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2.0 Key data issues Questions for stakeholders:
Potential options for addressing shortcomings
2.2 Measuring level movements in key variables Description of the data issue (scope) The SMVU sample allocation is currently designed to minimise both provider load and the standard error of the level estimates of key variables. TSUG members have commented that users require an emphasis on the measurement of annual changes for the key variable of vehicle kilometres travelled by broad vehicle type (passenger vehicles, rigid freight vehicles, articulated freight vehicles) and geographic region (preferably national, state and capital city region). The current SMVU uses a non-overlapping sample. An overlapping sample is generally required to minimise the standard errors associated with movements in the estimate between years. Redesigning the SMVU sample to improve the accuracy of estimate movements between years can be done, however it may affect the accuracy of the level estimates. Questions for stakeholders:
Potential options for addressing the level movement data issue Options to address this issue could include:
These two options would need detailed investigation before we can determine which is feasible and which might be the most appropriate way to improve movement RSEs. Selecting the same respondents in consecutive years can affect the response rate which in turn affects the data accuracy, so this would also need to be considered. 2.3 Data volatility Description of the data issue (scope) Some users are unhappy with the current sample size for SMVU - approximately 16,400 vehicles in the 2003 survey. For example, the stability of the kilometres travelled time series has been criticised by these users, who highlight the correlation between increased variability (measured by the estimated standard errors) and smaller sample sizes. If necessary to achieve improved accuracy, users would accept some of the SMVU data items to be published less frequently and with data items reduced to a core set. External users want to be consulted for input to the re-specification of the survey's objectives and design. It should be noted that methodological changes aimed at reducing 'recall bias' have arguably reduced the non-sampling error associated with these estimates. Moreover an internal methodological review in 2002 identified some erroneous aspects of the SMVU survey frame and new vehicle provision methodology, both of which have now been rectified. Questions for stakeholders:
Potential options for addressing data quality issues Options to address this issue could include:
Further questions for state transport departments and motor vehicle registration authorities: The inclusion of odometer readings on the administrative data sets provided to the ABS would improve the accuracy of SMVU estimates. Can state stakeholders work with their counterpart motor vehicle registration authorities in identifying the barriers precluding the inclusion of vehicle odometer readings on the administrative data sets provided to the ABS? How can the respective organisations overcome the constraints of:
A dimension of data quality is constrained by the consistency of the survey frames the SMVU samples are selected from. The state motor vehicle registration authorities do not currently apply consistent classificatory standards to the stratification variable (vehicle type) used by the SMVU. This introduces an element of non-sampling error into SMVU estimates. Can state stakeholders work with their counterpart motor vehicle registration authorities in identifying a strategy for the adoption of common standards (used by all motor vehicle registration authorities) to be applied to the state vehicle registers? 2.4 Geographic patterns of activity Description of the data issue (scope) In describing transport activities, geographic detail is required by users below the level of national and state aggregates. Data for individual coherent physical regions (eg for each 'capital city and surrounding catchment'), would be valuable. This data requirement would require the use of geographic area classifications. How would the ABS definition of metro area compare with different users of this state specific data? Data at Statistical Division level would be useful to users but of lower priority for inclusion in nationally collected data sets. If modelling is to be used in order to impute estimates for low-level geographies, users would need some raw data to be available for the low level classifications being investigated. External data users have a need for some small area transport information to be available - including data that supports the national level modelling work that describes transport characteristics at geographic regions between local and capital city regions. TSUG have acknowledged that the primary role of the ABS is to provide stable national and state SMVU data. TSUG have also acknowledged that it is not the role of the ABS to provide intra-regional transport data or transport corridor data. The ABS may have a role in assisting the modelling work for some sub-state data by providing some limited regional data. To this end, the ABS would need to know which region(s) to collect data for, how to define the region, what frequency of data availability would be required and what the associated data quality requirements would be. These questions need to be determined by stakeholder input.
Potential options for addressing geographic issues
2.5 Impact of unregistered vehicles Description of the data issue (scope) Users have raised concerns that unregistered vehicles make a contribution to motor vehicle usage in Australia. These vehicles are not covered by the survey as they are not available for selection from the frame. The concerns centre around what contribution these unregistered vehicles make to total fuel consumption and general motor vehicle use. The extent of contribution of unregistered vehicles is not known. There have been reports that unregistered vehicles illegally used on Queensland roads, could make up 4.5% of vehicles used on the road, whereas in NSW it is estimated to be around 2%. The extent of legal use of unregistered vehicles in industry is also not known, and we do not know if it is stable between years. Does the proportion of unregistered vehicles provide valid explanation for the criticisms of the estimates of fuel use and perceptions of underestimating total kilometres travelled? To answer this, we need to know if the proportion of unregistered to registered vehicles is the same across all states and stable over time. Volatility in the number of unregistered vehicles may well be a causal factor in some of the volatility of the SMVU estimates. Some investigation into the unregistered vehicle issue is recommended. Questions for stakeholders: Anecdotal evidence from Victoria suggests the number of illegally unregistered vehicles (actually in use) in Victoria are between 5% and 10%.
Potential options to address the issue of unregistered vehicles Options to address this issue could include:
2.6 Quality of fuel use estimates Description of the data issue (scope) SMVU fuel use estimates are used in a number of different applications, and have significant ramifications for governmental income and expenditures. In order to forecast income from items such as fuel taxes, it is important that Commonwealth and State governments have data describing characteristics of the fuel use to which taxes and charges are applied. Each year the National Road Transport Commission (NRTC) sets heavy vehicle levies, on fuel and on registrations, for the purpose of covering the estimated damage these vehicles do to roads. Greenhouse gas emission (GHG) estimates are modelled on the basis of fuel use estimates. These uses represent a substantial ongoing need for SMVU data. Quality of the SMVU fuel estimates was not specifically mentioned at the TSUG meeting, however the ABS is aware of the criticism that differences exist between SMVU's fuel use figures and those from fuel excise data sources. Questions for stakeholders:
Potential options for addressing shortcomings The ABS needs to investigate the conceptual issues, feasibility and cost of improving fuel use estimates associated with the following potential options:
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