Bayesian Probabilistic Projection of International Migration

Azose, Jonathan J.; Raftery, Adrian E.

doi:10.1007/s13524-015-0415-0

Cited by 58 publications

(61 citation statements)

References 28 publications

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“…We do not incorporate knowledge of migration quotas, because these quotas do change over time, as is happening with European Union quotas in the wake of Syria's refugee crisis. Furthermore, our migration model gives results consistent with the historical frequency with which countries switch between being net senders and net receivers of migrants (21).…”

Section: Discussionsupporting

confidence: 78%

“…We project net migration for all countries with a Bayesian hierarchical model on net migration rates (21). We define the net migration rate, r ct , for country c in time period t as a ratio with the numerator being the net number of migrants in country c over a 5-year period starting at time t and the denominator being the population of country c at time t, divided by 5 so that it is expressed in migrants per year per 1,000 population.…”

Section: Methodsmentioning

confidence: 99%

“…We have developed a probabilistic methodology for projecting net migration for all countries (21). Based on this, we have developed a method for projecting populations for all countries by age and sex that takes account of uncertainty about migration.…”

mentioning

confidence: 99%

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Probabilistic population projections with migration uncertainty

Azose

Ševčíková

Raftery

2016

Proc. Natl. Acad. Sci. U.S.A.

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We produce probabilistic projections of population for all countries based on probabilistic projections of fertility, mortality, and migration. We compare our projections to those from the United Nations' Probabilistic Population Projections, which uses similar methods for fertility and mortality but deterministic migration projections. We find that uncertainty in migration projection is a substantial contributor to uncertainty in population projections for many countries. Prediction intervals for the populations of Northern America and Europe are over 70% wider, whereas prediction intervals for the populations of Africa, Asia, and the world as a whole are nearly unchanged. Out-of-sample validation shows that the model is reasonably well calibrated.Bayesian hierarchical model | international migration | predictive distribution | United Nations | World Population Prospects I n this paper we describe a method for probabilistic projection of population for all countries, with a focus on accounting for uncertainty in projections of international migration. In particular, we are motivated by the needs of the United Nations (UN) Population Division in producing population projections for all countries until 2100 based on projections of fertility, mortality, and migration.A variety of forces contribute to the ebb and flow of international migration. Economic theories at varying levels of granularity indicate that migration flows can arise from individual attempts to maximize income (1, 2), household-level mitigation of risk (3, 4), or differences in global supply and demand for labor (5, 6). Individuals decide to migrate based on an assessment of push and pull factors (7), which may include migration policy (8), geopolitical conflict (9), and quality of the natural environment (10, 11). Networks of migrants provide a feedback mechanism such that migration flows tend to perpetuate themselves over time (12,13). Bijak (14) gives a thorough overview of theories and models of international migration. Despite their acknowledged role in driving migration, our model does not make use of push and pull factors, economic or otherwise, as covariates. Such factors are largely too difficult to predict in the long term to be of use. Instead, we appeal to the inertia of selfperpetuating migration patterns by modeling migration as an autoregressive process.Historically, most methods for projecting population have been deterministic. If the current population is known, broken down by age and sex, and future age-and sex-specific rates are projected for fertility, mortality, and migration, then the cohortcomponent method produces population projections (15). However, the UN Population Division now produces probabilistic projections of population, fertility, and mortality for all countries, but these projections still condition on deterministic migration projections (16, 17). The current methodology in the UN's World Population Prospects (WPP) differs from country to country but typically projects that net migration counts will remain cons...

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Section: Discussionsupporting

confidence: 78%

Section: Methodsmentioning

confidence: 99%

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Probabilistic population projections with migration uncertainty

Azose

Ševčíková

Raftery

2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…There are many social, economic, political and environmental drivers which impact migration flows (Massey et al, 1993), yet there is no single, robust migration theory that can be used for forecasting purposes (Arango, 2000). Besides, even if credible explanations of past migration flows existed, their tenets would be difficult to extrapolate into the futurefor that reason, most of the formal forecasting models rely on time series analysis, be it frequentist or Bayesian (Azose & Raftery, 2015;Bijak, 2010;Bijak & Wiśniowski, 2010;Cappellen, Skjerpen, & Tønnessen, 2015;de Beer, 1993de Beer, , 2008Wiśniowski, Smith, Bijak, Raymer, & Forster, 2015). Besides, even if credible explanations of past migration flows existed, their tenets would be difficult to extrapolate into the futurefor that reason, most of the formal forecasting models rely on time series analysis, be it frequentist or Bayesian (Azose & Raftery, 2015;Bijak, 2010;Bijak & Wiśniowski, 2010;Cappellen, Skjerpen, & Tønnessen, 2015;de Beer, 1993de Beer, , 2008Wiśniowski, Smith, Bijak, Raymer, & Forster, 2015).…”

Section: Introductionmentioning

confidence: 99%

Assessing time series models for forecasting international migration: Lessons from the United Kingdom

Bijak

Disney

Findlay

et al. 2019

Journal of Forecasting

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Migration is one of the most unpredictable demographic processes. The aim of this article is to provide a blueprint for assessing various possible forecasting approaches in order to help safeguard producers and users of official migration statistics against misguided forecasts. To achieve that, we first evaluate the various existing approaches to modelling and forecasting of international migration flows. Subsequently, we present an empirical comparison of ex post performance of various forecasting methods, applied to international migration to and from the United Kingdom. The overarching goal is to assess the uncertainty of forecasts produced by using different forecasting methods, both in terms of their errors (biases) and calibration of uncertainty. The empirical assessment, comparing the results of various forecasting models against past migration estimates, confirms the intuition about weak predictability of migration, but also highlights varying levels of forecast errors for different migration streams. There is no single forecasting approach that would be well suited for different flows. We therefore recommend adopting a tailored approach to forecasts, and applying a risk management framework to their results, taking into account the levels of uncertainty of the individual flows, as well as the differences in their potential societal impact.

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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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Bayesian Probabilistic Projection of International Migration

Cited by 58 publications

References 28 publications

Probabilistic population projections with migration uncertainty

Probabilistic population projections with migration uncertainty

Assessing time series models for forecasting international migration: Lessons from the United Kingdom

Multistate modelling extended by behavioural rules: An application to migration

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