Heartstrings and Economic Gravity

Heart-strings and Economic Gravity: Migration in Canada’s Local Health Regions

by

Ryan T. MacNeil

A research paper
presented to the University of Waterloo
in fulfillment of the
research paper requirement for the degree of
Master of Applied Environmental Studies
in
Local Economic Development

Waterloo, Ontario, Canada, 2006

© Ryan MacNeil 2006.

Heart-strings and Economic Gravity ii

AUTHOR'S DECLARATION

I hereby declare that I am the sole author of this research paper.

I authorize the University of Waterloo to lend this research paper to other institutions or
individuals for the purpose of scholarly research.

I further authorize the University of Waterloo to reproduce this research paper by
photocopying or by other means, in total or in part, at the request of other institutions or
individuals for the purpose of scholarly research.

Heart-strings and Economic Gravity iii

Abstract

This research seeks to extend the current understanding of migration behaviour in Canada. It
examines the effect of a community’s economic and social circumstances on its “migration
success”. Four migrant cohorts are considered: youth, young families, immigrants and older
migrants. The Canadian Community Health Survey (Statistics Canada, 2003) and Census
Profiles of Health Regions (Statistics Canada, 2005b) provide the independent variables. The
dependent variable (an estimate of the effect of regional net-migration) is calculated for each
cohort in each health region, using a formula derived from the cohort survival (Newkirk,
2002) and migration by residual (Goetz, 2005) methods. Pearson’s product moment
correlation coefficient is used to find relationships in the data. The results do not confirm
that social capital has an independent effect on migration. Instead, they reaffirm that
migration is primarily an economically-motivated behaviour. However, migration decisions
are clearly confounded by social considerations. Some migrants will trade off economic
wealth for social ties, but economic income provides a foundation for these “higher order”
needs. Community marketers can apply basic consumer behaviour principles to influence
migration decisions. While social capital cannot completely replace economic prosperity, it
can be a valuable tool for community economic development.

Heart-strings and Economic Gravity iv

Acknowledgements

This research paper should have been completed last August. But, as you will see, it has been
a self-reflective exercise in balancing multiple motivations.

In June, 2005, I was pleased to accept a position as a Development Officer at the Hants
Regional Development Authority in Windsor, Nova Scotia. The opportunity came at a time
when I should have been writing this paper, but I decided to put my money where my
mouth is and migrate back to Atlantic Canada. The RDA’s Executive Director, Amy
Melmock, and my colleagues Chantelle, Jacqueline, Karen, and Pat were very supportive as I
undertook this writing in the rare spare time our profession affords. Amy graciously provided
me with over a week of paid leave to write and present this paper (so that I might use
vacation time for a real vacation sometime soon).

This paper is the culmination of conversation and inspiration from Janet Larkman, Leslee
Fredericks, Karen Blotnicky, Rick Gilbert, and others. Thank you to my advisor, Paul Parker,
for his understanding of my extended timeline and for his own ex-patriot Bluenose
perspective. Thanks also to Peter Hall, Jean Andrey and my colleagues in the LED program
for ideas and perspectives that fed this work.

My family has been so supportive, despite their difficulty explaining what I do and what I
was writing about. I was delighted to delay this paper for a while when my niece, Emma
Catherine MacNeil-Comeau, was born on February 28, 2006. She has been one of my “other”
motivations.

But my favourite distraction has been Amanda, my love. Thank you for listening, reading,
calming me down, and kicking me in the “arse” when it came time to focus. Most of all,
thank you for teaching me to slow down and choose a more balanced life.

Heart-strings and Economic Gravity v

Table of Contents

Abstract .........................................................................................................................................iii

Acknowledgements ...................................................................................................................... iv

Table of Contents........................................................................................................................... v

List of Tables ................................................................................................................................ vii

List of Figures ..............................................................................................................................viii

Chapter 1 - Introduction ............................................................................................................... 1

Chapter 2 - Research Context ....................................................................................................... 6

Migration Theory: Cause & Effect ............................................................................................ 6

Migration in Canada .................................................................................................................. 9

Return Migration in Newfoundland....................................................................................... 15

Social Capital Theory and Migration...................................................................................... 18

Local Response Strategies ........................................................................................................ 20

Chapter 3 - Research Procedure ................................................................................................. 23

Data Sources ............................................................................................................................. 23

Population ................................................................................................................................ 24

Procedure ................................................................................................................................. 25

Limitations ............................................................................................................................... 34

Ethics Considerations .............................................................................................................. 36

Chapter 4 - Results....................................................................................................................... 37

Heart-strings and Economic Gravity vi

Cross Correlations.................................................................................................................... 37

Low Correlation Results .......................................................................................................... 39

Immigrants ............................................................................................................................... 40

Children (Families) .................................................................................................................. 42

Youth ........................................................................................................................................ 44

Older Migrants......................................................................................................................... 47

Chapter 5 - Conclusions .............................................................................................................. 52

Seducing Migrants ................................................................................................................... 52

The Role of Economics ............................................................................................................ 52

A Hierarchy of Needs .............................................................................................................. 55

The Role of Social Capital ....................................................................................................... 57

Works Cited ................................................................................................................................. 59

Appendix - Health Regions ......................................................................................................... 62

Heart-strings and Economic Gravity vii

List of Tables

Table 1. Typical Moorings of Everyday Life ................................................................................ 7

Table 2. Cohorts of Interest......................................................................................................... 27

Table 3. Social Capital Indicator Variables................................................................................. 28

Table 4. Economic and Market-size Variables ........................................................................... 31

Table 5. Pearson’s Product Moment Correlation Coefficient Interpretation........................... 34

Table 6. Participation in tennis, basketball and soccer is higher in wealthy urban regions (r-

values)................................................................................................................................... 38

Table 7. Variables with low correlation to migration success................................................... 39

Table 8. The Importance of Economics and Market-size for Four Migrant Cohorts. ............. 53

Heart-strings and Economic Gravity viii

List of Figures

Figure 1. Kotler, Haider and Rein (1993) summarize city decay dynamics. .............................. 3

Figure 2. Maslow’s Hierarchy of Needs........................................................................................ 9

Figure 3. Immigrant Concentration and Socio-economic Indicators (r-values). ..................... 41

Figure 4. Migration Success (Children – Families) and Socio-economic Indicators (r-values).

............................................................................................................................................... 43

Figure 5. Migration Success (Teenagers) and Socio-economic Indicators (r-values)............... 46

Figure 6. Migration Success (Early-twenty-somethings) and Socio-economic Indicators (r-

values)................................................................................................................................... 46

Figure 7. Migration Success (Late-twenty-somethings) and Socio-economic Indicators (r-

values)................................................................................................................................... 47

Figure 8. Migration success (Early-downshifters) and Socio-economic Indicators (r-values).48

Figure 9. Migration success (Late-downshifters) and Socio-economic Indicators (r-values).. 48

Figure 10. Migration Success (Preretirement) and Socio-economic Indicators (r-values)...... 49

Figure 11. Migration Success (Recently-retired) and Socio-economic Indicators (r-values).. 49

Heart-strings and Economic Gravity 1

Heart-strings and Economic Gravity: Migration in Canada’s Local Health Regions.

Chapter 1 - Introduction

Laughter is a pillar of the social construct on Canada’s east coast. Many self-

depreciating anecdotes begin with the line: “A New Brunswicker, a Nova Scotian and a

Newfoundlander were working on Bay Street in Toronto...” But there is a profound

dichotomy in these jokes. On one hand, the process of joke sharing and story telling is

representative of the tight-knit social fabric of Atlantic Canadian communities. On the other

hand, these jokes often reflect the exodus of Atlantic Canadians to “the big city.” In the

Atlantic region and beyond, there is a tug-of-war between economic reality and community

loyalty tearing apart Canada’s economically-depressed yet socially-vibrant communities.

