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The impact of employment specialisation on regional labour market outcomes in Australia.

Abstract

This article examines the industry composition of employment across Statistical Divisions in Australia utilising census data from 2001 and 2006. We find some evidence to support the hypothesis that peripheral regions tend to have a higher level of employment specialisation than metropolitan centres, but there is little indication that employment specialisation, in general, grew over the period. From a policy perspective, we provide support for the findings of previous Australian researchers that higher levels of employment specialisation are associated with better labour market outcomes in a region, and that when policymakers assess different regional-development policies, they should give some consideration as to whether or not the implementation of their preferred policy will have an impact upon employment specialisation in the particular region.

1. Introduction

Changing industry structure and an associated change in the industry composition of employment are ongoing features of all national economies. Commonly, these changes have been a decline in the importance of and employment levels in primary and secondary industries and a rapid growth in tertiary industries (see for example Robson (2006) and Garnet and Lewis (2007)). However, within each industry sector the experience of individual industries associated with that sector may be very different. For example in the Australian primary sector, drought may induce a decline in agriculture (Horridge et al., 2005), while external factors may stimulate mining (Connolly and Orsmond 2011). Further, across regions within national borders the nature of the changes taking place will display similar variability (Robson 2006).

Theoretical analysis (Krugman 1991; 1993; 2009; 2010) has attempted to explain both inter-temporal and geographic shifts in industrial-employment structures and the tendency towards either increasing or decreasing regional specialisation in employment. Government policy is capable of having an impact on the industrial structure, and consequently on the degree of employment specialisation across regions within an economy (Wren and Taylor 2009). Beer and Clower (2009) suggested that the desire to pursue policy intervention to change a region's industry structure is likely to evoke the question of whether or not the change generated is beneficial for that regional economy.

In the Australian context, this article explores the pattern of regional employment specialisation, how it has changed over a limited time span (between the 2001 and 2006 censuses), and whether--when formulating regional employment policy--the government should take into consideration the impact that their proposed policy would have on a region's employment specialisation. The study utilises a new dataset derived from the 2001 and 2006 Australian censuses. These data provide for a sub-state classification of regions and a detailed classification of employment by industry. Section 2 surveys previous research in Australia and internationally; it reviews the literature that deals with the theoretical underpinnings of the analysis. Section 3 considers issues of research method, including the definition of a region, selection of measures of employment specialisation, and description of the data. The research findings a re presented in Section 4 and are discussed in Section 5, after which some policy conclusions are drawn.

2. Literature Review

Empirical analysis of regional employment specialisation and its impact on regional economies is the focus of many international studies (for example Lilien 1982; Wren and Taylor 1999; Marelli 2004; Shearmur and Polese 2005; Suedekum 2006; Robson 2006; Belke and Heine 2006; Robson 2009). There is also a growing, but still relatively limited amount of literature that considers regional employment specialisation in an Australian context (Bradley and Gans 1998; Beer and Clower 2009; Stimson et al. 2009; Dixon and Freebairn 2009).

International research findings have been somewhat inconsistent. Lilien (1982) argued for regional policy intervention to offset identified disadvantages. Wren and Taylor (1999) found that all UK regions had increased in terms of their employment specialisation but that partly as the result of successful regional policy, the regions tended to converge towards the national employment pattern. Marelli (2004) found in his study of 145 European regions that specialisation had been decreasing, although he found evidence that increased specialisation had a positive impact on regional growth. Robson (2006) confirmed the increase in employment specialisation in UK regions, but found little evidence of convergence. Robson (2009) found a small but statistically significant relationship between an improved functioning of the regional labour market and increased regional employment specialisation. On the basis of their evidence, Shearmur and Polese (2005) concluded that diversification polices to enhance regional growth in Canada were difficult to justify However, Suedekum (2006) considered that his results based on German data supported diversification policies, despite not finding evidence of increasing employment specialisation in German regions.

Bradley and Gans (1998) attempted to explain the growth of Australian cities in terms of population and labour force growth. They included as one of their explanatory variables a measure of the industry specialisation of each of their selected cities, namely a modified Herfindahl index applied to city data segmented into 12 employment groups (Bradley and Gans 1998, p. 270). This broad classification of data was all that was available then. Their results indicated a negative relationship between growth and specialisation.

With a similar measure of employment specialisation, Beer and Clower (2009) utilised 2001 census data for 76 Australian cities (with populations greater than 10,000). While they found no relationship between labour force growth and their end-period employment specialisation measure (p. 383), they found a significant relationship between their measure of the change in employment specialisation and labour force growth (p.385).Their analysis suggests that the explanation for the difference between the findings of the two articles may be the substantially enhanced competitive nature of the prevailing economic environment at the time of the latter study. From the 1970s, with substantial reductions in tariff barriers, and moving through the floating of the Australian dollar in the 1980s and the microeconomic reforms of the 1990s, the Australian economy became more competitive globally and 'the economies of regional cities have reflected this transformation and those cities that have moved to greater levels of specialisation in their economies appear to have had the most certain economic growth' (Beer and Clower 2009, p. 386).

