As competition becomes intense, firms seek strategies to keep afloat. Among others, they carefully choose their activity lo- cation, recruit a talented workforce, and engage in innova- tion. In this thesis, we shed light on these three features em- pirically, mainly using econometric techniques. Our contri- bution is in the literature of firms’ competitiveness, industrial organization and economic geography. At first, we study regional productivity disparities and their interplay with local agglomeration advantages. To do so, we apply a density-based machine learning clustering algorithm to identify firms’ clusters at a fine-grained geographic scale on a sample of Italian firms. Then, we observe simultane- ously the extent to which clusters explain agglomeration economies and firm selection effects. Our findings suggest that dense clusters generate agglomeration externalities that are hetero- geneous across regions. In the second part of the thesis, we investigate the impact of foreign managers on firms’ compet- itiveness on a sample of firms operating in the United King- dom. We show that domestic firms become more efficient after recruiting foreigners to their management team due to previous industry-specific experience. In the last part, we as- sess the impact of patents on market share and labour pro- ductivity in the global Information and Communication Tech- nologies (ICT) sector. Using a recent difference-in-difference approach, we find that patenting increase market share with- out significantly affecting labour productivity. Our evidence indicates some concerns regarding the implications of prop- erty rights from innovation on market competition.

Essays on Firms’ Competitiveness

Exadaktylos, Dimitrios
2022

Abstract

As competition becomes intense, firms seek strategies to keep afloat. Among others, they carefully choose their activity lo- cation, recruit a talented workforce, and engage in innova- tion. In this thesis, we shed light on these three features em- pirically, mainly using econometric techniques. Our contri- bution is in the literature of firms’ competitiveness, industrial organization and economic geography. At first, we study regional productivity disparities and their interplay with local agglomeration advantages. To do so, we apply a density-based machine learning clustering algorithm to identify firms’ clusters at a fine-grained geographic scale on a sample of Italian firms. Then, we observe simultane- ously the extent to which clusters explain agglomeration economies and firm selection effects. Our findings suggest that dense clusters generate agglomeration externalities that are hetero- geneous across regions. In the second part of the thesis, we investigate the impact of foreign managers on firms’ compet- itiveness on a sample of firms operating in the United King- dom. We show that domestic firms become more efficient after recruiting foreigners to their management team due to previous industry-specific experience. In the last part, we as- sess the impact of patents on market share and labour pro- ductivity in the global Information and Communication Tech- nologies (ICT) sector. Using a recent difference-in-difference approach, we find that patenting increase market share with- out significantly affecting labour productivity. Our evidence indicates some concerns regarding the implications of prop- erty rights from innovation on market competition.
20-lug-2022
Inglese
RICCABONI, MASSIMO
RUNGI, ARMANDO
Scuola IMT Alti Studi Lucca
Lucca, Italia
169
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/378288
Il codice NBN di questa tesi è URN:NBN:IT:IMTLUCCA-378288