This paper analyzes the performance of global value chains during the trade collapse. To do so, it exploits a unique transaction-level dataset on French firms containing information on cross-border monthly transactions matched with data on worldwide intra-.rm linkages as defined by property...

We explore the nature of Business Groups, that is network-like forms of hierarchical organization between legally autonomous firms spanning both within and across national borders. Exploiting a unique dataset of 270,474 headquarters controlling more than 1,500,000 (domestic and foreign)...

In this paper, we test the contribution of foreign management on firms' competitiveness. We use a novel dataset on the careers of 165,084 managers employed by 13,106 companies in the United Kingdom in the period 2009-2017. We find that domestic manufacturing firms become, on average, between 7%...

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly...

We test the impact of import penetration on the productivity of a sample of roughly 35,000 Italian manufacturing firms operating in the period 1996-2003, considering the impact on productivity of both import penetration in the same industry and import penetration in the up-stream industries. We...

Backward and forward vertical integrations both shape the organization of Global Value Chains (GVCs). Yet, many studies make the unrealistic assumption that integration decisions are binary and one-directional, i.e., companies make the integration decision only once and they can go either...

In this contribution, we propose machine learning techniques to predict zombie firms . First, we derive risk of failure by training and testing our algorithm on disclosed financial information and non-random missing values by 304,906 firms active in Italy in the period 2008-2017. Then, we spot...