Let ditbe the PTA hub score, xit is a vector of control variables, and αi and νtare individual effects at the country- and year- level, respectively. The baseline panel model is
yit =
hub effect
z}|{ditδ +
main control
z }| {
XK k=1
xiktβk+
panel control
z }| {
αi+ νt +εit. (6)
However, conventional (e.g. ordinary least squares or maximum likelihood) estimates of δ from the above equation might not be a consistent estimate of dit because there may exist omitted confounding variables that affect dit and the dependent variable at the same time.
We introduce several layers of methodological treatments that reduce potential bias arising from the endogeneity.
We use the orthogonalized machine learning method (so called “double machine learning”, DML) (Belloni, Chernozhukov and Hansen,2014;Chernozhukov et al.,2017) for parameter estimation. The purpose of DML is to address the possibility of inconsistent estimation of nuisance parameters. DML is known to provide a √
N consistent estimate of the target pa-rameter in the presence of high-dimensional and somewhat inaccurate estimates of nuisance parameters (Chernozhukov et al.,2017). The key insight is to use the century old idea of the Frisch-Waugh-Lovell Theorem (Frisch and Waugh, 1933) or Neyman orthogonality (Cher-nozhukov et al.,2017). More recently,Semenova et al.(2018) show that the sample splitting and cross-fitting provides a debiased estimate for panel data by ensuring fitted values of each equation to be uncorrelated. In our model, the total number of predictors is 232, many of which are correlated. The inconsistent estimation of nuisance parameters will affect the estimation of δ. The sample splitting and cross-fitting DML method can be summarized as follows:
Author Manuscript
The Sample Splitting and Cross-fitting Panel DML Method
1: We model value-added exports by country i and at year t (yit) as
yit =
2: For the DML estimation, we rewrite the model by dropping our target variable in the first equation:
3: We group demeaned data into 2-fold partition (c and −c) by the year index and estimate coefficients for partition c using −c and vice versa using a regularized method. We select a regularization method among the ordinary least squares (OLS), Lasso (Tibshirani, 1996), adaptive Lasso (Zou, 2006) by comparing the residual sum of squares in each stage.
4: Compute the residuals for partition c using the cross-fit estimates from partition −c and data from partition c. Compute the residuals for partition −c similarly.
5: We pool the residuals from all partitions and estimate δ by regressing ˜yit(pooled residuals of Equation (9)) on ˜dit (pooled residuals of Equation (10)).
Author Manuscript
−3 −2 −1 0 1 2 Manufacture of wood and of products of wood and cork Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of coke and refined petroleum products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products Manufacture of rubber and plastic products Manufacture of other non−metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products Manufacture of computer Manufacture of electrical equipment Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles Manufacture of other transport equipment Manufacture of furniture other manufacturing Repair and installation of machinery and equipment Electricity Water collection Sewerage waste collection Construction Wholesale and retail trade of motor vehicles Wholesale trade Retail trade Land transport and transport via pipelines Water transport Air transport Warehousing and support activities for transportation Postal and courier activities Activities auxiliary to financial services and insurance activities Real estate activities Public administration and defence compulsory social security Education Human health and social work activities Other service activities Activities of households
Figure 3: Industry-level Cross-fitting DML Analysis for DVA: The size of dots is adjusted to be propror-tional to the export share of each industry. Red colors indicate positive effects and blue colors indicate negative effects. Grey colors indicate vague effects including 0 at the conventional significance level.
Author Manuscript
−3 −2 −1 0 1 2 Manufacture of wood and of products of wood and cork Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of coke and refined petroleum products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products Manufacture of rubber and plastic products Manufacture of other non−metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products Manufacture of computer Manufacture of electrical equipment Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles Manufacture of other transport equipment Manufacture of furniture other manufacturing Repair and installation of machinery and equipment Electricity Water collection Sewerage waste collection Construction Wholesale and retail trade of motor vehicles Wholesale trade Retail trade Land transport and transport via pipelines Water transport Air transport Warehousing and support activities for transportation Postal and courier activities Activities auxiliary to financial services and insurance activities Real estate activities Public administration and defence compulsory social security Education Human health and social work activities Other service activities Activities of households
Figure 4: Industry-level Cross-fitting DML Analysis for FVA: The size of dots is adjusted to be proprortional to the export share of each industry. Red colors indicate positive effects and blue colors indicate negative effects. Grey colors indicate vague effects including 0 at the conventional significance level.
Author Manuscript
● ●
Figure 5: Naive DML Estimates of Year-by-year PTA Hub Effects: The estimation method is the naive DML using the adaptive lasso method. The number of observations is 36, with 209 predictors (19 main predictors and 190 interaction terms).
