The Land of the ‘Fair Go’? Mapping Income Inequality and Socioeconomic Segregation Across Melbourne Neighbourhoods

Abstract

Despite enduring political rhetoric that promotes Australia as ‘the lucky country’ and ‘the land of the fair go’, recent decades have seen a noticeable increase in levels of income inequality. This growing economic divide has driven housing prices up and left lower-income families unable to access the housing market in inner-city locations. In contrast to other countries, Australia’s socioeconomic segregation does not overlap with ethnic segregation. Australia’s highly regulated immigration program has resulted in a relatively well-educated and employable foreign-born population who largely reside in middle-income neighbourhoods. These particularities make Australia an interesting context to explore patterns of socioeconomic segregation over time. In this chapter, we will utilise both traditional measures of segregation (such as the dissimilarity index) as well more spatialised measures (such as location quotients and Local Morans I) to assess socioeconomic segregation at the local level. Drawing on four waves of census data (2001, 2006, 2011 and 2016), we explore how socioeconomic segregation has changed over time across nearly 500 neighbourhoods in Melbourne. We further examine the degree to which socioeconomic segregation aligns with ethnic segregation patterns and levels in this city. We find patterns of socioeconomic segregation remain relatively unchanging over time in Melbourne. Additionally, our findings highlight important differences in patterns and levels of socioeconomic and ethnic segregation in the Australian context.

1 Introduction

In the 2019 federal election campaign, opposition leader Bill Shorten promised Australian voters a ‘fair go’ government if elected. While campaigning, Shorten assured voters, ‘if we win this election, our priority is not making the very rich even richer… it is getting wages moving again for working people’ (Benson 2019). Shorten went on to accuse current Prime Minister Scott Morrison of only ‘defending the top end of town’, positioning himself and the Australian Labour Party as the key to a fairer, more egalitarian society (Bagshaw 2019). This campaign strategy is not unprecedented. Australian politicians from both sides of the political spectrum have long capitalised on the general public’s attachment to the ‘fair go’ sentiment. In fact, polling data shows that over 90% of Australians perceive the ‘fair go’ to be a core Australian value (Gough 2006).

Given the importance of the ‘fair go’ to Australians, many have voiced concerns about growing levels of income inequality. In fact, 78% of Australians believe the current gap between the rich and the poor is too large (Douglas 2018). Public perceptions of growing inequality are reflected in the data. Income inequality, as measured by the Gini coefficient, has increased from 0.313 in 1981 to 0.358 in 2014 (World Bank 2019). At the same time, house prices in Australia’s capital cities have skyrocketed—growing by 250% since the 1990s (OECD 2017). Growth in house prices has outpaced growth in incomes, leaving many lower-income households struggling to access the housing market, especially in inner-city locations where jobs and services are concentrated (Spiller 2014). Consequently, lower-income households and younger generations are increasingly forced to live on the outskirts of the city in neighbourhoods that offer fewer employment opportunities, particularly in high skilled jobs (Randolph and Tice 2014). This trend reflects a growing spatial divide between the haves and the have nots—a process described by Randolph and Tice (2014: 385) as the ‘suburbanisation of disadvantage’.

Set against this backdrop of growing inequality and increasing house prices, this chapter examines trends in socioeconomic segregation over time across Melbourne—Australia’s second-largest city. Using occupational categories as a proxy for socioeconomic status, we draw on both traditional measures of segregation (such as the dissimilarity index) as well as more spatialised measures (such as location quotients and Local Morans I) to assess socioeconomic segregation across nearly 500 neighbourhoods over time. We further consider whether trends in socioeconomic segregation align with ethnic segregation patterns in the Australian context. The findings demonstrate relatively stable, albeit slightly increasing trends in socioeconomic segregation over time in Melbourne and highlight differences in patterns of socioeconomic segregation and ethnic segregation.

2 Welfare in Australia

There are two primary objectives that underpin tax-transfer systems globally. The first is referred to by Barr (2001: 1) as the ‘piggy bank objective’ whereby the system is designed to provide insurance against unemployment, disability or sickness—that is, periods during an individual’s life cycle when they have greater needs or lower-income (Barr 2001). The second is described as the ‘Robin Hood objective’ (Barr 2001: 1) whereby taxation is used to redistribute wealth amongst residents by taking from the rich to give to the poor. To achieve this, high-income earners are taxed at a much higher rate than low-income earners and lower-income households receive a greater proportion of social benefits.

