The cross-school Team 13 won $100,000 for a data-led project that addressed how international trade impacts global plastic pollution. The group included Silantyev, MFE 22; David Buch and Jennifer Kampe, PhD students from Duke University; and undergraduate Julian LaNeve of Southern Methodist University (SMU).
The team presented their project at the end of the week-long global Data Open Championship sponsored by Citadel LLC and Citadel Securities in partnership with Correlation One last December.
At the start of the competition, the team was given a dataset on international trade and asked to assess the impact that trade has on global plastic pollution. They were tasked with coming up with a hypothesis based on the data set.
The team’s winning question: “Should the world ban the global waste trade, and should the U.S. sign on to the Basel Convention, an international treaty designed to reduce the amount of waste shipped between countries?” Their work recommended an introduction of trade restrictions based on specialization in different types of plastic waste treatment to counter the global pollution crisis.
Silantyev said the team benefited from having complementary skills.
“David and Jennifer were the masterminds behind the statistical model we used,” he said. While LaNeve served as the data engineer “capable of testing any hypothesis about the data in minutes.” Silantyev provided case studies to support the team’s narrative.
To work on the project in person with all four team members, Silantyev and LaNeve flew to Durham, NC, renting an Airbnb and working 12-hour days. “While working together online has its benefits, nothing beats working together in person,” Silantyev said. “We could easily bounce ideas off each other.”
The global championship was the culmination of the year-long competition, where over 6,000 students from more than a dozen countries participated in nine regional events. Team 13 was invited to the championship after placing in the top 3 in the Europe Regional Datathon and the East Coast Datathon.
Cities are a source of fascination for new Berkeley Haas Asst. Prof. Nick Tsivanidis, and an endless source of data: He’s used satellite images and machine learning to study how Indian slums have changed over time, and cell phone data to look at Syrian refugees’ migration to Amman, Jordan—where they’ve doubled the size of the capitol from 2 million to 4 million.
“By 2050, it’s projected that about 2.5 billion more people will move into cities, mostly in Africa and East Asia,” Tsivanidis says. “There’s a huge opportunity there to get the backbones of these cities right, to get the benefits of urbanization and avoid the demons of density.”
Tsivanidis is the newest member of the Berkeley Haas faculty, joining the Real Estate Group with a joint appointment in the Department of Economics. His focus is using granular data and natural experiments to understand and potentially shape how cities develop. He’ll begin teaching macroeconomics in the MBA program in the spring.
“We’re thrilled to welcome Nick to our real estate faculty,” says Prof. Catherine Wolfram, associate dean for academic affairs and chair of the faculty. “He’s pushing the frontier to deepen our understanding of urbanization in developing economies. These are super important issues, given just how fast these cities are predicted to grow.”
Interest in developing economies
Tsivanidis, who grew up in London, first got interested in development when he taught English at a primary school in Tanzania during a gap year between high school and college. He returned home to study philosophy, politics, and economics at Warwick University as an undergraduate, before heading to Yale University for a master’s in international and development economics.
He got more on-the-ground experience during a year in Rwanda studying the spread of technology among coffee farmers. He then moved to Chicago, spending another year as a researcher before beginning his PhD at the University of Chicago, where he switched his focus to urban economics.
“Having grown up in a city, and seeing the differences between cities and the countryside, it’s clear that cities are the future lands-of-opportunity for these countries,” he says. “That’s what urban economics is all about: Can we measure and understand the benefits of population concentration in cities? How does this trade off with the downsides of density? And how can economic policy help us maximize those benefits and minimize the costs?
For his doctoral thesis, Tsivanidis looked at innovative ways that cities can improve their commuting infrastructure, studying the world’s largest bus rapid transit system in Bogotá, Colombia. Bus rapid transit systems are far cheaper and faster to build than subway systems, and nearly as fast. He analyzed multiple data sources to look at the combined benefits of Bogotá’s system, from more leisure time for commuters to higher land prices along the transit route.
Tsivanidis says he was drawn to Berkeley for the strength of its real estate group, its focus on urban economics and energy economics, and the opportunity to collaborate with exceptional researchers in economics, agricultural economics, the School of Information, and across campus.
