Conversable Economist

Om 2026, the federal government is on track to spend $7.4 trillion, and $1.9 trillion–about one-quarter of the total–will be newly borrowed money. It’s one thing to have deficit spending during a pandemic or a recession, but neither situation applies here. The accumulated federal debt owed to the public is now equal to 100% of US GDP, approximately matching the previous high set during spending and borrowing to finance World War II, and headed higher. Serious politicians would put some proposals on the table for debate for spending cuts and tax increases to reduce the gap. Kimberly Clausing and Natasha Sarin seek to launch a discussion for the tax proposal side of the debate in “$15 Trillion on the Table in 2029: Ordinary Taxes to Meet an Extraordinary Fiscal Moment.” The essay will appear in a book forthcoming later this year, In The American Economy in a New Era, edited by Melissa S. Kearney and Luke Pardue, and published by the Aspen Institute.

Rather than grapping with the challenges and mechanics of attempting to create a new tax, like a wealth tax, Clausing and Sarin instead on adjustments to existing taxes. However, the taxes they propose will have more effect on those with higher incomes and wealth. They offer five “buckets” of tax revenue.

Bucket 1: Reforming corporate tax to meet the challenges of a modern global economy

The corporate rate could be raised for the largest-profit taxpayers to 27 percent for fewer than 2 percent of firms, with a small surtax of 2.7 percentage points for fewer than 0.5 percent of firms. … The corporate tax base can be broadened to better tax pure profits … Full expensing—which allows investors to completely write off investments as they occur rather than taking deductions as the investment depreciates—achieves the goal of moving the corporate tax to a tax on pure profits, since it does not distort investment decisions in the expensed assets. However, if expensing is allowed, it should be coupled with a complete disallowance of interest deductions. Otherwise, debt-financed investments receive a net subsidy due to the tax system; some investments are undertaken post-tax that would not be profitable absent tax considerations. … The US international minimum tax regime could be strengthened by including a much stronger country-by-country tax on foreign income. Indeed, higher headline corporate rates would need to be coupled with serious international tax reforms to prevent increased international profit shifting from eroding the corporate tax base.

Bucket 2: Investing in the Internal Revenue Service

[A]udit rates for high-income taxpayers collapsed from 16.5 percent to 2 percent between 2010 and 2021. The tax gap has grown to an estimated $870 billion per year, or 2.7 percent of GDP, and the true number is almost certainly larger, given the difficulty of measuring noncompliance at the top of the income distribution.

Bucket 3: Expanding tax efficiency and revenue through new “sin” taxes

Sin taxes have historically referred to taxes on goods like alcohol and cigarettes, where the policy goal was both to collect some revenue and also to discourage the behavior. Clausing and Sarin suggest new sin taxes on gambling and on carbon emissions.

To help curb these kinds of negative effects, we include a gambling tax of 5 percent on the value of sports wagers; we pair this tax with one on prediction market winnings. This gambling levywould generate more than $130 billion over the 2030–2039 budget window. One could also extend sin taxes to other forms of gambling. … The excise tax that raises the most revenue in our suite of options is a tax on carbon emissions. … One approach would be a carbon fee that begins at $40 per metric ton, rises by 5 percent in real terms over the budget window, and then increases with inflation thereafter. This carbon fee is more modest than many legislative proposals that have been floated in US congressional bills … The fee is also lower than that used in many developed economies, including the European Union and Canada …

Bucket 4: Adjusting income tax parameters

Clausing and Sarin propose that US income tax brackets be returned to 1997 levels–that is, a period of rapid economic growth just before the US federal budget was actually in surplus for a couple of years.

Bucket 5: Reforming capital income taxation

Clausing and Sarin propose a package of changes listed here, together which how much revenue would be raised by each in 2030.

An obvious question is why the authors focus on the tax side. What about some proposed spending cuts? One answer is that the authors study tax policy, so they are writing about what they jknow. Another answer is that the US has relatively high debt and relatively low tax revenues. (The number below include not just national-level debt and taxes, but also lower levels of government.)

A final answer is that the authors are certainly open to considering an equivalent set of proposals for spending cuts. Their tax increases total about 3.3% of GDP, or about $1 trillion in the context of the current US GDP. The big categories of federal spending are Medicare, Social Security, defense, and interest payments on past debt. If defaulting on past debt by cutting interest payments is off the table, then it is a heavy lift to propose $1 trillion in spending cuts at a time when an aging population is pushing up Social Security and Medicare spending and an unruly world order seems to call for sustained defense spending. Some subset of these tax increases would be a heavy lift as well. But when the federal government is borrowing one-quarter of what it spends, some real and dramatic changes are needed.

