Wednesday, 9 September 2026

The political language of economists, and the associated principal-agent problem for government

Governments often employ economists as policy analysts or consultants, to provide advice on policy. That relationship between the government and the economist is a principal-agent relationship (regardless of whether the economist is a government employee or a consultant), as my ECONS102 class covered this week. 

In a principal-agent interaction, one person or group (the agent) is given the power to decide how to use resources that ‘belong’ to someone else (the principal). In the case of economists working for government, the government is the principal and the economist is the agent. The 'resource' that the agent is using is their work time, which has been paid for by the government.

Now, the government wants the economist to use that work time to provide unbiased economic or policy advice. [*] However, the economist has their own goals and motivations. They might use that work time to scroll TikTok, and then use ChatGPT to write the advice. Or, they might use the time to provide biased advice, based on their own political preferences. Either way, this is detrimental to the interests of the government. This is the essence of the principal-agent problem (or the agency problem).

Do economists act that way? Many economists (including, at times, myself) argue that we are non-partisan, and unbiased in our policy advice, even if we hold particular political views. But is that actually the case?

That is the question addressed in this 2024 article by Zubin Jelveh (University of Maryland), Bruce Kogut, and Suresh Naidu (both Columbia University), published in The Economic Journal (ungated earlier version here). They first link over 53,000 economists from the American Economic Association member registry in 1993, 1997, 2002, and 2009 to two measures of political activity: (1) their campaign contributions (between 1979 and 2012) drawn from the Federal Election Commission's website; and (2) their signing of one or more of 35 petitions aligned with the political left or right.

Next, Jelveh et al. obtain the full text of over 62,000 academic articles and over 17,000 NBER Working Papers published by a subset of 2471 of the economists from the larger sample, between 1973 and 2011. They then rank each of the articles and papers in terms of 'ideological valence', based on the phrases that appear in it, and use that to derive several different 'ideology scores' for the writing of each economist.

Jelveh et al. then start to do some analysis of the ideology scores, finding first that:

...the fields of finance, macroeconomics and industrial organisation are more conservative, while labour is considerably more liberal than the average. Other fields, such as history and international trade, show less political valence. We further see that faculty at business schools are more conservative, as are professors affiliated with ‘freshwater’ schools, while ‘saltwater’ schools have a left-wing bent. Professors of European origin also seem to be somewhat more conservative, and there seems to be no association with Latin American origin, full professor rank or top five department ranking.

The 'saltwater schools' are predominantly those on the east coast of the US, like Harvard or MIT, while the 'freshwater schools' are predominantly those in the Midwest, such as Chicago or Minnesota. Those results, and the results by field of economics, will not surprise many people who know those schools or those fields.

The more interesting results involve the next stage of the paper, where Jelveh et al. look at several economics debates, where there is a clear left-right divide in terms of expected effects. For example, conservative economists may be more likely to believe that the minimum wage reduces employment, while liberal economists may be more likely to believe that it doesn't. Jelveh et al. take several meta-analyses on the minimum wage, and similarly political topics, and look at the relationship between the estimated elasticity in each paper in the meta-analysis, and the estimated political ideology of the authors. They find a statistically significant relationship - elasticities reported by more conservative economists tend to be consistent with more conservative policy prescriptions, while elasticities reported by more liberal economists tend to be consistent with more liberal policy prescriptions.

Economists have political leanings (as does everyone else), Jelveh et al. show that those political leanings are correlated with the results that economists report in their research. This isn't to say that economists are engaged in falsifying data to support their political beliefs. Jelveh et al. have no evidence of that. However, their results could arise if economists choose to apply methods or investigate datasets that are more likely to lead to results that are consistent with their beliefs. Or, economists may simply choose not to publish results that are inconsistent with their beliefs. Either way, this research provides some evidence that knowing the political preferences of economists may be important in interpreting the results from their research.

In my ECONS102 class, we discuss various ways that the principal can act to reduce the principal-agent problem. Those options include stricter monitoring of the agent, paying efficiency wages, or performance-based pay (or delayed payment). In this case, the government might ask the economist agent to fully document their research, or subject it to careful peer review. This would constitute stricter monitoring. However, the relationship between the government and the peer reviewer opens up an additional layer of potential principal-agent problems (what are the political preferences of the peer reviewer?). The government might pay a wage to the economist that is much higher than the equilibrium wage, hoping that would motivate them to produce higher-quality and less biased work (because if they didn't, they would lose their job and have to work elsewhere for less). Government might also use performance-based pay, or may hold back payment until after a peer review, or a replication of any analyses. However, for all of those solutions, some form of monitoring is still required in order to determine the quality of the work. There is an additional problem, though. Efficiency wages and performance-based pay work best when the agency problem involves the effort the agent puts into their work. Those solutions may be less effective when the problem arises from sincerely held beliefs about which models, methods, or evidence are most appropriate.