According to Whiteford (2010), Australia’s welfare system strongly emphasises the latter objective, offering a safety net to those unable to support themselves. Those eligible for welfare payments in Australia include the elderly, persons with a disability, carers, families with children, veterans, and unemployed persons (AIHW 2017). Income support payments are fully funded by government revenue generated through the tax system (AIHW 2017). As such, Australians are not required to make individualised social security contributions and income support payments are set at a flat rate rather than linked to past earnings (Whiteford 2015).

Expenditure on welfare in real terms has risen over the past decade. In 2016, the Australian government spent $6,566 AUD per resident, up from $5,663 AUD in 2006 (AIHW 2017). Yet compared to other OECD countries, Australia is a relatively low social spender (Whiteford 2017), with social spending as a proportion of GDP well below the OECD average (17.8% compared to 20%) (OECD 2019). Whiteford (2017) attributes this low social spending to Australia’s relatively efficient welfare system, which utilises means-testing to determine eligibility. While means-testing is not unique to the Australian context, it is more widely used in Australia compared to any other OECD country, with approximately 80% of cash benefits means-tested (AIHW 2017). In 2011, the most disadvantaged households in Australia (the lowest quintile) received 42% of social benefits, while the most advantaged households (the highest quintile) received only 3.8% of social benefits (Whiteford 2017). In other words, the poorest 20% received 11 times more in social benefits than the richest 20% (Whiteford 2015). Given Australia’s social spending is largely directed towards the poorest quintile, Whiteford (2017: 1) argues that ‘an across-the-board reduction in social security spending in Australia would increase income inequality more than in any other OECD country’.

3 Income Inequality in Australia

Since the 1980s, Australia’s income distribution has gradually grown more unequal—rising from 0.313 in 1981 to 0.358 in 2014 (World Bank 2019). Globally this places Australia behind France (0.323), the United Kingdom (0.34) and Canada (0.34) but ahead of the United States (0.41¹) (World Bank 2019). Although household incomes² experienced growth between 1993–94 and 2013–14, the degree of growth was not uniform across all quintiles. The highest quintile experienced the greatest amount of growth, with incomes rising by 80% (AIHW 2017), doubling the gap between the highest quintile and the lowest quintile over this time period (AIHW 2017). While Australians wish to see the gap close (Douglas 2018), there is limited support for the tax increases necessary to allow for greater social spending. In fact, 50% of Australians believe they pay too much tax and an additional 34% believe they pay enough (Herscovitch 2013).

4 The Australian Housing System

This growing inequality directly threatens the ‘great Australian dream’ of homeownership—a widely held aspiration amongst Australians dating back to the mid-twentieth century (Gurran and Phibbs 2016). Colic-Peisker and Johnson (2010: 352) suggest that ‘the importance of homeownership in Australia is closely associated with a perception of an egalitarian society where everyone can become a homeowner’. Unfortunately, this dream has become increasingly unattainable in recent years. The last two decades have seen astronomical increases in house prices—growing by 250% since the 1990s (OECD 2017). Even after adjusting for growth in income between 1980 and 2015, a 78% increase in Australian house prices remains (AIHW 2017). As rising house prices have outpaced household incomes, homeowners have become increasingly reliant on mortgages to finance housing. In 2016, 32.8% of households owned their home outright, 35.7% owned their home with a mortgage and 31.5% were renting (ABS 2016).

Overall rates of homeownership have moderately declined over the last twenty years in Australia (Burke et al. 2014). Indeed, 71% of Australians owned their home either with or without a mortgage in 1994–95 compared to 67% in 2013–14 (AIHW 2017). Sharper decreases in homeownership are evident amongst young people. While 60% of persons between 25 and 34 years owned their own home in 1988–89, this number fell to just 39% in 2013–14 (AIHW 2017). This downward trend suggests housing affordability issues have created a barrier to accessing the housing market for younger generations, giving rise to intergenerational inequities (Saunders 2017).

