1. Discriminatory Grading? Analysis of Eighth-Grade
Teacher’s Assigned Math Scores in S˜ao Paulo, Brazil
Kai Wei Tan †
Senior Economics Thesis
29 April 2016
Abstract
I evaluate the work of Botelho and Rangel (2015) to examine if there is any discriminatory
grading between black and white students by eighth-grade mathematics teachers in S˜ao Paulo,
Brazil. I revise Botelho and Rangel (2015) causal-based model from their 3rd order polynomial
model to a linear model. The result turns out to be almost the same as theirs–0.02 of one
standard deviation gap between the black and white students after controlling for child demo-
graphics, performance standardized tests, past math grades, family background and behavioral
traits. Next, to further check for the robustness of the model, borrowing the theory of learning
by Altonji and Pierret (1997), I adjust the interacting regression from the original paper to
study whether having the same mathematics teacher again reduces discriminatory grading. My
result aligns with the theory of learning–a 0.016 of one standard deviation difference between
having a same teacher and a new teacher for their mathematics class. To investigate how
the Oaxaca decomposition performs against the causal-based model, I perform the Oaxaca De-
composition, from a single dimension to multiple-dimensions (child demographics, performance
standardized tests, past math grades, family background and behavioral traits) and conclude
that discrimination, the unexplained variation, accounts 56% of the difference in the math-
ematics grade between the black and white students, in contrast to the causal-based model
that explains 6%. Nevertheless, I recognize that the unexplained variation is sensitive to the
construct of the renormalization.
†
I would like to thank Professor David Card, Graduate Student Instructor Carl Nadler and Kaushik
Krishnan for their time, tremendous insight, guidance and encouragement throughout this process; Professor
Jane Mauldon who has inspired me to choose racial discrimination as a topic for my research paper; lastly,
the staffs in the Data Lab, Doe Library, for my countless visits to the facility in replicating this paper’s
causal based model.
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