MODELLING ENDOGENOUS EMPLOYMENT PERFORMANCE ACROSS AUSTRALIA'S FUNCTIONAL ECONOMIC REGIONS OVER THE DECADE 2001 TO 2011.
Modelling regional economic performance has long been a concern of regional scientists. In recent times, there has been considerable emphasis on focusing on endogenous growth (see, for example, Stimson, Stough and Roberts, 2006; Johansson et al., 2001; Stimson, Stough and Nijkamp, 2011; Stimson and Stough (with Salazar), 2009) providing a framework for measuring and modelling spatial variation in endogenous regional economic performance over time.
The modelling approach requires:
(a) specification of a dependent variable that measures how change in economic performance over time (both growth and decline) might be attributable to factors and processes that are endogenous to the region; and
(b) deciding on a set of independent variables that might provide explanation for the variation across regions in the incidence of that dependent variable, which is achieved using spatial econometric modelling.
That approach proposed by Stimson and Stough [with Salazar] (2009) has been adopted in several studies investigating the endogenous economic performance of regions across Australia over successive inter-census decadal periods (see Stimson, 2012; Stimson, Robson and Shyy, 2009a; 2009b; 2011; Stimson, Mitchell, Rohde and Shyy, 2011), and it is continued in this paper for the decade 2001-2011. It has also been used in the paper by Plummer et al. (2014). Importantly, in the research reported here, functional regions rather than de jure regions are used as the spatial base for the modelling. This has been shown to largely overcome the issue of spatial autocorrelation that is inherent in spatial econometric modelling based on using de jure regions as the spatial base for regional demarcation. The modelling reported in this paper employs a framework in which:
(a) the spatial base is 134 Functional Economic Regions (FERs) across both the capital city metropolitan regions and the non-metropolitan regions of Australia that have been derived by the authors (and reported in Stimson et al., 2016);
(b) the dependent variable, measuring endogenous regional employment performance, is the regional (or differential) component derived from a shift-share analysis of employment change over the decade 2001-2011; and
(c) the independent (explanatory) variables that potentially might explain variation in the dependent variable, are a set of 27 measures derived from census data that relate to factors and processes that regional scientists have been suggesting might influence endogenous regional performance, plus five locational variables.
The paper is structured as follows. The next section briefly reviews past approaches to research investigating regional economic performance in Australia. That is followed by an outline of the data and methodology used in the analysis. Next, the spatial patterns of endogenous regional performance--the dependent variable--over the decade 2001-2011 are mapped and described. The bulk of the paper then presents the results of the spatial econometric modelling performed to identify those factors that might explain the variations in endogenous regional employment performance across Australia's FERs. Finally, there is a brief discussion of the policy implications of the model findings.
2. OVERVIEW OF APPROACHES TO RESEARCH INTO REGIONAL PERFORMANCE IN AUSTRALIA
Since the 1970s Australia has undergone as series of significant structural economic transitions. The impacts of these shifts have not been homogeneous over space and there is considerable variation in the economic performance of regions across Australia, both within the major large metropolitan cities and beyond into regional Australia.
Stimson (2012) has provided a detailed review of research investigating regional economic performance in Australia, most of which has been based on using de jure rather than functional regions--such as Local Government Areas or Statistical Local Areas (SLAs), or aggregations of them--as the spatial unit of analysis.
The nature of those economic and social 'divides', as they were emerging in the decade or so up to the late 1990s, was discussed in a book by O'Connor et al. (2001) on Australia's changing economic geography. Divides have also been identified in other studies (such as Baum et al, 1999; Baum et al, 2006). Spatial mismatches were shown to be evident in regional shares of population and population change and in shares of investment in economic activity.
The O'Connor, et al. (2001) study raised a series of challenging implications for people-based and for place-based policy responses in addressing those spatial disparities. In particular, the infrastructure needs required to enhance the performance of those segments and places in the space economy that are significant contributors to national wealth and competitiveness were a focus.
Stimson (2012: p. 162) pointed out that:
"... Understanding the dynamics underlying the spatial differences that exist in the economic development and performance of Australia's regions is a complex task."
Regional research studies conducted over the last two to three decades have identified a range of factors influencing patterns of regional development and performance for specific periods of time. But:
"... the specific conclusions reached and the relationships identified are not necessarily consistent because of the different focus of the studies and their different methodologies, variations in the spatial units of analysis used, and the different time periods that are analysed" (p. 162).
Stimson pointed out that many studies have investigated:
"... regional differentials in, and inter-relationships between, regional population size and growth, employment changes, structural shifts in industry employment, income levels, resource endowments, and the locational characteristics of regions" (p. 162), along with aspects of human capital.
Among other things, they had shown that a region's industry structure, its occupation mix, and its human capital structure are affected not only by the size of the region's economy and its resource endowments, but also by its level of remoteness in the context of the nation's settlement system. Some examples of such regional research include the following:
* work by the Commonwealth Government's Bureau of Transport and Regional Economics (BTRE, 2004a; 2004b) has modelled relationships between regional shifts in industry structure diversification/specialisation, structural change in employment, unemployment, human capital, and the size of regional economies;
* research by Trendle and Shorney (2003) investigated the relationship between regional industry diversity and unemployment in the State of Queensland;
* studies by Bradley and Gans (1998) and Hogan et al. (1999) focused on analysing increasing regional industrial diversity;
* a study by Garnaut et al. (2001) investigated regional influences on employment and population growth;
* studies by Harrison (1997) and Garnett and Lewis (2000) focused on relationships between regional education participation rates and qualifications, migration, and labour markets;
* a study by the National Centre for Social and Economic Modelling (Lloyd et al., 2000) investigated the 'hollowing out' of income across regions; and
* a study by Plummer, et al. (2014) investigated uneven development and local competitiveness across Western Australia's regional cities.
However, as Stimson (2012) has noted, relatively few studies have:
"... incorporated an explicit focus on the nature of occupational structure, occupational status, and education qualifications and skills, all of which are important components in the consideration of human capital differentials in regional development and performance" (p. 162).
Much of the published research on regional performance has been on the level of income and tended to focus on modelling variation in aggregate employment change or the incidence of unemployment. Some of the research on differentials in regional performance has been restricted to measuring and modelling an aspect of patterns of economic performance within Australia's capital city metropolitan regions or within a specific city, while other research has been focused specifically on the nation's non-metropolitan regions.
