Education adequacy and foreign direct investment inflows in developing countries

Transcription

1 Education adequacy and foreign direct investment inflows in developing countries Élisé Wendlassida Miningou, November 26, 2014 Abstract The current paper is an attempt at studying the relationship between the education system adequacy with regard to the needs of the economy in terms of skills and the FDI inflows in developing countries. We firstly estimate a proxy for the education adequacy using a stochastic frontier model and secondly, we investigate the relationship between the education adequacy measure and the FDI inflows as shown in a theoretical model that we developed. Empirical results suggest that among other determinants of FDI inflows included in this study, education adequacy appears to be one of the most important factors for attracting FDI in developing countries. In contrast, the mean years of schooling does not have any effect on FDI. In addition, results from this paper reveal that secondary vocational education could play a role in improving the adequacy level of the education system. Keywords: Human capital, Education adequacy, Labor market, FDI inflows, GRÉDI, Département d Économique, Université de Sherbrooke, 2500 Université boulevard, Sherbrooke, Quebec, J1K 2R1, Canada. 1

2 1 Introduction It is well known in the economic literature that there is a relationship between FDI and economic development. FDI could help countries not only increase the stock of capital, but also improve the productivity of the economy. In fact, the literature shows that that there exist a cross-country technological diffusion that could improve the economic productivity and FDI is one of the major channels (Helpman, 1997; Bwalya, 2006; Liu, 2008). Therefore, Brooks, Hasan, Lee, Son, and Zhuang (2010) show that many developing countries believe that FDI could allow them to escape the poverty trap. However, Borensztein, De Gregorio, and Lee (1998) and Xu (2000) find that FDI has a positive effect on productivity growth as well as income growth only if the recipient country has reached a minimum human capital threshold. FDI could thus be an important factor for economic growth and the level of human capital could affect the relationship between FDI and growth. In addition to allowing countries to take better advantage of technological diffusion, it is shown in the literature that the level of human capital could also affect the attractiveness of countries with respect to FDI. Several studies have thus investigated the determinants of FDI inflows and some of them focused on the role of human capital. However, the evidence of the relationship between FDI and human capital in developing countries remains mixed. According to Assuncao, Forte, and Teixeira (2011) the literature shows three main determinants of FDI: location aspects (infrastructure, human capital, etc.), institutions (corruption, political instability, etc.) and factors related to the new trade theory (openness of the economy, factor endowments, etc.). Human capital could thus be considered as one of the determinants for the location of FDI flows. This relationship has been demonstrated in many empirical stud- 1

3 ies in the literature. For instance, Brooks et al. (2010) show that human capital could be a relevant determinant for FDI inflows, especially in skilled labour intensive sectors where the level of education facilitates technological innovation and improves productivity. In addition, Noorbakhsh, Paloni, and Youssef (2001) find that human capital is one of the most important determinants for FDI flows toward developing countries. Noorbakhsh et al. (2001) also show that the importance of human capital for FDI becomes increasingly greater through time. However, other empirical findings suggest that there is no effect of human capital on FDI flows. For instance, Root and Ahmed (1978) find that human capital is not an important determinant for FDI. Another empirical investigation made by Narula (1996) suggests that even though human capital comes up with a positive sign, it is not a significant determinant of FDI flows in developing countries. Using a secondary education index to proxy the level of human capital, Cleeve (2008) also finds that the relationship between FDI and the human capital level is not conclusive. In addition, Cheng and Kwan (2000) use chinese regional level data to show that labour quality, in a variety of measures, is insignificant in explaining the regional distribution of FDI in China. Hong (2008) finds an insignificant impact of labour quality on the location of China s inward-fdi. Overall, empirical attempts in the literature to investigate human capital as a determinant of FDI lead to mixed results. The purpose of the current paper is to explore new evidence regarding the impact of human capital on FDI inflows in developing countries. Indeed, we believe that the quality of the labor force training with regard to the needs of the economic activities matters in attracting FDI. We think that labor force has to be well trained in the the most relevant fields in order to be able of attracting FDI. This adequacy between the education system and the needs 2

