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Survey Analysis for Indigenous Policy in Australia

2. Mobile people, mobile measures: Limitations and opportunities for mobility analysis

John Taylor and Martin Bell

As the third in a (now) regular round of National Aboriginal and Torres Strait Islander Social Surveys (NATSISS), the 2008 survey is an important addition to the ever-growing armoury of statistical information available to governments and others in their analysis of the social, cultural and economic circumstances of Indigenous Australians. This survey activity is important because it provides the basis for determining change in individual and group circumstances since the first survey in 1994 and it lays a foundation for considering this into the future. Current results may also now be added to the volume of information available from the last two census rounds to contribute to what has become an almost constant flow of national and jurisdictional data.

Indeed, such is the accumulating volume of statistical information on Indigenous Australians that a Closing the Gap Clearinghouse has now been established in order to make some systematic sense of the findings from numerous evaluations that make use of these data in attempts to explain progress (or otherwise) in the pursuit of policy goals (Australian Institute of Health and Welfare/Australian Institute of Family Studies n.d.). This practical development is no surprise as it was foreshadowed in discussions leading up to the first National Aboriginal and Torres Strait Islander Survey (NATSIS) in 1994 where the concern was that the NATSIS would go to great lengths to gather data that would turn out to be in excess of the system’s available capacity to absorb and utilise it (Altman 1992: 163). Viewed historically, we have therefore shifted over the past three decades from a situation where the main problem was a lack of information – a concern raised by the Miller Report (1985) and then again by the Royal Commission into Aboriginal Deaths in Custody in 1991 (Commonwealth of Australia 1991) and that eventually spawned the first NATSIS (Altman 1992) – to one where there is now almost a surfeit of information. The emphasis has shifted to a consideration of unfolding cross-sectional analyses and what the cumulative evidence from these indicates about patterns and trends in Indigenous outcomes. This chapter takes its cue from this shift by considering the utility of the NATSISS in examining such trends with particular reference to dimensions of population mobility.

The 2002 NATSISS was the first household survey to include a question on Indigenous mobility. With a follow-up mobility module in the 2008 NATSISS we are now in a position to say something about how, and possibly why, mobility and its socioeconomic correlates change over time if, indeed, they do. At least that is the proposition. Some uncertainty exists here because the NATSISS questions that provide for a measure of mobility changed somewhat between 2002 and 2008 raising questions about comparability. As with a number of other questions on the questionnaire, the 2008 survey also asked for the first time about children’s mobility. However, this is not considered here as the focus is on adult mobility partly because of the lack of a 2002 comparison for children.

In this chapter we have two main objectives. First, we examine the importance of mobility as a policy issue and test the capacity of the NATSISS to measure change in the intensity and direction of residential movement over time as a contribution to understanding policy impacts. We do this by establishing one-year mobility rates for select characteristics of movers in 2008 and comparing these with equivalent rates for 2002. This reveals a limitation of the NATSISS as a means to establishing underlying trends. The bottom line is, the mobility modules differ between the two surveys to the extent that they produce quite different measures of movement. Second, we focus on the new mobility questions in the 2008 survey and explore the dimensions of mobility analysis that are now accessible. Aside from establishing socioeconomic correlates of movement, we also explore the possibilities for analysis of the tempo of movement that these new data present. Here we draw a distinction between chronic short-term and more stable long-term movers and attempt to estimate probabilities of residential change.

Mobility as a policy issue

Movement of population is a significant concern of Indigenous public policy (Snipp 2004). Of the three demographic components of population change it is the one that is most difficult to conceptualise and measure but also the one that is most likely to impact on regional and local population growth or decline. While the demographic outcome therefore goes directly to the issue of estimating the variable size and composition of identified social policy needs, social scientists have struggled to develop adequate measurement and predictive models of mobility, not least in regard to Indigenous populations where conceptual understanding of change in usual residence can be quite different (Taylor and Bell 2004).

