We analyze the resilience of U.K. regions to employment shocks. Two basic notions of resilience are distinguished. With engineering resilience, there is an underlying stable growth path to which a regional economy rebounds following a shock. With ecological resilience, shocks can permanently affect the growth path of the regional economy. Our data set consists of quarterly employment series for 12 U.K. regions (NUTS I) for the period 1971-2010. Using a seemingly unrelated regression (SUR) model specification, we test for the relevance of (engineering) resilience of U.K. regional employment to the four recessionary shocks in our sample. It turns out that U.K. regions do indeed differ in their resilience, but that these differences mainly concern the initial resistance to these shocks and not so much the recovery stage. The SUR model does not allow shocks to have permanent effects and it also does not take the possibility of time differentiated shock spillovers between the 12 regions into account. To this end, we also estimate a vector error-correction model (VECM) specification where employment shocks can have permanent effects and where also interregional employment linkages are included. We find that employment shocks typically have permanent effects when it concerns the own-region effects. Permanent effects can also be found for the impact on other regions but the interregional effects are typically only significant for nearby regions.

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

Recent contributions to the regional science literature have considered spatial effects in empirical growth specifications. In the case of spatial dependence, following theoretical arguments from new economic geography, and endogenous growth models, this phenomenon has been associated with the existence of externalities that cross regional borders. However, despite the general consensus that interactions or externalities are likely to be the major source of spatial dependence, they have been modelled in a rather ad hoc manner in most existing empirical studies. In contrast, we advocate basing the analysis on structural growth models which include externalities across economies, applying the appropriate spatial econometrics tools to test for their presence and estimate the magnitude of these externalities in the real world

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