 Regular article
 Open access
 Published:
Can Google Trends predict asylumseekers’ destination choices?
EPJ Data Science volume 12, Article number: 41 (2023)
Abstract
Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on firsttime asylum applications and a set of pushpull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow FixedEffects or by AutoRegressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration.
1 Introduction
The potential for Google Trends (GT) data to predict asylumseekers’ destination choices is a burgeoning area of research, drawing upon the nexus among big data analytics, migration studies, and the digital sociology of search behaviors. Asylumseekers, like many other demographic groups, use online tools to inform their migration decisions, including information on potential destinations. GT, which collates search volumes of specific terms over time and by geographic location, could, in theory, provide insights into where asylumseekers plan to move, revealed by what they are searching online. Empirically, the inclusion of GTbased indicators for migration intentions tend to enhance the performances of migration models [1–5]. However, what has been less explored in this literature is when GT cannot improve the accuracy of migration forecasts.
A distinctive feature of GT, compared to other digital traces, is that it may capture ex ante rather than ex post migration outcome. For example, georeferenced emails, social media posts, LinkedIn profiles, among others, can help map actual migration flows by observing the changes in users’ locations (see e.g., [6–13]). GT, on the other hand, may capture migration intentions rather than actions through their revealed demand for information [14]. This ex ante feature could be valuable for predictive analysis, e.g., a growing demand for migrationrelated information may predict an increase in migration volume. However, empirically, the predictive power of GT is not unequivocal; while some demonstrate that GT can outperform any of the established predictors of migration flows [1], others argued that it is not always the case [3, 4]. For example, GT’s ability to predict can differ depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others.
The predictive power of GT may also be sensitive to the complexity of migration models. For example, in [1], it is evident that GT can only increase the Rsquare values for relatively simple models, but not for those with a large set of fixedeffects (i.e., including originyear and origindestination constants). To gain more insights into such sensitivity, this article conducts a systematic examination of how GT’s predictive power may vary depending on the capacity of migration models. Our analysis follows two steps. First, we estimate three classes of models that are commonly used in the migration literature: i) pooled regression (PL), see e.g., [15, 16]; ii) autoregressive regression (AR), see e.g., [4]; and iii) flow fixedeffects regression (FE), see e.g., [17, 18]. We then add GT to these models and compare how their performances are changing, respectively.
It is noteworthy that these three model classes are inherently different in terms of complexity. PL is most parsimonious; it only includes a set of push and pull factors. AR is slightly more complex; it allows the migration outcome variable to be linearly dependent on its own previous values, which can potentially capture some unobserved timevarying processes of migration flows. FE is much more complex than PL and AR, as it includes a constant for each dyad flow to capture some timeinvariant factors, such as colonial ties, language proximity, geographical distance, among others. Given these differences, we argue that the extent to which GT may predict migration flows might not be universal. Specifically, if unobserved factors (such as destination preferences or migration intentions) captured by GT are timeinvariant, they will be fully absorbed into the dyadspecific constants in the FE model. Moreover, if unobserved factors that GT captures are timevarying but proportional to the migration outcome in the last period, they will be adjusted for by the AR component. In such cases, GT might only add predictive power to the relatively simple PL model.
Using GT and a set of pushpull indicators gathered from various data sources, we train three model classes described above to predict refugees’ choices of EU destinations (using EUROSTAT statistics on firsttime asylum applications). The results suggest that the inclusion of GT can only enhance the performance of relatively simple model (PL), but not the more complex ones (AR and FE).
2 Gravity models: micro foundation and empirical specifications
Migration models have undergone a substantial transformation in the last decade, with a pronounced shift towards microfounded gravity models. Such models build upon a theoretical foundation that combines the gravity model of international trade with concepts from the random utility framework. This blending of principles allows researchers to interpret macrolevel migration flows through the lens of microlevel decisionmaking, as well as to infer individual migration behavior while using aggregated dyad flow data (see e.g., [15, 17, 18]).
2.1 Micro foundation
