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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

Nominal News Reader Questionnaire

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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.

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Governments provide various assistance programs to address social issues such as poverty, poor health and poor educational outcomes. Many of these policies often provide certain financial benefits. One would expect uptake of these programs to be near perfect – why wouldn’t a person want ‘free money’. But, often, uptake of such policies is mixed.

A recent study by Yu, Stolove, Riddell, and Mahumane (2026) (“YSRM”) on HIV medication adherence may explain why these theoretically sound policies may have underwhelming results in practice.

HIV Medicine Adherence Incentives

YSRM conducted a field experiment in Mozambique to test several hypotheses around HIV medication adherence.

HIV Medication Adherence

Anti-retroviral therapies (ART) can keep the effects of HIV at bay, allowing HIV positive individuals to live a full life. Typically, as part of ART, an individual needs to take a daily pill. Missing a pill can severely weaken the benefits of the therapy, and a 95% adherence rate is needed for ART to be successful. In Mozambique, although ART is highly available and free to people, adherence is generally low – in the YSRM study, base adherence was about 22%. So what can be done to improve adherence?

The Experiment

YSRM enrolled eligible participants – individuals 18 years or older, living with HIV, who have not been on ART recently and who have a smartphone (the smartphone was needed for payments and medication reminder calls). As part of the ART, individuals were given pills for 30 days, and told to come back for a refill before they ran out of pills.

Individuals were then split into several groups:

1. Control – this group was not given any additional incentive;

2. Financial Incentive – this group was given a payment if they refilled their medication on or before the refill date (30 days after first pill);

3. Reminder – this group was given a call 0 to 6 days before their expected refill date to remind them of refilling their medication;

4. Combination – this group was given both treatments of the Financial Incentive and Reminder groups;

5. Information – this group was given a short video explaining the risks of HIV and importance of ART;

6. Stigma-Relieving – this group was told about how their local community perceives living with HIV, and that, in most cases, individuals overestimated the stigma associated with living with HIV.

Behaviors Tested

The purpose of these hypotheses was to assess the interventions and also to test two behaviors:

• Inattentiveness; and

• Forgetfulness (or Imperfect Memory).

Inattentiveness occurs when individuals do not take the pill every day, as they are not paying attention to daily pill taking.

Forgetfulness refers to the fact that individuals may not remember when they are supposed to go get the refill (no later than 30 days after the first pill). Individuals can guess they may need to refill their medicine based on the number of pills they have remaining, since they had exactly 30 pills to start. However, they also might not remember how many days they didn’t take the pill.

Results – Group Averages

The main outcome YSRM looked at is the Medication Possession Ratio (MPR) – the ratio of total number of pills possessed over the total number of days in the study (since for 100% adherence, you need one pill per day). As mentioned above, ART works best with 95%+ adherence, so for example, an individual will need to have (and take) at least 95 pills for 100 days, which would give an MPR of 95% or more. In order to satisfy that, an individual needs to ensure they refill their medicine on time.

The chart below shows the percentage of individuals that maintained at least 95% MPR/adherence under the different treatments:

After 180 days, the adherence rates (i.e. MPR of >95%) were:

1. Control – 22.6%

2. Financial Incentive – 31.8%

3. Reminder – 31.7%

4. Combination – 47.4%

5. Information – 28.3%

6. Stigma-Relieving – 35.7%

The Combination group clearly performed better than other groups in the raw data.

What Improves Adherence?

Using a regression-based analysis to get more granular impact, YSRM found that financial incentives increase adherence by about 10.1 percentage points. Reminders increase adherence by approximately 11.6 percentage points. The Combination increases adherence by 24.3 percentage points, which is a bit more than the two interventions additively.

The fact that the Combination result is greater than the sum of the two interventions is actually an important result. It means that the two interventions are valuable on their own. Often, each additional intervention has less of an impact when combined with other interventions, since the interventions are targeting the same population. If one intervention leads to adherence, giving a second intervention would be wasteful. Thus, typically we should not expect two interventions to have an additive impact.

Why Do People Not Adhere to ART?

YSMR were also able to determine that inattentiveness and forgetfulness do occur. The Reminder group showed increased adherence by showing up for medicine refills even before the reminder call was made! This means that just the knowledge of a reminder call meant individuals were paying more attention to taking pills and thus, saw based on their pill count, that they needed to refill their medicine.

With regards to forgetfulness, YSMR noted that in the Financial Incentive group, ART adherence increased mainly by individuals who thought they were refilling their medicine before the due date, when in fact they were refilling after the due date (and thus did not receive the cash incentive). This tells us that the financial incentive made individuals think 30 days haven’t passed yet, when in fact 30 days did pass. Thus, the financial incentive acted as a type of reminder that one ought to go to get a refill.

Implementing Policies – Dealing With People’s Behaviors

Free ART on its own is a really beneficial policy – both for individuals and society. Uptake of this policy is, however, quite low, even though it’s extremely beneficial to the individual. Thus, another policy is introduced – financial incentive for ART adherence. Again, the impact of this was relatively muted. The YSMR study shows us that this lack of adherence is driven partially by human behaviors – inattentiveness and forgetfulness. Thus, what may look like a ‘failed’ policy, is actually a policy implementation issue. Adding reminders significantly boosted the adherence rate.This finding has also been established in other research. Banerjee et al. (2025) conducted a large-scale experiment in India on immunizations and found that a policy mix of financial incentives, reminders and outreach ambassadors increased immunization by 44%.

This has implications for many similar policies – especially financial incentives. Many policies are usually based on some form of financial incentive – lowering tax, increasing a transfer, providing a voucher or pre-loaded debit card. Take up of policies can fail as people are inattentive to repeated tasks or forget deadlines for applying for these programs. YSMR shows that it can be relatively cheap to improve uptake of these policies by adding a reminder mechanism such as a call (or maybe an app notification).

Governments on every level may find it beneficial to focus more on connecting citizens to already existing programs, rather than creating new programs or policies to address issues. New programs may similarly suffer from low uptake of old programs, as they do not solve the issue of inattentiveness or forgetfulness. Instead, focusing on outreach or incentives that reminds citizens to existing programs may be much more effective. The creation of such civilian outreach programs has been shown to have large positive impacts on communities, as citizens are made aware of what’s available to support them.

Let me know what else you think reduces the effectiveness of policies in the comments below.

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Interesting Reads from the Week

1. Mind the Failure Gap: discusses research about how people underestimate how often people and systems fail. This has implications for policy, especially in how people may view punishments for infractions.

2. Nobody could tell me what they’d pay for a robot: asks an interesting question, to which the answer is not as easy as it seems – how much would you pay for a robot that can do house chores? Let her know your response.

3. The Bureau of Economic Analysis (BEA) made a change (without announcing it) to how one of the inflation measures is computed. It lowered reported inflation by 0.1 percentage points. The lack of information about this change, however, is concerning:

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Venture capital (VC) – a form of financing high risk/high reward early stage firms – has led to the creation of many large companies like WhatsApp, eBay, Uber, etc. Although VC investments can be highly lucrative, one of the main concerns of investing in early stage startups is the alignment of interests between investors and company founders, commonly referred to as the ‘principal-agent problem’. VC investments alleviate this problem through complex contracts1 between investors and founders. However, since the 2000s, changes in these contracts may have led to an increase in founder fraud. Dyck, Fang, Hebert and Xu (2026) (“DFHX”) took a closer look into this.

What is Venture Fraud and Why Should We Care?

Venture fraud occurs when the company, most often its founders, deliberately conducts fraudulent activity that harms the VC investor. This is often done when founders misrepresent financials (Spartan Micro), claim to have more customers than they do (Frank vs JP Morgan), or lie about what their product does (Theranos).