How does this tug-of-war affect migration trends? Will individuals not gravitate to

the best economic opportunities over time? It is surprising to some that out-migration rates

are relatively high in the oil-rich Western Provinces and low in the fish-starved Atlantic

Provinces. Looker (2001) attributes this to what marketers might call brand loyalty – “the

low rates of out-migration in [the Atlantic] region reflect the strong ties [of residents]…to

their communities” (p. 28). Newfoundland and Labrador has some of the lowest rates of out-

migration (Looker, 2001 and House et al, 1990) and highest rates of return migration (House

et al, 1990) in the country. But it also has the highest unemployment rates and lowest

average earnings. In theory, these disadvantages should push “economically rational” people

away from depressed provinces. However, some sociologists are critical of labour market


theories of migration. House, White and Ripley (1990) studied two outport Newfoundland

communities and found evidence in support of the idea that social ties and lifestyle values

reduce out-migration and encourage return migration. Gmelch and Richling (1988) have also

attributed these trends to the social capital found in the island province’s small communities.

It has been suggested that such findings are applicable to small communities beyond

Newfoundland and Labrador (House et al, 1990).

While a wealth of non-economic factors give Newfoundland and Labrador some of

the lowest out-migration rates in the country, poor economic circumstances prevent in-

migrants from choosing the province. Paired with declining birth-rates and an aging

population, a lack of in-migration diminishes labour markets. Labour market shortages (and

skills shortages) in turn affect economic productivity. Bruce and Lister (2005, 6) explain that,

For many rural communities and regions in Atlantic Canada, falling population levels
have resulted in problems filling vacant job positions (especially in seasonal and
primary sector activities), and maintaining sufficient thresholds to support health and
education service provision.

These factors are tied together with many others in a circular decay that Myrdal calls

“cumulative causation”. Myrdal (1957) uses his cumulative causation theory to explain

regional economic inequality. He presents an example where a major employer burns down

and is not rebuilt in the community. The newly unemployed have less money to spend,

throwing more people out of work in local businesses. Kotler, Haider and Rein (1993)

include migration in their summary of Myrdal’s observation (see Figure 1). However they

limit their treatment of “city decay dynamics” to a steady flow of market factors.


Place Becomes Unattractive

1. Major company or industry is hurt or exits
2. Economic recession hurts business
3. Unemployment climbs
4. Infrastructure breaks down
5. City budget deficit increases

Tourism,
Outward Outward
Convention
Migration Migration
of People Business of Business
Fall Off

Banks Tighten Credit,
Bankruptcies increase,
Crime Increases,
Social Needs Rise,
City Image Deteriorates

Government Raises Taxes

Figure 1. Kotler, Haider and Rein (1993) summarize city decay dynamics.

Unfortunately there is a certain “economic reductionism” at play in many theories

that consider migration. Migration’s economic bias is the unfortunate by-product of labour

market models focused on supply, demand and price.

Migration is customarily conceptualized as a product of the material forces at work in
our society…the migrant is seen either as a “rational economic man” choosing
individual advancement by responding to the economic signals of the job and housing
markets, or as a virtual prisoner of his or her class position, and thereby subject to
powerful structural economic forces set in motion by the logic of capitalist
accumulations (Fielding, 1992, p 201).


Interdisciplinary dialogue (see Halfacree, 2004) is revealing that migration decisions are

indeed multi-dimensional. “Place” is not purely economic in the way it affects an individual’s

life. Geography plays a holistic socio-economic role in each individual’s biography. For

example, a young person’s migration decision could be motivated by a range of

considerations: expanded social networks (including greater choice in life partner), new

worldly experiences, and better economic opportunities. In some cases (e.g. the care of an ill

family member), social considerations drive the decision. In others (e.g. staggering

unemployment and isolation), economic needs trump social ones. It may be that migration

motivations are tiered, as in Maslow’s hierarchy of needs (Maslow, 1943). If so, individuals at

different needs levels can be expected to make different migration decisions. Regardless of

the decision criteria, a decision to migrate is not taken lightly. Therefore migration cannot

be reduced to a search for the best paycheque.

Much of Canada is now facing a labour shortage and a few provinces are developing

strategies to recruit, retain and repatriate talented workers. The most poignant example of

this work comes from New Brunswick’s Premier, Bernard Lord, who has traveled the

country, treating “economic refugees” to down-east hospitality, including smoked salmon

and Moosehead beer. “He admits his strategy here is as much to pluck heartstrings and praise

Maritime simplicity as it is to offer New Brunswickers specific economic incentives to move

home” (Brean, 2004, A2). Lord’s successful repatriation campaign lured 139 New

Brunswickers home in the first year. This strategy demonstrates that provinces and

communities can appeal to migrants’ broad set of human needs.


This research seeks to extend the current understanding of migration behaviour in

Canada. It asks three questions:

1.) How strongly do economic and social considerations weigh on the migration patterns

of youth, young families, immigrants, older migrants?

2.) How do a community’s social capital and economic circumstances determine its

success in the competition for migrating talent?

3.) How can economic developers appeal to multiple migration motivations?

Communities are becoming less focused on attracting industry and more interested in

attracting talent. But there is little academic literature to support the kinds of socio-cultural

appeals which are being used to encourage migration. Evidence of multiple migration

motivations could validate new tools for less advantaged regions.


Chapter 2 - Research Context

Migration Theory: Cause & Effect

Population decline is a key element in a domino process of economic decline for

struggling communities. There are two direct effects of population decline. First, a declining

population directly reduces demand in real estate markets: there is a depression in housing

sales and prices and rising vacancy rates in rental properties. Over a slightly longer time

frame, a declining population has considerable impact on labour supply. Businesses face

significant challenges when labour is unavailable to fill key positions. In turn, a struggling

business community and declining real estate market affect government revenues. An

eroding tax base threatens government capacity to provide necessary infrastructure and

social services. Education and health services can be lost in small communities where

population falls below demand thresholds. This is consistent with Myrdal’s (1957)

observation of cumulative causation, and with Kotler, Haider and Rein’s city decay dynamics

outlined in Chapter 1.

However, it has been argued that migration is more than an element in an economic

domino rally. There are decision criteria beyond employment and efficient public services

that weigh on a migration decision. Indeed, migration is also social and cultural. In volume

one of the important Migration Processes and Patterns series, Tony Fielding (1992, 201)

called for greater attention to the culture of migration:


There is something strange about the way we study migration. We know, often from
personal experience, but also from family talk, that moving from one place to another
is nearly always a major event. It is one of those events around which an individual’s
biography is built. The feelings associated with migration are usually complicated, the
decision to migrate is typically difficult to make, and the outcome usually involves
mixed emotions…Migration tends to expose one’s personality, it expresses one’s
loyalties and reveals one’s values and attachments (often previously hidden). It is a
statement of an individual’s world view, and is, therefore, an extremely cultural
event.

Halfacree (2004, 241) has also called for researchers to recognize “the multiple currents that

feed into the decision-making process”, to listen for migrants’ “multiple reasons, even if

entangled and often partial”, and to situate “migration inextricably within culture.” He

argues that the non-economic elements of migration are often overshadowed by “a narrow

economism.” Researchers cannot neglect “how culture is shaped by migration as well as how

migration is rooted in culture” (Halfacree, 2004, 241).

Sociologists and demographers have considered multiple migration motivations,

particularly in the study of specific migrant groups. At a macro-level, Moon’s theory of

“moorings” (1995, 515) attempts to summarize the many ways individuals define their well-

being and in turn become bound to a particular place (see Table 1).

Table 1. Typical Moorings of Everyday Life
Life-course
Household/family structure, Career opportunities, Household income, Educational
opportunities, Caring responsibilities
Cultural
Household wealth, Employment structure, Social networks, Cultural affiliation,
Ethnicity, Class structure, Socio-economic ideologies
Spatial
Climate, Access to social contacts, Access to cultural icons, Access to recreational places
Source: Moon, 1995, 515.


