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This morning, the Bureau of Labor Statistics (BLS) released the Consumer Price Index (CPI) data. The headline CPI came in at 0.4% month-over-month, 3.4% year-over-year, while Core CPI (not including food and energy) came in at 0.3% month-over-month and 2.4% year-over-year.1

Another data release published on Thursday - the Producer Price Index (PPI, the price that sellers receive) - also came in hot. Headline PPI came in at 5.4% on a year-on-year basis, while Core PPI came in at 4.7% year-on-year.

Although CPI is not the main inflation measure used by the Federal Reserve - that is the Personal Consumption Expenditure Price Index (PCE) - the CPI and PPI data feed heavily into PCE. Based on the releases so far, forecasters are assuming a high print for August PCE as well.

Moreover, the latest PCE readings for the month of July were far above the Federal Reserve target of 2% - headline PCE was 3.7%, while Core PCE was 3.3%. With inflation being above target, the focus has turned to the interest rate decision that will be made on September 16, 2026 by the Federal Reserve. The big questions are whether the Federal Reserve will hike, and whether they should hike.

Below I lay out how to think through this decision using a standard economic model. I will also address why I think many opinions are off the mark in this current interest rate discussion:

• “It’s a supply shock, we shouldn’t hike into a supply shock”

• “Inflation is actually low and falling so we shouldn’t hike”

• “The labor market is weak and thus we shouldn’t hike”

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Hello,

Today, as the last part of the Nominal News re-launch, I am debuting a Paid Subscription Tier, in addition to the current Free Tier. I want to give you an update of what to expect, and explain what will be provided in the Paid Tier.

Paid Tier

The Paid Tier Subscription will, in addition to the weekly deep dives that are part of the Free Tier, provide the following:

• Special edition articles ~twice a month, covering breaking economic developments, as they occur;

• Audio coverage of certain deep dive articles to give further information and opinion on the topic covered;

• Early access to my own data analyses;

• Prioritizing responses to any questions about the weekly deep dives.

Special Edition Articles

In these articles, I will focus on applying academic economic research to breaking developments, as well as provide more of my opinion on the subject.

As an example of the type of article, over the last few weeks, I have provided 4 Special Edition Articles:

• Special Edition: Federal Reserve FOMC Meeting Update

• Special Edition: Responding to Stephen Miran’s Tariff Op-Ed

• Special Edition: A Bizarre Press Conference After the FOMC Holds Interest Rates Steady

• Special Edition – US Treasury Increases Bond Buybacks

As you can see above, the topics covered were interest rate announcement meetings, tariff arguments and the most recent US policy of expanded bond buybacks.

I will publish around 2 Special Edition articles per month for Paid Tier subscribers and, as relevant for major economic developments. The next one, for example, will cover the FOMC Meeting on interest rates on September 16.

Pricing

The paid Tier Subscription will cost $100 for a yearly subscription. But, as part of a launch promotion, I am offering a 40% discount – so $60 for the first year (which works out to be $5 a month). This new subscriber offer will only last through September 18.

I will also offer a monthly version of the Paid Tier that will cost $30 (discounted to $18 per month for the whole year, as part of the launch through September 18), in case you’d like to try it out first.

The reason why the stand-alone monthly subscription is higher than the annual is two-fold:

1. I believe the most value will be derived from being a subscriber for a year, as articles will connect thematically with each other, and;

2. By having longer-term subscribers, I will be able to focus my writing on topics that I deem important rather than focusing on topics that will retain short-term subscribers and get clicks.

Lastly, if you’d really like to champion my mission of sharing economic knowledge and insights with the wider community, please consider becoming a Founding Member at $1,000 per year. Your support will help accelerate Nominal News and allow me to enhance the offering, of which you will be the first to know! You will receive a personalized Nominal News stein mug as well.

Upgrade to Paid

Free Tier

Under the Free Tier, you will continue to receive weekly emails that will mostly focus on deeper dives into economics research and policy. There will be no change here and you will continue to receive weekly emails – most likely on Mondays. As one of my goals with Nominal News is to share my economics knowledge with the wider public, I would not want to paywall this.

I am already very grateful for your subscription, and if you’d like to support me in other ways – likes, comments and sharing the articles are always extremely helpful! Actually, I have enabled the Substack reward program, which will grant you a reward for referrals here:

• 5 referrals – 1 month Paid Subscription

• 20 referrals – 1 year Paid Subscription

• 50 referrals – a Nominal News themed stein mug (I use it for tea and beer!)

Thank You

As always, thank you very much for supporting my work and my mission. I would not be able to launch all this without your support! I am very grateful and excited to continue the Nominal News journey with you!

If you have any questions/comments, please do not hesitate to reach out on substack or via email, nominalnews at gmail.com

Now onto a quick topic:

II. Inflation Measures By Income Groups

Inspired by a piece written by Substack Writer Michelle Teheux on poverty lines and how a single measure fails to capture how people experience poverty, I went ahead and did my own analysis on how inflation impacts people in different income brackets.

It should not be surprising that a much richer person might have a different consumption basket than a person living at the poverty line. One could assume that a higher income person will more likely spend a higher share on various services, while a lower income individual might spend more on necessities such as food, rent and energy.

As an exercise, I would like to ask you to think about how a consumption basket between a top 5% income individual and bottom 20% individual might differ, and who experienced higher inflation in the last 12 months (and by how much).

Consumption Basket Differences

To understand the spending patterns by different income groups, I relied on the work of Zheli He, Xiaoyue Sun and Efraim Berkovich of the Penn Wharton Budget Model. From their analysis:

Lower income individuals (left most column) do spend a lot more on food, shelter and energy. The lowest income group spends around 61% of their income on these items, while the highest income group (right most column) spends only 48%.

Measuring Inflation

Since each income group has slightly different spending baskets, it should not be surprising that each group may experience inflation a bit differently. For example, if food inflation is higher, then lower income groups experience higher inflation.

To analyze this, I used the above the spending percentages1, and applied the actual recent inflation in each of the spending categories from the Bureau of Labor Statistics.2

Based on the spending patterns of each of the income group, I approximated the inflation rates faced by the lowest income group and highest income group over the last 12 months:

The inflation rate faced by high income individuals lately has been much lower than the officially stated rate. Moreover the gap itself is quite significant – almost a 0.6 percentage point difference in inflation rates.

Rethinking Real Wage Growth

The above analysis also points to a slightly different perspective on how much real wages grew for every household. The Federal Reserve of Atlanta’s Wage Growth Tracker shows that as of July 2026, the lowest income quartiles saw a 3.7% growth in wages, while the highest income quartile saw a 3.8% growth in income. With the above inflation measures, that means the lowest income households saw nearly no real wage growth in the last year, while the highest income households witnessed a 0.8% real wage growth.

This suggests that many in the lower quartile of income have seen their incomes stagnate over the last year. The adjustment for inflation by income could also adjust our view of what real wage growth workers saw since the pandemic. Post pandemic, low income households saw some of the strongest wage growth. However, it might have been muted by particularly strong inflation in categories they spend. This is something I’ll look into.

If you would like to support us in reaching our subscriber goal of 7,000 subscribers, please consider sharing this article and pressing the like❤️ button at top or bottom of this article!

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I have recently started writing full time and am turning on a paid feature in September, in addition to the current free offering. If you’d like to help me with some feedback (and a chance to win a 3-Month Paid Subscription tier) – please fill out the questionnaire below:

Nominal News Reader Questionnaire

Hello,

After going full-time on Nominal News earlier this month, I would like to introduce myself:

My name is Pawel and I am the writer behind Nominal News. To give a bit of background, I completed my PhD in Economics at New York University and live in New York City. My research at NYU focused on macroeconomics, trade and labor.

Specifically, my main research paper focused on the issue of how trade liberalization can unevenly impact people’s lifetime earnings, generating a situation where some people (in this case, it was manufacturing workers) are explicitly worse off under trade liberalization, even though the majority are better off.1 Here you can find my personal site with my research (I also hope to progress these papers as I have not had the opportunity to work on them since 2021!).

Why Nominal News

During my PhD, I had the privilege of hearing from many brilliant economists.2 Through these experiences, I got to learn the economic intuition and way of thinking that I realized is often restricted to the confines of academia. This inspired me to share this knowledge and experience with others, which is why I launched Nominal News.

With Nominal News, I want to teach you the economic tools and way of thinking that I learned during my time in PhD, rather than just focusing on giving ‘an answer’. Economic thinking can be used in nearly all settings, as, to me, economic thinking is modeling human behavior – how do people respond to things and to each other.

For example, suppose a government proposes a new tax-advantaged savings account for children. The initial thought might be that such an account will help people save on behalf of their children. But a deeper dive would show us that such an account is unlikely to generate new savings (wealthier people will save less in other accounts; lower income individuals often have insufficient income to save). This type of understanding can shine a completely different perspective on a policy and what it will actually achieve.

With Nominal News, I hope that this type of ‘deeper-dive’ thinking becomes second nature when considering economic policies,or any social policy.

Beyond Economics – How Nominal News Can Help Elsewhere

These tools and methods can be applied to anything that involves human behavior – economic policy, business decisions and even personal life choices. Some examples of questions that economic thinking may help:

• If I’m setting prices for products, what should they be going forward? – understanding how government policies impact inflation will be important

• Suppose I implement a new inventory or payments system, how can I determine if this system is generating benefits?3

• Should I pursue a very technical, narrow degree or go for a more broad liberal arts degree?

Economic thinking can help you with these questions and also ensure that you ask the right questions around these problems.

What To Expect

Going forward with Nominal News, I will continue to remain research-based. I will aim to share the methods and assumptions behind the research, and not just the results (as these results can be driven by the selected assumptions).

Any insights or opinion on my end will always be driven by research. I will also aim to keep the distinction between the research and my opinions separate. I think these are important tenets to be driven by, as a lot of public economic commentary can often seem like it’s based on the best available research, but really, it’s just a person’s personal policy preferences.

Article: The US Census – Hosting An Unnamed ‘Analysis’

On August 18, 2026, the US Census Bureau, a federal statistical agency, posted on its website, a report called: “Noncitizen Voting in the 2020 Election, A Beginning Analysis”.4 The report touches on a politically charged topic of whether ineligible people voted in US elections. But the report itself had two large red flags – it had no author and it lacked critical methodological elements.

The US Census Bureau

The US Census Bureau is a highly regarded institution. Its role in the US is critical, as the data it collects ends up being used to allocate seats across the US for the House of Representatives. It also has staff researchers that publish analyses based on the highly sensitive Census data. Every report typically has a specific author and a well described methodology section. Here is one such example “Monthly and Episodic Poverty: 2024” authored by Marissa Nicole Davila.

On the other hand, the Noncitizen Voting in the 2020 Election, A Beginning Analysis report contains no authorship nor any serious discussion of methodology. Both of these issues immediately raised concern in the economics community, as it suggested that the level of analysis is not something that would be considered acceptable.

Besides the lack of authorship, experts highlighted that the key term used in the report – non-citizen – is not actually a legal definition. This is a serious issue: in a February 2026 investigation of a Texas county, individuals were identified as non-citizens (and thus, the implication that they are not eligible to vote). However, it turned out that many identified individuals were citizens.5

The concerns raised by the research community turned out to be vindicated, as NPR found that the published report was not written by anyone at the US Census, but rather written by a politically linked think-tank:

“Work on the analysis was not conducted by career civil servants at the Census Bureau, and the team behind it included individuals affiliated with the America First Policy Institute, a think tank started by officials from Trump’s first administration, according to a Census Bureau employee who NPR has agreed not to name because they fear retaliation at work.”

The Importance of Institutions

The reason this issue is significant is that the US Census is a trusted source for rigorous data collection and research. It’s not a surprise that people would want to use the US Census as a seal of approval on their ‘research’. Such undermining of the institution can have long run consequences, as politicizing such institutions can only lead to bad outcomes.

It was promising to see that no one at the US Census put their names on this poor quality analysis. Moreover, the broad and swift criticism of this analysis is another good sign, as it helps protect the integrity of the institution. Regardless, it’s very unfortunate that such an analysis was published on their site.

Next week, I will officially launch a paid tier. My post next week will cover how it’ll work (Note: most importantly, weekly deep dives will remain free!)

If you would like to support us in reaching our subscriber goal of 7,000 subscribers, please consider sharing this article and pressing the like❤️ button at top or bottom of this article!

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Going forward, special edition articles will be accessible to paid tier subscribers. My next special edition article will be covering the FOMC meeting and press conference on September 16. Subscriptions will go live September 8. If you’d like to enter a chance for a free 3-month paid subscription, please fill out my questionnaire.

US Treasury Secretary Scott Bessent announced on August 19, 2026 that the US Treasury will boost buybacks of US treasury bonds with maturities between 10 years to 30 years. The US Treasury is expected to buy up to $4bln worth of bonds per transaction, 4 times a quarter. So what does it mean?

Rising Yields – i.e. Rising Interest Rates

The US has been experiencing a rise in yields on long-dated bonds. A yield can be thought of as the interest rate an investor would require to earn in order to buy the bond. Below is the yield of a 30-Year US Treasuries over the last 5 years:

Higher yields mean that when the government issues new 30 Year Treasuries (government debt), it will have to offer the yield as a fixed interest rate for the duration of the bond. This means issuing debt for the government has gotten more costly over the last 5 years.

At the same time, US Treasuries are often used as a baseline cost of debt for other markets. For example, the 10-Year Treasury is often relied upon to set interest rates for home mortgages. Other investments also rely on this as a benchmark.

This recent increase in yields and persistently higher yields appears to have concerned the government, which has been calling for lower interest rates for a while. The buyback initiated by the US Treasury appears to have been motivated by the desire to lower interest rates.

Buybacks – Balancing the Yield Curves

By buying back long-dated bonds, the yield on these bonds fall.1 In exchange, the US Treasury issues short-dated bonds (for example, 2-Year bonds), which have a lower yield than the long-dated bonds. This action does increase the yield of these short-dated bonds. In the end, the difference between yields of 2-Year bonds and 10-Year bonds shrinks.

Why Do This?

One potential reason to do such an operation is if the government believes the market is mis-pricing the bond. That is, if long-dated bonds have yields that are too high compared to what they think they should be, it might make sense to briefly issue more short-dated bonds, until the yields on long-dated bonds adjust.

However, this presumes that there’s a reasonable chance these long-dated yields will drop in the foreseeable future.

What Happened After The Buyback?

Right after the announcement, the 30-Year bond yields fell. But – yields quickly rebounded in a few days, as can be seen from the chart below (the red line being the day prior to the announcement)

What “Went Wrong”?

First, the amount of bonds being bought back is quite low, which is insufficient to truly move the market. Second, more importantly, the market disagrees with the US Treasury’s perception that the bonds are mis-priced. The yields, which are driven heavily by the Federal Reserve Interest Rate, are expected to be elevated until inflation is brought down. Basically, because inflation is expected to be higher for longer (due to multiple other government policies – tariffs, anti-immigration, war), interest rates are expected to be higher for longer as well.

In turn, investors demand higher interest rates (yields) to hold long-dated government debt. That’s because the yield of long-date bonds has to equal the yields of a sequence of short-dated bonds (the interest earned on a 30 Year bond should equal the interest earned on a 2 Year bond that’s renewed 15 times in the future).

