







































AUSTRALIAN POPULATION STUDIES  2017 | Volume 1 | Issue 1 | pages 41–54 

 

© Bernard, Forder, Kendig and Byles 2017. © Published under the Creative Commons Attribution-NonCommercial licence 
3.0 Australia (CC BY-NC 3.0 AU). Journal website: www.australianpopulationstudies.org 

Residential mobility in Australia and 
the United States: a retrospective study 

Aude Bernard*  The University of Queensland 

Peta Forder  The University of Newcastle 

Hal Kendig  Australian National University 

Julie Byles  The University of Newcastle 

*Corresponding author. Email: a.bernard@uq.edu.au. Address: Chamberlain Building, Queensland 

Centre for Population Research, School of Earth and Environmental Sciences, The University of 

Queensland, St Lucia QLD 4072 

Paper received 11 August 2017; accepted 27 October 2017; published 20 November 2017 

Abstract 

Background 

Levels of internal migration vary significantly between countries. Australia and the United States 

consistently record among the highest levels of migration anywhere in the world. Very little is known, 

however, about the factors underlying mobility differentials. We argue that this is because existing 

evidence is almost exclusively based on period measures applied to cross-sectional data.  

Aims  

We seek to advance understanding of cross-national variations in levels of residential mobility by 

drawing on a newly proposed suite of cohort migration measures, coupled with the recent release of 

internationally comparable retrospective residential history data.  

Data and methods 

Focusing on the early cohort of baby boomers born between 1947 and 1951, the paper examines 

residential mobility levels and patterns in early and mid-adulthood in Australia and the United States 

and compares them with 14 European countries. Differences in completed levels of residential 

mobility are assessed in terms of four components: the proportion of a cohort who moved at least 

once; mean age at first move; mean age at last move; and average interval between moves.  

Results  

While cohort analysis confirms high levels of mobility in Australia and the United States, it does not 

support the notion of a common ‘new world’ mobility regime distinct from other advanced 

economies. 

Conclusion 

A cohort perspective offers refined insights into population mobility. The increasing availability of 

retrospective survey data means that researchers can now apply cohort measures to a wide range of 

countries.  

Key words 

Retrospective life histories; residential mobility; cohort migration; completed migration rate; 

completed migration distribution; migration progression ratio; Australia; United States.  

 

http://www.australianpopulationstudies.org/
mailto:a.bernard@uq.edu.au


42 Bernard A et al. Australian Population Studies 1 (1) 2017 

 

1. Introduction 

‘New World’ countries such as Australia, Canada, New Zealand and the United States are firmly 

established as some of the most mobile countries in the world with 40 to 55 per cent of their 

populations changing residence over a five-year period (Bell et al. 2017). In a seminal cross-national 

comparison, Long (1991) attributed this high mobility to institutional frameworks, flexible housing 

and labour markets and peripatetic traditions inherited from immigrant forbears. More recently, in a 

comparison of 23 Organisation for Economic Co-operation and Development (OECD) countries, 

Sánchez and Andrews (2011) showed that Australia ranked second after Iceland, and the United 

States fourth after Sweden in terms of the proportion of households changing residence over a two-

year period. Similar variations in levels of internal migration were found in a global comparison of 

139 countries (Esipova, Pugliese and Ray 2013). These movements have occurred against a 

background of globalisation, urbanisation and population ageing (McDonald 2017). In most countries, 

internal migration has become a leading agent of demographic change, shaping patterns of human 

settlement and affecting the age distribution of populations.  

This paper adopts a cohort perspective to advance understanding of levels and patterns of residential 

mobility in Australia and the United States. A cohort approach provides distinct advantages over 

existing studies, which use period measures applied to cross-sectional data. First, by following 

individuals over their entire life course, a cohort perspective provides detailed information about the 

migration trajectories of different birth cohorts (Bogue 1950; Shyrock and Larmon 1965). This can 

reveal how moves are distributed in the population, which cannot be achieved with the dichotomy 

between movers and non-movers commonly used in national censuses and surveys (Xu-Doeve 2006). 