A unique combination of moorings becomes salient for each individual at a particular point

in their biography. For example, in some rural communities young people associate staying

in the community with failure (Bruce and Lister, 2005, 18). These youth develop “low

perceptions or expectations of their communities as places where they can lead a fulfilling

life” (Ibid., 17). There may be a difference between real opportunities for employment,

education and life experiences and the perception created by powerful socially and culturally

engendered attitudes. In this case socio-economic ideology can be as important a factor for

migration as a real dearth of opportunity. Another example can be found in research on

return migration. Here some moorings are stronger than others. In the case of return

migrants, the primary motivation “tends to be personal and family related rather than work

or economic related” (Ibid., 32).

Migration motivations can also be nested. Studies of immigration to Canada have

shown the importance of a critical mass of immigrants in attracting new immigrants. Bruce

and Lister (Ibid., 25) cite several studies that show “most, but not all immigrants choose their

destinations first based on the presence of kinship and ethnic networks, and then on

potential employment opportunities.”

Finally, migration motivations can be based on tiered needs, as in Maslow’s hierarchy

of needs (Maslow, 1943). Maslow argues that individuals seek to meet ‘basic needs’ and then

fulfill successively higher orders of needs. Figure 2 is a standard representation of this

hierarchy. Maslow’s hierarchy is used in many disciplines to explain human behaviour.

However, the specifics of his theory have been heavily critiqued. Wahba and Bridwell


(1976), for example, reviewed literature testing Maslow’s hierarchy. They were unable to

find evidence to support Maslow’s classification or ranking of needs. They add that, “theories

of self-actualization, particularly that of Maslow, suffer from vagueness in concept, looseness

in language, and lack of adequate empirical evidence” (Wahba and Bridwell, 1976, 233).

They corroborate the idea of a needs hierarchy, or a tiering of needs, but not in the specific

way Maslow has proposed.

5 Actualization

4 Esteem

3 Love/Belonging

2 Safety/Security

1 Physiological

Figure 2. Maslow’s Hierarchy of Needs.

The research outlined in this section can be applied to better understand the

motivations behind migration decision making. Unfortunately, these insights are not

influencing Canada’s economic development policies.

Migration in Canada

The greatest challenge to endogenous development in any community is the out-

migration of talent. Innovation and entrepreneurship are human activities that require


creative people. Governments and communities in Canada became acutely aware of this new

economy challenge when academics began sounding the “brain drain” alarm. But as Diane

Looker points out, “much of the [brain drain] research focuses on the issue (the “problem”) of

youth out-migration from rural areas…there is little information on in-migrants” (2001, 29).

As has been noted for Newfoundland, the real problem of migration is not that some

communities have significantly higher rates of out-migration but that they have significantly

lower rates of in-migration and return migration (Dupuy, Mayer and Morissette, 2000).

Researchers fall into a trap when they do not dissect the elements of net migration.

There is a volume of existing research that examines the geographic and economic

patterns of interprovincial migration in Canada (Finnie, 1998, 2000 and 2004; MPHEC, 2002;

Looker, 2001; Dupuy et al., 2000; R.A. Malatest & Associates Ltd., 2002). This research

consistently demonstrates that Canada’s most profound migration is from rural to urban areas

and from the Atlantic Provinces westward.

Ross Finnie of Queen’s University and Statistics Canada is currently the country’s

most prolific researcher on the topic of interprovincial migration. Finnie’s interest over the

past 6 years has been in identifying the demographic trends related to migration. He

consistently asks the question, “Who Moves?” (2000, 2004). Finnie’s past research (1998)

employed longitudinal data pulled from census records. But his more recent work (2000,

2004) has employed a panel logit method using data derived from Revenue Canada tax

records. The logit model is an extension of the basic discrete choice model. In the migration

context it views the decision to change province of residence as a Boolean choice (either


move or do not move). This probability can be tested for the influence of a number of

variables. Finnie (2004) has found that interprovincial mobility is: “

(i) inversely related to the home province’s population size, presumably reflecting local
economic conditions and labour market scale effects, while language also plays an
important role;
(ii) more common among residents of smaller cities, towns, and especially rural areas
than those in larger cities;
(iii) negatively related to age, marriage, and the presence of children for both men and
women;
(iv) positively related to the provincial unemployment rate, the individuals’ receipt of
unemployment insurance (except Entry Men), having no market income (except for
Entry Men and Entry Women), and the receipt of social assistance (especially for
men);
(v) (slightly) positively related to earnings levels (beyond the zero earnings point) for
prime aged men, but not for others; and
(vi) more or less stable over time, with men’s rates declining slightly and women’s
holding steadier or rising slightly, indicating a divergence in trends along gender
lines” (Finnie, 2004, 1759).

Beyond the broad issue of interprovincial mobility, there is considerable academic

interest in rural – urban migration variations over time. Looker (2001) explains that the

“rural turnaround” in the 1970s was attributed to higher rates of in-migration and lower out-

migration. This trend was reversed by falling in-migration in the 1980s and was then

corrected again by lower out-migration rates in the 1990s (Looker, 2001, 29). Rothwell et al

(2002a and b) break down these national trends in detail. They find that, in rural and small

town (RST) Canada, in-migration is greater than out-migration for all those aged 25 – 69

(Rothwell et al, 2002a, 1). For RST Canadians in their late teens and early twenties, out-

migration exceeds in-migration. But,

At the provincial level, rural and small town regions of British Columbia, Alberta and
Ontario have net in-migration. Quebec, Manitoba, Saskatchewan and Newfoundland


and Labrador have net out-migration. Migration has little overall effect on the rural
and small town populations of Nova Scotia, Prince Edward Island and New
Brunswick (Ibid.).

Newfoundland and Labrador’s rural in-migration is the lowest in the country: one

quarter the in-migration rates of British Columbia. “Provinces with the highest rates of RST

out-migration also tended to have the highest rates of RST in-migration” (Rothwell et al,

2002b, 9). Therefore, RST migration efficiency (turnover) is highest in British Columbia,

Alberta and Ontario (the most affluent provinces). RST migration efficiency is lowest in

Atlantic Canada (the least affluent provinces).

There is new emerging evidence that migration motivations are changing the rural-

urban migration picture. In Nova Scotia for example, “two-thirds of rural census subdivisions

declined in populations between 1991 and 2001” (Millward, 2005, 180). Meanwhile, a small

number of metro-adjacent rural areas experienced considerable population growth through

counter-urbanization (Ibid.). Some of these metro-adjacent rural communities are growing

faster than the cities they abut. Bruce and Lister (2005, 4) claim this is true for all regions of

the country. They cite two reasons: urban families seek a better quality of life, and more

distant rural dwellers seek proximity to urban services and employment. Millward (2005,

191) argues,

isolation from urban opportunities should be regarded as an independent cause of net
out-migration, so that depopulation in remote districts can occur even in the presence
of a buoyant local economy.

However, urban opportunities can also bring negative experiences, especially for women,

including “loneliness, harder work requirements and greater patriarchal control” (Halfacree,


2004, 243). Three key migrant groups gravitate toward urban areas and away from rural ones.

Seventy-five percent of Canada’s immigrants choose Toronto, Vancouver or Montreal each

year. In 2001, immigrants made up 27 percent of urban regions but only six percent of rural

regions. The predominantly rural Atlantic Provinces receive only one-point-three percent of

Canada’s annual immigration (Bruce and Lister, 2005). Older seniors (70 years of age and

older) also choose urban communities. They seek better health care services and more

appropriate “aging-in-place” housing. Meanwhile, young adults are drawn to urban

education, employment and life experience.

