Feature on Prof Yang Chenying, Assistant Professor of Economics
School of Economics, Singapore Management University
A Passionate Trade: Yang Chenying, Singapore Management University (SMU)
Assistant Professor Yang Chenying’s passion for international trade was sparked when she was just a little girl, inspired by stories her grandfather shared with her. Now, her research helps policymakers make better-informed decisions on carbon tax implementation for industries that are heavily exposed to international competition.
From the COVID-19 pandemic to the recent Russia-Ukraine war, the global supply chain has faced its greatest challenges yet in modern times—and they are often multifaceted.
With Ukraine being a major “breadbasket”, one of the most notable impacts of the war is the disruption to its agricultural export industry, which has sent ripple effects across global food supply chains. But the problem doesn’t end there.
“What many might not be aware of is that Ukraine is also a major exporter of automobile parts,” says Yang Chenying, Assistant Professor of Economics, Singapore Management University (SMU).
In particular, Ukraine specialises in producing the wire harness, with its exports accounting for seven percent of the product in the European Union. Due to the war, major European carmakers including BMW, Porsche and Volkswagen are now facing supply chain issues.
“When it comes to international trade, there are so much complexities due to how interconnected the modern world is—there is often more than meets the eye,” she adds.
Not all doom and gloom
Asst Prof Yang also explains that despite the media attention on how the COVID-19 pandemic has caused huge supply chain disruptions, to some extent, the issue has been overstated. “Data has shown otherwise. Despite the uncertainty in supply from China, the US container import volume has continued to set new record since March 2021. The supply chain has not broken down, at least in the US.”
So what is causing the delays in shipping and goods delivery? “It is mainly due to a substantial increase in demand,” she elaborates. “People are buying much more things than they were used to, and there are a couple of reasons for that. One, governments have been encouraging consumption through the distribution of subsidies. Second, people are starting to stock up on durable goods.” Notably, compared to pre-pandemic times, customers are increasingly purchasing specific products instead of consuming services.
“From the perspective of a trade economist, it’s really interesting when you get to the bottom of the issue and examine the dynamics of the different factors to realise how they come together,” Asst Prof Yang enthuses.
A lifelong passion rooted in childhood
On what inspired her to pursue the field, Asst Prof Yang shares that she grew up in a coastal city in China, where she used to always see container ships lined up at the port.
“From a very young age, I was filled with wonder about what was inside those containers, and where they were headed to. My grandfather would explain to me, in simple terms, how the world is connected through international trade,” shares Asst Prof Yang, who was inspired by those stories to pursue economics in school. “I wanted to be a trade economist. It’s a field I want to spend the rest of my life working in.”
A blending of purpose and passion
A current project of Asst Prof Yang looks at how multinational firms respond to carbon tax and how governments can use trade policies to combat climate change. She aims to quantify the magnitude of “carbon leakage”—which happens when businesses relocate their productions to another country with more lax emission constraints when they encounter higher operational costs as a result of government policies.
Asst Prof Yang explains that carbon leakage will hurt the welfare of the taxing country, and even more so if firms in the taxed industry hold significant market power. “Governments should be careful in deciding which industry to impose carbon tax and by how much,” she says. “Trade policies such as carbon border adjustment can help to correct some market failures caused by carbon leakage. But ultimately, we need international coordination in efforts to fight carbon emissions.”
Prior to joining academia, Asst Prof Yang was a consultant at the Asian Development Bank. Elaborating on the work she did there, Yang explains that many countries in the region play important roles in completing certain tasks along a global value chain, as firms in the US or Europe often outsource aspects of their production to factories here. “We came up with a measurement to quantify the statuses of specific Asian economies in the global value chain, the degree to which they are integrated in various sectors, and what can be done to help these developing economies move up the value chain,” she shares.
Asia’s way forward
Home to 60 percent of the youths of the world, Asia is a key driver of the global economic development moving forward, according to Asst Prof Yang. “There are a few key areas in which I believe Asian economies could contribute significantly,” she says. “These include the development of greener technology, as well as ensuring the political stability and safety of domestic economies in order to remain attractive to foreign investors.”
