📢Thrilled to continue the young scholars series on the @IdeasofIndia podcast at @mercatus, where we invite scholars entering the academic job market whose research focuses on India to discuss their work. We have a handful of 30-minute recording slots for 2026. Please spread the word! To be considered, please use this google form (takes about 5-7 minutes). forms.gle/f5nVA9hiUbcVGw9d9 Applications are open until August 10, 2026. All submissions received by the deadline will be considered together. We will make our decisions by August 18 and send out invitations thereafter. Recordings will take place in August and September. Past editions of the series have featured some fantastic scholars across disciplines. The 2025 series featured Kartik Srivasatva, Sunny Rai @snyrai_  Chetana Sabnis, @asad__tariq, Karthik Narayan @asknarayan, @nayantara_ Biswas, @Lavanya__Ammu The 2024 series featured @RollyKapoor, @DeepikaPadmanab, @sukrit_puri, @deepti_sharma8, Steven @BrownstoneEcon, Aarushi Kalra @chidiya_, Abishek Choutagunta @kaapi_croissant, @Kushagr_Bakshi, Atanu Chatterjee @atanu_xahs The 2023 series featured @rajatkochhar, Vani Swarupa Murali, @KartikeyaBatra, @kumar_rithika, @Vanisha49, @dunc_webb, @i_sarathpillai, @vatsalecon. The 2022 series featured Khushdeep Malhotra, Anoop Jain @Fruit_noops, @aliz_toth, @raamadhok, @mahimavasishth, @BanerjeeShweta, @nish_vats. The 2021 series featured @arkdevghosh, @tiwari_chhavi, Gaurav Mittal @geo_mittal, Bhumi Purohit, @AshishKumarSed1, @karsha, @Dr_NehaG, @KaranBabbar19, @KimFeCramer, @ArchanaDang1, @apuravbhatiya, @radhi_jain. And it all started in 2020 when we featured work by @vaishnavisur, @RohitTicku, @tanu_kumar1, @DrRayProma, @VaidehiTandel, Kunal Mangal @mrgunalan. I answered a few questions related to the job market series in 2024 (Please take note of points 1 and 3). Here’s the link nitter.net/srajagopalan/status/18…

Excited to share our new working paper: how much did the US standard of living really rise over the twentieth century — and when? With @bhwittenbrink Using 5.1 million Sears catalog listings, we build a quality-adjusted price index for consumer goods, 1900–1990. Two headline findings: growth was much larger than official statistics imply, and its peak came before WWII, not after. (1/9)

