Estimation of unordered-choice dependent variables requires a multinomial logistic model (Greene, 2000). It is intended for use when the dependent variable takes on more than two outcomes and the outcomes have no natural ordering. In our study, the family formation variable, "fertility and marital status at the age of 33", takes on six outcomes without natural ordering. Similar to logistic regression, the multinomial logistic regression provides a measure of the probability of one outcome relative to the reference outcome, known as relative risk. However, it is more difficult to interpret the relative risk from multinomial logistic regression since there are multiple equations. As an alternative, prediction is used to aid interpretation. We use the "method of recycled predictions", in which we vary characteristics of interest across the whole data set and average the prediction (STATA Reference, version 7). For example, in our data set we have those who initiated sex at ages 11-15, 16-17, 18-19, and those who had not initiated sex by age 19. We first assume that all respondents initiated sex at ages 11-15 but hold their other characteristics constant. We then calculate the probabilities for each fertility and marriage outcome. We repeat this exercise for the other three initiation groups. The difference between any two sets of calculated probabilities, then, is the difference due to different ages of sex initiation, holding other characteristics constant. For example, the predicted probabilities of "never married with children" for the four age categories of sex initiation are 0.112, 0.087, 0.075, and 0.042, respectively.