Quantitative assessment of asylum-related migration : a survey of methodology.
Date: 2017-08-29
Institutions: European Asylum Support Office
Subjects: migration, political asylum, quantitative analysis, research report

European Asylum Support Office

Quantitative 
assessment of 
asylum-related 
migration
A survey of methodology

August 2017

SUPPORT IS OUR MISSION

Acknowledgements

This work has been prepared by Jakub Bijak, Jonathan J Forster and 
Jason Hilton from the ESRC Centre for Population Change, University 
of Southampton, and was funded by the European Asylum Support 
Office (EASO), under the contract EASO/2015/290. The authors would 
like to thank Christoph Curchod, Andres Delgado, Louise Waldenström, 
Johanna Delac, Haidar Al-Talibi, and other colleagues from SEM and 
Migrationsverket for sharing their insights into the Swiss and Swedish 
models.

Disclaimer

The opinions expressed in the report are those of the authors and 
do not necessarily reflect the views of the European Asylum Support 
Office. EASO does not guarantee the accuracy of the data included in 
this study. No person acting on EASO’s behalf may be held responsible 
for the use which may be made of the information contained therein.

European Asylum Support Office

Quantitative 
assessment of  
asylum-related 
migration
A survey of methodology

AUGUST 2017

SUPPORT IS OUR MISSION

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Quantitative assessment of asylum-related migration. A survey of methodology — 3

Contents

Report Highlights .......................................................................................................................................................... 5

Executive Summary ...................................................................................................................................................... 6

1. 

2. 

Introduction and Background .............................................................................................................................. 8

State of the art: The conceptual framework for analysing asylum-related migration ...................................10

3.  Review and assessment of the existing sources of data on asylum-related migration .................................13
3.1  Assessment framework and criteria .........................................................................................................13
3.2  Data on asylum-related migration ............................................................................................................ 14
3.3  Data on drivers: push and pull factors, and intervening obstacles .........................................................18
3.4  Meta-information on asylum policies across Europe...............................................................................19

4.  Review and assessment of the existing methods for modelling asylum-related migration ..........................21
4.1  Model evaluation framework and criteria ................................................................................................21
4.2  Statistical and econometric methods and models ...................................................................................22
4.3  National practice in predicting asylum-related flows in Europe .............................................................26
4.4  Notes on the validation of models ............................................................................................................ 31

5. 

Towards a bespoke EASO model of asylum-related migration: A feasibility study........................................34
5.1  Synthesising the possible modelling frameworks ....................................................................................34
5.2  Design of the bespoke EASO model structure ..........................................................................................35
5.3  Identifying the data/information gaps and limitations ...........................................................................39
5.4  A simple illustration of the framework .....................................................................................................40

6.  Conclusions and recommendations .................................................................................................................. 42

Glossary .......................................................................................................................................................................44

Bibliography ................................................................................................................................................................ 45

Appendix A. Inventory of selected data and meta-data sources ............................................................................50

Appendix B. Detailed assessment of data and methods .......................................................................................... 51

Tables and Figures

Table 1. Evaluation of the existing data on asylum-related flows and their main drivers .........................17
Table 2. Evaluation of the existing methods for modelling and predicting asylum-related flows .............24
Table 3. National practice in asylum-related forecasting and links with contingency planning ................27
Table 4. Results of the validation exercise of Disney et al. (2015) for the forecasts of UK asylum 
seekers, for 2004–2013 ...............................................................................................................................32

Table B 1. Framework for evaluating the data on asylum-related migration – quality descriptors ..........51
Table B 2. Framework for evaluating the existing models and methods – quality descriptors .................51
Table B 3. Specification of selected quantitative models of asylum-related migration ............................52

Figure 1. Conceptualising asylum-related migration within a broader area of migration studies ..............9
Figure 2. Conceptual framework for modelling asylum-related migration ...............................................12
Figure 3. First-time asylum applications to 12 European states .................................................................16
Figure 4. Diagrammatic representation of the Swiss State Secretariat prognosis for asylum flows .........28
Figure 5. Diagramatic representation of the Swedish forecasts for asylum flows .....................................30
Figure 6. Elements of the proposed modelling approach ..........................................................................35
Figure 7. A stylised cusum analysis – the process (left) and the corresponding cusums (right).................36
Figure 8. An algorithmic representation of the workflow in the early warning asylum model .................38
Figure 9. Cusum control plots for Syria and Iran .........................................................................................40

Quantitative assessment of asylum-related migration. A survey of methodology — 5

Quantitative assessment of asylum-related 
migration: A survey of methodology

Report Highlights

•  Asylum-related migration is an analytical category with rather elusive boundaries, which does not 
necessarily map well onto established definitions of migration. In this report, it is understood as 
international forced displacement, caused by persecution, armed conflict, violence, or violations of 
human rights. As such, this is one of the most uncertain types of migration flows.

•  There are many explanation of migration processes, including forced migration, but they lack an 
overarching framework. Here, we endorse utilising a pragmatic approach for modelling purposes, 
which concentrates on individual push, pull and intervening factors operating across a range of levels 
of analysis: from individuals, to groups, to whole countries and societies.

•  The majority of data on asylum applications offer similar estimates, and the key advantage of the 
EASO data is their timeliness. Since 2008, European data have been harmonised, and are thus of 
higher quality and better comparable than before. Data on drivers of asylum are of varying quality, 
and their use in predictions may be problematic, except for crucial policy variables.

•  Quantitative studies of asylum typically rely on aggregate-level statistical or econometric models of 
varying complexity. There are trade-offs between explanatory panel regressions and predictive time 
series models. On the other hand, examples of the best national practice include some form of expert 
opinion. There is a knowledge gap in terms of learning from ex-post model validations.

•  For practical use, we recommend building an early warning model based on EASO data series, 
supplemented by synthetic asylum policy indices, expert opinion on individual asylum-related flows, 
and stakeholder views on what migration levels should trigger a warning. The model should be ideally 
expressed in terms of changes of asylum-related migration levels.

•  In the short-term, work efforts need to be concentrated on specification of the model based on user 
needs. Longer-term modelling endeavours can explore interactive simulations and the use of ‘big 
data’ from social media and other sources, to enhance the early warning capacity of the models. Clear 
communication of model aims, uncertainty and limitations, is crucial for its success.

•  Given the importance of the challenges related to asylum in Europe, a successful development of an 
early warning system is a priority, and would change the perspective in policy and decision-making 
at the EU level from reactive to pro-active. At the same time, contingency planning would become 
naturally embedded at the heart of the modelling process.

6 — Quantitative assessment of asylum-related migration. A survey of methodology 

Executive Summary

The report presents an overview and typology of existing empirical models of asylum-related migration, 
followed by an analysis of empirical data available for modelling purposes, and by an evaluation of models 
prepared in specific countries and by international organisations. Its key aims are to identify, review and 
assess the various quantitative models for explaining and predicting asylum-related migration, and to aid 
the development of a bespoke European asylum model in the longer term, as a part of the wider work 
programme of the European Asylum Support Office (EASO).

In this study, asylum-related migration is defined as international flows of people who are – or who claim 
to be – forcibly displaced due to persecution, armed conflict, violence, or violations of human rights. 
This definition excludes internally displaced persons (IDPs), who seek protection within their countries 
of nationality, as well as people who are forced to migrate by other extreme factors, for example 
famines, environmental crises, or development projects, except when these factors are related to forced 
displacement defined above. The category thus encompasses, but is not limited to, refugees, asylum 
seekers, people with temporary protected status, or others in refugee-like situations, and is independent 
from the duration of stay in the destination country (Section 1).

An overarching theoretical framework for analysing asylum-related migration does not exist despite 
the vast literature on the factors and drivers behind the forced population flows. Moreover, there is 
a disconnection between the explanations offered for asylum-related migration and for migration flows 
generally, and the corresponding bodies of literature rarely intersect. An analysis of push and pull factors of 
migration, together with individual-level and other intervening variables, even though reductionist, remains 
a pragmatic choice for operationalising the conceptual framework to guide the subsequent analysis. The 
specific character of asylum-related flows is related to the presence of extreme push factors behind the 
migration, which render the resulting processes less predictable than other migration types. Uncertainty 
and chance are thus central features of the asylum-related decisions and moves, what is reflected in the 
proposed analytical framework (Section 2).

The majority of data on asylum applications in Europe ultimately derives from national state sources, and 
so major providers of data (UNHCR, Eurostat, EASO, and national statistics offices or migration ministries) 
tend to give estimates that are extremely similar. EASO data has the advantage of providing monthly 
information in a timely fashion, about two weeks after the reference period. Coherent definitions and 
harmonisation due to new European legislation mean that data from 2008 onwards is of higher quality 
and better comparable across countries. Before this date, definitional problems and missing data is in 
evidence. A trade-off between the length of the data series needed to make good generalisations and data 
quality must thus be considered (Sections 3.1 and 3.2). Data on drivers including economic factors, conflict 
and human rights are available from a number of sources and vary in quality. In many cases, the inherent 
unpredictability of many of these factors makes it difficult to include them as covariates for prediction. 
Information on policy ‘pull’ factors is more accessible; the IMPALA database provides comparable and 
detailed indicators on relevant asylum policies and processes (Sections 3.3 and 3.4).

The existing academic approaches for modelling and predicting asylum-related migration have been 
reviewed and assessed according to six criteria: adequate specification, detailed resolution, justifiable 
assumptions, predictive potential, uncertainty assessment, and model generalisability. (Section 4.1). In 
statistical terms, almost all models of asylum-related migration can be seen as instances of generalised 
linear models (GLM) – generalisations of simple linear regression models. Three groups of models are 
analysed: simple regression models, panel regression models, and more complex approaches – time series, 
structural and log-linear models. The available models differ with regard to their complexity, yet all of 
them describe macro-level asylum processes, typically for countries or groups of countries, with usually 
yearly time resolution. There are trade-offs between the models from different groups, with the panel 
models being more comprehensive in terms of drivers and assumptions, and the more complex models 
better suited for prediction. Important features of different models involve political and policy variables, 

Quantitative assessment of asylum-related migration. A survey of methodology — 7

for which there exist encouraging examples for modelling and forecasting of synthetic indices of policy or 
conflict (Section 4.2).

However, the national practice in predicting asylum and preparing contingency plans – even though varying 
widely across Europe with respect to the levels of complexity – typically involves significant expert and 
contextual knowledge. The existing approaches do not rely on advanced statistical techniques but are 
more flexible and timely and thus potentially better suited to match the needs of their users (Section 4.3). 
The existing models are usually not formally assessed ex post, based on new data, although some national 
models get updated up to several times a year. This shows the potential for building a comprehensive 
forecasting framework, which would incorporate expert opinion and allow the predictions and models to 
be dynamically adjusted based on the results of periodic validation (Section 4.4).

For practical purposes, we recommend building an early warning model based on EASO data series on 
weekly or monthly changes in the level of asylum applications, supplemented by asylum policy indicators. 
Other important variables, such as armed conflict intensity measures for different regions of the world, 
could also be used, alongside the expert opinion on the envisaged changes of individual asylum-related 
flows, as well as stakeholder views on the warning thresholds – what levels of migration should trigger 
a model reassessment or a policy action. The asylum data used in the model would be origin-and-destination 
specific, the expert knowledge and conflict measures would be related to countries of origin, and the policy 
indices to the European countries of destination. A two-stage model structure would reflect the distinction 
between the push and pull factors. The various elements would be combined together by the means of 
Bayesian statistics (Sections 5.1 and 5.2).

The key knowledge gaps are related to asylum policy developments across Europe, with synthetic policy 
indices ideally updated in real time – existing projects, such as the IMPALA database, offer an excellent 
blueprint here. Other challenges are related to expert knowledge elicitation, reliant on a pool of experts on 
the countries of origin, and to specifying the warning thresholds for the models, ideally in collaboration with 
the stakeholders. One of the main issues here are related to the trade-offs between the costs of generating 
false alarms by the early warning system and the costs of its inaction in the face of real challenges (Section 
5.3). In this report, we provide a simplified illustration of how the modelling framework may operate, by 
using the examples of Syria and Iran (Section 5.4).

In terms of recommendations, we propose that the work programme concentrates on specification of 
early warning models tailored to user needs. Formal migration models in general can help with explaining 
the data, but not with prediction, which is difficult in the light of the many unpredictable push and pull 
factors. Longer-term modelling endeavours can also explore interactive simulations, where qualitative 
and other survey information could form additional input. From an academic perspective, there are also 
knowledge gaps with respect to people who remain immobile during large-scale crises and impacts of 
asylum on sending countries. Another area worth exploring is related to the use of ‘big data’ from social 
media and other sources to enhance the early warning capacity of the models. Key prerequisites of success 
of any formal modelling include an appropriate communication of the model aims, results, uncertainty 
and limitations to stakeholders (Section 6).

8 — Quantitative assessment of asylum-related migration. A survey of methodology 

1.  Introduction and Background

This study is intended to contribute to a wider work programme of the European Asylum Support Office 
(EASO), aimed at investigating the possibility of building a quantitative model of asylum-related migration 
in Europe. Within this programme, the key objectives of this report are threefold: we aim to identify, review 
and assess the various quantitative models for explaining and predicting asylum-related migration, with 
the view of aiding the development of a bespoke asylum model in the longer term. In particular, the report 
covers the following three topic areas:

1. A comprehensive overview and typology of existing empirical models of asylum-related migration;
2. An analysis of empirical data available for modelling purposes and gaps in the existing information;
3. A critical evaluation of models prepared in specific countries and by international organisations.

Within this framework, we present a feasibility study concerning the possibility of building a Europe-wide 
quantitative models of refugee and other asylum-related migration flows, accompanied by a discussion 
of the opportunities and limitations of different modelling approaches. The review also includes a set 
of recommendations and guidelines on the construction and interpretation of these models, and on 
communication of their results and limits to European policy users.

According to the 1951 United Nations (UN) Convention and the 1967 Protocol Relating to the Status of 
Refugees, the term refugee is very precisely defined in legal terms as someone, who:

“owing to well-founded fear of being persecuted for reasons of race, religion, nationality, membership 
of a particular social group or political opinion, is outside the country of his nationality and is unable 
or, owing to such fear, is unwilling to avail himself of the protection of that country; or who, not 
having a nationality and being outside the country of his former habitual residence as a result of such 
events, is unable or, owing to such fear, is unwilling to return to it.” (UNHCR 1951/1967; Art. 1 A (2))

The focus of this report, however, is on asylum-related migration, and is kept deliberately broad in order 
to encompass a variety of flows of people seeking different forms of humanitarian protection outside their 
respective countries of nationality. The key distinct groups here include refugees and asylum seekers – 
people who are seeking refugee status or another form of protection in a country other than their own; 
which includes foreigners seeking or being granted temporary protected status, or others in refugee-like 
situations, admitted on humanitarian grounds (UN 1998: 34).