Asst Prof Yang also highlighted the importance for countries in the region to invest in the infrastructure sector. “Take Singapore for example; its consistent investments in maintaining and transforming its infrastructure is one of the main reasons why the country as a global trade entrepot has been successful in tackling supply chain disruptions,” she adds.
Feature on Prof Lin Ma, Assistant Professor of Economics
School of Economics, Singapore Management University
Reading the road signs of urban city development
How do we know the best size for a city and, even if we do know, how do we reach that size while enhancing the well-being of residents? Assistant Professor Lin Ma uses data from high-growth cities to find the answers.
Urban growth is a conundrum for inhabitants as well as planners. ‘Is bigger better?’ we might wonder as we push our way into a crowded train to work. Even while squeezed among the other commuters, we might wish for the things that come with more people, such as better job options or a better arts scene. A policy maker arriving at work, meanwhile, might ponder whether a new road to a growing suburb will make life better or worse for the retailers there. A few floors above, a politician might practice a speech on immigration policy. All three characters are essentially grappling with the same issue: how do we balance urban growth with quality of life?
Revealing the numbers and patterns that answer this question is Assistant Professor Lin Ma, Assistant Professor of Economics and Lee Kong Chian Fellow at the SMU School of Economics. He turns data about the flow of people and transport into insights that help planners know whether to open or slow the flow of people into cities, and even how to guide those flows through transport networks.
An expert in urban economics, Professor Ma focuses on ‘destination cities’–those which attract migrants—and how to ensure they grow in a way the promotes the residents’ social and economic well-being. He arrived at the frontiers of urban development through the more traditional economics subjects of international trade and globalisation. “Over time, I tilted from the mobility of goods towards the mobility of people, particularly in the context of China” says the Professor, who himself was raised in Beijing before spending seven years as in the United States.
Now at the SMU School of Economics, he traces migration flows and, most recently, the evolution of transport networks, to unravel the factors that make cities grow and work.
“Urban economics tells us how everyday life—getting around the city, an affordable house—is connected and how one thing affects the other,” says Professor Ma. It’s an exciting time to study these dynamics, says Professor Ma, because the available data is richer than ever before. His data mostly stems from China, which he likens to a goldmine for empirical and qualitative research.
“To study how policy or infrastructure affects urban development we need to observe change,” Professor Ma says, noting that there has been little such change in the infrastructure of Europe and the United States in recent decades. In contrast, China has changed dramatically, with its urbanisation rate increasing from 10 percent to 50 percent in recent times. “China’s growth not only gives us variations to study, but they also document it enthusiastically. There are records and data about everything,” he enthuses.
Mega-cities under the microscope
The rich economic stories of cities like Beijing and Shanghai, each having doubled their size in less than a decade, informs complex topics such as immigration policy. To help pinpoint the right balance of pros and cons, Professor Ma developed a spatial equilibrium model, which identifies optimal city-size by looking at migration-fuelled productivity and downsides such as congestion. He developed the model with Assistant Professor Yang Tang from the Nanyang Technological University. They reported their research in the paper “Geography, Trade, and Internal Migration in China”.
“We found that even though inhabitants may face greater levels of congestion and higher housing prices, there is still a 90 percent improvement in their overall levels of welfare. In Beijing and Shanghai, for example, we estimated that number is around sixteen million. Any larger and the congestion factor might dominate,” explains Professor Ma.
But we also need to look beyond the mega-cities, says Professor Ma. “Growing cities have positive spillover effects for neighbouring places. Mega-cities produce goods cheaply and these benefit smaller cities along the same supply chain.”
Incorporating around 300 cities within China, the model also computes the optimal size of cities from a national perspective. “If the aim is to maximise national welfare, the positive spillover effect for other cities in the supply chain would bump up the optimal size of Beijing and Shanghai to around twenty-two million,” says Professor Ma. The model illustrates the divergent interests between local and national governments. “Without the national picture, Beijing or Shanghai local planners might prefer a smaller size,” he adds.
Roads to or away from growth?
Transport networks—such as roads and railways—profoundly affect the movement of people. Even with the right migration policies, you need to design transport networks that move people and affect neighbourhoods in the way that you intend. Professor Ma studies the distributional impacts of the new roads which stretch across China’s immense economic regions in a recent working paper, “The Distributional Impacts of Transportation Networks in China”, together with Dr. Yang Tang from Nanyang Technological University.
