Here - Tilburg University

greatly inefficient. Only very few events are surrounded by a very large amount
of non events. These facts call for a method, which takes this rareness of the
dependent variables into account. As a consequence, a logistic regression for
rare events data has to be conducted to analyze the reasons for starting and
expanding a family. King & Zeng (2001a; 2001b) developed a method that
combines these two issues, enabling both types of corrections to work
simultaneously.
For the first time in the field of Swiss demography, a comparison of a
conventional logistic regression and a logistic regression for rare events data is
conducted. This helps to statistically explore and research the different
structures and factors that influence fertility behavior. The results indicate the
importance of using a logistic regression specifically designed for rare events
data. This study examines the aforementioned comparison on the basis of the
Swiss Household Panel (SHP) by way of statistical methods for rare events data
and conventional logistic regression. In so doing, it reveals important
background factors and motives for fertility behavior, which do not
underestimate the event of having a baby. In terms of empirical considerations,
a new dimension is developed.