The COVID-19 pandemic has caused significant economic disruption in countries across the globe. Within Australia, once initial case numbers began to climb, the Federal Government introduced a range of measures in an attempt to stop the spread of the virus. While these were aimed at dealing with the public health emergency, the unintended economic consequences were wide-ranging, especially impacting on labour markets as the national economy was largely locked-down.
From a regional science perspective, the COVID-19 imposed lockdown provides an interesting case study on the impacts of such an endogenous shock on regional economic performance and in particular on the performance of labour markets. It is clear, for example, that in the period following 30th March 2020, when national public health stay at home orders came into effect, employment across the country took a significant hit. The Australian Bureau of Statistics Payroll jobs index (Australian Bureau of Statistics, 2020b) recorded a change in total wages between 14 March and 4 April of -6.7 per cent and an estimated unemployment rate of 6.2 per cent and an underemployment rate of 13.7 per cent (Australian Bureau of Statistics, 2021).
Placing this declining labour market performance into a regional science context, we might ask how have different regions responded in employment terms. Have some regions done better than others? Have they been less affected by the broader economic slowdown, or put another way, have some regions shown a higher level of resilience to the downturn than others? Over the past decade, especially since the Global Financial Crisis, understanding the ways different regions react to economic shocks has been an area of increasing regional science focus, often being framed within the context of regional resilience. Borrowing from a long-established tradition in biology and environmental science, regional resilience is defined as
The capacity of a regional or local economy to withstand or recover from market, competitive and environmental shocks to its developmental growth path, if necessary by undergoing adaptive changes to its economic structures and its social and institutional arrangements, so as to maintain or restore its previous developmental path, or transit to a new sustainable path characterised by a fuller and more productive use of its physical, human and environmental resources (Martin & Sunley, 2015).
As a concept, regional resilience has been presented as a useful lens with which to view the heterogeneous nature of economic shocks across regions (Giannakis & Bruggeman, 2017). Empirically, the study of regional resilience has taken many forms ranging from the use of case studies, and the development of resilience indices, to more complex time-series and structural economic models (Martin & Sunley, 2015), and have utilised a number of approaches to measure and operationalise resilience.
While some resilience measures are straightforward, comparing regions according to the percentage rise or fall in a particular indicator (i.e. employment), others, conceptualising regional resilience as regional changes relative to national changes, have used different approaches. For example, Han and Goetz (2013) compare actual regional output with trend output, with the difference being considered an illustration of the level of resilience in any one region. The less a region’s actual output deviates from the trend output, then the higher the relative level of resilience. Martin, Sunley, Gardiner, and Tyler (2016), using a slightly different approach, measured regional resilience in terms of how regional change deviated from national-level change. In doing so they argued
since of interest is how different regions (or localities or cities) are affected by a common (nationwide) recession, a particular type of expected or ‘counterfactual’ reaction suggests itself, namely, the resistance and recovery of the national economy as a whole (Martin et al., 2016, p. 565).
Indicators such as these allow for comparison between regions and the tracking of resilience over time, and in the case of an economic shock allow the researcher to being to understand how different regions might be expected to perform.
Over-and-above the issue of measurement, an important question about regional resilience is why it might vary between regions (Grabner, 2021). In a sense this is the most important question from a policy perspective as understanding the drivers of resilience provide insights into the kinds of policy prescriptions might be most appropriate. In the wake of the Global Financial Crisis, a significant body of work emerged that attempted to provide a greater understanding of regional resilience by considering the regional determinants of variations in resilience.
For instance Martin et al. (2016) considering the impact of recessionary shocks on regions in the United Kingdom identified a number of key findings including, that different shocks resulted in varying outcomes across regions at different time points and that factors including industry structure and region-specific or competitiveness effects could be viewed as important factors in explaining differences in regional resilience. In a similar long-run analysis of economic shocks across Italian regions between 1970 and 2011 Lagravinese (2015) found that among other things, the presence of large concentrations of manufacturing industry and the presence of temporary workers were associated with weaker regional resilience, while in contrast larger concentrations of public sector employees and service industries were associated with greater levels of resistance (resilience) in the face of economic downturns.
Considering the question of resilience in the United States following the Global Financial Crisis, Ringwood, Watson, and Lewin (2019) used monthly employment data for U.S. counties and found that counties dependent on agriculture significantly outperformed nonfarming-dependent counties when controlling for urban-rural hierarchy. In addition, manufacturing-dependent metro counties outperformed other places, but in total all manufacturing counties were outperformed by not manufacturing-dependent metro-based counties. In a similar paper, focusing on US metropolitan regions (MSAs) Doran and Fingleton (2018) compared actual and predicted employment paths to develop a measure of the impact of the Global Financial Crisis. In explaining the differences in resilience and recovery between metropolitan regions, the authors find that MSAs that exhibited higher levels of specialisation were more adversely impacted by the economic crisis, but that the same high levels of specialisation helped during the period of recovery. In addition, they note that for regions recording significant structural change during the crisis period negative impacts were reduced and that the contextual effects of the broader region a MSA is located in is also important for explaining resilience and recovery.
In Australia, Courvisanos, Jain, and K. Mardaneh (2016) investigated regional resilience across Local Government Areas following the Global Financial Crisis and major drought and identified groups of regions that were differentiated by both weak and strong regional resilience and industry. Strong resilience was found in high-income regions across rural, regional and metro-core and periphery and was associated with industries including mining, construction transport and utilities, especially in rural localities. Weaker resilience was especially dominant in the metro-core which reported far more functional regions than other areas.
Similar questions about regional resilience have begun emerging in a small but growing collection of literature focusing on the economic shocks associated with the COVID-19 pandemic. Importantly, these emerging studies find that regional economic resilience is influenced not only by factors that may be, a-priori, thought to help or hinder resilience but also by a number of factors peculiar to the pandemic such as the extent of public health measures and the introduction of specific government support packages. For instance, Turgel, Chernova, and Usoltceva (2021) analysing data for urbanised regions in Russia find that there are both significant differences between the impact of the COVID-19 pandemic on the economic performance of regions and that these differences can be explained by a number of factors including the severity of health-related restrictions on enterprises and the level of regional support. They found that regions with significant population shares and large numbers of small and medium-sized businesses were the most vulnerable to the COVID induced economic slowdown, while regions where agro-industrial and industrial organisations were strongest and whose enterprises were able to continue operating, showed greater stability. Focusing on regional economic resilience in Northeast China, Hu, Li, and Dong (2022) find that regional resilience was shaped by a region’s industry structure, the level of regional innovation, industry specialisation, openness and the level of government support. Importantly, they note that government public-health measures to contain the spread of the pandemic were especially important in shaping resilience and the level of recovery any one region experienced. Brada, Gajewski, and Kutan (2021) focusing on employment changes in Central and Eastern Europe find that the level of regional economic resilience is driven by the ability of regions to be able to alter their economic structure during downturns and part because of the presence of strong spatial spillover effects where highly resilient regions feed off other highly resilient regions.
The issues outlined above set the context for the remainder of the paper. Using data from the Australian Bureau of Statistics Payroll Jobs Index series (Australian Bureau of Statistics, 2020b) the paper develops an analysis of employment trajectories for Australian regions focusing on the initial period of national lockdown (DATES). The paper has several aims:
1. To identify the/ patterns of employment change across Australian regions and in turn scope out variations in levels of regional resilience.
2. Consider the variable that may help understand the patterns identified.
3. Illustrate the usefulness of the Payroll Jobs Index series to measure employment trajectories across regions.