ranked Choice Voting. I was thrilled to hear this. What an opportunity to exhibit the nuance and beauty of this collective choice method! They agreed to adopt whatever the outcome would be. As my friend told me this, I was giddy with anticipation. What an exciting story! More good experiences with ranked choice voting may improve its popularity and make widespread its adoption.
If you don’t know, Ranked Choice Voting involves everyone ranking the candidates in order of preference. In this case 1 is most preferred and 3 is least preferred. Then, the candidate with the fewest first-ranked votes is eliminated from the running. The voters whose first preference was nixed now have their votes reallocated to their 2nd preferred candidates. Since only two candidates remain, one of them has won the majority and the election ends with an outcome that is usually considered better than the simple ‘just choose your favorite’ version that most of us use at our local polls.
A reporter recently told me she thought there is a national trend toward hospitals issuing more bonds. I tried to verify this and found it surprising hard to do with publicly available data. But once I had to spend an hour digging through private Compustat data to find the answer, I figured I should share some results. Here’s the average debt in millions of companies by sector:
Source: My graph made from Compustat North American Fundamentals Annual data collapsed by Standard Industrial Classification code into the Fama-French 10 sectors
This shows that health care is actually the least-indebted sector, and telecommunications the most indebted, followed by utilities and “other” (a broad category that actually covers most firms in the Fama-French 10). But are health care firms really more conservative about debt, or are they just smaller? Let’s scale the debt by showing it as a share of revenue:
My graph made from Compustat North American Fundamentals Annual data collapsed by SIC code into the Fama-French 10 sectors(dltt/revt).
It appears that health care firms are the most indebted relative to revenue since 2023. But which parts of health care are driving this?
Hospitals in 2023 followed by specialty outpatient in 2024. However, seeing how much the numbers bounce around from year to year, I suspect they are driven by small numbers of outlier firms. This could be because Compustat North America data only covers publicly traded firms, but many sectors of health care are dominated by private corporations or non-profits.
I welcome suggestions for datasets on the bond-market side of things that are able to do industry splits including private companies, or suggestions for other breakdowns you’d like to see me do with Compustat.
SPOILER ALERT if you are watching the TV Series Yellowstone: at the start of Season 5, John Dutton (played by Kevin Costner) is sworn in as Governor of Montana. One of his first proposals in his inaugural address is that the state legislature “double property taxes for non-residents” who have been buying up vacation homes in the state, and contributing to the increase in property values in the state (a fact which drives many plotlines throughout the series). This episode aired in November 2022.
This week, the real governor of Montana signed a pair of bills which effectively did what the fictional governor John Dutton proposed: significantly increasing property taxes on non-residents. Starting in tax year 2026, the property taxes for non-primary residences (which will include non-Montana residents and Montanans who own vacation homes) will be based on 1.9% of market value, while Montana residents will pay a graduated rate structure for their primary residence: 0.76% for property up to the state median (currently about $340,000), 0.9% up to two times the state median, 1.1% for the value between 2 and 4 times the state median, and 1.9% (the same as non-residents) for the value of homes above 4 times the state median ($1.36 million currently). Currently residential property is taxed at 1.35% of market value, meaning that while the rate hasn’t fully doubled for non-residents, most non-residents will be paying twice or more in property taxes than Montana residents.
I was a non-resident member of the Montana Property Tax Task Force, and served on the “Tax Fairness” subcommittee where the plan for HB 231 originated, so I have somewhat of a unique perspective on these changes to property tax rates. I will offer a few thoughts, some of which are critical, but let me first say that it was a great honor to be asked to serve on the Task Force by Montana’s Governor. Also, everyone on the Task Force was very friendly and receptive to ideas from outsiders (I was one of three non-Montanans on the Task Force), and so my comments here are not critical of the Task Force process nor anyone on it. As I did when I served on the Task Force, my goal in this post is to try, as best as I can, to objectively analyze how this proposal (now law) will impact Montana.
Satire news shows are, in my opinion, one of the higher forms of art that my country has produced (and an example of our exports). “Meet Tariff Tilly, the perfect replacement for the 37 dolls your kid does not need” from The Daily Show
“Tariff Tilly” builds. There is even a comment on interest rates (addressed in my previous post).
This post is co-written with John Olis, History major at Ave Maria University.
There is a popular myth that manufacturing jobs of the past provided a leg-up to young people. The myth goes like this. Manufacturing jobs had low barriers to entry so anyone could join. Once there, the job paid well and provided opportunities for fostering skills and a path toward long-term economic success. There is more to the myth, but let’s stop there for the moment. Is the myth true?
One of my students, John Olis, did a case study on Connecticut in 1920-1930 using cross sectional IPUMS data of white working age individuals to evaluate the ‘Manufacturing Myth’. We are not talking causal inference here, but the weight of the evidence is non-zero. The story above has some predictions if not outright theoretical assertions.
Manufacturing jobs paid better than non-manufacturing jobs for people with less human capital.
Manufacturing jobs yielded faster income growth than non-manufacturing jobs.
Implicitly, manufacturing jobs provided faster income growth for people with less human capital.