A Unique Intervention

The US government buying back Treasuries is a relatively rare phenomenon. The most recent large buyback occurred in the 2000 to 2002 period, when $67.5bln of bonds were bought back. At the time, the outstanding debt was around $5.6trln, meaning around 1% of bonds were bought back. A recent paper by Connolly and Struby (2024) (“CS”) investigated that episode and found that the impact of the buyback was noticeable – yields in the bonds that were bought back fell by about 0.95%.

The current Treasury buyback policy, which started around 2023, is of a much lower scale. For it to be comparable to the 2000-2002 episode, around $400bln worth of bonds need to be bought (compared to the current $16bln per quarter buyback cap). CS, in their paper, actually discussed this specific buyback policy (prior to the announced increase) and found that the impact on yields will be zero. Which is basically what happened.

This new policy is unlikely to ‘fix’ what Scott Bessent thinks is the problem. Realistically, yields on long-dated bonds will only fall if the government enacts policies that reduce inflation. This includes ending the tariffs, easing immigration and ending the war. His government right now, however, appears to not be willing to do that. More concerningly, the government appears to not realize that these other factors are the problem.

If you would like to support us in reaching our subscriber goal of 7,000 subscribers, please consider sharing this article and pressing the like❤️ button at top or bottom of this article!

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Recently, the media outlets have stated that polls have again missed certain US elections with a special focus on the Democratic primaries in Michigan and Wisconsin. Media outlets are even questioning the reliability of polls. Given that elections are a popular topic, I wanted to clarify how polls should be interpreted and why the media headlines, as well post-election analyses, demonstrate a serious misunderstanding of what polls are.1

What Polls Actually Measure

The biggest confusion with polls is that polls are assumed to be direct reflections of what will happen on election day – if a poll shows a candidate ‘leading by 10%, then on election day the candidate should more or less win by such a margin’. This is incorrect – a poll is not a reflection or a forecast of what will happen on election day.

Polling Questions

A poll typically asks who would you vote for if the election were to happen today. Firstly, unlike an actual election, a poll is a low-stakes affair, and therefore people might not have fully considered what they would actually do on election day. Secondly, some people pick ‘undecided’ in polls. On election day, there is generally no such thing as an ‘undecided’ voter.2 Lastly, some people in polls pick non-leading candidates. On election day, they might change their vote to a leading candidate for strategic reasons.

Weighing the Sample

Once the pollster collects responses, the pollster then needs to appropriately ‘weigh’ the sample. That’s because the specific people that responded to a poll might not be representative of the group the pollster is trying to capture (for example, maybe too few young people responded in the sample).

But one question that arises – what group is the poll trying to capture – is it the general public or is it the people that will vote on election day? This is an important distinction, as it can change poll numbers significantly.

Poll Numbers

Once the pollster re-weighs the responses it got, the pollster publishes the polling numbers. Based on the steps above, the poll numbers are a snapshot of how the wider public (or voting public) feels about the election candidates at the time of the poll.

Election Day Forecasting

Instead of treating polls like a barometer, both the media and pollsters write headlines that view polls as if they are equivalent to what will happen on election day. For example:

The above language implies a forecast of what will happen on election day and that a candidate, El-Sayed, is ‘leading’ by 7 points. This headline is especially nonsensical, as a closer look at the poll shows that one candidate has 46%, while the other candidate has 39%.

So what about the 15% of undecideds – what will they do on election day? The headline above assumes that they will split perfectly evenly between the two candidates. Do you think that’s a logical assumption?

Forecasting

This is where forecasting comes in. Forecasting is modeling what exactly will happen on election day. That means predicting how many people will turnout, which people will turnout, and how each of them will vote. Polling can help inform our predictions, but it is NOT a prediction itself. A poll should be an input into a forecast model, and not the model itself.

But the media (and even pollsters) present polls as a forecast. There are plenty of elements that need to considered for a good election day forecast:

• Will everyone vote as they said in a poll? Will we see consolidation towards leading candidates?

• Who are the undecideds and will they show up to vote?

• Are any groups more likely to have a higher/lower turnout than typically assumed?

• Between the current poll and election day, what will candidates do (election campaigning, endorsements, new policy promises, etc.)?

Examples

If we take the above poll and headline as an example (let’s simplify by assuming 100 voters):

• Candidate A: 46% – (46 voters)

• Candidate B: 39% – (39 voters)

• Undecided: 15% – (15 voters)

Suppose Candidate A has a younger voter base, who are typically assumed less reliable to show up on election day. Let’s assume 3 of Candidate A’s voters do not go to the election. Next, let’s assume that, of the undecideds, 4 do not go to the election at all, while 8 vote for Candidate B and 3 vote for Candidate A. The election result will be as follows:

• Candidate A: 46 votes (49.5%)

• Candidate B: 47 votes (50.5%)

Candidate B wins the election. The media headlines will focus on “how did the poll miss this election”, as originally Candidate A was leading by 7 points, but saw an 8 point reversal.3

To belabor the point, let’s change one of our assumptions – suppose only 1 Candidate A voter does not go to the election. The result would then be:

• Candidate A: 48 votes (50.5%)

• Candidate B: 47 votes (49.5%)

Media headlines will again claim polling misses. But in both examples (and especially the second one), I hope it’s clear that the poll itself did nothing wrong. There was no polling mistake. The mistake was using the poll as a forecast!4

How I Use Polls

Hopefully the above clarifies how polling vs forecasting differs. Now, I doubt many of us have time to do deep dives and build our own forecasts. So here is how I quickly interpret polls for myself:

• If polls consistently show a candidate getting above 50%, then the candidate is almost guaranteed a win;

• If polls consistently show a candidate in the high 40s (47%+) and there are at least 6% undecided, the candidate has a very high chance of winning come election day;

• Anything below 47% (regardless of “lead”) is unknown and would require a deep dive to forecast the outcome.

I know the above guidelines are very crude and probably could be refined more, but that’s for a different writer to analyze. , whom I recommend on this topic, put together a historic table for US elections, which shows why ‘polling leads’ often do not mean much. The table below presents how often a candidate with a given polling support level (first column) and polling ‘lead’ actually won.

The conclusion one should draw from this is that ‘leading’ by 1% and having 49% support is better than leading by 7% and having 40% support – something that might seem counterintuitive.

In the appendix, I discuss two recent examples of alleged ‘polling misses’ and how I applied my rule of thumb.

Don’t Criticize Pollsters For Margin Misses

The point of this piece is to discourage criticism of polls by looking at polling margins. Polls are either conducted properly or improperly. They should not be judged against the actual results. These types of criticism also create incentives for pollsters to start targeting margin as the main metric rather than conducting a proper poll. And this may have actually happened recently, with a pollster adjusting results to more closely align with what they think will truly happen on election day:

TPSI [The Public Sentiment Institute] said in its ‘accuracy review’ of the survey that an error occurred after the firm applied an ‘experimental layer’ that applied previous survey results to the poll after its results ‘did not align with our read of the race’.

‘Acting on that conclusion, we identified respondents who selected [Florida Lt. Gov Jay] Collins but whose broader profile aligned with a competing [candidate], and leaned those respondents toward James Fishback’, the firm wrote in this document.

Just like pollsters, economists are also not in the forecasting business. We often get criticized, because our results ‘did not align with what happened’. As with polls, the real issue is misunderstanding what economists do and how to apply their work, not with the work itself.

Please let me know what you thought of the article length:

Appendix – Real Life Examples

Two recent elections restarted many of the polling-miss headlines – the senate democratic primary in Michigan and the governor democratic primary in Wisconsin.

Michigan

In Michigan, one candidate had a ‘polling lead’ of double digits, but in the end, the leading candidate won by 1 point. Here were the polls:

Using my own rule of thumb, as El-Sayed was polling in 50%+, I would say he was ‘guaranteed’ to win. Which he did. The fact that the result was ‘closer’ than the polls, simply suggests that undecideds split heavily in favor of Stevens.

Wisconsin

In Wisconsin, one candidate had an average ‘polling lead’ of over 15%. In the end, they lost by 0.5%. Here were the polls.

Polls had Hong in the high 30s/low 40s. Using my rule of thumb, I would not be able to say whether Hong would win or not without taking a deeper dive. Even if we use the naive5 historic trends, as in the table provided by Carl Allen above – Crowley had an ~1 in 4 chance of winning.

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Changing jobs can be both exciting and scary. As I am currently in that process, I have definitely already experienced both feelings. Financial security is always on one’s mind and I am fortunate to have some, as it allows me to explore going full time on Nominal News.

Governments provide the public such financial security through unemployment insurance (UI). It turns out also that UI can be an important factor driving economic growth as shown in a recent paper by Clymo, Denderski Mercan and Schoefer (2026) (“CDMS”).

Excessive Caution

An employed individual may naturally be worried about switching jobs. Going to a new job comes with inherent risks, one of which is what if the new job doesn’t work out and ends up terminating you. Faced with this uncertain risk of termination at a new job, a worker may be less inclined to switch jobs. This excessive, yet reasonable, caution can lead to worse outcomes for all.

UI may help with alleviating some of this risk. How?

Unemployment Insurance

To model the impact of UI, the authors, CDMS, assume a job is defined by two characteristics:

• how productive is it (for example, how much revenue it will generate)

• what is the likelihood the job will be terminated (e.g. how likely is the company to survive)

The wage a worker will receive is a function of these two things.1

The worker can decide whether to accept a job offer or not. If the worker accepts the offer, they receive a wage; if not, they receive UI.

Whilst on the job, the worker can receive job offers from other firms as well. As before, the job offer they get is described by the productivity level and job security level.

Intuition

It is intuitive that a worker can be indifferent between two jobs – one job that offers higher wages but lower job security is of equivalent value to a job that offers lower wages but higher job security.

Since the biggest worry for a person taking a higher risk job is becoming unemployed, UI can help mitigate this fear by providing a worker with some money in case they do become unemployed. The higher the UI, the more likely a worker should be willing to take a riskier job.

CDMS formally analyzed this with a survey2 to elicit how people view the wage vs security trade off. In this survey. CDMS found that:

• for a 1 percentage point higher probability of job loss, workers on average demand 1.63% higher wage;

• For an identical 1 percentage point higher probability of job loss, workers in higher paying jobs require a 1.9% wage increase compared to workers in lower paying jobs, who only require a 1.3% wage increase;

• For an identical 1 percentage point higher probability of job loss, workers in safer jobs require a 2.1% increase in wage increase compared to workers in riskier jobs who only require a 0.96% wage increase.

Consumption and Saving

Lastly, as is classic with most economic models, the worker chooses how much to consume each period and how much to save. Saving acts as a basic form of self-insurance from unemployment.

The CDMS model distills the world into a simple yet reasonable reflection of the world, as workers:

• Make a trade off between wages and job security;

• Consume or save.

Job Creation

The really interesting element of the CDMS model is that the decisions workers make also influence job creation. If workers are unlikely to switch jobs, firms are less likely to form and post job offers (posting a job entails a cost), as they are less likely to find a worker willing to join them.

At the same time, with higher unemployment insurance, workers are also less likely to accept jobs, again discouraging firms from posting jobs. This implies that there is an optimal level of UI, as both effects need to be balanced.

Bringing in Data

As the last step, CDMS calibrated the model to US data. In the US, unemployment insurance typically replaces around 40% of an unemployed person’s previous wage (this is called the replacement rate).

CDMS ran a counterfactual analysis by dropping unemployment insurance to about a 10% replacement rate. In this scenario, overall US productivity would fall by about 1.3%, while output would also fall by around 0.4%.

This would be primarily driven by the fact that job to job transitions collapse. The cost of becoming unemployed is simply too high. In turn, there are fewer risky-type firms entering the economy, which is why productivity and output fall.

Recessions

The really interesting situation arises during recessions. During recessions, the likelihood of becoming unemployed typically rises significantly for every type of job. This makes safer jobs even more desirable and reduces the willingness to switch jobs, as workers ‘flee to safety’. In turn, this rational, albeit conservative, behavior may further exacerbate the recession. Thus, CDMS propose a policy where the replacement rate of unemployment insurance is increased during recessions, to offset the effect of flight to safety.

For example, during the 2008 recession, to fully offset the flight to safety, the UI replacement rate of income would need to be increased temporarily by 50% to a 60% replacement rate.This higher replacement rate encourages workers to switch into riskier jobs, restoring productivity growth. It does result in a higher level of unemployment, since workers now are compensated better in unemployment, and thus, are more picky with accepting jobs.3

Unemployment Insurance – An Important Policy

Unemployment benefits are often criticized. Any expansions of UI, such as those during the Covid pandemic, are even more so criticized. The work by CDMS, however, shows how UI expansion in recessions might actually be an optimal policy. This is because people are generally risk averse, which can lead to sub-optimal, albeit correct, decision making. UI is needed to ‘correct’ for that risk aversion and encourage people to make decisions that benefit everyone.4

Moreover, CDMS show us that reducing UI benefits may actually reduce the number of firms willing to offer jobs. That’s because firms understand that employed workers are more fearful of switching jobs given a higher risk of unemployment.

The CDMS paper has also shown us how important it is to really consider people’s behavior. If we were to completely ignore the fact that people take into account the likelihood of getting laid off, our view of UI would be completely distorted and make the program look less beneficial.5

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I recently had a conversation on Substack Notes about economists being bad at predictions. I agreed. But that’s because economists are not in the business of prediction, but something slightly different – causation.

Prediction vs Causation

New York City recently implemented congestion pricing (a toll to enter the city center). Prior to the launch of congestion pricing, 730,000 vehicles would enter the congestion zone daily. If I were to tell you that economics research tells us that the implementation of the toll would reduce the number of cars entering the zone by 10% – how would the number of vehicles entering the zone change one year after the implementation of the toll:

• A: 10% lower

• B: the same

• C: More than 10% lower

• D: Can’t tell

The correct answer, from this list, is D – can’t tell. How come?

A Subtle Nuance

Most economics questions can be boiled down to one formula1:

• Y is the outcome variable that we are interested to see how it changes. In our current example, it’s the number of cars entering the congestion zone.

• X is the policy variable that we can control. In our case, the toll.

• Z is everything else that also impacts the number of cars entering the zone. For example, this can be things like population changes, big events like the world cup/concerts, weather patterns, etc.

• Beta is ultimately what economists want to establish – what is the effect of changing policy X on Y. In our hypothetical example, I said that if we enact the $9 congestion toll, the number of cars entering the zone will fall by 10%. That would be the estimate of Beta.

So why can’t we say that the year after congestion pricing is enacted, the number of cars entering the congestion would go down by 10%? That’s because the things that are in Z can change next year, independently of our policy. Thus, the number of cars entering the zone can really take on any value depending on how Z changes.

However, if somehow, nothing in Z changed year to year – Z remained constant – then Y would fall by 10%. This is exactly what economists do say – the $9 toll would reduce congestion by 10%, holding all else constant (i.e. nothing in Z changes).

So what if we implement the congestion toll and next year we observe 600,000 vehicle entries? Since the toll only reduced vehicle entries by 10% of 730,000, the congestion toll only accounts for 73,000 of the 130,000 fall in vehicle entries. That means that something in Z must have also changed that pushed vehicle entries down.

Counterfactual

Another way to think about this is through a counterfactual approach. Had we not implemented the congestion toll, there would have been 73,000 extra vehicle entries. So instead of 600,000 that we actually observed, we would have had 673,000 vehicle entries.

