For hundreds of years, a tiny group of négociants or wine brokers have determined the price that distributors, importers, and eventually consumers will pay for France’s top wines. These prices are based on the barrel scores of elite tasters, along with the brokers’ own expertise—and a generous splash of guesswork about the market.
That tradition-bound system is getting a data-driven shakeup this month with the debut of a new pricing algorithm on London’s Liv-ex fine wine market.
Visiting Prof. Burak Kazaz
The algorithm was developed by Burak Kazaz, a Berkeley Haas visiting professor (and the Steven R. Becker professor at Syracuse University’s Whitman School of Management), and Hakan Hekimoğlu of Rensselaer Polytechnic Institute. They established “realistic prices” for the 2018 en primeur (or wine futures) campaign—wines from last year’s vintage that are aging in the barrel and will hit shops and restaurants next year.
“This is the most important progression in making a transparent market for wine futures since the négociant system was established more than 300 years ago,” says Kazaz, who pioneered the field of wine analytics. “The realistic pricing will tell buyers and consumers whether a wine is underpriced or overpriced, leading to more effective and transparent purchase decisions. It will also tell winemakers how they can determine their own selling price to négociants.”
The algorithm incorporates temperatures, precipitation, market conditions based on the Liv-ex 100 index of top wines, and price trends based on barrel scores for wines from Bordeaux’s leading chateaus determined by tasting experts Lisa Perrotti-Brown (of The Wine Advocate) and James Suckling (formerly of The Wine Spectator). These influential tasters sample young wines a year after they are barreled, and one year before they are released to the market, scoring them on a 100-point scale.
The “realistic pricing” algorithm allows distributors and consumers to translate a Bordeaux’s score into a dollar amount, getting a clear idea of whether they’re getting a good deal or overpaying. For the 2018 vintage, the algorithm predicts a 3% price increase per additional point compared with the 2017 vintage.
The algorithmic pricing system is expected to shake up the wine futures market, but also to make it less risky, since investors will have a data-driven price for the first time and winemakers will know what price to set for their young wines. Kazaz and Hekimoğlu’s study was recently featured in Robert Parker’s influential Wine Advocate.
While the wine futures market doesn’t exist in the U.S., where individual wineries tend to set their prices, Kazaz is exploring the idea of bringing data driven-pricing to the U.S. market.
Kazaz teaches operations and supply chain management in the Berkeley Haas MBA program.
The list, now in its seventh year, aims “to identify the world’s best young business school professors in terms of research prowess, teaching chops, and impact they have on current students, former students, colleagues, business education research, and society and the world in general.” Competition for the list was especially fierce this year, with nominations submitted by 2,643 people for 188 qualified professors. Schroeder received nearly 80 nominations from students and members of the Haas community.
Asst. Prof. Juliana Schroeder, Management of Organizations
Schroeder, who has been at Berkeley Haas since 2015, is a rising star in psychology for her work on how we make sense of other people’s minds, and a rockstar to her students for her dedication to their success.
Over and over again in teaching evaluations, students describe Schroeder as passionate, accessible, incredibly organized, and inclusive of students with diverse backgrounds (international students and under-represented minority students) and different personality types (extroverts as well as introverts). More than a few describe her negotiations course among their faves at Berkeley Haas.
“Juliana is a source of powerful positive energy—enough to keep a large class awake in the session that starts at 8am and goes for three hours! She is great at artfully engaging the audience during the class discussion and spreading the dynamics of the conversation uniformly across the class,” wrote one student.
Though she’s near the beginning of her career, Schroeder has already racked up almost 30 awards and honors, including the Association for Psychological Science’s Rising Star Award and the International Social Cognition Network’s 2018 Early Career Award. She was named as a 2018 Schwabacher Fellow, the school’s highest honor for assistant professors, and last year students in the full-time MBA program honored her with a Cheit Award for Excellence in Teaching.
“I really care about my students and I try to get to know each one of them. Since I teach negotiations, I try to understand how each student’s negotiation skills fit into the broader context of his or her personality and life,” Schroeder says. “I stay in touch with a lot of my students and continue to coach them as they recruit for post-MBA jobs.”
To compile the “Best 40 Under 40” list, Poets & Quants judged professors on their teaching (70%), based on the number and thoughtfulness of nominations received, and their research (30%), based on Google scholar citations, awards and grants, and media appearances.
Asst. Prof. Drew Jacoby-Senghor, Management of Organizations
Asst. Prof. Drew Jacoby-Senghor (Photo: Copyright Noah Berger, 2019)
Jacoby-Senghor was mentioned by Bosun Adebaki, MBA 19, as a favorite professor. Adebaki noted that Jacoby-Senghor, who teaches Negotiations and Conflict Resolution, knows how to connect with students and breathes life into mantras like “Focus on interests and not positions.”
Note: The “Classified” series spotlights some of the powerful lessons faculty are teaching in Haas classrooms.
Prof. Don Moore passes around a jar filled with the titles of research papers on the psychology of scarcity. Psychology PhD student Ryan Lundell-Creagh selects the paper that he’ll have to replicate.
