Count data models for demographic data∗

Key demographic variables, such as the number of children and the number of marriages or divorces, can only take integer values. This papers deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality. Most empirical work in population economics has neglected the fact that the dependent variable is a nonnegative integer. In the few cases where this feature was recognized, the authors advocated the use of the Poisson regression model. The Poisson model imposes, however, the equality of conditional mean and variance, a restriction which is often rejected by the data. We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over‐ and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility.     

The plight of international child support enforcement

Arising from the landscape of global development and fueled by the demographics of increasing rates of mobility and divorce, the international enforcement of child support is a challenging and growing problem. Current initiatives to rectify this critical problem cut across geopolitical divides and have resulted in the creation of uniform procedures for child support enforcement that are awaiting ratification in the USA: the 2007 Hague Convention on the International Recovery of Child Support and Other Forms of Family Maintenance. In the following paper, we will trace the evolution of these efforts and extrapolate a framework for social work practice illustrated by examples drawn from a case study of a family's plight with international child support enforcement. While thousands of families are impacted by this issue, there is a gap in the social work literature about this critical issue.