Author Manuscript
References
Advisory Council on Economic Growth. 2017. Positioning Canada as a Global Trading Hub.
Government of Canada.
Amador, Jo˜ao and S´onia Cabral. 2016. “Networks of Value Added Trade.” ECB Working Paper 1931 (https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1931.en.
pdf?2afd42b5cb109caacb305e3a7ed58112).
Andreas, D. 2016. “The Design of Trade Agreements (DESTA) CODEBOOK.” 9(June).
Antr`as, Pol and Robert W Staiger. 2012. “Offshoring and the Value of Trade Agreements.”
American Economic Review 102(7):3140–3183.
Baier, Scott L and Jeffrey H Bergstrand. 2007. “Do free trade agreements actually increase members ’ international trade ?” 71:72–95.
Baier, Scott L. and Jeffrey H. Bergstrand. 2009. “Estimating the effects of free trade agree-ments on international trade flows using matching econometrics.” Journal of International Economics 77(1):63 – 76.
Baier, Scott L., Yoto V. Yotov and Thomas Zylkin. 2019. “On the widely differing effects of free trade agreements: Lessons from twenty years of trade integration.” Journal of International Economics 116:206 – 226.
Baldwin, Richard. 2013. Global Supply Chains: Why They Emerged, Why They Matter, and Where They Are Going. In Global Value Chains in A Changing World, ed. Deborah K.
Elms and Patrick Low. World Trade Organization.
Baldwin, Richard. 2016. The Great Convergence. Harvard University Press.
Baldwin, Richard E. 2006. “Multilateralising Regionalism: Spaghetti Bowls as Building Blocs on the Path to Global Free Trade.” The World Economy 29(11):1451–1518.
Baldwin, Richard E. 2008. “The Spoke Trap: hub and spoke bilateralism in East Asia.”
China, Asia, and the new world economy pp. 51–85.
Ballester, Coralio, Antoni Calv´o-Armengol and Yves Zenou. 2006. “Who’s Who in Networks.
Wanted: The Key Player.” Econometrica 74(5):1403–1417.
Barrat, Alain, Marc Barthelemy and Alessandro Vespignani. 2007. The Architecture of Complex Weighted Networks: Measurements and Models. In Large Scale Structure And Dynamics Of Complex Networks: From Information Technology to Finance and Natural Science. World Scientific pp. 67–92.
Belloni, Alexandre, Victor Chernozhukov and Christian Hansen. 2014. “Inference on Treat-ment Effects after Selection among High-Dimensional Controls.” The Review of Economic Studies 81(2):608–650.
Author Manuscript
Bonacich, Phillip. 1972. “Factoring and weighting approaches to status scores and clique identification.” The Journal of Mathematical Sociology 2(1):113–120.
Bonacich, Phillip. 1987. “Power and Centrality: A Family of Measures.” American Journal of Sociology 92(5):1170–1182.
Brin, Sergey and Lawrence Page. 1998. “The anatomy of a large-scale hypertextual Web search engine.” Computer Networks and ISDN Systems 30(1):107 – 117. Proceedings of the Seventh International World Wide Web Conference.
Burt, Ronald S. 2001. Structural Holes versus Network Closure as Social Capital. In So-cial Capital: Theory and Research, ed. Nan Lin, Karen S. Cook and Ronald S. Burt.
Transaction Publishers.
Burt, Ronald S. 2009. Structural Holes: The Social Structure of Competition. Harvard University Press.
Burt, Ronald S. 2010. Neighbor Networks: Competitive Advantages Local and Personal. New York: Oxford University Press.
B¨uthe, Tim and Helen V Milner. 2008. “The Politics of Foreign Direct Investment into De-veloping Countries: Increasing FDI through International Trade Agreements?” American Journal of Political Science 52(4):741–762.
Chase, Kerry A. 2009. Trading blocs: States, firms, and regions in the world economy.
University of Michigan Press.
Chauffour, Jean-Pierre and Jean Christophe Maur, eds. 2011. Preferential Trade Agreement Policies for Development: A Handbook. World Bank.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen and Whitney Newey. 2017. “Double/Debiased/Neyman Machine Learning of Treatment Effects.” American Economic Review 107(5):261–265.
Chinn, Menzie D. and Hiro Ito. 2006. “What Matters for Financial Development? Capital Controls, Institutions, and Interactions.” Journal of Development Economics 81(1):163–
192.
Conconi, Paola, Manuel Garc´ıa-Santana, Laura Puccio and Roberto Venturini. 2018. “From Final Goods to Inputs: The Protectionist Effect of Rules of Origin.” American Economic Review 108(8):2335–65.
