Economists watching A.I.: an open letter, and an edited version

My sense is that many economists are optimistic about the long term development of A.I., while being cautious about some of the shorter term transitions that it will initiate. (This is a different set of worries than the species-extinguishing fears that can also be heard.)

Yesterday an open letter was published, signed by many economists: 

We Must Act Now: A Statement on AI’s Transformation of the Economy 

1. AI may become radically more powerful over the next 10 years.

2. This could drive an unprecedented transformation of our economy, larger than the Industrial Revolution, but unfolding over a vastly shorter time frame. It could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards.

3. Economists, policymakers and technology leaders must act now to understand the economics of transformative AI and to build the incentives, guardrails, and institutions needed to steer AI in a direction that complements humans and benefits society.

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Here's the coverage from Stanford:

Stanford Digital Economy Lab / July 13, 2026  “We Must Act Now”: Sixteen Nobel Laureates Join Leading Economists and AI Researchers in Call to Prepare for AI’s Economic Transformation

"STANFORD, Calif. – July 13, 2026 – Today, a group of leading economists and AI researchers, including sixteen Nobel Laureates, released “We Must Act Now: A Statement on AI’s Transformation of the Economy,” calling for urgent preparation for the economic impacts of radically more powerful AI.

The statement, organized by economists Erik Brynjolfsson, Ajay Agrawal, Anton Korinek, and Tom Cunningham, warns that increasingly capable AI systems could reshape the economy at unprecedented speed. While AI offers enormous opportunities to improve productivity and living standards, it also raises important questions for workers, firms, and public institutions.

The statement calls on economists, policymakers, and technology leaders to deepen research on AI’s economic impacts and to begin building the policies and institutions needed to ensure AI complements human capabilities and benefits society.

“AI capabilities are advancing far faster than our understanding of the economic implications. In that gap lie the greatest opportunities of our era. We must act now to guide AI to complement humans rather than simply imitate them — and to generate prosperity for the many, not just the few,” said Erik Brynjolfsson, the Jerry Yang and Akiko Yamazaki Professor at Stanford University and Director of the Stanford Digital Economy Lab.

“The scale, scope, and speed of the advances in AI, combined with a high level of uncertainty about the magnitude and timing of the impacts across many parts of the economy, call for an ‘all hands on deck’ approach to steering AI in beneficial directions,” said Michael Spence, Nobel Laureate and Professor Emeritus at New York University.

“I’m so happy to join other leading experts in calling for the urgent need to redirect AI so that its risks are minimized and it can work for the benefit of workers and society,” said Daron Acemoglu, Nobel Laureate and Institute Professor at MIT.

“Steam, electricity, and computers each gave societies decades to adapt; AI may give us only a few years. We cannot improvise our strategy and institutions in the middle of the transformation; waiting for certainty means arriving too late,” said Anton Korinek, Professor at the University of Virginia, currently on leave at Anthropic.

“Whether rapidly advancing AI broadly elevates global living standards or severely concentrates wealth is not predetermined; it depends on how we choose to re-architect our political and economic systems today. We cannot afford to wait for the full transformation to arrive and in the meantime rely on institutional scaffolding that was optimized for a pre-high-fidelity-prediction world,” said Ajay Agrawal, Professor at the University of Toronto’s Rotman School of Management.

“We are driving in the fog, and it is extraordinarily difficult to anticipate what will happen next. It’s the right time for a coordinated effort to bring clarity to a confusing situation.” said Tom Cunningham, Researcher at METR.

The statement has been signed by more than 200 economists and AI researchers from leading universities and AI research organizations around the world. The full statement and the current list of signatories are available at http://wemustactnow.ai/." 

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Here's the story in the NYT:

Nearly 200 Economists and Tech Leaders Warn of A.I. Threats
A letter calls for policymakers to do more to understand and respond to potential disruptions from artificial intelligence. 
By Ben Casselman

"“A.I. may become radically more powerful over the next 10 years,” the researchers wrote in a statement released on Monday, adding that the technology “could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards.”

