Exploring Migration Determinants: a Meta-Analysis of Migration Drivers and Estimates

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  • Published: 20 October 2023

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  • Akira Soto Nishimura   ORCID: orcid.org/0009-0000-9876-863X 1 &
  • Mathias Czaika 1  

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This comprehensive study delves into over 100 empirical articles, examining the influence of structural drivers on both internal and international migration. Employing a meta-analysis approach, we dissect these studies to pinpoint the prevalent migration drivers frequently subjected to quantitative scrutiny. Our investigation extends to scrutinizing major migration drivers in terms of their statistical impact, directional tendencies, and statistical significance. Our findings underscore that indicators such as income or GDP, education, migrant networks, gender, age, and family characteristics are the most commonly scrutinized factors shaping migration patterns. Notably, geographical distance, gender, and migrant networks emerge as highly consistent drivers, exhibiting a remarkable uniformity in both effect direction and statistical significance across the most frequently studied factors. Numerous migration drivers exhibit statistical significance roughly around 50% of the time, while several others fall considerably below this threshold. Intriguingly, we delve into the complex variations characterizing the impact of destination country GDP per capita. Our exploration reveals that articles reporting a negative effect for destination country GDP per capita are more likely to focus on irregular or asylum migration flows. However, an intriguing subset of articles that also explore asylum migration flows finds a positive effect. These nuanced disparities are further influenced by variations in sample composition, control variables, statistical models, and the operationalization of GDP per capita. In sum, our in-depth analysis sheds light on the multifaceted landscape of migration drivers, offering critical insights into both the consensus and divergence within migration research.

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Introduction

The field of migration studies has witnessed a remarkable surge in scholarly output since the turn of the millennium (Pisarevskaya et al., 2020 ). Recent years have seen concerted efforts to systematically distill insights from this wealth of research, particularly focusing on the fundamental question of why individuals migrate (Czaika & Reinprecht, 2020 ; Pitoski et al., 2021 ). Systematic reviews of empirical findings play a pivotal role in crystallizing the landscape of migration research, illuminating both its breadth and the consistency of its findings across diverse studies. This article capitalizes on a newly available dataset (Soto Nishimura, 2022 ), encompassing essential indicators and findings extracted from more than 100 articles pertaining to international and internal migration. Footnote 1 This dataset constitutes an invaluable resource for the study of migration drivers, elucidating how each article operationalizes these drivers and scrutinizes their statistical significance and effect direction.

Migration drivers, the factors that influence migration decisions, hold the power to shape broader population movements by either enabling, facilitating, triggering, constraining, or preventing migration (Van Hear et al., 2018 ). These drivers not only affect the likelihood of migration as a behavioral choice but also dictate the prominence of specific migration routes and the desirability of particular destinations. However, migration drivers seldom function in isolation; they typically operate in concert with other structural factors, collectively crafting intricate migration driver environments (Czaika & Reinprecht, 2020 ).

Empirical research into migration drivers often grapples with the challenge of transforming abstract ideas (latent variables) believed to influence migration choices into quantifiable variables that can be measured and analyzed. Researchers, constrained by practical limitations such as data availability, may need to proxy more complex factors. For instance, when investigating the impact of civil conflict on migration, a researcher might resort to a related measure like the number of casualties. Operationalizing migration drivers varies across studies, with choices including different indices for similar concepts and decisions on whether to lag or transform variables to address issues like skewness. The sheer volume of variables available in databases such as the World Development Indicators necessitates careful selection to avoid over-specification, multicollinearity, and other statistical pitfalls. Hence, the migration driver data inventory serves as a record of how researchers have tackled the operationalization of migration drivers.

This article leverages this dataset to explore which migration drivers are most frequently under scrutiny and their statistical robustness. Here, “statistical robustness” refers to how often a migration driver is deemed statistically significant and how consistently its effect direction is observed. Thus, this review seeks to summarize and quantify empirical findings and estimates concerning the core question: “what drives human migration?” This meta-review not only brings into focus research imbalances and biases in the frequency of studying certain migration drivers but also scrutinizes the robustness of variable estimates in terms of their statistical significance and effect direction. In complement to existing literature reviews on migration drivers (such as Czaika & Reinprecht, 2020 ; Kuhnt, 2019 ), this meta-review offers a quantitative synthesis of results. Furthermore, it distinguishes itself from other meta-analyses on migration, such as Pitoski et al. ( 2021 ), by categorizing variables across individual, origin country, destination country, and dyadic levels. Additionally, we draw a clear distinction between internal and international migration. Consequently, our analysis provides a more granular examination compared to Pitoski et al. ( 2021 ).

This article is structured as follows: “Previous Meta-Analyses on Migration Drivers” section provides an overview of recent meta-analyses in the field of migration. In “Research Frequency of Migration Drivers and Significance of Estimates” section, we introduce the Quantmig Migration Drivers Data Inventory Records. The results section is divided into three parts: “Research Frequency of Migration Drivers” section assesses the frequency of migration driver analysis and the diversity of driver types considered, while “Statistical Significance of Estimates” section evaluates the robustness of migration variables in terms of effect direction and statistical significance. The “Understanding Variations in the Direction of Effect: the Income Variable” section conducts an in-depth analysis of the most frequently studied variable, GDP per capita, to elucidate variations in effect direction. Finally, the “Conclusion” section contextualizes our findings within recent meta-analyses on migration, highlighting infrequently analyzed variables and those lacking statistical robustness, offering insights for future research in migration studies.

Previous Meta-Analyses on Migration Drivers

There have been essentially two types of meta-analyses in the field of migration. There are the articles that take a wide approach and do not focus on a specific type of migration or driver such as Czaika and Reinprecht ( 2020 ), Pitoski et al. ( 2021 ), and Aslany et al. ( 2021 ). Then there are the articles that take a narrow approach and focus on a specific type or driver of migration such as Hoffmann et al. ( 2020 ), Beine and Jeusette ( 2021 ), and Soon ( 2013 ).

Pitoski et al. ( 2021 ) embarked on a meta-analysis that delves into the statistical effects of multiple migration drivers. Their exhaustive review of over 100 articles yielded a ranking of migration drivers based on their statistical robustness. The ranking was predicated on the frequency with which a particular migration driver's effect manifested in a certain direction, coupled with instances where authors explicitly asserted the impact of a factor on migration. Their top five ranked drivers encompassed origin country education level, origin country unemployment rate, origin country population size, destination country migrant communities, and geographical distance. Remarkably, only geographical distance consistently exhibited a dampening effect on migration, setting it apart from the other drivers.

Aslany et al. ( 2021 ) conducted a similar analysis to that presented in this article, albeit with a focus on migration aspirations. Their study complements our work by enabling a comparison between the two domains of literature in terms of frequently analyzed migration drivers. It also offers insights into whether drivers statistically robust in their impact on migration aspirations bear a similar robustness in actual migration outcomes. Their findings highlight age and migration networks as the most consistent drivers in terms of effect direction, followed by gender, marriage/cohabitation, urban residency, socio-economic status, and educational attainment. Violence/insecurity, while consistent in direction, was studied less frequently than the aforementioned drivers.

Czaika and Reinprecht ( 2020 ) did not analyze statistical effect and direction but on trends with regards to methodology, migration drivers analyzed, types of data used, locus of the migration driver, and level of analysis (macro, micro, meso). They found that economic and socio-cultural drivers were most frequently studied. The relative popularity of economic drivers and socio-cultural drivers declined from the year 2000 to 2018 while environmental and individual drivers increased in popularity.

Hoffmann et al. ( 2020 ) and Beine and Jeusette ( 2021 ) centered their investigations on articles examining the influence of environmental conditions on migration. A key consensus arising from both studies is that environmental conditions exert a more pronounced impact on migration in developing countries. Furthermore, they concurred that there was not a discernible systematic difference between articles investigating internal migration as opposed to international migration. The type of natural disaster was found to be a non-determining factor. Interestingly, both studies underscored the importance of employing panel data and addressing measurement errors, as these factors seemed to enhance the evidential weight of the environmental driver's effect on migration.

Soon ( 2013 ) took a different avenue by analyzing 22 articles that estimated the influence of education on migration. In his findings, higher levels of education correlated positively with an increased likelihood of migration, at least within the base models. An intriguing trend emerged, revealing that more recent publications were more inclined to establish a positive link between education and migration. Moreover, Soon’s analysis unveiled a stronger educational effect when studies focused on skilled migration or employed broad categorizations of education rather than specific years of schooling.

In synthesis, past reviews on migration drivers provide crucial context for our study. They collectively suggest that we should anticipate positive effects on migration from education, origin country unemployment rate, and origin country population size, while geographical distance is likely to exert a negative influence on migration. They also suggest that economic and socio-cultural drivers should be among the most ubiquitous drivers. These insights serve as valuable reference points as we delve into the statistical assessment of migration drivers in this article.

A Meta-Assessment of Migration Drivers: Data and Methodology

This meta-review capitalizes on the comprehensive resource of the Quantmig Migration Drivers Data Inventory Records (QMD). The QMD serves as a repository cataloging the spectrum of variables scrutinized across more than 100 articles, imparting invaluable insights into the field of migration research. This inventory distinguishes variables across four distinct levels of aggregation, as illustrated in Fig. 1 , with the most encompassing level positioned at the summit—the Driver dimension.

figure 1

Conceptualizing and measuring migration drivers at four levels of aggregation

The overarching design of the QMD is rooted in the schema conceptualized by Czaika and Reinprecht ( 2020 ), spanning two foundational dimensions: the driver dimension and driving factor level. The driver dimension is a scaffolding of nine overarching categories, including demographic, economic, environmental, human development, individual resources, politico-institutional, security, socio-cultural, and supranational. Nested within these categories are 24 driving factors, which in turn are meticulously deconstructed into an intricate tapestry of over 150 distinct drivers. Navigating further down, we arrive at the variable level, where the landscape flourishes with over 1000 unique variables. For elucidation, consider the example of variables such as “own land,” “own car,” and “own home.” Each of these variables converges under the specific driver “individual/household material assets,” situated within the driving factor “personal resources & migration experience,” which in turn finds its place within the driver dimension “individual resources.”