Presently, in policy debate, much is expected and alleged of Indigenous population movement. On the one hand is the proposition that migration to jobs and higher order services is an inevitable requirement for closing the gap and that this invariably involves or requires a shift to an urban centre or growth town (Hughes 2007: 21–23). In previous analysis of NATSISS data, the issue of how to match labour supply and demand in this way was raised with an observation about the likely role of welfare reform in literally ‘mobilising’ labour (Gregory 2006). There appears no doubt that such a rural-urban redistribution is underway. For some time now, census-based evidence points to a net step-wise movement up the settlement hierarchy from remote areas to regional towns and city areas (Biddle 2010; Gray 1989; Taylor 2006).

In relation to this, the period 2002 to 2008 is ripe for comparative analysis. Since the 2002 NATSISS there have been a number of economic and policy developments that are likely to have encouraged the flows mentioned above and, in the process, might have stimulated an overall rise in residential mobility. Not least has been the substantial and steady rise in non-CDEP (Community Development Employment Program) employment, especially in the private sector (Biddle, Taylor and Yap 2009: 271; Gray and Hunter 2011). Aside from the macroeconomic effects of a buoyant labour market, this increase is also likely to reflect the gradual impact of Indigenous Employment Strategy (IES) programs aimed at raising private sector engagement alongside the removal of remote area exemptions for jobseekers, the scrapping of CDEP in urban areas and its partial transformation elsewhere into a Job Services program, the enhancement of programs to encourage school participation to Year 12 and equivalent further emphasis given to raising levels of tertiary and vocational education and training (VET) qualification enabling more flexibility in the labour market. Indeed, the whole push towards ‘closing the gap’ and its decade-long precursor of ‘practical reconciliation’ implies the prospect of significant demographic change including that of enhanced movement for education and employment participation. At the same time, other policies, such as those encouraging home ownership, may have operated to reduce movement propensity as, indeed, would any lowering in levels of unemployment or renting of private dwellings given that these have been found in the past to be strongly associated with mobility (Taylor and Kinfu 2006).

Has the intensity and geography of mobility changed?

So what has happened to the level and composition of mobility against this background of likely influences? On the face of it, this seems to be a simple question to answer, and it is, as long as we have consistent measurement over time. The problem is we don’t, at least not from the NATSISS. In the 2002 NATSISS, the focus of attention in measuring mobility was on the multiple locations that an individual may have stayed in over a 12 month period. In particular, the survey asked (in non-remote and remote non-community sample areas), ‘In the last 12 months, have you lived in any other dwellings?’ and (in remote community sample areas), ‘In the last year have you lived in any other houses or places?’ In both areas, the survey then went on to elicit how many dwellings, houses/places people had lived in over the course of the designated year.

In deliberating over the content of the 2008 survey, the technical reference group convened by the Australian Bureau of Statistics (ABS) decided to change this question to one that focused on duration of residence in the current and previous dwelling of residence with no limit on time period (although only those who moved in the last five years were coded). The mobility questions became ‘how long have you lived in this house?’ and ‘how long did you live in the house immediately before this one?’ These are very different questions to those asked in 2002 and they provide a very different perspective on mobility. According to the ABS the initial proposal for the mobility module was to retain the 2002 questions and even add additional questions to expand the scope of the module. However, following field testing, the extended module was found to be overly long and a decision was made in favour of a shortened module with a revised mobility question that aligned with the question asked in the General Social Survey (GSS) to provide for Indigenous/non-Indigenous comparison. This is an example of where the demands of postcolonial demography for comparative data (Taylor 2011) can override attempts to generate specifically Indigenous survey data and it raises a question about the appropriate degree to which the content of the NATSISS should be driven by the content of the GSS and who should decide (Taylor 2008: 117–120; Yu 2011). This is no trivial matter as, in this instance, we can see that whilst the change in questions opens up new insights, it also closes down the possibility of measuring change over time.

