Seven decades of change in the demographics and research styles of top economics research

Back in 2013, Daniel Hamermesh (University of Texas at Austin) published this article in the Journal of Economic Literature (ungated earlier version here), which summarised changes in the demographics and research styles of top economics research, based on articles published between 1963 and 2011 in three top journals: the American Economic Review (AER), the Journal of Political Economy (JPE), and the Quarterly Journal of Economics (QJE). A new update last year (open access) from Hamermesh extends the analysis to include articles up to 2024.

In terms of demographics, the trends show a continuation and in terms of gender, Hamermesh notes that:

The progression that occurred from the 1960s and 1970s, when only a minute fraction of authors were women, to the early twenty-first century has, if anything, accelerated.

This will be welcome news, given the persistent gender gap in economics (see this post and the links at the end of it). It likely reflects the changing demographics of young economists, with a growing proportion of the young 'stars' in economics being women (and noting that it is young stars who often get published in the top journals that Hamermesh is considering).

In terms of the age structure of authors, Hamermesh reports that:

The changes from 2011 to 2024 continued those that started in the 1980s, but the rate of change has not accelerated. Indeed, most noticeable from 2011 to 2024 was a continuing sharp and statistically significant drop in the representation of the youngest group (and a nearly equal sharp rise among those 36–50)...

...the average age of authorship has increased steadily since 1973. 

Can I change my comment above about the young stars in economics? The increasing median age of authors in top journals seems to be a general trend across academia. Hamermesh then turns to research 'style', documenting a continued dramatic rise in the proportion of articles in those journals that are co-authored:

There were no four-authored papers as recently as 1983; today they account for 17 percent of articles. There were no papers with more than four authors in 2003; today nearly 12 percent of articles have five or more authors (with five articles written by six authors each and one by seven authors). Obversely, sole-authored papers are now quite scarce; and even two-authored papers today only account for slightly more than one-fourth of all articles (compared to a majority as recently as 2003).

Unsurprisingly, the increase in the number of co-authored articles means that the age diversity of author collaborations has increased over time as well. In terms of the types of research, he reports that:

The big changes are the continuing rise in empirical work based on original non-laboratory data and the rapid and even accelerating increase in experimental work. Today these two methods, which both involve collecting original data, account for over half of all published papers, compared to less than 4 percent four decades ago...

These trends are not all unrelated, of course. Experimental research, and the increasing use of large datasets, typically both require larger research teams. They also often require more detailed methods, which may involve both larger teams, and more experienced researchers. Larger teams might be more likely to include female team members. And larger teams often need someone to lead and coordinate all of the team members, and those leaders tend to be more experienced (and older) academics. So, it would not surprise me, if more detailed analysis was conducted, to see that the trends are interconnected.

Now, the interesting thing will be what happens going forward, given the increasing use of generative AI in research (see here, for example). Since generative AI can now do a lot of the work that research assistants and early career researchers previously did, will the trend towards larger research teams be reversed? How will that interact with the gender gap in research (given that the age of female economists skews younger at the moment). And how will it affect the age distribution of researchers (given that men, and younger people, are somewhat more likely to use generative AI). I'll be looking forward to Hamermesh's next update. Hopefully, we don't have to wait another 12 years.

[HT: Marginal Revolution, last year]

Changing their minds could be a good thing for economists

People don't like to change their minds. This may partly be an expression of loss aversion - we really want to avoid losses, including the loss of an idea that we previously thought was true. This leads to status quo bias - we prefer not to change things, and keep them the same, because changing things entails a loss. But what if changing our minds could make us better off? Would we be so reluctant to do so?

