Trade disruptions represent a second area of potential vulnerability. The Canadian, Ontarian, and GGH economies have become increasingly integrated with those of other countries, including through negotiated trade agreements, global supply chain management, and e-commerce. Although Canada has entered into new trade agreements, most recently the United States Mexico Canada (USMCA) agreement,[1] an integrated, global economy remains vulnerable to disruptions and crises around the world. Threats include possible trade wars, industry-specific tariff increases, political instability, or disruptions to transportation due to major climate change events.
As with automation, the impacts of potential trade disruptions will be uneven on the economy and the GGH's economic landscape. Not only would trade disruptions directly affect traded goods and services, but there would also be knock-on effects in other sectors. For example, shifting trade patterns could cause realignments of the geography of supply chains, with implications for warehousing and logistics facilities. And as with automation, any potential upside has not been quantified - for example, increasing trade uncertainty may lead to the reshoring of manufacturing, as producers reduce uncertainty by locating production closer to final markets.
Method
We drew on a 2017 analysis by Daniel Schwanen and Aaron Jacobs of the C.D. Howe Institute, The NAFTA constellation: Which Canadian industries are most vulnerable? Their analysis identified industries that would be most affected by a collapse of Canada-United States free trade. The analysis assumes that the higher the current level of trade, the greater the potential impacts.
We used the same indicator that they did to identify the industries that are most trade-dependent and therefore most vulnerable to trade disruptions: the share of an industry's jobs that rely directly on exports. In our case, we considered global exports, not just exports to the U.S., as trade disruptions could occur with any trading partners. We applied this indicator to Ontario data to identify the most vulnerable industries in the province,[2] and then quantified and mapped employment in those industries in the GGH.
Results
The industries identified as most vulnerable to trade disruptions, and their employment levels in the GGH are presented in Table 23.[3] We used a cut-off of 50 percent, that is, selecting those industries in which the share of employment directly relying on exports was 50 percent or greater.[4] We then mapped employment in these industries (Map 35).
Total employment in the most vulnerable industries in the GGH amounts to almost 200,000 jobs. Many are in manufacturing industries, especially the auto sector. Other vulnerable sectors are advanced manufacturing industries producing semi-conductors, computers and communications devices, and aerospace equipment. Disruption to trade would impact some of our most advanced, productive industries. Of service sector employment, only office administration and lessors of non-financial intangible assets (such as holders of patents, trademarks, brand names, etc.) are included.
Because vulnerability strongly affects manufacturing, the geography of employment vulnerable to trade disruptions reflects the manufacturing districts in the GGH. Auto manufacturing locations figure prominently, such as those in Guelph, Oakville, Alliston, Cambridge, and Oshawa. The three megazones are also highlighted, along with areas in Burlington and Scarborough. Other concentrated areas appear in the Meadowvale SKID and in Downsview, with its concentration of aerospace employment.
Table 23: Employment in industries with the highest share of jobs relying directly on exports, GGH, 2016
Sorted from highest to lowest vulnerability, based on share of jobs directly relying on exports | ||||
NAICS 2012 | Industry | Direct/ all (%) | Employment 2016 | |
3344 | Semiconductor and other electronic component manufacturing | 91.0 | 5,845 | |
3341 | Computer and peripheral equipment manufacturing | 82.2 | 2,795 | |
3342 | Communications equipment manufacturing | 80.9 | 4,145 | |
3361 | Motor vehicle manufacturing | 80.7 | 31,985 | |
3364 | Aerospace product and parts manufacturing | 79.1 | 13,150 | |
315 316 | Clothing manufacturing Leather and allied product manufacturing | 77.5 | 7,530 | |
3262 | Rubber product manufacturing | 76.1 | 2,700 | |
3339 | Other general-purpose machinery manufacturing | 72.0 | 9,425 | |
3314 | Non-ferrous metal (except aluminum) production and processing | 71.6 | 850 | |
3343 3345 3346 | Audio and video equipment manufacturing Navigational, measuring, medical, and control instruments manufacturing Manufacturing and reproducing magnetic and optical media | 71.0 | 6,910 | |
3399 | Other miscellaneous manufacturing | 65.6 | 14,280 | |
3333 | Commercial and service industry machinery manufacturing | 65.0 | 3,610 | |
3332 | Industrial machinery manufacturing | 63.2 | 4,280 | |
3326 | Spring and wire product manufacturing | 62.7 | 885 | |
3366 | Ship and boat building | 62.0 | 360 | |
3256 | Soap, cleaning compound, and toilet preparation manufacturing | 61.2 | 5,430 | |
313 314 | Textile mills Textile product mills | 60.9 | 4,115 | |
3221 | Pulp, paper and paperboard mills | 58.5 | 1,765 | |
3313 | Alumina and aluminum production and processing | 58.1 | 1,665 | |
3352 | Household appliance manufacturing | 57.2 | 1,280 | |
5611 | Office administrative services | 57.0 | 3,985 | |
3252 | Resin, synthetic rubber, artificial and synthetic fibres, and filaments manufacturing | 55.8 | 845 | |
3254 | Pharmaceutical and medicine manufacturing | 55.3 | 14,070 | |
3362 | Motor vehicle body and trailer manufacturing | 54.2 | 2,295 | |
3353 | Electrical equipment manufacturing | 54.2 | 5,325 | |
1114 | Greenhouse, nursery, and floriculture production | 53.5 | 6,445 | |
3363 | Motor vehicle parts manufacturing | 52.9 | 38,955 | |
533 | Lessors of non-financial intangible assets (except copyrighted works) | 51.3 | 465 | |
3336 | Engine, turbine, and power transmission equipment manufacturing | 50.4 | 1,595 | |
3117 | Seafood product preparation and packaging | 50.3 | 370 | |
TOTAL OF ABOVE |
| 197,355 | ||
ALL GTA INDUSTRIES |
| 3,710,915 | ||
Overall, vulnerable employment is distributed across the region. Some municipalities have higher concentrations of vulnerable industries, including Cambridge, Guelph, Milton, Oakville, Caledon, Newmarket, and Vaughan (see Table 24).