Stimson et al. (2009) used data from the 1991 and 2001 censuses to construct location quotients for mainland Local Government Areas as an index of employment specialisation over three employment categories. Based on these data they found limited support for a link between employment specialisation in a region, and in regional growth.

Finally, Dixon and Freebairn (2009) looked at regional differences in the composition of employment across 52 industry groups in regional Australia. They found that their measure of specialisation--the Coefficient of Regional Specialisation--indicated that the industrial composition of Australian states had tended to become less specialised over time. They attributed this to the tariff reductions which occurred throughout the period of their study.

In summary, past research based on different measures of employment specialisation produced evidence of constant, decreasing, and increasing levels of employment specialisation across regions. Within Australia, work undertaken at the city and Local Government Area levels with a relatively small number of industry groups returned findings of increasing specialisation; research at the state level, with a much larger number of industry categories, returned findings of falling specialisation. Evidence relating to the hypothesis that increasing regional employment specialisation will result in enhanced economic outcomes for a region has been found in some studies but not in others.

The Krugman hypothesis

The 'core-periphery' model, established in Krugman (1991; 1993) and revisited in Krugman (2009), is said to be driven by the choices open to both firms and individuals (RSAS 2008). Firms are attracted to the larger market by the potential economies of scale offered by the larger market and the likely saving in transport costs. On the other hand, individuals have an incentive to move to the region offering higher real wages and a greater variety of goods and services. The result of this is that the larger, more diverse core tends to grow and increase in diversity while regions at the periphery tend to become more specialised.

Increased specialisation at the periphery results from the interplay of the forces considered important in the Krugman model. Internal economies of scale accrue to the firm as it grows in size in one location and are associated with market size. External economies of scale, on the other hand, result from the organisation of the industry into agglomerations of firms that are mutually beneficial. When transport costs are high, firms remaining or establishing and growing in the periphery, will generally be those for which internal economies of scale are less important than the location of inputs (especially raw materials) and the presence of external economies. If transport costs fall, some firms in the periphery will relocate close to the large local market in order to take advantage of internal economies. However, as transport costs continue to decline, access to the market becomes less important and rising factor costs motivate firms to return to peripheral regions (Martin and Sunley 1999).

Associated with this, but often neglected in empirical work, is the prediction that specific industries tend to concentrate in particular regions (Krugman 2009). However, as Krugman notes: 'It seemed, from both an aesthetic and historical point of view, that a model in which agglomeration did not have to happen, in which results depend on parameters, would be more interesting than one in which emergence of an industrial core was preordained' (Krugman 2010, p.10). Thus if the 'centrifugal forces' of dispersed resources dominated the 'centripetal forces' of large markets, decentralisation could be the outcome.

While some research into the regional specialisation of employment has been set in the context of Krugman's 'core-periphery' model (for example Marelli (2004), Suedekum (2005), Coombes and Overman (2004), Dixon and Freebairn (2009)), this has not been the case with all such empirical work (for example Robson (2006; 2009), Shearmur and Polese (2005) and Lilien (1982)).

3. Method

The Definition of Regional Types

Garnett and Lewis (2007), in their review of the major attempts at classifying regions in Australia, argue that many regional classifications failed to show the full impact of the changes taking place. They regarded an ideal classification of regions as one which groups areas so that they are internally as homogeneous as possible across all of the variables of interest. However, they also recognise that 'all methods of classification have flaws and anomalies' (p. 31). Following their brief review of various regional-classification systems that have been used in Australia they opted to utilise the classification system developed by ABARE and reported in Garnaut et al. (2001). This system is based on the ABS Statistical Local Areas (SLA's). According to ABS (2011), SLA's are the smallest ABS spatial units identified in the Australian Standard Geographical Classification (ASGC) that, in sum, cover the whole of Australia. In terms of hierarchy, SLAs are combined to form Statistical Subdivisions (SSDs), which in turn can be combined to form Statistical Divisions (SDs)--the largest sub-state classification. The ABARE (and hence Garnett and Lewis) approach defines each SLA in Australia as being either Capital City (of which there are eight), Other Metropolitan (where the SLA contains whole or part of an urban centre with a population of at least 100,000), Coastal (within 80 kilometres of the coastline--except for remote coastal areas in northern Western Australia and Queensland), Remote (as determined by the Accessibility/Remoteness Index of Australia) and Inland (the remainder).