This 2025 paper by Matt Knepper (University of Georgia) and Brian Wheaton (UCLA) suggests that economists, at least, should not be afraid to change their minds, because doing so increases the number of citations to their research. Knepper and Wheaton investigate authors who undergo an 'ideological reversal' - previously publishing research that could be considered right-wing, before switching and publishing a paper that draws a left-wing-consistent conclusion, or the reverse (switching from left-wing to right-wing). Their main data source is every economics paper ever published in the top 100 economics journals indexed in Web of Science - some 200,000 articles. They also have a narrower dataset of papers referenced in meta-analyses on policy topics, including:

...the minimum wage, the economics of unions, the taxable income elasticity, the fiscal multiplier, intergenerational transfers, trade and productivity, trade and domestic employment, crowd-out, the gender wage gap, unemployment insurance, disability benefits, universal preschool, childcare and employment, immigration and wages, and more.

Knepper and Wheaton use this narrower dataset to train a machine learning model to categorise the rest of the papers in the dataset, as to how left-wing (or right-wing) the conclusions are. For instance, a paper that concludes that the minimum wage reduces employment is more right-wing, whereas one that concludes that there is no disemployment effect of the minimum wage is more left-wing. Knepper and Wheaton define an author as left-wing if they published more left-wing papers than right-wing ones over the previous five years, and the reverse for right-wing authors. They then use the larger dataset to investigate what happens to each economist who undergoes an 'ideological reversal'. They first outline some descriptive facts based on their dataset, including:

• Fact #1: The typical author mostly publishes results on one side of the political spectrum.

• Fact #2: Ideological reversals are not rare; they occur at least once for 40% of authors.

• Fact #3: Ideological reversals become much more common later in an author’s career, with authors essentially never undergoing a reversal in the first decade of their career.

• Fact #4: Most ideological reversals do not represent a permanent defection to the other side of the political spectrum, but rather the beginning of repeatedly publishing results on both sides of the spectrum.

• Fact #5: Ideological reversals occur much more frequently amongst authors who are (initially) classified as right-wing.

That does seem like a surprisingly high proportion of economists who undergo at least one ideological reversal. However, perhaps we should take comfort in that - if the results point in a particular direction, our conclusions should say that, even if that conclusion is inconsistent with our previous conclusions on the same topic.

Do these ideological reversals matter though? Knepper and Wheaton employ a difference-in-differences analysis, comparing the difference in citations (and other metrics) between authors who did, and did not, undergo an ideological reversal, between the time before, and after, the reversal occurred. In other words, they look at whether citation counts rise more for economists who have an ideological reversal than for otherwise similar economists who do not. The results are striking, with:

...a sharp clear increase in citation count following an ideological reversal with essentially no evidence of pre-trends... The citation boost accumulates to approximately 9 over a one-decade period and 30 over a two-decade period.

The results remain consistent when Knepper and Wheaton limit the analysis to papers published before the ideological reversal, and when they limit the analysis to papers in the meta-analysis only (showing that the machine learning approach doesn't drive the results). Knepper and Wheaton also find evidence consistent with no change in the quality of papers before and after the ideological reversal, and that:

Both left-to-right and right-to-left reversals are rewarded by increased citations of roughly the same magnitude. The boost in citations received subsequent to a left-to-right reversal is mostly driven by citations from right-wing authors, and the boost in citations received subsequent to a right-to-left reversal is mostly driven by citations from left-wing authors. Encouragingly, however, the new right-wing (left-wing) audience garnered by a left-to-right (right-to-left) reversal... also engages with and cites the author's previous left-wing (right-wing) papers. This dynamic suggests that ideological reversals help prevent the formation of echo chambers in economics academia and expose authors to opposite ideological findings.

This last result is particularly important, and I believe it allows us to conclude that economists need not fear ideological reversals. In doing so, they can attract a new audience from the other side of the ideological spectrum, bringing the two sides closer together. Hopefully through that, we end up with higher-quality research overall.

[HT: Marginal Revolution, last year]

Artificial intelligence and the 'age of leisure'

My ECONS101 class covered constrained optimisation last week, and one of the models we looked at was the labour-leisure trade-off for workers. Now artificial intelligence, and in particular generative AI, is likely to have large impacts on the labour-leisure trade-off. As the Financial Times reported last year (paywalled):

The idea that technological progress can enable people to work fewer hours is not outlandish...

