Surveys on migration aspirations, plans and intentions: a comprehensive overview

Survey data on migration aspirations, plans and intentions is important for understanding the drivers and dynamics of migration. Such data has been collected since the 1960s but has expanded massively in recent decades. This paper provides the first comprehensive overview of existing survey data in an inventory of 212 surveys with recorded metadata on geographic and temporal coverage, survey population, sample size, and other characteristics. ‘A survey’ is not always a clear-cut unit of analysis, but we adopted procedures that enable systematic comparisons, and identified surveys through systematic searches and follow-up investigation. The paper has three objectives. First, it facilitates reuse of survey data and secondary analysis, albeit with limitations in data access, which we document. Second, it helps consolidate a sprawling field and thereby contribute to methodological and theoretical strengthening. Third, it informs debates on the ethics, politics and biases of data collection by documenting broad patterns in the body of knowledge. The inventory of survey data on migration aspirations and related concepts gives migration researchers a new tool for locating existing data and strengthening the foundations for collecting new data.


Introduction
The study of migration has benefitted from collecting and analysing survey data on individuals' thoughts and feelings regarding the possibility of moving elsewhere.In line with recent developments in migration theory, we use 'migration aspirations' as the umbrella term to cover these mental constructs in their various forms, including desires, intentions, plans and expectations for migration (Carling, 2002;Carling & Schewel, 2018;de Haas, 2021;Koikkalainen & Kyle, 2016).
Within migration studies, survey data on migration aspirations complements population data by allowing for incorporation of fine-grained information on attitudes and behaviour.Moreover, it complements survey data on migrants, which offers limited insight on the drivers of migration because it is sampled on the dependent variable.
Data on migration aspirations has in part been used in attempts to predict or forecast migration flows (Tjaden et al., 2019).Even though most prospective migrants face daunting obstacles and end up staying, variations in the incidence of migration can shed light on the evolution of migration flows.Moreover, there are additional reasons for studying migration aspirations (Aslany et al., 2021;Carling, 2019).First, if we want to understand what motivates migration, it is insufficient to study actual migration.Factors such as poverty, corruption, crime, or environmental degradation could affect peoples' wish to move elsewhere (Aslany et al., 2021;Czaika & Reinprecht, 2022).Whether or not they lead to people crossing borders is a separate issue, governed not least by restrictive migration policies and other obstacles.Visa restrictions have, for instance, been found to decrease emigration (Czaika & de Haas, 2017).Second, migration aspirations could affect behaviour in other ways than migration, especially when the desire to leave remains unfulfilled for many years.People who wait for a chance to leave could, for instance, be less inclined to invest in local livelihoods, skills or relationships, with consequences for their own lives and societies.
From a policy perspective, insights on migration aspirations are essential for influencing migration flows and reducing the negative consequences of migration.As we will show, many surveys specifically target health workers and medical students and could provide insights that help stem the loss of human capital through emigration.More generally, factors that are strongly associated with a wish to leave can help set priorities for social policy.
In this paper we present a first of a kind systematic inventory of surveys that have collected data on migration aspirations.The inventory provides an overview of survey data and metadata from the past five decades to encourage further use and inform future research.(We are separately examining survey items and questionnaire design, and developing a question bank on migration aspirations; see Carling & Mjelva, 2021 for a preliminary version).
The study of migration aspirations touch upon several areas of migration research.This diversity is reflected in our references.In the inventory of surveys, we cite a total of 250 sources, of which 205 are journal articles.The articles are spread across 72 journals of which only 24 occur more than once.Table 1 lists them with their respective number of articles.Not surprisingly, the largest number of articles using data on migration aspirations are published in major migration journals.Other journals represent the fields of population studies, urban studies, development studies, rural studies and health policy.
In what follows, we present the inventory of surveys and its contents.In Section 2 we discuss the construction and organisation of the inventory, while Section 3 gives an overview of the metadata of the surveys.In our concluding remarks in Section 4, we discuss some recommendations and encouragements for survey reporting.