Prediction

People are generally interested in prediction rather than causality. In our case, the news headlines will always simply report the total vehicle entries and that’s what people will focus on. From this number, everyone will erroneously make judgments.

In our little formula above, it means the news and people are really interested in variable Y, and not Beta (the number economists care about).

To predict Y accurately, you not only need to know Beta and X, but also take a stab at forecasting what will happen with all the Z variables. Economists typically do not do that and our tools are generally not good for this. If you want to get predictions of Y, do not ask economists. We can only tell you how changing X will impact Y (the Beta). Our tools are designed to estimate this Beta number well.

Causality

I hope the above explains the nuance between causality and prediction. Causality can inform predictions, but it is not a prediction itself.

I like to think of causality as fundamentally similar to the rules of physics. The causal impact estimated by economists in any context is no different from, say, Newton’s laws of gravity. I do not view the law of gravity as a form of prediction.

The only reason why economists hesitate to call anything ‘laws’ in our field is because establishing laws in economics is much harder than in physics. That’s because we cannot control many things like physicists can – if they want to run an experiment, physicists can often build a physical tool that removes all or most of Z (the large hadron collider in Europe is a prime example of such an item).

Since in economics, we study people, such experiments cannot be run.

Human Nature

The problem economists face is that the economics’ way of thinking is not intuitive. People always want to know what Y will be and not how X impacts Y (Beta). One of the first questions economists get in social settings is always of the type “where do you think the economy is going?”2. I cannot answer that question. I can, however, tell you that if you enact congestion pricing you will have 10% fewer cars, holding all else constant.

People want to know what GDP will be next year, what inflation will be, what their income will be. They directly attribute what they end up observing to the policies that were enacted.

But this is incorrect. Outcomes are impacted by policy, but the specific level of the outcome (actual GDP, inflation, income, etc.) depends on a lot more than just one policy. Often, these non-policies dominate the impact of any policy.3 It still makes the policy worthwhile, but we shouldn’t expect massive changes when things are influenced from a thousand different sources.

P.S. Economists and Predictions

One thing I wanted to add after finishing this piece, is that lately there has been a noticeable increase in economists making predictions, especially around genAI. Recently, 200 economists signed a letter warning about the dangers of AI. It is also not uncommon to find futuristic predictions from economists in social media. I don’t have issues with people discussing the future, but these predictions are personal opinions, rather than causal statements backed by economics research. But, as was the case with the letter, there is significant emphasis on the fact that economists are the ones making these predictions, with the purpose of giving these statements additional validity. This implicitly presumes that economists are better futurists than any other profession, which we are not.

As I laid out in this piece, there’s nothing about economics that really makes us any better at prediction.

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Summary

• The FOMC (the interest rate setting committee) decided by a split vote (9-3 ) to keep interest rates between 3.5% and 3.75%.

• The 3 voters wanted to increase interest rates to 3.75% - 4.00%.

• Warsh’s press conference after the decision had a lot of contradictions and somewhat concerning statements.

Commentary

The Decision

In advance of today’s meeting, the probability of rate hike was quite high (between 30% and 40%), which made this meeting more important than normal.

The decision by the FOMC to keep rates steady was widely expected, although the fact that 3 voting members dissented is something we do not see often. The reason for the dissent was most likely driven by the fact that forecasts for June inflation remain elevated.

Current forecasts suggest that 12‑month inflation will be at 3.3% for core (excluding food and energy) PCE (Personal Consumption Expenditure) and 3.7% for headline PCE. On a month-to-month basis, Core PCE inflation is expected to be up in June by 0.18%, which gives an annualized inflation rate of around 2.2%. Note, that although the monthly rate is in-line with the the 2% target, recent US government decisions around the war in Iran and tariff re-enactment suggest future inflation pressures.

Last time, when the FOMC met in June, I mentioned that it was surprising that the vote to hold interest rates steady was unanimous (12-0). This time, no such surprise. I could believe that the vote could have been even closer given some recent statements made by FOMC voters.

What’s interesting is that Kevin Warsh did not vote for the hike. Although his individual vote to hold rates steady was expected, there is a bit of tension between his rhetoric of committing to a 2% inflation target, which would probably necessitate an interest rate increase, and his prior statements that interest rates need to be cut.

For example, from a statement he made 1 year ago.

“Their hesitancy to cut rates, I think, is actually ... quite a mark against them,” Warsh told CNBC. “The specter of the miss they made on inflation, it has stuck with them. So one of the reasons why the president, I think, is right to be pushing the Fed publicly is we need regime change in the conduct of policy.”

This tension between his prior statements and current economic reality may be his influencing voting behavior.

The Statement

Below is the latest FOMC statement, with changes highlighted in comparison to the June FOMC statement:

The statement has only minor changes referencing the fact that there were dissenters.

Although last time I interpreted the language around productivity growth and capital investment as suggesting what was discussed specifically at the meeting, my current reading is that it is a boilerplate statement. Therefore, not much can be gleamed from this statement.

Kevin Warsh Press Conference

Kevin Warsh had held his press conference after the decision. It was quite astounding - and not in a good way.

Prepared Statement (57:20 - Video)

The beginning of the prepared statement closely followed the officially published statement. Kevin Warsh reiterated that he will not provide forward guidance, because it would not be prudent during “these uncertain times” (note - this contradicts something he said later).

Kevin Warsh then bizarrely focused on criticizing the work done by the Federal Reserve over the last 5 years. Some quotes:

• “5 years of high inflation has left a mistaken impression that’s hard to shake'“

• “we have begun a new chapter and we understand that the 5 plus years of inflation above target cannot be cured in 9 weeks”

First, many of the current FOMC members were also FOMC members during these 5 years. Thus, Warsh appears to be insinuating that they’ve done a bad job.

Moreover, Warsh completely ignores the fact that when controlling for lagged rent data (something Warsh would advocate for with his emphasis on real-time data), inflation did drop to 2%. Thus, the criticism can be considered unfair.

The criticisms did feel more political in nature, rather than economic.

On Forward Guidance

From the press conference, it is clear that Warsh is laser focused on one thing - removing ‘forward guidance’.

As a quick reminder, forward guidance is the practice where central banks telegraph future monetary policy decisions. The purpose of forward guidance is increase central bank credibility in front of market actors. With this credibility, central banks do not have to make drastic policy changes to get inflation under control. That’s because market participants will anticipate what the central bank will do.

Warsh believes markets should not be informed about the exact choices the Federal Reserve will make. That’s because he believes it should be the other way around - market decisions should be act as an information signal for the Federal Reserve. Forward guidance influences market decisions and, thus, contaminates this signal that markets send to the Federal Reserve.

The problem I have with this idea is that central bank credibility is hard to establish, while the market signal can probably be extracted by other ways (e.g. directly talking to businesses). Moreover, I’m not sure why markets reacting pre-emptively is inherently a bad thing.

Separately, in a latter part of the conference, Warsh recounts how during a crisis (referencing the 2008 financial crisis), forward guidance was needed to help ease concerns. But his belief is that during ‘benign’ periods, forward guidance is bad.

This seems contradictory to me, especially since he has called the current times as ‘uncertain’.

Market Reaction - Another Contradiction (1:05:20 - Video)

Warsh puts a lot of emphasis on not having forward guidance because he wants unfiltered market data (i.e. not contaminated by the Fed’s future decisions).

A reporter then asked about how he interprets the financial market data which currently appears to suggest that interest rates should be higher. Warsh responds:

“Interpreting markets is an imperfect business”

And does not provide any additional relevant response to the question.

This wasn’t a promising answer. Quite the contrary - it’s a bit concerning.

If he wants financial market data, but he doesn’t know how to interpret it, then I’m not sure what’s the point…

Data Dependence - Another Contradiction (1:08:45 - Video)

Warsh was also confusing about using data. From the previous conference, Warsh emphasized the need for new data. During this press conference, Warsh says he does not like “data dependence”, but he cares about “trends on the data”.

Again, I’m stumped. What is the difference between data dependence and the trends in the data? Economists always look at trends and not just indvidual data points, if that is what he’s referencing.

The concerning interpretation of this statement is that Warsh is likely to pick and choose what data fits a particular narrative, he may want.

Comments on the FOMC Members

Lastly, throughout the press conference, Warsh kept reiterating how he has become more confident in the group working with him at the FOMC. He feels more certain than ever that the FOMC group will get inflation to the 2% rate.

This sounds like a harmless statement, but I found it a bit peculiar.

Many of the FOMC members have been on the FOMC for some time. By emphasizing that he feels more confident in the group, Warsh makes it sound like he was skeptical when he joined the Federal Reserve that these people would be good professionals. The repeated emphasis that he now trusts them after 9 weeks on the jobs seemed peculiar to me.

An Even More Concerning Press Conference Than in June

The decision to keep rates steady was not a big surprise. Neither was the fact that there were dissenters. The press conference, however, was something much more surprising.

It appears that Warsh is concerned by inflation, but does not want to raise interest rates, believing that just higher US treasury rates will suffice in slowing down the economy (which in turn, would reduce inflation). If that were the case, then we wouldn’t even need the Federal Reserve, as markets could control inflation by themselves. But we know that this is not true.

More broadly, although mistakes in monetary policy today might not have grave consequences, my concerns are what will Kevin Warsh do during a more pronounced crisis - such as the Great Recession of 2008 or the Covid pandemic.

His answers to questions left a lot to be desired, and it was quite clear that there were lots of contradictions in these answers. This does not bode well for crisis situations.

Perhaps confusion was the intent - if what the Fed says is contradictory, then we can’t have ‘forward guidance’.

Finally, let me just leave you with the reaction of the US 10 Year Treasury yield at the time of writing this article:

I think the signal here is that the markets really didn’t like this press conference.

This has been a special edition Nominal News article covering a very recent development. Please let me know your thoughts on this feature, and what would you like to see covered, in the comment section below.

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Tariffs continue to dominate US economic headlines, especially as the US has recently reinstated them. This entire tariff episode, which started in 2017, has reinvigorated the economics debate around ‘free trade’. All of a sudden, even economists that would previously unequivocally support ‘free trade’, are starting to suggest that tariffs may have a place as a polity tool. One recent paper by Werning and Costinot (2026) (“WC”) goes over the theoretical instances when tariffs may be beneficial.

The Problems with the Term “Free Trade”

First – I’m glad that ‘free trade’ is being revisited. In 2023, I raised the issue of ‘free trade’ being a misnomer, as any trade agreement is more than just tariffs or quotas. From our piece:

“The concept of ‘free trade’ is too vague to be meaningful. Trade agreements involve a lot of negotiation on issues that are beyond what we used to consider as ‘free trade’ – removing tariffs and quotas. The other components, especially regulatory issues, are just as important, not only for the negotiating countries, but even for the wider environment. But these tools are also manipulated by countries and special interest groups to create benefits for themselves. Economists should no longer be quick to say ‘free trade’ is good.”

So I am glad to see that the economic discussion is now focusing on whether maximizing trade improves welfare, and WC, in their paper, highlight what to consider before we can determine that.

Setting the Stage

To start off, it’s important to set the definition of a tariff:

When an activity is taxed, the activity (assuming everything else does not change) is reduced. Thus, tariffs reduce the amount of imports that occur. Many criticisms of tariffs focus on this – we should not be dissuading ‘trade’, as more ‘trade’ is typically welfare improving.1 So when should we dissuade trade?

Situations of Social Harm

WC start off by looking at cases where trade may cause some social harm. This social harm is something not taken into account by the individuals entering into a trade arrangement. A classic example of this is pollution. If I can import a good cheaper, I will do so, even if its production generates more pollution than if I had bought it domestically. I do not take into account the social harm of pollution, which is a cost on all members of society.

This means that absent a tariff (tax) on the import of this good, I will be consuming too much of this imported good from a society perspective given how much pollution I generate. A tariff would reduce my consumption of the good, which would in turn reduce the amount of pollution generated.

There are many examples of things we do not take into account when entering any trade transaction:

• Pollution;

• Personal moral standards – for example, if the good utilized child labor in its production;

• Geopolitical considerations – a topic that has recently become popular in tariff contexts with regards to ‘national security’;

• Reduced domestic knowledge – if in an industry, workers learn by doing (like semiconductor manufacturing), trade may reduce domestic production, which decreases learning on the job.

Tariffs, in theory, can mitigate some of these ‘social harms’ from trade. Before we evaluate the impact of tariffs, let’s turn to another case where tariffs may play a role.

Domestic and International Redistribution

Another example of when tariffs may be beneficial, mentioned by WC, is if they are intended to redistribute income. This income can be redistributed in three ways:

• From consumers to government – similar to a standard tax;

• From one group of consumers to one group of producers/workers;

• From one country to another.

The first case is straightforward – a tariff raises revenues for the government by collecting money from consumers.

To understand the second case, let’s assume the government puts tariffs on textile products. This ‘protects’ domestic textile workers. By increasing the price of imported textiles, domestic textiles, which were previously more expensive, may now be relatively cheaper. This in turn increases demand for domestic textiles, which increases wages for textile workers (and profits for textile manufacturing owners). Thus, domestic consumers end up paying more for the textiles, but the textile industry benefits.

The final case can be thought of commonly with the ‘foreigners will pay for the tariffs’ argument (a common argument used by tariff proponents). Basically, by imposing a tariff, a foreign exporter may need to reduce the price they offer their good to the importers. In this way, ‘foreigners’ transfer money to the domestic country, as they reduce the price. It is worth noting that recent research on current tariffs has shown very little to no response to price by foreign exporters.

So Can Tariffs Be Good? A Rebuttal

The above cases mentioned by WC give a good overview of situations of when tariffs may be welfare enhancing. But let’s see if tariffs are actually a solution in these instances.

Social Harm Cases

If we look at the first instance of social harm (e.g. pollution), tariffs can reduce social harms. BUT, this is only true if a similar tax is levied on domestic goods. For example, if domestic production is more polluting than foreign production, then a tariff will actually make the social harm worse.

The key element to note is imports and domestic production are never truly the same. Even if the product itself is the same, for example, steel, if the domestic or imported version was produced with more pollution, can they really be considered the same? Basically, are low pollution steel and high pollution steel interchangeable or should we differentiate them? Ensuring taxes and regulations are uniform on all goods is a complex issue, and are often the reason trade negotiations fail. But without this standardization of goods, trade agreements may lead to harmful outcomes, as was the case with San Diego fisheries.2

So just tariffs are not a good solution – a far better solution is a standardized tax and regulations to make goods truly similar.

Learning By Doing

In the case where there is a learning benefit from producing a good domestically, tariffs are probably a suboptimal solution. Instead, providing subsidies to the domestic industry would likely be superior. That’s because tariffs protect domestic producers from actual competition, which discourages domestic innovation.

We can think of tariffs as adding a weighted vest to your running competitor – it slows them down, but doesn’t improve you. On the other hand, subsidies, especially ones with an expiry date, encourage domestic producers to get better at production, and compete with foreign producers.

Therefore, although tariffs can help with developing nascent domestic industries, other solutions are likely to be superior.

Domestic Re-Distribution

Regarding domestic re-distribution, tariffs can help re-distribute income domestically. But as WC say in their paper, citing Dani Rodrik (another economist):

“Saying that trade policy exists because it serves to transfer income to favored groups is a bit like saying Sir Edmund Hillary climbed Mt. Everest because he wanted to get some mountain air. There was surely an easier way of accomplishing that objective.”