Some industries appear in both of the most-vulnerable lists, notably auto-related manufacturing and assembly. This industry also has a high level of employment in the GGH. Similarly, some municipalities are at the top of both lists, suggesting heightened vulnerability - including New Tecumseth, Cambridge, Guelph, Caledon, and Vaughan. In the following chapter we turn to land use strategies to address these vulnerabilities.
Table 24: Employment in industries most vulnerable to trade disruption as a share of total industry employment, municipalities with over 10,000 jobs, GGH, 2016
Employment in industries in which >50% of jobs rely directly on exports, sorted from highest to lowest vulnerability | |||
| Employment in Most Vulnerable Industries | Total Employment | % of Total Employment |
Greater Golden Horseshoe | 197,355 | 3,710,915 | 5.3 |
New Tecumseth | 6,660 | 16,515 | 40.3 |
Cambridge | 11,705 | 62,130 | 18.8 |
Guelph | 12,770 | 69,670 | 18.3 |
Milton | 3,180 | 30,490 | 10.4 |
Oakville | 8,295 | 81,240 | 10.2 |
Caledon | 1,910 | 19,770 | 9.7 |
Newmarket | 2,915 | 35,220 | 8.3 |
Vaughan | 12,845 | 158,280 | 8.1 |
Ajax | 2,045 | 25,500 | 8.0 |
Burlington | 5,865 | 78,665 | 7.5 |
Oshawa | 3,465 | 48,340 | 7.2 |
Brampton | 10,915 | 156,125 | 7.0 |
Woolwich | 860 | 12,540 | 6.9 |
Halton Hills | 1,205 | 17,845 | 6.8 |
Aurora | 1,455 | 22,355 | 6.5 |
Markham | 7,535 | 127,400 | 5.9 |
St. Catharines | 2,970 | 51,275 | 5.8 |
Brantford | 2,100 | 36,910 | 5.7 |
Brant | 595 | 10,820 | 5.5 |
Mississauga | 20,955 | 394,660 | 5.3 |
Whitby | 1,900 | 37,060 | 5.1 |
Niagara-on-the-Lake | 520 | 10,240 | 5.1 |
Pickering | 1,475 | 29,515 | 5.0 |
Richmond Hill | 2,620 | 54,890 | 4.8 |
Kitchener | 3,345 | 83,365 | 4.0 |
Haldimand County | 500 | 12,955 | 3.9 |
Waterloo | 2,180 | 59,025 | 3.7 |
Peterborough | 1,385 | 38,435 | 3.6 |
Clarington | 715 | 20,295 | 3.5 |
Whitchurch-Stouffville | 350 | 10,155 | 3.4 |
Hamilton | 5,985 | 187,500 | 3.2 |
Barrie | 1,660 | 56,110 | 3.0 |
Toronto | 38,930 | 1,342,435 | 2.9 |
Welland | 390 | 14,780 | 2.6 |
Kawartha Lakes | 450 | 17,425 | 2.6 |
Orangeville | 250 | 10,930 | 2.3 |
Niagara Falls | 720 | 35,560 | 2.0 |
Orillia | 270 | 15,015 | 1.8 |
Map 35: Employment in Industries with Highest Vulnerability to Trade Disruption, GGH, 2016
[1] At the time of publication (late October 2018), this agreement had not been ratified.
[2] Data for this indicator not available sub-provincially or for the GGH.
[3] In the Table, the share of jobs relying directly on exports is calculated based on Ontario data; these data are not available at the sub-provincial level. In some cases there is a single percentage number for a single or group of 3-digit industries; this is because the source data uses a different industry classification that treats these as one group and it is not possible to break out the shares at the 4-digit NAICS level. In some cases, the source data were available only at a 5-digit level equivalent, so we have aggregated to obtain a percentage value at the 4-digit level. This is the case for NAICS 3361 and 3363, but all of the 5-digit categories are captured in the 4-digit data. Employment figures are for the GGH.
[4] The aim of both automation and trade vulnerability analyses was to identify the most vulnerable industries and jobs. We used a cut-off of 50 percent or more for trade, resulting in almost 200,000 jobs. Using a cut-off of 60 percent or more for automation resulted in close to 700,000 jobs identified. So the vulnerability scale differs between the two factors. Had we used a 50 percent cut-off for automation, we would be including over 300,000 additional jobs, for a total of in excess of 1,00,000; a total that would not highlight the most vulnerable jobs.