For the average Australian household, housing costs (either rent payments or mortgage repayments) account for approximately 18% of total household expenditure and represent the largest household expense (ABS 2011; Saunders 2017). Homeowners that spend over 30% of their gross income on housing costs are deemed to be experiencing mortgage stress. In 2017, one in four households with mortgages were considered in financial distress (Hughes 2017). Low-income renters are also struggling. In 2013–14, 50% of lower-income rental households reported spending more than 30% of their gross income on housing compared to 42% in 2005–06 (AIHW 2017). There are no national-level rent control systems for the private rental market in Australia, with the rental market overseen at the state level.

For those unable to afford housing through the private market, public housing is provided by the state and federal government (Morris 2018). Low-income households are prioritised for public housing, particularly those who also have special needs such as Indigenous Australians, young people, old people, persons with disabilities, victims of domestic and family violence and the homeless (AIHW 2017). Rents are monitored to ensure that eligible tenants pay no more than 30% of their gross income (AIHW 2017). Yet demand for public housing far exceeds supply, with almost 200,000 households on waiting lists in 2015. Further, wait times are long, with almost 50% of households waiting for more than two years (AIHW 2017).

5 Greater Melbourne

Melbourne is the capital of the state of Victoria and is the second most populated city in Australia. Melbourne has been ranked as one of the world’s most liveable cities by the Economist Intelligence Unit (EIU) (The Economist 2018). Indeed, Melbourne held the number one ranking across 140 cities for seven years. Greater Melbourne spans nearly 10,000 km² and is home to a residential population of approximately 4.5 million persons (ABS 2016) (see Fig. 12.1). Similar to other Australian cities, Melbourne is a low-density city—with the average household comprising 2.7 persons and the majority of residents (66%) living in detached houses on suburban blocks (ABS 2016).

Fig. 12.1

Greater Melbourne

Between 2001 and 2011, the median house price in Melbourne increased by 163% (Department of Transport, Planning and Local Infrastructure (DTPLI) 2012). At the same time, wages increased by just 57% (DTPLI 2012). As a consequence of soaring house prices, inner-city neighbourhood housing is largely inaccessible for homebuyers on low to moderate household incomes (Goodman 2018). While those on an average household income could afford to buy a residence within 10 kms from Melbourne’s central business district (CBD) in 1994, by 2009 this distance increased to 40 kms (DTPLI 2014). Low-income renters are similarly struggling to find housing close to the city centre. According to Hulse, Reynolds and Yates (2014), the shortage of affordable and available rental dwellings exceeds 20,000 in the middle suburbs of Melbourne. Given the majority of jobs and services in Melbourne are concentrated in and around the CBD, lower-income households are left at a significant disadvantage (Spiller 2014).

The Residential Tenancies Act (1997) provides the legislative framework which guides the rental market in Melbourne. In 2017, these rental laws were reviewed with a series of reforms set for implementation by July 2020 (Victoria State Government 2019). Most notable of these reforms was a reduction in how often landlords can increase rent (previously every six months, now every 12 months). Further, several reforms focused on improving the long term suitability of rental properties for households by introducing long term leases, permitting minor modifications and allowing pets (Victoria State Government 2019).

As a longstanding gateway city for new immigrants arriving in Australia, Melbourne is renowned for being a progressive city with a strong history of ethnic diversity and inclusion (ABS 2014). In many countries, socioeconomic and ethnic segregation patterns are largely indistinguishable with certain ethnic groups living in the more disadvantaged areas (Iceland and Wilkes 2006). However, Australia’s highly controlled immigration program has resulted in a relatively well-educated and employable foreign-born population who largely reside in middle-income neighbourhoods (Sydes 2018). Between 2006 and 2016, the proportion of Melbourne’s population born overseas increased from 28.9% up to 33.9% (ABS 2016). In Australia, immigrants who arrive through the skilled stream are required to speak English proficiently, and thus the number of linguistically isolated immigrants is relatively small. While 38% of Melbourne’s population spoke a language other than English in 2016, just 5.6% reported not speaking English well or not at all (ABS 2016). In 2016, the top five countries of birth residing in Melbourne included India (3.6%), China (3.5%), England (3%), Vietnam (1.8%) and New Zealand (1.8%) (ABS 2016). In contrast to the immigrant population, Indigenous Australians experience high levels of disadvantage (Altman, Biddle, and Hunter 2018). In 2016, Indigenous Australians comprised just 0.5% of the total population in Melbourne—a much lower percentage compared to other capital cities such as Sydney (1.5%) and Brisbane (2.4%) (ABS 2016).

