Kalinda Ukanwa

Kalinda Ukanwa
University of Southern California | USC · Marshall School of Business

BS-Ind Engineering, MS-Ind Eng, MBA PhD Marketing (Quantitative)

About

7
Publications
4,474
Reads
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744
Citations
Introduction
Kalinda Ukanwa is Assistant Professor of Marketing at the University of Southern California. A quantitative modeler, Professor Ukanwa researches how algorithmic bias, algorithmic decision-making, and consumer reputations impact firms. She is the winner of the 2018 Eli Jones Promising Young Scholar Award and a finalist for the 2018 INFORMS Service Science Best Student Paper Award, 2019 Howard/AMA Doctoral Dissertation Award, and the 2020 AMS Mary Kay Doctoral Dissertation Award.
Education
August 2013 - May 2019
University of Maryland, College Park
Field of study
  • Marketing (Quantitative)
September 1999 - June 2001
Stanford University
Field of study
  • Business
April 1993 - January 1995
Stanford University
Field of study
  • Industrial Engineering

Publications

Publications (7)
Article
Full-text available
This research examines how school choice impacts school segregation. Specifically, this work demonstrates that even if parents do not take the racial demographics of schools into account, preference differences between Black and White parents for other school attributes can still result in segregation. These preference differences stem from motivat...
Article
This article addresses two seemingly incompatible claims about identity: (a) complex, multivalent identities are advantageous because they afford greater flexibility versus (b) simple, focused identities are advantageous because they facilitate valuation. Following Faulkner, it is hypothesized that a focused identity is helpful in gaining entree in...
Article
Full-text available
Why are similar workers paid differently? I review and compare two lines of research that have recently witnessed great progress in addressing “unexplained” wage inequality: (i) worker unobserved heterogeneity in, and sorting by, human capital; (ii) firms’ monopsony power in labor markets characterized by job search frictions. Both lines share a vi...

Questions

Question (1)
Question
I have a Hierarchical Bayes probit model, but I'm seeking answers in general for any random coefficients model.   Depending on what level I calculate DIC (sum the DICs per unit a vs. DIC for the average respondant), I get different answers in model comparison to a regular probit model.  The by unit way is worse than the regular probit, the other is better.  The hit rate for the HB model is superior the regular probit, so I don't why the sum of the by unit DICs is worse than regular probit.  

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