Local home bias: Theory and new empirical evidence from Italy

This thesis examines the literature on local home bias, i.e. investor preference towards geographically nearby stocks, and investigates the role of firm’s visibility, profitability, and opacity in explaining such behavior. While firm’s visibility is expected to proxy for the behavioral root originating such a preference, firm’s profitability and opacity are expected to capture the informational one. I find that less visible, and more profitable and opaque firms, conditionally to the demand, benefit from being headquartered in regions characterized by a scarcity of listed firms (local supply of stocks). Specifically, research estimates suggest that firms headquartered in regions with a poor supply of stocks would be worth i) 11 percent more if non-visible, non-profitable and non-opaque; ii) 16 percent more if profitable; and iii) 28 percent more if both profitable and opaque. Overall, as these features are able to explain most, albeit not all, of the local home bias effect, I reasonably argue and then assess that most of the preference for local is determined by a successful attempt to exploit local information advantage (60 percent), while the rest is determined by a mere (irrational) feeling of familiarity with the local firm (40 percent). Several and significant methodological, theoretical, and practical implications come out.

Local Earthquake Tomography by trans-dimensional Monte Carlo Sampling

In this study a new, fully non-linear, approach to Local Earthquake Tomography is presented. Local Earthquakes Tomography (LET) is a non-linear inversion problem that allows the joint determination of earthquakes parameters and velocity structure from arrival times of waves generated by local sources. Since the early developments of seismic tomography several inversion methods have been developed to solve this problem in a linearized way. In the framework of Monte Carlo sampling, we developed a new code based on the Reversible Jump Markov Chain Monte Carlo sampling method (Rj-McMc). It is a trans-dimensional approach in which the number of unknowns, and thus the model parameterization, is treated as one of the unknowns. I show that our new code allows overcoming major limitations of linearized tomography, opening a new perspective in seismic imaging. Synthetic tests demonstrate that our algorithm is able to produce a robust and reliable tomography without the need to make subjective a-priori assumptions about starting models and parameterization. Moreover it provides a more accurate estimate of uncertainties about the model parameters. Therefore, it is very suitable for investigating the velocity structure in regions that lack of accurate a-priori information. Synthetic tests also reveal that the lack of any regularization constraints allows extracting more information from the observed data and that the velocity structure can be detected also in regions where the density of rays is low and standard linearized codes fails. I also present high-resolution Vp and Vp/Vs models in two widespread investigated regions: the Parkfield segment of the San Andreas Fault (California, USA) and the area around the Alto Tiberina fault (Umbria-Marche, Italy). In both the cases, the models obtained with our code show a substantial improvement in the data fit, if compared with the models obtained from the same data set with the linearized inversion codes.

Topics in Local Economic Development

This thesis contributes to the current debate in literature about local economic development by considering two different topics: quality of institutions, and the role of clusters in innovation and productivity growth. The research is built upon three papers. The first paper deals with the analysis of the effect of administrative continuity on administrative efficiency. The analysis underlines the importance of different typologies of social capital. Findings reveal a positive impact on administrative efficiency (AE) by administrative continuity (AC) when it is coupled by bridging and linking social capital. On the contrary, bonding social capital influences negatively the effect by AC on AE. The second paper investigates the spatial interaction in levels of quality of government (QoG) among European regions. Notwithstanding the largely recognised role by institutions in the design of regional policies, no study has been conducted about the mechanisms of interaction and diffusion of QoG at regional level. This research wants to overcome this knowledge gap in literature. Findings reveal a heterogeneity in spatial interaction among groups of regions, i.e. ‘leader regions’ (Northern regions) and ‘lagging regions’ (Southern regions), when considering different mechanisms of interaction (learning / imitating competition and pure competition). Moreover, the effect of wealth on the levels of QoG is nonlinear. Finally, the third paper analyses the relation among specialization and productivity within the agricultural sector. In literature, the study of clusters dynamics has long neglected agriculture. The analysis describes the changes in sectorial specialization for eight main crop groups in Italian regions (NUTS 3), assessing the existence of spatial autocorrelations by using an exploratory data analysis. Furthermore, the effect of specialization on productivity is analysed within the main crop groups using a spatial panel data model. Findings reveal a marked tendency to specialization in the Italian agriculture, and a heterogeneous effect by specialization on productivity.