1. Gravitating Towards Europe: The Effects of EU Membership on Foreign Direct Investment Randolph Bruno (UCL, Rodolfo De Benedetti Foundation and IZA-Bonn) Nauro Campos (Brunel University London, ETH-Zurich and IZA-Bonn) Saul Estrin (LSE, CEPR and IZA-Bonn) Meng Tian (LSE and Peking University) The Economics of the UK-EU Relationship Workshop Brunel University London June 3 rd 2016 Dr Randolph L Bruno, Randolph.Bruno@ucl.ac.ukRandolph.Bruno@ucl.ac.uk SCHOOL OF SLAVONIC AND EAST EUROPEAN STUDIES

2. Outline Objectives of the study Literature Review Methodology Results Conclusions

3. Objectives of the Study 1.Understanding the impact of EU Membership on inward FDI when taking into account distance and economy size within the New Structural Gravity Approach. 2.Testing the robustness of results when using state of the art empirical modeling. 3.Analyzing the impact of EU Membership on FDI in the UK by comparing the impact on different EU countries.

4. Objectives (cont. ed) The merit in this study is that bilateral FDI inflows studies, for all OECD member countries, over a relatively long time span are lacking in the literature. In fact the existing literature on FDI: – It focused on one or a few countries –It mainly used cross-sectional data or shorter period of time In general, the empirical application of gravity model in FDI studies is still underdeveloped. Our paper develops panel gravity modelling specifically for FDI analysis.

5. Literature 1: FDI matters Havranek, T. and Z. Irsova. "Estimating vertical spillovers from FDI: Why results vary and what the true effect is." Journal of International Economics 85.2 (2011): 234-244. Iršová, Zuzana, and Tomáš Havránek. "Determinants of horizontal spillovers from FDI: Evidence from a large meta-analysis." World Development 42 (2013): 1-15.

6. Baldwin “The World Trade Organization and the Future of Multilateralism”, Journal of Economic Perspectives 2016:  20 th century trade = trade in final goods follows comparative advantage  21 st century trade = trade in parts and components following absorptive capacity….  …via Global value chains or global supply chains  UNCTAD WIR 2013 reports that 60% of global trade now in intermediate goods and services A.The gravity theory and econometrics for trade also applies to FDI B.Further deepening integration needs institutions, “needs EU”. Literature 2: FDI in the XXI Century

7. Baldwin (JEP 2016)

9. Literature 3: Applications of the Gravity Model Anderson, James “The Gravity Model” Annual Review of Economics. 3.1 (2011): 133-160. Anderson and Wincoop “Gravity with Gravitas: A Solution to the Border Puzzle” American Economic Review (2003), –border effects have an asymmetric effect on countries of different size. More precisely have a larger effect on small economies. Straathof et. al “The internal market and the Dutch Economy” CPB document (2008) Baier and Bergstrand “Do free trade agreements actually increase members’ international trade” Journal of International Economics (2007) Baier, Bergstrand, Egger and McLaughlin “Do Economic Integration Agreements Actually Work? Issue in Understanding the Causes and Consequences of the growth of Regionalism” World Economy (2008) –EU increased members’ trade by 127-146 % after 10-15 years. Membership in EFTA raised trade only 35 %, similar to the effect of membership in the European Economic Area (EEA)

10. Newton’s “Law of Universal Gravitation” (1687): The attractive force (F ij ) between i and j M i, M j are the masses D is distance between two objects G is gravitational constant The Origin of the Gravity Equation MjMi D F

11. Model many social interactions (migration, tourism, trade, FDI) F ij is the flow from i to j M’s are measure of economic mass D is the distance Economists and Gravity

12. Estimation of the Gravity Equation Take logs:

13. An important methodological evolution: Estimation of the Gravity Equation with Dyadic dummies

14. The GRAVITY model in the literature: high overall explanatory power R 2 between 0.65 and 0.95 It Suggests using gravity as a benchmark for volume of trade, FDI, migration, etc. We can then use gravity based benchmark to evaluate economic policy.

15. The GRAVITY model in the literature: The role of economic mass Usually measured using GDP Most theoretical explanations predict coefficient equal to one Estimates for trade are often not significantly different from 1, but range is from 0.7 to 1.1 Estimates for other economic variables can assume different ranges.