"The statement, titled “We Must Act Now,” was signed by nearly 200 people, including 15 Nobel laureates and the chief economists of two of the leading A.I. labs, Open AI and Anthropic. Other notable signatories include Jack Clark, a co-founder of Anthropic; Eric Schmidt, the former chief executive of Google; and Vinod Khosla, a prominent venture capitalist." 

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And here's an edited version of the letter, that I also like. (Inevitably, when you're asked to sign open letters, they don't read exactly as you would have written them yourself. (Even if you are one of the main authors of an open letter, it may reflect compromises that were required to reach consensus among your constituency.) 

Why I Didn't Sign the AI Open Letter: Instead, I edited it. by Andrew McAfee 

Here's his version (the big change is in item 3; his explanation is at the link):

1. AI is likely to become radically more powerful over the next 10 years.

2. Like previous world-changing technologies, AI will bring major gains in living standards. But it will also bring new risks, harms, and disruptions. And because of its extraordinarily fast improvement, AI’s benefits and shocks might come quickly.

3. So economists, policymakers and technology leaders must act now to understand the economics of transformative AI, and to build the capabilities needed to respond quickly and effectively to the challenges it will bring.

 

 

Survey of economists, concerning Living-Donor Kidney Transplants

Romesh Vaitilingam writes to draw my attention to the recent survey of economists, concerning Living-Donor Kidney Transplants, conducted by the Clark Center for Global Markets at Chicago Booth.

He says 

" I’m writing now as I thought you might be interested in the results of this survey, which was inspired by reading your recent Wash Post column."*

Below are the three questions they asked, and the results to each one. At the survey link above you can find the responses of the individual economists surveyed.

 

 

 Only one economist appeared to be skeptical about kidney exchange, and I was surprised at who it was (respondents may answer these questions very quickly...).

 

The next question concerns the End Kidney Deaths Act, which was introduced to the respondents at these links:

"There is draft legislation in Congress to increase the supply of human kidneys by encouraging donations to strangers: https://www.congress.gov/bill/119th-congress/house-bill/2687

"It is summarized here: https://www.hawaiibusiness.com/bipartisan-bill-aims-to-prevent-kidney-deaths-by-compensating-donors/ "

 

 

 The End Kidney Deaths Act gets a good deal of support (above) while an unspecified decentralized market gets considerably less support, below.

 

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*Earlier posts

Friday, May 8, 2026 It’s time to carefully but urgently rethink payments to kidney donors. My op-ed in the Washington Post

 

Peter Rousseau comments on our field experiment involving the Econ job market, in PNAS

 My post yesterday was about the experiment about social media and the job market for economists.  I only noticed later that the PNAS also posted a comment on our article, by Professor Peter Rousseau, the secretary of the American Economic Association, who has a long and intimate familiarity with that job market, which the AEA has played a giant role in organizing.

 Improving the job market in economics (and beyond…) by Peter L. Rousseau  PNAS   May 4, 2026  https://doi.org/10.1073/pnas.2609971123

Here is the part of his comment directly connected to our paper:

" the authors make a welcome and useful contribution to the market design literature with a fascinating experiment designed to substitute for and even improve upon the informal information channels lost to the economics job market in the new postpandemic normal. Given that some job candidates are less active self-promoters than others and that, conversely, excessive self-promotion can in some cases be viewed as a negative by prospective recruiters, the authors’ proposed mechanism offers serious promise for leveling the playing field, even if just modestly, for economics job candidates in terms of their visibilities, and perhaps even for expanding the number of jobs actually filled over the course of a recruiting season.
 