Academic and government interest in migration tends to focus on youth and young

adults. Throughout the mid-nineties, Nova Scotia, New Brunswick, Newfoundland and

Labrador, Prince Edward Island, Manitoba and Saskatchewan all saw net losses of rural

youth. Ontario, Alberta and British Columbia experienced net gains. Except in

Newfoundland and Labrador, rural leavers were headed to urban areas within their province

of origin. In Newfoundland the main destination of rural leavers was an urban area outside

the province. Urban youth are similarly likely to move to an urban area outside their

province, except in Quebec, Ontario, and British Columbia where urban youth move to

other urban areas within their province (Dupuy, Mayer and Morisette, 2000, 17). The Coastal

Communities Network says the trend is driven by “youth who leave rural areas to get a post-

secondary education and then remain in urban areas where economic opportunities are seen

as better than any available ‘back home’” (2004, 1).


The Maritime Provinces Higher Education Commission (2002) has found that nearly

half (45%) of Prince Edward Island residents, one-in-seven New Brunswickers, and one-in-

fifteen Nova Scotians left their home provinces to go to university. Those graduates who had

attended university outside their home province were 16% more likely to move at least once

after graduation, and nearly one-in-seven left the region completely. Unfortunately

MPHEC’s study may be limited because it only considers those Maritime residents who

graduated from universities within the region.

Despite these findings, universities and colleges cannot shoulder the blame for the

loss of their graduates. Looker disputes the link by saying that it is not clear “whether those

with higher education are more likely to move, or if those who are likely to move obtain

higher levels of education” (2001, 30). She notes that out-migration rates climb with

incremental educational attainment, but so do in-migration rates. MPHEC (2002) confirms

that lower earnings, job dissatisfaction, unemployment and underemployment (all beyond

the control of post-secondary institutions) motivate graduates to migrate. But Dupuy, Mayer

and Morisette counter that even if young people could hold the job they desire in a rural

community 40 percent would be willing to move to an urban centre. They say that “this is

evidence that other factors, such as one’s desire to experiment with different life experiences

or to fulfill one’s aspirations, play a role in explaining migration out of rural community

[sic]” (2000, 2). This supports the idea that a particular personality-trait might be common

among the majority of leavers and the majority of higher learners.


The economic motivation is much clearer, and it is grounded in a startling reality.

Rural unemployment in 2000 was highest in Newfoundland (40%) and lowest in Alberta

(11%) (Dupuy, Mayer and Morisette, 2000, 5). Seventy percent of urban, non-student, 15 –

29 year old Atlantic Canadians are employed. Only fifty-six percent of rural, non-student, 15

– 29 year old Atlantic Canadians can say the same. The urban wage premium for women in

their early twenties is $558. Young men (20 – 24) earn $1447 more in rural areas, but then

earn approximately $1900 less than their urban peers after reaching their thirties (Ibid.).

According to the Maritime Provinces Higher Education Commission, post-secondary

graduates who stayed in the Maritimes saw a 26% wage increase after graduation. Those who

left saw a 78% wage increase (MPHEC, 2002, 6).

These facts cause grief for young people in Atlantic Canada who are making career

decisions. They create a tug-of-war between economic reality and community loyalty. This

conflict is well documented (see references in Looker, 2001, 28). Many of the young people

who decide to move say things like “this place will always be home to me,” and “even if I

leave I am coming back” (Ibid., 31). But in the 1990s only one-quarter of those young people

who had moved away had also returned to their communities ten years later (Dupuy, Mayer

and Morissette, 2000).

Return Migration in Newfoundland

The introductory section of this paper made reference to a book on return migration

to Newfoundland by House, White and Ripley (1990). These researchers studied two small


coastal communities on the Great Northern Peninsula of Newfoundland: Anchor Point and

Bird Cove. Their findings do not completely fit with the theory of an economically rational

migrant. It is argued that, to Newfoundlanders, economic self-interest includes non-cash

wealth and benefits from the informal economy. Also, rational migration decisions extend

beyond immediate economic considerations to include past experience and present

personality. The authors explain, “In real life, people respond to situations in terms of their

personal biographies, which vary according to where they were born and raised, their family

relationships and a host of other influences” (House, White and Ripley, 1990, 3).

It has already been noted that out-migration from Newfoundland and Labrador is not

the source of the province’s net out-migration.

What distinguishes Newfoundland is not that Newfoundlanders choose to leave their
home province at a greater rate than people in other provinces, but rather that other
Canadians choose to move to Newfoundland at a lower rate than they do any other
province (House, White and Ripley, 1990, 12).

Despite this depressingly low level of in-migration, Newfoundland and Labrador has a

remarkably high level of return migration – the highest rates in the country. Over half of the

migrants into Newfoundland and Labrador are former residents. This trend has been

consistent for at least the past 20 years (Ibid.).

Gmelch and Richling (1988) tell the story of return migration to outport

Newfoundland not in terms of the return migrants’ failure in the urban economy, but rather

their desire to rediscover intimate social relations, community spirit and a rural household

economy (including the self-sufficient nature of hunting, fishing, and barter).


As veterans of urban-industrial Canada, having tasted the ‘modern life’, their
voluntary return to the province’s rural villages offers an unambiguous message that
outport society and culture are still vibrant and appealing (Gmelch and Richling,
1988, 14).

This seems to ring true for many of the over 13,000 former Newfoundlanders currently

living in Fort McMurray, Alberta. They represent one-third of the city’s population. The

mayor jokes that his city is, “Newfoundland’s third largest” (Burns, 2003, 49). This is

reminiscent of immigrants’ ethnic enclaves. A relative reports back to home that there is

plenty of work available for all the nieces, nephews and cousins. Employment is the

motivator, and yet many Newfoundlanders are looking only for a temporary move,

a few years of good wages to help get them on their feet to set up a business or
establish themselves more securely back home (Ibid.).1

House, White and Ripley agree that return migration to Newfoundland is not a result of

labour market failure, rather the result of a new awareness of Newfoundland’s advantages.

After some time away, the grass is no longer greener on the other side. Clearly, social

considerations are integral to the migration decisions of rural Newfoundlanders. Social

capital is one of the key incentives to return migration. House, White and Ripley suggest that

this observation likely applies in varying degrees to other Atlantic Canadian communities

and beyond.

1 Unfortunately a high cost of living often prevents any significant cash savings.


Social Capital Theory and Migration

Social capital is not a panacea for troubled communities. It can lead to isolationism

and destructive norms. But the message of social capital theory is, as Woolcock (2001)

explains, “that how we associate with each other, and on what terms, has enormous

implications for our well-being” (p. 15). Social capital can help people deal with uncertainties

like job loss, and it can provide opportunities for collaborative outcomes (Woolcock, 2001,

14). Strong social capital has been shown to predict low murder rates, low death rates

(controlling for blood chemistry, age, gender, jogging, and other risk factors), happiness, and

socio-economic equality (Putnam, 2001, 51). “The well-connected are more likely to be

housed, healthy, hired and happy” (Woolcock, 2001, 12). As evidence of these outcomes has

been mounting, the significance of social capital has become clearer.

However, critics of social capital emphasize the isolationist viewpoint. Florida (2002)

finds that his creative class is not only disinterested in community connectedness but is

avoiding tight-knit communities in favour of quasi-anonymity. He says,

The people in my focus groups and interviews rarely wished for the kinds of
community connectedness Putnam talks about. If anything, they were trying to get
away from those kinds of environments….they did not want friends and neighbors
[sic] peering over the fence into their lives (Florida, 2002, 268).

This critique strikes at the heart of socially vibrant communities. The creative class theory is

suggesting that tight social networks might be preventing in-migration. It is possible that

social capital is a double-edged sword: it might discourage out-migration and encourage

return migration, but simultaneously discourage in-migration.


