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Building a panel dataset to investigate the impact of exchange rate regimes on FDI flows

Building a panel dataset to investigate the impact of exchange rate regimes on FDI flows. Andrew Abbott & Glauco De Vita  a.abbott@bath.ac.uk & gde-vita@brookes.ac.uk AIM:

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Building a panel dataset to investigate the impact of exchange rate regimes on FDI flows

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  1. Building a panel dataset to investigate the impact of exchange rate regimes on FDI flows Andrew Abbott& Glauco De Vita a.abbott@bath.ac.uk & gde-vita@brookes.ac.uk AIM: To investigate the impact of different ER regimes upon FDI flows using panel data from 27 OECD & non-OECD countries for the period 1980-2003  We gratefully acknowledge financial support by the Economic and Social Research Council (grant RES-000-22-2350).

  2. Context • From 1980 to 2003, 450% increase in real world FDI flows • Much research on the determinants of FDI and its growth enhancing effects but no attention paid to ER regimes and FDI • Striking given the voluminous literature on ER regimes and trade (Rose, 2000 onwards) • Schiavo’s (2007, OEP) gravity model investigates the impact of EMU on FDI flows. OLS & Tobit results show that EMU has increased FDI flows by 160 to 320% (caution: EMU data 1999-2001)

  3. Our contribution • CUs are only one regime among feasible policy set. First set of estimates of effect of a wide menu of ER regimes on bilateral FDI flows between country-pairs (CU-CU; CU-FLT; CU-FIX; FIX-FIX; DFIX; FIX-FLT; and FLT-FLT) • Consider which ER regime the effect is benchmarked against. We compare the specific effect of each regime combination vis-à-vis the more plausible alternative of ‘double-float’

  4. Our contribution • In terms of the categorisation of ER regimes, comparative use of 3 different classifications: Reinhart and Rogoff (2004); Shambaugh (2004) and IMF (various issues of ERAR reports) • We explicitly control for simultaneity bias and reverse causality – instrumental variable estimation within SYS-GMM framework exploits time series variation while accounting for country specific effects

  5. Model • Our unbalanced panel (27 OECD and non-OECD countries over 1980-2003), yields over 7,000 country-year observations across almost 350 country-pairs • Drawing from standard variables typically entering the gravity equation, our baseline model is expressed (in long-run form) as: where fdi is the log of total bi-lateral real FDI flows between countries i and j at period t. Sum of inward and outward FDI flows, calculated from the OECD’s International Direct Investment Statistics database

  6. Data sources • Gravity type variables (dis; LANG; COL; COMLAN) - Source: Centre d'Etudes Prospectives et d'Informations Internationales, see http://www.cepii.fr/ • ER regime dummies (CU-CU; CU-FIX; CU-FLT; FIX-FIX; DFIX; FIX-FLT) calculated from classifications produced by Reinhart & Rogoff (2004), Shambaugh (2004) and various issues of the IMF’s ARERAR

  7. References Reinhart, C., Rogoff, K. (2004) The modern history of exchange rate arrangements: a reinterpretation. Quarterly Journal of Economics 119, 1-48. Rose, A. (2000) One money, one market: the effect of common currencies on trade. Economic Policy 15, 7-33. Schiavo, S. (2007) Common currencies and FDI flows. Oxford Economic Papers (Advance Access), March 3, 1-25. Shambaugh, J.C. (2004) The effect of fixed exchange rates on monetary policy. Quarterly Journal of Economics 119, 1, 300-351.

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