The inventory of surveys
In general, survey datasets exist in a variety of forms, with disparate degrees of public documentation and data availability.As a rule, they are not systematically indexed in databases in the way that, for instance, journal articles are.These factors make a review of surveys very different from a systematic review of literature.
We used publications as a gateway to establish an overview of surveys.Since we were looking for surveys that included items on migration aspirations, we conducted a search through Web of Science for literature that is survey-based and includes migration aspirations or related terms such as migration intentions or desires in the title or abstracts. 1This search produced 287 hits, which were subsequently screened to identify publications that used relevant data.In addition, we searched the authors' reference library of several thousand migrationrelated references, of which many relate specifically to migration aspirations.This library contains both articles, books, reports, and other publication types.Finally, the reference lists of selected articles were reviewed to identify additional potentially pertinent literature.Throughout the process, we did not discriminate by publication type or publication year.
In total, we identified 289 publications that used survey data on migration aspirations, stemming from 212 surveys.
Inclusion in the inventory of surveys is contingent on three requirements.First, the survey must be of a quantitative nature, meaning that it must be structured with pre-formulated, standardised questions.However, no threshold concerning sample size was set to distinguish quantitative from qualitative surveys.
Second, the survey must contain at least one question inquiring about respondents' migration aspirations.The question could concern residential mobility, domestic migration, international migration or migration at different geographical thresholds.It must, however, address the prospect of future migration, not respondents' experience with migration in the past.
Third, it must be possible to obtain a minimum of information about the survey and survey instrument, beyond the fact that a survey exists.We did not have strict rules as to which metadata had to be available but needed some information, for example about the topic of the survey, the survey population, or geographic coverage, for it to be meaningful to include the survey in the inventory.Metadata on surveys is occasionally missing for data collection method (16%), sampling method (10%), data collection period (6%), survey design (6%) and sample size (1%).For the purpose of gaining an overview of relevant surveys, we included surveys with satisfactory survey-level information even if the information about specific survey items was faulty.The inventory of surveys contains the best information available in the referenced publications or survey documentation.
Each row in the inventory refers to one survey.Many surveys have rounds that vary in methodology, sample size, geographic coverage, or content of the survey instrument.Consequently, it is sometimes difficult to distinguish between rounds and independent surveys.This difficulty is compounded by the uneven availability of metadata, depending on how various rounds or parts of surveys have been used in publications.
We have coded surveys as multi-round whenever they are described as such in the reference or survey documentation.
Each survey is given a numeric ID, assigned in the order of the first year of data collection, and then alphabetically by survey name among surveys with the same start year.If publications did not contain information about the data collection period, we assigned IDs with the assumption that data was collected three years before the publication year.In a few cases, information about additional rounds emerged during the review, with the result that not all IDs reflect the chronology of data collection.
Additionally, each survey in the inventory has a unique descriptive name.Some, like Afrobarometer or Gallup World Poll, have well-established official names.Others -especially one-off surveys carried out for a particular project -often lack a specific designation.In these cases, we have used the available information to formulate a name, such as 'Migration Intentions among University Students in Slovakia' or 'Hubei Province Migration Survey'.
The inventory of surveys includes references to publications that have used each survey, typically the publication(s) through which each survey was identified in the first place.Hence, the list does not necessarily include all references that have used the survey but shows where we discovered the data.Some publications use several surveys and are therefore listed in several rows.
Although the search for surveys has been extensive and the list of surveys is long, we cannot assume that it is exhaustive.In particular, surveys carried out by international organizations, civil-society organizations, or private-sector actors are less likely to be used in scientific publications and could therefore more easily have been missed.

Overview of surveys
In what follows, we assess the geographic, temporal and population coverage, survey methods and data availability of the surveys.As we discuss them in this section, we refer to examples by their ID number, and refer to the underlying data for the full reference.

Geographic coverage
The inventory includes several measures of geographic coverage: geographic scale, number of countries covered, distribution across countries, and distribution across world regions.
We have classified the geographic scale of surveys as subnational, national, multi-subnational, multinational, and diasporic, as defined in Table 2.The multinational surveys cover between 2 and 155 countries, with a median of 6.Three surveys have a globally diverse coverage: the Gallup World Poll (76), which covers more than 150 countries, the International Social Survey Programme (18), which covers 42 countries and the Pew Global Attitudes Survey (51), which covers 25 countries.Almost all the remaining multinational surveys span a set of neighbouring countries within the same region.
Very few multinational surveys concentrate on migration issues.All those that cover more than seven countries are thematically broad surveys that have, at most, a handful of migrationrelated questions.By contrast, most of the multi-subnational surveys -which cover two to eight countries -are surveys that focus on migration or migration aspirations.
This variation in geographic scale cuts across variation in the survey population, which we discuss in Section 3.4.In other words, a national survey can target a highly specific population, such as British doctors in New Zealand (112) or Russianorigin immigrants in Israel (165).When we classify surveys as national or multinational -that is, with the aim of being nationally representative -we rely on descriptions in the cited publications or other survey documentation and have not evaluated the actual representativeness.However, surveys differ in the compromises they must make in the attempt to be representative at the national level.
The largest group of surveys are subnational, followed by the national ones.Only one in five cover more than one country.This distribution is unsurprising considering the lower resource requirements for subnational surveys.Some are products of graduate research, for instance.
The variation in geographic scale is partly linked to differences in the form of migration that is the focus of the survey.Many surveys explicitly address international migration, while others address internal migration or local residential mobility, and yet others do not discriminate between internal and international destinations.
The surveys cover countries from all parts of the world, though with clear imbalances.Classifying the geographical coverage of surveys is, in most cases, straightforward, though not always in surveys that cover migrant populations or that vary across rounds. 2 To map the distribution across regions we use the World Bank's regional classification, presented in Table 3.The multiregional category describes surveys that include countries from more than one region, though they are, in some cases, a contiguous group of countries.The Afrobarometer (168), for instance, is multiregional because it spans the regions Sub-Saharan Africa and Middle East and North Africa.
Table 3 also displays the distribution of surveys across world regions.Europe and Central Asia top the list and strikingly account for half of all the surveys.At the bottom of the list is South Asia, which is represented by only three surveys: two from Pakistan and one from Afghanistan.South Asia, like other seemingly underrepresented regions, is also covered in multi-regional surveys.

