Comparing internal migration across the countries of Latin America: A multidimensional approach

Abstract

While considerable progress has been made in understanding the way particular aspects of internal migration, such as its intensity, age profile and spatial impact, vary between countries around the world, little attention to date has been given to establishing how these dimensions of migration interact in different national settings. We use recently developed measures of internal migration that are scale-independent to compare the overall intensity, age composition, spatial impact, and distance profile of internal migration in 19 Latin American countries. Comparisons reveal substantial cross-national variation but cluster analysis suggests the different dimensions of migration evolve systematically to form a broad sequence characterised by low intensities, young ages at migration, unbalanced flows and high friction of distance at lower levels of development, trending to high intensities, an older age profile of migration, more closely balanced flows and lower friction of distance at later stages of development. However, the transition is not linear and local contingencies, such as international migration and political control, often distort the migration-development nexus, leading to unique migration patterns in individual national contexts.

Fig 1. Aggregate crude migration intensity.

Source: IMAGE Repository, global mean across sample of 61 countries encompassing all world regions from [2] Note: Intensities greater than or equal to 0.5 standard deviation above the global mean are classified as high, intensities 0.5 standard deviation above or below are classified as intermediate, and intensities lower than or equal to 0.5 standard deviation below the global mean are classified as low.

Fig 2. Age at migration peak against normalised migration intensity at peak.

Source: IMAGE Repository. Note: Measured were derived from migration data disaggregated by single years of age, normalised to unity, and smoothed using Kernel regression [53]. Global mean was estimated for sample of 25 countries encompassing all world regions [3]. Gridlines are located 0.5 standard deviations from the global mean.

Fig 3. Index of Net Migration Impact.

Source: IMAGE Repository, global mean across a sample of 71 countries from [4] Note: INMIs greater than or equal to 0.5 standard deviations above the global mean are classified as high, INMIs 0.5 standard deviations above or below are classified as intermediate, and INMIs lower than or equal to 0.5 standard deviations below the global mean are classified as low.

Fig 4. Migration effectiveness against migration intensity.

Source: IMAGE Repository, global mean across a sample of 71 countries from [4].

Fig 5. Distance decay parameter for regions with mean populations of 200,000.

Source: IMAGE Repository, global mean across a sample of 29 countries from [5] Note: Distance decay parameters greater than or equal to 0.5 standard deviation above the global mean are classified as high, distance decay parameters 0.5 standard deviation above or below are classified as intermediate, and distance decay parameters lower than or equal to 0.5 standard deviation below the global mean are classified as low.