Economic Insights
The Effect of Labour Demand on Regional Demographics

by René Morissette
Social Analysis and Modelling Division

Release date: January 24, 2018

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This article in the Economic Insights series assesses the degree to which changes in labour demand affect the working-age population and the regional demographic dependency ratio, based on a range of administrative data and Statistics Canada’s population estimates. The results suggest that over periods of seven years, a 5.0% decline in regional labour demand reduces the regional population aged 15 to 64 by 4.5% to 6.0%. Because working-age individuals are leaving economically declining regions, a 5.0% decline in labour demand raises the demographic dependency ratio (the number of youth and seniors divided by the number of individuals aged 15 to 64) by between 1.1 and 1.5 percentage points, from a baseline rate of roughly 50.0%.

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Introduction

When the demand for labour falls within a local region, what happens to the size of the working-age population and the demographic dependency ratio in that area? Simple models of labour supply and demand suggest that populations will decline as working-age individuals leave to find employment elsewhere, and that the demographic dependency ratio (the number of youth and seniors divided by the number of individuals aged 15 to 64) will rise. An increase in labour demand is expected to have the opposite effects—drawing in working-age individuals and their families. While this theory provides guidance on the expected direction of change, the magnitude of such a change is not well understood.Note 1 If regional labour demand decreases by, for example, 5.0%, how large a decline in the working-age population and how significant an increase in the demographic dependency ratio might be observed? The goal of this paper is to answer this question.

Using administrative data, the study quantifies demographic changes observed in 76 economic regions that were characterized by different trends in labour demand from 2001 to 2015. The study takes advantage of the substantial differences in employment growth across economic regions. For example, from 2001 to 2008, the 8 economic regions in Alberta experienced paid employment growth that averaged about 19.0%—more than three times the amount registered in the economic regions of Quebec and Ontario (Chart 1).Note 2 From 2008 to 2015, the 14 economic regions in Newfoundland and Labrador, Nova Scotia and New Brunswick saw paid employment decline by 4.0% or more, while the 24 economic regions in Manitoba, Saskatchewan and Alberta posted gains averaging 2.0% or more. The study uses this spatial variation in paid employment growth to measure the parameters of interest.

Data table for Chart 1
Data table for Chart 1
Table summary
This table displays the results of Data table for Chart 1 2001 to 2008 and 2008 to 2015, calculated using average paid employment growth (percent) units of measure (appearing as column headers).
2001 to 2008 2008 to 2015
percent
Nunavut 16.4 11.8
Northwest Territories 5.9 0.3
Yukon 12.5 7.8
British Columbia 7.3 0.5
Alberta 19.3 6.1
Saskatchewan 11.1 2.3
Manitoba 6.3 3.8
Ontario 4.3 -1.0
Quebec 6.2 -0.5
New Brunswick 1.4 -4.0
Nova Scotia 2.1 -5.6
Prince Edward Island 2.6 -1.0
Newfoundland and Labrador 3.6 -6.0

Employment growth might be driven not only by changes in regional labour demand, but also by changes in regional labour supply. For example, if worker preferences for amenities change in a way that shifts labour supply towards regions with high job vacancy rates, employment in these regions will increase for reasons unrelated to labour demand. The challenge is to extract changes in labour demand from the observed employment growth.

To do so, this study uses variation in regional labour demand induced by national changes in the distribution of employment by industry. The underlying idea is simple: if, for example, factors external to a given region cause a decline in manufacturing employment nationwide, regions where employment was heavily concentrated in manufacturing at the beginning of the reference period should fare worse than other regions on various indicators. The regional employment growth that would have occurred if industries in a given region had grown at the same pace they grew nationally is used to extract information about regional changes in labour demand. Using this empirical strategy, the study quantifies the impact of changes in local labour demand on the size of the working-age population and the dependency ratio within economic regions (see the “Data and methods” section in the annex).Note 3 This informs discussions about labour mobility, population aging and the functioning of local labour markets.

Descriptive evidence

From 2001 to 2015, the population aged 15 to 64 grew at markedly different rates across economic regions. As a result of the oil boom of the 2000s, the number of individuals in this age group increased by 38.0% or more in the economic regions of Calgary, Edmonton, Red Deer and Wood Buffalo–Cold Lake (Table 1-2). In contrast, the population aged 15 to 64 fell by 10.0% or more in the following economic regions: South Coast–Burin Peninsula, West Coast–Northern Peninsula–Labrador, Notre Dame–Central Bonavista Bay, Cape Breton, Southern Nova Scotia, Campbellton–Miramichi, Edmunston–Woodstock, Gaspésie–Îles-de-la-Madeleine, Côte-Nord, Parklands, Cariboo, North Coast and Nechako (Tables 1-1 and 1-2).

The demographic dependency ratio also evolved differently, falling by about 2 to 10 percentage points in all economic regions in Saskatchewan but increasing in virtually all economic regions in Atlantic Canada, Quebec, and British Columbia.

