The determinants of US Foreign Direct Investment (FDI) outflows, a static and dynamic panel data approach

1. The determinants of US Foreign Direct Investment (FDI) outflows, a static and dynamic panel data approach ESDS International Annual Conference 2006 Salas Martínez Milton Hugo27th - November – 2006 ESDS Annual Conference

2. CONTENTS • OBJECTIVE • MOTIVATION • CONTRIBUTION • LITERATURE REVIEW • MODEL • DATA • ESTIMATIONS • CONCLUSION

3. OBJECTIVE • To study the determinants of USA Foreign Direct Investment (FDI) flows from USA to 52 countries in the period 1982-2003 • We use: • Static panel data approach • Dynamic panel data approach

4. MOTIVATION • Significant increase of FDI flows around the world (15%) • According to the UNCTAD, FDI helps the economies to: • to generate employment, • to raise productivity • to enhance exports, • to transfer skills and technologies, • to contribute to the long-term economic development of the world's developing countries. • Table 1 shows that FDI is becoming a more significant resource of Investment into the countries.

5. Table 1: Inward FDI Flows as a Percentage of Gross Fixed Capital Formation, by Host Region and Economy, 1970 – 2004Source: UNCTADin the ESDS database

6. CONTRIBUTION • A dynamic panel data approach to study the determinants of FDI (scarcely employed) • We extend the Knowledge Capital Model of Markusen (1998) by introducing market capital imperfections in the knowledge capital model. • Exchange Rate • Volatility of the Exchange Rate • We use different approaches of the variables that are estimated in the Knowledge Capital Model by other authors • We add “policy” variables in the model: Inflation, Infrastructure and Trade Openness

7. LITERATURE REVIEW • 2 fields in the FDI literature: International Trade Theory and Exchange Rates. • International Trade Theory • Markusen (1997): Knowledge Capital Model • Analyze both types, our main reference. • Market size • Skilled labour difference • Difference is market size (RGDP) • Transport Costs • Investment costs • Helpman, Melitz and Yeaple (2003): Heterogeneity in industries is an important factor to attract FDI. • Markusen et al (1998) estimate the Knowledge Capital Model

8. LITERATURE REVIEW • Exchange rates and FDI: • Froot and Stein (1991) • Model of FDI and exchange rates • They claim that when the capital markets are imperfect (information) the exchange rate plays a very important role as a determinant of FDI • Their main argument is the Wealth approach • Asymmetries in assets’ payoffs • Chakabrarti and Scholnik (2001): Exchange Rate Dynamics and FDI • Lucas (1993): Production costs • Blonigen (1997): Wealth approach • Goldberg and Kostald (1994): risk aversion, volatility of the Exchange Rate

9. THE MODEL • We estimate three models: • 1.- The standard model of Markusen: • FDI(i,t) =β0 +β1(rgdp(i,t)) + β2(rgdpdiff^2(i,t))+β3(skilleddiff(i,t))+ β4(rgdpdiff*skilleddiff(i,t)) +β5(freight(i,t)) + β6(freight*skilleddiff^2(i,t)) +β7(polcons(i,t)) + u(i,t) • 2.- The augmented model: • FDI(i,t) =β0 +β1(rgdp(i,t)) + β2(rgdpdiff^2(i,t))+β3(skilleddiff(i,t))+ β4(rgdpdiff*skilleddiff(i,t)) +β5(freight(i,t)) + β6(freight*skilleddiff^2(i,t)) +β7(polcons(i,t)) + β8(realxr(i,t)) +β9(volrealxr(i,t)) + u(i,t)

10. THE MODEL (CONT) • 3.- The final model • FDI(i,t) =β +β1(rgdp(i,t)) + β2(rgdpdiff^2(i,t))+β3(skilleddiff(i,t))+ β4(rgdpdiff*skilleddiff(i,t)) +β5(freight(i,t)) + β6(freight*skilleddiff^2(i,t)) +β7(polcons(i,t)) + β8(realxr(i,t)) +β9(volrealxr(i,t)) + β10(inflation(i,t)) + β11(openness(i,t)) +β9(infrastructure(i,t)) u(i,t) • Where i=52 and t=22 • The period is from 1982 to 2003 • Number of observations: 1144

11. DATA • Disaggregated USA FDI inflows: US Bureau of Economic Analysis • Real GDP (Market size): World Bank Development Indicators of the ESDS, constant prices 2000 • Freight: CIF/FOB imports, taken from the DOTS (Direction of Trade Statistics) IMF Dataset, ESDS • Real Exchange Rate: The reporting economy/USD nominal exchange rate, IFS of the IMF, in the ESDS data base. [(e)(p*)]/p. • Volatility of the Exchange Rate: Monthly observations. Standard Deviation

12. DATA • Skilled labour Differences: • Heads et al (2002) suggests changing the approach for skilled differences that Markusen et al (1998) use in their model. • Barro Lee Data Set: Educational attainment of the population over 25 years. We interpolate this data because they are reported every 5 years in order to have yearly observations. World Bank. • Political Constraints: Heinz (2003), credible commitment, how the political actors are constrained when willing to change political decisions • Inflation: World Bank Development Indicators, ESDS. • Openness in constant prices: (X + M)/RGDP, World Bank Development Indicators, ESDS. Missing data were taken from the V. 6.2 World Penn Table. • Infrastructure: Number of telephone lines per 1000 habitants, World Bank Development Indicators, ESDS

13. ESTIMATION • We estimate the model by: • Fixed Effects • Random Effects • FGLS • Arellano Bond

14. TABLE 2: The determinants of FDI. Fixed Effects Regression

15. TABLE 3: The determinants of FDI. Random Effects Regression

16. ESTIMATION (cont) • By using H test (Hausman test), our model predicts it is better to choose the random effects model • However, our model is not trustable for two reasons: • Heteroscedasticty: White test, Qi and Stengos (1994) • Heteroscedasticity across the panels: Wooldridge (2002) • So we have to use FGLS to correct both problems.

17. TABLE 4: The determinants of FDI. FGLS Regression

18. ARELLANO BOND Our estimates could be biased given the autocorrelation in the panels • Autocorrelation within the panels: Druker (2003) and Woldridge (2003) • Our 3 models present this problem • Estimating a dynamic model with the lagged value of FDI with FE and RE would give us biased estimates • It also allows us to correct the possible endogeneity between exchange rates and volatility of the exchange rate

19. ARELLANO BOND • There is also an issue we can explore with these estimator: Campa (1993) argues that the firm’s choice is not to look at the market today, at any moment in time the firm has to decide to enter the foreign economy, it incur a cost but does not know the final outcome at the period t+1 given that the investment is done in period t. • So we introduce the lagged values of the exchange rate and volatility of the exchange

20. TABLE 5: The determinants of FDI. Arellano Bond Estimator

21. TABLE 5: The determinants of FDI. Arellano Bond Estimator (cont) Constant suppressed. Numbers reported in parentheses are the standard errors; * and ** indicates significance at the 5% and 1% respectively

22. CONCLUSION • The Knowledge Capital model has empirical evidence when we use the new approach of the skilled differences variable suggested by Heads et al (2002) • By extending the model with the exchange Rate and Volatility of the Exchange Rate we found empirical evidence supporting a positive relationship of exchange rate and FDI and a negative relationship of the volatility of the exchange rate with FDI • We also found that the index of political constraint is important for the decision of Foreign Direct Investors • However, we don’t found a strong evidence of our policy variables