Economic and Social Reports
Mapping the importance of urban and rural economies in Canada: Experimental grid square-based gross domestic product and gross domestic income

Abstract

This paper investigates the importance of urban and rural regional economies in Canada. Taking advantage of newly developed experimental measures of gross domestic product and gross domestic income at the scale of 1 kilometre by 1 kilometre grid squares, it provides a picture of the importance of urban and rural economies in 2019. It shows that 23.1% of Canada’s output is produced in rural areas, where 19.5% of employee compensation is received, with the remainder located in urbanized areas. It also shows that 60.6% of rural production occurs in areas that are relatively close to major markets, such as southern Ontario, central Alberta between Calgary and Edmonton, and the lower mainland of British Columbia.

Keywords: gross domestic product, gross domestic income, rural economies, urban economies

Authors

Mark Brown, Matthew Brown and Jesse Tweedle are with Economic Analysis Division, Analytical Studies and Modelling Branch, Statistics Canada. Jiang Li, is with Strategy, Research and Results Branch, Innovation, Science and Economic Development Canada. Authors’ names appear in alphabetical order.

Acknowledgments

This paper is funded through the Innovation, Science and Economic Development Canada Enhancing rural data accessibility and advancing rural data development and analysis project.

Introduction

The challenges facing rural communities are unique, including access to services, industry diversification and climate change, to name a few. These economic vulnerabilities can be further exacerbated by digitization, automation and the green transition, which drive structural economic change. To address persistent and emerging challenges in rural Canada and to help rural Canada seize new opportunities, an evidence-based approach is crucial for governments at all levels. Geographically disaggregated data provides additional perspective when developing evidence-based policy and programs.

One of the limitations is that existing data on standard economic measures lack geographic granularity. For example, the most granular data available for rural gross domestic product (GDP) is at the level of non-census metropolitan areas within each province (Statistics Canada, 2024). The lack of geographic granularity makes it very difficult to directly compare inherently heterogenous rural and urban economies. There are also practical challenges with mapping GDP and gross domestic income (GDI) in rural Canada (see Bemrose, Brown and Macdonald, 2023). Chief among them are the scale and boundary flexibility of standard geographies. Population-based census geographies can be difficult to repurpose for rural analysis because their geographic units are often too large and have boundaries that change over time, further complicating their analysis. As a result, analysts have turned increasingly to grid squares (e.g., 1 kilometre by 1 kilometre squares) to measure the location of economic activity (see, for example, Ru et al. 2023, Ghosh et al. 2010, and Nordhaus 2008, 2006). They provide a way to represent the economy that is independent of where people reside and, from a rural perspective, offer an opportunity to better represent where rural economic production takes place.

To this end, Statistics Canada has experimented with grid square-based measures of economic activity, ranging from firm counts to GDP (see Bemrose and Macdonald, 2022, and Bemrose, Brown and Macdonald, 2023). This paper pushes this work a step further to produce grid square-based GDP and GDI for all industries across Canada in 2019. These experimental estimates provide the foundation to measure the economic importance of urban and rural Canada

Indeed, this paper’s primary contribution is the development of geographically fine-grained measures of the economic importance of rural economies across Canada. This is accomplished by applying an international standard used to define urban and rural areas and defining whether those areas have relatively high or low proximity to markets. About 23.1% of Canada’s GDP is produced in rural areas, nearly matching their population share. However, most rural production occurs in areas with high market proximity^Note  (14.0% of Canada’s GDP) whose GDP share outpaces their population share (12.7%). In contrast, rural areas with low market proximity account for a smaller share of GDP (9.1%). Compared with their population share (10.1%), this suggests they are producing less than their population share would otherwise indicate.

The remainder of the paper is organized into three sections. The next section describes the data and methods used in the analysis, including the measurement of GDP and GDI by grid square and how urban and rural Canada is defined. This is followed by a discussion of the results, which describes the relative importance of urban and rural economies in Canada. The paper ends with a brief conclusion.

Data and methods

The analysis of the importance of rural and urban economies rests on developing geographically fine-grained economic measures and defining the rural and urban parts of Canada. This section discusses how these measures are developed and applied.

Gross domestic product and gross domestic income by grid square

From a data development perspective, this work extends Bemrose, Brown and Macdonald’s (2023) measurement of Yukon’s industry-level GDP by grid square to all of Canada. Across industries, economic output can be thought of as taking place at points, along lines and in areas. All three of these approaches will be used to allocate GDP to the grid.

