This paper uses the first wave of HILDA in an analysis of the determinants of fertility, focusing in particular on the role of education. Estimating lifetime fertility from micro data sets is generally quite difficult since a large proportion of the sample, because of their age, will have incomplete fertility. The HILDA survey allows this problem to be addressed, however, because as well as measuring the actual number of children a person has, there is also information on the additional number of children a person expects to parent. Thus it is possible to estimate the determinants of fertility in three dimensions: the actual number of children a person has, the expected future number of children, and total intended lifetime fertility, the sum of the first two. The analysis is conducted in several stages. First, total intended lifetime fertility is modelled as a function of education and a host of other variables reflecting the opportunity costs and consumption elements of child rearing. The HILDA sample allows control for a host of other factors, reflecting both attitudes and values, and their roles are examined as well. The main result is that education lowers total lifetime fertility, although the strength of this relationship falls importantly with the addition of a range of variables, such as marital history and equivalised household income. A second set of estimations concerns the determinants of the expected future number of children, controlling for the number of children a person already has. The estimations reveal that more educated people tend to have significantly higher fertility expectations than others, and that the effect is non-linear. The juxtaposition of the results of the two approaches could be interpreted to mean that higher education per se does not lower people’s fertility expectation while the more educated tend to defer their fertility and may end up with fewer children due to some unexpected constraints such as deterioration or breakdown in relationship and fecundity problems at later stage. Realising these risks before hand along with appropriate institutional and financial supports from the government may help the educated people to achieve their fertility expectation. In addition to education, all fertility measures are influenced importantly by, among others: household income (negative for the first and positive for the second); partnering (positive); the significance of religion in people’s lives (positive); and values concerning motherhood (positive). Many different specifications were explored with the main conclusions being robust. It is recognised, however, that fertility decisions are likely to be made in combination with a host of other life-cycle issues, such as investment in education, and that the results of the estimations need to be qualified by this reality.