Other preliminary research validates the idea that social capital is a valuable

community feature. Cordes and his colleagues (2003) surveyed residents in a small Nebraska

community using a contingent valuation framework. This approach involved asking

respondents questions that revealed the salary increase they would accept in return for

moving from the community. It is not surprising that 61% of respondents said they would

move from the community and leave behind local relatives and friends for financial gain.

Surprisingly though, when respondents were asked to state the required salary incentive, the

average value was incredibly high: $30,000. The yes/no decision to accept-compensation was

found to correlate with other questionnaire responses on the size of the respondent’s close

personal network, emotional support, and general attitude about the town’s social

environment. Unfortunately the results are somewhat questionable because willingness-to-

accept questions are frowned upon in contingent valuation research. Willingness-to-pay

questions are more commonly accepted but Cordes et al were unable to devise a realistic

migration situation that included a willingness-to-pay element. Despite this drawback, the

researchers are confident that they have produced the first evidence that social capital results

in tangible and measurable attachment value. They have reinforced the idea that social

capital reduces gross out-migration and could induce return migration if the necessary trade-

offs are not realized.


Local Response Strategies

New Brunswick Premier Frank McKenna was the first Canadian political leader to

buy into the concept that technology infrastructure could attract clusters of investment. But

he went beyond the generic dot-com recruitment campaign that consumed many

communities, states and provinces. He looked at local strengths and aggressively promoted

his province as a skilled bilingual haven for the emerging call centre industry (Savoie, 2001).

McKenna’s economic success in New Brunswick was partly a result of call-centre job

creation but mostly the indirect result of the brand loyalty he created for the province

(Ibid.). The new conservative Premier, Bernard Lord, has reinvented McKenna’s brand-

strategy by using it to recruit ex-patriot New Brunswickers back to the province. Lord has

traveled the country, treating “economic refugees” to down-east hospitality, including

smoked salmon and Moosehead beer. “He admits his strategy here is as much to pluck

heartstrings and praise Maritime simplicity as it is to offer New Brunswickers specific

economic incentives to move home” (Brean, 2004, A2). Yet not every ex-patriot New

Brunswicker finds this appeal attractive. One woman left an event saying, “Don’t feed me the

lifestyle,…that’s not going to pay my rent” (Ibid.). Despite this criticism, Lord’s successful

repatriation campaign lured 139 New Brunswickers home in the first year.

Other provinces in Eastern Canada are also beginning to recruit people, particularly

those in their twenties. Newfoundland and Labrador turns student loans into grants for those

university and college graduates who stay in the province to work. Students are therefore not


forced to leave Newfoundland after graduation in order to repay their debt (Government of

NL, 2004). The province of Quebec is farther into the game with its “Place aux Jeunes”

program. To demonstrate that opportunities do exist in rural Quebec, university students are

given all-expenses-paid trips home for three winter weekends. One day out of each weekend

is spent meeting employers, economic developers, and successful local young people (Place

aux Jeunes, 2004).

While a number of intervention programs exist at the provincial level, the Coastal

Communities Network suggests that work must also be done at the local level. CCN

Executive Director Ishbel Munroe suggests some solutions for the Nova Scotia government,

namely forgiving a portion of the provincial student loan and realigning the education

system to highlight local opportunities. But she is quick to focus on what she has heard from

young people: “Currently, young people are told all the things they can’t do…Tno

skateboarding, no standing around on the main street, no sitting in the park after dark,” she

says, “…when they’re told to move along enough times, they do” (Coastal Communities

Network, 2004, 2). Munroe’s comments point to an attitude problem at the local level and

suggest communities will win the migration game if they can engage their youth. She cites

an earlier CCN study that found young volunteers are more likely than non-volunteers to

return to their communities after their post-secondary education. Unfortunately the study

also found that most young people never volunteer because they are never asked. The local

challenge is for communities to demonstrate, in as many ways as possible, that they value

their young people.


The Colchester Regional Development Agency in Truro, Nova Scotia, does this by

sending “Colchester Cares Kits” to local students who are away at university. Each kit

contains items donated by members of the local chamber of commerce. The intent is to

maintain community loyalty when young people leave to pursue an education (Von Kintzel,

2004).

The New Rural Economy (NRE) Project (a collaboration among government,

community, business, and 15 academic researchers) has been studying capacity building in 32

communities across Canada since 1998. Two of the NRE researchers published, “Strategic

observations for rural community decisions (ideas for communities that have decided that

they want to grow)” (Reimer and Bollman, 2004). Part of this document addresses the topic,

“youth are leaving…but young families return” (p. 4-5). The authors suggest that attractive

communities can bring young families back with a high probability that they will become

self-employed. They propose five strategies for addressing return migration that nurture and

capitalize on social ties: “

a) Five-year (or seven-year) high school reunions where you have researched the
interests and capacities of each former student and you make them an offer they
cannot refuse.

b) Community bulletins sent regularly to past residents to keep them informed about
local activities.

c) Use your diaspora for market intelligence and opportunities.

d) Small community venture capital funds for returning youth.

e) Mentors for new entrepreneurs” (Reimer and Bollman, 2004, 4 – 5).


Chapter 3 - Research Procedure

Data Sources

No comprehensive source of data on sub-provincial migration was available at the

time of this research. To build an appropriate data set, this study draws on three sources, all

provided by the Government of Canada. The files are made available to students at the

University of Waterloo through Tri-University Data Resources.

The first two data sets are CANSIM manipulations of 2001 Census records. Table 109-

5215 (Statistics Canada, 2005a) provided population counts for each health region by 5-year

age groupings. The table included data for 1996 and 2001. These figures were fed into a

cohort survival analysis to derive the dependent variables (see Procedure, step 1).

Meanwhile, Table 109-0200 (Statistics Canada, 2005b) provided economic indicators for each

health region. Many of these statistics were used as independent variables in the study.

The third data set is from a less familiar and relatively new source. Statistics Canada,

the Canadian Institute for Health Information and Health Canada engaged in a consultation

in 1998 to identify current and future needs for health information. As a result, Statistics

Canada fielded the Canadian Community Health Survey (CCHS) in 2000. This survey

provides data on the status and determinants of population health in Canada’s many sub-

provincial health regions. Since social capital has become recognized as a key determinant of

health, the CCHS includes data on this otherwise elusive phenomenon. For a select few

health regions four indicators of social support were calculated, including a measure of


tangible support and a measure of positive social interaction (Statistics Canada, 2003).

Unfortunately these indicators of social support cannot be used in this study because they are

available for so few regions. However, for all health regions, the CCHS questionnaire

included questions regarding Canadians’ sense of belonging, volunteer activity, and

participation in social activities. This is the first accessible data set with which researchers

can compare such social capital indicators in local communities. Additional independent

variables are drawn from this data set.

Population

The unit of analysis for this study is the community (health region) rather than the

individual migrant. While migration decisions appear to occur primarily at the individual

level, there is a need for research that can inform community economic development

interventions at a regional level. Communities need to better understand how their socio-

economic attributes influence the migration patterns of key demographic groups.

There are 118 health regions in Canada. The boundaries for these regions are defined

by the provincial ministries of health. These are legislated administrative areas in all

provinces except Nova Scotia. The Nova Scotia Department of Health uses statistical zones

that are aggregations of the province’s nine district health authorities. To meet data quality

guidelines in the Canadian Community Health Survey, Statistics Canada has aggregated 31 of

the provincial health regions in 16 larger regions. The same aggregation was applied to the

CANSIM data sets (see Appendix, Table 2). This aggregation created one geographically large


health region that encompasses the Yukon, Northwest Territories and Nunavut. This

northern health region was excluded from the analysis. The remaining 102 health regions are

listed in the Appendix.

Procedure

The data analysis made use of SPSS and Microsoft Excel. It involved five incremental

analysis steps.

Migration Variables (Step 1).