As a young researcher, Kristin Donnelly was captivated by the work of social psychologists who published striking insights on human behavior, such as a finding that people walked more slowly after being exposed to the words gray, Florida, and Bingo. That was one of many surprising studies that had crossed into mainstream pop culture—thanks to books like Malcom Gladwell’s Blink—but there was a problem: No one could reproduce them.
“It was a sad, dark time to enter the field,” says Donnelly, who is now a Berkeley Haas PhD student in behavioral marketing. “I was pursuing similar ideas to people who had these incredible studies, but I couldn’t get any significant results. I became very disillusioned with myself as a researcher.”
Psychology has been rocked by a full-blown replication crisis over the past few years, set off in part by a 2011 paper co-written by Haas Prof. Leif Nelson. It revealed how the publish-or-perish culture—which rewards novel findings and did not reward attempts to replicate others’ work—led researchers to exploit gray areas of data analysis procedures to make their findings appear more significant.
Professors Leif Nelson and Don Moore are leaders in the open science movement.
Now Nelson, along with Prof. Don Moore, is working to train a new generation of up-and-comers in methodologies that many see as key to a rebirth of the field. This semester, they’re leading Donnelly and 22 other doctoral students from various branches of psychology in what may be a first for a PhD seminar: a mass replication of studies around one psychological theory: to see how well they hold up.
“We aren’t doing this because we want to take down the literature or attack the original authors. We want to understand the truth,” says Prof. Don Moore, an expert on judgement and decision-making who holds the Lorraine Tyson Mitchell Chair in Leadership and Communication. “There are many forces at work in the scientific publication process that don’t necessarily ensure that what gets published is also true. And for scientists, truth is at the top of the things we ought to care about.”
Examining the psychology of scarcity
The theory they’re examining is the “psychology of scarcity,” or the idea that being poor or having fewer resources actually impairs thinking. Moore and Nelson chose it not because of an inherent flaw, but because it’s relatively new (defined by a 2012 paper), high profile, and relevant to the students’ interests. Each student was randomly assigned a published study, and, after reaching out to the original researchers for background details, is attempting to replicate it. Results will be combined in a group paper.
“At Berkeley, we’re at the epicenter of this new methodological and statistical scrutiny, and as a young researcher I want to do good work that will replicate,” says Stephen Baum, also a PhD student in behavioral marketing at Haas. “Most people were willing to take things at face value before 2011. Things have changed, and we all have to do better.”
Berkeley Haas PhD student Derek Schatz chats with Graduate Student Instructor Michael O’Donnell and professors Leif Nelson and Don Moore during a class break.
Moore and Nelson are leaders in the growing open science movement, which advocates for protocols to make research more transparent. Nelson, along with Joseph Simmons and Uri Simonsohn of Wharton, coined the term “P-hacking” in 2011 to describe widespread practices that had been within researchers’ discretion: removing data outliers, selectively reporting data while “file drawering” other results, or stopping data collection when a threshold was reached. These practices, they argued, made it all too tempting to manipulate data in pursuit of a P-value less than 0.05. That translates to a less than 5% chance that the results were due to pure chance, and it’s the standard for demonstrating statistical significance and the threshold for getting published.
Building confidence through pre-registration
At a recent session of their PhD seminar, Moore and Nelson led a discussion of one of the key ways to combat P-hacking: pre-registering research studies. It sounds arcane, but it’s simply the grown-up equivalent of what grade-school teachers require students to do before starting on their science fair project: Write out a detailed plan, including the questions to be answered, hypothesis, and study design, with key variables to be observed.
“How many of you are working with faculty who pre-register all their studies?” asks Nelson, a consumer psychologist in the Haas Marketing Group and the Ewald T. Grether Professor in Business Administration and Marketing. Less than half the class raises their hands.
Nelson and Moore estimate that only about 20% of psychology studies are now pre-registered, but they believe it will soon become a baseline requirement for getting published—as it has become in medical research. Although there’s no real enforcement body, the largest pre-registration portal, run by Brian Nosek of the Center for Open Science, creates permanent timestamps on all submissions so they can’t be changed later. Nelson co-founded his own site, AsPredicted, which now gets about 40 pre-registration submissions per day. It’s patrolled by a fraud-detecting robot named Larry that dings researchers for potential cheats like submitting multiple variations of the same study.
“Without pre-registration, statistics are usually, if not always, misleading,” Moore tells students. “They aren’t entirely worthless, but they’re worth less.”
The class is the largest PhD seminar that Moore has ever taught.
Gold Okafor, a first-year PhD student studying social and personality psychology, says she plans to pre-register all her future studies. Though it requires a bit more work up front, it may save time in the end. “I think if you don’t use some of these methods, you could be called out and have your work questioned,” she says.
Students are also learning techniques such as P-curving, which is a way to determine the strength of a study’s results and whether data manipulation may have occurred. They’re also learning from guest lectures from other open science leaders, including Economics Prof. Ted Miguel and UC Davis Psychology Prof. Simine Vazire, who edits several journals.
