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The Local Structure of Globalization

The Network Dynamics of Foreign Direct Investments in the International Electricity Industry

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Abstract

We study the evolution of the network of foreign direct investment (FDI) in the international electricity industry during the period 1994–2003. We assume that the ties in the network of investment relations between countries are created and deleted in continuous time, according to a conditional Gibbs distribution. This assumption allows us to take simultaneously into account the aggregate predictions of the well-established gravity model of international trade as well as local dependencies between network ties connecting the countries in our sample. According to the modified version of the gravity model that we specify, the probability of observing an investment tie between two countries depends on the mass of the economies involved, their physical distance, and the tendency of the network to self-organize into local configurations of network ties. While the limiting distribution of the data generating process is an exponential random graph model, we do not assume the system to be in equilibrium. We find evidence of the effects of the standard gravity model of international trade on evolution of the global FDI network. However, we also provide evidence of significant dyadic and extra-dyadic dependencies between investment ties that are typically ignored in available research. We show that local dependencies between national electricity industries are sufficient for explaining global properties of the network of foreign direct investments. We also show, however, that network dependencies vary significantly over time giving rise to a time-heterogeneous localized process of network evolution.

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Notes

  1. The programs PNet [100] and RSiena [73] may be used for tie-based processes. Currently both programs have limited functionality and the former only uses the method of moments. Estimation in the current paper is carried out in Matlab.

References

  1. Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)

    Article  ADS  MATH  Google Scholar 

  2. Albert, M., Cederman, L.-E., Wendt, A.: New Systems Theories of World Politics. Palgrave Macmillan, London (2010)

    Google Scholar 

  3. Aldous, D.: Minimization algorithms and random walk on the d-cube. Ann. Probab. 11, 403–413 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  4. Anderson, J.E.: The gravity model. Ann. Rev. Econ. 3, 133–160 (2011)

    Article  Google Scholar 

  5. Anderson, J.E., van Wincoop, E.: Trade costs. J. Econ. Lit. 42, 691–751 (2004)

    Article  Google Scholar 

  6. Antal, T., Krapivsky, P.L., Redner, S.: Social balance on networks: the dynamics of friendship and enmity. Physica D 224, 130 (2006)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  7. Bacon, R.W., Besant-Jones, G.J.: Lobal electric power reform, privatization and liberalization of the electric power industry in developing countries. Annu. Rev. Energy Environ. 26, 331–359 (2001)

    Article  Google Scholar 

  8. Baker, M., Foley, C.F., Wurgler, J.: Multinationals as arbitrageurs: the effect of stock market valuations on foreign direct investment. Rev. Financ. Stud. 22(1), 337–369 (2001)

    Article  Google Scholar 

  9. Baltagi, B.H., Egger, P., Pfaffermayr, M.: Estimating regional trade agreement effects on FDI in an interdependent world. J. Econom. 145, 194–208 (2008)

    Article  MathSciNet  Google Scholar 

  10. Bandelj, N.: Embedded economies: social relations as determinants of foreign direct investment in Central and Eastern Europe. Soc. Forces 81, 411 (2002)

    Article  Google Scholar 

  11. Bergstrand, J.H., Egger, P.: A knowledge- and physical-capital model of international trade flows, foreign direct investment and multinational enterprises. J. Int. Econ. 73, 278–308 (2007)

    Article  Google Scholar 

  12. Besag, J.E.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc. B 36, 192–236 (1974)

    MathSciNet  MATH  Google Scholar 

  13. Bhattacharya, K., Mukherjee, G., Saramaki, J., Kaski, K., Manna, S.: The international trade network: weighted network analysis and modeling. J. Stat. Mech. Theory Exp. 2, P02002 (2008)

    Article  Google Scholar 

  14. Blonigen, B.A., Davies, R.B., Waddell, G.R., Naughton, H.T.: FDI in space: spatial autoregressive relationships in foreign direct investment. Eur. Econ. Rev. 51, 1303–1325 (2007)