While it is technically possible to construct a group of survey respondents in 2002 and again in 2008 who can be said to have changed their dwelling of residence at least once during the 12 month period prior to the survey, our proposition is that the 2002 survey question on the number of dwellings/houses/places lived in over the past 12 months was designed to capture more movement than the 2008 question on duration lived in a single current dwelling. Because of this we would expect higher movement rates to emerge from the 2002 NATSISS and this is exactly what we find.

Propensities by age

At the 2002 NATSISS, 31 per cent of respondents aged 15 years and over indicated that they had changed residence during the 12 month period prior to the survey. This compares to just 21 per cent of respondents in the 2008 NATSISS. Taken at face value this looks like a dramatic decline in the overall intensity of population movement since 2002 but we would caution against drawing such a conclusion. It is true that one-year movement rates since 1996 derived from census data also point to a decline in the overall intensity of mobility, but the 2002 NATSISS level appears to be an aberration from this time series and we would suggest that it reflects the fact that 2002 data incorporate additional temporary movement. Interestingly, the 2008 NATSISS level is very close to that reported by the 2006 Census.

Fig. 2.1 Census and survey based age-specific Indigenous movement rates, Australia, 1996 to 2008

Conf_Mono_Fig2.1.tif

Source: Taylor and Kinfu 2005: 60; 2008 Remote Access Data Laboratory (RADL)

This same variation in the intensity of movement between different collections is evident across the age distribution. In Fig. 2.1 we plot one-year age-specific movement rates using data from the 2002 and 2008 NATSISS alongside one-year rates derived from the 1996, 2001 and 2006 Censuses with due deference to the differences in methodology between the survey questions as outlined above and the fixed-period usual residence questions deployed in the census. In this chart only the three census-based sets of rates are truly comparable over time and they are provided here for triangulation. What this reveals is that the age pattern of movement derived from both NATSISS collections is in broad agreement with census results in so far as movement peaks among young adults aged 20–24 years and steadily declines thereafter. Overall, these patterns conform with more or less universal observations that have long been made by for the United States and Europe (Rogers and Castro 1981), and subsequently in Australia by Bell (1992, 1995). The peak in the age profile of mobility is associated with the combined influence of departure from the parental home, the start of tertiary education, entry into the labour force and the establishment of independent living arrangements; subsequent decline is related to home ownership and prolonged attachment to the labour force.

To the extent that Indigenous people participate in these same lifecourse events the message from Fig. 2.1 is that they are evident regardless of which measure of mobility is used. The other, equally important observation is that movement rates derived using 2002 data are considerably out of step with the other series by being consistently much higher at all ages. If we focus on differentials between the 2002 and 2008 NATSISS rates these are the extremes with substantial gaps of 20 to 30 per cent in rates among 15–19 years and 25–29 year olds and up to as much as 40 per cent in some older age groups.

Spatial pattern

One of the constraints on mobility analysis using NATSISS data to date has been the lack of a spatial dimension describing the direction of population flows – whether up or down the settlement hierarchy. This is useful to know since movement in each direction is known to correlate with key socioeconomic characteristics and, as we have seen, movement up the hierarchy is an implicit goal of much social policy. Tantalisingly, in the 2008 survey, questions were asked about the current and previous location of dwellings therefore opening up the possibility of such analysis. In the processing of data, however, current dwellings were coded by remoteness category whereas previous dwellings were coded by ‘same locality/capital city/remainder of state’ or by ‘section of state’ – neither of which concord with remoteness.