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2 For diasporic surveys we use the code for the country of origin (see Table 2).For surveys of migrants in a single country of destination, we use the code for the country of destination.When the geographic coverage varies across rounds in a single survey, we list all the countries that, to our knowledge, have been included in at least one round.
Figure 1    but tend to be less attuned to context-specific dynamics.
In Figure 2, we display the frequency to which each country appears in national and subnational surveys.Here we see that much of Latin America, Africa, the Middle East and Asia are not covered in any national or subnational survey.In contrast, the United States and China stand out with particularly many surveys of this type.Most of these surveys address internal migration.In the case of the United States, some focus on residential mobility in a single metropolitan area.

Temporal coverage
The inventory of surveys covers data that has been collected from the 1960s until 2020.Most collect data in a single round only, which could take anywhere from a few weeks to several years to complete.Other surveys collect data on the same population in multiple rounds -an aspect of survey design that we will discuss in Section 3.4.Data collection for such surveys can cover much longer periods, up to several decades.In the inventory of surveys, we have included the first and last year of data collection, to the best of our knowledge. 4igure 3 displays the data collection period for each survey.
The period is the interval between the first and last year of data collection, regardless of the frequency of data collection in between.In multi-round surveys, data might be collected annually during this time span, or less often, or at less regular intervals.
All surveys covering a time span of ten years or more are labelled in the figure.Two thirds of these long-running surveys cover either Europe and Central Asia or North America.The two longest-running surveys are the Panel Study of Income Dynamics (2) and the American Housing Survey (7), both of which are national surveys in the United States.

Survey population
Most of the surveys cover general populations, but almost as many are targeting specific groups.Each survey draws a sample from a pre-defined population with certain characteristics, and the difference in population is a key form of variation between the surveys.Table 4 displays the distribution of surveys across population categories.Just over half of the surveys cover the general population, though some are limited to specific age groups.
The most common specific population category is students.
The prominence of students has several possible explanations.Some surveys are linked to the growing interest in international student mobility, especially in Europe.Other surveys among students may be motivated by concerns about human capital losses (so-called 'brain drain', see for instance Gibson & McKenzie, 2011 for more information about the concept).
Finally, student populations can be appealing for logistical and financial reasons when sampling and recruitment can be organized through schools, universities or associations.Migration aspirations is particularly prominent among youth and young adults, who, in many countries, are likely to be students.Consequently, a sample of students could reflect an interest in the age group, combined with logistical sampling considerations, rather than a specific emphasis on respondents being students.
A second prominent category includes migrant populations, which in some surveys include children of migrants (78,90,127).It is common in surveys of migrants to include questions about plans or wishes for return or onward migration, which can be seen as a particular form of migration aspirations.

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The third most frequent category is health workers, often defined as physicians or nurses.The emigration of health workers is a major policy concern in many countries, and understanding their migration aspirations, and the underlying motivations can therefore be important.The majority of these surveys were undertaken in European countries with significant out-migration, such as Ireland, Poland and Portugal.
The prominence of student, migrant, and health worker populations was reflected in several overlaps between these categories.We have therefore singled out health worker migrants and health sciences students as population categories.
In addition to differences in population type, the surveys differ considerably in the age range of their samples -as well as in the level of detail on age that is provided in publications.We have used the available information to classify the surveys into three broad groups (Table 5).The most important difference between surveys is the upper bound of the age range, which we use as the criteria for distinguishing between adult, young adult, and youth samples.The lower bound of the age range also varies, though not always in expected ways.For instance, some surveys covering the adult population include individuals down to the age of 14.
Almost one third of the surveys are limited to youth or young adults.Many of these surveys cover students, and some focus on migration aspirations of youth and young adults from rural districts.Migration aspirations decline with age (Aslany et al., 2021), and surveys that specifically address this topic can therefore benefit from concentrating available resources on a younger sample.

