Essays in spatial panel econometrics

  • Silvia Palombi

Student thesis: Doctoral Thesis

Abstract

This thesis develops and applies state-of-the-art spatial panel econometrics methods in order to model and analyse labour-market outcomes within and across small areas in Great Britain, with respect to three particular aspects; namely the resilience of local economies to periodic shocks, the determinants of spatial disparities in local wages, and the relationship between output and unemployment over the economic cycle. The contribution is thus provided in four essays.The first essay (Chapter 2) explores the relative ability of local economies to preserve their long-run growth dynamics when faced by the destabilising effects of major shocks. It borrows concepts from the regional economic resilience literature to characterise the different reactions of different places to recessions. In particular, economies are distinguished on the basis of their ability to resist to and recover from shocks thus maintaining stability around their counterfactuals, a notion known as ‘engineering resilience’, and to resume (or improve) their underlying growth trajectory by the end of the recessionary period under consideration thus showing ‘ecological resilience’ (or ‘positive hysteretic effects’). Related to these notions are the ideas of adaptability and ‘path dependence’, which help explain why some economies are more vulnerable to shocks than others (over and above the static causes of interregional heterogeneity incorporated in the model via random effects). Taking annual wage series for nineteen British towns over the historical period 1871-1906, I fit a spatial panel data model to 1871-1890 data by Spatial Two-Stage Least Square / Generalised Method of Moments (S2SLS / GMM), and use estimated coefficients in combination with trend forecasts to obtain counterfactual predictions of wage levels after the 1890 shock through to 1906.This allows to analyse how actual wages in different towns performed in relation to their counterfactual paths, and to gauge their relative resilience to economic shocks. The key finding, and the main lesson that can be drawn from the historical experience of British towns, is that the sectoral composition of local employment is important for economic resilience; my evidence suggests that excessive and increasing specialisation in declining industries means lack of the structural flexibility needed to replace these industries with competitive and productive activities (shock-proneness), whereas economies with a diversified industrial mix have greater scope for restructuring and renewal (shock-resilience); moreover, towns dominated by mature, staple sectors but who have also developed new growth industries are more able to adapt to and tolerate shocks.The second essay (Chapter 3) considers the relative success of alternative, non-nested wage equations from the perspective of Great Britain’s 408 unitary authority and local authority districts (UALADs) over the period 1999-2009. The negative relationship between wages and unemployment, embodied within the so-called Wage Curve, has an extensive literature and has been referred to as ‘an empirical law of economics’. However there are newer theories that seek to explain regional wage variations without reference to unemployment, namely Urban Economics (UE) and New Economic Geography (NEG). The aim is to discriminate between competing models of wage determination in order to establish whether the wage curve can be accepted as superior to its non-nested rivals. To do so I adopt an ‘Inclusive Regression’ approach (Davidson and MacKinnon, 1993; Hendry, 1995), combining the wage curve and either UE or NEG within an Artificial Nesting Model (ANM); this incorporates a spatial autoregressive process involving both the dependent variable and the error components and is estimated by S2SLS / GMM.The main conclusion is that, at least when the level of geographical resolution is relative
Date of Award1 Jan 2013
LanguageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorRoger Perman (Supervisor) & (Supervisor)

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