MIGRATION AND THE CANADIAN URBAN SYSTEM:

PART II, SIMPLE RELATIONSHIPS

J.W. Simmons

Research Paper No. 98

Centre for Urban and Community Studies

University of Toronto

July 1978

ABSTRACT

This paper develops a series of simple regression models to

explain the patterns of migration rates and flows which were described

in research paper No. 85. In brief the exercise was only partly

successful, with large amounts of unexplained variation remaining.

Demographic, social and envirorunental variables appear to be at least

significant economic measures in determining population growth.

Introduction

Migration Rates

Migration Flows

Migration and Growth

Conclusions

References

CONTENTS

- Review

- The Census Data and Methods of Analysis

- Out-Migration

- In-Migration

- Other Mobility Rates

- The Flow Matrix

- The Allocation Model

- Net Migration

- Immigration

- Natural Increase

- Net Migration

- Path Models of Population Growth

Appendix Independent Variables

LIST OF ILLUSTRATIONS AND TABLES

ILLUSTRATIONS page

1 The Canadian Urban Sys tern ..••...•...•...•••.•.•...••............ 3

2 Age Distribution of Intermunicipal Migrants ..•...••....•....... 16

3 Fluctuation in Wage and Employment Indices ...•...•...•••.....•. 18

4 Annual Variations in Growth Components .••••.••••...•.•••....•.. 52

5 Path Analysis: Population Growth ....••.....•••...•..••.....•.• 62

6 Alternative Specifications of the Growth Model ............•.•.• 64

TABLES

1 L.ist of Urban Regions .•.•...•....••............•..•.........•... 4

2 Measures of Mobility •...•.......•....••.••....••..•....••...••.. 8

3 Correlations among Mobility Rates .....•....•..•..•••...•......• 10

4 The Correlates of Out-Migration Rates ....••..•••.•..•.•.•..•.•• 19

5 Out-Migration: Selected Regression Models .•......•.....•...•... 22

6 The Correlates of In-Migration .•••.......•.....•.•............. 25

7 In-Migration: Regression Models ....•••.••.....................• 2 7

8 Regression Models: Net and Gross Migration •..•....••..•..•...•. 30

9 Correlates of Migration Flow Measures •......................... 37

10 Regression Models: Migration Flow Matrix .•....•...........•.•.. 28

11 The Correlates of Migration Flow Ratios ...•...............•.... 42

12 Regress ion Models: Allocation .........•.........•........•...• 42

13 The Dimensions of Social Variations in Canada ..•....••.......•. 45

14 The Correlates of Net Migration Flows .••.•......••.•.•....•..•. 48

15 Regression Models: Net Migration Flows .•...•................••. 49

16 The Components of Population Growth, by Hierarchical Order in the Urban Sys tern by Region ................................ 53

17 The Correlates of Net Immigration and Natural Increase Rates ... 55

18 Regression Models: Immigration and Natural Increase .•.•.•..••• 59

Tables continued:

A 1 Independent Variables: Measures obtained from 1971 Census

2 Other Independent Variables

3 Access Measures

4 Variables Used in the Analysis of Flow Matrices

PREFACE

This research paper continue3 the discussion and analysis of

the 1966-1971 Canadian migration patterns begun in Research Paper

No. 85 in this series. That paper was entirely descriptive, whereas

this study links the patterns of migration to the social and economic

characteristics of urban regions as described in the 1971 Census.

Hypotheses linking migration flows to city size, distance, and differ­

entials in income and employment are examined for the 124 urban-centred

regions which comprise the Canadian urban system.

I would like to acknowledge the support of the Centre for Urban

and Community Studies and the Department of Geography in preparing the

paper. Siegfried Schulte gave me invaluable technical advice. Jane

Davies created the figures and Bev Thompson typed the manuscript.

Larry Bourne and Neil Field provided useful suggestions.

This project was funded by a research grant from Canada Council.

JWS

- iii -

MIGRATION AND THE CANADIAN URBAN SYSTEM: PART II, SIMPLE RELATIONSHIPS

INTRODUCTION

Since the work of Ravenstein (1885, 1889) in the nineteenth century,

researchers have sought order in the patterns of migration among places

and regions. Why do people migrate in the first place? How do they

choose among alternative locations? And, increasingly, how does this

migration contribute to differential rates of population growth and capi-

tal investment? This paper explores some of the causal factors put forth

in the literature over the years to explain migration patterns, as they

apply to the Canadian urban system. The descriptions of migration rates,

flows of migrants, and net transfers among the urban regions of Canada,

for the same period, 1966-1971, have already been set forth in an earlier

paper (Simmons, 1977). In the present paper a number of simple regression

models are constructed to link migration patterns to various urban char-

acteristics as defined in the 1971 Census.

The rest of this section briefly reviews the data and units of

analysis used in the study. The second section focusses on the various

definitions of migration rates: including in, out, gross and net migration

as well as migration efficiency. Each of these measures can be related to

various characteristics of the 124 urban regions themselves, such as size,

isolation, economic base and demographic structures. The third section

looks at a more complex problem, the pattern of movements among the over

15,000 (124 x 124) pairings of urban regions in the Canadian urban system.

The final section pulls together the results of the previous analyses and

discusses their implications for understanding change in the urban system.

- 2 -

Review

The description of migration in the Canadian urban system presented

in Sinnnons (1977) was built upon a series of hypotheses about the spatial

organization of the urban system, which were then applied to a particular

data set from the 1971 Census. The spatial units of analysis are extended

urban regions, consisting of one or more counties or census divisions

centred on an urban node, such as a city, urbanized area or census metro-

politan area. The 260 census divisions of Canada were then allocated to

124 extended urban regions (Figure 1). 1 Given the set of urban areas

(Table 1), the definition of the urban system was completed by assigning

each urban area to one of five levels in an urban hierarchy, and linking

each urban region to a single high order centre. The result is a simple

urban hierarchy, consisting of two 5th order sub-systems (Toronto, Montreal)

eleven 4th order sub-systems (i.e. Vancouver, Winnipeg) and so on, per-

mitting us to examine systematic differences in migration characteristics

by both city size and membership in a regional subsystem.

