This highlight showcases the preliminary results from the application of the previously outlined machine learning-based methodology for defining industrial clusters in Québec. The novel approach introduced by Lucien Chaffa and Thierry Warin uses geospatial data on industries from the Registre des Entreprises du Québec (REQ) to quantitively define and identify clusters.

The authors first derive industry growth rates to group industries based on their co-movement over time. Growth rate is defined as the rate at which firms enter (and exit) an industry between 1990 and 2022. From a total of 328 industries classified using 3-digit CAE codes, 190 industries are selected for their variability in size. In this context, industry size refers to the number of firms within it, with a cutoff set at 50 firms.

The strength of inter-industry correlations is assessed using the correlation matrix shown below. Similarities in growth trajectories reveal the shared effects of economic forces, which could be explained by common supply chains, complementary market demands or usage of similar technology.

Unsupervised machine learning algorithms are applied to the natural groupings of industries derived from the correlation matrix to group industries with the most similar growth patterns. The perimeter of each group is defined by the strength of inter-industry linkages, as captured by the correlation of growth rates among the industries within each group.

To illustrate this, the following example filters out industries to include only those with a correlation higher than 0.95 with at least one other industry. This results in 15 industries, with the network diagram below depicting the pattern of linkages between industries within this group. While these industries are likely to experience similar impacts from economic shocks and policy regulations, they may not belong to the same cluster in terms of their activities.

Community detection analysis is performed on the network graph above using the Louvain clustering algorithm to identify the most closely related clusters of industries within the general group. The algorithm does this by hierarchically measuring the difference between the average correlation of industries within a cluster and those outside of it, allowing for the assignment of nodes within each cluster.

This results in five distinct clusters, as shown in the network graph below. The size of each node is proportional to the industry it represents. Broadly speaking, these clusters can be classified as Farming & Livestock, Food & Retail, Services & Training, Professional Services, and Other Services. The clustering algorithm largely groups related industries, though a few seemingly unrelated industries are also included. While this may initially seem counterintuitive, hidden linkages that are not immediately apparent may also be captured.

Next, the geographic distribution of identified clusters is determined at the census division level (MRC in Québec). A cluster is assumed to be present in an MRC if the number of employees within it exceeds the 90th percentile of the employment distribution. The following interactive map shows the presence of these clusters. The size of each cluster is proportional to the average size of the industries within it. A concentration of clusters within a particular MRC suggests that firms within a cluster also benefit from agglomeration economies with those outside their own cluster.

By turning to the temporal aspect of the REQ data, the evolution of clusters can be analyzed over time. This is computed by tracking the annual entry and exit of firms in each cluster. The clusters seem to exhibit generally similar patterns of growth, stagnation and decline. However, the Food & Retail cluster grows at a faster pace than the others. In contrast, the Farming and Livestock cluster shows a flatter trajectory, likely due to the regulated nature of the industry and the inelasticity of its output.

Focusing on firm entries reveals that entries peaked across all identified clusters in the mid-1990s. Since then, the annual number of firms entering the market has remained relatively stable. The Food & Retail cluster shows greater variation relative to others, with between 2,000 and 2,500 annual entrants since the early 2000s. In contrast, the Other Services cluster has seen fewer entrants since 2011. The abrupt decrease in 2022 may be attributable to the cutoff in the data compiled from the REQ.

On the other hand, firm exits appear to have climbed sharply shortly after the peak in firm entries during the mid-1990s. While 2017 saw the highest number of closures across clusters, there was also a shorter peak in 2010. The numbers have since been trending back down toward their usual levels.

In essence, the preliminary results reveal the presence of emergent clusters that provide valuable insights into industrial distribution and inter-industry dynamics. The co-movement of individual industries within the identified clusters underscores their significance to the regional economy and highlights the necessity for context-specific support to ensure their continued growth. Further investigation will focus on validating these results and exploring potential policy interventions that could strengthen these dynamically identified clusters and promote sustainable growth.

Cluster theory forms a key dimension of our Data Diamond model, but how does existing research measure up when defining the boundaries of a cluster as industries evolve? Building on foundational cluster theory, researchers Lucien Chaffa and Thierry Warin have developed a novel quantitative method to identify and dynamically redefine the boundaries of clusters and their constituent industries.

Academics have used various methods to operationalize Porter’s definition of a cluster—a concentration of interconnected businesses, suppliers, and associated institutions within a region—by relying on factors like co-location, shared inputs and similarities in employment and patents to measure industry connectivity. While these factors capture visible inter-industry linkages, unsupervised learning algorithms can uncover hidden structures and relationships that may not be immediately apparent.

Firm creation, previously identified as a measure of interconnectedness, is used by the authors to develop a metric for industry growth rates. Industry growth rate is an interesting metric, as it reflects the various economic and non-economic factors influencing an industry. The authors employ a k-means clustering algorithm, along with other unsupervised machine learning techniques, to group industries with similar growth patterns into clusters, while also defining dynamic cluster boundaries that indicate strong inter-industry interconnectedness. This approach captures shifts and co-movements in industry performance over time, transforming static cluster definitions into dynamic ones.