He’s also looking forward to exploring the Bay Area’s natural beauty with his wife and one-year-old. “We loved Chicago, but it was very flat, so we’re looking forward to doing a lot of hiking.”
A team of Berkeley MBA students bested groups from seven schools to win the annual Berkeley Haas Tech Challenge for their plan to educate city government officials on how new technologies can support initiatives that improve quality of life and efficiency.
The 2018 Berkeley Haas Tech Challenge, called “Big Data and the City of Tomorrow,” was held Nov. 8-10.
The winning Haas team included Bryan Chiang, Catherine Hsieh, Max Kubicki, and Cori Land, all MBA 19s. Haas took home the $5,000 first-place award for the second year in a row.
The challenge called on students to come up with a plan to entice city government officials to adopt Amazon Web Services (AWS) to create smart cities. Smart cities use data and communications technologies to increase efficiency, share information with the public, and improve the quality of government services and public safety. Example projects include monitoring and managing traffic signals remotely, using a software platform that tracks the real-time availability of spaces in parking lots, and implementing a lighting-management system that allows cities to monitor energy efficiency and maintenance needs.
The Haas team began the Tech Challenge case by asking: Who are the customers, what do they care about, and how can AWS meet them where they are?
“We tested each of our ideas against whether or not it ultimately solved a problem for people,” Land said. “If not, we rejected the idea, and it helped us focus our recommendations.”
The Haas team made recommendations for a website redesign that would provide easy-to-understand smart cities information to non-technical city planners, as well as new certification programs to educate government officials on how they could use AWS.
(Left to right) Max Kubicki, Bryan Chiang, Cori Land, and Catherine Hsieh prepare their presentation. Photos: Benny Johnson.
The team also proposed a dashboard tool to help city officials compare their city services to others that have adopted smart cities technology—and measure the potential return-on-investment for their proposed projects.
Confidence without attitude may have been what set the Haas team apart from competitors. “One judge kept thanking us for admitting we still had some work to do when we better understood some gaps in our plan,” Hsieh said. Another Haas strength was leveraging broader perspectives by assembling a team with different areas of expertise, ranging from finance and energy to design thinking and change management.
Evan Cory and Charlie Cubeta, Tech Challenge co-chairs who organized the competition for the Haas Tech Club, said they received 108 team applications from 15 schools for the competition, a 25 percent increase over last year.
Competing teams included Yale, Kellogg, Columbia, Chicago Booth, MIT Sloan, UCLA, and Wharton. Eight Amazon executives served as coaches and judges.
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.”
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.
A team of Master of Financial Engineering (MFE) students took first place in “The Data Open” this month, besting 25 teams who competed across UC Berkeley in the regional data science competition.
The winning team—Li Cao, Pierre Foret, Teddy Legros, and Hosang Yoon, all MFE 19—won $20,000 in the competition semi-final, sponsored by Citadel LLC, a global asset management firm, and operated by data scientist recruiting firm Correlation One.
The Berkeley Haas semi-finalists will travel to the New York Stock Exchange later this fall to compete against top universities for a $100,000 grand prize.
The Sept. 8 competition asked teams to solve a complex problem by analyzing complex data sets.
More than 400 students across UC Berkeley applied to participate, and 100 were accepted. Event organizers grouped the contestants into teams, many of whom—including the winners—had not worked together before.
Choosing a question
The night before the event, the teams were given a description of data sets—data that applied to everything from 311 calls in New York state to restaurant inspection data. The teams were challenged choosing a question and finding the answer in the data.
“Most data science competitions focus on achieving the best possible score for a predefined metric,” Foret said. “In contrast, we had to come up with our own question, which is a lot closer to the type of problems a data scientist has to tackle in the industry.”
The team outlined two goals: identifying the top factors that influence public health in New York state and providing local government officials with policy recommendations based on their analysis.
Working through the night, they prepared code that allowed them to test their hypotheses quickly, so they were able to spend most of the competition time running experiments, analyzing the data, and choosing the most relevant problem to solve.
