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Unemployment and social disadvantage: a tale of five cities.

INTRODUCTION

The issue of the Australian labour force, both in terms of those employed as well as those unemployed but actively looking for work, is the subject of much interest. At the national and state level there are regular releases of labour force data compiled through the monthly Labour Force Survey conducted by the Australian Bureau of Statistics (ABS, 2000 to 2013); detailed labour force information is also collected through the national census of population conducted by the Australian Bureau of Statistics every five years (ABS(a), 2001, 2006 & 2011). In addition local labour force statistics are compiled by the Department of Employment, formerly the Department of Education Employment and Workplace Relations, (DOE, 2000 to 2013) and the Australian Bureau of Statistics (ABS(b), 2011, 2006 & 2011). Despite a wealth of detailed labour force information available, it would appear that most interest is in national or state aggregates and the influence that various macro-economic forces (eg economic growth either positive or negative) have on the labour force. Very little interest appears to be directed to regional or local labour markets and the levels of employment (or otherwise) found within these areas and that micro-economic issues may be of more relevance.

The study reported on in this paper explores issues related to small area labour force data within Australia and whether or not different levels of social disadvantage experienced within these small areas have an impact on employment. Specifically, the study analyses time series labour force data for the cities of Blacktown, Fairfield, Parramatta, Ryde and Willoughby which are all located within the Sydney metropolitan region. These cities were specifically selected because of their size (and so possess viable local labour markets), their location within the one geographic region, and their diversity in terms of social disadvantage or the lack thereof. As the analysis will show, there is a relationship between social disadvantage and rates of unemployment for these five cities. The paper also considers the policy implications emerging from the findings of this analysis.

THE MULTI-DIMENSIONAL NATURE OF UNEMPLOYMENT

There is a narrative associated with unemployment in Australian coupled with a perception of uniformity, with the exception being references to the so-called 'two speed' Australian economy, that oversimplifies the nature of this problem and so its solution. Whenever labour force statistics are released by the Australian Bureau of Statistics we hear through various media outlets, for example, that the unemployment rate has dropped to 5.8% from the level of the previous month or that it is expected to rise to 6.5% over the next six to twelve months. Within the general public therefore there is a perception that the experience of unemployment is reasonably uniform across Australia. That is, with an unemployment rate of 5.8% there are 5.8 people within every 100 in the labour force that are not working at the moment and would like to work anywhere in Australia. However, unemployment and unemployment rates are far from uniform even at a state level as indicated in Figure 1 below.

As is evident from Figure 1, unemployment rates are at different levels across the different states and changes in unemployment rates are also often in different directions. When commentary about the different rates between states does occur, it often refers to the low unemployment rates being driven by the 'resources boom' in the states of Queensland and Western Australia. Surprisingly, as Figure 1 demonstrates, the unemployment rates in these resources states is not significantly different from the other states, despite the fact that there have been two so-called 'mining booms' in Australia during the period covered in Figure 1. Indeed, the overall proportion of people employed in the mining industry within Australia averaged only 1.38% for the quarters May 2000 to May 2013; Western Australia's average proportion for the same period was 5.22% while Queensland's proportion for this period was 1.38%. These low proportions reflect the capital intensive nature of mining; so even with substantial growth in the output of mining enterprises there would not be any significant lowering of unemployment.

Further, when looking at Figure 1 above, it is interesting to note that there are states-based factors at work driving different levels of unemployment. For example, Figure 1 shows that Victoria has an unemployment rate consistently above the other states and territories (maybe due to the level of manufacturing in this state); while the Australian Capital Territory has an unemployment rate consistently below the other states and territories (maybe due to the level of service industries in this territory). Again this is another indication that unemployment is complex and multi-dimensional rather than simple and uni-dimensional. We will reconsider this idea of diverse factors impacting on unemployment when considering the varying levels of unemployment across the five cities selected later in this paper. Gender is another classic divide in terms of differing unemployment rates; as indicated in Figure 2 below males usually experience lower (by not significantly lower) unemployment rates when compared with females.