One could argue that venture fraud is a private matter since most damages occur between two private parties – investors and founders. There may even be an ‘optimal’ level of fraud, since to perfectly prevent fraud, investors would need to spend a lot of money and time to ensure fraud doesn’t occur, or simply undertake fewer investments. However, the effect of venture fraud may spillover more broadly into society, which then makes it a topic of interest.

The Case of Theranos

Theranos claimed to have designed a compact blood testing machine that, using a small amount of blood, would give quick results. Several US pharmacies planned to open in-store rapid blood testing using this product. However, it turned out that the machine was entirely fraudulent. This ended up hurting patients, who were misled by the tests. The damages to patients, as well as the wasted time/capital of pharmacies are specific social damages – commonly known in economics as negative externalities.

Investors, beyond their own financial windfall, do not take into account possible negative externalities – i.e. they do not internalize these negative externalities. This means that from a society perspective, venture fraud is more damaging than to the investor, and therefore, too much venture fraud occurs.

Preventing Fraud

The main way investors prevent fraud from occurring is by having control in the firm they invest in. This is usually done via having voting rights in the board of the firm. In this role, investors can vote on changing CEOs (who are often the original founders), as well as setting compensation structures for the CEOs. Company boards also approve or block key business decisions. Thus, investors may often want to have control of the board (i.e. more than 50% of the voting rights). But in the last few decades, this investor control appears to have reduced.

Causes of Venture Fraud

The Theories

DFHX posit three different hypotheses on what may make fraud more likely to occur:

• Founder-friendly board structures – the original founders have more voting power in the boards of their companies, allowing them to mask fraud;

• The number of investors in a firm has increased – each new investor in a board may have a different goal (company growth vs. cash distributions to investors) leading to more board disagreements, which in turn reduce the quality of oversight in a firm, allowing fraud to occur;

• Founder characteristics – certain traits of founders, whether innate or developed over time, can lead to fraud occurring more often.

DFHX also looked at whether market conditions mattered for fraud. DFHX hypothesized that under “Hot Markets” – if macroeconomic conditions are such that firm valuations are higher, board control may be weaker, as investors are not as focused on company control. Moreover, there may be more investors in the market, competing for a limited pool of founders, which in turn allows founders to have friendlier board structures.

Data Collection

To test the theories, DFHX turned to data. The data collection process here is actually quite interesting, as there is no simple data set that can be easily looked into. DFHX looked at several sources – Securities and Exchange Commission (SEC) and US Department of Justice (DOJ) official filings, a Class Action lawsuits database, the Westlaw research platform and the Pitchbook investments news data feed.

The main issue for DFHX with data collection is the concern of not capturing all venture capital fraud events. This can skew the results if, for example, only specific venture fraud cases are reported. That is why DFHX looked at multiple databases to capture as many fraud cases as possible. Overall, DFHX built a sample of 614 US based venture fraud cases since 2000.2

Now, to study if venture-capital backed firms are more or less likely to have fraud cases, DFHX looked at a sample of 4,094 VC-backed firms and non-VC-backed firms that had an Initial Public Offering (IPO). IPOs (being listed on the stock market) have stringent disclosure requirements; often, fraud gets discovered in this phase. The fraud discovered during the IPO (or soon after it) can be assumed to have been caused by the company’s management team (i.e. often the founder) prior to the IPO.

Separately, to study what may be driving the differences in fraud level amongst venture capital firms, DFHX looked at a sample of 78,852 VC-backed firms in the 2000-2020 time period.

Predictors of Venture Fraud – Results

DFHX found that VC-backed firms that IPO’d are 54% more likely to have committed fraud than non-VC backed firms. To put it into context, about 8% of firms that IPO’d had a fraud lawsuit, while the rate for VC-backed firms was closer to 12-13%.

When looking specifically at VC-funded firms (not necessarily IPO’d), DFHX found that companies where the founder has control over the board are 88% more likely to commit fraud. Moreover, every additional investor on the board increases fraud likelihood by 8%. If the investors are less involved in oversight, like mutual funds or hedge funds who do not actively participate in board meetings, fraud is also more prevalent.

When looking at founder characteristics (age, gender, education, etc.), DFHX found that these did not matter for the likelihood of fraud. What did matter is whether the market was ‘hot’ when the VC funding round happened. That’s most likely driven by the fact that investors offered founder-friendly terms during these times, as there were more investors chasing fewer founders.

Lastly, one additional question DFHX looked at is whether fraud-involved founders are ‘disciplined’ by the market. Typically, one would assume that if someone committed fraud, this person would be less likely to receive investment in the future. Surprisingly, DFHX found that there is no market discipline – founders that committed fraud are just as likely to receive investment in the future as founders that did not.

Dealing With Fraud

The DFHX paper has several interesting implications. First, it shows the importance of the often-studied topic in economics of the principal-agent problem. The reduction in oversight by investors appears to have led to a real increase in fraud by founders (shown in next section below). It also shows the weakness of market mechanisms, as it appears that the market is not discipling fraudulent founders. This is a surprising result, as a common argument against regulations is that market actors will self-discipline. Here, it appears that investors are willing to take unnecessary losses.3

Regulatory Scope and The AI Boom

If we look at the VC market today, it appears that we’re in a ‘hot’ genAI market. Based on the DFHX research, this suggests that in the future, we may see quite a bit more fraud than usual associated with these VC-backed investments. As shown here by Ewens and Malenko (2020), founder/entrepreneur control has gone up over time at every stage of the company’s financial lifecycle in recent years (lines specify which round of fundraising it was):

It’s worth noting that, historically, founders rarely had 50% control after the first round of funding, as can be seen in the middle chart.

It is worth noting, that in other contexts, such as in the Private Equity-backed companies, DFHX found that fraud has not increased, and actually declined, clearly showing that oversight by investors, specifically in the VC space, is lacking:

From a policy perspective, it may make sense for governments to enhance oversight over VC-backed companies by potentially increasing financial reporting requirements, as VC investors appear to be more tolerant of fraud than socially optimal. This suggests that there may be scope for regulations to correct this market inefficiency.

Let me know your thoughts on this topic and whether you think intervention may be needed to reduce the amount of fraud.

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Interesting Reads from the Week

1. A Simple Idea That Could Change Things for Kids: Child Impact Statements – provides an interesting idea of requiring policy changes to have a ‘child impact statement’: that is, an explicit statement on how this policy change will impact children.

2. Does Where You’re Born Matter More Than How Hard You Work? – discussed why it appears that the physical location where one grows up explains a large amount of a person’s outcome: “Roughly 60 percent of the variation in outcomes across neighborhoods reflects genuine causal effects of place – not just differences in the families who happen to live there.”

3. Note: An economics professor argues that economists should not come up with solutions. That is not something I agree with – as discussed in our article a few weeks ago.

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Financial well-being, especially of children, is an issue that many countries grapple with. US President, Donald Trump, announced a policy in which newborns born between 2025 to 2028 will receive an investment account with $1,000 in it provided by the US Treasury (i.e. US tax payers). The money in the “Trump Account” can be invested in certain stock market funds, but the funds themselves can only be accessed after the child turns 18.1

Now, on paper, this policy sounds good, as we’re boosting savings for families with children. But, upon closer inspection, this is a policy that does not actually help much at all – and it’s not because the amount of money invested is small. Let’s dive in.

Alleviating Child Poverty

Before we start assessing the Trump Account policy, we should first determine what it is that we want to achieve with the policy. A policy is just a tool to achieve a goal. The goal, at least to me, is to improve outcomes/welfare for children and, perhaps, teach children and families to save/invest via exposure to markets.

Child poverty in the US is a serious problem. Around 12%-14% of children live in poverty2:

On the above chart, you may notice that, in 2021, there was a dramatic fall in child poverty, which then rebounded to the current highs. The reason child poverty fell in the US was thanks to the numerous government programs helping low income families, of which the 2021 Expanded Child Tax Credit3 (eCTC) was the most important. We’ve discussed this policy extensively here, and argued that it is a policy that pays for itself. The eCTC was a 40 times larger program than “Trump Accounts”, but the reason it worked had to do more with the fact that it immediately alleviated issues for families in need. Per every $100 a family received:

• $28 was spent on food;

• $31 on housing; and

• $15 on child related goods and services.