For the purpose of this study, asylum-related migration has therefore to jointly meet two criteria: first, 
it needs to be international in nature, and second, it has to be – or claimed to be – related to forced 
displacement, defined as forced migration due to persecution, armed conflict, violence, or violations of 
human rights1. This definition thus excludes internally displaced persons (IDPs), who seek protection within 
their countries of nationality, as well as people who are forced to migrate by other extreme factors, for 
example famines, environmental crises, or development projects2, except when these factors are related 
to forced displacement defined above.

As this definition needs to be related to the operational requirements of EASO, it does not distinguish between 
asylum seekers who have fled conflict or persecution, or those who only claim to have done so while seeking 
protection. Similarly, it does not distinguish between the channels of migration, and includes people subject 
to human smuggling or victims of human trafficking as appropriate, insofar they meet the main criteria 
listed above. Figure 1 presents a conceptual mapping of the various terms and definitions used in this study.

1 

2 

 Compare, for example, the following definition of the World Bank: “Forced displacement refers to the situation of persons who are forced to leave or 
flee their homes due to conflict, violence and human rights violations” (World Bank; http://www.worldbank.org/en/programs/forceddisplacement, as of 
1 June 2016).

 See, for example, the following definition, attributed to the International Association for the Study of Forced Migration (IASFM), which defines forced 
migration as “a general term that refers to the movements of refugees and internally displaced people (those displaced by conflicts) as well as people 
displaced by natural or environmental disasters, chemical or nuclear disasters, famine, or development projects” (Forced Migration Online; http://www.
forcedmigration.org/about/whatisfm, as of 1 June 2016).

Quantitative assessment of asylum-related migration. A survey of methodology — 9

Migration

International migration

Asylum-related migration

Persecution, conﬂict, violence, 
human rights violations

Forced migration

Figure 1. Conceptualising asylum-related migration within a broader area of migration 
studies

Additionally, given the asymmetry in resource implications, unless noted otherwise the focus in this study is 
at inflows of asylum-related migrants, with subsequent migrations being outside the scope of the current 
enquiry. Finally, it is worth noting that asylum-related flows are not related to any specific time criterion 
for the actual or intended duration of stay, which apply to other groups of migrants (UN 1998)3.

The structure of the remainder of this report is as follows. In Section 2, a conceptual framework for 
a quantitative analysis of asylum-related migration is introduced, building on the earlier study on migration 
factors and drivers (Maastricht University & IOM Berlin 2016). In Section 3 the various data sources on 
asylum-related migration and a range of its potential explanatory variables are discussed and evaluated, 
with focus on macro- and mezzo-level factors and drivers. Section 4 is devoted to evaluation of formal 
approaches aimed at modelling and prediction of asylum-related migration. A subsequent synthesis of the 
information from a range of sources and modelling practices, presented in Section 5, enables a feasibility 
study to be carried out related to constructing a bespoke EASO model of asylum flows. The report concludes 
with a set of practical recommendations related to short- and longer-term perspectives for model-building, 
which are discussed in Section 6 alongside the current gaps in data, and the limitation of the model uses. 
The report is accompanied by a glossary of key terms, an inventory of data available for modelling purposes 
(Appendix A), as well as a detailed assessment of various data and models (Appendix B).

3 

 For a more general discussion of issues related to defining migration in Europe, see Poulain et al. (2006) or Raymer et al. (2013).

10 — Quantitative assessment of asylum-related migration. A survey of methodology 

2.  State of the art: The conceptual framework 
for analysing asylum-related migration

This section is devoted to sketching a conceptual framework for quantitative studies of asylum-related 
migration. Its aims are, first, to critically assess the state of the art in the conceptual analysis of asylum-
related processes, and second, to propose a framework for operationalising the various push and pull 
factors, as well as intervening obstacles, which will guide the evaluation of individual data sources and 
models presented in the subsequent sections of this report.

What remains beyond doubt is that the key characteristic feature of migration – and asylum-related 
migration in particular – is its very high volatility and uncertainty, resulting from the susceptibility of 
migration flows to a broad range of factors, drivers, and shock events (Bongaarts and Bulatao 2000; Bijak 
2010). This high volatility leads to the very limited predictability of migration flows overall (Pijpers 2008; 
Bijak 2010), with horizons of prediction of a few years at most for forecasts made on an annual basis (Bijak 
and Wiśniowski 2010). The flows of refugees and asylum seekers, by their very nature, are the most volatile 
of all migration flows, yet can have extremely high impact, not only on the countries of origin but also on 
the receiving ones (Disney et al. 2015).

Despite a long history of attempts to generalise the observed migration patterns, dating back to Ravenstein 
(1885), a theoretical framework of migration studies remains patchy. The main criticisms of the existing 
approaches are that they are fragmented, entrenched in the individual disciplines of scientific enquiry, 
and lacking an all-encompassing view of migration processes (Arango 2000), as well as predictive capacity 
(Kupiszewski & Bijak 2013). Besides, specifically in the context of asylum, forced migration rarely features 
in theoretical enquiries on migration flows (FitzGerald 2015).

With notable exceptions, such as Zolberg (1989) or a few economic theories related to the provision of 
asylum and choices among asylum seekers (for a review, see Suriyakumaran & Tamura 2016), attempting 
to theorise about forced migration often boils down to namechecking armed conflict and violence among 
the key drivers of population flows. As observed by FitzGerald (2015), specialised areas of research, such 
as “refugee studies” or “forced migration studies” rarely intersect with the mainstream of international 
migration enquiries, especially in the theoretical domain. Given this disconnection, FitzGerald calls for more 
work on the transferability of statements on migration in general to the asylum processes, and on the 
interrelations between asylum and other types of migration, such as economic factors or social networks.

Where theories of asylum-related migration exist, they can be difficult to operationalise in a formal model 
without losing the depth of the original thought. For example, Zolberg’s (1989) rich narrative brings together 
a comprehensive analysis of the dynamics of world’s political and economic systems, with political theories 
of the state and social order. His framework can be formalised with a range of proxy variables but this 
approach would not do justice to the complexity of the theoretical model. Similar problems are faced in 
the economic domain: complex theoretical explanations of asylum processes are usually simplified when 
it comes to the modelling (Suriyakumaran & Tamura 2016).

Still, such a reductionist approach, whereby broad theories are distilled into a suite of several explanatory 
variables, is at least a pragmatic choice. In this context, a convenient overarching structure for analysing any 
migration flows – including asylum-related ones – is the classical ‘push-pull’ theoretical model of Everett 
Lee (1966). Briefly, in this framework, migration is a result of interplay between four types of factors: those 
‘pushing’ migrants from their origin countries or regions; those ‘pulling’ them to the areas of destinations; 
the ‘intervening factors’ facilitating or obstructing migration; and finally the individual characteristics of 
migrants. The framework on its own is not predictive – not least because it would require predicting all the 
factors working at different levels – but it can nevertheless help with framing the modelling endeavours.

As remarked by Öberg (1996), the push factors differ in their severity and importance. Those labelled 
as ‘hard’ include overt violence, the presence of armed conflict, or natural disasters, and lead to very 
strong and immediate pressure to migrate. On the other hand, the ones referred to as ‘soft’ – such as the 

Quantitative assessment of asylum-related migration. A survey of methodology — 11

economic factors – are associated more with conscientious choices related to migration. As argued by 
Öberg (1996), the ‘soft’ factors are associated with more stable long-term migration trends, whereas the 
‘hard’ ones are very hard to predict, if at all. Besides, the dominant factors shape the composition of the 
migrant groups: more self-selected when ‘soft’ factors are chiefly at play, and much more indiscriminate 
when the ‘hard’ ones dominate.

Of course, sometimes it can be very difficult to tease out the various reasons for migration, even for 
individual migrants or households. In an influential article, King (2002) has argued that some of the 
dichotomies present in migration studies, such as the one of voluntary versus forced migration, are largely 
obsolete given the complex nature of contemporary migration flows. Instead, it may be more helpful to 
look at the “a complex continuum of coercion and free-will in migration decisions” (King 2002, p. 92). For 
example, armed conflict may pose a very direct and immediate threat to people’s lives, or may result in 
prolonged economic collapse and loss of livelihood of people affected.

Similar conclusions were reached in a slightly different, yet related context of environmentally-driven 
migration flows (Government Office for Science 2011): as the various migration factors and drivers are 
intrinsically linked, it is difficult to isolate the ‘pure’ environmental effects. Importantly, the Government 
Office for Science (2011) pointed out to the often-overlooked problems related to immobility of those 
referred to as ‘trapped populations’ – vulnerable people who are unable to move in the face of losses of 
livelihood or even more immediate dangers. This links to one key gap in the literature mentioned by Arango 
(2000), who argued that migration scholars focus too much on those who move, at the expense of those 
who do not, and the drivers behind lack of mobility.

The different drivers of asylum-related migration have been recently reviewed by Maastricht University 
and IOM Berlin (2016), where a detailed survey of these factors is presented. A graphical synthesis of the 
analytical framework, as seen through the lens of the push-pull approach is offered in Figure 2, with the 
addition of several additional factors identified by Böcker and Havinga (1998). In particular, the roles of 
‘image’ and ‘reputation’ of the countries of destination – in other words, perceptions of, rather than actual 
political, social or economic situation – can be crucial in shaping the decisions of the asylum seekers. In the 
age of mass Internet access and social media, this aspect has become very important – and very dynamic, 
adding to the already high volatility of asylum processes. Besides, crucially given the uncertain character 
migration, Böcker and Havinga (1998) mention chance as an inherent element associated with the decision 
processes in asylum-related migration, in addition to the higher-level uncertainties of the various societal 
processes and drivers.

Hence, as shown in Figure 2, the general framework for analysing asylum-related migration flows 
encompasses various push and pull factors, operating at the most general (macro) levels, mediated by the 
decisions at the individual (micro) level, and a range of cross-level intervening factors. The operationalisation 
of these factors by using specific variables and data sources is discussed next.

12 — Quantitative assessment of asylum-related migration. A survey of methodology 

Push factors - origin

•  ‘Hard’: humanitarian crises: 
  environmental disasters, armed 

conﬂict and violence

•  ‘Soft’: economic hardship and 
  poverty, inequalities, exclusion, 
  demographic factors, climate

Micro level – individual decisions 

Personal characteristics

•  Perceptions or threat of 

violence, attitudes to risk, 
resources, family status, 
  human and social capital, 
  aspirations, preferences, 
  access to information

Macro level – processes and drivers

CHANCE

Macro/mezzo levels

Pull factors - destination

•  Security, wage diﬀerentials, job 
  opportunities, structural labour 
  demand, return on investment, 
  migration and asylum policies, 
  diasporas & migrant networks, 
image/reputation of a country

Intervening variables

•  Proximity (geographic, cultural, 
  historical), costs, presence of 

institutions and intermediaries 
(legal and illegal), routes, risks, 

  availability of information, 
  barriers / border enforcement

Figure 2. Conceptual framework for modelling asylum-related migration
Source: Böcker & Havinga (1998); Maastricht University and IOM Berlin (2016); framework after Lee (1966) and Öberg (1996)

 
 
 
 
 
 
Quantitative assessment of asylum-related migration. A survey of methodology — 13

3.  Review and assessment of the existing 
sources of data on asylum-related migration

3.1  Assessment framework and criteria

Any potential predictive model of asylum requires appropriate data. In order to facilitate the systematic 
assessment of the available data sources related to asylum flows, this section sets out consistent criteria 
that specifically relate to the intended goal of modelling and predicting asylum. Measuring asylum-relating 
migration is often difficult, not least because practical and definitional issues may arise in the collection 
and analysis of data (Bakewell 1999). Furthermore, the asylum system is hugely political, and many groups 
with competing interests attempt to attach their own narrative and interpretations to the ‘ground truth’ 
(Crisp 1999; Bakewell 1999).

Due to the specific focus of this project on asylum flows into Europe and asylum applications specifically, 
it is possible to sidestep some of the more severe difficulties associated with data on forced migrants in 
developing countries (see Bakewell 1999, UNHCR 2002). European countries are much better able to devote 
resources to enumerating arrivals, and the formalised nature of European labour markets together with 
the attractiveness of refugee status in European countries mean that those fleeing conflict are likely to 
be absorbed relatively quickly into the asylum application system (Bakewell 1999). The legal nature of the 
process of applications for asylum also means that the state has direct information about numbers, both of 
applicants and grants of status, which means that asylum applicants are in some cases better measureable 
than other forms of migration (Disney et al. 2015).

Despite this, there remain issues to consider when examining present and historical asylum data. Firstly, 
domestic political concerns, geopolitics, and national reputational issues may lead to secrecy or selective 
reporting of national statistics (Crisp 1999). For instance, during the Cold War, Western states viewed 
large asylum flows as propaganda victories over the communist bloc. Conversely, domestic opinion is 
often hostile to asylum seekers, and so this may incentivise states to focus on their ability to return failed 
claimants (ibid).

Incentives for individual asylum seekers may also lead to problems with asylum data. Those who are 
unsuccessful with a first attempt at gaining protected status may attempt to re-apply elsewhere, leading 
to the possibility of double counting in asylum statistics, particularly if applicants adopt false identities 
(Singleton 2016). The EURODAC database4, which allows the sharing of information (including fingerprints) 
about applicants, facilitates the re-identification of asylum seekers and thus reduces the incentives to take 
this route.

Furthermore, differences in administrative procedures across 28 member states may lead to additional 
problems. The length of the application process may differ over the continent, so that migrants who 
begin the application process at the same time in different states may show up in the statistics in different 
periods, which may be problematic when trying to relate outflows to inflows in over time (Singleton 2016). 
Definitions of data reported may also differ between sources; some may relate number of cases rather 
than number of individuals, while others describe numbers of first-time applicants while excluding repeat 
applicants counted by other states (UNHCR 2002).

