Intergenerational mobility measurement with latent transition matrices

Journal article

AbstractWe propose a multivariate approach for the estimation of intergenerational transition matrices. Our methodology is grounded on the assumption that individuals’ social status is unobservable and must be estimated. In this framework, parents and offspring are clustered on the basis of the observed levels of income and occupational categories, thus avoiding any discretionary rule in the definition of class boundaries. The resulting transition matrix is a function of the posterior probabilities of parents and young adults of belonging to each class. Estimation is carried out via maximum likelihood by means of an expectation-maximization algorithm. We illustrate the proposed method using National Longitudinal Survey Data from the United States in the period 1978-2006.

Authors: Bavaro, M; Tullio, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Journal of Economic Inequality
• Volume: 21
• Issue: 1
• Pages: 25-45
• Publication date: 2022-12-31
• DOI: 10.1007/s10888-022-09554-6
• EISSN: 1573-8701
• ISSN: 1569-1721
• Language: English
• Keywords: Estimation; Basis (linear algebra); Function (biology); Transition (genetics); Econometrics; Class (philosophy); Psychology; Economics; Artificial intelligence; Mathematics; Maximum likelihood; Unobservable; Multivariate statistics; Computer science; Matrix (chemical analysis); Expectation–maximization algorithm; Statistics