Management of technologies, innovation and sustainability

Programme 1. Introduction The impact of technological change on labor markets: competing views Classical, neoclassical, Keynesian and Evolutionary perspectives SBTC, RBTC model and beyond Robots and employment References Calvino, F., & Virgillito, M. E. (2018). The innovation‐employment nexus: a critical survey of theory and empirics. Journal of Economic surveys, 32(1), 83-117. Montobbio, F., Staccioli, J., Virgillito, M. E., & Vivarelli, M. (2023). The empirics of technology, employment and occupations: Lessons learned and challenges ahead. Journal of Economic Surveys. Notes provided in class 2. Using meta-analysis to synthesize the empirical literature on the robot-employment nexus Why meta-analsis can be helpful to synthesize the empirical literature on critical research questions and how it works Assessing the literature focusing on the robot-employment nexus Identifying the 'pubblication bias' Investigating the role of heterogeneity Irsova, Z., Doucouliagos, H., Havranek, T., & Stanley, T. D. (2023). Meta‐analysis of social science research: A practitioner's guide. Journal of Economic Surveys, 1-20. Guarascio, D., Piccirillo, A., & Reljic, J. (2024). Will Robots Replace Workers? Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. References 3. The impact of AI on labor markets The impact of AI on labor markets: theoretical foundations Measuring the diffusion of AI in labour markets Estimating the impact of AI on employment: measurement and identification strategies Guarascio, D., Reljic, (2024). Artificial intelligence and employment

Students should know the fundamentals of political economy, economic policy and econometrics. In particular, neoclassical theories of production and consumption as well as the standard representation of the labor market are prerequisites. Regarding the empirical part and applied classes, the basics of descriptive and inferential statistics as well as econometric techniques commonly used to study phenomena concerning the labor market (multivariate regression models, causal analysis and policy evaluation) constitute prerequisites.

All materials will be provided in class Calvino, F., & Virgillito, M. E. (2018). The innovation‐employment nexus: a critical survey of theory and empirics. Journal of Economic surveys, 32(1), 83-117. Montobbio, F., Staccioli, J., Virgillito, M. E., & Vivarelli, M. (2023). The empirics of technology, employment and occupations: Lessons learned and challenges ahead. Journal of Economic Surveys. Irsova, Z., Doucouliagos, H., Havranek, T., & Stanley, T. D. (2023). Meta‐analysis of social science research: A practitioner's guide. Journal of Economic Surveys, 1-20. Guarascio, D., Piccirillo, A., & Reljic, J. (2024). Will Robots Replace Workers? Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. Guarascio, D., Reljic, (2024). Artificial intelligence and employment

Strongly encouraged

Students will produce a short paper that can be either a review of the scientific literature on a topic agreed upon with the lecturer; or an original empirical analysis (descriptive and/or inferential) also to be agreed upon in advance with the lecturer. Students will be expected to present and discuss the submitted article, and evaluation will be based on both the quality of the article and the presentation.

Classes will include frontal lectures, laboratories and discussion classes.

Programme 1. Introduction The impact of technological change on labor markets: competing views Classical, neoclassical, Keynesian and Evolutionary perspectives SBTC, RBTC model and beyond Robots and employment References Calvino, F., & Virgillito, M. E. (2018). The innovation‐employment nexus: a critical survey of theory and empirics. Journal of Economic surveys, 32(1), 83-117. Montobbio, F., Staccioli, J., Virgillito, M. E., & Vivarelli, M. (2023). The empirics of technology, employment and occupations: Lessons learned and challenges ahead. Journal of Economic Surveys. Notes provided in class 2. Using meta-analysis to synthesize the empirical literature on the robot-employment nexus Why meta-analsis can be helpful to synthesize the empirical literature on critical research questions and how it works Assessing the literature focusing on the robot-employment nexus Identifying the 'pubblication bias' Investigating the role of heterogeneity Irsova, Z., Doucouliagos, H., Havranek, T., & Stanley, T. D. (2023). Meta‐analysis of social science research: A practitioner's guide. Journal of Economic Surveys, 1-20. Guarascio, D., Piccirillo, A., & Reljic, J. (2024). Will Robots Replace Workers? Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. References 3. The impact of AI on labor markets The impact of AI on labor markets: theoretical foundations Measuring the diffusion of AI in labour markets Estimating the impact of AI on employment: measurement and identification strategies Guarascio, D., Reljic, (2024). Artificial intelligence and employment

Students should know the fundamentals of political economy, economic policy and econometrics. In particular, neoclassical theories of production and consumption as well as the standard representation of the labor market are prerequisites. Regarding the empirical part and applied classes, the basics of descriptive and inferential statistics as well as econometric techniques commonly used to study phenomena concerning the labor market (multivariate regression models, causal analysis and policy evaluation) constitute prerequisites.

All materials will be provided in class Calvino, F., & Virgillito, M. E. (2018). The innovation‐employment nexus: a critical survey of theory and empirics. Journal of Economic surveys, 32(1), 83-117. Montobbio, F., Staccioli, J., Virgillito, M. E., & Vivarelli, M. (2023). The empirics of technology, employment and occupations: Lessons learned and challenges ahead. Journal of Economic Surveys. Irsova, Z., Doucouliagos, H., Havranek, T., & Stanley, T. D. (2023). Meta‐analysis of social science research: A practitioner's guide. Journal of Economic Surveys, 1-20. Guarascio, D., Piccirillo, A., & Reljic, J. (2024). Will Robots Replace Workers? Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. Assessing the Impact of Robots on Employment and Wages with Meta-Analysis. Guarascio, D., Reljic, (2024). Artificial intelligence and employment

Strongly encouraged

Students will produce a short paper that can be either a review of the scientific literature on a topic agreed upon with the lecturer; or an original empirical analysis (descriptive and/or inferential) also to be agreed upon in advance with the lecturer. Students will be expected to present and discuss the submitted article, and evaluation will be based on both the quality of the article and the presentation.

Classes will include frontal lectures, laboratories and discussion classes.