This means that the only analysis of spatial change in mobility enabled by the 2008 survey is to compare mobility rates over time at the jurisdictional level but even here this probably only serves to highlight that the 2002 and 2008 surveys produce different measures of mobility. The percentage of Indigenous adults in each jurisdiction who changed residence over the 12 month period prior to each survey in 2002 and 2008 is shown in Fig. 2.2. The results are quite striking – in 2002, movement rates in the Northern Territory and Tasmania/Australian Capital Territory were significantly lower than in all other jurisdictions whereas in 2008 there is no significant difference between reported rates in all jurisdictions due to what appears to be a substantial decline in mobility in all areas except the Northern Territory. On the face of it, it would seem that mobility in the Northern Territory slightly increased while in all other jurisdictions it decreased substantially. Consequently, in 2008 no significant difference in propensity to move was evident between any of the jurisdictions given the spread of the upper and lower bounds of each set of estimates. Consistent with this is the observation that no difference was evident between movement rates in remote and non-remote areas in 2008 (with rates of 22.5 and 20.8 respectively) whereas in 2002 very remote areas displayed significantly lower movement than all other regions and rates elsewhere were generally much higher than in 2008 at 30–35 per cent.

Fig. 2.2 Indigenous movement propensities by State and Territory, 2002 and 2008a

Conf_Mono_Fig2.2.tif

a. The error bars around the point estimates indicate a 95% confiedence interval.

Source: 2002 and 2008 NATSISS

Modelling mobility

From a theoretical perspective and using empirical evidence from around the world, it is argued that the intensity, spatial pattern, and composition of population mobility rises in level, spatial scale, and complexity over time in tandem with transitions in modernisation and economic development that involve shifts in the nature of consumption and production (Zelinksy 1971). While there is variation in the nature of response, the basic relationship nonetheless holds (Skeldon 1990). Ultimately, then, the proposition exists that spatial behaviour tends towards universal norms with urbanisation an inevitable co-requisite of the development process (Skeldon 1997). This general model is confirmed for Australia as a whole (Hugo 1988), and underlying this are the changing locational needs and preferences of individuals and households. These vary according to life cycle stage and correlate with a range of human capital attributes that are ultimately associated with participation in production (labour force status) and consumption (housing, amenity, welfare) (Bell and Maher 1995). If the nature of social and economic participation is reflected in mobility, what then of sub-populations, such as the Indigenous population, who are less engaged with mainstream institutions and who may articulate different priorities?

The situation of Indigenous Australians within this general model has been explored first by Hugo (1988) and then by Taylor and Bell (1996, 2004). They highlight the relative lack of urbanisation among Indigenous people compared to the rest of the population, the contemporary fragmentation of their rural settlement, and the continuity of short-term circular movement for non-economic reasons as distinguishing features that contrast with mainstream mobility. While these findings stem from census analyses and ethnographic studies, the NATSISS now provides a sample survey basis for exploring some of the dimensions of this different mobility by considering the probability of movement according to some of the human capital variables found to be associated with population movement among Indigenous Australians and more generally (Kinfu 2005). It also provides for an examination of the reasons provided by survey respondents to account for their mobility.

Social and economic correlates of movement

We begin by examining the propensity to move across a range of social, economic and cultural characteristics that are selected here for their likely association with movement propensity. As the respondent population differed in age distribution to the overall Indigenous population (in 2008, for example, it was noticeably older) observed results are standardised by age against the total Indigenous population. In some instances this varies the outcome up or down (Table 2.1). Propensity to move was highest among private renters followed by those unemployed. Owner-occupiers had the lowest movement rate. Unfortunately sector of employment was not coded in 2008 even though data to enable this were gathered. This means that the influence on mobility of the substantial rise in Indigenous private sector employment that was observed using 2001 and 2006 Census data (Biddle, Taylor and Yap 2009: 271–73) cannot be tested.

Table 2.1 Movement propensitiesa according to select social, economic and geographic characteristics, 2002 and 2008