16. The GRAVITY model in the literature: the role of distance Distance usually measured using great circle distance based on latitude and longitude Head (2000) averages results from 62 regressions in eight papers, for sample years ranging from 1928 to 1995 –Average distance effect is 1.01 –E.g. doubling distance halves trade

17. Distance and trade/FDI costs (I) Trade costs: –Direct (transport) –Indirect (government policy; language; etc.) Is distance just capturing the effect of trade costs (acting as a proxy) or does it play an additional role?

18. Distance and trade/FDI costs (II)  Distance – transportation costs, communication  Cultural affinity – cultural ties → economic ties  Geography – common borders, landlocked countries, ocean harbors, lack of mountains  Multi-national corporations – trade among divisions from different countries  Tariffs, customs, different language/money (studies on EMU)  Regulation, legal origin, transaction costs.

19. Distance and gravity: towards Dyadic dummies Distance explains around 45% variation in transport costs. Regressions of trade flows on both distance and proxy of trade costs still gives significant coefficient on distance (although the magnitude is lower). In other words distance must be a proxy for both trade costs and other information/transaction costs. The use of dyadic dummies control for a full batteries of potential time invariant trade bilateral trade/FDI costs.

20. Estimation of the Gravity Equation with Dyadic dummies: Empirical modelling Hence, our panel gravity model for FDI inflows from country i to country j in year t is as follows in the log form + The dummies represent various fixed effects specifications such as bilateral fixed effects, target FE, sender FE and year fixed effect. The latter in all specifications.

21. Data and Methods We restricted our sample to 34 OECD members, accounting for 70% of global FDI inflows. This study uses both bilateral country level FDI inward flows as well as bilateral country level FDI stock (as a robustness check). However, the main variable of interest is bilateral FDI inward flow. The main specifications use panel FDI data between 1985-2013.

22. Data and Methods The 34 OECD countries included are Austria, Australia, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, Norway, New Zealand, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, UK and the USA. “Target” indicates the country which is the recipient of the FDI and “sender” indicates the country is the sender of the FDI.

23. 3 Data and Methods Bilateral FDI inflow variable ItemPercentageCount Theoretical observations100.0032538 (34*33*2 9) Reported positive observations39.8612970 Reported zeros8.982923 Reported negative observations11.973895 Reported missing observations3.161027 Not reported in the dataset36.0311723

24. Data and Methods FDI flows with a negative sign indicate that at least one of the three components of FDI (equity capital, reinvested earnings or intra-company loans) is negative and not offset by positive amounts of the remaining components. These are instances of reverse investment or disinvestment.” Negative flows have real economic meaning, and, because of their numerical importance, we cannot get rid of them without losing consistency. Negative stock values are generally the consequences of accounting methods (they also be recorded when continuous losses in the direct investment enterprise lead to negative reserves) and we will treat them as zero.

25. Data and Methods In addition, a range of country specific factors are controlled for in the empirical approach. We collected the most recently available data on bilateral FDI flows, GDP and GDP per capita (sender and target), bilateral distance and the shares of manufacturing output, exports and imports in total GDP.

26. Estimation methodAdvantagesDisadvantages OLSSimpleLoss if information Elimination of zeros Biased coefficients HeckmanWidely usedValidity of exclusion restrictions Panel fixed effectsSimple Control for unobserved heterogeneity Loss of info (constants dropped) Elimination of zero flows Sample selection bias Assumption: Serially Uncorrelated error (if random walk -> First Differencing) PPML (Poisson Pseudo Maximum Likelihood) Deal with zeros Unbiased estimates All observations are weighted equally It may present limited dependent variable bias when a significant part of the observations are censored

27. Panel estimates of the effects of EU membership on FDI inflows- Baseline (1)(2) Dependent variable: Panel Fixed Effects PPML (Poisson) ln(1+FDI inflows) FDI inflows EU member (target)0.285***0.320* (0.077)(0.163) EU member (sender)-0.010.828*** (0.079)(0.191) Ln(GDP, target)0.473***3.799*** (0.056)(1.432) Ln(GDP, sender)0.500***3.903*** (0.154)(1.462) Ln(GDP per capita, target)0.18-1.489 (0.158)(1.513) Ln(GDP per capita, sender)1.450***-1.125 (0.154)(1.623) Observations32,52832,147

