Foreign Direct Investments – A Force Driving to Economic Growth.

1. Academy of Economic Studies Doctoral School of Finance and Banking Foreign Direct Investments – A Force Driving to Economic Growth. Evidence from Eastern European Countries MSc Student: Oana Simona Caraman Supervisor: Professor Moisă Altăr,PhD Bucharest, 2010

2. Contents • Introduction • Literature Review • Model • Data and Methodology • Empirical Results • Conclusions • Suggestions for Further Research • References

3. 1. Introduction • In this paper we intend to call into question the existing of a direct and positive impact of foreign direct investments on economic growth. We will resort to a panel data approach in order to capture the continuously evolving country-specific differences, thus eliminating many of the difficulties encountered in other types of estimations. • We will focus on the economy of seven Eastern European countries, namely: Romania, Bulgaria, Hungary, Poland, Moldova, Czech Republic and Slovak Republic, for the period 1993-2008, considering, by applying the methodology of panel cointegration and causality, the presence of heterogeneity in the estimated parameters and dynamics across countries.

4. 2. Literature Review 2.1. Theoretical background • Neoclassical growth model (Solow, R (1957)) - FDI is conceived as an addition to the capital stock of the target economy. Considering this, we could state that the influence of FDI on growth is similar to that of domestic capital: given the diminishing returns on capital, FDI has just a temporary impact on the target country’s growth rate. • Endogeneous growth model (Romer, P. (1986); Lucas, R. (1988)) - underlines the role of science and technology, human capital and externalities in economic development. FDI impacts economic growth by acting as an engine of technological diffusion coming from the developed world and being directed towards the target country.

5. Positive and significant correlation between FDI and economic growth (Bende-Nabende and Ford (1998); Soto (2000); Lu and Liu (2005)) • Positive results, but conditional on home country’s levels of human capital, infrastructure, financial market development, and so on (Borensztein, De Gregorio, and Lee (1998); Olofsdotter (1998); Nair-Reichert and Weinhold (2001); Bengoa and Sanchez-Robles (2003) ; Lai, Peng and Bao (2006); Kinoshita and Lu (2006)) • Insignificant or no relationship between foreign direct investments and economic development (De Mello (1999); (Bende-Nabende Ford, Santoso and Sen (2003); Laureti and Postiglione (2005); Carkovic and Levine (2005); Onaran and Stockhammer (2008); Lee and Chang (2009)) 2.2. Empirical studies

6. 3. Empirical Model where: εit is stochastic error term and β1,β2, β3, β4 are the parameters to be estimated andGDP - gross domestic product per capita FDI - net overall inflows of foreign direct investmentsDI - domestic investmentsTG - technological gap INF - Infrastructure

7. 4. Data All data used in this paper were obtained from the World Development Indicators 2009 of the World Bank. In order to standardize our data we have used some variables in natural logarithm (l_GDP, l_FDI and l_DI). • GDP – gross domestic product per capita expressed in US dollars – absolute values • FDI – net foreign direct investments inflows expressed in US dollars – absolute values • DI – domestic investments expressed in US dollars – absolute values • TG – technological gap rendered as an economic gap, according to Li and Liu (2004), as: • INF – infrastructure reflected by appealing to PCA based on road density, energy consumption and telephone lines.

8. 5. Empirical Analysis and Results 5.1. Basic information

9. For GDP, the highest ascension is to be attributed to Slovak Republic and the lowest to Moldova. • For FDI increase, top position comes to Bulgaria (as revealed by the graphs), the lowest position belonging to Poland.

10. Hereinafter is presented the correlation between the variables considered in this paper, that is l_GDP, l_FDI, l_DI, TG and INF.

11. A more interesting graphic clearly rendering the relationship between GDP and FDI is obtained by grouping in a single graph the gross domestic product and the foreign direct investments series, by stacking cross-sections.

12. 5.2. Series Stationarity We have started by performing a panel unit root test – Im, Pesaran, Shin (IPS) which specifies a separate ADF regression for each cross section: where the null hypothesis (the series contains a unit root I(1)) might be rendered as follows: while the alternative hypothesis (some cross-sections do not have unit root) shall be:

13. The hypothesis that the variables contain a unit root cannot be rejected. When first difference is used, unit root non-stationarity is rejected at the 1%, respectively 10% significance level, resulting in all series being I(1).

14. 5.3. Parameter estimation After having analyzed the series stationarity, we have proceeded to the analysis of the parameter significance while resorting to the following estimation methods: • Ordinary Least Squares (OLS) • Generalized Method of Moments (GMM) Considering the specific features characterizing each country, it is not quite suitable to use panel estimation methods with none effects. For this reason, we also resort to fixed effects (FE) and random effects (RE) estimates for both OLS and GMM methods, followed by a Hausman test which may help us in selecting the most appropriate model.