Thus, because it views migration as an incremental process where individuals progress from one 

move to the next, a cohort approach allows the association between the timing of migration and the 

number of lifetime moves to be explored, which can shed light on the demographic mechanisms 

underlying differences in migration levels (Bernard 2017b).  

Second, a cohort perspective has the advantage of eliminating any potential tempo effects, which inflate 

or deflate the period measure of a demographic event due to a rise or fall in the mean age at which the 

event occurs (Bongaarts and Feeney 2008). Cohort migration is free of this interpretive difficulty. If 

cohort migration falls, it is a pure quantum effect: people are moving less. Third, because individuals 

may live through periods of high and low migration, a cohort approach has the advantage of smoothing 

out temporal variations in migration levels. This is particularly important when comparing countries 

because the economic cycles, housing market conditions and government policy regimes that underpin 

short-term variations in migration level are unlikely to be in phase in different national contexts (Bell et 

al. 2002). Finally, comparing the migration trajectories of successive cohorts can reveal the influence of 

social change on the evolution of migration behaviour, while comparisons between countries can shed 

light on the effects of national economic, social and policy developments (Kendig and Nazroo 2016). 

To facilitate a structured approach to the cohort analysis of migration and residential mobility, 

Bernard (2017a) has recently proposed a comprehensive suite of robust cohort measures that 

capture the level and distribution of completed migration and the timing and spacing of moves. 

Application to European countries (Bernard 2017b) has revealed that differences in cohort migration 

levels are attributable to variations in the extent of repeat movement, which is underpinned by 

differences in mean ages at first and last move. 



Australian Population Studies 1 (1) 2017 Bernard A et al. 43 

 

We now examine the demographic mechanisms underpinning variations in residential mobility levels 

in two ‘New World’ countries. We draw retrospective residential history data from the Life Histories 

and Health (LHH) survey in Australia and the Life History Mail Survey (LHMS) in the United States. 

While the LHMS surveyed a nationally representative sample of Americans born before 1966, the 

LHH survey focused on early baby boomers born between 1947 and 1951 and living in New South 

Wales at the time of the survey. The latter may therefore not be representative of all Australians. 

The surveys collected retrospective lifetime residential mobility histories in 2011–2012 and 2015–

2016, respectively, using life-history grids. This approach involves showing respondents a schematic 

form that depicts the year in their life, from birth to present, alongside national and world events to 

help them recall past moves (Belli 1998; Blane 1996). While the life-history calendar only took the 

form of a mail survey in the United States, it was complemented in Australia by computer-assisted 

telephone interviews to assist recall (Kendig et al. 2014). Despite these differences, response rates 

were similar in the two countries, sitting at 45 per cent in Australia and 48 per cent in the United States. 

To position Australia and the United States internationally, we complement these datasets with 

retrospective residential histories from the English Longitudinal Study of Ageing (ELSA) and the Survey 

of Health, Ageing and Retirement in Europe (SHARE). Together these provide directly comparable 

residential histories for 14 European countries (Börsch-Supan 2010; Marmot et al. 2016). The paper 

explores variations in levels and patterns of residential mobility for the cohort of baby-boomers born in 

each case study country between 1947 and 1951 by comparing completed migration rates, completed 

migration distribution and the cumulative distribution of movers by age and move order. 

2. Cohort measures of migration: methods and data 

The analysis presented here is confined to a subset of six measures recommended by Bernard 

(2017a) to compare migration between cohorts and countries. While we apply these measures to 

residential mobility (i.e. changes of address), we use the term ‘cohort migration’ proposed by 

Bernard (2017a). Table 1 lists each measure in summary form, providing a definition and an algebraic 

representation, where M corresponds to the number of moves, P to the number of individuals and X 

to the age at move. Subscript i refers to the order of each move (first, second, etc.) and n to an 

individual. Thus, 𝑃𝑖 refers to the number of individuals who have moved i times, 𝑀𝑖 to the number of 

moves of order i for all i>0, and 𝑋𝑛 corresponds to the age at move of individual n. 

