Table 2 summarises State and Territory IRSAD scores, alongside average approved budgets and average rates of utilisation in June 2020 and June 2021. IRSAD scores ranged between 697 and 1131, with higher scores indicating higher levels of relative advantage. In 2020, the national average approved NDIS budget was $75,047 (SD $29,932), and an average only 57.14% of budgets were being used (SD 8.70). Average budgets decreased slightly in 2021 ($73,538, SD $21,399), and average utilisation slightly increased (61.87%, SD 6.22). The Northern Territory had scores indicating the greatest disadvantage across the country, as well as the highest average approved NDIS budget. The Northern Territory has the highest proportion of people living in remote and very remote communities, as well as the highest proportion of Aboriginal and Torres Strait Islander residents – both factors associated with greater need for health and social supports. The ability to access appropriate NDIS services appear to be lacking in the Northern Territory – the average utilisation rate across all disabilities for the Territory was 42% in 2020; increasing to 54% in 2021. In contrast, the Australian Capital Territory (ACT) is the smallest Territory within the country and had the highest average scores of relative advantage across the country. It also had substantially higher average rates of utilisation across all disabilities (66% in 2020, 68% in 2021), while also having a lower average approved budget of $62,000, compared to the national average of $75,047.62. However, the average approved budget for ACT service area jumped substantially in 2021, doubling to $124,000. The ACT is counted as one NDIS service area, whereas other States and Territories include at least 4 service areas where budgets are averaged across, however this substantial increase in approved funding is worth noting and seeking further investigation if addressing socioeconomic inequity is a concern for the NDIS program.
Hypothesis 1: Higher average approved plan budgets would be associated with higher levels of socioeconomic advantage.
Linear regression results are shown in Table 3. Linear regression showed a significant negative relationship between IRSAD scores and approved budgets, whereby higher IRSAD scores, or higher levels of relative advantage, predicted lower average approved budgets in both 2020 (β = -0.325, p < 0.001, R2 = 0.106) and 2021 (β = -0.227, p = 0.043, R2 = 0.051) (see Figs. 1 and 2, Table 3). While most average plan budgets clustered between $50,000 and $100,000, a number of higher approved budgets were outliers, particularly in service districts with higher levels of socioeconomic disadvantage.
Hypothesis 2: Higher average rates of plan utilisation would be associated with higher levels of socioeconomic advantage.
However, when looking at the relationship between relative advantage and disadvantage and fund utilisation rate, a significantly different relationship was observed (see Figs. 3 and 4). Higher levels of advantage (β = 0.530, p < 0.001, R2 = 0.281) predicted higher average rates of utilisation for ‘all’ disability types (excluding SIL and SDA clients), explaining 28% of model variance in 2020 (see Table 3). Similarly, higher levels of socioeconomic advantage (β = 0.550, p < 0.001, R2 = 0.302) predicted higher average rates of plan utilisation in 2021. That is, for an area such as the ACT, with the highest average IRSAD in the country (1089), clients with NDIS fund are also, on average, using a greater proportion of their allotted budget (66% in 2020, 68% in 2021). In contrast, a state such as Tasmania, which has an average IRSAD score (929.55) compared to the national average, predicts that clients on average utilise less of their budgets (59% in 2020, 62.75% in 2021). As Figs. 3 and 4 show, the relationship between socioeconomic advantage and higher rates of plan utilisation showed a clearer linear relationship, where participants living in areas of higher socioeconomic disadvantage were utilising a lower proportion of their approved plans. These two analyses indicate that while clients living in areas of greater socioeconomic disadvantage are being approved for higher individual funds, they utilise a smaller proportion of their funds compared to clients living in more advantaged areas.
Hypothesis 2a: Higher rates of plan utilisation and higher levels of socioeconomic advantage would remain significant once average approved plan budget is controlled for.
When predicting utilisation rates that excluded SIL and SDA (SILSDA), the average approved budget amount had a significantly negative contribution to the model above what was already predicted by average IRSAD scores in 2020 (β = -0.221, p = 0.025). This relationship was not significant in 2021 (β = 0.026, p = 0.792, see Table 4). This suggests that the approved budget amount may be contributing significantly to predicting the degree of fund utilisation across all disability types, for services that are not focused on supported independent living or disability accommodation. However, the relationship is not consistent across the two years of data and requires further investigation.
When looking at average fund utilisation rate that includes clients using funding for SILSDA, the average score for socioeconomic advantage was still a significant predictor, such that living in more advantaged areas predicted higher rates of fund utilisation in 2020 (β = 0.416, p < 0.001), however explained less model variance (17.3%) compared to predicting utilisation when SILSDA clients were excluded. This relationship between socioeconomic advantage and fund utilisation was stronger in 2021 (β = 0.464, p < 0.001), explaining 21.5% of model variance. This suggests that socioeconomic advantage may be less of an explanatory factor for utilisation for clients who are receiving support through SILSDA, however this relationship is still significant. Average approved budget amount did not significantly predict utilisation rates in 2020 (β = -0.005, p = 0.963), nor did it significantly contribute to the amount of variance accounted for by the regression model. In 2021, average approved budget amount explained additional variance for fund utilisation over and above socioeconomic advantage (β = 0.231, p = 0.024). This provides initial evidence that in 2021, having a higher approved budget may contribute to higher levels of utilisation. However, this inconsistent relationship requires further exploration rather than implying there is a consistent trend for higher approved budgets being related to higher rates of utilisation over and above level of socioeconomic disadvantage.
Hypothesis 2b: The relationship between rates of plan utilisation and higher levels of socioeconomic advantage would not significantly vary across disability support class types.
Linear regressions, modelling the relationship between average utilisation rate and average ISRAD scores for service districts, were conducted across three disability support class types – core, capacity building, and capital-, and were calculated when utilisation included SILSDA plans. In both June 2020 and June 2021 data, there was a significant positive relationship between utilisation rates and higher levels of socioeconomic advantage (see Table 5). All disability support class types were significantly associated with socioeconomic advantage, except for capital support in June 2020. Beta values were consistently high for capacity building activities (β ranging from 0.550 in June 2021 excluding SILSDA plans, to 0.592 when SILSDA plans were included), compared to core and capital support activities.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.