Finally, levels or rates of unemployment are also influenced significantly by the participation rate and whether or not unemployed people are still actively looking for work or whether they have become quite discouraged about the prospect of finding another job and so have given up the search, and so are considered to have left the labour force. Again it is interesting to note the differences between the states in terms of the participation rates that they have experienced over the last decade or so and shown in Figure 3 below: For example, the territories (ACT and NT) consistently have the highest participation rates (maybe reflecting the fact that these territories have a younger demographic compared to the states), followed by the resource boom states of Western Australian and Queensland. It is also interesting to note that both New South Wales and Victoria, with more developed economies, have participation rates below that for Australia as a whole, while Tasmania is consistently the lowest (which may reflect low population growth and poor economic performance over a number of decades). Again no uniformity, but rather, diversity.

In addition to geographical and gender based variations, unemployment levels and rates can also be analysed from other perspectives. For instance there are the differences between frictional unemployment versus cyclical unemployment versus structural (or long-term) unemployment (Myrick, 2012; Holzer, 1993; Schwartz, Cohen & Grimes, 1986; Gilpatrick, 1966). Frictional unemployment is related to the fact that labour markets are not perfectly efficient (or 'frictionless') in which the demand for labour equals at all times the supply of labour. Indeed, without frictional unemployment the concept of full employment would have to mean that there is not a single unemployed person in any labour market. For example, frictional unemployment would occur where a worker had left a full-time job one day and did not have another full-time job to commence on the following day. In most cases, people currently in a job (either full-time or part-time) would avoid this frictional unemployment by looking for and gaining an offer for another job before leaving their current one. Alternatively, someone in a part-time position finds a full-time job before leaving their current part-time job. Indeed, a true measure of unemployment should eliminate counting those people who are considered 'frictionally unemployed' as their unemployment experience should in most cases be quite ephemeral. We should also expect that during times of economic downturns, the rate of frictional unemployment should go down as people become more reluctant to leave their current job (either fulltime or part-time) before securing another one.

As indicated above, frictional unemployment can have various faces: a fulltime worker leaves a job but can only find a part-time job quickly rather than a full-time one and so decides to take the part-time job. Or a person leaves either a full-time position or a part-time position and does not immediately find another job (either part-time or full-time) and so begins to engage in job search. It could be that this job seeker does have an offer of a job but decides to decline this offer due to issues of working conditions (hours of work, work safety issues, etc.), job location or level of remuneration. That is, frictional unemployment can arise from decisions made by the job searcher just as much as the current number of job vacancies in the labour market. Another way frictional unemployment could arise is where a worker leaves a job (either voluntarily or not) and finds that their current knowledge and skills are no longer needed by employers and so needs to retrain. Finally, frictional unemployment may occur when employers decide to contract work out to independent contractors or other firms rather than employ someone themselves.

On the other hand structural unemployment (Lalive, 2007; Mondschean & Oppenheimer, 2007; Wood, 1988; Standing, 1983) is normally applied to people who lose their job because the job ceases to exist (say due to technological change). For example, people who used to be involved in the manufacture of Polaroid or other types of non-digital cameras, and therefore who now are no longer needed, would be seen as structurally unemployed. In most cases, those people who are structurally unemployed (for whatever reason), would be seen as part of the true unemployed more than is the case for those frictionally unemployed. Another example of structural unemployment arises where a firm makes a decision to close down its operations in one country (such as those decisions made by Mitsubishi and Ford to close their Australian car manufacturing operations) and begin sourcing their products from another country (Coorey, 2013; Colvin, 2008). The term 'job offshoring', (Kostopoulos & Bozionelos, 2010; Dunn, Kohlbeck & Magilke, 2009) is also another cause or trigger of structural unemployment.