Finally, given that the aim of this report is to make recommendations regarding a model to aid decision-
making and planning among European member states, consideration of whether potential data sources 
meet operational requirements is also vital. In particular, it is important that data arrive quickly enough 
for model outputs to be relevant and actionable, and that the frequency of the dataset is granular enough 
to allow responsive interventions to changes in the asylum system.

4  Details at http://ec.europa.eu/dgs/home-affairs/what-we-do/policies/asylum/identification-of-applicants/index_en.htm 

14 — Quantitative assessment of asylum-related migration. A survey of methodology 

Potential data sources are thus judged against the following criteria:

1. Frequency – How frequently are the data updated? Equivalently, what is the time period between 

consecutive measurements?

2. Definitions – To what extent do the data fit with the required definition of asylum-related migration, 

as discussed above?

3. Coverage – What time periods and spatial area or areas does the data describe, and how does this 

relate to what is required by stakeholders?

4. Accuracy – How far can the data be relied upon to describe the true state of reality with respect to 
asylum-related migration? What biases may the data have, and how uncertain are the data estimates?

5. Timeliness – How soon after the period which the data describe are the statistics released?
6. Quality assurance processes – What processes are in place to ensure that the data meet predefined 

quality standards and that possible errors are caught prior to publication?

Clear side-by-side comparison of the various data-sources is facilitated by the use of a traffic-lights scoring 
system. Each data series is given a rating for each criterion against a three-point colour scale, where 
‘red’ indicates that the data are problematic with respect to that measure; “amber” indicates that some 
problems exist; while “green” indicates that the variables in question meet standards needed for the task 
at hand. Table B1 in Appendix B gives specific descriptions of each point on the scale for every dimension 
of assessment. For an application of a similar evaluation framework, see Disney et al. (2015). Links to all 
data sources described are given in Appendix A.

3.2  Data on asylum-related migration

Based on the criteria developed in the previous sub-section, this section contains a review of existing data 
on asylum-related migration flows. Applying for asylum is a legal process, and so the main source of direct 
information about numbers of applications and numbers granted status is from the EU member states. 
As a result, data on asylum-related migration to European countries might be expected to be replicated 
between different series, as the ultimate source is likely to be the same (Singleton, 2016). However, this 
is not always the case, as Mouzourakis (2014) describes in relation to discrepancies between EUROSTAT 
data, EASO data, Frontex data, and that provided by individual member states. Recent moves by Eurostat 
and EASO to better harmonise definitions and reporting is likely to have alleviated some of these problems 
for more recent periods, however.

The most extensively used source of data regarding asylum and refugees is that provided by the UNHCR. 
In its online database, the organisation provides data from 1951 onwards on stocks of refugees, asylum 
seekers, internally displaced persons, returnees and stateless individuals, together with other ‘persons of 
concern’. These data are comprehensive in terms of its temporal and spatial coverage, detailing both sources 
and destinations of asylum-related migration globally. However, Crisp (1999) suggests that while there were 
significant improvements in quality after the end of the Cold War, prior to this time, a combination of 
political pressures and lack of harmonisation across administrative areas (country ‘desks’) lead to substantial 
problems with data quality. A desire to overstate numbers in order to maximise resources may also have 
been a factor in past crises, particularly as often not all promised supplies would arrive on time. However, 
more recently, UNHCR data collection and quality assurance procedures have improved dramatically (ibid); 
its website provides an Emergency Information Management Toolkit aimed at providing field operatives 
with guidance and tools on exactly what to collect (http://data.unhcr.org/imtoolkit/).

The UNHCR also provides details of asylum applications to industrialised nations from the early 1980s. 
These data are more difficult to access than the stock data described above, being located across statistical 
annexes of reports from several different years5. The information is disaggregated across both sending 
and receiving countries, although a cross tabulation showing specific flows is only available for aggregated 
multi-year periods, rather than for all individual years. For developed nations, the data are based on reports 

5 

Specifically, the UNHCR Statistical Yearbook (2001, 2008-2014), the Asylum Trends report (2005-07) (as detailed in Hatton and Moloney (2015)), and for 
2015, the Global Trends report.

Quantitative assessment of asylum-related migration. A survey of methodology — 15

from receiving governments. More detailed information is provided on specific emergency situations, 
such as the 2015 European migration crisis, but use of such information for continuous monitoring on an 
EU-wide basis is not plausible.

As would be expected, individual national statistical agencies or government agencies responsible for 
migration also publish data on asylum applications and recognition independently. For instance, the 
German Federal Agency for Migration and Refugees provides data on applications and first applications 
from 1995 onwards, while the UK Home Office provides similar data from 1979. The main problem with 
these data sources are inconsistencies in coverage and definitions between data collected by different 
agencies. Although recent moves towards harmonisation within the European Union have improved 
matters somewhat, historical data may still exhibit differences in definitions and reporting, as a review 
in the UNHCR Statistical Yearbook 2001 makes clear (UNHCR, 2002; Table VI.2). The need to consider the 
European system holistically is evident from the discussion in Section 2, and from the review of drivers 
conducted by the Maastricht University & IOM Berlin (2016), so data sources which are consistent would 
be preferred.

These differences between national sources have lessened with the introduction in 2007 of European 
regulations creating obligations on the part of member states to collect statistics on asylum in accordance 
with specific definitions. Eurostat collates these harmonised data from all member states, including not only 
applications, but also status decisions, numbers of unaccompanied minors and resettlements. Furthermore, 
since 2008, details of the age and sex composition of the applicant flow are also available. Eurostat data 
are only available on a consistent and comparable basis from 2008 onwards, with data before this period 
missing for some countries and country/period combinations. However for many of the primary recipient 
countries, the data are present for a substantial period prior to 2008, and metadata is provide detailing 
possible issues. Eurostat does provide disaggregation of applications by month, allowing seasonal patterns 
to be identified. To ensure that data meet the required standards, quality assurance procedures are in 
place for submissions to Eurostat, including the use of reporting templates to ensure consistency and 
checks for internal consistency6.

EASO’s Early-warning and Preparedness System (EPS) data on applications are derived from the same sources 
as the Eurostat data, but its focus on operational support to member states means that the turnaround on 
the data is much quicker than for Eurostat, being provided only 15 days after the reference period (versus 
several months at least for Eurostat data) (EASO, 2016). However, this faster turnaround means that the 
data do not undergo the same validation processes and are not signed off as official statistics, and thus 
may differ from revised data published later. However, EASO provides detailed guidance to member states 
regarding definitions, illustrated by practical examples, and the incentives are on member states to reduce 
duplication by getting the data right first time, so differences between EPS data and equivalent Eurostat 
data might be expected to be limited. Some additional series not provided by Eurostat are included in 
EASO’s collection efforts. In particular, recent moves to begin collecting data on “applications made” (as 
opposed to the currently reported “applications lodged”) may provide a more comparable indication of the 
timing of arrivals to the country in question, as the former is less affected by differences in administrative 
procedure in different countries.

Figure 3 displays the time series of application data from UNHCR, Eurostat, the UK Home Office and the 
German Federal Agency for Migration and Refugees for a selection of top recipient countries. As can 
be seen, for the most part, the data overlap almost exactly, with some moderate differences reflecting 
revisions and/or definitional differences.

6 

The annexes to the metadata for the asylum data series provide details of these procedures: http://ec.europa.eu/eurostat/cache/metadata/en/migr_
asyapp_esms.htm

16 — Quantitative assessment of asylum-related migration. A survey of methodology 

Austria

Belgium

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1990

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Eurostat

National

UNHCR

Switzerland

United Kingdom

100

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1990

2000

2010

1990

2000

2010

Year

Figure 3. First-time asylum applications to 12 European states
Source: Eurostat, UNHCR and, for the UK and Germany, national reporting bodies. Note that often the data coincide exactly and 
thus only the top-most line can be observed. A vertical line at 2008 indicates the date from which Eurostat data are harmonised

Frontex (the European Agency for the Management of Operational Cooperation at the External Borders of 
the Member States of the European Union) collects data on detected crossings at the EU’s external borders. 
Of course, not all border crossing are detected, and furthermore migrants may make multiple attempts to 
cross a border and may cross external EU borders more than once during their journey (Singleton, 2016). 
Thus, while Frontex data provide well-defined and timely operational data on attempted crossings, it can 
only approximate the flows or crossings themselves. It is also possible that some borders are intrinsically 
more difficult to police than others, so that the proportion of crossings detected differs according to border 
type and intensity of policing operations, making interpretation difficult.

 
Quantitative assessment of asylum-related migration. A survey of methodology — 17

Table 1. Evaluation of the existing data on asylum-related flows and their main drivers

Variable

Source

Frequency Definitions Coverage Accuracy Timeliness

Quality 
Assurance

Class

a. Asylum-related migration

Refugee 
Stocks

UNHCR1

National Agencies

Asylum  
Applica-
tions

EUROSTAT

EASO2

UNHCR

Border 
Crossings

Frontex3

b. Drivers of asylum-related migration

Conflict

Human 
Rights

UNCDP Battle 
Deaths Dataset

PRIO Armed 
Conflict Database

Political Terror 
Scale

Freedom House

Economy World Bank

Distance

CEPII

Key to assessment: 
For category descriptors, see Table B1 in Appendix B.
Notes:  1. UNHCR receives low ratings for accuracy mainly due to concerns about historical data;  

 [Amber] Average aspects; 

 [Green] Good aspects; 

 [Red] Problematic aspects. 

2. EASO data are rated ‘amber’ for coverage due to the relative short period for which data are available; 
3.  Frontex data are considered poor as a measure of total border crossings, as it can only capture 

detected crossings.

Sources: See table and Appendix B

18 — Quantitative assessment of asylum-related migration. A survey of methodology 

Overall assessments of potential sources of input data are given in Table 1, with evaluations made 
against the criteria previously set out. Given the high degree of similarity between the various sources of 
application data, the smaller delay between the reference period and data delivery suggests that EASO data 
is the preferable source of information for the dependent variable in any attempt to produce actionable 
predictions of asylum-related migration. This timeliness comes at the cost of a drop in the level of quality 
controls applied to the data but it is not expected that this will be significant.

The extent of historical data is also at issue. Using longer time series provides a greater pool of data for 
analysis and thus potentially better inference but this needs to be traded off against the potentially lower 
data quality. Data from before 2008 are available from UNHCR and Eurostat, although the latter is lacking 
more information than the former, but definitions are not harmonised between sources. Frontex data, 
while necessarily imperfect, provide a complementary sources, as it is an important as a leading indicator 
of short-term flows and about routes taken to access application procedures.

3.3  Data on drivers: push and pull factors, and intervening obstacles

Drawing on the work in the Section 2 conceptualising the processes of asylum-related migration, this 
section identifies metrics and/or proxies for the drivers of flows into Europe and assesses their reliability 
and utility as covariates.

Economic variables are often used to proxy for the attractiveness of host countries; more prosperous 
countries are more likely to be considered attractive as potential destinations for asylum seekers. The 
World Bank provides a comprehensive and widely used repository of economic indicators. However, some 
care must be taken when including economic covariates of this type. Thought must be given to which 
of these indicators are most relevant for those seeking to claim asylum, and also one must consider 
whether top-line indicators such as GDP reflect either the actual experience of those in the country or the 
information potential asylum seekers and refugees have about destination countries (c.f. Haldane 2016). 
Unemployment rates and quantiles of the real wage distribution might provide more information about 
such targets. Economic metrics are of course measured only with uncertainty and are notoriously difficult 
to predict into the future.

CEPII, a French research centre for international economics, provides a range of information on the 
distance between states. As well as geographic distance, other less physical indicators such as linguistic 
similarities and ‘gravity’ indicators based on trade links are also provided. However, CEPII does not 
appear to be currently maintained – this is not a problem for distance and linguistic ties, but the latest 
‘gravity’ indicators date from 2006. Relatedly, the size of diasporas already at a destination country can 
be considered a potential pull factor for migratory flows (Beine, 2011). The OECD Database on Immigrants 
in OECD Countries (DIOC) provides detailed information on migrant populations in industrialised nations 
for 2000/01, 2005/06 and 2010/11, including breakdowns by age and education, based on census data 
and labour force surveys. Although these are not provided by individual years, and only recent data are 
available, migrant stocks presumably change more slowly than flows, and so such data may still be useful 
as covariates.

Measures of human rights can be considered good indicators of push factors in particular states. Freedom 
House provides yearly data from 1972 on both civil liberties and political rights for all countries. These two 
metrics are rated on a 7-point scale by a team of in-house experts, using a broad range of sources. The 
extremes of the scale reflect ratings of ‘Free’ and ‘Not Free’. The focus is placed on realised rather than 
theoretical rights, so that the final score reflects not just what the law is but what is actually practiced. 
The Political Terror Scale adopts a similar approach by synthesising the information found in reports 
from Amnesty International, the US State Departments and Human Rights Watch, giving each report 
a quantitative rating out of 5. The difficulties of providing consistent ratings across different cultural and 
legal contexts and gathering information from such a wide range of countries notwithstanding, these 
datasets each appear to provide the best proxies that can be reasonably expected for measuring abstract 
concepts such as these. Hatton and Moloney (2015) found that both the civil liberties component of the 
Freedom House index and the Political Terror Scale were significant in their explanatory regressions.

Quantitative assessment of asylum-related migration. A survey of methodology — 19

Conflict is also obviously a major driver of asylum flows. The Peace Research Institute Oslo (PRIO) and the 
Uppsala Conflict Data Program (UCDP) provide the Armed Conflict Dataset, which details every post-World-
War-II conflict resulting in more than 25 conflict-related deaths together with information on the intensity 
and type of conflict. These numbers are based on summary estimates of conflict deaths by experts, and 
so their accuracy is questionable, but the database is relatively comprehensive and has large amounts 
of historical data. UCDP also produces estimates on the number of battle deaths in conflicts since 1989 
using reports from news media and from Non-Governmental Organisations on the ground. These latter 
estimates are conservative, designed to provide a lower bound on the total number of deaths in a particular 
conflict, and thus are certainly an underestimate of total deaths. Given that the ability to confirm deaths 
may differ in unknown ways between different conflicts, the uncertainty in comparisons of deaths across 
conflicts must be borne in mind.

Summaries of the evaluations of the described variables are given in Table 1, section b. Although the driver 
variables described above are likely to show correlations with asylum flows, utilising them in predictive 
modelling remains difficult, due to the problems involved in predicting such variables into the future. In 
some cases, lagged variables may prove useful in one-step-ahead predictions, particularly if we consider 
that asylum seekers will not necessary have up-to-date knowledge of current conditions in destination 
countries (Neumayer 2004). For other variables that change slowly (if at all), such as migrant stock and 
geographic and linguistic distance, this will not be as much of a problem.

