Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-20T05:37:37.816Z Has data issue: false hasContentIssue false
  • English
  • Français

Gravity's Rainbow: A dynamic latent space model for the world trade network

Published online by Cambridge University Press:  15 April 2013

MICHAEL D. WARD ,
JOHN S. AHLQUIST  and
ARTURAS ROZENAS
MICHAEL D. WARD
Affiliation:
Department of Political Science, Duke University, Durham, NC 27708, USA (e-mail: [email protected])
JOHN S. AHLQUIST
Affiliation:
Department of Political Science, University of Wisconsin, Madison, WI 53706, USA (e-mail: [email protected])
ARTURAS ROZENAS
Affiliation:
ISM University of Management and Economics, Vilnius, Lithuania (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The gravity model, long the empirical workhorse for modeling international trade, ignores network dependencies in bilateral trade data, instead assuming that dyadic trade is independent, conditional on a hierarchy of covariates over country, time, and dyad. We argue that there are theoretical as well as empirical reasons to expect network dependencies in international trade. Consequently, standard gravity models are empirically inadequate. We combine a gravity model specification with “latent space” networks to develop a dynamic mixture model for real-valued directed graphs. The model simultaneously incorporates network dependencies in both trade incidence and trade volumes. We estimate this model using bilateral trade data from 1990 to 2008. The model substantially outperforms standard accounts in terms of both in- and out-of-sample predictive heuristics. We illustrate the model's usefulness by tracking trading propensities between the USA and China.

