Prof Yang Zhenlin
Feature on Prof Yang Zhenlin, Professor of Economics & Statistics
School of Economics, Singapore Management University
Making space for econometrics analysis
Economic phenomena are bound by space and time. However, the traditional models of econometrics often neglect spatial relationships in their analyses. Through his research, SMU Professor of Economics and Statistics Yang Zhenlin explores the phenomena of how “near things are more related than distant things”.
Population density is a double-edged sword. Empirical research has shown that the more populated an area is, the higher the innovative output of its economy. On the one hand, the proximity of a population plays an important role in creating the flow of ideas that can generate innovation and growth. But on the other hand, when two people are close enough to change creative ideas face-to-face, they are also close enough to contract a contagious disease.
“This downside of cities was an imminent concern of city governance at the outbreak of the COVID-19 pandemic,” says Yang Zhenlin, Professor of Economics and Statistics at Singapore Management University (SMU).
In one research, Prof Yang studied the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. The study used a data model to map out the simultaneity of infection and death outcomes, the spatial pattern of the transmission, the inter-temporal dynamics of the disease, and the unobserved city- and time-specific effects. He explains, “We found that population density does indeed increase the level of infections. But fortunately, government efficiency was able to significantly mitigate the negative impacts of urban density.”
Prof Yang’s research focus is on spatial econometrics, a subfield of economics in which the theoretical models used leverage spatial data that includes coordinates or distances between the units. The essence of spatial econometrics is Tobler's First Law of Geography, that “everything is related to everything else, but near things are more related than distant things.”
Elaborating on the difference between traditional theoretical methodologies and those used in spatial econometrics, Prof Yang says, “Classical analysis methods are fairly straightforward – for instance, given one unit increase of your education, you would expect that you will earn this much more in the future. This is a very clean and neat explanation.”
It’s when the spatial econometrics lens is applied thatthings get much more complicated. “In reality, a person’s future performance not only depends on their personal characteristics, but also their social capital, and their peers’ social capital. Everything is interconnected,” he adds.
The economics of space
Prog Yang shares that the field of spatial econometrics has received increasing attention in recent years, due to growing recognition that the standard econometrics technique often fails in the presence of spatial interaction. This applies to key issues in economics including demand analysis, labour, public economics, and the international economics of trade. “For example, when two countries have a huge trade volume between them, they are inevitably somehow dependent on each other. This economic relationship should take geography into account, meaning the proximity of the two countries and how it affects the nature of their trading relations.”
This concept may seem more apparent in fields such as agriculture or environmental economics, but now it’s being increasingly applied to areas such as finance as well. Prof Yang says,“As a trader, when you invest in 100 stocks for example, you would be analysing and tracking their movements over time. But these stock movements don’t happen in their individual vacuums; there is definitely a certain level of dependence among a group of similar stocks. This is something that more people are starting to take into account when they do financial analysis.”
One interesting application of spatial econometrics is on housing prices. For example, in the US, the median price of an apartment in one county is dependent on certain economic factors, but at the same time, it is affected by the price in the neighbouring county. “Traditional statistical methodologies would have made independent observations about the prices in the two counties – but spatial econometrics examines the correlations between the two pricing levels,” he explains.
Putting housing policy effects into perspective
Given the limited land resources in Singapore, short-term policy interventions are key to managing the demand for public housing in the islandstate. In one research, Prof Yang applied the spatial econometrics technique to examine the short-term impact of home purchase restrictions in Singapore.
In August 2013, the Singapore government introduced a new rule in which permanent resident (PR) households must wait three years from the date of obtaining PR status before they can purchase resale public housing flats. Using quarterly housing data over Q4 of 2012 to Q2 of 2014, Prof Yang found that public housing prices decreased by three to five per cent in the four quarters following policy implementation. However, the transaction volume did not change. “What this shows is that the effects are likely driven by a decrease in the housing demand and the inelastic housing supply in the short run – and not directly caused by the new policy itself. From this example, we can also show that econometrics models that ignore spatial and dynamic effects can overestimate policy effects.”
On what excites him about spatial econometrics, Prof Yang shares that it is the process of gaining knowledge and creation that motivates him to keep going.
“Of course, I am happy when my papers get accepted, recognised, or when they gain traction. But more importantly, what gives my work purpose is the fact that it has real policy implications and the potential to help policymakers understand the social phenomena more deeply,” he says.