Cyclical unemployment (Diamond, 2013; Miyamoto, 2011; Min Zhang, 2008) can be caused by many different circumstances. For example, the work may be seasonal or cyclical: the classic case is that of the iconic shearer or the itinerant fruit picker (a job of great appeal to foreign back-packers). The work is there in the shearing season, but once the sheep are shorn there is no more work until next year. Whether these people should be counted as unemployed during these off season times is an interesting question to think about but it is beyond the scope of this paper. Another example of cyclical unemployment is related to business volumes increasing or decreasing. The jobs most often involved in this type of cyclical unemployment are part-time or contract jobs where the business sees these employees as a variable (or operational) cost to the business rather than an asset. With respect to the second example, those staff who no longer have a part-time position with their former employer due to a downturn in business volume should be seen as unemployed as they will not need to wait for the coming season but rather an improvement in business conditions which cannot be predicted with any degree of certainty.

In a recession, frictional unemployment tends to drop since people become afraid of quitting the job they have due to the poor chances of finding another one. People who already have another job lined up will still be willing to change jobs, though there will be fewer of them since new jobs are harder to find. However, they aren't counted as part of the unemployed. Thus, the fall in frictional unemployment is mainly due to a fall in people quitting voluntarily before they have another job lined up. On the other hand, both structural and cyclical unemployment will rise during a recession and fall during periods of economic growth. As indicated by the unemployment numbers and rates, the drop in frictional unemployment during a recession is more than compensated by a rise in either cyclical or structural unemployment. Indeed, as the nature of employment moves towards employers using more part-time workers, so this may lead to increasing volatility in employment (and unemployment levels) as the economy moves from growth into recession and then back to growth.

As can be seen from Figure 4 below, there has been some movement in the levels of frictional, cyclical and structural unemployment but at no point for the period covered (April 2001 to June 2013) do these series cross one another, indicating clear boundaries between these categories. It should be noted that for the period covered, most people unemployed can expect a reasonably short duration of unemployment (that is, they are only frictionally unemployed). It should also be remembered that at the beginning of their period of unemployment a person will expect that they will quickly find another job. That is, an unemployed person does not immediately become a cyclical or structural unemployed person overnight and so Figure 4 below needs to be considered over many periods of time rather than one particular period of time. What Figure 4 shows is that the circumstances of the unemployment have an impact which may mean that over time they become either cyclically or structurally unemployed; these cohorts should not be seen as entirely pure or coherent. For example, those cyclically unemployed who are waiting for economic times to improve, may become structurally unemployed as economies stay in recession for longer than expected. For structurally unemployed, this cohort is mainly composed of people endeavouring to acquire skills to replace those that they have currently but for which there is no job currently available. However, with good economic times, some structurally unemployed may still find a job without necessarily needing to retrain. Again diversity and heterogeneity, rather than uniformity or homogeneity.

While the issues discussed above are of current relevance and interest, this paper will explore issues related to social disadvantage and its impact on unemployment and the broader labour market. Specifically the paper will consider the spatial distribution of unemployment and responses within the labour market to changing economic conditions for five cities located within the Sydney metropolitan region, namely Blacktown, Fairfield, Parramatta, Ryde and Willoughby. Using statistics provided by the population censuses of 2001, 2006 and 2011 conducted by the Australian Bureau of Statistics the paper will consider various aspects of social advantage or disadvantage in terms of income levels through indexes compiled using population census data.

SOCIAL DISADVANTAGE AND UNEMPLOYMENT

Does unemployment cause or significantly contribute to social disadvantage or does social disadvantage cause or significantly contribute to unemployment? It is possibly a little like the chicken and the egg but as will be shown below there is a very strong relationship or interaction between these two things. Mocan (1999) follows a theme much discussed in the labour market economics literature by considering levels of unemployment related to income inequality and inflation. Income inequality was measured using the Gini index. Other measurement devices do exist, such as the UN human development index (HDI), or the Theil, Atkinson and Kolm indexes (Martinez, 2012) but these were not used. See also Checchi and Garcfa-Penalosa (2008) who discuss the difficulties in accurately measuring income inequality. Mocan (1999) claims that an increase in income inequality will also increase unemployment rates; however, as indicated above, economies do not experience only one uniform unemployment rate but rather many and so these broad overall indexes of income inequality do not explain a lot about unemployment. Indeed the study by Martfnez (2012) indicates that income inequality has reduced over the last thirty years but there is still entrenched and high unemployment in many European countries since the global financial crisis rather than low unemployment. The fact that inflation does not significantly influence income inequality suggests that periods of inflation are not necessarily linked to high unemployment rates nor vice versa (Mocan, 1999, p. 125).