The migration data were obtained from a sample of one in three house-

holds in the 1971 Census. Respondents were asked whether they had lived

1The migration data were obtained directly from Statistics Canada, but other census measures on income, ethnicity, etc., were obtained through the Institute for Behavioural Research (I.B.R.) at York University. Statistics Canada did not generate a summary tape for census divisions, nor did I.B.R., but the latter were able to provide a tape using enumera­tion areas which could be aggregated. For some reason, however, I.B.R. excluded the Territories from their master tape. Statistics Canada, for their part, have horribly intermingled the published data for the Yukon and Northwest Territories for many variables, and it was difficult to obtain a consistent set of measures for these units. Nonetheless measures for urban regions centred on Whitehorse and Yellowknife have been obtained and inserted into the data file manually.

- 3 -

e

i ~--.,,_ ....._ ........_,.._., II

I I

I I

/ I

/

J. 2. 3. I,.

5. 6.

Corner:r.rool CranJ ;·;, 11.;

St. Joh;1'~-

7. A;.-,1-,erst 8. Truro 9. Ne·,.: G 1 a,:;:::•\·:

l 0. Sydney 11. Yar1;1out~1

12 . fad if [l}: 13. l:cn tvi 11 l.

14. Bathurs~ 15. Cat:Jj' be J l l C»l

] (,. Fdr:1rn1dP tcT<

] 7. ])redr~• ri~ C' t

18. }h · n c tc;:1

19. ~~f'r,,.;·c:ist1t-2 0 . s t . ,k ! l1 )

21. Chi cc.:; l i:::i 22. Bai(' Ccr:c;_:l1

23. NGU!'.H.:

21;. R.i ;•1cit:s Li 25. Eiv:i L•l<• (it; Lo.c;' 26 _ Ec1 :_iyn

27. \"~11 d'CJ1· 2L~ Dru~:1:non-.~y~f 11-.' 29. Th<tford ~ljnc~:

3 0 ~ c~"'i\~' ,] n s \~ i 11 L'.

3]. \'aJL:dicld 12. ML111trc.:l 33. Quc1:cc 34, St. Jc;:1-. 35. St. Jero':h.: 3G. St. lly;-icinthc 37. Sorf!1 3g. Lachut~·

40. Hull 41 . M :·,; ·o g l; 7 . S Ii c· r b r cK· !. ' · 43. Grnnby

- 4 -

TABLE 1

LIST OF URBAN REGIONS

I, !., .. J ti J i t: L ~ 1

l,5. Trojs I:"iYi~~-cs

11(. }1onl L;!·Jr"it:r

t; 7 . ~-: L " G (' ( l ~~I :_~

4f'.. l1.1ic1: :l "f"L;,·;; 49. VicLori~villc

50. Ot Ur""'· 51. Corm-•:.1J 52. Hroc..kvlll.C' SJ. l'._,11bro!:' 54. H3v:l;u;l,1:ry 55. Kinge::tc;, .56. Pcterh1.Jrc·uyh 57. Lindsay 58. F~ell.::·.;ilJ.:

)9. Cohou~· t;

60. NortL 6L K:i rl·.1.->r:J L"h" 62. Ti8';1ins 63. Sudbl:ry 64. Sa~lt SLe. ~ari1 (,5. Barri c· 66. Osl-.a«.'a 67. Toronto 6 8. Owen Sc•un d 69. Wine.is.Jr 7 0. Chat l.c 71. Sarnia 72. St' ThC•i:i.'.i".

7 3. Lo::.C: on 71.*. StratforJ 7 S. Ri tcho:1c:;~

71. Orn:n:·(\·; lle 78. Goderich 79. Guel;·h 80. Simcoe 81. Brantford 82. St. C;•t L1rirics 83. Woodsto }: 84. Thunder B;iy 85. Ke;10 r u

86. Dauphin 87. Bra:1dr'n

t8. f;C;

90. 91. 92.

J'rin•" A]br; t

>:er-LL B:1ttlt,fJ1-;~

E\,;·j f ,_ Curr·~~l! t

}~()·~~ ( j ,-:\·-

95. Sc::,c ~· atc•on

96. Rq;i n2

97. Esllvan 9.S. \·:c-yb'...1rn

99. LctL:.,ri cg·.::: JOO. Hc.:dicir,c lict 101. :Re:d Deer 102. Gr ii:. de· Pr ai ri c 103. Cdf;::lry i 0:1.. Er~r .. :::~ 0:1

l OS. Pt. Alber:· 'i JOC. !';;:;n::i1~,·:::

107. Pc;;tjctc~ 108. K~ir'Jtiups

109. Vc:;~non

llO. Tr;:;il 111. Cnrnbrook 112. Kclowna 113~ '\'~ct_oria

114. Vauccuvcr 115. l~cJ.son

116. Pr i nee Ge~~ r;:,« 117. Di·h·son Creek llE. I'r:Lr~cc Ru;:H::rt 11 9 . \.' i1 l i n::1 <; Lah· 120. Courtenay 121. Ch:i lliwack J 22. J'O\.:cll ki ver 123. \·,'h·: tchorsc 12l. Y~llo~knifc.