However, as WC point out, even though tariffs are a bad tool for this type of re-distribution, there might not be any other politically feasible way. Opening up to trade creates winners and losers domestically. In a perfect world, we would re-distribute the trade gains from the winners to the losers, but often, this does not occur.

In one of my research papers, I worked on the idea of gradual trade liberalization (for example, by slowly lifting a quota over several decades) and showed that it may help protect workers in an industry until they retire. Through this gradual liberalization, you effectively reduce the gains of winners and transfer them to the ‘losers’ from trade.

Trade Protectionism

The WC research is a very helpful theoretical guide, but I view the research piece as a double-edged sword. It has its benefits, as the paper acts as a “theoretical compass” to evaluate when a government may need to tax or subsidize trade. As mentioned previously, I have never been a fan of ‘free trade’, as the term hides a lot of nuance around regulations, rights and real costs, such as pollution. ‘Free trade’ oversimplifies what actual trade is.

At the same time, however, some ideas in this paper may be ripe for abuse. WC quote economist, Francis Edgeworth, commented on researching the theoretical benefits of tariffs:

“The direct [benefit]... of the theory is likely to be small. But it is to be feared that its abuse will be considerable . . . Let us admire the skill of the analyst, but label the subject of his investigation POISON.”

Tariffs are almost always a suboptimal policy – solely placing a tax on imports is costly and welfare-reducing compared to alternative policies (subsidies, re-distribution). This is especially true in a globalized world, where no importing or exporting country is sufficiently large to impact the market.

In my opinion, a government choosing to implement tariffs, even in their best use case, already signals policy defeat, as the government failed to convince the public of the benefits of superior policies. Rather than focusing their energies on inherently poor policies, governments and politicians should push for far superior solutions, of which economists have plenty.

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Dear Reader!

I wanted to share an update with you – in the next few months, I will be transitioning into working on the Nominal Newsletter full time. It’s a big decision, but something I am very excited about. In the last few years, I have very much enjoyed writing and sharing the economic research that I read. I also look forward to formally introducing myself! Going full-time will enable me:

• Write more consistently;

• Spend more time finding research;

• Improve graphically;

• Continue to always have AI-free writing!;

• Explore other ways to provide content.

As part of the transition into full time work, I will be turning on a paid option in the newsletter in case you would like to support me and Nominal News. However, I do not intend to pay-wall my weekly deeper dive essays, as the aim of Nominal Newsletter is educational, and I would like that to remain accessible to all.

At the same time, I would like to offer something for Paid Subscribers. This is where you – my reader – come in!

Below is a link to a Google Questionnaire, would truly appreciate if you could take a few minutes to complete it. This is intended for you to let me know what would you like to see more of at Nominal News, and, especially, what would you like to see in case you’re interested in becoming a Paid Subscriber. (Although not finalized, I intend to charge around $100 for a yearly subscription, and $30 for a monthly subscription).

Nominal News Reader Questionnaire

As a thank you for completing the questionnaire, I will randomly select 10% of the completed questionnaires and offer a free Paid Subscription for 3 months (once the feature is on).

Paid Option Ideas

Here are some ideas I have mulled over for a paid option:

• Special Edition Newsletters – like these that I recently published (here and here). The Special Edition would come out once or twice a month, in case a bigger economic/market event occurs, where I will share my immediate thoughts backed by economic research;

• An audio version of my newsletter that will cover the main ideas of the newsletter, and, perhaps, further discussion;

• An occasional podcast where I talk with economic researchers about their work;

• An in-person meet-up event (this would be in New York City, as this is where I live).

The above are some ideas that I mentioned in the questionnaire.

Thank you and Looking Forward

With Nominal News, I set out to share the knowledge I gained during my PhD training. Moreover, I also want to share the methods of economic thinking, as I think they can be applied to any personal, social or job setting. Economics is fundamentally a study of decision making – and we make decisions in all kinds of situations, so it’s natural that economics can be very useful.

I look forward to continuing this journey, as I have always enjoyed sharing and teaching. Thank you for being a valued reader of Nominal News. Your readership has helped this project keep going over the last several years!

All the best,

Nominal News

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On June 26, 2026, Stephen Miran, former chair of the Council to Economic Advisers to President Trump and former member of the Federal Reserve Board of Governors, penned an opinion piece in the Wall Street Journal arguing in defense of tariffs (i.e. taxes on imports). He also posted a twitter thread covering the key points from his opinion piece. Today’s special edition will offer a quick response to some of the points made in the opinion piece.

Who Bears Tariff Cost

Miran in the op-ed says:

“Two centuries of optimal-tariff research show that tariffs are unique in that foreigners bear a material portion of the tax burden…Traditional estimates for the U.S. indicate that in the long run exporters will bear 70% of the burden of tariffs”

Response: Current data suggests this is false. Theoretically, the foreign exporter could bear some of the tariff cost by lowering the price of their goods to the importer. However, more granular research has shown that this is unlikely to happen. Prices faced by consumers are often increased by even more than the tariff amount. This is not surprising, as in a globalized economy, foreign exporters simply have other markets to sell goods to and do not need to reduce prices for US importers.

The difference between what economists are debating – do foreigners bear any of the ‘cost’ – and Miran’s claim that 70% of the cost is borne by foreign exporters could not be more stark.

Is Welfare Higher?

Miran in the twitter thread says:

“That means that when raising tariffs from low levels, overall national welfare including revenue increases. Eventually, the domestic share of distortions outweighs the benefit of revenue [Note: as tariffs are a tax they have an economic cost, but this cost is netted off against the revenue the tax raises]--the point at which this happens is called the “optimal tariff.”

Response: This question has now been repeatedly addressed by economists. Tariffs reduce domestic welfare – i.e. US consumers are worse off under tariffs. From our recent write up:
“Fajgeballum and Khandelwal (2026) (“FG”) find very interesting results – the overall impact on welfare in the US is extremely negligible with a 0.13% reduction in welfare measured in GDP terms – the reduction in welfare (happiness) is equivalent to having GDP lower by 0.13%.

But this lower overall welfare reduction hides the fact that the impact is very uneven:

• Consumers lose out by about 2.6%;

• Domestic produces gain around 1.4%, as they receive higher prices;

• The government gains around 1.1% due to tariff revenue.”

What tariffs successfully do is re-distribute from US consumers to US producers, a de facto government subsidy.

Distortions and Intermediary Goods

Miran says:

“It’s even more of a victory given how the 2025 tax cut incorporated full expensing of equipment. If a tariff drives up the cost of an input or intermediate good, the purchaser is very likely able to get that back on tax returns—making tariffs even less of a burden on Americans. Effectively, intermediate goods are largely untariffed, as economists have long advocated.”

Response: First, Miran is correct that taxes (i.e. tariffs) on intermediate goods often end up leading to large negative economic effects. Tariffs on these goods are actually the key reason we’ve witnessed slightly higher inflation.

Why is tariffing intermediate goods bad? A tax distorts a firm’s production decision. Making inputs cost more means the producer will have to deviate from their optimal production plan (produce less of what they make or use a different mix of inputs), reducing firm efficiency.

This, in turn, makes the firm’s final good more costly. Such tariffs (aka taxes) can reduce employment, as firms may need to reduce production. Taxes on final goods (i.e. when the goods are sold directly to the consumer) do not make firms less efficient – the producing firm still gets to choose its inputs as it wants.

Coming back to the point made by Miran, where he argues that, because new legislation allows for ‘full-expensing’1, the tariff impact on the goods gets reimbursed back to the firm. The issue here is that both domestic and foreign-produced goods get ‘fully-expensed’, meaning the tariff distortion is still present, leading to lower imports, and therefore less efficient output.

Moreover, raw material imports are also taxed, which again distorts the production decision of firms. This all reduces US efficiency and reduces economic output.

Replacement of Other Taxes

Miran says:

“Far from creating marginal deadweight loss, raising them toward optimal levels is actually welfare-enhancing. And tariff revenue can be used to reduce highly distortionary taxes on capital or labor. Doing so is particularly effective because deadweight loss increases faster with higher tax rates—going from a 40% rate to 50% has a much higher cost than jumping from 10% to 20%. The median lifetime marginal rate on income is an astonishingly high 39%, according to recent research.”

Response: This is a misunderstanding of several economic concepts.

Deadweight loss is an economic concept that aims to capture how much overall welfare is lost due to a market distortion, such as a tax. Basically, how much less are we getting as a whole society, by an imposition of a particular tax (note – not all taxes have to create losses). Miran argues that since labor taxes are at a high level (the US highest marginal tax rate is 37%), by reducing the labor tax from a high level and offsetting it with a higher tariff-tax that is currently at a ‘low level’ (near zero), we will have more welfare. The problem with this notion is that deadweight loss doesn’t work that way. Taxes interact with each other. You cannot count the deadweight loss from each tax separately and then add it together.

Using an analogy made by Alan Cole of the Tax Foundation, imagine the labor tax creates a distortion of size X, while the tariffs creates a distortion of size Y. The deadweight loss would be the area – i.e. (X+Y) squared, which is X-Squared + Y-Squared + 2XY. Miran, in his piece, is ignoring the 2XY piece and simply assumes that (X+Y) squared is X-Squared + Y-Squared. He would, therefore, need to demonstrate that the joint change in tax rates would lead to lower deadweight loss.

Tax distortions interact with each other – we cannot just look at marginal tax rates and determine what the impact on deadweight loss will be.

Final Thoughts

It is good that Miran published his thoughts and assumptions. I have always advocated for being transparent about one’s own economic thinking. Thanks to the opinion piece, we can clearly see where the misunderstanding is, and the issue that economics research can further address.

Nonetheless, the assumptions made by tariff proponents like Miran sound good on paper, but in practice are incorrect. This includes:

• overestimating how much foreign exporters ‘pay the tariff’;

• underestimating how much of the tariff US consumers pay; and

• underestimating the distortions created by tariffs.

Tariffs are simply an inefficient tax – we have better tools to achieve any goal that tariffs are supposed to achieve.

P.S. The “National Security” Argument

Separately, one argument I do not discuss much regarding tariffs is the “national security” argument. This argument has recently gained traction in public discourse, even with renowned economist Paul Krugman mentioning it regarding placing tariffs on Chinese vehicles. The reason I don’t talk much about the ‘national security’ argument is because it is not defined from an economic perspective – everyone appears to have their own definition of it. If, for example, as some say that the goal of tariffs is to have a US manufacturing base – what exactly does that mean? US manufacturing produces $3 trillion worth of goods, accounting for around 15% of the world’s manufacturing output (the US accounts for less than 5% of world population). Is this too small of a manufacturing base or big enough?

However we define ‘national security’, I do not see how tariffs help with achieving any of the commonly stated goals, and, on the contrary, they often make things worse, as they drain resources from efficient sectors.

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‘Greedflation’ is a contentious topic that rose in popularity during the inflation surge of the Covid pandemic. Many economics commentators have criticized the idea that ‘greed’ mattered for inflation during the Covid pandemic. But recent news on price collusion between egg producers, reinvigorated the debate around ‘greedflation’ as an important driver of recent inflation. Although collusion is an easy example of what many perceive as ‘greedflation’, recent research by two Federal Reserve economists suggests that there may be more to ‘greedflation’ than previously thought.

Defining ‘Greedflation’

One problem with the ‘greedflation’ debate is that the term itself is undefined.

Many economics commentators interpret greedflation as inflation that is caused by firms’ ‘greediness’ and often somewhat sarcastically argue that ‘when prices fall, firms must have gotten less greedy’.

I am not a fan of this definition. Unlike economics commentators, who do not view ‘greedflation’ and firm power as related, the public outrage about ‘greedflation’, appears to be focused on that firms may have too much power in dictating prices and that current competition levels do not sufficiently rein their pricing power in. Investigating this issue is something economists should do and many are doing. Did pricing power go up? If so – what could have caused it? Have competition levels deteriorated? Are firms better able to extract consumers’ willingness to pay? Is collusion up?

All of the above are valid questions, which I would capture under the umbrella of the word ‘greedflation’. Baslandze and Fuchs (2026) (“BF”) looked at one potential channel of pricing power – supply chain disruptions may have enabled firms to increase prices above their own increase in cost.

Covid Pandemic and Supply Chain

BF first built a model that focuses on two elements that may affect an importing firm’s decision on pricing their product – the cost of producing product and the likelihood of receiving the needed shipments to make the product.

A Simple Example – ‘Perfect’ World

To understand the intuition, let’s start off with a hypothetical ‘perfect’ world scenario of a basketball-selling firm that imports the basketballs. In this perfect world, the basketball firm always has a basketball available to sell if a customer wants to buy it. Under this scenario, the price the basketball firm would charge depends on the cost to import the basketball and how much people really want basketballs in comparison to other sports equipment (this is typically referred to as the ‘elasticity of substitution’). Both of these parameters – the cost and the ‘elasticity’ – are outside of the control of the basketball firm, so they set their price using these two external factors.

A Simple Example – Adding Shipping

Now, in a more realistic world, the firm would have storage costs to keep all the basketballs, and so the firm would have a limited stock of these basketballs. To replenish the stock of basketballs, the firm would rely on import shipments. If we assume that the firm sells 100 basketballs a day, and receives a shipment every month, the firm needs to receive 3,000 basketballs per shipment.

In normal times, since these shipments are predictable, the firm would de facto charge a similar ‘perfect world’ price, as in the example above.

A Simple Example – Shipping Delays

Suppose, however, all of a sudden shipments are delayed by 3 months. Now the firm is in danger of running out of stock, as it will sell its entire 3,000 basketball inventory in 1 month. In response to this risk, the firm may resort to increasing prices, in order to reduce demand for the basketball. Basically, the firm needs to now sell a third of the basketballs per day, or around 33 basketballs a day – and the only way it can make that happen is by raising the price. (I imagine that this behavior would often be called ‘greedy’ by the public).

A Simple Example – Adding Competitor Shipping Delays

BF alter the above model with another interesting element – how competitor shipping delays may influence the pricing. Suppose, there is another basketball seller in the market. Normally, the two basketball firms sell a combined 200 basketballs a day. Each firm also receives shipments in the exact same way – every month, 3,000 basketballs are delivered to each firm.

Now, just like in the above example, suppose your competitor gets hit by the 3 month shipping delay, but you actually do not get a delay. Since in a typical 3 month period, 18,000 basketballs get sold (200 per day as each firm sells 100 a day), due to the delay impacting one of the firms, there will only be 12,000 basketballs sold for the 3 month period.1 Due to this competitor delay, even though you yourself do not have a delay, you will also need to increase your price to such a level that about 133 basketballs are sold each day in total, as that is the only way the two firms combined do not run out of basketball before the next shipment. Thus, even though you were not directly impacted by the shipping delays, you may still raise prices.

The examples mentioned here are theoretical and aim to simply illustrate the mechanism in which one’s own shipping delays, as well as a competitor’s shipping delays, can impact one’s own prices. To test whether prices respond in this way and to what extent, BF test it with data.

Empirical Testing

Data Sources

BF used very detailed data that allowed us to observe quantities imported, import prices, as well as shipping/freight costs. Import quantities tell us about any shipping delays, as one would expect firms to maintain similar import quantities. Prices of imports and shipping costs are also needed, as we want to differentiate how goods prices respond to changes in production costs (unit import cost + shipping cost) and how much prices respond to shipping delays. Below is a graph that shows how the three things – delays, unit import prices, freight prices – responded during the pandemic. The values are normalized to 2019 – i.e. we are looking at deviations in these variables from 2019.