6 Neighbourhoods in the Australian Context

The unit of analysis used in this chapter is the state suburb—an approximation of localities gazetted by the Geographical Place Name authority in each State and Territory (ABS 2019). State suburbs are the most equivalent Australian unit to census tracts in the United States and Middle Layer Super Output Areas in the United Kingdom. In the Australian context, the state suburb is synonymous with the neighbourhood and is widely used in neighbourhood-based research (Benier and Wickes 2016; Foster et al. 2010; Wickes et al. 2013; Zahnow et al. 2013). Census data are also available at the state suburb level at each census. In 2016, 561 state suburbs comprised the Greater Melbourne region. However, some neighbourhoods are inappropriate for inclusion due to small residential populations. To avoid skewing the results, neighbourhoods were considered non-residential if they had less than 300 persons, usually resident (Sydes 2018). This process resulted in a total neighbourhood sample of N = 474 in 2001, N = 487 in 2006, N = 479 in 2011, N = 486 in 2016. The total persons residing within these neighbourhoods ranged from 305 to 50,479 persons, with an average population of 7,993 residents. Recognising that neighbourhood boundaries shift over time, data from 2001, 2006 and 2011 census were concorded to the 2016 state suburb boundaries using a proportional approach (ABS 2018).

7 Change in Occupational Structure in Melbourne

In this chapter, we use occupation categories as a proxy for measuring neighbourhood advantage and disadvantage. The ABS broadly classifies occupations under eight categories: (A) labourers; (B) machinery operators and drivers; (C) sales workers; (D) clerical and administrative workers; (E) community and personal service workers; (F) technicians and trade workers; (G) professionals; and (H) managers (ABS 2016). We collapsed these broad occupation categories into three key socioprofessional groups. To represent the top socioprofessional group, we combined managers and professionals. To represent the bottom socioprofessional group, we pooled labourers and machinery operators and drivers (also referred to as unskilled workers). All other remaining categories were merged to represent the middle socioprofessional group.

To demonstrate the utility of occupation categories as a proxy for measuring advantage/disadvantage, weekly personal income by group was examined. In doing so, we find clear differences in personal weekly income across the three groups. In 2016, 29.8% of the top socioprofessional group in Melbourne reported receiving a weekly income greater than $2,000 per week compared to just 2.9% of the bottom socioprofessional group. Alternatively, while over a quarter of the bottom socioprofessional group received an income of less than $500 per week, just 7.5% of the top socioprofessional group reported such earnings (ABS 2016).

Figure 12.2 demonstrates the changes in Greater Melbourne’s occupational structure over the period of study. Here it is evident that the size of the bottom socioprofessional group has reduced over time. In 2001, the bottom group comprised 16.5% of Melbourne’s workforce. By 2016, this figure dropped to just 13.9%. Both occupation categories that comprise the group—labourers and machinery operators and drivers—experienced a reduction in group size between 2001 and 2016. Reductions in size are also seen in the middle socioprofessional group—moving from 57.0% in 2001 to 47.2% in 2016. All occupation categories within the middle group declined in size over time with the exception of community and personal service workers. Rather, this group experienced growth, increasing from 7.6% in 2001 to 10.4% in 2016. The size of the top socioprofessional group increased by almost 5 percentage points between 2001 and 2016. While both managers and professionals increased as a share of the labour force overtime, the greatest amount of growth was apparent amongst professionals—increasing from a 21.6% share in 2001 to a 25.4% share in 2016. Taken together, these results show a gradual move towards professional and managerial positions—a trend that likely reflects growing education levels amongst the Australian population more broadly over time (AIHW 2017).

