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Socio-economic effects of an earthquake:does sub-regional counterfactual sampling matter in estimates? An empirical test on the 2012 Emilia-Romagna earthquake

Listed author(s):
  • Margherita Russo

    ()

  • Francesco Pagliacci

    ()

Estimates of macroeconomic effects of natural disaster have a long tradition in economic literature (Albala-Bertrand, 1993a; 1993b; Tol and Leek, 1999; Chang and Okuyama, 2004; Benson and Clay, 2004; Strömberg, 2007; UNISDR, 2009; Cuaresma, 2009; Cavallo and Noy, 2009; Cavallo et al., 2010; The United Nations and The World Bank, 2010). After the seminal contribution of Abadie et al. (2010) in identifying synthetic control groups, with DuPont and Noy (2015) a new strand has been opened in estimating long term effects of natural disaster at a sub-regional scale, at which the Japan case provides plenty of significant economic variables. Although the same methodology has been applied in estimating the impact of earthquakes in Italy (Barone et al. 2013; Barone and Mocetti, 2014), the analysis has been limited to the regional scale. In our paper, due to a lack in long-term time series data at municipality level, this paper cannot adopt the methodology suggested by Abadie et al. (2010). Nevertheless, it provides a test bed for assessing the relevance of a sub-regional counterfactual evaluation of a natural disaster's impact. By taking the 2012 Emilia-Romagna earthquake as a case study, we propose a comprehensive framework to answer some critical questions arising in such analysis. Firstly, we address the problem of identifying the proper boundaries of the area affected by an earthquake. Secondly, through a cluster analysis we show the importance of intra area differences in terms of their socio-economic features. Thirdly, counterfactual analysis is assessed by adopting a pre- and post-earthquake difference-in-difference comparison of average data in clusters within and outside the affected area. Moreover, three frames to apply propensity score matching at municipality level are also adopted, by taking the control group of municipalities (outside the affected area): (a) within the same cluster, (b) within the same region, (c) in the whole country. The four variables considered in the counterfactual analysis are: total population; foreigner population; total employment in manufacturing local units; employment in small and medium-sized manufacturing local units (0 to 49 employees). All the counterfactual tests largely show a similar result: socio-economic effects are heterogeneous across the affected area, where some clusters of municipalities perform better, in terms of increase of population and employment after the earthquake, against some others. This result sharply contrasts with the average results we observe by comparing the whole affected area with the non-affected one or with the entire region.

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Paper provided by Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi" in its series Center for the Analysis of Public Policies (CAPP) with number 0139.

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Length: pages 27
Date of creation: Apr 2016
Handle: RePEc:mod:cappmo:0139
Contact details of provider: Web page: http://www.capp.unimore.it

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  1. Matthew E. Kahn, 2005. "The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 271-284, May.
  2. Albala-Bertrand, J. M., 1993. "Political Economy of Large Natural Disasters: With Special Reference to Developing Countries," OUP Catalogue, Oxford University Press, number 9780198287650, December.
  3. Eduardo Cavallo & Andrew Powell & Oscar Becerra, 2010. "Estimating the Direct Economic Damages of the Earthquake in Haiti," Economic Journal, Royal Economic Society, vol. 120(546), pages 298-312, 08.
  4. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
  5. Marco Ranuzzini & Francesco Pagliacci & Margherita Russo, 2015. "L'informatizzazione delle procedure per la ricostruzione: prime evidenze dai contributi concessi per le abitazioni," Center for the Analysis of Public Policies (CAPP) 0127, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
  6. David Strömberg, 2007. "Natural Disasters, Economic Development, and Humanitarian Aid," Journal of Economic Perspectives, American Economic Association, vol. 21(3), pages 199-222, Summer.
  7. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, 02.
  8. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2011. "Synth: An R Package for Synthetic Control Methods in Comparative Case Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i13).
  9. Barone, Guglielmo & Mocetti, Sauro, 2014. "Natural disasters, growth and institutions: A tale of two earthquakes," Journal of Urban Economics, Elsevier, vol. 84(C), pages 52-66.
  10. Francesco Pagliacci & Paola Bertolini, 2015. "Le specificità del sistema agro-alimentare nella ricostruzione post-sisma," Center for the Analysis of Public Policies (CAPP) 0125, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
  11. Vittorio Piazzi & Francesco Pagliacci & Margherita Russo, 2015. "Analisi cluster delle caratteristiche socio-economiche dei comuni dell'Emilia-Romagna: un confronto tra comuni dentro e fuori dal cratere del sisma," Center for the Analysis of Public Policies (CAPP) 0120, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
  12. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
  13. Albala-Bertrand, J. M., 1993. "Natural disaster situations and growth: A macroeconomic model for sudden disaster impacts," World Development, Elsevier, vol. 21(9), pages 1417-1434, September.
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