- 5 -

in a different house, municipality, or county five years earlier, and

if so, where. The movers among the extended urban regions defined here

include 11.3 per cent of the population, five years of age and older;

in-movers from abroad amount to an additional 4.2 per cent. Approximately

3.0 per cent of those who had moved did not indicate where they had form­

erly lived. Thus the spatial measures of mobility slightly underestimate

the total flows.

The Census Data and Methods of Analysis

In order to test a number of simple hypotheses about the determinants

of migration and the allocation of migrants to various locations, it was

first necessary to develop a series of measures of population size and

other socio-economic characteristics. The most appropriate sources for

the majority of these measures is the 1971 census, which contains a wide

range of relevant variables which are also available for the same set of

spatial units as the migration data. Over 1000 counts of various sub­

populations were available, and after selection of the most relevant ones,

and the aggregation of many categories, about two hundred potential measures

remained. From this list those measures put forth in testing the following

hypotheses about migration were selected. These variables will be dis­

cussed as they are introduced in various analyses, but Appendix A (Table A.l)

defines them precisely.

Although it may be inappropriate to use measures defined at the end

of the migration period as proxies for regional characteristics throughout

the migration period, one has little choice. It can be argued that most

of the characteristics -- such as economic base, ethnicity and educational

- 6 -

structure -- will remain relatively the same over time, although some

measures such as the rate of unemployment fluctuate rapidly (see below).

In most centres the inter-urban migrants replace only 10 to 20 per cent

of the population, and do not of themselves radically alter the population

composition,at least in the short run. Nonetheless, some measures, such

as demographic composition, are sensitive to differences in migrant levels.

Rapidly growing or declining places will be affected to the greatest degree.

In addition a number of non-census measures were developed for parti­

cular analyses in order to evaluate the role of various natural amenities

and accessibility in explaining migration. These measures are defined in

the Appendix (Table A.2). Another set of variables developed to analyze

the dyadic matrices (124 x 124) of flows among urban places is presented

in Table A.4.

The data analyses consist of a series of correlations and regressions

carried out using the SPSS package. While some attempt was made to formu­

late and test a series of hypotheses, the process rapidly became more in­

ductive than deductive. The following results describe migration as it

took place in Canada during the period 1966-1971, but generalizations to

other places and other points in time would likely be invalid. Moreover,

several of the fundamental statistical assumptions required for inference

from regression have been violated. The collinearity among the 'independ­

ent' variables will be referred to frequently, and its confusing effects

will become obvious. More serious and less apparent is the root cause of

some of these collinearities; that is, the spatial autocorrelation of many

of the variables. The fundamental differentiation of the data observations

- 7 -

(and of Canada) into two distinct spatial groups, one region with high

levels of migration turnover (Montreal and West) and one of low turnover

(East of Montreal), leads to an association between variables that have

the same spatial structure, but no necessary causal relationship. Other

kinds of basic spatial structure may also intervene, such as the core­

periphery difference. In addition, a variance-sensitive analysis such as

this picks up the peculiarities of economic development and population

growth during the study period. The Canadian urban system has a large

random component through time, but this random (over time) component can

itself contain a powerful spatial structure.

This paper does not undertake to review the migration literature.

An excellent and recent review of migration theories is provided by

Greenwood (1975), and phenomena peculiar to Canada are discussed in a

report by Canada Manpower and Immigration (1975). For the most

part though, it will be evident that the hypotheses emerge from a wide

variety of sources rather than any single study.

MIGRATION RATES

From the migration information now available it is possible to gen­

erate measures of many different aspects of mobility (Table 2). For the

set of 124 urban regions the number of movers of a particular kind has

been converted into a movement rate by subdividing by a base population

(POPBAS) which is defined as follows: (the 1966 population + the 1971

population age 5 and over)/2. Many of these movement rates were mapped

in the earlier study (Simmons, 1977).

CODE

1. MOVER

2. IN MIG

3. OUT MIG

4. GROSS MIG

5. NET MIG

6. MIG EFF

7. NAT INC1

8. IMMIG

9. LOG POP 66

10. POPBAS

11. GROWTH

- 8 -

TABLE 2

MEASURES OF MOBILITY

DEFINITION

(Population over 5 in 1971 - the number of non-movers since 1966)/POPBAS

(In-movers from other urban regions)/POPBAS

(Out-movers to other urban regions)/POPBAS

IN MIG + OUT MIG

IN MIG - OUT MIG

NET MIG/GROSS MIG (Migration Efficiency)

(Births - Deaths)/POPBAS

Net Immigration = (GROWTH - NET MIG - NAT INC)/POPBAS

Log 10 (1966 Population)

(1966 Population + 1971 Population over 5)/2

(Population 1971 - Population, 1966)/Population 1966

1obtained from Statistics Canada, 1973

- 9 -

Table 3 suTIIlilarizes the relationships among the various measures of

mobility. It differs from Table 2 in the previous study in several minor

ways: the use of the averaged base population instead of the 1966 popula­

tion; the correction of minor data errors such as the level of in-migration

to Cobourg, and correcting for the effects of boundary changes over time;

the use of estimates of natural increase from vital statistics data (Statis­

tics Canada, 1973); and the calculation of net iTIIliligration as a residual

of population growth after natural increase and net internal migration have

been removed.

Despite these changes the results are essentially the same as before.

A typographical correction reverses the sign of the correlation between

city size and turnover. Correlations with net immigration are strengthened

except for the association with city size. Correlations with natural in­

crease are weaker, in particular the relationship with growth. A strong

collinearity exists among the various measures of mobility. All, except

perhaps the rate of out-migration, are positively correlated with each

other, and when the rates are mapped the spatial distribution suggests two

different migration regimes: first, east of Montreal almost all cities

are slow-growing and extremely stable with low rates of mobility of all

kinds. Second, Montreal and the rest of the country present a more complex

pattern with high levels of mobility and considerable diversity in the

various components of population growth.

