The spread of Christianity in Austronesian societies

What explains the success of Christianity as a religion, especially in (relatively) modern times? For example, modern Pacific Island cultures are predominantly Christian, and none of them would have begun converting until the first missionaries arrived in the 17th Century. An article last year by Joseph Watts (Max Planck Institute for the Science of Human History and University of Oxford) and co-authors, published in the journal Nature Human Behaviour (sorry I don't see an ungated version), looks into this question.

Watts et al. used data on Austronesian cultures (which are spread from Tahiti in the east to Sumatra in the west, plus Madagascar) and the timing of the conversion of half of each culture to Christianity over the period from 1668 to 1950, to test three hypotheses:

1. "whether cultures with greater political organization are faster to convert to Christianity, as predicted by top-down theories of conversion" - more politically complex societies are also more inter-connected, and the theory is that an innovation such as Christianity will spread faster;
2. "whether cultures with higher levels of social inequality are faster to convert" - more unequal societies have a social stratification, and Christianity brings a more egalitarian ideal that might appeal to those at the 'bottom'; and
3. "whether larger populations are slower to convert" - with larger populations, it simply takes longer for innovations to be adopted, particular when an innovation (such as Christianity) requires interaction between people (conversion).

They found that:

...population size was found to be significantly positively correlated with conversion times, indicating that larger populations took longer to convert to Christianity. Consistent with the top-down theory, political complexity was negatively associated with conversion times... Counter to the bottom-up theories, there was no reliable support for an association between conversion time and social inequality...

You might be wondering why I've posted about this particular research paper. It isn't because of the theory, or the results. Instead, I found the methods quite interesting. Health warning: the following description might be a little too technical for some readers.

A particular problem emerges in regression models when observations are not independent. For instance, regions that are close together spatially (e.g. neighbouring regions) are likely to be similar, and demonstrate similar relationships between variables. Treating the regions as independent observations (when they aren't, because they are neighbouring and therefore similar) leads the estimated standard errors from a regression model to be too small. The consequence of that is that we are more likely to have models tell us that coefficients are statistically significant, than they would be if we correctly treated the observations as not being independent. To deal with this in the case of regions, there are spatial econometric models.

Watts et al. don't deal with the problem of spatial dependence, but they do deal with a closely related problem that I have been thinking about for the last year or more - cultural dependence. We often run cross-country regression models (e.g. for happiness studies), treating the country-level observations as independent. However, countries that have similar cultures are not independent observations, and we should be accounting for that. Watts et al. do this in their model:

Standard regression methods assume that cultures are independent from one another, despite them being related through common descent and patterns of borrowing... This non-independence can lead to spurious correlations, and the difficulty of distinguishing such spurious correlations from correlations that result from actual causal relationships between variables has come to be known as Galton’s problem. The PGLS-spatial method developed by Freckleton and Jetz makes it possible to address Galton’s problem using a phylogeny to control for non-independence due to common ancestry, and geographic proximity to control for non-independence due to diffusion between cultures.

Their phylogenetic approach uses a language-based family tree to define how close (or far away) each Austronesian culture is from others. This is a nice approach to dealing with the issue of cultural dependence between the observations, and something we should make more use of in regression models. The irony is that in the case of this paper, the phylogenetic and spatial dependencies turned out to be statistically insignificant. I guess that, sometimes, a lack of independence between observations isn't as big a problem as we may worry it is.

[HT: New Zealand Herald, last year]