Migration data can be derived from general population statistics using a “residual

method” (see Goetz, 2005). Since births, deaths and migration are the three components of

population change, net migration can be calculated with the accounting identity in Equation

1 (Goetz, 2005).

Equation 1
M j , = Pop j ,t B j, + D j,

Here, the change in population due to migration is MU. PopU is the population at the

beginning of the time period while Popt-U is the population at the end of the period. BU is the

number of births over the period and DU is the number of deaths. The subscript “j” denotes a

particular demographic group, such as 20-24 year olds. This formula yields an absolute value

for the net number of migrants.

Absolute migration values are not useful for a comparison among communities of

different sizes. In response, an estimate of the effect of regional net-migration was developed.


A basic “cohort survival estimation”2 (see Newkirk, 2002) was combined with the “migration

by residual” method outlined above. Equation 2 was developed for the purposes of this study.

It was applied to each health region for each age-cohort of interest.

Equation 2
Pop ,r , j Pop ,r , j
M r, j = =
Est ,r , j Popt ,r , j 1 D ,j

This estimate of the effect of net-migration (M) indicates the relative impact of

migration for a given age group (j) in a given health region (r). The actual population in 2001

(PopU,r,j) is divided by a population estimate (EstU,r,j) based on cohort survival (neglecting

migration). The result captures the magnitude of the difference between the actual

population in 2001 (survivors and migrants) and the predicted population for 2001 (only

survivors). A score of 1.00 indicates that migration had no effect on the region’s population.

A score greater than 1.00 indicates a net positive population growth as a result of migration,

and a score less than 1.00 indicates a net negative result.

Unfortunately mortality/survival rates were not available for individual health

regions. In response, it was assumed that survival rates are relatively uniform throughout

each province. This assumption introduces the possibility of error since some of the variation

in migration levels may represent variations in mortality rates.

2 Each
age group is progressed through time and a survival rate is applied. For example, the
total number of 5-9 year olds in 1996 becomes the estimated number of 10-14 year olds in
2001, less any deaths.


These estimates (M) were calculated for the cohorts identified in Table 2.

Table 2. Cohorts of Interest
Summary of M values (all health regions)
Cohort Age
Min Q13 Mean Q34 Max

Children (Families) 5–9 0.948 1.006 1.051 1.085 1.329
Teens 15 - 19 0.896 0.983 1.010 1.058 1.240
Early-Twenties 20 - 24 0.621 0.807 0.894 1.065 1.458
Late-Twenties 25 - 29 0.677 0.860 0.928 1.033 1.351
Early-downshifters 50 - 54 0.880 0.984 1.005 1.027 1.083
Late-downshifters 55 - 59 0.867 0.987 1.002 1.036 1.149
Pre-retirement 60 - 64 0.821 0.984 1.003 1.035 1.150
Recently-Retired 65 - 69 0.884 0.977 1.002 1.018 1.131

Immigrants are also a group of interest to this study. A measure of immigration was

developed independently of the other migration variables. This measure considers

immigrants as a proportion of a health region’s population (based on the number of

individuals with permanent resident status identified in the CCHS).5

Social Indicators (Step 2).

The CCHS was used to estimate the social capital in each health region. Since social

support indicators were not available for all health regions, some of the indicators pioneered

by Putnam (2000) have been used. The social capital variables include participation in

3 The first quartile.

4 The third quartile.

5 Min = 0.080; Q1 = 2.607; Mean = 7.894; Q3 = 14.356; Max = 57.970.


voluntary organizations, participation in social activities, and sense of belonging to the

community. They are outlined in Table 3.

Table 3. Social Capital Indicator Variables

Summary of Values (all health regions)
Variable Description
Min Q1 Mean Q3 Max

Proportion of
respondents who
indicated a
Sense of Belonging somewhat strong or 44.24 63.32 67.27 71.90 82.60
very strong sense of
belonging to their
community.
Proportion of
respondents aged
15-19 who indicated
Teen Sense of
a somewhat strong 44.99 59.36 66.85 71.77 86.02
Belonging
or very strong sense
of belonging to their
community.
Proportion of
respondents aged
Twenty- 20-29 who indicated
something Sense a somewhat strong 40.18 51.31 57.90 63.53 85.00
of Belonging or very strong sense
of belonging to their
community.
Proportion of
respondents who
Volunteer
were members of a 19.36 34.67 38.12 41.00 48.68
Membership
voluntary
organization.


Min Q1 Mean Q3 Max

Proportion of
respondents aged
Teen Volunteer 15-19 who were
10.98 29.98 36.57 43.59 60.78
Membership members of a
voluntary
organization.
Proportion of
Twenty- respondents aged
something 20-29 who were
9.28 20.59 27.07 32.17 42.66
Volunteer members of a
Membership voluntary
organization.
Proportion of
respondents who
Volunteer
volunteered at least 6.19 16.06 18.56 20.88 26.26
Participation
once in the past
week.
Proportion of
respondents who
Social Dance participated in a 10.94 18.35 20.65 23.43 28.15
social dance in the
past 3 months.
Proportion of
respondents who
Ice Hockey participated in ice 2.64 5.64 6.78 7.93 12.26
hockey in the past 3
months.
Proportion of
respondents who
Golfing participated in 1.87 9.31 13.44 14.98 20.15
golfing in the past 3
months.
Proportion of
respondents who
Bowling participated in 3.48 9.26 10.83 12.35 16.63
bowling in the past
3 months.


Min Q1 Mean Q3 Max

Proportion of
respondents who
Baseball/Softball participated in 3.00 5.22 7.30 9.08 12.51
baseball/softball in
the past 3 months.
Proportion of
respondents who
Tennis participated in 1.12 2.41 3.32 4.78 9.49
tennis in the past 3
months.
Proportion of
respondents who
Volleyball participated in 2.10 5.89 7.25 8.44 11.39
volleyball in the
past 3 months.
Proportion of
respondents who
Basketball participated in 4.83 7.79 9.58 10.84 14.35
basketball in the
past 3 months.
Proportion of
respondents who
Soccer participated in 3.72 6.22 7.63 9.38 12.97
soccer in the past 3
months.

Economic and Market-size Indicators (Step 3).

The first economic indicator, income adequacy, was drawn from the CCHS. “Income

Adequacy” is the proportion of respondents in a health region who were identified as having


a low household income6. Another measure of low income was available in CANSIM Table

109-0200 (Statistics Canada, 2005b). “Economic Families – Incidence of Low Income (2000

income) (percent)” is the proportion of economic families with incomes below the Statistics

Canada low-income cut-off7. The remaining economic indicators were drawn from this same

CANSIM table and are listed in Table 4.

Table 4. Economic and Market-size Variables
Min Q1 Mean Q3 Max

Proportion of labour
Long-term force aged 15 and
Un- over who did not
1.50 2.62 3.40 5.20 17.99
employment have a job any time
Rate during the current
or previous year.
Average personal
income (pre-tax,
Average
post-transfer) for
Personal 19,804 24,978 27,518 29,410 43,691
persons aged 15 and
Income
over who reported
income ($).

6
The CCHS classifies low-income as less than $15,000 for household of 1 or 2 people, less than $20,000 for
households of 3 or 4 people, and les than 30,000 for households of 5 or more people. (Min = 3.459; Q1 = 6.128;
Mean = 7.683; Q3 = 9.632; Max = 19.173).
7 Min = 5.400; Q1 = 9.425; Mean = 11.200; Q3 = 12.800; Max = 22.700.