3.4  Meta-information on asylum policies across Europe

Sources and meta-information of information on institutional, policy and legal arrangements relating to 
asylum are also potentially crucial drivers of asylum flows7. As Suriyakumaran & Tamura (2016) describe in 
their review, economic models of asylum suggest that asylum provision can be considered a public good. 
Free-riding by unscrupulous states is thus possible by setting tougher standards for incoming claimants 
than do their neighbours, and thus allowing them to bear the costs of supporting the refugees while 
benefiting from the externalities of knowing that asylum-seekers have gained sanctuary. Comparable 
measures of policy strictness are thus important in this regard. Collating information across member states 
is a significant task, so it will be necessary to rely on existing work in this area.

The current developments in the area of policy analysis most notably include the International Migration 
Policy and Law Analysis (IMPALA) Database8 – a result of a project aiming to assemble a comparative 
database of various migration policy and law indicators in 20 OECD countries. One of the areas of interest 
is explicitly linked to humanitarian and asylum migration. Beine et al. (2015) present the tentative findings 
from IMPALA for 1999–2008 for nine countries (Australia, France, Germany, Luxembourg, the Netherlands, 
Spain, Switzerland, United Kingdom, and the US). The indicators considered for the humanitarian and 
asylum domain include country-specific numbers of legal ‘tracks’ under which a person can be admitted 
(between 6 and 43 in the countries under study in 2008), as well as a number of questions related to 
possible regulatory instruments (Beine et al. 2015: 9), which are subsequently used in creating synthetic 
measures (indices) of policy ‘stringency’.

The Asylum Information Database provides detailed comparisons of 20 major European countries across 
three policy areas: Asylum Procedure; Reception Conditions; and Asylum Detention. In each area, countries 
are rated on a large number of measures on everything to availability of interpreters for asylum interviews, 
to the availability of appeal procedures, to whether family members are allowed to visit detention centres. 
While the database is comprehensive and up-to-date, reports only go back to 2013 and thus obtaining 
time-varying data may be hard. Synthesis along the lines of that conducted by the IMPALA team would be 
required in order to utilise such information for modelling.

7 

8 

For a critical evaluation of asylum policies in the developed countries, see Gibney and Hansen (2004).

See http://www.impaladatabase.org/, as of 5 July 2016.

20 — Quantitative assessment of asylum-related migration. A survey of methodology 

An alternate approach is to rely on less direct proxies for the stringency of asylum policy. The ‘recognition 
rate’ is sometimes used in the literature, being the proportion of positive decisions in any particular time 
period (e.g. Neumayer 2004). Such a rate is easily calculable from data provided from Eurostat or EASO 
and can be lagged to account for endogeneity between policy changes and applications. However, this 
approach is not without problems; very stringent rules might be assumed to deter migrants who do not 
have genuine claims, meaning countries with tough policies may end up with higher recognition rates than 
those with loose conditions, but lower volumes. Thus recognition rates may not truly capture the effect 
of policy as expected.

Synthetic policy indices provide possible proxies for the comparative attractiveness of asylum-granting 
countries. One potential consideration is that policy changes are often predictable in advance, due to their 
basis in legislative processes which take time to reach conclusions, and the fact that changes in policies 
may come into effect only after a delay. Linking a consistent coding of asylum practices with information 
about planned future changes in legislation and policy might be possible within existing mechanisms such 
as EASO’s Information and Documentation System.

Quantitative assessment of asylum-related migration. A survey of methodology — 21

4.  Review and assessment of the existing 
methods for modelling asylum-related 
migration

4.1  Model evaluation framework and criteria

Throughout this section, the existing quantitative approaches for modelling and predicting asylum-related 
migration are reviewed and assessed, with the aim to provide guidance for the development of a bespoke 
EASO model. We begin by setting out the process for evaluating the main approaches available in the 
literature and other independent sources, looking at a range of factors that may be important for practical 
uses. Thus, for evaluating the models, the following six criteria are used:

1. Specification – how well are the individual models specified in relation to the state of the art in 

quantitative social sciences?

2. Resolution – how detailed is the spatial and temporal resolution of the model?
3. Assumptions – how detailed are the assumptions on the drivers of asylum-related processes, and 

how many aspects are taken into account?

4. Predictiveness – are the models intended to be predictive or chiefly explanatory, yet with some 

predictive potential?

5. Uncertainty – is the predictive uncertainty assessed in the models in a formal way, or at least discussed 

in the narrative?

6. Generality – how transferable are the models to other situations, countries, or periods?

As in Section 3, for each criterion, the particular model classes are rated by using traffic-lights codes, 
ranging from ‘green’ where the particular aspects of the models are well-designed and conform to the 
best modelling practice, through ‘amber’ for average, yet still acceptable quality, to ‘red’ for the presence 
of problematic aspects. As the aim of the assessment is to evaluate the available modelling approaches 
rather than individual models, the assessments have been aggregated for all models in a particular class. 
In borderline cases, mixed ratings (e.g. ‘amber/green’) have been used to indicate that some models in 
a given model class have demonstrated potential to go beyond what is the dominant approach, or that 
there are differences between the models in a given class.

The quality descriptors for the three assessment levels for each of the six criteria are presented in Table 
B2 in the Appendix. Additionally, Table B3 in the Appendix contains more specific notes on individual 
models or suites of models prepared by the same authors or teams, including the listing of variables that 
have been used in model construction. The variables are grouped into six categories, encompassing the 
various drivers of asylum-related migration conceptualised in Figure 2. These categories include: policy and 
politics, conflict, culture, demography and migration, economy, and other (encompassing geography and 
infrastructure). Within each category, individual variables used in particular studies have been mapped onto 
a few selected broader indicators, to help identify synergies and conceptual overlap between the models. 
Thus, for example, visa requirements and points-based systems would be classified together under the 
‘Policy: immigration overall’ header, while the asylum recognition rates, rejection rates and other variables 
measuring the restrictiveness of the asylum process would be grouped into the ‘Policy: asylum-related’.

Finally, there are two additional aspects that are no less important: data quality and transparency. They 
have been left out from the formal assessment process presented in this section, since data quality as such 
has been extensively discussed in Section 3, and transparency should be a sine qua non condition for the 
publication of any piece of scholarly research. This is the case for a vast majority of models examined in 
this study, and any exceptions to that rule are specifically discussed.

22 — Quantitative assessment of asylum-related migration. A survey of methodology 

4.2  Statistical and econometric methods and models

There exist several methodological reviews of modelling and forecasting methods for overall migration 
flows (Keilman 2008; Bijak 2010, 2012; Disney et al. 2015). On the other hand, in the academic literature, 
it has long been argued that asylum seekers and refugees should be modelled and forecast separately 
from other migration types (de Beer 2008). Comprehensive studies on the Analysis and forecasting of 
international migration by major groups, carried out for Eurostat in the 1990s and early 2000s (Salt & 
Singleton 1995; van der Gaag & van Wissen 1999; Hilderink et al. 2002) provide – among other insights – 
a conceptual and empirical background for such models.

In particular, at the theoretical level, Salt and Singleton (1995) identified the “political restructuring” in 
the third countries as a macro-level process driving asylum seekers into Europe. However, at the empirical 
level, the predictive potential of asylum trends was found to be largely limited to ‘now-casting’ of the very 
short-term trends (van der Gaag & van Wissen 1999). The updated analysis (Hilderink et al. 2002) did not 
identify specific spatial patterns of flows with regard to destination countries, but in some cases did so for 
broad groupings of countries of origin.

A useful overarching framework for analysing the different effects for all migration flows, including asylum-
related, is offered by well-known and established statistical and econometric methods, falling under the 
umbrella of generalised linear models (GLM) – various generalisations of simple linear regression models. 
In migration studies, Willekens and Baydar (1986) proposed an operationalisation of this approach, 
distinguishing the general effects related to regions of origin, destination, their interactions, and socio-
economic and political environment. An example of application is given by Willekens and Baydar (1986: 
207), who proposed the following model:

(1) 

mijt = Nt wit pijt.

In (1), the variable under study is mijt, migration from country i to country j in year t, while Nt denotes the 
overall migration level in that year; wit is the generation effect, the probability that a migration starts in 
i; and pijt is the distribution effect, the probability that migrations from i end up in j. In this framework, all 
elements of (1) can be additionally explained by using socio-economic and other covariates (Willekens & 
Baydar 1986). Other recent examples of the use of the GLM approach are gravity models for the whole 
world, where the key driving forces behind migrations are the population sizes of countries (Cohen 2012).

The underlying theoretical framework behind constructing and analysing GLMs is related to the push 
and pull factors (Lee 1966), conceptualised in Section 2. The background level of forced migration, Nt, 
is related to the overall political stability worldwide. The push factors or migration triggers – armed 
conflict, persecution – are related to the generation effects, wit. Then, the pull factors attracting asylum 
seekers and refugees to particular destinations – policies, legal frameworks, welfare provision, access to 
certain rights – are linked to the distribution effects, pijt. Each of these variables can be modelled with 
appropriate explanatory factors. The overall forced migration levels Nt can be approximated by some 
random (stochastic) processes, for example autoregressive ones, justified by the self-perpetuating nature 
of many migration flows, including asylum-related.

In practice, the dominant approaches to quantitative modelling of asylum-related flows present in the 
academic literature apply some forms of GLMs. The available models differ with regard to their complexity 
and to the attention paid to particular drivers, factors and the underpinning mechanisms of asylum 
processes but they share some general-level commonalities. First, all existing models describe macro-
level asylum processes. Second, typically the unit of analysis is asylum-related migration into a particular 
country or a group of countries, with a notable exception of sub-national flows in Czaika (2009). Third, the 
temporal resolution is usually yearly, again with a few exceptions: monthly in Stanley (1987) and Jennings 
(2010), and weekly in Schellman and Stewart (2007). The detailed specification of selected quantitative 
models of asylum-related migration reviewed in this report is presented in Table B 3, in Appendix B, which 
summarises the relevant drivers and factors of migration taken into account in different models.

Among the formal models of asylum-related migration, three broad groups can be distinguished. Firstly, 
there are simple regression models (univariate or multivariate), estimated for individual countries, usually 

Quantitative assessment of asylum-related migration. A survey of methodology — 23

with at most a few explanatory factors. Examples include Stanley (1987), Böcker and Havinga (1998), simple 
asylum-related models presented in a wider study of van der Gaag & van Wissen (1999), or the automatic 
asylum forecasts of Trading Economics (n.d.). These models do not have a high predictive potential, and 
their applicability is usually limited to the individual task at hand. In simple models, the uncertainty of 
predictions is usually not analysed in a formal way. The exact model specification behind the Trading 
Economics forecasts is unknown – judging by the outcomes, it looks like a polynomial trend, but the model 
is not documented and is not transparent. From the point of view of modelling asylum-related migration, 
such models are of limited use.

The second group of methods encompasses panel regression models, whereby panels of country-level 
indicators are modelled across time. Examples of models from this group include Moore & Shellman (2004); 
Neumayer (2004, 2005a, 2005b); Hatton (2004, 2009); Czaika (2009); Keogh (2013); Czaika & Hobolth 
(2014), and Hatton & Moloney (2015). The models in this category are typically explanatory, and have 
been designed with the aim of assessing the various drivers of asylum-related variables. On the one hand, 
the universe of these models collectively covers a very broad group of various macro-level trends and 
drivers of migration from different domains (see Table B3 in Appendix B). On the other hand, the predictive 
properties of such models are limited, and any predictive applications would need to involve predicting the 
drivers first, which can carry substantial uncertainty on its own (Bijak 2010). The uncertainty of estimates 
is usually not reported, beyond significance tests for impacts of particular drivers on asylum processes.

Finally, the third group covers the most complex approaches – time series, structural and log-linear 
models. This group is represented by formal time series analysis, either standalone (Schmeidl 1997; Jennings 
2010; and Toshkov 2014), or embedded within larger-scale structural model with economic elements 
(Schellman & Stewart 2007), as well as log-linear models for counts (van Wissen & Jennissen 2008; Vink & 
Meijerink 2003). Additionally, the studies by de Beer (2008) and van Wissen and Jennissen (2008) present 
decomposition of overall asylum-related flows into distribution and respectively substitution or distribution 
effects, akin to the framework presented in (1). Most of the models in this group have clear predictive aims, 
and sometimes even present quantifiable uncertainty assessment of the estimates (here, a good recent 
example is Toshkov 2014). The range of explanatory variables in such models is typically limited, though, 
and they most reliably include past (lagged) asylum or wider migration flows. Only Vink and Meijerink 
(2003) rely on a completely theory-free model with country-specific and time-specific effects, as well as 
their interactions.

A detailed assessment of the different classes of models with respect to the criteria introduced in Section 
4.1 is presented in Table 2. From the analysis of the properties of various model groups, it becomes apparent 
that simple regression models are too crude to quantifiably explain or predict asylum-related migration. 
As for the models from the second and third groups, there are obvious trade-offs between them. While 
the panel models are typically more comprehensive in terms of drivers, their predictive capacity remains 
only a potential possibility. On the other hand, the more complex models, involving time series or more 
advanced log-linear structures, are better suited for prediction, but at the expense of being able to identify 
the key drivers of asylum-related processes.

24 — Quantitative assessment of asylum-related migration. A survey of methodology 

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Quantitative assessment of asylum-related migration. A survey of methodology — 25

As mentioned before, the GLM framework is one of the most universal and flexible approaches for analysing 
and predicting asylum-related migration. In the most general form, the GLM family encompassing a vast 
majority of the existing approaches to modelling asylum flows from country i to country j in time t, yijt, 
can be operationalised as:

(2), 

 ,

xt

where f is a link function (for example, logarithmic or logistic), c is the constant, B is the backshift operator 
(Bm
= xt–m), x denote vectors of explanatory variables, β their respective regression coefficients, φ the 
autoregression parameters, and εijt are (some) error terms. This framework is flexible enough to encompass 
panel regression models, with or without lagged explanatory variables, panel and other time series, as well 
as log-linear models for counts (with all values of x set to 1, and with no lags).