"In the experiment, an AI-based algorithm, supplemented with some human checking and reassignments, matched selected economists on social media (i.e., the “influencers”) with willing job candidates based on the closeness of their research. About 43 percent of willing candidates were selected for this treatment. The key to the experiment lies in the matches themselves, which were assigned in a manner that did not take the relative prominence or institutional ranking of an influencer directly into account. All candidate participants were invited to post a tweet about their job market papers on a social media site created for this purpose, and the influencers were asked to post neutral quote-tweets about the members of the treatment group to which they had been assigned. If executed according to design, recruiters viewing the quote-tweets receive information about the closeness of a given candidate’s research interests to those of the influencer. This may function as a partial substitute for the painstaking process of deducing such information across the hundreds of application packets that recruiters receive with only a brief period for making initial decisions. Knowing that a candidate’s research is close to that of Professor “X” is a tangible signal that could make that candidate more likely to be interviewed or receive a campus flyout or job offer from an institution seeking an entry-level economist like Professor X. The experiment indicates that individuals in the treatment group did indeed receive more campus visits and job offers than candidates assigned to the control group, and that the effect on job offers was especially strong for women. It also finds, however, that these effects were more pronounced for candidates matched to influencers with relatively higher citation counts than for those matched to influencers with relatively more followers, as these two measures of prominence in the profession are not that highly correlated. 

" The question of scalability then becomes paramount. Considering the experiment’s positive findings, it is natural to assume that, if universally available, all job candidates would choose to participate and receive the treatment. The process would otherwise go on as stated with perhaps additional influencers being selected by the organizers to serve the larger pool of candidates. Two observations seem reasonable at this point: first, in such a setup, better information about matches could lead to more open positions being filled, which would be a better aggregate outcome; and second, the treatment might in practice benefit candidates from outside the very top departments the most. This is because candidates from the highest ranked departments, who are often perceived by recruiters as having a higher probability of eventually becoming a star, will typically receive more interviews, campus visits, and offers, but in the end can still only accept one offer. With an enlarged set of viable matches, this means that some candidates who may have been otherwise overlooked will find jobs. Of course, the job market may take longer to clear under this mechanism as candidates will have more options to consider before departments go to second or third rounds of offers.
 

"Casual observations of the job market among economics departments and their chairs do suggest that a number of recruiters are unable to fill positions they have posted. The AEA does not currently collect information on just how many, but the very existence of the “AEA Job Market Scramble,” where recruiters and unmatched candidates can post their availabilities on an online message board each March, is indicative of the challenge (3). The design of a job signaling mechanism by the AEA and its implementation in December of each year (4), where job candidates can list two departments to which they would like to express interest in an interview, is another such intervention aimed at easing the congestion.
Another interesting result is that women appear to benefit most from the treatment, while this benefit does not extend to members of other groups traditionally underrepresented in economics. The authors point to existing evidence indicating that women on average tend to be less active promoters of their own research on social media than others and suggest that the additional visibility provided by the quote-tweets could be leading to more job offers. This potential channel, of course, could also be viable for any candidate with a tendency to self-promote less. To explain a special advantage for women, one could note the possibility of forces in the 2022–2023 job market where departments seeking to improve the gender balances of their faculties became aware of candidates through the mechanism who they may have otherwise overlooked. If this is the case, the next question to ask is why does the effect not carry over for members of other underrepresented groups? The answer, though no doubt a speculative one, may lie in the preexistence of other mechanisms and informal channels for promoting such candidates, rendering the marginal effects of the authors’ particular intervention not statistically significant.
Finally, while having the potential to increase the number of matches and raise their average quality, the effects of the authors’ intervention will be subject to some randomness based on the assignment of a given candidate’s influencer. For example, when any influencer posts a quote-tweet about a candidate who has been independently and objectively determined to have close research interests, that candidate’s post tends to receive more views and likes on X than those in the control group, and the extent of this visibility correlates with the size of the influencer’s following. Yet these effects do not seem to transfer downstream to job outcomes, where candidates receiving quote-tweets from highly cited influencers are the ones tending to see more offers. In a real sense, the adage “all publicity is good publicity,” often applied to economics research, may not be always true. The assignment of influencers to candidates, even if randomized, will matter for individual outcomes even though the aggregate effects of the intervention are positive. Given the potential individual benefits compared to nontreatment, however, job candidates would likely embrace the residual uncertainty and participate in the mechanism.
 