While the QMD adopts the migration driver categories as proposed by Czaika and Reinprecht ( 2020 ), it is worth noting that alternative typologies exist, and the boundaries demarcating categories are not invariably distinct. The intricacies become evident, for instance, when attempting to differentiate between the driving factors “public infrastructure, services, and supply” and “migration policy and other public policies.” Often, the influence of public policy extends its reach to the realm of public infrastructure, services, and supply, blurring the lines of demarcation. As such, the QMD stands as an invaluable tool, fostering a comprehensive understanding of migration drivers. It is mindful of the dynamic interplay between categories and factors, which underscores the complexity inherent in the field of migration research. The articles considered for inclusion in this dataset originated from an initial selection conducted by Czaika and Reinprecht ( 2020 ). In their assessment, they scrutinized 660 English-language research documents pertaining to migration drivers. Czaika and Reinprecht ( 2020 , p. 7) stipulated that a key selection criterion was the presentation of novel empirical evidence or influence within the field of migration studies, especially if they hailed from respected organizations. These documents were sourced from a variety of outlets, including peer-reviewed journals, books, reports, and working papers. Their identification involved utilizing various search engines, such as Google Scholar and Scopus, literature datasets, cross-referencing documents, and convening an expert workshop (Ibid). From this initial collection, all articles published from the year 2000 onwards were considered for inclusion into the Quantitative Migration Dataset (QMD) (Soto Nishimura & Czaika, 2022 ). Articles were excluded if they were purely theoretical, lacked substantial large-N quantitative datasets, or relied on small sample sizes, a common characteristic of qualitative articles that rely on interviews and focus groups (Ibid). Footnote 2 In total, the dataset encompasses 176 articles available online between 2000 and 2019.

Crucially, the QMD contains information on the results of articles in terms of statistical significance ( p < .05) and the direction of effect (positive, indicating increased migration, and negative, indicating decreased migration). Articles often feature multiple models, wherein the significance and effect direction of a variable may differ among them. For each article, we recorded results from only one of the models per variable (Soto Nishimura & Czaika, 2022 ). This model was typically either the main one, as indicated by the author/s of the article, or the one with the most control variables (Ibid). In cases where a variable did not appear in the main model but did appear in a different model, such as a robustness check model, we derived the result from that specific model. In certain instances, the analysis was stratified by gender, country, or race/ethnicity. In such scenarios, the results from the “male sample” were recorded. When the analysis was stratified by both country and race/ethnicity, we recorded the value from the group whose regression table appeared first. Alternatively, in cases involving multiple split analyses, we relied on the majority value (Ibid). In articles featuring multiple dependent variables, such as internal and international migration or migration intention and migration behavior, we consistently prioritized “international” over “internal” and “behavior” over “intention.”

Our methodology diverges from traditional literature reviews in that we summarize past literature findings using descriptive statistics presented through graphs and tables. The QMD enables us to quantify how frequently variables were analyzed, how often they exhibited statistical significance, and the direction of their effect. Our primary analytical focus centers on migration factors and specific driver levels of aggregation. To prevent overwhelming readers with an excessive volume of results, we concentrate on the most prevalent migration drivers.

It is worth noting that the initial selection of articles was based on specific criteria: they were chosen if they “presented new empirical findings or were influential in the field of migration studies and/or came from respected organizations” (Czaika & Reinprecht, 2020 , p. 7). Articles were further selected if they featured quantitative analysis, typically involving regression tables. However, it remains uncertain whether and to what extent this selection process might have introduced biases into the results, affecting both the migration factors examined and their robustness. Additionally, the Quantitative Migration Dataset (QMD) lacked comprehensive information regarding interactions among migration factors or whether variables underwent transformations such as lagging or logarithmic conversion. Including such details would be a valuable enhancement, given that future research on migration drivers should increasingly emphasize the intricate interplay and complex dynamics among these factors (Czaika et al., 2021 ).

Research Frequency of Migration Drivers and Significance of Estimates

This meta-analysis presents an overview of the frequency with which migration driving factors are examined and evaluates the robustness of their statistical significance and effect direction.

Research Frequency of Migration Drivers

Figure 2 illustrates the five most frequently examined driving factors, categorized by the type of migration—internal or international. It is evident from Fig. 2 that there are disparities between internal and international migration regarding the driving factors under scrutiny. Notably, “population dynamics” and “labor markets and employment” are exceptions to this pattern.

figure 2

Frequency analysis of driving factors studied in N = 176 articles. Source: own elaboration based on QMD.

Figure 3 , akin to Fig. 2 , presents the analysis at the level of specific drivers’ aggregation. Fig. 3 reveals that, for internal migration, age, gender, and family structure emerge as the most extensively investigated specific drivers. These three specific drivers fall within the broader driver category of “population dynamics,” which, as depicted in Fig. 2 , ranks as the most frequently analyzed driving factor. In contrast, specific drivers linked to income hold the distinction of being the most frequently analyzed factors in international migration. Collectively, these figures imply that individual and familial elements such as age, gender, and education are frequently examined concerning internal migration, while international migration research predominantly leans toward country-level variables such as distance and migration stock.

figure 3

Frequency analysis of specific drivers studied in N = 176 articles. Source: own elaboration based on QMD. Source: own elaboration based on QMD. *In the original schema of Czaika and Reinprecht ( 2020 ), these factors are not under the Individual driver dimension. Article count: Internal = 48, international = 131

This pattern becomes even clearer in Table 1 , which shows the most frequently occurring driver dimensions and driving factors overall. Table 1 reflects how many times a driving factor (dimension) was operationalized, recognizing that a single article can analyze numerous variables related to one driving factor (dimension). Notably, Table 1 demonstrates that all nine driver dimensions and all 24 driving factors were examined at least once.

The demographic dimension holds prominence in both internal and international migration research, although the driving factor “family size and structure” garners more attention in the context of internal migration than in international migration. While the table does not explicitly convey this, it is likely, at least among the articles included in the QMD, that analyses of internal migration more frequently utilize individual survey data. This allows for the operationalization of variables related to family size and structure, contributing to its greater prominence. The heightened significance of the “individual resources” dimension in internal migration supports this notion. Conversely, the “security” dimension ranks as the third smallest dimension in international migration and the smallest for internal migration. This observation may reflect that, in many countries, security-related drivers are not considered highly relevant, or it could be due to the challenges in obtaining data, especially for internal migration, in regions facing conflict. Therefore, it is important to note that Table 1 should not be interpreted as an indication of the relative importance of migration drivers within the literature on migration.

Figure 4 illustrates the number of different driving factors analyzed per article. Notably, for both internal and international migration, approximately 19% of the articles examined seven distinct migration factors. Interestingly, more than 75% of the articles analyzed only one-third or fewer of the 24 driving factors.

figure 4

Number of migration driving factors analyzed per article. Internal articles n = 48, international articles n = 131

It is important to note that the limited analysis of driving factors in the articles is not necessarily problematic. Presumably, migration researchers are cognizant of the need to exclude certain migration factors from their analyses. Factors may be omitted if they are theoretically irrelevant to the research context or sample of cases. For example, the consideration of the “political situation, repression, and regime change” driver is likely unnecessary in an analysis of internal migration within Austria over the past 10 years. Another explanation for the restricted analysis of driving factors could be data availability. Articles relying on administrative data, which are frequently aggregated beyond the individual level, may lack data pertaining to drivers within the “individual resources” dimension. Multicollinearity and over-specification could also influence the decision to limit the number of analyzed migration drivers.

Statistical Significance of Estimates

Figures 5 and 6 depict the frequency with which specific drivers of international and internal migration, respectively, attain statistical significance ( p < .05). It is important to note that, at this stage, we do not consider the direction of the effect, as variables with different expected directions often fall under the same specific driver category. We present here only the most frequently studied drivers due to the sheer volume of data (a comprehensive table is available upon request).

figure 5

Statistical significance of most frequent specific drivers in international migration at the specific driver level. Note: statistically significant result at p < .05. Own elaboration based on QMD

figure 6

Statistical significance of most frequent specific drivers of internal migration at the specific driver level. Note: statistically significant result at p < .05. Own elaboration based on QMD

In both plots, the Y-axis represents the percentage of cases in which a factor achieved statistical significance ( p < .05), regardless of the effect direction. The X-axis corresponds to the number of articles that included the factor in their analysis. The size of each data point reflects the total number of times the driver was examined. Certain drivers may appear multiple times in a given analysis, as multiple variables are often categorized under the same specific category of influence. For instance, an article might incorporate both GDP per capita and GDP growth variables in the same regression, both of which would fall under the specific driver “GDP/Income.” In such cases, while the number of drivers is two, the number of articles is only one.

Starting with the drivers of international migration (Fig. 5 ), several specific factors achieve statistical significance in over 70% of cases, with “geographic distance” leading at approximately 85%. Surprisingly, “conflict/violence” registers statistical significance only around 45% of the time. This disparity might suggest that conflict and violence play a less significant role as causes for international migration compared to internal migration. Alternatively, there could be a discrepancy between the theoretical relevance of the variable and its practical operationalization (Pettersson, 2022 ). Notably, “geographic distance” consistently exhibits statistical significance. Not surprisingly, this specific driving factor, historically one of the earliest studied in migration research (Ravenstein, 1885 ), maintains high statistical significance. However, more recently, it and other similarly robust factors like “borders/common region” have become less frequent in analyses, as these time-invariant variables are often accounted for by fixed effects in regression models.

The results for internal migration (Fig. 6 ) reveal that only “family structure and characteristics” achieves a statistically significant frequency above 70%. Many other specific factors hover at or below the 50% mark. Surprisingly, the socio-demographic factors “dependent children” and “male” exhibit the lowest frequencies at 25% and 23%, respectively. This finding is perplexing, given that “male” is typically operationalized as a binary variable (male or female), and “dependent children” follows the same pattern. It is possible that the “male” variable might register statistical significance more often if the articles in the driver dataset only analyzed data prior to the 1970s when the share of female migrants was smaller compared to the twenty-first century (Gabaccia, 2016 ). Fig. 6 does not indicate whether the significance of the “male” variable has decreased or increased since 2000.

