The study was commissioned to IRPET by the ERDF Managing Authority and was carried out as part of the joint activities between IRPET and the aforementioned Managing Authority planned for 2024 (ERDF Single Fund Activity No. 2.2024). It was carried out by Giuseppe Caruso (University of Florence), Martina Colucci (University of Florence), Natalia Faraoni (IRPET), Marco Mariani (IRPET), Alessandra Mattei (University of Florence), and Fiammetta Menchetti (University of Florence), within the IRPET’s \u201cProduction Sectors and Businesses\u201d area. Paolo Chini and Valentina Patacchini collaborated on behalf of IRPET. The editorial layout was curated by Elena Zangheri (IRPET).<\/p>\n\n
The study focuses on Action 1.1.5 POR-FESR 2014-2020 of the Tuscany Region, and in particular on calls for proposals 1 and 2 of 2014, dedicated to the financing of R&D projects within the regional S3 framework, oriented towards technological priorities that include digitization and smart factories. Drawing on recent empirical literature on the consequences of automation and digitization, the objective of this analysis is to evaluate, over the medium term, the effects of the above-mentioned action on the productivity and work of participating companies. The analysis also focuses on the project texts, distinguishing first between automation and digitization projects and then between investments aimed at innovation in production processes and those aimed at product innovation. The causal analysis approach follows the characteristics of the case study, which is characterized by specific eligibility conditions based on changes in companies’ turnover prior to their application to participate, differentiated according to whether this application was made individually or in partnership with others. This constitutes a fuzzy Regression Discontinuity (RD) design. In the initial phase of designing the causal study, a sub-population of companies is selected for which we can assume that certain fundamental assumptions for identifying causal effects are met. In the subsequent analysis phase, the principal stratification approach is used to define local effects, which are then estimated and inferred using a Bayesian approach.<\/p>\n","protected":false},"excerpt":{"rendered":"
Edited by G. Caruso (Universit\u00e0 di Firenze), M. Colucci (Universit\u00e0 di Firenze), N. Faraoni (IRPET), M. Mariani (IRPET), A. Mattei (Universit\u00e0 di Firenze), F. Menchetti (Universit\u00e0 di Firenze). Collaboration of P. Chini and V. Patacchini (IRPET)<\/p>\n","protected":false},"author":5,"featured_media":27371,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[999,1001],"tags":[1538,1054,1539,1540],"staff":[993,985,953,946,989],"temi-pubblicazioni":[1006,968],"class_list":["post-30789","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-report","category-studies-for-community-planning","tag-incentives-en","tag-occupation","tag-productivity-en","tag-rs-projects","staff-elena-zangheri","staff-mariani-marco-en","staff-natalia-faraoni","staff-paolo-chini","staff-valentina-patacchini","temi-pubblicazioni-enterprise-and-production-sistems","temi-pubblicazioni-work-en"],"yoast_head":"\n