Microfounded gravity models treats migration flows as collective outcomes of individual decisions made under uncertainty. These models estimate the probability of migrating to a specific destination as a function of the characteristics of the origin and destination locations, the distance as well as costs associated with moving between different locations, and the characteristics of the individual migrant or household [18]. Furthermore, they account for the multilateral resistance to migration, which refers to the way in which the attractiveness of a potential destination is influenced not only by its characteristics and those of the origin country, but also by the characteristics of other alternative destinations [19].
Microfounded gravity models provide a more nuanced understanding of migration decisions and can offer more accurate forecasts of migration flows. They allow researchers to explore how changes in variables such as income levels, employment rates, or immigration policies in one country can affect migration flows to and from other countries. However, as with any model, they are simplifications of reality and should be used with an understanding of their assumptions and limitations.
In general, there are two versions of the microfounded gravity model. A restrictive version assumes that the unobserved component of the random utility function is independent and irrelevant across alternative destinations, i.e., the IIA assumption [20]. Under this assumption, an individual’s preference of migrating to a given destination d and that of remaining in the origin o can be respectively represented by,
where, V’s are the deterministic utility and W’s are the unobserved utility.
Since W’s are assumed to be random draws from the type I extreme value distribution with the same mean and variance \(\pi ^{2}/6\) [21], the probabilities of migrating to d and remaining in o are,
To link the probabilities above to macro flow data, we take the ratio of \(\Pr (M_{o,d,t})\) to \(\Pr (M_{o,o,t})\) which gives the migration rate, i.e., the number of people who decided to migrate from o to d relative to those who remained in o,
By taking logarithm, Eq. (3) can be linearized as,
where, \(W_{o,d,t}W_{o,o,t} \) is expected to have a zero mean, as the difference between two random variables from the type I extreme value distribution with the same mean has itself a mean of zero [21].
A less restrictive version of the microfounded gravity model was introduced in [17] which relaxes the IIA assumption and assumes that the unobserved utility are correlated across alternative destinations. Under this assumption, Eq. (1) becomes,
where, τ is a dissimilarity parameter with range \((0, 1]\).
The dissimilarity parameter τ is inversely related to the correlation in the unobserved utility \(W_{o,d,t}\) across alternative destinations [18]; the higher τ is, the less correlation in \(W_{o,d,t}\). When the destinations in the choice set are completely different, the dissimilarity parameter τ will equal to one, and hence Eq. (5) will be the same as Eq. (1).
Substitute Eq. (5) into Eq. (2), the probabilities of migrating to d and remaining in o becomes,
Due to the presence of τ, the denominators in Eq. (6) are no longer the same, and therefore cannot be cancelled out when taking the ratio of the two probabilities, the migration rate becomes,
Using logtransformation, Eq. (7) can be linearized as,
\(\Omega _{o,d,t}\) is known as the multilateral resistance to migration [17], which implies that the unobserved utility of moving from o to d can be influenced by the attractiveness of alternative l in the choice set. Ignoring \(\Omega _{o,d,t}\) may generate biases in the estimation of the coefficients in the deterministic utility V’s [18]. More importantly, this could, in turn, bias the prediction of the expected migration rate. One way to account for the influence of l’s attractiveness is to obtain information about people’s actual preference for d, which can then be modeled as a deterministic utility. Specifically, we can decompose Eq. (9) as,
where, \(1/\tau V_{o,d,t}^{*}  V_{o,o,t}^{*}\) represents a deterministic component of the multilateral resistance to migration. \(\eta _{o,d,t}\) is an unobserved component. If \(1/\tau V_{o,d,t}^{*}  V_{o,o,t}^{*}\) can be specified sufficiently, the remaining unobserved \(\eta _{o,d,t}\) is essentially white noise [21].
In this article, we seek to examine whether Google Trends (GT) may help account for people’s actual preferences for migration destinations, hence adjust for the multilateral resistance \(\Omega _{o,d,t}\). The intuition behind GT is that people’s preference for a given country may be revealed by their demand for this country’s information, and this demand could be potentially captured by the intensity of Google searches.
2.2 Empirical specifications
Based on the discussion above, we specify two baseline models in this article. The first model uses GT as linear predictors for migration rates,
where, τ is a dissimilarity parameter with range \((0, 1]\). \(\epsilon _{o,d,t}\) is assumed to be normally distributed with zero mean and \(\sigma _{\epsilon _{o,d,t}}\). \(Y_{d,t1}\) and \(Y_{o,t1}\) are economic variables in destination and origin, respectively. \(D_{o,t1}\) is a measure of conflicts or geopolitical tensions in origin. \(G_{o,d,t1}\) and \(G_{o,o,t1}\) are the volume of Google searches in origin o about destination d and origin o, respectively. \(G_{tr,d,t1}\) and \(G_{tr,tr,t1}\) are the volume of Google searches in transit country tr about destination d and transit country tr, respectively.