The “dependent children” driver exhibits variations in operationalization, as some articles differentiate regarding the age, number, and/or sex of children. These results suggest that it may be worthwhile for researchers to reevaluate whether a binary “dependent children” variable is the best approach or whether it would be more theoretically meaningful to consider the age, number, and/or gender of children. Fig. 6 does not provide insights into whether variables related to dependent children are more likely to attain statistical significance when they account for the age, number, and/or gender of the children.

One intriguing discovery is that for international migration, the “male” variable is statistically significant approximately 75% of the time, whereas for internal migration, it is statistically significant only about 25% of the time. This observation suggests that this gender-related driver holds greater relevance for international migration than for internal migration. Similar graphs at the driving factor level can be found in the Appendix.

Next, we delve into the analysis at the variable level. It is important to note that the variable level may not always precisely mirror the terminology used within the articles but is instead an approximation. Both Figs. 7 and 8 should be interpreted in the following manner: a positive effect implies an increase in migration. When examining destination country variables, this signifies a higher volume of migration to the destination country. For country-of-origin variables, it indicates increased migration from the country of origin. At this level of specificity, considering the variable perspective, it becomes more appropriate to examine both the direction of the effect and its statistical significance. Even at this level of granularity, there can still be considerable variation in the precise operationalization of variables. For example, education can be measured in years or as ordinal categories, with the specific categories varying from one article to another. However, as previously mentioned, some variables, such as “male” and “common border,” maintain consistent operationalization.

figure 7

Driver of international migration: most common variables, effect direction and statistical significance. Source: own elaboration based on QMD. Numbers at end of bar indicate total number of observations. Variables with less than 15 observations are not shown. Non-binary variables unemployment rate, population, migrant stock by country, GDP per capita, age, and education are coded such that more of the variable leads to given effect direction

figure 8

Driver of internal migration: most common variables, effect direction and statistical significance. Source: own elaboration based on QMD. Numbers at end of bar indicate total number of observations. Variables with less than 9 observations are not shown. Non-binary variables age, education, and income are coded such that more of the variable leads to given effect direction

Figure 7 highlights variables that have been employed in analyses of international migration at least 15 times. For international migration, we further divide the analysis into four perspectives. For instance, the “unemployment rate,” which is a destination country variable, appears at least 15 times, as does the same variable as a source (origin) country variable. Consequently, it is featured in both headings. In contrast, the “shared border” variable only appears in the heading of dyadic variables, which pertain to variables related to both the country of origin and the country of destination. The individual-level variables encompass micro-level factors, such as the gender of the migrant or potential migrant.

One characteristic of Fig. 7 is that more variables found a positive significant effect than a negative significant effect. This reflects the bias in framing mobility over immobility. Bilateral migration stock facilitates immigration, but framed differently the absence of this stock hinders immigration. This bias toward mobility has in recent years been challenged as immobility is emphasized (Schewel, 2020 ). In terms of the direction of effects, ignoring statistical significance, the destination and dyadic variables follow what one would expect from theory and tend to be heavily in one direction. The least biased in one direction was “shared borders” which was still in the positive direction 69% of the time. The individual and especially origin level variables were less biased in one direction. The effect direction of origin country GDP per capita and unemployment rate was closely split to where around half the time a positive effect was found and around half the time a negative effect was found. This pattern may be reflective of the inverted U-shaped relationship between emigration and development (Zelinsky, 1971 ).

Concerning internal migration (Fig. 8 ), we exclusively present results related to individual-level variables, as other perspectives were not sufficiently represented. One notable difference between Fig. 8 and Fig. 7 is that in Fig. 8 there is a lack of positive significant effects. The highest percentage reached for a positive significant effect is 25% for the education variable. The two most consistent variables in terms of direction were owning a home and being male. At the individual level, similar variables are considered for both domestic and international migration. Figs. 7 and 8 demonstrate that the influential factor “male” exhibits a predominantly positive effect but is more pronounced in the context of international migration, where it is significantly more likely to have a statistically significant impact (76% versus 23%). Being married for both internal and international migration was most often found to have a negative effect, but only 57% of the time for internal migration and 62% of the time for international migration.

Understanding Variations in the Direction of Effect: the Income Variable

Determining the specific reasons behind why some variables consistently exhibit a particular direction of effect compared to others is a complex task. Multiple factors come into play, encompassing the operationalization of the variable, the nature of the dependent variable, the statistical model chosen, the inclusion of other control variables, and the breadth of the analysis concerning observation periods and countries involved. To elucidate this intricacy, we will delve into a detailed examination of the specific impact of “per capita income” at the destination country level on international migration.

Figure 7 underscores a noteworthy finding among the 21 analyzed articles: destination country GDP per capita demonstrated a positive effect on international migration in (only) 75% of cases. To delve deeper into the reasons behind the varying findings within studies employing GDP per capita as an indicator, we will closely examine the differences among them.

Table 2 provides a comprehensive overview of these variations, encapsulating aspects such as sample selection, choice of dependent variable, listing of all articles in the QMD dataset that reported either a negative or a positive effect for destination country GDP per capita. Footnote 3 Even for seemingly straightforward variables like per capita income, there exist disparities in operationalization. However, regarding sample and operationalization, a consistent pattern distinguishing articles reporting positive from negative effects was elusive. Instead, the primary distinction lay in the choice of dependent variable. Notably, three out of the six articles reporting a negative effect employed asylum applications or irregular migration as their dependent variable, while a similar measure was used by three out of fourteen articles reporting a positive effect.

It is worth noting that Table 2 does not encompass all variations. Discrepancies exist both between and within articles concerning the statistical models employed. The QMD database lacks complete coverage of all results for a variable across various models within an article. This is a crucial point because upon closer examination of the findings within these articles, four out of six articles that the QMD reports as having found a negative effect for per capita income also identified a positive effect in at least one of their models (Backhaus et al., 2015 ; Docquier et al., 2014 ; Toshkov, 2014 ; Yoo & Koo, 2014 ). In contrast, only two out of fourteen articles that the QMD reports as having found a positive effect for per capita income found a negative effect in at least one of their models (Hatton & Moloney, 2015 ; Ortega & Peri, 2013 ). This finding aligns with the notion that, for asylum seekers and irregular migrants, economic factors tend to assume a secondary role compared to other determinants (Czaika & Reinprecht, 2020 ). Furthermore, there are additional variations not presented in Table 2 , such as whether dependent and independent variables are lagged and the duration of the lag.

Another factor contributing to the observed variation could be the choice of control variables in individual studies. Table 3 provides a compilation of the most frequently utilized variables and combinations thereof. These variables were incorporated into models that generated the estimates presented in Table 2 . With the exception of the unemployment rate in the destination country, there is minimal overlap in the control variables employed between studies that reported a negative effect for GDP per capita and those that identified a positive effect. None of the other variables reached a usage rate of at least 50% in either group. Furthermore, no combination of variables achieved such prominence in either group.

Among the variables considered, the most common combination across both groups was the inclusion of GDP growth in the destination country and the unemployment rate in the destination country. These variables featured in 33% of the models within the negative effect group and 57% of the models within the positive effect group. Even when examining individual groups, there is limited concurrence in terms of variables or combinations of variables employed. Table 3 enumerates all variables and combinations that appeared in at least two of the six articles reporting a negative effect. In contrast, for articles demonstrating a positive effect, not all variables and combinations are listed to avoid an excessively lengthy table. Nonetheless, within this latter group, there is scant overlap as the maximum number of common variables across at least three articles is four, with examples including distance, common border, unemployment rate, and origin of unemployment rate, all used as control variables in three distinct articles.

Variations exist not only in the control variables employed between studies reporting either a negative or positive effect for per capita income but also within these groups. Consequently, it is challenging to attribute the disparities in the effect direction of per capita income solely to differences in control variables. Consider the case of two articles, Adsera and Pytlikova ( 2015 ) and Backhaus et al. ( 2015 ), which share numerous similarities yet arrive at different conclusions regarding the effect of GDP per capita. Both studies cover similar time periods, use year dummies, destination countries, and dependent variables, and they operationalize GDP per capita similarly. Furthermore, when it comes to driver dimensions, both studies share a variable categorized under the demographic, economic, and supranational dimensions. However, distinctions emerge in the variables incorporated in each article. Specifically, Backhaus et al. ( 2015 ) emphasize environmental variables and feature fewer variables overall, whereas Adsera and Pytlikova ( 2015 ) exclude environmental factors. Backhaus et al. ( 2015 ) also include variables related to security dimensions, while Adsera and Pytlikova ( 2015 ) use variables related to human development, political-institutional, and sociocultural dimensions. Backhaus et al. ( 2015 ) employ a first-difference estimator, while Adsera and Pytlikova ( 2015 ) opt for a Poisson fixed-effects model for the country of origin and the country of destination. It is not possible to decern how much of the divergence in their findings are due to differences in variables used or the statistical model.

The systematic meta-analysis conducted here, which examines studies that conducted quantitative analysis on migration drivers published over the past two decades, reveals a distinct research emphasis on certain key factors considered pivotal. These studies predominantly delve into economic conditions, migrant communities/networks, labor markets, population dynamics, personal resources, transnational ties, family structures, and public infrastructure and utilities. However, there is a noticeable dearth of attention given to areas like cultural norms and ties, health services, educational and training opportunities, and geopolitical changes. These factors are comparatively underexplored in migration research. Intriguingly, some of these less-explored factors, such as the role of health services, have displayed statistical significance (refer to Figure 9 in the Appendix ), albeit with relatively small sample sizes. The infrequent analysis of these factors might be attributed, in part, to the challenges associated with their operationalization.

In the realm of both internal and international migration, it becomes evident that numerous factors exhibit statistical significance roughly around 50% of the time, while several others fall considerably below this threshold. The underlying reasons for this variance remain challenging to pinpoint, but the outcomes strongly hint at substantial room for enhancement. This variability may arise from a multitude of factors, including the potential inadequacy in the conceptualization of migration theory informing the inclusion of these drivers. More plausibly, it could be attributed to difficulties encountered when translating theoretical frameworks into operationalized variables (Carling et al. 2020 ). Alternatively, the limited number of influencing factors considered might also play a role. Notably, our investigation reveals that the majority of articles related to both internal and international migration encompass variables that address merely seven out of the possible 24 driving factors. Hence, it is advisable for researchers to broaden their scope and encompass a more comprehensive array of migration factors encompassing various dimensions of migration drivers, rather than being overly fixated on singular aspects like economic or environmental factors. It is also essential to be mindful of issues such as overidentification and multicollinearity, as these can have detrimental effects on the statistical significance of variables (Cinelli et al.,  2022 ).