A variety of methodologies have been used to investigate aspects of regional economic performance in Australia, but predominately the preferred approach has been to use a multiple regression model, most typically an Ordinary Least Squares (OLS) model, and sometimes a backward step-wise regression model.
Modelling approaches other than straight regression analysis have also been used in research investigating regional performance in Australia. Examples include the following:
* using a binomial logit model in a cross-sectional study investigating the relationship between education, skills and qualifications and the economic performance of the five mainland States of Australia (Lawson and Dwyer, 2002);
* using Principal Components Analysis (PCA) to model regional variations in human capital (Stimson, Baum, Mangan, van Gellecum, Shyy and Yigitcanlar, 2004);
* developing typologies of community opportunity and vulnerability and using Multi-Discriminant Analysis (MDA) to describe the characteristics of the categories in those typologies e.g. the study by Baum et al. (1999) and Baum et al. (2006);
* developing typologies to produce functional classifications of regional cities and towns across Australia and showing how those have evolved over time (Beer, 1999; Beer and Maude, 1995); and
* using Shift-Share Analysis and the national shift component of employment change by industry sector to shed light on the proposition that an explanation of differences in regional employment growth was that some strongly performing regions are more specialized in rapidly growing industry sectors--like mining--across the Australian Bureau of Statistics Labour Market Regions over the period 1996-2001 (BTRE, 2004a; 2004b).
To take account of the spatial autocorrelation problem that is inherent in using aggregated spatial data, especially where it is based on de jure regions, it is important for modelling to use a spatial dependence test, along with a multicollinearity test. In addition, a Spatial Error Model (SEM) and a Spatial Autoregressive (SAR) Model should be employed. This was the approach used by Stimson, Mitchell, Rohde and Shyy (2011) in their study of variations in endogenous regional employment performance of FERs over the decade 1996-2006, and it is the approach used in this paper.
There have also been attempts to forecast the growth of regions into the future (Adams, 2002; Beer, 2002) looking at the effects of:
(i) national shifts in employment on regional growth;
(ii) initial industry structure on regional growth;
(iii) industrial diversity; and
(iv) the level of education, skills and qualifications.
It is only during the last decade that regional research in Australia has specifically focused on measuring and modelling endogenous regional performance, initiated by Stimson, Robson and Shyy (2004; 2005; 2006), and used also by Plummer, et al. (2014).
It is worth noting that, while traditionally it has been common for economists to theorize about regional convergence occurring over time in phenomena such as income, it is very clear from the empirical research investigating regional economic performance across Australia that, rather than regional convergence occurring, there appears to be divergence with a considerable degree of unevenness of performance being the rule across regions.
3. DATA AND METHODOLOGY
The analysis of endogenous regional employment performance across Australia over the decade 2001-2011 reported in this paper follows the methodology used by Stimson, Mitchell, Rohde and Shyy (2011) in the previous analysis for the decade 1996-2006.
The spatial unit of analysis used are Functional Economic Regions (FERs) that have been compiled by the authors, as reported by Stimson et al. (2016), using the Intramax procedure developed by Masser and Brown (1975). The building blocks for the FERs are the Australian Bureau of Statistics' Statistical Areas Level 2 (SA2s). It uses the 2011 census journey-to-work data to analyse commuting patterns with FERs being demarcated to maximise within-region coincidence between where people live and where they work. The advantage of using FERs is that they tend to overcome, or at least minimise, the spatial autocorrelation problem encountered in the use of de jure regions (such as Local Government Areas), as has been demonstrated in the Stimson, Mitchell, Rohde and Shyy (2011) paper.
It is worth noting that the method used to demarcate the FERs was not constrained by restricting them to be fall within State and Territory borders, as is the case with the Australian Bureau of Statistics Labour Market Regions. That means some FERs cross over the state border between New South Wales and Victoria, along the eastern part of the border between New South Wales and Queensland, and along some of the border between Victoria and South Australia.
Within and around the capital city metropolitan city regions there are multiple FERs: 10 across the Sydney-Newcastle-Wollongong conurbation; seven across the Melbourne-Geelong region; six across the Brisbane-South East Queensland region that extends north to the Sunshine Coast and south to the Gold Coast; four across the greater Perth region; but only two across the greater Adelaide region. That reflects the emergence over time of a multi-centre spatial structure for Australia's big cities and the regionalisation of metropolitan labour markets.
Beyond the metropolitan city regions, the FERs tend to become much larger as the degree of remoteness and sparsity of settlement (and thus remoteness) increases. In addition, they are often elongated in shape along the main roads, which is not surprising.
Following the framework proposed by Stimson and Stough [with Salazar] (2009) and Stimson et al. (2009b), the modelling approach here uses, as the dependent variable, a surrogate measure of endogenous regional employment performance over time. This is measured as the differential (or regional) component derived from a Shift-Share Analysis of regional employment change over the decade 2001-2011, standardised by the size of the regional labour force at the beginning of the period.
The set of independent (or explanatory) variables used is the same set of 32 variables used in the previous studies cited above, 27 of which are derived from census data, and five of which are explicit locational variables (see Table 1).
As per the Stimson, Mitchell, Rohde and Shyy (2011) analysis for the decade 1996-2006, a range of models are applied to investigate the potential causes of the spatial variation in endogenous regional employment performance over the decade 2001-2011:
1. First, an OLS full model was run without allowing for spatial effects. Spatial dependence tests were then carried out, including the Lagrange Multiplier (LM) tests, and the Moran 's I test which was run on residuals (see Anselin et al, 1996; Anselin, 1988). A multicollinearity test was also completed.
2. Second, a backward step-wise regression (based on AIC) was employed to derive an OLS specific model. Again, spatial dependence tests and a multicollinearity test were implemented.
3. Third, using the same variables, a Spatial Error Model (SEM) was run, which includes a lagged spatial error term.
4. Finally, for completeness and for comparison, a Spatial Autoregressive (SAR) Model was carried out on the same variables, which includes a lagged dependent variable.
