María Constanza Street

Work Intensity and Non-Completion of University: Longitudinal Approach and Causal Inference.

Researchers focused upon the work–dropping out connection tend to show a U-shaped relationship between the likelihood of dropping out and the number of hours worked outside school, with a higher exit rate for both non-working students and for students whose working hours pass a critical threshold. Yet the data typically used by these researchers are drawn mainly from cross-sectional surveys, and as a result does not allow for any causal interpretation. The present article uses an event history analysis of Canadian longitudinal data covering seven years of a cohort, and offers original findings on the causal work–dropping out relationship at the university level. We find evidence showing that the evolution of the exit rates and the factors influencing the decision to quit a particular university programme differ substantially between students who want to enrol in another programme and those who do not. For the latter, we observe a critical threshold of 24 h of work, beyond which negative effects in terms of non-completion start to appear. We find no negative effects arising from not working vs. working a few hours. Our findings thus tend to show that the higher exit rate among non-working students evidenced in cross-sectional data should be attributed to the fact that academic difficulties cause some potential university dropouts to stop working and to devote more time to school.

Household Surveys as a Source of Data for Event History Analysis The Study of Family-Related Life Events in Argentina Using the Encuesta Permanente de Hogares

The authors introduce a method that allows the use of data from rotating panel surveys, a design used in many household or labour force surveys, to realize statistical analyses similar to event history analysis. The method is illustrated with two examples, one on the dynamics of poverty — the effect of demographic and socioeconomic factors on the hazard of becoming poor in Argentina — and the other on family dynamics — the conversion of consensual unions into marriages. Both examples use data from the Argentinean Encuesta Permanente de Hogares, a national survey that is not designed to collect prospective or biographical data. The method allows for the use of time-varying independent variables and thus allows one to estimate the effect of an event on the hazard of another event, as in conventional event history analysis; several examples are provided.