28. Robustness checks  Heckman  Lags  First differencing  FDI stocks

29. Panel estimates of the effects of EU membership on FDI inflows Heckman Dependent variable:Ln(1+FDI) Dummy 1(FDI>0) EU member (target)0.132*** (0.050) EU member (sender)0.199*** (0.050) Ln(GDP, target)0.686*** (0.226) Ln(GDP, sender)0.766*** (0.226) Ln(GDP per capita, target)-0.010.230*** (0.255)(0.017) Ln(GDP per capita, sender)1.655*** (0.254) Manufacturing value added/GDP0.005*** (target)(0.002) Exports/GDP-0.013*** (target)(0.001) Imports/GDP0.011*** (target)(0.002) Mills’ Ratio1.043*** (0.164) Observations32,528

30. VARIABLESlog(1+FDI) EU member_recipient0.47750***0.48156***0.48485***0.47971***0.03956 (0.11919)(0.13189)(0.13142)(0.13091)(0.11219) EU member_sender-0.21377**(0.15597)(0.13994)(0.12660)0.16993 (0.10610)(0.11888)(0.11864)(0.11832)(0.10385) lag1EUmember_sender0.36161***-0.24870**-0.25004** (0.09549)(0.11551)(0.11917)(0.11922)(0.11927) lag1EUmember_recipient-0.33219***0.04467(0.02608) (0.10476)(0.12207)(0.12894)(0.12899)(0.12905) lag2EUmember_sender0.58412***0.034220.02915 (0.09623)(0.11858)(0.12706)(0.12712) lag2EUmember_recipient-0.37082***0.00573(0.10340) (0.10916)(0.12750)(0.13333)(0.13339) lag3EUmember_sender0.57328***0.037760.03003 (0.10252)(0.11443)(0.11475) lag3EUmember_recipient-0.29740***0.41898***0.41000*** (0.11347)(0.13511)(0.13502) lag4EUmember_sender0.55228***0.49703*** (0.11373)(0.11419) lag4EUmember_recipient-0.61537***-0.65023*** (0.11680)(0.11627) lead1EUmember_sender-0.30928*** (0.11329) lead1EUmember_recipient0.44527*** (0.12597) Joint F-test EU membership of recipients F 8.140007.210004.660007.750007.00000 p-values 0.000300.000100.001000.00000 Constant1.72163***6.04786***7.08343***7.15384***3.01739*** (0.20174)(0.04815)(0.20906)(0.20800)(0.17251) Observations31,41630,29429,17228,05026,928 R-squared0.452490.450740.448650.446710.4602 Year FEYes Bilateral FEYes Clusteredpairid

31. VARIABLESlog(1+fdi)[t-t(t-1)] D.EUmember_recipientt-(t-1)0.29890***0.47226***0.45682*** (0.111)(0.124)(0.125) D.EUmember_sendert-(t-1)-0.19203*0.043560.02877 (0.107)(0.120)(0.121) LD.Eumember/recipient(t-1)-(t-2)0.34448***0.47786*** (0.113)(0.129) LD.Eumember/sender(t-1)-(t-2)-0.36655***-0.10008 (0.112)(0.128) L2D.Eumember/recipien t(t-2)-(t-3)0.50283*** (0.113) L2D.Eumember/sender(t-2)-(t-3)-0.12123 (0.111) Observations25,03023,97022,931 R-squared0.53540.543130.5499 Bilateral FEYes Clusteredpairid

32. VARIABLESlog(1+FDIstock)FDI_instocklog(1+FDIstock)FDI_stseenmills logGDP_sender0.46807***1.029950.15248 0.07770.841380.16234 logGDP_recipient0.51009***1.140440.07738 0.077650.85870.16265 logGDPercapita_sender1.61543***2.86921***3.11448*** 0.143240.932120.18589 logGDPercapita_recipient-0.02720.70833-0.086440.69983*** 0.146710.917310.1870.01823 EUmember_sender0.05140.93230***0.33922*** 0.10760.167690.03872 EUmember_recipient0.165810.34052***0.17918*** 0.10890.112550.03789 indshare0.01258*** 0.0018 expshare-0.03281*** 0.00144 impshare0.02870*** 0.00165 lambda-0.15486** 0.07525 Constant-26.48975***-63.14862***-26.86458***-7.02773*** 2.0302111.077432.515360.19724 Observations34,51030,39934,510 R-squared0.648020.83714 Year FEYes Bilateral FEYes Clusteredpairid No *** p<0.01, ** p<0.05, * p<0.1

