Ş. Pelin Akyol
Department of Economics
06800 Ankara / TURKEY
Information about this author at RePEc
NBER Working Papers and Publications
|August 2018||Taking PISA Seriously: How Accurate are Low Stakes Exams?|
with Kala Krishna, Jinwen Wang: w24930
PISA is seen as the gold standard for evaluating educational outcomes worldwide. Yet, as it is a low-stakes exam, students may not take it seriously resulting in downward biased scores and inaccurate rankings. This paper provides a method to identify and account for non-serious behavior by leveraging information in computer-based assessments in PISA 2015. We show that this bias is large: a country can rise up to 15 places in rankings if its students took the exam seriously. We ask where the bias is coming from and show that around half of it comes from the proportion of non-serious students, while 36% comes from their ability, with the remaining coming from the extent of non-seriousness
|July 2016||Hit or Miss? Test Taking Behavior in Multiple Choice Exams|
with James Key, Kala Krishna: w22401
We model and estimate the decision to answer questions in multiple choice tests with negative marking. Our focus is on the trade-off between precision and fairness. Negative marking reduces guessing, thereby increasing accuracy considerably. However, it reduces the expected score of the more risk averse, discriminating against them. Using data from the Turkish University Entrance Exam, we find that students' attitudes towards risk differ according to their gender and ability. Women and those with high ability are significantly more risk averse: nevertheless, the impact on scores of such differences is small, making a case for negative marking.
|March 2014||Preferences, Selection, and Value Added: A Structural Approach|
with Kala Krishna: w20013
This paper investigates two main questions: i) What do applicants take into consideration when choosing a high school? ii) To what extent do schools contribute to their students' academic success? To answer these questions, we model students' preferences and derive demand for each school by taking each student's feasible set of schools into account. We obtain average valuation placed on each school from market clearing conditions. Next, we investigate what drives these valuations by carefully controlling for endogeneity using a set of creative instruments suggested by our model. Finally, controlling for mean reversion bias, we look at each school's value-added.
We find that students infer the quality of a school from its selectivity and past performance on the university entrance exam. Ho...
Published: Şaziye Pelin Akyol & Kala Krishna, 2016. "Preferences, Selection, and Value Added: A Structural Approach," European Economic Review, . citation courtesy of