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DECISIONS TO MOVE AND DECISIONS TO STAY: LIFE COURSE

EVENTS AND MOBILITY OUTCOMES

William A.V. Clark University of California, Los Angeles William Lisowski Independent Scholar ABSTRACT The focus in life course studies has been on events rather than states and in in the case of mobility, moving rather than the duration of stay between moves, or on the nature of the stayers themselves. Because most people in fact move quite infrequently and indeed often spend very long periods in one community, and even one house, it is natural to ask – can we identify who stays and why they stay? The value of turning our attention to stayers is that it refocuses attention on the people/place connection. That is, it focuses our attention on people staying in places and the connections they have to their local environments. The thesis, which is tested using decade long panel data from the Housing Income and Labor Dynamics in Australia (HILDA), is that staying is embedded in the life course and that it is a direct response to long term family and household stability. Clearly, aging matters, we are less likely to move as we age, but the research shows that both family structure and place (though with modest impacts) play roles in staying though with varying outcomes for couples and singles. As expected home ownership and the presence of children play important roles in staying.

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DECISIONS TO MOVE AND DECISIONS TO STAY: LIFE COURSE

EVENTS AND MOBILITY OUTCOMES

There has been a long interest in the dichotomy of movers and

stayers. This research, dating back to the 1970s, was concerned in the

main with differences between these two groups. In a sense the agenda

was to identify the extent to which they were different populations. Over

time, we came to realize that it was not so much a matter of two

populations but rather of a continuum of behaviors which included both

moving and staying. The turn to the life course as a conceptual

mechanism for understanding behavior in the city took us a long way

towards understanding the continuum of moving and staying, that in fact

moving was an event followed by a spell or a process of staying. Using

this conceptualization it was much easier to bring to bear a theoretical

approach which incorporated both mobility and immobility, that in fact they

were part of the same life course process and did not represent distinct

populations. Even so most of the work has been focused on the mobility

event and not on the time between events. That is, the focus has been

primarily on the event of moving, rather than the spell of staying. To the

migration specialist, stayers were simply those who did not move, and

whose role was in essence to provide the potential stock of future movers.

In this paper we wish to redirect attention to the staying process and to

understand who stays and why they stay in the context of the life course.

Individuals are rooted in place and it is only with some stimulus that

they change locations, and identifying that stimulus is at the heart of

studies of residential change. Unlike the focus on moving, the study of

staying has been largely a ‘study of residuals’ rather than a study of the

logic of staying. Now with overall declines in mobility there has been a

move to study why mobility is declining, and thus by extension why

stability is growing. There is also interest in creating a theory of staying

though we will argue that it is the continuum of staying and moving which

provides us with the richest understanding of how households negotiate

their locations as they move through the life course.

By virtue of their predominance in the population it is stayers who

exercise the major influence on neighborhood social structure simply by

aging in place. As residential mobility slows down, there is a need to

better understand both the internal demographic and external economic

dynamic of staying and its consequences for the stability and social

cohesion of residential communities on the one hand, and labor market

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flexibility on the other. In this study we specifically focus on the nature of

staying and we eliminate families and individuals with life event change –

marriage, divorce, separation – and consider only how stable couples and

stable singles behave over the life course. In this way we focus on where

stayers fit into the larger life course experiential structure. We create

stable families and examine the distinction between those who stay for

decade long periods versus those same stable households who move.

In the context of our emphasis on the life course we hypothesize

that staying is (a) strongly related to ownership, (b) the presence of

children and (c) high levels of satisfaction with the dwelling or

neighborhood. In contrast, the stable families who move are almost all

involved in creating families or in children leaving for college or jobs. In the

analysis we are able to demonstrate the extent to which excluding the

focus on family change – that is on marriage, divorce and separation,

helps us understand the staying process. Additionally, by focusing on the

extended life course rather than the just the time proximate to an event or

transition we can place the process of staying and moving in a more

contextualized setting. In effect we can think of three components of the

moving and staying continuum – (a)the behavior of stable families to stay

(b) stable families to move and (c) the behavior of households in family

composition transition who are also in fact almost always movers.

THE THEORETICAL CONTEXT AND PREVIOUS RESEARCH

Much of the research on mobility, even those studies which use

longitudinal data, in essence study only the event and do not examine the

continuum of events over, at a minimum several years. Theoretically

however, it is these segmented effects of staying punctuated by moves

that are at the heart of understanding how mobility and immobility

intersect. In the analysis in this paper we have three important dimensions

which underlie the analysis of events and spells. We are interested in the

staying spell, who stays for long periods, and shorter periods (that is

interrupted by moves), second, what is the role of family stability in the

process of staying and moving, and third, what is the intersection of

stability and immobility. These questions are especially relevant in the

context of declining mobility, something that has been happening both

spatially and inter-generationally in the US over the past two decades

(Hanson 2005, Frey, 2009, Cooke, 2011, 2013).

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Both external and internal demographic drivers have been invoked

to explain the decline in mobility. The growth of an older population

makes mobility less likely and there is evidence that metropolitan job

markets are becoming more alike and so movement is less necessary to

achieve increased returns to wages (Molloy, et al 2011). The discussions

of external changes invoke a range of explanations including the recent

economic disruptions, changes in family work participation and changing

technology. External forces clearly play a role in the likelihood of staying.