The bedrock of the scientific method
Then there’s reproducibility, one of the bedrocks of the scientific method and the heart of the course. The American Psychological Association now promotes systematic replications, where multiple researchers around the world all re-create the same study. (PhD student Michael O’Donnell, who is assisting Nelson and Moore in teaching the course, recently led one such effort that cast doubt on a study finding that people who were asked to imagine themselves as a “professor” scored higher on a trivia quiz than those who imagined themselves as a “soccer hooligan.”)
Baum, the marketing student, will be replicating a psychology of scarcity study that was published in the flagship journal Psychological Science. The researchers asked people to recall a time when they felt uncertain about their economic prospects, and then write about how much pain they were experiencing in their body at that moment. The finding was that those people reported feeling more pain than those in a control group prompted to recall a time when they felt certain about their economic prospects.
“If it replicates, I will be surprised, but I’ve been wrong before,” Baum says.
No matter what the results, the replications will offer important new insights into the psychology of scarcity—important to understand in a society plagued by growing inequality, Moore says. Beyond the one theory, the fact that the course has the highest enrollment of any PhD seminar he’s ever taught gives Moore great hope for the future.
“The stakes are high,” he says. “The most courageous leaders in the open science revolution have been young people—it’s the doctoral students and junior faculty members who have led the way. The next generation will be holding themselves, and each other, to higher standards.”
Donnelly is a case in point. “This whole movement has made me a better researcher. I’ve changed what questions I ask, I changed how I ask them, and I changed how I work,” she says. “It’s a brave new world, and we may be able to lay the foundation of a new science that will build on itself.”
Zappos says its 10 core values are a “way of life,” while Netflix details seven aspects of its culture, nine “highly valued behaviors and skills,” plus deal-breakers like “no brilliant jerks.” Nordstrom has just one rule of thumb: “Use good judgment in all situations.”
More and more companies are asking employees to adopt a set of core values, seeking to build a culture that will give them an edge. But while getting everyone on the same page makes it easier for people to work together, too much of the same thinking can stifle creativity. What’s the right balance of cultural values to drive profitability, growth, and innovation?
An analysis of 500,000 Glassdoor.com reviews of S&P 500 firms found that companies whose employees disagree on core values are, indeed, less profitable than similar companies where workers are culturally aligned. Meanwhile, the firms that are the most highly valued and innovative have something in common: They embrace a diverse range of cultural values throughout the organization.
Assoc. Prof. Sameer Srivastava co-directs the Computational Culture Lab and the Berkeley Culture Initiative.
“Past research has suggested there’s a tradeoff between diversity and productivity,” said Berkeley Haas Assoc. Prof. Sameer Srivastava, co-author of the study, which is forthcoming in Administrative Science Quarterly. “We suggest it’s a false tradeoff. You can have a multiplicity of ideas and values and also have cultural alignment on those ideas and values.”
A new way of thinking about cultural diversity
The paper defines a new way of thinking about diversity in organizations and reconciles a fundamental contradiction in current thinking. On the one hand, deep differences in how people think can create problems when they have to coordinate on tasks; research has found that a strong and unified culture increases productivity and efficiency. On the other hand, diverse viewpoints and perspectives can help people respond to change and uncertainty, and ultimately recombine ideas into something novel.
To get a more nuanced view of the cultures of different organizations and their relationship to business performance, Srivastava and collaborators Amir Goldberg of Stanford and Matthew Corritore of McGill drew on the power of the Computational Culture Lab, which Srivastava and Goldberg co-direct. The joint Berkeley-Stanford lab uses data science to develop new ways of measuring organizational culture. (Srivastava, Goldberg and co-researchers previously analyzed 10 million internal emails from a technology company to learn about culture fit within an organization.)
This time they looked at differences between organizations, turning to Glassdoor, a job search platform with 17 million monthly users who post anonymous reviews of their employers. The company has a data science team that agreed to share data with the research team to gain new insights.
The power of machine learning
The researchers used natural language processing and machine learning to identify hundreds of topics in comments about company culture—ultimately choosing 500 topics to cover as wide a range as possible. After “training” their statistical model to identify patterns in culture-related sentences, they scanned over 500,000 reviews of 492 S&P 500 firms posted between 2008 and 2015. They matched companies with similar characteristics for purposes of comparison, limiting the sample to companies with at least 25 reviews.
By looking at how many topics were mentioned in reviews of each company, how many times they were mentioned, and how much commonality there was between reviews, they were able to determine whether a company’s culture was diverse or uniform, and divided or aligned.
For example, they classified a company as divided when they found little overlap in topics mentioned by reviewers—as might happen at a company where customer service reps prioritize delivering “wow,” but engineers care only about technical progress, and the finance team is laser-focused on profits. They found other companies where everyone talked about the same few topics: They were culturally unified, but had little diversity.
It was the companies in which employees, on average, talked about many different culture-related topics that seemed to hit the sweet spot for innovation. That kind of diversity was strongly associated with a higher market valuation—as measured by Tobin’s Q. Those firms also produced more patents on average, as well as higher-quality patents that were built on by other companies, than similar firms where the typical employee mentioned relatively few topics related to culture.
Conversely, the researchers found that firms with highly divided, rather than unified, cultures were less profitable: those types of cultures were associated with lower returns on assets (ROA).