• Points:In the business sector, this involves the direct measurement of GDP where business activity can be attributed to specific locations (e.g., manufacturing plants or retail stores) derived from tax data.^Note  However, for some business sector industries, such as mining, quarrying, and oil and gas extraction, GDP is allocated to specific points based on supplementary information (e.g., greenhouse gas emissions). Point information is also used for parts of the public sector to allocate GDP (e.g., using the location and square footage of federal buildings for the federal government public administration).
• Lines: For transportation industries and electrical utilities, GDP is spread along networks (e.g., the road transportation network or the electrical grid).
• Areas: For economic activity that is land intensive (e.g., agricultural production or forestry), GDP is spread across areas.

GDI is allocated using a different methodology, because it is based not on where output takes place, but on where income is used. However, in practice, this interpretation is more nuanced. Therefore, the key components of GDI—compensation of employees, gross mixed income (hereafter, mixed income), and gross operating surplus plus taxes less subsidies (hereafter, operating surplus)—must be spread accounting for these locational differences.^Note  Compensation of employees and mixed income are spread using the location of the worker or firm, based on address information derived from T1 tax data. Operating surplus is allocated based on the reporting location of the enterprise, typically its head office. In all instances, these locations can be considered points.

Lastly, both GDP and GDI will be used to measure the relative importance of urban and rural economies. Comparing them sheds light on the geospatial differences between where production takes place and where income is received. Production (i.e., GDP) takes place when capital and labour is combined to transform inputs into outputs. GDI is a measure of the ability of economic agents (e.g. firms, individuals) to transform the income they earn through production into the goods and services they need for consumption or investment.

At the national level, GDP and GDI add up to the same value. However, when broken down by subnational geographies, this is not the case. GDP can occur in factories and stores, on roads or transmission lines, and at remote workplaces such as mines, forests and fields. The income from these activities, and the decisions about whether to spend or save that income, accrues to economic agents such as individuals and firms at their home addresses or head offices. As a result, there is a difference between where GDP takes place and were GDI accrues to when fine geographies are used for analysis.^Note 

Workers, for instance, do not necessarily live in the same grid square, urban–rural class or province where they work. Operating surplus is allocated to the enterprise location where financial decisions are made (e.g., how much operating surplus is held as retained earnings and potentially invested, and how much is paid out in dividends). GDI does not measure where investments are made nor where dividends are distributed. It does, however, measure the income available at the locations where those decisions are made.

Throughout the analysis, emphasis will be placed on GDP since it captures where production takes place. GDI (or what might be more aptly named ‘regional GDI’) will also be selectively reported since it can help contrast where output is being generated and where incomes are accruing to, particularly labour compensation, where there is a clearer link between where income is being allocated and where people live.

Defining urban and rural areas

While the economy can be measured at a very detailed geographical scale, these data need to be aggregated in meaningful ways to provide a picture of rural and urban economies. Here, the urban–rural continuum is defined using the degree of urbanization (DEGURBA) methodology, developed by the European Union and endorsed by the United Nations Statistical Commission (see European Union, 2020, and Dijkstra et al., 2021). At its most aggregate (Level 1), DEGURBA classifies 1 kilometre by 1 kilometre grid squares into urban centres, urban clusters and rural areas (see Table 1).

• Urban centres consist of spatial clusters of grid squares with a population density of 1,500 people or more and have a population of at least 50,000.
• Urban clusters have a population density of at least 300 people per square kilometre and have a population of at least 5,000.
• Rural grid squares constitute the remainder of Canada that does not meet the density and population levels to qualify as an urban centre or cluster.

There are obvious parallels between DEGURBA and Statistics Canada’s definition of rural based on population centres, which is built from dissemination blocks and, like DEGURBA, uses population and population density to define population centres (see Table 1). Overall, DEGURBA will tend to have a higher rural population share, because of the higher population threshold used to define urban areas. This is indeed the case, with 17.8% of Canada’s population classified as rural based on the non-population centre definition (Statistics Canada, 2022) and 22.8% classified as rural based on the DEGURBA definition (see Table 2). For this study, DEGURBA was chosen because it is based on a similar 1 kilometre by 1 kilometre grid system, it provides a means to include gridded population data, and it is internationally comparable.

Economic importance of urban and rural Canada

Applying the degree of urbanization method to measure gross domestic product and gross domestic income

Map 1 illustrates the application of DEGURBA to the 1 kilometre by 1 kilometre grid used to measure GDP and GDI.^Note  As is apparent from the map, based on the classification, a very small percentage of Canada’s land mass used to produce GDP is urban (i.e., urban centres and urban clusters). Urban areas amount to 1.7% of Canada’s land mass used to produce GDP, with the remainder dedicated to rural production (see Table 2 below). As would be expected, major urban centres, such as Toronto, Montréal and Vancouver (see Map 1), are treated as urban centres, as well as many smaller urban areas such as Halifax or Thunder Bay. Urban clusters, which are far more numerous, can be broken down roughly into two types. Some form the suburban and exurban fringes of urban centres and might reasonably be thought of as extensions of these centres. Others are more isolated and form their own urbanized area, such as Truro, Nova Scotia, or Vernon, British Columbia.