Delivery shortfalls (orange) in the two years from the start of the pandemic jumped up 20% – that is, import quantities dropped by around 20%. In 2021, import quantities started to stabilize. Unit import costs (blue line) moved very gradually and reverted back. Freight costs (green) also were very volatile, with a large jump till 2024, and a fall in freight costs since then.

How Did This Impact Prices

In order to estimate the impact of the above variables on the firm’s price setting, BF ran a regression. BF first looked at the impacts of how a firm’s price responds when the firm itself experiences an increase in delivery shortfalls or an increase in import cost. BF found that, in response to a 1% increase in delivery shortfall, a firm increases its price by 0.25% over the year. Moreover, the size of this effect is very similar to how firms change prices in response to higher import prices (for about 1% increase in the import cost, firms increase prices by around 0.2%).

BF then also estimated how competitor’s delays impact the pricing decision of a firm. The effect, somewhat surprisingly, was still large. To a 1% increase in delivery shortfall of its competitors, a firm increases its price by 0.1%, half the effect of its delivery shortfall.

What’s even more surprising, when BF looked at firms that do not import at all, if their competitors do import, the non-importing firms raise prices to all three effects as if they were importing!

Lastly, BF also looked at the data by focusing on goods sectors that saw higher overall inflation (compared to the average inflation). In sectors with higher inflation, firms more aggressively increase prices in response to delivery shortfalls – for a 1% increase in their own delivery shortfall, a firm raises prices by more than 1%! A higher inflation sector allows companies to increase prices more aggressively.

Putting It All Together

BF put all the findings together and looked at how prices specifically change when taking into account all the effects during the Covid pandemic.

In the chart below, on the left we see how much prices respond if we do not include the fact that firms respond to each other’s situations. On the right, these interactions between firms are included (shown by the lighter shaded areas).

Without including the competitive interactions, we would be underestimating the impact of Covid-driven supply chain disruptions by half.

Is This Evidence For ‘Greedflation’?

The Covid pandemic caused significant global trade disruption. The BF research shows us how non-monetary costs (shipping delays) can feed directly into higher prices. Moreover, even if a firm isn’t impacted by the shipping delays, if a majority of competitors have shipping delays, they may still raise prices significantly. These interactions can help explain some of the rise in inflation post-Covid and the rise of the term ‘greedflation’.

It’s worth noting that the price hikes are strategically correct and firms have always been doing them, so their ‘greed’ didn’t change. Economists would therefore not use the term ‘greed’ or ‘greedflation’ to describe the hikes. But, the mechanism described above would fit in with price increases that consumers may find ‘unfair’ and thus refer to it as ‘greed’.

Moreover, since economists haven’t really considered shipping delays as an important source of price increases, our current beliefs about appropriate levels of market competition may be incorrect. For example, if economists previously thought that a market with 4 firms is a competitive market, once strategic pricing behavior regarding shipping delays is included, the market may need 5-6 firms to be competitive.

Regardless of what we call it, studying these interactions is crucial, as the impact on prices and general inflation of such supply disruptions can be large. Prior to the Covid pandemic, to the best of my knowledge, we did not have such research that looked at both direct supply disruptions (one’s own shipping delays) and indirect supply disruptions (competitor shipping delays). In part, due to the fact that the public started using the term ‘greedflation’, economists appear to have intensified research on strategic firm interactions and competition (like the research by BF). Unlike economic commentators, economists actually do listen to public dissatisfaction and investigate it rather than dismiss it.

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We’re in the midst of the 2026 World Cup, with the first matches of the round of 32 now being played. As football fans worldwide know, the World Cup is an emotional event, with victories bringing immense joy to countries. I got to experience it first hand on the streets of New York – during a recent Egypt victory, the streets in Astoria erupted with celebrations. But, it turns out that the impact of football games may be more than just short-lived celebratory moments. Winning football matches may help with reducing ethnic tensions and boosting nationhood within countries.

Building Nations Through Shared Experiences: Evidence from African Football

Football has been anecdotally known to unite nations – the qualification of Ivory Coast to the 2006 World Cup is considered to be one of the reasons the First Ivorian Civil War came to an end. Depetris-Chauvin, Durante and Campante (2020) (“DDC”) decided to investigate how the performance of national teams in sub-Saharan Africa impacted citizens’ perspectives on ethnicities within their country and how it impacted ethnic violence.

Ethnic Perceptions Data

To look at the ethnic perceptions within the sub-Saharan countries, DDC used the Afrobarometer, which is a set of nationally representative surveys covering many African countries. The Afrobarometer is conducted in local languages and includes various questions on attitudes towards democracy, political actors, markets, and civil society. The questions DDC focused on asked about the individual’s identification towards the nation compared to their own ethnic group. To give an example of how attitudes change over time over ethnic vs. national identification, DDC showed the graph below:

The graph shows the fraction of people that identified more closely with their ethnicity than their nationality. In 2002, 30% of Malians identified themselves based on their ethnicity rather than the Malian nationality. Interestingly, the share dropped in 2013, when Mali achieved their highest finish in the African Cup of Nations (AFCON). Similarly, Zambia saw a drop in ethnic identification when they won the AFCON in 2012.

Football Matches Data

The main matches looked at by DDC involved games related to the African Cup of Nations (AFCON), the main African tournament, and the World Cup. To study the impact of matches on people’s identification, DDC looked at situations when the Afrobarometer interviews, by coincidence, were conducted between 15 days before or 15 days after one of these matches.

Results

If a person was interviewed after a victory of their national team, the individual was 5.3 percentage points less likely to state a strong sense of ethnic identity than a similar person (an individual of the same language group) that was interviewed in the 15 day window before the victory. This is a 37 percent reduction in the average probability of identifying as ethnic rather than as a national. Interestingly, if the national team drew or lost the game, there was no difference in ethnic identification. DDC conducted several other tests with the following results:

• Defeating a ‘traditional’ rival further reduced ethnic identification;

• Whether the match was played at home or away did not matter;

• A victory in a friendly match did not matter;

• Larger victories did not matter;

• The total number of goals in the game did not matter;

• If the composition of the winning national team was more diverse, ethnic identification was reduced.

This last point suggests that team diversity in itself can be an important determinant of national unity.

Reduced Interethnic Tensions

The above results focused on self-identification. So how did perceptions of others change? The Afrobarometer asked questions about trust in others within your country. DDC found that trust in fellow countrymen increased by 6.3 percentage points after a win of their national team. Even more promisingly, trust in people of other ethnicities in the country goes up by even more – by around 14 percentage points. This is a 30 percent increase in interethnic trust.

Hostility to Others?

The above evidence suggests that winning matches has real impacts on national unity, as people view other ethnicities more positively, and self-identify with the nation more than their own ethnicity. But could this positivity be driven by increased hostility to foreigners? Turns out, probably not. The Afrobarometer also asked questions about attitudes towards foreigners and neighboring countries. DDC found that although their estimate suggests an increase in dislike to foreigners, it was not statistically significant.

Reduction in Violence

Does this increased national unity translate into reduced violence? DDC looked at two groups of countries – those that just qualified for AFCON and those that just missed out. Below is an example of such a situation:

Prior to the final round of games, Mali, Zimbabwe and Cape Verde could qualify. After the final round of games – Mali just qualified, while Zimbabwe did not qualify and Cape Verde just failed to qualify (on goal difference).

The effect of just qualifying is quite sizable – countries that just qualify for the AFCON experience an 8.6 percent fall in the number of conflict episodes (any reported civil battles, riots, or killings) compared to countries that just failed to qualify. Moreover, this effect is long lasting – even 25 weeks after the victory, the number of conflicts is lower.

Are People Just Happier?

One could argue that these above findings are really just capturing general happiness from a win, and that there is no specific change regarding ethnic and national identity. Using the Afrobarometer, DDC found that football victories did not impact one’s perception of how the country was doing economically, whether one’s life will get better or whether they perceive their current living conditions as better. This suggests that football victories did not make people generally more optimistic.

More importantly, football victories also did not improve people’s perception of their current government. We often hear that sports may be used to build political support, but the work by DDC suggests this is not true.

Football Teams and Building Nations

The research by DDC shows that football victories can increase national unity and reduce ethnic tensions. This is especially true in countries that have weaker ‘state-presence’ (i.e. countries where there is less public infrastructure like roads, hospitals, or public schools). On the other hand, in countries with strong ‘state-presence’, football victories do not impact national unity much (as often, unity is already high).

Investment in sport in low state-presence countries can be a good way to build national unity, especially if the team itself has diverse ethnicities. Football, and sport more broadly, may seem like just entertainment, but the impacts on nationhood are real. With quite a few sub-Saharan teams in this year’s World Cup performing well, we may get to witness some positive spillovers in these countries.

Let me know in the comments below how you view the role of national sports teams. Have you seen changes in ethnic perspectives after sports accomplishments?

Breadth of Economics

I hope that today’s article also shows the breadth of topics studied by economics and how economic methods can be applied to unconventional questions. The idea of looking at already gathered survey responses that overlapped with football matches is quite ingenious. Some might say that this topic is ‘not economics’, but as I have tried to demonstrate, economics is really the study of human behavior and decision making.

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Interesting Reads from the Week

1. Note:

2. Fixed-Rate Mortgages Are Rent Control for Homeowners – explains how long tenor mortgages are similar to rent-control, which can lead to a distorted housing market. But it is something few are willing to talk about.

3. Kevin Warsh’s Press Conference Collides Into 30 Years of Michael Woodford – discusses the recent press conference and thoughts of Kevin Warsh, and why his new monetary policy ideas may be a bit contradictory.

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On May 15, Jerome Powell, who had been serving as the Federal Reserve Chair since February 5, 2018, was officially replaced by Kevin Warsh. His legacy as the head of the Federal Reserve will always be defined by the handling of the tumultuous time period that involved one of the largest economic shocks – the Covid pandemic. In my opinion, Jerome Powell was a great Federal Reserve Chair, and not just because of how he steered the US through the Covid shock.

“Covid” Shutdown and Inflation

The Covid pandemic led to an unprecedented global economic shutdown. The US unemployment rate jumped by 10 percentage points in one month and would have been much higher had it not been for certain government measures that incentivized keeping workers ‘employed’ like the Paycheck Protection Program (known colloquially as PPP loans).

To support the economy and prevent any recessions, the Federal Reserve under Jerome Powell kept interest rates at 0%, purchased assets to free up liquidity, as well as extended various loans to financial institutions and municipalities. Without a doubt, both fiscal and monetary policies kept the US economy afloat and the recession that did occur was extremely short-lived. However, this came with a cost – elevated inflation:

This elevated inflation is the main criticism levied at Jerome Powell. However, I believe Jerome Powell, and the other members of the Federal Open Market Committee, were correct in the way they dealt with the Covid economic shock.

We have previously discussed the Federal Reserve’s handling of the Covid shock in depth, so I will only briefly touch upon the discussion by highlighting two key points:

1. Inflation during the Covid pandemic may have been mis-measured due to the uniqueness of the Covid shock;

2. The best response to a ‘supply-shock’ like the Covid shock, was to run the economy ‘hot’ (generating inflation).

Regarding the first point, I will point to our “Causes of Current Inflation” article and the Cavallo (2021) paper, which argued that consumers drastically changed what they were spending on during the pandemic. If we were to correct for the consumption patterns of consumers, initial inflation during the pandemic was under-stated, while later inflation was over-stated:

The blue line is the adjusted inflation, while the grey line is the official inflation data. This means that peak inflation towards the end of the graph was probably a bit lower during the Covid shock (even though cumulative inflation was the same).

Regarding the second point, quoting from our previous article:

Did the Federal Reserve Do Too Much

If one agrees that the Federal Reserve was correct to stimulate the economy, a legitimate question is did the Federal Reserve stimulate too much, which resulted in much higher inflation than was potentially needed to offset the Covid shock. This is a hindsight-type question, making it subjective in nature.

If we knew at the onset of the pandemic in 2020 how exactly it would progress, then we could argue that the Federal Reserve should have not stimulated the economy as much as it did, and the Federal Reserve perhaps should have increased interest rates earlier. But, this framing ignores the uncertainty that we had during the Covid shock.

What if the Covid pandemic turned out to be a much larger economic shock, keeping business closed much longer? What if more aggressive strains of the virus were to evolve? Ultimately, the Federal Reserve had to make a decision under this uncertainty. So I don’t think it makes sense to judge the Federal Reserve decisions based on how the Covid pandemic developed, but, rather, it should be judged based on how the Covid pandemic could have evolved.

For example, if economic lockdowns lasted several months longer, would the Federal Reserve decisions have been appropriate? Perhaps if they didn’t stimulate the economy as much as they did, under this worse Covid scenario, a deeper recession would have occurred. Thus, the way the tenure of the Federal Reserve should be judged is by looking whether the Federal Reserve decisions were targeting a worse Covid scenario than the one that occurred.

The Federal Reserve may have erred on the side of preventing an economic recession at the expense of higher inflation, which occurred as Covid turned out milder. In the words of New York Knick and NBA champion, Jalen Brunson – “You’re allowed to think about the worst-case scenario, but you gotta go out there and do something about it.” The Federal Reserve chose to do something in case the pandemic turned out much worse.

Overall, I believe the Federal Reserve under Jerome Powell acted reasonably and well. Arguing that the Federal Reserve should have made better decisions is tainted by hindsight bias.

An Open-Minded Federal Reserve Chair

The main reason I found Jerome Powell to be a good Federal Reserve chair is his general openness to discussion and ideas. He was also very transparent in how he was thinking through the various economic shocks. For example, here’s a quote from his March 2026 press conference on how he was thinking about tariff inflation:

“...when tariffs are put into place, of course what they do is they raise prices to some extent… It takes 8, 9, 10, 11 months—a year— to go through the system. And we’re waiting for the tariffs, which were put in place over the course of the middle part and later last year, we’re waiting for that to go through the system so that goods inflation will return closer to what it’s always been. I mean, it used to be—for many, many years [goods inflation] was negative, and then the year before tariffs came in it was zero. And … goods inflation is [currently] running at 2 percent.”

Powell clearly states how he viewed the impact of tariffs on inflation.

“But we’re not seeing progress there. … what we want to see for this year, rather, is continued progress on housing services, finally seeing the goods inflation come back down, because the one-time effects of tariffs are through, and then also get some help from nonhousing services. That’s what we’d like to see. But it’s, you know, it’s a good question why we didn’t see much of [non-housing services inflation coming down] last year.”

It’s also worth pointing out that Powell’s level-headed approach to data has been vindicated recently. For example, in the March 2026 press conference, a journalist presented a question that suggested the labor market is doing worse and perhaps rate cuts were needed:

“Journalist: So in December we saw employment numbers revised down to negative 17,000, January revised down, February posted a loss of 92,000….is the employment side a far greater risk than the inflation side? Because we are seeing inflation—CPI is close to 2 percent, PCE has ticked down overall

Powell: … I wouldn’t say that that’s clear at all, that one is more at risk than the other. …you can point to the unemployment rate being stable, and in a world where both supply and demand for workers have come down very, very sharply over the course of the past year due to immigration policy largely, you know, a ratio is going to be a better thing to look at than job creation, for example. And the ratio is the unemployment rate, and it’s been stable since September…. …we’re at 3.0 percent core inflation, 2.8 percent headline, so we’ve been well above 2 percent, that amount, whatever it is, 0.7, 0.8, we’re a full percentage point above 2 percent for some time. And that’s a concern. We need to get back down to 2 percent. And we need to keep focused on that, even though we do now face some new inflation from energy. So I’d be hard pressed to say that one of them is obviously more at risk than the other.”