) 

(

In terms of drivers x, across all models, the most prevalent explanatory variables are related to migration 
and asylum policies, politics and governance, trends in asylum and migration from the past, as well as 
other demographic variables, armed conflict, and economic differentials, chiefly related to income as 
measured by the GDP per capita. On the other hand, cultural, spatial or infrastructural factors have been 
examined only in a handful of studies (see Table B3 in Appendix B). In most cases, the data on the drivers 
are of acceptable quality and come from reputable sources, such as established databases of international 
organisations (UN, UNHCR, Eurostat, World Bank).

Among explanatory variables, especially noteworthy are attempts to create synthetic indices, in the context 
of asylum policy (e.g. Theilemann 2004 and Czaika 2005 – standalone pieces of work on policy indices; as 
well as Hatton 2004 and Hatton & Moloney 2015) and armed conflict intensity (e.g. Stanley 1987; Moore 
& Shellman 2004; Neumayer 2004, 2005a, 2005b; Hatton 2009; Hatton & Moloney 2015). Importantly, 
the role of other countries’ policies has also been acknowledged in several studies, both from the point of 
view of analysing their impact explicitly (e.g. Czaika & Hobolth, 2014; Hatton & Moloney 2015), or implicitly, 
through distribution or substitution effects (de Beer 2008; van Wissen and Jennissen 2008).

In this context, an important aspect of the underlying policy analysis is related to the ‘burden sharing’ 
related to asylum between different destination countries. This points to differences in policy norms 
related to asylum and humanitarian protection across Europe (Theilemann 2003; 2004), which have 
converged somewhat in the recent years, although not to the point of becoming irrelevant (Toshkov & de 
Haan 2013). This implies that the policies of other countries within the EU, as well as possibly of the third 
countries, need to be taken into account in any formal modelling of asylum-related migration even for 
individual countries. In particular, the variables from the IMPALA database discussed in Section 3 have 
a great potential in providing quantifiable input to formal models of asylum-related migration, subject to 
annual time granularity.

A separate question related to models based on the various push and pull factors of migration is: how to 
predict the individual factors and drivers? The forecasting of demographic and macroeconomic predictors 
constitute very large topics in their own right, and as such remain beyond the scope of this report. Here, 
the relevant review studies include: Jennissen (2004) on explanatory economic models of migration; 
Suriyakumaran and Tamura (2016) on economic drivers of asylum; Bijak (2010), with not very successful 
attempts to use economic variables in migration forecasting; Keilman (1990) and Alho and Specer (2005), 
with a comprehensive overview of a range of statistical methods for population forecasting; Bijak (2012) 
and Disney et al. (2015) with recent overviews of migration forecasting methods. However, in the context 
of asylum-related flows, political and policy variables – in particular, the presence of armed conflict and 
policy developments – require at least as much if not more attention than structural macroeconomic or 
demographic ones.

Some good examples of models for various political and especially conflict-related push factors of migration 
are available from the political science literature. Examples include multinomial logit models for transitions 
between different levels of conflict (Hegre et al. 2013) at a country level, or spatio-temporal autoregressive 

26 — Quantitative assessment of asylum-related migration. A survey of methodology 

sub-national Bayesian models with binary response for conflict presence (Weidmann & Ward 2010)9, both 
formally acknowledging the predictive uncertainty. The obvious caveat is that the uncertainty of prediction 
from such models can be very high, but there is a literature consensus on the presence of four key factors 
leading to conflict: (1) social, economic or political grievances; (2) competition between various groups; 
(3) readiness to use violence; and (4) lack of non-violent alternatives (Regehr 2014: 16).

The policy-related pull factors can follow the general literature on the role of policies as drivers of migration 
(Zolberg 1989), including specific examples related to asylum-related migration (Neumayer 2004; Hatton 
& Moloney 2015). Of course, general caveats about the weak predictive power of migration theories 
(Arango 2000) remain in force: one key issue is that the drivers of asylum may be even less predictable 
than asylum as such (Bijak 2010). Besides, with most of the models (with a notable exception of Schellman 
and Stewart 2007), timeliness remains an issue – for the data may not become available until after the 
desired prediction horizon. This suggests that such models, which rely exclusively on formal relationships 
between asylum-related migrations, are not the best choice for operational and planning purposes. With 
that in mind, the examples of national practice of trying to predict asylum flows in selected European 
countries are evaluated next.

4.3  National practice in predicting asylum-related flows in Europe

The national practice in trying to predict the inflows of asylum seekers varies between the member states 
of the European Union (EU). Most of the approaches used to predict future asylum-related flows, and thus 
demand for the relevant state services, are based on some analysis of the quantitative data coupled with 
expert knowledge. In this section, several of the approaches developed by the relevant national authorities 
in selected European countries are reviewed, and are supplemented by more detailed meta-information on 
the specification of forecasting models used in two countries that are at the forefront of methodological 
developments: Sweden and Switzerland.

The information reported in this section largely comes from a publicly available Ad-Hoc Query on Forecasting 
and Contingency Planning Arrangements for International Protection Applicants of the European Migration 
Network (2014). This information is current as of 19 December 2014, and covers responses from 15 EU 
countries, eight of which reported having some mechanisms for predicting asylum flows in place, and 
whether relevant contingency plans are in place. The details of the responses of individual countries are 
summarised in Table 3, together with the relevant meta-information concerning Switzerland, based on 
the information obtained from the State Secretariat for Migration (SEM, Staatssekretariat für Migration). 
Similarly, the entry for Sweden is updated based on the meta-information provided by the Swedish 
Migration Agency (Migrationsverket).

The approaches nine countries presented in Table 3 rely on internally-developed predictions and early 
warnings, which usually combine quantitative data series on asylum trends with qualitative insights from 
experts, border intelligence, media, as well as different types of contextual information about the countries 
of origin and routes. The approaches are not too complex in statistical terms – sometimes being outright 
descriptive or relying on simple extrapolation of trends – but their main strength lies in the potential 
richness of different types of auxiliary information that may be difficult to operationalise and quantify in 
a timely manner. The forecast horizons are usually short – typically up to a year – and are usually updated 
several times a year. There is not much methodological overlap between the national practice and academic 
models reviewed in Section 4.2. The countries listed in Table 3 typically either have some contingency plans 
related to asylum – of a varying degree of complexity – or are in the process of developing and enhancing 
them. A detailed description of the approaches used in Switzerland and in Sweden is provided in Box 1 
and Box 2, respectively.

9 

This is a very advanced, very well specified model, which has been applied to the 1990s war in Bosnia and Herzegovina, although the robustness of its 
findings is difficult to judge, as the paper does not take into account the body of evidence assembled at the UN International Criminal Tribunal for the 
former Yugoslavia (Tabeau 2009), relying instead on crude indicators of conflict presence or absence in particular municipalities.

Quantitative assessment of asylum-related migration. A survey of methodology — 27

Table 3. National practice in asylum-related forecasting and links with contingency planning

Country

Type of approach

Quantitative data 
sources

Other sources of 
information

Contingency 
planning

Estonia

Risk assessment and 
data analysis

Various series of 
statistical data

Germany

‘Entry forecasts’ up to 
a year ahead

Monthly arrivals of 
asylum seekers

Border intelligence, 
information exchange 
with other countries

Experts, countries 
of origin, EASO COI* 
portal, government 
reports, NGOs, media

Under 
development

In place

Ireland

Informal forecasting 
(extrapolations), trend 
monitoring

Netherlands

A rolling forecast, 
updated monthly

Norway

Four forecasts a year, 
forecasting monthly 
arrivals

Poland

Under development

Sweden***

Switzerland***

Four-five forecasts 
a year: quantitative 
predictions with 
qualitative insights

One-year ahead 
expert-based asylum 
predictions, including 
two alternative 
variants

Refugee and asylum 
flows in Ireland and 
nearby countries, 
UNHCR, EASO

National data on 
refugees and asylum 
seekers

Eurostat, EASO, IOM 
/ IGC**, Frontex, other 
countries, asylum 
applications

International and 
national and data on 
refugees and asylum 
flows; UNHCR, EASO

International and 
national data; UNHCR, 
EASO, other countries 
of asylum, Frontex

Existent refugee 
communities

Basic

Under development: to 
include a wide range of 
contextual information

In place

Media, Frontex, EASO 
COI*, diplomatic 
missions

Advanced

Under development: to 
include a wide range of 
contextual information

Basic

Media, NGOs, EASO, 
Lifos: national COI*, 
diplomatic missions

Advanced

Past trends, 
quantification of 
expert opinion, border 
crossings

Expert opinion on 
individual countries 
of origin, migration 
drivers and routes

Advanced

United 
Kingdom

Asylum intake reports, 
regularly updated, with 
annual forecasts by 
nationality

Home Office asylum 
data, tracing the intake 
reports and provide 
early warnings

Via involvement in 
EASO activities

In place

* COI – Country of Origin Information database; ** IGC – Intergovernmental Consultations on Migration, 
Asylum and Refugees (IOM Geneva) 
*** For Sweden and Switzerland, see the detailed descriptions in Box 1 and Box 2, respectively
Sources: European Migration Network (2014); for Switzerland: SEM; for Sweden also Migrationsverket

28 — Quantitative assessment of asylum-related migration. A survey of methodology 

Box 1. Case study: Switzerland
Model design

The Swiss State Secretariat for Migration (SEM) has been producing year-ahead prognoses of the likely 
number of asylum applicants to Switzerland for more than a decade, making the SEM process one of the most 
established in Europe. The yearly prognosis is produced in January and updated in July once the migration 
‘season’ is underway. The SEM prognosis can be considered an expert-based model; formal statistical 
methods are not used to produce the central forecast. Instead, detailed in-house knowledge is brought to 
bear to assess how asylum flows into Switzerland may change relative to the previous year.

More particularly, the Swiss approach starts by considering the push factors in sending countries. Country 
experts from the SEM are surveyed to determine whether they believe flows to Europe as a whole will 
increase or decrease versus the previous year, and to what extent. As well as giving a range of increase or 
decrease on a 7 point scale, experts are also asked to justify their assessment.

This assessment of pull factors is complemented by information about changes along the various migratory 
routes into Europe, incorporating both quantitative information about border crossings and modifications 
in border controls. An extremely wide range of sources are used to gather this information, including 
information from partner agencies and countries, all of which are continuously monitored throughout the 
year. The whole prognostic process is illustrated in Figure 4.

Push Factors
Expert assessment of 
change in ﬂow to Europe

Routes (Meso level)
Information on 
changes in border 
crossing

Pull Factors
Policy change vs other 
European states; other 
socio-economic.

Synthesis of input 
information by SEM 
analysis team to 
produce year-
ahead prognosis.

Scenario

Scenario

Scenario

Figure 4. Diagrammatic representation of the Swiss State Secretariat prognosis  
for asylum flows

Source: State Secretariat for Migration (SEM)

Finally, pull factors in both Switzerland and other European destination countries also play an important role 
in the SEM’s thinking. As well as social and economic factors, asylum policy and the welfare arrangements for 
applicants are also crucial in the final reckoning.

This information on the various factors determining change in the asylum applicants is synthesised by the 
SEM analysis team to arrive at their final prognosis. Although production of the model does not involve 
statistical techniques, an indication of the extent of uncertainty surrounding the year-ahead estimates of 
application levels is provided in two ways. First, the prognosis describes different possible scenarios, each 
describing particular changes in determining factors that may lead to different outcomes in terms of asylum 
outcomes. Each outcome is given a specific (subjective) probability of being realised, based on the opinion 
of the SEM analysts. Secondly, a “plus or minus” range around each scenario estimate is provided, delimiting 
a range of approximately 20 % of the central value.

Quantitative assessment of asylum-related migration. A survey of methodology — 29

Box 1 (cont.)
Model assessment

The prognosis of the SEM migration analysis team is used by the Secretariat and by the Swiss Cantons for 
planning purposes, indicating it is trusted in government. In terms of validation against the number of 
asylum applicants who apply in Switzerland, the prognoses have generally performed well. Exceptions have 
largely been in years of extreme changes in the European migration system. Specifically, in the aftermath of 
the Arab Spring in 2011, asylum applicants to Switzerland were understandably underestimated, although 
this assessment was corrected in the mid-year revisions. Similarly, the migration crisis in 2015 also led to 
underestimations. In contrast, in 2013, the prognosis was an over-estimation in the number of applications, 
largely due to a large than expected effect of the disparity between Swiss and other European asylum policy.

Despite its success, there are some problems with advocating such approaches for a Europe-wide system of 
asylum forecasting. Firstly, although the SEM is admirably clear about its conceptualisation of the migratory 
system and the determining factors in change in asylum applicants, due to the reliance on the specific 
expertise and judgment of the analysis team, it is not clear that this could be reproduced by other bodies. 
Secondly, the difficulties of applying a judgment-based model rather than a statistical/mathematical model 
are magnified when more than one country needs to be considered – particularly with regard to interactions 
between asylum policies.

However, the strengths and sustained success of the Swiss model certainly suggest some areas from which 
we can learn. In particular, the method of surveying experts for opinions on future changes in flows from 
sources countries is worth pursuing, and could be formalised further as a process of Bayesian elicitation in 
order to obtain probability distributions over future flow changes.
Source: State Secretariat for Migration (SEM)

The use of expert judgment has also featured in several quantitative academic studies of migration, including 
predictions of immigration to several European countries (Bijak and Wiśniowski 2010), of environment-
related migration to the United Kingdom (Abel et al. 2013), and of possible migration to and from Scotland 
expected after the 2014 independence referendum (Wiśniowski et al. 2014), as well as in the estimates 
of intra-European migration flows (Wiśniowski et al. 2013). In most cases, this information was formally 
elicited from a heterogeneous group of experts within the framework of quasi-Delphi techniques, and was 
formally combined with quantitative data by the means of Bayesian statistical inference (see Bijak & Bryant 
2016 for a review). Expert judgment has also been incorporated – albeit implicitly – in Bayesian predictive 
models of migration for the whole world, prepared for the United Nations by Azose and Raftery (2015). 
Additionally, some purely expert-based studies have followed a futures approach, with comprehensive 
narratives about the possible scenarios of environment-related migration (Government Office for Science 
2011); cross-border flows in the EU (Ariely et al. 2011); or migration in the Horn of Africa (RMMS–IMI 2012).

30 — Quantitative assessment of asylum-related migration. A survey of methodology 

Box 2. Case study: Sweden
Model design

In recent years, the Swedish Migration Agency (Migrationsverket) has developed a comprehensive national 
migration-related database (Milos), and, with focus on asylum, an advanced mechanism for preparing 
forecasts of asylum applications. The forecasts are utilised by a number of stakeholders, from internal 
operations to the Ministry of Justice, and are prepared five times a year: four quarterly and one annual. The 
forecasts are broken down by weeks, allowing for seasonal variations, with a horizon of one to two years, and 
are used for planning, policy evaluation and resource allocation.

