The patterns and trends in global international migration flows since the 1960s: a revisit with new data and methods

Nikola D. Sander, Wittgenstein Centre (IIASA, VID/ÖAW, WU)
Philip H. Rees, University of Leeds
Guy J. Abel, Wittgenstein Centre (IIASA, VID/ÖAW, WU)

In the absence of harmonised data on global international migration flows, estimates of net migration and bilateral migrant stock data published by the United Nations and the World Bank are used as proxy measures. A growing body of literature draws on these datasets to suggest a steady increase in the volume of global migration, a diversification of destinations, and a growing impact of migration on human settlement in recent decades. In this paper, we argue that net migration estimates and data on the number of people living outside their country of birth (i.e. migrant stock) do not adequately capture the complex spatial patterns and trends in global migration flows. We use new estimates of 10-year bilateral flows between 193 countries from 1960 through 2010 to calculate a set of indicators of migration spatial structure at global and regional levels. As laid out by Bell et al. (2002) for comparative studies of internal migration, we argue that the spatial structure of global migration flows can be decomposed into the intensity of migration, the degree of connectivity (or spatial focussing), the distance of migration, and the impact on the settlement pattern. We compare five indicators of spatial structure across world regions, across time, and between stocks and flows. Our results show that the increase in the volume of global migration flows is much lower than absolute net migration and migrant stocks suggest. In fact, when related to the size of the global population, the volume of flows has been almost stable since the 1960s. We find that stock data tend to overestimate the intensity of migration in Europe relative to other regions and the impact of migration in the Americas. Our new visualisation method highlights contemporary trends in bilateral flows that are inadequately captured by stock data.