Deltas, George, Klaus Desmet and Giovanni Facchini. 2012. “Hub-and-spoke free trade areas:
theory and evidence from Israel.” The Canadian Journal of Economics / Revue canadienne d’Economique 45(3):942–977.
Easley, David and Jon Kleinberg. 2010. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. New York: Cambridge University Press.
Author Manuscript
Egger, Hartmut, Peter Egger and David Greenaway. 2008. “The trade structure effects of endogenous regional trade agreements.” Journal of International Economics 74(2):278 – 298.
Elms, Deborah K. and Patrick Low, eds. 2013. Global Value Chains in a Changing World.
World Trade Organization.
European Central Bank. 2010. “The External Environment of the Euro Area.” Montly Bul-letin (October).
Freeman, Linton C. 1977. “A Set of Measures of Centrality Based on Betweenness.” Sociom-etry 40(1):35–41.
Freeman, Linton C. 1978. “Centrality in social networks conceptual clarification.” Social Networks 1(3):215–239.
Frisch, Ragnar and Frederick V Waugh. 1933. “Partial Time Regressions as Compared with Individual Trends.” Econometrica 1(4):387–401.
Government of Japan. 2014. “Japan Revitalization Strategy: Japan’s Challenge for the Future (Revised 2014).” (http://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/
honbunEN.pdf).
Granger, Clive W. J. and Paul Newbold. 1974. “Spurious Regressions in Econometrics.”
Journal of Econometrics 2:111–120.
Granovetter, Mark. 1978. “Threshold models of collective behavior.” American Journal of Sociology 83(6):1420–1443.
Grossman, Gene M. and Esteban Rossi-Hansberg. 2008. “Trading Tasks: A Simple Theory of Offshoring.” The American Economic Review 98(5):1978–1997.
Guimera, Roger and Lu´ıs A Nunes Amaral. 2005. “Cartography of Complex Networks:
Modules and Universal Roles.” Journal of Statistical Mechanics: Theory and Experiment P02001(02):1–13.
Handley, Kyle and Nuno Lim˜ao. 2017. “Policy Uncertainty, Trade, and Welfare: Theory and Evidence for China and the United States.” American Economic Review 107(9):2731–83.
Hiscox, Michael J. 2002. International Trade and Political Conflict: Commerce, Coalitions, and Mobility.
Hur, Jung, Joseph D Alba and Donghyun Park. 2010. “Effects of hub-and-spoke free trade agreements on trade: A panel data analysis.” World Development 38(8):1105–1113.
Jensen, J Bradford, Dennis P Quinn and Stephen Weymouth. 2015. “The Influence of Firm Global Supply Chains and Foreign Currency Undervaluations on US trade Disputes.”
International Organization 69(4):913–947.
Author Manuscript
Katz, Leo. 1953. “A New Status Index Derived from Sociometric Analysis.” Psychometrika 18(1):39–43.
Kim, In Song, Helen V. Milner, Thomas Bernauer, Iain Osgood, Gabriele Spilker and Dustin Tingley. 2019. “Firms and Global Value Chains: Identifying Firms’ Multidimensional Trade Preferences.” International Studies Quarterly Forthcoming.
Kim, Soo Yeon, Clara Lee and Melvin Tay. 2019. “Setting up Shop in Foreign Lands: Do Investment Commitments in PTAs Promote Production Networks?” (http://wp.peio.
me/wp-content/uploads/2016/12/PEIO10_paper_60.pdf).
Kleinberg, J. M. 1998. “Authoritative sources in a hyperlinked environment.” Proc. 9th ACM-SIAM Symposium on Discrete Algorithms .
Koopman, Robert, Zhi Wang and Shang-Jin Wei. 2014. “Tracing Value-added and Double Counting in Gross Exports.” American Economic Review 104(2):459–494.
Kowalczyk, Carsten and Ronald J. Wonnacott. 1992. “Hubs and Spokes, and Free Trade in the Americas.” NBER Working Papers 4198.
Krugman, Paul. 1993a. The Hub Effect: or, Threeness in Interregional Trade. In Theory, Policy and Dynamics in International Trade, ed. Wilfred J. Ethier, Elhanan Helpman and J. Peter Neary. Cambridge University Press pp. 29–37.
Krugman, Paul R. 1993b. Geography and trade. MIT press.
Manger, Mark S., Mark A. Pickup and Tom A.B. Snijders. 2012. “A Hierarchy of Prefer-ences: A Longitudinal Network Analysis Approach to PTA Formation.” Journal of Conflict Resolution 56(5):853–878.
Mansfield, Edward D. and Helen V. Milner. 2012. Votes, Vetoes, and the Political Economy of International Trade Agreements. Princeton, New Jersey: Princeton University Press.