As our purpose was to examine labour market activity, we considered that daily movements of workers between SLAs made it unlikely that SLAs could adequately represent individual labour markets (note, however, that Edwards et al. (2011) argue that the SLA level of disaggregation is the most commonly used in labour market studies). While the problem of interregional flows of labour persists at the level of SD, it was likely to be less of an issue. For example it is common for workers to commute between Orange and Bathurst (SLAs in the Central West SD involving 50 minutes travel by car), but it is far less common for workers to commute between Bathurst and Wagga Wagga, (the latter being an SLA in Murrumbidgee, an SD contiguous to the Central West), as this would require a commute of four hours each way. Nevertheless, there will be flows between SDs, especially between towns that are close to the SD boundaries. Therefore, we use data based on place of employment rather than place of residence. Our choice of SDs was further supported by a number of technical issues related to the data-collection processes of the ABS (see ABS 2011, pp. 11-12). First, SDs a re defined by the ABS on the basis of socioeconomic criteria. Outside the capital cities, they are defined as relatively homogeneous and characterised by identifiable social and economic links between the inhabitants, and between the economic units within the region under the unifying influence of one or more major town or city. Second, the boundaries of SDs are changed infrequently (15 to 20 years), making them the most stable regional unit within each state (ABS 2011, p. 11). This point is particularly important in the light of our econometric procedure (discussed below), as changes in the boundaries of both SSDs and SLAs over the period would have greatly complicated the process of estimation. Third, while we are restricted to the use of the classifications in the ASGC, the ABS has adopted a new classification system: the Australian Statistical Geography Standard (ASGS) replaces the ASCG. In this new system, Statistical Areas Level 4 (SA4) will be the broadest sub-state classification and are therefore comparable in size to existing SDs. Further, SA4s will be used for the release of labour force statistics (ABS 2011, p. 36).

Given the arguments put forward in Garnett and Lewis (2007) we felt that it was important to retain the essence of the ABARE classification; we classified the SDs by utilising the ABARE classification. This was done primarily by an inspection of the map of regional classifications provided in Garnaut et al. (2001), and the maps of SDs provided in ABS (2011). In the few uncertain cases, the ABARE classification of SLAs within specific SDs was consulted. As SDs often contained SLAs classified differently under the ABARE system, we had to make a value judgement about which of the two (and sometimes three) classifications most appropriately represented the SD. Table 1 reflects the outcome of our classification decisions for SDs by regional type.

Finally, we decided against the use of the alternative ASCG classification that covers all Australian Statistical Region Sectors (SRS), Statistical Regions (SR), and Major Statistical Regions (MSR) for three reasons. First, the classification was not based on SLAs and would preclude further use of the ABARE classification of regional types; second, the classification is based on the minimum regional population levels required to yield reliable survey results (ABS 2011, p. 25)--which is not necessary when census data are used--and not upon the economic and social homogeneity of the region; third, as previously discussed, the SD classification has the advantage of greater stability overtime.

Measures of employment specialisation

Our first measure of specialisation is designed to answer the question: regardless of any change with respect to the distribution of employment in Australia as a whole, has the employment in the region become more or less specialised relative to what the structure of employment in the region was previously? To answer this we need a measure of absolute specialisation for each region which we can compare overtime and across regions. In the literature, such a measure is provided by the Coefficient of Absolute Regional Specialisation (CARS):

CARS = [[[[summation].sup.n.sub.i=1] [([[chi].sub.ir] - [[??].sub.ir]).sup.2]/(n-1)].sup.1]/2/[[??].sub.ir] (1)

where [[chi].sub.ir] is employment in industry i in region r, and [[??].sub.ir] is the mean employment level overall industries in the region.

This measure has been used by Wren and Taylor (1999) and Robson (2006; 2009). It is regarded as a measure of absolute regional specialisation because the numerator in equation 1 is the standard deviation [sd] of regional employment across all industries from the mean employment level of industries in the region. When we divide the sd through by [[??].sub.ir] we have CARS, the coefficient of variation (CV) of employment in each industry. This provides a relative measure of absolute variation that a I lows us to compare variations in the absolute dispersion of employment among industries, across both time and space for regions, when mean levels of employment by industry may vary. An increase in CARS for a region, overtime, indicates that the region has become relatively more specialised in its employment structure, as the sd of employment across industries (the numerator in the index) relative to the mean level of employment for all industries (the denominator in the index) has increased. This can only occur when employment in some industries becomes relatively more important, that is as employment in the region becomes relatively more specialised overtime. Similarly, a higher value for CARS in region A than in region B, at a given time, implies that the employment structure in region A is relatively more specialised than in region B at that time.

A second measure of regional specialisation endeavours to quantify the degree of specialisation in regional employment relative to the degree of specialisation of employment in Australia as a whole. This measure is the coefficient of relative regional specialisation (CRRS) (Wren and Taylor (1999) and Robson (2006)) given by:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

CRRS is regarded as a measure of relative regional specialisation because the numerator in the index is the standard deviation of the regional share of national employment in each industry from the mean of the regional share of national employment across all industries. Dividing the numerator through by the latter makes CRRS the CV of the regional share of national employment in each industry. A value of zero for CRRS would mean that the share of employment across all industries in the region concerned was the same as the share of employment across all industries in the Australian economy. The higher the value of CRRS, the more divergent the employment structure in the region is compared to the employment structure in Australia. CRRS is a relative measure of the variation in the regional share of national employment across all industries, which allows us to compare variations in the regional share of national employment among industries (across time and space) for regions when mean levels of employment by industry may vary. A decrease in CRRS for a region overtime indicates that the industry structure of the region has become more like the industry structure of Australia. In this sense, it provides us with the same information as the frequently used measure of the extent of convergence in regional employment structures known as the Coefficient of Regional Specialisation (CRS) (1). CRS is calculated as:

CRS = 1/2 [[summation].sup.n.sub.i=1] [absolute value of (([[chi].sub.ir]/[[chi].sub.r]) - ([[chi].sub.iA]/[[chi].sub.A]))] (B)

In our work we followed Wren and Taylor (1999) and chose to use CRRS rather than CRS as our measure of relative specialisation. This is because changes in industry specialisation within a region result from changes in specialisation that take place within a sector (primary, secondary, or tertiary), and changes in specialisation that take place between these sectors. Thus, the variance of employment within a region will be composed of the variance of employment that takes place in each of the sectors within that region and the variance of employment that takes place between these sectors. In future work we wish to examine sectoral shifts. As explained in Wren and Taylor (1999), the square of CRRS (and also CARS) is the population variance divided by the mean squared, making CRRS (and CARS) amenable to decomposition into intra-sectoral and cross-sectoral changes.