For example, rent control significantly increases household tenancy

duration and recent research on the impact of the great recession

suggests that owners defer moves when the market is destabilized

(Ferreira, et al 2010).

Several studies including those by Haurin & Gill (2002) and Chan

(2001) explore just how much mobility is slowed and duration increased in

response to changing house prices. Overall, increasing house prices does

have the effect of slowing mobility. And, studies of subprime mortgage

holders also suggest that for those with high initial loan-to-value ratios are

even more likely to stay. Households respond differently to external forces

and in some cases staying is interrupted by these external events as in

those who entered the home owner market at the height of the housing

price bubble. Some could not move and were forced to stay, while others

lost their houses in mortgage foreclosure. Not all households respond the

same way to these external shocks but these events play a macro role in

any study of staying. Still, in this study it is the internal forces which are

direct concern, what goes into the decisions to stay rather than move. Our

theoretical structure, rather than invoking external changes, draws

attention to population composition and the nature of families and their

stable and changing structures as a format for understanding staying more

broadly not just the likelihood of staying as a result of external events.

Not staying has always been dominated by the young and young

renters account for much of the totality of movement. Morrow-Jones

(1988) amongst many studies both emphasizes the high level of

movement between rental units in the early household formation and how

it created much of the overall mobility in a population. A whole series of

studies of family panels showed that the young are mobile and that a

household’s propensity to change place of residence is a function of the

need to adjust housing consumption and this often occurred in the

transition from being a renter to being an owner (Ioannides,1987; Clark

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and Dieleman, 1996; Mulder, 1993). Thus, changing proportions of young

renters and lower rates of family formation (age at marriage has

increased) all play a role in the level of mobility rates and by extension

relate to the generation of staying.

A slowdown in mobility may also be a combination of external

economic events and internal behaviors, especially the decisions by the

millennial generation. They are deferring the shift to ownership and are

often sharing rental accommodation and moving less often (Taylor, et al

2008). Amongst the reasons, although they are largely speculative, it may

be more difficult to get a mortgage with the stricter loans standards on

debt to income ratios, they are in markets where housing costs are high,

and they are starting families later. The average age at first birth for

women has increased from about 22 in the 1960s to closer to 28 in the

first decade of the 21st century.

Family structure and family connections also play a growing role in

the likelihood of staying. There are changing linkages between children

and older parents which in turn will likely increase the need for local

connections and by extension on staying in local areas if not in the same

residence (Michelin et al., 2008, Mulder and Malmberg 2014). Because

decisions about moving or staying are increasingly embedded in the larger

context of extended family structures, now households, with the extended

life spans of older parents have to make decisions about the needs of

elderly parents, the desires of grandparents and a raft of familial

connections (Taylor, et al 2008, Cohn and Morin, 2008). A Pew survey

finds that stayers overwhelmingly say they remain because of family ties

and because their hometowns are good places to raise children. Their life

circumstances match those explanations. Most stayers say at least half a

dozen members of their extended families live within an hour’s drive; for

40%, more than 10 relatives live nearby (Cohn and Morin, 2008). Stayers

also cite a feeling of belonging as a major reason for staying put.

Two changes in work residence links may also be playing a role in

changing duration, the increase in two worker households and a growing

increase in home based work at least for a segment of the employed

population. The ability to use complex technology without regularly

attending a specific location has made it possible “not to have to move”

and further increased the likelihood of staying locally. Second, we know

that today’s two earner married households are about 46 percent less

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likely to move across state lines than were their counterparts in the 1980s.

(Taylor, et al 2008).

Previous models of staying1

Morrison (1970) was amongst the first to suggest that rather than a

dichotomy of moving or staying, that the mover stayer model should be

refined so that it could be used to describe movers and stayers on a

continuum rather than as members of two discrete classes. This point

notwithstanding, in the long ensuing literature we have learned a great

deal more about movers: who they are, what triggers their move and

where they move to, and when, but relatively little about stayers (Hanson,

2005).

Goodman (2003) created a formal model of staying by focusing on

how moving and transactions costs tend to discourage moving especially

if you are a homeowner. From the Goodman perspective it takes more

than a small change in income to generate a move, other things being

equal, and thus staying is more likely on the whole than moving

(Goodman, 2002). For Goodman, echoing the earlier work by Morrison

(1970) on duration of stay, he concluded that “length of stay has

measurable and important effects” on the probability of future staying

(Goodman, 2003, p. 107).

There have been several calls for research on staying (Harsman &

Quigley, 1991; Pickles & Davis, 1991), but remarkably little sustained

quantitative work devoted to understanding staying. Where staying and

duration in the same occupancy has been important is in the studies of

attachment, both attachment to the individual dwelling and attachment to

the neighborhood (Clark, et al. 2015). These studies emphasize the

attachment links of household members to their neighborhood – their

familiarity with the area, their social ties, and their feelings of security – all

of which increase with length of residence. At the same time, as Grief

(2009) and others (VanHam and Feitjen, 2008) show, changes in the

social composition of the neighborhood can disrupt the tendency to stay.