2008

reported

2008

age standardised

Total

21.2

21.2

Sex

Males

21.6

21.6

Females

21.0

21.0

Marital Status

Married

19.9

24.8

Not married

22.4

22.4

Labour force

Unemployed

32.1

28.7

Not in labour force

20.6

21.9

Employed

19.8

19.4

Public

No data

No data

Private

No data

No data

CDEP

21.1

19.9

Education

Yr 11 and 12

22.9

20.4

Yr 10

22.0

21.4

Yr 9 or below

19.0

21.7

Training

Attended

22.5

21.4

Not attended

21.8

21.0

Housing tenure

Owner

8.7

9.0

Private rental

36.2

34.0

Public rental

20.0

19.8

Community rental

20.5

20.6

Place of residence

In homeland

18.7

18.8

Not in homeland

22.8

23.8

Neighbourhood problems

Has problems

20.1

20.0

Does not have problems

22.4

22.5

Remoteness

Non-remote

20.7

20.7

Remote and very remote

22.4

22.5

Health status

Excellent

22.6

21.1

Very good

21.4

19.8

Good

21.8

21.8

Fair

20.7

25.6

Poor

17.0

22.3

a Movers per 100 population

Source: Customised cross-tabulations from the 2008 NATSISS RADL

Net effects of these independent variables can only be assessed using multivariate analysis. For this purpose, we fit a logistic regression with the dependent variable taking the form of 1 if the respondent moved in the 12 months period prior to the survey and 0 otherwise. In this way, the results indicate the effects of all the selected factors simultaneously on the chances of moving or not. Net effects on these chances in relation to a common reference person in 2008 are indicated in Fig. 2.3 which also indicates the characteristics of the reference person.

After removing the effects of other variables, there remains a significant underlying pattern of higher mobility among those unemployed or not in the labour force compared to those in mainstream employment and emphatically among those in private sector rental accommodation compared to other forms of housing tenure. Indeed, home ownership operates as a distinct brake on mobility. The effects of educational attainment and training are evident in the clear gradient from higher mobility among those with qualifications and Year 12 schooling to lower movement among those with Year 9 or below and non attendance in VET courses. Contrary to what might be expected, respondents resident in neighbourhoods with perceived social problems are relatively immobile which may reflect their limited capacity for residential change. It is notable that residence on homelands is negatively associated with mobility compared to remote/very remote residence which is positively associated. This underscores the fact that the former locations are likely to be associated with some form of Indigenous land tenure whereas the latter are simply reflective of ABS taxonomy. Finally, the effect of self-assessed health status appears to operate as might be expected – excellent health leading to higher mobility most likely due to increased opportunity, and poor health leading to reduced mobility most likely due to incapacity.

Fig. 2.3 Net effects of socioeconomic, spatial and household characteristics on Indigenous mobility: Logistic regression results, 2008a

Conf_Mono_Fig2.3.tif

a. Reference person: male, married, employed in non-CDEP, no post-school qualifications, Year 10, attended vocational training, lives in public rental dwelling, does not live in homeland, does not have neighbourhood problems, does not live in a remote area, reports very good health. The results are not sensitive to the choice of characteristics of the reference person.

Source: 2008 NATSISS RADL

 

The tempo of mobility

So far, we have discussed mobility in a conventional sense using a mover/stayer framework. This bifurcation is overly simplistic because there is considerable heterogeneity in population movement although data that exposes this is quite rare. For some time now there has been growing recognition of a need for more biographical approaches to the study of population mobility (Halfacree and Boyle 1993). It is argued that these provide a richness of detail that enables the proper interpretation of population movement as culturally situated in social fields and individual and group life courses. While such approaches undoubtedly add texture and meaning to analyses of human mobility a major drawback is the limited capacity for comparison between individuals and groups. More recently, Bell (2004) and Taylor and Bell (2011) have shown how duration and frequency can be combined with information on the sequences of movements to generate a new comparative metric – that of periodicity. In practice, though, exploration of concepts such as periodicity has been restricted by an absence of suitable data on the timing of population movements and the limited development of techniques to derive summary indices. There are compelling reasons for further methodological development in this area, not least in respect of Indigenous populations, and this is where the 2008 NATSISS data are innovative.