Data

The 2001 and 2006 census data on employment by industry by place of employment for all SDs in Australia form the basic dataset on which our analysis is predicated (ABS 2006). The industry data for the 2001 Census were classified on the basis of the 1993 Australian and New Zealand Standard Industrial Classification (ANZSIC) into 52 distinct industry groups The 2006 Census industry data were classified in terms of the 2006 ANZSIC classification. The data for 2006 were harmonised to the 2001 equivalent classification of 52 industries using ABS (2008).The harmonisation process should be regarded as approximate, as the data are not presented at a sufficiently high level of disaggregation to achieve complete precision. Only data classified by industry (or sector) were used. Undefined (for industry), not classifiable, and not-stated data were omitted. In conducting this research we did not have access to similar data for earlier periods. Not withstanding the limitation that this places on our findings, we proceeded with the analysis on the basis of the importance this period has for regional Australia in particular. Between 2002 and 2006 Australia experienced one of its worst droughts on record (ABARES2012, p. 5; p.14). The drought covered most of Australia's important agricultural regions and significantly reduced farm production and, therefore, employment in the affected regions. Over approximately the same period mining--another primary industry--was experiencing one of its longest and strongest boom periods (Connolly and Orsmond 2011), substantially adding to employment in the affected regions.

4. Results

The regional employment pattern in Australia

To establish a background against which our analysis of specialisation takes place we first examine the structural composition of changes in employment across Australia. Given that we employ data in our analysis of specialisation classified by 60 SDs and 52 industries, we need a means of summarising the data in order to keep the analysis manageable. To do this we chose to use our concept of regional types. For this purpose--for Australia and for each of the regional types--employment is the sum of employment in the relevant geographic SDs (reported by the ABS). Omitted from the data are employment figures for non-geographic SDs such as Undefined (Statistical Division), Offshore Areas (Statistical Division), and No Usual Address (Statistical Division). In this article we identify three employment sectors. The primary sector consists of the agriculture, forestry, fishing, and mining industries. The secondary sector comprises all of the manufacturing industries; the tertiary sector comprises the remainder and includes utilities and construction.

A formal technique for conducting this analysis is the classical shift-share approach (seefor example Baxendine et al. 2004). Shift-share analysis enables us to categorise changes in regional employment within an industry group ([SS.sub.i]) into three components:

[SS.sub.i] = [NS.sub.i] + [IM.sub.i] + [RS.sub.i] (4)

The first change component is the national share for a given industry in the region ([NS.sub.i]), which is equal to employment in the specified industry in the region in the base period ([e.sub.i,t-1]) multiplied by the national rate of growth in total employment overthe period of change ([E.su.t] - [E.sub.t-1]/[E.sub.t-1]) to give:

[NS.sub.i] = [e.sub.i,t-1] x ([E.sub.t] - [E.sub.t-1]/[E.sub.t-1]) (5)

The resulting estimate indicates how regional employment in the industry would have changed if it had changed at the same rate as total employment in the nation over the period. When summed overall industries in the region, the national share component indicates the extent to which regional growth as a whole results from growth in the national economy.

The second component is the industry mix ([IM.sub.i]), which is equal to [es.up.i,t-1] multiplied by the national rate of growth in the industry over the period ([E.sub.i,t] - [E.sub.i,t-1]/[E.sub.i,t-1]) less ([E.sub.t] - [E.sub.t-1]/[E.sub.t-1]) to give:

[IM.sub.i] = [e.sub.i,t-1] x ([E.sub.i,t] - [E.sub.i,t-1]/[E.sub.i,t-1] - [E.sub.t] - [E.sub.t-1]/[E.sub.t-1]) (6)

The resulting number reflects the change in employment in the industry in the region resulting from the fact that national employment in that industry was growing faster, or slower, than total national employment. If national employment in the industry is growing faster than total employment, we would expect this component to be positive for the region. On the other hand, if national employment in the industry is growing more slowly (or is declining) compared with total employment, we would expect this component to be negative for the region. Summing all [IM.sub.i] across the region provides an assessment of the impact of the region's industrial mix on total employment growth in the region. Where a region's industrial mix is dominated by slow-growing or declining industries the sum of [IM.sub.i] over all industries will be negative. A positive result therefore indicates that the region's industrial structure is dominated by fast-growing industries.

