Seminar Vol. 161
Title: Handwriting Quality and Grader Bias – Statistical Discrimination, Taste-based Bias, or Cognitive Bias?
Speaker: Jianfeng Xu, ShanghaiTech University
Time: May 24rd, 2019 13:30–15:00
Venue: Conference Room 106B, Zhonghui Building (IESR, JNU College of Economics)
About the speaker:
Jianfeng Xu is an Assistant Professor in the School of Entrepreneurship and Management, ShanghaiTech University. He received a Ph.D. from University of Illinois at Urbana-Champaign in 2018. Jianfeng Xu's main research interests are in Labor Economics, Development Economics, Behavioral Economics and Economics of Education.
Abstract:
Grading of the content may be biased against poor handwriting, resulting in discrimination against students with poor handwriting. We are the first to estimate the bias and decompose it to uncover its sources. We find that the bias alters 1.9% of high school admission outcomes and contributes to 70% of the gender gap in 9th grade’s writing test. The challenge with identifying bias is that ability may be correlated with handwriting quality. To quantify handwriting quality, we conduct a field experiment in a prefecture of China utilizing special rubrics for handwriting quality. To break the intrinsic correlation between handwriting and content quality, we randomly create two handwritten versions for each of 800 essays. The estimated bias is about 0.44 of a standard deviation: 1 point in handwriting (0-5) results in 2.45 points bias in content scores (0-60). Further experiments break the mechanism of this bias into statistical discrimination and Becker’s taste-based bias. We find statistical discrimination is negligible. In addition to true taste-based bias, we propose two cognitive biases (halo effect and cognitive fluency effect) and suggest halo effect is the major source of bias. To correct the attenuation bias caused by measurement error, we develop a new instrumental variable estimator that improves the small sample performance of traditional instrumental variable estimators.