On the other hand, a savings account will have none of this impact, as the money is locked until age 18.

Some might argue that these families who receive the “Trump Account” could increase consumption by saving less in other accounts. The problem with this argument is that families in poverty do not have savings, and thus they cannot ‘tap’ into other savings.

Therefore, for low income families, this policy will not have any meaningful impact, at least for 18 years.

Shuffling Savings

Turning to non-low income families who already may have their basic needs met, “Trump Accounts” could in theory benefit them by increasing and encouraging more saving. However, as these families may already have savings, it is unclear whether their overall savings will increase by the full $1,000 amount. That’s because a family may choose to save less in other accounts (savings accounts, personal brokerages, etc).

Research on other types of tax-advantaged accounts (i.e. saving accounts that have certain tax benefits) have shown that adjusting savings is common. Benjamin (2003) showed that when a person becomes eligible to use a new tax-advantaged retirement saving account, only around half of the funds that are put into the new tax-advantaged account are truly new savings. The rest is simply shuffling savings from a different account. Other studies found even lower percentages – Fehr, Habermann and Kindermann (2008) showed that only 22% of funds in tax-advantaged accounts are truly new savings, while Pence (2001) argued that the percentage may actually be 0%.

Regardless, this tells us that many families will simply adjust their other savings in response to the “Trump Accounts”. Intuitively, it makes sense. Suppose you have a rule of thumb that you save 30% of your income. If you receive an unexpected $1,000 in income, you may end up spending $700 more now to maintain the same ratio of consumption to saving.

Therefore, the “Trump Savings” account will not increase saving by a significant amount. Furthermore, in effect, tax payers will be subsidizing the consumption of higher income families, as the funds in the “Trump Accounts” come from tax payers.

Negative Impact on Recipients

While researching for this piece, one thing that surprised me is that there may be “Trump Account” recipients that can end up worse off. This surprising outcome is driven by how different government programs interact. Dynarski (2003) showed how similar tax advantaged accounts – the 529 savings plan, which allows families to save money tax free to be used to cover children’s college costs – can end up harming users. That’s because the US also gives college applicants financial aid that depends on the financial situation of a family. Thus, if you save enough, you may no longer be eligible for financial aid, resulting in no benefit whatsoever from saving.

Although this may be somewhat of an extreme case, it’s worth noting that not all the accumulated savings in “Trump Accounts” may end up truly staying with the recipient.

Financial Literacy

So is there an aspect where this policy may have larger positive impacts? One way “Trump Accounts” may be helpful is that it exposes people to saving and stock market investing. This in turn could lead to more saving/investing in the future, as children exposed to savings/investments will become adults that are more financially literate.

The question therefore is – does having an account from birth lead to children and their parents becoming more financially literate? The evidence on this is mixed (and the amount of research on this topic is limited). There have been similar programs enacted at the state level in the US – for example, the SEED Oklahoma program, where several thousand children received a $1,000 savings account at birth that was to be used for college. Results were mixed, as several analyses showed positive impacts (more likely to go to college, higher savings), but under closer inspection, a lot of these outcomes were driven by wealthy families.

An Experiment

A potentially better method to improve financial literacy is to actually teach children in school. Horn, Jamison, Karlan and Zinman (2023) (“HJKZ”) undertook a financial literacy experiment in Uganda. HJKZ took 2800 Ugandan youths and split them into 4 groups:

• Group 1 was given no intervention;

• Group 2 was given access to a savings account at a bank;

• Group 3 received financial education; and,

• Group 4 got the account and financial education.

The outcomes that were studied included – overall financial literacy, trust in financial institutions and the amount that was saved. One year after the intervention, the two groups that received education showed higher financial literacy and trust in financial institutions, while the group that only got the bank account showed no change.

With regards to saving, a similar pattern occurred – groups receiving financial education had higher savings (Note: since the study had a relatively small number of participants, HJKZ could not conclusively reject that the group that only had the account saved less than the education groups).

HJKZ followed up on this study five years later. Surprisingly, the financial literacy of the two education groups reverted to pre-finance education levels, although trust in institutions remained. Savings behavior remained similar to what happened after one year (higher savings than the group that received no account/no financial education, but unclear whether higher than the group that just received the account).

Overall, with the caveats that this is just one study, it appears that any intervention (whether account opening or education) has some positive impact with increased saving. In general, financial education appears to be slightly better than just having an account. Either way, the cost of either helping youths open bank accounts or provide education is relatively low, and thus it may be beneficial to conduct these interventions either way.

How To Improve the “Trump Account” Idea

I will first note that the “Trump Accounts” program is a small program. With about 3.5mln births in the US per year, the direct cost would be $3.6bln annually (for comparison, the Child Tax Credit program currently costs $128bln).4

However, we should still evaluate the program based on whether this is an effective spend of money. I feel quite comfortable saying “no” – the “Trump Accounts” are a poor spend of money. If the aim is to improve outcomes for low income families, then direct unconditional assistance (such as the Child Tax Credit program) is superior. If the aim is financial education, then a better program could easily be designed at the same cost.

For example, opening savings/investment accounts for school children coupled with a financial literacy course would probably be better. These accounts could also start with a government investment, albeit at a lower amount like $100. Moreover, since financial literacy dissipates, perhaps, refresher courses every 3-4 years would be beneficial. As part of completing these courses, students could get another $100 added to their account. This would encourage participation in these financial literacy classes. Students exposed several times during their studies might come out far better equipped.5 This policy would likely be much more effective and cheaper than the “Trump Accounts”.

Financial literacy and saving is an issue faced by all societies. Although today we focused on the US (and Uganda), I believe these findings are applicable globally.

Let me know your thoughts on these accounts and what you think would improve financial literacy.

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Interesting Reads from the Week

1. Stop looking at economic averages! – discusses why focusing on average statistics does not paint the whole picture of the economy – distributions matter.

2. Political journalism needs fewer “takes” and more analytical legwork – highlights the current problems with political journalism, which is similar to what we see in economics.

3. Prediction – Jason Furman asked people what they think average annual productivity growth will be in the next decade, when taking into account AI. He provided a table of 5 possible outcomes, and asked readers for their probabilities. Here is Nominal News’ forecast in red:

   

   Generally, I do not expect any major surprises. I do worry that there might be an ‘accidental’ recession that could happen due to misuse of genAI (similar to the financial crisis of 2008 and the misuse of collateralization calculations). My main reason for forecasting a solid breakout is that energy costs may drop in the next decade allowing for greater production + genAI lowering the cost of trying out new ideas, especially in early phase applied research.

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Friday, February 20, 2026 was a big economic news day in the US.

1. The US Supreme Court deemed President Donald Trump’s tariffs issued under the International Emergency Economic Powers Act (IEEPA) as illegal.

2. The Bureau of Economic Analysis (BEA) published US fourth quarter Gross Domestic Product (GDP) data which showed that real GDP growth was 1.4% (while various official forecasts were suggesting between 2.5% to 3.5% growth).

3. The BEA also released the Personal Consumption Expenditure Price Index (PCE – the Federal Reserve’s preferred measure of inflation) for the month of December, which showed inflation at 0.4% (month-over-month) and 2.9% (year-over-year).

So how do all these pieces of news fit together?

Tariffs – Soon to Disappear?

The decision by the Supreme Court to deem the tariffs as illegal was widely expected. But this doesn’t mean the end of tariffs, as President Trump quickly reinstated tariffs under a different section of US law. The newly announced tariff rate – i.e. tax rate on imported goods – is 15% on all imports, regardless of origin. These new tariffs can only be in place for 150 days until Congress must approve them through a vote. At the same time, the legality of these tariffs is also being questioned.

