Page tools: Print Page Print All  
Introduction A4.1. Interstate migration is estimated using Medicare data on changes of address. Since 6 August 1996, interstate migration has been estimated using Medicare data on persons of all ages. For each State, sex and single year of age, the number of interstate arrivals and departures are estimated by multiplying the corresponding number of interstate arrivals and departures identified through Medicare by an 'expansion factor' to reflect that the Medicare data may not capture all interstate movements. This process can be summarised according to the following formulae:
A4.2. The component is often referred to as the 'expansion factor'. As noted in the above formulae, expansion factors are equal to one for all ages, except for males aged between 16 and 29 inclusive and females aged between 18 and 24 inclusive. A4.3. This method is detailed further in Demography Working Paper 99/2: Estimating Interstate Migration, 1996  2001 which is available through the Internet on https://www.abs.gov.au. Lagging Medicare data A4.3. The model assumes an average lag of 3 months between moving address and registering the move with the Health Insurance Commission. While it is not possible to verify the assumption of a 3 month lag with the information available, a lag of 3 months would have produced a lower intercensal error for the 1991 to 1996 intercensal period than no lag. Expansion factors Smoothing A4.5. By calculating the expansion factors individually for each single year of age by sex, separately for arrivals and departures for each State, the expansion factors can be relatively volatile. Furthermore, it is reasonable to expect that consecutive ages would have similar expansion factors. As such, several adjustments are made to the expansion factors to reduce this volatility and increase the similarity in expansion factors for consecutive ages. Step 1. A4.6. All the separate components used to calculate the expansion factors (ie. Census movers data, Medicare movers data, Medicare multiple movers data) are smoothed across single years of age for both male and female arrivals and departures for each State using a 3 term moving average. Step 2. A4.7. The expansion factors are smoothed using a 3 term moving average. Step 3. A4.8. All expansion factors which are calculated as being less than one (ie. 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 nonintuitive, instead reflecting inconsistencies between the Census and Medicare numbers. Other Ages A4.9. These three steps generate smoothed expansion factors for all ages. Then, one additional step is applied which assign expansion factors of one (ie. Medicare data represents actual movers exactly) for most age groups. Step 4. A4.10. Expansion factors for males aged less than 16 or greater than 29 inclusive are set to one (assuming a coverage of 100%). Expansion factors for females aged less than 18 or greater than 24 are set to one (assuming a coverage of 100%).