In the realm of migration factors, there exists a broad spectrum of methodologies for operationalizing variables, a diversity most evident when examining economic conditions, specifically per capita income. In our examination of 20 articles for this purpose, we identified four primary approaches to measuring per capita income: GDP per capita, GDP purchasing power parity, and the logarithmic equivalents of these two metrics. Difference in the directional effect of GDP per capita found between articles may be primarily due to differences in dependent variables. Half of the articles that find a negative effect for GDP per capita use asylum applications or irregular migration as the dependent variable. Moreover, many of the articles that find a negative effect for GDP per capita in their main model also find a positive effect in some of their specifications, while relatively few articles that find a positive effect for GDP capita in their main model also find a negative effect in their other specifications. Still, there are articles with conflicting results that cannot be explained by differences in dependent variable, operationalization of GDP per capita, or sample. In this case, differences are most likely due to differences in independent variables and statistical model. Regrettably, our analysis does not provide definitive guidance on the optimal approach for the analysis of the effect of GDP per capita on migration or any other migration factors. The decision-making process should ideally be theory-driven, encompassing not only operationalization but also the selection of migration factors and their combinations. It is plausible that many researchers adhere to a “good practice” principle, adopting established methodologies prevalent within their specific research domain. Nevertheless, our findings strongly indicate that, for numerous migration factors, a critical reevaluation of conceptualization and operationalization may be overdue.

In a broader context, our findings align with previous meta-analyses, demonstrating a certain consistency in the field. Specifically, factors related to education tend to exhibit predominantly positive effects, corroborating the observations made by Soon ( 2013 ). Moreover, our analysis underscores the robustness of several influential factors, including education, geographic distance, migrant networks, unemployment, and population size, which echoes the findings of Pitoski et al. ( 2021 ). These factors maintain their stability, particularly in the context of international migration. Notably, our study also reaffirms the significance of certain drivers of migration aspirations, such as migrant networks, which were previously identified as robust influencers by Aslany et al. ( 2021 ) and continue to play a substantial role in actual migration, as evidenced in our analysis.

In summary, the meta-analysis conducted in this study offers a valuable tool for future research endeavors. It not only highlights which migration factors have received insufficient attention but also identifies those factors that frequently lack statistical significance, thereby suggesting the need for a more refined approach in their operationalization.

Data Availability

The dataset and code to reproduce results are available upon request.

Available on the website of the Horizon 2020 project QuantMig: https://www.quantmig.eu/migration_driver_inventory/

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Acknowledgement

We are very grateful for insightful comments and advice from Heidrun Bohnet, Ali Safi, and Zina Weisner.

Open access funding provided by Danube University Krems University for Continuing Education. This research has has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 870299.

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figure 9

Statistical significance of most frequent driving factor of international migration, level

figure 10

Statistical significance of most frequent driving factor in internal migration, driving factor level

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Soto Nishimura, A., Czaika, M. Exploring Migration Determinants: a Meta-Analysis of Migration Drivers and Estimates. Int. Migration & Integration (2023). https://doi.org/10.1007/s12134-023-01091-z

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1. introduction, 2. studies of migration studies, 3. methodology, 4. metadata on migration studies, 5. topic clusters in migration studies, 6. trends in topic networks in migration studies, 7. conclusions, acknowledgements.

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Mapping migration studies: An empirical analysis of the coming of age of a research field

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Asya Pisarevskaya, Nathan Levy, Peter Scholten, Joost Jansen, Mapping migration studies: An empirical analysis of the coming of age of a research field, Migration Studies , Volume 8, Issue 3, September 2020, Pages 455–481, https://doi.org/10.1093/migration/mnz031

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Migration studies have developed rapidly as a research field over the past decades. This article provides an empirical analysis not only on the development in volume and the internationalization of the field, but also on the development in terms of topical focus within migration studies over the past three decades. To capture volume, internationalisation, and topic focus, our analysis involves a computer-based topic modelling of the landscape of migration studies. Rather than a linear growth path towards an increasingly diversified and fragmented field, as suggested in the literature, this reveals a more complex path of coming of age of migration studies. Although there seems to be even an accelerated growth for migration studies in terms of volume, its internationalisation proceeds only slowly. Furthermore, our analysis shows that rather than a growth of diversification of topics within migration topic, we see a shift between various topics within the field. Finally, our study shows that there is no consistent trend to more fragmentation in the field; in contrast, it reveals a recent recovery of connectedness between the topics in the field, suggesting an institutionalisation or even theoretical and conceptual coming of age of migration studies.

Migration studies have developed rapidly as a research field in recent decades. It encompasses studies on all types of international and internal migration, migrants, and migration-related diversity ( King, 2002 ; Scholten, 2018 ). Many scholars have observed the increase in the volume of research on migration ( Massey et al., 1998 ; Bommes and Morawska, 2005 ; Scholten et al., 2015 ). Additionally, the field has become increasingly varied in terms of links to broader disciplines ( King, 2012 ; Brettell and Hollifield, 2014 ) and in terms of different methods used ( Vargas-Silva, 2012 ; Zapata-Barrero and Yalaz, 2018 ). It is now a field that has in many senses ‘come of age’: it has internationalised with scholars involved from many countries; it has institutionalised through a growing number of journals; an increasing number of institutes dedicated to migration studies; and more and more students are pursuing migration-related courses. These trends are also visible in the growing presence of international research networks in the field of migration.

Besides looking at the development of migration in studies in terms of size, interdisciplinarity, internationalisation, and institutionalisation, we focus in this article on the development in topical focus of migration studies. We address the question how has the field of migration studies developed in terms of its topical focuses? What topics have been discussed within migration studies? How has the topical composition of the field changed, both in terms of diversity (versus unity) and connectedness (versus fragmentation)? Here, the focus is not on influential publications, authors, or institutes, but rather on what topics scholars have written about in migration studies. The degree of diversity among and connectedness between these topics, especially in the context of quantitative growth, will provide an empirical indication of whether a ‘field’ of migration studies exists, or to what extent it is fragmented.

Consideration of the development of migration studies invokes several theoretical questions. Various scholars have argued that the growth of migration studies has kept pace not only with the growing prominence of migration itself but also with the growing attention of nation–states in particular towards controlling migration. The coproduction of knowledge between research and policy, some argue ( Scholten, 2011 ), has given migration research an inclination towards paradigmatic closure, especially around specific national perspectives on migration. Wimmer and Glick Schiller (2002 ) speak in this regard of ‘methodological nationalism’, and others refer to the prominence of national models that would be reproduced by scholars and policymakers ( Bommes and Morawska, 2005 ; Favell, 2003 ). More generally, this has led, some might argue, to an overconcentration of the field on a narrow number of topics, such as integration and migration control, and a consequent call to ‘de-migranticise’ migration research ( Dahinden, 2016 ; see also Schinkel, 2018 ).

However, recent studies suggest that the growth of migration studies involves a ‘coming of age’ in terms of growing diversity of research within the field. This diversification of migration studies has occurred along the lines of internationalisation ( Scholten et al., 2015 ), disciplinary variation ( Yans-McLaughlin, 1990 ; King, 2012 ; Brettell and Hollifield, 2014 ) and methodological variation ( Vargas-Silva, 2012 ; Zapata-Barrero and Yalaz, 2018 ). The International Organization for Migration ( IOM, 2017 : 95) even concludes that ‘the volume, diversity, and growth of both white and grey literature preclude a [manual] systematic review’ of migration research produced in 2015 and 2016 alone .

Nonetheless, in this article, we attempt to empirically trace the development of migration studies over the past three decades, and seek to find evidence for the claim that the ‘coming of age’ of migration studies indeed involves a broadening of the variety of topics within the field. We pursue an inductive approach to mapping the academic landscape of >30 years of migration studies. This includes a content analysis based on a topic modelling algorithm, applied to publications from migration journals and book series. We trace the changes over time of how the topics are distributed within the corpus and the extent to which they refer to one another. We conclude by giving a first interpretation of the patterns we found in the coming of age of migration studies, which is to set an agenda for further studies of and reflection on the development of this research field. While migration research is certainly not limited to journals and book series that focus specifically on migration, our methods enable us to gain a representative snapshot of what the field looks like, using content from sources that migration researchers regard as relevant.

Migration has always been studied from a variety of disciplines ( Cohen, 1996 ; Brettell and Hollifield, 2014 ), such as economics, sociology, history, and demography ( van Dalen, 2018 ), using a variety of methods ( Vargas-Silva, 2012 ; Zapata-Barrero and Yalaz, 2018 ), and in a number of countries ( Carling, 2015 ), though dominated by Northern Hemisphere scholarship (see, e.g. Piguet et al., 2018 ), especially from North America and Europe ( Bommes and Morawska, 2005 ). Taking stock of various studies on the development of migration studies, we can define several expectations that we will put to an empirical test.

Ravenstein’s (1885) 11 Laws of Migration is widely regarded as the beginning of scholarly thinking on this topic (see Zolberg, 1989 ; Greenwood and Hunt, 2003 ; Castles and Miller, 2014 ; Nestorowicz and Anacka, 2018 ). Thomas and Znaniecki’s (1918) five-volume study of Polish migrants in Europe and America laid is also noted as an early example of migration research. However, according to Greenwood and Hunt (2003 ), migration research ‘took off’ in the 1930s when Thomas (1938) indexed 191 studies of migration across the USA, UK, and Germany. Most ‘early’ migration research was quantitative (see, e.g. Thornthwaite, 1934 ; Thomas, 1938 ). In addition, from the beginning, migration research developed with two empirical traditions: research on internal migration and research on international migration ( King and Skeldon, 2010 ; Nestorowicz and Anacka, 2018 : 2).

