Modelling and forecasting the spatial and temporal patterns of bilateral international migration flows

Lead Research Organisation: University of Manchester
Department Name: Social Sciences

Abstract

1.Introduction

The existing data on migration flows are incomplete/incomparable which limits the understanding of various types of migration and its impact (Abel &Sanders,2014; Willekens et.al.,2016; Wisniowski,2017). This limitation stems from the fact that migration does not have a unique definition and is, consequently, poorly measured (Bilsborrow et.al.,1997:15-28). Other measurement problems, including population coverage, systematic bias and lack of accuracy (Wisniowski et.al.,2013:585; Disney et.al.,2016:11). Due to these issues, there is the need for a consistent framework for estimating bilateral migration flows enabling the production of reliable forecasting. The proposal: to develop a set of statistical models for estimating and forecasting the spatial and temporal patterns of bilateral international migration flows. These models allow for the breakdown of migration by reasons for moving (De Beer,2008:292-302).

In recent years, there have been significant efforts in developing methods for measuring and estimating international migration flows. Methods have also been developed to forecasting migration (Bijak,2010; Bijak &Wisniowski,2010; Wisniowski et.al.,2015; Disney et.al.,2015:29).

2.Scope and data
This study will focus on data from South American countries including independent and dependent territories. 1990-2017 data will be used, including censuses, international surveys, national household surveys, governmental offices of migration. Depending on the source/territory, data on migrant stocks and flows are available. These data will be disaggregated by reason for moving for countries where such data are available. This issue of sparse and missing data can be mitigated by extending/applying statistical methods described in the next section.

3.Proposed methodology
The data available for South American countries are much more sparse and potentially of lower quality due to being derived (e.g.) from surveys. This calls for a bespoke statistical model that would be able to combine sparse data from various data sources which, in principle, do not aim at measuring migration.

Generalising the Raymer et.al.'s model (2013:803), multiple tables of migration data flows from source k can be combined in a measurement model that corrects for data inadequacies. I can specify a model for the true (unobserved) migration flow at time t to allow parameter estimates for the role of demographic, social and economic factors on movements (Abel,2010:802; Raymer et.al.,2013:817). This approach is usually based on a gravity-type model (Sen & Smith,2012:49-53) which assumes that the flows are proportional to the population size of the sending and receiving countries and inversely proportional to the distance between them with estimable parameter. The posterior distribution of the true (unobserved) migration flow at time t, will represent the final synthetic data used as outputs for the model for estimates of migration flows over time with associated uncertainty. By including a time-specific parameter, forecasts of future flows can be produced. Introduction of specific covariates can allow extending the model to estimate particular types of migration.

Where only data on migrant stocks rather than flows are available, the Abel (2013: 508-524) and
Abel &Sander's (2014:1521) methods can be used. The model requires migrant stocks data as input and can be estimated using maximum likelihood. In the proposed project, I will refine the model considering the criticism of Dennet (2016) and apply Bayesian inference to provide flow estimates with measures of uncertainty that will be fed into the flows model described.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/P000665/1 01/10/2017 30/09/2027
2070641 Studentship ES/P000665/1 01/10/2018 30/06/2022 Andrea Lisette Aparicio Castro