Duration has always been a focus in the research on stayers

(Gordon and Molho, 1995). Previous research on cumulative inertia

posited the view that the duration effects were cumulative and created

1 The review draws on a working paper (Morrison and Clark, 2015)

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long term stayers (Clark and Huff, 1977). But it is the role of attachment

which is a more complex and perhaps a more useful explanatory

interpretation of the desire to stay and the behavior of staying. Although

sometimes staying comes about because households have had to

abandon desires to move (Coulter, 2013), studies of place attachment that

ask why people stay, find that it is a sense of community which plays an

important role in their decisions. The studies that explore staying in any

depth point to the role of “roots’ in the neighborhood, connectedness to

family and friends in the local area and feelings of security and overall

well-being.

Research by Uzzell et al. (2002) and Woldoff (2002) emphasize the

role of spatial bonds because it is social interaction in space which

generates attachment. Place attachment varies by age, social status,

tenure and especially length of residence and it is the latter which has

uniformly been the best predictor of place attachment (Lewicka, 2005,

2011). We can think of place attachment as a positive bond that develops

between an individual or a group and their environment (Low and Altman

1992; Williams et al.1992). As these authors note place attachment

involves an inter-play of affect and emotions, knowledge and beliefs,

behaviors and actions in reference to a place. For our purposes in this

paper it is how it is formed through satisfaction with dwellings and places.

Fischer and Malmberg (2001) also draw attention to the role of

place and local attachment as explanation for staying. People with ties are

less prone to move and they point to what they call “location specific

insider advantages” in the decision to stay. The notion that over longer

periods advantages accrue to stayers who accumulate advantages that

are non transferrable and would be “sunk costs” in the case of migrating

but this is of course mostly true of migration which was the focus of the

Fischer and Malmberg study. Still, social ties and other forms of local

knowledge are a part of local attachment. Their study focuses on the

decision to stay conditional on previously staying for 12 months in contrast

to our focus on long term staying.

The turn to a focus on staying has been especially focused on the

very long term stayers and studies of elderly staying. in particular. These

studies often couched in terms of “sedentism” do provide another window

on the role of roots, both in terms of family and links to the locality (Hjalm

2014). A study along similar lines of very long term stayers across Europe

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also noted the importance of roots and homeownership, a finding that we

will explore in our analysis. They also reiterated that staying is not

straightforward and that older stayers can be both intentional and

unintentional stayers and for the latter clearly attachment is not the

motivating variable. (Fernández-Carro and Evandrou, 2014).

The models we develop of the probability of staying are set within

the context of family structure over the life course, not only on the very

long term stayers. Still, we are also interested in family change and

connections to dwellings and the neighborhood . We specifically structure

the sample to report the variations in staying and moving across different

populations and show how stable family structures are a central element

of the staying process. To reiterate our initial statement, we hypothesize

that staying is (a) strongly related to ownership, (b) the presence of

children and (c) high levels of satisfaction with the dwelling or

neighborhood. The stable families who move are almost all involved in

family formation or in children leaving for college or jobs. Families who are

not stable, who are in the process of family transition have very high rates

of mobility, consequently very low rates of staying. Overall, we confirm

and extend the finding that family change is the core process of the

continuum of moving and staying.

DATA AND VARIABLES

The data to examine socio-spatial mobility in Australia come from

the first 13 waves of the Household Income and Labor Dynamics in

Australia survey (HILDA). The survey is a longitudinal survey of

approximately 7,600 households with approximately 19,900 respondents.2

The survey is modeled on and is similar to surveys in the US (the Panel

Study of Income Dynamics, PSID) and the British Household Panel

Survey (now called the ‘Understanding Society’ study).

The HILDA survey has detailed data on household composition,

economic characteristics of households, mobility and migration, and a

wide range of subjective measures of place and data on family change.

The source of our data is HILDA Release 13 from November 2014,

containing data from the first 13 waves. The data comprise observations

2 Attrition in longitudinal surveys is always an issue and while the rates from the HILDA survey are somewhat

similar to other surveys like the BHPS there is a higher attrition of younger single households which will modestly

affect the outcomes (see Watson and Wooden, 2004 for a discussion). The HILDA survey also added a “top-up”

sample in 2011which is not included in the current study as we are examining long term staying.

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of 37,426 distinct individuals in one or more of the 13 waves who filled in

certain characteristics. We limit our modeling universe to individuals

included in both waves 1 and 13, and a total of at least 11 of the 13

waves. In order to focus on their behavior, we include only individuals

whose household role is as a member of a couple (with or without

children), a single parent, or a lone person.

Our unit of observation is the family (couple or individual). Families

are identified using the individual and partner cross-wave IDs, and come

into and out of existence throughout HILDA’s 13 waves. We classify

families into one of five categories. Members of stable couples exist as a

family from waves 1 through 13; stable singles also exist as a family (with

no partner) from waves 1 through 13. Transitioning singles begin in wave

1 as a single person family, but at some point become part of a couple,

and remain in that couple through wave 13. Symmetrically, individuals

classified as a transitioning couple begin in wave 1 with a partner, but at

some point the partner is out of the picture and the individual remains

single through wave 13. Finally, the wide variety, but small realized

numbers, of remaining possibilities are classified as other.