The exact impact of the recent changes in tariffs is still ambiguous. Some are forecasting a higher effective tariff rate than was currently present (the effective tariff rate is computed as the total amount paid to the government by importers over the total amount that is imported). On the other hand, the Yale Budget Lab estimates that the effective tariff rate will be lower. Nonetheless, there will definitely be a change in the distributional impact of these tariffs, since the new tariffs are placed on all countries, while the previous tariff regime was targeted at different countries.

Tariff Refunds?

One significant development emerging from the Supreme Court decision is whether the tariffs collected in 2025 will need to be refunded to the importers. From an economic perspective, if the tariffs are refunded, it may lead to the net impact of the tariff policy being a large transfer from consumers to firms.

To understand why, we first note that tariffs are simply a tax on imported goods paid by US based importers. This tax, however, is ultimately paid by local consumers since importers typically increase the price of these imported goods in the US to recoup the tariff (tax) they had to pay. Now, the benefit of this tariff (tax) is that the government (i.e. US citizens) gets the revenues. With the Supreme Court ruling, if this collected revenue gets refunded, it will go back to the importers, since they were the one that paid the tariff tax originally.

Therefore, the net impact of these IEEPA tariffs with the Supreme Court ruling could be a de facto transfer from local US consumers to US importing firms, making it a very regressive policy (transferring from poorer individuals who had to pay extra for goods to richer individuals – owners/shareholders of importing companies).

However, it is worth adding that even absent these potential refunds, the tariffs have adversely impacted GDP growth and increased inflation.

GDP Growth – Negative Impacts of Tariffs

US fourth quarter real GDP growth came in at 1.4%, which was lower than various forecasts suggested. It is worth adding an important caveat. One of the reasons real GDP growth came in lower than expected was due to the government shutdown that lasted 43 days (October 1 to November 12, 2025).

Aside – Government Services Adjustment

Government-provided services are part of GDP. During the government shutdown, the government stopped providing most services, so in calculating total GDP, it can be assumed that the government provided 43 days less worth of services. But at the same time, government employees were still paid for these 43 days. Therefore, during the fourth quarter, the US government provided fewer services, but it cost the exact same amount as in other quarters during the year.1 Thus, although nominal GDP came in at 5.1%, real GDP came in at 1.4% since the amount of real government services was 43 days fewer.

Absent the government shutdown, the fourth quarter real GDP growth would have been closer to 2.5%. This ‘missing’ real GDP will reverse in the first quarter of 2026, as the government will return to a normal level of government service provision.

Overall 2025 GDP Growth

For 2025, real GDP growth was 2.2% (adjusting for the government shutdown, it would have been 2.5%). In 2024, real GDP growth was at 2.8%. Last year, we wrote an article talking about research on the likely impact of tariffs, summarized in the table below:

The above table shows that tariffs were estimated to lower real GDP by around 0.75% in the period after tariff enactment. Interestingly, this means that absent tariffs, real GDP growth in 2025 would have been around 3.3% (after adjusting for the government shutdown and adding the impact from the table), which would be a bit higher than 2024 real GDP growth.

Although this high level comparison is not definitive, it does appear that these initial estimates in the Kalemli-Ozcan, Soylu and Yıldırım (2025) have captured reality well. Even the inflation forecast seemed quite good.

US Inflation

After the release of the December data, US PCE measured inflation for the year was 2.9%. Using the estimate in the table above, absent tariffs, inflation would have probably been around 2.4% (target inflation is ~2%). Some data suggests that inflation may have been even lower absent tariffs. The image below shows a different estimate:

Since March 2025, Consumer Price Index (CPI) measured inflation (a slightly different inflation measure) would have been 1 percentage point lower had tariffs not been enacted.

Tariffs and the Economy

The recent developments show us that tariffs (a domestic tax on imported goods) are really not a good policy. So far, tariffs:

• Led to higher inflation by around 0.5 pp to 1 pp;

• Reduced GDP by around 0.3%-0.5%; and

• May end up being a direct transfer from poorer consumers to richer importers.

I think it’s safe to say that these are not generally desirable outcomes from a policy perspective.

The only positive from this tariff policy is that it has shown that academic economists are pretty good at modelling the impacts of such a policy, and, perhaps, they should be listened to a bit more.

To read more on our coverage of tariffs:

• Yes – Tariffs Have Increased Prices and Inflation in the US (June 15, 2025)

• Modelling the Impact of Tariffs (April 30, 2025)

• Why Import Tariffs are Identical to Export Taxes (April 13, 2025)

• Do Tariffs Cause Inflation? Yes. (March 24, 2025)

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Interesting Reads from the Week

1. Trust in Numbers – tackles the concerns around the reliability of US government statistics, showing that the statistics are currently reliable, although funding cuts may make collecting data harder.

2. Removing Truth & Sanity from American History (United States) – conducts an interview with Michiko Quinones (Co-Founder and Director of Public History for the 1838 Black Metropolis Project), where they discuss the re-writing of history via the recent removal of display panels from the President’s House in Philadelphia, which included information about the role of the U.S. slave trade in the development of the country.

3. America’s Pensions Can’t Beat a Vanguard Account Or Finance a Transmission Line, But They Can Close Your Hospital – discusses how pension funds can invest in important projects for society (energy projects, additional housing). Pension funds in other countries often do this, while US pension funds appear to be less efficient with their use of capital, spending a lot more on fund managers.

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On February 4, Jon Stewart – a well-known American comedian/writer/political commentator – interviewed Nobel Prize-winning economist Richard Thaler. The discussion touched on several economic topics like healthcare and climate change. The interview itself was relatively inconsequential, but there was significant fallout post the interview with numerous economists and economics commentators criticizing Jon Stewart harshly for what was viewed as very dismissive comments about the field of economics.

I listened to the interview and I see it as a classic case of economists and non-economists talking past each other. But I don’t think the avalanche of criticism against Stewart heaped on by economists was justified.

Economics in Trouble

I am a PhD-trained economist, and I believe economics is both an important and useful subject. Economics studies human decision-making; these insights are invaluable for policy discussion. I also believe that the majority of PhD-trained economists conduct research in good faith, with the aim to gain a deeper understanding of human behavior, which often results in better policy making.

In the interview during the a discussion on climate change policies, however, Jon Stewart said the following about economists:

“So my point is, since when is economics about improving the human condition and not just making money for the companies that are extracting the fossil fuels from the Earth?”

Stewart’s view of economics is starkly different from mine. It is an incorrect view, but the reason Jon has that view is because, as a field, economics has failed to accurately distinguish what we actually do. Unfortunately, the blame for this should fall on us, economists, as we have failed to properly demonstrate what we do, allowing for others to use economics as cover for their opinions. Let me explain.

Economics Research, Policy and Politics

In the interview, Stewart and Thaler focus a lot of their discussion on climate change and reducing pollution. Stewart asks about what economists would recommend to deal with climate change, to which Thaler responds by mentioning carbon taxes as the solution. Stewart says that such a policy would never pass.

We have several issues occurring in this exchange, which is predominantly driven by talking about different things. Thaler responds to Stewart’s question by proposing a very good policy (a policy I also would personally support). Stewart, however, changes the question by bringing in a constraint – he’d like to hear a policy that would be politically feasible. Stewart was a bit vague with his initial question, which caused the confusion.

Economists, however, can tackle both of Stewart’s questions. If you, as a personal preference for a social outcome, want to see less pollution, economists may recommend the carbon tax policy. If you add a constraint to this preferred outcome – for example, I want less pollution but I don’t want taxes – economists may provide a different policy.