The second baseline model seeks to investigate the nonlinearity in the relationship between migration rates and Google searches. To do so, we expand \(V_{o,d,t}^{*}\) and \(V_{o,o,t}^{*}\) by p order polynomials fitted by the Generalized Additive Model (GAM),
2.3 Augmented models: flow fixedeffects (FE) and autoregressive (AR) effects
As stressed at the outset, the ability of GT to predict human migration is not unequivocal; even if within a single study, the predictive power of GT may be inconsistent. For example, in [1], the results indicate that GT can only increase the Rsquare values for the models that are relatively simple, but not for those with a large set of fixedeffects. To make our study comparable to [1], we augment our baseline models by including the origindestination fixedeffects, and assess whether the explanatory power of GT weakens when model complexity increases.
Another important note in [1] is that the authors merely showed how GT may improve the goodnessoffit to the training data without assessing its predictive power in the testing data. In this article, we will use crossvalidation to assess whether the ability of GT to predict migration flows becomes weaker when our models are augmented with flowspecific dummies. As recent research showed that the flow fixedeffects gravity models can neither explain, nor predict migration patterns [22, 23], we expect that both the explanatory and predictive power of GT in the augmented models will be weaker than that in the baseline models.
For the purpose of shortterm forecasting of migration flows, a good alternative to the flow fixedeffects could be to include an autoregressive component [4]. To build on this literature, we augment our baseline models Eq. (11) and Eq. (12) by including \(\rho \ln (m_{o,d,t1})\), i.e., allowing the migration rate to be linearly dependent on its own previous values. The autoregressive (AR) model is slightly more complex than our baseline models, however, it can potentially capture some unobserved timevarying processes of migration flows. It may also capture sudden shifts in migration trends, which is critical as migration flows, particularly asylumrelated, are highly volatile and uncertain [24, 25].
3 Data
To estimate our baseline models Eq. (11) and Eq. (12) as well as their augmented versions, we make use of various data sources. To measure the intensity of asylumrelated migration, we compute the Asylum Seeking Rate (ASR) using EUROSTAT statistics on firsttime asylum applications lodged to different EU member states normalized by the population sizes in origin countries. The economic push (\(Y_{o,t1}\)) and pull (\(Y_{d,t1}\)) factors are measured by per capita GDP from the World Development Index. To measure push factors associated with geopolitical tensions \(D_{o,t1}\), we make use of the Uppsala Conflict Data Program (UCDP).
To measure people’s preferences for migration destinations, we rely on Google Trends (GT). The rationale for using GT is that what people search for may reflect their intentions, preferences, and other human behavior through revealed demand for information [14]. Such data has been adopted for a variety of applications, e.g. analyzing patterns of influenza [26], consumer demand [27, 28], stock and commodity prices [29–31], unemployment [32, 33], and suicide occurance [34], among others.
As stressed earlier, the usability of GT can vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share, users’ characteristics and their search behavior, among others. To reduce some heterogeneity, we limit our analysis to five sending countries: AFG, ARM, GEO, IRQ, and SYR, which, to some extent, share certain characteristics, e.g., geographical location and/or language. More importantly, they are also the major sending countries in terms of asylumrelated migration to Europe during the last decade [23].
GT can be used in numerous ways as predictors for migration flows. Typically, researchers construct countryspecific timeseries based on a set of predefined keywords on GT. For example, migrationrelated terms (e.g. “passports”, “visa”, “asylum”, etc.) searched on Google can be proxies for interest to seek asylum or to migrate. When the intensities of these searches increase, it can be an indication of rising intentions to outmigrate from a given origin.
The intentions measured by Google Trends could be sensitive to the choice of keywords and language [1]. Moreover, the possible number of keywordlanguage combinations can approach infinity. Since this article emphasizes on asylumseekers’ destination preferences, we thus make use of the search trends related to country topics; Google’s proprietary algorithm generates countryspecific topic by grouping search keywords that share the same concept in any language. For example, the trends on the topic of “Sweden” includes all the search terms related to Sweden in all languages, e.g. Stockholm, Sverige (Sweden in Swedish), IKEA, among others.
It is important to note that GT normalizes the search frequencies of each keyword to an index between zero and one. While this may reveal how the interest in a given country evolve overtime, it masks how the level of interest may differ across destinations. For example, for an average Syrian, the level of interest in Germany might be higher than in other destinations. To recover the level differences, we normalize each timeseries of a country topic and then adjust its level as,
