Acemoglu, D. 2010. “Theory, General Equilibrium, and Political Economy in Development Economics.” Journal of Economic Perspectives 24(3):17–32.Google Scholar
Aitken, B., and A. Harrison. 1999. “Do Domestic Firms Benefit From Foreign Direct Investment? Evidence from Venezuela.” American Economic Review 89(3):605–518.Google Scholar
Alfaro, L., and A. Charlton. 2007. Intra-Industry Foreign Direct Investment. National Bureau of Economic Reserarch WP 13447. Cambridge, MA.Google Scholar
Alkawaz, A. 2006. Non-Performing Industries in the Arab World. Kuwait: Arab Planning Institute.Google Scholar
Arellano, M., and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” The Review of Economic Studies 58(2):277–97.CrossrefGoogle Scholar
Baltagi, B. H. (ed.). 2000. Non Stationary Panels, Panel Cointegration and Dynamic Panels, 161–78. Advances in Econometrics, Vol. 15. Amsterdam: JAI Press.Google Scholar
Barro, R., and J.-W. Lee. 2010. “A New Data Set of Educational Attainment in the World, 1950–2010.” NBER Working Paper No. 15902.Google Scholar
Barro, R. J., and X. Sala-i-Martin. 1995. Economic Growth. New York: McGraw-Hill.Google Scholar
Baum, C. F., and M. E. Schaffer. 2003. “Instrumental Variables and GMM: Estimation and Testing.” The Stats Journal 3(1):1–31.Google Scholar
Bhagwati, J. N. 1978. Anatomy and Consequences of Exchange Control Regimes. National Bureau of Economic Reserarch.Google Scholar
Blackorby, C., and R. R. Russell. 1989. “Will the Real Elasticity of Substitution Please Stand up? (A Comparison of the Allan/Uzawa and Morishima Elasticities).” American Economic Review 79(4):882–88.Google Scholar
Blundell, R., and S. Bond. 1999. “GMM Estimation with Persistent Panel Data: An Application to Production Functions.” The Institute of Fiscal Studies Working Paper No. 99/4.Google Scholar
Breitung, J. 2000. “The Local Power of Some Unit Root Tests for Panel Data.” In Advances in Econometrics 15: Non-Stationary Panels, Panel Cointegration, and Dynamic Panels, edited by B. Balatagi, 161–178. Amsterdam.Google Scholar
Buckly, P.J. and M. Gasson. 1976. “The Future of the Multinational Enterprise, London, McMillan.Google Scholar
Carkovic, M., and R. Levine. 2002. “Does Foreign Direct Investment Accelerate Growth?” In Does Foreign Direct Investment Promote Development? edited by T. H. Moran, E. M. Graham, and M. Blomstrom. Washington, DC: Institute for International Economics.Google Scholar
Cook, D. 2002. “Education and Growth Instrumental Variable Estimates.” Department of Economics Working Paper, Hong Kong University of Science and Technology.Google Scholar
Coase, R. H. 1937. “The Nature of the Firm.” Economica 4:1937.Google Scholar
Dickey, D. A., and W. Fuller. 1979. “Distributions of Estimators for Autoregressive Time Series with a Unit Root.” Journal of American Statistical Association 74:427–31.Google Scholar
Dunning, J. H. 1977. “Trade, Location of Economic Activity and the MNE: A Search for an Eclectic Approach.” In The International Allocation of Economic Activity. Edited by B. Ohlin, P.O. Hesseborn, and P. M. Wijkman, 395–418, London: McMillan.Google Scholar
Grossman, G., and E. Helpman. 1991. Innovation and Growth in the Global Economy, 119. Cambridge, MA: MIT Press.Google Scholar
Haddad, M., and A. Harrison. 1993. “Are There Positive Spillovers From Foreign Direct Investment? Evidence from Panel Data for Morocco.” Journal of Development Economics 42:51–74.CrossrefGoogle Scholar
Havranek, T., and Z. Irsova. 2012. “Publication Bias in the Literature on Foreign Direct Investment Spillovers.” Journal of Development Studies 48(10):1375–96.Google Scholar
Hymer, S. H. 1976. The International Operations of National Firms: A Study of Direct Foreign Investment. MIT Monographs in Economics 14. MIT Press: Cambridge, MA.Google Scholar
Jallab, M. S., N. B. P. Gbakou, and R. Sandretto. 2008. “Foreign Direct Investments, Macroeconomic Instability and Economic Growth in Arab Countries.” WP 08-17, Centre de la Recherche Scientifique, CNRS, France.Google Scholar
Jones, C., and P. Romer. 2010. “The New Kaldor Facts: Ideas, Institutions, Population, and Human Capital.” American Economic Journal: Macroeconomics 2010 2(1):224–45.Google Scholar
Jorgenson, D., and B. M. Fraumeni. 1992. “Investment in Education and U.S. Economic Growth.” Scandinavian Journal of Economics 94:51–70.Google Scholar
Kawai, H. 1994. “International Comparative Analysis of Economic Growth: Trade Liberalization and Productivity.” Developing Economies 32:372–97.Google Scholar
Mairesse, J., and B. H. Hall. 1996. “Estimating the Production Function of Research and Development in French and US Manufacturing Firms: An Exploration of Simultaneity Issues with GMM Methods.” In International Productivity Differences and Their Explanations, edited by K. Wagner and B. Van Ark, 285–315. Elsevier Science.Google Scholar