In subsequent decades, studies of migration studies describe a burgeoning field. Pedraza-Bailey (1990) refers to a ‘veritable boom’ of knowledge production by the 1980s. A prominent part of these debates focussed around the concept of assimilation ( Gordon, 1964 ) in the 1950s and 1960s (see also Morawska, 1990 ). By the 1970s, in light of the civil rights movements, researchers were increasingly focussed on race and ethnic relations. However, migration research in this period lacked an interdisciplinary ‘synthesis’ and was likely not well-connected ( Kritz et al., 1981 : 10; Pryor, 1981 ; King, 2012 : 9–11). Through the 1980s, European migration scholarship was ‘catching up’ ( Bommes and Morawska, 2005 : 14) with the larger field across the Atlantic. Substantively, research became increasingly mindful of migrant experiences and critical of (national) borders and policies ( Pedraza-Bailey, 1990 : 49). King (2012) also observes this ‘cultural turn’ towards more qualitative anthropological migration research by the beginning of the 1990s, reflective of trends in social sciences more widely ( King, 2012 : 24). In the 1990s, Massey et al. (1993, 1998 ) and Massey (1994) reflected on the state of the academic landscape. Their literature review (1998) notes over 300 articles on immigration in the USA, and over 150 European publications. Despite growth, they note that the field did not develop as coherently in Europe at it had done in North America (1998: 122).

We therefore expect to see a significant growth of the field during the 1980s and 1990s, and more fragmentation, with a prominence of topics related to culture and borders.

At the turn of the millennium, Portes (1997) lists what were, in his view, the five key themes in (international) migration research: 1 transnational communities; 2 the new second generation; 3 households and gender; 4 states and state systems; and 5 cross-national comparisons. This came a year after Cohen’s review of Theories of Migration (1996), which classifies nine key thematic ‘dyads’ in migration studies, such as internal versus international migration; individual versus contextual reasons to migrate; temporary versus permanent migration; and push versus pull factors (see full list in Cohen, 1996 : 12–15). However, despite increasing knowledge production, Portes argues that the problem in these years was the opposite of what Kritz et al. (1981) observe above; scholars had access to and generated increasing amounts of data, but failed to achieve ‘conceptual breakthrough’ ( Portes, 1997 : 801), again suggesting fragmentation in the field.

Thus, in late 1990s and early 2000s scholarship we expect to find a prominence of topics related to these five themes, and a limited number of “new” topics.

In the 21st century, studies of migration studies indicate that there has been a re-orientation away from ‘states and state systems’. This is exemplified by Wimmer and Glick Schiller’s (2002) widely cited commentary on ‘methodological nationalism’, and the alleged naturalisation of nation-state societies in migration research (see Thranhardt and Bommes, 2010 ), leading to an apparent pre-occupation with the integration paradigm since the 1980s according to Favell (2003) and others ( Dahinden, 2016 ; Schinkel, 2018 ). This debate is picked up in Bommes and Morawska’s (2005) edited volume, and Lavenex (2005) . Describing this shift, Geddes (2005) , in the same volume, observes a trend of ‘Europeanised’ knowledge production, stimulated by the research framework programmes of the EU. Meanwhile, on this topic, others highlight a ‘local turn’ in migration and diversity research ( Caponio and Borkert, 2010 ; Zapata-Barrero et al., 2017) .

In this light, we expect to observe a growth in references to European (and other supra-national) level and local-level topics in the 21t century compared to before 2000.

As well as the ‘cultural turn’ mentioned above, King (2012 : 24–25) observes a re-inscription of migration within wider social phenomena—in terms of changes to the constitutive elements of host (and sending) societies—as a key development in recent migration scholarship. Furthermore, transnationalism, in his view, continues to dominate scholarship, though this dominance is disproportionate, he argues, to empirical reality. According to Scholten (2018) , migration research has indeed become more complex as the century has progressed. While the field has continued to grow and institutionalise thanks to networks like International Migration, Integration and Social Cohesion in Europe (IMISCOE) and Network of Migration Research on Africa (NOMRA), this has been in a context of apparently increasing ‘fragmentation’ observed by several scholars for many years (see Massey et al., 1998 : 17; Penninx et al., 2008 : 8; Martiniello, 2013 ; Scholten et al., 2015 : 331–335).

On this basis, we expect a complex picture to emerge for recent scholarship, with thematic references to multiple social phenomena, and a high level of diversity within the topic composition of the field. We furthermore expect increased fragmentation within migration studies in recent years.

The key expectation of this article is, therefore, that the recent topical composition of migration studies displays greater diversity than in previous decades as the field has grown. Following that logic, we hypothesise that with diversification (increasingly varied topical focuses), fragmentation (decreasing connections between topics) has also occurred.

The empirical analysis of the development in volume and topic composition of migration studies is based on the quantitative methods of bibliometrics and topic modelling. Although bibliometric analysis has not been widely used in the field of migration (for some exceptions, see Carling, 2015 ; Nestorowicz and Anacka, 2018 ; Piguet et al., 2018 ; Sweileh et al., 2018 ; van Dalen, 2018 ), this type of research is increasingly popular ( Fortunato et al., 2018 ). A bibliometric analysis can help map what Kajikawa et al. (2007) call an ‘academic landscape’. Our analysis pursues a similar objective for the field of migration studies. However, rather than using citations and authors to guide our analysis, we extract a model of latent topics from the contents of abstracts . In other words, we are focussed on the landscape of content rather than influence.

3.1 Topic modelling

Topic modelling involves a computer-based strategy for identifying topics or topic clusters that figure centrally in a specific textual landscape (e.g. Jiang et al., 2016 ). This is a class of unsupervised machine learning techniques ( Evans and Aceves, 2016 : 22), which are used to inductively explore and discover patterns and regularities within a corpus of texts. Among the most widely used topic models is Latent Dirichlet Allocation (LDA). LDA is a type of Bayesian probabilistic model that builds on the assumption that each document in a corpus discusses multiple topics in differing proportions. Therefore, Document A might primarily be about Topic 1 (60 per cent), but it also refers to terms associated with Topic 2 (30 per cent), and, to a lesser extent, Topic 3 (10 per cent). A topic, then, is defined as a probability distribution over a fixed vocabulary, that is, the totality of words present in the corpus. The advantage of the unsupervised LDA approach that we take is that it does not limit the topic model to our preconceptions of which topics are studied by migration researchers and therefore should be found in the literature. Instead, it allows for an inductive sketching of the field, and consequently an element of surprise ( Halford and Savage, 2017 : 1141–1142). To determine the optimal number of topics, we used the package ldatuning to calculate the statistically optimal number of topics, a number which we then qualitatively validated.

The chosen LDA model produced two main outcomes. First, it yielded a matrix with per-document topic proportions, which allow us to generate an idea of the topics discussed in the abstracts. Secondly, the model returned a matrix with per-topic word probabilities. Essentially, the topics are a collection of words ordered by their probability of (co-)occurrence. Each topic contains all the words from all the abstracts, but some words have a much higher likelihood to belong to the identified topic. The 20–30 most probable words for each topic can be helpful in understanding the content of the topic. The third step we undertook was to look at those most probable words by a group of experts familiar with the field and label them. We did this systematically and individually by first looking at the top 5 words, then the top 30, trying to find an umbrella label that would summarize the topic. The initial labels suggested by each of us were then compared and negotiated in a group discussion. To verify the labels even more, in case of a doubt, we read several selected abstracts marked by the algorithm as exhibiting a topic, and through this were able to further refine the names of the topics.

It is important to remember that this list of topics should not be considered a theoretically driven attempt to categorize the field. It is purely inductive because the algorithm is unable to understand theories, conceptual frames, and approaches; it makes a judgement only on the basis of words. So if words are often mentioned together, the computer regards their probability of belonging to one topic as high.

3.2 Dataset of publications

For the topic modelling, we created a dataset that is representative of publications relevant to migration studies. First, we identified the most relevant sources of literature. Here we chose not only to follow rankings in citation indices, but also to ask migration scholars, in an expert survey, to identify what they considered to be relevant sources. This survey was distributed among a group of senior scholars associated with the IMISCOE Network; 25 scholars anonymously completed the survey. A set of journals and book series was identified from existing indices (such as Google Scholar, Web of Science, and Scopus) which were then validated and added to by respondents. Included in our eventual dataset were all journals and book series that were mentioned at least by two experts in the survey. The dataset includes 40 journals and 4 book series (see Supplementary Data A). Non-English journals were omitted from data collection because the algorithm can only analyse one language. Despite their influence on the field, we also did not consider broader disciplinary journals (for instance, sociological journals or economic journals) for the dataset. Such journals, we acknowledge, have published some of the most important research in the history of migration studies, but even with their omission, it is still possible to achieve our goal of obtaining a representative snapshot of what migration researchers have studied, rather than who or which papers have been most influential. In addition, both because of the language restriction of the algorithm and because of the Global North’s dominance in the field that is mentioned above ( Bommes and Morawska, 2005 ; Piguet et al., 2018 ), there is likely to be an under-representation of scholarship from the Global South in our dataset.

Secondly, we gathered metadata on publications from the selected journals and book series using the Scopus and Web of Science electronic catalogues, and manually collecting from those sources available on neither Scopus nor Web of Science. The metadata included authors, years, titles, and abstracts. We collected all available data up to the end of 2017. In total, 94 per cent of our metadata originated from Scopus, ∼1 per cent from Web of Science, and 5 per cent was gathered manually. One limitation of our dataset lies in the fact that the electronic catalogue of Scopus, unfortunately, does not list all articles and abstracts ever published by all the journals (their policy is to collect articles and abstracts ‘where available’ ( Elsevier, 2017 )). There was no technical possibility of assessing Scopus or WoS’ proportional coverage of all articles actually published. The only way to improve the dataset in this regard would be to manually collect and count abstracts from journal websites. This is also why many relevant books were not included in our dataset; they are not indexed in such repositories.

In the earliest years of available data, only a few journals were publishing (with limited coverage of this on Scopus) specifically on migration. However, Fig. 1 below demonstrates that the numbers constantly grew between 1959 and 2018. As Fig. 1 shows, in the first 30 years (1959–88), the number of migration journals increased by 15, while in the following three decades (1989–2018), this growth intensified as the number of journals tripled to 45 in the survey (see Supplementary Data A for abbreviations).

Number of journals focussed on migration and migration-related diversity (1959–2018) Source: Own calculations.

Number of journals focussed on migration and migration-related diversity (1959–2018) Source: Own calculations.

