Octavio E. Lima

MIT Department: Political Science
Faculty Mentor: Prof. F. Daniel Hidalgo
Undergraduate Institution: University of Oregon
Hometown: São Manuel, São Paulo, Brazil
Website: LinkedIn
Research Poster
Lightning Talk


Octavio was born and raised in Brazil, and he is passionate about Data Analysis, Macroeconomics, and Econometrics. He has also studied abroad in Mexico during his last year of high school. In the summer of 2020, he participated in the Public Policy and International Affairs Program at Princeton University. PPIA is a competitive national fellowship for minority students who are interested in policy research. While at Princeton, he worked on Policy research and took classes on Advanced Statistics and Global Systemic Risk. This further inspired him to participate in the MIT Summer Research Program during the summer of 2021. The goal of this internship is “to prepare and recruit the best and brightest for graduate education at MIT.” While in Cambridge, Octavio conducted research under the supervision of Prof. F. Daniel Hidalgo from the Department of Political Science. That being said, his current goal is to pursue either a Master’s or a Doctorate degree. He is particularly interested in the areas of income inequality, poverty alleviation, and education access. When not working or studying, he enjoys exercising, reading, and playing volleyball or chess.

2021 Abstract

Political Dynasties in Brazil:
Quantification through Text Mining
and String Matching

Octavio E. Lima1 and F. Daniel Hidalgo, PhD2
1Department of Economics, University of Oregon
2Department of Political Science, Massachusetts Institute of Technology

Political Dynasties remain common across a wide range of countries today. Nonetheless, their scope has been relatively unexplored. The greatest challenge of this type of research is quantifying dynasties. In that sense, the existing studies are encouraging but limited. Previous scholars aimed to quantify dynasties solely based on the last names of political candidates. This creates substantial room for errors; last names alone are not good predictors, especially because some are very common. Using Brazil as a case study, we overcome this problem by webscrapping the full names of the parents of Mayoral candidates. We then grouped these names by state to further increase the accuracy of the data. To find out which Brazilian states had the highest percentages of dynasties, we compared our webscrapped names with an existing dataset of past politicians using Approximate String Matching. The methodology used in this paper may bring light to the unknown consequences of dynasties. By making our dataset publicly available, we encourage further studies to be done in this field. Specifically, we suggest that future scholars investigate why Northeastern states in Brazil have the highest rates; how the Sarney family influences the results in Maranhão; and whether dynastic candidates are wealthier than non-dynastic ones on average.