The current thesis work offers a contribution to the existing literature regarding the impacts of extreme weather events on economy. In particular, the present analysis focuses on the consequences of these events on firm level economic performance, addressing SMEs in European area. To perform the analysis, two datasets were built. The first containing ten years frequency (2009-2018) balance sheet concerning financial information from more than eight million companies, more than 95% of which are small and medium sized enterprises. The second including extreme weather event information of more than 170 events across Europe in the time period from 2008 to 2018. Information from the datasets was then combined through NUTS3 regions classification and a Difference-in-Difference model was implemented. The model measured the treatment effect of floods, storms and wildfires over total assets, operating revenues, costs of operations and return over assets, controlling for company age, industrial sector and year effect. Significant (at least 10% level) DID coefficients from the analysis, estimated for all events singularly, returned an overall negative mean behaviour for total assets, operating revenues and costs of operations while reporting a slightly positive overall effect for return over assets. Anyway, these mean trends come from a distribution of estimates mostly balanced between positive and negative coefficients that shed light on a not unique response direction. Furtherly, case study events (one for each event category) were observed singularly out of the distribution context. All of them present DID coefficients at 1% significance level with severe and negative estimates values for all variables.
Il presente lavoro di tesi offre un contributo all’esistente letteratura riguardante gli impatti di eventi meteorologici estremi sull’economia. In particolare, la presente analisi si concentra sulle conseguenze di questi eventi sulla performance economica d’impresa, nello specifico riferendosi alle piccole e medie imprese dell’area europea. Due differenti banche dati sono state costruite per lo svolgimento dell’analisi. La prima contenente informazioni finaziarie da più di otto milioni di imprese, il 95% delle quali piccole e medie, nel periodo 2009-2018. Il secondo composto dalle informazioni su più di 170 eventi climatici estremi che hanno colpito l’Europa nel periodo tra il 2008 e il 2018. Le informazioni di queste banche dati sono state poi combinate attraverso l’uso della classificazione regionale NUTS3 e l’implementazione di un modello Difference-in-Difference. Il modello ha misurato gli effetti dell’esposizione ad alluvioni, tempeste e incendi su asset totali, ricavi operativi, costi operativi e rendimento degli asset, controllando per età dell’impresa, settore industriale ed effetto annuo. I coefficienti DID significativi (almeno al 10%), stimati dall’analisi per tutti gli eventi singolarmente, mostrano un effetto medio complessivamente negativo per asset totali, ricavi operativi e costi operativi, mentre il rendimento degli asset si distingue per un effetto medio moderatamente positivo. Ad ogni modo, questi trend medi derivano da una distribuzione di coefficienti di stima perlopiù bilanciata tra effetti negativi e positivi, che fa luce su una reazione non univoca delle variabili rispetto ai fenomeni in esame. Inoltre, tre eventi campione (uno per ogni tipo) sono stati osservati singolarmente come casi studio. Tutti hanno riportato coefficienti di DID significativi all’1% e negativi per tutte le variabili in esame.
Storm is brewing : extreme weather events and firm performance in Europe
MILAZZO, TIZIANO
2019/2020
Abstract
The current thesis work offers a contribution to the existing literature regarding the impacts of extreme weather events on economy. In particular, the present analysis focuses on the consequences of these events on firm level economic performance, addressing SMEs in European area. To perform the analysis, two datasets were built. The first containing ten years frequency (2009-2018) balance sheet concerning financial information from more than eight million companies, more than 95% of which are small and medium sized enterprises. The second including extreme weather event information of more than 170 events across Europe in the time period from 2008 to 2018. Information from the datasets was then combined through NUTS3 regions classification and a Difference-in-Difference model was implemented. The model measured the treatment effect of floods, storms and wildfires over total assets, operating revenues, costs of operations and return over assets, controlling for company age, industrial sector and year effect. Significant (at least 10% level) DID coefficients from the analysis, estimated for all events singularly, returned an overall negative mean behaviour for total assets, operating revenues and costs of operations while reporting a slightly positive overall effect for return over assets. Anyway, these mean trends come from a distribution of estimates mostly balanced between positive and negative coefficients that shed light on a not unique response direction. Furtherly, case study events (one for each event category) were observed singularly out of the distribution context. All of them present DID coefficients at 1% significance level with severe and negative estimates values for all variables.| File | Dimensione | Formato | |
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Storm is brewing - Tiziano Milazzo 912956.pdf
Open Access dal 27/03/2022
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2.61 MB
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https://hdl.handle.net/10589/173282