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Human-mobility networks, country income, and labor productivity

Published online by Cambridge University Press:  11 September 2015

GIORGIO FAGIOLO  and
GIANLUCA SANTONI
GIORGIO FAGIOLO
Affiliation:
Istituto di Economia, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33. 56127 Pisa, Italy (e-mail: giorgio.fagiolo@sssup.it)
GIANLUCA SANTONI
Affiliation:
CEPII, 113, rue de Grenelle. 75007 Paris, France (e-mail: gianluca.santoni@cepii.fr)
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Abstract

This paper asks whether the level of integration of world countries in the international network of temporary human mobility can explain differences in their per-capita income and labor productivity. We disentangle the role played by global country centrality in the network from traditional openness measures, which only account for local, nearest-neighbor linkages through which ideas and knowledge can flow. Using 1995-2010 data, we show that global country centrality in the international temporary human-mobility network enhances both per-capita income and labor productivity. Our results hold cross-sectionally, as well as in a dynamic-panel estimation, and take into account potential endogeneity issues. Our findings imply that how close a country is to the theoretical technological frontier, depends not only on how much she is open to temporary human mobility, but mostly on whether she is embedded in a web of relationships connecting her with other influential partners in the network. Our exercises also suggest that most of the gain in income and productivity can be attained if country centrality in the network comes mostly from influential partners that lie not too far away from, but neither too close to them in the network.

Keywords

international temporary human-mobility networkinternational technological diffusionPer-capita incomeproductivityopenness to mobility and tradecentralityO33O47C21C26F22
Type
Research Article
Information
Network Science , Volume 3 , Issue 3 , September 2015 , pp. 377 - 407
Copyright
Copyright © Cambridge University Press 2015 