4 of the economy in terms of skills could be measured by the efficiency with which the human capital level is translated into income in the domestic economy. Well trained labor must be able to properly translate the human capital level into income in the economy. In other words, the efficiency of the skilled labor could give information about the quality of the skills the education system delivers with regard to the requirements of the economy in terms of abilities. Higher adequacy would allow labor to take better advantage of their skills in the labor market and acquire higher level of income compared to the lower adequacy case. In the current paper, we are trying to investigate the relationship between the education adequacy and the FDI inflows in developing countries. Using data from 1990 to 2010 on about 90 developing countries, we apply an efficiency measurement tool in order to assess the efficiency with which the human capital level is translated into income in the domestic economies as a proxy of the education adequacy. We thus investigate the effects of this education adequacy on the FDI inflows in developing countries. For the remaining of this paper, we will first present a simple theoretical model from which an econometric equation is derived and estimated. Afterward, we will present results of our empirical investigation and finally, we will present a conclusion and discussion about the findings. 2 Theoretical framework In this section, we are presenting a simple theoretical model that will link education adequacy and FDI inflows. From this model, an econometric equation will be derived later and estimated. Consider a small open developing economy producing a composite good Y t at each period t. We 3

5 assume a constant returns to scale technology that can be captured by a Cobb-Douglas function: Y t = A(q t )K 1 α t L α t (1) with L t the stock of labor, K t, the stock of capital, 0 < α < 1, the Cobb-Douglas parameter and A(q t ) = q t A 0 the total factor productivity (TFP) that depends positively on the education adequacy level denoted by q t and a scale parameter A 1 0. We assume that education adequacy is the source of the labor externalities. In other terms, total factor productivity depends on the education adequacy level that is the source of the labor force externalities. The first order conditions with respect to factors yield: r t = (1 α)a(q t )k α t (2) and w t = αa(q t )k 1 α (3) with k denoting the capital to labor ratio. The economy is opened and capital is perfectly mobile internationally. However, international capital flows are such that the domestic interest rate is equal to the international interest rate augmented by a country-risk premium: r t = rt + π t. π is related to internal factors such as political instability, corruption, individual freedom, degree of trade liberalization, quality of governance, etc. Equations 2 gives the capital to the labor ratio equation: 1 We assume that the greater the education adequacy level, the greater will be the productivity of the labor and that of the whole economy. 4

6 [ ] (1 α)a(qt ) 1/α k t = rt + π (4) t Using equation 3 and 4 yields: [ ] 1 α 1 α w t = αa(q t ) 1/α α rt + π t (5) with the derivatives 2 k q > 0; w q > 0. The stock of capital in the economy at the equilibrium is thus given by: [ ] (1 α)a(qt ) 1/α K t = k t L t = rt + π L t (6) t The stock of capital can be shared between the domestic component Kt and the foreign component K t so that K t = K t + K t with K t = K t 1 + F DI t and K t = K t 1 + Īt where Ī t is the domestic investment. We thus have K t = K t 1 + Īt + K t 1 + F DI t. Let s denote by λ the proportion of the FDI in the stock of capital so that K t F DI t = 1 + K t 1 +Īt+K t 1 F DI = 1 λ. The relationship between K t and F DI t is thus given by: F DI = λ t K t yielding the following equation: [ ] (1 α)a(qt ) 1/α F DI t = r λl t (7) + π Where F DI t is the foreign direct investment net inflows at time t. 2 The intuition behind these derivatives is that since a higher adequacy level could induce a higher productivity of the labor, the wage level will increase with the education adequacy level. Because of the labor productivity, the economy will need a smaller level of labor per unit of capital. 5

7 Equation 7 allows us to envision a causal relationship between FDI and education adequacy. The first implication is that both the stock of the labor force and the adequacy level of the education system have positive impact on FDI. As shown in the literature, the location of the FDI flows depends on the availability of the labor force. In addition to what is already shown in the literature, our theoretical model also suggests a positive relationship between the education adequacy level and FDI net inflows. Perfect adequacy means that the workers are perfectly efficient in translating their education level into income in the economy. In other words, they could generate the highest level of production compared to their level of education and it is not possible to do better with the same level of education. This relationship is intuitive because a higher adequacy level is associated with a higher productivity of the labor force and this could attract foreign investors looking for productive labor force in developing countries. Secondly, the international interest rate as well as factors related to the country-risk have negative impact on the FDI. In addition, the model shows that the intensity of the capital in the production process captured by 1 α also has a positive relationship with FDI inflows. This latter result means that countries with a higher demand in capital in the production process would better attract FDI, all things being equal. 3 Empirical strategy We are trying here to find out empirical evidence to support the theoretical findings derived from the above model. Consider a set of K variables x k with k = 1...K. We can give the following functional form to the interest rate: r = r + π = e k β kx k where β is a set of parameters and 6