Multiple measures are required to comprehensively quantify cohort migration: 

• The first of these measures is the completed migration rate (CMR), which represents the average 

number of moves undertaken by members of a given cohort over the course of their lives, as 

defined by equation (1) in Table 1. It is readily comparable across countries and indicates 

whether the overall level of migration is high or low.  

• Because the actual migration behaviour of individuals is more heterogeneous than this summary 

statistic suggests, the completed migration distribution (CMD) decomposes the population 

according to the number of moves individuals have made, as indicated in equation (2), and hence 

reveals the proportion of lifetime non-movers, infrequent movers and frequent movers.  

• Migration progression ratios (MPRs) depict the underlying, incremental process of moving by 

measuring the proportion of individuals who, having made a given number of moves, proceed to 

move at least one more time, as shown in equation (3). Underpinning MPRs is the idea that 

variation in migration behaviour depends on the number of times individuals have moved. 



44 Bernard A et al. Australian Population Studies 1 (1) 2017 

 

• Mean migration age (MMA) summarises migration age patterns by showing whether populations 

are moving early or late in life. It can be computed for all moves, as indicated by equation (4), or 

by move order, as shown by equation (5). Of particular importance are (a) the mean age at first 

move, because it captures the start of the migration career of successive cohorts and (b) the 

mean age at last move, which indicates how early or late in life different populations stop moving. 

• The final measure, mean migration spacing (MMS), relates to spacing between consecutive moves, 

which indicates the extent to which moves are close to each other or spaced further apart, as 

shown by equation (6). Further information and worked examples can be found in Bernard (2017a). 

Table 1: Cohort measures of migration 

Source: Bernard (2017a). Notes: M is the number of moves; P is the number of individuals; X is the age at move; i refers to 
the order of the move; n refers to an individual. 

To maximise comparability between LHH and LHMS and avoid censoring bias, the analysis is confined 

to individuals born between 1947 and 1951 in each case study country. To ensure that migration 

careers are of comparable lengths for different birth years, the paper focuses on moves undertaken 

during early and middle adulthood, between the ages of 17 and 50 years (inclusive), and excludes 

tied moves in childhood that are qualitatively different to moves made as independent adults. In 

both surveys, respondents were asked to report the start and end years of residence for dwellings in 

which they had lived for more than six months since birth (up to 29 dwellings in LHH and up to 18 

dwellings in the LHMS). The postcode was collected in both surveys but, to ensure confidentiality, an 

annual indicator of change of residence and change of state was constructed for the LHMS, whereas 

geographic information was released at city level for the LHH. To ensure comparability, the analysis 

in this paper makes use of data for up to 18 moves and uses all changes of postcodes, independent of 

administrative units, in measuring migration. While this means that the distinction between short- 

and long-distance moves cannot be made, the results have the advantage of not being affected by 

differences across countries and over time in the number and shape of spatial units, which can bias 

cross-national comparisons (Courgeau 1973). 

Measures  Definition Method Equation 

Completed migration 
rate (CMR) 

Average number of 
moves per individual 
by the end of their 
migratory life 

𝐶𝑀𝑅 = 𝑀 𝑃⁄  
 

(1) 

Completed migration 
distribution (CMD) 

Proportion of cohort who 
have moved exactly i 
times 

𝐶𝑀𝐷(0,𝑖) =
𝑃𝑖

𝑃
 

(2) 

Migration progression 
ratios (MPRs) 

Proportion of individuals 
who  moved i times and 
who went on to move at 
least once more 

𝑀𝑃𝑅(𝑖,𝑖+1) =
𝑀𝑖+1

𝑀𝑖
 

(3) 

Migration mean age 
(MMA) 

Mean age at which 
individuals in cohort 
moved 

𝑀𝑀𝐴 = ∑ ∑ 𝑋𝑛,𝑖

𝐼

𝑖=1

𝑁

𝑛=1
𝑀⁄  

(4) 

Order-specific 
migration mean age 
(MMA) 