For our analysis, we chose to focus on stable couples and stable

singles, as discussed at length elsewhere in this paper. Rather than the

usual pooled cross-sectional analysis, which tends to measure causes

and effects in close proximity of time, we create variables that summarize

the family’s characteristics over the 13 waves of survey data. Doing so

presents two sets of challenges: summarizing values from multiple waves

and combining values for two members of a couple. Some variables are

relatively straightforward. For example, we measure age in wave 1, and

for a stable couple, the order of the two ages is used. Similarly, we

measure household income in wave 1, which is the same for both

members of a couple. For household income change over the 13 waves,

we calculate the difference between wave 13 income and wave 1 income,

adjusting to constant dollars. Other variables are less straightforward. We

measure whether job change since the previous interview has occurred in

any of waves 2-13, and for couples, for either member. For measures of

satisfaction with housing and neighborhood, we average the values (for

both members of a couple) over the 13 waves of data. All this is

summarized in the Appendix, Table A1.

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The dependent variable is whether or not a family (couple or

individual) were in wave 1 and wave 13 and did not make a residential

move in the duration of the survey. A very large number of respondents

had complete records and a small number were missing only one or two

year. We included those families in our sample as illustrated in Table 1.

(TABLE 1 HERE)

The variables that are predictors of the likelihood of staying include

demographic measures of the household (age, education and children’s

presence and role), variables that are measures of status and economic

position (income, tenure, occupation and job change) and measures of

location and satisfaction with the dwelling and the neighborhood. We

exclude households where there are family changes and do not measure

the triggering events like marriage and destabilizing events, divorce,

separation and widowhood as the uniformly led to residential change. The

independent variables that measure households and family

characteristics include age, sex family and marital status and socio-

economic status (income and education) and employment status. The

tenure variable captures the important issue of housing stability and a set

of measures on satisfaction are included to measure the link and

satisfaction with locality.

ANALYSIS

Descriptive findings- stability and change in the sample

To place our analysis in the context of the Hilda survey we re-

examine Table 1 and add two tables that analyze the family structure of

the Hilda survey and a subset of that survey, used in our study (Tables 1-

3). There are 11,196 families in the Hilda survey who were interviewed at

some point during the 13 waves of the survey between 2001 and 2013. Of

those 11,000+ families 3711 were not in wave one because they were

either part of a top up sample added in 2011, or they formed at some point

during waves after wave 1. Of those in wave 1, 3285 were not in wave 13

mainly due to family dissolution and attrition. As we are concerned with

continuity we restrict our data to families who were in 11+ waves. An

additional 197 were not in 11+ waves. Of the remaining families, 3145

were stable couples or stable singles and 858 experienced a family

change (Table 1). Thus, 7.7 percent of the sample families (including

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singles as family because some are in fact single parents) had transitions

of one or more steps. We provide similar data for the total sample (all

persons) as well.

The breakdown of the stable couples and stable singles (3145) by

familial status in Wave 1 indicates that nearly 70 percent were couples

without a child or a dependent child under 15 years of age and singles

without a child. Along with stable families, Table 2 includes single

transitions couples and singles (It is not possible to provide detail on two

or more transitions by family status, as obviously singles become couples

and couples become singles). The final table in our preliminary

presentation focuses on the mobility behavior of members of couples and

singles and these details are captured in Table 3. In this table, we show

data on single transitions and multiple transitions of two or more steps for

individuals who can be members of couples or singles and also the

proportion who stayed or moved over the 13 waves.

TABLES 2 and 3 HERE

Members of stable couples were less likely to move than members

of couples that change status a single time (which usually of course

involves one member of the couple moving out) and the finding is similar

for singles who change status. But in this case the probably of moving is

extremely high. What is revealing in this table is the very high proportions

of mobility related to family transitions. In most instances in the models of

mobility, we are able to pinpoint divorce, separation, and marriage as

important transitions that create mobility but we are not often able to

estimate the total impact of these changes. The table reveals that 1379

individuals moved as a result of a family status change and that about half

of the individuals in stable households also made a residential move over

the 13 waves. In sum 35% of all moves in the sample were created by

family composition change. This finding documents just how much mobility

is created by family composition change.

Descriptive findings – long term stayers and movers

Table 4 documents the variation of moving and staying across

demographic characteristics, status and satisfaction levels separately for

stable couples and stable singles. These three classes of variables are the

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independent variables discussed in an earlier section on data and are the

variables used to model the probability of staying. The variables are

organized by their three classes of contribution to staying – demography,

status and location and satisfaction.

TABLE 4 HERE

For both couples and singles, age, as we know from much research

on mobility, is powerfully associated with the decision to stay or to move.

Only 26% of those under 35 for couples stayed during the window and

less than 20% in the case of singles. At the other extreme is a very high

probability of staying for older couples and older singles. Education has

only a modest role to play in staying or moving. Those couples with a BA

were several percentage points more likely to stay than those with a high

school education, and for singles the education affect seems to be more

for those with less than high school. With the demographic variables it is

the role of children that dramatically differentiates staying and moving for

both couples and singles. Couples who had a child at the beginning of the

survey interval have high probabilities of staying but it was just the

opposite for singles. Where a child enters the family, for couples or singles

there is a much higher likelihood of moving. For couples we can see the

effect of having a child and the classic interpretation of needing more

space in the dwelling.

Within the status measures it is tenure that has the most powerful

impact on staying or moving but job change also plays a role. Stayers are

owners by and large, and we see the powerful role in terms of the

likelihood of staying over long periods when we recall that the stayers are

those in this case who have not moved over the 13 waves in the sample.

There are not particularly big differences across the occupations although

clerical workers who were couples are more likely to stay as are those

who are not employed, and the results are similar for singles. Members of

couples in clerical occupations may be members of dual income

households, and likely also to be owners and hence also likely to be

stayers.