4. SPATIAL PATTERNS OF ENDOGENOUS REGIONAL EMPLOYMENT PERFORMANCE
It is important to understand the economic context of the decade 20012011 in Australia. The decade began just after the 2000 Sydney Olympics. The long-boom years of economic growth that had begun following the recession of the early 1990s continued until later in the first decade of the new millennium.
Not surprisingly then, one would expect there to be marked variations in the direction and the magnitude of endogenous regional employment performance over the decade 2001-2011 across Australia's FERs, and that is the case as clearly shown in Figure 1.
When the world was impacted by the sharp downturn of the Global Financial Crisis (GFC), fortunately it had a relatively low aggregate impact on Australia. But it did have significant variable regional impacts. The decade was also characterised by the resources boom led by high commodity prices and an escalation in mining investments, output and exports, which was to create circumstances for what has been described as a 'two-speed economy'.
Following the elimination of some of the very remote and barely inhabited areas characteristic of a vast continent such as Australia, 134 FERs remained. Over the decade 2001-2011 only 46 of these FERs (or 34%) recorded a positive score on the endogenous regional employment performance measure, with only seven of those FERs having a strong positive performance. Thus, the big majority of the FERs (88 or 66%) recorded negative scores on the endogenous regional employment performance variable, with four of them having a strong negative performance.
Table 2 lists the FERs that were the top 25 positive performers and those that were the bottom negative performing FERs on the endogenous regional employment performance measure for the decade 2001-2011.
Some distinctive characteristics are evident from the pattern, across Australia, of positive and negative performance of FERs on the endogenous regional employment performance variable over the decade 2001-2011, and these are discussed below.
Positive Endogenous Regional Employment Performance
The positive performing FERs are located across much of the capital city metropolitan regions, including Melbourne, Brisbane, Perth, Darwin, Hobart and across the ACT. But this was not the case for Adelaide or Sydney.
Positive performance was also spread across some areas of coastal NSW, Queensland, eastern Victoria, Western Australia and much of the nation's inland wheat-sheep belt. Furthermore, positive performance is found in some of the mining regions and in some of the indigenous settlement regions of Western Australia, Queensland and the Northern Territory. The existence of positive endogenous regional employment performance for some of the FERs that are indigenous settlements is surprising, but probably explained by the concerted public policy efforts of governments to create indigenous employment, which is in fact an exogenous factor but is picked-up by default in the measure of the REG_SHIFT dependent variable.
Negative Endogenous Regional Employment Performance
The negative performing FERs are located widely across regional Australia beyond the capital cities and especially across the nation's vast remote areas. That includes farming and grazing regions of western Victoria, South Australia, parts of central and western Queensland, and Western Australia. Those regions largely form Australia's extensive wheat-sheep belt. Some of the negative performing FERs are also found in parts of coastal New South Wales, Queensland, Victoria, South Australia and Western Australia. Most of the FERs in Tasmania also had negative performance.
Within the capital city metropolitan regions, negative performance on the endogenous regional employment performance dependent variable was also present across all of Adelaide, all of Sydney, in eastern Melbourne, and in the north-west of Brisbane.
5. THE MODELLING AND RESULTS
As indicated earlier, several approaches were used to model the role the independent variables might play in explaining the variation in the dependent variable across the FERs. These models and their results are discussed below.
The Ordinary Least Squares Full Model
First the OLS regression full model was run without allowing for spatial effects (see Table 3). The [R.sup.2] is quite high (0.9414). Only nine of the independent variables are significant in explaining the variation in the dependent variable, six having a positive and three having a negative influence on the endogenous regional employment performance of FERs. The positive effects result from variables relating to:
* industry diversification/specialization at the beginning of the decade and the decade 2001-2011;
* the region's structural change index at the start of the decade;
* population change over the decade;
* the level of unemployment at the beginning of the decade;
* change in the incidence of information jobs; and
* the measure of remoteness.
The negative effects are from the variables relating to:
* the initial level of income; and
* change in the incidence both of people with bachelor qualifications and of people with technical qualifications.
It is noteworthy that some variables have a different direction (positive/negative) to model outcomes in the Stimson, et al. (2011) paper analysing the decade 1996-2006.
The Anselin Lagrange Multiplier (LM) tests--both the original and the robust tests--were used to test for spatial dependence, both error and lag (Table 4). In addition, the Moran's I test on residuals was run (Table 5). The full model shows no evidence of spatial dependence (lag or error) according to the LM tests or Moran's I on residuals.
A test for multicollinearity using variance inflation factors (VIF) was run (Table 3). Obviously, some of these are very high.
The Backward Step-Wise Regression Model
A backward step-wise regression (based on AIC) was run to derive an OLS specific model (Table 6). This reduces, to 15, the number of independent variables that are relevant to explaining the dependent variable. Once more, the [R.sup.2] is quite high (0.937).
Twelve variables are significant, 10 having a positive effect and two having a negative effect. Again, some variables have a different direction (positive/negative) to model outcomes in the Stimson, Mitchell, Rohde and Shyy. (2011) paper analysing the decade 1996-2006.
The independent variables having a significant positive effect on FER endogenous regional employment over the decade 2001-2011 relate to:
* industry diversification/specialisation at the beginning of the period and for the change in it over time;
* the region's structural change index at the beginning of the decade;
* population change over time;
* the level of unemployment at the beginning of the decade;
* the change in the degree of concentration of jobs in information and in finance;
* the incidence of volunteering (as a surrogate measure of social capital); and
* the degree of regional remoteness.
Negative effects on endogenous regional employment performance are related to:
* the level of income at the beginning of the decade; and
* the change in the incidence both of people with bachelor qualifications and of people with technical qualifications.
Results of the Anselin's LM and Morans I tests are presented in tables 7 and 8, respectively. There is no spatial dependence according to the Anselin LM tests, but there is significant (at the 0.05 level) error due to spatial dependence using the Moran's I. Multicollinearity test results are reported in Table 6. According to most of the literature, these results are quite acceptable, though some authors advocate for VIFs less than 6.
Spatial Regression Models
Spatial Error Model (SEM)
Given the possibility of spatial error dependence evidenced from the Moran's I test on residuals, the same variables were run in a Spatial Error Model (SEM), which includes a lagged spatial error term (Table 9).