where, \(S^{*}_{k,t}\) is a single download of Google Trend’s index value for country k. \(S^{**}_{k,t}\) is the index values for country k that are downloaded together with the index values for origin \(S^{**}_{o,o,t}\). The first term normalizes the index by its mean, and the second term adjusts the index level in relation to the reference (i.e., the index value for origin o). o and d are origin and destination country, respectively. tr denotes transit country, which, in this article, refers to Turkey (as it has been widely documented that many refugees from SYR and AFG chose Turkey as a transit country before moving to Europe, hence the searches about EU countries in Turkey might also capture refugees’ destination preferences).
While a rise of search intensity of a given country may indicate growing interest in migration to that country, it could also be driven by certain events unrelated to migration. For example, a sudden increase in the searches of Germany could be due to a winning game played by the German football team in a major tournament. To exclude the noises as such, we decompose \(S_{k,t}\) into two components,
where, F is a regression model which includes fourway interactions among the GT indices for Migration, Travel, Refugee, and Visa. This model seeks to capture part of the variation in \(S_{k,t}\) that is related to migration, whereas \(\Omega _{S_{k,t}}\) is the remaining variation that is assumed to be unrelated to migration.
Figure 1 depicts the difference between the observed searches (\(S_{k,t}\)) and the searches predicted by the function \(F(S^{\mathrm{Migration}}_{j,t}, S^{\mathrm{Travel}}_{j,t}, S^{\mathrm{Refugee}}_{j,t}, S^{\mathrm{Visa}}_{j,t},)\). In general, the predicted searches resemble the observed ones very well. This suggests that when people in AFG, ARM, GEO, IRQ, SYR as well as in the transit country (Turkey) Google different European countries, they are mostly looking for information related to migration, travel, refugee and visa.
Using the predicted values from Eq. (14), we define people’s preferences for migration destinations in Eq. (11) and Eq. (12) as,
Figure 2 illustrates how the Asylum Seeking Rates (ASR) are associated with GT searches about EU country topics, as well as with different push and pull factors. It is evident that ASR tend to increase with Google searches about EU destinations. This positive relation holds not only for the searches from origin country (Panel B), but also from transit country—Turkey (Panel D).
4 Crossvalidation
As stated at the outset, the overarching aim of this article is to systematically analyze how the predictive power of GT may vary depending on the capacity of migration models. To achieve this, we apply a crossvalidation procedure to the baseline models Eq. (11) and Eq. (12), as well as their augmented versions (AR and FE). We then compare the effects of including GT on model performances (measured by Rsquare).
Figure 3 depicts how we split the data into training and testing sets. This crossvalidation strategy is known as the “rolling forecasting origin” approach [35]. We choose this approach for two reasons. First, it can help us assess whether the improvements in models’ performances by augmenting the baseline models are consistent overtime (over different training periods). Second, the “rolling forecasting origin” approach is also considered to be more appropriate for predicting future values of the outcome of interest [35]. It is important to stress that the choice of this crossvalidation method is also to adapt to the flow FixedEffects (FE) model in which the values of the flowspecific constants are assumed to be fixed. Should these fixed parameter values be random draws from certain probability distributions, other crossvalidation procedures might be more appropriate, such as Kfold, LeaveOneOut, or Resampling with Replacement.
5 Results
Figure 4 illustrates how the model performances have changed before and after including Google Trends (GT), i.e., from models with No GT (“No.Goog”) to models with Linear GT (“Goog.Lin”) and with Nonlinear GT (“Goog.NL”). It is evident that the effects of the inclusion of GT are not consistent across different model classes. In the Pooled regression (i.e., our baseline models Eq. (11) and Eq. (12)), there are noticeable improvements in models’ performances; the RSquare values tend to increase in both training and testing sets. However, in the models augmented by an autoregressive (AR) component, and by the flow FixedEffects (FE), the improvements become small in the training set, and negligible in the testing set. Another important note in Fig. 4 is that the nonlinear model (“Goog.NL”) tend to increase explanatory power, but reduce the predictive power. This diverging pattern suggests that Eq. (11) is a preferred model, compared to Eq. (12), as it maintains a better balance between variance and bias.