The first challenge of discussing US rural areas is to define them. I remember once reading a poetic definition that a rural area is a place where the shape of the land matters. But the US Census takes a different approach. It classifies each of the 3,141 counties in the United States as metropolitan, micropolitan, or neither. The “neither” category does not have an urban area of at least 10,000 people and is not in the commuting area of a nearby larger city. The “micropolitan” counties have an urban core of between 10,000 and 50,000 in population, and are not in the commuting area of a larger urban area. The most common way for research to look at “rural” America is to combine these these two categories of “nonmetropolitan” counties. Using this definition, rural areas are 97% of the land in the US, where 14% of the population lives. Rural areas also tend, with some exceptions, to be places where incomes are low, and where there have been many efforts over the decades to encourage economic development. Alexandra E. Hill, Sarah A. Low, and Keith Taylor explore the topic in “The Economics of Rural Development in the United States” (Annual Review of Resource Economics, 2026, pp. 11-39).

As one starting point, it’s important to recognize that the economy of rural America is not primarily agricultural, and has not been so for some time. Hill, Low, and Taylor write:

Today’s rural America contributes to the national economy through a range of industries. The USDA estimates that, with regard to jobs and earnings, 17% of nonmetro US counties are classified as farm-dependent, 25% are manufacturing, 10% are recreation, 4% are mining, 4% are the government sector, and the remaining 40% do not have a primary industry (USDA ERS 2025). While small family farms still constitute the majority of US farming operations (85%), they are becoming less prevalent, accounting for only 14% of farm sales (USDA NASS 2025). Among rural households engaged in production agriculture, reliance on nonfarm income and benefits, such as health insurance, has never been higher: In 2017, 82% of farm household income came from off-farm sources, and 56% of principal farm operators had a main job off the farm (Spell et al. 2022). Recognizing this, many agricultural economists now argue against the Goldschmidt Hypothesis, which suggested that improving the farm economy is necessary and sufficient for improving rural economies, and instead advocate for policies that more broadly stimulate prosperity, health, quality of life, and economic diversification in rural communities … Nonmetro US counties have experienced lower employment growth than metro counties over the last two decades (Dumont 2024, Sanders 2022). Multiple factors contribute to this, including falling population levels, fewer employer businesses and rising numbers of nonemployee businesses (Conroy et al. 2025), growth in high-skilled and declines in low-skilled employment opportunities (Autor 2019), and more …

Painting a comprehensive picture of rural America also requires understanding the people who fill jobs in, reside in, and contribute to rural communities. Today’s rural America is increasingly diverse in terms of race and ethnicity. According to the most recent decennial census (from 2020), 24% of rural Americans are “people of color” (versus 20.2% in 2010), and their distribution across rural America is heterogeneous: In the rural South, the largest nonwhite group is mostly Black, whereas in the rural North, these groups include American Indian, Latino, and Asian …

A common perception of rural America is that of widespread poverty. While most sources agree that rural household incomes are, on average, lower than urban incomes (Shrider et al. 2021), the share of rural households with incomes below standard poverty thresholds has declined. In fact, over the last 60 years, the rural-urban poverty gap has narrowed. In 1960, the average poverty rate across nonmetro counties was roughly 32%, 17 percentage points higher than the 2019 rate (15%). As of 2019, the poverty rate gap between metro and nonmetro US counties is only 3.5 percentage points, whereas it was 17% in 1960

Of course, a laissez-faire public policy strategy for rural areas would be to assist the poor, and otherwise just accept that people have different preferences for living in different places, with different mixtures of economic opportunity, and leave it at that. The efforts that have been made to support economic development in rural areas basically fall into two categories: place-based and people-based. Place-based policies focus on attracting businesses and industrial clusters to rural areas, as well as encouraging local entrepreneurs, through investment in infrastructure and amenities, as well as financial incentives. People-based policies focus on education and skill development. Obviously, the two types of policies can interact and support each other.

Hill, Low, and Taylor point out that there seem to be new economic opportunities for rural counties with the spread of remote work and only occasional commuting, and that there are potential payoffs to investing in communication and transportation infrastruture, as well as education and health care, in rural areas. Such recommendations are sensible enough, but it seems fair to say that similar efforts in the past have had mixed results. A key question is how to build stronger and lasting connections from nonmetropolitan areas to the rest of the US economy.

A long-time concern in the US K-12 schooling system is that local property taxes within each school district are a primary source of funding. As a result, school districts in high-income areas with higher property values will tend to have more money to spend per student. While this was indeed a pattern 40-50 years ago, school finance issues are no longer that simple. Sarah Reber, Beyond Deng, and Gabriela Goodman explain in “School Spending and Equity since 1976″ (Center for Economic Security and Opportunity at Brookings, June 2026). They write:

Schools in high-poverty or predominantly non-white communities often fail to serve their students well. Many point to American schools’ reliance on local property taxes as the culprit, arguing the school finance system ensures that “affluent areas end up with well-funded schools and low-income areas end up with poorly funded schools,” (American University 2020). However, state governments now contribute about as much to school funding as school districts raise locally, and the federal government also contributes. Moreover, state and federal funding disproportionately benefits school districts serving low-income students, and decades of school finance reforms mean that schools’ spending is less tied to its property wealth than in the past. …

