Eleonora Cutrini (p), Roy Cerqueti

Boris A. Portnov

This paper aims to give statistical significance to the measurement of localization and spatial concentration through relative entropy measures. This work is in line with research on the simulation of confidence intervals for the Ellison and Glaeser index (Cassey and Smith, 2014) and the comparable advancements in the context of distance-based methods that have been developed primarily for absolute indices (Duranton and Overman, 2005; Marcon and Puech, 2003, 2010) and, more recently, for relative indices (Lang, Marcon and Puech, 2014).
As a first step to define an appropriate random location scenario, we establish how relative entropy indices can be embedded in a stochastic location choice model.
To distinguish systematic from random localization and spatial concentration, we introduce a test of significance. Null hypotheses are identified through a Montecarlo procedure. We apply this method to the European manufacturing economy and we found a significant overall localization, whereby significant geographical concentration is evident for low-tech industries usually considered as localized because of industry-specific Marshallian external economies (i.e. wearing apparel, textiles, publishing, printing) as well as for small-scale knowledge intensive industries (e.g office machinery and computers, radio, television and communication equipment) and industries characterized by internal scale economies (such as motor vehicles, basic metals, chemicals, other transport equipment). The decomposition analysis allows to ascertain that geographical concentration is almost always statistical significant between national borders. Instead, the within-country components of the concentration measures are statistically significant only in five industries –office machinery, motor vehicles, other transport equipment, basic metals, publishing and printing. Industries with a significant similarity with the distribution of manufacturing employment are rubber and plastic products, machinery, and non-metallic minerals, paper and furniture. Interestingly, most of these industries are populated by specialized suppliers producing intermediate products for other manufacturing industries, hence it is not surprising that their location pattern mimics the one of total manufacturing.

Jan Oosterhaven (p), Johannes Többen

Eleonora Cutrini

Firms react to shortages in the supply of their inputs by looking for substitutes. We investigate the impact of finding such substitutes on estimates of the size of the regional and national disaster impacts. To investigate this issue, we use the German multiregional supply-use table (MRSUT) for 2007, together with data on the direct impacts of the 2013 heavy floods of the German Elbe and the Danube rivers. We start with a non-linear programming model that allows for maximum substitution possibilities, and observe little to no indirect damages in the directly affected regions, whereas negative indirect impacts of a magnitude of 5%-7% and of up to 34% occur in other German regions and abroad, respectively. Adding the increasingly less plausible fixed ratios that are commonly used in standard Type I and extended Type II multiregional input-output and MRSUT models to our model, results in (1) substantial increases in the magnitude of negative indirect impacts and (2) a significant shift in the intra-regional versus interregional and international distribution of these impacts. Our conclusion is that input-output models tend to grossly overstate the indirect damages of negative supply shocks, which are part and parcel of most disasters.

Boris A. Portnov (p), Alina Svechkina

Jan Oosterhaven

Large metropolitan areas often exhibit multiple morbidity hot-spots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In the present study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns. Since the morbidity effect associated with a particular health hazard normally weakens with distance, to account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate the proposed hazard-identification approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, characterized by multiple health hazards. As the analysis shows, in our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the analysis identified a spot in the local industrial zone, which hosts several petrochemical facilities, as the main health hazard. Since the proposed approach does not require extensive input information, researchers can use it a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.