Conventional data collections bifurcate the population into two discrete categories – movers and stayers – and this is true whether migration is measured over a one or five year interval, as in the Australian census, or any other period. In practice, however, we know that the vast majority of people change their place of residence at some point in their lives, and many individuals move frequently. Data on duration of residence – captured in the 2008 NATSISS question ‘How long have you lived in this house’ – go some way to unlocking this detail. Fig. 2.4 graphs median duration of residence in current dwelling for the indigenous population and reveals marked variations by age. As might be expected, median durations are shortest among young adults and (of necessity) among very young children, but reach five years among children of school age, and rise steadily after age 30, climbing to nine years among those aged 55 and over. Fig. 2.4 also reveals some intriguing regional differences in mobility, with Indigenous young adults in remote areas much more likely to move than their counterparts in non-remote areas, as reflected in markedly shorter residence durations. As suggested elsewhere, this might reflect differences in attachment to locationally specific activities and fixed places of employment, as well as variations in the timing and tempo of access to services and customary pursuits in remote communities. Differences in duration of residence emerge again beyond age 40, with people in remote Australia once more recording shorter average durations of residence, probably reflecting the relative constraints of fixed employment and housing in more closely settled areas (Taylor and Bell 2004).

Fig. 2.4 Median duration of residence in current dwelling by age and remoteness, 2008

Conf_Mono_Fig2.4.tif

Source: 2008 NATSISS RADL

To some extent, Fig. 2.4 is a mirror image of the conventional age profile of mobility: median durations are low where the propensity to move is high, and vice versa. Thinking about mobility in terms of duration of stay, rather than propensity to go, provides a somewhat different perspective on migration and median duration thus provides an alternative to the conventional statistical indicator, the proportion who moved. However, it is the facility to segment the population by period of residence that represents the real benefit of the data on duration (Fig. 2.5). When this is done, it becomes readily apparent that there is considerable heterogeneity among the population in terms of mobility. Even among young adults, who have the shortest median durations and the highest rates of mobility, there is a significant group who have not moved for more than a decade. This was the case for fully one-quarter of 15–19 years olds and almost one-fifth of those aged 20–24. Conversely, at older ages, half of whom had been in the same dwelling for a decade or more, there was a significant minority of recent movers with more than one in five of those aged 55 and over changing residence in the last three years. While the NATSISS provides little information on distance moved, these variations point to very different sub-groups within individual age cohorts and suggest potentially quite diverse needs profiles based on differing levels of locational knowledge, community attachment, and so on. Classification by period of residence therefore provides a more nuanced framework for survey analysis against which to examine differences in population characteristics, housing and economic circumstances that circumscribe and shape individual and household wellbeing.

Fig. 2.5 Duration of residence in current dwelling by age, 2008

Conf_Mono_Fig2.5_REPROVIDED.tif

Source: 2008 NATSISS RADL

Other things being equal, mobility is subject to cumulative inertia: the propensity to move declines with increasing duration of stay. However, migration events are variously distributed through time and recent movers may themselves be poised for a lengthy stay while those seemingly more settled may be planning a move. One major drawback of data on current duration of residence is that they are right censored: the current duration of stay is incomplete and Fig. 2.4 and 2.5 therefore represent a somewhat biased picture of the mobility profile of this population. Here, however, the 2008 NATSISS provides an alternative solution through a follow up question which asks respondents to specify their duration of stay in their previous residence. In the literature on migration studies, this is an unusual question normally confined to surveys that trace complete residential histories. Inevitably the information is a little dated because respondents are reporting events that have occurred, in some cases, many years before. Recall errors may also prejudice accuracy. However, the data do have the singular merit of providing a picture of completed residence durations. This, in turn, sets the foundation for analysis that can provide further insights into population mobility.

