INTERSTATE MIGRATION METHOD

7.11 Post-censal quarterly estimates of net interstate migration are created for the states and territories using interstate change of address advised to Medicare Australia and to the Department of Defence in the case of the military. Medicare data are adjusted by means of expansion factors. These expansion factors are used to account for an under coverage of Medicare data by various ages and sex. For example, it is known that some people, particularly younger card holders, do not register changes of address with Medicare, or do so long after the fact.

7.12 Expansion factors are used in the calculation of post-censal quarterly estimates of net interstate migration and remain constant throughout the intercensal period until once again they are reviewed after final data from the following Census of Population and Housing becomes available. They are calculated for each state and territory, single year of age, sex and movement direction (i.e. arrivals or departures).


Calculating expansion factors for Medicare data

7.13 Expansion factors applied to Medicare data are based on the estimated proportion of the population covered. To calculate this proportion, Medicare movement data are compared with Census data (based on persons' usual residence one year ago). In the graph below the example is based on the 2006 Census. The period of Medicare data chosen (October 2005 to September 2006) reflects the assumption that there is a lag in the registration of change of address through Medicare.

7.14 Expansion factors were estimated for each state and territory, by single year of age, sex and movement direction (arrivals/departures). As shown in the equation below Census data were adjusted for multiple movers before dividing by the annual Medicare data. These expansion factors are used to account for an under coverage of Medicare data by various ages (particularly young adults) as seen in the previous graph.

7.15 Analysis showed, based on the 2006 Census, that for all states and territories expansion factors on average, were greater than 1 (i.e. suggesting 'undercoverage' in Medicare data) for males aged 17-30 years and females aged 17-25 years. These age ranges were chosen as they decreased intercensal discrepancy the most when compared to any other range and are used in the 2006-11 model. Expansion factors are applied universally for all states and territories to Medicare data with these age-sex characteristics. For all other age groups, it is assumed that Medicare data provides a full coverage (i.e. an expansion factor of 1 is used).

7.16 Based on the 2006 Census the complete process undertaken can be summarised according to the following equations:

7.17 For the 2006-11 model all ages other than males aged 17-30 years and females aged 17-25 years were set to 1 as a default value. In addition any expansion factors within these age ranges which may be calculated as being less than one for a particular state/territory, single year of age, sex or movement direction are also set to one. If an expansion factor has been adjusted to 1 (i.e. no expansion factor applied) then the following equation is used:

7.18 To calculate net interstate migration for quarter t for state s and age-sex a then the following equation is the next one to be used:

Defence force adjustment

7.19 As seen in the equations above adjustments to compensate for interstate defence force movements not covered by Medicare are applied to the quarterly interstate migration estimates. These adjustments are estimated using counts of defence force personnel by age, sex and state/territory, obtained from the Department of Defence, with 70% of any change in quarterly defence force numbers assumed to be due to interstate migration not otherwise covered by the Medicare model.


Multiple movers and Census data

7.20 Some people will move more than once during a given time period, including some who move to one location, then return to their original location. However, the Census asks for each person's address one year ago without reference to multiple moves which could have occurred over the one year period.

7.21 Since Medicare data do include information on multiple/return movements, these were used as a proximate to adjust data from the 2006 Census for calculating the expansion factors. The proportion of multiple movers identified using Medicare data was calculated by dividing all quarterly movements by the number of final annual movements for each state or territory, by sex, single year of age and movement direction (arrivals/departures). Calculations showed that 7% of all movements within the year were multiple movements. Those who had moved twice represented 6% of all movements whereas those who moved more than twice only represented 1% of all movements.


Lagging of Medicare data

7.22 The Medicare model assumes an average lag of 3 months (one quarter) between a person moving address and them registering the move with Medicare Australia. Analysis has shown that registration of changes of address through Medicare generally occurs some time after the actual move. Comparison of the outcomes of most scenarios tested for choosing the 2006 expansion factors indicated that the use of lagging was better as it reduced intercensal discrepancy.