At the heart of the issue is whether income inequality is a cause of unemployment or whether unemployment causes income inequality. We would suggest the latter given that one of the most significant effects of unemployment is loss of income. In Australia we need go no further than compare the amount an unemployed person receives under the Newstart allowance with average weekly earnings. As at 20 March 2013 these amounts ranged from $501.00 per fortnight for a single person with no children to $542.10 for a single person with children (Centrelink, 2013). In May 2013 average weekly earnings (ABS, 2013) for all persons (male or female) working either full-time or part-time for Australia was $1,105.20 (or $2,210.40 per fortnight). Food bills, power and telephone bills that were well within the family budget now become more difficult to pay, let alone the home mortgage payments. And the longer the term of unemployment the further these people and their families lag behind the equivalent working families, and so they become more socially disadvantaged.

If income inequality is seen as an outcome of unemployment then possibly we should also consider the effects or impact of unemployment in terms of the broader area of social inequality or social disadvantage. A lot of the literature covering social inequality or social equity overlaps with income inequality in which the disadvantage generated needs to be addressed rather than trying to identify root cause issues and attempting to rectify the problem of social disadvantage. Gioacchino and Sabani (2009) consider income inequality and poverty as a given for which states develop instruments such as progressive taxation and income redistribution (through, say, unemployment benefits) to address these inequities in what is seen as the 'welfare state'. When considering unemployment specifically, Gioacchino and Sabani (2009, p. 389) consider the risk or the probability of becoming unemployed. Where a worker is characterised by few skills that are not transferable, then this worker is at a high unemployment risk; similarly, workers in firms that are exposed to the effects of globalisation are also seen as high unemployment risk. Furthermore, when looking across a number of countries in which the level of social expenditure varies, the compensation through unemployment benefits is never the same as the income received while working.

The idea that social disadvantage is actually a cause of unemployment is considered by Dasgupta and Ray (1987) when they claim that social disadvantage leads to malnutrition. This can affect a worker's productivity and so increase the risk of unemployment. While their study was focussed on physical malnutrition in India we could also consider mental malnutrition within post-industrial or knowledge-based economies where some workers with poor educational outcomes find it increasingly difficult to perform productively and so face a higher risk of unemployment. With respect to the labour market in general, Dasgupta and Ray (1987) make the point that where the distribution of assets (both tangible and intangible) is not equal then not everyone within that labour market has an equally likely chance of finding another job.

On the other hand, there has been the rise of workfare in Australia and elsewhere which is based on the tenet that solving unemployment is each individual unemployed person's responsibility rather than a responsibility encapsulated within the welfare state. For instance, Euzeby (2012) discussed the UK model of workfare which is based on the principle that there should be no welfare assistance without some recognition by each unemployed individual of their responsibilities is get back into work. Workfare is normally associated with neoliberal free market philosophies, a low level of social expenditure and a labour market (or markets) that have the minimum amount of regulation. Given a reduced reliance on social expenditure and welfare this supposedly motivates the unemployed to try harder in terms of their becoming employed again. However, this view really considers that the unemployed are essentially an homogeneous group facing the same sort of economic circumstances which is at odds with the discussion earlier in this paper. Western and Pettit (2005) also mount a strong case against homogeneity amongst the unemployed and the likelihood or probability of every unemployed person returning to work being equal.

Finally, Andrews, Green and Mangan (2004) also support the contention that unemployment is heterogeneous and multi-dimensional in their study of youth unemployment in Australia. They contend that the neoliberal policies may have worked at the macro-level but have failed at the micro-level, viz. (Andrews, Green and Mangan, 2004, p. 16):

Since the 1970s, Australia has become an increasingly polarized society (Gregory and Hunter, 1995; Borland and Wilkins, 1996; Gregory, 1996; Harding, 1996; Harding and Richardson, 1998). An important aspect of this process of polarization has been the concentration of job destruction in low socio-economic status (SES) neighbourhoods (Gregory and Hunter, 1995).