The current model is based on a mix of qualitative and quantitative information. The quantitative data come 
from the Milos database and Statistics Sweden, supplemented by the information from European agencies 
(EASO, Eurostat, Frontex, Eurodac), UNHCR, and other important countries of asylum, with information 
sharing arrangements in place between the Nordic countries. The qualitative information, in turn, is collated 
from a wide range of sources, including national database on the countries of origin (Lifos), contact points 
at diplomatic missions overseas, media or NGOs. This information concerns various push and pull factors of 
migration flows, an assessment of possible migration routes, and – crucially – of asylum policies and their 
anticipated changes across Europe, as well as at the EU level. Of particular significance are the temporarily-
reinstated border controls in the Schengen area.

The qualitative insights are subsequently quantified by using expert judgment through an application of 
morphological analysis, to produce the forecasts, which are then updated on a quarterly basis. The method, 
dating back to Zwicky (1948, 1969), relies on a systematic approach to coherently assessing the structure of 
multi-dimensional complex problems. The process is summarised in Figure 5.

Qualitative data
Push and pull factors, 
routes, asylum policies, 
Schengen controls, EU

Morphological analysis
Quantiﬁcation of the 
qualitative insights: ﬂow 
increase/decrease 

Quantitative data
Data series: National 
migration data, EASO, 
Frontex, Eurodac…

Synthesis of 
information by the 
Swedish Migration 
Agency in 1-2 year 
ahead forecasts, 
including trends 
and seasonality. 
Weekly reports, 
visualisations, 
quarterly forecast 
updates.

Lower error 
bounds

Forecast

Higher error 
bounds

Figure 5. Diagramatic representation of the Swedish forecasts for asylum flows

Source: Swedish Migration Agency (Migrationsverket)

Based on the main forecast, the error bounds are subsequently derived, aiming to cover with 90 % probability 
the number of asylum applications in the next forecast period.

The Swedish Migration Agency is also developing a comprehensive system for predicting flows and modelling 
the circulation of migrants with Sweden, with the aim of optimising resource allocation and increasing the 
preparedness in case of sudden changes in the volume of flows. This experimental system will combine 
extrapolative models (time series, exponential smoothing, and possibly some machine learning techniques) 
with queuing theory and a statistical model for the spatial distribution of flows. Through building a suite of 
interconnected formal models, the agency aims to further improve the predictive capacity of the existing 
approaches.

Quantitative assessment of asylum-related migration. A survey of methodology — 31

Box 2 (cont.)
Model assessment

The forecasts prepared at the Swedish Migration Agency are routinely monitored against the real-life 
developments, and have so far proved to be relatively accurate ex post, especially as for such a volatile 
phenomenon as asylum-related migration. The main drivers of this accuracy are a comprehensive set of 
quantitative and qualitative input information, a high level of detail of the background analysis behind the 
forecasts, and periodic updating of predictions. The forecasting and monitoring tools also enable analysis of 
the asylum flows at a very high level of detail, for example by individual nationalities, routes or channels of 
entry into Sweden, etc.

The numbers of applications being monitored and forecast are slightly biased due to including multiple 
entries for the same persons, for example when they change ‘track’, switching from other types of migration 
to asylum, or in the case of circulation of asylum seekers between different EU countries. Children born to 
asylum seekers and refugees in Sweden are also included in the data, even though technically they cannot be 
classified as ‘new arrivals’. These issues are recognised, and the users of the forecasts need to be aware of 
these shortcomings.

One obvious limitation of the Swedish approach is that it is very resource intensive, both in terms of human 
effort, as well as the required data infrastructure. Still, the impact of the forecasting work done at the agency 
is not to be underestimated: by providing accurate predictions, and by contributing to national contingency 
planning, it can generate efficiencies throughout the Swedish asylum system.
Source: Swedish Migration Agency (Migrationsverket)

The review of national practice in asylum forecasting has revealed four important features of the methods 
used. First, even among those countries that carry out forecasts of asylum-related migration, there is 
a wide disparity in terms of complexity and sophistication of the methods used. However, the best available 
models and contingency plans – such as those prepared in Sweden or Switzerland, but also Norway or the 
United Kingdom – offer quite comprehensive tools to prepare resources for prospective asylum flows. 
As such, there is much scope for harmonisation of the various approaches across the EU and for learning 
from best practices. Second, most of the predictions rely on expert opinion but its level of formalisation 
varies from country to country. Third, the approaches usually take into account a wide range of contextual 
information about asylum processes. Fourth, with a few exceptions, the examples of national practice in 
predicting migration are typically more flexible and timely than academic studies, which are usually based 
on secondary data published with a time lag. These features, especially the formal inclusion of expert 
judgment and of the detailed intelligence about the context, as well as timeliness, are worth retaining in 
the eventual construction of the bespoke EASO model of asylum flows.

4.4  Notes on the validation of models

An important question pertaining to all the models and approaches reviewed in this section is how they 
and their performance were – or could have been – validated by using real data. There are two main 
approaches to validation: ex ante and ex post. In the ex-ante approach, the expected properties of the 
model, such as estimation errors, goodness of model fit, etc., are evaluated on the basis of the same dataset 
on which the model is estimated. In the ex-post approach, the model predictions are compared with the 
actual observations that go beyond the data sample, which allows for calculating different measures of 
error, based on the differences between the predictions and observations. Here, usually some summary 
measures are calculated, such as mean percentage errors, mean absolute errors, or (root) mean square 
errors, based on averaged across all out-of-sample points, on the basis of which the validation is made 
(cf. Bijak 2010: 161).

For probabilistic methods and models, which describe their predictions not in terms of single values, but 
rather whole probability distributions of the possible future outcomes (Alho & Spencer 2005; Bijak et al. 
2015), there are a few additional considerations. Firstly, an open question is which characteristics of the 
predictive distributions should be used for calculating the errors: typically these are mean or median 
values from the distributions, but other points can be also used, depending on the actual needs of the 
forecast users (idem). Secondly, the predictive distributions themselves need to be well calibrated, in that 
the distributions obtained from the modelling should match the empirical frequencies of the actual errors 

32 — Quantitative assessment of asylum-related migration. A survey of methodology 

(Gneiting et al. 2007). In other words, in well-calibrated models, the errors ex ante and ex post should 
match: for example, 50-per cent predictive intervals should cover the true values ex post around half of 
the time, and so on.

Among the academic models reviewed, only some ex-ante model validation has been usually done, mostly 
through determining how the various models fit the data at hand. In hardly any case an ex-post analysis of 
errors was attempted (with very rare exceptions, such as the conflict model of Weidmann & Ward 2010). 
Even though conducting an ex-post validation exercise for a wide range of models and for different data 
series is beyond the scope of this study, Disney et al. (2015) have undertaken such evaluation for a range of 
migration-related data series for the United Kingdom, including the number of asylum applications based 
on the Home Office data. Several autoregressive (AR) and autoregressive moving average (ARMA) models 
have been estimated based on the data for 1984 – 2003, and the resulting forecasts for the subsequent 
10 years, 2004–2013, were compared to the actual observations from that period, yielding the results 
shown in Table 4.

Table 4. Results of the validation exercise of Disney et al. (2015) for the forecasts of UK asylum seekers, for 
2004–2013

Model

Mean 
Percentage 
Error

Mean 
Absolute 
Error

Root Mean 
Square 
Error

Calibration: 
50-percent 
interval

Summary 
of errors

Interval 
calibration

Calibra-
tion: 
80-percent 
interval

Classical (frequentist) time series extrapolation models

Random walk

–110%

25,293

25,590

45%

100%

Very high

General AR(1)

–49%

11,352

12,185

82%

100%

High

ARMA(1,1)

–13%

4,116

4,867

100%

100%

Medium

AR(1) differences –70%

15,950

16,286

100%

100%

Very high

AR(1) de-trended –1,446%

328,091

391,522

0%

0%

Very high

Bayesian extrapolation models

Random walk

445%

99,096

113,061

General AR(1)

–98%

22,339

22,598

0%

0%

40%

Very high

100%

Very high

ARMA(1,1)

–85%

19,281

19,585

40%

100%

Very high

AR(1) differences –330%

73,577

82,831

AR(1) de-trended –368%

82,425

90,115

0%

0%

100%

Very high

100%

Very high

Some 
issues

Too con-
servative

Extremely 
wide

Extremely 
wide

Extremely 
tight

Too 
optimistic

Miscali-
brated

Some 
issues

Miscali-
brated

Miscali-
brated

Source: Adapted from Disney et al. (2015: Table B5, p. 60)

The interpretation of the above results provided by Disney et al. (2015) confirms the nature of asylum-related 
flows as one of the worst predictable migration processes. Still, in the context of practical applications of 
asylum modelling, validation can be more than a one-off exercise to confirm the suitability of a particular 
model. There is a gap with respect to how much is learned from especially ex-post validation exercises, 
and how useful such approaches could potentially be. A more comprehensive approach would have the 
validation exercise built into the forecasting as a part of a wider framework – regularly updated as new 
data become available, as in several examples of national practice discussed in Section 4.3. There exists 
advanced statistical theory that formalises such a framework – the prequential (probabilistic and sequential) 

Quantitative assessment of asylum-related migration. A survey of methodology — 33

forecasting (Dawid 1984). For practical applications, however, if the framework was to be adopted across 
Europe, a simpler pragmatic solution may be preferable, which would formalise what is already common 
practice in some countries, and augment it with expert opinion, within a joint statistical framework. The 
feasibility of such a modelling approach is discussed in the next section.

34 — Quantitative assessment of asylum-related migration. A survey of methodology 

5.  Towards a bespoke EASO model of asylum-
related migration: A feasibility study

5.1  Synthesising the possible modelling frameworks

As a first step in assessing the feasibility of various modelling approaches for asylum-related migration 
flows, in this section we present the communalities and desirable features of the various models and 
methods identified earlier. In particular, the aim of this section is to synthesise the meta-information 
on the available data and models, and to analyse the potential areas of overlap or synergy between the 
modelling approaches. This assessment is subsequently used to guide the assessment of feasibility of 
the various approaches, in the context of their appropriateness for the development of a bespoke EASO 
model of asylum flows.

As mentioned, the existing quantitative models of asylum-related migration are macro-level, with a varying 
time resolution (from weekly to yearly). They encompass explanatory approaches (panel models), predictive 
ones (time-series models), and expert-based endeavours (best examples of the national practice). In the 
synthesis, we recommend retaining some elements of all three types of methods while bearing in mind 
their practical limitations. To start with, the prediction of all individual drivers of asylum-related migration 
is not a feasible task, as it is likely to introduce even more predictive uncertainty to the model than the 
asylum-related flows alone (see also Bijak 2010). Still, some information on especially policy-related drivers 
is crucial.

In the light of very weak predictability of forced migration, the proposed approach needs to take a form of 
an early warning system (Clark 1989; Schellman & Stewart 2007; Regehr 2014; Disney et al. 2015), rather 
than making futile attempts at predicting longer-term asylum trends. Given the high level of uncertainty, 
the use of Bayesian statistical methodology is especially recommended. The Bayesian approach updates 
the prior beliefs about the phenomena of interest with the information from data, in order to produce the 
posterior estimates of model parameters and derive the forecasts on their basis (Bijak and Bryant 2016). 
As such, it is well suited for the description of estimation and forecast errors in such circumstances, while 
also allowing for including structured expert knowledge in the models in a formal, open and transparent 
way (Bijak 2010; Azose and Raftery 2015).

There are several important arguments for including expert opinion. First, when the data are sparse or 
time series are short, they can be formally supplemented by expert judgment. More generally, in the 
Bayesian approach, there are important trade-offs between the prior information and the data: the more 
data are available, the less influential the prior assumptions are, and the less weight they have in the final 
estimates – and vice versa (see e.g. Gelman et al. 2014 for a detailed discussion). Secondly, given the high 
uncertainty of migration flows, expert information can provide insights that are not included in the data 
sample. In that way, expert-based prior information, for example on low predictability of migration flows 
or on similarities between various types of flows, can lead to better calibrated assessment of forecast 
uncertainty and help the users avoid the pitfalls of spurious accuracy of prediction (Bijak 2010; Azose and 
Raftery 2015). In the proposed model, the role of experts would be also to provide better timeliness and 
agility of model amendments beyond what is permitted by the available data, as done in the Swiss and 
Swedish models (Section 4.3).

Hence, in general terms, a macro-level early warning model could be based on the time series of EASO data, 
supplemented by synthetic asylum policy indices for a range of countries and regions, in order to capture 
some of the distribution or substitution effects in asylum processes. Other important variables, such as 
armed conflict intensity measures for different regions of the world, could also be used. Another key input 
would be expert opinion on the envisaged changes of individual asylum-related flows within a pre-defined 
horizon, for example one year ahead, as well as stakeholder views on the warning thresholds – what levels 
of asylum-related migration should trigger a reassessment of the model or a policy action.

Quantitative assessment of asylum-related migration. A survey of methodology — 35

The asylum data used in the model would be origin-and-destination specific, the expert knowledge and 
conflict intensity measures would be related to countries of origin, and the policy indices to the European 
countries of destination. A two-stage model structure would reflect that, possibly through adopting a GLM 
approach in the spirit of Willekens and Baydar (1986), mirroring the conceptual distinction between the 
push and pull factors (see Figure 2, Section 2), and possibly modelling the intervening factors through 
origin-destination interaction terms. The various elements would be combined via Bayesian statistics, 
and inferences would be drawn on the basis of an overall model, taking the warning thresholds mentioned 
above as a basis for decisions.

The key ingredients of the proposed approach are summarised in Figure 6. An example of the model design 
and structure is described next.

Baseline data: 
EASO series + 
policy indices

Expert and 
contextual 
knowledge

Early warning 
asylum model

Two-stage 
model structure 
(push and pull)

Warning 
thresholds 
(stakeholders)

Bayesian 
statistical 
inference

Figure 6. Elements of the proposed modelling approach

5.2  Design of the bespoke EASO model structure

Based on the knowledge of the asylum processes and the state of the art in formal statistical modelling, 
in this section we propose a class of models that may be potentially used for designing the EASO model 
at the later stages of the research programme.