The third component is the regional shift ([RS.sub.i]), which is equal to [e.sub.i,t-1] multiplied by the rate of growth in the industry in the region over the period ([e.sub.i,t] - [e.sub.i,t-1]/[e.sub.i,t-1]) less ([E.sub.i,t] - [E.sub.i,t-1]/[E.sub.i,t-1] to give:

[RS.sub.i] = [e.sub.i,t-1] x ([e.sub.i,t] - [e.sub.i,t-1]/[e.sub.i,t-1] - [E.sub.i,t] - [E.sub.i,t-1]/[E.sub.i,t-1]) = (7)

The resulting number reflects the change in the region's employment in the industry resulting from the fact that in the region, the industry performed better (employment grew faster or declined more slowly--giving a positive figure) or performed worse (employment grew more slowly or declined faster--giving a negative figure) than the industry did nationally. This component is therefore an indicator of local competitive advantage in the industry compared with the industry nationally. An indication of the region's competitive advantage as a whole can be assessed by summing the [RS.sub.1] over all industries.

While the employment pattern of individual component SDs did not always follow those of the respective regional type, the data in Table 2 give us a general picture of the pattern of change in employment occurring between the two census dates.

Employment in the capital cities (Metro) grew strongly, fuelled by rapid national employment growth and a generally advantageous industrial mix. However, Metro's overall competitiveness declined over the period, largely because of the decline in the competitiveness of its secondary industry. National growth in the tertiary sector, supported by the concentration of tertiary activities in Metro's regional mix, and the group's relative competitive advantage in these activities, saw tertiary employment in Metro grow strongly; it more than offset the employment decline in both the primary and secondary sectors. However, it is apparent that employment growth in Metro was held back by the fact that secondary industry is declining nationally but still accounts for over 10 per cent of Metro employment.

Growth in the other metropolitan centres (Other Metro) benefited from national employment growth, an overall advantageous industrial mix, and increased competiveness in all industry sectors--including secondary. However, the shares of secondary industry (about 10 percent) and primary industry (about 4 per cent)--both industries being in decline--in the group's industrial mix disadvantaged Other Metro's employment-growth performance. As with Metro, it was growth in the services sector--where Other Metro benefits from national growth in the sector, the concentration of the tertiary sector in the regional group's industrial mix, and the regional group's competitive advantage in this sector--which accounted for the bulk of the group's total employment growth over the period.

Employment growth in coastal regions (Coastal) was driven by national employment growth and an increase in overall competitiveness--notwithstanding the decline in the competiveness of the primary sector. Coastal was disadvantaged by its industry mix, where the declining primary and secondary sectors each accounted for about 11 per cent of employment. On the other hand, tertiary employment grew strongly on the back of national growth in the sector, the importance of tertiary industry in the group's industrial mix, and the fact that in services this group was becoming increasingly competitive.

As a group, Inland had the second-worst employment outcome over the period--growing at just on 3 per cent. This low growth was due to the impact of national growth on the regional group, as it was disadvantaged by its industrial mix (primary accounted for over 13 per cent and secondary accounted for over 11 per cent), and its failure to increase its relative competitiveness. Competiveness in the primary and tertiary sectors both declined; however, competiveness rose in the secondary sector. The decline in primary-industry employment of over 13 percent in Inland can be largely linked to the consequences of the drought conditions experienced over the period of study. In particular, the decline in the competiveness of this industry in what are major primary-producing areas reflects the adverse growing conditions experienced throughout the period.

The Remote Group of regions experienced the lowest growth in employment. This group was entirely dependent on national growth for their overall increase in employment, as the aggregate industry-mix and regional-shift components were negative. Employment in primary industry declined. However, the decline was less than half a per cent compared with the 13.5 percent decline for Inland. This is probably explained by the fact that the Remote Group includes many of Australia's important mining areas and, although the farming and agricultural activities would have suffered because of drought, mining activity during this period was booming and the increased mining employment would therefore offset the decreased employment in farming and agriculture. This explanation is further supported by the fact that the primary sector in Remote became increasingly competitive over the period, reflecting that mining activity could only be undertaken where the particular resource was located.

Discussion of the last two groups highlights one of the major drawbacks with shift-share analysis. It is sensitive to the level of regional disaggregation and to industry detail. For example a shift-share analysis of the Pilbara (i)--one of the SDs making up the Remote classification--based on the 52-sector industry classification discussed above showed that total employment rose by 17 per cent over the period compared with a rise of just 2 per cent for Remote. Mining-related employment grew by nearly 65 per cent and nearly half of this was attributed to the regional shift component--reflecting the geographically fixed resource being mined. The growth in mining was accompanied by significant growth in construction and nearly three-quarters of this was from the regional-shift component, as the growth in mining provided a competitive advantage for construction activity in the region. Other industries in which employment grew and which experienced a positive regional-shift effect included transport and health services--both necessary inputs into mining and construction. This example illustrates the ability of increased industrial specialisation (in this case in mining) to call forth employment growth in other areas and thus sustain the employment growth of a region. We turn now to examine changes in employment specialisation more closely.