This is a great point, @arindube , and I am really guilty of not sharing more about what I have been up to on the teaching side of things, given that this is what they pay me for! Let's get back on track with a mega post. At @LSEEcon, we've been running a series of structured experiments to figure out exactly how GenAI changes the production function of economics education. We moved past the "cheating" panic early (although we didn't really have one in our programmes) and started actively rebuilding our pedagogy around these tools. Here's what we're doing and what we're learning, and btw we will be presenting our work at CTREE 2026 in Las Vegas in late May if you are in town. The AI Economics Professor With Ronny Razin, we built a specialised, course-aligned AI tutor. The key idea Ronny had: best way to verify if a student actually understands a concept is to ask them to explain it interactively. Clearly this does not scale to the class size we have at LSE (Ronny teaches his course to 850 first-year students). But we can scale with AI! The key pedagogical principle is that the chatbot uses a Socratic framework. It refuses to hand out final answers. Instead, students are prompted with an exercise, and the chatbot asks them to identify the next step in a mathematical or logical derivation themselves, guiding them through the reasoning rather than short-circuiting it. It adapts to the students' level, for example by clarifying concepts or notation if needed. This gives students access to 24/7 personalised tutoring, levelling the playing field for those who might hesitate to speak up in small classes or office hours, and solving Bloom's "2-sigma problem" in economics education. Notice that we didn't train the bot or fine-tuned t to our course material. We just provided a system prompt embracing the Socratic approach, and the solutions to the exercise students had to solve. That's it. Off the shelf LLM model (it was Gemini 2.5 Flash). We did run a small experiment for a game theory exercise, where students had to work out strictly dominated strategies, and pure and mixed strategy Nash equilibria. The feedback we received is overwhelmingly positive: students found it useful to work through the reasoning with the chatbot, and it helped them understand the material better. We are also in the process of establishing if the use of the chatbot improves marks in the final exam, although we don't have a full analysis yet. But I can say that this was a very good year for the distribution of marks in this course, way above the average of previous years. If this proves as good as it looks, next step is to scale this to more courses, potentially expand to similar disciplines in LSE, and potentially expand to other universities. Stay tuned. AI Feedback Experiment Providing high-quality, scalable formative feedback is one of the hardest problems in our job. It's incredibly labour-intensive, and the result is that students often get too little feedback, too late to act on it. Main problem, again, is scale. Can we use AI to enhance our feedback process? We did an experiment with @MichaelGmeiner2 in one of our MSc courses. Michael is a great teacher. In his Econometrics course, he teaches students how to write referee reports, and provides feedback to each one of them on 5 submitted referee reports. We thought, why don't we provide two feedback reports for each submission, one AI-generated and one human-generated? This will allow us to evaluate how good the AI feedback is with respect to human feedback (well, Michael's feedback, which is superhuman in my view, but ok). And so we did. We didn't say which is which to students, to avoid any kind of bias. And again, we just cooked up a prompt for the LLM to generate feedback on the referee report, we provided the AI with the paper to referee, the referee report submitted by the student, and nothing more. We found out that students rated the AI-generated feedback as less useful than the human-generated, although not by a lot. Main problem with the AI-generated feedback is that it is too generic, and does not address the specific TECHNICAL issues that the student may have missed in their report. It is also too positive, and does not provide the student with the critical feedback that they need to improve. In particular, students highlighted that the AI feedback did not enhance their critical thinking, and did not address methodological problems in the research article they were refereeing. Some of these aspects can be addressed with a better prompt, and we are working on it. The technical and methodological issues can also be addressed by providing a summary of what the teacher expects students to criticise in the paper, although there may be additional challenges in this approach (what if the student finds something else to criticise that the teacher did not think of? it happens all the time). Students also mentioned they think the two pieces of feedback are complementary, and they will be happier getting both that just one of them. This points in the direction of a hybrid approach, where AI is used to enhance the human feedback process, rather than substituting it. The caveat is, of course, that we haven't used the most recent models, we didn't try with mixture-of-experts and all the tricks in the book. Teaching Python & RELAI Principles Perhaps our biggest curriculum shift: with @JADHazell we pioneered teaching AI coding tools to first-year students. In the first year macro course that Joe teaches, we introduce students to Python coding for economic analysis. This year, we decided to move in a different direction: since the advent of AI coding agents, we believe it is more important to be able to READ and ORGANISE code than writing it. It is more important to be able to explain your intent to the AI coding agent, and verify that intent has been reflected in the code, than to be able to write the code yourself and test it. But how can you teach students that have never seen a line of code to do that? Introducing Reverse Engineering Learning with AI (RELAI). Start with a full snippet of Python code. The student is told to prompt the AI to explain what the code does. Once the student understands what the code does, it can asks about the syntax and the programming concepts behind the snippet. Then can ask a study plan for those concepts, if needed. Then can try to enquire the AI about what would happen if I change this line or this parameter. Then it can experiment itself by changing the code, and debug with the help of the AI. Finally, the student can ask the AI to produce new code, based on what was learned, and the new intent. I call this the EXPLORE approach: Examine the code, eXplain what it does, Probe deeper, Link to economics, Output prediction, Recreate understanding, Extend with modification. Once students are familiar with AI coding agents, they are assessed with a challenging coursework that Joe created. The assignment has a part that is difficult to do without AI, but should be feasible with AI. There are open ended questions where students have to go beyond the simple repetition of what was learned in the course, possibly explore new datasets and questions, etc. We think this approach can help integrate AI coding agents into the curriculum in a meaningful way, and help students develop a deeper understanding of coding tools in a faster and more efficient way. Coursework is on the way, so we will be able to evaluate the impact of this approach in the next few months. I personally believe RELAI can be adapted to other topics and subjects, and can become one of the way we interact with AI when learning something new. Read more about our approach here: python-ec1b1.vercel.app/ AI as a productivity tool This is where you can really go nuts. I have used AI to produce new teaching material for several workshops and courses. Slides, assignments, exercises, etc. The last few exams were written with AI tools, creating a series of questions first with suggested solutions, and then choosing the most appropriate ones. I use a coding agent (@cursor_ai ) with access to my teaching materials and past exams, so that it is aware of the content and style. You get a very good exam draft in minutes, and can edit, change questions, generate new ones, etc. It used to take me days to write a good exam, now it takes me a few hours in the afternoon. I used Cursor to do deep research about a new course I wanted to design. I asked for topics, examples, current research in the field that I may not have been aware of, similar courses' syllabi, and in general what was the state of the art in the field. I got a very long list of topics that I could choose from to design my own course, based on my taste, interest and what I think my students should know. I could generate different versions of the same course for different levels (UG, MSc and PhD). Conclusion We are still at the early stages of this journey. We are learning a lot, and we are still figuring out how to best use AI to enhance our teaching. One important thing you may have noticed is that we first define our pedagogical approach and then we integrate AI tools to support it. The other principle should be, design not for the tools you have now, but the ones you will have in a few months or years. If you have comments, or have been running similar experiments, I will be happy to hear from you.