It seems that the principal-agent problem for governments employing economists (and, possibly, other consultants) is challenging to solve. Perhaps the best that governments can do is insist on transparency around economists' assumptions, methods, and evidence, and subject their analyses to replication and peer review. Political preferences may still matter, but good institutions can make it harder for those preferences to determine the advice that governments receive.

[HT: Marginal Revolution, back in 2024]

*****

[*] Or, maybe the government wants the economist to provide economic or policy advice that accords with the preferred policy platform of the government, rather than independent or unbiased advice. In that case, the nature of the principal-agent problem changes. Nevertheless, in both cases, what really matters is whether the economist's goals and motivations are aligned with those of the government.

Tuesday, 8 September 2026

Will AI make personalised pricing a reality, and is that really a bad thing?

Personalised pricing (or first-degree price discrimination) occurs when the seller sells their good or service to every consumer at a different price, as I described in this 2023 post. If executed perfectly, the seller could extract all of the consumer surplus as profits, by charging a price to every consumer that is exactly equal to the maximum the consumer is willing to pay. Fortunately for consumers, such perfect personalised pricing has remained a theoretical possibility.

But technological tools are increasingly helping firms to learn more detailed information about consumer preferences, and that allows firms to home in on consumers' maximum willingness-to-pay. The latest worry for consumers is AI, as this article in The Conversation by Patrick Dodd and Hanoku Bathula (both University of Auckland) notes:

Digital platforms can observe thousands of individual decisions. A ride-hailing platform can see which jobs a driver accepts, when they work and which incentives bring them online. A retailer can see purchases, abandoned carts and responses to discounts.

There is no strong evidence major companies already know everyone’s precise financial breaking point. But algorithmically mediated pay, personalised worker incentives, discounts and consumer offers are already real.

Notice that Dodd and Bathula also take the logic of personalised pricing for consumers, and apply it to gig-economy workers as well. Platforms such as Uber or Lyft or Doordash can increasingly use what they know about their delivery workers' preferences to determine their minimum willingness-to-accept for each delivery. The target is different (minimising how much they pay to the delivery worker, rather than maximising the price they charge the consumer), but the underlying premise of personalised pricing is the same.

Algorithms have been around for a while, though. Dodd and Bathula do not clearly lay out why they think that recent developments in AI make personalised pricing more of a reality than before. Most of what they say about 'algorithms' applies equally to statistical algorithms that have been around for years (decades, even) as to more recent developments in AI and machine learning (AI/ML). So, let me extend their argument more explicitly.

AI/ML dramatically lowers the cost of estimating individual willingness-to-pay. It can combine huge numbers of relatively weak signals about a particular consumer, learn complex patterns from the behaviour of millions of other similar consumers, experiment continually with prices and discounts, and update its estimate each time that circumstances change. That gives firms far richer models from which to estimate each particular customer's maximum willingness-to-pay (or, for their workers, to estimate their minimum willingness-to-accept). Moreover, while older statistical algorithms allowed firms to segment customers into fairly coarse categories, AI/ML allows firms to make predictions for each individual, and in real time. The better estimates from these newer models therefore allow firms to price much closer to the perfectly price-discriminating ideal. It's still not completely perfect, but it is a further improvement on what they were previously able to achieve.

Dodd and Bathula finish their article by noting the unfairness of personalised pricing. Their argument is essentially that there is asymmetry in the relationship between consumers and firms. Firms using algorithms (and now AI/ML) know increasingly more about what consumers are willing to pay, but consumers know very little about what firms are willing to accept.

However, it is worth unpacking that a bit more. Firms that don't know consumer willingness-to-pay can't raise their prices without limit, as consumers with low willingness-to-pay would stop buying from them. In practice though, personalised pricing will never be perfect. Firms may charge higher prices to consumers that they estimate have high willingness-to-pay, while offering lower prices or discounts to consumers with lower willingness-to-pay. So, relative to offering the same price to everyone, personalised pricing need not make every consumer worse off. The high-willingness-to-pay consumers are likely to be worse off, but some low-willingness-to-pay consumers may actually be better off.

Now consider which types of consumers tend to have high willingness-to-pay, and which types tend to have low willingness-to-pay. For many goods, lower-income consumers are likely, on average, to have lower willingness-to-pay, so personalised pricing could result in some of them being offered lower prices. That won't always be true though. Some lower-income consumers with few alternatives or an urgent need may have high willingness-to-pay despite having a low income. Taken together, this means that the distributional effects of personalised pricing are not necessarily straightforward. However, in some instances preventing firms from price discriminating could be making low-income consumers worse off. With that in mind, is it really fairer that firms are not allowed to offer lower prices to consumers with low willingness-to-pay?