Employment specialisation in the regions of Australia

The general pattern of our findings is reflected in Appendix Table 1 (for CRRS) and Appendix Table 2 for (CARS), where the median value for each regional classification in each state for 2001 (in brackets) and 2006 is recorded. In order to establish whether our measures of employment specialisation varied across regional types, we estimated thefollowing model for each measure:

Specialisation = [alpha] + [[beta].sub.1] Time + [[beta].sub.2] Other Metro + [[beta].sub.3] Coastal + [[beta].sub.4] Inland + b[[beta].sub.5] Remote + [[epsilon].sub.rt] (8)

Specialisation is the value of the index of specialisation (CRRS or CARS), Time is a dummy variable taking the value of 0 for 2001 and 1 for 2006, and the remaining independent variables are dummy variables for four of the five regional classifications (defined above), with Metro being represented by the intercept, [alpha], and the [[beta].sub.i] are the relevant coefficients, and [[epsilon].sub.rt] is the error term. The results of this exercise are reported in tables 3 and 4.

The values for CARS are significantly higher in Inland and Remote areas, but are not significantly different from Metro in either the Other Metro or Coastal regions.

With respect to CRRS, the index is significantly higher for Coastal, Inland, and Remote. However, we note that Time is not significant for either version of the model, and so we can conclude only that for the Australian economy overall there was little change in the level of specialisation over the period.

When we examine the impact of regional type on the change in CARS, we observe that being Coastal or Inland tends to have been associated with decreases in the value of CARS.lhe explanation for this negative change will certainly differ between the two regional types. For coastal regions, the fall in absolute specialisation is derived more from the rise in tertiary activity and less from the fall in primary activity than would be the case for inland Australia where primary industry was devastated by drought throughout the period. In terms of relative changes, the change in CRRS is significantly (positively) related only to the remote areas of Australia.

The impact of changes in specialisation on labour market outcomes

The basic form of our model for analysing the impact of employment specialisation on labour market outcomes takes the form (after Robson (2009):

[Y.sub.rt] = [alpha] + [[beta].sub.1] [Specialisation.sub.rt] + [[epsilono].sub.rt] (9)

where [Y.sub.rt] is the dependent labour market performance variable that takes one of three forms in our analysis: the natural logs of (a) the unemployment rate, InUr; the non-employment rate, InNer; and the participation rate, InPr. The variable InNer is used because loss of a job in an area where there are few alternative employment opportunities (a predominantly agricultural region for example) might lead to a more rapid withdrawal from the labour force than in areas where the newly unemployed might reasonably hold out an expectation for employment in an alternative industry. The variable Specialisation refers to the use of either the natural log of CARS, InCARS; or the natural log of CRRS, InCRRS.

Our reported analysis was undertaken via a panel regression--accounting for fixed effects for all SDs and incorporating time-series dummies. The analysis also employs robust standard errors to account for any heteroskedasticity that may be present. Given the ABS definition of SDs as relatively homogenous economic units, we decided not to apply a spatial econometric modelling approach which would account for interrelationships between regions, as we expected these to be relatively weak. However, this assumption will be tested in future work, especially if we were to expand the study to smaller spatial units where such interrelationships are likely to become more important. With our model, we could find no statistical relationship between InPr and either explanatory variable; nor could we identify any statistical relationship between InCRRS and either InUr or InNer. However, as reported in tables 5 and 6, there exists a significant statistical negative relationship between InCARS and InUr, and between InCARS and InNer.

5. Discussion and Conclusions

We noted that despite considerable analysis at the international level, research on industry specialisation in employment in Australia is scarce. One analysis (Dixon and Freebairn 2009), based on state-level data, produced findings that appeared contrary to the usual predictions of the Krugman hypothesis. The evidence was of declining specialisation when increasing specialisation was expected. We use the same ABS industry classifications and census data from 2001 and 2006 (rather than employment survey data at the state level) and we re-examined the situation. While the restrictions of our data do preclude us from making any useful comment on the change of specialisation overtime--the issue considered by Dixon and Freebairn (2009)--we are able to examine specialisation across SDs classified by regional type, based on the classification introduced by Garnaut et al. (2001) and used by Garnettand Lewis (2007). We find that those regions that could be described as peripheral (Inland and Remote) do, on average, exhibit higher values for the measures of industry specialisation of employment and that this is in line with the expected results from testing the Krugman hypothesis.

In analysing employment data at the SD level we note that employment growth is associated with tertiary industry growth and, typically, a decline in employment in agriculture (and, to a lesser extent, in manufacturing). As the period examined (2001-2006) was one of extensive drought in Australia, it is likely that the decline in agricultural employment can be attributed to the adverse climatic conditions (for farming production) experienced over the period. It was further observed that the decline in agricultural employment--which typically would have led to a fall in our measures of specialisation--was frequently offset in SDs by substantial growth in tertiary employment and, particularly in remote regions, by the growth of the mining industry fuelled by the rise in Australia's exports to Asia--particularly China.

Finally, we examined the impact of industry specialisation in employment on labour market outcomes. We found no evidence of a significant impact of CRRS on any measure of employment outcome. This was not entirely unexpected, as CRRS is a relative measure of specialisation with the index indicating whether or not a regional employment structure was becoming more or less like the national structure. In itself, it does not say anything about whether or not the region was becoming absolutely more specialised. The impact of our measure of absolute specialisation, CARS, appeared to produce evidence in support of the prediction that both a lower unemployment rate and a lower non-employment rate were associated with greater absolute specialisation in a region. This finding supports earlier findings by Beer and Clower (2009) and Stimson et al. (2009).

