Category: Economics
Economics job market update
From John A. List:
AEA job market update. The numbers don’t lie, as this is the toughest market for PhD economists in recent memory.
JOE listings are down 20% from last year. Worse: they are 19% below COVID levels. Let that sink in.
The academic market took the biggest hit. Full-time US positions dropped 33% year-over-year. Liberal arts colleges and PhD-granting universities? Both down about a third. International academic postings fell 13% from last year, 25% from COVID.
Nonacademic isn’t much better: down 27% from last year, 45% below COVID. And federal government hiring? That’s where it gets ugly. Down 71% year-over-year, 79% below COVID. DOGE cuts plus the shutdown created a perfect storm.
One bright spot: private sector jobs in consulting, research, banking, and finance are holding steady at recent-year levels.
Bottom line for candidates: the data confirm what you’re feeling. It’s brutal out there. Universities facing their own financial pressures should still find ways to bridge unmatched candidates for another year. The talent is there—the opportunities aren’t. H/T John Cawley
Here is the link to the tweet.
Growth Matters
Between 2011 and 2023 India’s GDP per capita grew at a rate of about 4.8% per year so in those 12 years GDP per capita, a good measure of the standard of living, nearly doubled (77%). Shamika Ravi and Sindhuja Penumarty look at what this means on the ground.
The percentage of the budget spent on food has declined–dropping below 50% for the first time ever–and that has enabled significant purchases of consumer durables.
It will perhaps not be surprising that mobile phones have become universal among both the poor and the rich but vehicle ownership is also converging with rural ownership of a vehicle (2 or 4 wheeler) nearly tripling from (19% to 59%).
Another standout is refrigerators which reflects growing income and reliable electricity. In the 12 years across the survey, refrigerator ownership in rural areas more than tripled from 9.4% circa 2011 to 33.2% in 2023. In urban areas refrigerator ownership went from less than half (43.8%) to more than two-thirds (68.1%) of urban households. Overall, only two states Bihar (37.1%) and Odisha (46.3%), had less than 50% of urban households owning a refrigerator in 2023-24.
Economists are often accused of “line go up” thinking but the truth is that line go up matters. The 4.8% annual growth matters because it shows up as a broad, visible upgrade in how people live.
“AI is everywhere but in the productivity statistics…”
These people are saying it is there too. Though I am not quite sure what they (or anyone, for that matter) mean by AI:
First, we argue that AI can already be seen in productivity statistics for the United States. The production and use effects of software and software R&D (alone) contributed (a) 50 percent of the 2 percent average rate of growth in US nonfarm business labor productivity from 2017 to 2024 and (a) 50 percent of its 1.2 percentage point acceleration relative to the pace from 2012 to 2017. Second, taking additional intangibles and data assets into account, we calculate a long-run contribution of AI to labor productivity growth based on assumptions that follow from the recent trajectories of investments in software, software R&D, other intangibles, and productivity growth in both US and Europe. Our central estimates are that AI will boost annual labor productivity growth by as much as 1 percentage point in the United States and about .3 percentage point in Europe.
That is from Bontadini, Corrado, Haskel, and Jona-Lasinio, here is the complete abstract online.
How bad was British “austerity” anyway?
Chris Giles writes in the FT:
The main periods of measurement error came in the austerity years of 2012 to 2014, in 2017 during the early period after the Brexit referendum and in recent post-pandemic years. The truth is that a huge pessimistic bias in our national accounts has led us to be fed with contemporary reports of doom and gloom, which subsequently turn out to be nonsense.
But it is the first version of economic events that enters the national debate — and the national consciousness — for the entirely understandable reason that initial releases of economic data make news. You cannot expect people to care deeply about a revision to data that is three years old. Psychologically, they have made up their mind by then.
We are still told that 2010s austerity destroyed growth, but the data no longer supports that story: growth between David Cameron’s election victories of 2010 and 2015 now registers an annualised average of 2 per cent.