But in order to believe a similar trend is going to take hold again, you have to assume three things. First: that AI will deliver a substantial boost to economic productivity...

Second, you have to assume the economic gains will be widely distributed...

Third, you have to believe workers will “cash in” those proceeds in the form of extra leisure, rather than higher income. But will they? In many developed countries, there has been a slowdown in the reduction in working hours in recent decades...

Far from trading income for leisure, it is the people with the highest salaries who tend to work the longest hours.

Will workers trade off higher productivity for more leisure time? Are we about to enter an 'age of leisure'? The constrained optimisation model for the worker (see also this post) can help us clarify the possibilities. In this model, we'll assume that AI increases productivity, and that the increase in productivity is represented by higher wages for workers. [*] The model will then tell us whether workers might respond by consuming more, or less, leisure.

Our model of the worker's decision is outlined in the diagram below. The worker's decision is constrained by the amount of discretionary time available to them. Let's call this their time endowment, E. If they spent every hour of discretionary time on leisure, they would have E hours of leisure, but zero income. That is one end point of the worker's budget constraint, on the x-axis. The x-axis measures leisure time from left to right, but that means that it also measures work time (from right to left, because each one hour less leisure means one hour more of work). The difference between E and the number of leisure hours is the number of work hours. Next, if the worker spent every hour working, they would have zero leisure, but would have an income equal to W0*E (the wage, W0, multiplied by the whole time endowment, E). That is the other end point of the worker's budget constraint, on the y-axis. The worker's budget constraint joins up those two points, and has a slope that is equal to the wage (more correctly, it is equal to -W0, and it is negative because the budget constraint is downward sloping). The slope of the budget constraint represents the opportunity cost of leisure. Every hour the worker spends on leisure, they give up the wage of W0. Now, we represent the worker's preferences over leisure and consumption by indifference curves. The worker is trying to maximise their utility, which means that they are trying to get to the highest possible indifference curve that they can, while remaining within their budget constraint. The highest indifference curve they can reach on our diagram is I0. The worker's optimum is the bundle of leisure and consumption where their highest indifference curve meets the budget constraint. This is the bundle A, which contains leisure of L0 (and work hours equal to [E-L0]), and consumption of C0.

Now, let's say that the situation shown above is the situation before the advent of AI. After AI is introduced, productivity increases, and so wages increase (from W0 to W1). This causes the budget constraint to pivot outwards and become steeper (since the slope of the budget constraint is equal to the wage, the slope has increased from -W0 to -W1). The worker can now reach a higher indifference curve, and it is the position of that higher indifference curve that determines the worker's response in terms of whether they consume more leisure or not. If they move to the higher indifference curve I1, then the worker's new optimum is the bundle of leisure and consumption B, which contains leisure of L1 (and work hours equal to [E-L1]), and consumption of C1. For this worker (whose response is shown in red on the diagram), leisure hours decrease as a result of the higher wage. On the other hand, if they move to the higher indifference curve I2, then the worker's new optimum is the bundle of leisure and consumption C, which contains leisure of L2 (and work hours equal to [E-L2]), and consumption of C2. For this worker (whose response is shown in blue on the diagram), leisure hours increase as a result of the higher wage. [**]

Either of these possibilities could happen. In fact, both could happen, with some workers increasing leisure time and others decreasing leisure time. By itself, this model doesn't answer the question of what will happen, but shows that both increased leisure and decreased leisure are possible outcomes.

The key difference here comes down to the size of the income effect of the increase in wages. When wages increase, the opportunity cost of leisure increases. That makes leisure relatively more expensive, and workers should respond by consuming less leisure. That is what we call the substitution effect - workers substitute away from leisure as it becomes more expensive. However, increased wages also lead to an income effect. Leisure is a normal good, which means that as the worker's income increases, they would like to consume more leisure. Notice that the substitution effect and the income effect are working in opposite directions here. For workers who overall decrease their leisure, the substitution effect (which says they should consume less leisure) must be bigger than the income effect (which says they should consume more leisure). For workers who overall increase their leisure, the reverse is true - the substitution effect must be smaller than the income effect.