Let us start with the dependent variable: given very high volatility of asylum-related outflows, it is 
recommended to model weekly or monthly changes in the level of asylum applications. The changes will 
likely exhibit more stable properties than the flow levels (see also Disney et al. 2015). This approach requires 
timely and good quality baseline (current) data and up-to-date information on policies across different 
countries, as well as on the new developments in terms of push factors, migration routes, smuggling 
channels, etc. – all of which would be very difficult to operationalise in with quantitative indicators but 
could be approximated through expert knowledge.

36 — Quantitative assessment of asylum-related migration. A survey of methodology 

One of the key purposes of the model will be to provide early warning signals through constant monitoring 
of asylum-related flows. The question underlying the modelling process would be: to what extent are the 
asylum processes stable, disregarding the impact of events identifiable at the time of model estimation? If 
there is evidence that the processes become unstable this should lead to a policy action as well as to a re-
evaluation of the model. Hence, for the time resolution, the asylum data would need to be as detailed as 
possible: ideally weekly, or at least monthly. In our view, the time horizon of the model should be limited 
to one year ahead, first, this is a period typically related to budget and planning, and second, given the 
high volatility of asylum-related migration, it may not be possible to create reasonable forecasts in a longer 
perspective.

Convenient methods for answering the question above are offered by the statistical control theory, in 
particular the cumulative sum (‘cusum’) approaches (Page 1954, 1961). In this approach, the indicator 
of choice (in our case, asylum levels) is traced by cumulatively adding the subsequent observations over 
time, checking whether such cumulative sums (cusums) exceed certain thresholds, and making decisions 
on that basis. Several formalisations of the cusum model exist; for example, Harrison and Veerapen (1994) 
discuss a three-element decision space, with process being either ‘in control’ under a certain acceptance 
threshold, μ0, or ‘out of control’ above some rejection threshold, μ1 ≥ μ0, or requiring further monitoring 
for the values between μ0 and μ1. The thresholds can be determined based on the past performance of the 
indicator, or alternatively example by using some formal model. Denoting the process value at time t by 
yt, the cusum Ct can be defined recursively, through the following equation, starting from C
 (Harrison 
and Veerapen 1994: 30):

0

= 0

(3) 

Ct

 Ct–1 

 yt 

μ1 

 μ0

 t 

.

+

 =

– (

A stylised illustration of the approach is illustrated in Figure 7, where the process triggers a warning by 
> 0
crossing a rejection threshold in the 20th observation, shown by red vertical lines on the process graph 
(left panel) and the corresponding cusum chart (right panel). As stipulated already by Page (1954), 
a downward path of a cusum indicates satisfactory performance of the indicator, and an upward path 
denotes an unsatisfactory performance.

)/2 for

+

20

15

10

5

0

0

5

10

15

20

25

Indicator

Rejection

Acceptance

0

-10000

-20000

-30000

-40000

-50000

-60000

0

5

10

15

20

25

Cusum

Figure 7. A stylised cusum analysis – the process (left) and the corresponding cusums 
(right)

There are different formulations of the statistical control problem, including those involving Bayesian 
inference (West 1986; see also a recent review in Tartakovsky and Moustakides 2010), and also natural links 
with the statistical decision theory (Harrison & Veerapen 1994). One important premise of the application 
of the method is an appropriate setting of the thresholds, which rely on good calibration of the underlying 
model and on appropriate trade-offs between ‘false alarms’ (Type 1 errors) – issuing warnings based on 

 
Quantitative assessment of asylum-related migration. A survey of methodology — 37

observations that ultimately prove to be following the trend, and ‘complacency’ (Type 2 errors) – not 
generating warnings when there are genuine shifts in the asylum trends. As remarked by the original 
creator of the cusum method, ES Page, these errors can be measured by the expected length of time the 
process spends respectively below or above the threshold before the methods triggers a warning which 
leads to a possible action (Page 1954: 101).

To obtain the one-year-ahead forecasts, which we suggest should be monitored using the above techniques, 
we propose that for each sending country experts are asked to synthesise the relevant push-factor 
information (together with their assessment of likely changes) into an estimate of the change in applications 
N for the year ahead. Use of formal elicitation methods (e.g. O’Hagan 1998) allows uncertainty in this 
estimation to be accounted for, and thus obtaining a distribution over the possible extent of change, which 
can be assumed to be normal:

(4) 

ΔNit ~ N 

nit, σit

The subscripts i and t here refer to country of origin and year respectively. A normal distribution is assumed 
for illustrative purposes but in practice other distributions might be used. Given these changes and their 
implications for levels of future applications, the overall process can be modelled as exhibiting the following 
structure:

(

)

(5) 

 μ

Yijm ~ 

μijm

ijm = NitπijtSm
Poisson(
t

.

)

(

)

In (5), Nit represents the implied elicited outflow from country i in year t, and Sm(t) describes the distribution 
of this flow across months m of the year, easily transferrable to weekly or even daily data. The estimated 
dyad-specific effects πijt describe how flows are split across receiving countries j, and incorporates both 
policy indicators for recipient country j, and historical and geographical information describing the tendency 
for refugees from country i to apply for asylum in country j. As the model is probabilistic in nature, its 
outcome will consist of full probability distributions of the future values of Nit and Yijt. Based on those 
distributions, the various point estimates can be derived, such as predictions of the central tendency (mean, 
median) and of other contingencies (for example, based on quantiles), measures of dispersion (standard 
errors, inter-quantile ranges), and so on.

The matrix Π incorporating the dyadic effects πijt allows for the capturing of dependencies between flows. 
For example, if a recipient country j tightened its asylum policy toward country i, this would manifest itself 
in a reduction in the respective πijt term. Given that these terms describe the distribution over recipient 
countries of the expected inflows Nit from country i, this will result in a redirection of some of the flow from 
i to j to other countries, in proportion to their attraction to migrants from the sending country (represented 
by the other terms πi.t).

Several extensions can be proposed to this basic model. Firstly, the Poisson assumption can be relaxed 
to allow variances to be over-dispersed with respect to the mean count estimate. This is likely to give 
a more realistic account of the uncertainties inherent in the model. Secondly, in order to account for the 
importance to the eventual destination of the choice of routes into Europe, we would suggest separate 
Π matrices and seasonal distribution effects Sm(t) be estimated for each route. Then, migrant flows can 
be probabilistically distributed across routes according to past trends, Frontex data, and expert opinion.

Monitoring the model on a weekly or monthly basis means that parameters can be updated using Bayes’ 
rule as more information becomes available, thus allowing the predictions themselves to be altered as new 
data come in. This applies equally to several elements of the model: the prevalence of different routes into 
Europe can be updated as further Frontex information becomes available; and the seasonal and flow levels 
may also be updated in light of new EASO data. Similarly, other auxiliary information about the asylum-
related migration process might be incorporated into the model, either through periodically asking experts 
to re-evaluate their assessments, or through more formal arrangements. For instance, noisy data may be 
available on numbers of migrants present in a border country such as Greece or Italy but who have yet 
to make an application for asylum (perhaps because they prefer to apply in other member states). Such 

 
38 — Quantitative assessment of asylum-related migration. A survey of methodology 

data could inform expert assessments of change in flows, or it might be used to formally update elicited 
assessments based on past relationships in the data, or it might be utilised to inform about route choice, 
as with Frontex data.

In terms of the workflow, the process could be summarised in the following stages. At first, the necessary 
information needs to be assembled, in the form of quantitative data, expert knowledge and stakeholder 
input as to the sensitivity of the warning thresholds. On that basis, the model is estimated and is updated 
every time new data on asylum become available. At this stage, the statistical control mechanisms are also 
triggered: if the process is found to be still ‘in control’ (below the threshold) or if it requires monitoring, 
no action is taken until the successive data become available, when the situation is being reassessed. If, 
however, at any stage the process is deemed ‘out of control’ (above the threshold), this triggers a policy 
warning to the stakeholders and a re-evaluation of the whole model, including a re-assessment of the 
situation by the experts. The control is then re-set, and the evaluation cycle resumes as described above. 
The particular elements of the workflow are graphically summarised in Figure 8.

START

Stakeholder

Setting the
thresholds

Expert
elicitation

New data

Model & 
forecast
estimation

‘Out of control’
(Above the 
threshold)

Control

‘In control’
(Below the 
threshold)

Policy
warning

Figure 8. An algorithmic representation of the workflow in the early warning asylum model

Quantitative assessment of asylum-related migration. A survey of methodology — 39

5.3  Identifying the data/information gaps and limitations

In this section, we look at the specific data and information gaps for the particular models proposed 
above, and make suggestions on the ways of filling these gaps, through additional analytical work and data 
collection. We also look into possible resource implications of such endeavours.

As mentioned, the key quantitative variable in the model would be the EASO estimates of asylum flows 
at the highest available frequency. In terms of further model development, it would also enable EASO to 
update the model very quickly, making use of the synergy with data collection efforts that are already 
in place. One question that arises is whether the outcome measure for asylum applications should be 
applications made or applications lodged. As described in the data section, the ‘applications made’ metric is 
less likely to be affected by differences in administrative procedures between countries, and so should give 
a better indication of the timing of inflows. However, this metric has only begun to be collected consistently 
in recent periods, so there may not be enough data to inform the various elements of the model. However, 
as more data become available, using the ‘applications made’ measure may become more practicable, 
and it may also be possible to model the relationship between the two measures.

As for other quantifiable data, a key modelling priority is to keep abreast of the asylum policy developments 
across Europe and in other important countries of destination. This implies the need to create and update 
synthetic policy indices: an excellent starting point is offered by the IMPALA database, but for the purposes 
of the EASO model it would need to be extended in terms of geography, possibly at the expense of some 
of the detail. Then, in order to be useful in the modelling process, the indices would need to be regularly 
monitored and updated. All the related tasks could be performed in-house by EASO, by having a dedicated 
ca. 10 % of a researcher’s time.

A more complex task has to do with expert knowledge elicitation. This would require assembling a pool 
of reliable experts on country-specific issues with respect to origin regions of asylum seekers, whose 
expertise could be called upon at a relatively short notice. These constraints imply that there ideally should 
be a restricted-access online survey, pre-tested as a part of further lots of the EASO research programme 
to ensure as unambiguous questions as possible. Ideally, a wide range of areas of expertise should be 
considered, and the expert answers would enter into the model in a quantified form. In principle, it might 
be beneficial to allow for multi-round elicitation, such as in a Delphi framework, but practical considerations 
are likely to limit the time available for the elicitation exercise. Through a survey, the expert knowledge 
could be elicited quickly and efficiently. Examples of migration-related formal elicitation frameworks are 
given in Bijak and Wiśniowski (2010), Abel et al. (2013), and Wiśniowski et al. (2013, 2014).

Another important task related to the modelling process described above consists in specifying the warning 
thresholds for the models. Ideally, the thresholds would need to be specified through a separate elicitation 
process, this time from policy stakeholders. One of the main aims of the elicitation exercise would be to 
establish trade-offs between the costs of ‘false alarms’ and the costs of ‘complacency’. These trade-offs 
could be subsequently formalised by using statistical decision analysis, to translate them into probability 
levels for warning thresholds (see Alho & Spencer 2005: Chapter 12.4, and Bijak 2010: Chapter 11, for 
further discussion and examples). For example, warnings could be triggered by ‘once in two years’ events 
(1 % probability threshold for weekly data), ‘once in a decade’ events (ca. 0.2 %), or at other levels. As 
the specification of thresholds would be a one-off task, or at least would not require routine or periodic 
updating, we recommend convening a meeting with the representatives of the member states and other 
interested parties, where the issues related to thresholds could be discussed and agreed upon. This is 
especially crucial, as warnings may have important financial implications, so there needs to be a consensus 
amongst the model users, at which probability level the warnings should be triggered.

Given that the recommended models are macro-level early warning mechanisms, there is no pressing 
need for carrying out additional survey-based studies amongst the asylum seekers and refugees. While 
the information that could potentially be gathered is important for a variety of operational reasons – for 
example to identify priority areas of service provision – it is unlikely to give new, game-changing insights 
into the dynamics of the asylum processes under study. In that respect, longer-term model development 
goals may include monitoring of social media, as well as other approaches designed to harvest information 

40 — Quantitative assessment of asylum-related migration. A survey of methodology 

from various ‘big data’ sources (some suggestions are provided in Section 6). However, at the first phase of 
model development this is something that does not need to be done independently and could be subsumed 
within the broader task of expert elicitation.

5.4  A simple illustration of the framework

To demonstrate the workings, information requirements, envisaged outputs, and the resulting application 
of the proposed approach for decision making for single countries, we present the workings of a simple 
version of a prototype control model using data from the Swedish Migrationverket (migration agency) 
on flows into Sweden from Syria and Iran. Taking the differences in log-counts, or equivalently, the log 
ratios of successive observations, we monitor how flow growth evolves over time for these two sources 
of applications. In reality, elicitation exercises would allow meaningful boundaries to be set determining 
when the flows are in and out of control (Harrison and Veerapen, 1994). Lacking this information, the 
current exercise uses a standardised version of the cusum measure given in Equation 6 below, and takes 
the decision boundary beyond which a loss of control is flagged as lying five standard errors above zero 
(Page 1954; Scrucca 2004).

(6) 

St = 

St-1 + xt – k, 0

)

max(

This is similar to the cusum measure described above but acts to reset the cusum to zero each time a new 
minimum value is reached. The value k determines what size of shift is picked up by the cusum monitoring 
process. In this case, one standard error is used. Figure 9 below displays the evolution of this measure for 
both Iran and Syria. Each chart contains two measures, one above the x axis examining positive shifts in 
log-ratios (that is, increases in rate of growth of the number of asylum seekers), and the other displaying 
negative shifts, plotted below the axis. The red areas of the plot mark boundaries of the region considered 
out of control. When put into practice, monthly data would be used, and the latest observations would 
be model-generated forecasts, but for this example, actual, annual data are used to aid illustration.

Iran

Syria

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(cid:31) (cid:31) (cid:31) (cid:31) (cid:31) (cid:31) (cid:31) (cid:31)
(cid:31) (cid:31) (cid:31) (cid:31) (cid:31)

(cid:31)

(cid:31)

(cid:31)

m
u
s
u
C

6

3

0

−3

−6
6

3

0

−3

−6
6

3

0

−3

−6

a

b

c

1990

2000

2010

1990

2000

2010

Year

Figure 9. Cusum control plots for Syria and Iran

Examining Figure 9, we can observe how a monitoring process would work in practice. Starting with Iran 
(left column), we see that initial the process is in control (panel a, top row), despite some decreases in 

Quantitative assessment of asylum-related migration. A survey of methodology — 41

rate. Looking at the panel b (second row), we see that in 1994, a significant downward shift in the rate is 
detected and the model is recalibrated. In panel c, the process remains in control until a sharp increase 
triggers a policy warning in 2015.