Somehow I am not seeing people jumping all over this story? Is it even correct? I have not seen anyone refute or counter it. Here is the analysis from 5.2 Pro, largely confirming, though it suggests 1.8% to 1.9% is a better estimate than 2%. I am very open to alternative points of view here, but at the moment it appears the correct stance was a) the British economic problems were largely structural and would not just be fixed by an aggregate demand boost, and b) fiscal consolidation was necessary, and while done imperfectly, not a disaster relative to the alternatives available.
The dust has not yet settled, but perhaps most of you were basically just wrong on this one?
Origins and persistence of the Mafia in the United States
This paper provides evidence of the institutional continuity between the “old world” Sicilian mafia and the mafia in America. We examine the migration to the United States of mafiosi expelled from Sicily in the 1920s following Fascist repression lead by Cesare Mori, the so-called “Iron Prefect”. Using historical US administrative records and FBI reports from decades later, we provide evidence that expelled mafiosi settled in pre-existing Sicilian immigrant enclaves, contributing to the rise of the American La Cosa Nostra (LCN). Our analysis reveals that a significant share of future mafia leaders in the US originated from neighborhoods that had hosted immigrant communities originating in the 32 Sicilian municipalities targeted by anti-mafia Fascist raids decades earlier. Future mafia activity is also disproportionately concentrated in these same neighborhoods. We then explore the socio-economic impact of organized crime on these communities. In the short term, we observe increased violence in adjacent neighborhoods, heightened incarceration rates, and redlining practices that restricted access to the formal financial sector. However, in the long run, these same neighborhoods exhibited higher levels of education, employment, and social mobility, challenging prevailing narratives about the purely detrimental effects of organized crime. Our findings contribute to debates on the persistence of criminal organizations and their broader economic and social consequences.
That is a new paper in the works by Zachary Porreca, Paolo Pinotti, and Masismo Anelli, here is the abstract online.
Gans and Doctorow on AI Copyright
Josh Gans had written what I think is the first textbook of AI. Instead of the “big issues” like will AI result in the singularity or the end of the human race, Gans treats AI as a tool for improving predictions. What will better predictions do in legal markets, economic markets, political markets? He generally avoids conclusions and instead explores models of thinking.
I especially enjoyed the chapter on intellectual property rights which maps out a model for thinking about copyright in training and in production, how they interact and the net costs and benefits.
Gans’s chapter usefully pairs with Cory Doctorow’s screed on AI. It’s a great screed despite being mostly wrong. I did like this bit, however:
Creative workers who cheer on lawsuits by the big studios and labels need to remember the first rule of class warfare: things that are good for your boss are rarely what’s good for you.
…When Getty Images sues AI companies, it’s not representing the interests of photographers. Getty hates paying photographers! Getty just wants to get paid for the training run, and they want the resulting AI model to have guardrails, so it will refuse to create images that compete with Getty’s images for anyone except Getty. But Getty will absolutely use its models to bankrupt as many photographers as it possibly can.
…Demanding a new copyright just makes you a useful idiot for your boss, a human shield they can brandish in policy fights, a tissue-thin pretense of “won’t someone think of the hungry artists?…
We need to protect artists from AI predation, not just create a new way for artists to be mad about their impoverishment.
And incredibly enough, there’s a really simple way to do that. After 20+ years of being consistently wrong and terrible for artists’ rights, the US Copyright Office has finally done something gloriously, wonderfully right. All through this AI bubble, the Copyright Office has maintained – correctly – that AI-generated works cannot be copyrighted, because copyright is exclusively for humans. That’s why the “monkey selfie” is in the public domain. Copyright is only awarded to works of human creative expression that are fixed in a tangible medium.
And not only has the Copyright Office taken this position, they’ve defended it vigorously in court, repeatedly winning judgments to uphold this principle.
The fact that every AI created work is in the public domain means that if Getty or Disney or Universal or Hearst newspapers use AI to generate works – then anyone else can take those works, copy them, sell them, or give them away for free. And the only thing those companies hate more than paying creative workers, is having other people take their stuff without permission.