In Fig. 2.6, the data for two cohorts are arranged in a form of survival analysis to show the progressive increase in proportions moving away from their previous places of residence. As time increases on the x-axis the proportion remaining in their original dwelling drops away, rapidly at first, and then more gradually, so that after around 10 years only 10 per cent of the cohorts remain in their previous dwelling. The decline is significantly faster for those in the 15–19 age group than for 30–49 year olds. After two years, half of young adults had changed residence compared with just two-fifths of the older cohort, and after five years just one in five young adults remained in their original dwelling compared with 27 per cent of 30–49 year olds. While the differences are not stark, they do serve to reveal the way in which mobility processes steadily pervade the entire population. Such analyses cannot be performed reliably with information on duration in current residence because the data are effectively ‘censored’ since none of the observed durations are complete. But further refinement in analysis of the prior residence data is also possible. As presented in Fig. 2.6 the results depict the combined experience of individuals as aged at the end time of the survey but this conflates moves made a variety of points in time. An alternative would be to transform the data to measure duration of stay by age at the start of the previous move. Although such analysis would require a fine level of data disaggregation, it would deliver a precise measure of movements through the housing stock that has not previously been available from Australian mobility statistics.

Fig. 2.6 Completed durations of stay and proportions remaining, 2008

Conf_Mono_Fig2.6.tif

Source: NATSISS 2008 question on duration in previous place of residence; age at time of the survey

Coupling data on current and previous residence has the potential to provide further insights into mobility by segmenting long term stayers from chronic movers. A conceptual framework for this is provided by the matrix of long and short-term duration of stays shown in Fig. 2.7; this should be read in combination with Table 2.2 which sets out a convenient cross-classification of both variables categorised into three broad time intervals and identifies the proportion of respondents in each. Fully two-fifths can be classed as relatively long term residents having lived in their current and previous dwellings for four years or more (cell 9). On the other hand, combining cells 1, 2, 4 and 5, reveals that one-third of respondents had resided in both their current and previous dwellings for three years or less.

Table 2.2 Current duration by previous duration of stay, 2008

Time in previous dwelling

Time in current dwelling

Code

Share of population (%)

< 1 year

< 1 year

1

8.0

< 1 year

1–3 years

2

4.6

< 1 year

> 4 years

3

1.6

1–3 years

< 1 year

4

8.5

1–3 years

1–3 years

5

11.9

1–3 years

> 4 years

6

4.3

> 4 years

< 1 year

7

5.5

> 4 years

1–3 years

8

9.2

> 4 years

> 4 years

9

41.8

Never moved

10

4.5

Source: 2008 NATSISS RADL

Fig. 2.7 Segmenting chronic movers from long term stayersa

Conf_Mono_Fig2.7.tif

a. Cell numbers correspond with those in Table 2.2.

Source: 2008 NATSISS RADL

The difference in characteristics and composition of these two groups, and in the reasons for their last move is shown in Table 2.3. While the differences are not stark, for chronic movers housing reasons emerge as the strongest motive for migration; employment also features more strongly than among long term stayers. These differences might reflect an earlier life cycle stage among chronic movers with changes of residence triggered by housing transitions and entry to employment. However, Table 2.3 also points to greater vulnerability in this group, with a significantly larger proportion of chronic movers being unemployed and in private rental accommodation and correspondingly fewer in owner occupied housing.

Table 2.3 Percentage distribution of chronic movers and long term stayers by reasons for moving, labour force status and housing tenure, 2008

 

Long term

Short term

Reasons for move

Housing

44.4

47.0

Family

33.8

30.0

Lifestyle

9.0

6.5

Employment

4.5

8.0

Health

3.6

2.1

Education

0.9

0.7

Other

3.8

5.7

Total

100.0

100.0

Labour force status

Employed

52.1

50.3

Unemployed

6.2

13.2

NILF

41.7

36.5

Total

100.0

100.0

Housing tenure

Owner

45.0

25.6

Private rental

10.6

33.4

Public rental

26.2

27.1

Community rental

18.2

13.9

Total

100.0

100.0

 

Source: 2008 NATSISS RADL

Conclusion

The NATSISS is now an established tool of social analysis in Indigenous affairs. As with any sample survey its chief utility arises from the direction and strength of associations that can be established between characteristics at the individual and group levels. While we have examined some such associations – between movement and select correlates – our primary purpose has been to test the strength of the single variable (residential change) to inform discussion about the relationship between policy and population movement and to provide further insight into the nature of mobility. In respect of the first of these tasks we find the 2008 NATSISS somewhat limited in capacity; with regard to the second it has opened up new possibilities.