33. UK in the wider EU context  We re-estimate the model with countries target Fixed Effects (Germany as omitted country)  We report the value for these FE target dummies in a PPML regresssion for the biggest EU economies (vis-à-vis Germany)  UK  Italy  France

34. PPML VARIABLESFDI inflows EU member (target)0.43204***0.52749***0.52228** (0.15483)(0.16262)(0.21373) EU member (sender) 0.70281***0.64160***2.14222*** (0.26451)(0.24252)(0.35328) UK_target 0.658811.69040*1.95906** (1.04006)(0.94501)(0.95009) IT_target 1.559453.02326*3.60701** (1.76599)(1.60887)(1.61262) FR_target 0.677221.54149*1.77123** (0.90944)(0.82448)(0.82863) NAFTA (target) -1.25810***-1.26764*** (0.30208)(0.31117) NAFTA (sender) -0.10138-0.13929 (0.32661)(0.33183) EFTA (target) -0.05714 (0.34074) EFTA (sender) 2.00750*** (0.41237) ln (GDP, target) 4.39908**6.88251***6.88330*** (1.81233)(1.94985)(1.98005) ln (GDP, sender) 3.13317*3.32090**3.48405** (1.61244)(1.68975)(1.70589) ln(GDPercapita, target) -1.36098-4.35390**-4.35378** (1.87510)(2.04976)(2.11090) ln(GDPercapita, sender) -0.86455-1.11639-1.40904 (1.85824)(1.97327)(2.00566) Observations30535 R-squared0.482040.491480.492 Year FEYes Bilateral FEYes Clusteredpairid

35. PPML VARIABLESFDI inflows EU member (target)0.41632***0.52749***0.49049** (0.15778)(0.16262)(0.21290) EU member (sender)0.68614***0.64160***2.08118*** (0.25032)(0.24252)(0.33709) UK_target2.74008**2.11058*3.88027*** (1.06655)(1.14285)(1.08791) IT_target2.514751.738774.14640** (1.71787)(2.32089)(1.71731) FR_target2.01093**1.162642.96490*** (0.94543)(0.96389)(0.95585) NAFTA (target)-1.25810***-1.31727*** (0.30208)(0.34789) NAFTA (sender)-0.10138-0.3551 (0.32661)(0.30115) EFTA (target)-0.05534 (0.33388) EFTA (sender)1.83700*** (0.41008) ln (GDP, target)4.08158**6.88251***6.60223*** (1.86703)(1.94985)(2.01239) ln (GDP, sender)2.87373*3.32090**3.25592* (1.59033)(1.68975)(1.70653) ln(GDPercapita, target)-1.00508-4.35390**-4.03957* (1.94729)(2.04976)(2.14621) ln(GDPercapita, sender)-0.62783-1.11639-1.19173 (1.85991)(1.97327)(2.02504) Observations30,82230,53530,822 R-squared0.420090.491480.42958 Year FEYes Target FE Yes Sender FE Yes Clusteredpairid

37. Conclusions  This paper reports a positive, large and highly significant estimates of the FDI premium from EU membership using OECD spanning from 1985 to 2013.  The estimates show that EU membership increased Foreign Direct Investment inflows by 28% over the period 1985 to 2013 confirming that EU membership robustly increases net FDI inflows.  The paper tests this hypothesis with state of the art empirical modelling by highlighting how 28% falls in the middle of the most conservative estimate impact, 14% (Heckman) and the highest estimates impact, 38% (PPML).  However, and more importantly, the paper endorses FDI as a channel (in addition to trade) for possible payoffs from European Union deep economic integration.

38. Conclusions (II)  The modelling strategy follows the new structural gravity approach. The gravity model has been a staple of international economics. It explains bilateral cross-border flows (trade, migration, investment, etc.) based on the relative size and distance between countries or regions.  Is the participation to the European Free Trade Association (EFTA), or the European Economic Area (EEA) or even NAFTA a more rewarding alternative to the European Union?  This research rules out this options out by a full set of counterfactual dummies: there is no empirical evidence that being part of EEA or EFTA or NAFTA could have earned higher benefit in terms of FDI inflows.

39. Conclusions (III)  The premium for being in the EU is even higher for UK vis-à-vis Germany.  In our preferred PPML estimate we gauge that UK could have lost up to 37% investment had it not decided to join EU in 1973.  We think that our results have important policy implications particularly within the on-going debate, (in UK and beyond) on the economic advantages and disadvantages of being part of the European Union.