Empirical research frequently involves regression analysis with binary categorical variables, which are traditionally handled through dummy explanatory variables. This paper argues that separate category-wise models may provide a more logical and comprehensive tool for analysing data with binary categories. Exploring different aspects of both methods, we contrast the two with a Monte Carlo simulation and an empirical example to provide a practical insight.
Holgersson, H.E.T., Nordström, L. & Öner, Ö. (2014). Dummy Variables vs. Category-wise Models. Journal of Applied Statistics, 41(2), 233-241. DOI: 10.1080/02664763.2013.838665