Manufacturing change and policy response in the contemporary economic landscape: how cities in Ontario, Canada, understand and plan for manufacturing

Figures & data

Table 1. Study area regions and cities.

Region	City	Population, 2016^a (type^b)	Manufacturing jobs, 2016^a	Manufacturing jobs, 2001^a	Percentage change, 2001–16
National Capital Region (NCR)	Brockville	21,346 (S)	1010	1990	−49%
	Cornwall	46,589 (S)	1965	4845	−59%
	Ottawa	934,243 (L)	15,670	35,275	−56%
Eastern Ontario	Kawartha Lakes	73,432 (S)	2780	4650	−38%
	Kingston	123,798 (M)	2295	3810	−40%
	Pembroke	13,882 (S)	240	500	−52%
	Peterborough	81,032 (M)	2730	4245	−36%
	Prince Edward County	24,753 (S)	835	1430	−42%
Greater Toronto Area (GTA)	Barrie	141,434 (M)	7705	9205	−16%
	Brampton	593,638 (L)	45,780	43,100	6%
	Cambridge	129,920 (M)	14,455	19,330	−25%
	Guelph	131,794 (M)	14,310	15,460	−7%
	Kitchener	233,222 (M)	21,370	28,155	−24%
	Markham	328,966 (M)	14,165	16,015	−5%
	Mississauga	721,599 (L)	43,080	61,780	−35%
	Orillia	31,166 (S)	1105	1550	−29%
	Oshawa	159,458 (M)	7950	15,070	−47%
	Pickering	91,771 (M)	3470	6216	−44%
	Toronto	2,731,571 (L)	106,385	186,870	−44%
	Vaughan	306,233 (M)	16,990	15,730	8%
	Waterloo	104,986 (M)	5930	9415	−37%
Golden Horseshoe	Brant	36,707 (S)	3095	3705	−16%
	Brantford	97,496 (M)	8900	12,220	−27%
	Burlington	183,314 (M)	9930	13,320	−25%
	Haldimand County	45,608 (S)	3255	4445	−27%
	Hamilton	536,917 (L)	31,550	49,005	−32%
	Niagara Falls	88,071 (M)	3285	5835	−44%
	Norfolk County	64,044 (S)	1140	1775	−36%
	Port Colborne	18,306 (S)	1125	1840	−39%
	St. Catherines	133,113 (M)	5760	10,875	−47%
	Welland	52,293 (S)	2340	5145	−55%
Southwestern Ontario	London	383,822 (L)	19,335	25,375	−24%
	Owen Sound	21,341 (S)	1130	1565	−28%
	Sarnia	71,594 (S)	3570	5150	−31%
	St. Thomas	38,909 (S)	3395	4955	−31%
	Stratford	31,465 (S)	3545	4900	−28%
	Windsor	217,188 (M)	19,655	29,145	−33%
	Woodstock	40,902 (S)	740	4570	10%
Northern Ontario	Dryden	7749 (S)	310	985	−69%
	Elliot Lake	10,741 (S)	115	165	−30%
	Greater Sudbury	161,531 (M)	3675	4865	−24%
	Kenora	15,096 (S)	335	720	−53%
	North Bay	51,553 (S)	1140	1775	−36%
	Sault Ste. Marie	73,368 (S)	3520	5290	−33%
	Temiskaming Shores	9920 (S)	315	470	−33%
	Thunder Bay	107,909 (M)	2850	5930	−52%
	Timmins	41,788 (S)	740	1255	−41%

Notes: Clarence-Rockland (National Capital Region – NCR), Belleville and Quinte West (Eastern Ontario), and Thorold (Southern Ontario) did not have official plans; additionally, Thorold was the only one of these four cities that experienced an increase in manufacturing employment (6%).

^a2001 and 2016 were selected as the Canadian census was conducted in these years.

^bS, small city, population < 75,000; M, mid-sized city, between 75,000 and 350,000; L, large city, > 350,000.

Figure 1. Study area (regions and cities).

Table 2. Chi-square results: manufacturing contextualization by city size.