"The intervention designed by Qiu et al. may hold even greater promise outside of the economics discipline. In the natural sciences, for example, recruiting for scarce academic postdoctoral positions among new PhDs at a similar career stage, which are markets typically saturated with candidates, often moves directly to a very limited allocation of campus visits based in no small part on letters and other communications from mentors, some of whom could be less than ideally matched with their students or less well known than would-be assigned influencers. These cases are ones in which an enhanced visibility of candidates, when coupled with independent information about the closeness of their work to what senior researchers and their groups might be seeking, could lead to the greater advancement of science more generally.
 : 
"Competing interests P.L.R. has served since 2012 as Secretary-Treasurer of the American Economic Association, a 501(c)(3) non-profit deeply committed to improving the job market for new Ph.D. economists, and for which one of the companion article’s co-authors (A. E. Roth) served as President in 2017.

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 Peter's comment and our paper appeared online, but won't appear in print until next week in the May 12, 2026 | vol. 123 | no. 19 issue of PNAS.

 

Yesterday's post: 

Tuesday, May 5, 2026  Social media, job market outcomes, and ethics of field experiments, by Qiu, Chen, Cohn and Roth in PNAS

 

Social media, job market outcomes, and ethics of field experiments, by Qiu, Chen, Cohn and Roth in PNAS

 One of the fun things about our paper published in today's PNAS is that, as a working paper, it prompted a vigorous discussion of the ethics of doing field experiments in economics.  We discuss this more fully in the published version, below:

J. Qiu, Y. Chen, A. Cohn, & A.E. Roth, Social media promotion improves job market outcomes, Proc. Natl. Acad. Sci. U.S.A. 123 (19) e2528289123, https://doi.org/10.1073/pnas.2528289123 (2026). 

Abstract: Social media has transformed how academics disseminate research, but its effect on academic job outcomes remains unclear. Previous research has shown correlations between social media exposure and metrics like citation counts, but these relationships may be confounded by unobserved factors such as researcher quality or access to professional networks. We examine whether social media promotion causally affects job market outcomes in economics through a field experiment on Twitter (now X). We first collect tweets about job market papers from 519 candidates and post them from a dedicated account. We then randomize half of the posts to be quote-tweeted by established economists in the candidates’ fields, and measure the effects on both online visibility and hiring outcomes. We find that posts in the treatment group receive 441% more views and 303% more likes than those in the control group. Candidates whose posts were assigned to be quote-tweeted receive one additional flyout invitation compared to the control group average of 5.4 flyouts. Furthermore, women in the treatment group receive 0.9 more job offers than women in the control group, who receive 3 offers on average. Exploring mechanisms, we find that academic reputation drives these results, with stronger effects for quote-tweets from highly cited scholars and for candidates from top institutions. Our findings suggest social media promotion causally increases research visibility and improves academic job market outcomes.

 

  ...

"Ethical considerations.
"After the release of our working paper on the Social Science Research Network (SSRN) on May 20, 2024, a vigorous discussion arose on both social and mainstream media, particularly on Twitter, about the ethics of our experiment and of field experiments more generally (e.g., ref. 30). The main concern suggested that job markets are essentially constant sum, so that randomly promoting some candidates through having their JMPs quote-tweeted by influencers would necessarily (and unethically) disadvantage both those who were in the control condition of the experiment and those who did not participate in the experiment.
 

"We understand the importance of considering the ethical implications of any experiment and that ethicality is connected to the underlying economics of the job market. In this latter respect, given the information friction and congestion in the interview process, job markets are unlikely to be constant sum. Aside from the possibility of welfare gains from improved match quality, we note that, typically in matching markets, many employers fail to fill all their positions while at the same time qualified candidates fail to find one, so that welfare can also be improved by filling more positions. [In the 2022–2023 job market, the total number of jobs listed on JOE was 3,608, including 933 (1,083) full-time academic jobs in (outside) the United States and 718 full-time nonacademic jobs (any location). On the supply side, 1,386 Ph.D. students and postdocs applied to at least one job through JOE from August to December 2022 (31).] In economics, the job market often has unfilled positions by the end of February, leading to a scramble round each year starting in March. Similarly, the annual National Resident Matching Program (NRMP) for new physicians in the United States also leads to some positions being unfilled, despite having far more applicants than available positions. [For example, in 2024, 38,494 positions were offered to 44,853 active applicants and 2,510 positions were unfilled (6.5%), at the end of both the main match (a deferred acceptance algorithm, see ref. 32) and a centralized postmatch scramble called the Supplemental Offer and Acceptance Program (33).]
 