Within all 40 journals in the dataset, we were able to access and extract for our analysis 29,844 articles, of which 22,140 contained abstracts. Furthermore, we collected 901 available abstracts of chapters in the 4 book series: 2 series were downloaded from the Scopus index (Immigration and Asylum Law in Europe; Handbook of the Economics of International Migration), and the abstracts of the other 2 series, selected from our expert survey (the IMISCOE Research Series Migration Diasporas and Citizenship), were collected manually. Given the necessity of manually collecting the metadata for 896 abstracts of the chapters in these series, it was both practical and logical to set these two series as the cut-off point. Ultimately, we get a better picture of the academic landscape as a whole with some expert-approved book series than with none .

Despite the limitations of access, we can still have an approximate idea on how the volume of publications changed overtime. The chart ( Fig. 2 ) below shows that both the number of published articles and the number of abstracts of these articles follow the same trend—a rapid growth after the turn of the century. In 2017, there were three times more articles published per year than in 2000.

Publications and abstracts in the dataset (1959–2018).

Publications and abstracts in the dataset (1959–2018).

The cumulative graph ( Fig. 3 ) below shows the total numbers of publications and the available abstracts. For the creation of our inductively driven topic model, we used all available abstracts in the entire timeframe. However, to evaluate the dynamics of topics over time, we decided to limit the timeframe of our chronological analyses to 1986–2017, because as of 1986, there were more than 10 active journals and more articles had abstracts. This analysis therefore covers the topical evolution of migration studies in the past three decades.

Cumulative total of publications and abstracts (1959–2017).

Cumulative total of publications and abstracts (1959–2017).

Migration studies has only internationalised very slowly in support of what others have previously argued ( Bommes and Morawska, 2005 ; Piguet et al., 2018 ). Figure 4 gives a snapshot of the geographic dispersion of the articles (including those without abstracts) that we collected from Scopus. Where available, we extracted the country of authors’ university affiliations. The colour shades represent the per capita publication volume. English-language migration scholarship has been dominated by researchers based, unsurprisingly, in Anglophone and Northern European countries.

Migration research output per capita (based on available affiliation data within dataset).

Migration research output per capita (based on available affiliation data within dataset).

The topic modelling (following the LDA model) led us, as discussed in methods, to the definition of 60 as the optimal number of topics for mapping migration studies. Each topic is a string of words that, according to the LDA algorithm, belong together. We reviewed the top 30 words for each word string and assigned labels that encapsulated their meaning. Two of the 60 word strings were too generic and did not describe anything related to migration studies; therefore, we excluded them. Subsequently, the remaining 58 topics were organised into a number of clusters. In the Table 1 below, you can see all the topic labels, the topic clusters they are grouped into and the first 5 (out of 30) most probable words defining those topics.

Topics in migration studies

After presenting all the observed topics in the corpus of our publication data, we examined which topics and topic clusters are most frequent in general (between 1964 and 2017), and how their prominence has been changing over the years. On the basis of the matrix of per-item topic proportions generated by LDA analysis, we calculated the shares of each topic in the whole corpus. On the level of individual topics, around 25 per cent of all abstract texts is about the top 10 most prominent topics, which you can see in Fig. 5 below. Among those, #56 identity narratives (migration-related diversity), #39 migration theory, and #29 migration flows are the three most frequently detected topics.

Top 10 topics in the whole corpus of abstracts.

Top 10 topics in the whole corpus of abstracts.

On the level of topic clusters, Fig. 6 (left) shows that migration-related diversity (26 per cent) and migration processes (19 per cent) clearly comprise the two largest clusters in terms of volume, also because they have the largest number topics belonging to them. However, due to our methodology of labelling these topics and grouping them into clusters, it is complicated to make comparisons between topic clusters in terms of relative size, because some clusters simply contain more topics. Calculating average proportions of topics within each cluster allows us to control for the number of topics per cluster, and with this measure, we can better compare the relative prominence of clusters. Figure 6 (right) shows that migration research and statistics have the highest average of topic proportions, followed by the cluster of migration processes and immigrant incorporation.

Topic proportions per cluster.

Topic proportions per cluster.

An analysis on the level of topic clusters in the project’s time frame (1986–2017) reveals several significant trends. First, when discussing shifts in topics over time, we can see that different topics have received more focus in different time frames. Figure 7 shows the ‘age’ of topics, calculated as average years weighted by proportions of publications within a topic per year. The average year of the articles on the same topic is a proxy for the age of the topic. This gives us an understanding of which topics were studied more often compared with others in the past and which topics are emerging. Thus, an average year can be understood as the ‘high-point’ of a topic’s relative prominence in the field. For instance, the oldest topics in our dataset are #22 ‘Migrant demographics’, followed by #45 ‘Governance of migration’ and #46 ‘Migration statistics and survey research’. The newest topics include #14 ‘Mobilities’ and #48 ‘Intra-EU mobility’.

Average topic age, weighted by proportions of publications (publications of 1986–2017). Note: Numbers near dots indicate the numeric id of topics (see Table 1 for the names).

Average topic age, weighted by proportions of publications (publications of 1986–2017). Note: Numbers near dots indicate the numeric id of topics (see Table 1 for the names).

When looking at the weighted ‘age’ of the clusters, it becomes clear that the focus on migration research and statistics is the ‘oldest’, which echoes what Greenwood and Hunt (2003 ) observe. This resonates with the idea that migration studies has roots in more demographic studies of migration and diversity (cf. Thornthwaite, 1934 ; Thomas, 1938 ), which somewhat contrasts with what van Dalen (2018) has found. Geographies of migration (studies related to specific migration flows, origins, and destinations) were also more prominent in the 1990s than now, and immigrant incorporation peaked at the turn of the century. However, gender and family, diversity, and health are more recent themes, as was mentioned above (see Fig. 8 ). This somewhat indicates a possible post-methodological nationalism, post-integration paradigm era in migration research going hand-in-hand with research that, as King (2012) argues, situates migration within wider social and political domains (cf. Scholten, 2018 ).

Diversity of topics and topic clusters (1985–2017).

Diversity of topics and topic clusters (1985–2017).

Then, we analysed the diversification of publications over the various clusters. Based on the literature review, we expected the diversification to have increased over the years, signalling a move beyond paradigmatic closure. Figure 9 (below) shows that we can hardly speak of a significant increase of diversity in migration studies publications. Over the years, only a marginal increase in the diversity of topics is observed. The Gini-Simpson index of diversity in 1985 was around 0.95 and increased to 0.98 from 1997 onwards. Similarly, there is little difference between the sizes of topic clusters over the years. Both ways of calculating the Gini-Simpson index of diversity by clusters resulted in a rather stable picture showing some fluctuations between 0.82 and 0.86. This indicates that there has never been a clear hegemony of any cluster at any time. In other words, over the past three decades, the diversity of topics and topic clusters was quite stable: there have always been a great variety of topics discussed in the literature of migration studies, with no topic or cluster holding a clear monopoly.

Average age of topic clusters, weighted by proportions (publications of 1986–2017).

Average age of topic clusters, weighted by proportions (publications of 1986–2017).

Subsequently, we focussed on trends in topic networks. As our goal is to describe the general development of migration studies as a field, we decided to analyse topic networks in three equal periods of 10 years (Period 1 (1988–97); Period 2 (1998–2007); Period 3 (2008–17)). On the basis of the LDA-generated matrix with per-abstract topic proportions (The LDA algorithm determines the proportions of all topics observed within each abstract. Therefore, each abstract can contain several topics with a substantial prominence), we calculated the topic-by-topic Spearman correlation coefficients in each of the time frames. From the received distribution of the correlation coefficients, we chose to focus on the top 25 per cent strongest correlations period. In order to highlight difference in strength of connections, we assigned different weights to the correlations between the topics. Coefficient values above the 75th percentile (0.438) but ≤0.5 were weighted 1; correlations above 0.5 but ≤0.6 were weighted 2; and correlations >0.6 were weighted 3. We visualised these topic networks using the software Gephi.

To compare networks of topics in each period, we used three common statistics of network analysis: 1 average degree of connections; 2 average weighted degree of connections; and 3 network density. The average degree of connections shows how many connections to other topics each topic in the network has on average. This measure can vary from 0 to N − 1, where N is the total number of topics in the network. Some correlations of topics are stronger and were assigned the Weight 2 or 3. These are included in the statistics of average weighted degree of connections, which shows us the variations in strength of existing connections between the topics. Network density is a proportion of existing links over the number of all potentially possible links between the topics. This measure varies from 0 = entirely disconnected topics to 1 = extremely dense network, where every topic is connected to every topic.

Table 2 shows that all network measures vary across the three periods. In Period 1, each topic had on average 21 links with other topics, while in Period 2, that number was much lower (11.5 links). In Period 3, the average degree of connections grew again, but not to the level of Period 1. The same trend is observed in the strength of these links—in Period 1, the correlations between the topics were stronger than in Period 3, while they were the weakest in Period 2. The density of the topic networks was highest in Period 1 (0.4), then in Period 2, the topic network became sparser before densifying again in Period 3 (but not to the extent of Period 1’s density).

Topic network statistics

These fluctuations on network statistics indicate that in the years 1988–97, topics within the analysed field of migration studies were mentioned in the same articles and book chapters more often, while at the turn of the 21st century, these topic co-occurrences became less frequent; publications therefore became more specialised and topics were more isolated from each other. In the past 10 years, migration studies once again became more connected, the dialogues between the topics emerged more frequently. These are important observations about topical development in the field of migration studies. The reasons behind these changes require further, possibly more qualitative explanation.

To get a more in-depth view of the content of these topic networks, we made an overview of the changes in the topic clusters across the three periods. As we can see in Fig. 10 , some changes emerge in terms of the prominence of various clusters. The two largest clusters (also by the number of topics within them) are migration-related diversity and migration processes. The cluster of migration-related diversity increased in its share of each period’s publications by around 20 per cent. This reflects our above remarks on the literature surrounding the integration debate, and the ‘cultural turn’ King mentions (2012). And the topic cluster migration processes also increased moderately its share.

Prominence and change in topic clusters 1988–2017.

Prominence and change in topic clusters 1988–2017.