15. 5.3.1. Fixed effects model Suppose we have the following equation: In order to see how the fixed effects model works, we can decompose the disturbance term, uit, into an individual specific effect, λi(encapsulating all of the variables that affect yitcross-sectionally but without varying over time) and the ‘remainder disturbance’, vit, which varies over time and entities (capturing everything that is left unexplained about yit). Therefore we can rewrite the initial equation and obtain:

16. 5.3.2. Random effects model Under the random effects model, the intercepts for each cross-sectional unit are assumed to arise from a common intercept α (the same for all cross-sectional units and over time), plus a random variable ηithat varies cross-sectionally but is constant over time, where ηimeasures the random deviation of each cross-section’s intercept term from the intercept term α. Unlike the fixed effects model, the random effects one does not capture the heterogeneity in the cross-sectional dimension by means of dummy variables but via the ηiterms.

17. 5.3.3. Hausman test The generally accepted way of choosing between fixed and random effects is running a Hausman test. The Hausman test checks a more efficient model against a less efficient but consistent model to make sure that the more efficient model also gives consistent results. H0: both estimators are consistent, but the random effect estimator is more efficient (has smaller asymptotic variance) than the fixed effect one. H1: one or both of these estimators is/are inconsistent. If we accept the null hypothesis, the random effects model shall prevail.

18. OLS and GMM Estimation with no effects

19. OLS and GMM Estimation with fixed effects

20. OLS and GMM Estimation with random effects

21. As we have just seen, foreign direct investments, direct investments and infrastructure are significant and exert a positive influence on the gross domestic product in each and every case, while higher the technological gap between a leading country and country i determines, as expected, lower gross domestic product per capita. • As it can be seen from the tables above, the results are highly similar and significant for both OLS and GMM estimation, no matter if none, fixed or random effects are used, therefore indicating the robustness of such results.

22. Yet, we have tried to see whether the fixed or random effects models are more appropriate for our analysis, resorting for this end to the Hausman test. As p-value indicates us that in both cases the null hypothesis is to be accepted, we assume that the random effect model is both consistent and more efficient and it shall prevail.

23. 5.4. Panel Cointegration Testing Given that all series considered are I(1), we have tested the cointegration relationship, by appealing to Pedroni cointegration test, which has extended the framework of Engel-Granger in order to test cointegration in panel data into two steps: • It starts with computing the residual from the regression equation: • If the series are cointegrated, this term should be a stationary variable. Thus, stationarity is achieved by testing whether ρit is unity in:

24. The null hypothesis, associated with Pedroni's test procedure is: The alternative hypothesis for between dimension would be: While for the within dimension would be: Pedroni has developed seven tests for cointegration in panel data, where there is more than one independent variable in the regression equation: • four such tests are based on within dimension statistics (panel v-stat, panel rho-stat, panel pp-stat and panel adf-stat) • three on between dimension statistics (group rho-stat, group pp-stat and group adf-stat)

25. The non-parametric and parametric tests (panel pp-stat and group-pp stat, panel adf-stat and group adf-stat) are deemed to be more powerful for smaller time dimensions (Bonham and Gangnes (2007); Salotti (2008)).

26. Given that our time series observations are restricted to 16 years (1993-2008), we shall consider the non parametric and parametric results - panel pp-stat and group pp-stat, respectively panel adf-stat and group adf-stat. • The conclusion drawn is that, for a significance level of 10%, 5% respectively 1%, the null hypothesis of no cointegration is to be rejected, resulting in a cointegration relationship of the variables concerned.

27. 5.5. Granger Causality • The approach of Granger (1969) relating to whether x causes y is to see how much of the current y may be explained by the past values of y and subsequently to see whether, by adding lagged values to x, we succeed in improving the explanation of y. • Granger causality runs, for all possible pairs of (x,y) series in the group, bi-variate regressions of the form: • The null hypothesis is, for the first regression, that x does not Granger – cause y and, for the second regression, that y does not Granger – cause x, meaning:

28. As revealed above, at a significance level of 1%, respectively 5%, there is a bi-directional causality between GDP and FDI.

29. 6. Conclusions In this paper we have examined, by using the panel cointegration and causality approach, the relationship existing between foreign direct investments and economic growth for seven Eastern European countries, drawing the following conclusions. • There is a positive impact of FDI on economic growth. • The analyzed variables are cointegrated, witnessing for a long-run relationship. • The causality between FDI and GDP per capita is bi-directional.