“A new highway will almost certainly benefit the main cities connected by the route, but the benefits for smaller in-between locations is uncertain. Increased connectivity can deliver new goods and ideas, or can send people and their money to larger cities,” observes Professor Ma.
To bring evidence to such important transport decisions, Professor Ma is building a significant data set that traces the evolution of China’s transportation networks, tracking the emergence of roads and railways at the pixel level, over time. “The data set will enable empirical work for theoretical frameworks to help design transportation networks,” he explains, with an eye to the future.
Feature on Prof Madhav Aney, Associate Professor of Economics (Education)
School of Economics, Singapore Management University
When teaching inspires new research directions
Associate Professor Madhav Aney began his career in economic theory, but teaching at the Singapore Management University led him to a path of understanding how incentives shape societal behaviours in Asia.
Whether we are conscious of them or not, many factors sway our decisions, from our internal beliefs to the prospect of financial gain (or ruin). Even as a society, we respond collectively to the systems and institutions that organise public life, such as laws and government policy.
These intricate strings that pull our decision-making levers have been the study focus of Associate Professor Madhav Aney since a little over a decade ago, when he completed a PhD degree in the principles of mechanism design at the London School of Economics.
“Mechanism design tells us about how people react to the incentives in systems that aim to produce certain outcomes,” he says. “A good tax code, for example, needs to consider the possibility that high taxes may give people more incentive to avoid paying the tax.
Although his early career was grounded in “pen and paper type research”—in other words theory and applied mathematics—Associate Prof Aney’s career took a new turn when he arrived at SMU.
“I was asked to teach economic development in Asia, which involves a lot of empirical content. I was very intrigued by it, which spurred me to actively seek out opportunities to work on empirical projects.” His enthusiasm paid off—he met other academics with similar interests and has not looked back since.
Curiosity fuels a lifetime of learning
Associate Prof Aney has diverse research interests, from politics and the military to market failures and even traffic safety. His continual curiosity about new areas was apparent in his younger days. Having come from a family of lawyers, reading law naturally his first choice; however, his eventual decision was influenced by the different classes he attended out of curiosity
“After I earned my law degree, I thought of exploring my options before deciding what I wanted for my career,” he shares. “An opportunity came along to study economics, which eventually led to pursuing a master’s program in that field. Even then, I thought I would go back to law eventually—I qualified to practice as a lawyer—but I liked economics so much that I stuck with it.”
While he pursued his interests and career in the field of economics, he has not been myopic in his outlook on research and teaching. His advice for students and early career academics is to be open to exploring different interests. “I initially saw teaching and research as two separate activities. However, staying open-minded helped me nurture a genuine interest in the topics I have been teaching.”
Applying statistical analysis to judicial analysis
Given Associate Prof Aney’s background in law, it may not be surprising that his recent research involves examining constitutional institutions in India.
In the paper Decree power in parliamentary systems: Theory and evidence from India, Associate Prof Aney and Professor Shubhankar Dam from the University of Portsmouth examined how governments use their ability to make executive orders—laws that are made by the executive rather than the legislature—to bypass the legislature.
“Governments with a majority in the legislature would likely be less fearful of keeping the law-making there. Meanwhile, governments that do not have such numbers in the legislature may resort to this executive route more often and more strategically,” he explains, noting their study supported this theory.
His latest published paper examines the incentives influencing the Indian judiciary. In the study Jobs for Justice(s): Corruption in the Supreme Court of India, Associate Prof Aney and his colleagues uncover statistical evidence that Supreme Court judges in India are influenced by the possibility of gaining prestigious post-retirement roles.
“This incentivises the judges to decide in favour of the government in important cases,” he explains. “In turn, these favourable decisions increase the likelihood that the judges would be appointed to such post-retirement roles.”
Now, the team is collecting more evidence of similar patterns that show how structural mechanisms intertwine with individual behaviours and societal outcomes.
“In a sense, what we have found is another confirmation that the incentives operating within a society affect the behaviour of its people,” Associate Prof Aney adds.
Preparing students for the real word
An influence that Associate Prof Aney did not expect to discover was his own influence on his students. The ripple effects of teaching and its power to shape lives did not yet feel concrete to him as he had just entered academia. But after interacting with his students and learning more about their aspirations, he could not help but be filled with gratification in watching them graduate.