Description for Map 1

Map 1 depicts a map of Canada with 1 kilometre by 1 kilometre grid squares used to measure gross domestic product (GDP), coloured by the degree of urbanization (DEGURBA) category that each grid square belongs to—rural, urban cluster or urban centre. Definitions for each DEGURBA category are provided in Table 1. The background of the map (i.e., area that does not contribute to gross domestic product) is light grey, and provincial and territorial borders are visible. Labels with place names are included at various city locations across the map. There is a map legend in the bottom right that provides a key for each DEGURBA category. Grid squares with the “rural” classification are green and cover the largest total area of any category. Grid squares with the “urban cluster” classification are light pink and exist as small to mid-sized towns or in peripheral regions of major cities. Grid squares with the “urban centre” classification are deep pink and cover areas of major cities across the country.

The map has two inset maps that zoom into specific areas in Canada. In the bottom left, there is an inset map showing the area containing Calgary and Edmonton. In the top right, there is an inset map that zooms in on the area between Ottawa and the city of Québec. Both inset maps include an extent indicator showing their precise zoom areas.

This map highlights that a small percentage of Canada’s land mass that is used to produce GDP is urban (i.e., urban centres and urban clusters). In fact, urban areas comprise around 1.7% of the total land area in the map, with the remainder being classified as rural. The largest area of rural production can be observed in the western provinces, particularly in Alberta and Saskatchewan. Both larger (e.g., Toronto, Vancouver) and smaller (e.g., Halifax, Thunder Bay) cities are treated as urban centres. Urban clusters, which are numerous compared with urban centres, consist of two types of areas. Some form the suburban or exurban edges of major cities (e.g., Flamborough, Ancaster or Winona, surrounding Hamilton), whereas others are more isolated and form their own urbanized areas, such as Truro, Nova Scotia. Overall, the map illustrates the significant geographic concentration of urbanized areas within Canada.

The key question being posed is how much economic output (GDP) and income (GDI) are located in rural areas. Rural regions, which account for 22.8% of Canada’s population, also account for 23.1% of GDP (see Table 2). In terms of GDI, rural areas receive 17.4% of GDI in total, which is driven down by the allocation of operating surplus to enterprise headquarters in urban centres. Still, in terms of compensation of employees, which is more strongly linked to where people live, rural areas account for 19.5%. In contrast, most of Canada’s output takes place in relatively dense urban centres, and most of its income is used and controlled in these urban centres. They account for 56.6% of Canada’s population and 60.5% of GDP and 65.0% of GDI. While this may reflect higher productivity on the part of workers in urban centres (see Beckstead et al., 2010), it may also reflect the concentration of jobs in urban centres that are, in part, filled by people living in nearby urban clusters and rural areas. Indeed, urban clusters account for 20.6% of Canada’s population but 16.3% of GDP. However, they do account for 21.3% of employee compensation.

Accounting for market access

While providing a geographically fine-grained, “bottom-up” definition of urban and rural areas, DEGURBA is limited in that it does not take into consideration location relative to markets. As a result, rural areas and urban clusters that are on the doorstep of major domestic markets (e.g., rural parts of southern Ontario) are treated the same as more remote rural areas (e.g., northern Ontario). Considering market proximity is important, because it is associated with higher income levels (see Hanson, 2005, and Beckstead et al.,2010)^Note  and firm location choice (see Head and Mayer, 2004) and, therefore, the GDP and GDI of regions.

To account for this, the market proximity of grid squares, or what has traditionally been called their market potential, is also measured (Harris, 1954; see also Fujita, Krugman and Venables, 2001). Based on the Harris (1954) formulation, the market proximity ($m_i$) of grid square $i$ is calculated as

$$m_i={\displaystyle \sum }_{j\in J, i\ne j}\frac{gd{p}_j}{d_{ij}^{\beta }},$$

where $gd{p}_j$ is the GDP of all grid squares $j\left(\in J\right)$ excluding grid square $i$ and $d_{ij}^{\beta }$ is the distance between $i$ and $j$ with the parameter $\beta =1$.^Note  Market proximity will be higher the closer the grid square is to grid squares with higher levels of GDP and lower the farther apart they are. Map 2 plots the market proximity for Canada, which is divided between those grid squares whose market proximity ranks in the top 5% (high proximity) and all other grid squares (low proximity). The market proximity of rural areas along the Windsor to Québec corridor, in Alberta between Calgary and Edmonton, and in the lower mainland of British Columbia and southern Vancouver Island is high. So too is that of rural areas on the outskirts of urban centres (e.g., Winnipeg).