8 x a matrix containing proxy variables for the country-risk and the international interest rate. The idea underlying this functional form is that the cost associated to risks -the interest rateis increasing more rapidly with respect to risk factors 3. Log-linearizing equation 7 yields the following equation for each observation i at time t: Log(F DI it /L it ) = 1 α Log(1 α) + Log(λ) + 1 α Log[A(q it)] 1 β k x itk (8) α By assuming fdi it = Log(F DI/L) it, Log[A(q it )] = Log(q it ) + Log(A 0 ), θ 0 = 1 αlog(1 α) + Log(λ) + Log(A 0 ), θ 1 = 1 α,, and θ 2k = 1 α β k and adding error terms u it, we can obtain the following econometric model that could be estimated: k fdi it = θ 0 + θ 1 Log(q it ) + k θ 2k x itk + u it (9) q it is a proxy for the education adequacy after controlling for all factors that could affect the inadequacy of the education system. Before estimating the equation 9, we need to estimate the education adequacy variable. 4 Education adequacy As already mentioned, a proxy for education adequacy could be the efficiency with which the education level is translated into income in the labor market. There is an optimal value of income that well trained workers from a given country could reach with their level of education. 3 The assumption here is somehow related to a risk aversion hypothesis: given an increase in the risk level, economic agents claim higher than proportional interest paiement. 7

9 Deviations from this optimal income could be a proxy for the education inadequacy 4. There are two main approaches for measuring efficiency (parametric and non parametric approaches). All these methods are based on the estimation of a frontier and the measurement of the technical efficiency through deviations from this frontier. Non parametric approaches do not allow to distinguish between inefficiency and noise, all deviations from the frontier being associated with inefficiency. However, Stochastic Frontier Analyse (SFA) is a parametric approach allowing a proper estimation of the technical efficiency, although this method needs assumption about the frontier function, the efficiency terms and the error terms. Let s define by gni it the level of income generated in the country i at time t proxied by the GNI per unit of employment, and η a set of parameters. Following Battese and Coelli (1995), and by assuming a Cobb Douglas function for the frontier, the SFA model that will be estimated is the following 5 : Log(gni it ) = η 0 +η 1 Log(MY S it )+η 2 Log(P RI it )+η 3 Log(SEC it )+η 4 Log(T ER it )+µ it ω it (10) where MY S is the mean years of schooling; P RI, SEC and T ER are control variables for the level of education: respectively, the gross enrollment rate in primary, secondary and tertiary education. ω is the technical inefficiency terms that has a normal truncated distribution and µ is the error terms that is normally distributed. The Battese and Coelli (1995) model allows 4 It seems important to isolate technical efficiency from some variables that could be associated to it. Otherwise, the value of the efficiency measure could be imperfect. So in the efficiency measurement, we will control for other economic variables that could affect the efficiency. 5 We have tried a translog specification and found that quadratic parameters are not significant. The Cobb Douglas function is thus convenient for the frontier. Recall that the Cobb Douglas function is a special case of the translog functional form, when the quasi-convexity assumption is relaxed. 8

10 the inefficiency terms to vary over time. Another specificity of this model is that ω it could be considered as random time varying individual effects. Heterogeneity between countries is thus somehow controlled for. The factors that could explain inefficiency are given by the inefficiency model as following: ω it = z it γ + ν it (11) where the distribution of ν it is normal truncated. z it is a matrix of explanatory variables that could explain the inefficiency of the observation i at time t. After controlling for the inefficiency explanatory factors, the technical efficiency (the education adequation proxy) is given by: q it = exp( ω it ) = exp( z it γ ν it ) (12) GNI per unit of employment captures worker s income at the level of each country. This output variable is used as a proxy for what the labor force generates in terms of income. The mean years of formal schooling is used as input capturing the workers level of education. Even though the mean years of schooling could indicate the duration of education, this variable does not give any information about the level in the education system (primary, secondary or tertiary education) the labor force has attained. This is the reason why we use the enrollment rate in primary, secondary and tertiary education as other input variables in order to control for differences in terms of level of education. As factors that could explain the inefficiency, we use variables controlling for the education specificities in each country and other key economic 9