“It makes me very happy—and grateful—to see many of my students go on to achieve great things including holding leadership positions and pursuing PhD programs,” he says. “At SOE, we constantly re-examine and update our curriculum to ensure that our students will be equipped with skills that make them job-ready.”
Similarly, in his 13 years of teaching at SOE, Associate Prof Aney has continually renewed his course curriculum, including new research that might interest students, in the same way that economics interested him then as a would-be lawyer.
Feature on Prof Sunha Myong, Assistant Professor of Economics
School of Economics, Singapore Management University
Pursuing an even playing field for education investment
Assistant Professor Sunha Myong aims to give children more equitable education opportunities through understanding how families invest in their education.
Why does inequality between wealthy and poor families persist across generations? This question deeply intrigues Assistant Professor Sunha Myong, and drives her research into how families invest in their children’s education.
While societies have made substantial progress in providing equal education opportunities to everyone, Asst Prof Myong notes that various frictions in the market and factors like family background can be a hindrance in making optimal investments in education. As such, her research tackles policies and mechanisms that can reduce the inequalities associated with these frictions.
“Understanding the role of education policy in social mobility is crucial to addressing inequality,” says Asst Prof Myong, citing the influential work of Nobel Laureate Gary Becker, father of the theory of human capital. This theory says that the most important driver of a person’s success is investment in their skills.
Researching how people invest in their human capital is just as important as research into financial investments. Most intriguing is that these fateful investment choices are made by parents and families based on matters that are out of their control or conscience. This is where we can uncover clues about social mobility. “Why do wealthy and poor families invest differently in children's education? Economics provides some answers, which in turn help us to judge the acceptable level of inequality in education outcomes,” explains Asst Prof Myong.
If inequality results from people of greater ability getting better “marginal returns” from educational investments, then we might accept that a better outcome for those people is fair. “But we should not accept inequality that results from poor financial resources or misguided parental choices,” says Asst Prof Myong.
Finding hidden barriers
Economic data are valuable in promoting good education choices, says Asst Prof Myong, who likens the problem to financial markets. “In a ‘perfect market’, a student could say, ‘I want to invest this much because I can achieve this much’ and then borrow against their future income,” she says. “But the market is not perfect—there are barriers. I use data to test what factors these barriers are related to.”
Policy makers also want to identify and understand the barriers, so they can know whether their polices, such as loans and scholarships, address those barriers.
In the study Self-financing, Parental Transfer and College Education, Asst Prof Myong and fellow SMU Assistant Professor Jungho Lee, found that policies that restrict student loans to tuition costs, and not other costs associated with study, pushed students into work that compromised the investment on their education. According to Asst Prof Myong, this is a significant barrier.
“Investment in human capital is not only about money but also time and effort. If we look at who works more hours, it is students with fewer financial resources, especially from parents. This is another way that inequality persists to over generations,” she says.
To that end, Asst Prof Myong suggests that student loans should cover other costs besides tuition, and that other ways be found to reduce students’ working hours.
“Students work because they need the money, not because they want to. If we can change the policies, we can redirect students’ time and effort back into their education,” she says.
More childcare sharing, more babies
Another area of Asst Prof Myong’s research with policy implications for human capital looks at earlier in the investment cycle: the impact of gender roles on fertility patterns. Low birth rates are common among Asia’s developed economies, with Singapore, South Korea, Hong Kong and Taiwan all having low fertility rates, in spite of significant efforts to boost birth rates. This is another area where Asst Prof Myong applies economics to understand family decision making.
“Standard economic theory tells us that opportunity cost in terms of salary affects childcare decisions. In Western society, highly educated women spend less time on childcare than other women. But in East Asian societies education has little impact on decisions about who will look after the children. Highly educated females in East Asia spend as many hours on childcare as those with less education.”
The difference may be due to gender norms about caring for children, according to the findings of Asst Prof Myong’s study, Social Norms and Fertility, which she undertook with international collaborators. “In those four countries, wives shoulder 80 percent of childcare, in comparison to 60 percent in Western countries,” she explains. Women who can expect to drop their career for several years are facing a big opportunity cost that traditional fertility policies may fail to counter.