McMillan, D. P. 2013. Quantile Regression for Spatial Data. Springer Briefs in Regional Science. Springer.Google Scholar
Miller, E. 2008. “An Assessment of CES and Cobb-Douglas Production Functions.” Congressional Budget Office, WP 05.Google Scholar
Nelson, R., and E. Phelps. 1966. “Investments in Humans, Technology Diffusion, and Economic Growth.” American Economic Review 56(2):69–75.Google Scholar
Pedroni, P. 2004. “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis.” Econometric Theory 20:597–625.Google Scholar
Phillips, P. C. B. 2003. “Laws and Limits of Econometrics.” Economic Journal 113(486):c26–52.Google Scholar
Razzak, W. A. 2007. “A Perspective on Unit Root and Cointegration in Applied Macroeconomics.” The International Journal of Applied Econometrics and Quantitative Studies 4(1):77–102.Google Scholar
Razzak, W. A. 2010. “An Empirical Glimpse on MSEs in Four Arab Countries.” Journal of Economics and Econometric 53(1):59–89.Google Scholar
Rudebusch, G. 1993. “The Uncertain Unit Root in Real GNP.” American Economic Review 83:264–72.Google Scholar
Rugman, A. M. 1975. “Motives for Foreign Investment: The Market Imperfections and Risk Diversification Hypotheses.” Journal of World Trade Law 9:567–73.Google Scholar
Rugman, A. M. 1980. Multinationals in Canada: Theory, Performance and Economic Impact. M. Nijhoff: Boston, MA.Google Scholar
Thirlwall, A. 2012. “The Rhetoric and Reality of Trade Liberalization in Developing Countries.” Keynote Lecture at the 53rd Annual Conference of the Italian Economic Society, University of Matera.Google Scholar
Varum, C. A., V. C. Rocha, G. Alves, and L. Piscitello. 2011. “The Enhancing Effect of Human Capital on the FDI and Economic Growth Nexus.” Working Paper, University of Aveiro, Portugal, Presented at the First Workshop on the Economics and Econometrics of Education in Lisbon, January 2011.Google Scholar
White, H., and W. J. Granger. 2011. “Consideration of Trends in Time Series.” Journal of Time Series Econometrics 3:1.Google Scholar
Wieser, R. 2007. “R&D Productivity and Spillovers: Empirical Evidence at the Firm Level.” Journal of Economic Surveys 19(4):587–621.Google Scholar
About the article
Published Online: 2013-12-12
Rugman (1980) provides a number of examples.
For example see Balasubramanyam, Salisu, and Sapsford (1996) and Kawai (1994) on trade-FDI-growth. Carkovic and Levine (2002) found the effect of FDI on growth is not robust in the presence of openness of trade. Carkovic and Levine (2002) had an interaction term of per capita income and FDI and reports no growth effect. Alfaro et al. (2007) argue that FDI has significant growth effect in countries with relatively more developed financial markets. See also Aitken and Harrison (1999), Haddad and Harrison (1993), and Mansfield and Romeo (1980). The main problem in this literature apparently is an estimation problem pointed out by Carkovic and Levine (2002) and that is not taking into account simultaneity bias, country-specific effects, and the use of lagged dependent variables in growth regression.
There is a literature on estimating the rate of return on investments in human capital and research and development, see Wieser (2007) for a survey of the literature.
The concept of thick-modeling was first introduced by Granger and Yongil (2004).
The Cobb-Douglas Production function is , subscripts aside at this stage, is real output, is a constant exogenous technical progress, is the stock of physical capital, is labor input, and is the error term, which has classical properties. To account for FDI in the production function we assume that the effective stock of capital consists of , which denotes the domestic stock of capital, and , which is the foreign stock of capital, i.e. FDI stock. The production function would have an extra term , where is the rate of disembodied technical change. If the data have unit roots this linear time trend would be a misspecification issue. If, however, we find the data to be trended, but the trend is not stochastic we would have to have this linear trend term back in the specification. It is, however, extremely difficult to discern one from the other.
It would be important to include a measure of the quality of human capital too. Measurement of quality is tricky. There are some data, but the time series are short. Future research must take this variable into account.
The Jorgenson and Fraumeni (1992) method constructs a stock of human capital, which is based on lifetime earnings.
Jallab, Gbakou, and Sandretto (2008) is the only paper we are aware of on the issue of FDI and growth in the Arab countries using a proper estimation technique.