Survey methodology
In this section we address four aspects of survey methodology: (1) the overall design in terms of data collection in one or more rounds over time, (2) the method of sampling respondents, (3) the size of the sample, and (4) the form in which respondents provide information.
A fundamental aspect of survey design is the way of which data is collected over time.There can be one or more rounds of data collection, and if there are several rounds, respondents can be the same or be replaced in each round.For simplicity, we use three main categories, presented in Table 6: single-round surveys, multi-round surveys, and longitudinal surveys.In addition, a few surveys have a mixed design with consecutive, disconnected panels.For about a dozen surveys, there is insufficient information about the survey design to allow for categorization.
Overall, about a third of the surveys have multi-round or longitudinal designs, allowing for analyses of trends or dynamics over time.These are primarily surveys of the general population.There is only one such survey among the 21 that cover workers and students in the health sector.Survey respondents can be sampled in diverse ways, which we have classified in three broad categories (Table 7).Random or quasi-random sampling methods seek to give each individual in the population the same probability of being included in the sample.In practice, randomness is a matter of degree, depending on compromises that are made in the design and execution of the survey.At the same time, standards for describing a survey as 'random' vary across research communities.We therefore use a broad category that also includes quasi-random designs in which the deviations from randomness are explicit.Two thirds of the surveys in the inventory of surveys fall into this category.
The second method is what we have called institutional sampling, in which individuals are sampled on the basis of an institutional affiliation.Examples include students at a university, employees of a company, members of an association, and similarly aggregated samples from multiple institutions of the same type.In some cases, the gross sample is the same as the population.For instance, if the population is defined as all medical students in a country, the entire population might be contacted via their universities, and the difference between the population and the sample would be accounted for by non-response.Overall, 17% of the surveys used institutional sampling.This proportion was twice as high in surveys of students and represented the vast majority of surveys on health workers.
Third, several surveys used explicitly non-random sampling methods.These include snowball sampling, by which respondents refer to other potential respondents.Surveys that authors describe as non-probabilistic have been placed in this category.Non-random sampling was used in only 6% of the surveys.
Basic information about sampling methods was missing for 10% of the surveys.In most cases, the publications or documentation mentioned sampling but described it too briefly or superficially for classification.Without proper information about sampling method, it is impossible to assess the representativity of surveys.
The sample size of the surveys varies by a factor of 4,000 from the smallest (40 respondents) to the largest (161,000 respondents).For multi-round surveys we have recorded the sample size as reported in the publications that are cited as sources for each survey.If information is available for more than one round, we have used the largest sample size.
Figure 4 displays the distribution of surveys by sample size and population category.Only one survey (76, the Gallup World Poll) has a sample of more than 100,000 respondents, while another 36 surveys have samples of 10,000 respondents or more.As can be seen in Figure 4, surveys of the general population dominate among these large surveys, although there are surveys of every other main population category with samples of at least 10,000 respondents.Several of the largest surveys are multinational and their samples for each country are not necessarily large.
Survey data can be collected in a number of ways that have diverse benefits and disadvantages, for instance in terms of costs and accuracy.The distinction that matters most for data

N % Description
Random or quasirandom 142 67 The survey uses sampling that approximates the ideal that each individual in the population has the same probability of being included in the sample.

Institutional sampling 36 17
The survey recruits respondents via institutional affiliation, sometimes with a gross sample that is the same as the population.
Non-random 13 6 The survey samples respondents in ways that cannot be described as random, for instance through respondent-to-respondent referrals (snowball sampling).
Missing 21 10 The sampling method was not possible to verify based on the information provided in publications or survey documentation content and reliability is whether the data was collected in conversation with an enumerator or entered directly by the respondent in a questionnaire or on a screen.We have classified the surveys based on this distinction and labelled the data collection method as either interview or self-administered (Table 8).
The majority of surveys collect data by means of interviews, either in person or by phone.Compared to self-administered data collection, interviews provide greater opportunities for quality assurance, though this potential depends on interviewer skills and training.The feasibility of self-administered data collection depends, among other things, on the qualifications of respondents and the complexity of the survey.
Even with a simple distinction between two broadly defined data collection methods, 33 surveys (16%) were not possible to classify based on the available information.A few surveys combined the two formats.In some cases, publications stated that data was collected by means of questionnaires but failed to specify whether they were completed by interviewers or respondents.

Survey items on migration aspirations
Survey items that enquire about migration aspirations can be broken down in terms of their mindset and action.Here, we discuss the nature of the mindset of the items.For more information about the composition of survey items on migration aspirations, see Carling (2019) and Carling & Mjelva (2021).
The nature of the mindset can broadly be described as a person's thoughts and feelings about the prospect of migration.Carling (2019) identified eight types of mindsets: consideration, preference, willingness, necessity, planning, intention, expectation, and likelihood.A description of these mindsets is found in Table 9, while Table 10 shows the distribution of the mindsets across the surveys.A list of the types of mindsets used in each survey is found in the underlying data.
Some surveys have more than one question enquiring about migration aspirations, and thus account for more than one mindset.These are marked as 'Multiple' in Table 10.Moreover, 2% of the surveys had items that could not be classified according to our framework because they combine several types of mindsets in the same question and/or response categories.These are labelled 'Other' in Table 10.Lastly, 10% of the surveys are coded as missing. 5 Consideration generally refers to cognitive behaviour in the past, simply differentiating between those who have given migration some thought and those who have not.Preference, willingness and necessity reflect some form of comparison between the expected outcomes of leaving and staying.If migration is seen to be 'necessary' it could be interpreted as an extreme version of preference in which the option of staying is so firmly rejected that it is considered impossible.Intention and planning both represent the next step, from evaluation towards action, and therefore appear to be more tangible than preferences, for instance (Tjaden et al., 2019;van Dalen & Henkens, 2008), though these concepts are marred by other shortcomings (Carling & Mjelva, 2021).Finally, expectation and likelihood stand out because they concern beliefs about future outcomes.Regardless of whether individuals would prefer to migrate and intend to do so, they could see it as most likely that they end up staying.The most widely used mindsets are preference and intention.

