Quantifying Global International Migration Flows

Abel, Guy J.; Sander, Nikola

doi:10.1126/science.1248676

Cited by 453 publications

(368 citation statements)

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“…Estimates of both the ve and ten-year ows during the earlier periods no longer show a steady increase. Instead, global 8 Estimates for ows in Abel and Sander (2014) incorrectly treat UN stock data for Serbia, Montenegro, Sudan and South Sudan in 1990 and 2000 as separate countries 9 Demographic data, not provided for the unied areas were obtained by combining data from the separate countries Consequently, the percentage of estimated migrants, shown in the bottom panel of Figure 12 during this period falls more sharply than estimates based on a xed number of countries.…”

Section: Changes In Political Geographymentioning

confidence: 99%

“…In this paper, the methodology of Abel (2013) and Abel and Sander (2014) is extended once more and applied to estimate ve and ten-year migrant ows separately by gender between 1960 and 2010. The application of the revised methodology allows for an updated view of international migration over a far longer time period.…”

Section: Introductionmentioning

confidence: 99%

“…Abel (2013) used global bilateral stock tables from the World Bank to derive global bilateral ow estimates between 1960 and 2000 over four ten-year periods via a proposed ows from stocks methodology. The methodology was altered slightly, and then applied by Abel and Sander (2014) to estimate bilateral migration ows over four ve-year periods between 1990 and 2010, based on the changes in global bilateral stocks of the United Nations. The alteration in the methodology allowed the dierence of the estimated immigration and emigration ow totals to match the net migration estimates of United Nations Population Division (2011).…”

Section: Introductionmentioning

confidence: 99%

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Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015¹

Abel

2018

International Migration Review

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu Terms of use: Documents in EconStor may / 2015Guy J. Abel Estimates of Global Bilateral Migration Flows by Gender Between 1960 and 2010 AbstractMeasures of international migration flows are often limited in both availability and comparability. This paper aims to address these issues at a global level using an indirect method to estimate country to country migration flows from more readily available bilateral stock data. Estimates are obtained over five and ten-year periods between 1960 and 2010 by gender, providing a comprehensive picture of past migration patterns. The estimated total amount of global international migrant flows is shown to generally increase over the 50 year time frame. The intensity of migration flows over five and ten-year periods fluctuate at around 0.6 and 1.25 percent of the global population respectively, with a noticeable spike during the 1990-95 period. Gender imbalances in the estimated flows between selected regions were found to exist, such as recent movements into oil rich Gulf States from South Asia. The sensitivity of flow estimates to alternative input stock and demographic data as well as changes in political geography are explored.

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Section: Changes In Political Geographymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015¹

Abel

2018

International Migration Review

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“…We use an individual-based model rather than a population-based model such as the popular cohort-component model. For an overview of approaches to forecasting migration, see Bijak (2011), as well as other recent contributions from Hatton and Williamson (2011), Azose and Raftery (2013), and Abel and Sander (2014. This individual-based micro perspective enables us to incorporate behavioural mechanisms and social processes that influence demographic behaviour and population change into our model.…”

Section: Introductionmentioning

confidence: 99%

Multistate modelling extended by behavioural rules: An application to migration

et al. 2017

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We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age-and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.Keywords: international migration; life course modelling; Theory of Planned Behaviour; multistate modelling; microsimulation; agent-based modelling IntroductionFor policymakers and researchers alike, it is of paramount interest to be able to predict how people make demographically relevant decisions. In this paper, we present a novel approach to projection by incorporating decision-making and interaction between individuals in a demographic projection model. We use an individual-based model rather than a population-based model such as the popular cohort-component model. For an overview of approaches to forecasting migration, see Bijak (2011), as well as other recent contributions from Hatton and Williamson (2011), Azose and Raftery (2013), and Abel and Sander (2014. This individual-based micro perspective enables us to incorporate behavioural mechanisms and social processes that influence demographic behaviour and population change into our model. We can thus predict the effect of external shocks, such as policy changes, by making the causal mechanism through which shocks alter behaviour explicit. Comparing model predictions with empirical outcomes facilitates the subsequent drawing of conclusions on the plausibility of the assumed behavioural mechanism. In this way, the model can be improved gradually over time, by refining the parameters and functional forms of its main component, in this case the decision-making on international migration.In the model, as in real life, demographically relevant decisions are embedded in the human life course. Choices are motivated by aspirations and preferences in different domains of life, such as work and family, and constrained by available resources (not only financial, but also cognitive and social resources, and time). We take into consideration that people do not have perfect foresight and usually lack the time and cognitive abilities to acquire the full and unbiased information necessary to make a rational choice. Preferences vary over the life course, as do both the availability of and need for resources, because of events or changing conditions (e.g., available social support). As a result, t...

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“…Population pyramids were first published by Walker (1874), whose statistical atlas of the ninth United States census contains a wide range of chart types presenting demographic data. Today, web applications like Gapminder World (Rosling 2006), the Global Flow of People (Abel and Sander 2014), or the Human Mortality Explorer (Schöley 2016) allow users to actively explore the data by manipulating interactive plots.…”

Section: Introductionmentioning

confidence: 99%