Resumo do evento

Biography:

Pedro Gil has a PhD degree in Economics from the School of Economics and Management, University of Porto (2010). He started his professional career as an economic analyst at the Research Department of the Portuguese Business Association (AEP, from 1998 to 2007), assisting the CEO and the Board regarding the assessment of the business cycle, the analysis of the monetary and fiscal policy as well as of the structural change of the Portuguese and Euro Area economy. Presently, he is a senior adviser at PlanAPP – Competence Centre for Planning, Policy and Foresight in Public Administration and an Associate Professor at the School of Economics and Management, University of Porto, teaching courses of Macroeconomics (undergraduate level and PhD – Doctoral Programme in Economics) and Economic Growth (undergraduate and Master in Economics). He is a researcher and deputy director at CEF.UP, Center for Economics and Finance at University of Porto. He was a member of the board of CEF.UP acting as Research Director of the research group MACGROW (Macroeconomy and Growth) between 2013 and 2020. As an academic consultant, he has been involved in the production of technical reports for, e.g., the Porto City Council, the Porto Metropolitan Board (Junta Metropolitana do Porto), the Ministry of Finance of Portugal, the Cohesion and Development Agency, and the European Commission.

Abstract:  

We address two main structural changes occurring in developed countries: the rise of automation and population ageing. We use an R&D-based growth model in an OLG framework with endogenous education and fertility, and automation in the production process. Our model is able to combine the growth of real wages over time and either a fall or an increase in birth rates, consistent with recent data

regarding the birth rate by skill group. Moreover, our model allows for the study of the interplay between population ageing and automation. The results show a dynamics consistent with the US trends for the period covering 1970 to 2019. We also show the model’s response to automation and demographic (fertility vs longevity) exogenous shocks and to policy instruments aiming to mitigate inequality.

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