Diversity of values, distribution of values
Interestingly, these statistical relationships were true no matter which specific values were mentioned (e.g. collaboration, adaptability, playfulness). The important factors were the variety of culture-related topics discussed and how consistently people mentioned those topics throughout the organization.
The paper suggests that in assessing a company’s culture, it’s important to look beyond which values are emphasized to how they are distributed in a group. While diversity may arise from differences between people—which the researchers call “interpersonal diversity”—it’s also true that individual people often hold multiple values, which may even be contradictory. They define this as “intrapersonal diversity.” This view builds on research that finds when people have a broad “toolkit” of cultural resources, they have greater capacity for creativity and adaptability.
New technique for measuring culture
In addition, the power of data science and natural language processing offers an exciting new way for organizations to understand what makes for a successful culture. Traditional approaches such as collecting demographic information like age, gender, or ethnicity may or may not relate to underlying beliefs, and surveys are not only expensive but also relatively static. This approach allows researchers to examine topics that people are actually talking about and how these topics vary over time.
“This gives us a much more granular measure of culture over time,” said Srivastava, who also co-leads the Berkeley Culture Initiative, which he founded with Prof. Jennifer Chatman to develop new approaches to organizational culture research.
Srivastava cautions that a limitation of Glassdoor data is that people are writing for an external audience, and they choose to write reviews for a complicated set of reasons—including in response to campaigns by their employers. To the extent possible, the researchers did account for these dynamics in their analyses, he said.
To mark Equal Pay Day, we’re featuring new work by Prof. Laura Kray, an expert on gender and negotiations, along with Margaret Lee, a postdoctoral research fellow with the Center for Equity, Gender, and Leadership. Equal Pay Day was created in 1996 by the National Committee on Pay Equity to mark extra days that American women would have to work, on average, to earn what male counterparts earned last year.
Professor Laura Kray has doubled down on helping women develop ace negotiation skills: She’s spent much of her career studying gender dynamics in negotiations, and has also taught many hundreds of MBA students and seasoned women executives how to negotiate like pros.
Prof. Laura Kray
But when it comes to strategies to close the stubborn pay gap that has women earning about 80 percent of what men earn (a statistic that varies by race/ethnicity and how it’s measured), she takes issue with telling women they can simply negotiate their way out of it. That not only puts the onus on women rather than the systemic issues that keep their salaries low, but it perpetuates stereotypes that may not be true, she said.
“We know that people who negotiate get more than those who don’t, but that’s not a ‘women’s issue’—two-thirds of men don’t negotiate,” said Kray, the Warren E. and Carol Spieker Chair in Leadership. “Women are asking, but they’re not always getting what they ask for, and they’re more likely to be told things that aren’t true.”
Kray has long peeled back the surface to look at the deeper structural issues that lead to gender inequality, from implicit bias to lack of transparency to inflexible mindsets. Recently, she’s uncovered a new front in the pay gap battle: team size. Kray and Margaret Lee, a postdoctoral research fellow sponsored by the Center for Equity, Gender, and Leadership (EGAL), are examining how deep-seated biases about leadership may lead to men being put in charge of larger teams than equally qualified women, and being paid more because of it.
Since supervising more people can be more work and indeed justify a higher salary, it’s important to unravel the reasons why men manage larger teams and how that drives higher salaries, she said. Combined with other findings, this new line of research offers another layer of insight into the causes of the gender pay gap—and possible solutions.
“We’re most interested in the structural issues, and the psychological processes of decision makers that produced them,” Kray said, at a recent EGAL presentation on her work with Lee.
Do women ask—and do they get?
Kray points to a 2017 study by McKinsey & Co. and Lean In that asked 70,000 respondents across 222 companies whether they had asked for a raise or negotiated for a promotion. While the percentages varied slightly by race, there were no significant differences between men and women overall.
She and Lee took a closer look at how this plays out among Berkeley Haas MBA students. Analyzing the results of a negotiation exercise completed by 346 MBA students who were asked to structure their own job offer, she found that the women did not sell themselves short, and asked for virtually identical base salaries as men.
In a more disturbing finding from a 2014 paper, Kray looked at the results of a sales negotiation exercise completed by pairs of 298 MBA students, where one acted as seller and one as buyer, with opportunities to lie or misrepresent the truth. Men reported they had lied to female partners in 24 percent of cases, versus just 3.4 percent of negotiations with another man—in other words, seven times as often. And although women reported lying less overall, they also were slightly more likely to lie to other women as to men.
Based on that and other experiments in the paper, Kray concluded that female negotiators are perceived as less competent and more gullible than male negotiators, which leads to them being lied to or manipulated more often—another reason why she believes the problem goes far beyond teaching women to negotiate.
“In this classroom simulation, MBA women were not getting the same treatment in negotiations, regardless of whether they were asking or not,” Kray said. “It’s important to explore if—and how—this plays out in organizational contexts.”
