Social inequality is ubiquitous in contemporary human societies, and has deleterious social and ecological impacts. However, the factors that shape the emergence and maintenance of inequality remain widely debated. Here we conduct a global analysis of pathways to inequality by comparing 408 non-industrial societies in the anthropological record (described largely between 1860 and 1960) that vary in degree of inequality. We apply structural equation modelling to open-access environmental and ethnographic data and explore two alternative models varying in the links among factors proposed by prior literature, including environmental conditions, resource intensification, wealth transmission, population size and a well-documented form of inequality: social class hierarchies. We found support for a model in which the probability of social class hierarchies is associated directly with increases in population size, the propensity to use intensive agriculture and domesticated large mammals, unigeniture inheritance of real property and hereditary political succession. We suggest that influence of environmental variables on inequality is mediated by measures of resource intensification, which, in turn, may influence inequality directly or indirectly via effects on wealth transmission variables. Overall, we conclude that in our analysis a complex network of effects are associated with social class hierarchies.

Recent work suggests that not all aspects of learning benefit from an iconicity advantage (Ortega, 2017). We present the results of an artificial sign language learning experiment testing the hypothesis that iconicity may help learners to learn mappings between forms and meanings, whilst having a negative impact on learning specific features of the form. We used a 3D camera (Microsoft Kinect) to capture participants’ gestures and quantify the accuracy with which they reproduce the target gestures in two conditions. In the iconic condition, participants were shown an artificial sign language consisting of congruent gesture–meaning pairs. In the arbitrary condition, the language consisted of non-congruent gesture–meaning pairs. We quantified the accuracy of participants’ gestures using dynamic time warping (Celebi et. al., 2013). Our results show that participants in the iconic condition learn mappings more successfully than participants in the arbitrary condition, but there is no difference in the accuracy with which participants reproduce the forms. While our work confirms that iconicity helps to establish form–meaning mappings, our study did not give conclusive evidence about the effect of iconicity on production; we suggest that iconicity may only have an impact on learning forms when these are complex.

Understanding the relationship between gesture, sign, and speech offers a valuable tool for investigating how language emerges from a nonlinguistic state. We propose that the focus on linguistic status is problematic, and a shift to focus on the processes that shape these systems serves to explain the relationship between them and contributes to the central question of how language evolves.

The production of this forecast is supported by the Institute's Corporate Members: Bank of England, HM Treasury, Mizuho Research Institute Ltd, Office for National Statistics, Santander (UK) plc and by the members of the NiGEM users group.

The production of this forecast is supported by the Institute's Corporate Members: Bank of England, HM Treasury, Mizuho Research Institute Ltd, Office for National Statistics, Santander (UK) plc and by the members of the NiGEM users group.

The production of this forecast is supported by the Institute's Corporate Members: Bank of England, HM Treasury, Mizuho Research Institute Ltd, Office for National Statistics, Santander (UK) plc and by the members of the NiGEM users group.

The production of this forecast is supported by the Institute's Corporate Members: Bank of England, HM Treasury, Mizuho Research Institute Ltd, Office for National Statistics, Santander (UK) plc and by the members of the NiGEM users group.

The production of this forecast is supported by the Institute's Corporate Members: Bank of England, HM Treasury, Mizuho Research Institute Ltd, Office for National Statistics, Santander (UK) plc and by the members of the NiGEM users group.

The production of this forecast is supported by the Institute's Corporate Members: Bank of England, HM Treasury, Mizuho Research Institute Ltd, Office for National Statistics, Santander (UK) plc and by the members of the NiGEM users group.