The Commission has developed a single metric of regional adaptive capacity. This was challenging due to the complexity of identifying, measuring and weighting the large set of factors that influence a community’s ability to deal with change.
The metric can be used to explore some broad themes and patterns of adaptive capacity in Australia’s regions. It provides a ‘litmus test’ to identify regions potentially at risk of failing to adjust to pressures for change. However, the metric, on its own, is not suited to guiding policy decisions.
The analysis shows that peoplerelated factors (including education, skills, employment, income and community cohesion) strongly shape adaptive capacity, particularly for communities in urban areas. For communities in remote areas, these and other factors associated with remoteness, such as accessibility to services and infrastructure, have the strongest influence on results.
The proportion of regions that are in the least adaptive category increases with remoteness. As these regions are sparsely populated, very few people live in regions with low adaptive capacity. In contrast, the most adaptive regions are concentrated in major cities — where most of the population resides. Pockets of disadvantage still exist in major cities.
Regions that have a high share of employment in mining and related industries typically have relatively lower adaptive capacity, as estimated with the metric. One reason is that a high concentration of mining employment has a negative impact on the index. Other factors, such as physical infrastructure and social capital are also important.
Regions with a large share of employment in agriculture have a diverse range of adaptive capacity outcomes, with some having above average adaptive capacity and some below average. This reflects differences across agricultural regions in financial capital, human capital and social factors, such as safety at night and volunteering.
The use of sensitivity analysis shows that the relative rankings of regions is subject to a degree of uncertainty and therefore the ranking of regions could change significantly when different variables are included in the index. Thus the published list of 77 functional economic regions ranked from least adaptive to most adaptive should be treated with a degree of caution.
Even if relative adaptive capacity could be measured accurately, on its own it does not identify whether regions will be successful in transitioning to a more sustainable economic base following a disruption.
The Commission was asked to develop a metric, combing a series of indicators, to assess the degree of economic dislocation/engagement, transitional friction and local economic sustainability of regions across Australia and to rank those regions to identify those most at risk of failing to adjust.
The approach to this task is to measure the relative adaptive capacity of regional communities and to use this as a ‘litmus test’ to identify regions potentially at risk of failing to adapt to pressures for change. This is a challenging task due to the complexity of identifying (and measuring) the large set of systemic factors that influence a community’s ability to deal with disruption to its economic circumstances.22 There is no agreed approach to measuring adaptive capacity and the concept itself is contested (chapter 2).
The framework and the factors that have been used as indicators of adaptive capacity are outlined in chapter 2. The 2016 Census of Population and Housing was a key data source for many variables included in the Commission’s index of relative adaptive capacity. Additional detail on the index and the methodology used to construct the index are available in appendix E.
Broadly speaking, regions with a high index value of adaptive capacity are in a relatively better position to respond to changes in their economic circumstances. However, the realised outcomes in a region arising from a disruption depends on a number of matters, including:
how sensitive and exposed the region is to a particular set of changes
the opportunities available to communities to transition into other economic activities or to build on existing strengths and comparative advantages
whether there are any impediments to adjustment, such as policy or regulatory barriers.
Ultimately, the changes experienced in communities are driven by the individual decisions of workers, business owners and others in the community as they seek to do what is in their best interests given the circumstances.
62.4.1 How should the metric be interpreted?
The Commission’s index of relative adaptive capacity (box 4.1) is based on a widely accepted methodology. This index can be used to identify regions that may have problems responding to economic shocks. That said, caution is required in interpreting and applying it to policy making aimed at building resilience.
The index of relative adaptive capacity cannot capture the unique attributes of each regional community. As noted by James Cook University:
Each region has its own culture, natural environment, climate, identity and a unique competitive advantage. The remote, Indigenousled Arnhem Land, for example is a very different region from Queensland’s sugar and tourismdriven Wet Tropics. (sub. 24, p. 2)
Further, factors that increase adaptive capacity in response to one type of disruption might decrease adaptive capacity for a different type of disruption. As such, any single measure of relative adaptive capacity for all Australian regions will have limitations.