Mean age at which 
individuals in cohort 
moved for the ith   time 

𝑀𝑀𝐴𝑖 = ∑ 𝑋𝑛.𝑖

𝑁

𝑛=1
𝑀𝑖⁄  

(5) 

Mean migration 
spacing (MMS) 

Average interval between 
all moves for individuals 
who have moved at least 
twice 

𝑀𝑀𝑆 = ∑ (𝑀𝑀𝐴𝑖+1 − 𝑀𝑀𝐴𝑖

𝐼

𝑖=1
) ∑ 𝑀𝑖

𝐼

𝑖=2
⁄  

(6) 



Australian Population Studies 1 (1) 2017 Bernard A et al. 45 

 

3. Cohort residential mobility in Australia and the United States  

Figure 1 ranks Australia and the United States alongside 14 European countries from the highest to 

lowest completed migration rate (CMR). While both countries display a CMR well above the 

European mean of 2.9 moves, Australia reports the highest level of residential mobility among the 16 

countries, with an average of 5.1 moves per individual. The United States ranks fifth after Australia, 

Denmark, England and Sweden, with one less move on average than Australia.  

 

Figure 1: Completed migration rates: Australia, United States and select European countries 

Source: Authors’ calculations from ELSA, SHARE, LHH and LHMS data. Note: Changes of address between ages 17 to 50 years. 

To describe the actual range of mobility behaviours in each country, Figure 2 (next page) reports the 

completed migration distribution (CMD), which decomposes populations according to the exact 

number of times people moved in their lives. The proportion of non-movers, infrequent movers and 

frequent movers in Australia and the United States is compared to the European mean. The United 

States stands out for its significant proportion of non-movers. Fully 15 per cent of American 

respondents reported having never changed residence in adulthood, which is more than twice the 

average proportion in Europe and the second highest level after Austria (Bernard 2017b). In contrast, 

immobility in Australia is very rare, with less than 2 per cent of respondents reporting no change of 

postcode, which is the third lowest level after Denmark and Sweden.  

Both Australia and the United States, however, display a high level of repeat movement. Approximately 

five in 10 Australians and four in 10 Americans moved at least five times between the ages of 17 and 

50, compared to an average of three in 10 people in Europe. Very frequent movers are especially 

characteristic of the Australian mobility landscape, with 20 per cent of respondents reporting eight 

moves or more, compared with 13 per cent in the United States and a mean of 9 per cent in Europe. 

5.1
4.9

4.6 4.5

4.1

3.7 3.7
3.4

2.8 2.7

2.1 2.0 1.9
1.7

1.6 1.6

0

1

2

3

4

5

6

A
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ra

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en

m
ar

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it
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46 Bernard A et al. Australian Population Studies 1 (1) 2017 

 

 

Figure 2: Completed migration distribution: Australia, United States and European mean  

Source: Authors’ calculations from ELSA, SHARE, LHH and LHMS data. Notes: Changes of address between ages 17 to 50 
years; European mean obtained from the 14 countries in ELSA and SHARE. 

Bernard (2017b) showed that variations in the extent of repeat movement are underpinned by 

differences in mean ages at first and last moves that together delineate the average length of 

migration careers. Age at first move signals the start of an individual’s migratory life and is of 

particular importance because it influences the probability of subsequent moves. In countries where 

young adults first move early (i.e. early twenties), they subsequently move at younger ages than in 

countries where first-time movers are older (i.e. mid-twenties). In addition, younger adult movers 

are more likely to proceed to a subsequent move and consequently report higher numbers of moves 

throughout adulthood than late starters. We test this proportion for Australia and the United States 

by plotting age at first move against the CMR. 

 

Figure 3: Age at first move by completed migration rate: Australia and United States  

Source: Authors’ calculations from LHH and LHMS data. Note: Changes of address between ages 17 to 50 years. 

