1. What Happens Before? A Field Experiment Exploring How Pay and Representation Differentially Shape Bias on the Pathway into Organizations by Katherine Milkman (University of Pennsylvania – The Wharton School) and Modupe Akinola (Columbia University – Columbia Business School) and Dolly Chugh (New York University (NYU) – Leonard N. Stern School of Business)
Our personal experiences guided this work as we each regularly receive emails from prospective doctoral students and often wonder whether or not to respond. The three of us attended graduate school together and shared an amazing mentor/advisor, Professor Max Bazerman. Working with him highlighted the importance of great mentors and we were very cognizant of the fact that many prospective doctoral students write faculty before applying to a doctoral program, as we did, in hopes of mentorship while on this informal “pathway” leading up to the official “gateway” of the admissions process. We are a racially diverse, all-female research team that cares deeply about diversity in organizations, including our own, and we often hear our colleagues explicitly express similar egalitarian views. Still, we are acutely aware of the robust body of research documenting the prevalence of implicit (unintentional) bias in most of us. So, we were curious to see if we, as academics, collectively apply the same decision making criteria across all prospective students, regardless of their race and gender, or if some form of bias (either intentional or unintentional) might be influencing our decisions.
We were pleasantly surprised that 67% of our colleagues responded to an email from someone they did not know. We have also been pleasantly surprised by the large number of downloads of our paper which makes it clear that this topic of bias in academia is one of general interest to a broad audience. Additionally, so many current and former academics have written us emails, some with their own stories of bias, others grappling with what our data means, and still others with sharp critiques. We have appreciated each of these emails and their quantity and intensity suggests that we have struck a chord. Increasing diversity, promoting equity, and reducing bias are all topics that are important to us, so to know that our research has furthered and even spurred dialogue on these topics is incredibly rewarding. Most important, we hope this research will result in more research, awareness, interventions, policies, and programs designed to level the playing field for women and minorities in academia and beyond.
-Katherine Milkman, Modupe Akinola and Dolly Chugh
2. Fact, Fiction and Momentum Investing by Clifford Asness (AQR Capital Management, LLC) and Andrea Frazzini (AQR Capital Management, LLC) and Ronen Israel (AQR Capital Management, LLC) and Tobias Moskowitz (University of Chicago – Booth School of Business)
3. On the Biases and Variability in the Estimation of Concentration Using Bracketed Quantile Contributions by Nassim Taleb (New York University-Poly School of Engineering) and Raphael Douady (Riskdata)
4. Profiting from Machine Learning in the NBA Draft by Philip Maymin (NYU Poly – Department of Finance and Risk Engineering)
Sports analytics is a hot and growing area. There are several new and exciting conferences appearing each year, including the wildly popular Sloan Sports Analytics Conference. Teams in all sports now recognize the value of analytics and technology and invest in it more than ever before. New sources of high quality and high frequency data are emerging. Worldwide competitions offer fame or money to whoever can do better at predicting sports outcomes.My own background has been in applying algorithmic approaches to study social behavior and risk in economics, finance, and sports. See http://algorithmicfinance.org for our Algorithmic Finance journal, with archives available on SSRN as well. The algorithmic analyses and technology in sports and finance are similar in using state-of-the-art tools and techniques from computer science and statistics applied to big data to improve human decision making, risk taking, and valuation. The algorithmic approach allows one to specify and generalize and backtest, and as a result, empower what people do.As an example, this working paper uses a machine learning approach to evaluate how each NBA team should have drafted over the past ten years or so. It turns out teams are on average missing out on almost ten million dollars per year in lost productivity, and there is a wide disparity. One team, for example, in aggregate missed out on nearly a quarter of a billion dollars worth of productivity. Just as today’s leading financial firms were the ones who committed to quants and an algorithmic approach a few decades ago, it will be interesting to see if a similar phenomenon will now happen in sports.
5. Learning by Thinking: How Reflection Aids Performance by Giada Di Stefano (HEC Paris – Strategy & Business Policy) and Francesca Gino (Harvard Business School) and Gary Pisano (Harvard Business School) and Bradley Staats (University of North Carolina Kenan-Flagler Business School)
Productivity and time efficiency are significant concerns in modern Western societies, with time being perceived as a precious resource to guard and protect. In our daily battle against the clock, taking time to reflect on one’s work may seem to be a luxurious pursuit. Though reflection entails the high opportunity cost of one’s time, in this paper we argue and show that deliberate reflection is no wasteful pursuit: it can powerfully enhance the learning process. Learning, we find, can be augmented if one deliberately focuses on thinking about what one has been doing. Results from our analyses show that employees who spent the last 15 minutes of each day in their training period writing and reflecting on the lessons they had learned that day did 23% better in the final training test than employees who didn’t take time to consider how they had approached the task. This improvement, we find, is explained by greater self-efficacy, i.e. confidence in one’s ability to complete tasks competently and effectively.