Harvard Economics Department

3 year exciting Pre-Doctoral fellowships. For more information please visit: https://lnkd.in/dcpBkxG6 1. Pre-Doctoral Fellowship (Prof Pallais) Professor Amanda Pallais is looking to hire a highly-skilled and motivated predoctoral fellow who will start work in the summer of 2025 and will work for one to two years. The job entails close collaboration with Professor Pallais on research in labor economics. Examples of past projects include identifying the impacts of remote work, determining how much workers value job flexibility, and understanding and developing interventions to combat the loneliness epidemic. This position is ideal for someone who has a long-term interest in economics research and is planning to go to graduate school in economics or a related field. The position will be based at Harvard University. 2. Pre-Doctoral Fellowship (Prof Kreindler) Professor Gabriel Kreindler is seeking a predoctoral fellow. The fellow will work on projects in the areas of urban, spatial and development economics, which involve structural modeling and estimation and large-scale randomized experiments. This position will last for one year, from July 2025 until June 2026. The fellow will be an active participant in the Harvard research community and will have opportunities to develop their own research agenda. The fellowship may lead to co-authoring papers with the PI.More information on Professor Kreindler can be found at this link: https://lnkd.in/dg6Jd9vJ *The position is fully paid, and a visa can be arranged. 3. Pre-Doctoral Fellowship in Behavioral-Economic Theory (Prof. Rabin) Professor Matthew Rabin at Harvard University seeks full-time, pre-doctoral research fellows. The position is ideal for candidates interested in pursuing PhD studies in Economics, particularly those with an interest in psychologically-grounded economic theory. Fellows will aid Professor Rabin and others in drawing out the theoretical implications of recent models that attempt to improve the psychological realism of economic models. Such models attempt to rigorously capture improvements to the psychological realism of our models of individual behavior, to include, e.g., errors in statistical reasoning, errors in interpreting others’ behavior, self-control problems, and the effects of expectations, self-image, and other beliefs on our utility. (The agenda motivating this research is outlined in Rabin 2013, AER and JEL.) The goal is to develop and evaluate models that can broadly improve economics, rather than over-stating the successes or failures of theories by cherry picking examples, or ignoring the cases where classical economics gets things right. The research assistance needed will reflect this goal: participants will be asked to work out the implications of new models, realistic and unrealistic, interesting and boring, to better understand the capacity of these models to help explain economic behavior.

Using an analysis of over 8,000 economics PhDs and their advisors to find what determines economics PhD student research success, from Joshua Angrist and Marc Diederichs https://lnkd.in/eyQjqEsx

🔊 Why do protests matter? Exploring their causes and lasting impacts How can we measure protests and their impact? How has social media changed the nature and frequency of protests? Where is more research needed? Today on VoxDevTalks, Tim Phillips speaks to David Y. Yang (Harvard Economics Department) and Noam Yuchtman (Department of Economics, University of Oxford) about these questions & more: https://lnkd.in/dK_s8cwY

We look forward to the 3rd Zurich Workshop in AI + Economics, including the keynote by Melissa Dell (Harvard Economics Department)! Organized by Elliott Ash & Sergio Galletta (CLE / ETH Zurich) and David Yanagizawa-Drott (University of Zurich Department of Economics), the workshop will take place in person on December 6 & 7 at Universität Zürich | University of Zurich.

If the US is an outlier on something like entrepreneurship, then why is data from the States still treated as the gold standard in management research? Are we attempting to make inferences based on an outlier? #irony #academicresearch

Our Research Director Lynne Sacks recently attended Opportunity Insights's (OI) third annual Conference on Economic Opportunity that brought together scholars with diverse perspectives on the determinants of economic opportunity and intergenerational mobility. The conference featured opening remarks from OI's Director Prof. Raj Chetty and a keynote from Henrik Kleven, Professor of Economics and Public Affairs at Princeton University. In 2024, we partnered with OI to launch the Upward Mobility Doctoral Fellowship, a research Fellowship that combines the power of “big data” research with the expertise of practitioner organizations working in the #cradletocareer field to improve evidence on upward mobility for children and youth. The Fellowship is training a new generation of researchers in collaborative research that can impact practice and policy for cradle-to-career place-based interventions. Blue Meridian Partners Communities In Schools National Office Partners for Rural Impact Purpose Built Communities StriveTogether Clare Suter Harvard Economics Department Harvard Kennedy School

"Without sincere recognition of [their] own mistakes, a leader will never build a culture of innovation in the company. Creating such a culture not in words, but in deeds, requires great effort...But it's worth it." I had such a wonderful conversation with Тома Мироненко for Forbes Ukraine on #failure, experimentation, and how to encourage taking smart risks. Read the article here: https://lnkd.in/ekun2sRC

I'm pleased to share the latest episode of the The Brookings Institution Podcast on Economic Activity, where I hosted a conversation with Harvard Economics Department's Oleg Itskhoki and the Peterson Institute for International Economics' Elina Ribakova. We delved into their research on the impact of economic sanctions on Russia following its invasion of Ukraine. We also talked about the effectiveness of these measures, their economic implications, and what the data reveals about their influence on the Russian economy.   You can find the full episode here: 

Congratulations to Professor Acemoglu! Thank you for impacting our students!

When I graduated in 2007, I had made econometrics my field (meaning I sat for the field exam), and one of my other fields was labor. But then when I was on the job market, I was always feeling like an outsider, but it was on so many margins where I felt it that I couldn't quite put my finger on which things were which. But one of them was how my understanding of econometrics differed from what we would now just call "causal inference". I ended up making this picture for a talk I gave back in August at Northwestern. I was trying to help the audience understand that there really isn't one "causal inference" as it's not a monolithic thing. Rather there is only "this causal inference" and "that causal inference". And one of the causal inference schools, you might call it, is the design tradition. But even then within the design tradition, the particular form it took on in economics was in the fusion of the quasi-experimental approach within labor economics, specifically rooted in the work by economists (faculty and students) in Princeton's Industrial Relations Section in the 1970s and 1980s, and the Rubin causal model. They were themselves completely distinct from one another until Angrist and Imbens get to Harvard. I've tried to write about all this on my substack, but it's always hard to fully wrap my arms around it while also making it easier for someone else to understand. But this graph is my way of making something that might do that. Basically, one of the things that people should understand about "this causal inference" is it was largely influenced by an empirical crisis in the 1970s in labor economics. And this is also I think partly why you see economists holding Pearl's work at more of a distance which to people who are not in "this causal inference" can find annoying (and vice versa). This causal inference was born out of a major empirical crisis. That empirical crisis had nothing to do with formalized econometrics or the Rubin causal model. It had everything to do with over-confidence in modeling assumptions leading to misuse of causal modeling that resulted in empirically detectable false results -- even very false results -- the most famous being Bob Lalonde's 1986 article in the AER where he undertook an ingenious way to compare the results of an RCT with non-experimental version of the same RCT and showed that nearly every way in which a person would approach this problem would yield not just the wrong answer -- it would oftentimes yield the wrong *sign* on the treatment effect. I think a lot of the new entrants to causal AI are just going to have to have their own empirical crisis before they have their own come to Jesus moment. But over confidence in one's ability to fully write down all aspects of the data generating process is where a lot of the bodies have been buried. https://lnkd.in/eKe2j7eE