Compared with the first period, the topic cluster of gender and family studies grew the fastest, with the largest growth observed in the turn of the century (relative to its original size). This suggests a growing awareness of gender and family-related aspects of migration although as a percentage of the total corpus it remains one the smallest clusters. Therefore, Massey et al.’s (1998) argument that households and gender represented a quantitatively significant pillar of migration research could be considered an overestimation. The cluster of health studies in migration research also grew significantly in the Period 2 although in Period 3, the percentage of publications in this cluster diminished. This suggests a rising awareness of health in relation to migration and diversity (see Sweileh et al., 2018 ) although this too remains one of the smallest clusters.

The cluster on Immigrant incorporation lost prominence the most over the past 30 years. This seems to resonate with the argument that ‘integrationism’ or the ‘integration paradigm’ was rather in the late 1990s (see Favell, 2003 ; Dahinden, 2016 ) and is losing its prominence. A somewhat slower but steady loss was also observed in the cluster of Geographies of migration and Migration research and statistics. This also suggests not only a decreasing emphasis on demographics within migration studies, but also a decreasing reflexivity in the development of the field and the focus on theory-building.

We will now go into more detail and show the most connected topics and top 10 most prominent topics in each period. Figures 11–13 show the network maps of topics in each period. The size of circles reflects the number and strength of links per each topic: the bigger the size, the more connected this topic is to the others; the biggest circles indicate the most connected topics. While the prominence of a topic is measured by the number of publications on that topic, it is important to note that the connectedness the topic has nothing necessarily to do with the amount of publications on that topic; in theory, a topic could appear in many articles without any reference to other topics (which would mean that it is prominent but isolated).

Topic network in 1988–97. Note: Numbers indicate topics' numerical ids, see Table 1 for topics' names.

Topic network in 1988–97. Note: Numbers indicate topics' numerical ids, see Table 1 for topics' names.

Topic network in 1998–2007. Note: Numbers indicate topics' numerical ids, see Table 1 for topics' names.

Topic network in 1998–2007. Note: Numbers indicate topics' numerical ids, see Table 1 for topics' names.

Topic network in 2008–2017. Note: Numbers indicate topics' numerical ids, see Table 1 for topics' names.

Topic network in 2008–2017. Note: Numbers indicate topics' numerical ids, see Table 1 for topics' names.

Thus, in the section below, we describe the most connected and most prominent topics in migration research per period. The degree of connectedness is a useful indicator of the extent to which we can speak of a ‘field’ of migration research. If topics are well-connected, especially in a context of increased knowledge production and changes in prominence among topics, then this would suggest that a shared conceptual and theoretical language exists.

6.1 Period 1: 1988–97

The five central topics with the highest degree of connectedness (the weighted degree of connectedness of these topics was above 60) were ‘black studies’, ‘mobilities’, ‘ICT, media and migration’, ‘migration in/from Israel and Palestine’, and ‘intra-EU mobility’. These topics are related to geopolitical regions, ethnicity, and race. The high degree of connectedness of these topics shows that ‘they often occurred together with other topics in the analysed abstracts from this period’. This is expected because research on migration and diversity inevitably discusses its subject within a certain geographical, political, or ethnic scope. Geographies usually appear in abstracts as countries of migrants’ origin or destination. The prominence of ‘black studies’ reflects the dominance of American research on diversity, which was most pronounced in this period ( Fig. 11 ).

The high degree of connectedness of the topics on ICT and ‘media’ is indicative of wider societal trends in the 1990s. As with any new phenomenon, it clearly attracted the attention of researchers who wanted to understand its relationship with migration issues.

Among the top 10 topics with the most publications in this period (see Supplementary Data B) were those describing the characteristics of migration flows (first) and migration populations (third). It goes in line with the trends of the most connected topics described above. Interest in questions of migrants’ socio-economic position (fourth) in the receiving societies and discussion on ‘labour migration’ (ninth) were also prevalent. Jointly, these topics confirm that in the earlier years, migration was ‘studied often from the perspectives of economics and demographics’ ( van Dalen, 2018 ).

Topics, such as ‘education and language training’ (second), community development’ (sixth), and ‘intercultural communication’ (eighth), point at scholarly interest in the issues of social cohesion and socio-cultural integration of migrants. This lends strong support to Favell’s ‘integration paradigm’ argument about this period and suggests that the coproduction of knowledge between research and policy was indeed very strong ( Scholten, 2011 ). This is further supported by the prominence of the topic ‘governance of migration’ (seventh), reflecting the evolution of migration and integration policymaking in the late 1980s and beginning of the 1990s, exemplified by the development of the Schengen area and the EU more widely; governance of refugee flows from the Balkan region (also somewhat represented in the topic ‘southern-European migration’, which was the 10th most prominent); and governance of post-Soviet migration. Interestingly, this is the only period in which ‘migration histories’ is among the top 10 topics, despite the later establishment of a journal dedicated to the very discipline of history. Together these topics account for 42 per cent of all migration studies publications in that period of time.

6.2 Period 2: 1998–2007

In the second period, as the general degree of connectedness in the topic networks decreased, the following five topics maintained a large number of connections in comparison to others, as their average weighted degree of connections ranged between 36 and 57 ties. The five topics were ‘migration in/from Israel and Palestine’, ‘black studies’, ‘Asian migration’, ‘religious diversity’, and ‘migration, sexuality, and health’ ( Fig. 12 ).

Here we can observe the same geographical focus of the most connected topics, as well as the new trends in the migration research. ‘Asian migration’ became one of the most connected topics, meaning that migration from/to and within that region provoked more interest of migration scholars than in the previous decade. This development appears to be in relation to high-skill migration, in one sense, because of its strong connections with the topics ‘Asian expat migration’ and ‘ICT, media, and migration’; and, in another sense, in relation to the growing Muslim population in Europe thanks to its strong connection to ‘religious diversity’. The high connectedness of the topic ‘migration sexuality and health’ can be explained by the dramatic rise of the volume of publications within the clusters ‘gender and family’ studies and ‘health’ in this time-frame as shown in the charts on page 13, and already argued by Portes (1997) .

In this period, ‘identity narratives’ became the most prominent topic (see Supplementary Data B), which suggests increased scholarly attention on the subjective experiences of migrants. Meanwhile ‘migrant flows’ and ‘migrant demographics’ decreased in prominence from the top 3 to the sixth and eighth position, respectively. The issues of education and socio-economic position remained prominent. The emergence of topics ‘migration and diversity in (higher) education’ (fifth) and ‘cultural diversity’ (seventh) in the top 10 of this period seem to reflect a shift from integrationism to studies of diversity. The simultaneous rise of ‘migration theory’ (to fourth) possibly illustrates the debates on methodological nationalism which emerged in the early 2000s. The combination of theoretical maturity and the intensified growth in the number of migration journals at the turn of the century suggests that the field was becoming institutionalised.

Overall, the changes in the top 10 most prominent topics seem to show a shifting attention from ‘who’ and ‘what’ questions to ‘how’ and ‘why’ questions. Moreover, the top 10 topics now account only for 26 per cent of all migration studies (a 15 per cent decrease compared with the period before). This means that there were many more topics which were nearly as prominent as those in the top 10. Such change again supports our claim that in this period, there were more intensive ‘sub-field’ developments in migration studies than in the previous period.

6.3 Period 3: 2008–17

In the last decade, the most connected topics have continued to be: ‘migration in/from Israel and Palestine’, ‘Asian migration’, and ‘black studies’. The hypothetical reasons for their central position in the network of topics are the same as in the previous period. The new most-connected topics—‘Conflicts, violence, and migration’, together with the topic ‘Religious diversity’—might indicate to a certain extent the widespread interest in the ‘refugee crisis’ of recent years ( Fig. 13 ).

The publications on the top 10 most prominent topics constituted a third of all migration literature of this period analysed in our study. A closer look at them reveals the following trends (see Supplementary Data B for details). ‘Mobilities’ is the topic of the highest prominence in this period. Together with ‘diasporas and transnationalism’ (fourth), this reflects the rise of critical thinking on methodological nationalism ( Wimmer and Glick Schiller, 2002 ) and the continued prominence of transnationalism in the post-‘mobility turn’ era ( Urry (2007) , cited in King, 2012 ).

The interest in subjective experiences of migration and diversity has continued, as ‘identity narratives’ continues to be prominent, with the second highest proportion of publications, and as ‘Discrimination and socio-psychological issues’ have become the eighth most prominent topic. This also echoes an increasing interest in the intersection of (mental) health and migration (cf. Sweileh et al., 2018 ).

The prominence of the topics ‘human rights law and protection’ (10th) and ‘governance of migration and diversity’ (9th), together with ‘conflicts, violence, and migration’ being one of the most connected topics, could be seen as a reflection of the academic interest in forced migration and asylum. Finally, in this period, the topics ‘race and racism’ (fifth) and ‘black studies’ (seventh) made it into the top 10. Since ‘black studies’ is also one of the most connected topics, such developments may reflect the growing attention to structural and inter-personal racism not only in the USA, perhaps reflecting the #blacklivesmatter movement, as well as in Europe, where the idea of ‘white Europeanness’ has featured in much public discourse.

6.4 Some hypotheses for further research

Why does the connectedness of topics change across three periods? In an attempt to explain these changes, we took a closer look at the geographical distribution of publications in each period. One of the trends that may at least partially explain the loss of connectedness between the topics in Period 2 could be related to the growing internationalisation of English language academic literature linked to a sharp increase in migration-focussed publications during the 1990s.

Internationalisation can be observed in two ways. First, the geographies of English language journal publications have become more diverse over the years. In the period 1988–97, the authors’ institutional affiliations spanned 57 countries. This increased to 72 in 1998–2007, and then to 100 in 2007–18 (we counted only those countries which contained at least 2 publications in our dataset). Alongside this, even though developed Anglophone countries (the USA, Canada, Australia, the UK, Ireland, and New Zealand) account for the majority of publications of our overall dataset, the share of publications originating from non-Anglophone countries has increased over time. In 1988–97, the number of publications from non-Anglophone European (EU+EEA) countries was around 13 per cent. By 2008–17, this had significantly increased to 28 per cent. Additionally, in the rest of the world, we observe a slight proportional increase from 9.5 per cent in the first period to 10.6 per cent in the last decade. Developed Anglophone countries witness a 16 per cent decrease in their share of all articles on migration. The trends of internationalisation illustrated above, combined with the loss of connectedness at the turn of the 21st century, seem to indicate that English became the lingua-franca for academic research on migration in a rather organic manner.