Economic regions with strong growth in paid employment generally experienced higher-than-average population growth (Chart 2). For example, Wood Buffalo–Cold Lake saw its paid employment and its population aged 15 to 64  rise by about 38.0% and 49.0%, respectively, from 2001 to 2015 (Table 1-2), while about  twenty economic regions experienced declines on both measures. Across all 76 economic regions, paid employment and the population aged 15 to 64 grew by 7.9% and 7.3%, respectively, on average.Note 4

Table 1-1
Selected socio-economic indicators, by economic region, 2001 and 2015 — Newfoundland and Labrador to Ontario
Table summary
This table displays the results of Selected socio-economic indicators Population aged 15 to 64, Demographic dependency ratio, Growth in paid employment, 2001, 2015, Growth and Change, calculated using counts, percentage change, percent and percentage points units of measure (appearing as column headers).
Population aged 15 to 64 Demographic dependency ratio Growth in paid employment
2001 2015 Growth 2001 2015 Change
counts percentage change percent percentage points percentage change
Newfoundland and Labrador
Avalon Peninsula 176,242 192,564 9.3 40.4 44.2 3.9 15.6
South Coast–Burin Peninsula 31,853 23,671 -25.7 39.5 53.4 13.9 -14.3
West Coast–Northern Peninsula–Labrador 79,395 70,359 -11.4 41.7 50.4 8.7 -4.9
Notre Dame–Central Bonavista Bay 81,690 69,515 -14.9 44.1 56.5 12.4 -6.2
Prince Edward Island 91,415 96,576 5.6 49.5 51.9 2.4 1.6
Nova Scotia
Cape Breton 100,820 83,988 -16.7 50.0 57.9 7.9 -10.6
North Shore 108,361 98,248 -9.3 49.9 56.7 6.8 -7.2
Annapolis Valley 83,436 81,009 -2.9 49.1 53.9 4.8 2.0
Southern 83,704 72,049 -13.9 49.5 58.5 9.0 -9.1
Halifax 262,818 296,466 12.8 40.5 41.0 0.4 7.1
New Brunswick
Campbellton–Miramichi 122,486 99,541 -18.7 43.1 54.4 11.3 -12.3
Moncton–Richibucto 131,222 142,420 8.5 43.3 48.7 5.4 7.8
Saint John–St. Stephen 116,726 113,656 -2.6 47.9 50.5 2.7 -1.3
Fredericton–Oromocto 88,961 94,968 6.8 43.4 47.4 4.0 3.0
Edmundston–Woodstock 58,856 50,692 -13.9 46.5 53.2 6.7 -9.8
Quebec
Gaspésie–Îles-de-la-Madeleine 67,432 59,016 -12.5 46.2 56.5 10.2 -5.2
Bas-Saint-Laurent 139,817 127,051 -9.1 46.1 57.5 11.4 -1.2
Capitale-Nationale 460,690 487,351 5.8 41.4 50.4 9.0 10.0
Chaudière-Appalaches 270,939 273,968 1.1 44.3 54.2 10.0 6.1
Estrie 199,061 208,432 4.7 46.4 54.2 7.8 2.9
Centre-du-Québec 152,163 155,916 2.5 46.4 55.4 9.0 7.6
Montérégie 912,916 1,012,919 11.0 43.9 50.3 6.5 11.4
Montréal 1,280,726 1,374,565 7.3 44.5 44.8 0.3 2.6
Laval 240,391 284,037 18.2 45.7 49.8 4.1 14.9
Lanaudière 274,333 334,032 21.8 44.5 49.0 4.5 22.2
Laurentides 326,931 400,807 22.6 44.7 48.2 3.6 20.2
Outaouais 227,469 264,958 16.5 42.0 45.4 3.4 11.6
Abitibi-Témiscamingue 102,538 98,808 -3.6 44.9 49.8 4.9 6.7
Mauricie 177,646 171,627 -3.4 46.4 56.0 9.6 -0.6
Saguenay–Lac-Saint-Jean 199,030 181,707 -8.7 42.3 52.8 10.5 -2.1
Côte-Nord 70,931 62,941 -11.3 40.3 49.0 8.8 -10.0
Nord-du-Québec 26,007 29,460 13.3 51.2 52.0 0.7 1.9
Ontario
Ottawa 804,056 907,330 12.8 45.2 46.7 1.6 8.2
Kingston–Pembroke 293,707 307,847 4.8 50.5 52.7 2.2 2.8
Muskoka–Kawarthas 227,646 241,741 6.2 55.7 58.5 2.7 2.1
Toronto 3,588,880 4,480,826 24.9 43.8 43.5 -0.3 20.1
Kitchener–Waterloo–Barrie 740,125 893,520 20.7 48.3 47.1 -1.2 17.2
Hamilton–Niagara Peninsula 871,968 968,324 11.1 50.5 50.5 0.0 2.9
London 409,426 450,685 10.1 49.0 49.1 0.1 5.4
Windsor–Sarnia 424,900 419,948 -1.2 49.3 52.3 3.0 -4.4
Stratford–Bruce Peninsula 191,821 188,883 -1.5 55.1 59.6 4.6 -2.3
Northeast 386,300 367,240 -4.9 48.7 52.6 3.9 -2.9
Northwest 164,865 158,008 -4.2 49.5 51.2 1.7 -11.4
Table 1-2
Selected socio-economic indicators, by economic region, 2001 and 2015 — Manitoba to Nunavut
Table summary
This table displays the results of Selected socio-economic indicators Population aged 15 to 64, Demographic dependency ratio, Growth in paid employment, 2001, 2015, Growth and Change, calculated using counts, percentage change, percent and percentage points units of measure (appearing as column headers).
Population aged 15 to 64 Demographic dependency ratio Growth in paid employment
2001 2015 Growth 2001 2015 Change
counts percentage change percent percentage points percentage change
Manitoba
Southeast 57,942 74,080 27.9 53.7 55.2 1.5 36.0
South Central 32,987 40,741 23.5 62.6 60.8 -1.8 27.6
Southwest 66,856 73,701 10.2 58.3 55.0 -3.3 16.4
North Central 30,639 31,878 4.0 59.2 59.5 0.3 8.6
Winnipeg 431,647 496,516 15.0 48.1 45.7 -2.4 9.6
Interlake 55,718 60,665 8.9 52.2 53.3 1.1 4.2
Parklands 27,222 24,284 -10.8 66.9 68.2 1.4 -3.8
North 53,142 58,407 9.9 59.6 59.8 0.2 -12.6
Saskatchewan
Regina–Moose Mountain 183,520 221,157 20.5 50.8 47.3 -3.5 21.3
Swift Current–Moose Jaw 66,402 65,206 -1.8 60.1 57.5 -2.6 5.1
Saskatoon–Biggar 194,490 251,137 29.1 49.9 44.7 -5.3 29.2
Yorkton–Melville 54,062 51,784 -4.2 67.4 64.7 -2.8 7.7
Prince Albert 125,534 135,296 7.8 61.1 59.1 -1.9 12.6
Northern 19,403 25,112 29.4 69.2 58.9 -10.3 4.5
Alberta
Lethbridge–Medicine Hat 160,279 194,412 21.3 52.9 53.3 0.4 20.0
Camrose–Drumheller 119,535 134,485 12.5 56.3 54.5 -1.8 15.7
Calgary 749,758 1,098,808 46.6 39.9 40.1 0.1 37.7
Banff–Jasper–Rocky Mountain House 58,477 65,177 11.5 41.0 42.1 1.1 11.7
Red Deer 106,430 147,164 38.3 47.7 45.4 -2.3 32.6
Edmonton 700,861 990,270 41.3 43.0 41.4 -1.6 34.4
Athabasca–Grande Prairie–Peace River 151,519 185,388 22.4 50.6 49.0 -1.6 23.2
Wood Buffalo–Cold Lake 73,660 109,653 48.9 45.3 38.4 -6.9 37.9
British Columbia
Vancouver Island and Coast 479,848 512,319 6.8 49.5 55.1 5.6 11.9
Lower Mainland–Southwest 1,678,206 2,018,449 20.3 42.0 42.8 0.8 24.2
Thompson–Okanagan 316,733 346,922 9.5 53.1 56.7 3.6 22.3
Kootenay 102,100 94,993 -7.0 48.0 56.8 8.7 4.1
Cariboo 118,527 105,475 -11.0 41.2 48.5 7.3 -2.5
North Coast 45,480 38,849 -14.6 44.3 47.7 3.5 -10.5
Nechako 29,591 26,147 -11.6 46.2 51.0 4.8 -0.9
Northeast 43,865 48,548 10.7 44.5 43.8 -0.7 15.1
Yukon 22,176 26,988 21.7 36.0 38.6 2.6 21.2
Northwest Territories 28,332 31,704 11.9 44.2 39.6 -4.6 6.2
Nunavut 17,262 23,842 38.1 63.0 53.2 -9.8 30.1

