Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg

Fantazzini, Dean and Pushchelenko, Julia and Mironenkov, Alexey and Kurbatskii, Alexey (2021): Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg. Published in: Forecasting , Vol. 4, No. 3 (2021): pp. 774-804.

Abstract

This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors.

• Item Type: MPRA Paper
• Language: English
• Keywords: Migration; Forecasting; Google Trends; VAR; Cointegration; ARIMA; Russia; Time-varying VAR; Multivariate Ridge regression.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; F - International Economics > F2 - International Factor Movements and International Business > F22 - International Migration; J - Labor and Demographic Economics > J1 - Demographic Economics > J11 - Demographic Trends, Macroeconomic Effects, and Forecasts; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O15 - Human Resources ; Human Development ; Income Distribution ; Migration; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R2 - Household Analysis > R23 - Regional Migration ; Regional Labor Markets ; Population ; Neighborhood Characteristics

References

Aaronson, Daniel, Scott Brave, Andrew Butters, Michael Fogarty, Daniel Sacks, and Boyoung Seo. 2021. Forecasting unemployment insurance claims in realtime with Google Trends. International Journal of Forecasting, in press.

Abashin, Sergei 2014. Migration from Central Asia to Russia in the new model of world order. Russian Politics & Law 52(6): 8-23.

Ahrens, Achim, and Arnab Bhattacharjee. 2015. Two-step lasso estimation of the spatial weights matrix. Econometrics 3(1): 128-155.

Alonso, William. 1986. Systemic and log-linear models: from here to there then to now and this to that. Discussion Paper 86—10, Center for Population Studies. Harvard University, Cambridge, Massachusetts.

Algan, Yann, Fabrice Murtin, Elizabeth Beasley, Kazuhito Higa, and Claudia Senik. 2019. Well-being through the lens of the internet. PloS one 14(1): e0209562.

Altissimo, Filippo, Riccardo Cristadoro, Mario Forni, Marco Lippi, and Giovanni Veronese. 2010. New Eurocoin: Tracking eco-nomic growth in real time. The review of economics and statistics 92(4), 1024-1034.

Andrienko, Yuri, and Sergei Guriev. (2004). Determinants of interregional mobility in Russia. Economics of transition 12(1), 1-27.

Aprigliano, Valentina, Claudia Foroni, Massimiliano Marcellino, Gianluigi Mazzi, and Fabrizio Venditti. 2017. A daily indicator of economic growth for the euro area. International Journal of Computational Economics and Econometrics 7(1-2): 43-63.

Aruoba, S. Borağan, Francis X. Diebold, and Chiara Scotti. 2009. Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417-427.

Artola, Concha, and Enrique Martínez-Galán. 2012. Tracking the future on the web: construction of leading indicators using in-ternet searches. Banco de Espana Occasional Paper 1203.

Bedrina, Elena, Yevgeniya Tukhtarova, and Natalia Neklyudova. 2018. Migration from Uzbekistan to Russia: Push-Pull Factor Analysis. In, The International Science and Technology Conference “FarEastСon”, pp. 283-296. Springer.

Bengtsson, Linus, Xin Lu, Anna Thorson, Richard Garfield, and Johan Von Schreeb. 2011. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS medicine 8(8): e1001083.

Bijak, Jakub. 2011. Forecasting international migration in Europe: A Bayesian view. Springer Science & Business Media, vol. 24.

Bijak, Jakub, George Disney, Allan M. Findlay, Jonathan J. Forster, Peter WF Smith, and Arkadiusz Wiśniowski. 2019. Assessing time series models for forecasting international migration: Lessons from the United Kingdom. Journal of Forecasting 38(5): 470-487.

Billari, Francesco, Francesco D'Amuri, and Juri Marcucci. 2016. Forecasting births using Google. In CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València.

Böhme, Marcus H., André Gröger, and Tobias Stöhr. 2020. Searching for a better life: Predicting international migration with online search keywords. Journal of Development Economics 142: 102347.

