Tax cut windfall: Regional NSW loses out
New analysis from the Australia Institute shows that regional NSW would receive below average benefits compared to the average Australian household from income tax cuts, outlined in the 2018 federal budget.
The figures represent the change in household disposable income (after tax income) as a percentage of change in the national average. Modelling also took into account tax cuts and other budgetary measures announced in the 2018 budget and then averaged out the benefit in dollar terms to each federal electorate.
“Our analysis shows eighteen regional electorates, that all come in below average.” said Matt Grudnoff, Senior Economist at The Australia Institute.
“Once the numbers are averaged across the nation, families in the lowest benefitting regional electorate of Lyne, only receive 76 cents for every dollar received by the average household. [See figure 2]
Sydney electorates would receive on average 118 cents for every dollar, while regional electorates would receive 88 cents.
“This tax cut is highly selective. In the highest paid electorate, the Prime Minister’s seat of Wentworth, households receive almost double the national average benefit,
“The top 10 electorates all get at least one and a half times more than the national average. This is a tax cut that truly delivers for the top-end and leaves everyone else behind, [See figure 1]
“By political party, it is clear that in NSW the least benefit flows to National Party and Labor seats. And on a national scale, one quarter of Nationals’ seats are in the bottom 10 electorates, in fact all but one of their seats are ranked in the bottom half of electorates.” [See figure 3]
Figure 1.
|
Top 10 electorates |
Percentage of average |
Party |
1 |
Wentworth, NSW |
192% |
LIB |
2 |
North Sydney, NSW |
180% |
LIB |
3 |
Warringah, NSW |
172% |
LIB |
4 |
Sydney, NSW |
167% |
ALP |
5 |
Melbourne Ports, VIC |
160% |
ALP |
6 |
Higgins, VIC |
159% |
LIB |
7 |
Bradfield, NSW |
158% |
LIB |
8 |
Kooyong, VIC |
156% |
LIB |
9 |
Grayndler, NSW |
154% |
ALP |
10 |
Goldstein, VIC |
150% |
LIB |
|
Bottom 10 electorates |
Percentage of average |
Party |
141 |
Longman, QLD |
77% |
ALP |
142 |
Cowper, NSW |
77% |
NAT |
143 |
Port Adelaide, SA |
76% |
ALP |
144 |
Lyne, NSW |
76% |
NAT |
145 |
Wide Bay, QLD |
76% |
NAT |
146 |
Wakefield, SA |
73% |
ALP |
147 |
Braddon, TAS |
72% |
ALP |
148 |
Lyons, TAS |
72% |
ALP |
149 |
Hinkler, QLD |
71% |
NAT |
150 |
Blaxland, NSW |
70% |
ALP |
Figure 2.
Rank |
Electorate |
Percentage of average |
Party |
1 |
Wentworth |
192% |
LIB |
2 |
North Sydney |
180% |
LIB |
3 |
Warringah |
172% |
LIB |
4 |
Sydney |
167% |
ALP |
5 |
Bradfield |
158% |
LIB |
6 |
Grayndler |
154% |
ALP |
7 |
Mackellar |
132% |
LIB |
8 |
Kingsford Smith |
131% |
ALP |
9 |
Berowra |
130% |
LIB |
10 |
Reid |
125% |
LIB |
11 |
Cook |
121% |
LIB |
12 |
Mitchell |
121% |
LIB |
13 |
Bennelong |
117% |
LIB |
14 |
Hughes |
116% |
LIB |
15 |
Eden-Monaro |
102% |
ALP |
16 |
Macquarie |
101% |
ALP |
17 |
Newcastle |
101% |
ALP |
18 |
Banks |
100% |
LIB |
19 |
Cunningham |
98% |
ALP |
20 |
Robertson |
97% |
LIB |
21 |
Barton |
97% |
ALP |
22 |
Hume |
97% |
LIB |
23 |
Greenway |
96% |
ALP |
24 |
Parramatta |
94% |
ALP |
25 |
Lindsay |
92% |
ALP |
26 |
Shortland |
90% |
ALP |
27 |
Clare |
89% |
NAT |
28 |
Hunter |
89% |
ALP |
29 |
Whitlam |
88% |
ALP |
30 |
Parks |
86% |
NAT |
31 |
Farrer |
86% |
LIB |
32 |
Riverina |
85% |
NAT |
33 |
Dobell |
85% |
ALP |
34 |
Macarthur |
85% |
ALP |
35 |
Richmond |
84% |
ALP |
36 |
Paterson |
83% |
ALP |
37 |
New England |
83% |
NAT |
38 |
Gilmore |
82% |
LIB |
39 |
Werriwa |
81% |
ALP |
40 |
McMahon |
80% |
ALP |
41 |
Chifley |
78% |
ALP |
42 |
Watson |
78% |
ALP |
43 |
Page |
77% |
NAT |
44 |
Cowper |
77% |
NAT |
45 |
Lyne |
76% |
NAT |
46 |
Blaxland |
70% |
ALP |
Total |
New South Wales |
105% |
Figure 3.
Rank |
Percentage of average |
Party |
1 |
127% |
LIB |
2 |
100% |
ALP |
3 |
82% |
NAT |
Total |
105% |
*The electorate of Fowler was excluded because it failed NATSEM’s validation test
Source: Australia Institute calculations; Analysis from NATSEM’s STINMOD+Tas/Transfer model and Spatial MSM18 spatial microsimulation model
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