Legros said their data analysis tracked factors that affect residents’ health, including both traditional socioeconomic ones and several lifestyle considerations—such as smoking habits, eating habits and living conditions—that evolve over time. “Next, we identified clusters of regions where some of these determining lifestyle factors are more dominant. This can help policymakers develop regional proposals to improve the health of residents.”
“It was one of the most intense 24 hours I have spent,” Yoon said. “While the competition itself was short, I really learned a lot from our preparation.”
The judges, announcing the winner, said they impressed by the originality of the team’s question and the strategy to focus on comprehensive view of the factors involved.
While developing a sophisticated algorithm requires technical expertise, leading a successful business analytics project can’t be done without paying attention to people. That’s the theme of the inaugural Berkeley-Fisher Center Summit for Business Analytics, to be held at the California Memorial Stadium on Sept. 27.
The new event, which is free and open to the Haas community, will explore how data-driven technologies like artificial intelligence (AI) and machine learning (ML) affect people in the workforce, and how organizations can align people with processes to drive the success of analytics initiatives.
“Many business analytics initiatives fail, and our research shows it is due to misalignment with people in the workforce, rather than issues with the technology,” says Gauthier Vasseur, executive director of the Fisher Center for Business Analytics, which is part of Berkeley Haas’ Institute for Business Innovation. “Part of our mission is to educate management, finance and operations leaders on how to help their people become supporters of business analytics projects.”
The event, which kicks off with a dinner gala and awards ceremony on the evening of Sept. 26, is expected to attract an audience of about 100 students, industry leaders, alumni, and faculty to share experiences, learn from the latest research and real-world case studies, and network among peers.
“This summit is Berkeley Haas’ first big event around business analytics, and it matches thought leaders from the private sector with academic researchers and talented students who can join together to solve the biggest business challenges,” Vasseur says.
Why people affect the success of business analytics initiatives
Vasseur shared an example of what happens when an organization overlooks the importance of people. A manufacturing company wanted to use AI and ML technology to solve a very expensive problem: the lost time and productivity that occurs when a machine stops working due to a broken or worn-out part.
The company built a highly sophisticated predictive-analytics platform to assess when parts needed to be replaced. The technology worked very well, but the maintenance workers didn’t trust the predictions and opted against replacing the recommended parts. The problem was that no one had considered their expertise and input, and the company had not shared enough information to earn their confidence in the new technology.
“In the end, the platform provided no benefit to the manufacturer, despite the significant investment and the accuracy of the technology,” adds Vasseur. “Our goal is to give businesses the research findings and tools to avoid these situations.”
Business analytics evolves beyond IT
Launched this year, the new summit reflects how Berkeley Haas has evolved its focus, research and curricula to address one of today’s most urgent business needs: analytics expertise. Formerly known as the Fisher Center for IT and relaunched last February, the center has held conferences for the past six years, focusing on the changing role of the chief information officer (CIO) and recognizing innovative CIOs with an award. This year’s event continues the tradition of presenting the Fisher-Hopper CIO Lifetime Achievement Award and introduces two new award categories: Excellence in Driving Transformation Award and Business Analytics Woman of the Year.
Awards recognize business analytics achievements
This year’s Fisher-Hopper CIO Lifetime Achievement Award goes to David E. Smoley, CIO of AstraZeneca, a global, science-led biopharmaceutical business. At the summit, he will share how he led a performance turnaround that reduced drug development cycle times while reducing IT spending by more than $800 million (48 percent) over three years.
Other awards include the Excellence in Driving Transformation Award, which recognizes The Boeing Company’s CIO and Senior Vice President Ted Colbert, and the Business Analytics Woman of the Year Award, which honors Hewlett Packard Enterprise’s Global Vice President for Big Data, AI, and Innovation Beena Ammanath.
Demystifying business analytics through research and case studies
Vasseur says the goal of the summit is to put data analytics into an actionable context that comprises data, people and processes for business success. Speakers will help unpack what AI and ML are, and how these technologies can be most effectively applied when including people’s support in their design.