From a policy perspective, our findings suggest that one consideration in deciding upon regional policy should be whether or not the chosen policy will encourage the development of industries that result in greater absolute specialisation of employment in a region, as this is likely to have a greater impact on reducing regional unemployment and non-employment than will policies that result in an increased diversity of employment. However, further research is required into the types of industries in which regions should specialise, and the appropriateness of policies that might bring this about. In particular, policies that encourage a clustering of industries in a region (as has occurred in the Pilbara) are preferable to policies that disrupt the industrial structure of the nation.

Appendix Table 1: Median Value of CRRS, 2006 and 2001

                   NSW      Victoria    Queensland         South
                                                       Australia

Metro            44.91         45.07         35.37         44.91
               (46.43)       (46.07)       (31.57)       (36.79)

Other            65.04         56.47            NC            NA
Metro
               (71.99)       (57.19)            NC            NA

Coastal          89.38        105.09        192.08        161.18
                  (84)      (109.52)      (163.87)      (146.81)

Inland           98.50        106.50         73.38        152.65
               (96.14)       (94.42)       (71.38)      (146.98)

Remote          217.94            NA        186.99        303.22
              (149.56)            NA      (150.08)      (297.25)

               Western      Tasmania            NT           ACT
             Australia

Metro            74.46         84.97        138.85        170.55
               (77.46)       (79.86)      (131.52)      (154.05)

Other               NA            NA            NA            NA
Metro
                    NA            NA            NA            NA

Coastal         115.52        169.42            NA            NA
              (107.48)      (129.62)            NA            NA

Inland          161.52            NA            NA            NC
              (153.92)            NA            NA            NC

Remote          320.11            NA        130.08            NA
              (279.83)            NA      (142.35)            NA

Notes: NA indicates that no examples of the relevant regional type
exist in the state; NC indicates that no calculation was made
because of data difficulties associated with the identification of
regional boundaries between the two censuses; brackets enclose the
2001 values.

Appendix Table Table 2: Median Value of CARS, 2006 and 2001

                   NSW      Victoria    Queensland         South
                                                       Australia

Metro           132.31        130.82        129.73        132.54
              (125.92)      (125.26)      (121.78)      (125.81)

Other           137.69        131.40        141.86            NA
Metro
              (130.84)      (125.18)      (143.92)            NA

Coastal         139.55        139.20        126.73        148.36
              (133.88)      (140.06)      (126.71)      (157.39)

Inland          145.13        140.94        136.18        186.94
              (148.70)      (140.88)      (142.66)      (212.12)

Remote          152.40            NA        163.50        148.57
              (147.85)            NA      (134.10)      (146.71)

               Western      Tasmania            NT           ACT
             Australia

Metro           132.54        146.68        155.49        224.93
              (124.75)      (133.24)      (131.01)      (191.97)

Other               NA            NA            NA            NA
Metro
                    NA            NA            NA            NA

Coastal         143.37        121.68            NA            NA
              (135.46)      (118.67)            NA            NA

Inland          218.39            NA            NA        417.95
              (199.27)            NA            NA      (234.38)

Remote          148.45            NA        179.85            NA
              (155.40)            NA      (155.01)            NA

Notes: NA indicates that no examples of the relevant regional type
exist in the state; brackets enclose the 2001 values.


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(1) Dixon and Freebairn (2009) provide an abridged list of authors utilising the concept from Hoover (1948). Dixon and Freebairn (2009) present the Krugman index, which is a simple transformation of CRS.

(i) The shift-share calculations for the Pilbara SD are available from the authors on request.

John Hicks, P.K. Basu and Christopher Sherley *

* Charles Sturt University

Table 1: Classification of Statistical Districts into Regional Types

           NSW               Victoria            Queensland

Metro      Sydney            Melbourne           Brisbane

Other      Hunter            Barwon              Gold Coast
Metro      Illawarra                             Sunshine Coast
           Richmond-Tweed                        West Moreton

Coastal    Mid-North Coast   Western District    Wide Bay-Burnett
           South Eastern     East Gippsland      Fitzroy
                             Gippsland

Inland     Northern          Central Highlands   Darling Downs
           Central West      Wimmera
           Murrumbidgee      Mallee
           Murray            Loddon
                             Goulburn
                             Ovens-Murray

Remote     North Western                         South West
           Far West                              Mackay
                                                 Northern
                                                 Far North
                                                 Central West
                                                 North West

           South                   Western          Tasmania
           Australia               Australia

Metro      Adelaide                Perth            Hobart

Other
Metro

Coastal    Outer Adelaide          South West       Southern
           South East              Lower            Northern
                                   Great Southern   Mersey-Lyell

Inland     Yorke and Lower North   Midlands
           Murray Lands

Remote     Eyre                    Upper Great
           Northern                Southern
                                   South Eastern
                                   Central
                                   Pilbara
                                   Kimberley

           NT        ACT

Metro      Darwin    Canberra

Other
Metro

Coastal

Inland               Rest ACT

Remote     Rest NT

Source: Authors' assessments based on ABS (2011) and Garnaut et al.
(2001).