Fig. 12.2

Distribution of occupational groups and change over time in Melbourne

8 Socioeconomic Segregation in Melbourne

In their landmark review, Massey and Denton (1988) identified five different dimensions of residential segregation—evenness, exposure, concentration, centrality and clustering. With these different dimensions in mind, Massey and Denton (1988: 312) argue that segregation should be measured by ‘a battery of indices’ rather than a single measure. Recognising the need to tap into these multiple dimensions of segregation, we utilise a wide range of segregation measures in this study, including the Dissimilarity Index, Location Quotients (LQs) and Local Moran’s I (LM-I) to capture socioeconomic segregation patterns in Melbourne.

8.1 Dissimilarity Index

Tables 12.1 and 12.2 present the dissimilarity indices across the four census waves (2001, 2006, 2011 and 2016). In comparing the dissimilarity indices for the top, middle and bottom groups, there appears to be little difference over time. The dissimilarity indices for the top–bottom comparison ranges from 0.38 to 0.39 overall time points. In other words, almost 40% of the bottom group would have to move to another neighbourhood to make the top and the bottom group evenly distributed across all neighbourhoods. The dissimilarity score for the top-middle (0.21) and middle-bottom (0.20) comparison is much lower and does not change over time.

Table 12.1 Dissimilarity index (multiplied by 100) between the occupational groups

Table 12.2 Dissimilarity index (multiplied by 100) between top, middle and bottom groups

Limited change over time is also apparent when examining the occupation categories. The greatest score in dissimilarity is between professionals and machinery operators and drivers (slightly increasing over time from 0.44 to 0.46); followed by managers and machinery operators and drivers (also increasing over time from 0.38 to 0.41). While Tables 12.1 and 12.2 provide some insight into socioeconomic segregation in Melbourne, traditional segregation indices like the dissimilarity index are often criticised (Wong 2016). First, the dissimilarity index is aspatial in nature, with each neighbourhood unit treated independently without considering the characteristics of nearby areas. Second, the dissimilarity index is global and thus provides a single score to summarise segregation patterns for the entire city and thereby fails to capture variations at the local level. Third, the dissimilarity index represents just one dimension of residential segregation—evenness. Given these limitations, we next consider other more spatialised measures of local segregation to more fully explore trends in socioeconomic segregation over time across Melbourne.

8.2 Location Quotients (LQs)

As a measure of relative concentration, LQs provide a clear visualisation of residential distributions—tapping into both the concentration and evenness dimensions of segregation (Brown and Chung 2006) (please refer to Chap. 1 for further information on LQs). Figure 12.3 shows the residential distributions of the top socioprofessional group and the bottom socioprofessional group in 2001 and 2016. As illustrated in Fig. 12.3, the top socioprofessional group are heavily overrepresented in neighbourhoods in and around the city centre at both time points. This trend is not surprising given that neighbourhoods closer to the city centre boast higher real estate values and are conveniently located near jobs and services (Goodman 2018). However, while a number of neighbourhoods outside of the CBD are highlighted as having more than their expected share of managers and professionals in 2001, in 2016, areas with high LQ values are restricted largely to the inner city region. This suggests that these slightly more distal neighbourhoods have become less popular for managers and professionals over time. By comparison, areas with low LQ values tend to be middle to outer ring neighbourhoods. Areas with low LQ values are relatively consistent over time.

Fig. 12.3

Location quotients

Compared to the top socioprofessional group, unskilled workers occupy vastly different areas of Melbourne. In fact, neighbourhoods with an overrepresentation of the bottom socioprofessional group tend to be located on the fringes of the city—reflecting more affordable housing options. Some areas, particularly in Melbourne’s east and/or on the outskirts of the city, have seen increasing levels of overrepresentation over time. Neighbourhoods with less than their expected share of unskilled workers (in other words, have LQ scores of less than 0.75) are largely co-located and situated in Melbourne’s CBD area. Over time, there appears to be a spatial spillover with LQ values increasing in nearby neighbourhoods. Overall, these maps present clear trends in residential segregation linked to socioprofessional status. The areas least populated by the bottom socioprofessional group are the same areas that are most populated by the top socioprofessional group and vice versa.