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References

Abeysinghe, T., & Forbes, K. (2005). Trade linkages and output-multiplier effects: A structural VAR approach with a focus on Asia. Review of International Economics, 13 (2), 356–375.Google Scholar
Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91 (5), 1369–1401.CrossRefGoogle Scholar
Alcalá, F., & Ciccone, A. (2004). Trade and productivity. The Quarterly Journal of Economics, 119 (2), 612–645.Google Scholar
Alesina, A., Harnoss, J., & Rapoport, H. (2013, Jan.). Birthplace diversity and economic prosperity. NBER Working Papers 18699. National Bureau of Economic Research, Inc. CrossRefGoogle Scholar
Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93 (1), 170–192.Google Scholar
Andersen, T., & Dalgaard, C.-J. (2011). Flows of people, flows of ideas, and the inequality of nations. Journal of Economic Growth, 16 (1), 1–32.Google Scholar
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58 (2), 277–97.Google Scholar
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68 (1), 29–51.Google Scholar
Arrow, K. J. (1969). Classificatory notes on the production and transmission of technological knowledge. American Economic Review, 59 (2), 29–35.Google Scholar
Balcan, D., Colizza, V., Goncalves, B., Hu, H., Ramasco, J. J., & Vespignani, A. (2009). Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences, 106 (21484).Google Scholar
Ballester, C., Calvo-Armengol, A., & Zenou, Y. (2006). Who's who in networks. Wanted: The key player. Econometrica, 74 (5), 1403–1417.Google Scholar
Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101 (11), 3747–52.CrossRefGoogle ScholarPubMed
Beine, M., Docquier, F., & Ozden, C. (2011). Diasporas. Journal of Development Economics, 95 (1), 30–41.Google Scholar
Bertoli, S., & Fernandez-Huertas Moraga, J. (2013). Multilateral resistance to migration. Journal of Development Economics, 102 (C), 79–100.Google Scholar
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87 (1), 115–143.Google Scholar
Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92 (5), 1170–1182.Google Scholar
Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23 (3), 191–201.Google Scholar
Bond, S. (2002, Apr.). Dynamic panel data models: A guide to microdata methods and practice. CEMMAP Working Papers. Centre for Microdata Methods and Practice, Institute for Fiscal Studies.Google Scholar
Brahmbhatt, J., & Menezes, R. (2013). On the relation between tourism and trade: A network experiment, network science workshop (nsw), IEEE 2nd.Google Scholar
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Proceedings of the 7th International Conference on World Wide Web. WWW7. Amsterdam, The Netherlands: Elsevier Science Publishers, pp. 107–117.Google Scholar
Brockmann, D., Hufnagel, L., & Geisel, T. (2006). The scaling laws of human travel. Nature, 439(7075), 462–465.Google Scholar
Brockmann, D., & Theis, F. (2008). Money circulation, trackable items, and the emergence of universal human mobility patterns. IEEE Pervasive Computing, 7 (4), 28–35.Google Scholar
Chinazzi, M., Fagiolo, G., Reyes, J. A., & Schiavo, S. (2013). Post-mortem examination of the International Financial Network. Journal of Economic Dynamics and Control, 37 (8), 1692–1713.Google Scholar
Comin, D., Hobijn, B., & Rovito, E.. (2008). Technology usage lags. Journal of Economic Growth, 13 (4), 237–256.CrossRefGoogle Scholar
Conley, T. G., & Ligon, E. (2002). Economic distance and cross-country spillovers. Journal of Economic Growth, 7 (2), 157–87.Google Scholar
Csaji, B., Browet, A., Traag, V. A., Delvenne, J.-C., Huens, E., Dooren, P. V, . . . Blondel, V. D. (2013). Exploring the mobility of mobile phone users. Physica A, 392 (6), 1459–1473.Google Scholar
Davis, K. F., D'Odorico, P., Laio, F., & Ridolfi, L. (2013). Global spatio-temporal patterns in human migration: A complex network perspective. PloS One, 8 (1), e53723, http://dx.doi.org/10.1371%2Fjournal.pone.0053723.CrossRefGoogle ScholarPubMed
De Domenico, M., Solè-Ribalta, A., Cozzo, E., Kivela, M., Moreno, Y., Porter, M. A., . . . Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3, 041022.Google Scholar
De Montis, A., Barthélemy, M., Chessa, A., & Vespignani, A. (2007). The structure of inter-urban traffic: A weighted network analysis. Environment and Planning B: Planning and Design, 34 (5), 905–924.Google Scholar
Dees, S., & Saint-Guilhem, A. (2011). The role of the united states in the global economy and its evolution over time. Empirical Economics, 41 (3), 573–591.Google Scholar
Duernecker, G., Meyer, M., & Vega-Redondo, F. (2012). Being close to grow faster: A network-based empirical analysis of economic globalization. Economics Working Papers ECO2012/05. European University Institute.Google Scholar
Duernecker, G., & Vega-Redondo, F. (2012). Building bridges or enhancing cohesion? Social networks in the process of globalization. Working Paper ECON 12-18. University of Mannheim.Google Scholar
Fagiolo, G. (2006). Directed or undirected? A new index to check for directionality of relations in socio-economic networks. Economics Bulletin, 3 (34), 1–12.Google Scholar
Fagiolo, G., & Mastrorillo, M. (2013a). International migration network: Topology and modeling. Physical Review E, 88, 012812.Google Scholar
Fagiolo, G., & Mastrorillo, M. (2013b). Migration and trade: A complex-network approach. Plos One, 9 (5), e97331.Google Scholar
Frankel, J., & Rose, A. (2002). An estimate of the effect of common currencies on trade and income. The Quarterly Journal of Economics, 117 (2), 437–466.Google Scholar
Frankel, J. A., & Romer, D. H. (1999). Does trade cause growth? American Economic Review, 89 (3), 379–399.Google Scholar
Gallup, J. L., Mellinger, A. D., & Sachs, J. D. (1999). Geography datasets. Center for International Development at Harvard University.Google Scholar
Gambardella, A., Mariani, M., & Torrisi, S. (2003). How provincial is your region? Effects on labour productivity and employment in europe. LEM Papers Series 2003/06. Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.Google Scholar
Gaston, N., & Nelson, D. R. (2013). Bridging trade theory and labour econometrics: The effects of international migration. Journal of Economic Surveys, 27 (1), 98–139.Google Scholar