Borup, Daniel, and Erik Christian Montes Schütte. 2020. In search of a job: Forecasting employment growth using google trends. Journal of Business & Economic Statistics, in press.

Burkhauser, Richard, Hahn, Markus, Hall, Matthew, and Nicole Watson. 2016. Australia Farewell: Predictors of emigration in the 2000s. Population Research and Policy Review, 35(2) 197-215.

Burnham, Kenneth, and Anderson, David. 2004. Model selection and multi-model inference. Second editionю NY: Springer-Verlag 63.

Casas, Isabel, and Ruben Fernandez-Casal. 2018. tvreg: Time-varying coefficients linear regression for single and multiple equations [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=tvReg (R package version 0.5.4)

Casas, Isabel, Eva Ferreira, and Susan Orbe. 2017. Time-varying coefficient estimation in SURE models. Application to portfolio management. Journal of Financial Econometrics nbz010.

Choi, Hyunyoung, and Hal Varian. 2012. Predicting the present with Google Trends. Economic Record 88: 2-9.

Chort, Isabelle. 2014. Mexican migrants to the US: What do unrealized migration intentions tell us about gender inequalities? World development 59: 535-552.

Chudinovskikh, Olga., and Mikhail Denisenko. 2017. Russia: A Migration System with Soviet Roots. Washington, DC: Migration Policy Institute. https://www.migrationpolicy.org/print/15920

Chudinovskikh Olga, and Mikhail Denisenko. 2020. Labour Migration on the Post-Soviet Territory. In, Migration from the Newly Independent States. Societies and Political Orders in Transition. Springer: pp. 55-80.

Clemen, Robert T. 1989. Combining forecasts: A review and annotated bibliography. International journal of forecasting 5(4): 559-583.

Constant, Amelie. and Klaus Zimmermann, 2011. Circular and repeat migration: counts of exits and years away from the host country. Population Research and Policy Review, 30(4): 495-515.

Dahlhaus, Rainer. 1997. Fitting time series models to nonstationary processes. Annals of Statistics 25: 1–37

D’Amuri, Francesco, and Juri Marcucci. 2017. The predictive power of Google searches in forecasting US unemployment. Interna-tional Journal of Forecasting 33(4): 801-816.

Demidova, Anastasia, Olga Druzhinina, Olga Masina, and Alexey Petrov. 2020. Computer research of the controlled models with migration flows. In, Proceedings of the 10th International Conference in Information and Telecommunication Technologies and Mathemat-ical Modeling of High-Tech Systems (ITTMM-2020), volume 2639: 117–129.

Demintseva, Ekaterina, and Vera Peshkova. 2014. Migranty iz Srednei Azii v Moskve. Demoscope Weekly: 597–598. http://www.demoscope.ru/weekly/2014/0597/tema01.php

Demintseva, Ekaterina, and Daniel Kashnitsky. 2016. Contextualizing Migrants’ Strategies of Seeking Medical Care in Russia. International Migration 54(5): 29–42.

Demintseva, Ekaterina. 2017. Labour migrants in post-Soviet Moscow: patterns of settlement. Journal of ethnic and migration studies, 43(15): 2556-2572.

Denisenko Mikhail, Mkrtchyan Nikita, and Olga Chudinovskikh. 2020. Permanent Migration in the Post-Soviet Countries. In, Migration from the Newly Independent States. Societies and Political Orders in Transition. Springer: pp. 23-53.

Docquier, Frédéric, and Hillel Rapoport. 2012. Globalization, brain drain, and development. Journal of economic literature 50(3): 681-730.

Docquier, Frédéric, Giovanni Peri, and Ilse Ruyssen. 2014. The cross-country determinants of potential and actual migration. International Migration Review 48(1): 37-99.

Elliott, Graham. 1998. On the robustness of cointegration methods when regressors almost have unit roots. Econometrica 66: 149-158.

Dustmann, Christian, and Anna Okatenko. 2014. Out-migration, wealth constraints, and the quality of local amenities. Journal of Development Economics 110: 52-63.

