Abstract
In the current chapter, a survey of various models and methods used in migration predictions to date is offered. The rationale is that socio-economic predictions can be based not only on general, well-grounded laws and theories, but also on descriptive models designed to suit specific research questions. The presented overview follows a distinction between deterministic and probabilistic approaches, presented respectively in Sections 4.1 and 4.2, depending on the way the uncertainty issue is treated. The presented models and methods are finally compared and evaluated from the point of view of their usefulness for the purpose of the current and possible future studies.
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Notes
- 1.
Informally referred to as ‘the underlying story’ (a term likely coined by Dutch demographer Harri Cruijsen).
- 2.
The mentioned issues have been discussed for example during the ‘Meeting of the Working Group on Population Projections’ in Eurostat (Luxembourg, 15–16 July 2004), and the ‘Joint Eurostat – UN ECE Work Sessions on Demographic Projections’ (Vienna, 21–23 September 2005 and Bucharest, 10–12 October 2007).
- 3.
In Eurostat (2005), the methodology differed for the ‘old’ 15 EU member states and for the 12 accession and candidate countries as of 2004. For the latter, the projections were purely based on judgemental scenarios, while for the former they involved averaging of forecasts yielded using three methods: extrapolation of trends, and econometric analysis of migration determinants, as well as national forecasts (Lanzieri, 2004).
- 4.
Apparently, the majority of population flows concerning the ex-USSR in the period 1992–1998 proved to be internal migration, and migration between the former republics. For example, in Russia alone, over 20.5 million people migrated internally in that period, 5.5 million immigrated from abroad, and 2.6 million emigrated (Wegren & Cooper Drury, 2001, pp. 16, 39). It is, however, worth noting that the two last figures relate to a large extent to the population exchange with the other republics of the former Soviet Union (idem).
- 5.
I am grateful to Izabela Koryś for drawing my attention to this.
- 6.
- 7.
A detailed overview of selected micro- and macro-level migration models, with special focus on the latter (mainly Poisson regression, gravity models and spatial interactions), is also offered in Stillwell and Congdon (1991).
- 8.
- 9.
Paradysz (2004, pp. 130) noted that an earlier study addressing the same problem was due to Korčak-Čepurkivs’kij (1970), which, however, was published only posthumously. Its author was persecuted under the Stalinist regime and spend 18 years in a Gulag forced labour camp (cf. the interview with A. Višnevskij in Demoscope weekly 197–198, 4–17 April 2005; http://www.demoscope.ru/weekly/2005/0197/analit01.php, accessed on 25 August 2006).
- 10.
For a mathematical discussion of multi-regional and multi-state models, see Jóźwiak (1992, pp. 51–94, 95–111).
- 11.
Such models are also sometimes referred to as ‘hierarchical’ due to the presence of a hierarchy of regions, although in the current study only the term ‘multi-level’ is used, in order to avoid confusion with hierarchical models in a statistical sense (i.e., models with random parameters or with latent variables, see also Chapter 12).
- 12.
A discussion on the stochastic versions of population dynamics models is offered by Jóźwiak (1992, pp. 113–121).
- 13.
Cf. Charemza and Deadman (1992/1997, pp. 13–22, 151–166); some issues are also addressed in Chapters 6 and http://10.
- 14.
See ‘Markov chain’ and ‘Markov process’ in the Springer online Encyclopaedia of Mathematics (eom.springer.de/M/m062350.htm, eom.springer.de/M/mm062490.htm, accessed on 25 August 2006), and the review of probabilistic literature of the Kolmogorov Library (http://www.kolmogorov.pms.ru/uspensky-predvarenie.html, accessed on 25 August 2006), albeit the latter quoting the 1907 edition of the seminal work of Markov (1906).
- 15.
Overview presented after Kupiszewski (2002b, pp. 28–34). Paradysz (2006, pp. 232) labels such mobility as ‘vertical’, as opposed to ‘horizontal’ (moves in geographic space). Whereas the former can also concern mobility within other social structures (educational, occupational, national, ethnic, etc.), the latter explicitly refers to migration.
- 16.
Many thanks go to Katarzyna Bijak for drawing my attention to Hidden Markov Models, and for a brief tutorial.
- 17.
- 18.
One well-known example is the Fertility and Family Survey (FFS), carried out in the 1990s in 20 European countries under the auspices of the Population Activities Unit (PAU) of the UN Economic Commission for Europe, following a common research design. Migration-related questions have been included in one of the optional modules of the original FFS. According to the knowledge of the author, up till the time of writing of this book, the FFS has been repeated only once in a handful of originally-participating countries. Although the PAU also conducts another European-wide survey on Generations and Gender, it does not include any information about migration. More detailed information is available on the PAU website: http://www.unece.org/pau (accessed on 9 August 2006).
- 19.
A brief description of the project, its rationale, basic assumptions, and methodology are offered for example by van der Gaag, de Beer, and Willekens (2005). More details can be found on the project website: http://www.micmac-projections.org (accessed on 30 April 2006).
- 20.
More complete and detailed literature surveys of existing econometric forecasts are presented for example in Alvarez-Plata et al. (2003), in a report of the CPB (2004), as well as in Brücker and Siliverstovs (2005).
- 21.
Some authors underline the association of migration with family formation and dissolution processes, concerning marriages, cohabitations, divorces, and, indirectly, childbearing (Paradysz, 2006, pp. 235). The direct impact of (internal) migration on fertility has been assessed for example by Kulu (2005), who found empirical support for a hypothesis that migrants might adjust their fertility to the levels observed in the host community, rather than preserve levels from their regions of origin. In the case of international migration, however, the situation can be more complex, for example due to the presence of ethnic enclaves (‘ghettos’) which do not facilitate the integration of immigrants with the host society. Moreover, a similar notion of convergence applies to mortality: it can be argued that migrants ‘adjust’ their mortality patterns to the ones prevailing at the destination, via two channels: access to the same health care services and exposure to the same environmental hazards as the host population (e.g., Bijak et al., 2007).
- 22.
More information is available from the project website: http://www.stat.fi/tup/euupe (accessed on 5 May 2006).
- 23.
I am very grateful to Jacek Osiewalski for drawing my attention to this problem.
- 24.
A summary of various probability models that can be used for migration forecasts is offered in Willekens (2008), who argues for using Poisson regression for counts of migrants, logit or logistic models for proportions of migrants in a given population, and Poisson models with offset for occurrence-exposure rates. The discussion further distinguishes models for state occupancies, transition probabilities, and transition rates.
- 25.
Despite a very general title (‘Demographic forecasting’), the book by Girosi and King (2008) explicitly deals in very much detail with various methods for Bayesian forecasting mortality rates by age, time, cause of death, etc.
- 26.
‘Model’ can be defined as ‘a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs’ (the Merriam-Webster Online Dictionary; http://www.m-w.com, accessed on 25 April 2006).
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Bijak, J. (2011). Forecasting Migration: Selected Models and Methods. In: Forecasting International Migration in Europe: A Bayesian View. The Springer Series on Demographic Methods and Population Analysis, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8897-0_4
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