11 variables 6. Table 3 7 gives descriptive statistics of all the variables used in order to estimate the education adequacy measure. All these data are collected from the World Bank s World Development Indicators database. Maximum likelihood method suggested by Battese and Coelli (1993) is applied in order to estimate the model described. Results from this estimation are presented in table 1 8. After controlling for some variables related to the education system as well as other control variables, results show that all the inputs have a positive effect on the output. The enrollment rate in the secondary education level seems to have the highest effect on the income per worker while the tertiary education enrollment rate appears to have the smallest effect. However, the sum of the coefficients is higher than 1 suggesting that an increase in all inputs could lead to a higher than proportional increase in the income per worker level. In addition, a higher secondary education duration as well as a higher share of vocational pupils at the secondary education level seem to be negatively associated with the inefficiency level. This means that promoting secondary vocational education could play a role in reducing the inadequacy of the education system in developing countries. Table 2 shows that the average technical efficiency is estimated to be about 55 %. This means that developing countries could use in average 45% less inputs to achieve the same level of GNI per person employed if the education system in developing countries was perfectly adequate. In other terms, with the actual education resources used, countries could achieve higher levels of 6 The choice of variables here is constrained by data availability 7 cf. annexes 8 A negative coefficient in the inefficiency model means that the considered variable is negatively linked to the inefficiency level. 10

12 Technical efficiency model Table 1: Education adequacy: econometric results Model 1 Model 2 Model 3 Intercept *** 5.512*** 2.593*** (0.322) (0.968) (0.931) Mean years of schooling *** 0.384*** (0.060) (0.103) (0.093) Gross primary education enrollment *** *** 0.122*** (0.072) (0.227) (0.227) Gross secondary education enrollment *** 1.118*** (0.051) (0.107) (0.117) Gross tertiairy education enrollment 0.250*** 0.084* 0.102** (0.018) (0.048) (0.045) Inefficiency model:explanatory factors for inefficiency Primary school starting age (1.028) (1.071) Secondary school starting age (1.023) (1.070) Primary education duration (1.021) (0.068) Secondary education duration * ** (0.082) (0.068) Secondary education vocational pupils (% of total pupils) *** *** (0.008) (0.005) Employment rate 0.046*** (0.006) Gross capital formation (% of GDP) *** (0.007) Control for regions NO NO YES *significant at 10%; ** significant at 5%; *** significant at 1 % Table 2: Descriptive statistics of the efficiency scores Obs Mean Std. Dev. Min Max Efficiency income. These results show that the education inadequacy level seems to be relatively high. 11

13 5 FDI and education adequacy With the adequacy proxy estimated in the above section, we will try to estimate the equation 9. As we have already mentioned, the matrix x contains other variables that could affect the FDI flows. According to Assuncao et al. (2011) there are 3 main factors that could affect FDI flows in a country: factors related to the localisation, factors related to institutions and factors related to openness. We will thus try to control for these factors by including appropriate variables in the matrix x. Table 3 shows descriptive statistics of variables used. Econometric results are shown in tables 4, 5 and 6. The pooled OLS estimations show a positive relationship between the education adequacy level and the FDI net inflows per unit of employment. This result holds even after controlling for each one of the 3 categories of control variables we considered or all of these control variables at the same time. In order to account for the panel structure of the data and also control for individual heterogeneity, we implement a fixed effects estimation of the equation 3. The fixed effects model also suggests a positive relationship between the education adequacy and the FDI inflows 9. In the literature on the determinant of FDI, some studies suggest that past FDI flows could also affect the actual FDI flows (Egger, 2001; Carstensen & Toubal, 2004; Naude & Krugell, 2007). For this reason, dynamic equations are sometimes estimated. In addition, authors in the literature find that FDI flows in a given country could have an impact on the country education achievements (Blomstrom & Kokko, 2003; Zhuang, 2008; Mughal & Vechiu, 2010). This may induce a reverse causality between the education adequacy and the FDI inflows raising 9 A Haussman specification test shows that the fixed effects model is preferred to the the random effects model 12