Speakers include Haas faculty members Thomas Lee, associate adjunct professor and director of data science at the center; Andreea Gorbatai, assistant professor in the Management of Organizations Group; and Gregory La Blanc, distinguished teaching fellow with the Finance Group. Additional presentations by industry leaders include:
Sabrina Menasria, Chanel’s head of business intelligence and master data governance
Todd Wilson, Clif Bar’s senior vice president of IT
Robert Brown, Cognizant Technology Solutions’ associate vice president for the Center for the Future of Work
Alexandre Robicquet, Crossing Minds’ co-founder and CEO
Matteo Melani, Ellipsis Company’s CEO
Maik Henkel, Global Foundries’ senior manager and deputy director of finance
Tickets for the Sept. 26 dinner gala are still available here for a donation of $250 or more. The Sept. 27 summit is free to all who register at the bottom of this page.
“Classified” is a series spotlighting some of the more powerful lessons faculty are teaching in Haas classrooms.
On a recent spring morning, the future of business education was on full display in Connie & Kevin Chou Hall on the Haas School of Business campus. Standing before a 5th-floor classroom packed with aspiring MBAs, Assoc. Prof. Jonathan Kolstad was talking advanced data science as part of his new course, “Big Data and Better Decisions.”
Kolstad asked students to pick between two common models for designing a predictive algorithm. “Tell me a situation where you would want to use a regression tree over a linear regression,” said Kolstad. A student spoke up: “When you’re using Facebook likes to analyze Metallica fans versus people who like to eat hamburgers.”
“Nice,” said Kolstad. “You can reach a subtler conclusion by using a tree that puts hamburger ‘likes’ and Metallica ‘likes’ together—you can see when a combination of these factors matters, rather than looking at each one alone.”
If ever there was a moment that captured how far business education has come from its 19th century roots, this was it. With data now pervasive across company operations—from marketing and sales to human resources and finance—there’s a demand for business managers and leaders who know a lot more than the basics. Companies are embracing data-driven machine learning to forecast everything from consumer behavior to worker productivity. They’re also relying more on randomized trials, a process long associated with medicine, to test sales promotions or advertising campaigns before they launch.
“There’s a growing need within companies for MBAs trained in data analytics,” says Prof. Paul Gertler, an economist who is co-teaching the course. “This class is designed to prepare students to be part of the modern labor force and leaders of industry.”
New breed of data-driven MBAs
Kolstad and Gertler aren’t looking to train data scientists. Their goal is to enable MBAs to bring a question-the-status-quo mindset to data-driven initiatives within their organizations—whether they are themselves part of a data team, managing one, or tasked as a C-suite leader with setting strategy based on what the numbers suggest. Students learn to blend business strategy with new tools from machine learning.
Yet equipping students with the right data skills means one thing: delving into the nitty-gritty of data analytics—even if, as Gertler says “they never touch another piece of data in their lives.”
“This isn’t a theory course,” said Gertler, who is the Li Ka Shing Professor at Haas with a joint appointment at the UC Berkeley School of Public Health. “This is a get-your-hands-dirty-up-to-your-elbows-in-data course.”
This meant, among other things, requiring students to learn a programming language called R, which is popular for data mining. They also had to complete a prerequisite, “Data and Decisions,” and a core statistics course.
“This isn’t a theory course. This is a get-your-hands-dirty-up-to-your-elbows-in-data course.” —Prof. Paul Gertler
To be sure, this hands-on approach made the class a stretch for students without experience in big data analytics—but also made it particularly rewarding for those who pushed through the technical challenges.
Laura Andersen, MBA 19, appreciated that the course was the real deal. She says data is changing the game in her chosen field of social services, but non-profits and government agencies often lack the resources to take full advantage of it.
“I need to know the vocabulary and to be able to pull some of the best practices around data from the private sector,” says Anderson, who plans to learn more by tapping into university-wide resources like UC Berkeley’s D-Lab, which offers classes and other services that support data-intensive research in social sciences. “This class has been a great investment, and experiencing it on a campus with some of the best academic resources in the world on these topics has been a great way to get started.”
