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

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; Str\uf6mberg, 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\u2019s 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

Explicit Solutions for the Wave Equation on Homogeneous Trees

AbstractIn this paper we consider the discretized version of the wave equation, in which a manifold is replaced by a homogeneous tree and the time line is replaced by the natural numbers. We give two methods for finding a closed form of the solution. One of these methods is found by first solving the Radon transform of the solution, which has a much simpler form. We also find a simple formula for the Radon transformation of the solution to the heat equation on homogeneous trees

Earthquake hazard in Italy Cluster analysis of socio-economic data to inform place-based policy measures

The Italian Government launched the Piano Casa Italia immediately after the series of massive earthquakes that struck Central Italy in 2016, following the 2009 earthquake in L'Aquila and the one in 2012 in Emilia-Romagna. The cumulative impact of human losses and economic and social uncertainty produced by the last disaster in 2016 has spurred political decision-makers to advocate an ambitious long-term intervention, aimed at restructuring Italian public buildings and houses over the next decades. Italy has experienced only one other era of similar schemes with the controversial interventions lasting for more than thirty years of the Cassa del Mezzogiorno, which started in the 1950s to cope with the country's dual economic condition. Since then, no other long-term ambitious plan has been attempted in Italy and a similar planning perspective is nowadays far from the experience of most public managers, policy makers and even scholars of economics and development. The ongoing challenges that the Piano Casa Italia has to face are multifaceted: political, economic and social. Challenges pertaining to the agents asked to design the scheme, to implement it and to accept it. The overall perspective of structural change will mark its implementation. This paper is a first contribution within a broader framework to outline the conditions characterizing those challenges and the paths of change that will be initiated by realizing the Plan. The paper suggests taking an analytical perspective to support informed policy measures, in four complementary domains: emergency (National Civil Protection), recovery (Struttura Commissariale), risk reduction (Piano Casa Italia), socio-economic development (National Strategy for Inner Areas). The present contribution starts with a preliminary step, in line with the Sendai Framework for disaster risk reduction (UNISDR 2015): a detailed analysis of the socio-economic, demographic and geographic conditions across Italian territorial areas, at a municipality level. This work explicitly aims to single out these features, by focusing both on seismic zones and on regions. The paper also returns the results of a cluster analysis performed at municipality level across Italy and concludes discussing some implications for place-based policy interventions

An inversion formula for finding technology distribution of production functions

AbstractWe solve the problem of finding the technology distribution for profit functions (equivalently production functions) in a discrete setting. This is done by finding an inversion formula for the profit function, making use of a sequence of recursively defined polynomials whose behavior is studied

Analisi cluster delle caratteristiche socio-economiche dei comuni dell'Emilia-Romagna: un confronto tra comuni dentro e fuori dal cratere del sisma

The socio-economic features of the area hit by the 2012 earthquake in Emilia-Romagna (Italy) represents a first step in building a more comprehensive framework, which could help in better interpreting earthquake effects in both short and medium period. Actually, this analysis falls under that broader field of research, which is aimed at providing counterfactual evaluations of both natural disasters’ impacts and the adoption of public policies to support reconstruction. This paper moves from the idea that those municipalities, which lie close to the epicentre of 2012 Emilia-Romagna earthquake, are not particularly homogenous in terms of socio-economic features. Analysing those major differences is a key element in order to assess the way human activities and other specific economic features at municipality level may either increase or limit the effects of an earthquake. Firstly, this paper tackles the problem of properly identifying the boundaries of the area directly hit by the 2012 earthquake. Indeed, different acts have provided different definitions of those boundaries. Eventually, a cluster analysis has been performed covering all municipalities in Emilia-Romagna, according to a set of demographic and economic variables, available at municipality level. This analysis highlights the existence of different typologies of municipalities, even in the area hit by the earthquake. In particular, these results are of particular interest, allowing further assessments on the effects of the earthquake. Actually, according to cluster analysis results, specific counterfactual examples (not hit by the earthquake) will be identified

Enhancing the resilience of social infrastructures: issues on agents, artefacts and processes. Proceedings of the 2016 Modena Workshop

In the social sciences domain, the term 'resilience' is usually associated to a wide set of changes that affect people and their communities. In particular, both the Hyogo Framework for Action 2005–2015 and the Sendai Framework explicitly focus on the way in which communities face both natural and man-made hazards. To this respect, both material and non-material infrastructures play a critical role, hence deserving a specific focus when assessing local communities' level of resilience. Among them, this paper focuses on: health services, social services, government (according to a multi-level perspective, from the national to the local level), communication infrastructure (i.e. specific tools to interconnect all aforementioned networks). Firstly, this paper discusses some of the most important issues and theoretical frameworks that should be addressed in the analysis of the processes of enhancing the resilience of social infrastructures. Secondly, the discussion that took place in a workshop promoted in May 2016 as the outcome of a one-year dialogue across a group of EU researchers is returned. The debate moves from some theoretical perspectives on resilience and it eventually returns some case studies and real experiences, such as the actions of local governments and the role of risk communication