14 an endogeneity issue. In order to properly control for this endogeneity problem and include FDI lags in the matrix x, we implement dynamic panel GMM estimation technique introduced by (Arellano & Bond, 1991). This method allows controlling for the endogeneity by using lags of the endogeneous variables as instruments in the econometric equation and also control for autocorrelation issues that could appear after introducing lags of the dependant variable as regressors. We think that FDI inflows could also affect economic growth and openness 10. We thus control for the endogeneity associated with these two variables using the lags of these endogeneous regressors as instruments 11 As shown in table 7, results from the Arellano and Bond (1991) dynamic panel GMM estimation show that after controlling for the endogeneity of the education adequacy variable and including lag of the FDI in the right side of the equation 3, education adequacy is still a significant determinant for the FDI flows in developing countries. All these results are robust after controlling for any category of the FDI determinants as described above and education adequacy remains a significant determinant of FDI net inflows in developing countries 12. After controlling for localisation, institutions and openness, results suggest an increase of about 2.34 % in the FDI net inflow per unit of the labor force after a 1% improvement in the education adequacy level. These results suggest that improving the adequacy of the education system by giving 10 A given country could have a higher economic growth because of he economic activity that the FDI flows would cause. In addition, FDI could contribute in developing some activities that require more exchange with the rest of the world. 11 We could also add other instruments in addition to variable labs and use for example Arellano and Bover (1995) but the main constraint is the unavailability of data for some relevant instruments that we could use. 12 Sargan s test for overidentifying restrictions confirms that instruments are valid. We also implement a test for zero autocorrelation in first-differenced errors. Results show that there is no evidence of model misspecification. 13

15 the appropriate abilities to workers according to the requirements of the economy could be a way for improving the attractiveness of developing countries with regard to foreign investments. Other variables such as the economic growth and the quality of the institutions also appear to have statistically significant effects on the FDI. In addition, empirical investigation supports the evidence that adequacy of the education system is more important than the labor force level in explaining FDI flows. Finally, we do other analysis applying dynamic panel model and show results in table 8. We investigate the relationship between FDI and the education level. We replace the education adequacy variable by the MYS in the dynamic panel model and find no statistically significant effect of the MYS on FDI inflows (model 1). This result holds when controlling for any endogeneity issue regarding the MYS (model 2). The MYS is still not significant when adding this variable and the education adequacy variable as regressors at the same time, after controlling for any endogeneity issue regarding these two variables (model 3). These findings support the evidence that the education level seems to not be important for FDI but the education adequacy matters in attracting FDI in developing countries. We also investigate the joined effect of the MYS and the education adequacy variable and find that these two variables together have a significant impact on FDI inflows (model 4). These results mean that increasing the level of education only does not have any effect on countries attractiveness to FDI, but improving both the years of schooling and the education adequacy could have a positive impact on FDI inflows. 14

16 6 Conclusion The current paper is an attempt to study the relationship between the education adequacy and the FDI inflows in developing countries. Education adequacy proxy has been estimated using a stochastic frontier model. We find that vocational secondary education as well as secondary education duration appear to have a positive effect on the education adequacy level. This result suggests that secondary education could play a role in improving the adequacy of the education system. Our results also support the evidence that the education adequacy has a positive impact on FDI in developing countries while the years of schooling alone do not have any effect on FDI inflows. This means that the capacity for the labor force to properly translate its education into income in the labor market seems to be more important than the level of education itself, in attracting FDI. However, education adequacy and the years of schooling have a positive joined effect on FDI inflows in developing countries. From these findings, it appears that developing countries could enhance their attractiveness with respect to FDI by implementing some policies in the secondary education system. For instance, improving the access and the quality of some specific programs at the secondary vocational education level could be a good policy in order to better attract FDI. However the current paper did not identify the fields of study that could be important for education adequacy. So it seems interesting for further studies to investigate the fields of study in the secondary education system that are more relevant in order to better attract foreign investments. Some studies in the literature also show that there is a human capital minimum threshold that a country must reach in order to allow FDI to positively affect economic growth. It could be also interesting to investigate the role of education adequacy in the 15

17 relationship between FDI and economic growth in comparison with that of the human capital level. 16

