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Activity Number:
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410
- Social Issues, Trends, Inequality, and Employment
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Social Statistics Section
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Abstract #322498
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Title:
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A Bayesian Approach to Misclassified Binary Response: Female Employment and Intimate Partner Violence in Urban India
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Author(s):
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Joon Jin Song* and Yoo-Mi Chin and James Stamey
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Companies:
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Baylor University and Baylor University and Baylor University
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Keywords:
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Bayesian misclassification model ;
propensity score matching ;
intimate partner violence ;
female employment
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Abstract:
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We examine the effect of female employment on the odds of physical spousal violence using a Bayesian misclassification model combined with propensity score regression estimation. While a classical propensity score model finds a significant violence-provoking effect of female employment, our model finds no evidence of a significant effect. This suggests that misleading inferences are caused by falsely small standard errors in a model that does not account for uncertainties around propensity scores. Further, we confirm our misclassification model as a preferred specification using Deviance Information Criterion (DIC).
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Authors who are presenting talks have a * after their name.