"The phenomenon of unfilled positions in a thick labor market may reflect congestion in the interview process. In such a market, since many positions receive more applications than the number of candidates who can feasibly be interviewed, the matching of interviews to jobs may be imperfect in the sense that an employer can find that none of the people interviewed can be successfully hired, but could have filled the position if more appropriate interviewees had been chosen. To mitigate this issue, signaling mechanisms have been introduced in both the economics and medical markets to facilitate a better matching of interviewees and employers (29, 34). In our context, the quote-tweeting of JMPs may similarly serve to help employers find better matches with their selection of interviewees who can be hired.
 

"We also propose that highlighting suitable candidates from underrepresented groups for a position could potentially expand the overall number of job openings. A notable example is the President’s Postdoctoral Fellowship Program, implemented across multiple institutions including the University of Michigan and the University of California system. This program seeks to recruit future faculty members “with the potential to bring to their research and undergraduate teaching the critical perspective that comes from their nontraditional educational background or understanding of the experiences of groups historically underrepresented in higher education.” (See, e.g., https://presidentspostdoc.umich.edu/, retrieved on August 29, 2025.)
 

"Finally, we consider trends in the broader context of job search in evaluating the ethical considerations related to our study. Social media has become a common channel for academics to advertise the JMPs of their students. Thus, we are not introducing a new channel for candidate promotion, nor are we excluding others outside of our experiment from availing themselves of this channel. Our goal is to understand the extent to which this channel may create visibility or improve outcomes for job candidates, especially since not all candidates may have equal access. Our paper belongs to the class of natural field experiment (35), a class that has seen a growing number of studies in which field experiments are used to assess the effects of market interventions. [A natural field experiment is one “where the subjects do not know that they are in an experiment” (35). In our context, participants were told only that we would arrange for their JMPs to be tweeted, but not that there would be a quote-tweet treatment.] One of the main benefits of conducting a natural field experiment is that it minimizes possible Hawthorne effects (36). These studies are widely accepted and even recognized, with the 2019 Nobel Prize for experiments in development economics. If it is ethical for economists to use experiments to evaluate interventions in other markets, it should also be ethical for economists to study the market for economists. And if it is ethical to promote students who are on the job market, then it should be ethical to study the effects of such promotion.
 

"In sum, from a normative perspective (should scholars promote candidates?), we argue that such promotion can reduce information friction and job market congestion, potentially leading to more efficient matching. From a positive perspective (does promotion matter?), we demonstrate in Results that it increases candidate visibility and improves job market outcomes, especially for women who are traditionally underrepresented in economics." 

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Earlier (a blog post about reference 30, above): 

Saturday, June 8, 2024  The ethics of field experiments in Economics, in the Financial Times

 

Kidney exchange now has a broad literature across multiple disciplines

 One pleasure of following an area of research for a long time is getting to see how its academic literature becomes both deeper and broader.  That's certainly been the case with kidney exchange, which now has (of course) a big medical literature, but has also spurred research in the economics and operations research communities.  Here's a recent survey of the OR literature:

Barkel, M., Colley, R., Delorme, M., Manlove, D., & Pettersson, W. (2025). Operational research approaches and mathematical models for kidney exchange: A literature survey and empirical evaluation. European Journal of Operational Research. 

Abstract: "Kidney exchange is a transplant modality that has provided new opportunities for living kidney donation in many countries around the world since 1991. It has been extensively studied from an Operational Research (OR) perspective since 2004. This article provides a comprehensive literature survey on OR approaches to fundamental computational problems associated with kidney exchange over the last two decades. We also summarise the key integer linear programming (ILP) models for kidney exchange, showing how to model optimisation problems involving only cycles and chains separately. This allows new combined ILP models, not previously presented, to be obtained by amalgamating cycle and chain models. We present a comprehensive empirical evaluation involving all combined models from this paper in addition to bespoke software packages from the literature involving advanced techniques. This focuses primarily on computation times for 49 methods applied to 4320 problem instances of varying sizes that reflect the characteristics of real kidney exchange datasets, corresponding to over 200,000 algorithm executions. We have made our implementations of all cycle and chain models described in this paper, together with all instances used for the experiments, and a web application to visualise our experimental results, publicly available. "

 

"The first papers to study algorithms or mechanisms for KE-Opt were the landmark papers of Roth et al., 2004, Roth et al., 2005. When the objective is to maximise the number of transplants, KE-Opt is 
-hard in general (Abraham et al., 2007).