“Given such gender norms, childcare subsidies may not lead to more children, unless fathers pick up a greater share of childcare,” says Asst Prof Myong. “We have to acknowledge the interactions between social norms and economic decisions to understand what is going on within a family and to design effective policies; otherwise, we might waste a lot of resources without any impact or change to show for it.”
Cultivating a vibrant academic culture
Speaking of investment in her own human capital, South Korea-born Asst Prof. Myong says she was drawn to SMU by its thriving research environment, which she describes as enriching for an early-career researcher like herself, especially as SMU attracts many distinguished seminar speakers and visiting scholars.
“We have gained valuable feedback from these visiting scholars as they commented on our research work when it was still in the early stages,” she says.
Asst Prof Myong is already having a positive impact on the younger generation herself. She teaches economic development in Asia, where she relishes learning from the students’ perspectives which they gain from investigating case studies about poverty and economic policies in Asia.
“It’s very fulfilling to share my ideas in a real-world context and see the practical policy implications emerge,” Asst Prof Myong concludes.
Feature on Prof Ismail Baydur, Assistant Professor of Economics
School of Economics, Singapore Management University
Combatting unemployment to improve lives
Job hunting can be frustrating and mysterious, especially when calls offering interviews become fewer and farther between. Assistant Professor Ismail Baydur researches the larger forces that might be at work when the phone stops ringing, with the aim of creating a fairer job market.
The two things that make non-economists sit up and take notice of labour market trends are economic crises and long-term unemployment. In the case of Assistant Professor Baydur, Turkey’s economic difficulties in 2001 turned his head from a career in engineering—the popular choice among his peers—to one in macroeconomics and labour markets.
“The economic crisis happened when I was about to enter university. Unemployment was high, especially for particular groups, even when the economy began to recover later,” recalls Asst Prof Baydur. The imbalance of the situation spurred him to study the macroeconomic aspects of labour markets. He eventually gained a master’s from Turkey’s Koc University and a PhD in labour markets from the University of Virginia, joining ADA University in Azerbaijan before moving to SMU’s School of Economics in 2016.
“People in long-term unemployment face discrimination. The longer communities and individuals remain in unemployment, the lower their prospects are for finding a job,” says Asst Prof Baydur, whose research focus shows empathy for the more vulnerable in the labour market.
Asst Prof Baydur cited a 2013 paper by Kory Kroft and his colleagues—a study that his current work built on. In the paper entitled, “Duration dependence and labour market conditions: evidence from a field experiment”, the authors found that the likelihood of receiving a call-back for an interview significantly decreases with the length of an applicant’s unemployment duration.
In the study, fictitious resumes were used to apply for real job vacancies and the proportion of applications which received a call-back for an interview was measured. The key difference between the resumes was in the unemployment length—from zero months where the applicant was currently employed, to three years.
This study demonstrated statistical discrimination against unemployed workers and the role companies play in generating “negative duration dependence” —the adverse effect of a longer unemployment spell. “These individuals who suffered from the discrimination won’t stand a chance if they can’t even get an interview,” explained Asst Prof Baydur.
To that end, Asst Prof Baydur and his colleague Asst Prof Xu Jianhuan worked to build a case to address statistical discrimination. They first developed a model which accounts for the different sources of negative duration dependence. In turn, by adjusting the parameters associated with each source, they were able to assess the impact of potential policy interventions—shutting down the statistical discrimination channel increases the job finding rate of a worker with an unemployment period of 24 months by about three-fold, on average from 0.121 to 0.335.
“The Great Renegotiation” is not something new
As an expert in job transition, the effects of the COVID-19 pandemic are of great interest to Asst Prof Baydur, who expects the ongoing impacts will—not unlike any economic shock—require many people to change their work lives. He is particularly interested to see data on job transition during the pandemic as it emerges. Until then, he has a nuanced view on talk about “the great resignation” and “the great renegotiation” brought about by the disruption, which has seen people change jobs and change their expectations of what their work life should offer them.
“This is not a completely new phenomenon,” Asst Prof Baydur says. “Job-to-job transitions where people quit to pick a better job has always been present. Historically, particularly in the US, we observe this taking place more frequently during booms and less so during (a) recession.”
