Introduction
The issue of Indigenous Australian crime and punishment rates is a topic that has been at the forefront of research since the initial investigation of the Royal Commission into Aboriginal Deaths in Custody. Since the inceptive inquiry, an adoption of stereotypes of Indigenous Australians has been appropriated throughout research that depicts the Aboriginal community as more susceptible to recidivism and more likely to receive incarceration punishments for crimes than non-Indigenous offenders. For the most part results have shown that higher crime rates are linked to social inequality and a culture that has been affected by inter-generational trauma.
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The interactivity between social inequality and crime rates has been one that is predominantly discussed among researchers however the discussion has become less prominent in regard to Indigenous Australians despite continued statistical analysis. The current gap in research relates to the focus on the Northern Territory and Queensland even though the highest Indigenous population is in New South Wales. The 2016 Census reported 216,276 Indigenous population within New South Wales making up 2.9% of the state population and 33.3% of the total Australian Indigenous population. Current scholarly research also predominantly focuses on the gross overrepresentation of Indigenous Australians in the prison system (Broadhurtsm R, 1997 pp 409). My research demonstrates that while crime and poverty indicators do influence the Indigenous population it is not universally disproportionate.
Theories and Hypotheses
The appeal of deprivation or strain theory focuses on the effects of high unemployment, poor education and high crime all testify to deprivation and thwarted opportunity (Aborigines and Crime (Broadhurtsm R, 1997 pp 413-414) as the poverty cycle is highly correlative to race and crime. Key data on the NSW Aboriginal population was released in 2019 showing that in 2016 on 66% of Aboriginal people compared to 89% had completed Year 12 or higher, they were 11.6 times more likely to be imprisoned with 67% of Aboriginal people in prison had experienced prior imprisonment compared to 46% of non-Indigenous. Indigenous Australians are also are more likely to not achieve at or above the national minimum standard in reading or numeracy. Research indicates that due to inadequate housing and onerous bail conditions Indigenous Australians are held on remand at a rate 583 per 100,000 population compared to the overall NSW rate of 49 per 100,000 ( Schwartz 2013, p 39). This significant loss of individuals from the Aboriginal community creates social and economic stress and decreases the capacity of that community in multiple regards, increasing likelihood of continuous offending and recidivism which is destabilising for families and communities as a whole ( Schwartz 2013, p 39). Systematic racism has been a common theme in literature in relation to Indigenous incarceration. Racial discrimination is defined as ‘when someone is treated less fairly because of their race, colour, descent, national origin or ethnic origin than someone of a different race (Australian Human Rights and Equal Opportunity Commission). Many dispute that it is this systematic issue of Indigenous imprisonment and rate of criminal charges that continues to perpetuate this cycle of poverty and recidivism.
I hypothesise that within New South Wales, regions with reported higher Indigenous populations will have lower mean income, higher unemployment, low education rates and higher rates of reported criminal activity. The null hypotheses is that there would be no change or decrease of crime or poverty rates with high reported Indigenous population (H1: u1 >9; H0: u1 Data
To investigate this hypothesis the NSW Bureau of Crime Statistics and Research Crime Dataset of NSW Local Government Areas (NSW) will be used alongside the information provided by the 2016 Census to identify the Indigenous population rates of each region.
Independent Variables
The variable of names of local government area (lga) is the independent variable. This variable was formed from the NSW Bureau of Crime Statistics and Research relating to the government areas of New South Wales. It is a nominal value. Data from the 2016 Australian Census was then used to identify and analyse the Indigenous Australian population of each area.
Dependent Variables
There are several dependent variables, all sourced from the NSW Bureau of Crime Statistics and Research. Variables one to four include crime statistics from domestic violence (astdomviol), sexual offences (sexoff), robbery (robbery) and offensive language (offlang). Variables five to nine related to basic socioeconomic indicators including mean total income (meaninc), Gini coefficient of total income (giniinc), percentage of residents who completed year 12 or equivalent (hsdegree), unemployment (unemploy), average family size (avgfamsize) and percentage of residents who rent dwelling (pcrent).
Method
IBM SPSS Statistics 22 software was utilised to conduct statistical analysis through descriptive statistics, a correlation matrix denoting Pearson’s R correlation coefficient and the significance level (p-value) for a two-tailed test. There were two alpha levels used with primary alpha of 0.05 and secondary alpha of 0.01. To identify strength rankings of the Pearson’s R was the division of relationships of ± 0.00-0.30 = weak; ± 0.31-0.60= moderate; ± 0.61-1.00 = strong. For each variable data was arranged in descending or ascending order to identify the three most affected areas which was then analysed as per the Census data to demonstrate the possible effect on Indigenous Australians.
Results
The mean for domestic violence assault was 437.14 per 100,00 individuals with a standard deviation of 294.84 and was the identified as the most frequent crime. The three most affected areas included Walgett Shire (2292.40), Moore Plains (1611.50) and Coonamble (1298.40). Correlation with Census data identified the Indigenous population of Walgett to be 1,798 (29.4% of total population), Moore Plains to be 2,845 (21.6% of total population) and Coonamble 1,180 (30.1% of total population).
Sexual offences demonstrated a mean of 207.62 with a standard deviation of 101.76 with the most effected areas being Moore Plains (596), Walcha (477.10) (6% of total population Indigenous) and Cobar (461.8) with an indigenous population 13.7%. Robbery had the lowest mean of 28.26 with a standard deviation of 28.22, the most affected areas included Moore Plains (220.8), Sydney (122.20) with an Indigenous population of 1.2% and Coonamble. Offensive language had a mean of 64.37 with a standard deviation of 58.65. Top areas were reported as Coonamble (343), Moore Plains (287) and Edward River (245) with an Indigenous population of 357 (6% of total population).