Since strong population growth is positively correlated with growth in paid employment (Chart 2) and negatively correlated with changes in the demographic dependency ratio (Chart 3), economic regions that had strong growth in paid employment experienced smaller increases (or larger declines) in their demographic dependency ratio (Chart 4).

Data table for Chart 2
Data table for Chart 2
Table summary
This table displays the results of Data table for Chart 2. The information is grouped by Economic region (appearing as row headers), Grown in paid employment and Growth in the population aged 15 to 64, calculated using percentage change units of measure (appearing as column headers).
Economic region Grown in paid employment Growth in the population aged 15 to 64
percentage change
Avalon Peninsula 15.6 9.3
South Coast–Burin Peninsula -14.3 -25.7
West Coast–Northern Peninsula–Labrador -4.9 -11.4
Notre Dame–Central Bonavista Bay -6.2 -14.9
Prince Edward Island 1.6 5.6
Cape Breton -10.6 -16.7
North Shore -7.2 -9.3
Annapolis Valley 2.0 -2.9
Southern -9.1 -13.9
Halifax 7.1 12.8
Campbellton–Miramichi -12.3 -18.7
Moncton–Richibucto 7.8 8.5
Saint John–St. Stephen -1.3 -2.6
Fredericton–Oromocto 3.0 6.8
Edmundston–Woodstock -9.8 -13.9
Gaspésie–Îles-de-la-Madeleine -5.2 -12.5
Bas-Saint-Laurent -1.2 -9.1
Capitale-Nationale 10.0 5.8
Chaudière-Appalaches 6.1 1.1
Estrie 2.9 4.7
Centre-du-Québec 7.6 2.5
Montérégie 11.4 11.0
Montréal 2.6 7.3
Laval 14.9 18.2
Lanaudière 22.2 21.8
Laurentides 20.2 22.6
Outaouais 11.6 16.5
Abitibi-Témiscamingue 6.7 -3.6
Mauricie -0.6 -3.4
Saguenay–Lac-Saint-Jean -2.1 -8.7
Côte-Nord -10.0 -11.3
Nord-du-Québec 1.9 13.3
Ottawa 8.2 12.8
Kingston–Pembroke 2.8 4.8
Muskoka–Kawarthas 2.1 6.2
Toronto 20.1 24.9
Kitchener–Waterloo–Barrie 17.2 20.7
Hamilton–Niagara Peninsula 2.9 11.1
London 5.4 10.1
Windsor–Sarnia -4.4 -1.2
Stratford–Bruce Peninsula -2.3 -1.5
Northeast (Ontario) -2.9 -4.9
Northwest -11.4 -4.2
Southeast 36.0 27.9
South Central 27.6 23.5
Southwest 16.4 10.2
North Central 8.6 4.0
Winnipeg 9.6 15.0
Interlake 4.2 8.9
Parklands -3.8 -10.8
North -12.6 9.9
Regina–Moose Mountain 21.3 20.5
Swift Current–Moose Jaw 5.1 -1.8
Saskatoon–Biggar 29.2 29.1
Yorkton–Melville 7.7 -4.2
Prince Albert 12.6 7.8
Northern 4.5 29.4
Lethbridge–Medicine Hat 20.0 21.3
Camrose–Drumheller 15.7 12.5
Calgary 37.7 46.6
Banff–Jasper–Rocky Mountain House 11.7 11.5
Red Deer 32.6 38.3
Edmonton 34.4 41.3
Athabasca–Grande Prairie–Peace River 23.2 22.4
Wood Buffalo–Cold Lake 37.9 48.9
Vancouver Island and Coast 11.9 6.8
Lower Mainland–Southwest 24.2 20.3
Thompson–Okanagan 22.3 9.5
Kootenay 4.1 -7.0
Cariboo -2.5 -11.0
North Coast -10.5 -14.6
Nechako -0.9 -11.6
Northeast (British Columbia) 15.1 10.7
Yukon 21.2 21.7
Northwest Territories 6.2 11.9
Nunavut 30.1 38.1