    Article  Google Scholar 

  15. Blume, L.E.: The statistical mechanics of strategic interaction. Games Econ. Behav. 4, 387–424 (1993)

    Article  MathSciNet  Google Scholar 

  16. Burda, Z., Jurkiewicz, J., Krzywicki, A.: Network transitivity and matrix models. Phys. Rev. 69, 026106 (2004)

    ADS  Google Scholar 

  17. Butts, C.T.: A relational event framework for social action. Sociol. Method. 381, 155–200 (2008)

    Google Scholar 

  18. Butts, C.T.: Space and Structure: Methods and Models for Large-Scale Inter-personal Networks. Springer, Berlin (2010, expected)

    Google Scholar 

  19. Caimo, A., Friel, N.: Bayesian inference for exponential random graph models. Soc. Netw. 33, 41–55 (2011)

    Article  Google Scholar 

  20. Chatterjee, S., Diaconis, P.: Estimating and understanding exponential random graph models. arXiv:1102.2650v3 (2011)

  21. Corander, J., Dahmström, K., Dahmström, P.: Maximum likelihood estimation for Markov graphs. Research report, 1998:8, Stockholm University, Department of Statistics (1998)

  22. Corander, J., Dahmström, K., Dahmström, P.: Maximum likelihood estimation for exponential random graph model. In: Hagberg, J. (ed.) Contributions to Social Network Analysis, Information Theory, and Other Topics in Statistics; A Festschrift in honour of Ove Frank, pp. 1–17. Department of Statistics, University of Stockholm, Stockholm (2002)

    Google Scholar 

  23. Crouch, B., Wasserman, S., Trachtenberg, F.: Markov Chain Monte Carlo maximum likelihood estimation for p social network models. Paper presented at the Sunbelt XVIII and Fifth European International Social Networks Conference, Sitges (Spain), May 28–31, 1998

  24. Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., Baum, S.: Networks and geography: modelling community network structures as the outcome of both spatial and network processes. Soc. Netw. 34, 6–17 (2012)

    Article  Google Scholar 

  25. Disdier, A., Head, K.: The puzzling persistence of the distance effect on bilateral trade. Rev. Econ. Stat. 90, 37–48 (2008)

    Article  Google Scholar 

  26. Dueñas, M., Fagiolo, G.: Modeling the international-trade network: a gravity approach. arXiv:1112.2867v1 [q-fin.GN] (2011)

  27. Durlauf, S.: Statistical mechanics approaches to socioeconomic behavior. In: Arthur, B., Durlauf, S., Lane, D. (eds.) The Economy as an Evolving Complex System II. Sante Fe Institute, Sante Fe (1997)

    Google Scholar 

  28. Egger, P., Mario, L.M.: Interdependent preferential trade agreement memberships: an empirical analysis (incomplete) (2006)

  29. Erdős, P., Rényi, A.: Publ. Math. Inst. Hung. Acad. Sci. 5, 17 (1960)

    Google Scholar 

  30. Fagiolo, G., Schiavo, S., Reyes, J.: World-trade web: topological properties, dynamics, and evolution. Phys. Rev. E 79, 036115 (2009)

    Article  MathSciNet  ADS  Google Scholar 

  31. Fagiolo, G., Schiavo, S., Reyes, J.: The evolution of the world trade web: a weighted-network approach. J. Evol. Econ. 20, 479–514 (2010)

    Article  Google Scholar 

  32. Feld, S.L.: The focused organization of social ties. Am. J. Sociol. 86, 1015–1035 (1981)

    Article  Google Scholar 

  33. Fidrmuc, J.: Gravity models in integrated panels. Empir. Econ. 37, 435–446 (2009)

    Article  Google Scholar 

  34. Fienberg, S.E., Wasserman, S.: Categorical data analysis of single sociometric relations. In: Leinhardt, S. (ed.) Sociological Methodology, pp. 156–192. Jossey-Bass, San Francisco (1981)

    Google Scholar 

  35. Fisher, S.: Globalization and its challenges. Am. Econ. Rev. 93, 1–30 (2003)

    Article  Google Scholar 

  36. Frank, O., Strauss, D.: Markov graphs. J. Am. Stat. Assoc. 81, 832–842 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  37. Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo in Practice. Chapman and Hall, London (1996)

