No abstract

This paper examines the hypotheses that a greater stock of migrants in New Zealand from a particular country leads to more trade between that country and New Zealand, and a greater stock of New Zealanders living overseas in a particular country leads to more trade between that country and New Zealand. It also examines the relationships between migration, diaspora and tourism exports. The literature suggests that migrants can stimulate trade by lowering transaction costs, and by bringing with them preferences for goods produced in their home country. Our approach is to apply panel data techniques, within the framework of a standard gravity model of trade, to a dataset consisting of information on 233 countries in each of the 26 years between 1981 and 2006. We estimate a random effects panel sample selection model using correlated random effects. Our results indicate that migration does indeed stimulate trade. In our benchmark specification, merchandise exports from and imports to New Zealand both have a statistically significant relationship with numbers of migrants in New Zealand; imports also have a statistically significant relationship with numbers of New Zealanders overseas. Our results also suggest that migration stimulates imports more than exports.

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.

No abstract