Data table for Chart 3
Data table for Chart 3
Table summary
This table displays the results of Data table for Chart 3. The information is grouped by Economic region (appearing as row headers), Growth in the population aged 15 to 64 and Change in the demographic dependency ratio, calculated using percentage change and percentage points units of measure (appearing as column headers).
Economic region Growth in the population aged 15 to 64 Change in the demographic dependency ratio
percentage change percentage points
Avalon Peninsula 9.3 3.9
South Coast–Burin Peninsula -25.7 13.9
West Coast–Northern Peninsula–Labrador -11.4 8.7
Notre Dame–Central Bonavista Bay -14.9 12.4
Prince Edward Island 5.6 2.4
Cape Breton -16.7 7.9
North Shore -9.3 6.8
Annapolis Valley -2.9 4.8
Southern -13.9 9.0
Halifax 12.8 0.4
Campbellton–Miramichi -18.7 11.3
Moncton–Richibucto 8.5 5.4
Saint John–St. Stephen -2.6 2.7
Fredericton–Oromocto 6.8 4.0
Edmundston–Woodstock -13.9 6.7
Gaspésie–Îles-de-la-Madeleine -12.5 10.2
Bas-Saint-Laurent -9.1 11.4
Capitale-Nationale 5.8 9.0
Chaudière-Appalaches 1.1 10.0
Estrie 4.7 7.8
Centre-du-Québec 2.5 9.0
Montérégie 11.0 6.5
Montréal 7.3 0.3
Laval 18.2 4.1
Lanaudière 21.8 4.5
Laurentides 22.6 3.6
Outaouais 16.5 3.4
Abitibi-Témiscamingue -3.6 4.9
Mauricie -3.4 9.6
Saguenay–Lac-Saint-Jean -8.7 10.5
Côte-Nord -11.3 8.8
Nord-du-Québec 13.3 0.7
Ottawa 12.8 1.6
Kingston–Pembroke 4.8 2.2
Muskoka–Kawarthas 6.2 2.7
Toronto 24.9 -0.3
Kitchener–Waterloo–Barrie 20.7 -1.2
Hamilton–Niagara Peninsula 11.1 0.0
London 10.1 0.1
Windsor–Sarnia -1.2 3.0
Stratford–Bruce Peninsula -1.5 4.6
Northeast (Ontario) -4.9 3.9
Northwest -4.2 1.7
Southeast 27.9 1.5
South Central 23.5 -1.8
Southwest 10.2 -3.3
North Central 4.0 0.3
Winnipeg 15.0 -2.4
Interlake 8.9 1.1
Parklands -10.8 1.4
North 9.9 0.2
Regina–Moose Mountain 20.5 -3.5
Swift Current–Moose Jaw -1.8 -2.6
Saskatoon–Biggar 29.1 -5.3
Yorkton–Melville -4.2 -2.8
Prince Albert 7.8 -1.9
Northern 29.4 -10.3
Lethbridge–Medicine Hat 21.3 0.4
Camrose–Drumheller 12.5 -1.8
Calgary 46.6 0.1
Banff–Jasper–Rocky Mountain House 11.5 1.1
Red Deer 38.3 -2.3
Edmonton 41.3 -1.6
Athabasca–Grande Prairie–Peace River 22.4 -1.6
Wood Buffalo–Cold Lake 48.9 -6.9
Vancouver Island and Coast 6.8 5.6
Lower Mainland–Southwest 20.3 0.8
Thompson–Okanagan 9.5 3.6
Kootenay -7.0 8.7
Cariboo -11.0 7.3
North Coast -14.6 3.5
Nechako -11.6 4.8
Northeast (British Columbia) 10.7 -0.7
Yukon 21.7 2.6
Northwest Territories 11.9 -4.6
Nunavut 38.1 -9.8