Income as expected creates greater opportunities to move and

consequently the higher income quintile has lower rates of staying. For

singles those in lower and higher income quintiles are likely to stay, but

again by only a modestly small higher rate. Income change plays some

role mainly for those with a very significant income gain that of course

makes moving possible and opens up a wide range of alternatives to

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those of staying in the house or neighborhood. While tenure equals

staying, job change equals moving and confirms work from other studies

of the link between mobility and job change (Clark and Withers, 1999).

Finally, we examine the impact of where the couple or a single

person lived and the levels of satisfaction with the dwelling and the

neighborhood on the likelihood of moving. There is not much variation

across the five quintiles of neighborhood socio-economic advantage.

Staying is slightly more likely towards the top end of the quintiles of

locational advantage but the differences are small. In contrast the

measures of satisfaction are linearly related to the probably of staying.

High levels of satisfaction are associated with high proportions staying and

they increase monotonically from those with low levels of satisfaction

(Table 4). The table provides a nice explication of the role of satisfaction in

staying. The findings are replicated for both the dwelling and the

neighborhood and for couples and singles.

. Multivariate analysis of staying

The models of staying are first presented as a sequence in which

first demographic variables are entered followed by status measures and

satisfaction measures. This approach allows us to calculate the additive

impact of adding status and satisfaction measures after the demographic

explanations have been entered (Table 5). Tests of collective significance

are presented for variables (e.g. age) rather than separate tests for

individual measures (e.g. for age and for age squared). A second table

(Table 6) provides the log odds ratios for the individual measures in the

model. The logistic models are duration effect models that estimate the

effects of status and levels of satisfaction after controlling for age,

education and occupation.

TABLE 5 HERE

As expected age and age squared are powerful predictors of the

probability of staying or moving and in combination with the presence of

children provide the context within which staying takes place. Education is

not significant. When we add status measures to the demographic

measures, both tenure and job change play important roles in whether

someone stays or moves. Ownership is clearly the most important

predictor of the likelihood of staying and job change as measured by the

log odds in the individual variables (Table 6). Conversely, job change is

likely to generate relocation. For couples, both income change and

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satisfaction are significant at the .10 level which confirms that satisfaction

does matter, but the additional explanation from including satisfaction is

modest. That said, the ownership variable is likely to be masking any

direct effect of satisfaction of the dwelling. For singles neighborhood is

significant and adds modestly to the overall explanation of the models.

Our hypothesis of strong place effects is not supported, the place effects

are modest and it is the measures of family status and ownership which

capture the staying process.

TABLE 6 HERE

Satisfaction in the descriptive tables is clearly related to both

housing and neighborhood satisfaction. Still, it is not a uniform outcome

and many with high levels of satisfaction move both house and

neighborhood. This is not an unexpected finding because you can be

satisfied on environmental levels for example but still need more space

and in turn it lowers the explanatory power of the satisfaction variables.

Those with low levels of satisfaction are much less likely to stay

suggesting that it is a push factor rather than a holding factor. There is

some association between neighborhood and housing satisfaction but in

the illustration for couples we note considerable dispersal across the

matrix (Table 7).

TABLE 7 HERE

Staying is a demographic process linked closely to ownership and

in this sense the paper has reiterated findings of the role of tenure, but it

has added important contributions on how children play a role in the

likelihood of staying. Viewing the table of coefficients for the individual

measures on children we find that couples who have children at the initial

wave have relatively high probabilities of staying (Table 7). We can infer

that they had made decisions about living space and the birth of children

in the window before the first wave. In both the initial and ending waves

where there are children there are very high log odds of staying. Children

were a continuing part of the process of staying. The continuing presence

of children then is a powerful predictor of staying as is demonstrated in

this analysis using longitudinal data rather than cross sectional analysis.

This is a new finding of just how children play a role in the likelihood of

staying

For singles the results are similar with respect to the role of

children. However, the overall levels of prediction are slightly lower. It is

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also useful to specifically focus on the role of children for singles. Where

children are present in waves one and 13 the odds are significantly

greater that the single household will not move, but the entry of a child

between wave one and waves 13 has a significantly negative effect on the

likelihood of staying. Clearly this is an adaptive process to restructuring

households.

OBSERVATIONS AND CONCLUSIONS

This paper provides a long-term window on the process of staying

and sets it within the framework of the life course. The models explore the

associations with who stays and the relative role of demographic

characteristics, status and satisfaction in the process of staying. The study

is innovative in that we pursue behavior over a longer window that is

common in studies of either moving or staying, and it is also innovative by

linking family stability to behavior. Using the full range of a 13 wave study

of households in Australia we are able to show that about 60 percent of all

households were stable over the 13 years and of those, approximately 45

percent moved. An innovative finding from the research was that about a

third of the total moves in the sample were directly related to household

transitions. This is a new finding in the sense that while we know that

household transitions create mobility we had not previously had a sense of

the long-term impact of these transitions on staying and moving.

A second important finding from the research is the way in which

children play a role in the process of staying. We were able to show that

households who had children at the beginning and the end of the window

in the survey were very unlikely to move and even more unlikely to move if

they were owners. For couple households in which the child entered after

the first wave there were also significant probabilities of staying. In

contrast for single households it was the presence of children at both the

beginning and end of the survey that was important. The entry of a child

stimulated the end of staying. This analysis is truly longitudinal in that we

are following households over a 13 year period.

