Evidence of the Impact of Exchange Rate Regimes on FDI Flows Abbott, G. & De Vita, G.  Scottish Economic Society Conference, Perth, 21 st -23 rd of April 2008 AIM: To investigate the impact of different ER regimes upon FDI flows using panel data from 27 OECD & non-OECD high-income countries for the period  We gratefully acknowledge financial support by the ESRC (grant RES ).

Context From 1980 to 2003, 450% increase in real, world FDI flows Much research on determinants of FDI and its growth enhancing effects but no attention paid to ER regimes and FDI flows 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 )

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; 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’

Contribution In terms of the categorisation of ER regimes, comparative use of three different classifications: Reinhart and Rogoff (2004); Shambaugh (2004) and IMF (ERAR reports, various issues) 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

Model Our unbalanced panel (27 OECD and non-OECD high-income countries over ), 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: fdi ijt = β 0 + β 1 tbt ijt + β 2 y it +β 3 y jt + β 4 RXRVOL ijt + α 5 dis ij + α 6 LANG ij + α 7 COL ij + α 8 COMLAN ij + α 9 FTA ijt + α 10 CU-CU ijt + ε ijt 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

Data Gravity type variables (dis*; LANG; COL; COMLAN) - see Centre d'Etudes Prospectives et d'Informations Internationales, * Based on bilateral distances between the biggest cities of the two countries, with intercity distances being weighted by the share of the city in the overall country’s population (Mayer & Zignago, 2006). > proximity : >trade but as dis increases : > incentive for FDI Melitz (2001;2005) showed that distance can reflect CA. > distance might raise (diminish) not diminish (raise), trade (FDI) ER regime dummies (CU-CU; CU-FIX; CU-FLT; FIX-FIX; FIX- FLT) calculated from classifications produced by Reinhart & Rogoff (2004), Shambaugh (2004), and IMF’s ARERAR

Why SYS-GMM estimation? Appropriate both conceptually and for its statistical virtues: Lagged values of FDI can be included to account for speed of adjustment (important since FDI might adjust slowly to changes in the regressors) FDI or one or more of the regressors may be simultaneously determined (with SYS-GMM every regressor is instrumented so issues of endogeneity bias are overcome) Including both level and first-difference equations in a stacked system allows us to investigate whether time-invariant variables (dis & COMLAN) play a role in the determination of FDI

Extensions and perturbations Re-estimated excluding USA & UK. Regime dummies proved robust to new sample. Reliability corroborated by economically sensible changes to some control variables (e.g. the investing country’s output measure and LANG coefficient now insignificant; COMLAN now +ve and significant) Then: (i) replaced real per capita GDP with real GDP (used in Schiavo, 2007); (ii) added three regressors: 1. a long-run measure of exchange rate volatility; 2. a proxy for informational flows; 3. the real ER Presence of many zeros in bilateral FDI matrix constitutes an econometric issue since log-linear structure of gravity model precludes estimation of obs for which the natural log does not exist. In our SYS-GMM we converted the series to logs by adding a +ve constant. We now also re-estimate using Tobit

Conclusions By exerting greater or lesser stability of ERs, ER regimes do affect FDI flows CU membership constitutes regime type most conducive to cross-border investment. EMU membership also appears to increase FDI flows with extra-EMU countries floating their currency (vis-à-vis double float country-pairs) FIX-FIX combination has most –ve impact on FDI flows Effects of other ER regime combinations are found to be either statistically indistinguishable from that of floating currency country-pairs or significantly –ve In comparing ER regime classifications, R+R emerges as the least robust and least able to capture year-to-year instability of FDI data

Selected references Mélitz, J., Geography, trade and currency unions. CEPR Discussion Paper 2987, October. Mélitz, J., North, south and distance in the gravity model. Mimeo, University of Strathclyde. Reinhart, C., Rogoff, K., The modern history of exchange rate arrangements: a reinterpretation. Quarterly Journal of Economics 119, Rose, A., One money, one market: the effect of common currencies on trade. Economic Policy 15, Schiavo, S., Common currencies and FDI flows. Oxford Economic Papers (Advance Access), March 3, Shambaugh, J.C., The effect of fixed exchange rates on monetary policy. Quarterly Journal of Economics 119, 1,