Data table for Chart 4
Data table for Chart 4
Table summary
This table displays the results of Data table for Chart 4. The information is grouped by Economic region (appearing as row headers), Growth in paid employment and Change in the demographic dependency ratio, calculated using percentage change and percentage points units of measure (appearing as column headers).
Economic region Growth in paid employment Change in the demographic dependency ratio
percentage change percentage points
Avalon Peninsula 15.6 3.9
South Coast–Burin Peninsula -14.3 13.9
West Coast–Northern Peninsula–Labrador -4.9 8.7
Notre Dame–Central Bonavista Bay -6.2 12.4
Prince Edward Island 1.6 2.4
Cape Breton -10.6 7.9
North Shore -7.2 6.8
Annapolis Valley 2.0 4.8
Southern -9.1 9.0
Halifax 7.1 0.4
Campbellton–Miramichi -12.3 11.3
Moncton–Richibucto 7.8 5.4
Saint John–St. Stephen -1.3 2.7
Fredericton–Oromocto 3.0 4.0
Edmundston–Woodstock -9.8 6.7
Gaspésie–Îles-de-la-Madeleine -5.2 10.2
Bas-Saint-Laurent -1.2 11.4
Capitale-Nationale 10.0 9.0
Chaudière-Appalaches 6.1 10.0
Estrie 2.9 7.8
Centre-du-Québec 7.6 9.0
Montérégie 11.4 6.5
Montréal 2.6 0.3
Laval 14.9 4.1
Lanaudière 22.2 4.5
Laurentides 20.2 3.6
Outaouais 11.6 3.4
Abitibi-Témiscamingue 6.7 4.9
Mauricie -0.6 9.6
Saguenay–Lac-Saint-Jean -2.1 10.5
Côte-Nord -10.0 8.8
Nord-du-Québec 1.9 0.7
Ottawa 8.2 1.6
Kingston–Pembroke 2.8 2.2
Muskoka–Kawarthas 2.1 2.7
Toronto 20.1 -0.3
Kitchener–Waterloo–Barrie 17.2 -1.2
Hamilton–Niagara Peninsula 2.9 0.0
London 5.4 0.1
Windsor–Sarnia -4.4 3.0
Stratford–Bruce Peninsula -2.3 4.6
Northeast (Ontario) -2.9 3.9
Northwest -11.4 1.7
Southeast 36.0 1.5
South Central 27.6 -1.8
Southwest 16.4 -3.3
North Central 8.6 0.3
Winnipeg 9.6 -2.4
Interlake 4.2 1.1
Parklands -3.8 1.4
North -12.6 0.2
Regina–Moose Mountain 21.3 -3.5
Swift Current–Moose Jaw 5.1 -2.6
Saskatoon–Biggar 29.2 -5.3
Yorkton–Melville 7.7 -2.8
Prince Albert 12.6 -1.9
Northern 4.5 -10.3
Lethbridge–Medicine Hat 20.0 0.4
Camrose–Drumheller 15.7 -1.8
Calgary 37.7 0.1
Banff–Jasper–Rocky Mountain House 11.7 1.1
Red Deer 32.6 -2.3
Edmonton 34.4 -1.6
Athabasca–Grande Prairie–Peace River 23.2 -1.6
Wood Buffalo–Cold Lake 37.9 -6.9
Vancouver Island and Coast 11.9 5.6
Lower Mainland–Southwest 24.2 0.8
Thompson–Okanagan 22.3 3.6
Kootenay 4.1 8.7
Cariboo -2.5 7.3
North Coast -10.5 3.5
Nechako -0.9 4.8
Northeast (British Columbia) 15.1 -0.7
Yukon 21.2 2.6
Northwest Territories 6.2 -4.6
Nunavut 30.1 -9.8

While Charts 2 to 4 suggest that employment growth tends to increase the population and decrease the dependency ratio of a given region, they are subject to two limitations. First, these charts do not distinguish the degree to which employment growth at the regional level is driven by increases in labour demand, rather than increases in labour supply. Second, they display only bivariate relationships and thus do not control for other confounding factors. To overcome these limitations, multivariate analyses are required.

Multivariate analyses

The extent to which changes in labour demand affected the population aged 15 to 64 in regions from 2001 to 2015 is addressed in Table 2. Results are shown using ordinary least squares (OLS) and instrumental variable (IV) estimators. Unlike the OLS, the IV methods do not confound the effects of labour demand and labour supply when measuring the impact of changes in labour demand on socio-economic outcomes.Note 5 For this reason, the preferred multivariate analyses and the ensuing discussion are based on IV methods.

Two sets of regressions are considered regardless of the estimator. In the first set, changes in the logarithmic value of the population aged 15 to 64, measured over two seven-year periods (2001 to 2008 and 2008 to 2015), are regressed on a binary indicator for the 2008-to-2015 period and on changes in the logarithmic value of regional paid employment (as measured by the number of employees aged 15 to 64, estimated from the Canadian Employer–Employee Dynamics Database). Province indicators are added in the second set of regressions.Note 6Note 7

Whether province indicators are included or not, the elasticity of the population aged 15 to 64 with respect to labour demand—based on the IV estimator—varies between 0.9 and 1.2. This suggests that, on average, a 5.0% drop in regional labour demand reduces the population aged 15 to 64 by between 4.5% (5.0% times 0.9) and 6.0% in a given region over a seven-year period.