We present a comprehensive descriptive analysis of how school spending varies across districts depending on child poverty and racial composition and how that has changed since 1976. To make sense of these patterns, we also decompose differences in average spending into their between-state and within-state components. Average spending varies tremendously across states—some spend nearly three times what others do (Reber and Goodman 2025)—so we ask: When some districts spend more than others, is it because they are in states where all districts spend more or because they spend more than other districts in the same state? …

The analysis shows that between 1976 and the early 1990s the average level of spending increased, but the distribution of spending changed only modestly, becoming somewhat less progressive. After about 1995, spending within states was increasingly targeted to high poverty districts, though between-state differences increasingly favored lower-poverty districts. That is, low-poverty districts were more likely to be in states where average spending was higher, but they had lower spending relative to high-poverty districts in the same state. We also find that low-poverty and predominantly white districts were more protected from spending cuts during the Great Recession, and high-poverty or predominantly non-white districts were the least protected. Finally, differences in average spending between states remain a major driver of spending inequality overall, even though policy conversations often focus on within-state differences. …

The analysis does not speak to whether high-poverty or minority-serving schools have “enough” funding—or whether or under what conditions additional funding would improve student outcomes. Instead, we document what happened: Which districts spent more, and which spent less? How has the distribution of school spending changed? This nearly five-decade national perspective captures how spending patterns evolved as states reformed their finance systems, sometimes multiple times, during the school finance reform era.

Here are a few figures to illustrate their main themes. This figure looks at school districts across the country in 1976-77 and 2020-21, split up by quintiles in terms of poverty rates and share of nonwhite students. Per student spending for each group is shown above the bars. New York City is a national outlier in terms of how much is spent per student. As the authors write: “Before the 1990s, NYC actually spent less than its own-state average but still had spending well above the national average (because its own-state average was high). By the 2010s, spending in NYC was well above average for New York state, which itself was nearly double the national average spending. Throughout the period we study, spending in New York state grew faster than average (from a relatively high starting point), and spending became more progressive (at least for NYC) faster than in the average state. Together these trends moved NYC from a moderate to more extreme high-spending outlier.” Thus, the white rectangles show wha tthe numbers at the top end would be like if New York City was excluded. The 2020-21 data suggest that, at the national level, there is no linear relationship in which school districts with more poverty or a greater share of nonwhite student have the lowest level of per capita spending. Instead, there is a U-shaped relationship, although the lowest quintile is below the top quintile.

Some of the difference here results from large differences in K-12 spending across states: in 2020, for example, “ranging from about $9,000 per pupil in the lowest spending states to almost $30,000 in the highest spending states.” In addition, “There is some geographic correlation between average spending and the child poverty rate. For example, both high-spending states and low-poverty districts are concentrated in the Northeast. … Nearly all states have at least some school districts in each poverty category, though states
with higher average incomes and school spending have fewer high-poverty districts and vice versa.” Differences in per student K-12 spending can be divided up into variation within states and variation between states. Consider this table:

For 1976 and for 2020, the per pupil spending numbers in the first two rows match the number from the bar chart above (with New York City left out). The third row subtracts the second row from the first. The final row shows how the spending gap would change if every school in each state had the same per pupil spending–but the differences across states were the same as they they are now. In the 2020 data, for example, “Average spending for
the highest-poverty districts was $17,208, compared to $15,921 for the lowest-poverty districts, a difference of $1,287. If every district spent at its own-state average, that gap would have been $1,084 in the other direction. Within states, highest-poverty districts spent $2,371 more, on average, compared to their lowest-poverty counterparts.”

Thus, within states, high-poverty school districts have higher per pupil spending. But across states, high-poverty school districts also tend to be in states with lower per pupil spending, so if one looks at national-level data, the within-state efforts to spend more on low-income students are less apparent.

The bottom line is to be wary of common claims that school districts with greater poverty or more nonwhite students have lower per pupil spending. Within states, this claim is generally wrong. Across states, the claim is more complex, because It costs more to hire teachers and to staff a school in higher-income states, so some of the difference across states is about differences in cost-of-living, not actual resources (like numbers of teachers) available to students. The report digs into such adjustments in some detail. It turns out that studies that make claims about fewer resources for higher-poverty schools have a tendency to do idiosyncratic cost-of-living adjustments and also to exclude federal K-12 spending, which is weighted toward students from lower-income households.

Financial literacy is meant to be basic stuff. For an example of how basic, here are eight questions from what is called the GFLEC Personal Finance Index. More on the index and the results in a moment, but take a moment to read the eight questions and look at the share of correct answers.

These eight questions are the public face of a longer 28-question survey, which has now been given to large groups for a decade. Paul J. Yakoboski, Annamaria Lusardi, Andrea Sticha, and Fran Mastry provide and overview and background in “A decade of tracking
financial literacy in America: Findings from the 2026 TIAA Institute–GFLEC Personal Finance Index” (June 2026).