Their study also considers unemployment from a spatial dimension in which the social disadvantage of some neighbourhoods--in the form of poor education outcomes, high levels of family issues and discontent, and social networks that are composed mainly of local peers--leads to poor labour market outcomes that in turn feed back into greater social disadvantage. Not only do people in these neighbourhoods make poor decisions about things such as education--considering university is not for them or that there is no value in gaining a university or post-secondary school qualification, they may also make poor decisions in the type of job search they employ or the way they present themselves to potential employers. For example, the social disadvantage that has caused high local unemployment may mean that there is little local information about jobs leading to too many people applying for too fewer jobs; job searchers in these neighbourhoods face more negative outcomes per job search and so over time become disengaged, believing that there are no suitable jobs available for them.

ANALYSIS: UNEMPLOYMENT TIME SERIES FOR SMALL AREAS IN AUSTRALIA

Alonso-Villar and Del Rio (2008) claim that there has been little interest to date in the spatial dimension of labour markets, and where there has been it has mainly concentrated on countries or regions and not areas within a country. Some exceptions are the studies by Wheaton and Lewis (2002), Glaeser and Mare (2001) and Yankow (2009) who considered differences between urban and rural workers with advantages in re-employment for the former over the latter. Jurajda and Tannery (2003) examined the labour markets within the cities of Pittsburgh, Illinois and Philadelphia, Pennsylvania covering the period 1983 to 1986. At the start of this period the US economy was in recession but by the end it was again showing signs of economic growth. The unemployment rates experienced by these two cities were different with local economic conditions being seen as part of the explanation of this difference. For example, the downturn in steel-making within the US and the reliance on durable goods manufacturing in Pittsburgh meant that this city experienced higher levels of unemployment when compared with Philadelphia. So again there is an argument to consider unemployment as a diverse and multidimensional issue rather than being caused by only one factor, economic downturn or recessions.

Similar to these studies discussed above, the analysis presented below further considers the spatial dimension relevant to unemployment within Australia and specifically within the single region, namely the greater Sydney metropolitan region. These data are derived from statistics compiled by the Department of Employment on small area labour markets (DOE, 2000 to 2013). As will be seen this study considers a longer timeframe when compared with the Pittsburgh-Philadelphia study (1983 to 1986 compared with June 2000 to June 2013). The time period for the Australian study includes the years of growth driven by resources boom Mark I, the impact of the global financial crisis (GFC) and the emergence of the Australian economy out of the GFC and the impact of resources boom Mark II. Furthermore, this study, by incorporating data from the 2001, 2006 and 2011 censuses of the Australian population, looks more at social disadvantage in a broader context rather than focussing on the disadvantage experienced between males and females in attempting to return to work (Alonso-Villar & Del Rio, 2008).

The current study is focussed on five major cities within the Sydney metropolitan region, which itself is the most populous region of the state of New South Wales and indeed within all of Australia. As indicated in the table below, all these cities have substantial populations and so substantial labour markets. One difference between these cities is the level of social disadvantage measured by the three Socio-Economic Indexes for Areas (SEIFA) reported in Table 1 below:

Table 1: SEIFA Indexes, 2001, 2006 and 2011 Censuses of
Population and Housing

                                            Index of ...

                  Year of      Resident       Relative
LGA Name           Census     Population   Socio-Economic
                                            Disadvantage

Blacktown (C)       2001        255075           952
                    2006        271710           973
                    2011        301125           968
Fairfield (C)       2001        181308           849
                    2006        179893           876
                    2011        187793           854
Parramatta (C)      2001        142901           990
                    2006        148323           987
                    2011        166935           984
Ryde (C)            2001        95242           1064
                    2006        96949           1054
                    2011        103039          1050
Willoughby (C)      2001        59354           1106
                    2006        63604           1100
                    2011        67378           1083

                                    Index of ...
                  Year of
LGA Name           Census      Economic    Education and
                              Resources      Occupation

Blacktown (C)       2001         1022           950
                    2006         991            949
                    2011         995            954
Fairfield (C)       2001         958            902
                    2006         951            911
                    2011         938            913
Parramatta (C)      2001         1040           1031
                    2006         977            1030
                    2011         959            1037
Ryde (C)            2001         1105           1106
                    2006         1046           1104
                    2011         1012           1107
Willoughby (C)      2001         1191           1173
                    2006         1098           1176
                    2011         1041           1165

Source: ABS, SEIFA, June 2011, Australian Bureau of Statistics,
Canberra, last viewed June 2013,
<<http://www.abs.gov.au/websitedbs/censushome.nsf/home/seifa>>.