Since younger workers tend to be more mobile than older workers, they would be expected to leave economically declining regions or migrate to expanding regions at a faster pace than older workers. Table 2 confirms this hypothesis. It shows that for individuals under 35, the elasticity of population with respect to changes in labour demand varies between 1.4 and 1.5. The corresponding elasticity for individuals aged 35 to 64 varies between 0.3 and 1.0. Thus, while a 5.0% decline in labour demand tends to reduce the regional youth population by between 7.0% and 7.5%, the same decline in labour demand will reduce the population of older workers by between 1.5% and 5.0%.Note 8

Declines in regional labour demand reduce the working-age population, suggesting that such declines will also increase the regional demographic dependency ratio.

Table 2
Elasticity of regional population with respect to labour demand
Table summary
This table displays the results of Elasticity of regional population with respect to labour demand Ordinary least squares, Instrumental variables, Model 1 and Model 2, calculated using parameter estimates and number units of measure (appearing as column headers).
Ordinary least squares Instrumental variables
Model 1 Model 2
parameter estimates
Elasticity of
Population aged 15 to 64
Not controlling for province of residence 0.97Note *** 1.17Note *** 1.16Note ***
Controlling for province of residence 0.78Note *** 0.90Note *** 0.87Note ***
Population aged 15 to 34
Not controlling for province of residence 1.19Note *** 1.41Note *** 1.40Note ***
Controlling for province of residence 0.96Note *** 1.53Note *** 1.53Note ***
Population aged 35 to 64
Not controlling for province of residence 0.82Note *** 0.95Note *** 0.94Note ***
Controlling for province of residence 0.63Note *** 0.37Note * 0.30
number
First-stage F statistic
Not controlling for province of residence Note ...: not applicable 37.8 31.6
Controlling for province of residence Note ...: not applicable 23.9 18.0
Number of observations 152 152 152

Table 3 confirms this hypothesis. Results from IV methods indicate that a 5.0% drop in regional labour demand will increase the demographic dependency ratio by between 1.1 percentage points (i.e., 5.0% times -0.22) and 1.5 percentage points (i.e., 5.0% times -0.29) from a baseline rate of 49.0% in 2001.Note 9Note 10 A 5.0% increase in regional labour demand is expected to decrease the demographic dependency ratio by the same amount. Table 3 also shows that most of the change in the demographic dependency ratio comes from changes in the ratio of the number of individuals over 64 years of age divided by the population aged 15 to 64, rather than from changes in the ratio of the number of children divided by the population aged 15 to 64.

Table 3
Changes in regional demographic dependency ratios and labour demand
Table summary
This table displays the results of Changes in regional demographic dependency ratios and labour demand Ordinary least squares, Instrumental variables, Model 1 and Model 2, calculated using parameter estimates and number units of measure (appearing as column headers).
Ordinary least squares Instrumental variables
Model 1 Model 2
parameter estimates
Population under 15 and over 64 divided by the population 15 to 64
Not controlling for province of residence -0.11Note *** -0.28Note *** -0.29Note ***
Controlling for province of residence -0.11Note *** -0.22Note *** -0.24Note ***
Population over 64 divided by the population 15 to 64
Not controlling for province of residence -0.11Note *** -0.19Note ** -0.20Note **
Controlling for province of residence -0.08Note *** -0.13Note *** -0.14Note ***
Population under 15 divided by the population 15 to 64
Not controlling for province of residence -0.01 -0.09Note * -0.09Note *
Controlling for province of residence -0.03Note * -0.09Note ** -0.10Note *
number
First-stage F statistic
Not controlling for province of residence Note ...: not applicable 25.3 21.5
Controlling for province of residence Note ...: not applicable 29.8 24.0
Number of observations 152 152 152

Conclusion

Although declines in regional labour demand are expected to reduce the working-age population and increase the dependency ratio in a given region, the magnitude of these effects had not yet been estimated in Canada.

Using administrative data and Statistics Canada’s population estimates, this study fills that gap. It shows that, over periods of seven years, a 5.0% decline in regional labour demand reduced the regional population aged 15 to 64 by 4.5% to 6.0%. Because working-age individuals are leaving economically declining regions, a 5.0% decline in labour demand raises the demographic dependency ratio by between 1.1 and 1.5 percentage points, from a baseline rate of roughly 50.0%.Note 11 Conversely, increases in labour demand—such as those witnessed in many economic regions of Alberta and Saskatchewan during the 2000s—tend to increase the working-age population and decrease the demographic dependency ratio.

In a context where population aging will pose a number of challenges, these results highlight the key role that employment growth may play to alter the demographics of regions.

Annex: Data and methods

This study combines data from the Canadian Employer–Employee Dynamics Database (CEEDD) and population estimates from the Demography Division to produce the estimates shown in Tables 1 to 3. The three following data sets from CEEDD are used: the T1 personal master file (T1PMF), the T4 Statement of Remuneration Paid (T4) and the Longitudinal Employment Analysis Program (LEAP). The 100% versions of T1PMF and T4 are used.

This paper examines changes in the working-age population and demographic dependency ratios across 76 economic regions (including Yukon, the Northwest Territories and Nunavut) over two periods of seven years (2001 to 2008 and 2008 to 2015).Note 12

Population estimates and demographic dependency ratios are obtained from the Demography Division of Statistics Canada. Three demographic dependency ratios are used. The first ratio is the number of individuals under 15 or over 64 years of age divided by the number of individuals aged 15 to 64. The second is the number of individuals over 64 divided by the number of individuals aged 15 to 64. The third is the number of individuals under 15 divided by the number of individuals aged 15 to 64.