As we hypothesized tenure is a dramatically important variable in

creating staying and was twice as important for couple households as

single households, but in both cases the log odds were the highest for this

measure. While tenure increases the likelihood of staying job change

disrupts staying. Those with a job change are only half as likely to stay as

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those without a job change. Satisfaction did add moderately modestly to

the explanation but is clearly subsumed in other measures

Overall the paper attempts to redress the focus on mobility and to

provide a context for analyzing the likelihood of staying. The paper

confirms the cross-sectional findings of tenure but provides important new

findings on how children play a role in the duration of stay.

17

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19

Table 1: Selection of Families and individuals in the HILDA Sample N Families 11,196 Not in universe Not in wave 1 3,711 Not in wave 13 3,285 Not in 11+waves 197 Stable 3,145 Transitioning 858 Individuals 37,426 Not in universe Child, other 16,961 Not in wave 1 8,278 Not in wave 13 4,785 Not in 11+ waves 295 Stable 5,327 Transitioning 1,780

20

Table 2: The structure of Stable Families and Transitioning Families in the Hilda Survey Stable Transitioning N Percent N Percent Couples Couple without child 889 28.3 107 12.5 Couple with child under 15 934 29.7 214 24.9 Couple with dependent student 151 4.8 15 1.7 Couple with independent child 165 5.2 10 1.2 Singles Lone person 714 22.7 323 37.6 Lone parent with child under 15 167 5.3 158 18.4 Lone parent dependent student 46 1.5 16 1.9 Lone parent with independent child 79 2.5 15 1.7 Total 3,145 100.0 858 100.0

21

Table 3: Staying and moving by individuals in Families in the HILDA sample N Stayed % Moved % Stable Couples (both members) 4,321 2,286 (53.5) 2,035 (46.5) Couples who changed status once 756 267 (35.3) 489 (64.7) Stable Singles 1,006 519 (51.6) 487 (48.4) Singles who change status once 349 52 (14.9) 297 (85.1) Other status transitions 675 82 (12.3) 593 (87.7)

22

Table 4: Demographics of Stable Couples and Stable Singles with 11 plus waves in the HILDA survey Stable Couples Stable Singles All Stayers % All Stayers % Total Population 2139 1130 52.8 1006 519 51.6 Average number of moves for non-stayers 1.8 2.2 Age Distribution <35 356 86 24.2 112 22 19.6 35-45 601 312 51.9 220 99 45.0 45-55 559 334 59.7 239 117 49.0 55-65 425 256 60.2 216 136 63.0 65+ 198 142 71.7 219 145 66.2 Male Single Invidual 305 166 54.4 Female Single Individual 701 353 50.4 Child –None 697 397 57.0 692 381 55.1 Child Wave 1 (beginning) 423 252 59.6 153 61 39.9 Child Wave 1 and 13 (continuing) 827 434 52.5 139 68 48.9 Child Wave 13 (child added) 192 47 24.5 22 9 40.9 Education less than HS 746 359 48.1 165 77 46.7 High School Grad. 267 152 56.9 92 53 57.6 Diploma 765 406 53.1 267 144 53.9 BA 361 213 59.0 482 245 50.8 Occupation Professional 1056 522 49.4 187 95 50.8 Technical 434 215 49.5 125 58 46.4 Clerical 240 127 52.9 147 56 38.1 Laborers 161 99 61.5 149 62 41.6 Not employed 248 167 67.3 398 248 62.3 Income quintile low 145 86 59.3 404 219 54.2 291 158 54.3 259 121 46.7 394 209 53.0 206 102 49.5 627 315 50.2 102 59 57.8 High 682 362 53.1 35 18 51.4 Income change low 428 240 56.1 202 103 51.0 428 250 58.4 201 100 49.8 428 246 57.5 201 117 58.2 428 205 47.9 201 107 53.2 High 427 189 44.3 201 92 45.8 Owner 1779 1068 60.0 612 384 62.7 Renter 360 62 17.2 394 135 34.3 Single employer 782 518 66.2 623 381 61.2 Multiple employer 1357 612 45.1 383 138 36.0

23

Table 4: Demographics of Stable Couples and Stable Singles with 11 plus waves in the HILDA survey (cont’d) Stable Couples Stable Singles All Stayers % All Stayers % Location (Socio-economic Status) low 382 193 50.5 269 141 52.4 419 214 51.1 232 111 47.8 425 229 53.9 150 80 53.3 426 239 56.1 159 87 54.7 High 487 255 52.4 196 100 51.0 Satisfaction with dwelling Low 428 183 42.8 210 86 41.0 428 200 46.7 215 98 45.6 430 207 48.1 190 99 52.1 433 269 62.1 190 103 54.2 High 420 271 64.5 201 133 66.2 Satisfaction with Neighborhood low 437 202 46.2 211 90 42.7 422 198 46.9 209 96 45.9 437 240 54.9 209 93 44.5 427 231 54.1 192 119 62.0 High 416 259 62.3 185 121 65.4

