Stephen Morgan (2009) collected a dataset on Chinese prisoners in Australia (among many other datasets) who were mostly convicted for a number of small crimes, with relatively few of them convicted for major crimes. As Morgan explains in his article, this dataset is potentially subject to two different selectivity biases, one of them being migrant selection: The Chinese migrants who went to Australia might have differed from the remaining population, plus the degree and type of selectivity might have changed over time. A large push of immigration from China to Australia occurred during the Australian Gold Rush of the 19th century, as Morgan (2009) explains, and most migrants came from the Southeastern Guangdong province. The second potential selectivity issue can occur when the individuals analysed were imprisoned. Poorer (and often shorter) people could be expected to be more likely to be imprisoned (although in reality, this is not always true, perhaps because tall stature is an asset in physical criminal activities, see Baten and Blum, 2012). There might also be changes in prisoner selectivity over time (for example, during economic boom phases, the opportunity costs of criminal activity are higher, and vice versa in crisis periods). In order to approach these selectivity issues, Morgan compared his source on Chinese migrants in prison with other datasets, and in other studies; non-migrants in a large sample of railway employees, and non-criminal migrants, for example. These have been analysed jointly, for instance, by Baten, Ma, Morgan and Wang (2010), who indicate that the resulting trends are relatively similar across datasets, even after coming from very distinct institutional contexts and different recruitment periods. The Chinese migrant prison dataset contains a large time dimension, spanning almost the entire 19th century. The regional focus is clearly on southern China and the province of Guangdong in particular. Occupations are recorded in the dataset, with labourers and miners mentioned most frequently. The dataset only consists of men, since it comes from prison inmates in jails that only imprison male criminals.