7

15

2

36

18

18

28

24

26

20

30

34

9

13

20

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

European mean

United States

Australia

0 moves 1 to 2 moves 3 to 4 moves 5 to 7 moves 8 moves +

R² = 0.7837

R² = 0.9271

0

1

2

3

4

5

6

7

17 18 19 20 21 22 23 24 25 26 27 28 29 30

C
o

m
p

le
te

d
 m

ig
ra

ti
o

n
 r

at
e

 

Age at first move (years)

United States Australia



Australian Population Studies 1 (1) 2017 Bernard A et al. 47 

 

Figure 3 reveals for both countries a clear negative association, confirming that later ages at first 

move in adulthood are associated with reduced lifetime mobility. The strength of this association is 

supported by a Pearson’s correlation coefficient of -0.89 for the United States and -0.96 for Australia. 

Individuals who first moved at age 17 changed place or residence on average 5.9 times in the United 

States and 6.5 times in Australia, compared with four times or less for individuals who first moved at 

age 25 or later in both countries. 

We further explore age differentials by reporting the cumulative distribution of movers by age and 

move order. We first analyse results for Australia separately in Figure 4, before comparing Australia 

with the United States in Figure 5. The results for Australia show that the first and second moves are 

strongly concentrated in early adulthood, which conforms to the well-established age patterns of 

migration peaking in the mid-to-late twenties (Bernard, Bell and Charles-Edwards 2014a, 2014b; 

Rogers and Castro 1981). Half the cohort have moved at least once by age 20 and more than 80 per 

cent by age 23. A year later, the same proportions have moved at least a second time. For both 

moves, the proportion of movers starts plateauing after age 30, which indicates that individuals who 

have not moved by that age never went on to move. The third and fourth moves occur a bit later in 

life and are spread across a broader age range. It is not until age 30 that 80 per cent of individuals 

who have moved twice proceed to a third move and it is not until age 36 than 80 per cent of 

individuals who have already moved three times undertook their fourth move. The curve for the 

third move plateaus around age 35, while the curve for the fourth move continues to increase at a 

slow rate, indicating a broader dispersion of the fourth move across the age spectrum.  

 

Figure 4: Cumulative distribution of movers by age and move order in Australia  

Source: Authors’ calculations from LHH and LHMS data. Note: Migration between ages 17 to 50 years. 

The cumulative proportion of movers at age 50 corresponds to the migration progression ratio 

(MPR). That is, the proportion of the cohort who having moved i times went on to move at least i +1 

more times. While MPRs decrease with move order, they remain high for all moves: as many as 89 

per cent of individuals who moved three times moved at least one more time. This pattern underpins 

the high level of repeat movement identified in Australia in Figures 1 and 2. 

0

25

50

75

100

17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

C
u

m
u

la
ti

ve
 d

is
tr

ib
u

ti
o

n
 o

f 
m

o
ve

rs
 (

%
)

Age at move (years)

first move second move



48 Bernard A et al. Australian Population Studies 1 (1) 2017 

 

We now compare age patterns between Australia and the United States. Figure 5 reveals that the key 

difference is in the progression to the first move. While the two countries first follow a similar 

progression in early adulthood, in the United States the proportion of first-time movers starts to 

plateau at around 80 per cent from age 25 onward. Thus, Americans who have not moved by age 25 

never proceed to a move. Conversely, in Australia the proportion of first-time movers continues, 

increasing to 98 per cent by age 40. On the other hand, second-, third- and fourth-time movers 

display very similar age patterns in the two countries and comparable proportions of movers by age 

40.  These results confirm that it is the proportion of non-movers that is the key factor underpinning 

differences in completed migration rates in Australia and the United States.  

                                                   Australia                               United States 

  

  

Figure 5: Cumulative distribution of movers by age and move order: Australia and United States 

Source: Authors’ calculations from LHH and LHMS data. Note: Migration between ages 17 to 40 years. 

