I completed my Ph.D. in Applied Mathematics at the University of Lisbon in 2019. I hold an M.Sc. in Econometrics and a B.Sc. in Economics from the University of Lisbon. My research is primarily driven toward econometrics, time-series, causal inference, and data science. I also complementarily invest in themes related to policy evaluation, scientometrics, and applied economics in general. My work has been published in several leading journals, such as PLoS ONE, Habitat International, Physica A: Statistical Mechanics and its Applications, Statistics and Probability Letters, Research Evaluation, Telecommunications Policy, among others. Additionally, I have taught for the past ten years and am currently a lecturer of statistics and econometrics at NOVA IMS (Nova University of Lisbon). I serve as a statistical consultant and trainer for numerous private and public organizations, including the OECD, European Commission, European Court of Auditors, and various other institutions, including Ministries, National Statistical Authorities, Regulators and Central Banks (all from eight countries).
My Research Statement
I understand my mission as advancing knowledge for the sake of science and the common good. My professional center of gravity is firmly rooted in research and innovation activities; nonetheless, I see these in connection to policy and regulatory concerns, which in turn have the potential to enrich and leverage my learning and academic efforts.
- Metzger, P., Mendonça, S., Silva, J. A., & Damásio, B. (2023). Battery innovation and the Circular Economy: What are patents revealing?. Renewable Energy.
- Vaz, E., Damásio, B., Bação, F., Shaker, R. R., & Penfound, E. (2023). Urban habitats and food insecurity: Lessons learned throughout a pandemic. Habitat International, 135, 102779.
- Damásio, B., & Mendonça, S. (2023). Leader-follower dynamics in real historical time: A Markovian test of non-linear causality between sail and steam (co-) development. Applied Economics, 55(17), 1908-1918.
- Lyra, M. S., Damásio, B., Pinheiro, F. L., & Bacao, F. (2022). Fraud, corruption, and collusion in public procurement activities, a systematic literature review on data-driven methods. Applied Network Science, 7(1), 83.
- Lehmann, C., Cruz-Jesus, F., Oliveira, T., & Damásio, B. (2022). Leveraging the circular economy: Investment and innovation as drivers. Journal of Cleaner Production, 132146.
- Mendonça, S., Damásio, B., Santiago, F., Chen, M., Santos, A. B., Cunha, M. P., & Nicita, A. (2022). Strategic Encounters in Innovation and Regulation: Healthcare transformation in the Era of Digital Connectivity; Comment on “What Managers Find Important for Implementation of Innovations in the Healthcare Sector–Practice Through Six Management Perspectives”. International Journal of Health Policy and Management, (Articles in Press).
- Paredes, A., Mendonça, J., Bação, F., & Damásio, B. (2022). Does R&D tax credit impact firm behaviour? Micro evidence for Portugal. Research Evaluation, 31(2), 226-235.
- Mendonça, S., Damásio, B., de Freitas, L. C., Oliveira, L., Cichy, M., & Nicita, A. (2022). The rise of 5G technologies and systems: A quantitative analysis of knowledge production. Telecommunications Policy, 46(4), 102327.
- Vasconcelos, C., & Damásio, B. (2022). GenMarkov: Modeling Generalized Multivariate Markov Chains in R. arXiv preprint arXiv:2202.00333.
- Curado, A., Damásio, B., Encarnação, S., Candia, C., & Pinheiro, F. L. (2021). Scaling behavior of public procurement activity. Plos one, 16(12), e0260806.
- Lyra, M. S., Curado, A., Damásio, B., Bação, F., & Pinheiro, F. L. (2021). Characterization of the firm–firm public procurement co-bidding network from the State of Ceará (Brazil) municipalities. Applied Network Science, 6(1), 1-10.
- Vaz, E., Damásio, B., Baçao, F., Kotha, M., Penfound, E., & Rai, S. K. (2021). Mumbai’s business landscape: A spatial analytical approach to urbanisation. Heliyon, 7(7), e07522.
- Vaz, E., Cusimano, M. D., Bação, F., Damásio, B., & Penfound, E. (2021). Open data and injuries in urban areas—A spatial analytical framework of Toronto using machine learning and spatial regressions. Plos one, 16(3), e0248285.
- Vaz, E., Bação, F., Damásio, B., Haynes, M., & Penfound, E. (2021). Machine learning for analysis of wealth in cities: A spatial-empirical examination of wealth in Toronto. Habitat International, 108, 102319.