Team size and salary
In their new work, Kray and Lee looked at the results of a Berkeley Haas alumni survey of almost 2,000 full-time professionals who graduated between 1994 and 2014. Respondents had between two and 18 years post-MBA work experience, with an average of seven years. The researchers found that while men’s base salaries were about 8 percent higher than women’s, it’s in the extras—bonuses, share values, and options, which tend to not be tracked as publicly as salaries—where the men’s salaries dwarfed the women’s. These MBA women’s overall compensation averaged about $290,000, or about 66 percent of men’s $439,000 average.
That echoes findings from a recent study by the Forté Foundation, revealing that the salary gap is even higher for MBA women than for women overall (and highest for minority women), and that it only increases with seniority. A 2010 study of Chicago Booth MBA grads found a similar result: thirteen years out, women earned 56 percent of what men earned overall (they traced a large part of that to the career interruptions of motherhood, but found at least 10 percent of it to be unexplained).
Analyzing the Berkeley Haas alumni survey, which contained information on direct reports, Kray and Lee became interested in whether team size is contributing to pay disparities.
They compared the number of subordinates men and women reported managing and found men averaged 11 direct reports while women averaged six. After controlling for multiple factors such as experience and industry, that was reduced to an average of 10 for men and slightly less than 8 for women, but still significant. They then conducted further analysis parsing out team size from salary, and concluded that team size did account for a portion of the pay gap—above and beyond other individual job characteristics.
The researchers then delved further into why men are given larger teams. They conducted surveys of Berkeley Haas undergraduates, and also of subjects recruited through an online platform, and found no differences in men’s and women’s preferences on the number of people they’d feel comfortable managing. Even so, both groups said they preferred male managers for large teams, and female managers for smaller teams.
In another study, they found that people were more likely to associate stereotypically male attributes (e.g. assertive, forceful, aggressive, demanding) with leaders of larger teams, and associate stereotypically feminine attributes (e.g. patient, polite, kind) with leaders of smaller teams. They also found that people do believe that leaders of large teams earn more than leaders of small teams.
Kray and Lee are now more deeply pursuing research on why a team-size bias exists—based not only on stereotypes of who is a more appropriate leader but also on how complex and challenging the jobs of leading teams of various sizes are believed to be. The ultimate goals is to examine how implicit biases about team size justify part of the difference in men’s and women’s pay, and especially the gap that widens with seniority.
What can women do?
In the meantime, Kray—who also serves as faculty director for EGAL—advises women entering a job negotiation to pay close attention not only to their salary and bonuses, but also to how many direct reports they’ll be managing.
“For women who are aiming to maximize their earnings, it is important to make sure they have the headcount to justify what they’re asking for,” she said. “My advice for these aspiring women is: Don’t overlook team size as a factor that could make a difference in your paycheck, especially in the long run.”
EGAL Founding Director Kellie McElhaney said Kray and Lee’s new research is exactly the type of work she wanted the center to support when it launched in 2017.
“This works on two critically important paths: Dispelling long-held and damaging myths that are used to justify inequitable behavior, like unequal pay, and introducing new explanations that need further research, like team size,” McElhaney said.
When Molly Turner started as Airbnb’s first policy liaison back in 2011, most people in urban planning and government were still thinking of tech as an industry—rather than a force that was about to unleash a barrage of services and technologies that would disrupt the very fabric of city life.
Five years later, Turner took what she had learned on the front lines of this disruption to create the Berkeley Haas course on the topic, “Tech and the City: How to get urban innovation right.”
Lecturer Molly Turner
“I was both inspired and terrified by how much money was pouring into what I call ‘real-world tech startups,’ because I noticed that the entrepreneurs and the investors building them didn’t know very much about the cities they were disrupting,” says Turner, a lecturer in the Haas Business & Public Policy Group the whose background is in urban planning. “It felt like a very good time to go and teach the future tech leaders at the business school a little bit about cities.”
Now, Turner’s course has inspired a new podcast, “Technopolis,” produced by The Atlantic’s CityLab and which she co-hosts with Jim Kapsis, a Washington D.C.-based start-up advisor. The first eight-episode season launched today.
Remaking, disrupting, overrunning
Each episode is inspired by “a technology that is remaking, disrupting, or overrunning our cities in some way, good or bad,” Turner says. In some cases it’s a specific company, in others it’s a concept such as autonomous vehicles.
“We start by asking what we know about it right now, and then we bring in guests to broaden our thinking and ask the questions people aren’t asking about this stuff,” Turner says. “What could this mean for cities 50 years from now? What are some of the impacts that no one is planning for, and some of the unintended consequences, both good and bad? And what does it mean for our lives in cities, and how cities govern.”
Technopolis Co-Host Jim Kapsis
The show’s guests come from some of the hottest tech companies and from city government, and also include academics and researchers who provide historical, philosophical, or futurist perspectives. The first season is sponsored by WeWork—though the company has nothing to do with the content, she says.
Turner says it was Kapsis, a friend who had served as a climate advisor in the Obama administration and with whom she often discussed these topics, who proposed the idea that they host a podcast together. So they pitched it to CityLab, “the best publication covering what’s going on in cities and what the future of cities look like,” she says. CityLab provides an editor-in-chief, seasoned radio producers, and access to the deep knowledge and connections of its reporting staff.