For these eight questions, roughly half or less are answered correctly–a level that hasn’t changed much for a decade. Here’s a breakdown of the scores on the 28-question version of the test in the last decade. In more detailed breakdowns, some groups do better than other–for example, those who are older tend to do better–but no subgroup is especially covered in glory.

In the modern US economy, people have access to a wide range of financial decisions. Here’s a short list. Do you systematically look over your household budget and set some priorities, or does the money just vanish? How much credit card debt are you carrying over every month? Under what conditions is college tuition a good long-term investment? When buying a car, new vs. old, and borrow, pay in cash, or lease? How to search for a bank that pays higher interest? What is your credit score and how does it affect your options? Is sports gambling a fun way to spend some entertainment money, or a plan to build wealth? How large a house mortgage should you take out? Should you buy life insurance, or long-term care insurance? How much to put into a retirement account–and what kind of account? How should savings intended for shorter-term uses or an emergency fund be invested, as compare with savings intended for medium-term uses, or saving intended for long-term retirement? At what age should you start taking benefits from Social Security? Should you opt out of traditional Medicare in favor of a Medicare Advantage plan? After retirement, how much should you withdraw from your retirement account each year?

These questions don’t have cookie-cutter correct answers. But the answers can have substantial real-world consequences, and if you can’t answer the basic financial literacy questions, the chances of answering the questions in a way that works best for you are not especially good. Those with less financial literacy report spending more time worrying about financial questions–and have good reason to do so. Figuring out how to help people make informed personal finance decisions, from the high school and college classroom and on through the key financial decision-making nodes that life brings, is an undervalued social opportunity.

Tyler Cowen serves up the questions in “Gita Gopinath on Trade, Currencies, and Economic Transformation” (Conversations with Tyler, September 25, 2026). Gopinath has just returned to Harvard after a stint at the IMF, and as usual is full of interesting and wide-ranging commentary. Here are a few of the many remarks that caught my eye:

Because world trade is mostly conducted and invoiced in US dollar, not just in the US but everywhere, changes in US dollar exchange rates have a muted effect on levels of imports and exports.

The value-added component of trade has declined over time, and because of the dollar’s dominant role in the trading system, everybody is pricing their goods to each other in dollars. In a way, the way you want to think about it is that when China is exporting some certain goods to the rest of the world, if its imports are priced also in dollars, then those inputs that are going into its production function are priced in dollars and sticky in dollars, then you have an incentive to just price in dollars, and your dollar price is not going to move that much because the exchange rate adjustment is not doing much, because an important part of your cost of production is also in dollars.

That’s the reason why we see a fair amount of dollar pricing in the world. The sense that somehow China should be able to, or any other country should be able to, cut their dollar prices by a lot when their currency depreciates is not the case, because they are also importing inputs from the rest of the world that are priced in dollars, and they don’t have that much of a margin to squeeze. Now, of course, there’s variation across goods. Some goods rely more on inputs that are dollar-priced, some depend less, and you do see in the data that that variation matters.

Can Argentina continue the progress it has made against inflation?

That’s actually a lesson we learn every time with countries that are in this disinflation process where you’re starting off with inflations of three-digit numbers. Two years ago, I think Argentina’s inflation was around 150%, so coming down to 30 percent, that was tough. Bringing it down is impressive, but it’s always the last mile going from here to anything like a single-digit number, always takes much, much longer. This is nothing special about Argentina. Now, in the case of Argentina, what would it take to bring it down much faster? Firstly, I think there’s got to be a lot more confidence in policy continuity. I think what the Milei administration has done very well compared to the previous administrations is recognize that the problem was fiscal, that as long as Argentina was running the kinds of deficits that they were doing and using monetary financing, basically money printing to pay for it, there was no possibility of getting out of this trap. He has been running primary surpluses since he came to power. He’s absolutely completely committed to it. I remember I had conversations with him when I was at the IMF. This is something he absolutely will not budge from, but there are questions whether the rest of the political class has signed on to this. 

What are the economic reasons for concern about trade imbalances?

[T]he trade imbalance in and of itself is not something that we should be focused on. I think what we care about is welfare, and welfare involves jobs and consumption, what’s inflation, purchasing power, and so on. People do not wake up in the morning saying, “Okay, my current account deficit is too big, or my current account surplus is too big.” If all of your policies that were delivering good outcomes for your country were to bring along a deficit or a surplus, that’s perfectly fine. There are lots of good reasons to be running deficits and surpluses we know. There’s nothing that tells you that you shouldn’t.