Basically, if you have a preference for a social outcome for anything involving people such as less pollution, more technological innovation, more factories, etc., economists can give you a list of policies that would achieve the stated goal. Moreover, each policy would have a particular cost associated with it. In the climate change case, it may look like this:

• Carbon Tax: short/medium run things will be more expensive, which may have distributional consequences;

• Subsidy: taxes need to be raised to fund subsidies; it is difficult to set subsidies to achieve desired goals;

• Regulations: costly to enforce (need to create a new body); regulation evasion can be significant.

Armed with this, a politician/policy maker/voter can pick their preferred policy. None of the policies above are wrong, as long as it is aligned with the preferred outcome of a politician or individual. Economists, in their capacity as experts, should focus on how to best achieve your stated goals/outcomes (we can think of this as the ‘fiduciary’ responsibility of economists).

However, economists are also people, and they often have their own preferred goals/outcomes, which is where things get murky.

When Policy Recommendation and Personal Preferences Get Murky

The problem for economics occurs when economists or economics commentators start intentionally mixing policy recommendations with their own preferences. Unfortunately, these are rarely easy to spot.

One notable example to me is the recent proposal by New York City mayor of lowering transit cost by providing free buses. Numerous articles and comments came out criticizing the program. But the reality is that economics research is quite clear that free buses (and free transit) is a good policy to address this issue. However, the field of economics has not pushed back against the false narrative being spun around the policy.

Another topic where the policies and personal preference got murky is the debate around wealth inequality. Many economists and commentators are often very critical of any changes to taxation or the introduction of wealth taxes. These commentators often point to the costs of implementing the policy, as if the costs themselves are sufficient reason for not implementing changes. Instead, it should be the choice of the public about what costs they want to bear and trade-offs they want to undertake.1 It is not for economists to decide which policy to choose – just to inform about the cost/benefits to the best of our knowledge.

Thus, the problem for the field of economists is that many of these economists/economic commentators covertly pass on their personal preferences and views, under the guise of economics. This is why Jon Stewart ultimately viewed the field in the negative context – he’s seen plenty of headlines and news pieces, which do that, and assumed that the entire field of economics must be like this. Economists, unfortunately, ceded the conversation on what economics does and let it be usurped by fancy looking opinion pieces. We shouldn’t be surprised that an opinion like Stewart’s is out there – we haven’t done much to combat it.

What Should Economists Do?

Naturally the next question is what should economists do, to be accurately depicted.

First – an easy one – don’t pile on people like Stewart (or anyone for that matter – as mentioned by Claudia Sahm). Stewart has his beliefs and preferred social outcomes. We, as economists, should engage people like Stewart and can help explain which policies can achieve his preferred social outcomes (Thaler did a good job with this in the podcast).

Moreover, since we economists are trained to think in particular frameworks which focus on preferences, in discussions like the one with Stewart, we should make sure we understand people’s preferences over social outcomes. Too often the debate focuses on a particular policy (free buses, wealth tax, etc), which is a tool and not the outcome the policy is supposed to generate.

But what else should we do? This is where it gets harder, but economists should start a conversation about how to properly communicate what we do. As mentioned earlier, the vast majority of academic economists and commentators act in the way I described economists should. Often, however, the most vocal ones do not. I do not have an easy answer on how to deal with this. Perhaps there is a need for a centralized body/committee that can clearly present economic findings, or present how different policies will affect the economy. Or maybe handbooks (similar to the ones in medicine) that may act as solid starting points for policies.

Taking One Step at a Time

This post is one of the reasons I started Nominal News, as I have seen this problem grow over the last decade. With Nominal News, I try my best to present economics as the important and useful field it is – a field that is filled with many great and brilliant economists and educators that have designed plenty of great tools to use under different circumstances.

I will finish this post with a humorous post by Professor Ben Ho:

Let me know if you have any thoughts on how economics can improve its image and what I, at Nominal News, could do better?

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Interesting Reads from the Week

1. What is Critical Thinking? And why is it Important? – discusses what it is ‘critical thinking’ and what steps we can take to improve our critical thinking skills.

2. Why Affordability and the Vibecession Are Real Economic Problems – synthesizes various strands in the affordability/vibecession debate. This includes the rising prices of essentials, higher costs to own a house, and the overall impacts of higher economic uncertainty.

3. AI and the Economics of the Human Touch – wrote an important piece highlighting how to approach AI from an economic standpoint, and uses historical examples to explain why technological change is unlikely to oust humans.

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In the US, a recent report by Challenger, Gray and Christmas Inc (the “Challenger Report”) made significant headlines, as it showed a surge in US layoff announcements in January 2026. The report stated that 108,435 job cuts were announced, a level not seen since 2009. Companies like Amazon and UPS have announced major layoffs. What to make of these layoffs, and, more broadly, are firms making the correct decision when conducting layoffs?

Layoffs in Context

First, it is worth putting the layoffs in context of the US Economy – weekly, around 100,000 to 300,000 new people file for unemployment. This suggests that these job cut announcements are not likely to impact national unemployment figures due to their relatively small size in comparison to the number of unemployed (around 7.5 mln currently). Moreover, as explained by labor economist here, the Challenger Report’s layoff announcement figures do not track actual layoffs well. It is directionally correct (i.e. layoffs have increased), but the magnitude in the Challenger Report is usually much

higher than what actually materializes. The reason for this discrepancy is unclear.

But how do layoffs, in general, impact:

• the wider economy;

• the individuals affected (which includes both laid off workers and workers that were not laid off); and

• the company making the decision to conduct layoffs.

I’ll start off from the latter.

Firms and Layoffs

The decision to layoff employees is an independent choice by the management of the company. Therefore, we would expect that this decision should bring some positive impact to the company, preferably in a quantifiable way. Answering this question is not as easy as it seems due to the fact that it is difficult to establish the counterfactual, i.e. what would have been had the company not laid off employees. Furthermore, as each company’s circumstances are unique, undertaking a ‘natural experiment’1 is not straightforward. It is difficult to argue that the company that did a layoff is similar to a company that did not do a layoff. However, attempts to address these issues and develop some insights have been made.

Layoff Impacts on Company Management and Stock Performance

Carriger (2016) summarized the research on the impact of downsizing (layoffs). There are two key ways to look at how downsizing might impact a firm – a management perspective, which focuses on how metrics such as profitability and return on equity or assets react to downsizing, and a finance perspective, which focuses on how a company’s publicly traded stock responds to the downsizing.

From a management perspective:

• De Meuse et al. (2004) found that the financial metrics of downsizing firms worsened in the years after a layoff event, but over time, improved back to match the financial performance of non–downsizing companies;

• They also found that companies that did fewer layoff events performed financially better than companies that had more layoffs;

• Guthrie and Datta (2009) found that downsizing reduces firm profitability, and is especially worse in industries characterized by large research & development investment, high growth, and low usage of tangible capital, such as machines (note: interestingly, this seems to describe the characteristics of the tech sector);

• Chalos and Chen (2003) found that the cause of downsizing matters. If the reason to downsize is cost-cutting (a reactive downsizing), then firms have worse financial performance; if it is revenue-refocusing (i.e. pro-active downsizing in response to future market trends), then financial performance improves.