I'm not really trying to defend price discrimination here. I'm not keen on personalised pricing for very selfish reasons - I don't want to pay more, even if I am willing to pay more! And like me, most consumers should probably not be keen on personalised pricing. But before we rail against the evils of firms price discriminating, we need to properly consider its distributional consequences. And that means thinking about which groups may be made better off by price discrimination, not just which groups are made worse off.

Read more:

Sunday, 6 September 2026

Can a simple email get students to study more economics?

This year, I started sending the top students in my ECONS101 class a 'student recognition letter', congratulating them on their performance and noting their overall grade and their ranking in the class. The purpose of this letter was not to try and sell these students on studying economics, but to provide them with some well-earned recognition, as well as a valuable signal for future employers that their A+ grade actually meant something (whereas in some papers, it clearly doesn't mean much).

I could have added a sentence or two encouraging those students to study more economics. They certainly have shown an aptitude for it. However, some years ago I did try to encourage the top students to study more economics, but it seemed like those efforts didn't have much impact. I stopped doing this in about 2018. And it seems I might have made a good choice, at least according to this forthcoming article in the journal Economics of Education Review (open access) by Olivia Edwards and Jonathan Meer (both Texas A&M University).

Edwards and Meer study the effect of a simple email from the professor to the top ten percent (or so) of the class in introductory economics at Texas A&M University:

...praising their performance and encouraging them to take more economics courses and considering majoring or minoring in economics if they were not already doing so.

Their analysis is based on 10,600 students who took the course between 2017 and 2023, of which 1802 received the encouragement email. Edwards and Meer apply a regression discontinuity design (RDD) approach to the analysis, essentially comparing students just above the cutoff mark for receiving the encouragement email with those just below the cutoff mark. The outcome variables Edwards and Meer look at are whether the student went on to study intermediate microeconomics, whether they majored or minored in economics, and whether they majored in an 'economics-adjacent field' (by which they mean agricultural economics or business). They also test whether the results differ by gender, under-represented minority (URM) status, and whether the student was first-in-family to study at university.

There did seem to be some effect on students going on to study intermediate microeconomics. For students around the cutoff point for receiving the encouragement email, there was:

...a relatively large and statistically significant discontinuity of 8.8 percentage points (s.e. = 3.3 percentage points), an increase of about 40 percent over the baseline level below the cutoff.

However, there was little evidence of an effect on majoring or minoring in economics, with both effects being statistically insignificant. Turning to whether there were heterogeneous effects based on demographic characteristics, the effect of the email on taking intermediate microeconomics did not differ significantly by gender, but the overall positive effect appears to have been concentrated among URM students and students who were first-in-family to attend university. The encouragement email had no effect on going onto an economics major or minor for any of the demographic groups.

Edwards and Meer describe the effect of the encouragement email as "modest". I would say it was disappointing, but not surprising. If Texas A&M is anything like Waikato, then once a student has chosen a particular major (or minor), it is actually quite difficult to induce them to switch, even if they later find that some other major or minor would be a better fit for them. In part, this is due to institutional barriers, such as the difficulty in navigating the change-of-enrolment process, or the necessity to take pre-requisites that students might have missed, or that the student may have 'spent' papers on a particular major that would be lost if they switch. It could also be that students simply don't like changing their minds (a 'status quo bias').

So, while a simple information intervention may not be enough to change students’ choice of major, it does seem capable of nudging some students into taking more economics. And on the plus side, sending an email doesn't cost the professor much, so even if it only encourages some additional enrolments in intermediate microeconomics, on a cost-benefit basis for the Economics Department, it is probably a net positive. Given these results, perhaps I was too hasty in stopping the explicit encouragement of my top students to study more economics? I may have to re-draft my student recognition letters for this trimester.

[HT: Tim Harford, last year]

Read more:

Friday, 4 September 2026

This week in research #142

Here's what caught my eye in research over the past week:

  • Sacerdote, Staiger, and Tine (with ungated earlier version here) find that test score–optional policies harm the likelihood of admission for high-achieving applicants from disadvantaged backgrounds, meaning that the availability of test scores on an application can promote rather than hinder social mobility
  • Armona et al. (with ungated earlier version here) develop and test a model of what is newsworthy to a media outlet
  • Crossin et al. (open access if you set up a free account) find using data from a nationally representative sample that an estimated 71.1 percent of the New Zealand population think politicians should do more to keep people safe from alcohol harm, with majority support across the political spectrum
  • Smit (open access) outlines the drawbacks to remote working that may have prevented greater internal migration from cities to the periphery in the Netherlands