Efimova, Elena, and Semen Mikhaltsov. 2017. Road Traffic as a Factor of Regional Development: Case of Saint Petersburg Region, Russian Federation. Procedia Engineering 187: 135-142.

Ette, Andreas, Heß, Barbara, and Lenore Sauer. 2016. Tackling Germany’s demographic skills shortage: permanent settlement intentions of the recent wave of labour migrants from non-European countries. Journal of International Migration and Integration 17(2): 429-448.

Ettredge, Michael, John Gerdes, and Gilbert Karuga. 2005. Using web-based search data to predict macroeconomic statistics. Com-munications of the ACM 48(11): 87-92.

Fantazzini, Dean, and Nikita Fomichev. 2014. Forecasting the real price of oil using online search data. International Journal of Computational Economics and Econometrics 4(1-2): 4-31.

Fantazzini, Dean, and Zhamal Toktamysova (2015). Forecasting German car sales using Google data and multivariate models. International Journal of Production Economics 170: 97-135.

Friedman, Milton. 1937. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association 32(200): 675-701.

Fuchs, Johann, Söhnlein, Doris, and Patrizio Vanella. 2021. Migration Forecasting—Significance and Approaches. Encyclopedia 1(3): 689-709.

Gerber, Theodore, and Jane Zavisca. 2020. Experiences in Russia of Kyrgyz and Ukrainian labor migrants: ethnic hierarchies, geopolitical remittances, and the relevance of migration theory. Post-Soviet Affairs 36(1 ): 61-82.

Golub, Gene H., Michael Heath, and Grace Wahba. 1979. Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter. Technometrics 21(2): 215–23.

Gospodinov, Nikolay, Herrera, Ana María, and Elena Pesavento. 2013. Unit roots, cointegration, and pretesting in VAR models. Advances in Econometrics 32: 81-115.

Hawelka, Bartosz, Sitko, Izabela, Beinat, Euro, Sobolevsky, Stanislav, Kazakopoulos, Pavlos and Carlo Ratti. 2014. Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science 41(3): 260-271.

Hayashi, Fumio. 2000. Econometrics. Princeton: Princeton University Press.

Heleniak, Timothy. 2009. Migration of the Russian Diaspora after the Breakup of the Soviet Union. Journal of International Affairs 57(2): 99–117.

Hoerl, Arthur E., and Robert W. Kennard. 1970. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics 12(1): 55–67

Hsiao, Cheng, and Shui Ki Wan. 2014. Is there an optimal forecast combination? Journal of Econometrics 178: 294-309.

Human Rights Watch. 2009. Are You Happy to Cheat Us? Exploitation of Migrant Construction Workers in Russia. https://www.hrw.org/report/2009/02/10/are-you-happy-cheat-us/exploitation-migrant-construction-workers-russia

Hyndman, Rob J., and George Athanasopoulos. 2018. Forecasting: principles and practice. OTexts.

Hyndman, Rob J., and Yeasmin Khandakar . 2008. Automatic Time Series Forecasting: The forecast Package for R. Journal of Sta-tistical Software 27(3): 1-22.

Iacus, Stefano, and Giuseppe Porro. Subjective Well-being and Social Media. CRC Press, 2021.

Inoue, Atsushi, and Lutz Kilian. 2020. The uniform validity of impulse response inference in autoregressions. Journal of Economet-rics 215(2): 450-472.

Johansen, Soren. 1995. Likelihood-based inference in cointegrated vector autoregressive models. Oxford: Oxford University Press.

Johansen, Soren. 2006. Cointegration: a survey. In: Palgrave handbook of econometrics: Volume 1, Econometric theory. Edited by Mills, T.C. and Patterson, K. Basingstoke (UK): Palgrave MacMillan, pp. 540–577.

Jun, Seung-Pyo, Hyoung Sun Yoo, and San Choi. 2018. Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change 130: 69-87.

Keilman, Nico, and Tomaš Kučera. 1991. The impact of forecasting methodology on the accuracy of national population forecasts: Evidence from the Netherlands and Czechoslovakia. Journal of forecasting 10(4): 371-398.