... 

 

"The main contributions of this survey paper are as follows:
 

•A detailed literature survey (with over 210 references) of OR approaches to KE-Opt, covering the following topics: algorithms and complexity for KE-Opt; hierarchical optimisation in KE-Opt; enabling equal access to transplantation; dynamic KEPs; uncertainty and robustness in KEPs; multi-hospital and international KEPs; recipients’ preferences; dataset generators and software tools; emerging topics; and other related surveys.
•A systematic exposition of all the key existing ILP approaches for KE-Opt, describing separately models for representing optimal solutions comprising only cycles from those comprising only chains. As a consequence, combined ILP models for KE-Opt can be obtained by mixing a cycle model with a chain model. We also use a running example (appearing in the Supplementary Material) to illustrate all models for the benefit of the reader. 

•A comprehensive empirical evaluation of all combined ILP models for KE-Opt that are described in this paper, together with “off-the-shelf” approaches involving advanced techniques such as column generation and branch-and-price, where we have been able to obtain and execute the third-party software. The main aim is to compare execution times of the different approaches considered on randomly generated datasets that reflect the characteristics of real data from the UK’s KEP. In particular, we tested 49 methods on 4320 instances, corresponding to over 200,000 algorithm executions, and amounting to over 10 years of computational processing time in total, across multiple cores running in parallel.
•An interactive tool to allow the reader to analyse the data resulting from our experiments that is publicly available at https://optimalmatching.com/kep-survey-2025, allowing custom heatmaps to be created by varying instance sets, models to be considered and measures of performance.
•All of the implementations of the combined cycle and chain ILP models presented in this paper are available for the reader to access at https://doi.org/10.5281/zenodo.14905243, and the benchmark instances used for the experiments are available for download at https://doi.org/10.5525/gla.researchdata.1878." 

My academic career to date, in two word clouds (covering 1974-1999 and 2000-2025)

 Here's a website that will make a word cloud based on your Google Scholar page: Scholar Goggler.

 I used it to create a kind of data-graphic of my career to date, by producing two word clouds from article titles on my Scholar page from 1974-1998 and from 1999-2025.  Those ranges have two properties: they are almost equally long, and so divide my career so far in half, and they also cover the period in which I mostly saw myself as a game-theorist and experimenter (studying bargaining, early in the period, and matching markets later), and the period in which I became something of a practical market designer drawing on those tools among others. (For context, my paper with Elliott Peranson on redesigning the medical residency match appeared in 1999*)

1974-1997 journal article titles

 

 

1998-2025 Journal article titles

 

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* Roth, A.E. and E. Peranson, "The Redesign of the Matching Market for American Physicians: Some Engineering Aspects of Economic Design,” American Economic Review, 89, 4, September, 1999, 748-780. https://www.aeaweb.org/articles?id=10.1257/aer.89.4.748  

A philosopher interviews economists about rational choice

A hardcover copy of this book of interviews (of economists, by a philosopher) arrived in my mail the other day, and I'm enjoying it. It's also freely available online at the links below. (Here is my interview (pp 371-395).