The problem arises when you have policies that countries have in place that are inconsistent with any kind of a balanced growth model, and they manifest themselves in that trade deficits and surpluses. This is what I’m saying as an economist … . [I]n both the case of the US and China, we’re very far from the world where countries are doing good policies … China’s [trade] surpluses are a reflection of things going wrong in China. It’s not a reflection of strength in China. It’s a reflection of weak consumption. It’s a reflection of misallocated resources going into different sectors. They did that with their property markets. Now they have a huge property market problem that they haven’t been able to fix in five years. They have this now with other markets, including EVs and the other sectors. They have the problem with inflation being too low. They’re trying to do so called anti-involution policies, bringing companies together and telling them, “You’ve got to keep prices higher than what you’re doing right now.” This is not the world where they’re playing good policies, and this is all comparative advantage, and this is the outcome that we see. I wouldn’t push the argument that this is, “Oh, we should be just happier with cheaper goods from China.”

Jeff Horwich serves as interlocutor in “Interview with Ariel Pakes: To understand the broader economy, unlock the inner workings of product markets” (Federal Reserve Bank of Minneapolis, September 25, 2026). Pakes is an industrial organization economist, so he is congenitally suspicious of macroeconomics aggregates and broad statements about “how markets work.” Instead, he wants to examine actual markets.

One of his most prominent papers, written with Steven Berry and James Levinsohn, was called “Automobile Prices in Market Equilibrium,” and published in Econometrica in 1995 (63(4), 841–890). The problem the authors faced was that if you want to think about how demand for one kind of car will be affected by a change in price, you also need to think about how demand for all other cars will be affected: for example, are there lots of close substitutes for the car where the price is rising, or not very many? But with 200 models of cars, this problem quickly becomes unwieldy. Here’s Pakes:

As an IO [industrial organization] economist, I have to analyze a market. In the auto market, there were 200 models. If I was to do a demand system—let’s say, how many people want to buy an Oldsmobile Ciera—you’d need the prices of all the goods. Just for the demand for the Olds Ciera, you’d need 200 price coefficients at least, even if you just did it linearly. That’s what we call, in IO, “product space.” The only thing you know is the products and their prices. And that’s how demand systems used to be analyzed. If you did that, I’d have Olds Ciera quantity on one side, 200 prices on the other, and a similar equation for every one of these 200 products. That’s 40,000 coefficients. There’s no data that could effectively estimate 40,000 coefficients.

What we did in the demand systems—the BLP [Berry, Levinsohn, Pakes] group—is we said, If we know the characteristics of the products, and the distribution of preferences over those characteristics—like you have a preference for car size that depends on your family size and price that depends on your income—then all I really need is the distribution of people’s preferences, some of which I can get from age and things like that. If that distribution is, say, normal, and there are 10 characteristics, there’s something like 50 covariance terms. And from that, I can give you 40,000 cross-price elasticities. I just determine what everybody would do if a price changed. I need to sum over households. But with modern computers, that’s easy even for a hundred million people. And then I say, What would happen if I changed the price? I see what you would buy if the prices changed, or what everybody else would buy, and I find out the price elasticity.

Working in “characteristic space” like this has two advantages. One, it allows you to estimate demand in a market with many products. Two, it allows you to do the second question in IO, which is, What products do I want to develop? If all I have is past price and quantity, I can’t tell you what would happen if I put out a new product. But if I know the characteristics of a new product, then I get some idea of what the demand for a new product would be. That’s the incentive for the development of new products.

Pakes had a follow-up story about this paper, when General Motors heard about it, that I had not previously heard:

There was a guy named Mustafa Mohatarem in charge of research who had a Ph.D. in economics and was a very smart guy. He asked us to come to General Motors headquarters and present on the basic stuff. It didn’t have any micro data, just aggregate characteristics and quantities demanded. Afterwards he said to us, “I know exactly the markups on every product, up to the options that are on the product. You guys got it almost right. What would happen if I gave you real data?” So, he gave us their micro data. They had, at the time, an annual 64,000-person sample from new purchases, new registrations across the U.S. And then they had another 64,000, asking the people what they liked and what they didn’t like and what their second choice was. So, we got an old copy of that.

Why I brought up the Olds was because one of the questions he asked us is, What would happen if we killed the Olds department of GM? This is because they had Pontiac, they had Chevy, they had many midsized cars or family cars, and they thought they were just cannibalizing each other. They were not expanding. They killed Olds before we got back to them because it took us a while to figure out how to use the micro data. But after we did this, we got a phone call from them asking for the programs. We put the programs together in a way they could use them, sent them to GM, and they never talked to us again about this. They just wanted the programs. I hope they ended up using them.

This idea of thinking about the product market in terms of characteristics of products, not just the products themselves, turns out to have wide applications, and Pakes touches on a number of them in this survey. For example, when calculating inflation, there is a problem with looking at the prices of products where technology is evolving rapidly: for example, the specific laptop computer you bought last year may no be on the shelf, because this year’s model has different characteristics. When calculating price change, one has to make a “hedonic” adjustment for the change in characteristics. Pakes was instrumental in getting the US government to use hedonic measurement for certain areas in the Consumer Price Index measure of inflation.

