Gilson Dutra
Doktorand vid Nationalekonomiska institutionen; Doktorander
- E-post:
- gilson.dutra@nek.uu.se
- Besöksadress:
- Ekonomikum
Kyrkogårdsgatan 10 - Postadress:
- Box 513
751 20 UPPSALA
Ladda ned kontaktuppgifter för Gilson Dutra vid Nationalekonomiska institutionen; Doktorander
Kort presentation
Ph.D. Candidate in Economics with a focus on Applied Microeconomics, Policy Evaluation, and Development.
Nyckelord
- Applied Microeconomics; Public Policy Evaluation; Human Capital; Development Economics.
Biografi
I am a PhD candidate in Economics at Uppsala University. My research lies in applied microeconomics, policy evaluation, and development with a focus on how public policies, access to services, and measurement systems shape health and human capital outcomes.
Before starting my PhD, I worked as a policy advisor at the National Congress in Brazil, with public policy evaluation at the Secretaria de Estado de Educação do Pará (SEDUC/PA), Brazil, and as a lecturer at the Federal University of Ouro Preto, Brazil.
I am affiliated with the Uppsala Center for Fiscal Studies (UCFS) at Uppsala University (Uppsala, Sweden) and the Center for Empirical Studies in Economics (CEEE) at Fundação Getulio Vargas (Rio de Janeiro, Brazil).
You can find my websites here: Gilson Dutra, Linkedin, and ResearchGate.
Forskning
Misdated at Birth: Ultrasound Timing Standardization and the Costs of a Slow Clock (JMP)
This study evaluates how Sweden’s nationwide rollout of ultrasound-based gestational dating (UTS) reshaped gestational-age assignment, obstetric management, and downstream outcomes.
Accurate Due Date Prediction without Ultrasound: A Machine Learning Approach Using Maternal Characteristics
With: Mikael Elinder and Oscar Erixson.
We develop machine-learning models to improve estimated due dates in settings where ultrasound is unavailable or difficult to access. Using data from more than 600,000 pregnancies in the Swedish Medical Birth Register, we show that models based on readily available maternal characteristics substantially outperform Naegele’s Rule and narrow the gap relative to ultrasound. The paper speaks to the design of low-cost prediction tools for resource constrained settings and the role of scalable technologies in maternal and child health systems.