Table 2: Shift-share Analysis by Broad Industry Sector and
Regional Type

Regional       Industry          2001    Percentage          2006
Group           sectors                    of total

Australia       Primary        361792           4.9        336087
              Secondary        945824          12.8        874856
               Tertiary       6097366          82.3       6775634
                  Total       7404982         100.0       7986577

Metro           Primary         56637           1.1         56287
              Secondary        684781          13.7        615225
               Tertiary       4270255          85.2       4750248
                  Total       5011673         100.0       5421760

Other           Primary         30545           4.9         28825
Metro         Secondary         71963          11.5         73652
               Tertiary        522559          83.6        613843
                  Total        625067           100        716320
Coastal         Primary         89365          13.6         80218
              Secondary         77067          11.7         78576
               Tertiary        489541          74.6        549524
                  Total        655973         100.0        708318

Inland          Primary        103341          16.4         89294
              Secondary         75694          12.0         73115
               Tertiary        450484          71.6        485394
                  Total        629519         100.0        647803

Remote          Primary         81904          17.0         81463
              Secondary         36319           7.5         34288
               Tertiary        364527          75.5        376625
                  Total        482750         100.0        492376

Regional       Industry    Percentage    Percentage      National
Group           sectors      of total        change         share

Australia       Primary           4.2          -7.1
              Secondary          11.0          -7.5
               Tertiary          84.8          11.1
                  Total         100.0           7.9

Metro           Primary           1.0          -0.6          4448
              Secondary          11.3         -10.2         53783
               Tertiary          87.6          11.2        335390
                  Total         100.0           8.2        393622

Other           Primary           4.0          -5.6          2399
Metro         Secondary          10.3           2.3          5652
               Tertiary          85.7          17.5         41042
                  Total         100.0          14.6         49093
Coastal         Primary          11.3         -10.2          7019
              Secondary          11.1           2.0          6053
               Tertiary          77.6          12.3         38449
                  Total         100.0           8.0         51521

Inland          Primary          13.8         -13.6          8117
              Secondary          11.3          -3.4          5945
               Tertiary          74.9           7.7         35381
                  Total         100.0           2.9         49443

Remote          Primary          16.5          -0.5          6433
              Secondary           7.0          -5.6          2853
               Tertiary          76.5           3.3         28630
                  Total         100.0           2.0         37916

Regional       Industry      Industry      Regional         Total
Group           sectors           mix         shift        change

Australia       Primary
              Secondary
               Tertiary
                  Total

Metro           Primary         -8472          3674          -350
              Secondary       -105165        -18175        -69556
               Tertiary        139631          4972        479993
                  Total         25994         -9529        410087

Other           Primary         -4569           450         -1720
Metro         Secondary        -11052          7089          1689
               Tertiary         17087         33155         91284
                  Total          1466         40694         91253
Coastal         Primary        -13368         -2798         -9147
              Secondary        -11835          7292          1509
               Tertiary         16007          5527         59983
                  Total         -9196         10021         52345

Inland          Primary        -15459         -6705        -14047
              Secondary        -11625          3101         -2579
               Tertiary         14730        -15202         34910
                  Total        -12353        -18806         18284

Remote          Primary        -12252          5378          -441
              Secondary         -5578           694         -2031
               Tertiary         11919        -28452         12098
                  Total         -5910        -22379          9626

Table 3: Impact of Regional Class on CARS

Variable             Coefficient   t statistic

Intercept                139.387        17.583    ***
Time                       4.335         0.761
Other Metro              -10.471        -0.817
Coastal                   -1.581        -0.168
Inland                    18.995         2.048    **
Remote                    23.352         2.549    **

R Square                   0.156
Adjusted R Square          0.115
Observations                 108

* = 10% level, ** = 5% level, *** = 1% level

Table 4: Impact of Regional Class on CRRS

Variable             Coefficient   t statistic

Intercept                 71.011         3.938    ***
Time                      13.836         1.119
Other Metro                0.266         0.011
Coastal                   61.978         2.919    ***
Inland                    42.203         2.012    **
Remote                   145.291         7.004    ***

R Square                   0.403
Adjusted R Square          0.377
Observations                 120

* = 10% level, ** = 5% level, *** = 1% level

Table 5: Impact of InCARS on InUr

Variable             Coefficient   t statistic

Intercept                  0.049         0.038
InCARS                    -0.537        -2.069    ***
Time                      -0.331       -15.589    ***

R Square                   0.939
Adjusted R Square          0.875
Observations                 108

* = 10% significance level, ** = 5% significance level,
*** = 1% significance level

Table 6: Impact of InCARS on InNer

Variable             Coefficient   t statistic

Intercept                  0.196         0.570
InCARS                    -0.204        -2.961    ***
Time                      -0.082       -16.762    ***

R Square                   0.990
Adjusted R Square          0.980
Observations                 108

* = 10% significance level, ** = 5% significance level,
*** = 1% significance level
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Date:Mar 1, 2014
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