Moving to Syria (right-hand column), we see that in the initial observations (panel a), we observe some slight 
downward shifts but not enough to trigger a recalibration. The process remains under control throughout 
the later 1990s and 2000s (panel b), but the effects of the civil war are picked up by the monitoring process, 
and trigger a warning in 2012 (panel c).

The above description gives examples of how model forecasts could be used to trigger policy warnings 
when significant shifts are detected in asylum flows. A final model would take into account uncertainty in 
predictions and flows and allow calibration of warning thresholds according to the preferences of policy 
makers.

42 — Quantitative assessment of asylum-related migration. A survey of methodology 

6.  Conclusions and recommendations

There are a few key conclusions from the data and methods review conducted in this study. Firstly, in the 
context of asylum-related migration prediction is difficult in the light of the presence of ‘hard’, unpredictable 
push and pull factors, such as armed conflict or political change (Öberg 1996). As is evident from the review, 
most formal models of migration can help explain the patterns seen in the data, but on their own do not 
have much predictive power. This confirms the findings of other studies related to modelling and forecasting 
of migration flows (Bijak 2010; Raymer et al. 2013).

For that reason, in terms of recommendations, we propose that the future EASO work programme 
concentrates on specification of early warning models tailored to the specific needs of user at the EU level. 
We propose a framework based on the tenets of statistical control theory, tailored to the specific needs 
of the model users. In particular, different features can be assessed for whether they remain ‘in control’: 
observations, for monitoring purposes; forecasts, for pro-active contingency planning; or model errors, 
for assessing, whether the model needs respecification. Depending on the specific needs of EASO, and on 
the available resources, the models can be estimated and evaluated at different levels, from all-European 
aggregates, to individual flows of asylum-related migration.

One of the key limitations of the proposed modelling work is that they cannot be seen as predictive in 
the formal sense. The practical implication of the low predictability of asylum-related migration is that 
the policy decisions will be always inevitably be made under the conditions of uncertainty. Even though 
there are techniques for supporting decision-making in uncertain circumstances, for example statistical 
decision analysis (see Alho & Spencer 2005; Bijak 2010), the fundamental uncertainty remains a crucial 
issue, which the policymakers need to be fully aware of. Hence, key prerequisites of success of any formal 
modelling approach include an appropriate communication of the model aims, results, uncertainty and 
limitations to stakeholders, in an accessible, yet at the same time honest and transparent way. The traffic-
lights approach and risk management matrices, as used in the migration context, for example by Disney 
et al. (2015), and adopted for the evaluation of data and methods in this study, can provide a very useful 
framework in that respect.

There are some natural extensions of the proposed approach that can be accommodated in the next stages 
of the EASO research programme. One area that is worth exploring in that context is related to ‘big data’ 
from social media, electronic communications, and other sources, to enhance the early warning capacity 
of the models. As is well documented in the accounts of the asylum seekers’ journeys (Kingsley 2016), new 
technologies are widely used by people seeking asylum to navigate through the challenges and obstacles 
along the way. The analytical potential of these sources remains so far unused but it can be potentially 
harnessed for the benefit of the whole European asylum system, provided that appropriate ethical and 
privacy safeguards are put in place, to protect the rights of individuals while allowing greater and timely 
insights into population-level phenomena.

Besides the practical recommendations listed above, there are many other aspects of asylum-related 
migration that would benefit from further and more detailed research, which goes well beyond the EASO 
work programme but should be of substantial academic interest. One aspect is related to the flip-side 
of asylum-related migration, which is immobility even in the face of dramatic events, armed conflict 
or persecution. Similarly to the findings of the Government Office for Science (2011) in the context of 
environment-related migration, it can be argued that the most vulnerable populations – those who do 
not have enough resources to move – remain ‘trapped’ in very adverse conditions. Migration and mobility 
are important strategies through which people are able to adapt to difficult circumstances, so it becomes 
crucial to examine the potential ways in which the EU member states and other international bodies can 
reach out and help the populations who are unable to move. Another aspect worth exploring is the impact 
of asylum-related migration and its consequences on countries of origin, which has been discussed only 
in a handful of studies (Koser and Van Hear 2004).

From the methodological point of view, there are also longer-term opportunities for policy-relevant model 
development, for example through the design of interactive simulations, such as agent-based models, 

Quantitative assessment of asylum-related migration. A survey of methodology — 43

whereby the behaviour of individual actors – in this case, refugees, as well as policy-making institutions – is 
modelled in silico, within computer programmes (Gilbert & Troitzsch 2005). In the context of environment-
related migration, an example of such a model has been proposed by Smith et al. (2008) with the framework 
discussed in policy terms by Ginnetti (2015), and a review of the current state of the art in this area has 
been offered by Klabunde and Willekens (2016). One particularly appealing feature of such policies is the 
testing of policy alternatives through simulations (examples may include the possible policy developments 
suggested by Gibney and Hansen 2004). Qualitative and other survey information on the mechanisms of 
decision making of the asylum seekers as fully autonomous agents (e.g. Robinson & Segrott 2002; Kingsley 
2016) could constitute important input into such models. Still, in terms of the work effort required, these 
approaches would go well beyond the EASO research programme but are worth considering as a longer-
term investment that could be co-funded and possibly co-produced with academic stakeholders.

At this stage, however, given the importance of the challenges related to asylum in Europe, the development 
of an early warning system is a priority. Just by doing that, the perspective in policy and decision-making 
at the EU level, including at EASO, would change from reactive to pro-active and contingency planning 
would become naturally embedded at the heart of the modelling, the role of which would shift from an 
one-off product to a constantly-updated process. We consider this to be the main appealing feature of 
the proposed approach to modelling asylum, which remains honest and realistic about the limitations of 
any formal models in the context of asylum-related migration.

The six key recommendations made throughout this report are summarised in Box 3.

Box 3. Summary of recommendations
Recommendation 1. The formal approach pursued as a part of the EASO research programme 
should be an early warning system, modelling changes in asylum flows based on internal EASO data.

Recommendation 2. The proposed way of operationalising this approach is through the statistical 
control theory, and by applying Bayesian methods for describing uncertainty in the model.

Recommendation 3. The main inputs into the model should include expert knowledge on countries 
of origin, possibly augmented by formal conflict intensity indices for countries of origin and policy 
variables for destinations, as well as stakeholder views on the sensitivity of the system.

Recommendation 4. The application of the approach should be iterative, and the model should be 
periodically revisited and reassessed, especially every time a change in asylum trends is suspected.

Recommendation 5. Communication is key: the users of the model should be made aware of the 
aim of the modelling process and limitations of the results, especially in relation to the uncertainty.

Recommendation 6. From the modelling point of view, further work should concentrate 
on harnessing the power of ‘big data’, for example from social media and other electronic 
communication means, while bearing in mind the associated ethical and privacy concerns.

44 — Quantitative assessment of asylum-related migration. A survey of methodology 

Glossary

Asylum-related migration: In this report, migration that is defined as international in nature, and 
being – or being claimed to be – related to forced displacement, defined as forced migration due  
to persecution, armed conflict, violence, or violations of human rights (page 6)

Bayesian approach: A statistical methodology based on prior beliefs about the phenomena of interest, 
updated with the information from data, in order to produce the posterior estimates of model parameters 
and derive the forecasts on their basis (page 32; more in Bijak & Bryant 2016)

Cusum: Cumulative sum – an indicator used in statistical quality control models and early warning systems, 
defined through cumulating the values of the underlying variable over time (pages 33–34)

Delphi technique: A multi-stage technique for expert elicitation, whereby results of the elicitation from 
one stage of the exercise are communicated anonymously back to the experts, who are then given the 
option of adjusting their views on that basis (page 27)

Early warning system: A statistical modelling framework designed to signal a need for undertaking action 
if some control indicators (such as cusums) exceed warning thresholds, which can be specified by taking 
into account the type 1 and type 2 errors related to making incorrect decisions (page 32)

Expert elicitation: An exercise aimed at gauging the views of a experts on certain aspects of the 
model, such as the prior distributions for parameters in the Bayesian approach, or target values  
for certain processes or indicators; carried out formally within a pre-defined framework (page 42)

Generalised linear models (GLM): Generalisations of simple linear regression models, where the error 
terms are allowed to have different probability distributions than normal. In this report, we consider models 
that enable distinguishing various effects: generation, distribution, etc. (page 20)

Log-linear models: A class of linear regression models with log-transformed response variables. In this 
report, these are mainly log-linear models for counts in multidimensional contingency tables, with 
explanatory variables including various individual effects and interactions (pages 21 and 50)

Panel regression: A type of regression models, whereby groups (panels) of the same variables or indicators 
are modelled across time for different objects, such as countries (page 21)

Push and pull factors: In Lee’s (1966) framework, these are drivers ‘pushing’ migrants from their origins 
and ‘pulling’ them to the destinations, mediated by the ‘intervening factors’ and individual migrant 
characteristics. Öberg (1996) additionally distinguished ‘hard’ and ‘soft’ factors (pages 8–9)

Probabilistic methods: Methods and models of forecasting, which describe their predictions not in terms 
of single values, but whole probability distributions of the possible future outcomes (page 30)

Synthetic indices: Artificial variables created by combining the information on different dimensions of 
the same phenomenon, for example stringency of the migration or asylum policy, or intensity of armed 
conflict (pages 18 and 23)

Time series analysis: A class of probabilistic methods of prediction, whereby the forecasts depend, among 
other things, on past values of the variables being predicted (page 21)

Traffic-lights approach: A method of evaluating various aspects of quality of the data or models, assigning 
them the ‘green’ (good), ‘amber’ (moderate) or ‘red’ (poor) ratings (pages 12 and 19)

Type 1 and type 2 errors: Errors in making incorrect decisions based on signals in data – either ‘false alarms’ 
(Type 1 errors), related to spurious warnings in data, or ‘complacency’ (Type 2 errors) – not generating 
warnings when the indications in the data are genuine (page 34)

Quantitative assessment of asylum-related migration. A survey of methodology — 45

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50 — Quantitative assessment of asylum-related migration. A survey of methodology 

Appendix A. Inventory of selected data and 
meta-data sources

•  Asylum – key series of interest

•  EASO
•  UNHCR (http://www.unhcr.org/uk/figures-at-a-glance.html)

•  National Statistical Institutes and ministries of interior (or equivalent authorities)
•  Germany: Bundesamt für Migration und Flüchtlinge (https://www.bamf.de)
•  The Netherlands: Statistics Netherlands (https://www.cbs.nl)
•  Sweden: Migrationsverket (http://www.migrationsverket.se)
•  Switzerland: Staatssekretariat für Migration (https://www.sem.admin.ch)
•  United Kingdom: National Statistics on asylum (https://www.gov.uk/government/publications/

immigration-statistics-april-to-june-2015/asylum)

•  Other EU and international agencies

•  Eurostat (http://ec.europa.eu/eurostat/web/asylum-and-managed-migration/data/-database)
•  Frontex (Selected publications at http://frontex.europa.eu/publications/)
•  Database  on  Immigrants  in  OECD  countries  (http://www.oecd.org/els/mig/

databaseonimmigrantsinoecdcountriesdioc.htm)

•  Data on drivers of asylum-related flows

•  World Bank (http://data.worldbank.org/)
•  Uppsala Conflict Data Program (http://www.pcr.uu.se/research/ucdp/datasets/)
•  CEPII (http://www.cepii.fr)
•  Freedom House (https://freedomhouse.org/)
•  Political Terror Scale (http://www.politicalterrorscale.org/)

•   Meta-data and research projects

•  Asylum Information Database (AIDA, http://www.asylumineurope.org/)
•  International Migration Policy And Law Analysis project database (IMPALA, http://www.

impaladatabase.org)

Quantitative assessment of asylum-related migration. A survey of methodology — 51

Appendix B. Detailed assessment of data and 
methods

Table B 1. Framework for evaluating the data on asylum-related migration – quality descriptors

Dimension

Frequency

Definitions

Coverage

Accuracy

Timeliness

Good aspects

Average aspects

Problematic aspects

Daily, weekly, or fortnightly 
series

Monthly, n-monthly, or 
yearly series

Occasional or one-off studies

Following international 
standards

Roughly corresponding to 
established concepts

Not defined or definitions 
imprecise

Full population of interest / 
extensive historical series.

Well-defined regions or 
subpopulations / shorter 
times series.

Unclear, unsystematic, 
opportunity

Good-quality 
comprehensive register

Other register or good-
quality survey

Poor-quality survey or 
unknown

Promptly after the 
reference period

Reasonable timeliness, 
without long delays

Infrequent production, one-off 
collections, long delays

Quality 
assurance

In place, rigorous, and well 
documented

Some, documentation may 
be patchy

Unclear or non-existent quality 
processes

Table B 2. Framework for evaluating the existing models and methods – quality descriptors

Dimension

Good aspects

Average aspects

Problematic aspects

Specification

Resolution

Assumptions

Appropriate method, well-
designed model

Some issues, but method 
generally fine

Design unclear or not fit for 
purpose, specification errors

Detailed spatial and/or 
temporal resolution

Broad spatial and 
temporal resolution

Coherent, logical, well 
explained, many aspects

Implicit, broad, some 
aspects considered

Case studies: single area 
and single time period

Missing, incoherent, not 
justified, single aspects

Predictiveness

Predictive model by design 
(e.g. time series)

Explanatory model, some 
pred. potential

Descriptive model; with no 
predictive potential

Uncertainty

Generality

Explicitly addressed & 
formally modelled

Model transferable to 
other problems

Discussed in the narrative 
/ limitations

Not acknowledged explicitly, 
dismissed or ignored

Model suited for a given 
class of problems

Model tailored only for a very 
specific problem

52 — Quantitative assessment of asylum-related migration. A survey of methodology 

Table B 3. Specification of selected quantitative models of asylum-related migration (For sources, see table)

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Table B 3. (cont.)

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HOW TO OBTAIN EU PUBLICATIONS

Free publications:

•  one copy:  

via EU Bookshop (http://bookshop.europa.eu);

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Priced publications:

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B
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