We now seek to quantify the relative contribution of immobility to differences in completed 

migration rates. Following Jain and McDonald’s (1997) approach to fertility analysis, Bernard (2017a) 

demonstrated that the CMR can be expressed mathematically as a function of four components, 

𝐶𝑀𝑅 =  𝑀𝑜[1 + (
𝐿−𝐹

𝐼
)]  (7) 

0

25

50

75

100

17 19 21 23 25 27 29 31 33 35 37 39

C
u

m
u

la
ti

ve
 d

is
tr

ib
u

ti
o

n
 o

f 
m

o
ve

rs
 (

%
)

Age at move (years)

First move

0

25

50

75

100

17 19 21 23 25 27 29 31 33 35 37 39

C
u

m
u

la
ti

ve
 d

is
tr

ib
u

ti
o

n
 o

f 
m

o
ve

rs
 (

%
)

Age at move (years)

Second move 

0

25

50

75

100

17 19 21 23 25 27 29 31 33 35 37 39

C
u

m
u

la
ti

ve
 d

is
tr

ib
u

ti
o

n
 o

f 
m

o
ve

rs
 (

%
)

Age at move (years)

Third move

0

25

50

75

100

17 19 21 23 25 27 29 31 33 35 37 39

C
u

m
u

la
ti

ve
 d

is
tr

ib
u

ti
o

n
 o

f 
m

o
ve

rs
 (

%
)

Age at move (years)

Fourth move



Australian Population Studies 1 (1) 2017 Bernard A et al. 49 

 

where 𝑀𝑜 is the proportion of individuals who moved at least once, 𝐹 is the mean age at first move, 

𝐿 is the mean age at last move and 𝐼 is the mean length of all intervals between consecutive moves 

for individuals who moved at least twice.  

Table 2: Components of completed migration rate: Australia and United States 

 

Proportion 
of movers 

(Mo) 

Mean age at 
first move  

(F) 

Mean age at 
last move  

(L) 

Mean length 
of migration 
interval (I) 

Completed 
migration 

rate (CMR) 

Australia  99.0 20.9 37.2 4.0 5.1 

United States 84.7 20.6 36.7 4.5 4.1 

% difference in CMR 
attributed to each 
component in United States 
compared to Australia  

-16.9 1.4 -2.7 -3.8 -22.4 

Source: Authors’ calculations from LHH and LHMS data. Note: Migration between ages 17 to 50 years. 

Table 2 reports each component of equation (7) for Australia and the United States and shows the 

estimated percentage differences in CMR in the United States compared to Australia. It reveals that 

the lower proportion of movers in the United States is overwhelmingly the main factor underpinning 

migration differentials between the two countries. Of the 22.4 per cent different in CMR with 

Australia, the proportion of movers accounts for 16.9 per cent. The impact of longer mean migration 

intervals on the CMR is much smaller, less than 4 per cent, as is the impact of the mean age at last 

move. The slightly younger mean age at first move has a small counteracting effect on CMR in the 

United States. Measured as the difference between the mean ages at first and last move, the average 

length of migration careers is about 16 years in both Australia and the United States. This is relatively 

long compared to that in other countries. In southern and eastern Europe, for example, individuals 

are mobile on average for about seven years (Bernard 2017b). 

 

Figure 6: Proportion of adult non-movers by birth cohort in the United States  

Source: Authors’ calculations from LHMS data. Note: Migration between ages 17 to 50 years. 

0

5

10

15

20

25

30

19
27

-1
9

31

19
32

-1
9

36

19
37

-1
9

41

19
42

-1
9

46

19
47

-1
9

51

19
52

-1
9

56

19
57

-1
9

61

P
e

rc
e

n
ta

ge
 w

n
o

 n
ev

er
 m

o
ve

d
 (

%
)



50 Bernard A et al. Australian Population Studies 1 (1) 2017 

 

Is the relatively high level of adult immobility a long-standing feature of the American landscape or a 

characteristic of the cohort born between 1947 and 1951? To answer that this question, Figure 6 

(previous page) examines trends in immobility between ages 17 to 50 for seven successive cohorts 

born between 1927–1931 and 1957–1961. It shows that immobility among the early cohort of baby 

boomers (born 1947–1951) forms part of a declining trend of high immobility. The proportion of 

adult non-movers was at its highest level for individuals born 1927–1931 (26%). It then fell for 

successive cohorts, reaching a low of 13 per cent for the cohort born 1952–1956, before increasing 

again for the 1957–1961 cohort.  