18 References Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58 (2), Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68 (1), Assuncao, S., Forte, R., & Teixeira, A. A. C. (2011). Location Determinants Of Fdi: A Literature Review (Tech. Rep.). Battese, G. E., & Coelli, T. J. (1993). A stochastic frontier production function incorporating a model for technical inefficiency effects (Tech. Rep.). Battese, G. E., & Coelli, T. J. (1995). A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics, 20 (2), Blomstrom, M., & Kokko, A. (2003). Human Capital and Inward FDI (Tech. Rep.). Borensztein, E., De Gregorio, J., & Lee, J. (1998). How Does Foreign Direct Investment Affect Economic Growth? Journal of International Economics, 45, Brooks, H. D., Hasan, R., Lee, J.-W., Son, H. H., & Zhuang, J. (2010). Closing Development Gaps: Challenges and Policy Options. Asian Development Review, 27 (2), Bwalya, S. M. (2006). Foreign direct investment and technology spillovers: Evidence from panel data analysis of manufacturing firms in Zambia. Journal of Development Economics, 81 (2), Carstensen, K., & Toubal, F. (2004, March). Foreign direct investment in Central and Eastern 17

19 European countries: a dynamic panel analysis. Journal of Comparative Economics, 32 (1), Cheng, L. K., & Kwan, Y. K. (2000). What are the determinants of the location of foreign direct investment? The Chinese experience. Journal of International Economics, 51 (2), Cleeve, E. (2008). How effective are fiscal incentives to attract fdi to sub-saharan africa? The Journal of Developing Areas, 42 (1), Egger, P. (2001, September). European exports and outward foreign direct investment: A dynamic panel data approach. Review of World Economics (Weltwirtschaftliches Archiv), 137 (3), Helpman, E. (1997). R&D and Productivity: The International Connection (Tech. Rep.). Hong, J. (2008, December). WTO accession and foreign direct investment in China. Journal of Chinese Economic and Foreign Trade Studies, 1 (2), Liu, Z. (2008, February). Foreign direct investment and technology spillovers: Theory and evidence. Journal of Development Economics, 85 (1-2), Mughal, M., & Vechiu, N. (2010). The role of Foreign Direct Investment in higher education in the developing countries (Does FDI promote education?) (Tech. Rep.). Narula, R. (1996). Multinational investment and economic structure: Globalisation and competitiveness. London: Routledge. Naude, W. A., & Krugell, W. F. (2007). Investigating geography and institutions as determinants of foreign direct investment in Africa using panel data. Applied Economics, 39 (10),

20 Noorbakhsh, F., Paloni, A., & Youssef, A. (2001). Human Capital and FDI Inflows to Developing Countries: New Empirical Evidence. World Development, 29 (9), Root, F. R., & Ahmed, A. A. (1978). The influence of policy instruments on manufacturing Direct Foreign investment in developing countries. Journal of International Business Studies, 9 (3), Xu, B. (2000, August). Multinational enterprises, technology diffusion, and host country productivity growth. Journal of Development Economics, 62 (2), Zhuang, H. (2008). Foreign direct investment and human capital accumulation in china. International Research Journal of Finance and Economics, 19,

21 Annexes Table 3: Descriptive statistics for the adequacy measurement Obs Mean Std. Dev. Min Max Inputs Mean years of schooling Gross primary education enrollment Gross secondary education enrollment Gross tertiairy education enrollment Output GNI per person employed Explanatory factors for the inefficiency Primary school starting age Secondary school starting age Primary education duration Secondary education duration Secondary education vocational pupils (% of total pupils) Employment rate Gross capital formation (% of GDP) Sub-Saharan Africa South Asia Europe and Central Asia Latin America and the Caribbean Arab States East Asia and the Pacific

22 Table 4: Descriptive statistics for the FDI model Obs Mean Std. Dev. Min Max FDI and adequacy variables FDI net inflows per unit of employment ( constant 2000 US $) Education adequacy Control variables Infrastructure Telephone lines (per 100 people) Institutions Corruption control Government effectiveness Political stability Regulation quality Rule of Law Voice and accountability Openness and international interest rate Exports (% of GDP) Interest payment (% of GNI) Other economic control variables GDP growth Inflation (GDP deflator) Population share (14-64 years old) Population share (65 years old and higher) Rural population share (% of total)