Min Q1 Mean Q3 Max

Proportion of all
income that came
from government
transfers (e.g.,
Guaranteed Income
Government Supplement/Old Age
Transfer Security, 5.90 11.82 14.01 16.10 27.10
Income Canada/Quebec
Pension Plan,
Employment
Insurance) for the
population 15 years
of age and over.
Proportion of the
population living
within a Census
Metropolitan Area, a
Census
Population in
Agglomeration or a 0.00 43.82 81.70 95.10 100.00
a CMA
strong Census
Metropolitan Area
and Census
Agglomeration
Influenced Zone.
Proportion of health
region population
living in a
continuously built-
Urban up area having a
21.80 49.62 64.90 81.50 100.00
Population population
concentration of
1,000 or more and a
population density
of 400/km2 or more.
Inverse of “Urban
Rural
Population” (not 0.00 18.42 35.10 49.40 78.20
Population
used in analysis).


Min Q1 Mean Q3 Max

Population per
Population square kilometer
0.26 3.38 15.95 48.68 4,238.75
Density (based on 1996
census).

Cross-correlation of independent variables (Step 5)

This study assumes that social capital is a phenomenon that acts on migration rates

relatively independently of economic indicators. That is, social capital is not simply a by-

product of economic market factors. To support this assumption the two sets of independent

variables were correlated against each other. Where logical, some correlation tests were also

conducted within the variable sets. For example, it was particularly prudent to determine the

degree to which “sense of belonging” is influenced by other social indicators.

Correlation tests (Step 6)

The final step in the data analysis was an array of correlation tests. The Pearson’s

product moment correlation coefficient (r) was used. Interpretation of r was based on the

thresholds outlined in Table 5. The basic analysis involved all Canadian health regions. A

total of 176 tests were conducted using 22 independent variables (16 social indicators and 6

economic indicators) and 8 dependent variables (cohort-specific estimates of the effect of

regional net-migration). All 176 tests were then recalculated twice to further understand

geographic variations. These secondary analyses focused on a set of Atlantic Canadian health


regions, and a set of rural health regions (those with more than 50% of their population

living in rural areas).

Table 5. Pearson’s Product Moment Correlation Coefficient Interpretation.
Association Absolute r-value

Perfect (P) 1.000
Strong (S) 0.750 – 0.999
Moderately Strong (M) 0.500 – 0.749
Weak (W) 0.001 – 0.499
None (N) 0.000

Limitations

At this point it is important to identify some key limitations and assumptions

underlying this study. These relate to the treatment of migration statistics, the geographic

level of analysis, and the selection of social capital indicators.

First, there are three important considerations with respect to the treatment of

migration and population change in this study. A key challenge with all census population

data is that the census methodology does not separate migrant births/deaths from those in

the pre-existing population. As a result, the infant children of migrants are not counted as

migrants, regardless of whether or not they were born after the migration event. Since

fertility rates were not available for health regions anyway, newborns were excluded from

this study. Deaths and cohort survival rates present a different challenge. Provincial survival

rates were applied to the health regions because data at the health region level was

unavailable. Unfortunately this introduces some bias into the residual method: variations in

survival rates can manifest as variations in migration rates. To minimize this error, the older


age cohorts (70+) were excluded. For those ages, survival rates are lowest and the inferred

bias would be high. Another limitation is the lack of data for dissecting in- vs. out-migration.

As demonstrated in Chapter 2, separating the components of migration introduces an

important level of understanding (as in NL’s low out-migration and lower in-migration).

Although this study is only able to estimate net-migration, it reaches for this deeper level of

understanding through an examination of age cohorts. Because in and out flows tend to be

tied to age groups (youth, families, seniors) there is a degree of richness in the results that

would not be found in a community-wide net-migration statistic. The research design

carefully assuages all three of these migration limitations.

The second set of limitations and assumptions relates to the geographic level of

analysis. For starters, health regions are a coarse basis for spatial analysis. A smaller level,

such as census divisions or sub-divisions, might yield greater variation in the data set.

However, a smaller geographic unit might also introduce a greater degree of spatial

dependency between neighbouring regions. Unfortunately this discussion is moot because

appropriate social capital data are not publicly available for any geographic aggregation

smaller than health regions. As Statistics Canada introduces new sources for social capital

data, studies at the CD level or lower may become possible. Meanwhile, regardless of the

geographic aggregation, researchers must contemplate whether or not “community” is an

appropriate unit of analysis. The question for theorists is whether social capital rests at the

individual or community level. Putnam operates on the premise that social capital is a

phenomenon shared between many individuals. He goes so far as to compare levels of social


capital between American states. This study assumes that social capital operates at a more

local level. Putnam’s high level of aggregation is rejected, but his basic premise (that social

capital is a community phenomenon) remains intact.

Putnam’s approach to social capital indicators has also been adopted here. This is the

third set of limitations and assumptions. In his book, Bowling Alone (2000), Putnam uses

membership in voluntary associations and participation in social activities as indicators of

social capital. This is because more sophisticated measures were not available at the time of

his pioneering work. A similar limitation applies in Canada today. Although social capital is

better understood, public data sources are not up-to-speed. For a select few geographies, the

CCHS asked questions leading to an index of “tangible social support” and “positive social

interaction”. These included questions about bonding ties between individuals. Bridging and

linking social capital are as yet unexplored in this dataset. Current theory encourages more

specific measures than those used in this study, but they may not be available until specific

surveys on social capital are fielded. In the meantime, Putnam-style indicators allow

significant insight and discussion.

Ethics Considerations

Further to its stringent ethics policies, Statistics Canada has removed all personal

information from the data files. Results that could identify any individual have been

suppressed. Because the research did not involve contact with human subjects there was no

need for a review by the Office of Research Ethics.


Chapter 4 - Results

Cross Correlations

The results indicate that the social and economic indicators employed here are two

relatively independent sets of variables. There are, however, two notable exceptions.

First, there is evidence that “sense of belonging” is more prevalent outside cities.

There is a moderately strong negative relationship between sense of belonging and urban

population (r = -0.566) and between sense of belonging and population within a CMA (r = -

0.543). Digging deeper, the least wealthy of these rural regions have a more prevalent sense

of belonging. For the set of rural health regions, “sense of belonging” is moderately strongly

(and positively) related to both long-term unemployment rates (r = 0.618) and government

transfer income (r = 0.541). For the set of Atlantic health regions this relationship is even

stronger: the correlation coefficient for sense of belonging and long-term unemployment is

0.880, and for sense of belonging and government transfer income it is 0.777. There is a

fascinating migration story in these statistics. It appears that people are bonded together by

economic hardship; supporting one another through tough times and relying on family,

friends and sometimes government income support. This connectedness makes it a little

easier to be unemployed or underemployed, allowing people to stay in the community to

which they are socially bonded. They can try to wait for better economic circumstances

rather than moving in search of them.


Three social activities provide a second exception to the independence of the social

and economic indicator sets. Participation in these three activities is related (moderately

strongly) to the economic circumstances and urbanization of a given community. Tennis,

basketball and soccer are all more prevalent in richer urban regions (see Table 6).

Surprisingly this was not the case for hockey which generally has a higher cost-of-entry (in

the form of higher equipment costs). Rather than cost-of-entry, this evidence may point to a

class-divide for these three sports, or perhaps an issue of access to facilities.

Table 6. Participation in tennis, basketball and soccer is higher in wealthy urban regions (r-
values).
Income Urbanization
Avg. Personal Gov’t Transfer % in a CMA % Urban
Tennis 0.668 0.592 0.602 0.722
Basketball 0.549 0.516 0.489 * 0.405 *
Soccer 0.674 0.626 0.518 0.459 *
* below the |.500| threshold.

Some cross-correlation within the social indicators revealed one further interesting

finding. The correlation between “sense of belonging” and “volunteer membership” is

positive and moderately strong (r = 0.716). It is likely that the causal relationship runs in

both directions. Across the country, people in strong “sense of belonging” regions are more

likely to support one another through membership in voluntary organizations. In turn,

communities where residents are actively engaged in voluntary organizations have a more

prevalent sense of belonging.