Given that in May we saw employment grow by 172,000, unemployment remained stable, while inflation was elevated, Powell was again correct in his nuanced take.

To me, Jerome Powell appeared to be an inquisitive and open-minded person, listening to the different strands of research. In the above quote, for example, Powell acknowledged that immigration policy may explain why we are seeing lower job creation numbers, something highlighted by here. Jerome Powell stayed very close to fresh economic research, and this type of approach is something a Federal Reserve Chair should have.

Jerome Powell is a great example of what an economist should be. And, what may be surprising to some, Powell does not have an economics degree (he has a law degree)! However, Powell demonstrates that, in essence, economics is a formalized approach to thinking about social issues, and that anyone, even without formal economics training, can be a good economist. This is something I strongly believe in (and hope to convey with Nominal News).

For this reason, I found Jerome Powell to be a great Federal Reserve Chair.

P.S. Kevin Warsh – the New Federal Reserve Chair

The most recent Federal Open Market Committee on June 17, 2026 was chaired by Kevin Warsh. I covered this meeting, as well as the press conference with Kevin Warsh in a special edition article here. I found his press conference quite concerning, and, in many ways, the opposite of the openness and clarity of Jerome Powell. We are in for some interesting times.

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Summary

• The FOMC (the interest rate setting committee) decided unanimously (12-0 vote) to keep interest rates between 3.5% and 3.75%.

• The FOMC also released their projections for the economy, inflation and interest rates.

Commentary

The Decision

The decision by the FOMC to keep rates steady should not be surprising. Current forecasts for May 2026 place 12‑month inflation at 3.4% for core (excluding food and energy) PCE (Personal Consumption Expenditure) and 4.1% for headline PCE. On a month-to-month basis, Core PCE inflation is expected to be up in May by 0.35%, which annualizes to an even higher inflation rate of around 4.3%. At the same time, unemployment has not increased significantly, although real wage growth has turned negative in the recent months. Unfortunately, the main way higher interest rates push inflation down is by lowering real wages.

What was perhaps more surprising is that the vote was unanimous - all 12 FOMC voters chose to keep the interest rate the same. We didn’t see a dissent either way - no voter wanted to increase or decrease interest rates. This does suggest that Kevin Warsh himself appears unlikely to cut interest rates pre-emptively. At the same time, we may have expected certain voters to advocate for an increase in rates.

The Statement

Below is the changes of the FOMC statement from April 2026 to today:

As you can see, the statement has been entirely rewritten and the amount of information contained in it is much smaller. There are few glimmers of what the discussions focused on in the FOMC meeting, as referenced by the comments on productivity and capital investment. Both of these elements, however, are not necessarily the most relevant to the current inflation level, suggesting a shift in priorities

FOMC Forecasts

The FOMC also published its views on the economy, commonly referred to as the “Fed Dot Plot”. Basically each member of the FOMC states what they believe the economy will look like in the next few years.

The main point of note is where the committee members believe the interest rates will be:

The above chart tells us that for 2026, 1 member believes there will be a cut (the dot between 3.25% and 3.5%), 8 believe in no change, while 9 believe a rate hike will be needed!

To me, this is not surprising, as inflation is pretty clearly too high and there are concerns that the elevated inflation could entrench itself.

It is also worth seeing how the FOMC participants perspective on inflation changed for 2026:

The dashed lines are what FOMC participants believe inflation would be in 2026 as of March 2026. The blue columns is what the participants currently believe.

Kevin Warsh Press Conference

Kevin Warsh had his first press conference - and it was definitely interesting, as well as concerning.

Forward Guidance

WIth the above FOMC statement and the press conference, Kevin Warsh was clear that he will not provide forward guidance. Forward guidance is the practice of signalling how the FOMC may make decisions in the future. To give an example, one question that Kevin Warsh was asked was how long is he comfortable seeing inflation above the 2% target. His response was that he will not give forward guidance. Kevin Warsh’s push against forward guidance is so determined that he also did not submit his own projections in the “Fed Dot Plot” above.

This is a significant change from past behavior of the FOMC, and I believe it’s for the worse. Central bank transparency is generally a stabilizing force, as it informs market participants what to expect. Less transparency does not feel like a good approach, and we should expect increased uncertainty.

Task Forces

Another big announcement from Kevin Warsh was the creation of 5 different task forces inside the Federal Reserve. These were:

1. Fed Communications;

2. Fed Balance Sheet;

3. Data Sources;

4. Productivity and Jobs;

5. Fed’s Inflation Frameworks

The ones that appear most interesting (and concerning) are the focuses on communications, data gathering and inflation frameworks.

Communications - this has already been changed, as discussed above, with much shorter statements and no forward guidance. Kevin Warsh also intimated that even the “Fed Dot Plots” may be suspended in the future. Whether the communication task force will actually improve communications is to be seen, but so far this looks somewhat worrisome.

Data Collection - this task force sounds good on paper, but again there are concerns. It is worth noting that economists have studied monetary policy extensively, and thus have examined a wide range of data and data sources. Kevin Warsh mentioned that he wants more ‘real-time’ data rather than lagged data. However, ‘real-time’ data is often not particularly accurate. At the same time, interest rate changes impact the economy slowly and thus the need for ‘real-time’ may not be as warranted. An example mentioned by Kevin Warsh was the Employment Situation Summary, where he criticized the fact that the numbers get revised several times. The concern here for me is that Kevin Warsh appears to overestimate how precisely one can measure a labor force of roughly 160,000,000 people. Revisions of 100,000 or 200,000 are minuscule.

Inflation Frameworks - this task force appears to be very vague, as it aims to focus on what ‘drives inflation’. Many economists study monetary policy and inflation. It’s a bit surprising that a task force would be expected to outperform the extensive research already available. This does not appear to be a project for a task force.

Big Changes Coming and Not Necessarily for the Better

Although rates remained the same, this FOMC meeting pointed to very large changes, which sound somewhat concerning. I don’t think reduced Fed communications are likely to improve monetary policy outcomes. The task forces on data and inflation frameworks might sound good on paper, but also do not really appear to be helpful (especially since we do not know yet who will lead them).

Overall - interesting times are coming to the Federal Reserve.

Let me know what you think about the new Federal Reserve Chairman’s comments.

This has been a special edition Nominal News article covering a very recent development. Please let me know your thoughts on this feature, and what would you like to see covered, in the comment section below.

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At Nominal News, we often talk about economics research that establishes causality. For example, how do immigrants impact innovation or how do free trade agreements impact people’s incomes and life expectancy. This causality is established using statistical methods, specifically, linear regression analysis.

Linear regression is an extremely powerful tool – nearly every existing economic policy can be linked to a research paper that uses linear regression to evaluate it. However, this tool can also be very easily abused to make it seem like there is ‘causality’ when there isn’t. The rise of genAI can make it much easier to manipulate data and present false research.

Linear Regression and Statistical Significance

Linear regression is a statistical method that tells us about the relationship between two variables. For example, how does educational attainment impact income; how do vaccines impact survival; how does adding a one road lane change congestion.

To properly conduct linear regression analysis, a researcher should follow the following steps:

1. Stipulate a hypothesis – e.g. a vaccine prevents the disease;

2. Establish the “null” hypothesis – e.g. the vaccine does not prevent the disease;

3. Collect data;

4. Undertake a linear regression analysis;

5. Conclude by rejecting or failing to reject the null.

The final step is what determines whether we have an outcome that is ‘causal’ or not: since if we ‘reject the null’, that means there is evidence for the alternative hypothesis – i.e. there is evidence that the vaccine does prevent the disease.

How Do We Reject/Fail to Reject the Null

So how exactly do we decide to reject/fail to reject the null hypothesis? Initially, we start off under the assumption that the null hypothesis is true – in our example, the vaccine does not prevent the disease. Once we collect the data, we perform the linear regression analysis. This analysis tells us how statistically likely we were to collect the data we did IF the null hypothesis is true. The probability of observing the data we did assuming the null hypothesis is true is called a “p-value”.

In our example, suppose based on the data collected, many of the vaccine recipients did not get the disease, while many non-vaccinated individuals did get the disease. Then, if the null hypothesis were true – i.e. the vaccine does not prevent the disease - the probability of collecting such data would be very low, and we would get a low p-value.

The decision to reject/fail to reject the null hypothesis depends on this p-value. If the p-value is lower than 5%, we reject the null, if it is greater, we fail to reject. It is worth pointing out that there’s no mathematical reason for this 5% cutoff – it is simply a scientifically agreed upon consensus. A p-value of 5% tells us that, if the null were true, there is a 5% probability that we would have observed the data numbers that we did.

Since 5% is assumed to be a low, unlikely event, we ‘reject the null’, meaning that our starting null hypothesis is rejected. In the case of the vaccine example, we would reject that hypothesis that the vaccine does not prevent disease. When we reject the null, it is often referred to as a ‘statistically significant’ result. It is important to note, however, that around 5% of the time we will wrongly claim statistical significance. That is, 5% of the time, we will have collected data that will make it look like there is a ‘causal relationship’ (statistically significant) between the variables in question, but, in fact, there is no such relationship.

‘P-Hacking’

This 5% cutoff is often crucial for researchers, as researchers cannot claim that they found a result – for example, that the vaccine reduces disease - unless the p-value is lower than 5%. Naturally, this creates an incentive to get one’s results below 5% by potentially manipulating the data. Any such actions are colloquially referred to as ‘p-hacking’.

The most obvious way to “p-hack” is to simply delete or change the data collected in such a way to get the p-value below 5%. This is a clear case of falsifying data. These types of ‘p-hacking’ have occurred, with a recent high-profile scandal at Harvard involving exactly this type of data manipulation. However, there are also more subtle ways to ‘p-hack’.

Iterating on the Collected Data

One way to ‘p-hack’ on data is to look at subsets of the collected data. For example, focusing only on certain demographic groups within the collected data might yield a ‘p-value’ of less than 5%, which would then allow the researcher to say that they found a statistically significant result.

In our case, suppose the vaccine data did not result in a p-value of 5%, but if we were to look at people aged 18-29, we would get a p-value of 5%. Thus, researchers might say that the vaccine works on people aged 18-29.

But here is the problem: if we look at all possible demographic sub-groups – of which there can be hundreds – we are bound to find one which will have a p-value of 5% or less. The researcher will present this finding and claim to have found a statistically significant result. But you are bound to find one such sub-group, because, by statistical design of the 5% p-value, 5% of the time, we will see data, which we will incorrectly conclude as statistically significant, because it has a p-value of less than 5%!

Why ‘AI’ May Make P-Hacking Easier

GenAI may make such p-hacking much easier. As genAI can be used to do data analysis, as well as create computer code, it will be much easier to find subsets of data that will satisfy the p-value of 5%. This will then be presented as novel research finding, rather than ‘p-hacking’.

Everything I mentioned today was also possible prior to genAI of course. But the rise and ease of doing this thanks to genAI may significantly increase the amount of research that is p-hacked.

How To Detect P-Hacking

Detecting p-hacking is difficult. Let’s start with what a good study should do:

• Explain why a hypothesis may make sense in the first place: for example, if a vaccine should work differently on people of different ages, what prior research or theory do we have that would support this idea.

• Pre-register the hypothesis: prior to running an experiment or collecting data, record the hypothesis publicly (like at the Open Science Framework).

• Make data available: all analysis and data from the study is publicly available and reviewable.

The above three elements give a significant boost of confidence that the research results are legitimate. Using the above points, some p-hacking red flags are:

• Statistically significant outcomes that do not seem intuitive or lack theory backing the hypothesis;

• Outcomes that appear to be confined to an oddly specific sub-group.

In a famous example, John Bohannon intentionally created a p-hacked research study titled ‘Chocolate with High Cocoa Content as a Weight Loss Accelerator’, which showed that eating chocolate resulted in statistically significant weight loss. This study was reported on by global media that did not question the outcome (or look into the easy to spot methodological flaws, as the study had only 15 participants!). Later, Bohannon said that he intentionally created this fake study to show how easily false conclusions may disseminate.

The Incentives of P-Hacking

The reason p-hacking occurs is due to the pressures researchers have in a ‘publish-or-perish’ world. Researchers, in order to get published, need to often show statistically significant results, as ‘null’ results (or statistically insignificant results) are treated as ‘uninteresting’ (even though they shouldn’t be, since a lack of a statistically significant result is just as important for science).

Until the incentives in academia are fully corrected to discourage ‘p-hacking’, situations with ‘p-hacking’ will occur and erroneous research will be picked up by the media. We can be vigilant when reading these headlines by being just a bit more skeptical when something does not seem right.

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On April 2, 2025, the US announced the “liberation day” tariffs which raised tariffs on all imports by at least 10%.1 One year later, the latest research shows that economists’ original estimates of the impacts of tariffs were pretty accurate – the tariffs were almost entirely paid by US taxpayers, inflation readings were elevated, while overall economic output was slightly reduced.

Tariff Pass-Through To US Consumers

One common argument made by tariff proponents is that the tariff – or tax on imports – will be effectively paid by foreign countries, specifically foreign exporters. Gopinath and Neiman (2026) (“GN”) looked at the 2019 tariffs and 2025 tariffs and found that the cost of the tariffs is nearly entirely borne by the American consumer.

First, GN show that the actual paid tariffs, or effective tariff rate (blue lines), are much lower than the statutory ones (red lines) established:

There are several reasons for the discrepancy between statutory and actual tariffs:

1. Shipment lags – goods that were in transit when the tariffs were announced were exempts from tariffs;

2. The existence of multiple product exemptions, as well as exemptions for specific companies;

3. Utilizing the USMCA exemption – the USMCA trade agreement between USA, Mexico and Canada, allows for tariff-free trade if a sufficient amount of the value of the goods is made in Canada or Mexico.

Using the actual tariffs, GN found that for the ten sectors with the largest increases in tariffs, the tariff pass-through to consumers ranged from 90% to 114%. Interestingly, this shows that in some sectors, tariffs resulted in price increases that went beyond the tariff size. A look at how tariffs impact the entire goods supply chain can help explain how this happens.

Tariffs and the Supply Chain

Flaaen, Hortaçsu, Tintelnot, Urdaneta and Xu (2025) (“FHTUX”) studied how tariffs on European wines fed through the US wine supply chain. FHTUX tracked the price of wine from the foreigner producer, to the importer, distributor, retailer and ultimately, the consumer. The US wine market is particularly well-suited for this type of analysis, as, by law, producers/importers cannot legally be distributors, and distributors cannot be retailers. This legal separation means that the prices set in each stage of the supply chain were market prices negotiated at arm’s length.

FHTUX found the following price response to a 25% US tariff levied on wine:

• Foreign wine producers reduced their prices by 5.2%;

• US importers increased their prices charged to distributors by 5.4%;

• US consumers ultimately paid a 6.9% higher price.

FHTUX tells us that many commonly mentioned narratives can be true at the same time:

• Foreign producers do ‘pay’ some of the tariffs as they reduced their prices;

• US consumers also pay the entire tariff (and more!).