	Small (n = 23)	Mid-sized (n = 19)	Large (n = 6)	Test result (χ^2)
Changes to the traditional economy	87%	79%	100%	1.71
Industry clusters	48%	89%	100%	11.56 *
Auto sector decline	17%	32%	0%	3.07
Labour issues around manufacturing	78%	89%	67%	1.82
Green industries	57%	74%	83%	2.24
Reusing industry facilities	35%	32%	33%	0.05
Primary industry	74%	32%	0%	13.78 *
Agri-business	74%	68%	33%	3.57
Creating an entrepreneurial environment	96%	95%	100%	0.32
Serviceable manufacturing	87%	79%	333%	1.40
Energy	78%	74%	83%	0.28
Increased investment into the tech and advanced manufacturing sector	87%	95%	100%	1.45
Infrastructure and logistics improvements	91%	89%	100%	0.67
Establishing and attending industry outreach programmes	96%	84%	100%	2.41

Note: *Significant at 0.05 (2 d.f.; CV = 5.99).

Table 3. Chi-square results: manufacturing conceptualization by region.

	National Capital Region (NCR) (n = 3)	Eastern Ontario (n = 5)	Greater Toronto Area (GTA) (n = 13)	Golden Horseshoe (n = 10)	Southwestern Ontario (n = 7)	Northern Ontario (n = 9)	Test result (χ^2)
Changes to the traditional economy	67%	80%	92%	90%	71%	100%	4.21
Industry clusters	33%	80%	100%	70%	71%	33%	12.33*
Auto sector decline	0%	0%	23%	20%	71%	0%	13.34*
Labour issues around manufacturing	67%	80%	85%	80%	100%	67%	3.04
Green industries	67%	60%	85%	80%	43%	44%	3.17
Reusing industry facilities	0%	40%	38%	40%	43%	22%	2.46
Primary industry	33%	20%	31%	40%	57%	100%	12.35*
Agri-business	33%	80%	62%	80%	71%	56%	3.07
Creating an entrepreneurial environment	100%	100%	100%	90%	100%	89%	2.81
Serviceable manufacturing	100%	60%	69%	100%	71%	89%	6.27
Energy	67%	60%	85%	70%	71%	89%	3.06
Increased investment into the tech and advanced manufacturing sector	100%	40%	100%	90%	100%	56%	17.36*
Infrastructure and logistics improvements	100%	60%	92%	100%	100%	89%	7.57
Establishing and attending industry outreach programmes	100%	60%	69%	100%	100%	100%	10.32

Note: *Significant at 0.05 (5 d.f.; CV = 11.070).

Table 4. Chi-square test results – manufacturing challenges by city sizes.

	Small (n = 23)	Mid-sized (n = 19)	Large (n = 6)	Test result (χ^2)
Manufacturing shift	48%	58%	67%	0.86
Aging workforce and labour shortages	52%	11%	17%	9.08 *
Lack of serviceable industrial lands	30%	37%	67%	2.67
Automation	17%	16%	17%	0.19
Globalized economy	65%	68%	67%	0.48
Competitive economy	70%	74%	67%	1.43
Lack of resources from higher levels of government	43%	21%	0%	5.36
Influx of workers after a large plant closure	17%	0%	0%	4.74
Changing community industrial image	17%	21%	17%	0.11
Local market base is too small	4%	0%	0%	1.11
Energy costs	22%	11%	33%	1.82

Note: *Significant at 0.05 (2 d.f.; CV = 5.99).

Table 5. Chi-square results – manufacturing challenges by region.

	National Capital Region (NCR) (n = 3)	Eastern Ontario (n = 5)	Greater Toronto Area (GTA) (n = 13)	Golden Horseshoe (n = 10)	Southwestern Ontario (n = 7)	Northern Ontario (n = 9)	Test result (χ^2)
Manufacturing shift	33%	20%	62%	70%	86%	33%	5.75
Aging workforce and labour shortages	100%	20%	8%	10%	43%	67%	14.25*
Lack of serviceable industrial lands	100%	40%	46%	60%	14%	0%	13.16*
Automation	0%	20%	23%	20%	14%	11%	1.19
Globalized economy	67%	20%	77%	60%	71%	78%	2.72
Competitive economy	67%	40%	77%	80%	71%	67%	2.72
Lack of resources from higher levels of government	0%	40%	23%	20%	43%	44%	3.42
Influx of workers after a large plant closure	0%	0%	0%	0%	14%	33%	10.17
Changing community industrial image	67%	0%	15%	0%	57%	11%	10.46
Local market base is too small	0%	0%	0%	0%	0%	11%	3.92
Energy costs	0%	0%	8%	30%	0%	56%	10.53

Note: *Significant at 0.05 (5 d.f.; CV = 11.070).

Table 6. Chi-square results: manufacturing sectors targeted by city size.

