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Vaibhav Anand

@vaibhavanand.bsky.social
60 followers 157 following 19 posts

Assistant Professor, Greenberg School of Risk and Insurance, St. John's University | www.vaibhavanand.com

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Vaibhav Anand @vaibhavanand.bsky.social · 03/06/2026
3. Total prevention spending may decline as predictions improve (the Snowville question above). This is especially for actions with rapidly diminishing marginal benefits. e.g., total spending on road salting may decline, whereas spending on flood barriers or power-grid protection may increase.
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Vaibhav Anand @vaibhavanand.bsky.social · 03/06/2026
2. Individual risk preferences matter less as forecasts improve. (Communities that respond differently to pandemic forecasts under uncertainty may converge toward similar actions as forecasts become more accurate.)
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Vaibhav Anand @vaibhavanand.bsky.social · 03/06/2026
The paper develops a framework for forecast-based prevention and provides three key insights: 1. Improved predictions lead to more intense but less frequent preventive actions. (Roads may be salted less often, but when they are, cities apply more salt and cover more road miles.)
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Vaibhav Anand @vaibhavanand.bsky.social · 03/06/2026
A. Increase, because accurate forecasts make pretreatment more appealing. B. Decrease, because accurate forecasts reduce unnecessary salting. C. Stay the same, because accurate forecasts do not change the overall risk.
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Vaibhav Anand @vaibhavanand.bsky.social · 03/06/2026
The paper cold-opens with a thought experiment: Consider the town of Snowville that relies on #weather #forecasts to pretreat roads with salt before snowstorms. As forecasts become more accurate, how might the city’s annual spending on salt change?
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Vaibhav Anand @vaibhavanand.bsky.social · 03/06/2026
How do improvements in predictions affect our investment in loss prevention? Glad to share my article in @informs.bsky.social journal Decision Analysis that examines this question. Open-access SSRN link: papers.ssrn.com/abstract=417... Journal link: pubsonline.informs.org/doi/full/10....
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AERE @aereorg.bsky.social · 05/12/2025
🌾How does extreme heat change diets of farming families in rural India? 🔥A new #JAERE study finds that when growing-season temperatures spike, crop yields drop and this drives strong undernutrition for many households, especially calories, zinc, thiamine, and niacin. 📊 #JaereFigureFriday 🌎 #EconSky
Six coefficient plots of the effect of temperature on the percentage of households consuming below
80% of the recommended levels of different nutrients. The x-axis contains 10◦F temperature bins from below 70◦F to above 110◦F. The nutrient in Panel A is protein, and no coefficients are statistically significant. The outcome in Panel B is iron, and only the coefficients for the 80 to 90◦F bin (0.14) and the 90 to 100◦F bin (0.16) are statistically significant. The nutrient in Panel C is zinc, and statistically significant coefficients range from 0.16 for the 80 to 90◦F bin to 0.25 for the above 110◦F bin. The nutrient in Panel D is thiamine, and statistically significant coefficients range from 0.13 for the 70 to 80◦F bin to 0.32 for the above 110◦F bin. The nutrient in Panel E is niacin, and statistically significant coefficients range from 0.13 for the 70 to 80◦F bin to 0.41 for the above 110◦F bin. The nutrient in Panel F is riboflavin, and no coefficients are statistically significant.
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Paul Goldsmith-Pinkham @paulgp.com · 02/11/2025
Dropping a beta version of this page while everyone is up and processing baseball! This tool lets you search the full text of papers from the American Economic Review, American Economic Journal series, and over 30,000 NBER working papers. paulgp.com/econlit-pipe...
paulgp.com
Economics Literature Search
Full-text search across 15,000+ papers from top economics journals and NBER working papers. Track how empirical methods have evolved over time.
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Vaibhav Anand @vaibhavanand.bsky.social · 30/10/2025
An insightful discussion of my paper by @mattkahn1966.bsky.social Thank you for highlighting the work. The post raises interesting questions on how tech innovations (e.g., remote work, autonomous cars) might interact with these traditional adaptation measures (e.g., weather advisories).
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Vaibhav Anand @vaibhavanand.bsky.social · 30/10/2025
Thanks @joshua-goodman.com for highlighting the paper. Here is the ungated version papers.ssrn.com/abstract=420...
papers.ssrn.com
Does Getting Forecasts Earlier Matter? Evidence from Winter Advisories and Vehicle Crashes
Significant investments are directed toward improving the accuracy and early availability of forecasts. However, the value of longer lead times on forecasts is
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Reposted by Vaibhav Anand