While the overall rate of mobility recorded by the 2008 NATSISS was much lower in all jurisdictions compared to 2002 this is not surprising since the methodology used to measure mobility differed in the two surveys in a way that lowered the chances of recording population movement in 2008. Basically, the 2002 questions appear to have picked up excess short term movement across the board, except in the Northern Territory where reported movement is consistently low regardless of the question. It would seem that the more open-ended questions regarding mobility as used in 2002 are more likely to elicit family-related reasons for movement whereas the single-move questions as used in 2008 emphasise housing reasons. Together with the lack of industry sector of employment coding in the 2008 survey output and limited scope for linking current and previous residence, this use of different mobility questions between surveys substantially hampers any attempt to examine the role of population movement in effecting labour force change. This resurrects an important long-standing question about the purpose of the NATSISS as a policy tool and the need to retain identical questions to ensure comparability over time (Altman and Taylor 1996: 198).

While temporal comparisons have been compromised, the new questions in the 2008 survey mobility module do have the benefit of providing novel insights. Questions on duration of residence have rarely been used in Australian migration research, but are comparatively common as part of the standard armoury of statistical agencies in other countries. Of 141 countries collecting migration data in the 2000 round of censuses, fully 82 asked questions on duration of residence while only 56 asked about place of residence one or five years previously (Bell et al. 2011) – the standard questions asked at the Australian census. The key benefit of duration of residence data is in superceding the conventional mover–stayer framework by segmenting the population into more detailed mover categories. Not only does this better recognise the near universality of migration, and the heterogeneous nature of populations, it also allows movement classification to be customised around particular topics of interest: chronic movers, or long term stayers, for example. Set alongside other socioeconomic variables, such as income or level of education, it is the bifurcation of mobility into two discrete categories (moved/did not move) that emerges as unusual, and duration data overcome this limitation. What is additionally unusual in the 2008 NATSISS is the question on duration of residence in the previous dwelling which provides, for the first time, a full picture of completed durations of residence.

Within the confines of this paper, we have been able to explore only a fraction of the potential offered by these data. We have established how the median duration of stay varies across the life course and offers an alternative to the conventional statistic reporting proportions who moved in a given time interval. We have shown how this measure varies across space, with generally shorter durations of residence in remote communities, at young adult ages and among older people. We have also illustrated the considerable heterogeneity which exists in regard to population movement. For example, among young adults (always identified as the most mobile group in the population) we find a substantial group who have not moved for more than a decade, whereas at older ages, conventionally regarded as the most stable, more than 1 in 5 had changed residence within the last three years. Data on previous residence allowed us to chart the rate at which individual cohorts progressively moved from their earlier dwellings, and coupling these data with duration in current residence provided the basis for a crude segmentation of chronic movers and longer-term stayers. One key benefit of surveys is in providing information on reasons for movement and, combining these responses with other housing and labour force statistics, we were able to tease out the differences between these two groups which represent polar opposites on the mobility continuum.

Population mobility is a complex phenomenon. Most people move multiple times during their lives, and these changes of residence occur in response to the interplay of opportunities and constraints in various life domains: family, work, education, health, and so on. Understanding the causes and consequences of these moves, and their underlying dynamics, calls for considerable detail as to their timing, context and fit within the lifecourse. Ultimately, such understanding requires detailed residential life histories which track individual moves through time situated within the family and household context so as to link these with contingent events such as family formation and changes in employment. The 2008 NATSISS falls well short of this aspirational goal, but nevertheless it serves a valuable purpose in providing an alternative perspective on Indigenous population mobility and opening the way for innovative methods of analysis. The challenge for the 2014 survey is to shape a more wide-ranging module on mobility that encompasses the material collected in both 2002 and 2008, and builds a broader framework linking mobility to other lifecourse events.

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