P&Q’s Favorite MBA Professors Of 2019

Mark Stabile, Insead

As mentioned above, the rapid rise of the so-called gig economy has impacted the way millions of people across the globe work. Researchers at universities — both inside and out of business schools — are trying to catch up on measuring and understanding what sort of impact the innovations are having. Insead professor Mark Stabile and Bénédicte Apouey of the Paris School of Economics-CNRS recently examined what sort of effects working in the gig economy have on people like Uber and Lyft drivers. The result? Uber and Lyft drivers — and workers in the gig economy — generally have better mental health than those that work outside of the gig economy.

“Our findings suggest that self and temporary employment, as identified through gig-economy activity, have large positive effects on mental health,” Stabile and Apouey write. “These effects exist for both men and women but are stronger for women and for older workers (ages 40-64).”

The paper, titled “The Effects of Self and Temporary Employment on Mental Health: The role of the Gig Economy in the UK,” was published by Insead last May. Stabile and Apouey examined Airbnb hosts, Uber drivers, and food delivery workers for the food delivery company Deliveroo, all based in the U.K. The findings “suggest that, contrary to some previous studies, self-employment and temporary employment are positively associated with mental health,” they write. They also found “a consistent pattern of improvements” in sleep, physical activity, use of medication, smoking and drinking, all key drivers of mental health.

The upshot: having control over your schedule and workload actually leads to better mental health. We applaud Stabile for his work in going outside the typical B-school research areas to look at the impacts of a new and evolving way of work on mental health.

Xiumin Martin, Washington University in St. Louis Olin

It’s a very common occurrence: the overly-positive CEO in front of investors. No matter the state of economy or firm, CEOs — and other top managers — often portray a rosy outlook. That should put up some red flags for investors says research from Washington University in St. Louis Olin Business School professor Xiumin Martin and her co-authors in a paper titled “Manager Sentiment and Stock Returns.”

“We find a negative predictive relationship between manager sentiment and subsequent future stock returns at both the aggregate level and at the firm level over longer horizons,” Martin and her co-authors write.

Martin and her co-authors examined the transcripts of around 113,000 conference calls from almost 6,000 firms from January 2003 through December 2014. They also sifted through 264,335 10K annual reports and 10Q quarterly reports filed by more than 10,000 firms with the Securities and Exchange Commission (SEC) over the same time period. They then calculated tone by subtracting negative terms from positive ones and compiled a monthly index based on average tone. Then they tracked stocks of those companies in the period after the communication was examined.

“We find that periods with high manager sentiment are accompanied by high aggregate investment growth in the short run-up to three quarters, but low subsequent aggregate investment growth in the long run-up to two years,” the researchers write. “Our findings indicate that high manager sentiment captures managers’ overly optimistic beliefs about future returns to investment which leads to overinvestment.”

So, investors everywhere, heed the researchers’ findings when meeting with top-managers of portfolio firms.

Adair Morse, UC-Berkeley Haas School of Business

Algorithms are now everywhere. Created to objectively crunch and process large sets of data, algorithms can be built subjectively. And according to research from Adair Morse, a leading financial technology researcher from the University of California-Berkeley’s Haas School of Business, fintech lending algorithms can and are built to discriminate against minorities. The paper, titled “Consumer Lending in the Era of FinTech,” was written with two other Haas researchers as well as a professor from UC-Berkeley’s School of Law.

Specifically, Morse and her colleagues looked at housing loan applications. They used machine-learning techniques to create a dataset that for the first time linked income, ethnicity, and loan-to-value ratio, as well as such specific factors as coupon and loan amount, for five million accepted loan applications and 3.7 million rejected loan applications between 2007 and 2015. Then, the researchers looked at only 30-year fixed-rate mortgages on single-family homes securitized by the GSEs, whose median loan amount was just over $100,000, and they zeroed in on borrowers who had FICO scores between 630 and 770.

Their findings revealed that fintech companies, while improving, discriminate just as much as traditional face-to-face lenders. Morse continues to be at the forefront of financial innovations and teaches courses like New Venture Funding and Impact Investing.

Amit Khandelwal, Columbia Business School

If done best, research coming out of top business schools gets media coverage and impacts the national or international narrative around a topic. That’s exactly what happened in the spring of 2019 when Columbia Business School’s Amit Khandelwal and his co-authors published a paper called “The Return to Protectionism.” The paper looked at President Donald Trump’s trade war and its impact on the U.S. economy.

So far, at the macro level, Khandelwal and his fellow researchers have found that there has been little impact to the U.S. economy, thanks to an offset and shift in Americans buying more domestically. “We find substantial redistribution from buyers of foreign goods to U.S. producers and the government, but a small net loss for the U.S economy as a whole,” they write.

But for rural America — where a lot of President Trump’s voting base lives — it hasn’t been as un-affected. “The counties hit hardest by the war are those concentrated in the Midwestern Plains,” the authors write. “Workers in very Republican counties bore the brunt of the costs of the trade war, in part because retaliations disproportionately targeted agricultural sectors.”

While President Trump’s administration focused initial sanctions to benefit swing-voter counties and regions, the retaliations — particularly from China — have greatly impacted rural farming communities more. “The Great Lakes region of the Midwest and the industrial areas of the Northeast received higher tariff protection, while rural regions of the Midwestern plains and Mountain West received higher tariff retaliation,” the authors conclude.

Khandelwal’s work was featured in many media outlets including Time and Reuters.