	Small (n = 23)	Mid-sized (n = 19)	Large (n = 6)	Test result (χ^2)
Energy sector	57%	47%	50%	0.36
High-tech industry	74%	89%	100%	3.19
Logistics and warehousing	48%	63%	67%	1.30
Health care	52%	53%	100%	4.90
Agri-business	70%	63%	33%	2.67
Forestry	39%	11%	0%	6.86 *
Mining	39%	16%	0%	5.31
Textiles	0%	11%	17%	3.24
Food and beverage production	61%	68%	83%	1.12
Aerospace	22%	26%	17%	0.28
Automotive	22%	21%	17%	0.08

Note: *Significant at 0.05 (2 d.f.; CV = 5.99).

Table 7. Chi-square results: manufacturing sectors targeted by region.

	National Capital Region (NCR) (n = 3)	Eastern Ontario (n = 5)	Greater Toronto Area (GTA) (n = 13)	Golden Horseshoe (n = 10)	Southwestern Ontario (n = 7)	Northern Ontario (n = 9)	Test result (χ^2)
Energy sector	67%	0%	62%	50%	43%	78%	9.94
High-tech industry	100%	60%	92%	80%	100%	67%	5.89
Logistics and warehousing	67%	40%	69%	60%	57%	33%	3.19
Health care	67%	40%	69%	70%	57%	44%	2.45
Agri-business	67%	80%	54%	90%	57%	33%	6.61
Forestry	0%	20%	8%	10%	0%	89%	22.61*
Mining	0%	20%	8%	20%	0%	89%	24.93*
Textiles	0%	0%	23%	0%	0%	0%	7.62
Food and beverage production	100%	60%	85%	90%	57%	11%	16.84*
Aerospace	33%	20%	54%	10%	14%	0%	12.59*
Automotive	0%	0%	23%	10%	71%	0%	15.51*

Note: *Significant at 0.05 (5 d.f.; CV = 11.070).

Table 8. Chi-square results: policies and strategies by city size.

	Small (n = 23)	Mid-sized (n = 19)	Large (n = 6)	Test result (χ^2)
Pair local education institutes with industry	87%	95%	100%	1.44
Pair arts and culture with industry	48%	47%	33%	0.43
Improve industry supply chains	57%	53%	67%	0.37
Reuse of existing manufacturing facilities	35%	32%	33%	0.05
Utilize technology for local industry	70%	79%	83%	0.74
Partnering with other advanced manufacturing networks	61%	63%	83%	1.08
Increased investment and creation of advanced manufacturing ecosystems	70%	74%	83%	0.47
Labour retraining for advanced manufacturing	39%	37%	83%	4.39
Increase public perception for manufacturing sector	39%	21%	17%	2.16
Diversify economy in areas with predominantly manufacturing	87%	58%	33%	8.09 *
Targeted marketing towards manufacturing firms	52%	47%	17%	2.45
Using existing material industry for value added production	52%	32%	0%	5.99 *

Note: *Significant at 0.05 (2 d.f.; CV = 5.99).

Table 9. Chi-square test results: policies and strategies of Ontario cities by region.

	National Capital Region (NCR) (n = 3)	Eastern Ontario (n = 5)	Greater Toronto Area (GTA) (n = 13)	Golden Horseshoe (n = 10)	Southwestern Ontario (n = 7)	Northern Ontario (n = 9)	Test result (χ^2)
Pair local education institutes with industry	100%	100%	100%	70%	86%	100%	8.21
Pair arts/culture community with industry	67%	40%	62%	20%	71%	33%	6.31
Improve industry supply chains	67%	60%	54%	50%	71%	56%	0.9
Reuse of existing manufacturing facilities	0%	40%	38%	40%	43%	22%	2.46
Utilize technology for local industry	67%	40%	85%	70%	100%	78%	6.14
Partnering with other advanced manufacturing networks	67%	20%	85%	60%	86%	44%	11.25*
Increased investment and creation of advanced manufacturing ecosystems	67%	60%	92%	50%	86%	67%	5.71
Labour retraining for advanced manufacturing	33%	20%	62%	30%	86%	22%	11.72*
Increase public perception for manufacturing sector	33%	20%	15%	20%	43%	44%	3.22
Diversify economy in areas with predominantly manufacturing	100%	60%	38%	60%	86%	100%	13.07*
Targeted marketing toward manufacturing firms	33%	20%	31%	50%	71%	67%	5.57
Using existing material industry for value added production	0%	0%	23%	30%	57%	89%	15.76*

Note: *Significant at 0.05 (5 d.f.; CV = 11.070).