Keywords

gravity modeltradenetworkslatent space
Type
Research Article
Information
Network Science , Volume 1 , Issue 1 , April 2013 , pp. 95 - 118
Copyright
Copyright © Cambridge University Press 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. E. (1979). A theoretical foundation for the gravity equation. American Economic Review, 69 (1), 106–116.Google Scholar
Anderson, J. E., & Marcouiller, D. (2002). Insecurity and the pattern of trade: An empirical investigation. Review of Economics and Statistics, 84 (2), 342–352.Google Scholar
Anderson, J. E., & van Wincoop, E. (2003). Gravity with gravitas: A solution to the border puzzle. American Economic Review, 93, 170–192.Google Scholar
Baier, S. L., & Bergtrand, J. H. (2007). Do free trade agreements actually increase members' international trade? Journal of International Economics, 71, 72–95.Google Scholar
Baier, S. L., & Bergtrand, J. H. (2009). Bonus vetus OLS: a simple method for approximating international trade-cost effects using the gravity equation. Journal of International Economics, 77, 77–85.Google Scholar
Baldwin, R., & Harrigan, J. (2011). Zeros, quality and space: Trade theory and trade evidence. American Economic Journal: Microeconomics, 3 (2), 60–88.Google Scholar
Barbieri, K., & Schneider, G. (1999). Globalization and peace: Assessing new directions in the study of trade and conflict. Journal of Peace Research, 36 (4), 387–404.Google Scholar
Bergstrand, J. H. (1985). The gravity equation in international trade: Some microeconomic foundations and empirical evidence. Review of Economics and Statistics, 67 (3), 474–481.Google Scholar
Bhattacharya, K., Mukherjee, G., Saramäki, J., Kaski, K., & Manna, S. S. (2008). The international trade network: Weighted network analysis and modeling. Journal of Statistical Mechanics, 2008, P02002.Google Scholar
Bliss, H., & Russett, B. (1998). Democratic trading partners: The liberal connection, 1962–1989. Journal of Politics, 60 (4), 1126–1147.Google Scholar
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power law distributions in empirical data. SIAM Review, 51 (4), 661–703.Google Scholar
deBenedictis, L. Benedictis, L., & Tajoli, L. (2011). The world trade network. The World Economy, 34 (8), 1417–1454.Google Scholar
Deardorff, A. V. (1998). Determinants of bilateral trade: Does gravity work in a neoclassical world? In Frankel, J. A. (Ed.), The regionalization of the world economy (pp. 7–32). Chicago, IL: University of Chicago.Google Scholar
Deardorff, A., & Stern, R. M. (1990). Computational analysis of global trading arrangements. Ann Arbor, MI: The University of Michigan Press.Google Scholar
Deardorff, A. V., & Stern, R. M. (1998). Measurement of nontariff barriers. Ann Arbor, MI: The University of Michigan Press.CrossRefGoogle Scholar
Desmarais, B. A., & Cranmer, S. J. (2012). Statistical inference for valued-edge networks: The generalized exponential random graph model. Plos One, 7 (1), e30136.Google Scholar
Fagiolo, G. (2009). The international-trade network: Gravity equations and topological properties. http://arxiv.org/abs/0908.2086, arXiv:0908.2086 [q-fin.GN]Google Scholar
Fagiolo, G., Reyez, J., & Schiavo, S. (2010a). The evolution of the world trade web. Journal of Evolutionary Economics, 20 (4), 479–514.Google Scholar
Fagiolo, G., Reyez, J., & Schiavo, S. (2010b). International trade and financial integration. Quantitative Finance, 10 (4), 389–399.Google Scholar
Feenstra, R. C. (2002). Border effects and the gravity equation: Consistent methods for estimation. Scottish Journal of Political Economy, 49, 491–506.Google Scholar
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832–842.Google Scholar
Gabriel, K. R. (1998). Generalised bilinear regression. Biometrika, 85 (3), 689–700.Google Scholar
Garlaschelli, D., & Loffredo, M. I. (2004). Fitness-dependent topological properties of the world trade web. Physical Review Letters, 93 (18), 188701 (1–4).Google Scholar
Garlaschelli, D., & Loffredo, M. I. (2005). Structure and evolution of the world trade network. Physica A, 335, 138–144.Google Scholar
Gill, P. S., & Swartz, T. B. (2001). Statistical analyses for round robin interaction data. The Canadian Journal of Statistics. La Revue Canadienne de Statistique, 29 (2), 321–331.Google Scholar
Gleditsch, K. S., & Ward, M. D. (2001). Measuring space: A minimum distance database and applications to international studies. Journal of Peace Research, 38 (6), 749–768.Google Scholar
Goenner, C. F. (2003). A Hierarchical Linear Model of Bilateral Trade: The Effect of Geography and Institutions. Department of Economics, University of North Dakota. URL: http://www.business.und.edu/goenner/research/papers.html.Google Scholar
Goenner, C. F. (2004). Uncertainty of the liberal peace. Journal of Peace Research, 41 (5), 589–605.Google Scholar
Goldstein, J., Rivers, D., & Tomz, M. (2007). Institutions in international relations: Understanding the effects of the GATT and the WTO on world trade. International Organization, 61 (Winter), 37–67.Google Scholar
Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners and trading volumes. Quarterly Journal of Economics, 123 (2), 441–487.Google Scholar
Hilgerdt, F. (1943). The case for multilateral trade. The American Economic Review, 33 (1), 393–407.Google Scholar
Hoff, P. D. (2005). Bilinear mixed effects models for dyadic data. Journal of the American Statistical Association, 100, 286–295.CrossRefGoogle Scholar
Hoff, P. D. (2007). Extending the rank likelihood for semiparametric copula estimation. Annals of Applied Statistics, 1 (1), 265–283.CrossRefGoogle Scholar
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97 (460), 1090–1098.CrossRefGoogle Scholar
Krivitsky, P. N. (2012). Exponential random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128.CrossRefGoogle ScholarPubMed
Li, H., & Loken, E. (2002). A unified theory of statistical analysis and inference for variance component models for dyadic data. Statistica Sinica, 12 (2), 519–535.Google Scholar
Linneman, H. (1966). An econometric study of international trade flows. Amsterdam: North-Holland.Google Scholar
Malloy, T. E., & Kenny, D. A. (1986). The social relations model: An integrative method for personality research. Journal of Personality, 54 (1), 199–225.Google Scholar
Maskus, K. E., & Ramazani, R. (1993). Testing the Heckscher–Ohlin–Vanek theorem in an industrializing economy: The case of Korea. Review of Economics and Statistics, 75 (3), 568–572.Google Scholar
Oguledo, V., & MacPhee, C. (1994). Gravity model: A reformulation and application to discriminatory trade arrangements. Applied Economics, 40, 315–337.Google Scholar
Pattison, P. E., & Robins, G. L. (2002). Neighbourhood-based models for social networks. Sociological Methodology, 32, 301–337.CrossRefGoogle Scholar
Plümper, T., & Krempel, L. (2003). Exploring the dynamics of international trade by combining the comparative advantages of multivariate statistics and network analysis. Journal of Social Structures, 4 (1), 1–22.Google Scholar
Poyhonen, P. (1963). A tentative model for the flows of trade between countries. Weltwirtschaftliches Archiv, 90 (1), 93–100.Google Scholar
Robins, G. L., Pattison, P. E., & Wasserman, S. (1999). Logit models and logistic regression for social networks III: Valued relations. Psychometrika, 64, 371–394.Google Scholar
Rose, A. K. (2004). Do we really know that the WTO increases trade? American Economic Review, 94 (1), 98–114.Google Scholar
Rose, A. K. (2007). Do we really know that the WTO increases trade? Reply. American Economic Review, 97(December), 2019–2025.Google Scholar
SantosSilva, J. M. C. Silva, J. M. C., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88 (4), 641–658.Google Scholar
Subramanian, A., & Wei, S.-J. (2007). The WTO promotes trade, strongly but unevenly. Journal of International Economics, 72 (1), 151–175.Google Scholar
Tinbergen, J. (1962). Shaping the world economy: Suggestions for an international economic policy. New York: The Twentieth Century Fund.Google Scholar
Tomz, M., Goldstein, J., & Rivers, D. (2007). Do we really know that the WTO increases trade? Comment. American Economic Review, 97 (5), 2005–2018.Google Scholar
van Duijn, M., Snijders, T. A. B., & Zijlstra, B. J. H. (2004). p2: A random effects model with covariates for directed graphs. Statistica Neerlandica, 58, 234–254.Google Scholar
Ward, M. D., & Hoff, P. D. (2007). Persistent patterns of international commerce. Journal of Peace Research, 44 (2), 157–175.Google Scholar
Ward, M. D., & Hoff, P. D. (2008). Analyzing dependencies in geo-politics and geo-economics. In Fontanel, J. & Chatterji, M. (Eds.), Contributions to conflict management, peace economics, and development, vol. War, Peace, and Security (pp. 133–160). Amsterdam: Elsevier Science.Google Scholar
Warner, R., Kenny, D. A., & Stoto, M. A. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology, 37, 1742–1757.CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis. New York: Cambridge University Press.Google Scholar
Wong, G. Y. (1982). Round robin analysis of variance via maximum likelihood. Journal of the American Statistical Association, 77 (380), 714–724.Google Scholar