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Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era

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  • Fingleton, Bernard
  • Palombi, Silvia

Abstract

We explore the relative ability of local economies to retain their long-run growth dynamics when faced by the destabilizing effects of major shocks. Taking annual wage series for nineteen U.K. towns over the historical period 1871–1906, we fit a spatial panel data model to 1871–1890 data and use estimated coefficients to obtain counterfactual predictions of wage levels after the 1890 shock to the end of the post-shock period. This allows us to analyze how actual wages in different towns performed in relation to their counterfactual paths, and to assess their relative resilience to the 1890 and subsequent crises. The key conclusion is that the sectoral composition of local employment is important for economic resilience; our evidence suggests that towns with excessive and increasing specialization in one dominant industry are relatively prone to shocks, because they lack the structural flexibility needed to replace declining sectors with productive and competitive activities, whereas economies with a diversified industrial mix have more scope for restructuring and renewal, and thus are more able to adapt to and tolerate shocks.

Suggested Citation

  • Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
  • Handle: RePEc:eee:regeco:v:43:y:2013:i:4:p:649-660
    DOI: 10.1016/j.regsciurbeco.2013.04.005
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    15. Lisa Gianmoena & Vicente Rios, 2018. "The Determinants of Resilience in European Regions During the Great Recession: a Bayesian Model Averaging Approach," Discussion Papers 2018/235, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
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    More about this item

    Keywords

    Economic resilience; Panel data; Spatial econometrics; Prediction; Counterfactual analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • N33 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: Pre-1913
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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