Doctoral Fellow develops methods to better understand regional recessions

sergei2Zooming-in without losing focus – understanding regional recessions and the importance of spatial interactions

By Sergei Shibaev, JDI Student Fellow, Queen’s University

Here is the scenario – you are an interested party (e.g. regional policy maker or researcher) in a small regional division in Canada (e.g. Central Okanagan Regional District of British Columbia).  You need to know if your region is likely to become economically at-risk or potentially distressed separately from the national economy, and to do so you require an informative assessment of any synchronicities (i.e. co-movements) with other regions in the country regarding how your small region’s economy has evolved in the last decade. Furthermore, you have existing knowledge regarding several types of connections to other regions that you know are important for your local economy (e.g. your largest regional trading partners), and you wish to explore and compare them through time. I develop and investigate a tool that is capable of learning by itself about these types of phenomena in a unified framework that collectively models a large number of small regions in a country.

Lack of suitable data is the usual culprit that prevents regional policy makers from receiving up-to-date evidence on the business cycle characteristics of their region. This problem prevails at the provincial level and is even more pronounced when the interest lies in a smaller regional division, such as a county, census division or regional district.

A question such as “How many recessions did the Central Okanagan Regional District experience in the last decade?” is likely to leave one scratching their head trying to think of the analog of the Gross Domestic Product (GDP) measure for a small region. As a result, monitoring fluctuations over the business cycle for small regions typically relies on employment data, which acts as the variable that moves with the business cycle. If interested in Central Okanagan, there is an abundance of tools at the disposal of economists to learn about economic downturns for a single specific area using regional employment data over time.

A cornerstone observation that motivates leaving behind case-by-case regional analysis – is that all regions that make up the national economy are connected (e.g. through trade of goods or assets). In this context, a more interesting question to pose is “Have there been any geographical concentrations (or clusters) of small regions in Canada, over the last decade, that have been impacted by recessions in similar ways, which have included Central Okanagan?” Answering this question provides evidence that enables regional policy makers to identify potential warning signs when neighbouring or distant regions, which have historically belonged to the same clusters, begin to show troubling signs of an economic downturn. The frontier of methods available to economists has made large strides towards being able to learn about the national economy to answer a question of this design. This has enabled analysts, researchers and policy makers to learn about an entire country by using a method that endogenously (i.e. within itself) groups small regions according to observed employment growth patterns and industrial labour concentrations over time.

This albeit powerful framework has great potential if empowered with the ability to understand how regions are connected in the economy. This is extremely relevant for regional policy work if it provides a quantifiable measure of spatial interactions with other regions in the economy. Continuing with the Okanagan example, a framework of this design can provide answers to question such as “What geographical or economic factors receive the highest degree of spatial interaction for Central Okanagan? Is it shared physical borders with neighbouring regions? Is it connections to Okanagan’s largest trading partners?” One can even ask – “Over the last several regional recessions, to which Central Okanagan belonged to, for which recession were the spatial spillovers at their highest?”

The tools I develop and investigate in my research will provide analysts, researchers and policy makers with answers to these types of questions by identifying geographical areas that would stand to benefit from industry-specific economic stimulus to prevent chronic economic distress. The results will shed light on areas that are economically at-risk to becoming either temporarily distressed or to entering a period of prolonged economic distress. All of these phenomena will be investigated in a unified framework that explores different types of connections between regions and identifies their magnitudes through time.