The presence of this collinearity among mobility rates complicates

the analysis of causal relationships as we shall see subsequently. Mobil­

ity begets mobility, because those individuals who have changed cities (or

- 10 -

TABLE 3

CORRELATIONS AMONG MOBILITY RATES

Variable 1 2 3 4 5 6 7 9 10

1. MOVER 1.000

2. IN MIG . 864 1.000

3. OUT MIG .417 .553 1.000

4. GROSS MIG • 775 .929 . 823 1.000

5. NET MIG . 705 . 761 -.119 .466 1.000

6. MIG EFF .708 • 718 -.107 .442 .940 1.000

7. NAT INC • 322 .138 .241 .201 -.023 -.045 1.000

8. IMMIG .661 .584 .252 .Sll .soo .558 -.005 1.000

9. LOG POP 66 -.047 -.387 -.480 -.478 -.088 -.029 -.023 .058 1.000

10. GROWTH • 853 .818 .107 .605 • 892 .850 .287 • 729 -.062 1.000

* * Mean 44.1 14.2 14.4% 28.6 4.5 15.0 5.6 0.7 6.3

Standard 11.6 7.5 4.9 11.0 6.5 18.4 2.9 3.5 9.5 Deviation

Coefficient of 0.26 0.53 0. 34 0.39 1.44 1.23 0.52 5.00 1. 70 Variation

* Absolute values

- 11 -

countries) a short time ago, are much more likely to move again -- by

virtue of their age (since mobility is highly concentrated in the period

of life-cycle changes, age 15-34), their migration experience, and their

relative lack of social or economic links to any given community. We can

think of rapidly growing communities as containing a large number of

socially mobile economic maximizers, willing to move on to the best offer

at any time. For instance, Cordey-Hayes (1975) presents a model in which

high levels of out-migration leave behind job opportunities which in turn

attract in-migrants. In the low migration regime, in contrast, we observe

communities in Eastern Quebec and the Haritimes which are composed of

households whose mobility appears to be less sensitive to economic condi­

tions. Older households, home-owning, linked closely by kinship, culture

and language to a region, are the least likely to move -- particularly

when economic opportunities are located far away, in a markedly different

cultural milieu.

Motivations for migration are seldom clearly defined, however,

either in the heads of migrants or in tables of migration flows. There

are those individuals who have to move "to get out of here" at any cost,

and those who will suffer all kinds of indignities to stay. Sometimes

the former coincide with those who have a drive to improve their lives;

sometimes the latter are people whose lives are already relatively com­

fortable. The literature on urban growth and migration (e.g. Miron, 1975)

defines two kinds of growth: household-initiated and industry-initiated,

and these growth types in turn may be related to the above migration

regimes. In household-initiated movements the emphasis is on consumption

- 12 -

rewards -- peace and quiet or urban stimuli, natural amenities, or per-

haps a familiar life-style. Such rewards can lead to a complex migration

pattern with movements to both rural and urban areas in all parts of the

country as people sort themselves out by life-style. Recently an inter­

esting phenomenon has been observed: an important migrant stream to Cali­

fornia -- that of the 'Okies' in the thirties -- was not household initiated

(Morrison, 1975). These were people who had been forced off their land

in the Eastern Plains by droughts and dust storms, and who looked to Cali­

fornia as a place of work. Forty years later, as they retire, they are

moving back to Oklahoma and Missouri seeking the way-of-life they have

missed. Vanderkamp (1973) suggests that as many as one quarter of Canada's

interprovincial movers are return migrants. Consumption choices are di­

verse, and ephemeral. They include the option of staying as well as the

option of moving, and like any consumer decision are linked inextricably

to the costs and rewards of moving.

It is also important to recall that very large adjustments to house­

hold preferences can occur without necessarily requiring or initiating job

creation. Only about 13.4 per cent of all moves (gross migration) result

in net migration: the othempermit adjustments of individual households

to life styles and amenities. As emphasized in the previous study these

adjustments reflect patterns of social integration, the distribution of

opportunities and the awareness of these opportunities.

The industry-initiated flow treats the household as responding simply

to differentials in levels of employment and wages between cities and re­

gions. The growth of jobs follows the logic of location theory. In

Canada these changes in jobs often reflect massive external stimuli. The

- 13 -

decline in copper prices leads to the closing of a mine, and people

move away. The household tends to choose locations of higher wages and

greater employment probability. Again, the migrant pattern can be quite

complex, only in this case we observe differential flows due to the diver­

sity in the kinds of jobs and occupations involved. Miners move here,

professors there, and executives to somewhere else. A career cycle of

moves may occur -- to military service, to college, to a large city head

office, to the branch plant, etc. Even where the direct inducement of

employment gain is not immediately apparent, the argument of generational

mobility may be involved, with "greater opportunities for the children",

linking social and geographical mobility over two generations. We have

other situations in which the temporal lag between such events and the

migration response is very long. A community of aging miners will not

necessarily change as the mine shuts down. Instead, over twenty years,

we may simply observe that all the young people leave year after year.

We have considerable evidence of both household- and industry-

ini tia ted movement. Sociologists (e.g. Morrison, 1975) have documented

the former; economists (Courchene, 1974; Grant and Vanderkamp, 1976)

the latter. A complex system of tradeoffs links the two sets of motives,

so that unattractive areas (an Arctic weather station) require high wages

and attract an atypical subset of workers, while attractive locations

(a Nova Scotia coastal village or a town in the B.C. interior) can be main­

tained with no visible economic support. As the general level of income

and the availability of transfer payments increase, we can posit a relax­

ation of the importance of industry-initiated forces in migration.