    MATH  Google Scholar 

  38. Handcock, M.S.: Statistical models for social networks: degeneracy and inference. In: Breiger, R., Carley, K.M., Pattison, P. (eds.) Dynamic Social Network Modeling and Analysis, pp. 229–240. National Academies Press, Washington (2002)

    Google Scholar 

  39. Handcock, M., Jones, J.: An assessment of preferential attachment as a mechanism for human sexual network formation. Proc. R. Soc. B 270, 1123–1128 (2003)

    Article  Google Scholar 

  40. Hanneke, S., Xing, E.P.: Discrete temporal models of social networks. In: Airoldi, E., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) Statistical Network Analysis: Models, Issues and New Directions (ICML 2006). Lecture Notes in Computer Science, vol. 4503, pp. 115–125. Springer, Berlin (2007)

    Chapter  Google Scholar 

  41. Helpman, E., Melitz, M.J., Rubinstein, Y.: Estimating trade flows: trading partners and trading volumes. Q. J. Econ. 123, 441–487 (2008)

    Article  MATH  Google Scholar 

  42. Hintze, J.L., Nelson, R.D.: Violin plots: a box plot-density trace synergism. Am. Stat. 52, 181–184 (1998)

    Google Scholar 

  43. Hoff, P.: Multiplicative latent factor models for description and prediction of social networks. Comput. Math. Organ. Theory 15, 261–272 (2009)

    Article  Google Scholar 

  44. Holland, P.W., Leinhardt, S.: Local structure in social networks. In: Heise, D. (ed.) Sociological Methodology. Jossey-Bass, San Francisco (1975)

    Google Scholar 

  45. Holland, P.W., Leinhardt, S.: A dynamic model for social networks. J. Math. Sociol. 5, 5–20 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  46. Holland, P.W., Leinhardt, S.: An exponential family of probability distributions for directed graphs (with discussion). J. Am. Stat. Assoc. 76, 33–65 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  47. Häggström, O., Jonasson, J.: Phase transition in the random triangle model. J. Appl. Probab. 36, 1101–1115 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  48. Igarashi, T.: Longitudinal changes in face-to-face and text message-mediated friendship networks. In: Lusher, D., Koskinen, J.H., Robins, G.E. (eds.) Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, pp. 248–259. Cambridge University Press, New York (2013)

    Google Scholar 

  49. Indlekofer, N.: Visualizing the fit of actor-based models. Paper presented at the 5th UK Social Network Conference, 3–5 July, 2009. University of Greenwich, London (2009)

    Google Scholar 

  50. Jansen, W.J., Stockman, C.J.: Foreign direct investment and international business cycle co-movement. European Central Bank. Working paper Series. WP N 401 (2004)

  51. Jonasson, J.: The random triangle model. J. Appl. Probab. 36, 852–867 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  52. Kim, S., Shin, E.-H.: A longitudinal analysis of globalization and regionalization in international trade: a network approach. Soc. Forces 81, 445–470 (2002)

    Article  Google Scholar 

  53. Koskinen, J.H., Snijders, T.A.B.: Bayesian inference for dynamic social network data. J. Stat. Plan. Inference 137, 3930–3938 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  54. Koskinen, J.H., Robins, G.L., Pattison, P.E.: Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation. Stat. Methodol. 7, 366–384 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  55. Krackhardt, D., Handcock, M.S.: Heider vs Simmel: emergent features in dynamic structures. In: Airoldi, E., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) Statistical Network Analysis: Models, Issues and New Directions (ICML 2006). Lecture Notes in Computer Science, vol. 4503, pp. 14–27. Springer, Berlin (2007)

    Chapter  Google Scholar 

  56. Krugman, P.: Fire-sale FDI. In: Edwards, S. (ed.) Capital Flows and the Emerging Economies: Theory, Evidence, and Controversies, pp. 43–59. University of Chicago Press, Chicago (2000)

  57. Leamer, E., Levinsohn, J.: International trade theory: the evidence. In: Grossman, G.M., Rogoff, K. (eds.) The Handbook of International Economics, vol. III. North-Holland, Amsterdam (1995)