Box 4.1 Relative adaptive capacity
Relative adaptive capacity is an unobservable attribute of a region. It is not a measure of resilience to disruptive events. Rather, it is a summary of the complex set of factors affecting resilience, including the skills and education of regional workforces, access to infrastructure and services, availability of natural resources, financial resources available to businesses and individuals, and industry diversity. For this report, a relative measure of adaptive capacity has been inferred, derived using data across all regions. Principal component analysis has been used to construct the metric. This is a method applied to develop similar metrics, such as the ABS SocioEconomic Indexes for Areas (SEIFA). In general, regions with higher adaptive capacity have attributes that are likely to increase the potential to transition successfully following an economic disruption.
Sensitivity analysis has been undertaken to understand and illustrate the degree of certainty in the estimated index.23 This provides insights into the extent that indicators included in the dataset, such as levels of education, incomes and remoteness, influence confidence in the index score for each region. The larger the confidence intervals for index scores of regions, the less reliable the score and ranking. The degree of confidence in the individual index scores tends to be lower for remote and very remote regions (figure 4.1 and box 4.2).
There are a large number of regions whose relative rankings could change substantially when indicators are included or excluded from the analysis. For example, Karratha (Western Australia) and Yorke Peninsula (South Australia) — both estimated to have below average adaptive capacity — have confidence intervals so large that their maximum index score within that confidence interval range would also place them in the most adaptive capacity category.
A number of the variables included in the metric analysis are imperfect proxies of the underlying factors thought to shape adaptive capacity (which is itself difficult to define). The ranges presented in figure 4.1 should therefore be thought of as lowerbound estimates of the sensitivity of the results.
Index values for each functional economic region and their 90 per cent confident intervals, sorted from lowest to highesta
a Regions are defined using functional economic regions (FERs). The top and bottom group of regions are defined as those above and below one standard deviation of the mean index value of adaptive capacity across all regions. Regions are ordered based on their final index value, where the whiskers represent the upper and lower 5 percentiles (90 per cent confidence intervals) of the region’s index value across sensitivity analysis. Remoteness of regions is represented in the colouring of the lines.
The index for each region is highly sensitive to a small number of key variables which, when removed, dramatically change the rankings of regions.
Variables related to remoteness play a key role in determining the sensitivity of a region’s ranking. This is primarily due to physical and natural capital factors. A number of remote and very remote regions are highly dependent on natural resources (in particular mining) or are particularly disadvantaged by multiple physical factors, including access to infrastructure and services.
For example, a mining region that has average scores for all variables except mining employment (which could be very high), could have a low score for most index calculations (that include the mining employment variable), but would have a more midlevel ranking for the small number of sensitivity runs where the mining employment variable is excluded.
Even if adaptive capacity could be measured accurately, it does not, on its own, identify whether regions will be successful in transitioning to a more sustainable economic base following a disruption. As noted earlier, this depends on how sensitive a region is to a particular disruption (which can vary by magnitude and probability), the opportunities available to regional communities and the actions of people within those communities. As such, on its own, the metric has limited suitability as a guide for policy decisions, particularly the allocation of funding. However, the metric can be used to explore broad themes and patterns of adaptive capacity in Australia’s regions, and can be used as a ‘litmus test’ to identify regions potentially at risk of failing to adjust to pressures for change.
The Commission’s index of relative adaptive capacity is based on a widely accepted methodology. The metric can be used as a litmus test to identify regions which may find it difficult to adjust to significant economic disruptions.
However, caution is required in interpreting the metric and using it as a basis for policy making. A single metric of relative adaptive capacity cannot fully capture the unique attributes of each regional community. Further, the metric does not predict the likely outcome of a region to a shock, which is based not only on the region’s adaptive capacity but also the nature of shocks it faces, the options available to people affected, and the decisions that they make.