7.23 Interstate migration models for both 1996-2001 and 2001-06 assumed an average registration lag of three months, so Medicare information for a particular quarter was used to estimate interstate migration for the previous quarter. It is not possible to lag these data further (i.e. to assume a delay in registration of more than three months) as this would impact on the production and publication of population estimates which are released within 6 months after the reference period.


Smoothing

7.24 Using Census data on address one year ago do introduce some potential problems because a relatively short (and therefore potentially more volatile and less representative) time period is used to estimate expansion factors. Smoothing used in generating the expansion factors helps to address these problems.

7.25 By calculating the expansion factors individually for each single year of age by sex, separately for arrivals and departures for each state and territory, the expansion factors can be relatively volatile. Furthermore, it is reasonable to expect that consecutive ages would have similar expansion factors. As such, smoothing adjustments are made to the expansion factors to reduce this volatility and increase the similarity in expansion factors for consecutive ages.

7.26 First, all the separate input components used to calculate the expansion factors (i.e. Census movers data, Medicare movers data and multiple movers data) are smoothed across single years of age for both male and female; arrivals and departures; and for each state and territory using a 3 term moving average.

7.27 Second, all expansion factors which are calculated as being less than one (i.e. fewer Census movers than Medicare movers) are set to one. Expansion factors less than one represent Medicare coverage of greater than 100% with movers registered through Medicare outnumbering adjusted Census movers. As such, expansion factors less than one are considered non-intuitive, instead reflecting inconsistencies between the Census and Medicare data.

7.28 These two steps generate smoothed expansion factors for all ages. Then, an additional step is applied which assign expansion factors of one for most age groups. From analysis based on the 2006 Census, the Medicare model expansion factors for males aged less than 17 or greater than 30 are all set to one (i.e. assumes a coverage of 100%). Likewise, expansion factors for females aged less than 17 or greater than 25 are also all set to one (i.e. assumes a coverage of 100%).

7.29 A further smoothing option is to smooth the actual output (expansion factors) using a 3 term moving average. This was used in the 1996-2001 method. However, for both the 2001-06 and 2006-11 methods smoothing of expansion factors produced only small improvements in the estimated levels of intercensal discrepancy; in some cases, results were worsened when the factors were smoothed.

7.30 Greater improvements were gained by smoothing the input data of Medicare, Census and multiple movers used to produce the expansion factors. For most states and territories, the best results were found from expansion factors using smoothed input data and un-smoothed output data (i.e. un-smoothed expansion factors).


Capping expansion factors

7.31 In the interstate migration model used for 1996-2001, expansion factors calculated as being greater than 2 (i.e. less than 50% coverage estimated for Medicare data) were set to 2. The rationale for 'capping' expansion factors was that this would reduce the influence of outlying extreme results, such as unusually low registrations in particular age/sex groups. In the 2001-06 model analysis of capping applied to the expansion factors did not produce improvements in the intercensal discrepancy and was therefore not used.

7.32 For the 2006-11 model analysis of capping applied to the expansion factors did produce improvements in the intercensal discrepancy and was therefore used. In the 2006-11 model the only outlying group of interstate movers for which capping did apply was males aged 23 to 25 departing the Northern Territory.


Medicare based model for 2006-11

7.33 The Medicare based model used for the 2006-11 intercensal period for calculating interstate migration contain the following characteristics:

• lagging of Medicare input data by three months (both for calculating expansion factors and for estimating progressive quarters of interstate migration)
• smoothing of input data (Census, Medicare and multiple movers) used to produce expansion factors
• capping applied to expansion factors
• expansion factors applied to males aged 17 to 30 years and females aged 17 to 25 years.

7.34 The ABS is using this model to produce interstate migration estimates each quarter for the intercensal period September quarter 2006 to June quarter 2011 and onwards until once again a review is undertaken after data from the 2011 Census of Population and Housing have been finalised. A defence force adjustment will continue to be applied to estimates produced by this model to compensate for movements of defence force personnel not covered by Medicare.

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