Or think about the official poverty line, which is a certain dollar amount, adjusted for family size, that rises by the rate of inflation each year. But the characteristics of what a low-income family can actually purchase have changed over time, so that a poverty line based on characteristics of goods and services might look quite a bit different over a few decades from one based on adjusting a dollar amount.

Or think about antitrust. If two large companies want to merge, how should government regulators evaluate the threat to competition? Rather than that looking at meaures like share of total industry sales, perhaps the more relevant measure is about the characteristics of goods that would be produced by the merged firm and the characteristics of goods produced by other firms in the market. Indeed, this approach is now being used by many antitrust regulators.

At the end of the interview, Pakes offers this thought on the interplay of data and theory:

I think it’s the philosophy of science, that you have to put together data and theory to understand the world. Data by itself cannot prove anything. You need the theory that generated the data to have an answer. The theory is not perfect either, but you’ve got to start somewhere. The issue isn’t that we get the answers right. The issue is that we get the answers better than the next best person could get them. The world is too complicated to get them exactly.

Many of us are inconsistent in our economic desires. I would like the price of my home to rise, because it increases my wealth, but I would also like the prices of all other homes to fall, so that I could sell my house and buy an even nicer house. Also, I would like to have the value of my house rise, but also pay lower property taxes. David Schleicher digs into the second issue in “The Great American Property Tax Freak Out” (posted online at SSRN on September 1, 2026). He writes:

In the last three years, a number of states have substantially reformed their property tax systems, providing huge tax benefits to owner-occupied homes and shifting the burden of paying for services like schools and police to commercial property owners (including rental apartment buildings), to other local taxes, and to state funding, which itself largely comes from sources like sales taxes and income taxes. A few, including Florida, Ohio, North Dakota, and Texas, have considered going further, either completely abandoning property taxation for owner-occupied housing or even abandoning property taxation entirely. … One might think that homeowners would be happy that their largest asset has appreciated substantially in value, as homes did, particularly in suburbs, in the post-COVID period. But because property taxes are wealth taxes, property owners have responded to their increasing wealth with political anger, pushing shifts in property tax policy, often leading to increasing rates on commercial property owners who have seen declining values during the same period. …

[T]hese reforms shift the property tax from a tool homeowners use collectively to provide for locally-wanted services towards a more standard form of redistributive taxation, charging commercial property owners to pay for services for homeowners. Further, they will lead to greater state authority over local governments, less stable funding for local governments (but also less tax foreclosure in recessions), and stricter zoning controls (but more building of homes where it is legal). Perhaps most notably, these property tax reforms will lead to higher housing costs, and thus constitute a substantial transfer of wealth towards people who already own homes and have already seen their net worth increase substantially in the post COVID housing market.

A few other facts and insights seem worth appending here.

1) there’s a lot of local variation across public finance in the United States. But as a broad statement, property taxes are the major own-source of revenue for local US governments, and in turn the main source of financing for local schools. Many local governments have traditionally relied on property taxes to pay for police as well. The political dynamic of reducing property taxes often involves state-level actions that limit or block property taxes at the local level. It is not clear that voters for lower property taxes have drawn a connection in their mind to the likelihood of reduced local public services.

2) the property tax is a form of wealth tax, and it has the standard problems of a wealth tax: specifically, you can have more wealth–say, the value of your home, your business, or your retirement account went up–without having immediate income to pay a higher level of taxes. Thus, older voters who tend to have higher accumulated wealth, partly in the form of home equity, but also lower current income, are a politically powerful and sympathetic group in the push to reduce or eliminate property taxes.

3) If property taxes are reduced or abolished, the cost of owning a home falls. (For example, imagine that you could guarantee that the roof, exterior, driveway, plumbing and HVAC systems in your house would never need repair or replacement, which would also lead to a fall in the cost of owning a home.) With lower costs of owning a home, the market value of the home rises. Thus, the ironic outcome is that higher housing prices cause voters to want to limit or repeal property taxes, which would then lead to even higher housing prices. Moreover, the current homeowners who have already benefited from higher home values will benefit further, while homeownership will look even costlier for potential future homebuyers.

When economists think about how much utility is gained from additional economic resources the basic assumption is that an additional amount of income or wealth increases utility, but by a diminishing amount–because additional income or wealth has diminishing marginal returns. However, there’s also a long-standing tradition in economics of thinking about why this relationship might have an S-shape. Benoît de Courson, Willem E. Frankenhuis, and Daniel Nettle discuss this past tradition and offer new thoughts in “Explaining the paradoxical effects of poverty on risk taking: The Desperation Threshold Model” (Behavioral and Brain Sciences, published online February 23, 2026, not yet typeset).

