Collaboration for innovation and project failure. A dynamic analysis

The aim of this paper is to investigate the hypothesis that there is a complex and bidirectional relation between collaboration and failure in innovation projects. On the one hand, collaboration in innovative activities may increase the likelihood of project failure. At the same time, the failure in innovation projects may induce the firm to collaborate in order to overcome the problems that determined the failure of innovation projects (induced collaboration). Up to now, we are not aware about the existence of any empirical paper analysing the interaction between these two mechanisms. This paper aims at filling this gap by providing a motivation for the induced collaboration and testing its empirical relevance in a dynamic framework. The empirical analysis is carried out by using two consecutive German Community Innovation Surveys referring to the period 2006–2010. The empirical results support the hypothesis of a bidirectional causal relationship between collaboration and failure.     

A note on the relationship between the total factor productivity and the network of firms

In this paper we study the robustness of the results found recently by Guzzini and Palestrini (J Econ Interact Coord 11:35–55, 2016). Since the original analysis was carried out in a static setting, we perform a dynamic panel analysis by using the same dataset. The inclusion of the lagged value of the endogenous variable, missing in the original paper, could be justified for several reasons. Firstly, the statistical relationship may have itself a dynamical nature; secondly the inclusion of lagged-endogenous variable is a way to mitigate the possibility of an omitted variable problem. We find that the results are only qualitatively the same, and we discuss the quantitative differences.

     

Growth in total factor productivity and links among firms

In this paper we study the relevance of links among firms in explaining the mean and the auto-correlation property of the aggregate total factor productivity rate of growth (Solow residual). Our approach relies on the interaction between idiosyncratic shocks of firms and the network structure of firms. We analytically study this phenomenon using the adjacency matrix of a complete network and we present a simulation with more general random adjacency matrices. We also check empirically, using Italian data, the relationship between the network structure and the Solow residual. In particular, in the empirical part, we find two main results: firstly, the relationship between the Solow residual and the measure of connectivity of firms is positive, in accordance with the analytical results. Secondly, we find that the measure of connectivity is pro-cyclical with the annual growth rate of industrial production.     

Early warning indicators and macro-prudential policies: a credit network agent based model

Credit network configurations play a crucial role in determining the vulnerability of the economic system. Following the network-based financial accelerator approach, we constructed an agent based model reproducing an artificial credit network that evolves endogenously according to the leverage choices of heterogeneous firms and banks. Thus, our work aims at defining both early warning indicators for crises and policy precautionary measures based on the endogenous credit network dynamics. The model is calibrated on a sample of firms and banks quoted in the Japanese stock-exchange markets from 1980 to 2012. Both empirical and simulated data suggest that credit and connectivity variations could be used as early warning measures for crises. Moreover, targeting banks that are central in the credit network in terms of size and connectivity, the capital-related macro-prudential policies may reduce systemic vulnerability without affecting aggregate output.     

Aggregation of Heterogeneous Interacting Agents: The Variant Representative Agent Framework

In this paper we have presented a variant of the stochastic aggregation approach which basically consists in exploring the evolution over time of higher moments of the economic units’ distribution. In a sense therefore, we propose to focus on the behavior of a Variant Representative Agent. An application to a classical growth model shows that changes in aggregate output usually associated with Total Factor Productivity in the aggregative interpretation of the framework may be due to changes in the distribution of agents in terms of capital intensity. The application to a model by Gatti et al. (Interaction and market structure, Springer, 2000) shows the efficacy of the method in capturing the evolution over time of the distribution of firms in terms of financial solidity (equity ratio). The method seems general enough to cover a wide range of economic situations in which heterogeneity is relevant and persistent. It seems also simple enough to deserve the attention of the macroeconomist dissatisfied with the RA who wants to derive meaningful and microfounded macroeconomic results.