Broader Array of Goods

Hinz, Lohmann, Mahlkow and Vorwig (2026) (“HLMV”) looked at a global shipping data set that provides daily price and quantity data on maritime shipments. HLMV observed over 25 million product shipments and were able to extract unit prices – value per kilogram of goods transported. In response to tariffs, HLMV found a near complete pass-through of tariffs – that is, foreign exporters did not reduce prices at all (at most the estimate is a 4% price reduction, although this was not statistically significant).2

Bulk Discounts

Ganapati and Hottman (2026) (“GH”) also looked at price pass-through but took bulk pricing into account. Typically when ordering more of a good, producers offer lower prices per unit. Thus, if the amount ordered is lower than before, one could expect the price per unit to go up. Since tariffs increase prices of imported goods, importers order fewer goods, which in turn should result in the producer/exporter charging a higher price. Therefore, if the producers do not charge a higher amount per unit, that would imply that the producer is ‘paying’ some of the tariff.

By taking this ‘bulk discount’ factor into account, GH find that the estimated tariff pass-through to US consumers is approximately 60%. GH show that if we do not take into account the potential for ‘bulk discounts’, the estimated tariff pass-through is 100%, as found by other studies. This suggests that taking into account order volume may matter.

I would, however, caution about the ‘bulk discount’ assumption, as currently, exporters may be keeping similar unit prices as part of long term deals or even as a strategy to maintain relationships with importers under the assumption that the tariffs may be rescinded in the near future. Thus, if exporters believed that the tariffs were a permanent policy for many years, these exporters perhaps would have increased prices given the lower order volume.

Broad Economic Impacts

The research above demonstrates that tariff pass-through to US consumers has been largely complete. So how has it impacted macroeconomic factors?

Tariffs and Inflation

As tariffs have increased prices, it should come as no surprise that inflation in the US has ticked up. Research by Cavallo, Llamas and Vazquez (2026), (“CLV”) continues to show a steady increase in observed inflation in tariffed goods (imported, yellow line).

More importantly, non-tariffed goods (domestic) also see a rise in inflation. This is not a surprise – inflation always spills over to other sectors and it’s really only the magnitude of the spillover that’s the question. In the goods sector, it appears the tariff spill-over on domestic good prices (blue line) has been just as large as the impact on prices of imported goods (domestic goods saw a price increase by 5.2 percentage points compared to 6.8 percentage points for imported goods).

Although we have predominantly seen goods sector inflation increases, there is, or will be, also some spillover to the service sector. This is due to the fact that physical goods are an input to services (e.g. food is an input for restaurants), and because workers will also demand higher nominal wages to offset the fact that the basket of items they purchase for personal consumption is now more expensive. The magnitude of this spillover into services may end up being small but it almost certainly exists nonetheless. CLV estimate that so far, tariffs have contributed almost 1 full percentage point to US inflation (which is currently at 3.3%). Absent tariffs, inflation would have been very close to the 2% inflation target set by the Federal Reserve.

Tariffs and US Economy

Beyond inflation, tariffs can also be expected to impact the wider economy. For this, we turn to the Fajgeballum and Khandelwal (2026) (“FG”) model. First, a few key inputs – FG estimated the effective tariff rate to be 9.6% (recall that effective rates may be much lower than officially stated ones) with a 90% tariff pass-through (this estimate is perhaps on the low side compared to other studies).

FG find very interesting results – the overall impact on welfare in the US is extremely negligible with a 0.13% reduction in welfare measured in GDP terms – the reduction in welfare (happiness) is equivalent to having GDP lower by 0.13%.

But this lower overall welfare reduction hides the fact that impact is very uneven:

• Consumers lose out by about 2.6%;

• Domestic produces gain around 1.4%, as they receive higher prices;

• The government gains around 1.1% due to tariff revenue.

Real wages also fall, particularly for workers in goods-producing sectors like manufacturing. This is because many of the tariffed goods are inputs into US manufacturing, making manufacturing in the US more costly, which in turn reduces employment and pushes real wages lower in these sectors.

The Tariff Experiment – A Predictable Failure

This recent batch of research quite unambiguously shows that tariffs have been a bad policy for the US.

Moreover, although the aggregate effect of tariffs on the US may be small, the impact has been uneven – consumers and workers lost out, while domestic producers benefited from the tariff policy. Additionally, after the tariffs were deemed ‘illegal’, US importing firms will be able to claim a tariff refund, meaning that the US taxpayer/consumer will be even worse off, as government revenues fall!

The research discussed today also shows how tariff-proponents may be very selective with data – for example, they may highlight that foreign producers may reduce prices slightly in response to tariffs, but conveniently omit that other participants in the supply chain can use tariffs as a reason to increase prices by even more than the tariff cost.

Overall, the fact that we have to write this article is a bit unfortunate – most of economics research has effectively shown that tariffs could not have a positive impact on the US economy, and undertaking this ‘experiment’ in 2025 was not worth it.

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Interesting Reads from the Week

1. Are People Happier When They Buy Experiences? – discusses research showing that how we define what is materialistic or experiential is impacted by our own well-being.

2. AI May, Paradoxically, Increase Demand for Higher Ed – talks about how AI may increase demand for higher education, although it may require institutions to re-organize a bit.

3. Estimating the Impact of Trump’s Policies on Consumers – , , and estimate that the average household is expected to lose more than $1,100 this year due to Trump’s policy choices, with bigger losses for low income households!

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Marital choice and partner selection are rich social interactions for economists to study as they involve many economic theory-based behaviors. For example, there is asymmetric information, where each potential partner has information about themselves that the other person does not know. There is also signaling, in which an individual intends to convey unobservable information to a potential partner. In a marriage, there are many decisions that need to be made by spouses regarding both selfish (i.e. personal) interests and communal interests, such as chores. Some form of implicit or explicit negotiation occurs, even if it does not seem like it to us.

It appears that many of the implicit impacts of spousal preferences differently impacts the genders, especially worsening the career outcomes of women.

Partner Selection – Gender Differences

As we anecdotally know and feel, men and women approach partner selection differently. But exactly how? It is usually difficult to get truthful responses from individuals just by asking them, especially on a potentially sensitive topic such as dating preferences. Thus, it is important to use a research method such that individuals will truthfully report their preferences. One such way is through setting up a field experiment.1

Fisman et al. (2006) conducted such a field experiment experiment in order to infer people’s preferences by hosting real speed-dating2 events. During these events, participants would record after each round whether they would like to see each other again. If a match did occur, contact information would be exchanged, thus participants had an incentive to be truthful. What Fisman et al. found is that women put more emphasis on intelligence and race, while men put more emphasis on attractiveness. Moreover, the men preferred women who are less intelligent and, especially, less ambitious than them. These field experiment findings on ambition are consistent with what is referred to as the ‘social structure theory’. This theory posits that social arrangements and societal roles are more important in determining individuals’ particular decisions rather than biological reasons. 

So why are economists concerned about these partner preferences – after all, they may simply be individual choices that have little bearing on others? The old economic adage is that individuals respond to incentives. This implies that simply knowing about other people’s preferences might alter your own behavior. As it turns out, further economic studies have shown this to be the case.

Real Costs of Preferences

Bursztyn, Fujiwara and Pallais (2017) conducted a field experiment on newly admitted MBA students. The authors used a survey to ask students certain career and personality questions. As with all survey-type studies, there is an issue of whether respondents will give truthful answers. We covered an example of how survey design was important to elucidating honest responses when we discussed gift giving. However, in this instance, the MBA surveys were perceived by the surveyed, as something more than just a meaningless questionnaire. As this was a newly joining MBA cohort, this was the first time they would be offering their information and preferences to the career center. The career center does use this information to set up internships – individuals that aren’t willing to travel as much would be steered away from consulting, while people that prefer shorter hours, would be less likely to get internships with investment banks.

Bursztyn, Fujiwara and Pallais conducted the surveys and randomly told some of the students that their results will be shared publicly with other students, while others were told their answers would remain private or anonymous. The results of this study were illuminating. Single women who believed their answers may be made public in front of their classmates, desired $19,000 lower annual salary ($112,000 vs $131,000), desired less work travel per month (6.6 days vs 13.5 days) and were willing to work fewer hours per week (48.3 hours vs 52.2 hours) than if they believed their answers would be anonymous! The same pattern was found for specific personality traits – single women were less likely to want to lead or state they were ambitious if their information would be shared in a public setting. 

Confirming Causality

The above experiment might not be sufficient to determine whether men’s preferences might be causing single women to act differently. Theoretically, there could be an unobserved variable that could make it more likely for women to both be single and alter their response if it would be presented in a public setting.

To address that issue, Bursztyn, Fujiwara and Pallais performed additional experiments and analysis. The authors conducted an experiment during which students, in smaller groups, were asked to rank which type of jobs they would prefer (higher salary and longer hours vs lower salary and shorter hours) and told that their responses may be discussed at the end of the session. When placed in all female groups, women were far more likely to choose jobs with higher salaries and longer hours, as well as jobs with faster promotion schedules and included more travel. Women were more likely to prefer lower salary outcomes in groups in which there were more single men as opposed to married men. This difference in response leads more credence to the idea that it is social expectations influencing behavior of single women.

The authors also noted that single women tended to have lower class participation grades than married women. In a class setting, their participation would be seen by others. However, their performance on midterms, final exams and problems was similar to married women, suggesting that skills and ability are not an explanatory factor.

An additional survey asked students about their experiences. Bursztyn, Fujiwara and Pallais found that 64% of single women avoided asking for raises or promotions to avoid looking “too ambitious, assertive, or pushy”. Only 39% of women who were married or in a serious relationship and 27% of men said the same. Similarly, single women tended to avoid speaking up in meetings to avoid looking too ambitious. All of these results may also be somewhat downward biased,3 as people pursuing an MBA are more likely to be more focused on career enhancement and getting a higher salary. 

These results demonstrate that the preferences of men for less ambitious women has real impacts on women’s decisions and behaviors. Whether consciously or subconsciously, evidence suggests that women alter their behavior to appear more ‘attractive’ to potential partners. What are the ways we can deal with this situation? One solution that can work in the near-term proposed by Bursztyn et al. is to encourage schools and workplaces to make certain actions less observable. As the studies have shown, single women mainly alter their decisions when it would be observable to others. Thus, removing this channel would potentially prevent this behavioral adjustment.

Ambitious Women and Divorce

The Bursztyn, Fujiwara and Pallais study, given that there were lower stakes at play, might still be seen with a dose of skepticism. To determine whether women’s ambition could impact marital outcomes, a more direct study would be preferable. Folke and Rickne (2020), using an extremely rich Swedish data set, were able to study the impact of promotions on divorce rates. Folke and Rickne have 30 years of data and are able to track individuals that are applying to be promoted to become a mayor or a parliamentarian, which are both seen as very high positions (both from a reputational and financial perspective) in Swedish society.

Firstly, Folke and Rickne find that divorce rates for promoted women go up, while there is no change for promoted men. Secondly, women that were promoted are twice as likely to get divorced than women that attempted, but failed to gain promotion. Folke and Rickne are also able to track pre-promotion data and thus, are able to rule out any differences between the women who got promoted or failed to get promoted, prior to the promotion. This pattern did not only hold in political roles – undertaking the same analysis on CEO promotions, the study found very similar results. The figures below visualizes the impact for political roles. As can be seen, in the 4 years prior to the election, women that would be promoted and women that failed to get promoted remain married at the same rates. After promotion, the marriage rate of promoted women drops by more than for women that failed to get promoted. No such effect can be seen for men. 

The rich dataset allowed Folke and Rickne to analyze which types of couples were more likely to divorce. They found that gender-traditional couples, where there was a larger spousal age-gap and more gender-skewed division of parental leave,4 were more likely to divorce after the promotion. Women in more gender-equal marriages did not divorce more often after a promotion. Folke and Rickne highlight the issue between couple formation and the glass ceiling for women in the labor market. High ability men are often in relationships where the focus is on the men’s career, while high ability women tend to be in dual-earner relationships where the woman is primary care-giver. Women in traditional marriages find it tougher to get higher responsibility jobs due to the adjustment costs within the marriage, which includes task renegotiation within the couple.

Addressing the Issue – Long-Run

The above research describes the current social and economic situation. In economics, this is called ‘positive’ economics, which is describing the world as it is currently. The issue that arises is that given that men today prefer less ambitious women, women may find it rational to act in a less ambitious way in order to find a spouse, if that is their goal. This behavior, although logical currently, is clearly costly for women in the future. 

One could, therefore, argue that in order to improve outcomes for women and the economy in general, the perspective of men on women’s ambitions should be changed. This is referred to as ‘normative’ economics – i.e. what the economy ought to be. Resolving the perception by men of women in the workplace, which is a cultural phenomenon, is not an easy question – the most likely solution is a long-run one. Economists have also studied what can lead to cultural change in the long run.

Fernandez et al. (2004) assess one cultural component that may be important in forming men’s attitudes towards working women – working mothers. Throughout history, Fernandez et al. point out how several changes impacted women in the labor force – consumer durables (most famously, the washing machine) that made housework easier, the ‘pill’ that enabled women to control their fertility, leading to controlling their career, and the growth of service sector ‘white-collar’ jobs. All of these changes had a dramatic impact on women’s participation in the labor force, changing the entire cultural perspective around women in the workforce. Regarding more recent times, Fernandez et al. argue both theoretically and empirically, that growing up with a working mother has changed men’s attitudes toward working women. They found that having a working mother increases the probability of the man having a working wife by 24 to 32 percentage points.

Societal Perceptions and Outcomes for Women

Finding a spouse clearly impacts men and women very differently. The preferences of men and women also unequally affect the genders. This results in real costs for women as both their careers and earnings potentially suffer. There may be ways to mitigate some of these effects, such as by anonymizing participants (for example, salary negotiations could be done without personally identifiable data) or obscuring certain actions (such as reviews) in the workplace to reduce observability.

However, any such stopgaps will not address the key long-run issue. Career decisions will always be publicly visible, so in order to enable women to reach their career goals and not having to worry about the implications it may have on their marital life, the perception of womens’ ambitions and desirability must change. That is a process that takes generations.

Finally, the above research has shown several important ways to assess the validity of studies which use survey data. When using survey data, it is critical to create incentives that will elucidate truthful responses. Surveys and social experiments in laboratories can typically suffer from incorrect data. If accessible, however, studies that use officially recorded data, such as the Folke and Rickne Swedish study of political candidates, will typically be better in establishing causality. This is due to the fact that the data they used was unlikely to be erroneous or suffer from any significant biases.5

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In almost any introductory economics class, ‘free’ trade is presented as a welfare-improving policy. However, in graduate economics classes, you learn that this increase in welfare is only certain in the long run. But what happens in the short-run? Here the picture gets murkier. Recently, a study by Finkelstein, Notowidigdo and Shi (2026) (“FNS”) and by Noghanibehambari and Fletcher (2026) (“NF”) looked at how the North American Free Trade Agreement (NAFTA) between the US, Mexico and Canada may have shortened lives of US manufacturing workers.