The results show there is little difference between the OLS and the SEM. Those variables that were significant in the OLS are still significant in the SEM, all coefficient directions are the same, and there are only minor variations in magnitudes. Regarding the SEM, the spatial error coefficient, lambda, is significant (p value 0.028) and the AIC is slightly lower, but the Likelihood Ratio test is not significant (p value 0.0733), thus pointing to the OLS as the preferred model.
Spatial Autoregressive Model
For completeness and comparison, the Spatial Autoregressive (SAR) Model was also run, which includes a lagged dependent variable (Table 10).
In the SAR model, LQ_MAN_CH becomes significant, but otherwise the results are similar to those for the OLS model and the SEM. Importantly, the lagged dependent variable coefficient (rho) is not significant (p value 0.548) and the AIC is higher than for the OLS model.
In summary, the OLS does appear to be the best model, negating the need for spatial models. This is an interesting finding, and it confirms the supposition that the use of a functional rather than a de jure spatial base should help overcome the issue of spatial autocorrelation.
By way of an aside, the Moran's I statistic for the REG_SHIFT variable is significant, meaning the REG_SHIFT itself shows some spatial dependence. However, it is no longer significant in the SAR model (Table 10), which is interesting.
6. POLICY ISSUES
Regional Policy Interventions in Australia
In Australia it has been common to have government involvement in implementing explicit regional development policy, but that has waxed and waned over time. Such involvement has tended to have been focused almost exclusively on non-metropolitan regions and rarely on metropolitan regions. Such policies are what O'Connor, et al. (2001) have referred to as 'place-specific' policies. The interventions have typically been about, inter alia, the following:
* investments in infrastructure (including transportation projects, dams and irrigation systems);
* grants for community facilities;
* providing higher education facilities; and
* industry attraction schemes, which are essentially about 'picking winners' and which have a long history of failure.
Regional economic development policy and programs are almost exclusively the concern of the state/territory governments.
In addition, some government policy and programs that are 'people-specific' can have regional impacts, one of the most notable being the immigration policy of Commonwealth governments, with immigrants overwhelmingly choosing to live in the major cities, especially Sydney and Melbourne and in specific areas within them.
Over the last two to three decades regional development policy has tended to be focused more on developing local capacity and enhancing competitiveness, which is about bolstering-up endogenous processes. But often the implementation of such policy approaches has been characterised by 'picking winners' as illustrated by the Western Australia experience discussed by Plummer, et al. (2014).
It is often the case that attention has been directed towards intervening in poorly performing/lagging regions, rather than making investments to further enhance the performance of successful regions.
Implications of the Modelling for Policy
What might be the implications for policy of the modelling undertaken for this paper? Several are evident if the purpose of regional policy programs is to enhance the endogenous performance of regions.
It is evident that marked differences persist in the pattern of endogenous regional employment performance across Australia, with the large majority of FERs displaying negative performance over the decade 2001-2011. There are marked divides between the regional and some of the metropolitan region FERs, but that is not universal. Across regional and rural Australia there are pockets of positively performing FERs, so it is not all 'gloom-and-doom' across Australia's regional and rural areas. But nor is it all booming across the metropolitan regions.
Of special concern from the experience of the 2000s is the negative performance of FERs in the Sydney metropolitan region, which was a reversal of the situation for prior inter-census decades. Was this a post-Olympics effect? And was it an outcome of planning policy restricting land release and a reaction to the Labor Premier of New South Wales, Bob Carr, declaring that Sydney was closed to expansion? For Australia's global city, this negative endogenous growth employment performance was a disturbing outcome.
It is also disturbing that the Adelaide metropolitan region continues to be a negative performer.
The modelling certainly highlights the difficulty for regional development policy to be formulated in a global sense. This suggests the need for a region-specific policy approach rather than a 'one-size-fits-all' approach.
It is evident that using functional in contrast to de jure regions as the target for regional policy would make more sense than continuing the common practice of directing programs and investment to Local Government Authorities. It is understandable that that there has been a focus on Local Government Authorities as the third-tier of government in Australia, and they are in fact the creatures of State governments. However, these de jure regions are largely historic in origin, although it is not uncommon for State governments to force Local Government amalgamations. At the least, regional development strategies and the investments associated with them should recognise that it is not often the case that a single local government entity equates with a functional economic region (a functional labour market). As a result, there should be an insistence that there be collaboration between the local government entities that might equate with a functional economic unit, and that a regional development strategy be formulated for such a functional entity.
From the modelling undertaken for this paper, it is clear key factors that seem to underpin positive endogenous regional employment performance relate to a region's industry diversity/specialisation, its structural characteristics, and the degree of concentration of employment in information jobs and in finance jobs. In addition, population growth seems to be positively associated with positive endogenous regional employment performance. Not surprisingly, the remoteness of a FER also seems to be a factor that enhanced the performance of some FERs as the decade 2001-2011 coincided with the remarkable resources boom experienced by Australia, with mining activities especially, being highly concentrated in very remote locations and with many highly productive agricultural and pastoral regions that are in relatively remote inland areas. As a hallmark of what was referred to as Australia's 'two-speed' economy, the resources boom sucked jobs out of the non-mining sector adversely impacting non-resource regions.
The reality is there is little that government interventions could do with respect to these factors that related to structural transition in the economy, with some regions being 'winners' while others were 'losers'. However, since the end of the 2000s, the resources boom ended abruptly, so it might be expected that future modelling focusing on endogenous regional employment performance for the current decade will reveal perhaps a different role being played by those regional factors.
The level of unemployment at the beginning of the decade 2001-2011 does appear to assume some significance in a positive way. This is possibly because many of the more remote regions would have experienced jobs growth relating to the resources boom over the decade, with many such regions traditionally having somewhat higher than normal unemployment.
It is interesting that the modelling for the decade 2001-2011 did not reveal population size per se to be a statistically significant factor enhancing endogenous regional employment performance, while population growth over the decade did significantly affect positive performance.
Thus, positive performance was not necessarily the prerogative of large regional labour markets, and nor were small size FERs necessarily poor performers.
Similarly, and perhaps also surprisingly, regional income levels at the start of the decade were shown to, in fact, have a negative effect on endogenous regional employment performance.