Here’s a basic diagram, showing the relationship between resources (like income or wealth) and utility. Focus first on the upper-right portion. This standard shape shows that greater resources lead to additional utility, but the marginal gains to utility are diminishing, and so the curve flattens out. Imagine some one who is considering a risky choice that could result in a gain or loss of an equal amount of income. The marginal utility of the gain in income is smaller than the marginal utility of the loss, so the person will only make this risky choice if the probability of gain is high enough, certainly greater than 50:50

Now consider the lower-left portion of the curve. The underlying assumption here is that people need a basic level of resources to survive. For those who have resources substantially below that level, additional income doesn’t increase their utility, because they still don’t have the resources to survive. However, imagine someone who has resources slightly below the basic survival level, who is considering a risky choice that could gain or lose an equivalent amount. For that person, the utility loss from this risky choice is low, because the person is already below the basic survival level, and so being further below that level has a minimal loss in utility. However, if the risky choice can propel this person to a place above the basic survival level, then the marginal utility gains are large.

The S-shaped graph thus seeks to explain why people with low income levels may be more prone to risky decisions, like crime or gambling. The authors are careful to call this a “mid-level theory,” by which they mean that it suggests a pattern but then “raises many further detailed questions: about its explanatory scope; its cognitive and neural implementation; and its ability to predict and explain in different socioecological contexts and at different scales.” That is, the purpose of the theory is not to assert that all people act in a certain way, but to offer a basis for additional discussion and appropriately hedged real-world insights. One obvious question, for example, is how people in different times, places, ages, and economic settings may perceive the basic level of resources. Perhaps some middle-class people (or upper-income people, for that matter) who are well above the poverty line do not perceive themselves as having what the regard as “basic” resources. The model also suggests that people who are just above the “basic” level (however defined) might avoid risk and act cautiously, while those just below the “basic” level might be risk-takers.

De Courson, Frankenhuis, and Nettle point out that the idea has entered economic theory in a number of situations. For example, why do many middle-class people both buy insurance (risk-reducing) and gamble (risk-increasing?). The authors write:

Friedman and Savage (1948) proposed a utility function with two inflection points, to explain why the same individual can sometimes both buy insurance and gamble. Their model predicts a mixture of risk taking and risk aversion, but not for reasons to do with basic needs. Rather, they predict maximal risk taking among people in the middle of the resource distribution. People in such a position were hypothesized to have strong incentives to move up into an elite, which they can only do through big gains.

What about investment managers? “Roy (1952) analysed the case of a portfolio manager trying not to maximize expected gain, but to minimize the risk of catastrophe (he cites ‘death, bankruptcy, and a prison sentence’ as examples, p. 433). He framed the problem as a step-like utility function.” Similarly, managers of banks that are insolvent have an incentive to “gamble for resurrection”: that is, if a high-risk strategy succeeds, they get to keep their position at the bank; if it fails, the bank was already going broke and the regulators were going to step in.

What about subsistence farmers in low-income countries? “Since Chayanov (1926), it had been commonplace to analyse peasant behaviour as aimed at guaranteeing a minimal harvest necessary for subsistence. The implications of this motivation for risk taking were formalised by multiple authors in the 1970s. Roumasset (1976) and Kunreuther & Wright (1974) proposed hierarchical goal models: the producer first tries to ensure that they do not fall below a ‘disaster level’, and only if sufficiently confident of succeeding, try to maximize profit. Masson (1974) represented this idea using utility functions with ‘jump discontinuities’. These authors had both the S-shaped utility function, and a concept of basic needs.”

There are other applications as well. For example, apparently some biologists have a “risk-sensitive foraging theory”: “When starvation is imminent, animals should take any level of risk to gain food (desperation prediction), whereas once it has been averted for the time being, they should avoid too much risk (caution prediction).” Once the S-shaped curve has entered your mind, you can see it in many places.

For some people, their pulse beats faster when their favorite sports team wins, or their favorite musician drops a new album. For others, pulse and respiration accelerate when confronted with “Spending, Taxes, and Deficits: A Book of Charts.” If you (like me) are in this second category, Jessica Riedl has got your back (Brookings Institution, April 2026). This is a book with a lot of pictures and not too many words.

Here’s the basic picture. Federal debt as a share of GDP is near the highest level in US history, which happened after the US financed the fighting of World War II with borrowed money. National debt is now about $235,000 per US household. The pattern of the last two decades shows that we fought the Great Recession of 2008-09 and the pandemic recession of 2020 with borrowed money, too. Assuming no future giant recessions, wars, or pandemics, past tax cuts expiring when they are scheduled by law to do so, and interest rates not rising further, this is the path we’re on.

Those of you with long memories may recall that back in 2000, the US government actually had budget surpluses for a couple of years. What has changed since then? The short answer is tax cuts, higher spending on Social Security and Medicare, and higher spending on interest payments.

I sometimes talk to people who believe that the defense budget is the biggest part of federal spending. I heard someone refer to the “military-industrial complex” the other day, a phrase form President Eisenhower’s 1961 farewell address. If you hold this belief, time to update your priors. At the time of Eisenhower’s speech, defense was about half of all federal spending, while Social Security/Medicare and anti-poverty programs wer about 16% of federal spending. Those categories have now (roughly) switched places. Social Security/Medicare and antipoverty progrmas ar now 53% of total federal spending, while defense is about 13%.