Low Correlation Results

Although “volunteer membership” is linked to “sense of belonging”, it is one of the

seven variables that are weakly correlated with migration success for the demographic

cohorts. Table 7 lists the indicators for which r-values fell below the |0.500| threshold.

Table 7. Variables with low correlation to migration success.
Variable Range of r-values

Volunteer Membership -0.245 to 0.067
Teen Volunteer Membership 0.018 to 0.276
Twenty-something Volunteer Membership -0.138 to 0.054
Volunteer Participation -0.128 to 0.325
Social Dance -0.101 to 0.057
Ice Hockey -0.275 to 0.294
Volleyball -0.237 to 0.227

These low correlation results should not be over-interpreted. Indeed, some

researchers in some studies would place their threshold for acceptable r-values much lower

than |0.500|. The results only indicate that these forms of social engagement have little effect

on migration at a community level. In unique cases, an individual may find any of these

connections to be a strong anchor/pull that influences her/his migration decision. It is also

likely that these variables simply have a less direct effect on migration than those variables

that yielded stronger results. For example, it has been noted that “volunteer membership”

has a moderately strong relationship to “sense of belonging”. “Sense of belonging” is, in turn,

strongly related to migration for some demographic cohorts. More intensive multi-variate

study of the social indicators is needed.


Immigrants

There is little evidence that general social factors play a strong role in the location

decisions of immigrants. There are strong positive correlation results for “immigrant

concentration” and “tennis” (r = 0.728), “basketball” (r = 0.509), and “soccer” (r = 0.568).

However, these are the social activities that are strongly linked to economic and market-size

factors. Indeed, these three results are indicative of the strong results for economic

indicators. Although unemployment rates had little effect on the concentration of

immigrants (r = -0.359), average personal incomes (r = 0.655) and government transfer

incomes (r = -0.620) did. Immigrant concentration is highest in urban areas, as evidenced by

strong results for all three indicators of urbanization. This is consistent with the literature,

which indicates Toronto, Montreal and Vancouver are the top three immigrant destinations

in Canada. Social factors may be unimportant to immigrants because many have already

detached from social ties in their countries of origin. It is also true that ethnic enclaves tend

to occur in the most densely populated urban centres, where geographic concentrations of a

particular socio-cultural minority can be found. The correlation results for the immigration

cohort are reported in Figure 3.

Although the economic motivations of immigrants come through in these results,

three social factors arise in the population sub-sets. For the set of rural health regions,

immigration success is strongly and positively related to participation in golf (r = 0.615) and

baseball (r = 0.558). These social indicators weigh on the relative success of rural regions for


other demographic cohorts as well. Because average personal income (r = 0.778) and

government transfer income (r = -0.800) have a strong effect on immigration for the set of

rural regions, it may simply be that golf and baseball are common to the wealthier rural

regions. A further anomaly is the strength of bowling participation in determining

immigrant concentration for the set of Atlantic health regions (r = 0.513). This may also be a

confounding variable since no other social variables had a strong effect in Atlantic Canada

while the economic and market-size indicators had a strong effect. Robert Putnam might be

disappointed to hear that, as a social capital indicator, bowling participation did not have a

strong effect on migration for any demographic cohort other than immigrants.

Immigrant Concentration and
Socio-economic Indicators (r-values)

1.000
0.800
0.600
0.400
0.200 All
r-value

- Atlantic
-0.200 Rural
-0.400
-0.600
-0.800
-1.000
e
G e r plo me

an In nt
/ s fing

A In e
S o is

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pu Po tion
g

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y
sk er

B all
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CM fer m
a

ng ce Ad lin

sit
nn

e
Ba cc

ti
r m Lo qua
b
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o
ym

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l

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et
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la pu
e

p
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ll

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ov so
-te o f
se

b
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't

Figure 3. Immigrant Concentration and Socio-economic Indicators (r-values).


Children (Families)

Like the immigrant cohort, migration for children (aged 5 – 9 years) had little to do

with social indicators. Soccer participation was the only social indicator with a strong result

in the set of all health regions (r = 0.516). However, soccer’s link to economic factors has

already been demonstrated. Indeed, the presence of poor economic conditions goes hand-in-

hand with poor migration results for children. The relationship between average personal

income and migration success for the “children” cohort was found to be in a positive

direction, but the r-value fell below the significance threshold by 0.005.

For the population subsets (rural and Atlantic regions), golf participation was found to

have an effect on migration results for the “children” cohort. This may again be spill over

from economic indicators which are particularly strong for this cohort in the subset

populations. It should be noted that market-size (CMA, urban, density) is weakly correlated

with migration success for this cohort. It would appear that density and urbanization have

little bearing on migration of children (families), while economic opportunities do (e.g.

income adequacy, incidence of low income, long-term unemployment rate, government

transfer income).

For the set of rural regions, economic indicators yielded r-values ranging from |0.534|

to |0.718|. The results were even stronger for the set of Atlantic health regions. There, the

relationship between migration of this cohort and long-term unemployment rates was strong

and in a negative direction (r = -0.829). In other words, the rural and Atlantic regions that


are most successful in attracting/retaining populations of children (hence, families) are those

experiencing the most economic prosperity. In Atlantic Canada, “sense of belonging” also

plays a strong role: it reduces migration success for this cohort. This is likely because “sense

of belonging” is strongly tied to economic circumstances. However, it might also be

indicative of tight-knit communities pushing away outsiders. In- and out-migration must be

dissected from net-migration to determine which of the former or latter conclusions are true.

Further research can also examine the role of children’s social connections in a family’s

decision to migrate. This study has used children aged 5 – 9 years as a proxy for young

families. However, this presents challenges because families often contain multiple children

and can be expected to never delegate the migration decision solely to the children.

Nevertheless, the results outlined above (and in Figure 4) provide some insight.

Migration Success (Children - Families)
and Socio-economic Indicators (r-values)

0.800
0.600
0.400
0.200 All
r-value

-
-0.200 Atlantic
-0.400 Rural
-0.600
-0.800
-1.000
t

e

e
en
y

e
ng

g

er

om
ac

m
m
fi n

cc

m
gi

co
co
qu
ol

nc
n

oy
So

In
lo

In
G

de

lI
pl
Be

r
ow

m

na
A

fe
ne

ns
e
of

o
L
m

rs
U

ra
of
e

co

Pe
ns

T
rm
e
In

nc
Se

't
g.
-te

ov
de

Av
ng

G
ci

Lo
In

Figure 4. Migration Success (Children – Families) and Socio-economic Indicators (r-values).


Youth

This section examines the results for three demographic cohorts: teenagers (aged 15 –

19 years), early-twenty-somethings (aged 20 – 24 years), and late-twenty-somethings (aged

25 – 29 years). Strong and moderately strong correlation coefficient values are reported in

Figure 5, Figure 6 and Figure 7 respectively. The cohorts are grouped for discussion purposes

because their M values responded similarly against the various independent variables. For

example, the social activities that arose from the analysis were the same economically-driven

ones seen in previous cohorts (tennis, soccer, golf and baseball). Similar economic factors also

appeared. The most successful regions economically (unemployment and income) are also

the most successful with respect to youth migration. Interestingly, the strength of this

relationship was found to increase with youth’s ages. Each progressive cohort of the three

has larger absolute r-values for economic indicators. Economic considerations become more

important and better understood as young people age and increasingly rely on their own

employment earnings.

Another interesting finding relates to the relationship between “sense of belonging”

and migration success for these three youth cohorts. The overall community “sense of

belonging” yielded larger absolute r-values than peer “sense of belonging” (“Teen Sense of

Belonging” and “Twenty-something Sense of Belonging”). Overall community “sense of

belonging” is a stronger deterrent to positive youth migration than peer-group “sense of

belonging”. This may be evidence of an underlying social story about how communities

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