R&I smart specialisation strategies: classification of EU regions’ priorities. Results from automatic text analysis

Building on automatic text analysis, this paper proposes an original categorization of Research and Innovation Smart Specialisation Strategy (RIS3) priorities and provides a common language (with detailed dictionaries) to classify priorities and then to associate EU regions to multiclass categories of priorities. This result is a powerful tool to interpret the current state of diversification across regions, with its potential of complementarities and synergies that might support territorial integrated development paths. It would also support regions in their future strategic programmes on RIS3. A case study on the Alpine macro-region shows innovation development paths to outline macroregion strategic planning

Multidimensional clustering of EU regions. A contribution to orient public policies in reducing regional disparities

This paper applies multidimensional clustering of EU-28 regions with regard to their specialisation strategies and socioeconomic characteristics. It builds on an original dataset. Several academic studies discuss the relevant issues to be addressed by innovation and regional development policies, but so far no systematic analysis has linked the different aspects of EU regions research and innovation strategies (RIS3) and their socio-economic characteristics. This paper intends to fill this gap, with the aim to provide clues for more effective regional and innovation policies. In the data set analysed in this paper, the socioeconomic and demographic classification associates each region to one categorical variable (with 19 categories), while the classification of the RIS3 priorities clustering was performed separately on “descriptions” (21 Boolean categories) and “codes” (11 Boolean Categories) of regions’ RIS3. The cluster analysis, implemented on the results of the correspondence analysis on the three sets of categories, returns 9 groups of regions that are similar in terms of priorities and socioeconomic characteristics. Each group has different characteristics that revolve mainly around the concepts of selectivity (group’s ability to represent a category) and homogeneity (similarity in the group with respect to one category) with respect to the different classifications on which the analysis is based. Policy implications showed in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and to the enhance complementarities and synergies within macro-regions

Energie Sisma Emilia. Guida all'utilizzo dei database dell'indagine 'Energie_I400'

After the series of earthquakes that occurred in Emilia-Romagna (Italy) in May 2012, the research team based in the Department of Economics of the University of Modena and Reggio Emilia started research activities to investigate the socio-economic impact due to the shock. When analysing the effects of the earthquake on the living and working conditions of the population, a critical issue is the availability of relevant data. Thus, within the applied research ‘Energie Sisma Emilia’, in 2015 the research team realized a survey on a sample of 400 families living in the area affected by the earthquake. The survey was based on the same questionnaire adopted in the previous research ICESmo3 (carried on in Summer 2012) investigating the socio-economic conditions of families living in the same area. The 2015 questionnaire includes additional sections on the effects of the earthquake on social capital and consumption behaviour. Data are under analysis and will be presented in public events and publications. The dataset will be made available on request at the end of the research project. The goal of this paper is to return the main methodological aspects, and to present the principles that guided management of data and that drive the use of databases. Section I describes the main issues addressed by the ‘Energie Sisma Emilia’ project. Section II introduces the general survey settings; the sample characteristics; and the elaboration of sample weights. Section III describes the details of the informational basis (organization of specific databases, codebook and command files developed to generate databases in format Stata13). Section IV presents the criteria that align the databases and the questionnaire: variables management was originally handled to allow researchers to easily retrieve information from the syntax of the variable labels, and to automatize future elaborations. Section V concludes with operational indications in using ‘Stata Survey’ to produce inferential analysis

Detecting multidimensional clustering across EU regions. Focus on R&I smart specialisation strategies and on socio-economic and demographic conditions

This paper applies multidimensional clustering of EU-28 regions to identify similar specialisation strategies and socioeconomic characteristics. It builds on an original dataset where the EU-28 regions are classified according to their socioeconomic and demographic features and to the strategic priorities outlined in their research and innovation smart specialisations strategy (RIS3). The socioeconomic and demographic classification associates each region to one categorical variable (with 19 modalities), while the classification of the RIS3 priorities clustering was performed separately on “descriptions” (21 Boolean categories) and “codes” (11 Boolean Categories) of regions’ RIS3. Three techniques of clustering have been applied: Infomap multilayer algorithm, Correspondence Analysis plus Cluster Analysis and cross tabulation. The most effective clustering, in terms of both the characteristics of the data and the emerging results, is that obtained on the results of the Correspondence Analysis. By contrast, due to the very dense network induced by the data characteristics, the Infomap algorithm does not produce significant results. Finally, cross tabulation is the most detailed tool to identify groups of regions with similar characteristics. In particular, in the paper we present an application of cross tabulation to focus on the regions investing in sustainable development priorities. Policy implications of methods implemented in this paper are discussed as a contribution to the current debate on post-2020 European Cohesion Policy, which aims at orienting public policies toward the reduction of regional disparities and the enhancement of complementarities and synergies within macroregions