Summary of survey characteristics
We have so far addressed key characteristics one by one and presented frequency distributions across categories in a series of tables.Figure 5 provides a visual display of these frequency distributions.For each characteristic, the most common category accounts for more than half of the surveys.In other words, a 'typical' survey that combines all the modal categories would be a single-round sub-national survey in Europe or Central Asia that covers the general population of adults with random or semi-random sampling and collects data through interviews.However, only five surveys (81,157,188,200,210) share this combination of characteristics.
To explore variation across characteristics, we present Figure 6, which displays all 212 surveys by geographic scale and population, differentiated by regional coverage.The figure also identifies surveys that used random or quasi-random sampling methods and gathered responses through interviews rather than

Consideration
The act of reflecting on the feasibility or desirability of migration

Preference
The evaluative conclusion that migrating would be preferable to staying

Willingness
The preparedness to migrate despite assumed disadvantage or hardship

Necessity
The assessment that migration is the only option

Planning
The preparation of a course of action towards migration

Intention
The will or commitment to pursue a course of action towards migration

Expectation
The belief that migration will most probably take place

Likelihood
The assessment of the probability that migration will take place  Note: Two surveys included an item with a necessity mindset.Both surveys had multiple survey items on migration aspirations, which means that the nature of the mindset for these surveys are coded as multiple.N = 212.
self-administration. Table 11 lists the surveys in the same order as the figure for easy reference.

Data availability in sampled surveys
It is increasingly the norm to make research data publicly available, though this is far from universally the case.We have coded the availability of survey data based on information in the publications or other documentation, in two broad categories.The survey data is deemed available if, according to the publications, it can either be downloaded or obtained upon request, with or without a fee, and with or without specific restrictions or conditions.Data from the remaining surveys is deemed not available (the two classifications are coded as 'yes' and 'no' in the stated data availability column of the underlying data).Overall, data was reported to be available for 25% of the surveys.
The data availability information is an indication, but no guarantee either way.When a publication from several decades ago states that data is available upon request, it might not be possible to obtain today.Likewise, if publications do not state explicitly that data is available, it could, nevertheless, be possible to obtain upon request.
Data availability varies systematically by survey type.To illustrate, Figure 7 replicates the structure of Figure 6 and uses stated data availability instead of regional coverage.We see that data is more likely to be available for surveys of the general population as opposed to specific population groups.Moreover, stated data availability is highest for national and multinational surveys.It is only among multinational surveys of the general population that a majority of datasets are available.

Concluding remarks
We have presented a first-of-a-kind inventory of surveys that enquire about migration aspirations.For understanding migration processes, the data produced by such surveys is an essential complement to data on migration itself (Carling & Schewel, 2018;de Haas 2021).The work of compiling the inventory of surveys yielded two overall conclusions (1) there is a rich diversity of datasets that address migration aspirations, and (2) the standards of documentation are disappointingly low.Surprisingly often, basic information about surveys was missing from publications or difficult to obtain through the sources that were referenced.These weaknesses point to potentials for improved practice at all stages of the survey research process.In the following, we briefly summarize the implications.
When surveys are carried out, precise information about the survey design, population parameters, geographic coverage, sampling procedures, data collection method and data collection period should documented.In the course of running the survey, such documentation might be scattered across e-mails, meetings notes, and internal memos, and require deliberate effort to compile for posterity.
When data collection has been completed, survey documentation should be made securely accessible to others.Even if the data itself remains restricted, there are rarely good reasons to limit access to metadata and documentation.An added advantage of publishing documentation and metadata is that these documents can be referred to in the methods sections of research articles, where the scope for detailed description is limited.
When research publications are written, authors should ensure that basic information about the survey -such as the parameters used in our inventory -is included.Authors should preferably also indicate where more detailed information is available.
Information on data availability ought to be included regardless of whether the publication appears in a journal with a policy on data availability statements.6 Survey items on migration aspirations are extremely sensitive to the exact wording, and analyses should therefore quote the question and response alternatives for key variables.This seems obvious, perhaps, but is often disregarded.Engaging actively with the wording of survey items helps ensure consistency between the data and the text.Publications should not refer to migration 'desires' or 'intentions', for instance, if the relevant survey item was phrased in terms of consideration or expectation.Similarly, publications should not infer likelihood to migrate from survey data on migration aspirations.Such misinterpretations are surprisingly widespread.
Regardless of the potential for better practice, the wealth of existing data represents promising opportunities now that an overview has been compiled.Information about existing data helps carry out secondary analyses, make new surveys more cost-effective, and add comparative dimensions to analyses.The inventory of surveys on migration aspirations seeks to stimulate such gains.

