Patenting in 4IR technologies and firm performance

We investigate whether firm performance is related to the accumulated stock of technological knowledge associated with the Fourth Industrial Revolution (4IR) and, if so, whether the firm’s history in 4IR technology development affects such a relationship. We exploit a rich longitudinal matched patent-firm data set on the population of large firms that filed 4IR patents at the European Patent Office (EPO) between 2009 and 2014, while reconstructing their patent stocks from 1985 onward. To identify 4IR patents, we use a novel twostep procedure proposed by EPO (2020, Patents and the Fourth Industrial Revolution: The Global Technology Trends Enabling the Data-Driven Economy, European Patent Office), based on Cooperative Patent Classification codes and on a full-text patent search. Our results show a positive and significant relationship between firms’ stocks of 4IR patents and labor and total factor productivity. We also find that firms with a long history in 4IR patent filings benefit more from the development of 4IR technological capabilities than later applicants. Conversely, we find that firm profitability is not significantly related to the stock of 4IR patents, which suggests that the returns from 4IR technological developments may be slow to be cashed in. Finally, we find that the positive relationship with productivity is stronger for 4IR-related wireless technology and for artificial intelligence, cognitive computing, and big data analytics

Salvage stereotactic body radiotherapy for isolated lymph node recurrent prostate cancer : single institution series of 94 consecutive patients and 124 lymph nodes

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Incentives to quality and investment : evidence from electricity distribution in Italy

This paper investigates the relationship between output-based incentives for service quality and the use of capital and non-capital resources to meet regulatory targets in the electricity industry. To conduct the empirical analysis we use a dataset collected with the support of the Italian energy regulatory authority, comprising micro data on monetary incentives and physical assets for the largest electricity distribution operator in Italy (86 % of the market). Our results show that physical assets and operational expenditures do affect service quality. Moreover, when we investigate causality in the relationship between incentives to quality and the use of capital and non-capital resources, we find that incentives Granger-cause capital expenditures ( and not vice-versa). Finally, our results reveal an asymmetric effect of rewards and penalties on capital expenditures' decisions across areas with different quality levels. From these findings, we derive several policy implications.Italian Regulatory Authority for Electricity, Gas and Wate

Setting Network Tariffs With Heterogeneous Firms: The Case Of Natural Gas Distribution

The appropriate treatment of firm heterogeneity plays a crucial role in the application of benchmarking analyses for regulatory purposes. Within the realm of two-step approaches, this paper challenges the widespread adoption of single-variable clustering: heterogeneity has often multiple sources, which calls for more sophisticated clustering methodologies. In fact, reliable cluster-specific rankings provide firms’ management with more realistic objectives as well as freedom to identify the appropriate strategies to improve efficiency. In order to provide regulatory guidance on this issue, we use a unique dataset of detailed accounting data and unbundled network-related costs for a panel of Italian gas distributors and we test two alternative methods: a hybrid clustering procedure (HCP) and a latent class model (LCM). Our results show that HCP and LCM perform better than size segmentation in the identification of classes, thereby leading to more reliable production frontiers, but do not support a conclusive preference for one or the other method. While both methods are sensitive to outliers, LCMs seem to provide deeper insights on the drivers of firm inefficiency. However, they also present stationarity and convergence issues, which might favour the implementation of HCP methods. Furthermore, the degree of discretionary judgement in the modelling decisions (e.g., model specification and choice of the partition) is slightly higher with LCMs than with HCP. In this respect, the HCP, with its lower modelling and analytical complexity, may feature as a more appealing option, facilitating the interactions between regulator and firm managers

Setting Network Tariffs With Heterogeneous Firms: The Case Of Natural Gas Distribution

The appropriate treatment of firm heterogeneity plays a crucial role in the application of benchmarking analyses for regulatory purposes. Within the realm of two-step approaches, this paper challenges the widespread adoption of single-variable clustering: heterogeneity has often multiple sources, which calls for more sophisticated clustering methodologies. In fact, reliable cluster-specific rankings provide firms’ management with more realistic objectives as well as freedom to identify the appropriate strategies to improve efficiency. In order to provide regulatory guidance on this issue, we use a unique dataset of detailed accounting data and unbundled network-related costs for a panel of Italian gas distributors and we test two alternative methods: a hybrid clustering procedure (HCP) and a latent class model (LCM). Our results show that HCP and LCM perform better than size segmentation in the identification of classes, thereby leading to more reliable production frontiers, but do not support a conclusive preference for one or the other method. While both methods are sensitive to outliers, LCMs seem to provide deeper insights on the drivers of firm inefficiency. However, they also present stationarity and convergence issues, which might favour the implementation of HCP methods. Furthermore, the degree of discretionary judgement in the modelling decisions (e.g., model specification and choice of the partition) is slightly higher with LCMs than with HCP. In this respect, the HCP, with its lower modelling and analytical complexity, may feature as a more appealing option, facilitating the interactions between regulator and firm managers

Setting Network Tariffs With Heterogeneous Firms: The Case Of Natural Gas Distribution

The appropriate treatment of firm heterogeneity plays a crucial role in the application of benchmarking analyses for regulatory purposes. Within the realm of two-step approaches, this paper challenges the widespread adoption of single-variable clustering: heterogeneity has often multiple sources, which calls for more sophisticated clustering methodologies. In fact, reliable cluster-specific rankings provide firms’ management with more realistic objectives as well as freedom to identify the appropriate strategies to improve efficiency. In order to provide regulatory guidance on this issue, we use a unique dataset of detailed accounting data and unbundled network-related costs for a panel of Italian gas distributors and we test two alternative methods: a hybrid clustering procedure (HCP) and a latent class model (LCM). Our results show that HCP and LCM perform better than size segmentation in the identification of classes, thereby leading to more reliable production frontiers, but do not support a conclusive preference for one or the other method. While both methods are sensitive to outliers, LCMs seem to provide deeper insights on the drivers of firm inefficiency. However, they also present stationarity and convergence issues, which might favour the implementation of HCP methods. Furthermore, the degree of discretionary judgement in the modelling decisions (e.g., model specification and choice of the partition) is slightly higher with LCMs than with HCP. In this respect, the HCP, with its lower modelling and analytical complexity, may feature as a more appealing option, facilitating the interactions between regulator and firm managers

Setting Network Tariffs With Heterogeneous Firms: The Case Of Natural Gas Distribution

The appropriate treatment of firm heterogeneity plays a crucial role in the application of benchmarking analyses for regulatory purposes. Within the realm of two-step approaches, this paper challenges the widespread adoption of single-variable clustering: heterogeneity has often multiple sources, which calls for more sophisticated clustering methodologies. In fact, reliable cluster-specific rankings provide firms’ management with more realistic objectives as well as freedom to identify the appropriate strategies to improve efficiency. In order to provide regulatory guidance on this issue, we use a unique dataset of detailed accounting data and unbundled network-related costs for a panel of Italian gas distributors and we test two alternative methods: a hybrid clustering procedure (HCP) and a latent class model (LCM). Our results show that HCP and LCM perform better than size segmentation in the identification of classes, thereby leading to more reliable production frontiers, but do not support a conclusive preference for one or the other method. While both methods are sensitive to outliers, LCMs seem to provide deeper insights on the drivers of firm inefficiency. However, they also present stationarity and convergence issues, which might favour the implementation of HCP methods. Furthermore, the degree of discretionary judgement in the modelling decisions (e.g., model specification and choice of the partition) is slightly higher with LCMs than with HCP. In this respect, the HCP, with its lower modelling and analytical complexity, may feature as a more appealing option, facilitating the interactions between regulator and firm managers