Melitz, Marc J and Gianmarco IP Ottaviano. 2008. “Market size, Trade, and Productivity.”
The Review of Economic Studies 75(1):295–316.
Moser, Christoph and Andrew K. Rose. 2012. “Why Do Trade Negotiations Take So Long?”
Journal of Economic Integration 27(2):280–290.
Mukunoki, Hiroshi and Kentaro Tachi. 2006. “Multilateralism and Hub-and-Spoke Bilater-alism.” Review of International Economics 14(4):658–674.
Mulgan, Aurelia George and Masayoshi Honma. 2015. The Political Economy of Japanese Trade Policy. Springer.
Newman, Mark E.J. 2018. “The Mathematics of Networks.”http://www-personal.umich.
edu/˜mejn/papers/palgrave.pdf.
OECD. 2013. Interconnected Economies: Benefiting from Global Value Chains. Paris.
Author Manuscript
Orefice, Gianluca and Nadia Rocha. 2014. “Deep integration and production networks: an empirical analysis.” The World Economy 37(1):106–136.
Osgood, Iain. 2018. “Globalizing the Supply Chain: Firm and Industrial Support for US Trade Agreements.” International Organization 72(2):455–484.
Osnago, Alberto, Nadia Rocha and Michele Ruta. 2015. “Deep Trade Agreements and Vertical FDI: The Devil is in the Details.” http:
//documents.worldbank.org/curated/en/739621468001166726/pdf/
WPS7464-REVISED-Deep-Trade-Agreements-and-Vertical-FDI.pdf.
Presidential Advisory Policy Committee. 2008. Expansion of Free Trade Agreement (FTA):
Choice for Larger Korea. Government of South Korea.
Quast, Bastiaan and Victor Kummritz. 2015. “Decompr: Global Value Chain Decomposition In R.” CTEI Working Papers Series 01-2015 (http://graduateinstitute.ch/files/
live/sites/iheid/files/sites/ctei/shared/CTEI/working_papers/CTEI-2015-01_
Quast,%20Kummritz.pdf).
Rogowski, Ronald. 1989. Commerce and Coalitions: How Trade Affects Domestic Political Alignments. Princeton: .
Semenova, Vira, Matt Goldman, Matt Taddy and Victor Chernozhukov. 2018. “Orthogonal ML for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels.”
arXiv (https://arxiv.org/abs/1712.09988).
Stolper, Wolfgang F. and Paul A. Samuelson. 1941. “Protection and Real Wages.” Review of Economic Studies 9(1):58–73.
Tibshirani, Robert. 1996. “Regression shrinkage and selection via the lasso.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) pp. 267–288.
Timmer, Marcel P, Bart Los, Robert Stehrer, Gaaitzen J de Vries et al. 2016. “An Anatomy of the Global Trade Slowdown Based on the WIOD 2016 Release.” http://www.ggdc.
net/publications/memorandum/gd162.pdf.
Timmer, Marcel P, Erik Dietzenbacher, Bart Los, Robert Stehrer and Gaaitzen J De Vries.
2015. “An Illustrated User Guide to the World Input-Output Database : the Case of Global Automotive Production.” Review of International Economics 23(3):575–605.
UNIDO. 2018. Global Value Chains and Industrial Developtment: Lessons from China, South-East and South Asia. United Nations Industrial Development Organization (UNIDO).
Wang, Zhi, Shang-Jin Wei and Kunfu Zhu. 2018. “Quantifying International Production Sharing at the Bilateral and Sector Levels.” NBER Working Paper 19677 (https://www.
nber.org/papers/w19677).
Author Manuscript
Wang, Zhi, Shang-Jin Wei, Xinding Yu and Kunfu Zhu. 2017. “Characterizing Global Value Chains: Production Length and Upstreamness.” (https://www.nber.org/papers/
w23261.pdf).
Wasserman, Stanley and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. Vol. 8 Cambridge university press.
Whalley, John. 1998. Why Do Countries Seek Regional Trade Agreements? In The Region-alization of the World Economy, ed. Jeffrey A. Frankel. Chicago: University of Chicago Press.
Wonnacott, Ronald J. 1975. “Canada’s Future in a World of Trade Blocs: A Proposal.”
Canadian Public Policy/Analyse de Politiques 1(1):118–130.
Wren, Anne, Mate Fodor and Sotiria Theodoropoulou. 2013. The Trilemma Revisited: In-stitutions, Inequality, and Employment Creation in an Era of ICT-Intensive Service Ex-pansion. In The Political Economy of the Service Transition, ed. Anne Wren. Oxford University Press.
Zou, Hui. 2006. “The Adaptive Lasso and Its Oracle Properties.” Journal of the American Statistical Association 101(476):1418–1429.