Causation + prediction
Led by Gertler, the first months of the class were devoted to the growing use of randomized trials in business and different tests for finding causation—a critical step in data analysis. “Businesses are no longer saying, ‘Let’s try this and see whether we feel like it worked or not,'” says Gertler, who is also the scientific director at the UC Berkeley Center for Effective Global Action. “They want hard evidence, not only that a strategy worked but also by how much.”
To that end, students learned how one randomized trial showed that buyer discounts drove car sales faster than boosting incentives for sales teams. They studied how eBay ran a series of random experiments to test its Google advertising and found zero upside to running ads alongside brand-specific searches. In a third case study, generic drug makers called off a planned $5 million marketing campaign after a randomized trial showed it would have no effect on its target audience: physicians prescribing brand-name drugs.
“Randomized trials are as much about finding what doesn’t work—the counterfactual—as it is about finding what does,” Gertler told the class. “That’s the dirty little secret about data analysis.”
How to 2nd-guess an AI
For Kolstad, who taught the machine learning portion of the class, a key theme was the “black box.” As artificial intelligence has grown, so, too, have instances where an algorithm makes accurate predictions yet nobody really understands why. For example, a program could know that a certain consumer will click on an ad, but the “black box” means it can’t tell whether that’s because the online user was young and female or liked Metallica and hamburgers.
“Analytics consultants often come to the C-suite and say, ‘We have these fancy models that predict really well,'” said Kolstad, who also co-directs the health initiative at the UC Berkeley Opportunity Lab and is co-founder of Philadelphia startup Picwell, which provides personalized insurance recommendations.
“Now that you understand what’s really behind these models, you know the right questions to ask,” Kolstad tells students. “You can say, ‘I don’t care just about predictive performance. I want to know my customers better.’ And you can ask, ‘How did you avoid ‘overfitting’ the data? Did you leave data out? Can I provide data of my own for you to test?’”
Students like KC Loder, MBA/MPH 18, signed up for the class because they, too, recognize that advanced data analytics is a must-have skill for today’s business leaders. Before coming to Haas, he worked in health care and saw firsthand how hospitals and other providers leverage data to keep costs down and improve patient care.
“Whether you’re working in health care or education or big tech, if you are the strategic thinker and you can’t understand what good data science is and what the data scientists on your team are doing, then you’re going to be obsolete,” says Loder.
Assoc. Prof. Jonathan Kolstad, who uses big data and behavioral economics to investigate the complexities of the health care system, has been named a “40 Under 40” business leader by the San Francisco Business Times.
The award honors 40 young leaders who “exemplify the creativity, passion and perseverance that have come to characterize the Bay Area,” according to the publication. The awards were published March 5.
Since joining the Haas Economics & Policy Group in 2015, Kolstad has become known as both a cutting-edge researcher and a popular teacher. His MBA courses include “Health Economics and Policy” and “Big Data and Better Decisions”—a new class that gives students advanced training in how to analyze and use large-scale data.
As a researcher, Kolstad employs machine learning and economic analysis through a behavioral lens. His studies demonstrate that conventional wisdom can lead businesses and policy makers astray when it comes to health care.
For example, he has studied the mistakes people make when they choose health insurance plans that lead to large financial losses and worse health coverage. He found that high deductible health plans—often thought to be a silver bullet to improve efficiency—actually lead to large reductions in high value and low value care, particularly for the sickest enrollees.
Kolstad is particularly interested in finding new models and unique data that account for the complexity of health care markets, including how health reform impacts hospital care, the labor market, and insurance premiums. In one study, he looked at what motivates doctors, and found that feedback can be more important than pay.
He has also combined his passion for solving thorny academic problems with developing real-world tools that help people avoid costly mistakes when choosing health insurance. He founded a startup, Picwell, to provide personalized recommendations for insurance. The tool now provides more than 1 million recommendations per year from Medicare to state exchanges and employer-based insurance.
In addition to his teaching and research, Kolstad serves as a research associate at the National Bureau of Economic Research, and co-leads a health care initiative at the UC Berkeley Opportunity Lab that uses data from private firms and government to understand population health and its role in inequality. He holds a PhD from Harvard University and a BA from Stanford University.