Shared Cultural History as a Predictor of Political and Economic Changes among Nation States

Abstract

Political and economic risks arise from social phenomena that spread within and across countries. Regime changes, protest movements, and stock market and default shocks can have ramifications across the globe. Quantitative models have made great strides at predicting these events in recent decades but incorporate few explicitly measured cultural variables. However, in recent years cultural evolutionary theory has emerged as a major paradigm to understand the inheritance and diffusion of human cultural variation. Here, we combine these two strands of research by proposing that measures of socio-linguistic affiliation derived from language phylogenies track variation in cultural norms that influence how political and economic changes diffuse across the globe. First, we show that changes over time in a country's democratic or autocratic character correlate with simultaneous changes among their socio-linguistic affiliations more than with changes of spatially proximate countries. Second, we find that models of changes in sovereign default status favor including socio-linguistic affiliations in addition to spatial data. These findings suggest that better measurement of cultural networks could be profoundly useful to policy makers who wish to diversify commercial, social, and other forms of investment across political and economic risks on an international scale.

Fig 1. Autocorrelation of state (1a) and change (1b) in autocracy-democracy ‘Polity score’ on the socio-linguistic affiliation network at biennial intervals.

Fig 2. The density of political changes (proportion of intervals with any change in polity score) layered across geography and the socio-linguistic affiliation network.

Network connection thickness is scaled to recency of ancestry. Only the 10 most recent connections are visualized for each country. Depicted network communities were inferred on the socio-linguist affiliation network through the Louvain algorithm [49] as implemented in Gephi [50].