Julian Reif @julianreif.bsky.social · 28/03/2025
Important advance for simulated instruments
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Scott Hosking @scotthosking.bsky.social · 20/03/2025
🌤️ Our #AI weather forecasting model has been published today in Nature 🤖 🥇the first AI weather model to learn directly from raw environmental observations 🌎 compared to existing AI models it is cheaper to train, uses less carbon and easier to deploy locally www.turing.ac.uk/news/fully-a...
turing.ac.uk
Fully AI driven weather prediction system could start revolution in forecasting
A new AI weather prediction system, Aardvark Weather, can deliver accurate forecasts tens of times faster and using thousands of times less computing power t
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Felix Pretis @felixpretis.bsky.social · 24/03/2025
Heads (and elbows) up: The call for abstracts for the 9th Conference on Econometric Models of Climate Change (University of Victoria, BC, Canada – Aug. 27-28, 2025) is open until April 17th! #Econsky Looking forward to seeing you in August! More info at: www.tinyurl.com/emcc2025uvic
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
9/ Ungated link to the paper: papers.ssrn.com/abstract=420...
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
8/ A big thanks to my advisors, friends, family, reviewers, and fellow researchers who provided valuable feedback and tremendous support without which this study wouldn't have been possible.
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
7/ Bottom line: Weather forecasts are valuable! Improving forecast lead times has material economic benefits. People and institutions pay attention to forecasts and advisories.
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
6/ My estimates suggest longer lead times on winter advisories reduce approximately 13.2 crashes per 100,000 people each year, resulting in an annual savings of approximately USD 190 million.
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
5/ I provide evidence of two mechanisms at work here: • Individual behavior: People visit fewer places when they get advisories earlier • Institutional response: Snowplow crews intensify road maintenance operations when advisories arrive earlier
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
4/ In fact, at the current timescale of forecast horizon, the marginal benefits of additional lead time don't decrease--longer the lead time, fewer the crashes.
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
3/ Winter advisories with longer lead times DO reduce vehicle crashes, even when they're less accurate than advisories with shorter lead times.
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
Research Question: Significant resources go into improving #forecast accuracy, but we know little about the value of longer lead times. Does getting warnings earlier actually provide economic benefits? I examined this question in the context of winter weather and crashes.
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Vaibhav Anand @vaibhavanand.bsky.social · 11/03/2025
Excited to share that my job market paper "Does Getting Forecasts Earlier Matter? Evidence from Winter Advisories and Vehicle Crashes" is forthcoming in AEJ Policy!
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Vaibhav Anand @vaibhavanand.bsky.social · 14/01/2025
Agree about the name--it is confusing. Though I didn't think about it earlier, sampling within a pre-defined insurance unit may reduce basis risk even more. e.g., in home ins, a unit could be homes that share similar characteristics (age, roof type, elevation). Thanks for highlighting this.
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Vaibhav Anand @vaibhavanand.bsky.social · 14/01/2025
Crop cutting experiments (CCE) based crop ins in India might be an example. I believe the sample farms for CCE are randomly chosen within an insurance unit (a village). CCEs determine the actual yield, which determines the payout. Although, not sure if the CCE farms are themselves insured or not.
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AERE @aereorg.bsky.social · 17/05/2024
Join us at #AERE2024 for an exciting plenary on America's Trillion Dollar Climate Investment. We'll hear from panelists Karen Palmer, Arik Levinson, Lori Bennear, and Nathaniel Keohane, moderated by Joe Aldy 📈📉
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Alexandra Witze @alexwitze.bsky.social · 26/03/2024
"Machine learning makes this science a lot more fun,” says Tapio Schneider. New climate feature in Nature by Carissa Wong:
nature.com
How AI is improving climate forecasts
Researchers are using various machine-learning strategies to speed up climate modelling, reduce its energy costs and hopefully improve accuracy. Researchers are using various machine-learning strategi...
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Brigitte Roth Tran @brothtran.bsky.social · 10/01/2024
More timely than expected: Check out my latest Economic Letter showing extreme weather events cause uncertainty for affected firms that lasts several months. We focus on hurricanes but find similar effects after other extreme weather events. #EconSky #ExtremeWeather www.frbsf.org/economic-res...
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