AI may lead us into an age of leisure. But only if productivity gains lead to higher wages, and the income effect of higher wages more than offsets the substitution effect.

*****

[*] The assumption that productivity gains will lead to higher wages is a strong assumption. Indeed, the FT article questions whether this assumption is valid. If productivity gains don't lead to higher wages, then this model doesn't help us evaluate whether we're about to move into an 'age of leisure', and the impacts might be more macroeconomic than microeconomic. That is, we may end up with leisure, but arising through weaker labour demand, reduced hours, or unemployment rather than through workers voluntarily choosing more leisure as wages increase.

[**] Notice that the indifference curves I1 and I2 are crossing, and indifference curves cannot cross. However, those two indifference curves are for different workers, so there is no problem. I could easily have drawn two different diagrams, one for each worker, but I've kept them both on the same diagram for efficiency.

This week in research #117

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

• Zhang et al. find that Uber’s entry into a US city significantly reduces crime rates, with larger effects in areas facing greater liquidity constraints (less bank credit supply, fewer local job opportunities, higher personal bankruptcy risk, and greater household financial stress)
• Sandorf and Navrud (open access) establish convergent validity between a contingent valuation survey and a discrete choice experiment (meaning that both measures are highly correlated), with the example they use being willingness-to-pay to reduce the spread of invasive crabs in Norway
• Desierto and Koyama (with ungated earlier version here) explain the economics of medieval castles in Europe
• Ordali and Rapallini (with ungated earlier version here) conduct a meta-analysis of the relationship between age and risk aversion, and confirm that there is a positive relationship in studies using survey data and lotteries
• Singh and Mukherjee conduct a replication of an earlier study that established 'action bias' among goalkeepers facing a penalty kick, and find that jumping left or right rather than staying in the centre of the goal is not a sub-optimal action for goalkeepers in FIFA World Cup matches, and so the high frequency of jumping is not indicative of action bias (it is good to see a replication study published in a good journal)
• Lindkvist et al. (open access) investigate attitudes toward research misconduct and questionable research practices among researchers and ethics reviewers across academic fields, and find that researchers and ethics reviewers in medicine, as well as more senior and female researchers and reviewers, took a more negative view of questionable research practices
• Lei et al. use China’s Compulsory Schooling Law as a quasi-natural experiment to investigate the effect of education on HIV/AIDS, finding that mass education significantly enhances knowledge about HIV/AIDS, and that each additional year of exposure to the law reduces HIV/AIDS and mortality rates by 6.51 percent and 2.15 percent respectively
• Daoud, Conlin, and Jerzak (open access) study the differential effects of World Bank and Chinese development projects in Africa between 2002 and 2013, using data across 9899 neighbourhoods in 36 African countries, and find that both donors raise wealth, with larger and more consistent gains for Chinese development projects
• Stoelinga and Tähtinen (open access) find that conflict exposure, on average, increases support for democracy in African countries, but the effects vary by ethnicity and regime type, but interestingly, violence increases trust in ruling institutions in autocratic regimes
• Ruiz et al. (with ungated earlier version here) find that, following the exodus of Cuban doctors from Brazil in 2018, the reduction in doctors was associated with persistent reductions in the care of chronic diseases, while service utilization for conditions requiring immediate care, such as maternal-related services and infections, quickly recovered
• Geddes and Holz (open access) investigate the effect of rent control on domestic violence in San Francisco, and find that there was a nearly 10 percent decrease in assaults on women for the average ZIP code (some good news for advocates of rent control, but it hardly offsets the bad outcomes)
• Clemens and Strain (with ungated earlier version here) add further to the literature on the disemployment effects of minimum wages, this time looking at the difference between large and small minimum wage changes, finding that relatively large minimum wage increases reduced usual hours worked per week among individuals with low levels of experience and education by just under one hour per week during the decade prior to the onset of the Covid-19 pandemic, while the effects of smaller minimum wage increases are economically and statistically indistinguishable from zero