- 14 -

There is also some evidence that the two stimuli are not perfectly

linked. Imperfections in the labour market, for example, due to the

costs and time requested in adjustment, maintain deficits or surpluses

of jobs in both attractive and unattractive areas. Governments attempt

to increase the efficiency of the market by encouraging the migration of

either jobs or workers; but they have also enlarged the imperfections by

separating the ties between production and consumption -- providing in­

come where there is no net output of goods and services. Moreover, com­

panies transfer workers with little reference to current labour market

conditions.

The sections to follow apply some of these hypotheses to the various

measures of mobility rates. First we explore factors linked to the basic

rates of out-migration and in-migration, followed by a brief discussion

of the composite measures of net migration, gross migration, and migration

efficiency. In the final section of the paper the various components of

population growth, including natural increase and immigration, are linked

together into a composite model. Note that Table 3 indicated much greater

levels of variation among the regions in net migration rates than in

either of the other two sources of population growth, natural increase

and net innnigration.

Out-migration

Prior to the 1960 Census of the United States, only very limited

data were available on the rates of in and out-migration for various

spatial units. Wolpert (1965), for example, used the 1960 data to develop

- 15 -

a series of hypotheses about migration decisions in which the age-

structure of the region played an important role. For Canada the age

distribution of inter-municipal movers is described in Figure 2. The

appropriate hypothesis to test this relationship is:

Hl: The rate of out-migration is directly proportional to the proportion of population aged 15 to 34.

Unfortunately our data file does not include the age-structure of the

population at the beginning of the time period (1966), and since in-

migrants are (naturally) disproportionately young, the measure used here

picks up part of the correlation between in-migration and out-migration

which was noted above (r = .553). Wolpert also introduces the notion of

place utility in which different kinds of households have differing in-

tensities of relationships with their environments. Grant and Vanderkamp

(1976, p. 25), concede the importance of 'taste differences' and specifi-

cally identify French language as a variable in Canadian migration patterns.

Our hypothesis is:

H2: The rate of out-migration is lower for areas which are a) French-speaking, b) spatially isolated, c) settled for a long time, d) characterized by low-levels of education/occupation.

The attempt here is to pick up the marked variations in migration between

the eastern and western parts of the country. One expects that these

characteristics will require much stronger pressures to initiate high

levels of out-migration; whether the relationship is causal or simply a

series of coincidental correlations remains moot.

Accessibility is suggested by Stone (1969) as a critical variable in

migration, which can be defined in any number of ways. The measure used

E N

- 16 -

FIGURE 2

E

I L61 'dnOJD aoo ~o UO!+o1ndOd /S+UOJD!W JO ·oN

0 U)

0 I()

0 v

0 !'(')

0 C\I

Q

0

c > .... Q.) -c:

-c: Q.)

E Q.)

> 0 E

..... 0 ~ c: c: c: ~ Q.) .c -0 Q.)

~ <(

- 17 -

here is a form of population potential:

POP POT. 1

E j

-~-b d ..

1]

where bis equal to 1.5 and d .. is assigned the value of 20 miles. The 11

reason for this choice is elaborated in Appendix A.2.

We might also expect the level of out-migration to be related to

the economic performance of a region -- as hypothesized (but not found)

in a number of previous studies (Sjaastad, 1962; Lowry, 1966; and by

Courchene, 1970 and Grant and Vanderkamp, 1976, in Canada). Two measures

are available -- wage rates and the level of unemployment. The former

tend to be a long-s~anding measure tied to the long-term growth trends

of regions. The latter is a highly volatile measure, as Figure 3 suggests,

fluctuating widely both seasonally and through the business cycle. As

Table 4 ind:i.cates (and Grant and Vanderkamp found), unemployment is an in-

effective predictor of migration and a third, intermediate measure was

tried, called the employment ratio (ER). It combines the participation

rate (with links to income, demography and industrial structure) and the

level of unemployment:

H3: Out-migration rates decline as wage rate and employment rate increase.

Renshaw (1975) has looked at the relationship between in-migration, out-

migration and economic growth in some detail, using annual dat~. By thus

controlling for age structure and localized sources of high mobility, he

is able to show that employment growth alters in-migration and out-migration

in opposite directions.

30C

20C

150

'" 150

- 18 -

FIGURE 3

SEPT I LE ( Saguenay County)

Average weekly wage ( $ )

( 1961 - 100 0)

100 1977

BRANTFORD (BRANT COUNTY) 300

Average weekly wage ( $ )

200

150

100

200 Employment index

150

( 1961 1000)

1969 1970 1971 1972 1973 1974 1975 1976 1977

RED DEER, ALBERTA

200 Average weekly wage ($)

150

iOO 90 80

250 Employment index

200

250 ( 1961 - 100.0)