General remarks
The study is a comprehensive review of studies dealing with migration aspirations, plans and intentions.The authors created a database of all studies on the topic by searching published articles.Each study was categorised according to the characteristics of the study itself.The paper is a useful analysis of surveys conducted since the 1960s.In my view, the paper is an important contribution to migration research on migrants' intentions, aspirations and plans.
The paper clearly presents the design and methodological approach.The results summarise the main aspects of previous studies, taking each characteristic into account, but also combining all characteristics in the same analysis.I found the summary in Figures 6 and 7 particularly useful.
The statistical analyses are descriptive (tables and maps) but accurate and the authors always provide an appropriate description of both tables and maps.
The conclusions emphasise a crucial aspect: the need to have adequate documentation for surveys, describing all aspects (target population, design, geographical context...).The authors provide useful suggestions for other researchers.Some suggestions In my opinion, the only aspect not directly addressed in this systematic review is the distinction between first and second migration, and even more so between onward migration and return migration.This distinction would enrich the analysis.

Luděk Jirka
Univerzita Hradec Kralove, Hradec Kralove, Hradec Králové Region, Czech Republic The article is well written; it provides a thorough and comprehensive overview of surveys on migration aspirations, and the authors deserve credit for their work.However, in my opinion, one small critical point could be emphasized.The article is based on surveys from articles published in English and in major migration journals.These journals are located in the "West" and this may limit the comprehensive focus on migration aspirations.For this reason, the article might be seen as Western-centric, as it neglects authors who had not published either in English or in major migration journals, but who still focus on migration aspirations.This is just a small caveat and I could imagine the amount of work that would be required to meet this criteria.However, the Western-centric focus also means that, for example, African scholars are under-represented (Figure 1).Authors should think about that.Furthermore, I wonder how authors could be so precise in assigning surveys to categories (Table 9) and specifications of the mindset (Table 10), when they conclude by stating that the consistency of the terminology could be questioned (sixth paragraph in conclusion).I understood that categories and specifications are more defined by the authors of this article rather than using the definitions of authors?Or authors categorise definitions of authors?I think this not entirely clear from the article(and it is important) and I recommend to authors to clearly state how they are working on this.
Is the work clearly and accurately presented and does it engage with the current literature?Partly

Sandra Morgenstern
University of Mannheim, Mannheim, Germany This is a really important topic and a valuable undertaking by the authors.Although academics and practitioners rely heavily on survey data when it comes to migration, a systematic review of existing surveys on migration aspirations and related concepts is rare.Another important contribution I see in this article is the rare in-depth focus on these surveys -particularly with regard to the survey methodology (3.4.) e.g. the sampling method or the data collection method, and the data availability (3.7.).
Section 2: The inventory of surveys / Methodological procedure Given that the core of the article is to judge surveys on their methodological procedures, I would propose to be a 'role model' in the methodological section.By this I mean that I would make the methodological section more detailed and precise.I am aware that this may not be of interest to all readers but pushing for improvements in the methodology, reporting and transparency of survey research on migration goes hand in hand with being a role model.
So I would suggest the following details: (a) a flowchart of surveys, where inclusion/exclusion goes through the search strategy, (b) more detail on inclusion criteria, e.g. in terms of language, and (c) reflections on the implications this might have (especially when thinking about paper 3: discussing biases), (d) more detail (e.g. in the flowchart) on the translation from collection of papers to collection of surveys (it is already in the text but might be confusing for readers), (e) justification for the search tool (web of science = focus/target group of academics?),... Section 3: Overview of surveys I enjoyed reading this section.The only point I could make is that there is a lot of information: geographical coverage, temporal coverage, survey population, ... It might be easier for the reader to digest this amount of information if there was an additional (short) paragraph at the beginning of this section summarising the different focuses that follow, and one sentence why each of these is relevant / chosen as a focus.This might also be an option for the abstract, where it currently says "recorded metadata on …, and other characteristics.",which could be too much ambiguity for some readers to read further.
Section 4: Concluding remarks In this section, the authors state the following overall conclusions: "The work of compiling the inventory of surveys yielded two overall conclusions (1) there is a rich diversity of datasets that address migration aspirations, and (2) the standards of documentation are disappointingly low.".I am aware that this is not a positive finding, but it is a really important one to encourage and motivate further research in this direction, particularly with regard to documentation standards and the highly interrelated quality of data.Therefore, I would suggest that these key findings be mentioned at the beginning of the article (e.g. in the abstract).
After the conclusion, the authors move on to suggestions and recommendations for future research.Given the nature of this article in reporting facts (facts = coverage, sampling, etc.), an interpretation of the state of the art in surveys of migration aspirations would be nice at this point.
For example, what are the implications of the lack of data availability?Or the non-transparent reporting of survey coverage?I see that this information is implicit in the recommendations for better research, but I think the in-between step here would be a valuable addition for readability.
Another suggestion (and similar to the previous one, again with the intention of readability/accessibility rather than a 'must do' argument) is to link the concluding remarks back to the three key contributions made at the beginning: "First, it facilitates reuse of survey data and secondary analysis, albeit with limitations in data access, which we document.Second, it helps consolidate a sprawling field and thereby contribute to methodological and theoretical strengthening.Third, it informs debates on the ethics, politics and biases of data collection by documenting broad patterns in the body of knowledge.".E.g. how exactly does the overview link back to ethics and/or biases?
One minor point: An additional argument for the relevance of survey data on migration aspirations (second paragraph of the introduction) that the authors could add is that surveys collect information not only on attitudes and intended behaviour, but also on the individual's perspective on the setting and their situation.