Figure 1: Descriptive statistics of dependent variables showing mean, standard deviation and number of cases.
The descriptive statistics were also gathered for the selected poverty indicators and showed a large correlation between unemployment and mean income and Indigenous communities.
Mean total income had a mean of $57,164.37 with a standard deviation of $19,081.013. The areas in which had the lowest mean income were Dubbo Regional ($28,822), Unincorporated Far West ($34,724) and Walgett ($37,112). Dubbo has a reported Indigenous population of 14.5%. This was a significant difference against the highest grossing area of Mosman with a mean income of $155,330. Data was not available for the Indigenous populations Unincorporated Far West making it difficult to determine the effects of this variable.
The Gini coefficient is calculated in a way in which 0 represents perfect equality and 1 perfect inequality. Local areas with the highest gini score were Woollahra (.659), Mosman (.658) and Hunters Hill (.642). These areas had very insignificant Indigenous populations with Mosman at .2%, Woollahra .3% and Hunters Hill .6%. Residents who completed year 12 or equivalent was arranged ascendingly and reflected that Dubbo (21.2%), Unincorporated Far West (22.7%) and Broken Hill (23.5%) had the lowest educational percentages of New South Wales. Although there is a high Indigenous population in Dubbo, lack of data for Unincorporated Far West and a low percentage of 8.5% for Broken Hill demonstrated this is an issue affecting a wider community than the Indigenous population. The unemployment rate was also analysed and reflected that the areas with the highest unemployment were Brewarrina (12,7), Nambucca (10.7) and Central Darling (10.1). Brewarrina has a significantly high Indigenous population with a reported 61.2% of the total population, Central Darling was also high with a percentage of 39.5% and Nambucca 7.6%. The areas with the highest reported family size included Liverpool (3.4), Fairfield and Cumberland (3.3). Each of these regions reported an insignificant Indigenous population with reported percentages at 1.5% or lower. The residents without dwelling reported the highest percentage in the areas of Sydney (54.9%), Brewarrina (48.6%) and North Sydney (46.5%). As per the above-mentioned data of Indigenous population and the low percentage of North Sydney at 0.6% it is demonstrated that whilst Brewarrina has a high Indigenous population this does not significantly disproportionately impact Indigenous Australians.
Figure 2: Correlation coefficient of dependent variables containing Person’s R correlation coefficient, p-value and significance levels.
The correlation matrix was used to show the relationship of poverty indicators with crime with promising results. There was significant statistical correlation at 0.01 between assault and every indicator except for the average household size. Similarly, sexual offences and offensive language had strong correlative relationships with every indicator except for Gini coefficient of total income. Robbery was had the weakest correlative relationship with only significant correlation with unemployment rate which was surprising as it is a common misconception that low-socioeconomic areas seem to face more crime.
Statistical analysis indicates that there is a significant correlative relationship between poverty indicators rise and crime. Analysis of the data demonstrated that whilst not all the top affected areas had high Indigenous proportions of their local populations, the majority did. The Pearson’s R correlation coefficient identifies that violent crimes have a stronger association than those of robbery with the poverty indicators chosen, however offences that typically relate to lack of adequate education such as offensive language is also significantly connected.
The null hypothesis cannot be rejected on the basis that whilst Indigenous Australians seem to be more susceptible to crime the data does not demonstrate that they face poverty indicators in a higher percentage than non-Indigenous Australians, therefore the hypothesis is rejected.
Conclusion and Discussion
There is a consensus in the scholarly and political community that there is need to address the growing inequalities and discrimination experienced by the Indigenous Australian community. My analysis has shown that while there is a significant correlative relationship between poverty and crime this does not necessarily mean that Indigenous Australians are disproportionately affected. The limitations of this research are predominantly the lack of in depth data and the broad interpretation of local government areas rather than the focus on the Indigenous population as a whole. Further research should be conducted to investigate Indigenous Australians poverty indicators on a more micro level. The distribution of Aboriginal and Torres Strait Islanders whilst is important to a general understanding of crime rates, to fully understand the effects of recidivism and the correlations with poverty research must be conducted on Indigenous offenders and the effects crime is having on Indigenous Australian communities, families and individuals. Similarly, to other research my findings do however indicate that there is a strong correlative relationship between inequality indicators and crime, in particular violent crimes and minor conduct crimes such as offensive language. This can be useful in further research as it can allow scholars to provide empirical analysis into these criminal and inequality indicators and determine appropriate measures to counter-act the growing issue of Indigenous incarceration.
Word Count: 1875
Bibliography
Snowball, S & Weatherburn, D 2006, ‘Indigenous over-representation in prison: the role of offender characteristics’, Crime and Justice Bulletin, no 99.
Australian Human Rights and Equal Opportunity Commission – definition of racial discrimination
Key Data: NSW Aboriginal people
Bureau of Crime Statistics and Research. (2018). ‘Crime Statistics’, https://www.bocsar.nsw.gov.au/Pages/bocsar_crime_stats/bocsar_detailedspreadsheets.as px
Australian Bureau of Statistics – Census of Population and Housing – Counts of Aboriginal and Torres Strait Islander
Broadhurst, R 1997, ‘Aborigines and Crime in Australia’, Crime and Justice, vol 21, pp 407-468.
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