The US Copyright Office’s position means that the only way these companies can get a copyright is to pay humans to do creative work. This is a recipe for centaurhood. If you’re a visual artist or writer who uses prompts to come up with ideas or variations, that’s no problem, because the ultimate work comes from you. And if you’re a video editor who uses deepfakes to change the eyelines of 200 extras in a crowd-scene, then sure, those eyeballs are in the public domain, but the movie stays copyrighted.
AI should not have to pay to read books any more than a human. At the same time, making AI created works non-copyrightable is I think the right strategy at the present moment. Moreover, it’s the most practical suggestion I have heard for channeling AI in a more socially beneficial direction, something Acemoglu has discussed without much specificity.
GDPR is worse than you had thought
We examine how data privacy regulation affects healthcare innovation and research collaboration. The European Union’s General Data Protection Regulation (GDPR) aims to enhance data security and individual privacy, but may also impose costs to data collection and sharing critical to clinical research. Focusing on the pharmaceutical sector, where timely access and the ability to share patient-level data plays an important role drug development, we use a difference-in-differences design exploiting variation in firms’ pre-GDPR reliance on EU trial sites. We find that GDPR led to a significant decline in clinical trial activity: affected firms initiated fewer trials, enrolled fewer patients, and operated at fewer trial sites. Overall collaborative clinical trials also declined, driven by a reduction in new partnerships, while collaborations with existing partners modestly increased. The decline in collaborations was driven among younger firms, with little variation by firm size. Our findings highlight a trade- off between stronger privacy protections and the efficiency of healthcare innovation, with implications for how regulation shapes the rate and composition of subsequent R&D.
That is from Jennifer Kao and Sukhun Kang, here is the online abstract for the AEA meetings.
Agentic interactions
Do human differences persist and scale when decisions are delegated to AI agents? We study an experimental marketplace in which individuals author instructions for buyer-and seller-side agents that negotiate on their behalf. We compare these AI agentic interactions to standard human-to-human negotiations in the same setting. First, contrary to predictions of more homogenous outcomes, agentic interactions lead to, if anything, greater dispersion in outcomes compared to human-mediated interactions. Second, crossing agents across counterparties reveals systematic dispersion in outcomes that tracks the identity and characteristics of the human creators; who designs the agent matters as much as, and often more than, shared information or code. Canonical behavioral frictions reappear in agentic form: personality traits shape agent behavior and selection on principal characteristics yields sorting. Despite AI agents not having access to the human principal’s characteristics, demographics such as gender and personality variables have substantial explanatory power for outcomes, in ways that are sometimes reversed from human-to-human interactions. Moreover, we uncover significant variation in “machine fluency”-the ability to instruct an AI agent to effectively align with one’s objective function-that is predicted by principals’ individual types, suggesting a new source of heterogeneity and inequality in economic outcomes. These results indicate that the agentic economy inherits, transforms, and may even amplify, human heterogeneity. Finally, we highlight a new type of information asymmetry in principal-agent relationships and the potential for specification hazard, and discuss broader implications for welfare, inequality, and market power in economies increasingly transacted through machines shaped by human intent.
Here is the full paper by Alex Imas, Kevin Lee, and Sanjog Misra. Here is a thread on the paper.
My Conversation with the excellent Gaurav Kapadia
Here is the audio, video, and transcript. Here is the episode summary:
Gaurav Kapadia has deliberately avoided publicity throughout his career in investing, which makes this conversation a rare window into how he thinks. He now runs XN, a firm built around concentrated bets on a small number of companies with long holding periods. However, his education in judgment began much earlier, in a two-family house in Flushing that his parents converted into a four-family house. It was there where a young Gaurav served as de facto landlord, collecting rent and negotiating late payments at age 10. That grounding now expresses itself across an unusual range of domains: Tyler invited him on the show not just as an investor, but as someone with a rare ability to judge quality in cities, talent, art, and more with equal fluency.
Tyler and Gaurav discuss how Queens has thrived without new infrastructure, what he’d change as “dictator” of Flushing, whether Robert Moses should rise or fall in status, who’s the most underrated NYC mayor, what’s needed to attract better mayoral candidates, the weirdest place in NYC, why he initially turned down opportunities in investment banking for consulting, bonding with Rishi Sunak over railroads, XN’s investment philosophy, maintaining founder energy in investment firms and how he hires to prevent complacency, AI’s impact on investing, the differences between New York and London finance, the most common fundraising mistake art museums make, why he collects only American artists within 20 years of his own age, what makes Kara Walker and Rashid Johnson and Salman Toor special, whether buying art makes you a better investor, his new magazine Totei celebrating craft and craftsmanship, and much more.
