Transitioning Regional Economies

113.E.5 Results discussion

This section provides a brief overview of the results from the Commission’s preferred index of adaptive capacity, using the single PCA approach. A more detailed discussion can be found in chapter 4. In addition, appendix B contains a list of regions by their ranking and category of adaptive capacity.

The least adaptive regions tend to be concentrated in more remote regions of Australia, while the most adaptive regions tend to be in major cities (figure E.4). This is further illustrated in figure E.8. There are no major city regions within the least adaptive or below average categories. In terms of population sizes, a relatively larger share of people in the least adaptive regions are living in regional or remote areas, compared with the share of people in those areas among the whole of Australia (figure E.8). However, in terms of the whole population, the number of people living within the least adaptive regions is small, due to the relatively sparse populations in these areas when compared with the populations in major cities (figure E.8).

Figure E.8 Regions and population by relative adaptive capacity and remoteness

Source: Productivity Commission estimates.

114.E.6 SA2 adaptive capacity results for 2016

Creating the index of adaptive capacity at the FER level has the potential to hide differences in indicators between the SA2s that make up the FERs. Therefore the index was also created at the SA2 level, using the single PCA approach. A total of 2070 SA2 regions were included in the calculation of the index. 126 were excluded due to missing data.

The indicators that were included in the analysis were the same as for the FER analysis, but at the SA2 level. There are a few exceptions — the measures of industry diversity and interregional flows are at the FER level for the SA2 analysis. These variables are more appropriate to consider at a larger level of geography than SA2s because people live, work and consume services across multiple SA2s (particularly in urban areas).

Weights of each indicator in the SA2level index are presented in table E.17 and the contributions of each capital domain are presented in table E.18.

Table E.17 Single PCA for SA2 regions, 2016 — weights of indicators in index Ordered from largest to smallest in absolute value

Indicator Weight in index Indicator Weight in index safeatnight 0.103 accesstransport 0.048 invinc 0.097 trademarks 0.042 providesupport 0.091 bsnsexitrate 0.042 ownhome 0.090 bsnsentryrate 0.039 volunt 0.086 buildappvalpp 0.038 nature 0.082 psychdistress 0.035 indig 0.080 yr12plus 0.034 skill1to3 0.077 remoteness 0.033 youtheng 0.077 agind 0.030 selfhealth 0.075 internet 0.029 homeless 0.071 highinc 0.026 propprice 0.071 dsp 0.026 wkage 0.068 miningind 0.024 housingstress 0.064 interflows 0.023 comprisk 0.060 wkagechange 0.019 ltnewstart 0.057 govtpyts 0.017 getsupport 0.053 herfinneg 0.009 emptolf 0.053 discriminated 0.009 culturalacceptance 0.051 participation 0.005 patents 0.049

Source: Productivity Commission estimates.

Table E.18 Single PCA for SA2 regions, 2016 — contributions of each capital domain Ordered from largest to smallest contribution

Capital domain Contribution to index (%) Human 41.30 Social 23.43 Financial 18.38 Physical 7.47 Natural 6.86 Other 2.55

Source: Productivity Commission estimates.

Human capital factors have the largest contribution to the index at 41 per cent (table E.18). However, this is smaller than the contribution of human capital to the single PCA index for FERs (50 per cent). Social capital factors make a larger contribution to the index for SA2 regions than FERs (23 per cent compared with 17 per cent). The contributions of financial, physical, natural and other factors to the SA2 index are similar to that of FERs.

After grouping SA2 regions into categories according to their index scores, 309 were classified as least adaptive, 740 as below average, 710 as above average and 311 as most adaptive. However, similar to the FER results, region’s index scores are sensitive to changes in the sample of regions and indicators included in the analysis.

Figure E.9 presents a map of SA2 regions by their relative adaptive capacity, according to each region’s index score. A comparison of figures E.4 and E.9 shows that large remote and regional areas tend to have relatively low adaptive capacity at both the FER and SA2 level. However, there are pockets of lower adaptive capacity within major cities that are not obvious at the aggregate FER level.

For the single PCA index for SA2 regions, the share of people in more remote regions within the least adaptive category is larger than the national share of people in more remote regions (figure E.10). However, most people within the least adaptive category of SA2s live in major cities. These people were masked in the FER results, because they reside within a FER that had relatively high adaptive capacity overall.

Figure E.9 Single PCA — relative adaptive capacity of SA2 regions, 2016

Source: Productivity Commission estimates.