Gaulier, G., & Zignago, S. (2010, Oct.). Baci: International trade database at the product-level. The 1994-2007 version. Working Papers 2010-23. CEPII research center.Google Scholar
Granovetter, M. S. (1973). The strength of weak ties. The American Journal of Sociology, 78 (6), 1360–1380.Google Scholar
Hale, G. (2012). Bank relationships, business cycles, and financial crises. Journal of International Economics, 88 (2), 312–325.Google Scholar
Halu, A., Mondragòn, R. J., Panzarasa, P., & Bianconi, G. (2013). Multiplex PageRank. PloS One, 8 (10), e78293.Google Scholar
Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2013). Geo-located twitter as the proxy for global mobility patterns. cs.SI 1311.0680. arXiv.org.Google Scholar
Hofleitner, A., Chiraphadhanakul, Ta V., & State, B. (2013). Coordinated migration. Facebook Data Science Team.Google Scholar
Hua, Y., & Zhu, D. (2009). Empirical analysis of the worldwide maritime transportation network. Physica A: Statistical Mechanics and its Applications, 388 (10), 2061–2071.Google Scholar
Jackson, M. O., & Zenou, Y. (2014). Games on networks. In Young, P. & Zamir, S. (Eds.), Handbook of game theory with economic applications, volume 4 (pp. 95–163) Elsevier Science.Google Scholar
Kali, R., Méndez, F., & Reyes, J. (2007). Trade structure and economic growth. Journal of International Trade & Economic Development, 16 (2), 245–269.CrossRefGoogle Scholar
Kali, R., & Reyes, J. (2010). Financial contagion on the international trade network. Economic Inquiry, 48 (4), 1072–1101.Google Scholar
Kaluza, P., Kolzsch, A., Gastner, M. T., & Blasius, B. (2010). The complex network of global cargo ship movements. Interface, 7 (48), 1093–1103.Google Scholar
Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18 (1), 39–43.Google Scholar
Keller, W. (2004). International technology diffusion. Journal of Economic Literature, 42 (3), 752–782.Google Scholar
Keum, K. (2010). Tourism flows and trade theory: A panel data analysis with the gravity model. The Annals of Regional Science, 44 (3), 541–557.CrossRefGoogle Scholar
Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46 (5), 604–632.Google Scholar
Lee, K., Yang, J., Kim, G., Lee, J., Goh, K., & Kim, I. (2011). Impact of the topology of global macroeconomic network on the spreading of economic crises. PloS One, 6 (3), e18443.Google Scholar
Lindner, I., & Strulik, H. (2014). From tradition to modernity: Economic growth in a small world. Journal of Development Economics, 109 (C), 17–29.CrossRefGoogle Scholar
Lu, X., Wetter, E., Bharti, N., Tatem, A. J., & Bengtsson, L. (2013). Approaching the limit of predictability in human mobility. Scientific Reports, 3 (Oct.). doi:10.1038/srep02923.Google Scholar
Lucas, R. E. B. (ed). (2014). International handbook on migration and economic development. Edward Elgar.Google Scholar
Lucas, R. E. Jr., (1993). Making a miracle. Econometrica, 61 (2), 251–72.CrossRefGoogle Scholar
Marshall, M., & Jaggers, K. (1999). “Polity iv project: Political regime characteristics and transitions, 1800–2007”. Center for international development and conflict management, University of Maryland.Google Scholar
Mayer, T., & Zignago, S. (2011, Dec.). Notes on CEPII's distances measures: The GeoDist database. Working Papers. CEPII research center.Google Scholar
Miguéns, J. I. L., & Mendes, J. F. F. (2008). Travel and tourism: Into a complex network. Physica A: Statistical Mechanics and its Applications, 387 (12), 2963–2971.Google Scholar
Minoiu, C., Kang, C., Subrahmanian, V. S., & Berea, A. (2013). Does financial connectedness predict crises? IMF Working Paper 13/267. International Monetary Fund.Google Scholar
Moreno, R., & Trehan, B. (1997). Location and the growth of nations. Journal of Economic Growth, 2 (4), 399–418.Google Scholar
Nelson, R. R., & Phelps, E. S. (1966). Investment in humans, technological diffusion and economic growth. American Economic Review, 56 (2), 67–75.Google Scholar
Ortega, F., & Peri, G. (2013, Apr.). Migration, trade and income. IZA Discussion Papers 7325. Institute for the Study of Labor (IZA).Google Scholar
Reyes, J., Schiavo, S., & Fagiolo, G. (2010). Using complex networks analysis to assess the evolution of international economic integration: The cases of east asia and latin america. The Journal of International Trade and Economic Development, 19 (2), 215–239.Google Scholar
Rodriguez, F., & Rodrik, D. (2001). Trade policy and economic growth: A skeptic's guide to the cross-national evidence. Nber macroeconomics annual 2000, volume 15 (pp. 261–338). NBER Chapters. National Bureau of Economic Research, Inc. Google Scholar
Roodman, D. (2009a). How to do xtabond2: An introduction to difference and system gmm in stata. Stata Journal, 9 (1), 86–136.Google Scholar
Roodman, D. (2009b). A note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71 (1), 135–158.Google Scholar
Santos Silva, J., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88 (4), 641–658.Google Scholar
Simini, F., González, M. C., Maritan, A., & Barabasi, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484, 96–100. doi:10.1038/nature10856.Google Scholar
Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70 (1), 65–94.Google Scholar
Stock, J., & Yogo, M. (2005). Testing for weak instruments in linear iv regression (pp. 80–108). New York: Cambridge University Press.Google Scholar
Tranos, E., Gheasi, M., & Nijkamp, P. (2012). International migration: A global complex network. Tinbergen Institute Discussion Papers 12-123/VIII. Tinbergen Institute.Google Scholar
Wang, X.-W., Han, X.-P., & Wang, B.-H. (2014). Correlations and scaling laws in human mobility. PloS One, 9 (1), e84954.Google Scholar
Ward, M. D., Ahlquist, J. S., & Rozenas, A. (2013). Gravity's rainbow: A dynamic latent space model for the world trade network. Network Science, 1 (1), 95–118.Google Scholar
Woolley-Meza, O., Thiemann, C., Grady, D., Lee, J. J., Seebens, H., Blasius, B., & Brockmann, D. (2011). Complexity in human transportation networks: A comparative analysis of worldwide air transportation and global cargo-ship movements. The European Physical Journal B, 84 (4), 589–600.Google Scholar
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