Anita Brzozowska
University of Warsaw, Warsaw, Poland The article presents a noteworthy effort in providing an inventory of surveys addressing migration aspirations and related mental constructs concerning both internal and international migration.
The strengths of the study lie in its attempt to create a valuable resource for researchers by compiling information on 212 surveys, offering the tool for potential extensive secondary analyses and methodological advancements.Thus, it succeeds in its primary goal of facilitating data reuse (even if the availability rate is only 25%, it can still increase the effectiveness in terms of cost and comparative dimension).
The article provides a clear overview of the methodology employed, allowing replication.The rationale behind using the Web of Science to search survey-based literature is clearly presented, and the authors acknowledged the limitations of using such a method.I just wonder whether checking the EMM (Ethnic and Migrant Minorities) Survey Registry, a database of quantitative surveys that have been undertaken with EMM (sub)samples across Europe and beyond, would give a more detailed picture of quantitative studies conducted in underrepresented regions.GENERAL ASSESSMENT This article is an extremely welcome exercise aimed ultimately at maximizing reuse of existing data by first giving it the visibility it needs (and currently lacks).More specifically, it extensively explores existing quantitative surveys on migration aspirations, delving into their significance, diversity, and implications.The resulting systematic inventory serves as an invaluable resource for researchers in the field, offering a comprehensive understanding of survey practices.Notably, the article raises crucial points regarding documentation standards in survey research, pinpointing areas for enhancement and suggesting practical solutions.Its conclusions provide clear and actionable recommendations for researchers, emphasizing detailed survey documentation, accurate data interpretation, and effective referencing, thus guiding future studies in this domain.
The emphasis on leveraging existing data and datasets on migration aspirations for secondary analyses and comparative studies showcases the authors' forward-thinking approach, promoting resourceful use of available information.

FURTHER SUGGESTIONS
The authors mostly focus on the geographic scope, the target population and methodologies used, while giving only a few hints with respect to the survey items operationalizing the umbrella term of 'migration aspirations'.A separate publication is planned to focus specifically on the various dimensions of such aspirations that different surveys measure via various items.However, by separating the two exercises, the ambition of the review is somewhat limited, as geographic or temporal patterns cannot be connected to dimensions of migration aspirations that are measured, which is a missed opportunity.More generally, even within the scope of this review, it may have been interesting to uncover some relationships: e.g.surveys in sub-Saharan Africa target mainly which types of population with which methods and with which methods?In addition, two aspects warrant attention for their unexpected nature.Firstly, the inclusion of 'residential mobility' within the category of migration aspirations is somewhat surprising, as it diverges from the conventional focus in migration literature.
Secondly, the incorporation of surveys among migrant populations introduces a particular dimension.Unlike surveys targeting non-migrants, these inquiries delve into aspirations to return or move onwards implicitly, and hence go beyond the typical focus on initial migration aspirations.This distinction adds complexity to the analysis of targeted populations which could be further explored.Furthermore, it could be interesting to examine whether different dimensions of migration aspirations (preference vs. consideration vs. intention) are then used in surveys that measure aspirations for onward/return migration.
A more explicit exploration of the rationale behind these two choices would contribute to a deeper understanding of the study's methodology and enrich the overall narrative.

Is the work clearly and accurately presented and does it engage with the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
Are all the source data and materials underlying the results available?Yes If applicable, is the statistical analysis and its interpretation appropriate?Not applicable

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Migration aspirations, Staying preferences, Immobility, Determinants of migration, Implications of (imm)mobility I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Figure 1 .
Figure 1.Frequency of coverage in surveys on migration aspirations.

Figure 2 .
Figure 2. Frequency of coverage in national and subnational surveys on migration aspirations.

Figure 3 .
Figure 3. Data collection periods by region.Note: Only surveys with a data collection period of at least ten years are labelled.Where the data collection period is not reported in the reference, we have estimated it by assuming that data was collected three years before the publication year of the reference.See the underlying data for details on each survey.

Figure 4 .
Figure 4. Distribution of surveys by sample size and population.Note: In the classification of survey populations 'students' include health sciences students and ' other' include health worker migrants.N = 212.

Note:
Dashed lines indicate closely related mindsets.

Figure 6 .
Figure 6.Surveys by survey population and geographic scale.Note: Numbers are survey IDs.Black type represents surveys with random or semi-random sampling and data collection by interviews.Blue type represents other surveys.In the classification of survey populations 'students' include health sciences students and ' other' include health worker migrants.

Figure 7 .
Figure 7. Stated data availability by survey population and geographic scale.Note: Numbers are survey IDs.Black type represents surveys with random or semi-random sampling and data collection by interviews.Blue type represents other surveys.In the classification of survey populations 'students' include health sciences students and ' other' include health worker migrants.

