The role of venture capitalist to enhance the growth of Spanish and Italian university spin-offs

The establishment of spin-offs to commercialise university knowledge/technology is a potential mechanism to promote economic and innovative development. Nevertheless, University Spin-Offs (USOs) are usually resource-constrained, especially in obtaining funding, limiting their growth. Venture Capital (VC) investors play an important role in the financing and the improvement of their managerial skills, which are critical for firm growth. This paper aims to explore both the effect of VC partners on the USOs’ growth and the cross-national differences in the role played by them. To study both issues, we empirically analysed 516 Spanish and 904 Italian USOs created by 50 Spanish and 57 Italian universities, respectively, and observed them between 2005 and 2013. The results showed different effects in the Spanish and Italian cases. While in Spain the presence of VC partners positively affects the USOs’ growth, in Italy there is not a significant effect. This evidence calls for systematic policies by public administrations and universities to foster USO growth.     

Natural resource rents and internal conflicts: Can decentralization lift the curse?

We study how natural resource rents affect the risk of internal conflict within countries and how the federal structure of countries influences this relationship. Natural resource abundance may induce excessive rent-seeking and thus increase the risk of internal conflict. Fiscal and political decentralization as an institutional arrangement for rent-sharing and political codetermination of regions within a country may limit the destructive effect of natural resource rents on internal stability. Using cross-country and panel data from more than 90 countries covering the period 1984–2004, we find evidence that natural resource rents indeed increase the risk of internal conflict, but this relationship is significantly mitigated by political decentralization.     

Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data

This paper discusses a factor model for short-term forecasting of GDP growth using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm, combined with a principal components estimator. We discuss some in-sample properties of the estimator in a real-time environment and propose alternative methods for forecasting quarterly GDP with monthly factors. In the empirical application, we use a novel real-time dataset for the German economy. Employing a recursive forecast experiment, we evaluate the forecast accuracy of the factor model with respect to German GDP. Furthermore, we investigate the role of revisions in forecast accuracy and assess the contribution of timely monthly observations to the forecast performance. Finally, we compare the performance of the mixed-frequency model with that of a factor model, based on time-aggregated quarterly data.