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Measuring the World Economy

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  • Harald Badinger

Abstract

Over the last decades there has been a sizeable increase in trade and financial openness, triggered by current and capital account liberalization as well as improvements in transport and communication technologies. As a consequence, the importance of spillover effects is likely to have increased at all levels of aggregation: among firms, industries, regions, and even countries. In other words, the relevance of geographic distance should have decreased over time, a view that has been popularized as ‘the death of distance’ by Cairncross (1997). Yet there is little overall quantitative evidence by how much globalisation has reduced economic distance between countries. A related albeit more specific strand of the literature considers either directly the evolution of trade costs over time or the sensitivity of trade flows to distance in gravity models. In their survey of the empirical trade literature, Leamer and Levinsohn (1995) conclude that the effect of distance on trade patterns has not diminished over time. More recently, Jacks et al. (2009) find that the role of distance has declined dramatically in the 19th century, but not over the period since 1950. This paper takes an alternative approach in addressing the question whether the world has become smaller in terms of economic distance. Instead of focussing on the transmission of single shocks (which requires high frequency data) or confining the analysis to a particular channel of interdependence (such as trade), it provides a bird-eye’s perspective, using a large cross-section of 135 countries and considering how cross-country interdependence of output volatility has evolved over the period 1955 to 2006. From a methodological perspective, we adopt a spatial econometric approach, relating a country’s output volatility to (distance weighted averages of) other countries’ output volatility, using descriptive measures, test statistics, and econometric estimates of cross-country interdependence. We also suggest a new method to approximate (unobserved) bilateral data from observed aggregate data in order to assess the importance of alternative channels of interdependence, in particular that of trade versus financial openness. We find that cross-country interdependence increased significantly till the mid 1980s and remained at high levels since then. In quantitative terms, estimation results for the most recent period suggest that a uniform shock to output volatility in the world economy roughly quadruplicates through spillover effects and the associated repercussions, which are equally transmitted through both trade and financial linkages. Keywords: financial openness, spillovers, trade, output volatility JEL No: C21, E32, F15, F40

Suggested Citation

  • Harald Badinger, 2012. "Measuring the World Economy," ERSA conference papers ersa12p240, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa12p240
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    References listed on IDEAS

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    1. Leamer, Edward E. & Levinsohn, James, 1995. "International trade theory: The evidence," Handbook of International Economics, in: G. M. Grossman & K. Rogoff (ed.), Handbook of International Economics, edition 1, volume 3, chapter 26, pages 1339-1394, Elsevier.
    2. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    3. Selen Sarisoy Guerin, 2006. "The Role of Geography in Financial and Economic Integration: A Comparative Analysis of Foreign Direct Investment, Trade and Portfolio Investment Flows," The World Economy, Wiley Blackwell, vol. 29(2), pages 189-209, February.
    4. David H. Romer & Jeffrey A. Frankel, 1999. "Does Trade Cause Growth?," American Economic Review, American Economic Association, vol. 89(3), pages 379-399, June.
    5. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    6. Jacks, David S. & Meissner, Christopher M. & Novy, Dennis, 2011. "Trade booms, trade busts, and trade costs," Journal of International Economics, Elsevier, vol. 83(2), pages 185-201, March.
    7. Antonio Fatás & Ilian Mihov, 2003. "The Case for Restricting Fiscal Policy Discretion," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1419-1447.
    8. James H. Stock & Mark W. Watson, 2005. "Understanding Changes In International Business Cycle Dynamics," Journal of the European Economic Association, MIT Press, vol. 3(5), pages 968-1006, September.
    9. Joshua Aizenman & Ilan Noy, 2009. "Endogenous Financial and Trade Openness," Review of Development Economics, Wiley Blackwell, vol. 13(2), pages 175-189, May.
    10. Harald Badinger & Peter Egger, 2011. "Estimation of higher‐order spatial autoregressive cross‐section models with heteroscedastic disturbances," Papers in Regional Science, Wiley Blackwell, vol. 90(1), pages 213-235, March.
    11. Lane, Philip & Milesi-Ferretti, Gian Maria, "undated". "External Wealth of Nations," Instructional Stata datasets for econometrics extwealth, Boston College Department of Economics.
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    Cited by:

    1. N. Antonakakis & H. Badinger, 2014. "International business cycle spillovers since the 1870s," Applied Economics, Taylor & Francis Journals, vol. 46(30), pages 3682-3694, October.
    2. Vadim V. Krivorotov & Alexey V. Kalina & Zhanna S. Belyaeva & Sergey Ye Erypalov, 2016. "Optimisation model for industrial complex competitiveness: a path to sustainable innovation process," World Review of Entrepreneurship, Management and Sustainable Development, Inderscience Enterprises Ltd, vol. 12(2/3), pages 254-269.
    3. Shinya Fukui, 2020. "Business Cycle Spatial Synchronization: Measuring a Synchronization Parameter," Discussion Papers 2009, Graduate School of Economics, Kobe University.

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    More about this item

    Keywords

    financial openness; spillovers; trade; output volatility jel no: c21; e32; f15; f40;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F15 - International Economics - - Trade - - - Economic Integration
    • F40 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - General

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