23 Table 5: Pooled OLS results Model 1 Model 2 Model 3 Model 4 Model 5 Education adequacy 1.184*** 0.654*** 0.537*** 0.885*** 0.494*** (0.185) (0.160) (0.179) (0.138) (0.102) GDP growth 0.044*** 0.054*** (0.014) (0.013) Inflation (GDP deflator) *** (0.002) (0.009) Population share (14-64 years old) *** (0.036) (0.020) Population share (65 years old and higher) (0.055) (0.025) Rural population share (% of total) * *** (0.008) (0.006) Telephone lines (per 100 people) 0.065** (0.032) (0.013) Corruption control *** (0.467) (0.298)) Government effectiveness *** (0.470) (0.308) Political stability (0.220) (0.112) Regulation quality 1.775*** 1.299*** (0.388) (0.229) Rule of Law (0.509) (0.356) Voice and accountability *** (0.216) (0.148) Exports (% of GDP) 0.885*** 0.012*** (0.138) (0.004) Interest payment (% of GNI) 0.020*** 0.180** (0.006) (0.094) Constant 5.434*** *** 3.493*** (0.242) (1.919) (0.196) (0.337) (1.168) Observations R square Year dummies NO YES YES YES YES *significant at 10%; ** significant at 5%; *** significant at 1 % 22

24 Table 6: Fixed effects results Model 1 Model 2 Model 3 Model 4 Model 5 Education adequacy 1.516*** 1.565*** 1.452*** ** (0.376) (0.374) (0.369) (0.394) (0.477) GDP growth 0.039*** 0.042** (0.013 (0.016) Inflation (GDP deflator) *** (0.002) (0.010) Population share (14-64 years old) 0.126** 0.143** (0.049) (0.061) Population share (65 years old and higher) 0.004** * (0.159) (0.206) Rural population share (% of total) *** (0.049) (0.060) Telephone lines (per 100 people) (0.019) (0.024) Corruption control (0.451) (0.423) Government effectiveness (0.449) (0.470) Political stability (0.191) (0.181) Regulation quality 0.740* 0.793* (0.419) (0.455) Rule of Law (0.483) (0.375) Voice and accountability (0.328) (0.333) Exports (% of GDP) (0.394) (0.007) Interest payment (% of GNI) (0.007) (0.111) Constant 5.706*** *** 5.270*** (0.308) (2.788) (0.291) (0.472) (0.197) Observations R square (overall) Year dummies NO YES YES YES YES *significant at 10%; ** significant at 5%; *** significant at 1 % 23

25 Table 7: Dynamic panel results Model 1 Model 2 Model 3 Model 4 Model 5 Log FDI per worker (t-1) 0.157*** 0.199*** 0.133*** 0.320*** 0.295*** (0.062) (0.073) (0.065) (0.053) (0.064) Education adequacy 2.522*** 1.493** 2.831*** 1.789*** 2.337*** (0.744) (0.586) (0.736) (0.421) (0.795) GDP growth 0.042*** 0.047*** (0.015) (0.013) Inflation (GDP deflator) (0.009) (0.012) Population share (14-64 years old) 0.244*** (0.074) (0.111) Population share (65 years old and higher) (0.173) (0.269) Rural population share (% of total) (0.063) (0.087) Telephone lines (per 100 people) (0.019) (0.023) Corruption control (0.427) (0.466) Government effectiveness * (0.498) (0.471) Political stability (0.214) (0.258) Regulation quality (0.447) (0.462) Rule of Law ** (0.477) (0.459) Voice and accountability (0.495) (0.374) Exports (% of GDP) 0.020*** (0.007) (0.014) Interest payment (% of GNI) (0.079) (0.073) Constant 5.899*** *** (0.828) (6.555) (0.799) (0.554) (11.275) Observations Year dummies NO YES YES YES YES *significant at 10%; ** significant at 5%; *** significant at 1 % 24

26 Table 8: Comparing MYS and education adequacy Model 1 Model 2 Model 3 Model 4 Log FDI per worker (t-1) 0.349*** 0.374*** 0.294*** 0.295*** (0.055) (0.060) (0.063) (0.069) Education adequacy 2.494*** (0.803) Mean years of schooling (0.262) (0.262) (0.214) Education adequacy mean years of schooling 2.144*** (0.828) Constant (7.383) (7.040) (10.419) (10.340) Observations Year dummies YES YES YES YES Control for location, governance and oppeness YES YES YES YES *significant at 10%; ** significant at 5%; *** significant at 1 % 25