“It’s a true partnership,” she says.
From VC explosion to batteries and more
Episode 1 of Technopolis starts at the beginning, in a sense: it’s all about venture capital, and why tech investors are so interested in cities all of a sudden. They look at what that means for city leaders, and how the venture capital influx has transformed jobs as city halls.
The second episode covers autonomous vehicles, exploring some of the impacts no one is thinking about, while the third episode looks at batteries, and whether they may soon be turning buildings into mini power plants.
What about electric scooters? “Of course the scooters have home up—I think they’ve been mentioned several times in the first three episodes already, because they’re such a visible example for everyone in cities about how technology is changing our lives,” Turner says. While they haven’t devoted an episode entirely to scooters, Turner says they do explore the different tactics scooter companies and other startups are using to deal with city government.
“Is it better to ask for permission or beg for foregiveness? Companies are definitely trying both,” she says.
It’s that time of year when inboxes and mailboxes are flooded with pleas from a parade of worthy causes—from fighting hunger overseas to aiding victims of the latest natural disaster to funding your local library. Faced with so many needs, do you spread your donation dollars around, or focus on one cause to maximize your impact?
If you’re like most people faced multiple requests for help, you’re likely to divide your donations among requesters because it feels more fair, according to a new Berkeley Haas study. The upshot? You may end up giving less to each requester, but more overall, the study found.
Juliana Schroeder
The paper, co-authored by Berkeley Haas Asst. Prof. Juliana Schroeder and PhD student Daron Sharps and forthcoming in the Journal of Personality and Social Psychology, offers new insights into how people give help. The results could help groups seeking the best strategy to maximize donations.
“People seem to be primarily driven by fairness concerns when allocating help, and when people see more individual requesters, they give more,” said Schroeder, a psychologist in the Haas Management of Organizations Group who studies social interactions and previously looked at how donors give different kinds of help to people they view as less competent. “That was surprising to both of us, since it seems to contrast with the ‘identifiable victim effect.’”
Identifiable victims
That effect is the tendency people have to offer more help to a single, identifiable person with a compelling need than to a large, vaguely defined group such as “earthquake victims”—a phenomenon leveraged by every charity sharing heart-tugging stories and photos. It’s also been found that people don’t tend to give more to larger groups, even though the need is greater: one study by Nobel Prize-winning psychologist Daniel Kahneman found that people will give the same amount to a group of 2,000 as to 200,000.
Daron Sharps
Yet prior studies did not look at whether people were considering each request separately—a psychological phenomenon known as “unpacking”.
“We know that you might get more donations for ‘Tommy’ who was affected by the earthquake than for a million people who were affected by the earthquake, but what about ‘Tommy’ compared with ‘Tommy, Ana, and John’?,” said Sharps, who served as lead author on the study. “We found the important part is the identifiable nature of the recipient, not that it’s one versus many requesters.”
A clear pattern
The researchers studied giving behavior across nine experiments involving 3,100 people, most of whom were recruited through an online platform. They found a clear pattern: When given a choice of how to allocate donations, almost 80 percent of people chose to distribute funds across multiple recipients, with the majority giving some to every requester—and half of those distributing funds equally (researchers only looked at up to ten requesters). Only about 20 percent of givers funneled all of their donation to one recipient.
In an initial experiment, participants were shown five real profiles of women seeking money to buy seeds for the upcoming farming season via Kiva.org—an online platform that allows people to lend money to low-income entrepreneurs in 80 countries. The participants were asked how they’d distribute $100. People not only preferred to spread the money among all the women, but rated that strategy as the fairest.
“People would rather give $10 each to 10 people than choose four people to give them $25 each,” said Sharps.
Deciding what’s fair
Even when people were presented with requesters with different levels of neediness, they tended to give more to those with greater needs but still thought it was most fair to distribute the money rather than concentrate on one needy person. When asked how they’d divide funds between women who were all trying to raise $600 for business equipment—but were starting out with unequal amounts from $100 to $400—just 16 percent of donors chose to give everything to the neediest requester, while 81 percent distributed funds among all the women. What’s more, only 4 percent split up the money so that the women ended up with the same amount of funding.
Having worked in philanthropy earlier in her career, Sharps said she was surprised that so many people chose breadth over depth, and that they paid more attention to distributing their donations equally than to equal outcomes. “Making an impact was a very important part of what we thought about in the world of philanthropy, and concentrating donations would seem to make a greater impact in some cases,” she said. “But people were more focused on allocating their help fairly than on the requesters’ actual outcomes.”
When asked about their motivations in one of the experiments, participants said that dividing funds equally was not only more fair, but was also more impactful and efficient, would be more appreciated, and would leave helpers with less guilt. A further analysis by the researchers found that only participants’ beliefs about fairness—rather than impact or efficiency or other motivations—statistically predicted their choice to distribute donations. This indicates that fairness may be the primary psychological driver of decision to distribute help, the authors concluded.
More requests = more giving
In another set of experiments, the researchers used real profiles from various online platforms that featured people as well as pets in need of medical care or support. The participants were given small amounts of real money to donate—some were told they had to donate it all, but in other cases were told it was optional and they could keep what they didn’t donate.
