Stochastic modelling of intersectional pay gaps in universities.

The gender and ethnicity pay gaps are well publicised for academics. The majority of research relies on observations representing a point in time or uses models to consider a standard academic lifespan. We use a stochastic mathematical model to ask what drives differences in lifetime earnings of university academics and highlight a new question: how best should we quantify a working lifetime? The model observes and accounts for patterns in age when entering and leaving the workforce, and differing salary trajectories during an academic career. It is parameterized with data from a national dataset in Aotearoa New Zealand. We compare the total lifetime earnings of different gender and ethnicity groups with and without accounting for the different lengths of time spent in academia. The lifetime earnings gaps are considerably larger when we account for different hiring and leaving ages. We find that overall, for every ethnicity, women have shorter careers and are more likely to leave academia. All minority ethnic groups-and women-earn considerably less than their male white, European colleagues.

Kia kaua te reo e rite ki te moa, ka ngaro: do not let the language suffer the same fate as the moa.

More than a third of the world's languages are currently classified as endangered and more than half are expected to go extinct by 2100. Strategies aimed at revitalizing endangered languages have been implemented in numerous countries, with varying degrees of success. Here, we develop a new model regarding language transmission by dividing the population into defined proficiency categories and dynamically quantifying transition rates between categories. The model can predict changes in proficiency levels over time and, ultimately, whether a given endangered language is on a long-term trajectory towards extinction or recovery. We calibrate the model using data from Wales and show that the model predicts that the Welsh language will thrive in the long term. We then apply the model to te reo Maori, the indigenous language of New Zealand, as a case study. Initial conditions for this model are estimated using New Zealand census data. We modify the model to describe a country, such as New Zealand, where the endangered language is associated with a particular subpopulation representing the indigenous people. We conclude that, with current learning rates, te reo Maori is on a pathway towards extinction, but identify strategies that could help restore it to an upward trajectory.