100 -.----~·-----,--...-1969 1970 1971 1972 1973 t:F4 1975

- 19 -

TABLE 4

THE CORREL.ATES OF OUT-MIGRATION RATES

Variable

1 2 3 4 5 6 7 8 9 10

1. AGE 15-34 1.000

2. FRENCH .503 1.000

3. ACCESS .190 .230 1.000

4. DECADES -.033 .230 .560 1.000

5. UNIVERSITY .081 -.469 .215 -.054 1.000

6. WAGE .488 -.139 .240 -.118 .509 1.000

7. EMPLOYMENT -.403 -.652 -.019 -.027 .277 .013 1.000

8. EMPLOYMENT RATIO -.163 -.694 .119 -.079 .544 .398 . 775 1.000

9. LOG POP 66 .170 .001 .578 .399 .435 .253 -.051 .130 1.000

10. PRIMARY -.079 -.094 -.573 -.602 -.301 -.327 -.152 -.040 -.449 1.000

11. OUT MIG -.114 -.375 -.483 -.649 -.096 .093 .253 .311 -.480 .495

12. Gi\OSS MIG -.051 -.465 -.357 -.601 .287 .306 .260 .423 -.478 .237

13. NET MIG .090 -.229 .128 -.039 .352 .391 .062 .256 -.088 -.356

14. MIG EFF .072 -.301 .210 .210 .054 .426 .471 .204 .411 -.421

Mean 31.1% 30.2% 4901 10.8 7.1 $4859 91.9% 51.2 20.0

Standard 2.6 36.0 4900 4.45 2.3 740 2.8 6.0 10.7 Deviation

Coefficient .08 1.19 1. 00 0.41 0.32 0.15 0.03 0.12 0.53 of Variation

- 20 -

The final hypotheses derive from various descriptions of the Canadian

urban system (Simmons, 1974a, b, 1976). It was observed in these studies

that the largest cities are the most stable, changing slowly and providing

the most diversified economies. The most unstable economies, prone to

rapid growth and decline, occur in the small resource-based regions. Also

we know that primary producing regions have been most directly affected

by the loss of employment due to technological change. The resulting

hypothesis is:

H4: Out-migration rates are ~elated inversely with population size, and directly with the propor­tion of employment in the primary sector.

A quick overview of the results is provided in Table 4. Out-migration

is obviously complex. Hypothesis one, involving the role of young adults

in out-migration rates, is apparently rejected as the sign is in the wrong

direction. This test is complicated, however, by the collinearity between

the variables AGE 15-34 and FRENCH language (r = .503) and the economic

measures WAGE and EMPLOYMENT. These relationships bedevil the basic be-

havioural hypothesis throughout the study. The high rate of natural in-

crease in French-speaking Canada stopped abruptly in the 1960's, leaving

a population bulge of young adults. The first part of the second hypothesis,

represented by the variable FRENCH (r = -.375), is weakly upheld subject

to the difficulties noted above, that UNIVERSITY and FRENCH are inversely

correlated. Although the level of education (UNIVERSITY) appears to be

an ineffective measure, age of settlement (DECADES) is strongly and negatively

correlated with out-migration, supporting that part of the hypothesis.

The correlation of out-migration with ACCESS is relatively high, but in

the opposite direction to that hypothesized. Northern and far Western

- 21 -

cities, the most remote in the system, are characterized by high levels

of turnover.

Hypothesis three is generally unsuccessful. WAGE has a negligible

effect and both measures of EMPLOYMENT are positively correlated with out­

migration, negating the hypothesis. It is apparent that only a closely

controlled analysis like Renshaw's (1975) can support the economic hypothesis.

In hypothesis 4 the correlation with population size is -.480 and with

employment in primary activities is .495. Both correlations are higher

than the correlation between the two independent variables, although both

are related to ACCESS. The city size effect suggests that the number of

opportunities -- socially and economically -- within the region is im­

portant (and logically, the presence of more internal opportunities means

fewer external opportunities). The relationship with the primary special-

ization and geographic isolation identifies the high turnover typical of

new resource towns, and also the large net transfer of aggregate employ-

ment from primary to tertiary activities.

From these clusters of relationships, and non-relationships, we can

construct some multiple regression models which incorporate combinations

of variables (Table 5), in order to gauge some of the indirect linkages

in migration. As other measures are taken into consideration, the effect

of age structure comes into line with that proposed in hypothesis one.

The rate of out-migration is consistently, albeit weakly, linked to the

proportion of young adults (although this may be an post effect, re-

fleeting the association between in-migration and out-migration rates

noted earlier), The low migration propensity of French-speaking Canadians

shows up consistently as does the age of settlement, and the variables

of hypothesis 4 remain significant. Experiments with combinations of

OUT-MIGRATION:

Variables AGE 15-34

Model 1. .100

2. .217

3. .242

4. .113

Final OUTMIG =

- 22 -

TABLE 5

SELECTED REGRESSION MODELS (Beta Values)

FRENCH DECADE LOGPOP66 PRIMARY

-.425

-.484 -.516

-.466 - .. 374 .326

-.344 -.377 -.292 .126

.217 + .454 (AGE 15-34) - 0.063 FRENCH -(.145) (.010)

+ 0.150 PRL~Y (.034)

R2

.148

.405

.488

.559

0.045 lO<?EOP 66 (. 009)

R2 = .488

- 23 -

economic measures, however, proved negative. Overall it is possible to

explain about half the variance in out-migration rates with these variables.

Out-migration appears to be due to long-term social and economic adjust-

ments in society rather than the conditions existing at any one point in

time. Even in a booming community many young people will still seek edu-

cational and occupational opportunities elsewhere.

In-Migration

In-migration rates vary more than out-migration rates. As a result

the variations from place to place in level of in-migration account for

the majority of the differences in net migration, turnover and, ultimately

in population growth. This greater variation eases the problem of regres-

sion modelling in some ways, but also forces us to examine a wider range

of independent measures. Stone (1969) undertook a brief exploration of

differences in in-migration rates among urban places in Canada. The re-

sults, however, were not promising. The independent variables, mostly

measures of economic specialization and income, explained only 28 per

cent of the variance. Moreover the differences in the results for urban

subsystems across the country were considerable. We can re-evaluate his

hypotheses here.

The first hypotheses centre around variations in economic opportuni-

ties. Migrants are supposed to seek locations which have high wages and

high levels of employment just as these conditions were supposed to

retard out-migration. In addition we must remember that out-migrants also

contribute to employment opportunities. Our hypothesis is:

HS: In-migration increases with the wage level, employment level and employment ratio.