To test their methodology, the authors use firm-level data from the Registre des Entreprises du Québec (REQ), which is updated every fortnight, to calculate industry growth rates. By leveraging near real-time data, changes in an industry’s health and competitiveness are captured almost immediately. This approach stands in stark contrast to traditional cluster definition methods that rely on outdated cross-sectional datasets, which overlook the importance of considering nascent industries.

The granularity of this dataset extends across three dimensions: industrial classification, geographical location, and temporal attributes. This allows clusters to be identified at various levels of industrial classification while also considering the geography they occupy. The geospatial data of firms is particularly valuable for understanding clustering behaviour beyond political boundaries. Lastly, the temporal dimension of the dataset offers insights into the evolution of clusters over time.

To sum up, this machine learning-based methodology not only modernizes cluster theory but also paves the way for more dynamic, data-driven analyses of economic geography and regional competitiveness. For policymakers, this approach provides crucial insights into the health of regional industries, enabling informed and targeted interventions to enhance regional economic development.

The aerospace industry in Quebec is a major workhorse, accounting for over 50% of Canada’s production, with sales of $15.3 billion in 2018¹. The industry’s employment is also concentrated in Quebec, representing about 60% of the Canadian total². The performance of the industry can be explained by the presence of major local and international players in the Greater Montreal region. These firms engage in activities throughout the aerospace supply chain, making it a competitive global cluster, rivalling Seattle and Toulouse. In fact, the concentration of firms has resulted in unique competencies in technologies related to decarbonization, autonomous aircraft and safety³.

Leveraging these innovations, the Government of Quebec has declared the Greater Montreal region as an innovation zone. Named Espace Aéro, the zone comprises of three poles in Montreal, Longueuil and Mirabel. Furthermore, the government has announced an initial investment of $85 million out of a larger industry-led investment of $415 million in the industry. Of this, nearly $240 million comes from Boeing, wanting to establish its presence alongside Bombardier and Airbus⁴. A common thread in these investments is that they are earmarked for research and development to further the region’s competencies. Within the wider innovation ecosystem, the region’s universities and research centres also play a crucial role.

This highlight seeks to examine the aerospace industry in Quebec using data from the Registre des entreprises du Québec (REQ). This dataset provides near real-time information on firms, including the number of employees, domicile address, primary and secondary industrial classification, description of activities, and location of establishments.

We begin by identifying firms (having more than 1 employee) that were active during 2022 with a primary or secondary industrial classification of 3211 (Industrie des aéronefs et des pièces d’aéronefs). This results in 179 firms, which is lower than the 262 firms identified by Montréal International. This discrepancy can be attributed to the limitation of relying on industrial codes and excluding firms with no employees. For example, Thales Canada Inc., a major designer and integrator of avionics suites and vision systems, has operations in Montreal. However, its industry classification in the REQ is 9799 (Autres services personnels et domestiques), which falls outside the selection criteria.

These firms are then categorized based on the number of their employees into Micro, Small and Medium (SME), and Large enterprises. Mapping these firms, we can see that the industry is clearly concentrated in the Greater Montreal region. Furthermore, most of the SMEs and large enterprises are also located here.

To better understand the role played by each firm in the industry, we need to identify their activities and its position in the aerospace industry's supply chain. For most of the firms, an indication of the activities undertaken can be extracted from the description provided in the REQ. These activities are then used to assign firms to a specific stage in the supply chain.

The aerospace supply chain is globally dispersed with significant inter-firm collaboration at different levels of the supply chain. With the product life cycle in the industry spanning several decades, firms that produce aircrafts (OEMs) enter into partnerships with their suppliers to leverage their specialized technological expertise, freeing up their resources to focus on other stages of the supply chain. These suppliers produce and assemble components and sub-components required to manufacture aircrafts. Finally, firms that provide technical consulting and maintenance, repair and overhaul (MRO) services are also integral to the industry's supply chain.

The following interactive chart illustrates the stages in the aerospace industry's supply chain. Hover over each stage to get a more detailed account of the activities undertaken by constituent firms.

The following map provides a closer look at the concentration of firms in the Greater Montreal region. In addition to the features outlined in the previous map, hovering over a firm reveals its address, number of employees, position in the supply chain and a description of its activities. The airports in Montreal, Mirabel and Longueuil are also mapped to highlight the proximity of this industry to these assets. Examining the geographic spread of these firms, we can see that the large enterprises and SMEs are mostly located within the three poles of the Espace Aéro. In terms of their position in the supply chain, a majority of these firms are Tier 1, 2 and 3 suppliers. Finally, the smallest firms in the industry are scattered around the larger firms and mostly provide technical services to them.

The classification of firms to a specific stage of the supply chain was done in two steps: first by feeding the firm name and description into ChatGPT, and then by manually refining the results to ensure consistency.

In conclusion, Quebec's aerospace industry plays a pivotal role in Canada's economy and global competitiveness. Using the description of activities from the REQ allows for a closer analysis of the industry from a supply chain perspective, offering valuable insight into its structure and dynamics. These insights support policymakers and industry leaders in making strategic investments to drive further growth and sustainability, capitalizing on the industry's unique competencies in decarbonization and autonomous aircraft.

References

1. Investissement Québec (n.d.)

2. State of Canada's Aerospace Industry Report (2023)

3. Gouvernement du Québec (2024)

4. Gouvernement du Québec (2024)