To assess the impact of labour demand on population size, a two-step procedure is used. First, changes in the logarithmic value of log population size are constructed for 20 age–sex cells for each of the 76 economic regions.Note 13 Denoting age groups, sex and regions by a MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyyaaaa@36DD@ , s MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4Caaaa@36EF@ and r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOCaaaa@36EE@ , respectively, these changes ( Δ Y asr MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaam ywamaaBaaaleaaieGacaWFHbGaa83Caiaa=jhaaeqaaaaa@3B3A@ ) are regressed on a full set of age–sex interactions ( θ as MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeqiUde3aaS baaSqaaiaadggacaWGZbaabeaaaaa@39B6@ ), as well as on a vector of regional fixed effects ( θ r MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8hUdm aaBaaaleaacaWFYbGaa8hiaaqabaaaaa@38FD@ ):

Δ Y asr = θ as + θ r + ε asr . MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaam ywamaaBaaaleaaieGacaWFHbGaa83Caiaa=jhaaeqaaOGaa8hiaiaa =1dacaWFGaGaeqiUde3aaSbaaSqaaiaadggacaWGZbaabeaakiaa=b cacaWFRaGaa8hiaiaa=H7adaWgaaWcbaGaa8NCaiaa=bcaaeqaaOGa a83kaiaa=bcacqaH1oqzdaWgaaWcbaGaa8xyaiaa=nhacaWFYbaabe aakiaayIW7caaMi8UaaiOlaaaa@4FC9@

(1)

Equation (1) is estimated separately for each of the seven-year periods defined above.Note 14 The parameter estimates for θ r MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8hUdm aaBaaaleaacaWFYbGaa8hiaaqabaaaaa@38FD@ measure changes in log population size that are standardized for regional differences in the composition of the population by age and sex. In a second step, these parameter estimates are used to form the dependent variable in the following equation:

θ ^ rt = α p + α t +βΔln E rt + u rt ;t=20012008,20082015, MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGafqiUdeNbaK aadaWgaaWcbaGaamOCaiaadshaaeqaaOGaeyypa0JaeqySde2aaSba aSqaaiaadchaaeqaaOGaey4kaSIaeqySde2aaSbaaSqaaiaadshaae qaaOGaey4kaSIaeqOSdiMaeuiLdqKaciiBaiaac6gacaWGfbWaaSba aSqaaiaadkhacaWG0baabeaakiabgUcaRiaadwhadaWgaaWcbaGaam OCaiaadshaaeqaaOGaai4oaiaayIW7caaMi8UaaGjcVlaadshacqGH 9aqpcaaIYaGaaGimaiaaicdacaaIXaGaeyOeI0IaaGOmaiaaicdaca aIWaGaaGioaiaacYcacaaMi8UaaGjcVlaayIW7caaMi8UaaGOmaiaa icdacaaIWaGaaGioaiabgkHiTiaaikdacaaIWaGaaGymaiaaiwdaca aMi8UaaGjcVlaacYcaaaa@6DF1@

(2)

where α p MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeqySde2aaS baaSqaaiaadchaaeqaaaaa@38B6@ is a vector of province and territory indicators,Note 15 α t MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeqySde2aaS baaSqaaiaadshaaeqaaaaa@38BA@ is a binary indicator for the period from 2008 to 2015 (2001 to 2008 is omitted), and Δln E rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaci iBaiaac6gacaWGfbWaaSbaaSqaaiaadkhacaWG0baabeaaaaa@3C26@ measures changes in regional log paid employment.Note 16 Since there are 76 economic regions and two seven-year periods, Equation (2) is initially estimated using the ordinary least squares (OLS) estimator on 152 observations. The parameter β MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeqOSdigaaa@3798@ measures the elasticity of population size with respect to regional labour demand.

To account for the possibility that Δln E rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaci iBaiaac6gacaWGfbWaaSbaaSqaaiaadkhacaWG0baabeaaaaa@3C26@ might be correlated with the error term u rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyDamaaBa aaleaacaWGYbGaamiDaaqabaaaaa@390C@ , Equation (2) is also estimated using the instrumental variable (IV) estimator. When doing so, variable IV 1 rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8xsai aa=zfaieaacaGFXaWaaSbaaSqaaiaa=jhacaWF0bGaa8hiaaqabaaa aa@3B11@ is used as an instrumental variable for Δln E rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaci iBaiaac6gacaWGfbWaaSbaaSqaaiaadkhacaWG0baabeaaaaa@3C26@ :

IV 1 rt = i Shar e ir0 Δln E it ;t=20012008,20082015 , MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamysaiaadA facaaIXaWaaSbaaSqaaiaadkhacaWG0baabeaakiabg2da9maaqafa baGaam4uaiaadIgacaWGHbGaamOCaiaadwgadaWgaaWcbaGaamyAai aadkhacaaIWaaabeaakiabgEHiQiabfs5aejGacYgacaGGUbGaamyr amaaBaaaleaacaWGPbGaamiDaaqabaGccaaMi8UaaGjcVlaacUdaca aMi8UaaGjcVlaadshacqGH9aqpcaaIYaGaaGimaiaaicdacaaIXaGa eyOeI0IaaGOmaiaaicdacaaIWaGaaGioaiaacYcacaaMi8UaaGjcVl aayIW7caaIYaGaaGimaiaaicdacaaI4aGaeyOeI0IaaGOmaiaaicda caaIXaGaaGynaaWcbaGaamyAaaqab0GaeyyeIuoakiaayIW7caaMi8 Uaaiilaaaa@6CF2@