4. Conclusion 

Despite wide and enduring variations in migration levels between countries around the world, 

explanation for the relatively high mobility observed in ‘New World’ countries remains tentative. This 

can be traced in part to the limitations inherent in application of cross-national data to period 

measures, which fail to account for heterogeneity in migration behaviour or for cohort differentials. 

This paper has drawn on a series of robust cohort measures of migration, coupled with 

internationally comparable retrospective migration histories, to compare residential mobility levels 

and patterns in Australia and the United States and identify key differences compared with 14 

European countries for early baby-boomers born between 1947 and 1951. 

The results have confirmed a high level of residential mobility in Australia and the United States, with 

the former reporting the highest level of residential mobility among the 16 OECD countries in our 

sample and the latter ranking fifth after Denmark, England and Sweden. High levels of mobility in 

both countries are attributable to the high incidence of repeat moves. Approximately five in 10 

Australians and four in 10 Americans moved at least five times in adulthood, compared with an 

average of three in 10 people in the 14 European countries. Very frequent moves were especially 

characteristic of Australia, where 20 per cent of respondents reported eight moves or more.  

Compared with Australia, the United States combines a high level of repeat moves with a substantial 

rate of immobility. While less than 2 per cent of Australians never moved in adulthood, as many as 

15 per cent of Americans remained immobile. This is more than twice the average proportion of non-

movers in Europe and is the second highest level among the 16 OECD countries. Immobility appears 

to be an enduring characteristic of the United States that has persisted across successive cohorts 

born between 1927 and 1961. Although its incidence has progressively diminished, it remains close 

to 15 per cent for the most recent cohort. 

Cohort analysis does not support the notion of a common ‘New World’ mobility regime distinct from 

other advanced economies. While Australia’s very high level of mobility may be inflated as a result of 

the LHH sample being drawn exclusively from the state of New South Wales, its mobility level and 

patterns correspond to the mobility regime of Northern and Western European countries. In these 

countries, high mobility is the product of the extremely low incidence of immobility and a high level of 

repeat movement. This, in turn, is underpinned by an early mean age at first move and a late mean age 

at last move, which together support long migration careers (Bernard 2017b). Because of its high level 

of immobility, the United States departs from this mobility regime and does not conform to the 

patterns identified in OECD countries with intermediate levels of residential mobility, such as the 

Netherlands, France, Switzerland and Belgium, where immobility is very low and the level of repeat 



Australian Population Studies 1 (1) 2017 Bernard A et al. 51 

 

movement moderate. This suggests that the United States has a distinct mobility regime characterised 

by a unique combination of a high level of repeat movement and a high rate of immobility. 

As with any retrospective data, residential histories face issues of recall and are based on survivors 

only. Although survivor bias is expected to be small, the completed migration rate should, strictly 

speaking, be interpreted as the average number of moves undertaken by members of a cohort 

conditional on survival to the date of the survey. Bearing these limitations in mind, a cohort 

perspective offers a step forward in the comparative analysis of residential mobility by revealing new 

insights into mobility behaviour. It provides a robust foundation for exploring the demographic 

mechanisms underpinning migration differentials that parallel methods long used in fertility and 

mortality analysis. This paper has shown that moving beyond population-level averages and 

considering the distribution of moves provides a realistic description of the mobility experience of 

each cohort and offers refined insights into mobility behaviour. Of particular importance is the 

negative association between age at first move and completed migration, which indicates that late 

starters in Australia and the United States move less throughout the course of their adult lives.  

Bernard (2017a, 2017b) showed that age at first move operates to affect completed migration by 

influencing the likelihood of progressing to moves of higher orders and that, in turn, variations in 

mean age at first move underpin differences in completed migration over time and between 

countries. The association between age at first move and completed migration may also result from a 

selection effect whereby individuals who view mobility more positively choose to start moving at 

younger adult ages, or that the experience gained from a previous move may facilitate subsequent 

migrations (Van Arsdol, Sabagh and Butler 1968). In this paper, we have shown that age also matters 

because individuals who have not moved by the age 25 in the United States and age 30 in Australia 

remained immobile through their adult lives. Thus, moves in early adulthood seem to have a lifelong 

imprint on mobility behaviour.  