30. 7. Suggestions for further research • Our regression could be extended by introducing also the schooling variable (SCH), therefore reflecting the level of education of the target country, and thus its absorptive capacity. • Interaction terms such as FDI*TG*INF, FDI*TG*SCH and FDI*TG*INF*SCH, meaning the technological spillover of FDI conditional on infrastructure, on educational level, respectively on both infrastructure and educational level could be used in order to render the indirect impact of FDI on GDP.

31. References -Akinlo, A. (2004). Foreign Direct Investment and Growth in Nigeria. An Empirical Investigation. Journal of Policy Modeling, 26, 627-639.- Alfaro, L., Chanda, A., Kalemli-Ozcan, S. and Sayek, S. (2004). FDI and Economic Growth: the Role of Local Financial Markets. Journal of International Economics, 64, 89-112.- Apergis, N., Lyroudi, K. and Vamvakidis, A. (2008). The Relationship Between FDI and Economic Growth: Evidence from Transition Countries. Transition Studies Review, 15, 37-51. - Balasubramanyam, V. N., Salisu, M., & Sapsford, D. (1996). Foreign Direct Investment and Growth in EP and IS Countries. Economic Journal, 106(1), 92-105. - Baltagi, B. (2005). Econometric Analysis of Panel Data. Thrid Edition. John Wiley & Sons Ltd, West Sussex. - Barro, R., Sala-I-Martin (1995). Economic Growth. New York: McGraw Hill.- Barrios, S., Görg, H. and Strobl, E. (2005). Foreign Direct Investment, Competition Industrial Development in the Host Country. European Economic Review, 49, 1761 – 1784. - Bende-Nabende, A., Ford, J., Santoso, B., & Sen, S. (2003). The Interaction Between FDI, Output and the Spillover Variables: Co-integration and VAR Analyses for APEC, 1965-99. Applied Economics Letters, 10(3), 165-172. - Bengoa, M. and Sanchez-Robles, B. (2003), Foreign Direct Investment, Economic Freedom and Growth: New Evidence from Latin America, European Journal of Political Economy, 19, 529-545.

32. Blomstrom, M., Lipsey, R. E. and Zejan, M. (1992). What Explains Developing Countries Growth? Working paper no. 4132 - National Bureau of Economic Research, Cambridge, MA 02138, NBER working paper series. - Bonham, C., Gangnes, B.(2007). Fragmentation and East Asia Information Technology Trade. Applied Economics 39 (2), 215-228.- Borensztein, E., De Gregorio J., Lee J.W. (1998). How Does Foreign Direct Investment Affect Economic Growth. Journal of International Economics 45, 115–135.- Brems, S. (1970). A Growth Model for International Direct Investment. The American Economic Review, 60(3), 320-331.- Brooks, C. (2008). Introductory Econometrics for Finance. Second Edition. Cambridge University Press, New York.- Carkovic, M. and Levine, R. (2002), Does Foreign Direct Investment Accelerate Economic Growth? Working paper, University of Minnesota.- Chakraborty, C., & Basu, P. (2002). Foreign Direct Investment and Growth in India: A Cointegration Approach. Applied economics, 34(9), 1061-1073. - De Mello, L. R. (1999). Foreign Direct Investment-led Growth: Evidence from Time Series and Panel Data. Oxford Economic Papers, 51(1), 133-151.- Driffield, N., and Henry, M., (2007). Trade, FDI and Technology Diffusion in Developing Countries: The Role of Human Capital and Institutions. Working Paper, Aston Business School. - Dunning, J., Narula, R. (1996). Foreign Direct Investment and Government. Routledge London.

33. - Durham, J. B. (2004). Absorptive Capacity and The Effects of Foreign Direct Investment and Equity Foreign Portfolio Investment on Economic Growth. European Economic Review, 48(2), 285-306. - Granger, C. W. J. (1969) Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37, 424-438.- Greenaway, D., Nam, C.H., (1988). Industrialization and Macroeconomic Performance in Developing Countries under Alternative Trade Strategies, Kyklos 41 (3), 419-35.- Gries, T. (2002). Catching – Up, Falling Behind and the Role of FDI: A Model of Endogenous Growth and Development. The South African Journal of Economics, 70(4), 181-211. - Herzer, D., Klasen, S., Nowak-Lehmann D.F. (2008). In Search of FDI-led Growth in Developing Countries: The way forward. Economic Modelling, 25(5), 793-810.- Kholdy, S. (1995). Causality Between Foreign Investment and Spillover Efficiency. Applied Economics, 27(8), 745-749. - Kholdy, S., Sohrabian, A. (2006). Financial Markets, FDI, and Economic Growth: Granger Causality Tests in Panel Data Model, EFA Moscow Meetings.- Kinoshita, Y., Lu, C.H. 2006. On the Role of Absorptive capacity: FDI Maters to Growth. Working Paper Series no. 845 - William Davidson Institute, University of Michigan Stephen, M. Ross Business School.- Kottaridi, C., Stengos, T. (2010) Foreign Direct Investment, Human Capital and Nonlinearities in Economic Growth. Journal of Macroeconomics (accepted manuscript).- Lai, M., Peng, S., Bao, Q. (2006). Technology Spillovers, Absorptive Capacity and Economic Growth. China Economic Review, 17(3), 300-320.

