An individualized inference of social mobility via generative analysis of discrete data

Music City Center 

Inspired by the concepts of individualized recommendation and personalized medicine, we propose an individualized inference method for social science to estimate intergenerational mobility-i-mobility-in American society. Leveraging the generative analysis framework introduced by Liu et al. (2021) and a kernel smoothing metric for similarity scoring, our approach enables the tracking of changes in subject profiles defined by specific combinations of characteristics. This, in turn, provides insights into social changes at the profile level or near the individual level. Additionally, our method addresses key estimation challenges posed by small sample sizes and the presence of mixed data in social surveys.

Intergenerational social mobility

Generative method

Personalized inference

Mixed data analysis 

Survey Research Methods Section