Differences in population composition are likely to account for some of the variation in residential 

mobility observed in this paper. The cohort measures employed here can be readily applied to specific 

groups to explore within and between country differences, differences between socioeconomic and 

ethnic groups and differences between native- and foreign-born populations. Better understanding 

of the role of population heterogeneity would represent an important step forward in the comparative 

analysis and understanding of migration and residential mobility. The distinctively high proportion of 

non-movers in the United States also invites investigation into reasons for immobility in that country 

and in other national settings to identify the underpinning factors. Attention to the determinants of 

repeat movement, which accounts for most of the differences in completed migration between the 

16 OECD countries, is needed also to establish the individual-level characteristics associated with 

repeat movement at a range of spatial scales and in different countries. 

Key messages 

• Cohort migration measures facilitate a structured and systematic analysis of retrospective 

residential history data  

• Application of these measures to residential histories collected in Australia and the United States 

has shown a high level of repeat movement in both countries, and a much a higher proportion of 

immobility in the United States. 



52 Bernard A et al. Australian Population Studies 1 (1) 2017 

 

• Individuals who had not moved by age 25 in the United States and age 30 in Australia remained 

immobile through their adult lives. This suggests that moves in early adulthood have a lifelong 

imprint on mobility behaviour. 

Acknowledgments 

The authors gratefully acknowledge the support of the Australian Research Council (ARC) through the 

ARC Centre of Excellence in Population Ageing Research (CE110001029), ARC Discovery Project 

(DP1096778) and ARC Discovery Early Career Researcher Award (DE160101574). The authors thank 

Emeritus Professor Martin Bell for providing comments on a draft version of the paper. 

The Life History and Health (LHH) Survey was approved by The University of Sydney Human Research 

Ethics Committee (#12744) and supported by an Australian Research Council Grant (DP1096778, 

‘Socio-economic determinants and health inequalities over the life-course: Australian and English 

comparisons’). Chief Investigators were Prof. Hal Kendig (ANU), Prof. Julie Byles (The University of 

Newcastle) and Prof. James Nazroo (The University of Manchester). Fieldwork and the sample are 

described in Kendig et al. (2014). This research was supported by the Australian Research Council 

Centre of Excellence in Population Ageing Research and by infrastructure and staff of the Priority 

Research Centre for Gender, Health and Ageing. 

The LHH research was completed as a sub-study of the 45 and Up Study. The 45 and Up Study is 

managed by the Sax Institute (www.saxinstitute.org.au) in collaboration with partners: Cancer 

Council NSW; National Heart Foundation of Australia (NSW); NSW Ministry of Health; NSW 

Department of Family and Community Services; Disability Council NSW; and Australian Red Cross 

Blood Service. We thank the many thousands of people participating in the 45 and Up Study.  

The 2015 Life History Mail Survey (LHMS) contains questions about residential history, education 

history and other important childhood and family events. The LHMS is part of The University of 

Michigan Health and Retirement Study, supported by the National Institute on Aging and Social 

Security Administration in the United States. 

The SHARE data collection has been primarily funded by the European Commission through FP5 

(QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, 

SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE 

M4: N°261982). Additional funding from the German Ministry of Education and Research, the Max 

Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-

13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, 

OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully 

acknowledged (see www.share-project.org). 

The ELSA datasets were made available through the UK Data Archive. ELSA was developed by a team 

of researchers based at NatCen Social Research, University College London and the Institute for Fiscal 

Studies. The data were collected by NatCen Social Research. The funding is provided by the National 

Institute of Aging in the United States and a consortium of UK government departments coordinated 

by the Office for National Statistics. The developers and funders of ELSA and the Archive do not bear 

any responsibility for the analyses or interpretations presented here.  

http://www.saxinstitute.org.au/
http://www.share-project.org/


Australian Population Studies 1 (1) 2017 Bernard A et al. 53 

 

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