While there is a spike in transition rates due to the pandemic, Asst Prof Baydur points out this takes place against the backdrop of an overall downward trend of job transition rates. “More data is needed to see if the overall levels will continue to fall,” he explains. “Decreasing transition rates could mean that labour markets are getting less fluid and re-allocation of labour is discouraged. To policymakers, this could be a problem as it could slow down productivity growth and it is important to understand the source of the decline.”
Feature on Prof Jia Li, Lee Kong Chian Professor of Economics
School of Economics, Singapore Management University
From astrophysics to algorithms: testing theories against real life
Lee Kong Chian Professor of Economics Jia Li helps regulators and traders know which economic models will best predict the economic future.
Economic forecasts and machine learning algorithms are, to most of us, as mysterious as the laws of the universe. But for Professor Jia Li, he has the right tools to unravel those mysteries—substituting theory and matter with models and data.
Prof Li’s first encounter with SMU was almost a decade ago, when he was invited to speak at the 2013 Asian Meeting of the Econometric Society hosted by SMU. However, it wasn’t until 2019 that he made an official visit to the university—and was deeply drawn to it.
“I was very impressed by the School of Economics and its world-class econometrics research group—above all, by the calibre of the students. They were passionate about their field and gave excellent presentations,” recalls Prof Li. When the opportunity to formally join the School of Economics (SOE) came, Prof Li swiftly seized it.
“The energy—and passion—of the faculty and students is compelling,” he says. “When it comes to passion, it doesn’t have to be the same as that of others. It also doesn't have to be about money. I’d say follow your heart—in every step of my career and my life I have done just that.”
Putting economic models to the test
Prof Li’s genuine passion shines through as he describes his academic journey from astrophysics to economics. “I was what one might called a ‘physics geek’ in high-school,” he says, recounting how his excellent performance in the subject won him prizes and a place in Peking University to study astrophysics. Before long, however, he was introduced to a whole new world by his roommate—the world of economics.
“He was very into social science; we would talk about it a lot. I found it all very interesting!” So much so that, it turned out, he eventually graduated with a double degree in economics and science. Eventually his professors invited him to join the China Center for Economic Research to pursue a master’s degree in economics.
But although he had ‘officially’ become an economist, he would “always approach economic research with the mindset of a physicist,” he says. This mindset naturally gravitated him towards econometrics, where data and statistics are used to understand economic relationships.
In the realm of econometrics, he approaches the technical aspects by working at the interface between theory and data, hypothesis and falsification—essentially the whole cycle of scientific research. In this case, the theory aspect is that of economics rather than of astrophysics, and he observes reality through people's economic behaviour rather than the movement of matter.
The data explored by Prof Li are the millions of trades and transactions in financial markets. With this vast amount of data, Prof Li tests the accuracy of economic models and tools. The primary questions he seeks to address are: “Do the models’ theories and rules match the real story told by the data?” and “Are the tools working reliably?” Answering those questions would allow him to measure market volatility in order to understand market risks, as well as the performance of models that measure that risk.
Currently Prof Li is devising an evaluation methodology, one that’s known as “Conditional Superior Predictive Ability” which compares different forecasting models. “A natural question to ask is ‘which model is better?’ But both could be very good. A more precise question to ask is which will work better in certain empirical scenarios, for example, one might perform better in a recession while the other might do well in better economic conditions,” he explains.
Opening black boxes to build trust
Fundamentally, Prof Li’s research helps us to know which models we can trust—an important task with the advent of machine learning and artificial intelligence (AI). One particular machine learning model that robustly forecasted inflation was adapted from the random forest method—named for its broad usage to classify images.
Nonetheless, Prof Li acknowledged the need to be cautious about relying on machine learning and algorithms, given we cannot tell them to ‘show their working’ in each case like we can to a maths student.
“A ‘black box’ may work ninety-nine percent of the time, but without a deep theoretical understanding of what is really happening within, there is a possibility that the other one percent may happen tomorrow,” Prof Li says.
To address this challenge, Professor Li is leading the establishment of a Centre for Machine Learning and Econometrics at SMU to nurture cutting-edge research on modern data analytics for economic applications.
