Methods - Indigenous workforce
Indigenous and key government agency representatives in the region engaged in the co-design of analytic output methods used in the Indigenous workforce case study. The need to understand the profile and characteristics for this region’s Indigenous workforce – particularly those relevant to the land and water sector - was identified as a key priority.
Data from the Multi-Agency Data Integration Project (MADIP) and the Business Longitudinal Analysis Data Environment (BLADE) were used to examine the employment status, occupations, income levels and income stability of Indigenous people in the Darwin region who were over 15 years old and not in school during the years covered by the last two censuses (2011 and 2016). Details of how information was obtained to determine education profile, employment status and occupations can be found in the methods report that accompanies this study (Stokes et al.2019).
There were 161,731 records for people in the Darwin study area (Indigenous workforce case study) in 2016 in the data-set (ABS 2019) (MADIP). Of these, there were 77,794 people aged 15 or above, who had 2016 Census data, were not attending school (based on 2016 Census) and were living in the Darwin study area (Indigenous workforce case study), excluding the Tiwi Islands. These 77,794 people are referred to in this case study as ‘workers’. There were 5,824 (around 7.5%) Indigenous workers out of this population. A total of 78.5% (4,572) of those Indigenous workers lived in urban areas and the rest 21.5% (1,252) in rural and conservation areas.
Using BLADE to provide insights relevant to Indigenous businesses in the region which are involved in land and water management activities proved unsuccessful, principally because the number of Indigenous businesses is low and often hard to identify as being Indigenous. Analysis of data from MADIP was more successful.
The project team focused on Indigenous workers in the study region (Indigenous workforce case study) – looking at relationships between variables such as gender, education, urban/rural residence, personal income tax, industry of employment, occupation and employment status. The analysis highlighted a few issues – many category responses were listed as ‘other’ suggesting the need for improved processes to encourage Indigenous people (many of whom will not have English as their first language) to better understand and respond to Census questions.
Some fields have policy-relevant potential that cannot currently be realised due to data constraints. For example, reconfiguring ‘the language spoken at home’ questions could highlight key relationships between Indigenous residential location, mobility, and traditional custodial affiliation. In the 2016 Census, the Indigenous identification and ‘language spoken’ questions are separated, and the Indigenous language questions focus on speech facility rather than linguistic affiliation or identity. Indigenous completion rates are poor for this question despite the significance to Indigenous people of linguistic affiliation. Linking indigenous identification and cultural or linguistic affiliation questions in a Census containing residential and employment data would be of considerable policy and planning value (for example motivation for on-country work, employee retention) and may also increase Indigenous completion rates.