8.3 Local Moran’s I (LM-I)

While LQs show how a population is distributed across a city, LM-I captures the co-location of neighbourhoods which share similar characteristics—depicting the clustering-exposure dimension of segregation (Brown and Chung 2006). LM-I examines each neighbourhood in relation to the characteristics of surrounding areas and highlights the incidences of spatial clustering (Anselin 1995; Brown and Chung 2006). The LM-I procedure was computed in ArcGIS and used a rook spatial contiguity matrix due to its more conservative approach in identifying neighbouring areas (Dubin 2009). The LM-I procedure allocates neighbourhoods to one of five clusters: High High (HH) clusters (high concentration surrounded by high concentration); Low Low (LL) clusters (low concentration surrounded by low concentration); High Low (HL) clusters (high concentration surrounded by low concentration); Low High (LH) clusters (low concentration surrounded by high concentration) and non-significant neighbourhoods.

Figure 12.4 presents the LM-I results for the top and bottom socioprofessional groups in 2001 and 2016. In 2001, 107 neighbourhoods are identified as a HH cluster for the top socioprofessional group. These neighbourhoods are located in one of three locations—in and around Melbourne’s CBD, in Melbourne’s east and in Melbourne’s south. However, the majority are situated close to the CBD. While HH clusters were largely located in Melbourne’s CBD again in 2016, the size of the HH grouping has reduced considerably—with only 77 neighbourhoods classified as a HH cluster. The areas previously highlighted as a HH cluster in 2001, that are no longer HH clusters in 2016, are typically further away from the CBD. No HH clusters in the east or south remain in 2016. While six HL clusters were identified in 2001 and seven in 2016—the neighbourhoods highlighted as a HL cluster are not consistent over time. While the neighbourhoods classified as HL clusters change over time, they are largely located on the outskirts of the city at both time points. Neighbourhoods categorised as a LL cluster are spread across the city—although they are largely located in middle and outer ring suburbs. In contrast to HH clusters, the number of LL clusters has increased over time from 61 neighbourhoods in 2001 to 83 neighbourhoods in 2016.

Fig. 12.4

Local Moran’s I

The LM-I results for the bottom socioprofessional group reveal notably different segregation patterns. In 2001, the neighbourhoods identified as HH clusters for unskilled workers are located in the middle and outer ring. Areas highlighted as a statistically significant LL cluster are mainly located in and around the city centre—directly overlapping the areas classified as HH clusters for the top socioprofessional group. Several HL clusters are identified on the outskirts of the city in 2001. In 2016, a greater number of LL clusters was found for the bottom socioprofessional group—increasing from 41 in 2001 to 131 in 2016. For the most part, these LL clusters again cover the area surrounding Melbourne’s CBD. Also increasing is the number of HH clusters for the bottom socioprofessional group in 2016, from 70 to 86. Compared to 2001, these HH clusters are slightly further away from the city centre and largely located on the fringes of Melbourne. No HL clusters for the bottom socioprofessional group are identified in 2016. Taken together, these findings likely reflect the inner city’s growing house prices over time and the limited affordable housing options for the bottom socioprofessional group.

8.4 Classification of Neighbourhoods by Socioeconomic Composition

Using the criteria outlined in Chap. 1, Fig. 12.5 displays the classification of Melbourne neighbourhoods by socioeconomic composition in 2001 and 2016. In 2001, most neighbourhoods within the Melbourne context meet the criteria of either a middle-income neighbourhood (N = 156) or a middle to high-income neighbourhood (N = 259). Only 9 neighbourhoods meet the criteria of a high-income neighbourhood. In 2001, 38 neighbourhoods are classified as low to middle income and 10 neighbourhoods are considered mixed. No low income or polarised neighbourhoods are identified. In 2016, middle income and middle to high-income neighbourhoods continued to represent the majority of neighbourhoods in Melbourne (N = 123 and N = 307, respectively). By comparison to 2001, the number of high-income neighbourhoods has increased in 2016 (N = 24) while the number of low to middle-income neighbourhoods has decreased (N = 26). Similar to 2001, no neighbourhood in 2016 is classified as a low income or polarised neighbourhood.