To test whether the differences seen from Fig. 4 are statistically significant, we conduct a simple regression analysis in which the RSquare value is a function of a set of dummies indicating whether a model includes linear GT (“Goog.Lin”) or nonlinear GT (“Goog.NL”). The results (shown in Fig. 5) clearly indicate that the effects of including GT is only significant for the Pooled (PL) regression, but not for those augmented by AR and FE. The test also confirms that in the PL model, the linear functional form of GT is more balanced in terms of variancebias tradoff, whereas the nonlinear one is prone to overfitting.
The model comparisons presented above shed some important light on when GT can and cannot improve the accuracy of migration forecasts. Specifically, they demonstrate that GT can significantly increase the prediction accuracy when the gravity model is kept relatively simple (e.g., when applying Pooled regression). However, when gravity model’s complexity increases (e.g., augmented by AR or FE), GT can neither add explanatory power, nor enhance predictive performances. These patterns imply that the information captured by GT might be, partially, overlapping with what captured by the AR component and the flowspecific constants. As a result, GT becomes nonpredictive in these more complex models.
Nevertheless, it is important to stress that, while the AR and FE models outperform the simple PL in terms of prediction accuracy, they are less valuable from an explanatory perspective. Specifically, many interesting determinants of migration are masked in these models. For example, language, cultural, and geographical proximity, and other timeinvariant factors are vanished into the flow FixedEffects. Moreover, many timevarying drivers, such as migrant networks, migration policies and costs, are blended into the autoregressive component. Should such factors be the primary interest, PL model is preferable. In this regard, the trends in Google searches about EU countries are highly valuable, as it can, to a large extent, capture asylumseekers’ destination preferences, and hence adjust for the biases that may arise from the multilateral resistance in Eq. (9).
6 Conclusion
Google Trends (GT) has been increasingly adopted in migration forecasting, as it may contain information about ex ante intentions to migrate which are valuable for predictive analysis. While some demonstrated that GT can outperform any of the established predictors of migration flows, others argued that it is not always the case. In particular, even if within a single study, the predictive power of GT may vary depending on models’ assumptions. Sensitivity as such highlights the need for recognizing the limits of GT’s ability to predict future migration. Unlike most previous studies attempting to demonstrate the usability of GT for forecasting migration flows, this article emphasizes the importance of model complexity and the contextual influences that can affect the efficacy of GT as a predictive tool. Specifically, we address a critical but less discussed issue: when GT cannot improve the accuracy of migration forecasts.
Using EUROSTAT statistics on firsttime asylum applications and a set of pushpull indicators gathered from various data sources, we train three classes of gravity models to predict refugees’ choices of EU destinations. We then examine how the inclusion of GT affect the performances of these three model classes. The results suggest that the effects of including GT is highly heterogeneous and contingent on the complexity of these models. Specifically, in a simple Pooled (PL) regression model, the inclusion of GT significantly increased the RSquare value in both training and testing sets. However, the corresponding tests show no significant improvements in RSquare when the models are augmented by an autoregressive (AR) component or by flow FixedEffects (FE). These results challenge the view that GT can outperform any of the established predictors of migration flows [1].
In a broader context, our findings in this study provide a counterbalance to the prevailing optimism about the potential of big data for advancing migration research, and hence call for a more comprehensive analysis of the strengths and limitations of big data in predicting migration flows. The empirical framework used here not only contributes to the theoretical understanding of migrants’ destination choices, it also offers a foundation for future research to explore a broader range of digital trace data and to more carefully evaluate their sensitivity to different methodological approaches. It is our hope that this nuanced perspective can spur further innovations in the field, and bring us closer to a comprehensive modeling framework of human migration patterns.
We would like to conclude this paper by stressing a key caveat: the findings presented here are specific to the asylumseeking population from selected countries (AFG, ARM, GEO, IRQ, and SYR). Consequently, it is essential to extend and replicate this analytical approach to different geographic and demographic contexts, as the usability and predictive power of GT may be contextually contingent. For instance, in regions with high Google usage, GT data may offer valuable predictive insights even for complex models. Conversely, in regions with limited Google services, alternatives such as China’s Baidu Index, Microsoft’s Bing keyword research, or Russiabased Yandex might prove to be more effective.
Availability of data and materials
The datasets generated and/or analysed during the current study are available in the https://github.com/andykin888/GTpredictrefugeeflows.git.
Abbreviations
 GT:

Google Trends
 PL:

Pooled
 AR:

Autoregressive
 FE:

Fixedeffects
 EU:

European Union
 IIA:

Independent and Irrelevant Alternative
 GAM:

Generalized Additive Model
 ASR:

Asylum Seeking Rate
 GDP:

Gross Domestic Product
 UCDP:

Uppsala Conflict Data Program
 AFG:

Afghanistan
 ARM:

Armenia
 GEO:

Georgia
 IRQ:

Iraq
 SYR:

Syria
References
Böhme MH, Gröger A, Stöhr T (2020) Searching for a better life: predicting international migration with online search keywords. J Dev Econ 142:102347
Carammia M, Iacus SM, Wilkin T (2022) Forecasting asylumrelated migration flows with machine learning and data at scale. Sci Rep 12(1):1–16
Wanner P (2020) How well can we estimate immigration trends using Google data? Qual Quant 55(4):1181–1202. https://doi.org/10.1007/s1113502001047w
Avramescu A, Wiśniowski A (2021) Nowcasting Romanian migration into the United Kingdom by using Google search engine data. Demogr Res 45:1219–1254
Bosco C, GrubanovBoskovic S, Iacus S, Minora U, Sermi F, Spyratos S (2022) Data Innovation in Demography, Migration and Human Mobility, EUR 30907 EN. Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/027157
Adema J, Guha M, Adema JAH (2022) Following the online trail of Ukrainian refugees through Google Trends. In: CESifo forum, vol 23, pp 62–66. ifo InstitutLeibnizInstitut für Wirtschaftsforschung an der …, München
Spence PR, Lachlan KA, Rainear AM (2016) Social media and crisis research: data collection and directions. Comput Hum Behav 54:667–672
Nakamura N, Suzuki A (2021) Covid19 and the intentions to migrate from developing countries: evidence from online search activities in southeast Asia. J Asian Econ 76:101348
Zagheni E, Weber I (2012) You are where you email: using email data to estimate international migration rates. In: Proceedings of the 4th annual ACM web science conference, pp 348–351
State B, Weber I, Zagheni E (2013) Studying international mobility through IP geolocation. In: Proceedings of the sixth ACM international conference on web search and data mining, pp 265–274
Zagheni E, Weber I et al. (2015) Demographic research with nonrepresentative internet data. Int J Manpow 36(1):13–25
Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geolocated Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41(3):260–271
State B, Rodriguez M, Helbing D, Zagheni E (2014) Migration of professionals to the US. In: Social informatics: 6th international conference, SocInfo 2014, Barcelona, Spain, November 11–13, 2014. Proceedings, Lecture notes in computer science. Springer, Cham, pp 531–543
Varian HR (2014) Big data: new tricks for econometrics. J Econ Perspect 28(2):3–28
Grogger J, Hanson GH (2011) Income maximization and the selection and sorting of international migrants. J Dev Econ 95(1):42–57. https://doi.org/10.1016/j.jdeveco.2010.06.003
Pedersen PJ, Pytlikova M, Smith N (2008) Selection and network effects—migration flows into OECD countries 1990–2000. Eur Econ Rev 52(7):1160–1186. https://doi.org/10.1016/j.euroecorev.2007.12.002
Bertoli S, Moraga JFH (2013) Multilateral resistance to migration. J Dev Econ 102:79–100. https://doi.org/10.1016/j.jdeveco.2012.12.001
Beine M, Bertoli S, Moraga JFH (2015) A practitioners’ guide to gravity models of international migration. World Econ 39(4):496–512. https://doi.org/10.1111/twec.12265
Anderson JE, Van Wincoop E (2003) Gravity with gravitas: a solution to the border puzzle. Am Econ Rev 93(1):170–192
Mcfadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics, vol 105. Academic Press, New York, p 42, Chap. 4
Train KE (2009) Discrete choice methods with simulation. Cambridge University Press, Cambridge
Beyer R, Schewe J, LotzeCampen H (2022) Gravity models do not explain, and cannot predict, international migration dynamics. Humanit Soc Sci Commun 9(1):56. https://doi.org/10.1057/s4159902201067x
Qi H, Bircan T (2023) Modelling and predicting forced migration. PLoS ONE 18(4):0284416
Bijak J, Disney G, Findlay AM, Forster JJ, Smith PW, Wiśniowski A (2019) Assessing time series models for forecasting international migration: lessons from the United Kingdom. J Forecast 38(5):470–487
Napierala J, Hilton J, Forster JJ, Carammia M, Bijak J (2022) Toward an early warning system for monitoring asylumrelated migration flows in Europe. Int Migr Rev 56(1):33–62
Ginsberg J, Mohebbi M, Patel R, Brammer L, Smolinski M, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014
CarrièreSwallow Y, Labbé F (2013) Nowcasting with Google Trends in an emerging market. J Forecast 32(4):289–298. https://doi.org/10.1002/for.1252
Vosen S, Schmidt T (2011) Forecasting private consumption: surveybased indicators vs. Google Trends. J Forecast 30(6):565–578
Da Z, Engelberg J, Gao P (2011) In search of attention. J Finance 66(5):1461–1499
Fantazzini D, Fomichev N (2014) Forecasting the real price of oil using online search data. Int J Comput Econ Econom 4(1–2):4–31
Vlastakis N, Markellos RN (2012) Information demand and stock market volatility. J Bank Finance 36(6):1808–1821
Askitas N, Zimmermann KF et al. (2009) Google econometrics and unemployment forecasting. Appl Econ Q 55(2):107–120
D’Amuri F, Marcucci J (2017) The predictive power of Google searches in forecasting US unemployment. Int J Forecast 33(4):801–816
MirandaGonzález A, Aref S, Theile T, Zagheni E (2020) Scholarly migration within Mexico: analyzing internal migration among researchers using scopus longitudinal bibliometric data. EPJ Data Sci 9(1):34
Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts
Acknowledgements
The article has benefited from valuable comments of the journal editor, and anonymous referees. Financial support by European Unions Horizon 2020 Research and Innovation Program under grant agreement No. 870661 and No. 101004535, and the Swedish Research Council Vetenskapsradet under grant agreement 2022060123 are gratefully noted.
Funding
Open access funding provided by Malmö University. Haodong Qi has received support from the Swedish Research Council Vetenskapsradet (grant agreement 2022060123) and from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement 101004535). Tuba Bircan has received support from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement 870661). The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Conceptualization: HQ and TB; Data collection: HQ and TB; Methodology: HQ; Formal analysis and investigation: HQ; Writing: HQ and TB; Funding acquisition: HQ and TB. Both authors read and approved the final manuscript.
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Qi, H., Bircan, T. Can Google Trends predict asylumseekers’ destination choices?. EPJ Data Sci. 12, 41 (2023). https://doi.org/10.1140/epjds/s13688023004190
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DOI: https://doi.org/10.1140/epjds/s13688023004190