It is possible that a new inflow of ideas came from the increased number of countries publishing on migration whose native language is not English. This rise in ‘competition’ might also have catalysed innovation in the schools that had longer established centres for migration studies. Evidence for this lies in the rise in prominence of the topic ‘migration theory’ during this period. It is also possible that the expansion of the European Union and its research framework programmes, as well as the Erasmus Programmes and Erasmus Mundus, have perhaps brought novel, comparative, perspectives in the field. All this together might have created fruitful soil for developing unique themes and approaches, since such approaches in theory lead to more success and, crucially, more funds for research institutions.

This, however, cannot fully explain why in Period 3 the field became more connected again, other than that the framework programmes—in particular framework programme 8, Horizon 2020—encourage the building of scientific bridges, so to speak. Our hypothesis is thus that after the burst of publications and ideas in Period 2, scholars began trying to connect these new themes and topics to each other through emergent international networks and projects. Perhaps even the creation and work of the IMISCOE (2004-) and NOMRA (1998-) networks contributed to this process of institutionalisation. This, however, requires much further thought and exploration, but for now, we know that the relationship between the growth, the diversification, and the connectedness in this emergent research field is less straightforward than we might previously have suggested. This begs for further investigation perhaps within a sociology of science framework.

This article offers an inductive mapping of the topical focus of migration studies over a period of more than 30 years of development of the research field. Based on the literature, we expected to observe increasing diversity of topics within the field and increasing fragmentation between the topics, also in relation to the rapid growth in volume and internationalisation of publications in migration studies. However, rather than growth and increased diversity leading to increased fragmentation, our analysis reveals a complex picture of a rapidly growing field where the diversity of topics has remained relatively stable. Also, even as the field has internationalised, it has retained its overall connectedness, albeit with a slight and temporary fragmentation at the turn of the century. In this sense, we can argue that migration studies have indeed come of age as a distinct research field.

In terms of the volume of the field of migration studies, our study reveals an exponential growth trajectory, especially since the mid-1990s. This involves both the number of outlets and the number of publications therein. There also seems to be a consistent path to internationalisation of the field, with scholars from an increasing number of countries publishing on migration, and a somewhat shrinking share of publications from Anglo-American countries. However, our analysis shows that this has not provoked an increased diversity of topics in the field. Instead, the data showed that there have been several important shifts in terms of which topics have been most prominent in migration studies. The field has moved from focusing on issues of demographics, statistics, and governance, to an increasing focus on mobilities, migration-related diversity, gender, and health. Also, interest in specific geographies of migration seems to have decreased.

These shifts partially resonated with the expectations derived from the literature. In the 1980s and 1990s, we observed the expected widespread interest in culture, seen in publications dealing primarily with ‘education and language training’, ‘community development’, and ‘intercultural communication’. This continued to be the case at the turn of the century, where ‘identity narratives’ and ‘cultural diversity’ became prominent. The expected focus on borders in the periods ( Pedraza-Bailey, 1990 ) was represented by the high proportion of research on the ‘governance of migration’, ‘migration flows’, and in the highly connected topic ‘intra-EU mobility’. Following Portes (1997) , we expected ‘transnational communities’, ‘states and state systems’, and the ‘new second generation’ to be key themes for the ‘new century’. Transnationalism shifts attention away from geographies of migration and nation–states, and indeed, our study shows that ‘geographies of migration’ gave way to ‘mobilities’, the most prominent topic in the last decade. This trend is supported by the focus on ‘diasporas and transnationalism’ and ‘identity narratives’ since the 2000s, including literature on migrants’ and their descendants’ dual identities. These developments indicate a paradigmatic shift in migration studies, possibly caused by criticism of methodological nationalism. Moreover, our data show that themes of families and gender have been discussed more in the 21st century, which is in line with Portes’ predictions.

The transition from geographies to mobilities and from the governance of migration to the governance of migration-related diversity, race and racism, discrimination, and social–psychological issues indicates a shifting attention in migration studies from questions of ‘who’ and ‘what’ towards ‘how’ and ‘why’. In other words, a more nuanced understanding of the complexity of migration processes and consequences emerges, with greater consideration of both the global and the individual levels of analysis.

However, this complexification has not led to thematic fragmentation in the long run. We did not find a linear trend towards more fragmentation, meaning that migration studies have continued to be a field. After an initial period of high connectedness of research mainly coming from America and the UK, there was a period with significantly fewer connections within migration studies (1998–2007), followed by a recovery of connectedness since then, while internationalisation has continued. What does this tell us?

We may hypothesise that the young age of the field and the tendency towards methodological nationalism may have contributed to more connectedness in the early days of migration studies. The accelerated growth and internationalisation of the field since the late 1990s may have come with an initial phase of slight fragmentation. The increased share of publications from outside the USA may have caused this, as according to Massey et al. (1998) , European migration research was then more conceptually dispersed than across the Atlantic. The recent recovery of connectedness could then be hypothesised as an indicator of the field’s institutionalisation, especially at the European level, and growing conceptual and theoretical development. As ‘wisdom comes with age’, this may be an indication of the ‘coming of age’ of migration studies as a field with a shared conceptual and theoretical foundation.

The authors would like to thank the three anonymous reviewers for their constructive feedback, as well as dr. J.F. Alvarado for his advice in the early stages of work on this article.

This research is associated with the CrossMigration project, funded by the European Union's Horizon 2020 research and innovation programme under the grant agreement Ares(2017) 5627812-770121.

Conflict of interest statement . None declared.

Bommes M. , Morawska E. ( 2005 ) International Migration Research: Constructions, Omissions and the Promises of Interdisciplinarity . Aldershot : Ashgate .

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2021 Theses Doctoral

Three Essays on International Migration

Huang, Xiaoning

Today, there are about 250 million international migrants globally, and the number is increasing each year. Immigrants have contributed to the global economy, bridged cultural and business exchanges between host and home countries, and increased ethnic, racial, social, and cultural diversity in the host societies. Immigrants have also been overgeneralized about, misunderstood, scapegoated, and discriminated against. Understanding what drives international migration, who migrate, and how immigrants fare in destination has valuable theoretical, practical, and policy implications. This dissertation consists of three essays on international immigration. The first paper aims to test a series of immigration theories by studying immigrant skill-selection into South Africa and the United States. Most of the research on the determinants of immigrant skill selection has been focusing on immigrants in the United States and other developed destination countries. However, migration has been growing much faster in recent years between developing countries. This case study offers insights into the similarities and differences of immigration theories within the contexts of international migration into South Africa and the US. This project is funded by the Hamilton Research Fellowship of Columbia School of Social Work. The second paper narrows down the focus onto Asian immigrants in the United States, studying how the skill-selection of Asian immigrants from different regions has evolved over the past four decades. Asian sending countries have experienced tremendous growth in their economy and educational infrastructure. The rapid development provides an excellent opportunity to test the theories on the associations between emigrants’ skill-selection and sending countries’ income, inequality, and education level. On the other hand, during the study period, the United States has had massive expansion employment-based immigration system, followed by cutbacks in immigration policies. I study the association between immigration patterns and these policies to draw inferences on how the changes in immigration policies have affected the skill selection of Asian immigrants. This research is funded by Columbia University Weatherhead East Asia Institute’s Dorothy Borg Research Program Dissertation Research Fellowship. The third paper centers on the less-educated immigrant groups in the US and investigates the gap in welfare use between less-educated immigrant and native households during 1995-2018, spanning periods of economic recessions and recoveries, changes in welfare policy regimes, and policies towards immigrants. I use “decomposition analysis” to study to what extend demographic factors, macroeconomic trends, and welfare and immigration policy could explain the disparities in welfare participation between immigrants and natives. This paper is co-authored with Dr. Neeraj Kaushal from Columbia School of Social Work and Dr. Julia Shu-Huah Wang from the University of Hong Kong. The work has been published in Population Research and Policy Review (doi.org/10.1007/s11113-020-09621-8).

Geographic Areas

  • South Africa
  • United States
  • Social service
  • Immigrants--Economic aspects
  • Immigrants--Social conditions
  • Race discrimination
  • Immigrants--Education

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International Migration and International Trade

This paper surveys key developments in the theory of international migration and international trade, and provides a few stylized facts. International migration, in many important cases, such as cross-country differences in productivity, can be a complement to international flows of commodities. In the presence of a productivity difference that is generated by an external economy effect of human, capital physical capital has weak incentives to flow from developed to underdeveloped countries while pressures for international migration from poor to rich countries are strong. The balancing factors underlying an efficient global dispersion of population are those which generate advantages to size, such as public goods, or increasing returns to scale on one hand, and those which generate disadvantages to size, such as immobile factors or congestion effects in the utilization of public services, on the other hand. The modem welfare state typically redistribute income from the rich to the poor in a way which attracts poor migrants from the less developed countries. Since migration could impose a toll on the redistribution policy of the Developed Country it may benefit from the extension of foreign aid to the Less Developed Country if this aid serves to finance a subsidy to workers in the Less Developed Country, thereby containing migration.

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EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

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As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. Once approved, these will be the world’s first rules on AI.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

  • Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
  • Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
  • Biometric identification and categorisation of people
  • Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

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All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle.

General purpose and generative AI

Generative AI, like ChatGPT, would have to comply with transparency requirements:

  • Disclosing that the content was generated by AI
  • Designing the model to prevent it from generating illegal content
  • Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Limited risk

Limited risk AI systems should comply with minimal transparency requirements that would allow users to make informed decisions. After interacting with the applications, the user can then decide whether they want to continue using it. Users should be made aware when they are interacting with AI. This includes AI systems that generate or manipulate image, audio or video content, for example deepfakes.

On December 9 2023, Parliament reached a provisional agreement with the Council on the AI act . The agreed text will now have to be formally adopted by both Parliament and Council to become EU law. Before all MEPs have their say on the agreement, Parliament’s internal market and civil liberties committees will vote on it.

More on the EU’s digital measures

  • Cryptocurrency dangers and the benefits of EU legislation
  • Fighting cybercrime: new EU cybersecurity laws explained
  • Boosting data sharing in the EU: what are the benefits?
  • EU Digital Markets Act and Digital Services Act
  • Five ways the European Parliament wants to protect online gamers
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