Even with the caveat that it is difficult to ascertain what would have happened had these firms not downsized, which would allow us to establish a causal link, most research shows that there is generally no evidence or correlations suggesting downsizing for cost-cutting purposes will improve financial performance. Turning to the financial perspective:

• Chen et al. (2001) found that on the layoff announcement day, stock prices respond negatively, especially if the cause behind the layoffs is lowering demand for the company’s goods or services. They do find that profitability and labor productivity increases for companies that conduct layoffs. However, these results occur for companies that have been underperforming to their industry peers for the prior three years. Three years after the layoff decision, operational metrics of firms that conducted layoffs outperform industry peers, while employment reverts back to pre-layoff levels. Interestingly, this improvement does not get reflected in stock market performance – the stock performance three years after the layoff period remains similar to its peers. Hiller et al. (2007) find similar results;

• Brookman et al. (2007) found that companies in which CEO’s pay is linked to the performance of the stock are more likely to announce layoffs; this layoff decision does generate positive returns to shareholders. They find that this is potentially due to CEOs “adopting operating strategies that improve operating profits and stock performance”. Furthermore, similar to the Chen et al. (2001) study, Brookman et al. (2007) found that company internal performance metrics improved within two years of the layoff announcement;

• Marshall et al. (2012) found that stock prices respond positively to layoffs in good times, but fall in bad times;

• Capelle-Blanchard and Couderc (2007) conducted an overview of the entire literature of stock prices reacting to layoff announcements and found that generally stocks respond negatively to these announcements. As with research mentioned above, if the layoffs are driven by ‘defensive’ reasons (the firm is facing a difficulty), then the stock performance is even worse.

From the above, it appears that layoffs have negative impacts in the short-term on company stock prices. If the layoffs are ‘defensive’ in nature, most evidence points that both company stock performance and internal performance will suffer. This does not necessarily determine causality – i.e. do layoffs make the firm worse, or was the firm going to be faring poorly regardless of layoff decision. If the layoffs are done as a strategic decision, evidence suggests that companies fare somewhat better after them. Again, the same causality issue applies as above.

Longer Time Horizon

To further address some of these contradictory results, Carriger (2016) tracked companies for a longer period of time during the 2008 financial crisis. He partitioned companies into 4 groups by two categories: financially healthy and unhealthy companies based on the amount of available cash they were generating, and whether companies downsized in staff or not, specifically in 2008.

He found that companies that downsized in 2008 did worse in the period 2008-2014 than non-downsizing companies, regardless of financial health. However, any difference between these two groups became negligible in 2014. Furthermore, companies that downsized in 2008, immediately began re-hiring, and within 3 years were back to their pre-downsize employee count.

One important issue with this analysis is “Survivorship Bias”. Survivorship bias is the fact that when doing data analysis, we can only use data points of companies that existed (i.e. survived) throughout the data analysis period (in this case, from 2008 to 2014). More specifically, in the case of this particular study on layoffs and firms, we do not observe firms that ceased to exist in 2014. Thus, we do not have data points on companies that either chose or didn’t choose to downsize, but went bankrupt in that time frame. For example, a company that downsized in 2008 and in 2014, appeared to have poor financial performance, maybe would have gone entirely bankrupt if it had not downsized.2 In this instance, survivorship bias may make downsizing and layoffs seem like a less beneficial tool to the firm than it really is.

Layoffs and Individuals

Layoffs impact specific people. Economists are very interested in this impact because layoffs can potentially create many costs. For example, being laid off destroys accumulated human capital3 that was specific to the firm (i.e. by working at a firm, you already know how everything works and because this knowledge is specific to the firm, upon getting laid off, this knowledge is wasted). Furthermore, there are potentially many impacts on laid off workers, including changes to long-run career prospects, health outcomes, and even impacts on their families.

Unexpected Job Loss

Davis and von Wachter (2011) summarize the research around the various impacts of job displacement, an unexpected job loss initiated by the firm, usually involving multiple people being laid off. In their own research, they find that for laid off workers during mass layoff events, lifetime earnings of displaced workers drops by approximately 1.4 years of pre-displacement earnings (i.e. the drop in lifetime earnings is equivalent to you not working for 1.4 years, regardless of length of unemployment). This effect is twice as large, if at the time of the layoff, the unemployment rate in the US is high, above 8%.

There is debate, whether the act of layoffs itself results in lower earnings, or whether a different underlying cause is responsible. Fallick et al (2021) find that it is the joblessness duration after the layoff that explains all the drop in earnings – the longer one is unemployed, the worse their future earnings outcome.

Why Length of Unemployment May Matter

There are several hypotheses why duration of unemployment may have a significant impact on future earnings. The mains ones are:

1. worker differences (there is something inherently different about workers that stay unemployed longer, which results in them earning less in the future);

2. human capital depreciation (the longer one is unemployed, the more they ‘forget’ about the job and become less effective and thus, receive a lower wage);

3. changes to local labor markets (if many of those laid off are in one market, there is an over-supply of workers, leading to a reduction in earnings); and

4. moving to a worse firm (this is called the job ladder model: good companies hire from worse companies, but worse companies hire from the unemployed).

The authors find that the most likely reason is number 4. Workers that stay unemployed for longer than four quarters see significant earning reductions because they end up at generally worse firms.

Layoff Impacts – Lower Consumption, Worse Health and Less Child Education

Browning and Crossley (2001), using Canadian job loss data, find that consumption drops significantly for laid off individuals who are in the lower end of the income distribution, as they do not usually possess savings to use for consumption during unemployment spells. Stevens (1997) finds that job displacement lowers job stability, increases earnings instability, increases job and industry switching, as well as makes future unemployment spells more likely. These effects are very persistent, lasting 10 years after the layoff, resulting in earnings 9% below expected levels. If, however, a displaced worker is able to find a job in the same industry very soon after being laid-off, they experience an increase in earnings in comparison to their previous job.

Regarding health, workers that experience job loss have higher incidences of strokes and heart attacks (Burgard, Brand, and House, 2007). Sullivan and von Wachter (2010) use administrative and social security data that allowed them to track workers that were displaced and found that mortality rates significantly increased for this group. Based on their analysis, a job loss during a recession reduced life expectancy for middle-aged men by 1 to 1.5 years.

Besides impacts on the individual, job losses also spread to the family of the impacted workers. Stevens and Schaller (2011) established that parental job loss reduced children’s educational achievement, with children being 15% more likely to repeat a grade. Wightman (2009) further shows that both educational attainment and cognitive development of the children suffer after job loss. Other studies have shown that job loss increases divorce rates4 (Charles and Stephens, 2004), reduces fertility, and decreases homeownership (von Wachter and Handwerker, 2009).

Layoffs have a significant cost on employees. These costs are both direct with falling earnings and consumption, but also indirect, as health, life, and family outcomes are also adversely affected.

Layoffs and the Wider Economy

The prior two sections focused on the impact of layoffs on firms and individuals. These impacts, however, also have costs to the economy. From the firm side, layoffs might lead to lower profitability, which means we are generally using resources less efficiently, and the government has lower tax revenues. The negative consequences to the workers are also costly to the economy; as a society, we have less effective workers due to losses in health, human capital, and job match. Furthermore, government services are also additionally strained due to the adverse health and family outcomes of impacted workers, as they will seek assistance from government programs.

Local Community Impact

Beyond these economic costs, how do layoffs affect the communities and areas they are located in? Foote et al. (2018) studied mass layoff events from 2000 to 2011 to establish how impacted local labor markets respond to such events. They found that, if 1% of a county’s population gets laid off, the county’s labor force drops by 0.19 percentage points. This is caused by two key channels: migration (that is local population moving to other parts of the US) and quitting the labor force (i.e. permanently not looking for a job, retiring, or going on disability). Typically migration was the main driver of local labor market adjustments to layoffs. However, the authors found that after the Great Recession of 2007, quitting the labor force grew substantially and became the predominant way local labor markets adjust to shocks.

Layoffs – Costly to Firms and Employees

Summarizing:

• Current layoff announcements are relatively small and their magnitude overestimates the true size layoffs;

• It is unclear if firms that do layoffs are better off after layoffs from both operational and stock price perspectives;

• Workers that are laid off see real negative impacts – lower consumption and lower health outcomes.

I would preface the above conclusions with the fact that research on layoffs and its impacts still appears to be in its infancy, in part due to data difficulties and assessing the counterfactual (i.e. what would have happened if the firm did or did not conduct layoffs). Overall, though, it appears that the impact of layoffs on the overall economy is very murky – it is unclear if firms truly know that layoffs will improve their own performance, while costs to workers are significant.