The City of Fairfield shows the most social disadvantage across all three population censuses, followed by the City of Blacktown. The City of Parramatta and the City of Ryde show only modest social disadvantage with scores slightly under or slightly over 1000 for all indexes, while the City of Willoughby shows the least amount of social disadvantage with all three SEIFA indexes scoring above 1000 in all three population censuses. The values of these indexes do not seem to be very different, but we need to recognise the sensitivity of these indexes. For instance, in the 2011 census the city of Fairfield was ranked the third most disadvantaged region in NSW while the City of Willoughby was ranked the 143rd most disadvantaged. This level of social disadvantage is also reflected in the different rates of unemployment experienced across these five cities as shown in Figure 5 below:

There are a number of things that we can deduce from Figure 5 above. First, despite the fact that there was positive growth in real domestic income since June 2000 (with the exception of the impact of the global financial crisis in 2008 and 2009) there has been no real improvement in the rate of unemployment, particularly for the cities of Blacktown and Fairfield. While the unemployment rates for these cities has been somewhat sensitive to changes in real domestic income (particularly the rise in the rate of unemployment during the GFC years) there is no overall downward trend. On the other hand, for the socially advantaged city of Willoughby, economic growth again has not resulted in a downward trend and surprisingly no change during the global financial crisis years of 2008 and 2009. It may be that Willoughby reflects the level of full employment (and so this city's entire unemployed being frictionally unemployed) in the sense that there is no discernible downward trend and that this level of full employment is somewhat immune to economic downturns such as the impact of the global financial crisis.

Second, these time series point to embedded unemployment for both Blacktown and Fairfield. The rates of unemployment rarely cross each other and do not intersect with any of the other cities of Ryde, Parramatta or Willoughby. While Parramatta and Ryde do cross over at times, there are also long periods in which they do not cross, with Ryde never intersecting with Willoughby. Finally, while Parramatta does intersect with Willoughby between December quarter 2001 and June quarter 2004, for other periods these series do not.

Third, social disadvantage, or the lack thereof, does seem to have an impact on the rate of unemployment with those cities most socially disadvantaged (Blacktown and Fairfield) having substantially higher levels of unemployment, those with moderate disadvantage (Parramatta and Ryde) having lower levels of unemployment, and that city with almost no disadvantage (Willoughby) having the lowest level of unemployment. As indicated by Figure 6 below, these entrenched levels of unemployment cannot be explained by fluctuations in the labour markets for these cities:

While there were some significant changes in the labour market for Willoughby in the June quarter 2001 and June quarter 2002 and for Fairfield in June quarter 2004, these labour markets have not significantly changed in size nor is there evidence of trend decline or trend increase. It would appear that these labour markets, despite having different levels of unemployment, do not change due to this fact but rather change as a result of overall economic factors. That is, again there is no support to say that improvements in the economy overall leads to any substantial difference in the levels or size of local labour markets.

Finally, social disadvantage appears to have an impact, although this is not clear cut, on the volatility of unemployment rates. Table 2 below presents variances in the rate of unemployment for these five cities over two periods (pre-GFC, namely June quarter 2000 to December quarter 2007, and post- GFC, namely March quarter 2008 to June quarter 2013) with benchmark data for the Sydney metropolitan region and for NSW presented:

Table 2: Variances in Local Unemployment Rates, June 2000
to June 2013

                  Variance in rate of unemployment

                  June 2000 to    March 2008 to
Region            December 2007     June 2013

Blacktown             0.403           0.606
Fairfield             1.516           1.254
Parramatta            0.321           0.813
Ryde                  0.570           0.273
Willoughby            0.466           0.214
Sydney                0.079           0.276
New South Wales       0.134           0.174


These data show that Willoughby, with the lowest level of social disadvantage, had the lowest volatility while Fairfield, with the highest level of social disadvantage, had the highest volatility. So the expectation of becoming unemployed for a person located in the City of Willoughby should be lower than that for a person located in the City of Fairfield. The volatilities of the other cities is not so clear cut, indicating that possibly other factors are also involved in the level of changes in the unemployment rate.

