Mons Chans uses big data to ask big questions

As an economist, Mons Chan is not one to shy away from big questions – the kind that animate countless think tanks, conferences, books and debates around the world. For example:

• Why does income equality exist?
• Are differences in wages due to workers’ education and skill levels, unions, the market power of companies, or government policies such as minimum wages?
• Why do some firms and countries grow faster than others?
• How do certain characteristics of workers affect a company’s productivity?
• How are all these factors interrelated, and how does their interplay influence national economies and international trade, and vice versa?

To answer such questions, you need lots of detailed data on companies and their employees. You need to know company revenues and profits, where firms are located, what they sell, and what their costs are. You need to know what workers earn, how much schooling they have, where they live, and more.

Fortunately, in parts of Europe – and particularly Denmark, where Chan spent a year after receiving his PhD from the University of Minnesota – such financial and demographic data are comparatively easy to come by, because the government collects it for tax and social security purposes. They also make it available to researchers and statistics wizards like Chan who can crunch the numbers to shed light on how the economy works and how government policies affect it.

Historically in North America, where taxes are collected differently, such fine-grained demographic data have tended to be either inaccessible or scattered across multiple government departments and agencies. The situation is improving, as Statistics Canada and other agencies are trying to put together Canadian versions of the Scandinavian datasets. This fall, when Chan starts teaching as an assistant professor at Queen’s Economics, he hopes to use some of them to examine productivity and trade in domestic industries such as dairy and softwood lumber.

Chan’s doctoral thesis examined the substitutability of different kinds of labor and different kinds of traded goods and services.

Let’s say you want to think about the effect of trade on wage inequality or productivity growth. To do that, you need to ask if it becomes cheaper to buy textiles from China or India, how the demand and wages for textile workers in Canada or the United States be affected? If tariffs rise or fall due to trade agreements, how do they affect the productivity of firms that buy textiles from these countries and the wages and well-being of the industry’s workers? Who wins as a result of all this? Who loses?

Another research theme Chan is interested in involves measuring productivity in huge global e-commerce firms like Amazon. Unlike, say, Apple, the insanely profitable high-tech behemoth that cranks out physical goods like iPhones and MacBooks, Amazon is primarily a service that connects buyers and sellers. At the same time, the two companies both employ workers, pay wages, and own assets such as warehouses and equipment.

Despite these similarities, Amazon’s business model makes it difficult for economists to measure its productivity, because it’s not possible to plot the number of gizmos it makes against the number of people it employs. “You can’t really put [Amazon’s] services into units the same way that you can iPhones or laptops,” says Chan. “This is a fairly new area of economics, because previous to the last five years or so we actually haven't really had good data on these kinds of firms.”

Ultimately, Chan hopes that his research will help companies and governments better understand how trade policies affect the risk of outsourcing for different workers, how labour market policies affect wages, productivity and welfare in different sectors, and how all of these things interconnect in determining how the economy grows in the future.