34. - Laureti, L. and Postiglione, P. (2005). The Effects of Capital Inflows on the economic Growth in the MED Area. Journal of Policy Modelling, 27, 839-851. - Li, X. and Liu, X. (2005). Foreign Direct Investment and Economic Growth: An Increasingly Endogenous Relationship. World Development, 33(3), 393-407.- Lee, C.C.,Chang, C.P. (2009). FDI, Financial Development, and Economic Growth: International evidence. Journal of Applied Economics, 12, 249-271.- Lucas, R.E. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22, 3-42.- Lutkepohl, H. (1982). Non-causality due to Omitted Variables. Journal of Econometrics, 19(2-3), 367-378.- Maddala, G.S., and Wu, S., (1999) A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test, Oxford Bulletin of Economics and Statistics, 61, 631-52.- Markusen, J.R., Venables, A.J.(1999). Foreign Direct Investment as a Catalyst for Industrial Development, European Economic Review, 43, 335-356.- Nadeem, M. (2010). Long Run Effect of Technology Spillover through FDI on Economic Growth: A Panel Cointegration Approach. Working Paper Series, Cand Oecon, Aarhus University, Denmark.- Nair-Reichert, U., Weinhold, D. (2001). Causality Tests for Cross-country Panels: A New Look at FDI and Economic Growth in Developing Countries. Oxford Bulletin of Economics and Statistics, 63(2), 153-171.- Olofsdotter, K. (1998). Foreign Direct Investment, Country Capabilities and Economic Growth. EltwitschaftHches Archive, 134(3), 534-547.

35. - Onaran, O., Stockhammer, E. (2008). The effect of FDI and Foreign Trade on Wages in the Central and Eastern European Countries in the Post-transition Era: A Sectoral Analysis for the Manufacturing Industry. Structural Change and Economic Dynimics, 19(1), 66-80. - Oxelheim, L. (1996). Financial Markets in Transition: Globalization, Investment and Economic Growth. Routledge,London. - Pedroni, P., (2001). Purchasing Power Parity Tests in Cointegrated Panels. Review of Economics and Statistics, 83, 727-731. - Pesaran, M. H. Y. Shin, and Smith, R. J. (2001) Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16, pp. 289-326. - Romer, P. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, 94(5), 1002-1037. - Romer, P. (1987). Growth based on increasing Returns Due to Specialisation. American Economic Review, 77(2), 56-62. - Romer, P. (1990). Endogenous Technology Change. Journal of Political Economy, 98(5), 71- 102. - Salotti, S. (2008). The Long Run Determinants of Household Savings: Is There a Role for Wealth?. Working Paper- University Of Siena. - Sims, C. (1972). Are Threte Exogenous Variables in the Short-Run Production Relations?. Annals of Economic and Social Measurement, National Bureau of Economic Research, 1(1), 16-35. - Sjoholm, F. (1999). Technology Gap, Competition and Spillovers from Direct Investment: Evidence from Establishment Data. Journal of Development Studies, 36(1), 53-73.

36. - Solow, R (1957). Technical Change and the Aggregate Production Function. Review of Economics and Statistics, 39, 312-320. - Soto, M. (2000). Capital Flows and Growth in Developing Countries: Recent Empirical Evidence. Working paper no. 160 – OECD Development Centre, Research programme on capital movement and development.- Xu, B. (2000). Multinational Enterprises, Technology Diffusion, and Host Country Productivity Growth. Journal of Development Economics, 62(2), 477-493.- Zhang, K. H. (1999a). Foreign Direct Investment and Economic Growth: Evidence from ten East Asian Economies. Economia Internazionale, 51(4), 517-535.- Zhang, K. H. (1999b). How does FDI Interact with Economic Growth in a Large Developping Country? The Case of China. Economic System, 21(4), 291-304.

37. Thank you!

38. Appendix