Patterns and trends in occupational attainment of first jobs in the Netherlands, 1930–1995: ordinary least squares regression versus conditional multinomial logistic regression
This paper brings together the virtues of linear regression models for status attainment models formulated by second-generation social mobility researchers and the strengths of log-linear models formulated by third-generation researchers, into fourth-generation social mobility models, by using conditional multinomial logistic regression (CMLR). These CMLR models are capable of capturing the discrete and multidimensional nature of social mobility patterns (a characteristic of third-generation output) while reducing the number of parameters leading to parsimonious models (a characteristic of second-generation output). Using data from eight pooled surveys in the Netherlands, an extended Blau–Duncan status attainment model is formulated and analysed. The corresponding CMLR model is formulated incorporating general and specific inheritance effects. The final CMLR model gives a relatively parsimonious description of Dutch mobility patterns, similar to the extended Blau–Duncan model, at the same time offering the possibility of including specific effects where necessary. Effects of gender and education appear to be too complex to be captured by a single parameter.