New paper out in Scientific Data! 📈 We show that wealth inequality within the United States has risen to a new peak, likely an all time high. This work is made possible by our use of ensemble machine learning to connect federal wealth surveys and public microdata. Write-up: https://lnkd.in/gUATBMDD Paper: https://lnkd.in/g2dneYE3
Curious to see what it would look like if you accounted for inflation; $1 in 1960 is equivalent to ~$10 today.
Interested in the FAIR principles of mathematical research data? Here is a recent paper https://lnkd.in/gQpBQ6qc discussing in particular our awesome database www.FindStat.org of combinatorial statistics and maps, originally build by Chris Berg and me and currently run by Martin Rubey and me.
The progress in causal inference research is inspiring: https://lnkd.in/emn76Hz3
Professor of Econometrics / University of Bologna and Distinguished Research Professor of Economics / University of Exeter Business School
*** 𝐂𝐚𝐮𝐬𝐚𝐥 𝐌𝐨𝐝𝐞𝐥𝐬 𝐟𝐨𝐫 𝐋𝐨𝐧𝐠𝐢𝐭𝐮𝐝𝐢𝐧𝐚𝐥 𝐚𝐧𝐝 𝐏𝐚𝐧𝐞𝐥 𝐃𝐚𝐭𝐚 *** Curious about the latest in causal inference with panel data models? 📈 Check out 📷the new release of this #econometrics survey by Дмитрий Архангельский (CEMFI) and guido imbens (Stanford University). It covers a lot of stuff, from difference-in-differences and two-way-fixed-effect estimators to nonlinear models, synthetic controls, and design-based inference. Don't miss out! ⭐️ 𝐋𝐢𝐧𝐤: https://lnkd.in/dBi4Jzgf
Award-Winning Market & UX Researcher, President of the Qualitative Research Consultants Association (QRCA)
Are you curious about the real impact of Artificial Intelligence on qualitative methods? 🤔 I’ll discuss it in great depth at The H* Factor Conference this June. Learn more over here: https://lnkd.in/eNf-cJA7
Professor of Econometrics / University of Bologna and Distinguished Research Professor of Economics / University of Exeter Business School
*** 𝐓𝐡𝐞 𝐄𝐜𝐨𝐧𝐨𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐨𝐟 𝐂𝐚𝐮𝐬𝐚𝐥 𝐏𝐚𝐧𝐞𝐥 𝐃𝐚𝐭𝐚 *** Curious about the latest in 𝐜𝐚𝐮𝐬𝐚𝐥 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 with 𝐩𝐚𝐧𝐞𝐥 𝐝𝐚𝐭𝐚 𝐦𝐨𝐝𝐞𝐥𝐬? 📈 Check out 👇the latest version of this #econometrics survey by Дмитрий Архангельский (CEMFI) and guido imbens (Stanford University). It covers everything from difference-in-differences and two-way-fixed-effect estimators to nonlinear models, synthetic controls, and design-based inference. A must-read for insights into this literature. ⭐️ 𝐋𝐢𝐧𝐤: https://lnkd.in/dBi4Jzgf
2023 was a period of continual change and transformation for marketing leaders. However, with the meteoric rise of artificial intelligence and ongoing economic uncertainty, the industry is preparing for further swings and shifts in 2024. So, where is marketing heading in the year to come? Join us on the 29th of February at 13:00 GMT to find out. In this panel discussion, we’ve invited top brand leaders to give us their take on the ‘why’ behind these exclusive research findings. Together we’ll explore key insights including: - AI fever – how are brands adopting and adapting? - The challenges of integration – has the world finally moved on from matching luggage? - Sustainability – is the ‘s’ word falling down the priority scale? - Embedding human understanding – how brands can elevate their campaign strategies by focusing on the human. Looking forward to seeing you there!
📊 We’ve surveyed CMOs from a myriad of brands and industries to zero in on the key opportunities to maximise performance for the year ahead. Join our host Nikki McCulloch in a webinar launching the insights on 29th February. First on the line-up, we have our Director of the Human Understanding Lab, Simon Collister PhD, bringing his expert perspectives on everything neuro, behavioural, and data science. 👋 Get on the list below. https://hubs.ly/Q02jS6fw0
Data permeates almost every aspect of our lives, but do we truly understand its effects? In this edition of Author Talks, Aram Sinnreich, co-author of 'The Secret Life of Data: Navigating Hype and Uncertainty in the Age of Algorithmic Surveillance,' explores data’s impact on our culture, the power of manipulated algorithms, and more. ➡️ https://mck.co/3SJO3jd
🙌🏼 Our latest issue (6.3) is out! 📖 For an in-depth preview of every article, start the issue by first reading the excellent editorial, "From COVID-19 to GPT-4o: The Groundbreaking Quinquennium for Harvard Data Science Review (and Humanity)" by Editor-in-Chief Xiao-Li MENG. 🧐 Check it out now! https://lnkd.in/eN3iPPT5
Weekend Reading: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy is a 2016 book by Cathy O'Neil A former Wall Street quant and data scientist. In this book, O'Neil exposes the dark side of algorithms and big data, and how they are used to reinforce existing biases, inequalities, and injustices in various domains of society, such as education, employment, law enforcement, health care, and politics. O'Neil argues that these algorithms, which she calls "weapons of math destruction" or WMDs, are opaque, unaccountable, and harmful, especially to the poor and the marginalized. She calls for more transparency, regulation, and ethical standards for the use of big data and algorithms, and urges us to question the assumptions and values behind them.
Visiting scholar at MIT Sloan || Assistant Prof. at IMT Lucca || Previously postdoc in Bocconi
Hi all, I have enhanced my previous blog post on IV assumptions with interesting study cases, a better notation, examples, the case of conditional assumptions. I think this post can be useful for us machine-learning enthusiasts in social sciences to not overlook the econometric pillars that have to guide our data-centric analysis. Take a look at https://lnkd.in/dA5ewhnz
Bridging Finance and Urbanism | ClimateTech
7moThank you for sharing this! At a first glance, Newark surprises me, and I'm curious to know if it's just the property values driving up the mean wealth despite the lower incomes. I'm very interested in digging into the data more. I went through several of the datasets but had trouble mapping to Newark. Can you point me to where I can find that?