Resilience Analysis of the 2008 Economic Crisis Using Entropic Measures

Abstract

This paper deals with the analysis of employment data of the 2008 economic crises. The analyses are done by using entropy measures that can help with predicting regional employment dynamics. Our finding suggests that the Shannon entropy and Tsallis entropy are significant predictors for the size of the employment downturn. The Rényi entropy is also a useful predictor of the rate of employment downturn in recession phase. When the Shannon entropy was growing through the recovery phase before the crisis, regions experience a higher rate of employment decrease in the following recession period and high Shannon entropy inferred the smaller employment downturn. The results indicate the different role of the Tsallis entropy that plays a different role in comparison to Shannon entropy. The higher the Tsallis entropy, the more severely the region was affected. In conclusion, the use of entropic measures as resilience indicators in terms of regional policy is a significant predictor of regional resilience.

Local resistance to rising unemployment in the context of the COVID‐19 mitigation policies across Great Britain

“…Employment change 2015–18 is significant, but only at the 95% level and with a positive sign (employment growth pre‐COVID associated with larger increases in unemployment during the lockdown). The positive sign on employment change is somewhat counter‐intuitive, but replicates similar findings during the Great Recession in the USA (Han & Goetz, 2015) and the EU (Ibl et al, 2018) and may be accounted for by skills mismatches introduced by growth and/or the shaking out of weak businesses by a recent downturn.…”

“…During the impact phase of a recessionary shock, local and regional capacity to resist is shaped in particular by the underlying strength of a local economy and the stability of its pre-shock growth (Fingleton et al, 2012;Han & Goetz, 2015;Ibl et al, 2018;Kitsos & Bishop, 2018;Webber et al, 2018). Sector composition can be important since some sectors are usually worse affected by recession, in particular construction and manufacturing (Angulo, Mur, & Trívez, 2018;Lagravinese, 2015), with the public sector and services more "recession proof" although this protective effect has eroded in the UK since 2010 due to public spending cuts (Martin, 2018).…”

“…Evidence from previous recessionary shocks suggests that it is the weakest local and regional economies that are likely to experience the greatest economic impact (Fingleton et al, 2012;Han & Goetz, 2015;Ibl et al, 2018;Kapitsinis, 2018;Kitsos & Bishop, 2018;Webber et al, 2018;Wójcik, 2009). Specifically, in relation to unemployment, pre-lockdown unemployment is likely to indicate underlying susceptibility to unemployment, including susceptibility to rising unemployment during the lockdown (Lee, 2014).…”

“…In testing these hypotheses, control variables are included for high skills, low skills, pre-lockdown unemployment trend, sectoral specialization/diversity, economic dynamism/growth, productivity and region. These controls are based on existing literature on the risk factors associated with rising unemployment and/or falling employment during previous recessions (Fingleton et al, 2012;Han & Goetz, 2015;Ibl et al, 2018;Kitsos & Bishop, 2018;Lee, 2014;Webber et al, 2018).…”

“…Initial indications are that mass unemployment is likely. In normal circumstances, weaker local economies tend to be hit harder and for longer by economic recessions, with protective effects deriving from sectoral specialization, diversity, dynamism and productivity (Fingleton, Garretsen, & Martin, 2012;Han & Goetz, 2015;Ibl, Svoboda, & Siegert, 2018;Kapitsinis, 2018;Kitsos & Bishop, 2018;Webber, Healy, & Bristow, 2018;Wójcik, 2009). Resilience approaches to understanding the impact of recessionary shocks suggest a degree of path dependency, with pre-shock trajectories displaying some inertia and resistance to the shock (Martin, 2018).…”