  Latent variable models are increasingly used in economics for
high-dimensional categorical data like text and surveys. We demonstrate the
effectiveness of Hamiltonian Monte Carlo (HMC) with parallelized automatic
differentiation for analyzing such data in a computationally efficient and
methodologically sound manner. Our new model, Supervised Topic Model with
Covariates, shows that carefully modeling this type of data can have
significant implications on conclusions compared to a simpler, frequently used,
yet methodologically problematic, two-step approach. A simulation study and
revisiting Bandiera et al. (2020)'s study of executive time use demonstrate
these results. The approach accommodates thousands of parameters and doesn't
require custom algorithms specific to each model, making it accessible for
applied researchers