24

Table 5: Explanatory models of staying d.f. 𝜒2 Signif. 𝜒2 Signif. 𝜒2 Signif. Stable Couples Demography Age and Age Squared 2 132.93 0.000*** 50.76 0.000*** 44.28 0.000*** Presence of Children 3 51.13 0.000*** 40.94 0.000*** 40.33 0.000*** Highest Education 3 5.39 0.145 3.87 0.276 3.06 0.382 Status Occupation 4 6.00 0.199 6.01 0.198 Household Income 4 2.11 0.716 2.22 0.695 Household Income Change 4 9.07 0.059 8.36 0.079 Tenure 1 120.01 0.000*** 116.60 0.000*** Job Change 1 19.27 0.000*** 18.55 0.000*** SEIFA 4 3.06 0.547 2.71 0.607 Satisfaction Housing Satisfaction 4 6.50 0.165 Neighborhood Satisfaction 4 4.02 0.403 Satisfaction Measures 8 13.77 0.088 Model Summary L.R. Chi Squared 235.11 424.95 438.78 Degrees of Freedom 8 26 34 Prob > chi2 0.000 0.000 0.000 Pseudo R-squared 0.079 0.144 0.148 Number of Observations 2139 2139 2139 Stable Singles Demography Age and Age Squared 2 79.10 0.000*** 27.43 0.000*** 23.48 0.000*** Sex 1 2.44 0.118 2.55 0.110 3.24 0.072 Presence of Children 3 14.38 0.002** 12.48 0.006** 13.02 0.005** Highest Education 3 5.04 0.169 3.96 0.266 3.76 0.288 Status Occupation 4 4.80 0.309 3.98 0.408 Household Income 4 1.54 0.820 2.20 0.699 Household Income Change 4 1.30 0.862 1.32 0.859 Tenure 1 33.45 0.000*** 31.03 0.000*** Job Change 1 5.83 0.016* 5.01 0.025* SEIFA 4 1.13 0.890 1.31 0.860 Satisfaction Housing Satisfaction 4 1.12 0.891 Neighborhood Satisfaction 4 12.67 0.013* Satisfaction Measures 8 17.71 0.023* Model Summary L.R. Chi Squared 112.10 170.62 188.59 Degrees of Freedom 9 27 35 Prob > chi2 0.000 0.000 0.000 Pseudo R-squared 0.080 0.122 0.135 Number of Observations 1006 1006 1006 Notes: * p<0.05; ** p<0.01; *** p<0.001

25

TABLE 6: Log odds for the independent variables in the staying models Stable Couples Stable Singles Odds Std. Odds Std. Ratio Err. Z Ratio Err. Z Age 1.131 0.040 3.44*** 1.172 0.043 4.37*** Age Squared 0.999 0.000 -2.21* 0.999 0.000 -3.85*** Single individual gender (ref Male) Female 0.730 0.128 -1.80* Children (ref in neither waves 1 nor 13) In wave 13 1.551 0.420 1.62 0.647 0.327 -0.86 In wave 1 1.747 0.268 3.63*** 0.680 0.153 -1.71* In waves 1 and 13 2.757 0.471 5.93*** 1.868 0.483 2.42* Education (ref less than HS) BA or more 0.789 0.137 -1.36 0.834 0.204 -0.74 Diploma 1.017 0.193 0.09 1.158 0.309 0.55 HS Grad or Cert. 0.913 0.138 -0.61 1.286 0.235 1.38 Occupation (ref Laborers/Operators) Professional 0.628 0.138 -2.12* 1.287 0.371 0.87 Technical/Service 0.607 0.135 -2.24* 1.201 0.337 0.65 Clerical/Sales 0.657 0.158 -1.75* 0.831 0.229 -0.67 Not employed 0.581 0.157 -2.01* 1.305 0.366 0.95 Household Income (ref middle quintile) Lowest quintile 0.924 0.228 -0.32 0.796 0.196 -0.93 Second quintile 0.780 0.148 -1.31 0.918 0.204 -0.39 Fourth quintile 0.846 0.127 -1.11 1.177 0.324 0.59 Highest quintile 0.884 0.145 -0.75 0.754 0.312 -0.68 Household Income Change (ref middle quintile) Lowest quintile 0.764 0.135 -1.52 0.779 0.209 -0.93 Second quintile 1.028 0.166 0.17 0.897 0.213 -0.46 Fourth quintile 0.843 0.136 -1.06 0.965 0.220 -0.16 Highest quintile 0.685 0.115 -2.26* 0.807 0.204 -0.85 Tenure (ref renter) Owner 5.758 0.933 10.80*** 2.425 0.386 5.57*** Job Change (ref single or no employer) 2 or more empl. 0.597 0.071 -4.31*** 0.647 0.126 -2.24* (cont’d)