Whether the donations were optional or mandatory, people spread the money around. For those who were told their donations were optional, the total amount they donated increased with the number of people or animals to which they could donate. People were just as likely to distribute donations to two requesters as to ten requesters—although they gave less per requester as the number increased.
In another scenario, one group of participants was shown profiles of four pets and asked to consider donations to each of them, while another group was asked to donate to the “Pets in Need” charity to support the same four animals. Those who considered the animals separately gave more overall. Researchers found the same outcome when people were asked to donate polio vaccines to five individual children versus a group that would vaccinate five children—they gave more to the five individual children than to the group.
This finding has several implications for organizations, Sharps said. Charities might consider ways to bring in the stories of multiple people into their donation appeals, and ask people to consider each one individually, rather than lumping them together. They might also find ways to amplify fairness concerns—such as a message for donors before they leave the page: “Are you sure you want to leave this person unhelped?”
“People don’t like to feel like they’re leaving some needy requesters unhelped,” Sharps said.
Unemployment is at its lowest rate in 50 years, there are almost 7 million job openings nationwide, and consumer confidence is at its highest level in a decade. There’s more venture capital flowing than at any point since 2000, and commercial real estate loan origination is at its highest point ever. So, what could go wrong?
“We’ve had a sugar high for the economy, and it will wear off,” said Rosen, delivering his annual forecast at the 41st Annual Real Estate & Economics Symposium in San Francisco this week. “We’re in the last stages of a very good recovery, but we’re buying this time by spending a lot of money that we don’t have.”
Kenneth Rosen
Even so, Rosen does not foresee a crash in the real estate market anything like 2004 to 2007 or the late 1980s, and there’s still plenty of “dry powder”—or cash available for investment, he said. While real estate is overvalued compared to historical prices, it’s not overvalued compared to everything else, he said.
“This is not a very dramatic forecast, but the risks have risen dramatically and this is just the beginning of volatility,” he said. “There will be correction, and the Bay Area is likely to have a bigger correction than the rest of the country, because we’ve gone up so much.”
“A sledgehammer to break open a walnut”
Rosen pointed to rising interest rates, a ballooning national deficit, increasingly restrictive immigration policies in a tight labor market, and the escalating trade war with China as trouble signs. With $250 billion in tariffs imposed and $257 billion more threatened by President Trump, Chinese retaliation is to be expected.
“We certainly have problems with China, but tariffs are an exceptionally a blunt tool. It’s like using a sledgehammer to break open a walnut,” said Rosen, who is also chairman Rosen Consulting Group, a real estate market research firm. “No one wins in a trade war.”
The combined economic stimulus of tax cuts and increased spending has overheated the economy, and left few tools in policy makers’ arsenal to recover from the next recession, he said. A gridlocked Congress would not be able to pass a spending increase bill, or another tax cut. “In a full employment economy, the deficit should be zero or positive, so we should be at a balanced budget today. We’re doing the opposite. We’re not going to be ready for the next big downturn.”
While he had plenty of critiques of President Trump and his administration, he said Congress is also at fault. “There are no fiscal hawks any more. There’s no one who believes in balanced budgets.”
Red-hot economy
In the immediate future, however, the economy remains red hot. There’s a boom in job creation nationwide, especially throughout the West. California as a whole is a bit lower than the rest of the region, but it still had 1.9 percent growth in job creation year-over-year. Unemployment in San Francisco hovers just over 2 percent.
“The tech cities are going strong: Seattle, Austin, Silicon Valley, Denver, San Francisco, and Oakland are still very strong. The only thing constraining these places is the fact that housing is so expensive that they can’t get people to come. We’d be growing faster if we can solve some of those issues.”
Real estate forecast
Nationwide, retail is still struggling while industrial properties are hot, with vacancy rates at their lowest point since the 80s. “It’s a red-hot sector because of e-commerce, which is driving the demand for this space and bypassing retail network.”
In terms of commercial real estate, Rosen predicts cap rates—or the rates of return on commercial property—are expected to rise after historic lows. He warned the crowd of 300 real estate and finance professionals in the room to not be too smug. “We’ve had big periods of appreciation in the last decade because cap rates went down. Don’t think it’s because you’re smart—it’s because they repressed interest rates. It’s going to reverse. We’ll have headwinds.”
His advice? “As a lender, I’d say now’s the time to be cautious. Don’t lend to inexperienced people. You’ve got to be able to hold through the next six years—don’t think you’re going to miss this recession,” he said. “As an investor, it’s now is the time harvest or hedge, don’t wait for the next cycle.”
Nationally there’s strong demand for office space, but it’s nothing like previous booms. And vacancy rates have stopped going down because businesses need less space per worker—thanks to open floor plans and co-working spaces.
“The WeWorks of the world are 50 percent more dense than traditional office spaces,” he said.
Although the rental market peaked several years ago and home ownership is creeping back up, rental vacancy rates are low. The proportion of young adults living with their parents has begun to drop from a high of 32 percent in 2016. In the Bay Area, apartment vacancy rates are below 4 percent, and rents are rising again after appearing to top out.