    Google Scholar 

  58. Lospinoso, J.A.: Statistical models for social network dynamics. Unpublished doctoral thesis. Department of Statistics, University of Oxford (2012)

  59. Lospinoso, J.A., Schweinberger, M., Snijders, T.A.B., Ripley, R.M.: Assessing and accounting for time heterogeneity in stochastic actor oriented models. Adv. Data Anal. Comput. 5, 147–176 (2011)

    Article  MathSciNet  Google Scholar 

  60. Lusher, D., Ackland, R.: A relational hyperlink analysis of an online social movement. J. Soc. Struct. 12(5) (2011)

  61. Lusher, D., Koskinen, J., Robins, G.: Exponential Random Graph Models for Social Networks: Theory, Methods and Applications. Cambridge University Press, New York (2013)

    Google Scholar 

  62. Macy, M.W., Willer, R.: Form factors to actors. Annu. Rev. Sociol. 38, 143–166 (2002)

    Article  Google Scholar 

  63. Mayer, T., Zignago, S.: Notes on CEPII’s distances measures. MPRA Paper 31243 (2006)

  64. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)

    Article  Google Scholar 

  65. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Sci. Signal. 298(5594), 824 (2002)

    Google Scholar 

  66. Park, J., Newman, M.E.J.: General methods of statistical physics—statistical mechanics of networks. Phys. Rev. C 70, 66117 (2004)

    MathSciNet  ADS  Google Scholar 

  67. Park, J., Newman, M.E.J.: Solution of the two-star model of a network. Phys. Rev. E 70, 066146 (2004)

    Article  MathSciNet  ADS  Google Scholar 

  68. Park, J., Newman, M.E.J.: Solution for the properties of a clustered network. Phys. Rev. E 72, 026136 (2005)

    Article  ADS  Google Scholar 

  69. Pattison, P., Robins, G.L.: Neighbourhood-based models for social networks. Sociol. Method. 32, 301–337 (2002)

    Article  Google Scholar 

  70. Pattison, P., Snijders, T.A.B.: Modelling social networks: next steps. In: Lusher, D., Koskinen, J.H., Robins, G.E. (eds.) Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, pp. 287–301. Cambridge University Press, New York (2013)

    Google Scholar 

  71. Power Deals: Annual Review. Price Waterhouse Coopers (2005)

  72. Preciado, P., Snijders, T.A.B., Burk, W.J., Stattin, H., Kerr, M.: Does proximity matter? Distance dependence of adolescent friendships. Soc. Netw. 34, 18–31 (2012)

    Google Scholar 

  73. Ripley, R., Snijders, T.A.B.: Siena—Simulation Investigation for Empirical Network Analysis. Contributed R-package

  74. Robins, P., Lusher, D.: Illustrations: simulation, estimation and goodness of fit. In: Lusher, D., Koskinen, J., Robins, G. (eds.) Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, pp. 167–186. Cambridge University Press, New York (2013)

    Google Scholar 

  75. Robins, G., Morris, M.: Advances in exponential random graph (p ) models. Soc. Netw. 29, 169–172 (2007)

    Article  Google Scholar 

  76. Robins, G.L., Pattison, P.E.: Random graph models for temporal processes in social networks. J. Math. Sociol. 25, 5–41 (2001)

    Article  MATH  Google Scholar 

  77. Robins, G., Elliott, P., Pattison, P.: Network models for social selection processes. Soc. Netw. 23, 1–30 (2001)

    Article  Google Scholar 

  78. Robins, G., Pattison, P., Woolcock, J.: Small and other worlds: global network structures from local processes. Am. J. Sociol. 110, 894–936 (2005)

    Article  Google Scholar 

  79. Robins, G.L., Pattison, P.E., Wang, P.: Closure, connectivity and degree distributions: exponential random graph (p ) models for directed social networks. Soc. Netw. 31, 105–117 (2009)

    Article  Google Scholar 

  80. Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971)

    Article  Google Scholar 

  81. Schelling, T.: Micromotives and Macrobehavior. Norton, New York (1978)

    Google Scholar 

  82. Serrano, A., Boguñá, M., Vespignani, A.: Patterns of dominant flows in the world trade web. J. Econ. Coord. 2, 111–124 (2007)