Description for Map 2

Map 2 is a map of Canada depicting 1 kilometre by 1 kilometre grid squares used to measure gross domestic product, coloured by urban–rural market proximity. This map combines the degree of urbanization category with the relative proximity to markets (i.e., high or low) for each grid square. The background of the map (i.e., area that does not contribute to gross domestic product) is light grey, and provincial and territorial borders are visible. Labels with place names are included at various city locations across the map.

In the top middle of the map, there is a map legend. “Rural with high market proximity” grids are green and are located primarily in the Windsor to Québec corridor, in Alberta between Calgary and Edmonton, and in the lower mainland of British Columbia and southern Vancouver Island. “Rural with low market proximity” grid squares are light green and comprise most of the land in Canada used to produce GDP. These grid squares, which consist mainly of agricultural land, appear in high concentration in Saskatchewan, Alberta and Manitoba, and more sparsely in the other provinces and territories. “Urban cluster with high market proximity” grids are medium pink and are typically found in urban areas on the suburban or exurban fringe of major cities. “Urban cluster with low market proximity” grid squares are light pink and correspond to more isolated urban areas away from major cities. “Urban centre” grid squares are deep pink and correspond to the same areas as in Map 1, i.e., major cities.

The map features three inset maps, showing zoomed in areas of the map. The top right inset map shows southern Ontario, which entirely comprises high market proximity grid squares, regardless of their classification. Here, the Greater Toronto Area stands out as a major urban centre, along with London, Windsor and various other cities in the region. The bottom right inset map highlights the area in Nova Scotia from Truro to Halifax. Here, Truro is classified as urban with low market proximity and is separated from Halifax by a large area of “rural with low market proximity” grids. Halifax itself is classified as an urban centre, while its suburban fringes are largely classified as an urban cluster with high market proximity. The bottom left inset map shows the lower mainland of British Columbia, where the areas surrounding Vancouver, Nanaimo and Victoria all have high market proximity. Each inset map includes an extent indicator showing its precise zoom area.

Overall, this map highlights that despite the small land area that urban areas cover within Canada, their influence on surrounding areas via market proximity is quite significant, especially in the Windsor to Québec corridor, in Alberta between Calgary and Edmonton, and in the lower mainland of British Columbia and southern Vancouver Island.

With the DEGURBA and market proximity classifications determined, each grid square can be classified across them, resulting in six urban–rural classes. In practice, there are only five classes, since very few grid squares were classified as urban centres with low proximity. Consequently, this urban–rural class will not be reported, with results for urban centre grid squares reported instead (see Table 3).

Revealing patterns emerge when urban and rural areas are classified by their market proximity. For urban clusters, regardless of market proximity, their GDP share is less than their population share, but their labour compensation share about matches their population share. These tend to be places where people live but less so where output is produced. In rural areas with high market proximity, their GDP share outpaces their population share. Conversely, their share of employee compensation is less than their population share. These are places with relatively high levels of output but fewer people and less income. Rural areas with low market proximity account for about 10.1% of Canada’s population but 9.1% of GDP and 7.2% of employee compensation. Rural areas with low market proximity are the only urban–rural category for which the GDP and labour compensation shares are less than their population share. The overall pattern is one where output, relative to population, tends to be concentrated in urban centres and rural areas with high market proximity (typically proximity to urban centres).

Conclusions and discussion

Rural communities face challenges that differ from urbanized areas. In part, these challenges stem from the nature of their economies in terms of their location relative to markets. The analysis shows that about 23.1% of Canada’s output is produced in rural areas, with the remaining 76.9% produced in urbanized areas. It also shows that 60.6% of rural production occurs in areas that are relatively close to major markets, such as southern Ontario, central Alberta between Calgary and Edmonton, and the lower mainland of British Columbia. In remoter rural areas, GDP and labour compensation shares are less than would be expected given their populations.

This is the first snapshot of urban and rural economies derived from such geographically detailed measures of economic output. While it provides a reasonably accurate picture of these economies, improvements to the data will further refine this picture. From an analytical perspective, this work would be best viewed as a first step, where more insights will be gained by applying different definitions of urban and rural areas and by investigating their different and potentially complementary industrial structures, as well as how urban and rural economies evolve through time and respond to economic shocks.

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