One grim sign, when you are in debt, is the vicious circle where your interest payments are so high that you can’t reduce your current borrowing, and so you sinkhole of debt keeps getting deeper and deeper. On its current trajectory, interest payments in the US federal budget are headed down this path. The purple bars show the rise in federal interest costs since 2018; the green bars show the path the budget is currently on. On this path, about 30% of all federal revenues will be going to interest payments on past debt by 2036–and interest payments would be a higher cost than Social Security.

Reidl uses the charts to make a case that addressing the long-standing long-term insolvency of Social Security and Medicare would go a long way to addressing these issues. US politicians have been ducking this topic for decades, and continue to do so. Based on selection of charts and labels, Riedl is clearly less enthusiastic about tax increases, but in my own mind, going back in the direction of the tax burdens from back in 2000s can be a reasonable step as well. For example, I would favor moving the estate tax back toward its 2000 levels, and I’d at least be willing to talk about eliminating all income tax reductions–which at this point only apply to about 10% of all taxpayers who tend to have higher incomes. But Riedl offers the useful reminder that the more generous welfare states of Europe are not in fact financed by taxing high incomes and wealth at conspicuously above US levels, but instead by value-added taxes (think a national sales tax) and higher payroll taxes. In short, they provide additional middle-class benefits by taxing the middle class.

The basic patterns of US manufacturing jobs are fairly well-known. Here’s a figure showing total US manufacturing jobs over the last half-century or so: more-or-less flat from the 1970s up through 2000, then a decline from 2000 to about 2010, and more-or-less flat since then. Part of what happened in the early 2000s was a surge of imports from China, after China became a member of the World Trade Organization. Another part was that US workers who might otherwise have been in manufacturing jobs shifted to house-building during that boom-and-bust of US housing in the lead-up to the Great Recession. Part of the reason was US manufacturing firms making much more widespread use of robotics.

Putting manufacturing jobs in the context of total jobs in the US economy gives something of a different impression. This figure shows that even when total manufacturing jobs seemed to be more-or-less holding their own from 1970 up to about 2000, other jobs in the US economy were actually growing. The result is that manufacturing jobs were about 26% of the US total in 1970, but the percentage had fallen by half to 13% of all US jobs by 2000. From this perspective, the decline of the role of manufacturing jobs in US employment is a long run process–a little faster in the decade after 2000 than in the decades before, but not much faster.

Vittoria Dicandia  provides a closer look at some long-term patterns in US manufacturing jobs in “Manufacturing in the United States” (Economic Commentary: Federal Reserve Bank of Cleveland, September 21, 2026).

1) The share of US manufacturing workers with no college has fallen substantially, and the share with a college degree has risen substantially.

2) College-educated manufacturing workers have a modest wage premium; non-college manufacturing workers do not. A “wage premium” is “defined as the average wage difference between workers in a given industry and those with similar observable characteristics, such as age, education,and occupation, in the rest of the economy.” Dicandia calculates:

3) The traditional story about US manufacturing jobs from 1970 up through about 2000 was that the number of manufacturing workers wasn’t going up, but the productivity of US manufacturing workers was rising substantially. That story no longer seems to be true. In the 1990s and even into the 2000s, productivity in manufacturing rose faster than the rest of the economy; since 2007, productivity growth in manufacturing has actually been slightly negative.

4) Over time, the US workforce has adapted to a smaller share of manufacturing jobs not so much by an outflow of existing manufacturing workers, but by a lack of inflow of younger workers into the sector. A result is that current US manufacturers are concerned that they would like to increase production, but lack the workers to do so. (Of course, the economist in me suggests that increasing the wage premiums would help them in attracting more workers.) Dicandia writes:

Alongside this persistent decline in employment share in the sector, manufacturers themselves report significant workforce challenges. The 2025:Q3 Quarterly Survey of Plant Capacity Utilization (US Census Bureau) reports that more than one-fifth of all manufacturers indicate that they were not able to work at full production capacity because of an insufficient supply of labor, while the National Association of Manufacturers survey in 2025:Q4 reports that more than half of manufacturers identify attracting and retaining qualified employees as a relevant challenge for their business.

Worker flows into and out of manufacturing have evolved over time but do not differ markedly from patterns observed in other sectors of the economy (results not shown, author’s analysis of US Census Bureau LED data). This suggests that worker mobility involving manufacturing largely reflects economy-wide labor market dynamics rather than sector-specific turnover. Instead, the key driver of manufacturing’s employment decline has been the lack of younger workers entering the industry (results not shown, author’s analysis of CPS data). Specifically, between 1960 and 2009, each successive cohort of workers has entered manufacturing at progressively lower rates.