- 24 -

The second set of variables describes various amenities that may act

as positive attractions to migrants, although the positive correlation

between in-migration and out-migration rates suggests that the character-

istics which keep people at home need not be attractive to residents of

other locations. Svart (1976) and Roberts (1974), for example, discuss

the kind of amenities which attract or deter migrants; such as the variety

of urban activities (described by population size), measures of climate

such as temperature, rainfall and sunshine, or terrain measures such as

the presence of the ocean or the mountains. An interesting table has

been prepared by Leven,~ al., (1970) to show that different age groups

respond differently to these environmental attractions. Unfortunately our

data set does not permit this kind of demographic disaggregation. The

final amenity variable used here follows from Robert's (1974) study which

found that mining and industrial cities were unattractive to prospective

migrants. The composite hypothesis is:

H6: In-migration is positively related to average temperature, sunshine, dryness, and the presence of mountains; cities which are large, non-industrial and non-mining attract migrants.

The final hypothesis introduces the complex notion of access.

Places which are relatively isolated must achieve larger thresholds of

attraction in order to attract migrants. Moreover, they draw upon a

limited number of origins.

H7: In-migration rates are higher in more accessible places.

The simple correlations presented in Table 6 support the economic

hypothesis (HS), but weakly -- much as Stone (1969) found. Both high

wage levels and participation rates attract migrants. The fact that

these variables were not significant to out-migrants suggests a pull

- 25 -

TABLE 6

THE CORRELATES OF IN-MIGRATION

Variable 1 2 3 4 5 6 7 8 9 10

1. WAGE 1.000

2. EMPLOYMENT .013 1.000

3. ER .398 . 775 1.000

4. TEMP .155 -.062 . 044 1. 000

5. RAIN . 075 -.261 -.290 . 574 1. 000

6. WET .099 -.312 -.376 .297 .731 1.000

7. MOUNTAINS .165 -.337 -.196 .309 .135 .182 1. 000

8. LOG POP 66 .253 -.051 .130 .060 .025 .060 .166 1.000

9. MFGMNG .408 -.085 .066 .046 .106 .082 -.175 .066 1.000

10. ACCESS .290 .050 .222 .164 .129 .054 -.225 .578 . 427 1. 000

11. IN MIG .389 .217 .417 .147 -.204 -.211 .330 -.387 -.067 -.208

12. GROSS MIG .306 .260 .423 -.003 -.263 -.258 .280 -.478 =-.174 -.357

13. NET MIG .391 .062 .256 .357 -.028 -.052 .297 -.088 .146 .128

14. MIG EFF .471 .204 .411 .391 .011 -.062 .171 -.029 .215 .210

Mean $4859 91. 9% 51.2 14.4 33.7 137.2 .282 16 .4 4902

Standard 740 2.8 6.0 10.7 13.3 25.6 .452 9.8 4900 Deviation --- --

Coefficient of Variation 0.15 0.03 0.12 .74 .40 .19 1. 60 --- .60 1.00

- 26 -

rather than push factor, confirming the empirical work of Lowry (1966),

Courchene (1970) and Grant and Vanderkamp (1976). The amenity hypothesis

(H6) is also confirmed, in part. The mean January temperature appears to

be an important variable, although neither the amount of annual rain nor

the number of rainy days come through strongly. The relationship between

in-migration and temperature (r = .147) is notable because the sign of

the association with out-migration (r -.233) is in the opposite direction,

producing a sizeable net impact. The presence of mountains, in contrast,

increases the levels of both out-migration and in-migration; and simply

reflects rapid growth in B.C. City size has a negative effect on in­

migration, contradicting that part of our hypothesis, but reflecting the

earlier conclusion that city size reduces the level of turnover in general.

The variable MFGMNG, measuring levels of manufacturing and mining activity,

seems to be irrelevant. Finally, it appears that accessibility is not a

major factor affecting in-migration rates; again the sign is opposite

from that hypothesized.

These variables in combination present a much stronger set of relation­

ships. As Table 7 shows, the successive addition of a new variable en­

larges the role of the earlier ones. The result is a reasonably powerful

regression model of in-migration, which includes p0pulation size (LOG POP 66)

as an explanatory variable, despite the fact that hypothesis H6 had been

rejected earlier. Our larger cities simply generate larger numbers of mi­

grants within their own boundaries. The combination of wage rates and city

size are particularly effective. The two variables are weakly collinear,

but operate in opposite directions in influencing in-migration rates. One

experiment, using OUTMIG as an independent variable, produced a low beta

- 27 -

TABLE 7

IN-MIGRATION: REGRESSION MODELS (Beta Coefficients)

EMPLOYMENT R2 Variables WAGE RATIO LOGPOP66 MFGMNG TEMP OUTMIG

1. .264 .312 .233

2. .391 .330 -.529 .494

3. .511 .301 -.538 -.259 .549

4. .468 .253 -.449 -.198 .159 .562

5. .494 .303 -.540 -.256 .101 .559

Final

Equation In-migration = 0.225 + .00005 WAGE + 0.301 EMPLOYMENT RATIO

(.0001) (0.084)

-0.259 MFGMNG - 0.538 LOGPOP66

(.052) (.012)

(Standard errors in parentheses)

- 28 -

value suggesting that the covariation between in-migration and out­

migration rates stems from common causes rather than a direct inter­

dependence. The major environmental variable (TEMP) did not help much

either, but MFGMNG continuously contributed in the hypothesized direction.

The final regression equation explains 56 per cent of the variance in in­

migration rates, not much better than the out-migration model, despite

the existence of greater variance than in the former. It is difficult to

specify a model which identifies those small and primary-production

oriented centres which are growing rapidly in the short term. As Stone

(forthcoming) points out, these migrations are responses to specific

'events' rather than prevailing conditions.