California—a victim of its own success?
California homes have become increasingly unaffordable–just 30 percent of people can afford a median priced home, compared with 50 percent nationally. In the Bay Area, prices were up almost 10 percent year-over-year in September. Recently, however, there are signs that home prices may be beginning to drop—whether it’s due to rising interest rates or people leaving the region.
All this leads Rosen to believe California may soon be a victim of its own success.
“We may be the cause of our own demise. With high housing prices and congestion, people are going to move elsewhere,” he said. “You’ve seen reports that up to a third of people are thinking of relocating in the next five years. Add to that the higher taxes that we keep on voting on ourselves, and I think we could be in a situation where we’ve hit our peak moment and it’s just a question of how fast we can slow down.”
Face-to-face meetings between mortgage officers and homebuyers have been rapidly replaced by online applications and algorithms, but lending discrimination hasn’t gone away.
A new University of California, Berkeley study has found that both online and face-to-face lenders charge higher interest rates to African American and Latino borrowers, earning 11 to 17 percent higher profits on such loans. All told, those homebuyers pay up to half a billion dollars more in interest every year than white borrowers with comparable credit scores do, researchers found.
The findings raise legal questions about the rise of statistical discrimination in the fintech era, and point to potentially widespread violations of U.S. fair lending laws, the researchers say. While lending discrimination has historically been caused by human prejudice, pricing disparities are increasingly the result of algorithms that use machine learning to target applicants who might shop around less with higher-priced loans.
“The mode of lending discrimination has shifted from human bias to algorithmic bias,” said study co-author Adair Morse, a finance professor at UC Berkeley’s Haas School of Business. “Even if the people writing the algorithms intend to create a fair system, their programming is having a disparate impact on minority borrowers—in other words, discriminating under the law.”
First-ever dataset
A key challenge in studying lending discrimination has been that the only large data source that includes race and ethnicity is the Home Mortgage Disclosure Act (HDMA), which covers 90 percent of residential mortgages but lacks information on loan structure and property type. Using machine learning techniques, researchers merged HDMA data with three other large datasets—ATTOM, McDash, and Equifax—connecting, for the first time ever, details on interest rates, loan terms and performance, property location, and borrower’s credit with race and ethnicity.
The researchers—including professors Nancy Wallace and Richard Stanton of the Haas School of Business and Prof. Robert Bartlett of Berkeley Law—focused on 30-year, fixed-rate, single-family residential loans issued from 2008 to 2015 and guaranteed by Fannie Mae and Freddie Mac.
This ensured that all the loans in the pool were backed by the U.S. government and followed the same rigorous pricing process—based only on a grid of loan-to-value and credit scores—put in place after the financial crisis. Because the private lenders are protected from default by the government guarantee, any additional variations in loan pricing would be due to the lenders’ competitive decisions. The researchers could thus isolate pricing differences that correlate with race and ethnicity apart from credit risk.
The analysis found significant discrimination by both face-to-face and algorithmic lenders:
Black and Latino borrowers pay 5.6 to 8.6 basis points higher interest on purchase loans than White and Asian ethnicity borrowers do, and 3 basis points more on refinance loans.
For borrowers, these disparities cost them $250M to $500M annually.
For lenders, this amounts to 11 percent to 17 percent higher profits on purchase loans to minorities, based on the industry average 50-basis-point profit on loan issuance.
“Algorithmic strategic pricing”
Morse said the results are consistent with lenders using big data variables and machine learning to infer the extent of competition for customers and price loans accordingly. This pricing might be based on geography—such as targeting areas with fewer financial services—or on characteristics of applicants. If an AI can figure out which applicants might do less comparison shopping and accept higher-priced offerings, the lender has created what Morse calls “algorithmic strategic pricing.”
“There are a number of reasons that ethnic minority groups may shop around less—it could be because they live in financial deserts with less access to a range of products and more monopoly pricing, or it could be that the financial system creates an unfriendly atmosphere for some borrowers,” Morse said. “The lenders may not be specifically targeting minorities in their pricing schemes, but by profiling non-shopping applicants they end up targeting them.”
This is the type of price discrimination that U.S. fair lending laws are designed to prohibit, Bartlett notes. Several U.S. courts have held that loan pricing differences that vary by race or ethnicity can only be legally justified if they are based on borrowers’ creditworthiness. “The novelty of our empirical design is that we can rule out the possibility that these pricing differences are due to differences in credit risk among borrowers,” he said.
Overall decline in lending discrimination
The data did reveal some good news: Lending discrimination overall has been on a steady decline, suggesting that the rise of new fintech platforms and simpler online application processes for traditional lenders has boosted competition and made it easier for people to comparison shop—which bodes well for underserved homebuyers.
The researchers also found that fintech lenders did not discriminate on accepting minority applicants. Traditional face-to-face lenders, however, were still 5 percent more likely to reject them.
Episode 1 of Technopolis starts at the beginning, in a sense: it’s all about venture capital, and why tech investors are so interested in cities all of a sudden. They look at what that means for city leaders, and how the venture capital influx has transformed jobs as city halls.