We use panel cointegration tests, the Johansen-Fisher test found in Maddala and Wu (1999), Kao’s (1999) residual test, and Pedroni’s (1999, 2004) residual test. The latter includes a number of tests, which allow for heterogeneous slope coefficients to vary across the panel (the panel v-test, panel test, panel Phillips-Perron test, panel ADF test, group test, group Phillips-Perron test and group ADF test). The null hypothesis is that the residuals are I(1) – no cointegration – and the alternative hypothesis is that the residuals are I(0). For the first 4 tests, the assumption is that under the alternative hypothesis, the residuals have a common AR coefficient. In the remaining 3 tests, the assumption is that the residuals under the alternative hypothesis have an individual AR coefficient. Kao (1999) test is similar to Pedroni’s test in principle, i.e. a residual-based test, but there are cross-section specific intercepts and homogenous coefficients in the first-stage regressors. The null hypothesis is that the residuals are I(1). The Maddala and Wu (1999) Johansen test is similar to Johansen’s time series tests, i.e. a maximum eigenvalue test.
Only the Pedroni (2004) test(s) for the Asian panel has high the p values.
We use a variety of common test statistics such as the Dickey-Fuller (1979), the ADF test, Said and Dickey (1984; Phillips and Perron (1988), and Elliot (1999). We also use different specifications (with and without trend), and test the lag structure using various testing criteria. We also tested the panel of the five countries for a unit root using a variety of common tests such as Levin, Lin, and Chu (2002), Breitung (2000), Im, Pesaran, and Shin (2003), Hadri (2000), Sarno and Taylor (1998) and Taylor and Sarno (1998).
We regress each of the three explanatory variables on a constant and the set of instruments, retrieve the residuals and then estimate the equations with the residuals as additional regressors. We test the hypothesis that the set of the coefficients of the residuals are zero using both F and Chi-squared.
The GMM estimator minimizes with respect to the coefficients matrix for a chosen weighting matrix where ; and is a matrix of instruments.
We found that the p values of the F statistics of the first-stage 2SLS estimator to be very significant, 0.0000 for all six regression specifications for the Arab and the Asian panels. Thus, there is a strong correlation between the instruments and the endogenous variables. See Baum and Schaffer (2003).
We do not use dynamic GMM (Arellano and Bond 1991; Arellano and Bover 1995 and Blundell and Bond 1999) because we are (1) interested in the long-run point elasticity to compute the rate of returns, and (2) because we have a short panel, i.e. N is small.
We also allowed the share of human capital to vary across countries, but we do not report the results to save space. The results are available upon request. We found the coefficient estimate to be insignificant for Algeria. We also found the coefficient estimate to be negative for Jordan and Morocco. A number of papers on growth-FDI seem to report negative coefficients for FDI or human capital in similar specifications (see for example, Kottaridi and Stengos (2010); Varum et al. (2011) and Borensztein, De Gregorio, and Lee (1998)). Finally we found the coefficient to be positive, sizable, and significant for Egypt and Tunisia.
Independent calculations of the ratios of gross operating surplus to GDP from National Income Accounts also reveal similarly high values. These estimates are between 0.35 and 0.78 depending on the specification.
We re-estimated the regressions (GMM) and fixed the coefficient for all Arab countries. The estimated elasticity (is an average elasticity across all Arab countries) is 0.22%.
This coefficient measures the distance from a constant returns to scale. There are different interpretations to this negative value. One is that the production function exhibits a decreasing return-to-scale. This suggests that the Arab markets are small, thus doubling output is costly and requires more than doubling inputs. It could also mean that output in the Arab markets is consistently priced below marginal cost. Basu and Fernald (1997) suggest that this interpretation and decreasing returns to scale sounds illogical for a profit-maximizing firm. However, there is evidence that the majority of firms in the Arab countries are small in size with negative value added, hence non-profitable firms, Alkawaz (2006). Of course a positive value means that the function exhibits an increasing return to scale.
Quantile regressions failed to produce any sensible results for Asia, which may indicate that distributional nonlinearity is insignificant.
A measure of cognitive skills as a proxy for the quality of human capital is found in Trends in International Math and Science (TIMSS), which is an international student’s assessment survey and reports country scores for students in 4th and 8th grades in more than 80 countries, every four years. In the first survey in 1995, the scores for Algeria, Egypt, Jordan, Morocco, and Tunisia were 394.15, 420.40, 439, 372.36, and 439 respectively. In the last published survey in 2007, these scores declined in all countries except Jordan, 381.75, 399.5, 454.5, 319, and 377 respectively. While Korea’s score increased from 568 in 1995 to 575 in 2007, both Thailand’s and Malaysia’s scores declined from 505.5 and 462 in 1995 to 472.5 and 456 in 2007 respectively.
Razzak (2010) estimated a CES production function using cross sectional data of thousands of observations for firms in Egypt, Lebanon, Morocco and Turkey.