Keilman, Nico, Dinh Quang Pham, and Arve Hetland. 2001. Norway’s uncertain demographic future. Statistics Norway Social and Economic Studies no. 105. Statistics Norway: Oslo

Kikas, Riivo, Dumas, Marlon and Ando Saabas. 2015. Explaining international migration in the skype network: The role of social network features. In Proceedings of the 1st ACM Workshop on Social Media World Sensors, pp. 17-22.

Korovkin, Andrei., Dolgova, Irina, and Ekaterina Edinak. 2013. Analysis of the relationship between internal migration and socio-economic differentiation of regions (on the example of the central Federal District). Scientific works: Institute for Economic Forecast-ing, Russian Academy of Sciences, pp 71-94.

Kruskal, William H., and W. Allen Wallis. 1952. Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260): 583-62.

Kuan, Chung-Ming, and Kurt Hornik. 1995. The generalized fluctuation test: A unifying view, Econometric Reviews, 14, 135–161.

Kuhlenkasper, Torben, and Max Friedrich Steinhardt. 2017. Who leaves and when? Selective outmigration of immigrants from Germany. Economic Systems 41(4): 610-621.

Lam, Clifford, and Pedro CL Souza. 2020. Estimation and selection of spatial weight matrix in a spatial lag model. Journal of Business & Economic Statistics 38(3): 693-710.

Lee, Namgil, Hyemi Choi, and Sung-Ho Kim. 2016. Bayes Shrinkage Estimation for High-Dimensional VAR Models with Scale Mixture of Normal Distributions for Noise. Computationl Statistics & Data Analysis 101: 250–76.

Lütkepohl, Helmut. 2005. New introduction to multiple time series analysis. Berlin: Springer Science and Business Media.

Maddala, Gangadharrao S., and In-Moo Kim. 1998. Unit Roots, cointegration, and structural change. Cambridge University Press.

Maravall, Agustin. 2011. Seasonality Tests and Automatic Model Identification in TRAMO-SEATS. Bank of Spain.

Mayda, Anna Maria. 2010. International migration: A panel data analysis of the determinants of bilateral flows. Journal of Popula-tion Economics 23(4):1249-1274.

McLaren, Nick, and Rachana Shanbhogue. 2011. Using internet search data as economic indicators. Bank of England Quarterly Bulletin, (2011), Q2.

Moise, Izabela, Gaere, Edward , Merz, Ruben, Koch, Stefan and Evangelos Pournaras. 2016. Tracking language mobility in the Twitter landscape. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 663-670.

Nikolopoulos, Konstantinos, Christos Tsinopoulos, and Chrysovalantis Vasilakis. 2021a. Operational research in the time of COVID-19: The ‘science for better’or worse in the absence of hard data. Journal of the Operational Research Society, 1-2.

Nikolopoulos, Konstantinos, Sushil Punia, Andreas Schäfers, Christos Tsinopoulos, and Chrysovalantis Vasilakis. 2021b. Fore-casting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. Euro-pean journal of operational research 290(1): 99-115.

Ni, Shawn, and Dongchu Sun. 2005. Bayesian Estimates for Vector Autoregressive Models, Journal of Business and Economic Statis-tics 23(1): 105–117.

Ollech, Daniel, and Karsten Webel. 2020. A random forest-based approach to identifying the most informative seasonality tests. Bundesbank Discussion Paper No. 55/2020.

Opgen-Rhein, Rainer, and Korbinian Strimmer. 2007. Learning Causal Networks from Systems Biology Time Course Data: An Effective Model Selection Procedure for the Vector Autoregressive Process. BMC Bioinformatics 8(2): 1-7.

Ortega, Francesco, and Giovanni Peri. 2013. The effect of income and immigration policies on international migration. Migration Studies 1(1): 47-74.

Pavlovskij, Egor. 2017. Arima Models in the Short-Term Forecasting of Internal Migration in Russia. Voprosy Statistiki, 1(10): 53-63.