(3)

where Δln E rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaci iBaiaac6gacaWGfbWaaSbaaSqaaiaadkhacaWG0baabeaaaaa@3C26@ measures employment growth in industry i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyAaaaa@36E5@ nationwide during the seven-year period t MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiDaaaa@36EF@ (t=20012008,20082015) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaaiikaiaads hacqGH9aqpcaaIYaGaaGimaiaaicdacaaIXaGaeyOeI0IaaGOmaiaa icdacaaIWaGaaGioaiaacYcacaaMc8UaaGOmaiaaicdacaaIWaGaaG ioaiabgkHiTiaaikdacaaIWaGaaGymaiaaiwdacaGGPaaaaa@4922@  and Shar e ir0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4uaiaadI gacaWGHbGaamOCaiaadwgadaWgaaWcbaGaamyAaiaadkhacaaIWaaa beaaaaa@3D4D@  measures the share of industry i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyAaaaa@36E5@ in total paid employment of region r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOCaaaa@36EE@ at the beginning of the seven-year observation period that is considered.   IV 1 rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8xsai aa=zfaieaacaGFXaWaaSbaaSqaaiaa=jhacaWF0bGaa8hiaaqabaaa aa@3B11@ measures the predicted growth in paid employment that would occur in region r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOCaaaa@36EE@ if each industry had grown at the same pace regionally as it had grown nationally.Note 17 A second instrumental variable, IV 2 rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8xsai aa=zfaieaacaGFYaWaaSbaaSqaaiaa=jhacaWF0bGaa8hiaaqabaaa aa@3B12@ , is also used. When IV 2 rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8xsai aa=zfaieaacaGFYaWaaSbaaSqaaiaa=jhacaWF0bGaa8hiaaqabaaa aa@3B12@ is used, employment growth in industry i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamyAaaaa@36E5@ nationwide excludes employment growth in that industry in the economic region r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOCaaaa@36EE@ that is being considered.

In Tables 2 and 3, columns “Model 1” and “Model 2” report instrumental variable results based on IV 1 rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8xsai aa=zfaieaacaGFXaWaaSbaaSqaaiaa=jhacaWF0bGaa8hiaaqabaaa aa@3B11@ and IV 2 rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8xsai aa=zfaieaacaGFYaWaaSbaaSqaaiaa=jhacaWF0bGaa8hiaaqabaaa aa@3B12@ , respectively.

To analyze regional changes (measured in percentage points) in various demographic dependency ratios, ΔD R rt MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeuiLdqKaam iraiaadkfadaWgaaWcbaacbiGaa8NCaiaa=rhaaeqaaaaa@3B1B@ , the following equation is estimated with the OLS estimator and the IV estimator:

IV 1 rt = i Shar e ir0 Δln E it ;t=20012008,20082015 , MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamysaiaadA facaaIXaWaaSbaaSqaaiaadkhacaWG0baabeaakiabg2da9maaqafa baGaam4uaiaadIgacaWGHbGaamOCaiaadwgadaWgaaWcbaGaamyAai aadkhacaaIWaaabeaakiabgEHiQiabfs5aejGacYgacaGGUbGaamyr amaaBaaaleaacaWGPbGaamiDaaqabaGccaaMi8UaaGjcVlaacUdaca aMi8UaaGjcVlaadshacqGH9aqpcaaIYaGaaGimaiaaicdacaaIXaGa eyOeI0IaaGOmaiaaicdacaaIWaGaaGioaiaacYcacaaMi8UaaGjcVl aayIW7caaIYaGaaGimaiaaicdacaaI4aGaeyOeI0IaaGOmaiaaicda caaIXaGaaGynaaWcbaGaamyAaaqab0GaeyyeIuoakiaayIW7caaMi8 Uaaiilaaaa@6CF2@

(4)

where SHARE_08_1 4 r0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa83uai aa=HeacaWFbbGaa8Nuaiaa=veacaWFFbGaaGimaiaaiIdacaWFFbac baGaa4xmaiaa+rdadaWgaaWcbaGaa8NCaiaa+bdaaeqaaaaa@4072@ ( SHARE_58_6 4 r0 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa83uai aa=HeacaWFbbGaa8Nuaiaa=veacaWFFbacbaGaa4xnaiaa+HdacaWF FbGaa4Nnaiaa+rdadaWgaaWcbaGaa8NCaiaa+bdaaeqaaaaa@4068@ ) equals the share of the population in region r MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOCaaaa@36EE@ aged 8 to 14 or 58 to 64 at the beginning of a seven-year period, θ p MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaeqiUde3aaS baaSqaaiaadchaaeqaaaaa@38CD@ is a vector of province and territory indicators, and θ t MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaacbiGaa8hUdm aaBaaaleaacaWF0bGaa8hiaaqabaaaaa@38FF@ is a binary indicator for the period from 2008 to 2015.

Because economic regions are the unit of analysis in this study and because sampling variability issues do not arise when using the 100% versions of T1PMF and T4, Equations (1) to (4) are unweighted (i.e., estimated without population weights). In all cases, standard errors are clustered by economic region.

References

Bound, J., and H.J. Holzer. 2000. “Demand shifts, population adjustments, and labor market outcomes during the 1980s.” Journal of Labor Economics 18 (1): 20–54.

Marchand, J. 2012. “Local labor market impacts of energy boom-bust-boom in Western Canada.” Journal of Urban Economics 71 (1): 165–174.


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