But the most surprising result from the modelling was that factors relating to levels of regional human capital were not significant in explaining positive endogenous regional employment performance. Indeed, the modelling showed that change in the incidence of people with bachelor qualifications and change in the incidence of people with technical qualifications, in fact, had a negative influence on regional endogenous performance (at least for the decade 2001-2011). These results are counterintuitive to much of the research on regional development which postulates that improved levels of human capital will improve economic performance. This finding poses questions about outcomes of public policy to encourage engagement in tertiary education, including; the increasing investment that has been occurring in post-school education and training, the massive rise that has occurred in the number of students attending tertiary education institutions and, as a result, the very large increase that has been occurring in the number of tertiary-educated young people of workforce age.
In contrast, the supposed positive effect of increasing social capital--as measured, albeit inadequately, by the incidence of volunteering--does appear to be a factor that is a significant positive factor for enhancing endogenous regional employment performance. Enhancing social capital has been receiving some attention by governments.
Perhaps the most important lesson to take from the modelling for the decade 2001-2011 is that policy interventions to enhance endogenous regional employment performance might be those that relate to the structural characteristics of a region and enhanced diversification of employment, along with enhancing social capital. Interventions to enhance human capital might be worthwhile goals in themselves, but do not appear to be having a positive impact on endogenous regional employment performance.
This paper continues the research thrust initiated a decade or so ago to investigate endogenous regional employment performance across Australia's regions. That work has operationalised a model framework along the lines proposed by Stimson and his collaborators, and as set out in detail in Stimson, and Stough [with Salazar] (2009) and Stimson et al. (2009b).
The study reported here has focused on:
(a) analysing the patterns of endogenous regional employment performance for the decade 2001-2011; and
(b) modelling the potential determinants of variations in that performance.
A functional as against a de jure spatial base was employed using a new functional geography of FERs (developed by the authors and reported in a published paper by Stimson, et al. (2016)) across both Australia's major metropolitan regions and beyond across the vast expanses of regional Australia. The econometric modelling described in this paper indicates that using FERs appears to overcome the spatial autocorrelation issue inherent in using a de jure regional demarcation, which was also found to be the case in the earlier work by Stimson, Mitchell, Rohde and Shyy (2011) which modelled the endogenous regional employment performance of FERs for the decade 1996-2006. A series of econometric models were run:
(a) first a full OLS model and then a backward step-wise regression OLS spatial specific model (for both models the Anselin Lagrange Multiplier (LM) and Moran's I spatial dependence tests were run, along with a multicollinearity test); and
(b) second spatial regression models were run, both a Spatial Error Model and also a Spatial Autoregressive Model.
The results from these models were discussed.
We judge that an OLS model would be the preferred modelling approach when using a functional spatial base to investigate potential factors explaining the positive or negative performance of FERs in regard to endogenous regional employment over the decade 2001-2011. This finding confirms what Stimson, Mitchell, Rohde and Shyy (2011) also found in their analysis for the decade 1996-2006. It certainly leads us to conclude that a functional spatial base is preferable to a de jure spatial base that has more commonly been used in econometric modelling investigating regional economic performance in Australia. Modelling based on de jure regions has typically been the focus for regional development policy interventions, which is probably not a suitable policy approach.
It is evident from the empirical findings of research investigating regional economic performance in Australia that considerable regional differentiation persists. The gaps are wide. That is particularly evident from the research explicitly focusing on measuring endogenous regional employment performance across the nation's FERs as reported in the paper by Stimson, Mitchell, Rohde and Shyy (2011) for the decade 1996-2006 and in this paper for the decade 2001-2011.
Regarding the findings from the modelling, and depending on which model is used, it appears that a positive influence on regional endogenous employment for FERs over the 2001-2011 period is significantly related to factors to do with:
* regional industry diversification/specialisation at the beginning of the decade;
* the structural change index for the region; population change over time;
* the incidence of employment in information jobs and possibly in finance jobs;
* the initial level of unemployment;
* the level of social capital as measured by the incidence of volunteering; and
* regional remoteness.
A negative influence is significantly related to factors to do with:
* the initial level of regional income; and
* the incidence of people with bachelor and technical qualifications.
There is a need for further work to be undertaken to enhance our understanding of endogenous regional employment performance across FERs in Australia. For example, it would be worthwhile to explicitly focus the modelling exclusively on FERs beyond the major metropolitan regions. It might also be worthwhile partitioning Australia into groups such as the capital city metropolitan regions and for regional Australia into size category or remoteness category FERs to explicitly analyse endogenous processes in the FERs encompassing the larger and smaller regional cities and towns. Additionally, segmenting the analysis into the two five-year inter-census periods that comprise a decade might be worthwhile.
We also need to be aware that, over time, there will be changes in the boundaries of FERs due to both improvements to the transport infrastructure and changes in the distribution of employment across space. In addition, we need to be cognisant that the macro-economic conditions within which the processes of endogenous regional performance play out do change over time, will be specific to an inter-census period and will have exogenous impacts on regional performance.
ACKNOWLEDGEMENT: The research on which this paper is based is funded by the Australian Research Council (ARC) Discovery Project Grant #DP150103437.
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Robert J. Stimson
Emeritus Professor, School of Geography, University of Melbourne, Vic, 3052 and School of Earth and Environmental
Sciences, University of Queensland, Qld, 4072, Australia.
Senior Research Assistant, Centre of Full Employment and Equity, University of Newcastle, Callaghan, NSW, 2308,
Emeritus Professor, Centre of Full Employment and Equity, University of Newcastle, Callaghan, NSW, 2308, Australia.
Email: bill. email@example.com. au.
Research Fellow, School of Geography, Planning and Environmental Management, University of Queensland, Qld, 4072,
Email: t. shyy@uq. edu. au.
Professor of Urban and Regional Analysis, Griffith University, Brisbane, Qld, 4111, Australia. Email:
Caption: Figure 1: Map of Positive and Negative Scores on the Endogenous Regional Employment Performance (REG_SHIFT) Dependent Variable Measure, 2011-2011. Source the Authors.