Opening Up to Trade

NAFTA, which removed tariff and non-tariff trade barriers between the US, Mexico and Canada, became law on January 1, 1994. NAFTA de facto created a single goods market that covered, at the time, 400 million people, and accounted for one third of the world’s output. The main impact of NAFTA was the removal of trade barriers between the US and Mexico (as Canadian-US trade barriers were effectively removed in 1987). Over 15 years, NAFTA reduced US tariffs on goods from Mexico from 2% to 0%:

The seminal paper by Caliendo and Parro (2015) found that the impacts of NAFTA on welfare were relatively muted – Mexico benefitted the most with a 1.3% increase in welfare, the US gained about 0.08%, while Canada saw a minor welfare decline of 0.06%. However, all countries saw an increase in real wages – i.e. workers were able to purchase more goods/services after the passing of NAFTA.

But the Caliendo and Parro paper looked at the question of trade liberalization mainly from a total income perspective. Since trade liberalization shocks may also impact workers’ well being in other ways, FNS and NF wanted to understand what happens to mortality after such a shock.

Concentrated Manufacturing Shock

To determine how mortality changed due to the impact of NAFTA, both papers had to first determine which areas in the US were most ‘exposed’ to NAFTA. To measure this exposure (or vulnerability), economists looked at three key factors:

1. Whether the industry had a high share of imports from Mexico – this captures the fact that Mexico had a comparative advantage in this industry than in other industries;

2. The size of tariffs prior to NAFTA in the industry – a higher tariff would mean there would be greater competition from Mexican producers once the tariffs are removed;

3. The share of US workers in a given region working in the industries that are influenced by 1 and 2 – the more workers working in industries exposed to Mexican imports, the bigger the shock the region will face.

The map below from FNS shows which US regions were most vulnerable by quartiles (the darkest color being the most vulnerable):

Using the fact that regions had different exposures to NAFTA, FNS estimated how mortality responded to NAFTA. FNS found that average annual mortality increased by 0.68% over the period 1994 to 2008. To put into context, average annual mortality between 1965 and 2000 fell between 1% to 1.5% (Cutler and Meara, 2004). This means that NAFTA may have eroded half the improvements in mortality.

Mortality Increases Among Working Men and The Elderly

It is important to note that the 0.68% increase in mortality is an average. NF, using similar methodology in their paper, found that the mortality increase in the most NAFTA exposed areas was 4.2%. FNS broke out the increase in mortality and found two major groups accounted for a large share of the increase:

• One-third of the increase in mortality was due to an increase in deaths of working age men, between 25 to 64 years old;

• Half of the increase in mortality was driven by people aged 65 or more.

The mortality increase of working age men suggests that direct job loss/displacement may be the cause of overall increased mortality.

Regarding the increase in elderly mortality, a potential explanation is that adults over 65 may have depended on their children for financial assistance. Thus, when a worker lost a job during NAFTA, it may have also adversely impacted their parents/grandparents. FNS found evidence for this – over 60% of the increase in the mortality of the elderly was driven by increased mortality of elderly female widows, who often depend financially more on their children.

Did NAFTA Reduce Welfare?

FNS conducted a high level welfare analysis which, unlike previous research that focused predominantly on the income impacts of NAFTA, also included the mortality impacts they estimated. As noted previously, prior estimates of NAFTA showed a 0.08% welfare increase in the US. FNS estimated that including the mortality impacts, US welfare most likely fell – the reduction in welfare due to NAFTA was around 0.11% to 0.69% (depending on assumptions).

But, there are certain caveats around this conclusion that need to be taken into account.

Understanding the Econometrics of the Estimation Method

FNS point out that their estimation relies on looking at differences in mortality between regions – that is, how did mortality change in a region that was more exposed to NAFTA compared to a region that was less exposed. In effect, we are looking at a difference, and in simple terms we have the following problem: If previously, we had a difference of 3 (for example, one group was 10 and the other group was 7, giving a difference of 3), and now we have a difference of 4, we could have two causes for it:

• One group went up by 1 – so 11-7 = 4; or

• One group went down by 1 – so 10-6 = 4.

(A mix of both is also possible). In our context, this means that the observed increase in mortality could be driven in part by the fact that mortality for non-NAFTA exposed individuals decreased thanks to NAFTA.

However, looking at other studies, FNS argue that it is very unlikely that mortality for non-NAFTA exposed individuals reduced meaningfully. Most studies find that income increases have no impact or, surprisingly, a negative impact on mortality. Thus, in my opinion, it does appear that NAFTA did increase mortality of vulnerable workers.

Uniqueness of Trade Shocks

The adverse impacts of NAFTA on mortality appear to be driven by job loss/displacement. One could expect that job loss would always increase mortality. Surprisingly, economists have found that during recessions, when job losses may even be greater, mortality falls (Ariizumi and Schirle, 2012). FNS confirmed this by looking at the impact of the 2008 Great Recession on mortality. They found that mortality reduced in areas more exposed to the adverse impacts of the Great Recession. But, trade shocks like NAFTA or China joining the World Trade Organization in 2001, increase mortality.

The reason behind these different mortality outcomes to what appear to be similar shocks is a bit of a puzzle. One proposed answer is that trade shocks adversely impact jobs that are unionized, which typically have higher wages and other benefits. FNS found that regions with higher unionization rates did exhibit larger mortality increases due to NAFTA. This is suggestive evidence for that hypothesis.

Can We Better Redistribute the Gains of Trade?

The research by FNS and NF shows that trade shocks like NAFTA do have strong negative impacts on a concentrated group of people exposed to these shocks. Are there ways to mitigate this issue?

Providing Cash/In-Kind Transfers

One solution might be to offer income support to workers that are exposed to job loss from trade shocks. However, studies by Chorniy, Finkelstein and Notowidigdo (2025), as well as Evans and Moore (2012), have found that such cash transfers increase mortality, often caused by alcohol or drug abuse. In-kind transfers, like food vouchers, appear to not increase mortality. Just giving cash may not be sufficient to alleviate the negative consequences.

As mentioned earlier, there was a significant increase in mortality among the elderly that may have been driven by reduced financial support from their children laid off from their jobs. Although exacerbated by trade shocks, assisting the elderly should be addressable by policy, whether through better design of social insurance or more direct financial assistance.

Key Takeaways

The FNS and NF research has three key takeaways – one for economists/researchers and two for policy designers.

For economists:

1. The impacts of trade liberalization on individuals is very varied in the short run. Typically, as economists, we have looked at trade liberalization from a long run perspective which showed unambiguous long run benefits. However, a lot of the adverse impacts occur during the transition phase of opening up to trade, concentrated on a narrow group. The focus on the transition is crucial to understand the consequences of trade liberalization, as well as informing the design of better economic policy.

For Policy Makers:

1. Alleviating the negative impacts of the trade shock is important, as certain groups face the brunt of negative consequences. This may entail redistributing via taxes into in-kind transfers, as well as more broad outreach to communities that may see the negative consequences.

2. Tariffs and trade protection, especially after trade liberalization already occurred, may result in the amplification of the negative outcomes from trade liberalization. Attempting to protect an industry from foreign competition via tariffs may inadvertently incentivize workers to enter the industry. Once the tariffs disappear (or foreign competitors get even better), a similar trade shock to the NAFTA shock may occur again, resulting in a mortality rate jump. Thus, ‘closing’ to trade will not undo the negative impacts of opening to trade, but will likely repeat them.

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Interesting Reads from the Week

1. When People Cut Back on Instagram, Where Do They Go? – discusses a recent research experiment on how people use the time freed up by turning off Facebook and Instagram.

2. Reflections on Vibe Researching – talks about using AI for research. This paragraph summarizes it very well:

My point is that the experiment – can we do research at high speed without much human input – was a failure. And it wasn’t just a failure because LLMs aren’t yet good enough. I think that even if LLMs improve greatly, the human taste or judgment in research is still incredibly important, and I saw nothing over the course of the year to suggest that LLMs were able to encroach on that advantage. They could be of great help and certainly make research a ton more fun, but there is something in the judgment that comes from research experience, the judgment of my peers and the importance of letting research gestate that seems more immutable to me than ever.

3. Short Note from me:

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Recently, The Georgia Department of Transport posted on their social media account the following statement with the below picture:

The widening of I-95 near the Savannah River will improve travel for thousands of drivers each day while supporting the continued growth of the Port of Savannah –one of the fastest-growing ports in the nation. A smarter, safer corridor is on the way. https://bit.ly/4ux3vjW

The picture suggests that widening the road by 1 lane will significantly reduce congestion and improve commute times. Economics research tells us that this extra lane will have no impact on congestion whatsoever.

The Fundamental Law of Road Congestion

Economist Anthony Downs, in 1962, posited the Law of Peak-Hour Traffic Congestion:

This Law states that on urban commuter expressways, peak-hour traffic congestion rises to meet maximum capacity.

Over time this law was rephrased as the Fundamental Law of Road Congestion. Economists have often studied this law to see if it actually holds, and research has shown this to be the case.

Evidence from US Cities

Duranton and Turner (2011) investigated whether the Fundamental Law of Road Congestion holds in the context of US cities. The main question Duranton and Turner were interested in answering is what impact would changing the number of lane kilometers (i.e. increasing the number of lanes on road) have on vehicle-kilometers traveled (VKT). VKT is a measure of the total number of kilometers traveled by vehicles on a given highway system. If one vehicle travels 1 kilometer, that would be considered to be 1 VKT.

Suppose a one lane highway is 100 kilometers and 10 cars travel it in its entirety. This would be a road with 100 lane kilometers (1 lane times 100 kilometers) and VKT would be 1000 (10 cars times 100 kilometers). If we add an extra lane to this highway, the overall distance does not change, so 10 cars would still travel 100 kilometers, resulting in 1000 VKT. Lane kilometers would double since we have two lanes (2 times 100 kilometers). If VKT remains at 1000, while lanes doubled, then we would consider this road to be less congested. On the other hand, if the extra lane results in 20 cars on the road (2000 VKT), then the additional lane will have no impact on congestion.

Data and Results

To estimate the impact of adding lanes, Duranton and Turner looked at 228 metropolitan areas in the US over a 30 year time period, between 1983 to 2003. The number of vehicles on roads in the US is collected quite accurately – for example, states must provide accurate daily interstate highway usage to the federal government.

Overall, VKT in the US doubled from 1983 to 2003, from 7,700 VKT to 15,900 VKT for interstate highways. For major urban roads, a similar pattern occurred – VKT went up from 15,000 to 30,000. In the same time span, the number of lane kilometers of interstate highways remained basically constant, while on major urban roads, it went up from 3,800km to 6,500km.

It is important to note that in order to determine the impact on VKT of adding additional roads (causality), we have to ensure we are not capturing other factors. For example, overall population growth will naturally result in higher VKT and it may seem like adding roads has no impact on congestion. Duranton and Turner do control for many factors and even use an innovative approach called “Instrumental Variables regression”.

So what is the impact of increasing the number of lane kilometers on VKT? Duranton and Turner estimate that increasing lane kilometers by 1% will increase VKT by 1.03%. Economists call this an elasticity1 of 1.03. Similarly, a 10% change in lane kilometers will increase VKT by, approximately, 1.03 times 10%, so 10.3%.

The Duranton and Turner result tells us that any feasible increase in roadways will have no impact on congestion – any increase in roads will be met by more vehicles on the road, more than offsetting the increase in roads.

Where Do The Extra Vehicles Come From

Duranton and Turner were also interested in figuring out what is the source of extra vehicles. They looked at four potential sources:

• Additional commercial traffic;

• People and economic activity migrating to places with additional roads;

• Diversion of traffic from smaller, local roads;

• Changes in individual behavior with people choosing to drive more.

Duranton and Turner studied how a hypothetical 10% increase in lane kilometers in interstate highways, that increases VKT by 10.3%, would be apportioned between the above 4 causes.

Duranton and Turner found that a 10% increase in interstate highways results in a 10 to 20% increase in truck road usage. As trucks account for approximately 13% of all vehicle traffic, increases in commercial truck traffic explains about 20% to 30% (thus, 2%-3% of the 10.3% increase) increase in VKT due to increased interstate highways.

Individual behavior is also a major contributor to increasing VKT. A 10% increase in interstate highways results in about a 1% increase in road usage by each individual driver annually. Although this may not sound like much, the largest source of VKT on the roads are individual drivers (87% of all VKT). Thus, a minor increase in car usage by each person, results in a significant increase of cars on the road. Individual behavior changes account for approximately 9% to 39% (1%-4% of the total VKT increase) of the increase in VKT due to additional interstate highways.

The other two reasons appear to have much less impact on congestion. Population growth from migration is minimal – a 10% increase in interstate lane kilometers increases population in the regions around the interstate by 2% over 20 years and can explain only around 5% to 20% of the increase in VKT. Diversion from other smaller local roads is almost non-existent – only 0% and 10% of the increase in VKT.

These results suggest that construction of additional roads have limited positive impacts – it does not reduce congestion on any roads nor does it have any meaningful increase on population growth, which would translate to economic development.

The increase in commercial vehicles could be a positive economic sign (more goods being transported) but it depends whether this increased truck traffic is driven by increased economic activity or a substitution from other forms of goods transportation like trains.

The main cause for increasing congestion when new lanes are built is that individual drivers choose to drive more. This fact has been termed often as “induced” or “latent” demand. Any time a new road is built or an old one is expanded, it quickly ends up getting congested, without any relief of congestion on other roads. However, the people undertaking these ‘new trips’ must have been doing something before the road expansion. They either used an alternative mode of transport or did not make this trip. This ‘pent-up’ demand keeps congestion constant, as any road expansion will immediately be filled up by cars.

Interestingly, this also means that some solutions that might work – won’t. Duranton and Turner looked at whether increasing public transit can reduce congestion. By studying the provision of buses, Duranton and Turner found that public transit has no impact on VKT. This is consistent with induced demand. Even if people shift some of their car trips to public transit, there are ‘new trips’ ready to take their place.

Duranton and Turner also found that, over-time, regions slowly converge to having the same average daily number of vehicles per lane. This suggests that there is a ‘constant’ level of congestion all roads tend to. People appear to migrate out of regions that have higher than average congestion levels, to areas that are lower, resulting in the convergence of congestion.

Solving Traffic

Research shows that expanding roads will not alleviate congestion. So what will? The only solution demonstrated to have had impacts on congestion is congestion pricing. Congestion pricing internalizes the externality2 caused by using a vehicle. The externalities caused by car usage include increased travel times, increased air and noise pollution, as well as increased probability of accidents. These costs are borne by both drivers and non-drivers. By putting a monetary cost on the travel, we internalize this cost, as the driver now pays for the costs that car usage entails. Research shows congestion pricing does reduce car usage – a holistic overview by Lehe (2019) has shown that congestion pricing does reduce the number of cars (depending on the implementation) and that people are very responsive to the additional congestion cost.

In the US, New York City implemented congestion pricing last year, and, as expected, the benefits have been significant, including a fall in commute times and pollution.

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Interesting Reads from the Week

1. Even Before the Iran War, There Was a Growing Inflation Problem – goes over the inflation picture in the US, and why inflation had been rising even before the Iran war.

2. In view of the above post, March inflation, as being forecast by the Cleveland Federal Reserve, may be very high – at 0.62 percent (normally it should be at 0.15%)

   

3. The Dirty Secret of Election Models: explains why judging election models based on hindsight (i.e. after the result) isn’t helpful for picking good models. What matters more is the transparency and validity of assumptions:

All models have assumptions. There’s no such thing as a “purely data-driven” model – only models whose assumptions are obvious…

The difference between a good model and a bad one isn’t whether assumptions exist. It’s whether those assumptions are valid, explicit, and aligned with the real-world process being modeled.