Interestingly, the US unemployment insurance program requires firms that have undertaken layoffs to pay larger unemployment insurance taxes for a period of time. The bigger the layoffs, the larger the tax. This policy appears to focus on making the layoff decision a bit less frivolous. During the Great Recession of 2007-2009, this mechanism is estimated to have reduced layoffs by around 825,000.

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Interesting Reads from the Week

1. When “Efficiency” Means Admitting You Were Wrong – writes in that the recent layoff announcements may be driven by ‘malinvestment’ (when capital gets allocated based on distorted market signals rather than genuine economic fundamentals). In other words, firms mistakenly over-hired several years ago and now are incorrectly conducting too many layoffs.

2. When “Preferences Changed” Stops Being an Explanation – tackles the commonly used explanation for observed economic phenomena (my preferred example being the increase in working from home) is that people’s “preferences” have changed. As Sebastian points out, one needs to explain how and what caused preferences to change before claiming they did.

3. Will Trump’s Fed Nominee Kevin Warsh Be Chair In Name Only? – discusses the appointment of the new Federal Reserve Chair with a slightly different perspective from mine, arguing that Kevin Warsh will be able to push through his preferred interest rate policy.

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What Happened?

On January 30, 2026, US President Donald Trump picked Kevin Warsh to replace Jerome Powell on May 15, 2026 as the Chair of the Federal Reserve.

Why Does It Matter?

The Federal Reserve (the US Central Bank) sets US monetary policy. Unlike other central banks, the Federal Reserve has a dual mandate:

• Maintaining price stability by keeping inflation stable (typically at 2%);

• Maximizing employment.

Decisions made by the Federal Reserve, therefore, influence inflation and employment. These monetary policy decisions are made via a vote in the Federal Open Market Committee (FOMC). There are 12 voting members in the Federal Reserve:

• 7 Federal Reserve Governors (of which the Chair is one);

• The head of the New York Federal Reserve;

• 4 rotating presidents of the regional Federal Reserve Banks (there are 11 such banks).

The Chair of the Federal Reserve has one vote like all other voting members.

Why Are We Writing About This?

Independence Concerns

The reason this nomination garnered more media attention than usual is due to comments from President Trump about wanting lower interest rates. These comments have raised concerns that the Federal Reserve may no longer act independently of the government, which may result in economic instability.

Monetary policy, as mentioned above, influences both inflation and employment – high interest rates, which lower inflation, also slow down economic activity, which increase unemployment. This can naturally create a problem for the government – a slow, high-unemployment economy can reduce the chance of winning an election. That is why monetary policy is often not controlled directly by the governments, as governments may be tempted to protect their electoral position by keeping interest rates lower. This, however, can result in runaway inflation, due to the ‘unanchoring of inflation expectations’.

‘Inflation expectations’ is one of the three key drivers of inflation (if you’d like to learn more, we have a guide on inflation here). In simple terms, what people and firms believe inflation will be in the future impacts today’s inflation. Thus, if people believe that the Federal Reserve will no longer focus on keeping inflation stable in the future, then today’s inflation will rise. Restoring the ‘anchor’ can take many years, as you need people to credibly believe that the Federal Reserve will prioritize keeping inflation in check. (Somewhat worryingly, here is an observation by that inflation expectations may already be trending higher).

Should We Be Concerned?

I personally don’t think that inflation expectations will un-anchor. Monetary policy is voted on by 12 voters and thus, it is unlikely that a majority of these voters would go against what economics would broadly suggest (i.e. allow for inflation expectation to un-anchor). Moreover, although Warsh has stated that he would prefer to lower interest rates today which could increase the already high inflation of 3%, some of his other monetary policy preferences would lead to lower inflation. Let me explain.

Kevin Warsh’s Thoughts and Preferences

In one article (“The Federal Reserve’s Broken Leadership”), Warsh argues against the ‘large’ Federal Reserve balance sheet and would prefer to shrink it (commonly referred to as “Quantitative Tightening”). This is actually a disinflationary policy.

An Aside on Federal Reserve Balance Sheet Operations

The Federal Reserve holds assets – predominantly US Treasuries (government bonds) and Mortgage Backed Securities (home loans). The reason the Federal Reserve holds these assets is to increase the amount of ‘liquidity’ in the financial system, as well as lower the interest rate that needs to be paid on long-dated borrowing (especially home loans).

To understand how this happens, let’s think of a home loan. Initially, a bank issues a loan to a home buyer. The bank now owns an asset – the loan – which pays interest and principal in regular installments. However, the bank is ‘stuck’ with this asset, as it only slowly gets the money from the home buyer.

In comes the Federal Reserve, which can buy this home loan from the bank. The bank instantly receives all the cash and can re-lend this money, stimulating the economy. The Federal Reserve now holds the asset (the home loan) and slowly gets paid back. Since the bank knows that the Federal Reserve will be committing to buying the home loan, the bank can also offer a lower interest rate on future home purchasers. In this way, the Federal Reserve can both increase liquidity in the market (the bank now has cash it can lend again) and also reduce the long term interest rates (home loans have lower interest rates).

Reducing the Balance Sheet

Turning back to Warsh’s article, he stated a preference of reducing the Federal Reserve balance sheet, which would entail selling off the assets that the Federal Reserve has. This will have a disinflationary effect, as it would do the opposite of buying assets – increasing borrowing costs and reducing the amount of cash in the market.

So it appears Warsh prefers two policies – lowering interest rates and reducing the balance sheet – that work in the opposite directions, meaning inflation is unlikely to un-anchor.

What Does Concern Me

Although I am not concerned about inflation spiralling out of control, I am a bit concerned by the approach Warsh has towards analyzing certain economic events. Here are a few examples:

On AI

Warsh believes “AI will be a significant disinflationary force, increasing productivity and bolstering American competitiveness.” This is a strong and risky belief, as evidence for it is very minimal. Warsh predicts a 1 percentage point higher productivity growth, which is nearly doubling the long run productivity growth of 1.5%. Again, this appears to be very unlikely and more wishful than academically rigorous.

On Recent Inflation

“Second, inflation is a choice, and the Fed’s track record under Chair Jerome Powell is one of unwise choices. The Fed should re-examine its great mistakes that led to the great inflation”.

Warsh puts blame squarely on the Federal Reserve for high inflation. However, as we have discussed before, keeping inflation at 2% at all costs during the pandemic would have ended up severely harming workers. The Federal Reserve followed the latest research in not increasing interest rates by as much. Its action during the Covid pandemic resulted in large real wage growth for many workers, especially on the lower end of the income distribution. At the time, the choice for the Federal Reserve was either higher than usual inflation or large unemployment, and they wisely chose the first.

On Tariffs and Inflation

In this op-ed, Warsh states “So long as Mr. Powell’s Fed hasn’t suffered from a loss of inflation-fighting credibility, the effect of a 10% tariff [on inflation] shouldn’t be statistically significant.”

This is a surprising statement, as we know today that the tariffs have led to at least 0.65% extra inflation:

Tariffs do lead to higher inflation, as we discussed here.1 Tariffs (and potentially current immigration policy) are some of the main reasons we are at 3% inflation today rather than 2% inflation.

Concluding Remarks

Overall, I don’t have any strong concerns on Warsh, as ultimately the Federal Reserve Chair is just one vote in a committee of 12. My main concerns surround how he appears to view certain economic developments through one perspective without looking at all the data, with the tariff example being especially relevant. This dogmatic view of economic events may put him in an interesting spot, where his preferred policy will not be chosen by the FOMC (the interest rate voting committee), leading to some very awkward press conferences.

Tell me in the comments what do you think of the appointment of Warsh. Am I being too harsh on him? Or am I not concerned enough?