Table 1. The Variables Used to Model Change in Endogenous Regional Employment Change over the Decade 2001-2011 Across Australia's Functional Economic Regions. DEPENDENT VARIABLE REG_SHIFT Regional Shift component of a Shift-Share Analysis of Employment change (2001 to 2011) /Labour Force (2001)/1000 INDEPENDENT VARIABLES Derived from Census Data SPEC_01 Specialisation Index for 2001 (Herfindahl- Hirschman Index) SPEC_CH Change in Specialisation Index from 2001 to 2011 (Herfindahl-Hirschman Index) SCI Structural Change Index (2001 to 2011) SCI_CH Change in the Structural Change Index (from 2001-2006 to 2006-2011) L_INC_01 Median Individual Income-2001 Annual (Log) (real) L_INC_CH Change in Median Individual Income-2001 to 2011 Annual (Log) (real) UNEMP_01 Unemployment rate in 2001 UNEMP_CH Change in Unemployment rate from 2001 to 2011 L_POP_01 Log of population (2001) L_POP_CH Change in Log of population (2001 to 2011) LQ_MAN_01 Location Quotient for the Manufacturing Industry in 2001 LQ_INF_01 Location Quotient for the Information media & telecommunications Industry in 2001 LQ_FIN_01 Location Quotient for the Financial & insurance services Industry in 2001 LQ_PRO_01 Location Quotient for the Professional, scientific & technical services Industry in 2001 LQ_MAN_CH Change in the Location Quotient for the Manufacturing Industry, 2001 to 2011 LQ_INF_CH Change in the Location Quotient for the Information media & telecommunications Industry, 2001 to 2011 LQ_FIN_CH Change in the Location Quotient for the Financial & insurance services Industry, 2001 to 2011 LQ_PRO_CH Change in the Location Quotient for the Professional, scientific & technical services Industry, 2001 to 2011 POSTGRAD_01 Proportion of labour force with a Postgraduate Degree or higher in 2001 BACHELOR_01 Proportion of labour force with a Bachelor Degree or higher in 2001 TECHQUALS_01 Proportion of labour force with technical qualifications in 2001 POSTGRAD_CH Change in the Proportion of labour force with a postgraduate degree or higher, from 2001 to 2011 BACHELOR_CH Change in the Proportion of labour force with a bachelor degree or higher, from 2001 to 2011 TECHQUALS_CH Change in the Proportion of labour force with technical qualifications, from 2001 to 2011 SYMBA_01 Proportion of Symbolic Analysts (Managers + Professionals) in Employment in 2001 SYMBA_CH Change in the proportion of Symbolic Analysts (Managers + Professionals) in Employment from 2001 to 2011 VOLUNTEER_11 Proportion of Volunteers in Working Age Population (15-64) in 2011 Location Variables A_COAST Border is adjacent to coastline (No = 0; Yes = 1) P_METRO Border is adjacent to metropolitan statistical division (No = 0; Yes = 1) D_URBAN Classified as Urban under Australian Classification of Local Governments system (1 = Yes, 0 = No) D_REMOTE Classified as Remote under Australian Classification of Local Governments system (1 = Yes, 0 = No) W_METRO Border is within metropolitan statistical division (No = 0; Yes = 1) Source: the Authors. Table 2. The Bottom 25 Negative Performing FERs on the Endogenous Regional Employment Performance Variable for the Decade 2001-2011. Bottom 25 negative performing FERs State/Territory, REG_SHIFT (name given) type of region score 1. Petermann-- NT: Remote -437.2739166 Simpson 2. APY Lands SA: Remote indigenous lands, -367.6052307 north west 3. Bourke-- NSW: Inland far west, remote -359.6419387 Walgett 4. Coober Pedy SA: Inland, remote -320.0340477 5. Renmark-- SA: Inland, irrigation area -290.1754182 Loxton 6. Victoria NT: Remote, indigenous -287.7707821 River 7. Dorset TAS: Coastal, rural, north -281.1617911 east 8. King Island TAS: Island, rural remote -265.9318968 9. Swan Hill-- NSW-VIC: Inland irrigation -263.4774996 Deniliquin-- area Wentworth 10. Snowy NSW: Inland south, rural -248.6266220 Mountains 11. Griffith-- NSW: Inland, irrigation area -244.0103606 Narrandera 12. Manjimup-- WA: Coastal, rural, south west -243.3089350 Bridgetown 13. Longreach and QLD: Inland far west, pastoral -217.2412607 surrounds 14. Carnarvon-- WA: NW remote mining -214.1387132 Exmouth 15. Moree-- NSW- QLD: Inland north, rural -213.3415212 Inverell --Goondiwindi 16. Charters QLD: Coastal and inland north -204.8155484 Towers--Ayr 17. Brookton-- WA: Inland, rural -202.4857934 Narrogin --Katanning 18. Parkes-- NSW: Inland western plains, -196.4080787 Cobar rural 19. Cape York QLD: Far North Cape, remote -195.2428712 Peninsula 20. Ingham-- QLD: Coastal north, rural -194.9280890 Innisfail 21. York-- WA: Inland, rural -193.7412466 Dalwallinu --Merredin 22. Carpentaria QLD: Coastal, far north, -192.9291385 remote 23. Tennant NT: Inland remote -189.3799572 Creek--Barkly 24. Northern NT: Metropolitan suburban -187.5386101 Darwin suburbs 25. Mildura and VIC-NSW: Inland, irrigation -185.2242600 surrounds area Top 25 positive performing FERs State/Territory, REG_SHIFT (name given) type of region score 1. Ashburton WA: North west, remote mining 1475.2003860 2. Thamarrurr NT: Indigenous area, remote 991.9831003 3. Port Hedland-- WA: North west, remote mining 751.6731816 Newman--East Pilbara 4. Karratha-- WA: North west, remote mining 708.2806127 Roebourne 5. Rockingham-- WA: Metropolitan outer 443.3334812 Mandurah suburban, south 6. Anindilyakwa NT: Indigenous land council 439.2604002 area, remote 7. West Arnhem NT: Remote, indigenous 309.9875564 8. Darwin City-- NT: Metropolitan inner 271.3055835 Inner suburbs 9. Western SA: Inland, remote 253.3676169 10. Weipa QLD: Coastal, far north, remote 243.8628282 11. Mackay-- QLD: Coastal, regional city 232.8954608 Whitsunday and rural 12. Gladstone QLD: Coastal, north 218.6443992 and surrounds 13. Sunshine QLD: Metropolitan outer 197.4943849 Coast suburban northern 14. Palmerston-- NT: Inland, remote 183.0851687 Litchfield 15. Melbourne West VIC: Metropolitan outer 162.6978148 --North West-- suburban, north and west Bacchus Marsh 16. Gold Coast QLD-NSW: Metropolitan outer 158.5154890 --Tweed suburban, south 17. Bunbury-- WA: South west coastal, 142.9204609 Margaret River regional city and wine area 18. Ipswich-- QLD: Metropolitan outer 138.8049684 Springfield suburban, west 19. Hervey Bay-- QLD: Coastal, regional city 136.4600045 Maryborough and rural 20. Midland-- WA: Inland rural 134.8232042 Mundaring-- Gingin 21. Brisbane North QLD: Metropolitan suburban, 128.5096330 --Moreton Bay north Region 22. Mornington VIC: Metropolitan outer 98.1366192 Peninsula-- suburban, southeast Dandenong-- Pakenham 23. North Perth-- WA: Metropolitan outer 98.0774790 Joondalup suburban, north 24. Greater QLD: Coastal regional city 91.2981568 Townsville 25. Fremantle-- WA: Metropolitan suburban, 90.9848153 South Eastern south Perth Source: the Authors. Table 3: Full OLS Model Results and Multicollinearity Test Results. Coefficient Estimate Std. t p value Error value (Intercept) 510.084 412.418 1.237 0.219025 SPEC_01 542.682 249.199 2.178 0.031756 * SPEC_CH 725.606 372.764 1.947 0.054366 SCI 799.754 192.563 4.153 6.87e-05 *** SCI_CH 67.315 212.352 0.317 0.751898 L_INC_01 -369.200 131.331 -2.811 0.005929 ** L_INC_CH 114.551 182.396 0.628 0.531398 UNEMP_01 13.761 5.238 2.627 0.009956 ** UNEMP_CH -6.573 5.943 -1.106 0.271349 L_POP_01 24.596 23.474 -1.048 0.297240 L_POP_CH 3441.275 153.338 22.442 < 2.0e-16 *** LQ_MAN_01 19.628 25.393 0.773 0.441342 LQ_INF_01 160.601 40.129 0.669 0.505141 LQ_FIN_01 402.623 203.362 1.980 0.050444 LQ_PRO_01 -133.266 124.184 -1.073 0.285772 LQ_MAN_CH -67.364 39.632 -1.700 0.092259 LQ_INF_CH 73.053 280.635 2.042 0.043759 * LQ_FIN_CH -170.392 218.090 0.781 0.436459 LQ_PRO_CH 96.262 98.370 0.979 0.330131 POSTGRAD_01 42.977 1296.986 0.033 0.973631 POSTGRAD_CH -1179.092 1739.547 -0.678 0.499439 BACHELOR_01 90.571 499.123 0.181 0.856370 BACHELOR_CH -2557.620 730.100 -3.503 0.000687 *** TECHQUALS_01 238.953 162.494 1.471 0.144524 TECHQUALS_CH -1484.946 294.507 -5.042 2.03e-06 *** SYMBA_01 259.703 315.795 0.822 0.412798 SYMBA_CH 183.322 632.459 0.290 0.772521 VOLUNTEER_11 2.412 2.367 1.019 0.310660 A_COAST -20.734 15.912 -1.303 0.195511 P_METRO 4.405 21.777 0.202 0.840116 D_URBAN -4.909 21.746 -0.226 0.821847 D_REMOTE 84.127 42.356 1.986 0.049723 * W_METRO -14.090 28.492 -0.495 0.621997 Coefficient Multicollinearity test (VIF) (Intercept) -- SPEC_01 8.907465 SPEC_CH 8.835060 SCI 8.300719 SCI_CH 2.712929 L_INC_01 7.720642 L_INC_CH 6.698806 UNEMP_01 6.533965 UNEMP_CH 10.313893 L_POP_01 8.789758 L_POP_CH 2.708974 LQ_MAN_01 3.479791 LQ_INF_01 13.368863 LQ_FIN_01 20.924515 LQ_PRO_01 26.257744 LQ_MAN_CH 1.823345 LQ_INF_CH 1.653523 LQ_FIN_CH 5.857960 LQ_PRO_CH 4.123870 POSTGRAD_01 13.713052 POSTGRAD_CH 17.156961 BACHELOR_01 28.413478 BACHELOR_CH 6.886694 TECHQUALS_01 4.083310 TECHQUALS_CH 6.622777 SYMBA_01 14.158755 SYMBA_CH 16.353417 VOLUNTEER_11 5.758360 A_COAST 1.778726 P_METRO 1.281645 D_URBAN 3.626887 D_REMOTE 3.107093 W_METRO 2.633234 Notes: Residual standard error: 65.91 on 101 degrees of freedom; Multiple [R.sup.2] = 0.9414; Adjusted [R.sup.2] = 0.9229; F Statistic = 50.73(32, 101); p value < 2.2 [e.sup.