Other Mobility Rates

Net migration, gross migration and migration efficiency are simply

combinations of in- and out-migration rates. It is unlikely that any

variables yet unproposed will contribute to their explanation, although

it is possible that some of the measures identified above will become ir-

relevant or accentuated in combination for instance, the relationship

between city size and in/out-migration is cancelled in the case of net

migration. An overview of the correlations was provided previously in

Tables 4 and 6. Each measure, but especially net migration and migration

efficiency, is linked more closely to in-movement rates than out-movement

rates. Net migration (NET MIG) and migration efficiency (MIG EFF) are so

highly collinear (r .940) that essentially the same relationships hold

for both, although the associations of MIG EFF tend to be slightly stronger.

When Stone (1969) looked at net migration (combining internal and inter­

national) patterns for urban centres in Canada over the period 1951-1961,

- 29 -

the strongest correlations were with population size and the demographic

structure - reflecting growth in the previous decade. When the analysis

was repeated using counties as units of observation, the size of popula­

tion (or urbanization) was the most significant measure.

The strongest linkages for NET MIG are economic measures, particular­

ly WAGE level (r = .391) and the participation rate (.256), while the

strength of the primary sector leads to negative levels of net migration

(-.356). TEMP (.357) is also significant. The education variable

(UNIVERSITY) is also positively linked with net migration, but a negative

association exists with FRENCH. As suggested earlier, population size,

AGE 15-34, ACCESS and MFGMNG appear to be irrelevant. Methodological

differences aside, the results differ markedly from Stone's (1969) find­

ings, suggesting that different growth factors dominate in different

decades.

Gross migration or turnover is strongly and inversely correlated

with population size (r =-.478), as might be expected, but is positively

correlated with the dominance of the primary sector (r = .237). The

weakly negative relation with the demographic variable AGE 15-34 (r = -.051)

suggests that we must wait and see how it operates in combination with

other variables before drawing any firm conclusions. The hypothesized

relationship with FRENCH ethnicity is supported (r = -.465), as is age

of settlement (DECADES, r = -.601). Weakly positive relationships with

the economic growth variables are also apparent.

In Table 8 a series of multiple regression models express some of

the indirect effects of these variables. Equation 2 in part a) of the table

again exhibits the complementarity between economic measures and population

a)

b)

- 30 -

TABLE 8

REGRESSION MODELS: NET AND GROSS MIGRATION (Beta Values)

Net Migration

WAGE EMPLOYMENT LOGPOP66 PRIMARY RATIO

1 .392 .126 -.203

2 .276 .177 -.371 -.425

3 .255 .175 -.364 -.352

TEMP

.239

NETMIG = 0.093 + .00002 WAGE + 0.183 EMPLOYMENT RATIO

(.00001) (.083)

-0.055 LOGPOP66 - 0.214 PRIMARY + .00141 TEMP

(. 013) (. 051) (. 00044)

Gross Migr«tion

EMPLOYMENT

R2

.204

. 377

.390

WAGE RATIO LOGPOP66 AGElS-34 FRENCH DECADE R2

1 .309 .379 -.605 .544

2 .348 .353 -.601 -.061 .546

3 .281 .128 -.588 -.137 -.406 .603

4 .244 .193 -.446 .053 -.250 -.320 .675

5 . 271 .198 -.441 -.215 -.328 .674

Final Equation

~ GROMIG = 0.607 + .00004 WAGE + .359 EMPLOYMENT RATIO '\ '\

(.00001) (.149)

-.120 LOG10POP - .0659 FRENCH - .00812 DECADE I\ "\ \

( .016) (.0237) (.00152)

- 31 -

size operating on NETMIG. In combination each variable gains some ex­

planatory strength. By and large non-economic predictors of net migration

are ineffective, however, and the final model is both parsimonious and

weak in explanatory power. It can be argued, though, that short-term

variables dominate the relationship.

The gross migration equations are richer in complexity and in ex­

planation. The addition of the variable FRENCH to the economic measures

permits age structure to operate as hypothe5ized, but the addition of

age of settlement negates the demographic relationship. Population size

remains as a major determinant of overall levels of migration, suggesting

that continuing increa5e5 in the level of urbanization of Canada may

reduce the amount of inter-regional adjustment which is required for

more balanced growth. French language and age of settlement also

dampen the overall turnover. The model in part b) of Table 8 is reason­

ably effective, by the standards of this paper, explaining about two-thirds

of the variation in turnover. Nonetheless the high levels of covariation

among independent variables make the overall results rather suspect. We

know that the level of turnover in Canada differs in our two macro regions,

but we may not understand it any better than we understand net migration

patterns among urban places.

- 32 -

MIGRATION FLOWS

Now that we have some feel for those factors which determine the

number of migrants who begin or end their moves in various locations,

we can investigate the links between the origins and destinations them­

selves. It would be possible to satisfy the movement requirements of

Canadians in an infinite number of ways: for instance, by linking only

neighbouring places, or integrating the entire nation. Alternatively we

could imagine migration to be largely confined to regional subsystems,

or perhaps with the most intense flows among major metropolitan centres

across the country.

The data mapped in the earlier paper (Simmons, 1977) describe the

significant patterns of migrant flows. The distribution of flows is

highly skewed, with a large number of zero linkages and a few very strong

linkages. Large cities dominate. Regional differences in migration rates

also affect flow patterns. The significant flows are concentrated in

three spatial clusters, around Montreal, around Toronto and in Western

Canada. Population size and the distance separating places, as combined

in the urban hierarchy, are clearly significant determinants of flow

patterns. Finally the matrix of migration flows is strongly symmetrical,

with net migrants accounting for less than 15 per cent of the total move-

ment.

Spatial interaction can be modelled in either of two ways: we can

observe the individual unit (e.g. the household) as a goal-seeking entity,

and we can try to see how it responds to its environment by moving or

otherwise adapting. These models are essentially behavioural, focussing