Sofía Jiménez (p), Rosa Duarte, Julio Sánchez-Chóliz, Alfredo Mainar, Patricia Fuentes

Nowadays new debate about economic growth and its drivers can be found in economic literature. Besides, with the start of globalization process regional patterns and its effects over development have been on the forefront of discussions.
With technological improvements that led to lower transportation costs and better communication among countries and regions, territorial and geographical aspects seem to be replaced in a second place. However, as Crevoisier (2004) claim development and economic growth cannot be understood separately of the spatial context. Besides, some recent works show the importance of spatial issues in order to do a country/region more attractive than another for capturing new investments, trade partners, etc.
However, we have to note that not only does matter spatial actors but also social and economic ones are important. In other words, economic growth is explained by a combination of economic, social and geographical drivers. In this context, the main aim of this study is to get more insight about these issues analyzing to what extent these different dimensions (economic, social and geographical) have affected economic growth in the last two decades.
In order to do that we are going make use of a panel data analysis where GDPpc will be our dependent variable and the analysis is going to be done in an international context at country level. We will basically explore the role of four variables that are geographical distance, economic similarity, social and cultural similarity and trade closeness that will be calculated by the application of nearest neighbor method. For the construction of most part of variables, mainly economic similarity and trade closeness, we will work in an input-output framework because of its capacity to capture linkages among countries. MRIO framework is a useful tool to study these kind of issues as this let us capture issues such as technological and spatial spillovers or technological regional patterns (see Capello (2013)). As a main database we will use EORA that covers 190 countries and the period 1990-2015 (see Lenzen et al. (2013)). The rest of variables will be built with data from the World Bank following the most common indicators used in economic literature (for instance, linguistic distance). We expected as a results to be all of them significant although economic similarity and trade closeness with higher coefficient and increasing trend of its effects.

Ditte Håkonsson Lyngemark (p), Ismir Mulalic , Cecilie Dohlmann Weatherall

Lately Denmark, like many other countries, has focused on attracting and sustaining foreign direct investments (FDI) because FDI is seen as an important source to knowledge, growth and jobs. However, studies have found both negative and positive effects of FDIs on domestic firms’ productivity. Where previous studies have investigated the vertical and horizontal spillover effects related to the sector, we analyze geographical proximity and investigate how proximity to FDIs influence the total factor productivity (TFP) spillovers to domestic firms. We do this by employing a unique dataset on FDIs including Greenfield Investments (GIs) and Mergers and Acquisitions (M&As) in Denmark and a full population of geo coded micro-level panel data for Danish firms. We find that it is important to take proximity into account when estimating the relation between GIs and M&As on domestic firms’ TFP. Also, we find positive significant correlations for vertical spillovers from both GIs and M&As on domestic firms’ TFP. For horizontal spillovers from M&As, we see a negative relationship between fulltime employees in M&As and domestic firms’ TFP, which could indicate a negative competition effect.

Nadine Levratto (p), Giusepe Arcuri, Aziza Garsaa, Lara Abdel Fattah

This paper investigates the role of regional context with regard to human capital and knowledge spillover effects in SMEs’ financial soundness. Our empirical setting is based on the multilevel analysis for panel data, which better allows for the treatment of hierarchical data. It is applied to firms belonging to the industrial sector and operating in four European countries over the 2010–2015 period. We find that a combination of individual- and regional-level characteristics explain firm soundness more accurately than individual features alone. Furthermore, we find that a higher local educational level and knowledge spillover improve the firm soundness.