    Article  Google Scholar 

  83. Simon, H.: On a class of skew distribution functions. Biometrika 42, 435–450 (1955)

    Google Scholar 

  84. Snijders, T.A.B.: The statistical evaluation of social network dynamics. In: Sobel, M.E., Becker, M.P. (eds.) Sociological Methodology, pp. 361–395. Blackwell, London (2001)

    Google Scholar 

  85. Snijders, T.A.B.: Markov chain Monte Carlo estimation of exponential random graph models. J. Soc. Struct. 3(2) (2002)

  86. Snijders, T.A.B.: Statistical methods for network dynamics. In: Luchini, S.R. (ed.) XLIII Scientific Meeting, Italian Statistical Society, pp. 281–296. CLEUP, Padova (2006)

    Google Scholar 

  87. Snijders, T.A.B., Koskinen, J.: Longitudinal models. In: Lusher, D., Koskinen, J., Robins, G. (eds.) Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, pp. 130–140. Cambridge University Press, New York (2013)

    Google Scholar 

  88. Snijders, T.A.B., Pattison, P., Robins, G., Handcock, M.: New specifications for exponential random graph models. Sociol. Method. 36, 99–153 (2006)

    Article  Google Scholar 

  89. Snijders, T.A.B., Koskinen, J.H., Schweinberger, M.: Maximum likelihood estimation for social network dynamics. Ann. Appl. Stat. 4, 567–588 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  90. Snijders, T.A.B., van de Bunt, G.G., Steglich, C.E.G.: Introduction to stochastic actor-based models for network dynamics. Soc. Netw. 32, 44–60 (2012)

    Article  Google Scholar 

  91. Solomonoff, R., Rapoport, A.: Connectivity of random nets. Bull. Math. Biol. 13, 107–117 (1951)

    MathSciNet  Google Scholar 

  92. Squartini, T., Fagiolo, G., Garlaschelli, D.: Randomizing world trade. I. A binary network analysis. Phys. Rev. E 84, 046117 (2011)

    Article  ADS  Google Scholar 

  93. Squartini, T., Fagiolo, G., Garlaschelli, D.: Randomizing world trade. II. A weighted network analysis. Phys. Rev. E 84, 046118 (2011)

    Article  ADS  Google Scholar 

  94. Stark, D., Vedres, B.: Social times of network spaces: network sequences and foreign investment in Hungary. Am. J. Sociol. 111, 1367–1411 (2006)

    Article  Google Scholar 

  95. Strauss, D.: On a general class of models for interaction. SIAM Rev. 28(4), 513–527 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  96. Tinbergen, J.: An analysis of world trade flows. In: Tinbergen, J. (ed.) Shaping the World Economy. The Twentieth Century Fund, New York (1962)

    Google Scholar 

  97. Tzekina, I., Danthi, K., Rockmore, D.: Evolution of community structure in the world trade web. Eur. Phys. J., B Cond. Matter Phys. 63, 541–545 (2008)

    ADS  MATH  Google Scholar 

  98. United Nations Conference on Trade and Development (UNCTAD): World Investment Report. UN, Geneva (1999–2003)

  99. Wang, P., Pattison, P., Robins, G.: Exponential random graph model specifications for bipartite networks: a dependence hierarchy. Soc. Netw. (2012), doi:10.1016/j.socnet.2011.12.004

    Google Scholar 

  100. Wang, P., Robins, G.L., Pattison, P.E.: PNet: program for the simulation and estimation of p exponential random graph models. Available from http://www.sna.unimelb.edu.au/ (2009)

  101. Wasserman, S., Pattison, P.E.: Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p . Psychometrika 61, 401–425 (1996)

    Article  MathSciNet  MATH  Google Scholar 

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We are grateful to Clare Hall College, University of Cambridge (U.K.) for hospitality and support during the work leading to this paper.

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Koskinen, J., Lomi, A. The Local Structure of Globalization. J Stat Phys 151, 523–548 (2013). https://doi.org/10.1007/s10955-013-0732-x

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