Excerpt:
COWEN: Now, I don’t intend this as commentary on any particular individual, but what is it that could be done to attract a higher quality of candidate for being mayor of New York? It’s a super important job. It’s one of the world’s greatest cities, arguably the greatest. Why isn’t there more talent running after it?
KAPADIA: It is something that I’ve thought about a great deal. I think there’s a bunch of little things that accumulate, but the main thing that happens in New York City is, people automatically assume they can’t win because it’s such a big and great city. Actually, the last few presidential elections and also the current mayoral election have taught people that anyone could win. I think that, in and of itself, is going to draw more candidates as we go forward.
What happened as an example, this time, people just assumed that one candidate had the race locked up, so a lot of good candidates, even that I know, decided not even to run. It turns out that that ended up not being the case at all. Now that people put that into their mental model, the new Bayesian analysis of that would be, “Oh, more people should run.”
The second thing: New York has a bunch of very peculiar dynamics. It’s an off-year election, and the primaries are at very awkward times. I believe there’s a history of why the primary shifted to basically the third week of June, in which there’s a very low turnout. The third week of June in New York City, when the private schools are out and an off-year election. You’re able to win the Democratic nomination and therefore the mayoral election with tens of thousands of votes in a city this big. That is absolutely insane.
A couple of things that I would probably do would be to make the primary more normal, change the election timing to make it on-cycle, even number of years. You’d have to figure out how to do that. Potentially have an open primary as well.
COWEN: If we apply the Gaurav Kapadia judgment algorithm to mayoral candidates, what’s the non-obvious quality you’re looking for?
KAPADIA: Optimism.
COWEN: Optimism.
KAPADIA: Optimism.
COWEN: Is it scarce?
KAPADIA: Extraordinarily scarce. I think there’s much more doomerism everywhere than optimism. At the end of the day, people are attracted to optimism. If you think about the machinery of the city and the state, having a clear plan — of course, you need all the basics. You need to be able to govern. It’s a very complicated city. There’re many constituents.
But I think beyond that, you have to have the ability to inspire. For some reason, almost all of the candidates, over the last couple of cycles, have really not had that — with the exception of probably one — the ability to inspire. I think that is the most underrated quality that one will need.
COWEN: I have my own answer to this question, but I’m curious to see what you say. What is, for you, the weirdest part of New York City that you know of that doesn’t really feel like it belongs to New York City at all?
Definitely recommended.
Did market power go up so much?
It seems not:
De Loecker et al. (2020) (DEU) estimate that markups increased significantly in the United States from 1955 to 2016. We find this result is sensitive to unreported sample restrictions that drop 27% of the available observations. Applying the methodology as described in the article to the full sample, markup increases are more muted until late in the sample period, and are almost entirely driven by Finance and Insurance firms. If these firms are removed, markup increases are modest. We conclude that the DEU methodology and data, as they are described in the article, do not support the conclusion that broad-based increases in market power have occurred in recent decades.
That is from a recent NBER working paper by Benkard, Miller, and Yurukoglu.
Three Nobel lectures in economic science
Crime and the Welfare State
Several recent papers claim that expanding programs like Medicaid reduces crime (e.g. here). I’ve been skeptical, not because of weaknesses in any particular paper, but just because the results feel a bit too aligned with social-desirability bias and we know that the underlying research designs can be fragile. As a result, my priors haven’t moved much. The first paper using a genuine randomized controlled trial now reports no effect of Medicaid expansion on crime.