Conversations on Rational Choice, a series of interviews conducted by Catherine Herfeld DOI: https://doi.org/10.1017/9781316344392   Published online by Cambridge University Press:  27 December 2025

 

 Contents:

Acknowledgments

1Introduction

2Pioneers of Rational Choice

1. 2.1Kenneth Arrow

2. 2.2Thomas Schelling

3. 2.3Martin Shubik

4. 2.4Gary Becker

3Formal Theories of Rational Choice

1. 3.1David Kreps

2. 3.2Itzhak Gilboa

3. 3.3Christian List

4Psychological Dimensions of Rational Choice

1. 4.1Robin Hogarth

2. 4.2Daniel Kahneman

3. 4.3Ernst Fehr

4. 4.4Gerd Gigerenzer

5Relating Neurons, Norms, and Networks to Rational Choice

1. 5.1Colin Camerer

2. 5.2Cristina Bicchieri

3. 5.3Rachel Kranton

6Rational Choice, Games, and Markets

1. 6.1Robert Aumann

2. 6.2Alvin Roth

3. 6.3Vernon Smith

7Critical Reflections on Rational Choice

1. 7.1Duncan Foley

2. 7.2Robert Sugden

3. 7.3Alexander Rosenberg

8Rational Choice in Philosophy

1. 8.1Isaac Levi

2. 8.2Patrick Suppes

3. 8.3Laurie A. Paul

9Epilogue

References

Index

 

Nick Bloom discusses work from home

 Econ To Go is a Stanford series in which Neale Mahoney, the director of SIEPR, interviews an economist.

In this one he interviews the inimitable Nick Bloom, who is perhaps the leading scholar of the growing pattern of work from home. 

 

x 

 

Claudia Goldin to Receive Talcott Parsons Prize from the American Academy of Arts and Sciences

 Here's the announcement from the American Academy of Arts and Sciences*:

Claudia Goldin to Receive Talcott Parsons Prize 

“To truly understand the American economy, one must recognize Claudia Goldin’s essential work,” said Laurie L. Patton, President of the American Academy of Arts and Sciences. “We commend her fearlessness, leadership, and commitment to understanding what is lost and what is gained for everyone when opportunities for women contract or expand. Her dedication to communicating that knowledge widely is equally courageous.”

“It is a great honor to receive an award named for Talcott Parsons that has been given to leading figures in linguistics, history, psychology, and sociology,” said Goldin. “I am immensely gratified that my work in economic history is seen as a bridge between economics and the other social sciences.” 

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I learned of this award from an email with the subject line "Announcing an Academy Award," which for a moment made me think that Claudia had been honored by the Academy Awards, and would receive an Oscar.

Fast and slow dissemination of new ideas in medicine and economics (one timeline:)

There are many differences between medicine and economics, but one of the most striking is the speed of publication. 

I publish papers in both fields, so I get to experience very different speeds, of publication and response. Publishing (and therefore also responding--both positively and negatively ) is much slower in economics than in medicine.  I've been noticing this because of recent attention to a paper I coauthored that was published in November, 2025, in the journal Transplantation. (It had been submitted in January, was revised and accepted in March, and was published online in May.)  In December the journal created and distributed to its subscriber list a narrated video abstract of the paper. You can find the video here https://vimeo.com/1146995735/486989e95c?fl=pl&fe=sh

 Our paper suggested ways that information revealed during deceased organ allocation could be used to evaluate organ quality, and expedite (i.e. speed up) the allocation process for organs at risk of being unused.  And the first published response, just three month later, suggests how such information could be used in India.

Early Refusal Pattern Phenotyping as a Surrogate for Organ Quality Assessment in Kidney Allocation
Kashiv, Pranjal MD, DM1,2; Pasari, Amit MD, DM2,3; Balwani, Manish MD, DM2,3; Kute, Vivek MD, DM4
Transplantation ():10.1097/TP.0000000000005664, February 09, 2026. | DOI: 10.1097/TP.0000000000005664

"We read with interest the recent article by Guan et al,1 which provides a comprehensive and methodologically thoughtful assessment of refusal behavior in deceased donor kidney allocation. Their distinction between single-patient and multiple-patient simultaneous refusals, derived through timestamp-based clustering, offers a methodologically robust framework that elevates routine offer-response data into a meaningful surrogate for real-time assessment of organ suitability. This approach is particularly valuable in allocation environments where decisions must be made under substantial time pressure and with incomplete ancillary information.

...

" Their observations offer global relevance and hold potential for strengthening allocation efficiency in India’s evolving deceased donor landscape." 

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Earlier:

Here's the blog post that accompanied the publication online... 

Friday, May 23, 2025  Deceased organ allocation: deciding early when to move fast