-16]. * = significant at 0.05 level; ** = significant at 0.01 level; *** = significant at 0.00 level. Source: the Authors. Table 4. Anselin Lagrange Multiplier test: Spatial Error and Spatial Lag Results. est [chi square] df p value LM error 1.0049 1 0.3161 LM lag 1.3566 1 0.2441 Robust LM error 2.6684 1 0.1024 Robust LM lag 3.02 1 0.08224 Source: the Authors. Table 5. Moran's I Test Results Moran's I z value p value 0.062399 1.814 0.06967 Source: the Authors. Table 6. Backward Step-Wise Regression OLS Specific Model and Multicollinearity Test Results. Coefficient Estimate Std. t p value Error value (Intercept) 486.664 257.791 1.888 0.061506 SPEC_01 534.036 206.619 2.585 0.010966 * SPEC_CH 895.148 288.790 3.100 0.002423 ** SCI 1006.150 128.078 7.856 2.05e-12 *** L_INC_01 -372.183 101.600 -3.663 0.000374 *** UNEMP_01 14.410 3.648 3.951 0.000133 *** UNEMP_CH -5.928 3.941 -1.504 0.135236 L_POP_CH 3340.952 111.646 29.924 2.00e-16 *** LQ_FIN_01 195.943 87.959 2.228 0.027800 * LQ_MAN_CH -66.334 33.987 -1.952 0.053339 LQ_INF_CH 511.158 232.272 2.201 0.029705 * BACHELOR_CH -2704.582 389.691 -6.940 2.26e-10 *** TECHQUALS_01 201.311 122.432 1.644 0.102783 TECHQUALS_CH -1531.763 218.177 -7.021 1.51e-10 *** VOLUNTEER_11 4.172 1.310 3.184 0.001858 ** D_REMOTE 96.245 32.977 2.919 0.004212 ** Coefficient Multicollinearity test (VIF) (Intercept) -- SPEC_01 6.646055 SPEC_CH 5.755288 SCI 3.985473 L_INC_01 5.015021 UNEMP_01 3.438619 UNEMP_CH 4.922926 L_POP_CH 1.558675 LQ_FIN_01 4.248533 LQ_MAN_CH 1.455343 LQ_INF_CH 1.229364 BACHELOR_CH 2.129349 TECHQUALS_01 2.515898 TECHQUALS_CH 3.944820 VOLUNTEER_11 1.915148 D_REMOTE 2.044094 Notes: Residual standard error: 63.26 on 118 degrees of freedom; Multiple [R.sup.2] = 0.937; Adjusted [R.sup.2] = 0.9289; F Statistic = 116.9(15, 118); p value < 2.2 [e.sup.-16]. * = significant at 0.05 level; ** = significant at 0.01 level; *** = significant at 0.00 level. Source: the Authors. Table 7: Anselin Lagrange Multiplier Test Results: Backward Step-Wise Regression OLS Specific Model. [chi Test square] df p value LM error 2.3964 1 0.1216 LM lag 0.4289 1 0.5125 Robust LM error 3.7228 1 0.0537 Robust LM lag 1.7553 1 0.1852 Source: the Authors Table 8. Moran's I Test on Residuals Results. Moran's I z value p value 0.096359 2.0674 0.0387 * Source: the Authors. Table 9. Spatial Error Model Results. Coefficient Estimate Std. Error z value p value (Intercept) 505.5062 241.1581 2.0962 0.0360679 * SPEC_01 586.9817 194.5074 3.0178 0.0025463 ** SPEC_CH 1054.3037 274.7729 3.8370 0.0001245 *** SCI 1010.0631 115.2328 8.7654 <2.2e-16 *** L_INC_01 395.5645 94.4138 -4.1897 2.793e-05 *** UNEMP_01 13.8385 3.4481 4.0134 5.986e-05 *** UNEMP_CH -5.0196 3.6979 -1.3574 0.1746421 L_POP_CH 3349.6546 109.5279 30.5827 <2.2e-16 *** LQ_FIN_01 208.8935 88.2814 2.3662 0.0179705 * LQ_MAN_CH -60.5119 31.2101 -1.9389 0.0525192 LQ_INF_CH 582.1127 214.9754 2.7078 0.0067729 ** BACHELOR_CH -2569.5230 370.7719 -6.9302 4.202e-12 *** TECHQUALS_01 218.6299 120.1337 1.8199 0.0687761 TECHQUALS_CH -1442.5091 201.6122 -7.1549 8.376e-13 *** VOLUNTEER_11 4.4870 1.2194 3.6796 0.0002336 *** D_REMOTE 83.4578 30.5998 2.7274 0.0063836 ** Notes: Lambda: 0.23906, LR test value: 3.2081, p-value: 0.073276; Asymptotic standard error: 0.10888; z-value: 2.1956, p-value: 0.028124 *; Wald statistic: 4.8205, p-value: 0.028124 *; Log likelihood: -735.7494 for error model; ML residual variance (sigma squared): 3393.7, (sigma: 58.256); Number of observations: 134; Number of parameters estimated: 18; AIC: 1507.5, (AIC for lm: 1508.7). * = significant at 0.05 level; ** = significant at 0.01 level; *** = significant at 0.00 level. Source: the Authors. Table 10. Spatial Autoregressive (SAR) Model Results. Coefficient Estimate Std. Error z value p value (Intercept) 463.0629 243.9175 1.8984 0.0576380 SPEC_01 541.2858 194.2695 2.7863 0.0053320 ** SPEC_CH 898.9836 271.0777 3.3163 0.0009121 *** SCI 1015.7014 121.0489 8.3908 <2.2e-16 *** L_INC_01 -366.9723 95.3511 -3.8486 0.0001188 *** UNEMP_01 14.5123 3.4177 4.2463 2.174e-05 *** UNEMP_CH -5.9810 3.6942 -1.6190 0.1054405 L_POP_CH 369.3973 114.2799 29.4837 <2.2e-16 *** LQ_FIN_01 192.5118 82.5667 2.3316 0.0197221 * LQ_MAN_CH 67.3119 31.8483 -2.1135 0.0345563 * LQ_INF_CH 469.4857 227.3773 2.0648 0.0389431 * BACHELOR_CH -2674.0478 371.5417 -7.1972 6.148e-13 *** TECHQUALS_01 207.3834 115.3710 1.7975 0.0722507 TECHQUALS_CH -1533.2985 204.4728 -7.4988 6.439e-14 *** VOLUNTEER_11 4.2163 1.2306 3.4262 0.0006120 *** D_REMOTE 96.8746 30.9031 3.1348 0.0017198 ** Rho: -0.026617, LR test value: 0.39186, p value: 0.53132; Asymptotic standard error: 0.044357; z value: -0.60006, p value: 0.54846; Wald statistic: 0.36008, p value: 0.54846. Log likelihood: -737.1575 for lag model; ML residual variance (sigma squared): 3513.3, (sigma: 59.273); Number of observations: 134; Number of parameters estimated: 18; AIC: 1510.3, (AIC for lm: 1508.7); LM test for residual autocorrelat * = significant at 0.05 level; ** = significant at 0.01 level; *** = significant at 0.00 level. Source: the Authors.
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|Author:||Stimson, Robert J.; Flanagan, Michael; Mitchell, William; Shyy, Tung-Kai; Baum, Scott|
|Publication:||Australasian Journal of Regional Studies|
|Date:||Jan 1, 2018|
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