Those involved with the criminal justice system have disproportionately high rates of mental illness and substance-use disorders, prompting speculation that health insurance, by improving treatment of these conditions, could reduce crime. Using the 2008 Oregon Health Insurance Experiment, which randomly made some low-income adults eligible to apply for Medicaid, we find no statistically significant impact of Medicaid coverage on criminal charges or convictions. These null effects persist for high-risk subgroups, such as those with prior criminal cases and convictions or mental health conditions. In the full sample, our confidence intervals can rule out most quasi-experimental estimates of Medicaid’s crime-reducing impact.
Finkelstein, Miller, and Baicker (WP).
It could still be the case that very targeted interventions–say making sure that released criminals get access to mental health care–could do some good but there’s unlikely to be any general positive effect.
A similar story is found in Finland where a large RCT on a guaranteed basic income found zero effect on crime
This paper provides the first experimental evidence on the impact of providing a guaranteed basic income on criminal perpetration and victimization. We analyze a nationwide randomized controlled trial that provided 2,000 unemployed individuals in Finland with an unconditional monthly payment of 560 Euros for two years (2017-2018), while 173,222 comparable individuals remained under the existing social safety net. Using comprehensive administrative data on police reports and district court trials, we estimate precise zero effects on criminal perpetration and victimization. Point estimates are small and statistically insignificant across all crime categories. Our confidence intervals rule out reductions in perpetration of 5 percent or more for crime reports and 10 percent or more for criminal charges.
Does studying economics and business make students more conservative?
College education is a key determinant of political attitudes in the United States and other countries. This paper highlights an important source of variation among college graduates: studying different academic fields has sizable effects on their political attitudes. Using surveys of about 300,000 students across 500 U.S. colleges, we find several results. First, relative to natural sciences, studying social sciences and humanities makes students more left-leaning, whereas studying economics and business makes them more right-leaning. Second, the rightward effects of economics and business are driven by positions on economic issues, whereas the leftward effects of humanities and social sciences are driven by cultural ones. Third, these effects extend to behavior: humanities and social sciences increase activism, while economics and business increase the emphasis on financial success. Fourth, the effects operate through academic content and teaching rather than socialization or earnings expectations. Finally, the implications are substantial. If all students majored in economics or business, the college–noncollege ideological gap would shrink by about one-third. A uniform-major scenario, in which everyone studies the same field, would reduce ideological variance and the gender gap. Together, the results show that academic fields shape students’ attitudes and that field specialization contributes to political fragmentation.
That is a recent paper from Yoav Goldstein and Matan Kolerman. Here is a thread on the paper.
Colors of growth
This looks pretty tremendous:
We develop a novel approach to measuring long-run economic growth by exploiting systematic variation in the use of color in European paintings. Drawing inspiration from the literature on nighttime lights as a proxy for income, we extract hue, saturation, and brightness from millions of pixels to construct annual indices for Great Britain, Holland, France, Italy, and Germany between 1600 and 1820. These indices track broad trends in existing GDP reconstructions while revealing higher frequency fluctuations – such as those associated with wars, political instability, and climatic shocks – that traditional series smooth over. Our findings demonstrate that light, decomposed into color and brightness components, provides a credible and independent source of information on early modern economic activity.
That is new research by Lars Boerner, Tim Reinicke, Samad Sarferaz, and Battista Severgnini. Via Ethan Mollick.
Planning sentences to ponder
Planning assistance caused municipalities to build 20% fewer housing units per decade over the 50 years that followed.
Here is the full abstract:
We study how the federal Urban Planning Assistance Program, which subsidized growing communities in the 1960s to hire urban planners to draft land-use plans, affected housing supply. Using newly digitized records merged with panel data across municipalities on housing and zoning outcomes, we exploit eligibility thresholds and capacity to approve funds across state agencies to identify effects. Planning assistance caused municipalities to build 20% fewer housing units per decade over the 50 years that followed. Regulatory innovation steered construction in assisted areas away from apartments and toward larger single-family homes. Textual evidence related to zoning and development politics further shows that, since the 1980s, assisted communities have disincentivized housing supply by passing on development costs to developers. These findings suggest that federal intervention in planning helped institutionalize practices that complicate community growth, with subsequent consequences for national housing affordability.
Hail Martin Anderson! The above paper is by Tom Cui and Beau Bressler, via Brad, and also Yonah Freemark.