Improved measures for the cross-national comparison of age profiles of internal migration

Abstract

We develop and demonstrate the application of a concise set of measures intended to encapsulate key features of the age profile of internal migration and highlight the significant differences that exist between nations in these profiles. Model schedules have been the most common method of comparing internal migration patterns but issues related to the estimation and interpretation of their parameters hinder their use for cross-national comparison. We demonstrate that the interpretation of exponential coefficients as rates of ascent and descent does not best reflect the slopes of migration age profiles, and we propose more consistent measures based on the rate of change in migration intensity. We demonstrate, through correlation and factor analysis, that most of the inter-country variance in migration age profiles is captured by the age at and intensity of peak migration. The application of these two indicators to 25 countries reveals significant differences between regions.

Smoothing internal migration age profiles for comparative research

“…Source: Based on Rogers and Castro, 1981 In a recent review of model migration schedules, Bernard et al (2014a) identified two main limitations related to the estimation of model schedules. The first limitation bears upon the difficulty of selecting the appropriate number of component curves.…”

“…The age and intensity at which migration peaks have widely been used to characterise and compare migration age patterns across countries, either alongside other metrics (Rogers and Castro 1981) or as the main or sole summary measures (Bell et al 2002;Bell and Muhidin 2009;Bernard et al 2014a;Bracken and Bates 1983;Rees et al 2000). The choice of a particular model and its specification affect the shape of the fitted curve, which in turn influences estimates of the age and intensity at peak.…”

“…While model schedules are based on accumulated evidence primarily from contemporary Europe and the United States, they constrain migration age patterns to follow a standard predicted shape that may not describe migration age patterns accurately in other regions or in historical populations. Furthermore, model schedules face limitations related to their estimation (Bernard et al 2014a), including the difficulty of selecting an optimal set of component curves specific to each age profile (Rees et al 2000), the instability of parameter estimates (Congdon 1993), and the sensitivity of estimates to initial parameter values (Rogers et al 2005). Researchers have therefore to employ a trial-and-error approach to decide on the set of component curves and initial parameter values that will yield the best fit, which directly influences the shape of the estimated curve and the value of the estimated parameters.…”

“…Rogers and Castro (1981) have demonstrated that this broad age profile is replicated across a number of countries and at various spatial scales. Subsequent analysts have proposed summary indicators of the age profile that can be used to make comparisons across countries and over time (Bell et al 2002;Bernard et al 2014a) and to examine the association of the migration age profiles with other demographic processes such as life-course age patterns (Bernard et al 2014b). Since its introduction by Rogers et al (1978), the model migration schedule has been widely adopted as the main method to smooth migration age profiles.…”

“…Our particular concern is to assess the performance of these three models on the key indicators used for comparing aggregate migration age patterns across countries and over time, namely the age at which migration peaks and the intensity at the peak (Bernard et al 2014a). This work forms part of the IMAGE project (Internal Migration Around the GlobE, http://www.gpem.uq.edu.au/image), an international programme of collaborative research.…”