Mentoring the future
In the pursuit of the perfect model, Prof Li never loses sight of nurturing the next generation. “It’s an honour to be an educator—you can have a major influence on a young person’s career,” says Prof Li, who not only enjoys sharing his knowledge through teaching, but also serves as an associate editor in several economic journals such as Econometrica and the Journal of Econometrics.
“As an editor, I aggregate views to assess whether a paper is significant or not. If a paper is assessed to have the potential for publication, we provide feedback for the authors to improve it,” reveals Prof Li. “This is a privilege because we have a part to play in directing where the literature is going, and hopefully in the right direction to propel the field to greater heights!”
Feature on Prof Denis Leung, Professor of Statistics
School of Economics, Singapore Management University
Making sense of data to make life better
Professor Denis Leung’s ability to plug data gaps and unlock the mysteries of patient data has led to more personalised treatments and the delivery new drugs with fewer clinical trials.
Over the last three decades, the speed and amount of data collected has grown exponentially. Playing a pivotal role in this ever-important space is Professor Denis Leung, who has been working alongside the researchers collecting the data, helping them to turn information into meaningful findings to improve systems and lives.
“For a long time, statisticians struggled with collecting sufficient data for them to work with,” says Prof Leung. “The big challenge was making sense of incomplete data: paper-based surveys and our reluctance to give precise answers about personal things like age and income left gaps for statisticians to replace with credible and trusted estimates.”
This is where Prof Leung’s 30 years of experience comes in, devising new tools to analyse data, or somewhat paradoxically, missing data, which he has done since his first day of work at the Chinese University of Hong Kong’s Faculty of Medicine.
“My first job was with clinicians and researchers, making sense of patient data and treatment outcomes. Why did some patients get better while others not? Why did some treatments work in one way but not in other ways?” says Prof Leung, who continued this work in New York for almost ten years where he applied his expertise in oncology and carcinogenesis.
He worked in a hospital with scientists and clinicians who subsequently became life-long research collaborators. “We worked very well together to solve problems for patients with severe diseases. Every week we'd get together—surgeons, pathologists, oncologists, basic scientists, statisticians, data scientists—to look at patient data and design patient management pathways,” he says. “These pathways allow clinicians to advise on appropriate treatment options, and also let patients and their families make informed decisions.”
According to Prof Leung, there's a certain point that any treatment will only make things worse. “Sometimes, you would rather see the patient spending more time with the family and living life rather than going for ineffective and toxic treatments—this is the very issue that has kept me motivated to do more,” he shares.
Indeed, he kept this very motivation when he moved to Singapore and SMU in 2001. He has since made significant contributions to the field of biomedicine and in 2020, a Stanford University report on the world’s most-cited researchers ranked Prof Leung among the top two percent of scientists in the field of oncology and carcinogenesis.
“Every problem in practice and data set has its unique characteristics. You often find that many issues cannot be comprehensively solved using existing methods,” he says. “Often, current knowledge is adapted to create new methods for addressing these specific challenges to provide better solutions.”
His time at SMU can be characterised as one of continual discovery of new statistical methods to problems and opportunities. In 2001, Controlled Clinical Trials 22(2) 126-138, Prof Leung developed a statistical rule to define a novel way of conducting phase one clinical trials. Traditionally, new drugs are first piloted with a small number of people to understand the drug’s adverse impacts. To expose fewer patients to these risks, he devised a rule which achieves the same degree of credibility but with fewer trial participants needed using conventional methods.
Keeping people at the heart of the digital age
Prof Leung remains deeply passionate about working on problems which would help people. His early experience in cross-disciplinary collaboration features prominently in his thoughts about how statisticians should adapt in the data age. Statistical methods, he believes, have an increasingly important and complementary role to play in uncovering the relationships and reasons behind conclusions generated through machine learning and artificial intelligence (AI).
“While algorithms can process massive amounts of data to derive a specific outcome such as diagnosing whether a person has a certain condition, they are unable to explain the mechanics or the ‘why’ behind this assessment,” he explains. “This is where statisticians come in—to determine the specific reasons behind the problem or the human element involved.”
Going forward, Prof Leung hopes that statisticians and computer scientists will collaborate deeply and learn from each other. “By combining the explanatory strength of statistics and the speed and predictive power of machine learning and AI algorithms, valuable insights can be derived to solve many complex real-world problems,” he says.