New paper out w/ my excellent co-author (and friend) @gabrielpfritsch : “High-frequency fiscal shocks”. We use LLM’s to construct a daily time-series of expectations about US fiscal deficits (1947-2025) and thereby identify “fiscal shocks” in the historical record. Thread:

All the best my dear! It was good working on your restoration. May you fare well. #VPHall

🚀 Draft chapters my forthcoming MIT Press book: 𝗛𝗲𝘁𝗲𝗿𝗼𝗴𝗲𝗻𝗲𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁 𝗠𝗮𝗰𝗿𝗼𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀: A Tractable New Keynesian Framework A modern, analytical roadmap to TANK & HANK models for researchers, students and policy institutions sites.google.com/site/florin… 👇

Advance notice on the publication of a major new reference work in economic history (expected to be out by the end of 2025 or early 2026, in two volumes). I will upload the contents page in a week.

I'm on the Job Market this year 🚀 Really happy to share my JMP: "Climate change beliefs and savings behavior: a macroeconomic perspective" I show how beliefs over structural shifts affect transitional macroeconomic dynamics, using the example of climate change 👇

Shruti Rajagopalan (@srajagopalan) talks with Karthik Narayan (@asknarayan) about how financial markets interpret monetary policy surprises, why scheduled and unscheduled RBI announcements move asset prices in opposite ways, what this reveals about central bank communication and credibility, and how better measurement of policy shocks can improve decision-making in emerging economies.

In the fifth episode of the 2025 job market series @IdeasofIndia @mercatus I spoke with Karthik Narayan @asknarayan , a doctoral candidate @NuffieldCollege @OxfordEconDept. We spoke about the effects of scheduled versus unscheduled monetary policy announcements in India, and much more.

Amazing session on one of my favorite topics at @GRAPE_ORG Summer Workshop on Macro and Finance: determinacy and uniqueness. @LukaszRachel and Tobias Kawalec (who will be on the job market).

New preprint! 🧠🤖 How do we build neural decoders that are: ⚡️ fast enough for real-time use 🎯 accurate across diverse tasks 🌍 generalizable to new sessions, subjects, and species? We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes! 🧵1/7

Resurrecting the Lucas Phillips curve. A post describing a new paper at Grumpy Economist. Link in first reply.

Govts, esp. in emerging economies, increasingly use eminent domain to stimulate private industrial development. How does this affect entry & employment in these large-scale sectors? Read the NEW WP by JI postdoc Megan Haasbroek @CamEcon #EconTwitter ➡janeway.econ.cam.ac.uk/publi…

Dr. Manmohan Singh (1932-2024) 1. His PhD thesis was one of the first to call for a greater export focus 2. Designed the package that brought down inflation from a peak of over 20 percent in 1974 3. Brought much of the eventual reforms team into government

Indian econ. development's failing is under-performance of labor-intensive manufacturing In fresh research (mids.ac.in/assets/doc/WP_244…) @abhishekecon @naveenjthomas & I find a new clue: multi-plants First of 2 @bsindia pieces explains some findings: business-standard.com/indust… 1/

** New WP alert ** Slack and Economic Development, with @mwwalkerecon, @nachi_365, @tedmiguel, @soliman_felix, @TilmanGraff We study the causes and macroeconomic consequences of slack in small firms. drive.google.com/file/d/1heK…

🚨🚨🚨 Excited to release a working paper "Do Deficits Cause Inflation? A High Frequency Narrative Approach" with superstar LSE PhD student @sj_hobler 🚨🚨🚨 Summary of the paper: (1/15)

I’ve done a big update of my notes on computation for heterogeneous-agent macro. They start with the endogenous grid method and end with by solving a HANK model using sequence space Jacobians. Codes are in Julia. alisdairmckay.com/Notes/HetA…

Now open for applications: Nuffield Postdoctoral Research Fellowships in Economics with @OxfordEconomics, for candidates who have recently finished (or will soon) a relevant doctorate. Deadline: Sunday 15 September More info at nuffield.ox.ac.uk/the-colleg…