Using only one state and two decades of data obviously makes the analysis highly specific. Expanding the breadth or the timescale could confirm or falsify the results. But historical Connecticut is a particularly useful population because 1) it had a large manufacturing sector, 2) existed prior to the post WWII boom in manufacturing that resulted from the destruction of European capacity, and 3) had large identifiable populations with different levels of human capital.
Who had less human capital on average? There are two groups who are easy to identify: 1) immigrants and 2) illiterate people. Immigrants at the time often couldn’t speak English with native proficiency or lacked the social norms that eased commercial transactions in their new country (on average, not always). Illiterate people couldn’t read or write. Therefore, having a comparative advantage in manual labor, we’d expect these two groups to be well served by manufacturing employment vs the alternative.
Being cross-sectional, the individuals are not linked over time, so we can’t say what happened to particular people. But we can say how people differed by their time and characteristics. Interaction variables help to drill-down to the relevant comparisons. There are two specifications for explaining income*, one that interacts manufacturing employment with immigrant status and one that interacts the status of illiteracy. The baseline case is a 1920 non-operative native or literate person. Let’s start with the below snapshot of 1920. The term used in the data is ‘operative’ rather than ‘manufacturer’, referring to people who operate machines of one sort or another. So, it’s often the same as manufacturing, but can also be manufacturing-adjacent. The below charts illustrate the effect of lower human capital in pink and the additional subpopulation impacts of manufacturing in blue.
In the left-hand specification, native operatives made 2.2% less than the baseline population. That is, being an operative was slightly harmful to individual earnings. Being an immigrant lowered earnings a substantial 16.8%, but being an operative recovered most of the gap so that immigrant operatives made only 6.1pp less than the baseline population and only 3.9pp less than native operatives. In the right-hand specification, unsurprisingly, being illiterate was terrible for one’s earnings to the tune of 23.4pp. And while being an operative resulted in a 1.2% earnings boost among natives, being an operative entirely eliminated the harm that illiteracy imposed on earnings.
Both graphs show that manufacturing had tiny effects for a typical native or literate individual. But manufacturing mattered hugely for people who had less human capital. So, prediction 1) above is borne out by the data: Manufacturing is great for people with less-than-average human capital.
The Mercatus Center has put together a page of “Snapshots of State Regulation” using data from their State RegData project. Their latest data suggests that population is still a big predictor of state-level regulation, on top of the red/blue dynamics people expect:
They also made pages with much more detail on each state, like what the most regulated industries in each state are and how each one compares to the national average:
Has it gotten easier or harder for Americans to afford the basic necessities of life? Part of the answer to this question depends on how you define “basic necessities,” but using the common triad of food, clothing, and housing seems like a reasonable definition since these composed over 80% of household spending in 1901 in the United States.
If we use that definition of necessities, here is what the progress has looked like in the US since 1901:
The data comes from various surveys that the Bureau Labor Statistics has collected over the years, collectively known as the Consumer Expenditure Surveys. The surveys were conducted about once every 1-2 decades from 1901 up until the 1980s, and then annually starting in 1984. Some of these are multi-year averages, but to simplify the chart I’ll just state one year (e.g., “1919” is for 1918 and 1919). The categories are fairly comprehensive: “food” includes both groceries and spending at restaurants; “housing” includes either mortgage or rent, plus things like utilities and maintenance; and “clothing” includes not only the cost of the clothes themselves, but services associated with them such as repairs or alterations (much more important in the past).
We can see in the chart that over time the share spent on these three areas of spending has declined dramatically, taken as a group. Housing is different, but it has been fairly stable over time, mostly staying between 22% and 29% of income (the Great Depression being an exception). There are two time periods when these costs rose: the Great Depression and the late 1970s/early 1980s. Both are widely recognized as bad economic times, but they are aberrations. The jump from 1973 to 1985 in spending on necessities was fully offset by 2003, and today spending on necessities is well below 1973 — even though for housing, it is a few percentage points greater.
A chart like this shows great progress over time, but it will inevitably raise many questions. Let me try to answer a few of them in advance.
Many regular Americans and policymakers say they want the US to manufacture more things domestically. But when they ask economists how to accomplish this, I find that our most common response is to question their premise- to say the US already manufactures plenty, or that there is nothing special about manufacturing. It’s easy for people to round off this answer to ‘your question is dumb and you are dumb’, then go ask someone else who will give them a real answer, even if that real answer is wrong.
Economists tell our students in intro classes that we focus on positive economics, not normative- that we won’t tell you what your goals should be, just how best to accomplish them. But then we seem to forget all that when it comes to manufacturing. Normally we would take even unreasonable questions seriously; but I think wondering how to increase manufacturing output is reasonable given the national defense externalities.
So if you had to increase the value of total US manufacturing output- if you were going to be paid based on a fraction of real US manufacturing output 10 years from now- how would you do it?
I haven’t made a deep study of this, but here are my thoughts. Better ideas at the top, ‘costly but would increase manufacturing output’ ideas at the bottom:
Economist Writing Every Day 