26

TABLE 6: Log odds for the independent variables in the staying models (cont’d) Stable Couples Stable Singles Odds Std. Odds Std. Ratio Err. Z Ratio Err. Z SEIFA (ref middle quintile) Lowest quintile 0.895 0.145 -0.69 1.032 0.237 0.14 Second quintile 1.012 0.157 0.08 0.930 0.219 -0.31 Fourth quintile 1.160 0.181 0.95 1.211 0.308 0.75 Highest quintile 0.971 0.153 -0.19 1.055 0.263 0.21 Housing Satisfaction (ref middle quintile) Lowest quintile 1.224 0.200 1.24 1.012 0.248 0.05 Second quintile 1.115 0.170 0.71 0.943 0.212 -0.26 Fourth quintile 1.438 0.222 2.35* 0.825 0.189 -0.84 Highest quintile 1.349 0.227 1.78* 1.004 0.254 0.01 Neighborhood Satisfaction (ref middle quintile) Lowest quintile 0.837 0.137 -1.09 0.992 0.232 -0.04 Second quintile 0.774 0.120 -1.65* 1.042 0.231 0.19 Fourth quintile 0.834 0.129 -1.18 1.893 0.436 2.77** Highest quintile 0.994 0.163 -0.03 1.834 0.461 2.41* Model Summary L.R. Chi Squared 438.78 188.59 Degrees of Freedom 34 35 Prob > chi2 0.000 0.000 Pseudo R-squared 0.148 0.135 Observations 2,139 1,006 Notes: * p<0.10; ** p<0.05; *** p<0.01

27

Table 7: Expressions of satisfaction (1-5) by neighborhood and Dwelling for couples Neighborhood satisfaction

Dwelling

satisfaction

1 2 3 4 5

1 219 97 59 31 22

2 115 124 105 65 19

3 64 122 107 85 52

4 28 58 114 129 104

5 11 21 52 117 219

28

Table A1: Construction of Variables for Analysis Category xwaveid

_hhpxid Family type and stability

Stable Couple Both members are each other’s partner (_hhpxid) for all waves

Stable Single Missing _hhpxid for all waves Transitioning Single Missing _hhpxid initially, then same for

remaining waves

Transitioning Couple Both members are each other’s partner initially, then missing _hhpxid for remaining waves

Other Transitioning All others Stayer _mhli Changed address during

waves 1-13 For either member of a couple

Did not change address 0 In every wave Changed at least once 1 In at least one wave DEMOGRAPHY Age ahgage Age as of last birthday, as of

June 30 2001 The older of the members of a couple

Single Individual Gender ahgsex Sex of single individual Missing for a couple Children ahhrih

mhhrih Child in household, in waves 1 and 13

In wave 13 4, 12 1, 2, 3, 5, 6, 7

ahhrih mhhrih

In wave 1 1, 2, 3, 5, 6, 7 4, 12

ahhrih mhhrih

In waves 1 and 13 1, 2, 3, 4, 6, 7 Both ahhrih and mhhrih In neither waves 1 nor 13 4, 12 Both ahhrih and mhhrih Education _edhigh1 Highest education level

achieved, in the earliest wave with a non-missing value

The highest of the members of a couple BA or more 1, 2, 3

Diploma 4 H.S. Graduate or Certificate 5, 8 Less than H.S. Graduate 9, 10 STATUS Occupation _jbmo61

_esbrd Occupation category, in the earliest wave for which the individual is employed

For a couple, professional if either member is; otherwise technical/service if either member is; etc.

Professional _jbmo61: 1, 2 Technical/Service _jbmo61: 3, 4 Clerical/Sales _jbmo61: 5, 6 Laborers/Operators _jbmo61: 7, 8 Not Employed _esbrd: 2, 3 In every wave Household Income ahifefp

ahifefn Household financial year gross regular income ($) in wave 1

Grouped into quintiles [see note 1]

29

Table A1: Construction of Variables for Analysis (cont’d) STATUS (cont’d) Household Income Change ahifefp

ahifefn mhifefp mhifefn

Change in household financial year gross regular income ($) between waves 1 and 13

Grouped into quintiles [see note 2]

Tenure ahstenr Household tenure in wave 1

[see note 3]

Owner Renter Job Change _pjsemp

_pjmsemp _pjemply

Changed employer (or primary employer) during waves 1-13

For either member of a couple

Changed at least once [see note 4] In at least one wave Did not change employers otherwise In every wave SEIFA 2001 ahhad10 SEIFA index of relative socio-

economic advantage or disadvantage in wave 1

Deciles collapsed into quintiles

SATISFACTION Housing Satisfaction _losathl Satisfaction with the home in

which you live, averaged over all waves

For both members of a couple Collapsed into quintiles

[see note 5] Neighborhood Satisfaction _losatnl Satisfaction with the

neighborhood in which you live, averaged over all waves

For both members of a couple Collapsed into quintiles

[see note 5]

Note 1: Quintiles of household income were computed across all households in wave 1 of HILDA. These were then merged back to the individual observations, so that the quintile definitions are independent of the selection of individuals for modeling universe.

Note 2: For each household in the modeling universe, the difference between income in wave 13 and in wave 1 (in constant dollars) was calculated; then quintiles of this change were computed separately in each category (stable couples and stable singles).

Note 3: The coding of the tenure question changed in the second wave with the separation of rent-to-buy schemes from rental. We recoded the first wave response (ahstenur) to be consistent with the subsequent waves, imputing rent-to-buy for renters who were asked the value of their house (ahsvalue). In our models, rent-to-buy was combined with ownership, and living rent free or with life tenure was combined with rental or paying board.

Note 4: The individual was considered to have changed employers if he or she was either (a) unemployed at the previous interview (_pjemply=2) and employed at the current interview (_esbrd=1) or (b) employed at the previous interview (_pjemply=1) and not working for the same employer at the current interview (_pjsemp=2 or _pjmsemp=2)

Note 5: Quintiles of average satisfaction were computed separately for each satisfaction measure (household and neighborhood) in each category (stable couples and stable singles).