©
2024 Ruyssen I.This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Ilse RuyssenAssociate Research Professor in Migration Economics at the Department of Economics, Ghent University, Ghent, Belgium SUMMARY This study introduces a comprehensive inventory of surveys examining migration aspirations, plans, and intentions dating back to the 1960s.It provides essential details such as geographic focus, data collection periods, survey population, and methodologies employed.Highlighting the significance of understanding migration aspirations (used as an umbrella term) through survey data, it emphasizes their role in complementing the comprehension of migration processes and urges exploration beyond actual migration motivations.Furthermore, it underscores the relevance of migration aspirations in policy-making, particularly in addressing migration-related challenges and establishing social policy priorities.The inventory's primary goal is to facilitate researchers in locating and reusing existing survey data while exposing patterns and biases that could inform improved data collection strategies.The study reveals the diverse nature of quantitative datasets focusing on migration aspirations.Surveys typically involve single-round data collection at the sub-national level, predominantly in Europe or Central Asia.They primarily target the general adult population using random or semirandom sampling and predominantly employ interview-based data collection methods.The article advocates for enhanced survey documentation, secure accessibility post-data collection, and detailed descriptions in research publications.It also stresses the significance of accurately citing survey questions and response options, avoiding misinterpretation of survey data.Moreover, it encourages the utilization of existing survey data for secondary analyses, cost-effective survey designs, and comprehensive comparative studies.

Table 2 . Distribution of surveys by geographic scale. Frequency Geographic scale N % Description
Note: N = 212.

Table 3 . Regional classification.
Note: Percentages do not add up to 100 due to rounding.N = 212.

Table 4 . Distribution of surveys by population category.
All residents in the geographic area covered.In some cases, data is obtained from heads of households, but also cover other household members.Students41 19 Pupils or students at any level of education, from high school to graduate programs.Populations are often restricted to specific grades or disciplines.Surveys of recent graduates are included in this category.Migrants, and sometimes children of migrants, or others with a migrant background.The populations may be defined by either internal or international migration and may cover migrants from a single origin country or of multiple origins.Defined by the overlap of the 'migrant population' and 'health workers' categories (all surveys in this category cover physicians who live in a country other than their country of citizenship or training).Populations not covered by any of the above groups.Examples include married women, employees at a particular company, or individuals who identify as LGBT.
Note: Percentages do not add up to 100 due to rounding.N = 212.

Table 5 . Distribution of surveys by age range.
Note: N = 212.Comparability and continuity across multiple rounds of a survey vary.First, the selection of countries or other aspects of the target population could differ.Afrobarometer (168) for instance, has collected data in multiple rounds since 1999 and covered a total of 40 countries, but the first round covered only 12. Similarly, the survey Living Conditions among Immigrants in Norway (78) has been carried out roughly every decade, covering a selection of immigrant groups that has changed from round to round.Second, questions about migration aspirations are not necessarily included, or formulated in the same way, in every round.

Table 8 . Data collection method.
Carling & Mjelva 2021)use the documentation was insufficient or because the items on migration aspirations did not relate to the basic dimension of staying versus leaving (seeCarling & Mjelva 2021).

Table 11 . Surveys listed in the order of display in Figure 6.
Note: Asterisks indicate official names.See the underlying data for additional information.

Is the work clearly and accurately presented and does it engage with the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes Are all the source data and materials underlying the results available? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests:
No competing interests were disclosed.

have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

the work clearly and accurately presented and does it engage with the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly Are all the source data and materials underlying the results available? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results? Partly Competing Interests:
No competing interests were disclosed.

have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Reviewer Report 05 February 2024 https://doi.org/10.21956/openreseurope.17068.r37360© 2024 Brzozowska A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

the work clearly and accurately presented and does it engage with the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes Are all the source data and materials underlying the results available? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are the conclusions drawn adequately supported by the results? Partly Competing Interests:
Looking at Table 1, I guess no datasets addressing migration aspirations were published in data journals.Nevertheless, this option could be included in the section devoted to recommendations as an incentive to enhance documentation standards, fostering better practices in the field.The article could benefit from a more detailed exploration of other specific strategies addressing documentation shortcomings identified as crucial obstacles in reusing the data.It would help scholars who are not familiar with the FAIR data principles and tools like the DCC Checklist for a Data Management Plan or a 5-star deployment scheme for Open Data to navigate and discover different and often challenging aspects of open science (of course, taking into account that it is not the main topic of the manuscript).On another note, I am aware that survey items and questionnaire design were examined in a separate paper.However, exposing patterns and biases in the focus of the migration aspiration research would help align the article with all the ambitious objectives, namely methodological and theoretical strengthening, and informing debates on the ethics and biases of data collection.A more thorough exploration of the challenges posed by survey items and potential pitfalls in data analysis would strengthen the paper.And one minor remark of an editorial nature: there is an unnecessary note to Table9.In summary, the inventory presented in the article can catalyse positive changes in research practices and facilitate data reuse.No competing interests were disclosed.

have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
https://doi.org/10.21956/openreseurope.17068.r36171