Qin, Yu, and Hongjia Zhu. 2018. Run away? Air pollution and emigration interests in China. Journal of Population Economics 31(1): 235-266.

Ravenstein, Ernest George. 1885. The laws of migration. Journal of the statistical society of London 48(2): 167-235.

Reeves, Madeleine. 2013. Clean Fake: Authenticating Documents and Persons in Migrant Moscow. American Ethnologist 40(3):508–524.

Reeves, Madeleine. 2015. Living from the Nerves: Deportability, Indeterminacy, and the ‘feel of Law’ in Migrant Moscow. Social Analysis 59(4): 119–136.

Ryazantsev, Sergey. 2016. Labour Migration from Central Asia to Russia in the Context of the Economic Crisis. Russia in Global Affairs, August 31. http://eng.globalaffairs.ru/valday/Labour-Migration-from-Central-Asia-to-Russia-in-the-Context-of-the-Economic-Crisis-18334

Salini, Silvia, Siletti Elena, and Porro Giuseppe. 2020. Controlling for Selection Bias in Social Media Indicators through Official Statistics: a Proposal. Journal of Official Statistics 36(2): 315-338.

Schenk, Caress. 2018. Why Control Immigration? Strategic Uses of Migration Management in Russia. Toronto: University of Toronto Press.

Sîrbu, Alina, Gennady Andrienko, Natalia Andrienko, Chiara Boldrini, Marco Conti, Fosca Giannotti, Riccardo Guidotti et al. 2021. Human migration: the big data perspective. International Journal of Data Science and Analytics 11(4): 341-360.

Stock, James H., and Mark W. Watson. 1993. A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica 61(4): 783–820.

Sun, Dongchu, and Shawn Ni. 2004. Bayesian Analysis of Vector-Autoregressive Models with Noninformative Priors. Journal of Statistical Planning and Inference 121(2): 291–309.

Tamgno, James K., Roger M. Faye, and Claude Lishou. 2013. Verbal autopsies, mobile data collection for monitoring and warning causes of deaths. In 2013 - 15th International Conference on Advanced Communications Technology (ICACT), pp. 495-501. IEEE.

Timmermann, Allan. 2006. Forecast combinations. Handbook of economic forecasting 1: 135-196.

Timoshkin, Dmitry. 2020. Construction of Horizontal Networks on “Migrant” Russian-Language Digital Platforms, Journal of Si-berian Federal University. Humanities & Social Sciences, 13(5): 688-699.

United Nations. 2017. International Migration Report 2017. New York: United Nations Population Division

Varaksin, Sergei, and Natal'ya Varaksina. 2017. Application of fuzzy linear regression for modeling the migration process in Russia. In, Economic and Social Development: Book of Proceedings: 332-340.

Vakulenko, Elena, Nikita Mkrtchyan, and Kirill Furmanov. 2011. Modeling registered migration flows between regions of the Russian Federation. Applied Econometrics 21(1): 35-55.

Vakulenko, Elena, and Nikita Mkrtchyan. 2020. Factors of Interregional Migration in Russia Disaggregated by Age. Applied Spatial Analysis and Policy 13(3): 609-630.

Zagheni, Emilio, Venkata Rama Kiran Garimella, Ingmar Weber, and Bogdan State. 2014. Inferring international and internal migration patterns from twitter data. In Proceedings of the 23rd International Conference on World Wide Web, pp. 439-444.

Zeileis, Achim. 2006. Implementing a class of structural change tests: An econometric computing approach. Computational Statis-tics & Data Analysis 50(11):2987–3008.

Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. 2005. Monitoring structural change in dynamic econometric models. Journal of Applied Econometrics 20(1): 99–121.

Welch, Bernard Lewis. 1951. On the Comparison of Several Mean Values: An Alternative Approach. Biometrika 38(3/4): 330-336.

Willekens, Frans. 1980. Entropy, multiproportional adjustment and the analysis of contingency tables. Systemi Urbani 2(3):171-201.

Wilson, Alan. (1970). Entropy in urban and regional modelling. Volume 1. London: Routledge.