The determinants of financial awareness are a widely researched area in consumer economics and related fields. Financial awareness is usually measured using a Likert scale-based variable, with the various levels of the variable indicating the underlying latent utility, which is unobserved. Given the discrete nature of the financial awareness variable, it is not possible to use methods such as quantile regression methods to capture how changes in a covariate affect different quantiles of the financial awareness variable in its Likert-scale form. In this study, we utilize ideas from the Bayesian econometrics literature to uncover the underlying latent utility for the Likert measured financial awareness variable. Specifically, Albert and Chib (1993) outlined how one can uncover the underlying latent utility in both a standard and ordered probit model using the Gibbs sampler. We then use quantile regression methods to examine the relationship between commonly used covariates and the transformed latent utility representation of financial awareness. Using a quantile regression method allows for a more nuanced explanation of how changes in covariates affect the dependent variable.

Accepted Oral Presentation