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Interesting Reads from the Week

1. Article – The Means-Testing Industrial Complex: shows how means-testing programs (such as work requirements) end up costing the government a significant amount of money.

2. Article – Young College Graduate Unemployment Is Worse Than the Slowdown Explains: explains that there may be truth to the fact that this slowdown might be worse for young college grads than previous ones.

3. Note: An interesting debate on X/Twitter, where Oliver Blanchard suggested that people in France prefer more leisure to Americans. This ended up with a discussion – do the French prefer more leisure or are they prevented from working more by laws/regulations?

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The US Supreme Court is the highest judicial body in the US, and opines on federal and constitutional issues. One unique feature of the Supreme Court is that it consists of 9 judges that are appointed for life, making judicial appointments extremely important. These Supreme Court justice appointments, which do not occur often, are usually made by the party in power, which has led to questions about the politicization of the Supreme Court. Surveys from the Pew Research Center show the public’s change in perception of the Supreme Court over time:

So is the public right in their view that the Supreme Court has polarized? Prat, Morton and Spritz (2026) (“PMS”) set out to answer that question.

Do Judges Rule in Favor of the Wealthy?

PMS started with a simple observation – a Supreme Court judge either rules in favor of the wealthier or poorer party of a court case. Different Supreme Court judges vote differently in the same court case. Many theories, involving legal interpretations, are proposed to explain this difference. However, what if instead, judges themselves have preferences over the wealth distribution? PMS decided to build a model of judges’ decision making.

Judges’ Preferences

A judge is assumed to have a preference over the ideal wealth distribution in society (in theory, every one of us has a preferred ideal wealth distribution). This preferred wealth distribution is referred to as the judge’s “ideal point”.

When faced with a court case, a judge can choose to side with the wealthier or poorer party. A judge gets utility (i.e. happiness) depending on the decision the judge makes. If the judge’s utility is higher when they side with the wealthier party over the poorer party, they’ll rule in favor of the wealthier party (and vice versa).

The amount of utility a judge gets from a decision depends on the court case specifics (i.e. it may be clearer which party should ‘win’ the court case) and how close the decision is to the judge’s “ideal point”.

Based on the above model, we can infer the judge’s “ideal point” based on their decisions. For example, if the outcome of a specific court case leans in favor of the wealthy party, while the judge rules in favor of the poor party, that would imply that the judge’s “ideal point” of the wealth distribution leans towards poorer individuals.

Judge Nominations

Supreme Court judges in the US get nominated by the majority party – the Republican or Democratic party. When the party in power gets to pick a judge, it usually chooses a judge from a pool of candidates. The pool of potential candidates is not random, as it inherently reflects the preferences of the party. In our particular case, we assume that both Republican and Democratic parties have preferences over the wealth distribution, and therefore the average judge in the pool of candidates from which they select would reflect this preference.

By looking at which judges are selected for the Supreme Court, PMS infer whether a party’s preferences over the wealth distribution have changed. For example, if judges selected by the Democratic party are more likely to side with the wealthier party in court, that would imply that the Democratic party’s preferences have moved towards the wealthy.

Now that we have the model, in order to determine how party preferences have changed, we need to look at data.

Data Collection

Contentious Court Cases

PMS, using undergraduate and graduate students, went through all Supreme Court rulings since 1953. First, PMS excluded all court cases in which the decision was unanimous, i.e. all 9 judges voted in the same way. The reason for this is that when all judges vote the same, it is not possible to infer the judges’ “ideal points” (preferences). To give a simpler example of why these court cases need to be excluded, if you and a group of friends all pick pizza when choosing dinner, then you cannot figure out from this observation who likes pizza the most (the technical term for this issue is we cannot ‘“identify” the preference).

PMS therefore focused solely on court cases where there is at least one Supreme Court judge that voted for the wealthier party and at least one judge voted for the poorer party.

Cases with Wealth Transfer

Next, PMS looked at court cases that resulted in a wealth transfer from one party to another. For example, if the case involved an employee receiving a payment from an employer, this court case would be selected for analysis, since the ruling would result in a direct transfer of wealth between the employee-employer.

Lastly, the selected court cases also had to have litigating parties that can be described as ‘wealthy’ or ‘poor’. For example, in the employee-employer case, the employer would be coded as ‘wealthy’, while the employee would be coded as ‘poor’. Court cases where this was not clear, such as a court case between two companies, were not included in the data set.

PMS laid out the protocol for classifying these cases quite clearly and imposed several checks, such as having multiple assistants classify the same cases to determine if there are any mistakes being made.

So Did Judges Become More Pro-Wealthy?

After classifying the cases, PMS looked at raw data:

The above chart shows Republican-selected judges (red) and Democratic-selected judges (blue). The y-axis shows the share of decisions made in favor of the wealthier party. The dots signify the date the judge was appointed. The line by party, at any point, represents the most recent 7-judge average (i.e. the red line is the average pro-wealthy vote share of the last 7 Republican judges).

From the graph, it can be seen that recently, there’s been a big shift towards pro-wealthy voting by Republican-nominated judges and slight fall in the pro-wealthy vote share for Democratic-nominated judges.

If we look at the specific type of cases (the case type is coded by the Supreme Court), the shift in voting occurs for all case types:

For example, in the top right, contentious cases involving unions have always been voted in favor of the wealthier party by the three most recent Republican-nominated judges.

Judges’ “Ideal Point” (Preference)

The above results show the share of decisions made in favor of the wealthy party. As mentioned in the model section, using the observed decisions, we can attempt to infer what is each judge’s ‘ideal point’ or preference for wealth distribution. The chart below shows that:

Values above 0 suggest a pro-wealthy preference, while values below suggest a pro-poor preference. The black line shows the current implied median justice preference. The implied “ideal point” of Republican nominated judges has increased significantly, which also implies that the pool of potential judges from which the Republican party selects has also become more pro-wealthy.

More specifically, the implied probability a Republican-appointed judge in 1953 would side with the wealthy party was 44% (for Democratic-appointed judges, it was 40%). In 2022, the probability that a Republican-nominated judge would side with the wealthier side is 74% (for Democratic-appointed judges, it is 27%).

What Does it All Mean?

The paper I discussed today, although political in nature, is mainly meant to demonstrate the variety of questions economic methods can be applied to. A recent post by talked about what is economics –

“Students arrive at class thinking it [economics] is “the study of money.” It’s not. Economics is the study of decision-making under scarcity.”

I liked this definition, and, moreover, I think that economics is more broadly the study of decision-making.

Today’s paper studied two decision processes – the decision of nominating a judge and judges’ voting decisions. As mentioned previously, judges’ decisions are often analyzed based on the legal school of thought (like textualism or originalism) judges prefer to use to interpret law. However, not all decisions by the same judge seem consistent with the school of thought. The research discussed today may explain this discrepancy, as it appears that judges’ wealth preferences may help explain why judges make different decisions in similar legal cases.

The Benefits of Clarity

Given the political nature of the question, there can be a lot of agreement/disagreement with the findings. The benefits of this paper by PMS is that the approach to the question is clearly laid out – which court cases were looked at, how the cases were coded, etc. This enables for a much more productive discussion, as any and all assumptions made by PMS are clearly stated.

One minor concern I noted is that the authors cannot control for the possibility that the cases brought to the Supreme Court may have fundamentally changed over time. For example, if in the 2000s cases, the law was more ‘objectively’ on the side of the wealthy parties, then some of the observed shift in Republican judge’s preferences may be due to the change in cases rather than an actual shift in preferences. However, some of this concern is alleviated by the fact that the Democratic-nominated judges sided more with poorer parties. Nonetheless, thanks to clarity of the model and the research, we can have such discussions.

The study by PMS is definitely not the last one on this topic and any future research will have to grapple with their findings of large shifts in judges’ decisions favoring the wealthy. Maybe this article will inspire one of our readers to tackle this issue!

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