DISCUSSION: IMPLICATIONS FOR 'SOLVING' UNEMPLOYMENT

This study further demonstrates that unemployment is both a diverse and multi-dimensional issue as well as being a highly contingent one. There are many factors at play including the presence of social disadvantage which was the focus on the study discussed above. Furthermore, the narrative about merely relying on economic growth to solve unemployment again has been challenged. Even though regions that have significant social disadvantage are affected by changes in economic growth these changes are more fluctuations from what is already a disadvantaged position rather than providing a solution in terms of a long-term downward trend in the rate of unemployment for these areas.

Emerging from this study there are lessons to be learned about how to address unemployment. That is, the blunt instruments of work for the dole and workfare, which have been tried in various forms over the period of study, do not appear to have had any effect on the rate of unemployment or growth (or otherwise) in local labour markets, particularly in regions of social disadvantage. The implications of this study indicate that there should be a change in the way unemployment is addressed in Australia: it is neither a national nor a state (the so-called two-speed economy) issue. It is a local issue in which the economy and the employment or unemployment found should not be viewed as a layered cake but rather as a patchwork quilt. Unemployment needs to be addressed and driven by locally focussed programs that endeavour to reduce levels of unemployment in cities such as Blacktown and Fairfield. It may be that the programs and efforts need to be focused below even the local government level.

LIMITATIONS OF THE CURRENT RESEARCH

While the current study has provided some interesting insights, the data are not able to address additional issues. For example, what types of unemployment do people experience by living in socially disadvantaged areas? Are these people more likely to be cyclically or structurally unemployed than frictionally unemployed? On the other hand, are people living in socially advantaged areas more likely to be frictionally unemployed rather than cyclically or structurally unemployed? These questions would be important in terms of formulating effective local programs to address unemployment and so would need improved or more detailed data collection to allow the tracking of unemployment by regions and over time, so that average lengths of unemployment can be compiled. This level of analysis is beyond the currently available small area labour force statistics.

Another important question that the current study could not address is how people in various regions react to their becoming unemployed. For instance, do people located in socially advantaged areas have better access to social networks in various forms that assist them in finding another job as compared to people located in socially disadvantaged areas that do not? Calvo-Armengol and Jackson (2004, p. 426) state:

   If staying in the labour market is costly (both financial and
   social costs) and one group starts with a worse employment status,
   then that group's drop-out rate will be higher and their employment
   prospects will be persistently below that of the other group.


So it would be beneficial if current labour small area force collections could be adapted to consider responses and plans developed by the unemployed in terms of their getting another job.

CONCLUSION

This paper analysed labour force statistics for the period June quarter 2000 to June quarter 2013 for five cities located in the Sydney metropolitan region. The analysis indicated that there were substantial differences in unemployment rates between these cities which were sustained over a long period of time. It was suggested that the levels of social disadvantage (or otherwise) experienced by these cities did influence the rate of unemployment. It was also demonstrated that at a regional or local level, pursuing macro-economic policies to ensure or improve overall economic growth appeared to have no impact on the rate of unemployment within these five cities. The analysis indicated that to successfully address unemployment, this issue should not be seen as either a national or a state issue but a local issue, and that programs should therefore address the contingencies within each local area experiencing high rates of unemployment to ensure that these regions reduce their rates, trending over time towards the national and state averages.

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Ian Caddy

Dennis Mortimer

University of Western Sydney
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Author:Caddy, Ian; Mortimer, Dennis
Publication:International Employment Relations Review
Geographic Code:8AUST
Date:Jul 1, 2013
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