Fig. 12.5

Classification of neighbourhoods by socioeconomic composition

8.5 Location of the Top Socioprofessional Group

Lastly, we examined the degree of spatial concentration of the top socioprofessional group in Melbourne (see Chap. 1 for further methodological explanation). Figure 12.6 illustrates the location of the top socioprofessional group in 2001 and 2016. In 2001, Q1 comprises 20 neighbourhoods. In other words, 20% of Melbourne’s top socioprofessional group resides in just 4% of Melbourne’s neighbourhoods. These neighbourhoods are largely proximate to one another and located nearby the city centre. In 2016, the number of neighbourhoods that fall within Q1 remains largely unchanged at 21. Again, these neighbourhoods are largely located near the city centre. However, two areas, further away from the CBD are highlighted as Q1 in 2016. These neighbourhoods—Berwick and Point Cook—were two of the five most highly populated Melbourne neighbourhoods in 2016, with populations exceeding 45,000.

Fig. 12.6

Location of the top socioeconomic group

9 Socioeconomic Segregation and Ethnic Segregation Patterns

To provide a visual representation of how socioeconomic and ethnic segregation trends overlap in the Australian context, an additional series of maps were created. Figure 12.7 highlights areas that are HH clusters for linguistically isolated residents (i.e. those who are not proficient in English) and Indigenous residents. As can be seen in Fig. 12.7, areas that are popular for persons who are not proficient in English are relatively consistent over time. Neighbourhoods identified as a HH cluster are largely co-located in one of three areas, all located in middle-ring suburbs. By comparison, neighbourhoods identified as a HH cluster for Indigenous residents are notably different in 2016 compared to 2001. Additionally, fewer HH clusters were found in 2016. Nevertheless, at both time points, HH cluster neighbourhoods for Indigenous residents tend to be located on the outskirts of the city. Interestingly, there is limited overlap between areas identified as a HH clusters for non-English proficient residents and Indigenous residents. In 2001, just four neighbourhoods were classified as HH clusters for both group. In 2016, there was no overlap between the groups. This suggests Indigenous Australians and linguistically isolated residents occupy different areas of the city.

Fig. 12.7

Socioeconomic segregation and ethnic segregation

Overlayed on these maps are areas highlighted as HH clusters for the top socioprofessional group and bottom socioprofessional group. While some neighbourhoods deemed to be HH clusters for Indigenous residents or non-English proficient residents are also identified as HH clusters for the bottom socioprofessional group, these neighbourhoods are not completely overlapping. However, it is clear that Indigenous residents and non-English proficient residents are largely absent from the top socioprofessional neighbourhoods. In 2001, nine neighbourhoods were classified as both a HH cluster for the top socioprofessional group and linguistically isolated residents. By 2016, this overlap was reduced to just three neighbourhoods. Further, no neighbourhood deemed a HH cluster for the top socioprofessional group is also classified as a HH cluster for Indigenous persons. Thus, while these ethnic groups are not necessarily restricted to residing in just the poorest neighbourhoods, they lack presence in the top socioeconomic areas.

10 Conclusions

While the overwhelming majority of Australians perceive the ‘fair go’ to be a core Australian value, the growing levels of income inequality, issues with housing affordability and increasing socioeconomic segregation outlined in this chapter present a threat to its longevity. Melbourne is expected to be the largest city in Australia in the coming decades and is predicted to grow to a population of 8 million people by 2051 (Victoria State Government 2016). Therefore a key priority for government and urban planning is to maintain liveability and ensure that access to housing and employment does not fall outside of the reach of ‘ordinary’ Australians.

The results of the analyses undertaken for this chapter highlight clear trends in socioeconomic segregation in Melbourne. Traditional measures of segregation like the dissimilarity index indicate that almost 40% of the bottom socioprofessional group would need to move to another neighbourhood in order to make the top and the bottom group evenly distributed. However, the dissimilarity indices show a limited change in socioeconomic segregation over time.

By comparison, the more spatialised measures reveal a growing spatial divide between the top and bottom socioprofessional groups. Looking first to location quotients, the top socioprofessional group are overrepresented in neighbourhoods closest to the centre of the city. Results also show that top socioprofessional group and unskilled workers inhabit different parts of city, with the bottom socioprofessional group residing in the city’s outer suburbs. Put simply, those areas least popular for the top socioprofessional group are the areas where unskilled workers are most likely to live. Similar patterns are found when looking at the LM-I results. Taken together this demonstrates a growing geographical distance between the ‘haves’ and the ‘have-nots’. This increasing segregation is likely to be further compounded by the expected population growth of Melbourne.